Дисертації з теми "LUNG CANCER DETECTION"

Doctor of Philosophy
Department of Chemistry
Stefan H. Bossmann
Cancer is a group of diseases that are characterized by uncontrolled growth and spread of cells. In order to treat cancer successfully, it is important to diagnose cancers in their early stages, because survival often depends on the stage of cancer detection. For that purpose, highly sensitive and selective methods must be developed, taking advantage of suitable biomarkers. The expression levels of proteases differ from one cancer type to the other, because different cancers arise from different cell types. According to the literature, there are significant differences between the protease expression levels of cancer patients and healthy people, because solid tumors rely on proteases for survival, angiogenesis and metastasis. Development of fluorescence-based nanobiosensors for the early detection of pancreatic cancer and non-small-cell lung cancer is discussed in this thesis. The nanobiosensors are capable of detecting protease/arginase activities in serum samples over a broad range. The functionality of the nanobiosensor is based on Förster resonance energy transfer and surface energy transfer mechanisms. The nanobiosensors for protease detection feature dopamine-coated Fe/Fe₃O₄ nanoparticles, consensus (cleavage) peptide sequences, meso-tetra(4-carboxyphenyl)porphine (TCPP), and cyanine 5.5. The consensus peptide sequences were synthesized by solid-supported peptide synthesis. In this thesis, improved consensus sequences were used, which permit faster synthesis and higher signal intensities. TCPP, which is the fluorophore of the nanoplatform, was connected to the N-terminal end of the oligopeptides while it was still on the resin. After the addition of TCPP, the TCPP-oligopeptide was cleaved off the resin and linked to the primary amine groups of Fe/Fe₃O₄-bound via a stable amide bond. In the presence of a particular protease, the consensus sequences attached to the nanoparticle can be cleaved and release TCPP to the aqueous medium. Upon releasing the dye, the emission intensity increases significantly and can be detected by fluorescence spectroscopy or, similarly, by using a fluorescence plate reader. In sensing of arginase, posttranslational modification of the peptide sequence will occur, transforming arginine to ornithine. This changes the conformational dynamics of the oligopeptide tether, leading to the increase of the TCPP signal. This is a highly selective technology, which has a very low limit of detection (LOD) of 1 x 10⁻¹⁶ molL⁻¹ for proteases and arginase. The potential of this nanobiosensor technology to detect early pancreatic and lung cancer was demonstrated by using serum samples, which were collected from patients who have been diagnosed with pancreatic cancer and non-small cell lung cancer at the South Eastern Nebraska Cancer Center (lung cancer) and the University of Kansas Cancer Center (pancreatic cancer). As controls, serum samples collected from healthy volunteers were analyzed. In pancreatic cancer detection, the protease/arginase signature for the detection of pancreatic adenocarcinomas in serum was identified. It comprises arginase, MMPs -1, - 3, and -9, cathepsins -B and -E, urokinase plasminogen activator, and neutrophil elastase. For lung cancer detection, the specificity and sensitivity of the nanobiosensors permit the accurate measurements of the activities of nine signature proteases in serum samples. Cathepsin -L and MMPs-1, -3, and -7 permit detecting non-small-cell lung-cancer at stage 1.

Aims: The aims are to measure radiologists’ performance in lung cancer detection using chest CT; compare the performance of radiology-residents to that of board-certified radiologists in lung cancer detection and to investigate the associations between lung cancer detectability and nodule characteristics including size, lobe location, surrounding-background and primary lung cancer cell type. Methods: 30 radiologists read 60 chest CT scans (30 lung cancer cases, 30 cancer-free). The readers were requested to identify and localize suspicious nodules and give each a confidence score from 1 to 5. The performance of different subgroups was compared using the two-sample t-test. Multiple logistic regression was used to estimate the association between the four characteristics and cancer detection sensitivity. Results: Radiologists’ performance had the following mean values: sensitivity=0.749, location sensitivity=0.666, specificity=0.81, AUC=0.846 and sensitivity at fixed specificity=0.744. In study two, readers had the following (radiologists, residents) pairs of values: sensitivity=(0.782, 0.687); location sensitivity=(0.702, 0.597); specificity=(0.8, 0.83); AUC=(0.844, 0.85) and sensitivity for fixed 0.8 specificity=(0.74, 0.73). In study three, the multivariable regression model demonstrated that the adjusted ORs for all four predictors were significant. Conclusion: Radiologists performance was similar to studies performed elsewhere. Senior-residents compared favorably with board-certified radiologists in the ability to discriminate between diseased and non-diseased patients; however they had significantly lower lung cancer detection sensitivity. Lung cancer features that contributed to reduced detection were: lower-lobe location, non-isolated nodules, NSCLC and smaller size nodules, with size having the largest effect on the predictive value.

Lung cancer is the most lethal cancer globally, in part because it is often diagnosed at an incurable stage. Furthermore, limited options exist for effective patient treatment stratification within the disease. Circulating cell free (cf) DNA provides an accessible non-invasive source of patient tumour DNA, potentially useful for the detection of early stage lesions, or for identifying biomarkers predictive of response to treatment in more advanced disease. The molecular chaperone Heat Shock Protein 90 (HSP90) is a promising therapeutic target in cancer, though predictive biomarkers remain elusive. Using cfDNA it was possible to identify genetic alterations predictive of patient survival in GALAXY-1, a clinical trial designed to assess the efficacy of the HSP90 inhibitor ganetespib. It was also found that low cfDNA concentration was prognostic of improved survival, and a high somatic tumour burden predicted improved survival under the treatment arm containing ganetespib. Furthermore, using FFPE samples it was also possible to identify somatic copy number alterations (SCNAs) predictive of survival, and a positive correlation between the number of SCNAs found and worsened survival was observed. Experimental workflows established that peptide nucleic acid (PNA) clamps can improve the sensitivity of mutation detection in next generation sequencing (NGS) workflows using cfDNA. It was also demonstrated that NGS data that is routinely discarded, such as information on synonymous mutations that alter tRNA pools, can give potentially useful information on somatic alterations in a patient’s cancer. Reduced cfDNA integrity was found to be prognostic of worsened survival in the GALAXY-1 patients. To explore the hypothesis that analysis of cfDNA could allow for the identification of early stage preneoplastic lung cancer the genetically engineered KRAS+/LSL-G12D mouse model was utilised. cfDNA levels were found to correlate with tumour burden, with tumour bearing mice having significantly greater levels of cfDNA versus mice without premalignant lesions, and versus earlier samples taken from the same mice before the development of a tumour burden. It was also shown that the recombined KRAS allele could be detected in circulation. Additionally, CCR1 inhibition was demonstrated to have potential therapeutic benefit in the KRASLSL-G12D model, by causing significant alterations in the recruitment of hematopoietic cells to KRAS mutant murine lungs.

Orientador : Lucas Ferrari de Oliveira
Dissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Elétrica. Defesa: Curitiba, 29/08/2016
Inclui referências : f. 72-76
Área de concentração: Sistemas eletrônicos
Resumo: Câncer é uma das causas com mais mortalidade mundialmente. Câncer de pulmão é o tipo de câncer mais comum (excluíndo câncer de pele não-melanoma). Seus sintomas aparecem em estágios mais avançados, o que dificulta o seu tratamento. Para diagnosticar o paciente, a tomografia computadorizada é utilizada. Ela é composta de diversos cortes, que mapeiam uma região 3D de interesse. Apesar de fornecer muitos detalhes, por serem gerados vários cortes, a análise de exames de tomografia computadorizada se torna exaustiva, o que pode influenciar negativamente no diagnóstico feito pelo especialista. O objetivo deste trabalho é o desenvolvimento de métodos para a segmentação do pulmão e a detecção de nódulos em imagens de tomografia computadorizada do tórax. As imagens são segmentadas para separar o pulmão das outras estruturas e após, detecção de nódulos utilizando a técnicas de superpixeis são aplicadas. A técnica de Rótulamento dos Eixos teve uma média de preservação de nódulos de 93,53% e a técnica Monotone Chain Convex Hull apresentou melhores resultados com uma taxa de 97,78%. Para a detecção dos nódulos, as técnicas Felzenszwalb e SLIC são empregadas para o agrupamento de regiões de nódulos em superpixeis. Uma seleção de candidatos à nódulos baseada em shape index e curvedness é aplicada para redução do número de superpixeis. Para a classificação desses candidatos, foi utilizada a técnica de Florestas Aleatórias. A base de imagens utilizada foi a LIDC, que foi dividida em duas sub-bases: uma de desenvolvimento, composta pelos pacientes 0001 a 0600, e uma de validação, composta pelos pacientes 0601 a 1012. Na base de validação, a técnica Felzenszwalb obteve uma sensibilidade de 60,61% e 7,2 FP/exame. Palavras-chaves: Câncer de pulmão. Detecção de nódulos. Superpixel. Shape index.
Abstract: Cancer is one of the causes with more mortality worldwide. Lung cancer is the most common type (excluding non-melanoma skin cancer). Its symptoms appear mostly in advanced stages, which difficult its treatment. For patient diagnostic, computer tomography (CT) is used. CT is composed of many slices, which maps a 3D region of interest. Although it provides many details, its analysis is very exhaustive, which may has negatively influence in the specialist's diagnostic. The objective of this work is the development of lung segmentation and nodule detection methods in chest CT images. These images are segmented to separate the lung region from other parts and, after that, nodule detection using superpixel methods is applied. The Axes' Labeling had a mean of nodule preservation of 93.53% and the Monotone Chain Convex Hull method presented better results, with a mean of 97.78%. For nodule detection, the Felzenszwalb and SLIC methods are employed to group nodule regions. A nodule candidate selection based on shape index and curvedness is applied for superpixel reduction. Then, classification of these candidates is realized by the Random Forest. The LIDC database was divided into two data sets: a development data set composed of the CT scans of patients 0001 to 0600, and a untouched, validation data set, composed of patients 0601 to 1012. For the validation data set, the Felzenszwalb method had a sensitivity of 60.61% and 7.2 FP/scan. Key-words: Lung cancer. Nodule detection. Superpixel. Shape index.

Lung cancer is the main cause of cancer-related deaths both in Europe and United States, because often it is diagnosed at late stages of the disease, when the survival rate is very low if compared to first asymptomatic stage. Lung cancer screening using annual low-dose Computed Tomography (CT) reduces lung cancer 5-year mortality by about 20% in comparison to annual screening with chest radiography. However, the detection of pulmonary nodules in low-dose chest CT scans is a very difficult task for radiologists, because of the large number (300/500) of slices to be analyzed. In order to support radiologists, researchers have developed Computer aided Detection (CAD) algorithms for the automated detection of pulmonary nodules in chest CT scans. Despite proved benefits of those systems on the radiologists detection sensitivity, the usage of CADs in clinical practice has not spread yet. The main objective of this thesis is to investigate and tackle the issues underlying this inconsistency. In particular, in Chapter 2 we introduce M5L, a fully automated Web and Cloud-based CAD for the automated detection of pulmonary nodules in chest CT scans. This system introduces a new paradigm in clinical practice, by making available CAD systems without requiring to radiologists any additional software and hardware installation. The proposed solution provides an innovative cost-effective approach for clinical structures. In Chapter 3 we present our international challenge aiming at a large-scale validation of state-of-the-art CAD systems. We also investigate and prove how the combination of different CAD systems reaches performances much higher than any best stand-alone system developed so far. Our results open the possibility to introduce in clinical practice very high-performing CAD systems, which miss a tiny fraction of clinically relevant nodules. Finally, we tested the performance of M5L on clinical data-sets. In chapter 4 we present the results of its clinical validation, which prove the positive impact of CAD as second reader in the diagnosis of pulmonary metastases on oncological patients with extra-thoracic cancers. The proposed approaches have the potential to exploit at best the features of different algorithms, developed independently, for any possible clinical application, setting a collaborative environment for algorithm comparison, combination, clinical validation and, if all of the above were successful, clinical practice.

