Academic literature on the topic 'Genomic biomarker'
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Journal articles on the topic "Genomic biomarker"
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Fox, Natalie S., Emilie Lalonde, Julie Livingstone, Julia Hopkins, Yu-Jia Shiah, Vincent Huang, Takafumi Yamaguchi, et al. "Integrated somatic subtypes of localized intermediate-risk prostate cancer." Journal of Clinical Oncology 35, no. 6_suppl (February 20, 2017): e560-e560. http://dx.doi.org/10.1200/jco.2017.35.6_suppl.e560.
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e560 Background: Approximately two thirds of intermediate risk prostate cancer patients are over- or under- treated because we cannot correctly prognose this risk group; therefore we require novel biomarkers to better direct patient therapies and avoid subjecting patients to side effects without benefit. One reason genomic biomarkers are not currently used in clinical settings is because they are notoriously difficult to validate in follow-up studies. Furthermore, the lack of clear prostate cancer subtypes prevents the development of subtype specific biomarkers as is standard practice in breast cancer. We aim to improve biomarker validation rates by defining prostate cancer subtypes that can be used to create subtype specific biomarkers. Methods: First, we assess large scale genomic patterns using whole genome sequencing and methylation data and create integrative subtypes for intermediate risk prostate cancer. Second, we assess associations between specific aberrations and subtypes, and determine whether certain types of molecular aberrations are more important background aberrations for subtype specific biomarker development. Finally, we assess biases in prognostic performance of the published Lalonde biomarker between groups associated with patient subtypes to show that subtype aware biomarkers are necessary. Results: We demonstrate that the Lalonde biomarker is biased by the cohorts’ proportion of TMPRSS2-ERG (T2E) aberrations illustrating the need to develop different biomarkers for patients with T2E and patients without T2E. Further, we suggest integrative subtypes can be used to select patients with similar genomic profiles for biomarker analysis to improve biomarker validation rates. Conclusions: This analysis provides direct guidance for future biomarker development and addresses an important barrier to clinical use of genomic biomarkers for prostate cancer.
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Matsui, Shigeyuki. "Genomic Biomarkers for Personalized Medicine: Development and Validation in Clinical Studies." Computational and Mathematical Methods in Medicine 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/865980.
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The establishment of high-throughput technologies has brought substantial advances to our understanding of the biology of many diseases at the molecular level and increasing expectations on the development of innovative molecularly targeted treatments and molecular biomarkers or diagnostic tests in the context of clinical studies. In this review article, we position the two critical statistical analyses of high-dimensional genomic data, gene screening and prediction, in the framework of development and validation of genomic biomarkers or signatures, through taking into consideration the possible different strategies for developing genomic signatures. A wide variety of biomarker-based clinical trial designs to assess clinical utility of a biomarker or a new treatment with a companion biomarker are also discussed.
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Fertig, Elana J., Robbert Slebos, and Christine H. Chung. "Application of Genomic and Proteomic Technologies in Biomarker Discovery." American Society of Clinical Oncology Educational Book, no. 32 (June 2012): 377–82. http://dx.doi.org/10.14694/edbook_am.2012.32.156.
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Overview: Sequencing of the human genome was completed in 2001. Building on the technology and experience of whole-exome sequencing, numerous cancer genomes have been sequenced, including head and neck squamous cell carcinoma (HNSCC) in 2011. Although DNA sequencing data reveals a complex genome with numerous mutations, the biologic interaction and clinical significance of the overall genetic aberrations are largely unknown. Comprehensive analyses of the tumors using genomics and proteomics beyond sequencing data can potentially accelerate the rate and number of biomarker discoveries to improve biology-driven classification of tumors for prognosis and patient selection for a specific therapy. In this review, we will summarize the current genomic and proteomic technologies, general biomarker-discovery paradigms using the technology and published data in HNSCC—including potential clinical applications and limitations.
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Lim-Fat, Mary-Jane, Lakshmi Nayak, and David M. Meredith. "Genomic Biomarker Assessment in Gliomas." Surgical Pathology Clinics 13, no. 2 (June 2020): 209–15. http://dx.doi.org/10.1016/j.path.2020.02.003.
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Glaser, Vicki. "Genomic Analysis Drives Biomarker Discovery." Genetic Engineering & Biotechnology News 33, no. 1 (January 2013): 26–27. http://dx.doi.org/10.1089/gen.33.01.15.
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Heiden, Lisa. "Biomarker Validation for Genomic Assays." Genetic Engineering & Biotechnology News 37, no. 10 (May 15, 2017): 1, 8–11. http://dx.doi.org/10.1089/gen.37.10.02.
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Brastianos, Harry, Jure Murgic, Adriana Salcedo, Melvin Lee Kiang Chua, Alice Meng, Michael Fraser, Michael Donald Brundage, et al. "The impact of intratumoral heterogeneity on prognostic biomarkers in localized prostate cancer." Journal of Clinical Oncology 37, no. 7_suppl (March 1, 2019): 46. http://dx.doi.org/10.1200/jco.2019.37.7_suppl.46.
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46 Background: Genomic biomarkers can identify patients that harbour aggressive disease. The utility of these biomarkers is uncertain due to genomic variation between prostate biopsy specimens. To quantify the robustness of genomic biomarkers, we performed spatio-genomic characterization of distinct tumor foci. We scored three validated DNA-based biomarkers of early biochemical recurrence: percentage of genome with a copy number aberration (PGA), a 100-loci biomarker, and an optimized 31- loci biomarker derived from the previous. For each biomarker, we determined their robustness to intratumoral heterogeneity in association with predicting early biochemical recurrence (eBCR; ≤18 months) and long term control (LTC; ≥48 months). Methods: We queried a registry of 1054 patients with high-risk prostate cancer who underwent a radical prostatectomy (RP). We developed a cohort (n = 42) risk matched by clinicopathologic prognostic indices. Half of the patients had eBCR, while the other half had LTC. We profiled multiple tumor foci per patient, analyzing 119 tumor foci. For each focus, CNA profiles were generated, and three biomarker scores were calculated. For each patient and biomarker, we calculated the score of the lowest-score region, the highest-score region, or sampling of all foci and use the mean score. Results: All three biomarkers distinguished LTC from eBCR. PGA scores separated the two groups with an area under the receiver operator curves (AUC) ranging from 0.75-0.80. The 100- and 31-loci signatures, had AUCs ranging from 0.76-0.85 and 0.76-0.80 respectively. Using Cox proportional hazards modeling, we found that PGA was associated with LTC (Hazard ratio (HR) range: 2.56-6.22; p < 0.05. This was replicated for the 100-loci signature (HR range: 3.55-5.23; p < 0.05). The 31-loci detected associations with eBCR independent of how different foci were summarized (log-rank p-value range: 5.1 x 10-4- 5.9 x 10-3). Conclusions: Despite divergence in biomarker scores, all three predicted eBCR. Our study suggests that genomic biomarkers can overcome intratumoral heterogeneity, making discrete samples potentially adequate in patients with high-risk disease to determine the risk of eBCR after radical treatment.
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Moore, Rowan E., Jennifer Kirwan, Mary K. Doherty, and Phillip D. Whitfield. "Biomarker Discovery in Animal Health and Disease: The Application of Post-Genomic Technologies." Biomarker Insights 2 (January 2007): 117727190700200. http://dx.doi.org/10.1177/117727190700200040.
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The causes of many important diseases in animals are complex and multifactorial, which present unique challenges. Biomarkers indicate the presence or extent of a biological process, which is directly linked to the clinical manifestations and outcome of a particular disease. Identifying biomarkers or biomarker profiles will be an important step towards disease characterization and management of disease in animals. The emergence of post-genomic technologies has led to the development of strategies aimed at identifying specific and sensitive biomarkers from the thousands of molecules present in a tissue or biological fluid. This review will summarize the current developments in biomarker discovery and will focus on the role of transcriptomics, proteomics and metabolomics in biomarker discovery for animal health and disease.
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Puliyel, Mammen M. "Genomic biomarker in sickle cell disease." Blood 129, no. 22 (June 1, 2017): 2956–57. http://dx.doi.org/10.1182/blood-2017-04-778951.
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Sabharwal, Robin, Parul Verma, MohammedAsif Syed, Tamanna Sharma, SantoshKumar Subudhi, Saumyakanta Mohanty, and Shivangi Gupta. "Emergence of micronuclei as a genomic biomarker." Indian Journal of Medical and Paediatric Oncology 36, no. 4 (2015): 212. http://dx.doi.org/10.4103/0971-5851.171541.
Full textDissertations / Theses on the topic "Genomic biomarker"
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Liu, Yiding. "Technologies for Proteomic and Genomic Biomarker Analysis." Cleveland State University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=csu1229461302.
