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{{Short description|Genetic characteristic of tumorous tissue}}
'''Tumour mutational burden''' (abbreviated as '''TMB''') is a genetic characteristic of tumorous tissue that can be informative to [[cancer research]] and treatment. It is defined as the number of [[Somatic mutation|non-inherited mutations]] per million [[Base pair|bases]] (Mb) of investigated genomic sequence,<ref name="ref1">{{cite journal |vauthors=Merino DM, [[Lisa McShane|McShane LM]], Fabrizio D, Funari V, Chen S, White JR, etal |date=2020 |title=Establishing guidelines to harmonize tumor mutational burden (TMB): in silico assessment of variation in TMB quantification across diagnostic platforms: phase I of the Friends of Cancer Research TMB Harmonization Project |journal=J Immunother Cancer |volume=8 |issue=1 |article-number=e000147 |doi=10.1136/jitc-2019-000147 |pmc=7174078 |pmid=32217756}}</ref> and its measurement has been enabled by [[Massive parallel sequencing|next generation sequencing]]. High TMB and DNA damage repair mutations were discovered to be associated with superior clinical benefit from immune checkpoint blockade therapy by Timothy Chan and colleagues at the Memorial Sloan Kettering Cancer Center.<ref name="ref2">{{cite journal |last1=Rizvi |first1=Naiyer |last2=Hellmann |first2=Matthew |last3=Snyder |first3=Alexandra |last4=Kvistborg |first4=Pia |last5=Makarov |first5=Vladimir |last6=Havel |first6=Jonathan |last7=Lee |first7=William |last8=Yuan |first8=Jianda |last9=Wong |first9=Phillip |last10=Ho |first10=Teresa |last11=Miller |first11=Martin |last12=Rekhtman |first12=Natasha |last13=Moreira |first13=Andra |last14=Ibrahim |first14=Fawzia |last15=Bruggeman |first15=Cameron |last16=Gasmi |first16=Billel |last17=Zappasodi |first17=Roberta |last18=Maeda |first18=Yuka |last19=Sander |first19=Chris |last20=Garon |first20=Edward |last21=Merghoub |first21=Taha |last22=Wolchok |first22=Jedd |last23=Schumacher |first23=Ton |last24=Timothy |first24=Chan |title=Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer |journal=Science |date=2015 |volume=6230 |issue=348 |pages=124–128 |doi=10.1126/science.aaa1348 |pmid=25765070 |pmc=4993154 |bibcode=2015Sci...348..124R }}</ref>

TMB has been validated as a predictive [[biomarker]] with several applications, including associations reported between different TMB levels and patient response to immune checkpoint inhibitor (ICI) therapy in a variety of cancers.<ref name="ref3">{{cite journal |vauthors=Kim JY, Kronbichler A, Eisenhut M, Hong SH, van der Vliet HJ, Kang J, etal |date=2019 |title=Tumor Mutational Burden and Efficacy of Immune Checkpoint Inhibitors: A Systematic Review and Meta-Analysis |journal=Cancers |volume=11 |issue=11 |page=1798 |doi=10.3390/cancers11111798 |pmc=6895916 |pmid=31731749|doi-access=free }}</ref><ref>{{cite journal |vauthors=Samstein RM, Lee CH, Shoushtari AN, Hellmann MD, Shen R, Janjigian YY, Barron DA, Zehir A, Jordan EJ, Omuro A, Kaley TJ, Kendall SM, Motzer RJ, Hakimi AA, Voss MH, Russo P, Rosenberg J, Iyer G, Bochner BH, Bajorin DF, Al-Ahmadie HA, Chaft JE, Rudin CM, Riely GJ, Baxi S, Ho AL, Wong RJ, Pfister DG, Wolchok JD, Barker CA, Gutin PH, Brennan CW, Tabar V, Mellinghoff IK, DeAngelis LM, Ariyan CE, Lee N, Tap WD, Gounder MM, D'Angelo SP, Saltz L, Stadler ZK, Scher HI, Baselga J, Razavi P, Klebanoff CA, Yaeger R, Segal NH, Ku GY, DeMatteo RP, Ladanyi M, Rizvi NA, Berger MF, Riaz N, Solit DB, Chan TA, Morris LG |title=Tumor mutational load predicts survival after immunotherapy across multiple cancer types |journal=Nature Genetics |date=2019 |volume=51 |issue=2 |pages=202–206 |doi=10.1038/s41588-018-0312-8 |pmid=30643254|pmc=6365097 }}</ref> TMB is also strongly predictive of overall as well as disease-specific survival, independently of cancer type, stage or grade. Patients with both low and high TMB fare notably better than those with intermediate burden.<ref>{{cite journal |vauthors=Smith JR, Parl FF, Dupont WD |date=2023 |title=Mutation burden independently predicts survival in the Pan-Cancer Atlas |url=|journal=JCO Precis Oncol |volume=7 |issue=7 |pages=e2200571 |doi=10.1200/po.22.00571 |pmid=37276492 |pmc=10309535 }}</ref>