Object detection on natural images has become a single-stage end-to-end process thanks to recent breakthroughs on deep neural networks. By contrast, automated pulmonary nodule detection is usually a three steps method: lung segmentation, generation of nodule candidates and false positive reduction. This project tackles the nodule detection problem with a single stage modelusing a deep neural network. Pulmonary nodules have unique shapes and characteristics which are not present outside of the lungs. We expect the model to capture these characteristics and to only focus on elements inside the lungs when working on raw CT scans (without the segmentation). Nodules are small, distributed and infrequent. We show that a well trained deep neural network can spot relevantfeatures and keep a low number of region proposals without any extra preprocessing or post-processing. Due to the visual nature of the task, we designed a three-dimensional convolutional neural network with residual connections. It was inspired by the region proposal network of the Faster R-CNN detection framework. The evaluation is performed on the LUNA16 dataset. The final score is 0.826 which is the average sensitivity at 0.125, 0.25, 0.5, 1, 2, 4, and 8 false positives per scan. It can be considered as an average score compared to other submissions to the challenge. However, the solution described here was trained end-to-end and has fewer trainable parameters.
Objektdetektering i naturliga bilder har reducerates till en enstegs process tack vare genombrott i djupa neurala nätverk. Automatisk detektering av pulmonella nodulärer är vanligtvis ett trestegsproblem: segmentering av lunga, generering av nodulärkandidater och reducering av falska positiva utfall. Det här projektet tar sig an nodulärdetektering med en enstegsmodell med hjälp av ett djupt neuralt nätverk. Pulmonella nodulärer har unika karaktärsdrag som inte finns utanför lungorna. Modellen förväntas fånga dessa drag och enbart fokusera på element inuti lungorna när den arbetar med datortomografibilder. Nodulärer är små och glest föredelade. Vi visar att ett vältränat nätverk kan finna relevanta särdrag samt föreslå ett lågt antal intresseregioner utan extra för- eller efter- behandling. På grund av den visuella karaktären av det här problemet så designade vi ett tredimensionellt s.k. convolutional neural network med residualkopplingar. Projektet inspirerades av Faster R-CNN, ett nätverk som utmärker sig i sin förmåga att detektera intresseregioner. Nätverket utvärderades på ett dataset vid namn LUNA16. Det slutgiltiga nätverket testade 0.826, vilket är genomsnittlig sensitivitet vid 0.125, 0.25, 0.5, 1, 2, 4, och 8 falska positiva per utvärdering. Detta kan anses vara genomsnittligt jämfört med andra deltagande i tävlingen, men lösningen som föreslås här är en enstegslösning som utför detektering från början till slut och har färre träningsbara parametrar.
La détection d’objets sur les images naturelles est devenue au fil du temps un processus réalisé de bout en bout en une seule étape grâce aux évolutions récentes des architectures de neurones artificiels profonds. En revanche, la détection automatique de nodules pulmonaires est généralement un processus en trois étapes : la segmentation des poumons (pré-traitement), la génération de zones d’intérêt (modèle) et la réduction des faux positifs (post-traitement). Ce projet s’attaque à la détection des nodules pulmonaires en une seule étape avec un réseau profond de neurones artificiels. Les nodules pulmonaires ont des formes et des structures uniques qui ne sont pas présentes en dehors de cet organe. Nous nous attendons à ce qu’un modèle soit capable de capturer ces caractéristiques et de se focaliser uniquement sur les éléments à l’intérieur des poumons alors même qu’il reçoit des images brutes (sans segmentation des poumons). Les nodules sont petits, peu fréquents et répartis aléatoirement. Nous montrons qu’un modèle correctement entraîné peut repérer les éléments caractéristiques des nodules et générer peu de localisations sans pré-traitement ni post-traitement. Du fait de la nature visuelle de la tâche, nous avons développé un réseau neuronal convolutif tridimensionnel. L’architecture utilisée est inspirée du méta-algorithme de détection Faster R-CNN. L’évaluation est réalisée avec le jeu de données du challenge LUNA16. Le score final est de 0.826 qui représente la sensibilité moyenne pour les valeurs de 0.125, 0.25, 0.5, 1, 2, 4 et 8 faux positifs par scanner. Il peut être considéré comme un score moyen comparé aux autres contributions du challenge. Cependant, la solution décrite montre la faisabilité d’un modèle en une seule étape, entraîné de bout en bout. Le réseau comporte moins de paramètres que la majorité des solutions.

Since the dawn of the genomics era, the genetics of numerous human disorders has been understood which has led to improvements in targeted therapeutics. However, the focus of most research has been primarily on protein coding genes, which account for only 2% of the entire genome, leaving much of the remaining genome relatively unstudied. In particular, repetitive sequences, called microsatellites (MST), which are tandem repeats of 1 to 6 bases, are known to be mutational hotspots and have been linked to diseases, such as Huntington disease and Fragile X syndrome. This work represents a significant effort towards closing this knowledge gap. Specifically, we developed a next generation sequencing based enrichment method along with the supporting computational pipeline for detecting novel MST sequences in the human genome. Using this global MST enrichment protocol, we have identified 790 novel sequences. Analysis of these novel sequences has identified previously unknown functional elements, demonstrating its potential for aiding in the completion of the euchromatic DNA. We also developed a disease risk diagnostic using a novel target specific enrichment method that produces high resolution MST sequencing data that has the potential to validate, for the first time, the link between MST genotype variation and cancer. Combined with publically available exome datasets of non-small cell lung cancer and 1000 genomes project, the target specific MST enrichment method uncovered a signature set of 21 MST loci that can differentiate between lung cancer and noncancer control samples with a sensitivity ratio of 0.93. Finally, to understand the molecular causes of MST instability, we analyzed genomic variants and gene expression data for an autosomal recessive disorder, Fanconi anemia (FA). This first of its kind study quantified the heterogeneity of FA cells and demonstrated the possibility of utilizing the DNA crosslink repair dysfunctional FA cells as a suitable system to further study the causes of MST instability.
Ph. D.

Aldehydes in lung cancer cell culture have been investigated using gas chromatography/mass spectrometry and solid-phase microextraction with on-fiber derivatization. In this study, the poly(dimethylsiloxane/divinylbenzene (PDMS/DVB) fiber was used and o-2,3,4,5,6-(pentafluorobenzyl) hydroxylamine hydrochloride (PFBHA) was first loaded on the fiber. Aldehydes in the headspace of lung cancer cell culture were extracted by solid-phase microextraction (SPME) fiber and subsequently derivatized by PFBHA on the fiber. Finally, the aldehyde oximes formed on the fiber were analyzed by gas chromatography/mass spectrometry (GC/MS). Using this method, acetaldehyde decrease was found in both non-small lung cancer cell cultures studied compared to the medium control study. The results of spiking the cell culture with acetaldehyde solution showed that 5 million SK-MES-1 cell lines could consume up to 4.5 uM acetaldehyde in 15-ml medium, and 5 million NCI-H522 cell lines could consume 5.9 uM acetaldehyde in 15-ml medium. The decrease of acetaldehyde may contribute to the metabolism of lung cancer cells. It was proved that GC/MS and SPME with on-fiber derivatization is a simple, rapid, sensitive and solvent-free method for the detection of aldehydes in lung cancer cell culture.

Abstract Today the golden standard method to find fusion genes is with fluorescent in situ hybridization. Massive parallel sequencing is a method that can analyze several genes and samples at the same time at a lower cost. The aim of this study was to compare massive parallel sequencing with fluorescent in situ hybridization for detection of fusion genes in patients with non-small cell lung cancer. Additionally, an evaluation of RNA extraction was performed to obtain RNA samples with quality. Four different purification methods were evaluated and method C, a semi-automatic method showed the highest quantity and a good quality. Method C was used to extract 23 samples from non-small cell lung cancer patients and analyzed with massive parallel sequencing and panel Oncomine Solid Tumour Fusion Transcript Kit (Thermo Fisher Scientific). A total of 11/14 samples showed concordant results with fluorescent in situ hybridization. Three samples either had too low quality or there was too little tissue left on the FFPE block to determine the tumor cell content. Unfortunately, there is a shortage of positive fusion genes samples since the qualities of the samples were uncertain and especially the proportion of tumor cells. No conclusion can be drawn if massive parallel sequencing can replace fluorescent in situ hybridization in the future for patients with non-small cell lung cancer. Further studies are required.

Le carcinome pulmonaire est une affection grave et fréquente dont la prise en charge a été bouleversée ces dernières années, tant sur le plan diagnostique que pronostique ou « théranostique » avec l’avènement des « thérapies ciblées ». Ces dernières permettent une nette amélioration de la survie et du confort des patients éligibles, mais ne sont pas sans compliquer le travail médical, depuis le diagnostic de la maladie jusqu’au suivi régulier du patient, sans oublier le choix des traitements ou les problèmes techniques posés par la multiplication arborescente des altérations moléculaires à rechercher à partir d’un tissu tumoral souvent peu abondant dans ce contexte particulier de l’oncologie thoracique. Ce travail de thèse collige 5 travaux de recherche selon deux angles d’approche : les marqueurs moléculaires pronostiques et « théranostiques » du cancer pulmonaire, et les procédures de diagnostic in vivo de cette pathologie. Le premier axe comporte 4 articles. Les deux premiers concernent l’évaluation d’une nouvelle technique moléculaire, la LD-RT-PCR, dans la détection des translocation géniques du cancer pulmonaire : la première étude est une étude de faisabilité, la deuxième est un travail de validation. Le troisième article explore l’association entre la présence d’une mutation STK11 dans les carcinomes pulmonaires et l’expression de PDL1. Enfin, le quatrième article est une étude de cas illustrant l’importance de l’approche morphologique du cancer pulmonaire. Le second axe est représenté par un travail comparant une technique d’imagerie in vivo par voie endoscopique utilisant la micro-endoscopie confocale par laser avec l’approche microscopique conventionnelle
Lung cancer is a serious and frequent condition for which the management strategies have been dramatically modified in recent years, from a diagnostic, prognostic and “theranostic” perspective, most notably with the introduction of “targeted therapies”. The latter have demonstrated dramatic improvement in both quality of life and survival rates of eligible patients, yet consequently highlight new complications in diagnosis, treatment options or technical considerations which can be attributed to the growing number of molecular alterations to be detected from limited tissue samples frequently encountered in thoracic oncology. This work combines 5 different research papers from 2 different angles: prognostic and “theranostic” molecular markers of lung cancer, as well as in vivo diagnostic procedures of lung cancer. The first angle encompasses 4 articles. The first two evaluate a new molecular technique, LD-RT-PCR, to detect gene translocation in lung cancer. The third article explores the association between STK11 mutations in lung cancer and the expression of PDL1. Finally, the fourth article is a case report illustrating the importance of a morphological approach to lung cancer. The second angle compares in vivo imaging techniques by endoscopy using confocal laser microendoscopy alongside a conventional microscopic approach