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ZANDA, VALERIA MARIA. "Development of new tools for genomic biomarker investigations." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2011. http://hdl.handle.net/10281/19712.
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During drug research and development, biomarkers are broadly used to improve the understanding of drug mechanism of action, to investigate drug efficacy and safety, to support the selection of target patient population and to optimize treatment schedule. Among different classes of biomarkers, genomic biomarkers are defined as a measurable DNA or RNA characteristics that are indicator of normal biologic processes, pathogenic processes, and/or response to therapeutic or other intervention.
A genomic biomarker can consist, for example, in one or more DNA characteristics such as single nucleotide polymorphisms (SNPs), insertions, deletions or RNA characteristics such as RNA expression levels, RNA processing (splicing and editing) and microRNA levels.
The present research aimed at developing new genomics-based tools using non-conventional biological samples that might support biomarker investigations in clinical settings.
Concerning DNA biomarkers, Single Nucleotide Polymorphisms (SNP) analysis was validated on DNA extracted from Formalin-Fixed Paraffin-Embedded (FFPE) tissue, from Hematoxylin and Eosin (H&E) stained FFPE slides, and from serum samples.
Unconventional samples represent a challenge for genetic analysis due to limitation of biological material and/or the poor quality of the DNA extracted. For example, due to fixation effect and formaldehyde interaction DNA extracted from FFPE samples are characterized by degradation, cross-link, limitation of material, methylol derivatives and PCR inhibitors presence. Therefore before analyzing these samples a method validation is necessary to prove data reliability in accordance with Regulatory Agencies guidelines that encourage the scientific community to perform a fit-for-purpose method validation to support any pharmacogenetic data submission.
SNPs were investigated by Real-Time PCR using TaqMan SNP genotyping assays. Polymorphisms in a panel of genes involved in EGFR pathway, which is directly associated with many type of cancer, were evaluated. In particular, each single assay was first validated for accuracy, intra-assay precision (repeatability under the same operating conditions) and ruggedness (reproducibility with different operators, different batches). These parameters were tested first on good quality DNA such as DNA extracted from cell lines and then on real sample to evaluate the non-conventional matrix.
On this purpose, the impact of fragmented DNA (FFPE samples) and H&E staining on FFPE samples was evaluated. DNA was extracted from different tissues of 10 commercial donors. DNA genotyping results of unstained FFPE and H&E staining were compared with the genotype obtained from high quality genomic DNA extracted from Fresh Frozen (FF) tissues obtained from the same donors (used as reference samples).
Overall, these results demonstrate that SNP genotyping can be performed on archived FFPE tissues providing reliable results.
As additional test serum was used as source of DNA to perform SNPs analyses. Serum is usually used to investigate protein biomarker and is generally collected in most of the clinical trials. It has been demonstrated indeed that free circulating DNA is present in serum: in particular, DNA is present in healthy individual at low concentration while levels are higher in cancer patients, in arthritis, hepatitis (Board et al., 2008; Gahana et al., 2008; Gormally et al., 2007).
To validate SNPs analysis on serum, two aliquots of whole blood were obtained from 35 healthy volunteers. For each subject one aliquot was used to extract good quality DNA, the other was used to prepare serum prior to DNA extraction.
As expected DNA quantity was very low for serum samples. As result, even though DNA was not degraded, genotype analysis was successful only on 70% of the samples.
Overall, the validation conducted showed that serum could be used as source of biological material to conduct genetic analyses. However limitation of DNA does not consent to perform a large panel of analysis. These could be further explored in patients since circulating DNA is present at higher levels in several diseases.
Part of the present thesis focused also on the validation of methods for KRAS mutation analysis. This gene encodes for a G-protein which plays a key role in the Ras/mitogen-activated protein kinase (MAPK) signaling pathway and located downstream Epidermal Growth Factor Receptor (EGFR) which is involved in colorectal cancer (CRC).
KRAS status can predict which patients benefit (KRAS wild-type) or do not benefit (KRAS mutated) from anti-EGFR therapy. Since KRAS analysis is also used for diagnostic analysis, an accurate validation of the method was required.
The aim was to compare and validate two different methods for KRAS mutation detection on FFPE tumor specimens, and on H&E stained FFPE which represent an unconventional source of samples for this type of analysis.
In particular, DxS ThreraScreen KRAS mutation kit, a Real-Time PCR assay, was compared to the PyroMark KRAS Kit, based on pyrosequencing technology.
The DxS ThreraScreen KRAS mutation test kit is able to detect 7 different mutations present in codons 12 and 13 of the KRAS gene while PyroMark KRAS Kit is able to detect 9 KRAS mutations in codon 12-13 and 5 mutations in codon 61.
Results from validation showed that both Pyrosequencing assay both DxS ThreraScreen assay are accurate and reproducible. Moreover no impact of degraded DNA obtained from FFPE or influence due to H&E staining was observed in both methods.
In conclusion PyroMark KRAS Kit showed advantages such as lower amount of DNA needed for analysis, detection of additional mutations in cod.12/13 and codon 61 than DxS TheraScreen KRAS kit; on the other hand DxS TheraScreen KRAS resulted more sensitive than pyrosequencing assays and less time consuming.
This thesis focused also on establishing a simple method to perform gene expression investigation on hair follicles (HF) and to evaluate its applicability in clinical trials.
Despite 80% of solid cancers arising from epithelial tissues, blood is still one of the most common peripheral tissues used for biomarkers and pharmacogenomic investigations in oncology. Hair follicles may offer a viable alternative since they can reflect biological response in epithelial tissue, they are easy to collect (non-invasive) and available from most individuals.
After the establishment of sample collection and RNA extraction, HFs were collected from 23 health donors to evaluate inter-individual variability of RNA yield and quality.
Gene expression analysis was then conducted on the extracted RNA. First it was evaluated a panel of 16 housekeeping genes to assess the feasibility of the analysis. Then it was shown that in HF a panel of epithelial specific genes were expressed. Indeed, Realtime PCR analyses showed that EGFR, Keratin 19 (KRT19), Collagen, Melan-A were expressed in HFs but not in RNA derived from blood. On the opposite, FPR1 and PRF1 genes were expressed only in blood. These results suggest that HF represents a valuable biological source to study pathways active in epithelial tissue.
Finally, gene expression analysis was conducted on an in vivo experiment to evaluate if a response to treatment could be observed in HFs. In particular, PD markers of Interferon treatment were investigated after in vivo subministration of Interferon-beta (IFN-β) in Macaca fascicularis. The expression of the known IFN-β responding genes MxA was investigated both in blood and in anagen HFs. Results showed that MxA induction was observed both in blood and HF: gene induction in blood was observed at 6 hours after subministration while in HF at 24 hours probably due to a different IFN-β distribution. These data suggest that gene expression analysis can be carried out in HF samples. However, it is important to highlight that in HF the response had a lower degree of induction and higher variability than in blood.
However this preliminary observations need to be further explored in pilot clinical studies to evaluate its applicability.
Overall the validation of different genomic analysis on unconventional sampled opens the possibility to conduct biomarker investigations on several clinical trials conducted in the past or to plan new investigations with non invasive methods.
In addition, from the deep evaluation of the current guidelines from the Regulatory Agencies (and from the open debate in the scientific community) a proper strategy to validate genomic analytical assays was proposed according to fit-for-purpose criteria.
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Stagni, Camilla. "Genomic analysis in cutaneous melanoma: a tool for predictive biomarker identification and molecular classification." Doctoral thesis, Università degli studi di Padova, 2017. http://hdl.handle.net/11577/3426683.
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Project 1. Identification of molecular signatures associated with response to MAPK inhibitors.
BRAF V600-mutated melanoma benefits from MAPK inhibitors-based therapy. Yet, the onset of resistance impacts long-term efficacy and can even be immediate. In this study, we examined the genetic alterations characterizing melanoma progression to identify predictive factors of response to MAPK inhibitors (MAPKi). Specifically, we evaluated BRAF copy number variation (CNV), BRAF mutant (BRAFmut) allele frequency, PTEN loss or mutations and TERT promoter mutations in pre-treatment melanoma specimens from MAPKi-treated patients (pts) and we analyzed their association with progression free survival (PFS). We also applied a comprehensive unbiased approach, using genome-wide CNV analysis, to identify additional genomic aberrations potentially associated with response to therapy.
We found that 65% pts displayed BRAF gains, often supported by chromosome 7 polysomy. In addition, we observed that 64% pts had a balanced BRAF mutant/wild-type allele ratio, while 14% and 23% pts had low and high BRAFmut allele frequency, respectively. Notably, a significantly higher risk of progression was observed in pts with a diploid BRAF status vs. those with BRAF gains (HR = 2.86; 95% CI 1.29‒6.35; p = 0.01) and in pts with low vs. those with a balanced BRAFmut allele percentage (HR = 4.54, 95% CI 1.33‒15.53; p = 0.016).