While both TMB and [[mutational signatures]] provide critical information about cancer behaviour, they have different definitions. TMB is defined as the number of somatic mutations/megabase whereas mutational signatures are distinct mutational patterns of single base substitutions, double base substitutions, or small insertions and deletions in tumors.<ref name="ref4">{{cite journal |vauthors=Alexandrov LB, Kim J, Haradhvala NJ, Huang MN, Ng AW, Wu Y, etal |date=2020 |title=The repertoire of mutational signatures in human cancers |url= |journal=Nature |volume=578 |issue=7793 | pages=94–101 |doi=10.1038/s41586-020-1943-3|pmid=32025018 |pmc=7054213 |bibcode=2020Natur.578...94A }}</ref> For instance, [[COSMIC cancer database|COSMIC]] single base substitution signature 1 is characterized by the enzymatic deamination of cytosine to thymine and has been associated with age of an individual.<ref name="ref4" />

Scientists postulate that high TMB is associated with an increased amount of neoantigens, which are tumour specific markers displayed by cells.<ref name="ref2" /><ref name="ref5">{{cite journal |vauthors=Owada-Ozaki Y, Muto S, Takagi H, Inoue T, Watanabe Y, Fukuhara M, etal |date=2018 |title=Prognostic Impact of Tumour Mutation Burden in Patients with Completely Resected Non-Small Cell Lung Cancer: Brief Report |url=https://www.jto.org/article/S1556-0864(18)30509-4/fulltext |journal=Journal of Thoracic Oncology |volume=13 |issue=8 |pages=1217–1221 |doi=10.1016/j.jtho.2018.04.003 |pmid=29654927|s2cid=4863075 |doi-access=free }}</ref> An increase in these [[antigen]]s may then lead to increased detection of cancer cells by the immune system and more robust activation of cytotoxic [[T cell|T-lymphocytes]]. Activation of T-cells is further regulated by [[immune checkpoint]]s that can be displayed by cancer cells, thus treatment with ICIs can lead to improved patient [[Survival rate|survival]].<ref name="ref6">{{cite journal|vauthors=Riviere P, Goodman AM, Okamura R, Barkauskas DA, Whitchurch TJ, Lee S, etal|date=2020|title=High Tumor Mutational Burden Correlates with Longer Survival in Immunotherapy-Naïve Patients with Diverse Cancers|url= |journal=Molecular Cancer Therapeutics|volume=19|issue=10|pages=2139–2145|doi=10.1158/1535-7163.MCT-20-0161|pmid=32747422|pmc=7541603}}</ref>

On June 16, 2020 the [[Food and Drug Administration|U.S. Food and Drug Administration]] expanded the approval of the immunotherapy drug [[pembrolizumab]] to treat any advanced solid-tumor cancers with a TMB greater than 10 mutations per Mb and continued growth following prior treatments.<ref name="ref7">{{cite web |url=https://www.fda.gov/drugs/drug-approvals-and-databases/fda-approves-pembrolizumab-adults-and-children-tmb-h-solid-tumors |title=FDA approves pembrolizumab for adults and children with TMB-H solid tumors |date=2020 |website=U.S. Food and Drug Administration |access-date=February 18, 2021}}</ref> This marks the first time that the FDA has approved a drug with its use based on TMB measurements.<ref name="ref8">{{cite web |url=https://www.cancer.org/latest-news/fda-approves-first-drug-for-cancers-with-a-high-tumor-mutational-burden.html |title=FDA Approves First Drug for Cancers with a High Tumor Mutational Burden |date=2020 |website=American Cancer Society |access-date=February 18, 2021}}</ref>
{{Clear}}[[File:TMB-Antigen Association.jpg|thumb|Mutations (red marks) in segments of the genome are reflected in proteins produced from them through transcription and translation. Some proteins are fragmented into peptides that can then be presented as antigens on the surface of cell membranes by major histocompatibility complexes (MHCs). If presented antigens accumulate enough mutations, they can bind and activate T-cells which can then initiate immune mediated cell death.|440x440px]]