Мета даного дисертацiйного дослiдження полягає в детальному розглядi, розробцi та удосконаленнi систем автоматичної комп’ютерної дiагностики раку легень використовуючи методи штучного iнтелекту, зокрема застосовуючи та удосконалюючи останнi досягнення в областi глибинного навчання. Для дiагностування раку легенiв в сучасних медичних закладах використовують комп’ютерну томографiю, що представляє собою тривимiрне зображення легенiв пацiєнта, отримане за допомогою рентгенiвського променю, що пошарово та поступово проходить через тканини людського тiла в рiзних напрямках, з рiзних кутiв та положень. Такий вид зображень використовується в роботi для аналiзу присутностi пухлини в легенях за допомогою згорткових нейронних мереж. Однак, такi особливi данi накладають свої складностi в розробцi систем медичного комп’ютерного дiагностування, оскiльки при роботi з ними необхiдно враховувати їхню тривимiрну природу та вiдповiднi просторовi зв’язки. Тому, в дисертацiйному дослiдженнi розглядається три основнi пiдходи для роботи з такими даними: 1. Використання двовимiрної згорткової нейронної мережi. Для кожного шару КТ знiмка застосовується згорткова нейронна мережа. Виходи мережi для кожного шару знiмку об’єднуються та фiнальний висновок робиться на основi правил навчання за набором зразкiв. 2. Використання тривимiрних згорткових нейронних мереж, якi враховують тривимiрну природу вхiдних даних та можуть вiднайти кориснi патерни використовуючи всi три просторовi вiсi. Часто, такi системи роздiляють задачу на декiлька етапiв, кожен з яких використовує тривимiрну згорткову нейронну мережу налаштовану пiд конкретну пiдзадачу. 3. Використання комбiнованої структури двовимiрної згорткової та рекурентної нейронних мереж. В такому пiдходi двовимiрну згорткову нейронну мережу використовують для представлення вхiдних даних в менш мiрному просторi шляхом навчання многовиду меншої розмiрностi. Завдяки цьому на кожному шарi КТ зображення будуть видiлятися тiльки найбiльш важливi високорiвневi ознаки. Отриманi ознаки обробляються двонаправленою рекурентною нейронною мережею з вентильним вузлом (англ. bidirectional gated recurrent neural network), яка навчається складним нелiнiйним функцiям, що описують просторовi залежностi та вплив мiж ними. Вихiд рекурентної мережi повертає ймовiрнiсть наявностi пухлини на знiмку. В рамках даного дисертацiйного дослiдження проводиться аналiз та виконується експерименти для кожного пiдходу, а отриманi результати порiвнюються з роботами iнших авторiв. Експерименти показують, що найбiльш точними є системи побудованi iз декiлькох тривимiрних згорткових нейронних мереж (одна мережа сегментує потенцiйнi проблемнi регiони, iнша класифiкує присутнiсть в таких регiонах пухлини). Однак, такi системи мають дуже великi обчислювальнi вимоги, через те що використовують операцiю тривимiрної згортки, вимоги до обчислювальної потужностi якої ростуть кубiчно зi збiльшенням розмiрностi вхiдного зображення. В такому випадку, запропонована архiтектура рекурентної згорткової нейронної мережi дозволяє отримати точнiсть роботи системи на достатньо високому рiвнi, в той же час використовуючи значно менш вимогливу до обчислювальних потужностей та пам’ятi операцiю двовимiрної згортки. Наукова новизна отриманих результатiв дисертацiї полягає в запропонованому здобувачем методi побудови комбiнованої структури системи комп’ютерної дiагностики, що полягає в поєднаннi двовимiрної згорткової та двонаправленої рекурентної нейронної мережi LSTM. На вiдмiну вiд iнших рiшень, така система враховує просторовi зв’язки мiж рiзними шарами знiмку комп’ютерної томографiї шляхом використання двонаправленої рекурентної нейронної мережi, на входi якої використовують високорiвневi ознаки сформованi за допомогою двовимiрної згорткової нейронної мережi. Високорiвневi ознаки будуються для кожного шару знiмку пацiєнта. За результатами експериментiв така архiтектура нейронної мережi змогла досягти значення AUC ROC на рiвнi 83%, що трохи нижче у порiвнянi з системами тривимiрних згорткових нейронних мереж, що показують значення AUC ROC на рiвнi 90-95%. Однак, отриманi результати є найвищими результатами для рекурентних нейронних мереж, що застосовуються для побудови систем комп’ютерної дiагностики раку легенiв. Також, запропонована архiтектура має вищу швидкодiю, що досягається шляхом використання операцiї двовимiрної згортки замiсть операцiї тривимiрної згортки, вимоги якої до обчислювальної потужностi та пам’ятi ростуть квадратично з розмiром вхiдних даних, а не кубiчно. Для ефективного навчання комбiнованої структури згорткової рекурентної нейронної мережi був запропонований механiзм м’якої уваги, що надав можливiсть нейроннiй мережi отримати iнформацiю про локацiю пухлини пiд час навчання. Згiдно проведених експериментiв, такий пiдхiд допомiг покращити показники метрики AUC ROC бiльш нiж на 8%. Практичне значення отриманих результатiв полягає в розширенi та удосконаленi iснуючих методiв побудови систем комп’ютерної дiагностики. Запропонована комбiнована структура згорткової нейронної мережi та двонаправленої рекурентної мережi дозволяє отримати достатньо високу точнiсть роботи системи та пiдвищує точнiсть роботи системи у порiвнянi з використанням звичайних рекурентних нейронних мереж. Також, така система вiдзначається використанням меншої кiлькостi ресурсiв чим у тривимiрної згорткової нейронної мережi. Проведенi експерименти та аналiз iснуючих методiв систем комп’ютерної дiагностики дозволив сформулювати необхiднi вимоги та пiдходи, якi потрiбно використовувати в залежностi вiд прiоритету швидкодiї чи точностi роботи системи. Запропонований механiзм м’якої уваги дозволяє значно пiдвищити ефективнiсть навчання комбiнованих архiтектур згорткових рекурентних нейронних мереж. Результати дисертацiйного дослiдження впроваджено в НДР за темою “Розроблення та дослiдження методiв обробки, розпiзнавання, захисту та зберiгання медичних зображень в розподiлених комп’ютерних системах” за номером держ реєстрацiї 0117U004267 (тема №2021п, код КВНТД I.1 01.05.02). Також, основнi результати роботи викладенi в 6 друкованих наукових роботах, з них двоє статей в наукових фахових виданнях України, 2 опублiковано в iноземних журналах, що iндексується в Googel Scholar та iнших базах даних, 1-а стаття у виданнi, що входить до Web of Science Core Collection та SCOPUS. Також опублiковано одну роботу в тезах доповiдей мiжнародної наукової конференцiї.

Nowadays, lung cancer assessment is a complex and tedious task mainly per- formed by radiological visual inspection of suspicious pulmonary nodules, using computed tomography (CT) scan images taken to patients over time. Several computational tools relying on conventional artificial intelligence and computer vision algorithms have been proposed for supporting lung cancer de- tection and classification. These solutions mostly rely on the analysis of indi- vidual lung CT images of patients and on the use of hand-crafted image de- scriptors. Unfortunately, this makes them unable to cope with the complexity and variability of the problem. Recently, the advent of deep learning has led to a major breakthrough in the medical image domain, outperforming conven- tional approaches. Despite recent promising achievements in nodule detection, segmentation, and lung cancer classification, radiologists are still reluctant to use these solutions in their day-to-day clinical practice. One of the main rea- sons is that current solutions do not provide support to automatic analysis of the temporal evolution of lung tumours. The difficulty to collect and annotate longitudinal lung CT cases to train models may partially explain the lack of deep learning studies that address this issue. In this dissertation, we investigate how to automatically provide lung can- cer assessment through deep learning algorithms and computer vision pipelines, especially taking into consideration the temporal evolution of the pulmonary nodules. To this end, our first goal consisted on obtaining accurate methods for lung cancer assessment (diagnostic ground truth) based on individual lung CT images. Since these types of labels are expensive and difficult to collect (e.g. usually after biopsy), we proposed to train different deep learning models, based on 3D convolutional neural networks (CNN), to predict nodule malig- nancy based on radiologist visual inspection annotations (which are reasonable to obtain). These classifiers were built based on ground truth consisting of the nodule malignancy, the position and the size of the nodules to classify. Next, we evaluated different ways of synthesizing the knowledge embedded by the nodule malignancy neural network, into an end-to-end pipeline aimed to detect pul- monary nodules and predict lung cancer at the patient level, given a lung CT image. The positive results confirmed the convenience of using CNNs for mod- elling nodule malignancy, according to radiologists, for the automatic prediction of lung cancer. Next, we focused on the analysis of lung CT image series. Thus, we first faced the problem of automatically re-identifying pulmonary nodules from dif- ferent lung CT scans of the same patient. To do this, we present a novel method based on a Siamese neural network (SNN) to rank similarity between nodules, overpassing the need for image registration. This change of paradigm avoided introducing potentially erroneous image deformations and provided computa- tionally faster results. Different configurations of the SNN were examined, in- cluding the application of transfer learning, using different loss functions, and the combination of several feature maps of different network levels. This method obtained state-of-the-art performances for nodule matching both in an isolated manner and embedded in an end-to-end nodule growth detection pipeline. Afterwards, we moved to the core problem of supporting radiologists in the longitudinal management of lung cancer. For this purpose, we created a novel end-to-end deep learning pipeline, composed of four stages that completely au- tomatize from the detection of nodules to the classification of cancer, through the detection of growth in the nodules. In addition, the pipeline integrated a novel approach for nodule growth detection, which relies on a recent hierarchi- cal probabilistic segmentation network adapted to report uncertainty estimates. Also, a second novel method was introduced for lung cancer nodule classification, integrating into a two stream 3D-CNN the estimated nodule malignancy prob- abilities derived from a pre-trained nodule malignancy network. The pipeline was evaluated in a longitudinal cohort and the reported outcomes (i.e. nodule detection, re-identification, growth quantification, and malignancy prediction) were comparable with state-of-the-art work, focused on solving one or a few of the functionalities of our pipeline. Thereafter, we also investigated how to help clinicians to prescribe more accurate tumour treatments and surgical planning. Thus, we created a novel method to forecast nodule growth given a single image of the nodule. Partic- ularly, the method relied on a hierarchical, probabilistic and generative deep neural network able to produce multiple consistent future segmentations of the nodule at a given time. To do this, the network learned to model the mul- timodal posterior distribution of future lung tumour segmentations by using variational inference and injecting the posterior latent features. Eventually, by applying Monte-Carlo sampling on the outputs of the trained network, we esti- mated the expected tumour growth mean and the uncertainty associated with the prediction. Although further evaluation in a larger cohort would be highly recommended, the proposed methods reported accurate results to adequately support the ra- diological workflow of pulmonary nodule follow-up. Beyond this specific appli- cation, the outlined innovations, such as the methods for integrating CNNs into computer vision pipelines, the re-identification of suspicious regions over time based on SNNs, without the need to warp the inherent image structure, or the proposed deep generative and probabilistic network to model tumour growth considering ambiguous images and label uncertainty, they could be easily appli- cable to other types of cancer (e.g. pancreas), clinical diseases (e.g. Covid-19) or medical applications (e.g. therapy follow-up).
Avui en dia, l’avaluació del càncer de pulmó ´es una tasca complexa i tediosa, principalment realitzada per inspecció visual radiològica de nòduls pulmonars sospitosos, mitjançant imatges de tomografia computada (TC) preses als pacients al llarg del temps. Actualment, existeixen diverses eines computacionals basades en intel·ligència artificial i algorismes de visió per computador per donar suport a la detecció i classificació del càncer de pulmó. Aquestes solucions es basen majoritàriament en l’anàlisi d’imatges individuals de TC pulmonar dels pacients i en l’ús de descriptors d’imatges fets a mà. Malauradament, això les fa incapaces d’afrontar completament la complexitat i la variabilitat del problema. Recentment, l’aparició de l’aprenentatge profund ha permès un gran avenc¸ en el camp de la imatge mèdica. Malgrat els prometedors assoliments en detecció de nòduls, segmentació i classificació del càncer de pulmó, els radiòlegs encara són reticents a utilitzar aquestes solucions en el seu dia a dia. Un dels principals motius ´es que les solucions actuals no proporcionen suport automàtic per analitzar l’evolució temporal dels tumors pulmonars. La dificultat de recopilar i anotar cohorts longitudinals de TC pulmonar poden explicar la manca de treballs d’aprenentatge profund que aborden aquest problema. En aquesta tesi investiguem com abordar el suport automàtic a l’avaluació del càncer de pulmó, construint algoritmes d’aprenentatge profund i pipelines de visió per ordinador que, especialment, tenen en compte l’evolució temporal dels nòduls pulmonars. Així doncs, el nostre primer objectiu va consistir a obtenir mètodes precisos per a l’avaluació del càncer de pulmó basats en imatges de CT pulmonar individuals. Atès que aquests tipus d’etiquetes són costoses i difícils d’obtenir (per exemple, després d’una biòpsia), vam dissenyar diferents xarxes neuronals profundes, basades en xarxes de convolució 3D (CNN), per predir la malignitat dels nòduls basada en la inspecció visual dels radiòlegs (més senzilles de recol.lectar). A continuació, vàrem avaluar diferents maneres de sintetitzar aquest coneixement representat en la xarxa neuronal de malignitat, en una pipeline destinada a proporcionar predicció del càncer de pulmó a nivell de pacient, donada una imatge de TC pulmonar. Els resultats positius van confirmar la conveniència d’utilitzar CNN per modelar la malignitat dels nòduls, segons els radiòlegs, per a la predicció automàtica del càncer de pulmó. Seguidament, vam dirigir la nostra investigació cap a l’anàlisi de sèries d’imatges de TC pulmonar. Per tant, ens vam enfrontar primer a la reidentificació automàtica de nòduls pulmonars de diferents tomografies pulmonars. Per fer-ho, vam proposar utilitzar xarxes neuronals siameses (SNN) per classificar la similitud entre nòduls, superant la necessitat de registre d’imatges. Aquest canvi de paradigma va evitar possibles pertorbacions de la imatge i va proporcionar resultats computacionalment més ràpids. Es van examinar diferents configuracions del SNN convencional, que van des de l’aplicació de l’aprenentatge de transferència, utilitzant diferents funcions de pèrdua, fins a la combinació de diversos mapes de característiques de diferents nivells de xarxa. Aquest mètode va obtenir resultats d’estat de la tècnica per reidentificar nòduls de manera aïllada, i de forma integrada en una pipeline per a la quantificació de creixement de nòduls. A més, vam abordar el problema de donar suport als radiòlegs en la gestió longitudinal del càncer de pulmó. Amb aquesta finalitat, vam proposar una nova pipeline d’aprenentatge profund, composta de quatre etapes que s’automatitzen completament i que van des de la detecció de nòduls fins a la classificació del càncer, passant per la detecció del creixement dels nòduls. A més, la pipeline va integrar un nou enfocament per a la detecció del creixement dels nòduls, que es basava en una recent xarxa de segmentació probabilística jeràrquica adaptada per informar estimacions d’incertesa. A més, es va introduir un segon mètode per a la classificació dels nòduls del càncer de pulmó, que integrava en una xarxa 3D-CNN de dos fluxos les probabilitats estimades de malignitat dels nòduls derivades de la xarxa pre-entrenada de malignitat dels nòduls. La pipeline es va avaluar en una cohort longitudinal i va informar rendiments comparables a l’estat de la tècnica utilitzats individualment o en pipelines però amb menys components que la proposada. Finalment, també vam investigar com ajudar els metges a prescriure de forma més acurada tractaments tumorals i planificacions quirúrgiques més precises. Amb aquesta finalitat, hem realitzat un nou mètode per predir el creixement dels nòduls donada una única imatge del nòdul. Particularment, el mètode es basa en una xarxa neuronal profunda jeràrquica, probabilística i generativa capaç de produir múltiples segmentacions de nòduls futurs consistents del nòdul en un moment determinat. Per fer-ho, la xarxa aprèn a modelar la distribució posterior multimodal de futures segmentacions de tumors pulmonars mitjançant la utilització d’inferència variacional i la injecció de les característiques latents posteriors. Finalment, aplicant el mostreig de Monte-Carlo a les sortides de la xarxa, podem estimar la mitjana de creixement del tumor i la incertesa associada a la predicció. Tot i que es recomanable una avaluació posterior en una cohort més gran, els mètodes proposats en aquest treball han informat resultats prou precisos per donar suport adequadament al flux de treball radiològic del seguiment dels nòduls pulmonars. Més enllà d’aquesta aplicació especifica, les innovacions presentades com, per exemple, els mètodes per integrar les xarxes CNN a pipelines de visió per ordinador, la reidentificació de regions sospitoses al llarg del temps basades en SNN, sense la necessitat de deformar l’estructura de la imatge inherent o la xarxa probabilística per modelar el creixement del tumor tenint en compte imatges ambigües i la incertesa en les prediccions, podrien ser fàcilment aplicables a altres tipus de càncer (per exemple, pàncrees), malalties clíniques (per exemple, Covid-19) o aplicacions mèdiques (per exemple, seguiment de la teràpia).