We identified PTEN gene mutations affecting the catalitic and C2 domains in 27% pts. Moreover, we observed a complete PTEN loss in 42% pts, partial loss in 35% pts and no loss in 23% pts. Of note, we found PTEN loss also in pre-treatment samples from pts with long PFS.
Sequencing of TERT promoter gene disclosed mutations in 78% pts. The -124C>T and the -146C>T mutations were equally frequent (36%) while the -138-139CC>TT was present only in 5% pts. Fifty-one % pts carried also the neighboring polymorphism rs2853669, which reportedly counteracts the activating effect of the above-mentioned mutations on TERT expression. Upon stratification of the TERT promoter mutant cohort based on presence/absence of the polymorphism, TERTmutant/SNPcarrier pts showed a trend toward better PFS (median PFS 11.5 mo., 95% CI 3.12‒19.88) compared to TERTmutant/SNPnon-carrier pts (median PFS 7 mo., 95% CI 4.27‒9.72). When stratifying based on mutation type, the -146C>T mutation correlated with shorter PFS (median PFS 5.45 mo., 95% CI 2.80‒9.20) compared to the -124C>T one (median PFS 15.2 mo., 95% CI 5.57‒).
Genome-wide CNV analysis pointed at chr3p24, chr3p21.2 and chr17p13.1, which are differently alterated between pts with long and short time to disease progression, as regions of potential interest to identify new genes involved in therapeutic resistance.
Our data suggest that quantitative analysis of the BRAF gene and sequencing of the TERT promoter gene could be useful to select the melanoma pts who are most likely to benefit from MAPKi therapy. In addition, chromosome 3 and 17 could be regions that warrant further investigation. Conversely, because PTEN loss was present in pre-treatment samples from pts with both short and long PFS, the assessment of PTEN gene status does not seem to provide information about patient responsiveness to treatment.
Project 2. Research of molecular biomarkers to classify the acral melanoma.
Acral lentiginous melanoma (ALM) is a rare subtype of cutaneous melanoma with specific morphological, epidemiological, and genetic features. Since the genomic landscape of ALM is still incompletely described, we used whole genome CNV analysis to characterize ALM and detail the genomic signatures that differentiate ALM from non-acral melanoma (NAM).
We observed that the most strikingly different copy number aberrations were a higher frequency of losses of chromosome 16q24.2-16q24.3 in ALM than in NAM (64.7% vs. 10%) and a lower frequency of gains of chromosome 7q21.2-7q33 in ALM than in NAM (26.5% vs.79.5%). We observed also that ALM more often (than NAM) harbored clusters of breakpoints and isochromosomes. Moreover, in ALM we identified focal amplification of TERT, CCND1, MDM2 and MITF. In NAM, instead, we found only two focal amplifications, involving BRAF and MITF. Focal homozygous copy losses affected especially the CDKN2A and PTEN genes, both in ALM and in NAM, even though they were more frequent in the latter group.
In keeping with previous observations that led to classify ALM as a distinct molecular subtype of melanoma, we observed a peculiar genomic landscape in ALM (vs. NAM). Our study provides insights into the molecular characteristics of ALM, which is key to full elucidation of its pathogenesis.Progetto 1: identificazione di signatures molecolari associate alla risposta al trattamento con inibitori del MAPK pathway. I melanomi portatori di una mutazione nel codone V600 del gene BRAF rispondono agli inibitori del MAPK pathway, ma l’efficacia a lungo termine di questa terapia è limitata dallo sviluppo di resistenza, talvolta immediata. In questo studio, abbiamo esaminato le alterazioni molecolari caratterizzanti la progressione del melanoma al fine di identificare fattori predittivi di risposta/resistenza ai MAPK-inibitori. Nello specifico, su una serie di campioni pretrattamento di pazienti affetti da melanoma, trattati con MAPK-inibitori, abbiamo valutato numero di copie del gene BRAF e percentuale di allele V600-mutato, delezione e mutazioni di PTEN, alterazioni del promotore di TERT, e ne abbiamo analizzato l’associazione con la risposta dei pazienti alla terapia. Inoltre, abbiamo determinato il copy number variation dell’intero genoma dei nostri campioni per individuare ulteriori aberrazioni non note potenzialmente associate con la risposta alla terapia. Abbiamo identificato un numero aumentato di copie (gain) del gene BRAF, spesso dovuto a polisomia del cromosoma 7, nel 65% dei pazienti; l’allele mutato è stato trovato in una percentuale compresa tra il 35% e il 65% nel 64% dei pazienti, inferiore al 35% nel 14% dei pazienti e superiore al 65% nel 23% dei pazienti. Dall’analisi di sopravvivenza, è risultato che i pazienti con BRAF diploide o una percentuale di allele mutato inferiore al 35% presentano un più alto rischio di progressione rispetto a coloro che presentano gain di BRAF (HR=2.86; 95% CI 1.29-6.35; p=0.01) o tra il 35% e il 65% di allele mutato (HR=4.54,CI 1.33-15.53; p=0.016), rispettivamente. L’analisi di PTEN ha rivelato la presenza di mutazioni nel 27% dei pazienti, localizzate a livello dei domini catalitico e C2 della proteina codificata; inoltre, il 42% dei casi valutati mostrava una delezione completa del gene, il 35% una delezione parziale, mentre nel 23% dei pazienti non è stata individuata alcuna aberrazione di PTEN. Da notare, delezioni di PTEN sono emerse sia nei casi di melanoma resistente alla terapia, che in quelli che avevano risposto a lungo. Il sequenziamento del promotore del gene TERT ha permesso l’identificazione di mutazioni nel 78% dei pazienti. Le mutazioni -124C>T e -146C>T mostravano la stessa frequenza (36%) nella nostra coorte, mentre la -138-139CC>TT è stata individuata solo nel 5% dei casi. Il 51% dei pazienti presentava inoltre lo SNP rs2853669, noto per contrastare l’effetto attivante delle mutazioni sull’espressione di TERT. Stratificando la coorte di pazienti mutati in base alla presenza/assenza del polimorfismo, i pazienti TERT mutati/SNP carriers mostravano un trend verso una migliore PFS (PFS mediana 11.5 mesi, 95% CI 3.12-19.88) rispetto ai TERT mutati/SNP non-carriers (PFS mediana 7 mesi, 95% CI 4.27-9.72). La mutazione -146C>T, inoltre, correlava con PFS più breve (PFS mediana 5.45 mesi, 95% CI 2.80-9.20) rispetto alla -124C>T (PFS mediana 15.2 mesi, 95% CI 5.57-). Dall’analisi del copy number variation (CNV) sull’intero genoma, le regioni chr3p24, chr3p21.2 e chr17p13.1 hanno mostrato pattern di alterazioni diverse in pazienti responsivi vs. non-responsivi alle terapie; risultano pertanto regioni di potenziale interesse per l’individuazione di nuovi geni coinvolti nella resistenza alla terapia. I nostri dati suggeriscono dunque che l’analisi quantitativa del gene BRAF e il sequenziamento del promotore di TERT costituiscono un utile strumento di selezione dei pazienti con maggiore probabilità di rispondere alla terapia con MAPK-inibitori, contrariamente alla valutazione dello status di PTEN. L’analisi genome-wide, invece, indica di approfondire lo studio dei cromosomi 3 e 17. Progetto 2: ricerca di marcatori biomolecolari per la classificazione del melanoma acrale. Il melanoma acrale lentigginoso è un raro sottotipo di melanoma cutaneo con specifiche caratteristiche morfologiche, epidemiologiche e genetiche. Poiché il genoma del melanoma acrale non è ancora stato pienamente caratterizzato, ne abbiamo analizzato il CNV per individuare quei caratteri genomici peculiari che lo differenziano dal melanoma non acrale. La nostra analisi genome-wide ha evidenziato una maggiore frequenza di delezioni della regione 16q24.2-16q24.3, gains meno frequenti nella regione 7q21.2-7q33, una più accentuata frammentazione genomica e numerosi isocromosomi come caratteri che distinguono il melanoma acrale dal non acrale. Abbiamo inoltre identificato amplificazioni focali nei geni TERT, CCND1, MDM2 e MITF, più rare nei non acrali, laddove interessavano altri geni, come BRAF e MITF. Delezioni focali sono state individuate soprattutto nei geni CDKN2A e PTEN in entrambi i sottotipi di melanoma, anche se più frequenti nei non acrali. I nostri dati, in accordo con il classificare il melanoma acrale come tipo distinto di melanoma, hanno consentito di delinearne alcune delle peculiarità genomiche, chiave per elucidarne anche la patogenesi.
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ZILIOTTO, Nicole. "Genomic, vessel wall transcriptomic, and plasma proteomic approaches to investigate multiple sclerosis." Doctoral thesis, Università degli studi di Ferrara, 2019. http://hdl.handle.net/11392/2487975.