== Importance ==

=== TMB as a Biomarker ===

One survival mechanism in tumors is to increase the expression of [[immune checkpoint]] molecules that can bind to tumor-specific T-cells and inactivate them, so that the tumor cells cannot be detected and killed.<ref name="ref9">{{cite journal |vauthors=Chan TA, Yarchoan M, Jaffee E, Swanton C, Quezada SA, Stenzinger A, etal |date=2019 |title=Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic |journal=Ann Oncol |volume=30 |issue=1 |pages=44–56 |doi=10.1093/annonc/mdy495 |pmc=6336005 |pmid=30395155}}</ref> ICIs have been shown to improve patients' response and the [[survival rate]]s as they help the immune system to target tumor cells.<ref name="ref1" /><ref name="ref8" /> However, there is a variation in response to ICIs among patients and it is crucial to know which patients can benefit from ICI therapy.<ref name="ref1" /> The expression of [[PD-L1]] (programmed death-ligand 1; one of the [[immune checkpoint]]s) has been demonstrated to be a good [[biomarker]] of [[PD-L1]] blockade therapy in some cancers.<ref name="ref8" /> However, there is a need for better biomarkers as there are some predictive errors with [[PD-L1]] expression.<ref name="ref8" /> Studies on TMB have illustrated that there is an association between patients' outcome (of ICI therapy) and the TMB value.<ref name="ref1" /> It has been proposed that TMB can be used as a predictive marker of response in ICI therapy across many cancer types.<ref name="ref8" /> Also, TMB can be helpful to identify individuals that can benefit from ICI therapy with cancers that generally have low TMB values.<ref name="ref8" /> Furthermore, it has been shown that tumors with higher TMB values usually result in a higher number of neoantigens, the [[antigen]]s that are presented on the tumor cells surface that are usually a result of missense mutations.<ref name="ref8" /> So, TMB can be a good estimator of neoantigen load and can help find the patients who can benefit from ICI therapy by increasing the chance of detecting the neoantigens.<ref name="ref8" /> However, it is important to note that different sequencing platforms and bioinformatics pipelines have been used to estimate TMB and it is important to harmonize TMB quantification protocols and procedures before it can be used as a reliable [[biomarker]].<ref name="ref1" /><ref>{{cite journal |vauthors=Addeo A, Banna GL, Weiss GJ |date=2019 |title=Tumor Mutation Burden-From Hopes to Doubts |url=https://jamanetwork.com/journals/jamaoncology/article-abstract/2734830 |journal=JAMA Oncol |volume=5 |issue=7 |pages=934–935 |doi=10.1001/jamaoncol.2019.0626|pmid=31145420 |s2cid=169038765 |url-access=subscription }}</ref> There have been some efforts to standardize these methods.<ref name="ref1" />

=== Treatment Response ===

TMB has been found to correlate with patient response to therapies such as immune checkpoint inhibitors (ICIs). An analysis of a large cohort of patients receiving ICI therapy revealed that higher TMB levels (≥ 20 mutations/Mb) corresponded to a 58% [[Response rate (medicine)|response rate]] to ICIs while lower TMB levels (<20 mutations/Mb) reduced response to 20%.<ref name="ref10">{{cite journal |vauthors=Goodman AM, Kato S, Bazhenova L, Patel SP, Frampton GM, Miller V, etal |date=2017 |title=Tumor Mutational Burden as an Independent Predictor of Response to Immunotherapy in Diverse Cancers |journal=Molecular Cancer Therapeutics |volume=16 |issue=11 |pages=2598–2608 |doi=10.1158/1535-7163.MCT-17-0386 |pmc=5670009 |pmid=28835386}}</ref> Researchers could also show a significant correlation between treatment response rate and TMB level in patients treated with anti-PD-1 or anti-PD-L1 (types of ICIs).<ref name="ref11">{{cite journal |vauthors=Yarchoan M, Hopkins A, Jaffee EM |date=2017 |title=Tumour Mutational Burden and Response Rate to PD-1 Inhibition |journal=N Engl J Med |volume=377 |issue=25 |pages=2500–2501 |doi=10.1056/NEJMc1713444 |pmc=6549688 |pmid=29262275}}</ref> Additionally, it has been reported that when ICIs were the only treatments used by patients, 55% of the differences in the objective response rate across cancer types were explained by TMB.<ref name="ref11" />