Lung cancer is one of the most severe and prevalent cancers worldwide. Last year, it was responsible for around two million new cases, and represented the leading cancer regarding mortality. Lung cancer subtype non-small cell lung cancer (NSCLC) constitutes the majority of total lung cancer cases, getting generally diagnosed in advanced stages. Current NSCLC diagnosis approaches lack of sensitivity and specificity. Moreover, most accurate NSCLC diagnosis comprises risky patient-invasive procedures like biopsies. The forward-looking demand for new innovative NSCLC diagnosis approaches that could reinforce ongoing procedures is crucial for an even more precise NSCLC staging and early diagnosis. Recently, new molecular biomarkers are emerging as potential non-invasive diagnostic agents for early-stage NSCLC screening, including multiple miRNAs that display an unusual expression profile in NSCLC. Furthermore, plasma and peripheral blood mononuclear cells (PBMCs) miRNA profile could relate to NSCLC diagnosis. Molecular beacons (MBs) are oligonucleotide probes with a stem-loop structural configuration capable of detect specific nucleic acid sequences throughout fluorescence. The following work studied the development of an in situ MB-based strategy for miRNA detection in NSCLC biological samples. Initially, plasma and PBMCs were isolated from whole blood samples from NSCLC and healthy individuals. miRNA expression profile was screened in PBMCs by RT-qPCR analysis, and further MBs were designed targeting selected miRNAs. Obtained results revealed an upregulated expression of miR-21-3p, miR-21-5p, miR-155-3p and miR-3662 in NSCLC PBMCs, whereas levels of miR-92b-5p, miR-150-3p, miR-155-5p and miR-181a-5p were reduced. Therefore, an in situ method involving miR-21 detection in PBMCs via MBs was shaped and optimized. Accomplished results demonstrated the developed MB approach potential towards miR-21 detection in PBMCs for NSCLC diagnosis.
O cancro do pulmão é considerado um dos mais severos e prevalentes a nível mundial. Em 2020 foi responsável por cerca de dois milhões de novos casos, assim como teve a mais elevada taxa de mortalidade. O cancro do pulmão de não-pequenas células (CPNPC) é o subtipo de cancro do pulmão mais comum, sendo geralmente diagnosticado em estadios avançados. A procura por novas metodologias para o diagnóstico do CPNPC que possam reforçar os procedimentos já praticados é crucial para um diagnóstico precoce e caraterização mais precisa do cancro. Recentemente, novos biomarcadores moleculares estão a surgir como potenciais alvos para o diagnóstico precoce e não-invasivo do CPNPC, nos quais se enquadram múltiplos microRNAs (miRNAs) com expressão alterada. Adicionalmente, o perfil de expressão de miRNAs em plasma e células mononucleares do sangue periférico está conectado com o diagnóstico e estadio do CPNPC. Sondas moleculares são sequências de oligonucleótidos com uma configuração estrutural em stem-loop que lhes permite detetar sequências específicas de ácidos nucleicos através de um sinal fluorescente. O trabalho apresentado estudou o desenvolvimento de uma abordagem in situ baseada em sondas moleculares para a deteção de miRNAs em amostras biológicas para o diagnóstico do CPNPC. O perfil de expressão de miRNAs foi analisado em células mononucleares do sangue periférico através da técnica de RT-qPCR, e posteriormente foram desenhadas sondas moleculares direcionadas para os miRNAs selecionados. Os resultados obtidos mostraram um perfil sob expresso para o microRNA 21-3p (miR-21-3p), miR-21-5p, miR-155-3p e miR-3662 em células mononucleares do sangue periférico de CPNPC, nas quais o miR-92b-5p, miR-150-3p, miR155-3p e o miR-181a-5p apresentaram uma expressão reduzida relativamente aos controlos. Consequentemente, uma abordagem in situ envolvendo sondas moleculares foi desenvolvida, a qual mostrou potencial para a deteção do miR-21 em RNA total de células mononucleares do sangue periférico para o diagnóstico de CPNPC.

In radiation therapy, a treatment plan is designed to make the delivery of radiation to a target more accurate, effective, and less damaging to surrounding healthy tissues. In lung sites, the tumor is affected by the patient’s respiratory motion. Despite tumor motion, current practice still uses a static delivery plan. Unexpected changes due to coughs and sneezes are not taken into account and as a result, the tumor is not treated accurately and healthy tissues are damaged. In this thesis we detail a framework of using an accelerometer device to detect and forecast coughs. The accelerometer measurements are modeled as a ARMA process to make forecasts. We draw from studies in cough physiology and use amplitudes and durations of the forecasted breathing cycles as features to estimate parameters of Gaussian Mixture Models for cough and normal breathing classes. The system was tested on 10 volunteers, where each data set consisted of one 3-5 minute accelerometer measurements to train the system, and two 1-3 minute accelerometer measurements for testing.

碩士
中臺科技大學
生命科學研究所
100
Mutations in the epidermal growth factor receptor (EGFR) gene are suggested to be strongly correlated with sensitivity to EGFR tyrosine kinase inhibitor, and play an important role in the targeted therapy for lung cancer. Nowadays direct sequencing is the gold standard for the detection of EGFR mutations, but its sensitivity is limited by the ratio of mutation cells in the specimen. Only when the percentage of tumor cells is above 25%, can direct sequencing detect mutations. To improve the detection efficiency, a novel method, PNA (Peptide Nucleic Acid )-ZNA (Zip nucleic acids) Clamp Clamp PCR, which was based on TaqMan® Real-Time PCR with the use of ZNA probes to amplify mutant alleles specifically and a PNA probe to block the amplification of wild type allele, was developed in this study. The experimental results demonstrated that PNA-ZNA Clamp PCR could increase the detection sensitivity to 0.1% (mutant allele/wild type allele). And when the ratio was 1%, 1.56-4.16 ng genomic DNA was sufficient for mutations detection. In addition, the clinical application assessment showed that PNA-ZNA Clamp PCR detection was more sensitive than DxS EGFR Mutation Test.

Dissertação de Mestrado em Bioquímica apresentada à Faculdade de Ciências e Tecnologia
O cancro de pulmão é a causa mais comum de morte por cancro, em todo o mundo, em ambos os sexos. Cerca de 80% a 85% dos casos de cancro de pulmão são pacientes com cancro de pulmão de não pequenas células (CPNPC), sendo os restantes 15% -20% cancro de pulmão de pequenas células (CPPC). O CPNPC é dividido em três grupos: adenocarcinoma, carcinoma de células escamosas e carcinoma de células grandes. Entre eles, os casos de adenocarcinoma representam cerca de 40 a 50% dos pacientes com CPNPC. O prognóstico para CPNPC é baixo, com uma taxa de sobrevivência de cinco anos inferior a 20% sendo esta, ainda, pior para o CPPC, com uma taxa de sobrevivência de cinco anos inferior a 5%.Durante muito tempo, os tratamentos de primeira linha foram a cirurgia, a quimioterapia ou a radioterapia. No entanto, a descoberta de várias mutações drivers da carcinogénese em pacientes com CPNPC, especialmente em casos de adenocarcinoma, permitiu o desenvolvimento de tratamentos personalizados com base nessas alterações moleculares específicas. Deste modo, as mutações no EGFR (Recetor do Fator de Crescimento Epidérmico) representam até 15% dos adenocarcinomas e ocorrem principalmente no domínio tirosina quinase (TK) do gene. Mais de 80% dessas mutações consistem em deleções in-frame no exão 19 e na mutação pontual L858R no exão 21. Tais mutações induzem uma ativação constitutiva do EGFR, tornando-se um potencial alvo terapêutico. Assim, os pacientes portadores de mutações no EGFR podem beneficiar de um tratamento específico de primeira linha, mais especificamente, de inibidores de TK (TKI) que, de forma competitiva, inibem a fixação da adenosina trifosfato (ATP) ao local de ligação catalítica do domínio TK. Foram, também, propostos outros driver biomarcadores em cancro de pulmão podendo, alguns deles, fornecer informações adicionais para a tomada de decisões clínicas.Desta forma, o objetivo principal deste projeto foi avaliar mutações noutros alvos potencialmente acionáveis - MET e ERBB2 - em pacientes com adenocarcinoma, através da sequenciação de Sanger, e desenvolver um ensaio multiplex de PCR em tempo real, para uma rápida e sensível avaliação do estado mutacional em tecido e em plasma. Este ensaio também dará a oportunidade de monitorizar a evolução do estado mutacional no plasma durante o tratamento, para a predição de recidiva e controlo do aparecimento de clones com mutações de resistência.Do total de 172 amostras, 161 (88,9%) foram classificadas como negativas para alterações nos exões 18, 19, 20 e 21 do EGFR, enquanto 19 (11,1%) foram classificadas como positivas. No total das 19 alterações encontradas no EGFR, 73,7% foram deleções no exão 19 e 21% relataram a mutação Leu858Arg. Um caso de uma alteração T790M foi, também, encontrado num paciente. Numa frequência mais baixa, um caso Leu861Gln também foi relatado. No gene MET, as mesmas 172 amostras foram, igualmente, analisadas. Destas, 9 amostras (5,2%) apresentaram alterações no gene, incluindo 2 variantes intrónicas, 2 mutações indel e 5 mutações pontuais, no exão 14. As alterações no ERBB2 foram analisadas em 69 amostras, tendo sido detetado um caso de inserção de 12 bases no exão 20.Este trabalho permitiu concluir que uma proporção importante de casos apresenta mutações no MET e ERBB2, sendo que tais pacientes poderiam ser tratados com fármacos aprovados para esses alvos.
Lung cancer is the most common cause of cancer death around the world, in both sex. About 80%–85% of lung cancer cases are non-small-cell lung cancer (NSCLC) patients, the remaining 15%–20% are small-cell lung cancer (SCLC). NSCLC is divided into three categories called: adenocarcinoma, squamous-cell carcinoma and large cell carcinoma. Among them, adenocarcinoma cases account for around 40-50% of NSCLC patients. The prognosis for NSCLC is low with a five-year survival rate of less than 20%, and is even worse for SCLC with a five-year survival rate of less than 5%.For a long time, the first-line treatments have been surgery, chemotherapy or radiotherapy. However, the discovery of several oncogenic driver mutations in patients with NSCLC, adenocarcinoma cases in particular, has allowed the development of personalized treatments based on these specific molecular alterations. Therefore, EGFR (epidermal growth factor receptor) mutations account for up to 15% of adenocarcinoma and primarily occurred in the tyrosine kinase (TK) domain of the gene. More than 80% of these mutations consist of in-frame deletions in exon 19 and the L858R point mutation in exon 21. Such mutations induced a constitutive activation of EGFR, making it a potential therapeutic target. Thus, EGFR-mutated patients can benefit from a specific first-line treatment specifically the TK inhibitors (TKI) that competitively inhibits fixation of adenosine triphosphate (ATP) in the catalytic binding site of TK domain. Other driver biomarkers in lung cancer have also been proposed and some of them might provide additional information for clinical decision-making. In this way, the main goal of this project was to evaluate mutations in other potentially actionable targets – MET and ERBB2– in patients with adenocarcinoma by Sanger sequencing and to develop a Real Time PCR multiplex assay for rapid sensitive assessment of mutation profile in tissue and plasma. This assay, will also give the opportunity to monitor the evolution of mutational status in the plasma during the treatment for the prediction of relapse and control the appearance of clones with resistance mutations.Of the total of 172 samples, 161 (88.9%) were classified as negative for alterations in exons 18, 19, 20 and 21 of EGFR, whereas 19 samples (11.1%) were classified as positive. In total of the 19 alterations in EGFR, 73.7% were deletions in exon 19 and 21% was related to Leu858Arg mutation. A case of a T790M alteration was also founded in a patient. At a lower frequency, a case of a Leu861Gln was also reported. In MET gene, the same 172 samples were, also, analyzed. Of these, 9 samples (5.2%) harbored alterations in MET gene, including 2 intronic variants, 2 indel mutations and 5 pontual mutations in exon 14. ERBB2 alterations were analyzed in 69 samples and one case of an insertion of 12 bases in exon 20 were detected.This work allowed us to conclude that an important proportion of cases harbors mutations in MET and ERBB2 and these patients could potentially be treated with approved drugs for these targets.

