What makes TMB an ambivalent biomarker for immunotherapy? A subtle mismatch between the sample-based design of variant callers and real clinical cohort
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Jiayin Wang | Xuqi Li | Xuanping Zhang | Xiao-e Xiao | Yixuan Wang | Xin Lai | Yuqian Liu | Shenjie Wang | Xiao-yi Zhu | Yifei Li
[1] Jiayin Wang,et al. Is an SV caller compatible with sequencing data? An online recommendation tool to automatically recommend the optimal caller based on data features , 2023, Frontiers in Genetics.
[2] Jiayin Wang,et al. TMBcat: A multi-endpoint p-value criterion on different discrepancy metrics for superiorly inferring tumor mutation burden thresholds , 2022, Frontiers in Immunology.
[3] Jiayin Wang,et al. PEcnv: accurate and efficient detection of copy number variations of various lengths , 2022, Briefings Bioinform..
[4] Yangguang Shi,et al. Abstract LB512: RATIONALE-304: The association of tumor mutational burden (TMB) with clinical outcomes of tislelizumab (TIS) + chemotherapy (chemo) versus chemo alone as first-line treatment for advanced non-squamous non-small cell lung cancer (nsq-NSCLC) , 2022, Cancer Research.
[5] Jiaxuan Li,et al. Ensemble of ML-KNN for classification algorithm recommendation , 2021, Knowl. Based Syst..
[6] A. Jewett,et al. Immunotherapy of Glioblastoma: Current Strategies and Challenges in Tumor Model Development , 2021, Cells.
[7] Manasi Sheth,et al. Exploring the relationship between Overall Survival (OS), Progression Free Survival (PFS) and Objective Response Rate (ORR) in patients with advanced melanoma. , 2020, Cancer treatment and research communications.
[8] T. Chan,et al. The FDA approval of pembrolizumab for adult and pediatric patients with tumor mutational burden (TMB) ≥10: a decision centered on empowering patients and their physicians. , 2020, Annals of oncology : official journal of the European Society for Medical Oncology.
[9] Y. Bang,et al. The association of tissue tumor mutational burden (tTMB) using the Foundation Medicine genomic platform with efficacy of pembrolizumab versus paclitaxel in patients (pts) with gastric cancer (GC) from KEYNOTE-061. , 2020 .
[10] S. Nomura,et al. TAS-116, an oral HSP90 inhibitor, in combination with nivolumab in patients with colorectal cancer and other solid tumors: An open-label, dose-finding, and expansion phase Ib trial (EPOC1704). , 2020 .
[11] A. Matakidou,et al. Molecular biomarkers to identify patients (pts) who may benefit from durvalumab (D; anti-PD-L1) ± tremelimumab (T; anti-CTLA-4) in recurrent/metastatic head and neck squamous cell carcinoma (R/M HNSCC) from HAWK and CONDOR studies. , 2020 .
[12] R. Ferris,et al. Plasma-based tumor mutational burden (bTMB) as predictor for survival in phase III EAGLE study: Durvalumab (D) ± tremelimumab (T) versus chemotherapy (CT) in recurrent/metastatic head and neck squamous cell carcinoma (R/M HNSCC) after platinum failure. , 2020 .
[13] Y. Bang,et al. The association of molecular biomarkers with efficacy of pembrolizumab versus paclitaxel in patients with gastric cancer (GC) from KEYNOTE-061. , 2020 .
[14] K. Bensalah,et al. Immunotherapy in Renal Cell Carcinoma: The Future Is Now , 2020, International journal of molecular sciences.
[15] M. Narukawa,et al. Magnitude of advantage in tumor response contributes to a better correlation between treatment effects on overall survival and progression-free survival: a literature-based meta-analysis of clinical trials in patients with metastatic colorectal cancer , 2020, International Journal of Clinical Oncology.
[16] L. Diaz,et al. Efficacy of Pembrolizumab in Patients With Noncolorectal High Microsatellite Instability/Mismatch Repair-Deficient Cancer: Results From the Phase II KEYNOTE-158 Study. , 2020, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[17] Tae-Min Kim,et al. False-negative errors in next-generation sequencing contribute substantially to inconsistency of mutation databases , 2019, PloS one.
[18] G. Colloca,et al. Analysis of response-related endpoints in trials of first-line medical treatment of metastatic colorectal cancer , 2019, International Journal of Clinical Oncology.
[19] Julianne K. David,et al. Burden of tumor mutations, neoepitopes, and other variants are weak predictors of cancer immunotherapy response and overall survival , 2019, Genome Medicine.
[20] Y. Kamatani,et al. Comprehensive evaluation of structural variation detection algorithms for whole genome sequencing , 2019, Genome Biology.
[21] C. Brennan,et al. Tumor mutational load predicts survival after immunotherapy across multiple cancer types , 2019, Nature Genetics.
[22] J. Lunceford,et al. Pan-tumor genomic biomarkers for PD-1 checkpoint blockade–based immunotherapy , 2018, Science.
[23] Thomas Colthurst,et al. A universal SNP and small-indel variant caller using deep neural networks , 2018, Nature Biotechnology.