=== Patient Prognosis ===

Associations have been reported between TMB and patient outcome in a variety of cancers. In one study, scientists observed differences in [[survival rate]]s, with high TMB individuals having a median progression-free survival of 12.8 months and a median overall survival not reached by the time of publication, compared to 3.3 months and 16.3 months respectively for individuals with lower TMB.<ref name="ref10" /> Another study examining patients who had not received ICI therapy found that intermediate levels of TMB (>5 and <20 mutations/Mb) correlate with significantly decreased [[Survival rate|survival]], likely as a result of the accumulation of mutations in [[oncogene]]s.<ref name="ref5" /> This relationship does not appear to be significantly disparate across different tissues types and is only modestly affected by corrections for confounders such as smoking, sex, age, and ethnicity.<ref name="ref5" /> This suggests that TMB is both an independent and reliable indicator of poor patient outcomes in the absence of ICI therapy.<ref name="ref5" /> Interestingly, very high levels of TMB (≥ 50 mutations/Mb) were reported to correlate with increased [[Survival rate|survival]], giving an overall parabolic shape to the trend.<ref name="ref5" /> While this association is still under investigation, it has been hypothesized that the decreased risk of death under very high TMB could result from reduced cell viability due to [[Genome instability|genetic instability]] or increased production of neoantigens recognized by the immune system.<ref name="ref5" />

== TMB in different cancers==

There is a large variation in TMB values across different cancer types as the number of [[somatic mutation]]s can span from 0.01 to 400 mutations per megabase of genome.<ref name="ref1" /><ref name="ref8" /><ref name="ref9" /> It has been shown that [[melanoma]], [[Non-small-cell lung carcinoma|NSCLC]] and other [[Squamous cell carcinoma|squamous carcinomas]] have the highest levels of TMB in this order, while [[leukemia]]s and [[Pediatrics|pediatric]] tumors have the lowest levels of TMB and other cancers like [[Breast cancer|breast]], [[Kidney cancer|kidney]], and [[Ovarian cancer|ovary]] have intermediate TMB values.<ref name="ref8" /> There is also variation in TMB across different subtypes of different cancers.<ref name="ref8" /> Due to high variability in TMB across different cancer types and subtypes, it is important to define different cut-offs to have an improved survival prediction and a better treatment decision.<ref name="ref1" /><ref name="ref8" /><ref name="ref9" /> For example, Fernandez et al. showed that TMB can range from 0.03 to 14.13 mutations per megabase (mean=1.23) in [[The Cancer Genome Atlas|TCGA]] prostate cancer cohort while this range is from 0.04-99.68 mutations per megabase (mean=6.92) in [[The Cancer Genome Atlas|TCGA]] bladder cancer cohort.<ref name="ref12">{{cite journal |vauthors=Fernandez EM, Eng K, Beg S, Beltran H, Faltas BM, Mosquera JM, etal |date=2019 |title=Cancer-Specific Thresholds Adjust for Whole Exome Sequencing-based Tumor Mutational Burden Distribution |journal=JCO Precis Oncol |volume=3 |issue=3 |pages=1–12 |doi=10.1200/PO.18.00400 |pmc=6716608 |pmid=31475242}}</ref> A recent study illustrated that different cut-offs are needed for different cancer types to find the patients who can benefit from ICI therapy.<ref name="ref1" /> In addition, it is crucial to understand that usually there are different clusters of cells in a tumor, known as [[tumor heterogeneity]], that can affect TMB and consequently the response to ICIs.<ref name="ref8" /> Another factor that can affect TMB is whether the source of the sample is primary or [[Metastasis|metastatic]] tissue.<ref name="ref13">{{cite journal |vauthors=Schnidrig D, Turajlic S, Litchfield K |date=2019 |title=Tumor mutational burden: primary versus metastatic tissue creates systematic bias |journal=IOTECH |volume=4 |pages=8–14 |doi=10.1016/j.iotech.2019.11.003|pmid=35755001 |pmc=9216665 |doi-access=free }}</ref> Most [[Metastasis|metastatic]] samples have been shown to be monoclonal (i.e. there is only one cluster of cells in the tumor), while primary tumors usually consist of a higher number of clusters and have higher overall genetic diversity (more heterogeneous).<ref name="ref13" /> Scientists have shown that [[Metastasis|metastatic]] tumors usually have a higher TMB level compared to primary tumors and this can be due to monoclonal nature of [[Metastasis|metastatic]] lesions.<ref name="ref13" />