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This study was designed to investigate, by several experimental approaches, genes, and proteins associated with multiple sclerosis (MS), an inflammatory and demyelinating disease of the central nervous system (CNS). The study design was aimed to prioritize, by the investigation in patients, potential targets and biomarkers for future mechanistic studies. Through the genomic approach(chpt.10), selected families were investigated by WES for candidate genes from GWAS. The identified low-frequency variants were further investigated in unrelated MS patients. A number of rare and novel mutations were detected, and particularly null variants in the C6orf10 3’ region, in combination with both intra and extra locus low-frequency SNPs. These findings provide the bases for expression studies.
The transcriptomic approach (chpt.6) was focused on the internal jugular vein wall, supported by the interaction between vascular and neurodegenerative mechanisms in MS. This original investigation produced a wealth of information on several biological pathways and permitted the combined transcriptome-protein analysis, which provided intriguing biological and clinical hints.
Analysis at protein level was conducted in plasma by multiplex assays in relation to clinical MS phenotypes and brain MRI measures, as quantitative and “intermediate” phenotypes evaluating disease progression. Higher CCL18 plasma levels were associated with more severe neurodegenerative features, a noticeable finding(chpt.7). The contribution of adhesion molecules, suggested by the transcriptomic analysis, was similarly explored(chpt.8 and 9). Correlation between plasma levels of specific adhesion molecules in MS patients highlights the leukocyte adhesion process in disease.
Increased blood-brain-barrier permeability, a key event in the MS pathophysiology, leads to the irruption of coagulation and hemostasis factors into the CNS, potentially causing an inflammatory response and immune activation. We investigated hemostasis components with main open questions in relation to MS. FXII, the key protease of the coagulation contact activation found deposited in patients’ brain, might participate in adaptive immunity during neuroinflammation. In plasma of MS patients(chpt.4), FXII protein levels were higher than activity, causing a decreased activity/antigen ratio. These findings, corroborated by specifically designed intrinsic thrombin generation assays, might support that FXII contribution in MS is not directly correlated with its “intrinsic” pro-coagulant activity.
Negative regulators of hemostasis (TFPI, ADAMTS13, HCII, TM) with anti-inflammatory properties were also studied, which detected specific patterns of correlations (chpt.5 and 11). Positive association of TFPI with TM, observed in MS patients and not in healthy subjects, would imply that endothelium perturbation acts on multiple release mechanisms. In patients, PAI-1, the key fibrinolysis inhibitor, was positively associated with FXII, and negatively associated with HCII, which suggest disease mechanisms influencing their expression in different tissues with implications in fibrin generation/impaired fibrinolysis, important contributors to neuro-inflammation/degeneration.
Correlations observed between hemostasis components plasma levels and MRI measures, of interest for brain disease mechanisms, did not overcome correction for multiple comparisons.
Extravascular leakage of blood components in MS patients, measured as cerebral microbleeds (CMBs) by MRI, was investigated in relation to plasma levels of hemostasis components. Interestingly, lower ADAMTS13 levels were detected in the MS cohort, in particular in patients with CMBs (chpt.5), who also showed higher VAP-1 levels(chpt.9). These novel findings support the investigation of the plasma protease ADAMTS13, and the amino oxidase/adhesion protein VAP-1, in relation to CMBs. This study provides novel MS disease biomarkers as well as potential drug targetsQuesto studio è stato progettato per indagare attraverso diversi approcci sperimentali i geni e le proteine associate alla sclerosi multipla (SM), una malattia infiammatoria e demielinizzante del sistema nervoso centrale (SNC). L’obiettivo era individuare mediante indagini su pazienti, potenziali bersagli e biomarcatori per futuri studi meccanicistici. Mediante l'approccio genomico(cap.10), le famiglie selezionate sono state studiate attraverso WES per geni candidati da GWAS. Gli SNPs identificati a bassa frequenza sono stati ulteriormente studiati in pazienti indipendenti con SM. L’indagine ha rilevato varianti rare e nuove, tra cui le nulle della regione 3' di C6orf10 in combinazione con SNPs a bassa frequenza a livello intra ed extra locus, fornendo le basi per studi di espressione. L'approccio trascrittomico(cap.6) focalizzato sulla parete interna della vena giugulare, era supportato dall'interazione tra i meccanismi vascolari e quelli neurodegenerativi nella SM. Questa indagine ha prodotto una grande quantità di informazioni su diversi percorsi biologici e ha permesso l'analisi combinata trascrittoma-proteine. L'analisi a livello proteico è stata condotta nel plasma mediante saggi multiplex in relazione ai fenotipi clinici di SM e alle misure cerebrali MRI considerate come fenotipi quantitativi e "intermedi" della progressione della malattia. I livelli plasmatici più alti di CCL18 erano associati a caratteristiche neurodegenerative più gravi(cap.7). Il contributo delle molecole di adesione, suggerito dall'analisi trascrittomica, è stato esplorato in modo analogo(cap.8 e 9). La correlazione tra i livelli plasmatici di specifiche molecole di adesione nei pazienti ha evidenziato il processo di adesione dei leucociti nella malattia. L'aumento della permeabilità della barriera emato-encefalica, evento chiave nella fisiopatologia della SM, porta all’irruzione di fattori emostatici nel SNC, causando una risposta infiammatoria e l’attivazione immunitaria. I componenti dell'emostasi con le principali domande aperte in relazione alla SM sono stati investigati. Il FXII, la proteasi attivatrice della coagulazione da contatto trovata depositata nel cervello dei pazienti, potrebbe partecipare all'immunità adattativa durante la neuroinfiammazione. Nel plasma di pazienti(cap.4) i livelli di proteina del FXII erano superiori all'attività, causando un ridotto rapporto attività/antigene. I risultati corroborati dai saggi di generazione intrinseca di trombina, supporterebbero il contributo del FXII nella SM non attraverso la sua attività pro-coagulante. Lo studio di alcuni inibitori dell'emostasi (TFPI, ADAMTS13, HCII, TM) con proprietà antinfiammatorie, ha rivelato specifici schemi di correlazione(cap.5 e 11). L'associazione positiva di TFPI con TM, osservata nei pazienti e non in soggetti sani, implicherebbe che la perturbazione dell'endotelio agisca su più meccanismi di rilascio. Nei pazienti il PAI-1, l'inibitore chiave della fibrinolisi, era associato positivamente al FXII e negativamente all'HCII, suggerendo meccanismi patologici che influenzano la loro espressione in diversi tessuti con implicazioni nella generazione di fibrina e nella compromissione della fibrinolisi. Le correlazioni osservate tra i livelli plasmatici dei componenti dell'emostasi con le misure di MRI, non hanno superato la correzione per confronti multipli. La perdita extravascolare di sangue misurata come micro sanguinamenti cerebrali (MSC) attraverso MRI è stata studiata nei pazienti in relazione ai livelli plasmatici di componenti dell'emostasi. Livelli più bassi di ADAMTS13 sono stati rilevati nella coorte di SM ed in particolare nei pazienti con MSC(cap.5) che mostravano anche livelli più alti di VAP-1(cap.9). Queste nuove scoperte supportano l'analisi della proteasi ADAMTS13 e l’aminossidasi/proteina di adesione VAP-1 in relazione ai MSC. Questo studio fornisce nuovi biomarcatori della SM e potenziali bersagli farmacologici
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Sanavia, Tiziana. "Biomarker lists stability in genomic studies: analysis and improvement by prior biological knowledge integration into the learning process." Doctoral thesis, Università degli studi di Padova, 2012. http://hdl.handle.net/11577/3422197.
Full textAbstract:
The analysis of high-throughput sequencing, microarray and mass spectrometry data has been demonstrated extremely helpful for the identification of those genes and proteins, called biomarkers, helpful for answering to both diagnostic/prognostic and functional questions. In this context, robustness of the results is critical both to understand the biological mechanisms underlying diseases and to gain sufficient reliability for clinical/pharmaceutical applications. Recently, different studies have proved that the lists of identified biomarkers are poorly reproducible, making the validation of biomarkers as robust predictors of a disease a still open issue. The reasons of these differences are referable to both data dimensions (few subjects with respect to the number of features) and heterogeneity of complex diseases, characterized by alterations of multiple regulatory pathways and of the interplay between different genes and the environment. Typically in an experimental design, data to analyze come from different subjects and different phenotypes (e.g. normal and pathological). The most widely used methodologies for the identification of significant genes related to a disease from microarray data are based on computing differential gene expression between different phenotypes by univariate statistical tests. Such approach provides information on the effect of specific genes as independent features, whereas it is now recognized that the interplay among weakly up/down regulated genes, although not significantly differentially expressed, might be extremely important to characterize a disease status. Machine learning algorithms are, in principle, able to identify multivariate nonlinear combinations of features and have thus the possibility to select a more complete set of experimentally relevant features. In this context, supervised classification methods are often used to select biomarkers, and different methods, like discriminant analysis, random forests and support vector machines among others, have been used, especially in cancer studies. Although high accuracy is often achieved in classification approaches, the reproducibility of biomarker lists still remains an open issue, since many possible sets of biological features (i.e. genes or proteins) can be considered equally relevant in terms of prediction, thus it is in principle possible to have a lack of stability even by achieving the best accuracy.
