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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.
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Kulasingam, Vathany, and Eleftherios P. Diamandis. "Proteomic and genomic technologies for biomarker discovery." Clinical Proteomics 2, no. 1-2 (March 2006): 5–11. http://dx.doi.org/10.1385/cp:2:1:5.
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Chakravarty, Debyani, Amber Johnson, Jeffrey Sklar, Neal I. Lindeman, Kathleen Moore, Shridar Ganesan, Christine M. Lovly, et al. "Somatic Genomic Testing in Patients With Metastatic or Advanced Cancer: ASCO Provisional Clinical Opinion." Journal of Clinical Oncology 40, no. 11 (April 10, 2022): 1231–58. http://dx.doi.org/10.1200/jco.21.02767.
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PURPOSE An ASCO provisional clinical opinion offers timely clinical direction to ASCO's membership following publication or presentation of potentially practice-changing data from major studies. This provisional clinical opinion addresses the appropriate use of tumor genomic testing in patients with metastatic or advanced solid tumors. CLINICAL CONTEXT An increasing number of therapies are approved to treat cancers harboring specific genomic biomarkers. However, there is a lack of clarity as to when tumor genomic sequencing should be ordered, what type of assays should be performed, and how to interpret the results for treatment selection. PROVISIONAL CLINICAL OPINION Patients with metastatic or advanced cancer should undergo genomic sequencing in a certified laboratory if the presence of one or more specific genomic alterations has regulatory approval as biomarkers to guide the use of or exclusion from certain treatments for their disease. Multigene panel–based assays should be used if more than one biomarker-linked therapy is approved for the patient's disease. Site-agnostic approvals for any cancer with a high tumor mutation burden, mismatch repair deficiency, or neurotrophic tyrosine receptor kinase ( NTRK) fusions provide a rationale for genomic testing for all solid tumors. Multigene testing may also assist in treatment selection by identifying additional targets when there are few or no genotype-based therapy approvals for the patient's disease. For treatment planning, the clinician should consider the functional impact of the targeted alteration and expected efficacy of genomic biomarker–linked options relative to other approved or investigational treatments. Additional information is available at www.asco.org/assays-and-predictive-markers-guidelines .
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Sarun, Kadir, Kenneth Lee, Marissa Williams, Casey Wright, Candice Clarke, Ngan Cheng, Ken Takahashi, and Yuen Cheng. "Genomic Deletion of BAP1 and CDKN2A Are Useful Markers for Quality Control of Malignant Pleural Mesothelioma (MPM) Primary Cultures." International Journal of Molecular Sciences 19, no. 10 (October 7, 2018): 3056. http://dx.doi.org/10.3390/ijms19103056.
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Malignant pleural mesothelioma (MPM) is a deadly cancer that is caused by asbestos exposure and that has limited treatment options. The current standard of MPM diagnosis requires the testing of multiple immunohistochemical (IHC) markers on formalin-fixed paraffin-embedded tissue to differentiate MPM from other lung malignancies. To date, no single biomarker exists for definitive diagnosis of MPM due to the lack of specificity and sensitivity; therefore, there is ongoing research and development in order to identify alternative biomarkers for this purpose. In this study, we utilized primary MPM cell lines and tested the expression of clinically used biomarker panels, including CK8/18, Calretinin, CK 5/6, CD141, HBME-1, WT-1, D2-40, EMA, CEA, TAG72, BG8, CD15, TTF-1, BAP1, and Ber-Ep4. The genomic alteration of CDNK2A and BAP1 is common in MPM and has potential diagnostic value. Changes in CDKN2A and BAP1 genomic expression were confirmed in MPM samples in the current study using Fluorescence In situ Hybridization (FISH) analysis or copy number variation (CNV) analysis with digital droplet PCR (ddPCR). To determine whether MPM tissue and cell lines were comparable in terms of molecular alterations, IHC marker expression was analyzed in both sample types. The percentage of MPM biomarker levels showed variation between original tissue and matched cells established in culture. Genomic deletions of BAP1 and CDKN2A, however, showed consistent levels between the two. The data from this study suggest that genomic deletion analysis may provide more accurate biomarker options for MPM diagnosis.
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Hayes, Daniel F., Muin J. Khoury, and David Ransohoff. "Why Hasn't Genomic Testing Changed the Landscape in Clinical Oncology?" American Society of Clinical Oncology Educational Book, no. 32 (June 2012): e52-e55. http://dx.doi.org/10.14694/edbook_am.2012.32.78.
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Overview: The “omics” revolution produced great optimism that tumor biomarker tests based on high-order analysis of multiple (sometimes thousands) of factors would result in truly personalized oncologic care. Unfortunately, 10 years into the revolution, the promise of omics-based research has not yet been realized. The factors behind the slow progress in omics-based clinical care are many. First, over the last 15 years, there has been a gradual recognition of the importance of conducting tumor biomarker science with the kind of rigor that has traditionally been used for therapeutic research. However, this recognition has only recently been applied widely, and therefore most tumor biomarkers have insufficiently high levels of evidence to determine clinical utility. Second, omics-based research offers its own particular set of concerns, especially in regard to overfitting computational models and false discovery rates. Researchers and clinicians need to understand the importance of analytic validity, and the difference between clinical/biologic validity and clinical utility. The latter is required to introduce a tumor biomarker test of any kind (single analyte or omics-based), and are ideally generated by carefully planned and properly conducted “prospective retrospective” or truly prospective clinical trials. Only carefully planned studies, which take all three of these into account and in which the investigators are aware and recognize the enormous risk of unintended bias and overfitting inherent in omics-based test development, will ultimately result in translation of the exciting new technologies into better care for patients with cancer.
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Kwee, Lydia Coulter, Megan L. Neely, Elizabeth Grass, Simon G. Gregory, Matthew T. Roe, E. Magnus Ohman, Keith A. A. Fox, et al. "Associations of osteopontin and NT-proBNP with circulating miRNA levels in acute coronary syndrome." Physiological Genomics 51, no. 10 (October 1, 2019): 506–15. http://dx.doi.org/10.1152/physiolgenomics.00033.2019.
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The genomic regulatory networks underlying the pathogenesis of non-ST-segment elevation acute coronary syndrome (NSTE-ACS) are incompletely understood. As intermediate traits, protein biomarkers report on underlying disease severity and prognosis in NSTE-ACS. We hypothesized that integration of dense microRNA (miRNA) profiling with biomarker measurements would highlight potential regulatory pathways that underlie the relationships between prognostic biomarkers, miRNAs, and cardiovascular phenotypes. We performed miRNA sequencing using whole blood from 186 patients from the TRILOGY-ACS trial. Seven circulating prognostic biomarkers were measured: NH2-terminal pro-B-type natriuretic peptide (NT-proBNP), high-sensitivity C-reactive protein, osteopontin (OPN), myeloperoxidase, growth differentiation factor 15, monocyte chemoattractant protein, and neopterin. We tested miRNAs for association with each biomarker with generalized linear models and controlled the false discovery rate at 0.05. Ten miRNAs, including known cardiac-related miRNAs 25-3p and 423-3p, were associated with NT-proBNP levels (min. P = 7.5 × 10−4) and 48 miRNAs, including cardiac-related miRNAs 378a-3p, 20b-5p and 320a, -b, and -d, were associated with OPN levels (min. P = 1.6 × 10−6). NT-proBNP and OPN were also associated with time to cardiovascular death, myocardial infarction (MI), or stroke in the sample. By integrating large-scale miRNA profiling with circulating biomarkers as intermediate traits, we identified associations of known cardiac-related and novel miRNAs with two prognostic biomarkers and identified potential genomic networks regulating these biomarkers. These results, highlighting plausible biological pathways connecting miRNAs with biomarkers and outcomes, may inform future studies seeking to delineate genomic pathways underlying NSTE-ACS outcomes.