In this project, Lung cancer remains an extremely important disease in the world that causes deaths. Early Diagnosis can prevent large amounts of deaths. Classifiers play an important role in detecting lung cancer by means of a machine learning set of rules in addition to CAD-based image processing techniques. For the classifier’s accuracy, there is the need for a good feature collection of images. Features of an image can help to find all relevant information for identifying disease. Features are the important parameter for finding results. Mostly, features are extracted from feature extraction techniques like GLCM or some datasets already have features of lung cancer images by using some techniques. For different models of classifier, dimension, storage, speed, time and performance create an impactful effect on the results because we have large amount features of the images. An optimized method like the feature selection technique is the one solution that leads to finding relevant features from datasets containing features or features extracted from feature extraction techniques. The lung cancer database has 32 case records with 57 unique characteristics. Hong and Young compiled this database, which was indexed in the University of California Irvine repository. Take out medical information and X-ray information, for example, are among the experimental materials. The data described three categories of problematic lung malignancies, each with an integer value ranging from 0 to 3. A new strategy for identifying effective aspects of lung cancer is proposed in our work in Matlab 2022a. It employs a Genetic Algorithm. Using a simplified 8-feature SVM classifier and four feature KNN, 100% accurateness is achieved. The new method is compared to the existing Hyper Heuristic method for the feature selection. Through the maximum level of precision, the projected technique performs better. As a result, the proposed approach is recommended for determining an effective disease symptom.

Dissertação de Mestrado em Bioquímica apresentada à Faculdade de Ciências e Tecnologia
O cancro de pulmão é uma das principais causas oncológicas de morte em todo o mundo, constituindo o segundo tipo de cancro mais frequentemente diagnosticado. Esta patologia tem um decurso “silencioso”, não produzindo sintomas característicos que permitam a sua detecção precoce, o que faz com que normalmente seja detectada tardiamente, já numa fase inoperável. Este diagnóstico tardio contribui para uma taxa de sobrevivência a 5 anos de apenas 18.7 %. No entanto, a sobrevida é significativamente melhor quando o cancro do pulmão é detectado em estágio inicial, com a taxa de sobrevivência a 5 anos a aumentar para 50-70%,. Assim sendo, o controlo da mortalidade por cancro do pulmão dependente do desenvolvimento de métodos específicos de diagnóstico precoce que possam ser empregues no rastreio de base populacional como forma de prevenção secundária. Nas últimas quatro décadas, o diagnóstico precoce tem sido amplamente investigado como uma opção para reduzir a mortalidade do cancro do pulmão. Nos anos 70 e 80 do século XX, foram praticados vários estudos de rastreio com recurso à radiografia ao tórax e citologia da expectoração em população de risco. No entanto, estes estudos não geraram qualquer redução na taxa de mortalidade e, por isso, não são recomendados como método de rastreio. Recentemente, a National Lung Screening Trial (NLST) demostrou que a tomografia computorizada de baixa dose levou a uma redução de 20% na taxa de mortalidade numa população de alto risco. O sucesso deste levou a que este método de rastreio fosse implementado nos Estados Unidos da América. Contudo, ensaios Europeus de rastreio do cancro do pulmão ainda não relataram uma vantagem clara na implementação deste método numa população de alto risco. Deste modo, no actual estado da arte não existe nenhum teste que seja consensualmente recomendado para fazer parte de um programa de rastreio em população assintomática. Com o aumento do conhecimento da biologia do cancro do pulmão é sabido que diversas alterações genéticas estão associadas ao desenvolvimento desta doença, particularmente: mutações em oncogenes, metilação nas ilhas CpG de genes supressores de tumores, perda de heterozigotia e instabilidade de microssatélites. Estas alterações genéticas e epigenéticas não têm apresentado sensibilidade e especificidade aceitável por si só, pelo que a utilização de um painel de marcadores moleculares que atinja uma sensibilidade e especificidade credível no diagnósticos precoce do cancro do pulmão é muito promissor.Constatou-se que no cancro há uma maior libertação de DNA por parte do tecido tumoral para os fluidos corporais. No que toca ao pulmão, as vias aéreas inferiores são revestidas por um fluido protector e estudos demonstraram que é possível obter DNA do ar exalado que se pensa provir deste fluido. Além disto, foi possível detectar alterações moleculares características do cancro do pulmão neste tipo de amostras, o que perspectiva a sua aplicação como material biológico de partida para a análise de marcadores moleculares do cancro. Neste sentido, o ar exalado condensado tem aberto fronteiras no desenvolvimento de metodologias para diagnóstico precoce na área do cancro do pulmão. Com base nestes estudos, a Starup Infogene desenvolveu e patenteou um dispositivo de sopro, designado Oncospro, portátil e de auto-colheita, que permite a recuperação de biomoléculas do ar exalado condensado através da sua captura num filtro. A presente tese tem como principal objectivo optimizar o protocolo laboratorial de recuperação da amostra biológico a partir do dispositivo de sopro, de modo a obter a maior quantidade possível de material biológico para análises moleculares posteriores. A eficiência dos diversos protocolos foi avaliada com base em 2 critérios: quantidade e qualidade de DNA, pela medição da absorvância a 260, 280 e 230 nm por espectrofotometria e por um método fluorométrico e; a integridade e qualidade do DNA obtido para amplificação, através de PCR e electroforese em gel de agarose. Através da modificação de diversos parâmetros no protocolo, obteve-se uma maior quantidade de DNA, cumprindo-se o objectivo estipulado. O cumprimento deste levou à posterior realização de um estudo populacional num grupo de controlos, constituído por pessoas saudáveis, fumadoras e ex-fumadoras, com idade superior a 50 anos. Neste estudo foi possível obter e amplificar o DNA proveniente do ar exalado em todas as amostras da população, revelando a eficácia do método de extração otimizado. Uma vez alcançada esta meta com sucesso, uma etapa futura passará pela identificação de um painel de marcadores moleculares genéticos sensíveis e específicos para a detecção precoce e previsão de resposta terapêutica do cancro do pulmão pelo ar exalado, através de um estudo clínico.
Lung cancer, the second most diagnosed type of cancer, is one of the primary oncological causes of death in the world. This pathology has a silent course, with characteristic symptoms that would allow its precocious detection being absent. This leads to a late detection that often occurs in a non-operable phase of the cancer, contributing to a 5 year survival rate of only 18,7 %. In the rare cases that the cancer is detected in an initial state, the survival rate augments significantly to 50-70 %. Therefore, controlling lung cancer-derived mortality relays on the development of specific methods for precocious diagnosis that are employable to the population basis as a secondary prevention measure. In the last four decades early diagnosis methods have been widely investigated as an option to reduce lung cancer mortality. In the 70’s and the 80’s several studies of screening were conducted with the use of thorax radiography and expectoration cytology methods in risk populations. However, these studies failed to show a reduction in mortality rate and, therefore, are not recommended as screening methods. Recently, National Lung Screening Trial (NLST) demonstrated that computerized tomography in low doses lead to a 20% reduction in the mortality rate in one risk population. The success of this method lead to its implementation in the USA. Nevertheless, European screening tests have yet to proof a clear advantage in the implementation of the latter method in high risk populations. Therefore, in the current state of the art there is no trial consensually recommended to be a part of a screening program in asymptomatic populations. With the rise in cancer biology knowledge it is now known that several genetic alterations are associated with the development of this disease, particularly: mutations in oncogenes, methilation in CpG islands of tumor suppressor genes, loss of heterozygoty and microsatellite instability. These genetic and epigenetic alterations have not shown enough sensibility and specificity alone but a panel of molecular markers that reaches a desirable sensitivity and specificity for precocious diagnosis is very promising. It was found that in cancer there is a higher release of DNA by tumor tissue into body fluids. In what concerns the lungs, the inferior airways are covered by a protector fluid and studies have demonstrated that it is possible to obtain DNA from exhaled air that it is thought to come from this fluid. Also, it was possible to detect molecular alterations characteristic of lung cancer in these types of samples, what puts into perspective its application as biological material for analysis of cancer molecular markers. In this sense, condensed exhaled air has opened frontiers in the development of methodologies for precocious diagnosis in the lung cancer field. Based on these studies, the startup Infogene developed and patented a portable and self-harvest blowing device denominated Oncosopro, which allows the recovery of biomolecules from condensed exhaled air through a small filter. The current thesis has as a main goal the optimization of the laboratory protocol for sample recovery by the patented blowing device, in order to obtain the major possible quantity of biological material for posterior molecular analysis. The efficiency of several protocols was evaluated based on two criteria: quantity and quality of the DNA, measured through espectrophotometry and another fluorometric method; and the integrity and quality of the DNA obtained for further amplification through PCR and electrophoresis. Through the modification of several parameters in the protocol, a higher quantity of DNA was obtained, meeting the stated goal. A posterior population trial in control groups formed by healthy people, smokers and ex-smokers over 50 years old showed this optimized extraction method is effective for recovery and amplification of the exhaled air DNA.A future phase will rely on the identification of a panel of genetic molecular markers sensible and specific for early detection of lung cancer as well as a method of prediction of the respective therapeutic answer using exhaled air. Clinical utility can be validated through determination of several parameters, such as specificity, sensibility, positive predictive value and negative predictive value of this DNA recovering method. Upon the clinical validation, this method might be employed into screening programs in order to reduce the mortality rate in this oncological disease.