This thesis represents a study of several computational aspects related to biomarker discovery in genomic studies: from the classification and feature selection strategies to the type and the reliability of the biological information used, proposing new approaches able to cope with the problem of the reproducibility of biomarker lists. The study has highlighted that, although reasonable and comparable classification accuracy can be achieved by different methods, further developments are necessary to achieve robust biomarker lists stability, because of the high number of features and the high correlation among them.
In particular, this thesis proposes two different approaches to improve biomarker lists stability by using prior information related to biological interplay and functional correlation among the analyzed features. Both approaches were able to improve biomarker selection. The first approach, using prior information to divide the application of the method into different subproblems, improves results interpretability and offers an alternative way to assess lists reproducibility. The second, integrating prior information in the kernel function of the learning algorithm, improves lists stability.
Finally, the interpretability of results is strongly affected by the quality of the biological information available and the analysis of the heterogeneities performed in the Gene Ontology database has revealed the importance of providing new methods able to verify the reliability of the biological properties which are assigned to a specific feature, discriminating missing or less specific information from possible inconsistencies among the annotations.
These aspects will be more and more deepened in the future, as the new sequencing technologies will monitor an increasing number of features and the number of functional annotations from genomic databases will considerably grow in the next years.L’analisi di dati high-throughput basata sull’utilizzo di tecnologie di sequencing, microarray e spettrometria di massa si è dimostrata estremamente utile per l’identificazione di quei geni e proteine, chiamati biomarcatori, utili per rispondere a quesiti sia di tipo diagnostico/prognostico che funzionale. In tale contesto, la stabilità dei risultati è cruciale sia per capire i meccanismi biologici che caratterizzano le malattie sia per ottenere una sufficiente affidabilità per applicazioni in campo clinico/farmaceutico. Recentemente, diversi studi hanno dimostrato che le liste di biomarcatori identificati sono scarsamente riproducibili, rendendo la validazione di tali biomarcatori come indicatori stabili di una malattia un problema ancora aperto. Le ragioni di queste differenze sono imputabili sia alla dimensione dei dataset (pochi soggetti rispetto al numero di variabili) sia all’eterogeneità di malattie complesse, caratterizzate da alterazioni di più pathway di regolazione e delle interazioni tra diversi geni e l’ambiente. Tipicamente in un disegno sperimentale, i dati da analizzare provengono da diversi soggetti e diversi fenotipi (e.g. normali e patologici). Le metodologie maggiormente utilizzate per l’identificazione di geni legati ad una malattia si basano sull’analisi differenziale dell’espressione genica tra i diversi fenotipi usando test statistici univariati. Tale approccio fornisce le informazioni sull’effetto di specifici geni considerati come variabili indipendenti tra loro, mentre è ormai noto che l’interazione tra geni debolmente up/down regolati, sebbene non differenzialmente espressi, potrebbe rivelarsi estremamente importante per caratterizzare lo stato di una malattia. Gli algoritmi di machine learning sono, in linea di principio, capaci di identificare combinazioni non lineari delle variabili e hanno quindi la possibilità di selezionare un insieme più dettagliato di geni che sono sperimentalmente rilevanti. In tale contesto, i metodi di classificazione supervisionata vengono spesso utilizzati per selezionare i biomarcatori, e diversi approcci, quali discriminant analysis, random forests e support vector machines tra altri, sono stati utilizzati, soprattutto in studi oncologici. Sebbene con tali approcci di classificazione si ottenga un alto livello di accuratezza di predizione, la riproducibilità delle liste di biomarcatori rimane ancora una questione aperta, dato che esistono molteplici set di variabili biologiche (i.e. geni o proteine) che possono essere considerati ugualmente rilevanti in termini di predizione. Quindi in teoria è possibile avere un’insufficiente stabilità anche raggiungendo il massimo livello di accuratezza. Questa tesi rappresenta uno studio su diversi aspetti computazionali legati all’identificazione di biomarcatori in genomica: dalle strategie di classificazione e di feature selection adottate alla tipologia e affidabilità dell’informazione biologica utilizzata, proponendo nuovi approcci in grado di affrontare il problema della riproducibilità delle liste di biomarcatori. Tale studio ha evidenziato che sebbene un’accettabile e comparabile accuratezza nella predizione può essere ottenuta attraverso diversi metodi, ulteriori sviluppi sono necessari per raggiungere una robusta stabilità nelle liste di biomarcatori, a causa dell’alto numero di variabili e dell’alto livello di correlazione tra loro. In particolare, questa tesi propone due diversi approcci per migliorare la stabilità delle liste di biomarcatori usando l’informazione a priori legata alle interazioni biologiche e alla correlazione funzionale tra le features analizzate. Entrambi gli approcci sono stati in grado di migliorare la selezione di biomarcatori. Il primo approccio, usando l’informazione a priori per dividere l’applicazione del metodo in diversi sottoproblemi, migliora l’interpretabilità dei risultati e offre un modo alternativo per verificare la riproducibilità delle liste. Il secondo, integrando l’informazione a priori in una funzione kernel dell’algoritmo di learning, migliora la stabilità delle liste. Infine, l’interpretabilità dei risultati è fortemente influenzata dalla qualità dell’informazione biologica disponibile e l’analisi delle eterogeneità delle annotazioni effettuata sul database Gene Ontology rivela l’importanza di fornire nuovi metodi in grado di verificare l’attendibilità delle proprietà biologiche che vengono assegnate ad una specifica variabile, distinguendo la mancanza o la minore specificità di informazione da possibili inconsistenze tra le annotazioni. Questi aspetti verranno sempre più approfonditi in futuro, dato che le nuove tecnologie di sequencing monitoreranno un maggior numero di variabili e il numero di annotazioni funzionali derivanti dai database genomici crescer`a considerevolmente nei prossimi anni.
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Zanjirband, Maryam. "The genomic and functional status of TP53 in ovarian cancer : biomarker for chemotherapy outcome and determinant of response to MDM2 inhibitors." Thesis, University of Newcastle upon Tyne, 2017. http://hdl.handle.net/10443/3831.