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Lee, Jung-Yun, Hee Seung Kim, Dong Hoon Suh, Mi-Kyung Kim, Hyun Hoon Chung, and Yong-Sang Song. "Ovarian Cancer Biomarker Discovery Based on Genomic Approaches." Journal of Cancer Prevention 18, no. 4 (December 30, 2013): 298–312. http://dx.doi.org/10.15430/jcp.2013.18.4.298.
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Robeson, RiLee H., Andrew M. Siegel, and Travis Dunckley. "Genomic and Proteomic Biomarker Discovery in Neurological Disease." Biomarker Insights 3 (January 2008): BMI.S596. http://dx.doi.org/10.4137/bmi.s596.
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Technology for high-throughout scanning of the human genome and its encoded proteins have rapidly developed to allow systematic analyses of human disease. Application of these technologies is becoming an increasingly effective approach for identifying the biological basis of genetically complex neurological diseases. This review will highlight significant findings resulting from the use of a multitude of genomic and proteomic technologies toward biomarker discovery in neurological disorders. Though substantial discoveries have been made, there is clearly significant promise and potential remaining to be fully realized through increasing use of and further development of -omic technologies.
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Root, Alex. "Mathematical Modeling of The Challenge to Detect Pancreatic Adenocarcinoma Early with Biomarkers." Challenges 10, no. 1 (April 3, 2019): 26. http://dx.doi.org/10.3390/challe10010026.
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Pancreatic ductal adenocarcinoma (PDAC) is an aggressive tumor type and is usually detected at late stage. Here, mathematical modeling is used to assess the feasibility of two-step early detection with biomarkers, followed by confirmatory imaging. A one-compartment model of biomarker concentration in blood was parameterized and analyzed. Tumor growth models were generated from two competing genomic evolution models: gradual tumor evolution and punctuated equilibrium. When a biomarker is produced by the tumor at moderate-to-high secretion rates, both evolutionary models indicate that early detection with a blood-based biomarker is feasible and can occur approximately one and a half years before the limit of detection by imaging. Early detection with a blood-based biomarker is at the borderline of clinical utility when biomarker secretion rates by the tumor are an order of magnitude lower and the fraction of biomarker entering the blood is also lower by an order of magntidue. Regardless of whether tumor evolutionary dynamics follow the gradual model or punctuated equilibrium model, the uncertainty in production and clearance rates of molecular biomarkers is a major knowledge gap, and despite significant measurement challenges, should be a priority for the field. The findings of this study provide caution regarding the feasibility of early detection of pancreatic cancer with blood-based biomarkers and challenge the community to measure biomarker production and clearance rates.
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Z Oikonomakou, Mariza, Despoina Gkentzi, Charalambos Gogos, and Karolina Akinosoglou. "Biomarkers in pediatric sepsis: a review of recent literature." Biomarkers in Medicine 14, no. 10 (July 2020): 895–917. http://dx.doi.org/10.2217/bmm-2020-0016.
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Sepsis remains the leading cause of death in infants and children worldwide. Prompt diagnosis and monitoring of infection is pivotal to guide therapy and optimize outcomes. No single biomarker has so far been identified to accurately diagnose sepsis, monitor response and predict severity. We aimed to assess existing evidence of available sepsis biomarkers, and their utility in pediatric population. C-reactive protein and procalcitonin remain the most extensively evaluated and used biomarkers. However, biomarkers related to endothelial damage, vasodilation, oxidative stress, cytokines/chemokines and cell bioproducts have also been identified, often with regard to the site of infection and etiologic pathogen; still, with controversial utility. A multi-biomarker model driven by genomic tools could establish a personalized approach in future disease management.
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Won Choi, Seung, Hwan-ho Cho, Harim Koo, Kyung rae Cho, Karl-Heinz Nenning, Georg Langs, Julia Furtner, et al. "NIMG-20. MULTI-HABITAT RADIOMICS UNRAVELS DISTINCT PHENOTYPIC SUBTYPES OF GLIOBLASTOMA WITH CLINICAL AND GENOMIC SIGNIFICANCE." Neuro-Oncology 22, Supplement_2 (November 2020): ii151. http://dx.doi.org/10.1093/neuonc/noaa215.633.
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Abstract BACKGROUNDS We aimed to evaluate the potential of radiomics as an imaging biomarker for GBM patients and explore the molecular rationale behind radiomics by radio-genomics approach. METHODS A total of 144 primary GBM patients were included in this study as a training cohort. Using multi-parametric MR images, radiomics features were extracted from multi-habitats of the tumor. We applied Cox-LASSO algorithm to build a survival prediction model and validated this model using an independent validation cohort (56 patients from Vienna). With the selected radiomics features, GBM patients were consensus clustered to reveal inherent phenotypic subtypes. The subtypes were further explored in terms of genomic signatures. RESULTS GBM patients were successfully stratified by the radiomics risk score, a weighted sum of radiomics features, corroborating the potential of radiomics as a prognostic biomarker. Using consensus clustering, we identified three distinct subtypes which significantly differed in the prognosis (‘heterogenous enhancing’, ‘rim-enhancing necrotic’, and ‘cystic’ subtype). Multi-variate cox regression analysis confirmed that radiomics subtype as an independent prognostic factor. Transcriptomic traits enriched in individual subtypes were in accordance with imaging phenotypes summarized by radiomics. For example, rim-enhancing necrotic subtype was well described by radiomics profiling (T2 autocorrelation & flat shape) and highlighted by the inflammatory genomic signatures, which well correlated to its phenotypic peculiarity (necrosis). CONCLUSIONS The present study confirmed the feasibility of radiomics as an imaging biomarker for GBM patients with comprehensible biologic annotation. Imaging subtypes derived from radiomics successfully recapitulate the genomic underpinnings of GBM tumors and in turn reinforce their potential as a prognostic biomarker.
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Velimirovic, Marko, Dejan Juric, Andrzej Niemierko, Laura Spring, Neelima Vidula, Seth A. Wander, Arielle Medford, et al. "Rising Circulating Tumor DNA As a Molecular Biomarker of Early Disease Progression in Metastatic Breast Cancer." JCO Precision Oncology, no. 4 (October 2020): 1246–62. http://dx.doi.org/10.1200/po.20.00117.