Yung, Kam Fai.
Thesis (M.Phil.)--Chinese University of Hong Kong, 2009.
Includes bibliographical references (leaves 107-129).
Abstracts in English and Chinese.
ABSTRACT --- p.ii
摘要 --- p.iv
ACKNOWLEDGEMENTS --- p.vi
TABLE OF CONTENTS --- p.vii
PUBLICATION --- p.ix
LIST OF TABLES --- p.x
LIST OF FIGURES --- p.xi
LIST OF ABBREVIATIONS --- p.xii
Chapter SECTION I: --- BACKGROUND --- p.1
Chapter CHAPTER 1: --- "The biology, diagnostics and management of lung cancer" --- p.2
Chapter 1.1 --- "Basic biology, classification and diagnostics" --- p.2
Chapter 1.1.1 --- Epidemiology and etiology of lung cancer --- p.2
Chapter 1.1.2 --- Clinical Presentation and Diagnostics of Lung Cancer --- p.3
Chapter 1.2 --- Treatment of lung cancer --- p.9
Chapter 1.2.2 --- Radiotherapy --- p.10
Chapter 1.2.3 --- Chemotherapy --- p.11
Chapter CHAPTER 2: --- Epidermal Growth Factor Receptor Mutations in Lung Cancer --- p.13
Chapter 2.1 --- The Epidermal Growth Factor Receptor --- p.13
Chapter 2.2 --- Overexpression of EGFR in NSCLC --- p.14
Chapter 2.3 --- The development of EGFR inhibitors --- p.15
Chapter 2.3.1 --- Monoclonal Antibodies --- p.16
Chapter 2.3.2 --- Small-molecule inhibitors --- p.17
Chapter 2.3.2.1 --- Gefitinib --- p.17
Chapter 2.3.2.2 --- Erlotinib --- p.19
Chapter 2.3.2.3 --- Other small-molecule inhibitors --- p.20
Chapter 2.4 --- Mutations of EGFR in NSCLC --- p.21
Chapter 2.4.1 --- Activating Mutations conferring sensitivity to tyrosine kinase inhibitors --- p.21
Chapter 2.4.2 --- Secondary mutations associated with resistance to tyrosine kinase inhibitors --- p.23
Chapter 2.5 --- EGFR gene amplification --- p.24
Chapter 2.6 --- Detection of EGFR mutations --- p.25
Chapter 2.7 --- Aim of the thesis --- p.31
Chapter SECTION II: --- DETECTION OF EGFR MUTATIONS IN TUMOR AND PLASMA SAMPLES BY MASS SPECTROMETRY AND DIGITAL PCR --- p.33
Chapter CHAPTER 3: --- Detection of EGFR mutations by mass spectrometric methods --- p.34
Chapter 3.1 --- Introduction --- p.34
Chapter 3.1.1 --- Principles of Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) --- p.34
Chapter 3.1.2 --- The MassARRAY Homogenous MassEXTEND (hME) assay --- p.35
Chapter 3.1.3 --- The Single-Allele Base Extension Reaction (SABER) and the Allele-Specific Base Extension Reaction (ASBER) --- p.36
Chapter 3.2 --- Materials and Methods --- p.36
Chapter 3.2.1 --- The protocol for the detection of EGFR exon 21 point mutation by Mass Spectrometric Methods --- p.37
Chapter 3.3 --- Results --- p.42
Chapter 3.4 --- Discussion --- p.49
Chapter CHAPTER 4: --- Evaluation of the detection limit and sensitivity of the digital PCR assays --- p.51
Chapter 4.1 --- Introduction --- p.51
Chapter 4.1.1 --- The theoretical basis of digital PCR quantification and the relationship with the Poisson distribution --- p.51
Chapter 4.1.2 --- Assessment of Assay Detection Limit --- p.54
Chapter 4.1.3 --- Comparing Digital PCR with sequencing after conformation sensitive gel electrophoresis (CSGE) --- p.59
Chapter 4.2 --- Materials and Methods --- p.59
Chapter 4.2.1 --- Design of digital PCR assay for the detection of EGFR exon21 L858R point mutation --- p.59
Chapter 4.2.2 --- Design of digital PCR assay for the detection of EGFR exon19 deletion --- p.60
Chapter 4.2.3 --- The protocols of digital PCR assays for EGFR mutation detection --- p.64
Chapter 4.2.4 --- Single molecule detection test --- p.65
Chapter 4.2.5 --- Artificial mixtures of mutant and wild-type DNA --- p.66
Chapter 4.2.6 --- Sequencing after CSGE --- p.66
Chapter 4.3 --- Results --- p.67
Chapter 4.3.1 --- Results of the single molecule detection test and artificial mixture analysis --- p.67
Chapter 4.3.2 --- Results of CSGE and sequencing compared with digital PCR --- p.73
Chapter 4.4 --- Discussion --- p.75
Chapter CHAPTER 5: --- Detection of EGFR mutations in prospectively collected tumor samples of NSCLC patients --- p.77
Chapter 5.1 --- Introduction --- p.77
Chapter 5.2 --- Materials and Methods --- p.78
Chapter 5.2.1 --- Sample preparation and DNA extraction of tumor tissues --- p.78
Chapter 5.3 --- Results --- p.79
Chapter 5.4 --- Discussion --- p.82
Chapter CHAPTER 6: --- Detection of EGFR mutations in prospectively collected plasma samples of NSCLC patients --- p.85
Chapter 6.1 --- Introduction --- p.85
Chapter 6.2 --- Materials and Methods --- p.87
Chapter 6.2.1 --- Sample preparation and DNA extraction of plasma samples --- p.87
Chapter 6.3 --- Results --- p.88
Chapter 6.3.1 --- Digital PCR analysis of EGFR mutations in plasma samples of NSCLC patient --- p.88
Chapter 6.3.2 --- Variations in plasma EGFR mutation concentration after TKI treatment --- p.93
Chapter 6.4 --- Discussion --- p.96
Chapter SECTION III: --- CONCLUDING REMARKS --- p.100
Chapter CHAPTER 7: --- Conclusion and future perspectives --- p.101
Chapter 7.1 --- Mass spectrometric analysis --- p.101
Chapter 7.2 --- Microfluidics Digital PCR --- p.102
Chapter 7.3 --- Future perspectives --- p.105
References --- p.107

Purpose: In the contemporary management of early-stage lung cancer with RadiationTherapy (RT), there is increased imaging utilization for the diagnosis and treatment and follow-up after completion of treatment. We evaluated whether this increased radiation exposure to patients with early-stage lung cancer that receive stereotactic body radiotherapy (SBRT) significantly increases the risk of radiation-induced carcinogenesis (RIC). Methods: Following IRB approval, one hundred and ninety-six consecutively treated lung cancer patients treated with SBRT were selected for analysis. Information collected included demographics and all ionizing imaging scans one year before SBRT treatment and one year following treatment. These included chest X-rays (CXR), computerized tomography scan (CT scan), positron emission tomography scan (PET-CT scan), bone scan, ventilation-perfusion scan (VQ scan), cone-beam CT scans. In addition to the lung cancer patients, comparative data on ten prostate and breast cancer patients each was collected to get an estimate of the radiation-induced risk (RIC) in other common malignancies. For each patient, the total effective dose (mSv) was calculated by the sum of all effective doses for all scans (1 year before SBRT to 1-year post-SBRT). After calculating the total effective dose, the summed dose was used to calculate the RIC using the RadRat tool. For the study, we decided that a 1% increase in the baseline risk of radiation-induced lung cancer will be considered a significant increase. Results: Among lung cancer patients, there were 87 males (44.4%) and 109 females (55.6%). The median number of Pre-SBRT CXRs (PA/lateral) was 2 (Range: 1-22), the median number of pre-SBRT CT scans was 2 (Range: 1-6), the median number of pre-SBRT PET-CT scans was 1 (Range: 1-4), the median number of Bone Scans or VQ scans pre-SBRT was 1. The median effective exposure dose from all scans was 72mSv (Range: 24-140.36mSv). The median excess lifetime risk (ELR) of developing lung cancer (a chance in 100,000) with a 90% uncertainty range was 57.15. The Excess Future risk (EFR), the risk from 2019 to the end of the expected lifetime of developing cancer (a chance in 100,000), showed a median of EFR mean of 73.75 (Range: 8.45- 416). The total future risk (TFR, a sum of baseline and excess risk) of developing cancer, from 2019 to end of an expected lifetime was 2732.5 (Range: 808-8290), the median of TFR upper bound was 2785.5 (Range: 856-8400) and median of TFR lower bound was 2679.5 (Range: 761- 8183). At 6 months, survival was 94.7% (144/152), at 1 year, 79% (94/119), at 3 years 32.5% (27/83). At five years, with survival data on 77 patients available, 9 (11.6%) were alive. Regarding the comparison of RIC from imaging before RT for patients with prostate cancer, the median total effective radiation dose from all pre-SBRT and post-SBRT scans was 20mSv (Range: 20-30mSv), and the median of mean ELR for development of RIC prostate cancer was 4.24 (per 100,000). Regarding early-stage breast cancer, the median total effective radiation dose from all pre-RT and post-RT scans was 16.56mSv (Range: 10.52-31.48mSv), and the median of mean ELR for development of RIC was 35.95 (per 100,000). Conclusion: The median excess cancer lifetime radiation-induced cancer risk for the lung cancer cohort was 0.05%, which is significantly less than the 1% risk that was determined to be clinically significant as per our study objective. The survival in this cohort of patients was poor. Enhanced imaging to enhance staging accuracy, safety during SBRT treatment, and adequate follow-up outweigh the RIC risk.

We present a new x-ray imaging technique, Correlation Imaging (CI), for improved breast and lung cancer detection. In CI, multiple low-dose radiographic images are acquired along a limited angular arc. Information from unreconstructed angular projections is directly combined to reduce the effect of overlying anatomy - the largest bottleneck in diagnosing cancer with projection imaging. In addition, CI avoids reconstruction artifacts that otherwise limit the performance of tomosynthesis. This work involved assessing the feasibility of the CI technique, its optimization, and its implementation for breast and chest imaging.

First a theoretical model was developed to determine the diagnostic information content of projection images using a mathematical observer. The model was benchmarked for a specific application in assessing the impact of reduced dose in mammography. Using this model, a multi-factorial task-based framework was developed to optimize the image acquisition of CI using existing low-dose clinical data. The framework was further validated using a CADe processor. Performance of CI was evaluated on mastectomy specimens at clinically relevant doses and further compared to tomosynthesis. Finally, leveraging on the expected improvement in breast imaging, a new hardware capable of CI acquisition for chest imaging was designed, prototyped, evaluated, and experimentally validated.

The theoretical model successfully predicted diagnostic performance on mammographic backgrounds, indicating a possible reduction in mammography dose by as much as 50% without adversely affecting lesion detection. Application of this model on low-dose clinical data showed that peak CI performance may be obtained with 15-17 projections. CAD results confirmed similar trends. Mastectomy specimen results at higher dose revealed that the performance of optimized breast CI may exceed that of mammography and tomosynthesis by 18% and 8%, respectively. Furthermore, for both CI and tomosynthesis, highest dose setting and maximum angular span with an angular separation of 2.75o was found to be optimum, indicating a threshold in the number of projections per angular span for optimum performance.

Finally, for the CI chest imaging system, the positional errors were found to be within 1% and motion blur to have minimal impact on the system MTF. The clinical images had excellent diagnostic quality for potentially improved lung cancer detection. The system was found to be robust and scalable to enable advanced applications for chest radiography, including novel tomosynthesis trajectories and stereoscopic imaging.

Dissertation

The diagnostic evaluation of ever smokers with pulmonary nodules represents a growing clinical challenge due to the implementation of lung cancer screening. The high false-positive rate of screening frequently results in the use of unnecessary invasive procedures in patients who are ultimately diagnosed as benign, clearly highlighting the need for additional diagnostic approaches. We previously derived and validated a bronchial epithelial gene-expression biomarker to detect lung cancer in ever smokers. However, bronchoscopy is not always chosen as a diagnostic modality. Given that bronchial and nasal epithelial gene-expression are similarly altered by cigarette smoke exposure, we sought to determine if cancer-associated gene-expression might also be detectable in the more readily accessible nasal epithelium. Nasal epithelial brushings were prospectively collected from ever smokers undergoing diagnostic evaluation for lung cancer in the AEGIS-1 (n=375) and AEGIS-2 (n=130) clinical trials and gene-expression profiled using microarrays. The computational framework used to discover biomarkers in these data was formalized and implemented in an open-source R-package. We identified 535 genes in the nasal epithelium of AEGIS-1 patients whose expression was associated with lung cancer status. Using matched bronchial gene-expression data from a subset of these patients, we found significantly concordant cancer-associated gene-expression alterations between the two airway sites. A nasal lung cancer classifier derived in the AEGIS-1 cohort that combined clinical factors and nasal gene-expression had significantly higher AUC (0.81) and sensitivity (0.91) than the clinical-factor model alone in independent samples from the AEGIS-2 cohort. These results support that the airway epithelial field of lung cancer-associated injury extends to the nose and demonstrates the potential of using nasal gene-expression as a non-invasive biomarker for lung cancer detection. The framework for deriving this biomarker was generalized and implemented in an open-source R-package. The package provides a computational pipeline to compare biomarker development strategies using microarray data. The results from this pipeline can be used to highlight the optimal model development parameters for a given dataset leading to more robust and accurate models. This package provides the community with a novel and powerful tool to facilitate biomarker discovery in microarray data.