Full textAbstract:
Background: Mutation and loss of TP53 function is one of the most frequent genetic abnormalities in ovarian cancer. TP53 genomic and functional status have been shown to provide potentially prognostic and predictive value in ovarian cancer; however, the results are controversial and evaluation in the context of a controlled clinical trial with single agent treatment have been lacking. Reactivation of p53 using MDM2-p53 antagonists is a promising therapeutic target for most patients with type I epithelial ovarian cancer and those left from type II harbouring wild-type TP53. BRCA1/2 mutations are present in 70-85% of germline mutations in patients with inherited ovarian cancer, and deficiencies in homologous recombination repair (HRR) account for up to 50% of epithelial ovarian cancer, indicating the possible sensitivity of ovarian cancer patients to PARP inhibitors. MDM2-p53 antagonists and PARP inhibitors are now undergoing clinical trials as targeted therapy for different types of cancer. The effect of RG7388 on its own and in combination with cisplatin, and combined treatment between MDM2-p53 antagonists and PARP inhibitors have not been investigated in ovarian cancer. Hypotheses: 1) Different genomic and functional status of p53 and some of its downstream targets such as p21WAF1, MDM2 and WIP1 can be used as prognostic and predictive biomarkers for the outcome of chemotherapy and overall survival in ovarian cancer. 2) Reactivation of p53 by inhibition of its negative regulator MDM2, using the MDM2-p53 antagonists Nutlin-3 and RG7388, will result in p53-mediated growth arrest and apoptosis in wild-type TP53 ovarian cancer cells, and combination of them with current therapeutic agents or rucaparib increases growth inhibition and/or apoptosis in ovarian cancer cell lines compared to either agent alone. Methods: TP53 was sequenced in 260 ovarian cancer samples from the ICON3 trial using Sanger sequencing and Next Generation Sequencing (NGS) methods. The prognostic value of the expression levels of p53, p21WAF1, MDM2 and WIP1 was investigated using immunohistochemistry (IHC). The effect of MDM2-p53 antagonists, Nutlin-3/RG7112/RG7388, and PARP inhibitor, rucaparib, as single agents and in combination with cisplatin or together were investigated on a panel of ovarian cancer cell lines. Sensitivity was measured by growth inhibition, clonogenic cell survival assay, apoptosis assays including caspase 3/7 activity and flow cytometry. The effect on the p53 molecular pathway and p53-regulated candidate gene expression were investigated by western blotting and Quantitative Reverse-Transcription Polymerase Chain Reaction (qRT-PCR) respectively. Results: Patients from the ICON3 clinical trial treated with carboplatin whose tumours harbour wild-type TP53 had a significantly better overall survival based on both univariate and multivariate analysis compared to those with mutant TP53 regardless of sequencing method. Adding paclitaxel to the platinum-based treatment showed a trend in favour of greater benefit for those with mutant TP53, although this failed to reach statistical significance (p > 0.05). Overexpression of p53 has potential prognostic value for overall survival of ovarian cancer patients. Ovarian cancer cell lines with wild-type TP53 were sensitive to MDM2-p53 antagonists, Nutlin-3/RG7112/RG7388, while those with mutant TP53 were resistant to MDM2 inhibitors. Among the individual cell lines, A2780 and MDAH-2774 were sensitive and other cell lines (IGROV-1, OAW42, CP70, MLH1-corrected CP70+ and SKOV-3) were resistant to rucaparib regardless of BRCA1/BRCA2 status or deficiencies in HRR reported for these cell lines. Combination of Nutlin-3/RG7388 with cisplatin or rucaparib has synergistic and/or dose reduction potential dependent on cell genotype and the type of MDM2-p53 antagonist. Combined treatments using Nutlin-3/RG7388 and cisplatin led to greater levels of p53 stabilisation and upregulation of p21WAF1 and MDM2, and higher expression of p21WAF1 was associated with a greater synergistic effect for growth inhibition. In combination treatment with rucaparib and Nutlin-3/RG7388, rucaparib showed no increase in the effect of MDM2 inhibitors on the p53 pathway, indicating that the mechanism of observed synergy does not involve enhancement of p53 pathway activation by MDM2 inhibitors. Nutlin-3/RG7388 in combination with cisplatin or rucaparib resulted in changes in cell cycle distribution, SubG1 events and caspase 3/7 activity in a cell type, time and compound-dependent manner. The fold changes in expression of candidate genes in response to MDM2 inhibitors were less in A2780 cells than IGROV-1 and OAW42. The balance of activity between growth inhibitory/pro-survival and pro-apoptotic genes dominates a small increase in the expression of several DNA repair genes as an explanation for the synergy observed for treatment with cisplatin and MDM2 inhibitors. Conclusions: The genomic and functional status of TP53 have potentially important prognostic and predictive values in ovarian cancer. Targeting the interaction between MDM2 and p53 using MDM2-p53 antagonists is a promising therapeutic strategy for ovarian cancer patients with wild-type TP53 tumours, and combination treatment with them and cisplatin or rucaparib.
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Pereira, Carolina Ruivo 1986. "Genomic profile of tumorgrafts identifies B2M as a novel tumor suppressor gene in lung cancer." Doctoral thesis, Universitat Pompeu Fabra, 2016. http://hdl.handle.net/10803/482055.
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El cáncer de pulmón es la forma más mortal de cáncer en el mundo. Recientemente, el estudio del perfil genómico a larga escala de tumores humanos ha impulsado el desarrollo de drogas que tienen como diana terapéutica genes alterados. Dado que las terapias dirigidas son escasas, el descubrimiento de nuevos genes implicados en cáncer de pulmón con relevancia clínica es crucial.
Por eso, este proyecto tuvo como base la secuenciación de exomas y transcriptomas de xenotransplantes de pulmón. La pureza tumoral alcanzada durante el injerto fue fundamental, sobre todo para identificar delecciones homocigóticas y amplificaciones génicas. El gen B2M (β2-microglobulina), encontrado inactivado en 5% de los tumores pulmonares, se caracterizó. Su pérdida genética se correlacionó con bajos niveles de infiltración intratumoral por linfocitos T citotóxicos. Además, la β2-microglobulina se asoció a supervivencia en pacientes tratados con agentes anti-PD1/PD-L1, evidenciando su rol potencial el predecir respuestas a inmunoterapias en neoplasias pulmonares.Lung cancer is the deadliest form of cancer worldwide. Recently, the large-scale genomic profiling of human tumors has fueled the development of efficient anticancer agents that target the activity of mutated genes. Given that directed therapies are still very scarce, the discovery of novel lung cancer-related genes with potential relevance within the clinical context is imperative. Thus, this project consisted on coupling high-throughput sequencing strategies (exomes and transcriptomes) with the use of lung tumorgrafts. The high tumor purity achieved through the engraftment was crucial, particularly to identify homozygous deletions and gene amplifications. The B2M gene (β2-microglobulin), found to be mutated in 5% of lung tumors, was characterized. Its genetic loss was correlated to lower cytotoxic T-cell intratumoral infiltration, probably impairing the immune-mediated tumor eradication. Moreover, β2-microglobulin was associated with survival in patients treated with anti-PD-1/PD-L1 agents, highlighting a potential role in predicting response to immunologically-based therapies in lung cancer.
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Jiao, Yunlong. "Pronostic moléculaire basé sur l'ordre des gènes et découverte de biomarqueurs guidé par des réseaux pour le cancer du sein." Thesis, Paris Sciences et Lettres (ComUE), 2017. http://www.theses.fr/2017PSLEM027/document.
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Le cancer du sein est le deuxième cancer le plus répandu dans le monde et la principale cause de décès due à un cancer chez les femmes. L'amélioration du pronostic du cancer a été l'une des principales préoccupations afin de permettre une meilleure gestion et un meilleur traitement clinique des patients. Avec l'avancement rapide des technologies de profilage génomique durant ces dernières décennies, la disponibilité aisée d'une grande quantité de données génomiques pour la recherche médicale a motivé la tendance actuelle qui consiste à utiliser des outils informatiques tels que l'apprentissage statistique dans le domaine de la science des données afin de découvrir les biomarqueurs moléculaires en lien avec l'amélioration du pronostic. Cette thèse est conçue suivant deux directions d'approches destinées à répondre à deux défis majeurs dans l'analyse de données génomiques pour le pronostic du cancer du sein d'un point de vue méthodologique de l'apprentissage statistique : les approches basées sur le classement pour améliorer le pronostic moléculaire et les approches guidées par un réseau donné pour améliorer la découverte de biomarqueurs. D'autre part, les méthodologies développées et étudiées dans cette thèse, qui concernent respectivement l'apprentissage à partir de données de classements et l'apprentissage sur un graphe, apportent une contribution significative à plusieurs branches de l'apprentissage statistique, concernant au moins les applications à la biologie du cancer et la théorie du choix socialBreast cancer is the second most common cancer worldwide and the leading cause of women's death from cancer. Improving cancer prognosis has been one of the problems of primary interest towards better clinical management and treatment decision making for cancer patients. With the rapid advancement of genomic profiling technologies in the past decades, easy availability of a substantial amount of genomic data for medical research has been motivating the currently popular trend of using computational tools, especially machine learning in the era of data science, to discover molecular biomarkers regarding prognosis improvement. This thesis is conceived following two lines of approaches intended to address two major challenges arising in genomic data analysis for breast cancer prognosis from a methodological standpoint of machine learning: rank-based approaches for improved molecular prognosis and network-guided approaches for enhanced biomarker discovery. Furthermore, the methodologies developed and investigated in this thesis, pertaining respectively to learning with rank data and learning on graphs, have a significant contribution to several branches of machine learning, concerning applications across but not limited to cancer biology and social choice theory
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Moreira, Elisa Rennó Donnard. "Estudo de variações genômicas para a identificação de biomarcadores personalizados e novos alvos terapêuticos em tumores colorretais." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/46/46131/tde-20012015-101640/.