[24] Martin Posch,et al. Methods for the analysis of multiple endpoints in small populations: A review , 2018, Journal of biopharmaceutical statistics.
[25] R. Bourgon,et al. Association of high tissue TMB and atezolizumab efficacy across multiple tumor types. , 2018 .
[26] D. Schadendorf,et al. Genomic correlates of response to immune checkpoint blockade in microsatellite-stable solid tumors , 2018, Nature Genetics.
[27] G. Getz,et al. Genomic correlates of response to immune checkpoint blockade in microsatellite-stable solid tumors , 2018, Nature Genetics.
[28] Arun Ahuja,et al. Genomic Features of Response to Combination Immunotherapy in Patients with Advanced Non-Small-Cell Lung Cancer , 2018, Cancer cell.
[29] Paolo A Ascierto,et al. Tumor Mutational Burden and Efficacy of Nivolumab Monotherapy and in Combination with Ipilimumab in Small-Cell Lung Cancer. , 2018, Cancer cell.
[30] J. Szustakowski,et al. Nivolumab plus Ipilimumab in Lung Cancer with a High Tumor Mutational Burden , 2018, The New England journal of medicine.
[31] L. Macconaill,et al. Frameshift events predict anti-PD-1/L1 response in head and neck cancer. , 2018, JCI insight.
[32] Mark W. Ball,et al. Genomic correlates of response to immune checkpoint therapies in clear cell renal cell carcinoma , 2018, Science.
[33] Chang Xu,et al. A review of somatic single nucleotide variant calling algorithms for next-generation sequencing data , 2018, Computational and structural biotechnology journal.
[34] Renke Pan,et al. TNscope: Accurate Detection of Somatic Mutations with Haplotype-based Variant Candidate Detection and Machine Learning Filtering , 2018, bioRxiv.
[35] T. Chan,et al. Tumor and Microenvironment Evolution during Immunotherapy with Nivolumab , 2017, Cell.
[36] P. Stephens,et al. Tumor Mutational Burden as an Independent Predictor of Response to Immunotherapy in Diverse Cancers , 2017, Molecular Cancer Therapeutics.
[37] M. Socinski,et al. First‐Line Nivolumab in Stage IV or Recurrent Non–Small‐Cell Lung Cancer , 2017, The New England journal of medicine.
[38] Levi Garraway,et al. Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden , 2017, Genome Medicine.
[39] Daniel Sinnett,et al. SNooPer: a machine learning-based method for somatic variant identification from low-pass next-generation sequencing , 2016, BMC Genomics.
[40] S. Chanock,et al. Burden of Nonsynonymous Mutations among TCGA Cancers and Candidate Immune Checkpoint Inhibitor Responses. , 2016, Cancer research.
[41] Philip J. Stephens,et al. Total mutation burden (TMB) in lung cancer (LC) and relationship with response to PD-1/PD-L1 targeted therapies. , 2016 .
[42] Emmanuel Barillot,et al. SV-Bay: structural variant detection in cancer genomes using a Bayesian approach with correction for GC-content and read mappability , 2016, Bioinform..
[43] Michael C. Heinold,et al. A comprehensive assessment of somatic mutation detection in cancer using whole-genome sequencing , 2015, Nature Communications.
[44] Jatinder Singh. International conference on harmonization of technical requirements for registration of pharmaceuticals for human use , 2015, Journal of pharmacology & pharmacotherapeutics.
[45] C. Drake,et al. Immune checkpoint blockade: a common denominator approach to cancer therapy. , 2015, Cancer cell.
[46] P. Sharma,et al. Immune Checkpoint Targeting in Cancer Therapy: Toward Combination Strategies with Curative Potential , 2015, Cell.
[47] Martin L. Miller,et al. Mutational landscape determines sensitivity to PD-1 blockade in non–small cell lung cancer , 2015, Science.
[48] R. Satya,et al. Comparison of somatic mutation calling methods in amplicon and whole exome sequence data , 2014, BMC Genomics.
[49] Ryan M. Layer,et al. LUMPY: a probabilistic framework for structural variant discovery , 2012, Genome Biology.
[50] V. Beneš,et al. DELLY: structural variant discovery by integrated paired-end and split-read analysis , 2012, Bioinform..
[51] George Coukos,et al. Cancer immunotherapy comes of age , 2011, Nature.
[52] Gholamreza Haffari,et al. Feature-based classifiers for somatic mutation detection in tumour–normal paired sequencing data , 2011, Bioinform..
[53] R. Wilson,et al. BreakDancer: An algorithm for high resolution mapping of genomic structural variation , 2009, Nature Methods.
[54] Raymond J. Carroll,et al. Measurement error in nonlinear models: a modern perspective , 2006 .
[55] Leslie G. Valiant,et al. Computational limitations on learning from examples , 1988, JACM.
[56] M. Postma,et al. Do Surrogate Endpoints Better Correlate with Overall Survival in Studies That Did Not Allow for Crossover or Reported Balanced Postprogression Treatments? An Application in Advanced Non-Small Cell Lung Cancer. , 2018, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.
[57] Leslie G. Valiant,et al. A theory of the learnable , 1984, CACM.