Methods of measurement for tumor mutational burden in tumor tissue.

Immunotherapies based on immune checkpoint inhibitors are emerging as an innovative treatment for different types of advanced cancers. While the utility of immune checkpoint inhibitors has been clearly demonstrated, the response rate is highly variable across individuals. Due to the cost and toxicity of these immunotherapies, a critical challenge in this field is the identification of predictive biomarkers to discriminate which patients may respond to immunotherapy. Recently, a high tumor mutational burden (TMB) has been identified as a genetic signature that is associated with a favorable outcome for immune checkpoint inhibitor therapy. The TMB is defined as the total number of nonsynonymous mutations per coding area of a tumor genome. Initially, it was determined using whole exome sequencing, but due to the high costs and long turnaround time of this method, targeted panel sequencing is currently being explored to measure TMB. In the near future, TMB evaluation may play an important role in immuno-oncology, but its implementation in a routine setting involves robust analytical and clinical validation. Standardization is also needed in order to make informed decisions about patients. This review presents the methodologies employed for determining TMB and discusses the factors that may have an impact on its measurement.

[1]  Joshua F. McMichael,et al.  Optimizing cancer genome sequencing and analysis. , 2015, Cell systems.

[2]  David T. W. Jones,et al.  Signatures of mutational processes in human cancer , 2013, Nature.

[3]  Li Ding,et al.  Genomic Landscape of Non-Small Cell Lung Cancer in Smokers and Never-Smokers , 2012, Cell.

[4]  M. Marton,et al.  Data Interoperability of Whole Exome Sequencing (WES) Based Mutational Burden Estimates from Different Laboratories , 2016, International journal of molecular sciences.

[5]  Emily H Turner,et al.  Targeted Capture and Massively Parallel Sequencing of Twelve Human Exomes , 2009, Nature.

[6]  J. Wolchok,et al.  Genetic basis for clinical response to CTLA-4 blockade in melanoma. , 2014, The New England journal of medicine.

[7]  M. Berger,et al.  Reliable Detection of Mismatch Repair Deficiency in Colorectal Cancers Using Mutational Load in Next-Generation Sequencing Panels. , 2016, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[8]  Nikhil Wagle,et al.  The impact of tumor profiling approaches and genomic data strategies for cancer precision medicine , 2016, Genome Medicine.

[9]  Jedd D. Wolchok,et al.  Cancer immunotherapy using checkpoint blockade , 2018, Science.

[10]  K. Kinzler,et al.  Cancer Genome Landscapes , 2013, Science.

[11]  J. Szustakowski,et al.  Nivolumab plus Ipilimumab in Lung Cancer with a High Tumor Mutational Burden , 2018, The New England journal of medicine.

[12]  D. Carbone,et al.  First-Line Nivolumab in Stage IV or Recurrent Non-Small Cell Lung Cancer , 2017 .

[13]  R. Salgia,et al.  Value-based genomics , 2018, Oncotarget.

[14]  Ahmet Zehir,et al.  Molecular Determinants of Response to Anti-Programmed Cell Death (PD)-1 and Anti-Programmed Death-Ligand 1 (PD-L1) Blockade in Patients With Non-Small-Cell Lung Cancer Profiled With Targeted Next-Generation Sequencing. , 2018, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[15]  Levi Garraway,et al.  Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden , 2017, Genome Medicine.

[16]  Anne-Mette K. Hein,et al.  Next-Generation Sequencing of RNA and DNA Isolated from Paired Fresh-Frozen and Formalin-Fixed Paraffin-Embedded Samples of Human Cancer and Normal Tissue , 2014, PloS one.

[17]  Yu Shyr,et al.  Targeted Next Generation Sequencing Identifies Markers of Response to PD-1 Blockade , 2016, Cancer Immunology Research.

[18]  Joshua M. Stuart,et al.  The Cancer Genome Atlas Pan-Cancer analysis project , 2013, Nature Genetics.

[19]  Jorge Sabbaga,et al.  Comprehensive cancer-gene panels can be used to estimate mutational load and predict clinical benefit to PD-1 blockade in clinical practice , 2015, Oncotarget.

[20]  J. Carpten,et al.  Deep Clonal Profiling of Formalin Fixed Paraffin Embedded Clinical Samples , 2012, PloS one.

[21]  P. Stephens,et al.  Tumor Mutational Burden as an Independent Predictor of Response to Immunotherapy in Diverse Cancers , 2017, Molecular Cancer Therapeutics.

[22]  J. Lunceford,et al.  Pembrolizumab for the treatment of non-small-cell lung cancer. , 2015, The New England journal of medicine.

[23]  T. Schumacher,et al.  Neoantigens in cancer immunotherapy , 2015, Science.

[24]  A. Broomer,et al.  Estimating tumor mutation burden using next-generation sequencing assay. , 2017 .

[25]  Dennis C. Friedrich,et al.  Whole-exome sequencing and clinical interpretation of formalin-fixed , paraffin-embedded tumor samples to guide precision cancer medicine , 2014 .

[26]  Alexander Dobrovic,et al.  Sequence artifacts in DNA from formalin-fixed tissues: causes and strategies for minimization. , 2015, Clinical chemistry.

[27]  S. Ramalingam,et al.  Tumor Mutation Burden: Leading Immunotherapy to the Era of Precision Medicine? , 2018, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[28]  David C. Smith,et al.  Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. , 2012, The New England journal of medicine.

[29]  Steven A. Roberts,et al.  Mutational heterogeneity in cancer and the search for new cancer-associated genes , 2013 .