Detecting lung cancer is di cult as it is hidden in the body, and current clinical methods are not elective at an early stage; the one-year survival rate after diagnosis in the World is just 29-33%. Acetyl amantadine (AcAm) is recognised as an exogeneous cancer biomarker because it is the product of a metabolic process known to be significantly up-regulated in cancerous cells. After ingestion, the an-tiparkinson and antiviral drug amantadine is acetylated in the body by the enzyme spermidine/spermine N1 acetyltransferase to give AcAm, which can be detected in patient’s urine. However, techniques previously used to quantify AcAm in urine, such as liquid chromatography-mass spectrometry (LC-MS), are undesirable for clin- ical adoption due to high costs and long run times. Further costs and delays result from the requirement for solid phase extraction (SPE). Therefore, it is highly desired to lower the costs and delays in processing by exploring different quantification approaches, ideally without the need for SPE processing. In this thesis, I investigate the use of surface enhanced Raman spectroscopy (SERS) to quantify AcAm in urinalysis. I prepare two kinds of Raman substrates with hydrophobic pocket surface capture agents beta -cyclodextrin (beta -CD) that work to extract the AcAm from the urine, followed by the surface enhanced Raman measurement using two kinds of Raman systems. The detection strategy is more economical than the currently used LC-MS approach, and enables development of an easy-to-use point-of-care tool that should provide a more rapid turnaround to the health care provider. The next step will be to use real samples. If it is achieved, it will be a promising step in early cancer diagnostics.
Graduate

Cancer is a leading cause of death and some types of cancer are hard to diagnose at early stages. An accurate method for subtype classification of cancer types is also critical for patients to receive effective treatments. Many cancer biomarkers (e.g., EGFR for lung cancers and CD19/CD20 for leukemia) have been found with potential of being used for cancer diagnosis and subtype cancer classification. A biosensing technique being able to detect biomarkers with a miniaturized system, based on extraordinary light transmission (EOT) through nanohole arrays on metal films, is promising for cancer diagnosis and subtype classifications. In this research, the detection of different biomarkers (EGFR, CD19 and CD20) was demonstrated using a surface plasmon resonance (SPR) setup with EOT. The concentration of EGFR from cell lysate solution was determined using the SPR setup and compared with a current analytical method (ELISA). The SPR setup gave a detection limit concentration of 0.77 g/mL for the EGFR. The EGFR concentration from the cell lysate was determined to be greater than 10 g/mL from SPR experiments; while a lower concentration of 0.604 g/mL was found from ELISA indicating some problems with the calibration curves obtained in the SPR experiments. A whole lung cancer cell capture experiment was also conducted using microscopy imaging and the SPR setup. A number of 11 2 cells/mm2 was captured from a pre-modified metal surface, which was confirmed by SPR.
Graduate
0752
0541

Non Small Cell Lung Cancer (NSCLC) represents the most leading cause of death for cancer worldwide.1 In addition to standard care (chemotherapy), several clinical trials demonstrated the efficacy in advanced stage (IIIB-IV) NSCLC patients of targeted treatment, represented by tyrosine kinase inhibitors (TKIs).2 For this reason, the College of American Pathologists (CAP), the International Association for the Study of Lung Cancer (IASLC) and the Association for Molecular Pathology (AMP) established, in addition to epidermal growth factor receptor (EGFR) molecular assessment, a panel of “must test genes” that includes also gene fusions, such as Anaplastic Lymphoma Kinase (ALK) and ROS Proto-Oncogene 1 Receptor Tyrosine Kinase (ROS1).3 On the overall, other clinically relevant gene rearrangements involving REarranged during Transfection (RET) and neurotrophic receptor tyrosine kinase (NTRK) genes 1, 2, and 3 showed their feasibility as targetable biomarkers in NSCLC patients.4,5 For predictive molecular analysis, tissue represents the most suitable sample type in diagnostic routine, but in NSCLC setting tissue specimens are often characterized by scant amount of nucleic acids on which molecular analysis may be performed. For this reason, very sensitive methodologies such as Next Generation Sequencing (NGS) and nanofluidic color-code barcode systems (Nanostring) should be implemented in clinical practice. Unfortunately, despite of NGS approach, a not negligible percentage of NSCLC patients (20-25%) cannot be analyzed for the quality and quantity limitations of “scant” samples. In this setting, “liquid biopsy” may represent a valid diagnostic tool to satisfy clinical needs.6,7 Liquid biopsy was clinically approved for the detection of sensitive mutations in EGFR when tissue is not available at basal or for acquired resistance mutation p.T790M after first line of TKIs treatments in NSCLC patients, unfortunately tissue specimen represents the only biological source to test clinically relevant gene fusions in clinical practice, this issues contributes to generate an incomplete molecular profile for the NSCLC patients. 4 For all these reason, the aim of this project is to validate the possibility to detect clinically relevant gene fusions in advanced NSCLC patients by adopting next generation sequencing (NGS) platform starting from RNA extracted from different specimens; secondary aim is the evaluation of the concordance rate between the two different NGS gene panels on RNA samples and an orthogonal platform with the same reference range. Results will be compared with those obtained from RNA extracted on corresponding tissue specimens for each patient analyzed by a validated customized multiplex panel on nCounter platform (Nanostring Technologies, Seattle, WA), in Pangaea institute.

碩士
國立臺灣師範大學
生命科學研究所
93
Purpose: Lung cancer is the leading cause of cancer deaths in Taiwan. Traditional radiography and sputum cytology have not been successfully reducing lung cancer mortality. It’s urgent to develop more sensitive molecular marker panel for large early lung cancer screening. Strategy: Carcinogenesis is a multi-step process resulting from the accumulation of errors in vital regulatory pathways. The present study was designed to select multiple DNA markers, which have high sensitivity and specificity to serve as diagnostic biomarkers for lung cancer detection. Methods: Part I, we examined the promoter hypermethylation of three tumor suppressor genes (FHIT, p16INK4a, and RARβ) by methylation-specific PCR (MSP), and the instability of eight microsatellite markers (D3S1234, D3S1285, D5S1456, D9S286, D9S942, GATA49D12, D13S170, and D17S786) by loss of heterozygosity (LOH) and microsatellite instability (MSI) analyses in lung tumor cells and matched sputum specimens from 79 lung cancer patients. Part II, we examined the promoter hypermethylation of six tumor suppressor genes (BLU, CDH13, FHIT, p16INK4a, RARβ, and RASSF1A) by MSP assay in lung tumor tissues and matched plasma specimens from 63 lung cancer patients. In addition, there were additional sputum and plasma specimens from 22 cancer-free individuals to be the negative control of part I and part II studies. Results: Part I sputum study, based on the results of sensitivity, specificity, and concordance from each marker analyzed, we selected seven biomarkers, which are LOH of D9S286, D9S942, GATA49D12, and D13S170, MSI of D9S942, and methylation of p16INK4a and RARβ. In addition, the odds ratio of D9S942 LOH in sputum was 4.9 (95% confidence interval, CI: 1.23~21.73, P=0.024), and the odds ratio of p16INK4a methylation in sputum was 3.29 (95% CI: 1.00~14.93, P=0.049). Using a definition that patient with cancer risk had alteration in more than two among seven selected biomarkers, we achieved a sensitivity of 81%, a specificity of 72%, and a concordance of 77%. In addition, the regression model calculated from the training set (53 cancer patients, 13 cancer-free individuals) had a match score of 80% while applying to the test set (26 cancer patients, 9 cancer-free individuals). The new regression model Y = -0.87＋0.79 (D9S286 LOH)＋1.96 (D9S942 LOH)＋2.24 (GATA49D12 LOH)＋12.19 (D13S170 LOH)＋11.02 (D9S942 MSI)＋0.70 (p16INK4a methyl)＋1.25 (RARβ methyl) thus was generated by calculating all cases (79 cancer patients, 22 cancer-free individuals) and this led to a sensitivity of 86%, a specificity of 22%, and a concordance of 78%. Part II plasma study, p16INK4a, RARβ, and RASSF1A genes had higher promoter hypermethylation frequencies. In addition, the odds ratio of p16INK4a methylation and RASSF1A methylation in plasma was 5.56 (95%CI: 1.41~37.22, P=0.012) and 5.48 (95% CI: 1.40~37.00, P=0.014), respectively. Using a definition of risk individual showing alteration in more than one of the three selected biomarkers, we achieved a sensitivity of 74%, a specificity of 78%, and a concordance of 75%. The regression model calculated from the training set (43 cancer patients, 13 cancer-free individuals) had a match score of 83% comparing to the test set (20 cancer patients, 9 cancer-free individuals). The new regression model Y = 0.19＋0.52 (BLU methyl)＋1.92 (p16INK4a methyl)＋1.52 (RASSF1A methyl), which calculated by overall cases (63 cancer patients, 22 cancer-free individuals), achieved a sensitivity of 77%, a specificity of 90%, and a concordance of 79%. Therefore, the new regression model will be used in the future clinical screening because its high sensitivity, specificity, and concordance. Conclusion: These selected early-etiologically associated biomarkers can potentially be tested as supplement biomarkers for early lung cancer detection in the future.

In this work, two different yet related hypotheses were tested by experimental means as follows: i) pre-cancerous and non-invasive (early) lung cancer can be detected and localized using the fluorescence properties of tumour localizing drugs at non-photosensitizing doses to skin tissue; ii) significant differences exist in laser-induced autofluorescence between normal, pre-cancerous and cancerous tissues such that these differences alone can be exploited to detect and delineate early lung cancer without using exogenous drug(s). For most cancers, including lung cancer, a five-year survival of over 90% can be realized if the malignancy is diagnosed at the carcinoma in situ stage. However, current techniques: chest x-ray, sputum cytology, and conventional bronchoscopy alone or in combination cannot detect these very small lesions which are usually only a few cell layers thick and a few millimeters in surface diameter. Exogenous fluorescent tumour markers such as hematoporphyrin derivatives (e.g. Photofrin) have been used to enhance the detection of these occult lung lesions. Photofrin is preferentially retained in tumour tissues compared to the surrounding normal tissues; it fluoresces at 630 rim and 690 run when excited at —405 rim. based on this principle several imaging and non-imaging devices have been developed. However, wider clinical applications were limited due to the skin photosensitivity property of Photofrin. We have postulated that this could be solved by employing a much lower dose of photofrin (0.25 mg/kg) which was believed to be less photosensitizing to human patients. This postulate was experimentally tested by ratio fluorometry and early lung cancers were detected with no false negative results and no apparent skin photosensitivity. An important finding in this study was that the mechanism for detection of early cancer was mainly due to the differences in the green autofluorescence between normal and malignant tissues, rather than fluorescence of tumour localizing drug. This discovery led to the second postulate of this thesis that tissue autofluorescence alone can be exploited for the detection of early lung cancer. In vivo spectroscopy using an optical multi-channel analyzer showed an overall decrease in autofluorescence in pre-cancerous and cancerous lesions when excited by 405 nm or 442 nm laser light. A stepwise discriminant function analysis was performed on a database of nearly 300 patients spectra to determine the optical emission wavelength(s) and algorithm(s) at which the normal, pre-cancerous and cancerous tissues can be differentiated. The results indicated that algorithm(s) could be developed to clearly delineate early lesions from the normal tissues. Several algorithms were then tested using a non-imaging ratio fluoronieter device and a prototype imaging fluorescence system to detect early lung cancer and dysplasia during standard bronchoscopy, therefore confirming the initial hypotheses even in a clinical setting. The major source of the autofluorescence in the normal bronchial tissue was determined to come from the sub-epithelial layers. The mechanism for the decrease in the autofluorescence in pre-malignant and malignant tissues was explored but not yet completely elucidated. Several factors such as decrease in fluorophores, increase in absorption of the excitation/fluorescent light or different redox state of the fluorophores may be responsible for the observed phenomena.

碩士
長庚大學
醫學生物技術研究所
97
Mutation of epidermal growth factor receptor (EGFR) in lung cancer is a predictive factor for response to therapeutic drugs, gefitinib (Iressa) and erlotinib (Tarceva). However, detection of EGFR mutation in clinical samples is difficult as the mutant allele usually exists in a low abundance in the background of the wild-type allele. We aim to develop a rapid and non-invasive method to detect EGFR mutations in lung cancer patients. The most important mutation types, G719X, T790M, L858R and exon 19 deletions of EGFR were chosen as the targets. A peptide nucleic acid (PNA) probe was designed to cover each mutation site. In a PCR, PNA probes selectively inhibit the wild-type amplification but allow the mutant amplification. Combining with PNA probe or DNA probe, the mutant products can be identified through melting curve analysis. The detection thus can be completed in a single tube without the need of subsequent assay steps. Mixtures of genomic DNA from cultured cells with wild-type or mutant EGFR were used to test the efficacy of the assay. In the preliminary test, the assay successfully detected 1/100 ~ 1/10000 of mutant DNA in wild-type background. The assay was then applied to detect EGFR mutations in lung cancer patients. In pleural fluid and plasma samples, the assay identified EGFR mutations, which was otherwise undetectable using conventional PCR and direct sequencing. This method is thus useful for detecting cancer-related mutations in body fluids.