[[File:Tmb_all_samples.png|thumb|center|350x350px|TMB Variation within and between Different Cancer Types found in TCGA (a colorblind palette was used to make this figure and the TCGA mutation file, mc3.v0.2.8.PUBLIC.maf.gz, was obtained in July 2020 from: https://gdc.cancer.gov/about-data/publications/mc3-2017)]]

== TMB calculation==

There are disparities between how TMB is calculated in clinical and research settings.<ref name="ref14">{{cite journal |vauthors=Xu Z, Dai J, Wang D, Lu H, Dai H, Ye H, etal |date=2019 |title=Assessment of tumor mutation burden calculation from gene panel sequencing data |journal=OncoTargets Ther |volume=12 |pages=3401–9 |doi=10.2147/OTT.S196638 |pmc=6510391 |pmid=31123404 |doi-access=free }}</ref> Broadly, [[whole genome sequencing]], [[Exome sequencing|whole exome sequencing]], and panel based approaches can be used to help to calculate TMB.<ref name="ref14" /> Studies of TMB from research perspectives typically incorporate [[Exome sequencing|whole exome sequencing]], and occasionally [[whole genome sequencing]] within their workflows while clinical applications use panel sequencing to estimate TMB primarily for their comparatively quicker speed and low cost.<ref name="ref14" /> Within panel based approaches, different strategies to calculate TMB have been adopted.<ref name="ref14" /> For instance, consider MSK-IMPACT developed by the [[Memorial Sloan Kettering Cancer Center]] and F1CDx developed by [[Foundation Medicine]].<ref name="ref15">{{cite journal |vauthors=Büttner R, Longshore JW, López-Ríos F, Merkelbach-Bruse S, Normanno N, Rouleau E, etal |date=2019 |title=Implementing TMB measurement in clinical practice: considerations on assay requirements |journal=ESMO Open |volume=4 |issue=1 |article-number=e000442 |doi=10.1136/esmoopen-2018-000442 |pmc=6350758 |pmid=30792906}}</ref><ref name="ref16">{{cite journal |vauthors=Cheng DT, Mitchell TN, Zehir A, Shah RH, Benayed R, Syed A, etal |date=2015 |title=Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT): A Hybridization Capture-Based Next-Generation Sequencing Clinical Assay for Solid Tumor Molecular Oncology |journal=J Mol Diagn |volume=17 |issue=3 |pages=251–64 |doi=10.1016/j.jmoldx.2014.12.006 |pmc=5808190 |pmid=25801821}}</ref> F1CDx utilizes tumor-only sequencing strategy while MSK-IMPACT requires sequencing of both the tumor and its matched normal sample. Additionally, F1CDx counts synonymous mutations while excluding hotspot driver mutations.<ref name="ref15" /> MSK-IMPACT calculates TMB with similar filtering criteria to those used in [[Exome sequencing|whole exome sequencing]], considering both [[Synonymous substitution|synonymous mutations]] and [[Somatic evolution in cancer|hotspot driver mutations]].<ref name="ref16" /> Ensembles of targeted panels and [[Exome sequencing|whole exome sequencing]] panels have been recommended for optimal results.<ref name="ref17">{{cite journal |vauthors=Shao C, Li G, Huang L, Pruitt S, Castellanos E, Frampton G, etal |date=2020 |title=Prevalence of High Tumor Mutational Burden and Association With Survival in Patients With Less Common Solid Tumors |journal=JAMA Netw Open |volume=3 |issue=10 |pages=e2025109 |doi=10.1001/jamanetworkopen.2020.25109 |pmc=7596577 |pmid=33119110}}</ref> As an approach that is potentially more expedient and cost effective than sequencing, TMB can be calculated directly from [[H&E stain]]ed pathology images using [[deep learning]].<ref name=":0">{{Cite journal|last1=Jain|first1=Mika S.|last2=Massoud|first2=Tarik F.|date=2020|title=Predicting tumour mutational burden from histopathological images using multiscale deep learning|url=https://www.nature.com/articles/s42256-020-0190-5|journal=Nature Machine Intelligence|language=en|volume=2|issue=6|pages=356–362|doi=10.1038/s42256-020-0190-5|s2cid=220510782|issn=2522-5839|url-access=subscription}}</ref>
[[File:Factors influencing TMB Calculation.png|thumb|500x500px|Factors such as tumor cell content, tissue preprocessing, choice of sequencing technology, downstream bioinformatic pipelines, and TMB cutoffs can influence TMB calculations.]]