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PURPOSE Accurate monitoring of therapeutic response remains an important unmet need for patients with metastatic breast cancer (MBC). Analysis of tumor genomics obtained via circulating tumor DNA (ctDNA) can provide a comprehensive overview of tumor evolution. Here, we evaluated ctDNA change as an early prognostic biomarker of subsequent radiologic progression and survival in MBC. PATIENTS AND METHODS Paired blood samples from patients with MBC were analyzed for levels of ctDNA, carcinoembryonic antigen, and cancer antigen 15-3 at baseline and during treatment. A Clinical Laboratory Improvement Amendments–certified sequencing panel of 73 genes was used to quantify tumor-specific point mutations in ctDNA. Multivariable logistic regression analysis was conducted to evaluate the association between ctDNA rise from baseline to during-treatment (genomic progression) and subsequent radiologic progression and progression-free survival (PFS). RESULTS Somatic mutations were detected in 76 baseline samples (90.5%) and 71 during-treatment samples (84.5%). Patients with genomic progression were more than twice as likely to have subsequent radiologic progression (odds ratio, 2.04; 95% CI, 1.74 to 2.41; P < .0001), with a mean lead time of 5.8 weeks. Genomic assessment provided a high positive predictive value of 81.8% and a negative predictive value of 89.7%. The subset of patients with genomic progression also had shorter PFS (median, 4.2 v 8.3 months; hazard ratio, 2.97; 95% CI, 1.75 to 5.04; log-rank P < .0001) compared with those without genomic progression. CONCLUSION Genomic progression, as assessed by early rise in ctDNA, is an independent biomarker of disease progression before overt radiologic or clinical progression becomes evident in patients with MBC.
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Ma, Shuangge, and Jian Huang. "Combining Clinical and Genomic Covariates via Cov-TGDR." Cancer Informatics 3 (January 2007): 117693510700300. http://dx.doi.org/10.1177/117693510700300015.
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Clinical covariates such as age, gender, tumor grade, and smoking history have been extensively used in prediction of disease occurrence and progression. On the other hand, genomic biomarkers selected from microarray measurements may provide an alternative, satisfactory way of disease prediction. Recent studies show that better prediction can be achieved by using both clinical and genomic biomarkers. However, due to different characteristics of clinical and genomic measurements, combining those covariates in disease prediction is very challenging. We propose a new regularization method, Covariate-Adjusted Threshold Gradient Directed Regularization (Cov-TGDR), for combining different type of covariates in disease prediction. The proposed approach is capable of simultaneous biomarker selection and predictive model building. It allows different degrees of regularization for different type of covariates. We consider biomedical studies with binary outcomes and right censored survival outcomes as examples. Logistic model and Cox model are assumed, respectively. Analysis of the Breast Cancer data and the Follicular lymphoma data show that the proposed approach can have better prediction performance than using clinical or genomic covariates alone.
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Joffe, Erel, Alexia Iasonos, and Anas Younes. "Clinical Trials in the Genomic Era." Journal of Clinical Oncology 35, no. 9 (March 20, 2017): 1011–17. http://dx.doi.org/10.1200/jco.2016.70.8891.
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Personalization of therapy to target specific molecular pathways has been placed in the forefront of cancer research. Initial reports from clinical trials designed to select patients for appropriate treatment on the basis of tumor characteristics not only have generated considerable excitement but also have identified several challenges. These challenges include the overcoming of regulatory and logistic difficulties, identification of the best selection biomarkers and diagnostic platforms that can be applied in the clinical setting, definition of relevant outcomes in small preselected patient populations, and the design of methods that facilitate rapid enrollment and interpretation of clinical trials by aggregating data across histologically diverse malignancies with common genetic alterations. Furthermore, because our knowledge of the functional consequences of many genetic alterations lags, investigators and sponsors struggle with choosing between ideal clinical trial designs and more practical ones. These challenges are amplified when more than one biomarker is used to select patients for a combination of targeted agents. This review summarizes the current status and challenges of clinical trials in the genomic era and proposes ways to address these challenges.
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Njoku, Kelechi, Davide Chiasserini, Anthony D. Whetton, and Emma J. Crosbie. "Proteomic Biomarkers for the Detection of Endometrial Cancer." Cancers 11, no. 10 (October 16, 2019): 1572. http://dx.doi.org/10.3390/cancers11101572.
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Endometrial cancer is the leading gynaecological malignancy in the western world and its incidence is rising in tandem with the global epidemic of obesity. Early diagnosis is key to improving survival, which at 5 years is less than 20% in advanced disease and over 90% in early-stage disease. As yet, there are no validated biological markers for its early detection. Advances in high-throughput technologies and machine learning techniques now offer unique and promising perspectives for biomarker discovery, especially through the integration of genomic, transcriptomic, proteomic, metabolomic and imaging data. Because the proteome closely mirrors the dynamic state of cells, tissues and organisms, proteomics has great potential to deliver clinically relevant biomarkers for cancer diagnosis. In this review, we present the current progress in endometrial cancer diagnostic biomarker discovery using proteomics. We describe the various mass spectrometry-based approaches and highlight the challenges inherent in biomarker discovery studies. We suggest novel strategies for endometrial cancer detection exploiting biologically important protein biomarkers and set the scene for future directions in endometrial cancer biomarker research.
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Milbury, Coren A., James Creeden, Wai-Ki Yip, David L. Smith, Varun Pattani, Kristi Maxwell, Bethany Sawchyn, et al. "Clinical and analytical validation of FoundationOne®CDx, a comprehensive genomic profiling assay for solid tumors." PLOS ONE 17, no. 3 (March 16, 2022): e0264138. http://dx.doi.org/10.1371/journal.pone.0264138.
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FoundationOne®CDx (F1CDx) is a United States (US) Food and Drug Administration (FDA)-approved companion diagnostic test to identify patients who may benefit from treatment in accordance with the approved therapeutic product labeling for 28 drug therapies. F1CDx utilizes next-generation sequencing (NGS)-based comprehensive genomic profiling (CGP) technology to examine 324 cancer genes in solid tumors. F1CDx reports known and likely pathogenic short variants (SVs), copy number alterations (CNAs), and select rearrangements, as well as complex biomarkers including tumor mutational burden (TMB) and microsatellite instability (MSI), in addition to genomic loss of heterozygosity (gLOH) in ovarian cancer. CGP services can reduce the complexity of biomarker testing, enabling precision medicine to improve treatment decision-making and outcomes for cancer patients, but only if test results are reliable, accurate, and validated clinically and analytically to the highest standard available. The analyses presented herein demonstrate the extensive analytical and clinical validation supporting the F1CDx initial and subsequent FDA approvals to ensure high sensitivity, specificity, and reliability of the data reported. The analytical validation included several in-depth evaluations of F1CDx assay performance including limit of detection (LoD), limit of blank (LoB), precision, and orthogonal concordance for SVs (including base substitutions [SUBs] and insertions/deletions [INDELs]), CNAs (including amplifications and homozygous deletions), genomic rearrangements, and select complex biomarkers. The assay validation of >30,000 test results comprises a considerable and increasing body of evidence that supports the clinical utility of F1CDx to match patients with solid tumors to targeted therapies or immunotherapies based on their tumor’s genomic alterations and biomarkers. F1CDx meets the clinical needs of providers and patients to receive guideline-based biomarker testing, helping them keep pace with a rapidly evolving field of medicine.