碩士
國立交通大學
生物科技學系
105
Prognosis of lung cancer at its early stages is vital for improving the survival rate of patients. MiRNAs are short non-coding ribonucleic acid molecules that play key roles in a class of genes involved in human tumorigenesis. Current methods for the detection of miRNA are facing different challenges and problems such as time-consuming, low-sensitivity and requirement for labeling. Polysilicon silicon nanowire field-effect transistor (pSNWFET) has great potential to be developed to a powerful biosensor because of its capability for highly sensitivity, real-time and label-free detection of nucleic acid. In this research, functionalized pSNWFET has been demonstrated to achieve specific and ultrasensitive detection of tumor-derived miRNAs, let-7a and miR-21, which may serve as biomarkers for lung cancer. Optimal conditions for specific target-probe hybridization on the nanowire surface were determined. Finally, miR-21 in A549 lung cancer cells extract was determined using pSNWFET and its sensitivity was found comparable to the standard qPCR method at femtomolar regime. We demonstrated in this study that a platform for the screening of miRNA associated with lung cancer in clinical serum sample can be achieved through a pSNWFET based miRNA biosensor. We expect that this platform can be an excellent choice for future application in biomedical and prognosis of lung cancer.

Lung cancer is the leading cause of death from cancer in the world. First, we hypothesized that microRNA expression is altered in the bronchial epithelium of patients with lung cancer and that incorporating microRNA expression into an existing mRNA biomarker may improve its performance. Using bronchial brushings collected from current and former smokers, we profiled microRNA expression via small RNA sequencing for 347 patients with available mRNA data. We found that four microRNAs were under-expressed in cancer patients compared to controls (p<0.002, FDR<0.2). We explored the role of these microRNAs and their gene targets in cancer. In addition, we found that adding a microRNA feature to an existing 23-gene biomarker significantly improves its performance (AUC) in a test set (p<0.05). Next, we generalized the biomarker discovery process, and developed a visualization tool for biomarker selection. We built upon an existing biomarker discovery pipeline and created a web-based interface to visualize the performance of multiple predictors. The “visualization” component is the key to sorting through a thousand potential biomarkers, and developing clinically useful molecular predictors. Finally, we explored the molecular events leading to the development of COPD and ILD, two heterogeneous diseases with high mortality. We hypothesized that integrative genetic and expression networks can help identify drivers and elucidate mechanisms of genetic susceptibility. We utilized 262 lung tissue specimens profiled with microRNA sequencing, microarray gene expression and SNP chip genotyping. Next, we built condition specific integrative networks using a causality inference test for predicting SNP-microRNA-mRNA associations, where the microRNA is a predicted mediator of the SNP’s effect on gene expression. We identified the microRNAs predicted to affect the most genes within each network. Members of miR-34/449 family, known to promote airway differentiation by repressing the Notch pathway, were among the top ranked microRNAs in COPD and ILD networks, but not in the non-disease network. In addition, the miR-34/449 gene module was enriched among genes that increase in expression over time when airway basal cells are differentiated at an air-liquid interface and among genes that increase in expression with the airway wall thickening in patients with emphysema.
2019-07-31T00:00:00Z

博士
國立中興大學
生物醫學研究所
103
Lung cancer is the leading cause of cancer-related death both worldwide and in Taiwan. In a recent decade, epidermal growth factor receptor (EGFR)-targeted therapy has emerged as a novel and effective strategy in lung cancer management. Activating mutations of EGFR gene are the most common genetic alterations in lung cancer in Eastern Asians, and serve as an important predictor of EGFR-tyrosine kinase inhibitors (TKIs) efficacy. Herein, we focused on the association between the detection of EGFR mutations and the efficacy of EGFR-TKIs in lung cancer patients. The present study contained three major parts. In the first part, we showed that plasma cell-free DNA could be an alternative specimen for EGFR mutation testing. Furthermore, changes in plasma EGFR mutation status can be successfully assessed using the PNA-ZNA PCR clamp method and can serve as an independent outcome predictor. In the second part, we demonstrated that a significant portion of the erlotinib responses in EGFR-wild type lung adenocarcinoma patients were related to the limitations of detection methods. We emphasized that not only direct sequencing but also mutant type-sepcific sensitive methods were unable to detect EGFR mutations in some patients. In the third part, we showed that a significant proportion of lung squamous cell carcinoma (SCC) patients would derive a clinical benefit from erlotinib treatment. The objective response rate of erlotinib in lung SCC patients was much higher than the frequency of EGFR mutations. One of the possible explantations is that erlotinib might target additional pathways other than the EGFR mutations. Further studies are needed to prove this concept. In the present study, we explored the limitations of current EGFR detection methods, evaluated peripheral blood as an alternative specimen for EGFR mutation testing and looked for patients other than EGFR-mutant population who could potentially benefit from EGFR-TKIs treatment. These results may lead to the application of EGFR-TKIs to more lung cancer patients and drive the era of targeted therapy going a step further.

碩士
中山醫學大學
醫學分子毒理學研究所
96
Our previous report indicated that HPV16/18 infection was associated with developing nonsmoking female lung cancer in Taiwan. We further found that the presence of HPV16/18 DNA in the blood circulation may serve as risk biomarker of lung cancer. A recent population study showed that the most prevalent of HPV subtype in Taiwanese cervical cancer and cervical intraepithelial lesion (CIN) was HPV16 subtype followed by HPV18 and HPV58 subtypes. In this study, we questioned whether HPV58 was infected in the blood circulation of lung cancer patients to be further acting as a risk biomarker of lung cancer as similar as HPV16. Thus, a case-control study was conducted enrolling 168 lung cancer patients and 198 non-cancer control subjects and the blood samples were collected for the detection of HPV16 or 58 DNA by nested PCR. Our data showed that the infection rate of HPV16 or HPV58 in patients’ blood was significantly higher than that of non-cancer controls (38.7% vs. 3.5%,P < 0.0001 for HPV16 ; 15.5% vs. 3.5%,P < 0.0001 for HPV58). Among the clinical paramethers, HPV16 infection was more common in nonsmokers, female, tumors with late-stage and adenocarcinomas compared with their counterparts (67.7% vs. 32.3% for smoking status, P=0.038; 47.7% vs. 52.3%, P=0.036 for genders; 72.3% vs. 27.7%, P=0.02 for tumor stage; 70.8% vs. 29.2% P=0.06 for tumor type). More interesting, HPV58 infection was more prevalent in male and nonsmokers than in female and smokers, respectively (73.1% vs. 26.9%, P=0.085 for genders; 80.8% vs. 19.2% for smoking status, P=0.01). Multivariate logistic regression analysis showed that subjects with HPV16 or HPV58 infection had 38.21 or 5.82-fold of lung cancer risk than those without HPV16 or HPV58 infection in blood circulation, and then both HPV16 and HPV58 infection had 19.4-fold of lung cancer risk. These results clearly indicated that the presence of HPV58 in the blood circulation may serve as a risk marker of lung cancer. Our present result from HPV16 infection was consistent with our previous report showing that HPV16 may be a risk marker of lung cancer. hTERT transcription has been shown to be upregulated by HPV16 E6 in keratinocytes and lung cancer cells. In this study, blood cDNA of 91 lung cancer case and 81 non-cancer controls was collected to evaluate hTERT mRNA expression level by real-time RT-PCR. Our results showed that a significant higher hTERT mRNA expression level was observed in HPV16-infected lung cancer patients compared with that of HPV non-infection (P=0.002). Additionally, hTERT mRNA expression level was positively correlated with HPV16 viral load (P= 0.013). Moreover, a marginal significant correlation was observed between HPV16 E6 and quantitative hTERT mRNA expression level (P=0.059). After adjusting the effects of age, gender, smoking status, subjects with higher hTERT mRNA level had 5.30-fold of lung cancer risk than those with lower hTERT mRNA level (P < 0.0001). In summary, HPV58 DNA detected in blood circulation was similar as HPV16 DNA may act as a potential risk marker of lung cancer. Additionally, evaluating hTERT mRNA expression may be more feasible to serve as an adjuvant diagnostic marker of lung cancer, specially for HPV-associated lung cancer.

Cancer is the second leading cause of death in the world, and therefore, there is an undeniable need to ensure early screening and detection systems worldwide. The aim of this project is to study the feasibility of a Cone Beam Computed Tomography (CBCT) scanner for simultaneous breast and lung imaging. Additionally, the development of reconstruction algorithms and the study of their impact to the image quality was considered. Monte Carlo (MC) simulations were performed using the PENELOPE code system. A MC geometry model of a CBCT scanner was implemented for energies of 30 keV and 80 keV for hypothetical scanning protocols. Microcalcifications were inserted into the breast and lung of the computational phantom (ICRP Adult Female Reference), used in the simulations for dose assessment and projection acquisition. Reconstructed images were analyzed in terms of the Contrast-to-Noise Ratio (CNR) and dose calculations were performed for two protocols, one with a normalization factor of 2 mGy in the breast and another with 5 mGy in the lungs. Both, MC geometry model and reconstruction algorithm were validated by means of on-field measurements and data acquisition in a clinical center. Dosimetric and imaging performances were evaluated through Quality Assurance phantoms (Computed Tomography Dose Index and Catphan, respectively). Results indicate that the best implementation of the reconstruction algorithm was achieved with 80 keV, using the Hanning filter and linear interpolation. More specifically, for a spherical lung lesion with a radius of 7 mm a 30% CNR gain was found when the number of projections varied from 12 to 36 (corresponding to a dose increase of a factor of 3). This research suggests the possibility of developing a CBCT modulated beam scanner for simultaneous breast and lung imaging while ensuring dose reduction. However further investigation regarding the number of projections needed for image reconstruction is required.

碩士
國立彰化師範大學
生物學系
93
Lung cancers have been identified to cause one of the major lethality in the world. Non-small cell lung cancer (NSCLC) is one of the two major types of lung cancers. Patients with NSCLC always have very poor prognosis mainly due to late diagnosis, and high rates of micrometastasis, which is the metastatic tumors cannot be detected with current methods in clinic. Current clinical methods of detecting metastasis are not sensitive enough, therefore it’s urgent to develop a more specific and sensitive method. The developing method is to search for microsatellite alterations in plasma DNA in order to use them as biomarkers for detecting micro-metastasis. Scientists have found that the plasma DNA of cancer patients has neoplastic characteristics, such as LOH and MSI. Microsatellite DNA could be detected fast and easily by PCR, which makes it be a good biomarker to detect LOH or MSI in the plasma DNA. According to the ratio of heterozygosity, microsatellite alterations (MA) in tumor and plasma DNA, microsatellite markers were classified into four grades. Markers with heterozygosity?70%, MA in tumor DNA?50%, and MA in both tumor and plasma DNA?30% were classified as the grade I markers; those with heterozygosity?70%, MA in tumor DNA?50%, and MA in both tumor and plasma DNA<30% were classified as the grade II markers; those with heterozygosity?70% and 25%?MA in tumor DNA<50% were classified as the grade III markers; while those with heterozygosity?70% and MA in tumor DNA<25% were classified as the grade IV, which was considered as unsuitable markers. The grade I markers were regarded as the idealist markers for detection of micro-metastasis. Besides, the markers for the early detection of tumor occurrence could be found by their MA% in the normal tissue DNA. Therefore the aims of this research were to search for grade I microsatellite markers and those had altered in the early stage of the development of NSCLC. 26 NSCLCs were screened with 17 microsatellite markers distributed on 15 chromosome arms. We found that grade I markers were D3S1300, D8S277 and TP53, and the marker altered at the earliest stage of the development of NSCLC was D18S61. These four markers were then tested in control groups, including 46 non-tumor individuals, 30 non-NSCLC patients, to see their specificity to NSCLCs. MA% in the plasma DNA at D8S277 and D18S61 had no significant difference between NSCLC patients and two control groups. And TP53 had no significant difference in MA% in the plasma DNA between patients with NSCLC and non-NSCLC patients. Therefore, D8S277 was further selected out from grade I markers, and D18S61 was considered unsuitable for early detection. Besides, we found MA at D3S1300 associated with older age and smoking, and TP53 related to poor differentiation. FRL index was the highest in the tumor DNA, indicating tumor cells have the highest extent of allelic loss. In conclusion, D3S1300 was thought to be the most proper and specific marker for NSCLC. However, TP53 was considered as a universal marker for detecting cancers. We suggested TP53 should be examined first to screen if having cancers or not, and then D3S1300 is tested to see if cancers are NSCLCs when practicing in clinic.