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O câncer colorretal é um dos tipos de tumores mais frequentes no mundo. A atual dificuldade na avaliação correta da resposta ao tratamento torna necessário o desenvolvimento de novas abordagens de detecção tumoral. Atualmente, o sequenciamento genômico em larga escala permite um estudo mais compreensivo das alterações estruturais e de sequência presentes no tumor. A aplicação destas abordagens de maneira personalizada permite o desenvolvimento de biomarcadores tumor específicos que podem facilitar a avaliação de resposta ao tratamento e a presença de doença residual, bem como revelar alterações de sequência em genes capazes de servir de novos alvos terapêuticos. Neste estudo foi desenvolvida uma metodologia eficiente para a identificação de biomarcadores baseados na existência de variações estruturais em genomas de tumores de reto, eliminando a necessidade de sequenciamento do genoma normal do mesmo paciente e diminuindo portanto o custo da abordagem. Os biomarcadores encontrados para cada um dos seis pacientes foram utilizados para avaliar a presença de doença residual após o tratamento através da detecção de DNA tumoral circulante nas amostras de plasma coletadas em momentos diferentes do tratamento. O sequenciamento em baixa cobertura personalizado é portanto uma alternativa viável e promissora para avaliar a resposta ao tratamento em pacientes com tumores de reto. Na segunda parte do estudo, a análise de linhagens celulares de tumores colorretais revelou uma grande quantidade de mutações pontuais somáticas (SNVs e InDels) em genes codificadores para proteínas de superfície celular (surfaceoma). Estas alterações no surfaceoma indicam potenciais novos alvos para drogas e vias regulatórias alteradas neste tipo de tumor. Além disso, estas mutações pontuais também são responsáveis pela geração de epítopos com potencial imunogênico e estes novos epítopos podem ser aplicados como vacinas antitumorais personalizadas e já haviam sido propostos como uma alternativa terapêutica. A presença de novos epítopos, principalmente nas linhagens com elevadas taxas de mutação (resultante da instabilidade de microssatélites e mutações em genes de reparo de DNA tipo mismatch ou POLE), sugerem também um potencial uso de drogas moduladoras do sistema imune em pacientes com tumores que apresentam estas mesmas características. Portanto, o estudo de alterações genômicas em tumores primários e linhagens de câncer colorretal permitiu a detecção de variações estruturais que foram utilizadas como biomarcadores personalizados em pacientes com tumores de reto assim como a identificação de genes contendo mutações pontuais em linhagens celulares de câncer colorretal, que revelam potenciais novos alvos terapêuticos a serem explorados na clínicaColorectal cancer is one of the more frequent tumor types in the world. To select the appropriate treatment course, it is necessary to develop more precise diagnostic approaches. The current availability of high throughput genome sequencing methods allows for a comprehensive characterization of the structural and sequence alterations present in each tumor. The use of tumor genome sequencing in a personalized setting can result in tumor specific biomarkers that help evaluate response to treatment and the presence of residual disease, improving the clinical management of these patients, and also reveal sequence alterations in genes capable of serving as new therapeutic targets. In this study we developed an efficient bioinformatics pipeline to identify biomarkers based on the existing structural alterations in rectal tumor genomes, eliminating the need to sequence the matched normal genome and therefore reducing the cost for this approach. The biomarkers found for each of the six patients were used to evaluate the presence of residual disease after treatment through the detection of circulating tumor DNA in plasma samples collected at different points during the treatment. Sequencing tumor genomes with low coverage is therefore a viable and promising alternative to follow up rectal cancer patient\'s response to treatment. In the second part of this study, the analysis of colorectal cancer cell lines revealed a large quantity of point mutations (SNVs and InDels) in genes coding for proteins located in the cell surface (surfaceome). These alterations in the surfaceome indicate potential new drug targets and altered pathways in this type of tumor. Furthermore, these point mutations are also responsible for the generation of new epitopes with immunogenic potential and these new epitopes can be applied as personalized tumor vaccines and had previously been proposed as a therapeutic alternative. The presence of new epitopes, especially in the cell lines with elevated mutation rates (resulting from MSI and mutations in DNA mismatch-repair genes or POLE), suggests a potential use of immune checkpoint target drugs in patients with tumors that share these genetic characteristics. With a large-scale bioinformatics approach, we detected new tumor epitopes resulting from point mutations, present in most of the cell lines used. The analysis of gene expression data puts into perspective both the somatic mutations found and which targets are promising as well as the development of therapies based on vaccines derived from tumor epitopes. In conclusion, the study of genomic alterations in primary tumors and colorectal cancer cell lines allowed the detection of structural variations that were used as personalized biomarkers in patients with rectal tumors as well as the identification of genes containing point mutations in colorectal cancer cell lines, that reveal potential new therapeutic targets to be explored in the clinical setting.
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Brown, Margaret M. "Application of genomic techniques to development of biomarkers for the aquatic environment." Thesis, Glasgow Caledonian University, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.443169.
Full textBooks on the topic "Genomic biomarker"
1
Eric, Blomme, ed. Genomics in drug discovery and development. Hoboken, N.J: John Wiley, 2008.
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Semizarov, Dimitri. Genomics in drug discovery and development. Hoboken, N.J: Wiley, 2009.
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Zhang, Xuewu. Omics technologies in cancer biomarker discovery. Austin, Tex: Landes Bioscience, 2011.
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Azuaje, Francisco. Bioinformatics and biomarker discovery: "omic" data analysis for personalised medicine. Hoboken, NJ: John Wiley & Sons, 2010.
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Azuaje, Francisco. Bioinformatics and biomarker discovery: "omic" data analysis for personalised medicine. Hoboken, NJ: John Wiley & Sons, 2010.
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Azuaje, Francisco. Bioinformatics and biomarker discovery: "omic" data analysis for personalised medicine. Hoboken, NJ: John Wiley & Sons, 2010.
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Bioinformatics and biomarker discovery: "omic" data analysis for personalised medicine. Hoboken, NJ: John Wiley & Sons, 2010.
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International Conference on Toxic Exposure Related Biomarker, Genomes, and Health Effects (2008 National Environmental Engineering Research Institute). International Conference on Toxic Exposure Related Biomarker, Genomes, and Health Effects: Under the aegis of NEERI's golden jubilee celebrations, 2007-2008, 10-11 January 2008. Nagpur: National Environmental Engineering Research Institute, 2008.
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Genomic Biomarkers for Pharmaceutical Development. Elsevier, 2014. http://dx.doi.org/10.1016/c2011-0-08165-6.
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Haghighi, Afshin Borhani, and Bernadette Kalman. Other Proven and Putative Autoimmune Disorders of the CNS. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199937837.003.0094.
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Behcet’s Disease (BD) is a multiorgan disorder characterized by oral and genital ulceration, uveitis, and dermatological symptoms. BD is most prevalent in the Mediterranean countries and East Asia, but also occurs in Europe and North America. The etiology remains unknown. Evidence suggests that BD is an autoimmune disorder with complex traits. Neuro-Behcet’s Syndome (NBS) develops in about 5% to 30% of patients with BD and presents with parenchymal or nonparenchymal pathology. The course of NBS is highly variable. Treatment strategies include modulations of the immune response and tissue degeneration, along with symptomatic medications. Main directions of current research include genomic studies, biomarker discovery, and inventive drug- development strategies.
Book chapters on the topic "Genomic biomarker"
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Simon, Richard. "Genomic Biomarker Clinical Trial Designs." In Textbook of Clinical Trials in Oncology, 289–95. Boca Raton, Florida : CRC Press, [2019]: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9781315112084-14.
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Choi, Yoonha, and Jing Huang. "Validation of Genomic-Based Assay." In Statistical Methods in Biomarker and Early Clinical Development, 117–36. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31503-0_7.
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Chang, Ken C. N., and Matthew J. Marton. "Clinical Genomic Biomarker Assay Development: Technologies and Issues." In Translating Molecular Biomarkers into Clinical Assays, 163–76. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40793-7_15.
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Zhou, Xiaofeng, Nagesh P. Rao, Steven W. Cole, and David T. Wong. "Comprehensive Genomic Profiling for Biomarker Discovery for Cancer Detection, Diagnostics and Prognostics." In Bioinformatics in Cancer and Cancer Therapy, 1–17. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-59745-576-3_7.
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Tong, Pan, and Hua Li. "Mining Massive Genomic Data for Therapeutic Biomarker Discovery in Cancer: Resources, Tools, and Algorithms." In Big Data Analytics in Genomics, 337–55. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41279-5_10.
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Braman, Nathaniel, Jacob W. H. Gordon, Emery T. Goossens, Caleb Willis, Martin C. Stumpe, and Jagadish Venkataraman. "Deep Orthogonal Fusion: Multimodal Prognostic Biomarker Discovery Integrating Radiology, Pathology, Genomic, and Clinical Data." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, 667–77. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87240-3_64.
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Kumar, Rakesh, and Rafael G. Amado. "Predictive Genomic Biomarkers." In Therapeutic Kinase Inhibitors, 173–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/82_2011_164.
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Santos, Deborah Conte. "Genomic Ancestry as Biomarkers." In Biomarkers in Diabetes, 669–80. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08014-2_35.
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Santos, Deborah Conte. "Genomic Ancestry as Biomarkers." In Biomarkers in Diabetes, 1–12. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-81303-1_35-1.
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Díez, Paula, Rosa Ma Dégano, Nieves Ibarrola, Juan Casado-Vela, and Manuel Fuentes. "Genomics and Proteomics for Biomarker Validation." In Biomarker Validation, 231–42. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2015. http://dx.doi.org/10.1002/9783527680658.ch12.
Full textConference papers on the topic "Genomic biomarker"
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Liu, Ziyu, Wei Shao, Jie Zhang, Min Zhang, and Kun Huang. "Transfer Learning via Optimal Transportation for Integrative Cancer Patient Stratification." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/380.