=== Factors that Influence TMB Calculation ===
Overall, 5 primary factors have been identified to influence TMB calculations.<ref name="ref18">{{cite journal |vauthors=Meléndez B, Van Campenhout C, Rorive S, Remmelink M, Salmon I, D'Haene N |date=2018 |title=Methods of measurement for tumor mutational burden in tumor tissue |journal=Transl Lung Cancer Res |volume=7 |issue=6 |pages=661–7 |doi=10.21037/tlcr.2018.08.02 |pmc=6249625 |pmid=30505710 |doi-access=free }}</ref>

==== Tumor Cell Content and Sequencing Coverage ====
Greater tumor cell content and sequencing coverage play a key role in the quality of TMB data.<ref name="ref18" /> For instance, targeted panels may enable deeper sequencing compared to [[Exome sequencing|whole exome sequencing]], enabling higher sensitivity, that have been shown to perform well even when tumor cell content is low (defined as <10%).<ref name="ref18" /> Targeted panels have shown to enable much greater coverage than in [[Exome sequencing|whole exome sequencing]].<ref name="ref18" /> For example, one recent study reached a mean sequencing [[Coverage (genetics)|coverage]] across all tumor samples of 744× when using the MSK-IMPACT panel, while the WES led to a mean target coverage of 232× in tumor sequences.<ref name="ref19">{{cite journal |vauthors=Rizvi H, Sanchez-Vega F, La K, Chatila W, Jonsson P, Halpenny D, etal |date=2018 |title=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 |journal=J Clin Oncol |volume=36 |issue=7 |pages=633–41 |doi=10.1200/JCO.2017.75.3384 |pmc=6075848 |pmid=29337640}}</ref>

==== Tissue Preprocessing ====
Typically, tumor tissues are fixated in formalin to preserve tissue and cellular morphology in the formalin-fixed paraffin-embedded (FFPE) protocols.<ref name="ref20">{{cite journal |vauthors=Chalmers ZR, Connelly CF, Fabrizio D, Gay L, Ali SM, Ennis R, etal |date=2017 |title=Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden |url= |journal=Genome Med |volume=9 |issue=1 |page=34 |doi=10.1186/s13073-017-0424-2|pmid=28420421 |pmc=5395719 |doi-access=free }}</ref> While FFPE offers a cost-effective method to store tissues for long durations of time, limitations must be considered as to how it will affect TMB calculations.<ref name="ref20" /> One limitation of this method is that it induces the formation of various crosslinks, whereby strands of DNA become covalently bound to each other, which may consequently lead to [[deamination]] of cytosine bases.<ref name="ref18" /> Cytosine deamination is the major cause of baseline noise in [[Next-Generation Sequencing|Next Generation Sequencing]], leading to the most prevalent sequence artifacts in FFPE (C:G > T:A).<ref name="ref18" /> This may generate artefacts that must be removed in the downstream pipeline.