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Carroll, Danielle, Rob McEwen, Iwanka Kozarewa, Elizabeth Harrington, Anne L'Hernault, Jayantha Ratnayake, Richard Mather, et al. "Correlation of circulating tumor DNA (ctDNA), tissue-based genomic profiling and clinical efficacy in the biomarker directed Ph1b trial in metastatic bladder cancer (BISCAY)." Journal of Clinical Oncology 37, no. 15_suppl (May 20, 2019): 4553. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.4553.
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4553 Background: BISCAY is a biomarker-directed Ph1b multi-arm platform study exploring the combination of targeted therapies with anti-PD-L1, Durvalumab, in advanced urothelial cancer. Methods: Next generation sequencing (NGS) of tumour tissue samples from > 380 patients(pts) was performed using the FoundationOne assay alongside IHC for PD-L1. ct DNA from pts enrolled in trial modules at treatment initiation was profiled using the Guardant Health OMNI platform assessing a panel of 500 genes. For a subset of pts, serial plasma samples were also analysed to monitor early signs of response vs. resistance and changes in ct DNA dynamics using a bespoke NGS panel of 10 genes. Results: To date 149 pts have been actively enrolled across 7 different biomarker selected and unselected treatment modules. Across all screened pts the most prevalent genomic alterations in tumour tissue were TERT promoter (65%), TP53 (59%), KMT2D 21%, KDM6A 21%, with the most common CNV CDKN2A/ B loss (32 %). All enrolled pts tested had detectable ctDNA in plasma. Similar genomic alterations, both frequency and type, were detected in both plasma ctDNA and tumour tissue with high concordance for module specific biomarkers used for patient allocation (80% (8/10) for ATM, BRCA1 and 2). Alterations in putative biomarkers predictive of response to anti-PD-L1, such as HRR/MMR alterations and high bTMB levels ( > 20mut/Mb) were observed in22% and 40% patient plasma samples, respectively. Correlations between biomarkers across modules treatment efficacy have been explored. Conclusions: All pts with advanced bladder cancer enrolled on BISCAY who were plasma profiled had detectable ctDNA; frequencies of genomic alterations (in both tumour tissue and plasma) were comparable to prior published data sets. ctDNA may be an attractive alternative to tissue-based NGS, providing comprehensive dynamic snapshots of genomic landscapes at the start and during therapy, and warrants further prospective investigation in trials.
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Tang, Benjamin M., Maryam Shojaei, Grant P. Parnell, Stephen Huang, Marek Nalos, Sally Teoh, Kate O'Connor, et al. "A novel immune biomarker IFI27 discriminates between influenza and bacteria in patients with suspected respiratory infection." European Respiratory Journal 49, no. 6 (June 2017): 1602098. http://dx.doi.org/10.1183/13993003.02098-2016.
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Host response biomarkers can accurately distinguish between influenza and bacterial infection. However, published biomarkers require the measurement of many genes, thereby making it difficult to implement them in clinical practice. This study aims to identify a single-gene biomarker with a high diagnostic accuracy equivalent to multi-gene biomarkers.In this study, we combined an integrated genomic analysis of 1071 individuals with in vitro experiments using well-established infection models.We identified a single-gene biomarker, IFI27, which had a high prediction accuracy (91%) equivalent to that obtained by multi-gene biomarkers. In vitro studies showed that IFI27 was upregulated by TLR7 in plasmacytoid dendritic cells, antigen-presenting cells that responded to influenza virus rather than bacteria. In vivo studies confirmed that IFI27 was expressed in influenza patients but not in bacterial infection, as demonstrated in multiple patient cohorts (n=521). In a large prospective study (n=439) of patients presented with undifferentiated respiratory illness (aetiologies included viral, bacterial and non-infectious conditions), IFI27 displayed 88% diagnostic accuracy (AUC) and 90% specificity in discriminating between influenza and bacterial infections.IFI27 represents a significant step forward in overcoming a translational barrier in applying genomic assay in clinical setting; its implementation may improve the diagnosis and management of respiratory infection.
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He, Yudong D. "Genomic approach to biomarker identification and its recent applications." Cancer Biomarkers 2, no. 3-4 (September 14, 2006): 103–33. http://dx.doi.org/10.3233/cbm-2006-23-404.
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Lucia Vecchione, Bentham Science Publisher, Bentham Science Publisher Elisa Gargiul, Bentham Science Publisher Paola Borgiani, Bentham Science Publisher Irene Predazzi, Bentham Science Publisher Ruggiero Mango, Bentham Science Publisher Francesco Romeo, Bentham Science Publisher Mauro Magnani, and Bentham Science Publisher Giuseppe Novelli. "Genotyping OLR1 Gene: A Genomic Biomarker for Cardiovascular Diseases." Recent Patents on Cardiovascular Drug Discovery 2, no. 2 (June 1, 2007): 147–51. http://dx.doi.org/10.2174/157489007780832506.
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Gold, David A., Jonathan Grabenstatter, Alex de Mendoza, Ana Riesgo, Iñaki Ruiz-Trillo, and Roger E. Summons. "Sterol and genomic analyses validate the sponge biomarker hypothesis." Proceedings of the National Academy of Sciences 113, no. 10 (February 22, 2016): 2684–89. http://dx.doi.org/10.1073/pnas.1512614113.
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Molecular fossils (or biomarkers) are key to unraveling the deep history of eukaryotes, especially in the absence of traditional fossils. In this regard, the sterane 24-isopropylcholestane has been proposed as a molecular fossil for sponges, and could represent the oldest evidence for animal life. The sterane is found in rocks ∼650–540 million y old, and its sterol precursor (24-isopropylcholesterol, or 24-ipc) is synthesized today by certain sea sponges. However, 24-ipc is also produced in trace amounts by distantly related pelagophyte algae, whereas only a few close relatives of sponges have been assayed for sterols. In this study, we analyzed the sterol and gene repertoires of four taxa (Salpingoeca rosetta,Capsaspora owczarzaki,Sphaeroforma arctica, andCreolimax fragrantissima), which collectively represent the major living animal outgroups. We discovered that all four taxa lack C30sterols, including 24-ipc. By building phylogenetic trees for key enzymes in 24-ipc biosynthesis, we identified a candidate gene (carbon-24/28 sterol methyltransferase, orSMT) responsible for 24-ipc production. Our results suggest that pelagophytes and sponges independently evolved C30sterol biosynthesis through clade-specificSMTduplications. Using a molecular clock approach, we demonstrate that the relevant spongeSMTduplication event overlapped with the appearance of 24-isopropylcholestanes in the Neoproterozoic, but that the algalSMTduplication event occurred later in the Phanerozoic. Subsequently, pelagophyte algae and their relatives are an unlikely alternative to sponges as a source of Neoproterozoic 24-isopropylcholestanes, consistent with growing evidence that sponges evolved long before the Cambrian explosion ∼542 million y ago.