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The Stratification of early-stage cancer patients for the prediction of clinical outcome is a challenging task since cancer is associated with various molecular aberrations. A single biomarker often cannot provide sufficient information to stratify early-stage patients effectively. Understanding the complex mechanism behind cancer development calls for exploiting biomarkers from multiple modalities of data such as histopathology images and genomic data. The integrative analysis of these biomarkers sheds light on cancer diagnosis, subtyping, and prognosis. Another difficulty is that labels for early-stage cancer patients are scarce and not reliable enough for predicting survival times. Given the fact that different cancer types share some commonalities, we explore if the knowledge learned from one cancer type can be utilized to improve prognosis accuracy for another cancer type. We propose a novel unsupervised multi-view transfer learning algorithm to simultaneously analyze multiple biomarkers in different cancer types. We integrate multiple views using non-negative matrix factorization and formulate the transfer learning model based on the Optimal Transport theory to align features of different cancer types. We evaluate the stratification performance on three early-stage cancers from the Cancer Genome Atlas (TCGA) project. Comparing with other benchmark methods, our framework achieves superior accuracy for patient outcome prediction.
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Li, Xingnan, Stefano Guerra, Huashi Li, Stephanie Christenson, R. Graham Barr, Christopher Cooper, David Couper, et al. "Genomic analysis of CC16 as a biomarker for COPD." In ERS International Congress 2018 abstracts. European Respiratory Society, 2018. http://dx.doi.org/10.1183/13993003.congress-2018.pa1273.
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Bild, A., Y. Sun, R. Soldi, T. Conner, D. Walker, T. Werner, A. Spira, I. Andrulis, S. Buys, and E. Johnson. "A Genomic Biomarker for Breast Cancer Development in High-Risk Women." In Abstracts: Thirty-Second Annual CTRC‐AACR San Antonio Breast Cancer Symposium‐‐ Dec 10‐13, 2009; San Antonio, TX. American Association for Cancer Research, 2009. http://dx.doi.org/10.1158/0008-5472.sabcs-09-4059.
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Sanga, Sandeep, Praveen Nair, Cyrus Mirsaidi, and Thomas Broudy. "Abstract 4279: Rapid biomarker discovery using large-scale, patient-derived cancer genomic cohorts." In Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA. American Association for Cancer Research, 2014. http://dx.doi.org/10.1158/1538-7445.am2014-4279.
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Massion, PP, Y. Zou, H. Uner, P. Kiatsimkul, HJ Wolf, T. Byers, S. Jonsson, et al. "Recurrent Genomic Gains in Preinvasive Lesions as a Biomarker of Risk for Lung Cancer." In American Thoracic Society 2009 International Conference, May 15-20, 2009 • San Diego, California. American Thoracic Society, 2009. http://dx.doi.org/10.1164/ajrccm-conference.2009.179.1_meetingabstracts.a2671.
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Smith, Bradley L., Philip Breitfeld, Jennifer Cubino, Victor Weigman, Donald P. Richards, and Ki Y. Chung. "Abstract 5187: Feasibility study of genomic biomarker profiling for patients with metastatic colorectal cancer." In Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA. American Association for Cancer Research, 2014. http://dx.doi.org/10.1158/1538-7445.am2014-5187.
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Hoog, JW, T. Treece, L. Blumencranz, W. Audeh, S. Sanati, MJ Ellis, and CX Ma. "Abstract P2-09-19: Genomic biomarker for resistance to palbociclib in the NeoPalAna trial." In Abstracts: 2017 San Antonio Breast Cancer Symposium; December 5-9, 2017; San Antonio, Texas. American Association for Cancer Research, 2018. http://dx.doi.org/10.1158/1538-7445.sabcs17-p2-09-19.
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Park, Jenny, Beatrice Chiu, Joe Delaney, Cate Paschal, Daniel Roche, James Shima, Anita Umesh, Robert Wisotzkey, Mamatha Shekar, and Ilya Kupershmidt. "Abstract 5151: Integrative genomic analysis identifies cancer-testis antigen LEMD1 as a prognostic biomarker." In Proceedings: AACR 104th Annual Meeting 2013; Apr 6-10, 2013; Washington, DC. American Association for Cancer Research, 2013. http://dx.doi.org/10.1158/1538-7445.am2013-5151.
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Bayani, Jane, Uros Kuzmanov, Ihor Batruch, Chan-Kyung Cho, Chris Smith, Paula Marrano, Cassandra Graham, et al. "Abstract 3037: Integrated Genomic, MicroRNA (miRNA) and Proteomic Profiling of Ovarian Carcinoma for Biomarker Discovery." In Proceedings: AACR 101st Annual Meeting 2010‐‐ Apr 17‐21, 2010; Washington, DC. American Association for Cancer Research, 2010. http://dx.doi.org/10.1158/1538-7445.am10-3037.
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Zajchowski, D. A., M. Whitlow, K. M. Zajchowski, and L. K. Shawver. "Abstract POSTER-TECH-1127: Genomic profiles inform treatment decisions and enable future drug/biomarker discovery." In Abstracts: 10th Biennial Ovarian Cancer Research Symposium; September 8-9, 2014; Seattle, WA. American Association for Cancer Research, 2015. http://dx.doi.org/10.1158/1557-3265.ovcasymp14-poster-tech-1127.
Full textReports on the topic "Genomic biomarker"
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Brosh, Arieh, Gordon Carstens, Kristen Johnson, Ariel Shabtay, Joshuah Miron, Yoav Aharoni, Luis Tedeschi, and Ilan Halachmi. Enhancing Sustainability of Cattle Production Systems through Discovery of Biomarkers for Feed Efficiency. United States Department of Agriculture, July 2011. http://dx.doi.org/10.32747/2011.7592644.bard.
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Feed inputs represent the largest variable cost of producing meat and milk from ruminant animals. Thus, strategies that improve the efficiency of feed utilization are needed to improve the global competitiveness of Israeli and U.S. cattle industries, and mitigate their environmental impact through reductions in nutrient excretions and greenhouse gas emissions. Implementation of innovative technologies that will enhance genetic merit for feed efficiency is arguably one of the most cost-effective strategies to meet future demands for animal-protein foods in an environmentally sustainable manner. While considerable genetic variation in feed efficiency exist within cattle populations, the expense of measuring individual-animal feed intake has precluded implementation of selection programs that target this trait. Residual feed intake (RFI) is a trait that quantifies between-animal variation in feed intake beyond that expected to meet energy requirements for maintenance and production, with efficient animals being those that eat less than expected for a given size and level of production. There remains a critical need to understand the biological drivers for genetic variation in RFI to facilitate development of effective selection programs in the future. Therefore, the aim of this project was to determine the biological basis for phenotypic variation in RFI of growing and lactating cattle, and discover metabolic biomarkers of RFI for early and more cost-effective selection of cattle for feed efficiency. Objectives were to: (1) Characterize the phenotypic relationships between RFI and production traits (growth or lactation), (2) Quantify inter-animal variation in residual HP, (3) Determine if divergent RFIphenotypes differ in HP, residual HP, recovered energy and digestibility, and (4) Determine if divergent RFI phenotypes differ in physical activity, feeding behavior traits, serum hormones and metabolites and hepatic mitochondrial traits. The major research findings from this project to date include: In lactating dairy cattle, substantial phenotypic variation in RFI was demonstrated as cows classified as having low RMEI consumed 17% less MEI than high-RMEI cows despite having similar body size and lactation productivity. Further, between-animal variation in RMEI was found to moderately associated with differences in RHP demonstrating that maintenance energy requirements contribute to observed differences in RFI. Quantifying energetic efficiency of dairy cows using RHP revealed that substantial changes occur as week of lactation advances—thus it will be critical to measure RMEI at a standardized stage of lactation. Finally, to determine RMEI in lactating dairy cows, individual DMI and production data should be collected for a minimum of 6 wk. We demonstrated that a favorably association exists between RFI in growing heifers and efficiency of forage utilization in pregnant cows. Therefore, results indicate that female progeny from parents selected for low RFI during postweaning development will also be efficient as mature females, which has positive implications for both dairy and beef cattle industries. Results from the beef cattle studies further extend our knowledge regarding the biological drivers of phenotypic variation in RFI of growing animals, and demonstrate that significant differences in feeding behavioral patterns, digestibility and heart rate exist between animals with divergent RFI. Feeding behavior traits may be an effective biomarker trait for RFI in beef and dairy cattle. There are differences in mitochondrial acceptor control and respiratory control ratios between calves with divergent RFI suggesting that variation in mitochondrial metabolism may be visible at the genome level. Multiple genes associated with mitochondrial energy processes are altered by RFI phenotype and some of these genes are associated with mitochondrial energy expenditure and major cellular pathways involved in regulation of immune responses and energy metabolism.