==== Sequencing Strategy ====
Different sequencing strategies enable different number of genes to be included in the calculation of TMB (with WGS and WES approaches allowing a greater quantity of genes to be analyzed). While panel based approaches analyze comparatively fewer genes than other strategies, one advantage of panel based sequencing is that genes of interest can be covered in much greater sequencing depths, and rare variants can possibly be identified.<ref name="ref18" /> The panel sizes vary across panels with 468 genes in the MSK-IMPACT panel, 315 genes in the Foundation Medicine panel, and 409 genes in the Life Technologies panel.<ref name="ref18" /> As panel sizes are smaller, uncertainty associated with TMB estimation becomes greater, with coefficient of variance increases rapidly when the size of the targeted panels is less than 1 Mb.<ref name="ref20" />

==== Bioinformatics Pipeline ====
In most calculations of TMB, [[Synonymous substitution|synonymous variants]] and germline variants are filtered out as they are unlikely to be directly involved in creating neoantigens.<ref name="ref18" /> However, some pipelines maintain [[Synonymous substitution|synonymous variants]].<ref name="ref20" /> To account for germline variants, ideally sequencing would have been performed on a matched non-tumor sample from each patient.<ref name="ref20" /> However, in a clinical practice, the availability of this matched sample may vary across different institutions and diverse organizational factors, and data unavailability may inhibit germline variants to be filtered.<ref name="ref20" /> The choice of variant callers and other software in the downstream analyses may also affect how TMB is ultimately calculated.<ref name="ref20" /> TMB can be calculated directly from histopathology images using a multiscale deep learning pipeline, avoiding the need for sequencing and variant calling.<ref name=":0" />

==== Cut-offs ====
Different studies have assigned different cut-offs to delineate between high and low TMB status.<ref name="ref18" /> In the lung, the median TMB across more than 18,000 lung cancer cases was 7.2 mutations/Mb, with approximately 12% of the patients showing more than 20 mutations/Mb.<ref name="ref20" /> The authors identified a tumor mutational burden greater than or equal to 10 mutations/Mb as the optimal cut-off to benefit from combination [[immunotherapy]].<ref name="ref20" /> However, in other cancer types, high TMB status has been classified as >20 mutations/Mb.<ref name="ref5" />

== Issues and future directions ==

One approved [[biomarker]] of ICI therapy is [[PD-L1]] expression, but the predictive power of this biomarker is affected by factors such as assay interpretation and lack of standard methods.<ref name="ref8" /> TMB is also affected by these factors in addition to accessibility issues.<ref name="ref8" /> Biological factors like specimen type and [[cancer]] type as well as technical factors like [[Massive parallel sequencing|sequencing technology]] can affect evaluation of TMB.<ref name="ref1" /> Thus, it is necessary to harmonize evaluation methods and there are still so many factors that can complicate this task.<ref name="ref1" /><ref name="ref8" /> For example, [[Fusion gene|gene fusions]] and [[Post-translational modification|post-translational changes]] in proteins contribute to tumor behaviour and consequently response to therapy while these factors are not considered in TMB estimation.<ref name="ref8" /> In addition, currently all mutations have the same weight in TMB calculation, while they can have very different effects on [[protein]]s and [[Biological pathway|pathways]] activity.<ref name="ref8" /> Furthermore, there is still no good answer to the question of how mutations in [[gene]]s that are known to influence ICI therapy should be treated in TMB evaluation.<ref name="ref8" /> It is also important to note that TMB is highly variable across cancer types and subtypes and different studies are being conducted to find distinct TMB thresholds.<ref name="ref8" />

Some studies argue that to have better prediction of response to ICI therapy, TMB should be used as a complementary marker with other biomarkers such as [[PD-L1]].<ref name="ref8" /> Other studies have shown that a combination of TMB and neoantigen load can be used as a biomarker to predict survival in patients with [[melanoma]] who received adaptive T cell transfer therapy.<ref name="ref8" /> Since TMB is a relatively new biomarker, there is still a need to perform more studies and many labs are being focused on different aspects of this biomarker.<ref name="ref8" /><ref name="ref9" />

== References ==
{{reflist}}

[[Category:Genetics]]
[[Category:Mutation]]
[[Category:DNA]]
[[Category:Tumor markers]]

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