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Kurian, Sunil M., Thomas Whisenant, Valeria Mas, Raymond Heilman, Michael Abecassis, Daniel R. Salomon, Adyr Moss, and Bruce Kaplan. "Biomarker Guidelines for High-Dimensional Genomic Studies in Transplantation." Transplantation 101, no. 3 (March 2017): 457–63. http://dx.doi.org/10.1097/tp.0000000000001622.
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Bonne, Nicolai J., and David TW Wong. "Salivary biomarker development using genomic, proteomic and metabolomic approaches." Genome Medicine 4, no. 10 (2012): 82. http://dx.doi.org/10.1186/gm383.
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Deng, Mario C. "The AlloMap™ genomic biomarker story: 10 years after." Clinical Transplantation 31, no. 3 (February 3, 2017): e12900. http://dx.doi.org/10.1111/ctr.12900.
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Koch, Sebastian, David Della-Morte, Kunjan R. Dave, Ralph L. Sacco, and Miguel A. Perez-Pinzon. "Biomarkers for Ischemic Preconditioning: Finding the Responders." Journal of Cerebral Blood Flow & Metabolism 34, no. 6 (March 19, 2014): 933–41. http://dx.doi.org/10.1038/jcbfm.2014.42.
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Ischemic preconditioning is emerging as an innovative and novel cytoprotective strategy to counter ischemic vascular disease. At the root of the preconditioning response is the upregulation of endogenous defense systems to achieve ischemic tolerance. Identifying suitable biomarkers to show that a preconditioning response has been induced remains a translational research priority. Preconditioning leads to a widespread genomic and proteonomic response with important effects on hemostatic, endothelial, and inflammatory systems. The present article summarizes the relevant preclinical studies defining the mechanisms of preconditioning, reviews how the human preconditioning response has been investigated, and which of these bioresponses could serve as a suitable biomarker. Human preconditioning studies have investigated the effects of preconditioning on coagulation, endothelial factors, and inflammatory mediators as well as on genetic expression and tissue blood flow imaging. A biomarker for preconditioning would significantly contribute to define the optimal preconditioning stimulus and the extent to which such a response can be elicited in humans and greatly aid in dose selection in the design of phase II trials. Given the manifold biologic effects of preconditioning a panel of multiple serum biomarkers or genomic assessments of upstream regulators may most accurately reflect the full spectrum of a preconditioning response.
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Yu, Irene S., Francine Aubin, Rachel Goodwin, Jonathan M. Loree, Cheryl Mather, Brandon S. Sheffield, Stephanie Snow, and Sharlene Gill. "Tumor Biomarker Testing for Metastatic Colorectal Cancer: a Canadian Consensus Practice Guideline." Therapeutic Advances in Medical Oncology 14 (January 2022): 175883592211117. http://dx.doi.org/10.1177/17588359221111705.
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The systemic therapy management of metastatic colorectal cancer (mCRC) has evolved from primarily cytotoxic chemotherapies to now include targeted agents given alone or in combination with chemotherapy, and immune checkpoint inhibitors. A better understanding of the pathogenesis and molecular drivers of colorectal cancer not only aided the development of novel targeted therapies but led to the discovery of tumor mutations which act as predictive biomarkers for therapeutic response. Mutational status of the KRAS gene became the first genomic biomarker to be established as part of standard of care molecular testing, where KRAS mutations within exons 2, 3, and 4 predict a lack of response to anti- epidermal growth factor receptor therapies. Since then, several other biomarkers have become relevant to inform mCRC treatment; however, there are no published Canadian guidelines which reflect the current standards for biomarker testing. This guideline was developed by a pan-Canadian advisory group to provide contemporary, evidence-based recommendations on the minimum acceptable standards for biomarker testing in mCRC, and to describe additional biomarkers for consideration.
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Hofman, Paul. "What Is New in Biomarker Testing at Diagnosis of Advanced Non-Squamous Non-Small Cell Lung Carcinoma? Implications for Cytology and Liquid Biopsy." Journal of Molecular Pathology 2, no. 2 (June 4, 2021): 147–72. http://dx.doi.org/10.3390/jmp2020015.
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The discovery and clinical validation of biomarkers predictive of the response of non-squamous non-small-cell lung carcinomas (NS-NSCLC) to therapeutic strategies continue to provide new data. The evaluation of novel treatments is based on molecular analyses aimed at determining their efficacy. These tests are increasing in number, but the tissue specimens are smaller and smaller and/or can have few tumor cells. Indeed, in addition to tissue samples, complementary cytological and/or blood samples can also give access to these biomarkers. To date, it is recommended and necessary to look for the status of five genomic molecular biomarkers (EGFR, ALK, ROS1, BRAFV600, NTRK) and of a protein biomarker (PD-L1). However, the short- and more or less long-term emergence of new targeted treatments of genomic alterations on RET and MET, but also on others’ genomic alteration, notably on KRAS, HER2, NRG1, SMARCA4, and NUT, have made cellular and blood samples essential for molecular testing. The aim of this review is to present the interest in using cytological and/or liquid biopsies as complementary biological material, or as an alternative to tissue specimens, for detection at diagnosis of new predictive biomarkers of NS-NSCLC.
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Markman, Maurie. "Genomic-Based Therapy of Gynecologic Malignancies." Acta Medica Academica 48, no. 1 (June 26, 2019): 84. http://dx.doi.org/10.5644/ama2006-124.245.
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<p>This paper will review the current status of genomic-based therapy of gynecologic malignancies. The routine “standard-of-care” delivery of targeted therapeutics based on the presence of specific molecular biomarkers in the management of the gynecologic malignancies has been delayed compared to the substantial progress made in several other tumor types. However, relatively recently reported and rather robust phase 3 trial data have confirmed a potentially major role for PARP inhibitors as both active treatment and maintenance therapy of advanced ovarian cancer. Further, data demonstrating the presence of a specific molecular phenotype (micro-satellite instability high – MSI-H) is a valid biomarker for the potential clinical utility of checkpoint inhibitor immunotherapy has relevance for all gynecologic malignancies, and particularly in the setting of metastatic or recurrent endometrial cancer.</p><p><strong>Conclusions. </strong>The introduction of PARP inhibitors into the oncology armamentarium has substantially impacted standard-of-care strategies in the management of ovarian cancer. It is anticipated that the results of ongoing and future trials will further define the role of genomic-based therapy in ovarian cancer and other gynecologic malignancies.</p>
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Ukmar, G., G. E. M. Melloni, L. Raddrizzani, P. Rossi, S. Di Bella, M. R. Pirchio, M. Vescovi, et al. "PATRI, a Genomics Data Integration Tool for Biomarker Discovery." BioMed Research International 2018 (June 28, 2018): 1–13. http://dx.doi.org/10.1155/2018/2012078.
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The availability of genomic datasets in association with clinical, phenotypic, and drug sensitivity information represents an invaluable source for potential therapeutic applications, supporting the identification of new drug sensitivity biomarkers and pharmacological targets. Drug discovery and precision oncology can largely benefit from the integration of treatment molecular discriminants obtained from cell line models and clinical tumor samples; however this task demands comprehensive analysis approaches for the discovery of underlying data connections. Here we introduce PATRI (Platform for the Analysis of TRanslational Integrated data), a standalone tool accessible through a user-friendly graphical interface, conceived for the identification of treatment sensitivity biomarkers from user-provided genomics data, associated with information on sample characteristics. PATRI streamlines a translational analysis workflow: first, baseline genomics signatures are statistically identified, differentiating treatment sensitive from resistant preclinical models; then, these signatures are used for the prediction of treatment sensitivity in clinical samples, via random forest categorization of clinical genomics datasets and statistical evaluation of the relative phenotypic features. The same workflow can also be applied across distinct clinical datasets. The ease of use of the PATRI tool is illustrated with validation analysis examples, performed with sensitivity data for drug treatments with known molecular discriminants.
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Choi, Seung Won, Hwan-Ho Cho, Harim Koo, Kyung Rae Cho, Karl-Heinz Nenning, Georg Langs, Julia Furtner, et al. "Multi-Habitat Radiomics Unravels Distinct Phenotypic Subtypes of Glioblastoma with Clinical and Genomic Significance." Cancers 12, no. 7 (June 27, 2020): 1707. http://dx.doi.org/10.3390/cancers12071707.
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We aimed to evaluate the potential of radiomics as an imaging biomarker for glioblastoma (GBM) patients and explore the molecular rationale behind radiomics using a radio-genomics approach. A total of 144 primary GBM patients were included in this study (training cohort). Using multi-parametric MR images, radiomics features were extracted from multi-habitats of the tumor. We applied Cox-LASSO algorithm to build a survival prediction model, which we validated using an independent validation cohort. GBM patients were consensus clustered to reveal inherent phenotypic subtypes. GBM patients were successfully stratified by the radiomics risk score, a weighted sum of radiomics features, corroborating the potential of radiomics as a prognostic biomarker. Using consensus clustering, we identified three distinct subtypes which significantly differed in the prognosis (“heterogenous enhancing”, “rim-enhancing necrotic”, and “cystic” subtypes). Transcriptomic traits enriched in individual subtypes were in accordance with imaging phenotypes summarized by radiomics. For example, rim-enhancing necrotic subtype was well described by radiomics profiling (T2 autocorrelation and flat shape) and highlighted by the inflammatory genomic signatures, which well correlated to its phenotypic peculiarity (necrosis). This study showed that imaging subtypes derived from radiomics successfully recapitulated the genomic underpinnings of GBMs and thereby confirmed the feasibility of radiomics as an imaging biomarker for GBM patients with comprehensible biologic annotation.
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Gutierrez, Martin E., Kristin S. Price, Richard B. Lanman, Rebecca J. Nagy, Irfan Shah, Shivam Mathura, Michael Mulcahy, Andrew D. Norden, and Stuart L. Goldberg. "Genomic Profiling for KRAS, NRAS, BRAF, Microsatellite Instability, and Mismatch Repair Deficiency Among Patients With Metastatic Colon Cancer." JCO Precision Oncology, no. 3 (December 2019): 1–9. http://dx.doi.org/10.1200/po.19.00274.
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PURPOSE Genomic testing is recognized in national guidelines as essential to guide appropriate therapy selection in metastatic colorectal cancer. Previous studies report adherence to testing guidelines is suboptimal, but current testing rates have not been assessed. This study reports testing rates in metastatic colon cancer (mCC) for guideline-recommended biomarkers in a US-based population. MATERIALS AND METHODS A retrospective review of data extracted from electronic medical records was performed to identify patients with pathologically confirmed mCC and describe patterns of guideline-aligned biomarker testing. Data were extracted from the electronic health records of 1,497 patients treated at 23 practices across the United States. Both community and academic centers were represented. RESULTS A total of 1,497 patients with mCC diagnosed between January 1, 2013 and December 31, 2017 were identified. Guideline-aligned biomarker testing rates for RAS, BRAF, and microsatellite instability/mismatch repair deficiency over this study period were 41%, 43%, and 51%, respectively. Patients were more likely to have guideline-aligned testing for RAS and BRAF if they were treated at an academic center, were diagnosed with de novo metastatic disease, and were female. In addition, patients < 65 years of age were more likely to have guideline-aligned RAS testing. Of the 177 patients (12% of cohort) who received anti–epidermal growth factor receptor therapy, only 50 (28%) had complete guideline-aligned biomarker testing. CONCLUSION Despite guideline recommendations and significant therapeutic implications, overall biomarker testing rates in mCC remain suboptimal. Adherence to guideline-recommended biomarker testing would potentially reduce exposure to expensive and ineffective therapies, resulting in improved patient outcomes.
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Wong, Geoffrey Yuet Mun, Connie Diakos, Thomas J. Hugh, and Mark P. Molloy. "Proteomic Profiling and Biomarker Discovery in Colorectal Liver Metastases." International Journal of Molecular Sciences 23, no. 11 (May 29, 2022): 6091. http://dx.doi.org/10.3390/ijms23116091.
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Colorectal liver metastases (CRLM) are the leading cause of death among patients with metastatic colorectal cancer (CRC). As part of multimodal therapy, liver resection is the mainstay of curative-intent treatment for select patients with CRLM. However, effective treatment of CRLM remains challenging as recurrence occurs in most patients after liver resection. Proposed clinicopathologic factors for predicting recurrence are inconsistent and lose prognostic significance over time. The rapid development of next-generation sequencing technologies and decreasing DNA sequencing costs have accelerated the genomic profiling of various cancers. The characterisation of genomic alterations in CRC has significantly improved our understanding of its carcinogenesis. However, the functional context at the protein level has not been established for most of this genomic information. Furthermore, genomic alterations do not always result in predicted changes in the corresponding proteins and cancer phenotype, while post-transcriptional and post-translational regulation may alter synthesised protein levels, affecting phenotypes. More recent advancements in mass spectrometry-based technology enable accurate protein quantitation and comprehensive proteomic profiling of cancers. Several studies have explored proteomic biomarkers for predicting CRLM after oncologic resection of primary CRC and recurrence after curative-intent resection of CRLM. The current review aims to rationalise the proteomic complexity of CRC and explore the potential applications of proteomic biomarkers in CRLM.
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Patel, Jai, Mei Fong, and Megan Jagosky. "Colorectal Cancer Biomarkers in the Era of Personalized Medicine." Journal of Personalized Medicine 9, no. 1 (January 14, 2019): 3. http://dx.doi.org/10.3390/jpm9010003.
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The 5-year survival probability for patients with metastatic colorectal cancer has not drastically changed over the last several years, nor has the backbone chemotherapy in first-line disease. Nevertheless, newer targeted therapies and immunotherapies have been approved primarily in the refractory setting, which appears to benefit a small proportion of patients. Until recently, rat sarcoma (RAS) mutations remained the only genomic biomarker to assist with therapy selection in metastatic colorectal cancer. Next generation sequencing has unveiled many more potentially powerful predictive genomic markers of therapy response. Importantly, there are also clinical and physiologic predictive or prognostic biomarkers, such as tumor sidedness. Variations in germline pharmacogenomic biomarkers have demonstrated usefulness in determining response or risk of toxicity, which can be critical in defining dose intensity. This review outlines such biomarkers and summarizes their clinical implications on the treatment of colorectal cancer. It is critical that clinicians understand which biomarkers are clinically validated for use in practice and how to act on such test results.
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Alexander, Brian M., Lorenzo Trippa, Sarah Gaffey, Isabel C. Arrillaga-Romany, Eudocia Q. Lee, Mikael L. Rinne, Manmeet S. Ahluwalia, et al. "Individualized Screening Trial of Innovative Glioblastoma Therapy (INSIGhT): A Bayesian Adaptive Platform Trial to Develop Precision Medicines for Patients With Glioblastoma." JCO Precision Oncology, no. 3 (December 2019): 1–13. http://dx.doi.org/10.1200/po.18.00071.
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PURPOSE Adequately prioritizing the numerous therapies and biomarkers available in late-stage testing for patients with glioblastoma (GBM) requires an efficient clinical testing platform. We developed and implemented INSIGhT (Individualized Screening Trial of Innovative Glioblastoma Therapy) as a novel adaptive platform trial (APT) to develop precision medicine approaches in GBM. METHODS INSIGhT compares experimental arms with a common control of standard concurrent temozolomide and radiation therapy followed by adjuvant temozolomide. The primary end point is overall survival. Patients with newly diagnosed unmethylated GBM who are IDH R132H mutation negative and with genomic data available for biomarker grouping are eligible. At the initiation of INSIGhT, three experimental arms (neratinib, abemaciclib, and CC-115), each with a proposed genomic biomarker, are tested simultaneously. Initial randomization is equal across arms. As the trial progresses, randomization probabilities adapt on the basis of accumulating results using Bayesian estimation of the biomarker-specific probability of treatment impact on progression-free survival. Treatment arms may drop because of low probability of treatment impact on overall survival, and new arms may be added. Detailed information on the statistical model and randomization algorithm is provided to stimulate discussion on trial design choices more generally and provide an example for other investigators developing APTs. CONCLUSION INSIGhT (NCT02977780) is an ongoing novel biomarker-based, Bayesian APT for patients with newly diagnosed unmethylated GBM. Our goal is to dramatically shorten trial execution timelines while increasing scientific power of results and biomarker discovery using adaptive randomization. We anticipate that trial execution efficiency will also be improved by using the APT format, which allows for the collaborative addition of new experimental arms while retaining the overall trial structure.
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Hill, Thomas, John Rooney, Jaleh Abedini, Hisham El-Masri, Charles E. Wood, and J. Christopher Corton. "Gene Expression Thresholds Derived From Short-term Exposures Identify Rat Liver Tumorigens." Toxicological Sciences 177, no. 1 (June 30, 2020): 41–59. http://dx.doi.org/10.1093/toxsci/kfaa102.
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Abstract Traditional methods for cancer risk assessment are resource-intensive, retrospective, and not feasible for the vast majority of environmental chemicals. In this study, we investigated whether quantitative genomic data from short-term studies may be used to set protective thresholds for potential tumorigenic effects. We hypothesized that gene expression biomarkers measuring activation of the key early events in established pathways for rodent liver cancer exhibit cross-chemical thresholds for tumorigenesis predictive for liver cancer risk. We defined biomarker thresholds for 6 major liver cancer pathways using training sets of chemicals with short-term genomic data (3–29 days of exposure) from the TG-GATES (n = 77 chemicals) and DrugMatrix (n = 86 chemicals) databases and then tested these thresholds within and between datasets. The 6 pathway biomarkers represented genotoxicity, cytotoxicity, and activation of xenobiotic, steroid, and lipid receptors (aryl hydrocarbon receptor, constitutive activated receptor, estrogen receptor, and peroxisome proliferator-activated receptor α). Thresholds were calculated as the maximum values derived from exposures without detectable liver tumor outcomes. We identified clear response values that were consistent across training and test sets. Thresholds derived from the TG-GATES training set were highly predictive (97%) in a test set of independent chemicals, whereas thresholds derived from the DrugMatrix study were 96%–97% predictive for the TG-GATES study. Threshold values derived from an abridged gene list (2/biomarker) also exhibited high predictive accuracy (91%–94%). These findings support the idea that early genomic changes can be used to establish threshold estimates or “molecular tipping points” that are predictive of later-life health outcomes.
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Wallis, Deeann, Anat Stemmer-Rachamimov, Sarah Adsit, Bruce Korf, Dominique Pichard, Jaishri Blakeley, and Kavita Y. Sarin. "Status and Recommendations for Incorporating Biomarkers for Cutaneous Neurofibromas Into Clinical Research." Neurology 97, no. 7 Supplement 1 (July 6, 2021): S42—S49. http://dx.doi.org/10.1212/wnl.0000000000012426.
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ObjectiveTo summarize existing biomarker data for cutaneous neurofibroma (cNF) and to inform the incorporation of biomarkers into clinical trial design for cNFs.MethodsThe cNF working group, a subgroup of the Response Evaluation in Neurofibromatosis and Schwannomatosis (REiNS) consortium, was formed to review and inform clinical trial design for cNFs. Between June 2018 and February 2020, the cNF working group performed a review of existing data on genetic biomarkers for cNFs in the setting of neurofibromatosis type 1. We also reviewed criteria for successful biomarker application in the clinic. The group then held a series of meetings to develop a consensus report.ResultsOur systematic literature review of existing data revealed a lack of validated biomarkers for cNFs. In our report, we summarize the existing signaling, genomic, transcriptomic, histopathologic, and proteomic data relevant to cNF. Finally, we make recommendations for incorporating exploratory aims for predictive biomarkers into clinical trials through biobanking samples.ConclusionThese recommendations are intended to provide both researchers and clinicians with best practices for clinical trial design to aid in the identification of clinically validated biomarkers for cNF.
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Lin, Victor T. G., and Eddy S. Yang. "The Pros and Cons of Incorporating Transcriptomics in the Age of Precision Oncology." JNCI: Journal of the National Cancer Institute 111, no. 10 (June 4, 2019): 1016–22. http://dx.doi.org/10.1093/jnci/djz114.
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Abstract The treatment of cancer continues to evolve toward personalized therapies based on individual patient and tumor characteristics. Our successes and failures in adopting a precision-oncology approach have demonstrated the utmost importance in identifying the proper predictive biomarkers of response. Until recently, most biomarkers were identified using immunohistochemistry for protein expression or single-gene analysis to identify targetable alterations. With the rapid propagation of next-generation sequencing to evaluate tumor tissue and “liquid biopsies,” identification of genomic biomarkers is now standard, particularly in non–small cell lung cancer, for which there is now an extensive catalog of biomarker-directed therapies with more anticipated to come. Despite these great strides, it has also become apparent that using genomic biomarkers alone will be insufficient, as it has been consistently shown that at least one-half of patients who undergo tumor genomic profiling have no actionable alteration. This is perhaps to be expected given the remarkable breadth of nongenetic factors that contribute to tumor initiation and progression. Some have proposed that the next logical step is to use transcriptome profiling to define new biomarkers of response to targeted agents. Recently, results from the WINTHER trial were published, specifically investigating the use of transcriptomics to improve match rates over genomic next-generation sequencing alone. In this review, we discuss the complexities of precision-oncology efforts and appraise the available evidence supporting the incorporation of transcriptomic data into the precision-oncology framework in the historical context of the development of biomarkers for directing cancer therapy.
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Zhang, Zhengjun. "Genomic Biomarker Heterogeneities between SARS-CoV-2 and COVID-19." Vaccines 10, no. 10 (October 2, 2022): 1657. http://dx.doi.org/10.3390/vaccines10101657.
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Genes functionally associated with SARS-CoV-2 infection and genes functionally related to the COVID-19 disease can be different, whose distinction will become the first essential step for successfully fighting against the COVID-19 pandemic. Unfortunately, this first step has not been completed in all biological and medical research. Using a newly developed max-competing logistic classifier, two genes, ATP6V1B2 and IFI27, stand out to be critical in the transcriptional response to SARS-CoV-2 infection with differential expressions derived from NP/OP swab PCR. This finding is evidenced by combining these two genes with another gene in predicting disease status to achieve better-indicating accuracy than existing classifiers with the same number of genes. In addition, combining these two genes with three other genes to form a five-gene classifier outperforms existing classifiers with ten or more genes. These two genes can be critical in fighting against the COVID-19 pandemic as a new focus and direction with their exceptional predicting accuracy. Comparing the functional effects of these genes with a five-gene classifier with 100% accuracy identified and tested from blood samples in our earlier work, the genes and their transcriptional response and functional effects on SARS-CoV-2 infection, and the genes and their functional signature patterns on COVID-19 antibodies, are significantly different. We will use a total of fourteen cohort studies (including breakthrough infections and omicron variants) with 1481 samples to justify our results. Such significant findings can help explore the causal and pathological links between SARS-CoV-2 infection and the COVID-19 disease, and fight against the disease with more targeted genes, vaccines, antiviral drugs, and therapies.
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Mahesh Kumar, TalkadSubbaiah, and Govindraju Poornima. "Genomic Alphabets of Saliva as a Biomarker in Oral Cancer." Journal of Indian Academy of Oral Medicine and Radiology 29, no. 4 (2017): 300. http://dx.doi.org/10.4103/jiaomr.jiaomr_90_16.
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Espejo-Freire, Andrea P., Andrew Elliott, Andrew Rosenberg, Philippos Apolinario Costa, Priscila Barreto-Coelho, Emily Jonczak, Gina D’Amato, et al. "Genomic Landscape of Angiosarcoma: A Targeted and Immunotherapy Biomarker Analysis." Cancers 13, no. 19 (September 26, 2021): 4816. http://dx.doi.org/10.3390/cancers13194816.
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We performed a retrospective analysis of angiosarcoma (AS) genomic biomarkers and their associations with the site of origin in a cohort of 143 cases. Primary sites were head and neck (31%), breast (22%), extremity (11%), viscera (20%), skin at other locations (8%), and unknown (9%). All cases had Next Generation Sequencing (NGS) data with a 592 gene panel, and 53 cases had Whole Exome Sequencing (WES) data, which we used to study the microenvironment phenotype. The immunotherapy (IO) response biomarkers Tumor Mutation Burden (TMB), Microsatellite Instability (MSI), and PD-L1 status were the most frequently encountered alteration, present in 36.4% of the cohort and 65% of head and neck AS (H/N-AS) (p < 0.0001). In H/N-AS, TMB-High was seen in 63.4% of cases (p < 0.0001) and PDL-1 positivity in 33% of cases. The most common genetic alterations were TP53 (29%), MYC amplification (23%), ARID1A (17%), POT1 (16%), and ATRX (13%). H/N-AS cases had predominantly mutations in TP53 (50.0%, p = 0.0004), POT1 (40.5%, p < 0.0001), and ARID1A (33.3%, p = 0.5875). In breast AS, leading alterations were MYC amplification (63.3%, p < 0.0001), HRAS (16.1%, p = 0.0377), and PIK3CA (16.1%, p = 0.2352). At other sites, conclusions are difficult to generate due to the small number of cases. A microenvironment with a high immune signature, previously associated with IO response, was evenly distributed in 13% of the cases at different primary sites. Our findings can facilitate the design and optimization of therapeutic strategies for AS.
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Li, Hainan, Changguo Shan, Chongzhu Fan, Shengnan Wu, Mingyao Lai, Linbo Cai, Dan Zhu, et al. "Genomic profiling identified novel prognostic biomarker in Chinsese glioma patients." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): e14538-e14538. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e14538.
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e14538 Background: Molecular charactersitcs are essential for the classification and grading of gliomas. However, majority of current understanding is based on public databases that might not accurately reflect the Asian population. Here, we studied the mutation landscape of Chinese glioma patients in hope to provide new insights for glioma prognosis and treatment. Methods: Tissue samples from 112 glioma patients underwent next-generation sequencing targeting 425 cancer-relevant genes. Gene mutations and copy number variations were investigated for their prognostic effect using overall survival data. Pathway-based survival analysis was peformed using top ten predefined oncogenic pathways. Results: We identified similar prevalence of currently established molecular diagnostic markers of glioma, including TP53 (33%), EGFR(26%), TERT (24%), PTEN (21%), ATRX (14%), BRAF (13%) and IDH1/2 (6%). Among all genetic abberations with more than 5% occurrence rate, four mutations and four copy number gains were significantly associated with poor overall survival (univariate, P < 0.05). Of these, TERT mutations (hazard ratio [HR], 3.14; 95% confidence interval [CI], 1.31 to 7.49; P = 0.01) and EGFR amplification (HR, 2.67; 95% CI, 1.20 to 5.95; P = 0.02) remained significant after adjusting for clinical parameters. Similarly, PIK3CA mutations, which was also frequently mutated in glioma but not used for clinical classification, were found to correlate with poor prognosis (HR, 2.61; 95% CI, 1.19 to 5.74; P = 0.02). Additionally, we have also identified MCL1 amplification as a potential novel biomarker for glioma (HR, 2.73; 95% CI, 1.47 to 5.07; P < 0.001), which was seldom reported in the TCGA database and might possibly be ancestral specific giving its high prevelance in our cohort (found in 32% patients). Pathway analyses revealed significantly worse prognosis with abnormal PI3K (HR, 1.81; 95% CI, 1.12 to 2.95; P = 0.02) and cell cycle pathways (HR, 2.04; 95% CI, 1.2 to 3.47; P < 0.001), both of which stayed meaningful after adjusting for clinical factors. Conclusions: In this study, we discovered PIK3CA mutations and MCL1 amplification as novel prognostic markers of glioma. We also demonstrated shorter survival with abnormal PI3K and cell cycle pathways that provided an intergrative understanding of glioma.