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Tumor mutational burden
Tumor mutational burden
Tumor mutational burden
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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 non-inherited mutations per million bases (Mb) of investigated genomic sequence,[1] and its measurement has been enabled by 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.[2]

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.[3][4] 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.[5]

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.[6] For instance, 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.[6]

Scientists postulate that high TMB is associated with an increased amount of neoantigens, which are tumour specific markers displayed by cells.[2][7] An increase in these antigens may then lead to increased detection of cancer cells by the immune system and more robust activation of cytotoxic T-lymphocytes. Activation of T-cells is further regulated by immune checkpoints that can be displayed by cancer cells, thus treatment with ICIs can lead to improved patient survival.[8]

On June 16, 2020 the 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.[9] This marks the first time that the FDA has approved a drug with its use based on TMB measurements.[10]

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.

Importance

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TMB as a Biomarker

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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.[11] ICIs have been shown to improve patients' response and the survival rates as they help the immune system to target tumor cells.[1][10] However, there is a variation in response to ICIs among patients and it is crucial to know which patients can benefit from ICI therapy.[1] The expression of PD-L1 (programmed death-ligand 1; one of the immune checkpoints) has been demonstrated to be a good biomarker of PD-L1 blockade therapy in some cancers.[10] However, there is a need for better biomarkers as there are some predictive errors with PD-L1 expression.[10] Studies on TMB have illustrated that there is an association between patients' outcome (of ICI therapy) and the TMB value.[1] It has been proposed that TMB can be used as a predictive marker of response in ICI therapy across many cancer types.[10] Also, TMB can be helpful to identify individuals that can benefit from ICI therapy with cancers that generally have low TMB values.[10] Furthermore, it has been shown that tumors with higher TMB values usually result in a higher number of neoantigens, the antigens that are presented on the tumor cells surface that are usually a result of missense mutations.[10] 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.[10] 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.[1][12] There have been some efforts to standardize these methods.[1]

Treatment Response

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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 to ICIs while lower TMB levels (<20 mutations/Mb) reduced response to 20%.[13] 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).[14] 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.[14]

Patient Prognosis

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Associations have been reported between TMB and patient outcome in a variety of cancers. In one study, scientists observed differences in survival rates, 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.[13] 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, likely as a result of the accumulation of mutations in oncogenes.[7] 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.[7] This suggests that TMB is both an independent and reliable indicator of poor patient outcomes in the absence of ICI therapy.[7] Interestingly, very high levels of TMB (≥ 50 mutations/Mb) were reported to correlate with increased survival, giving an overall parabolic shape to the trend.[7] 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 genetic instability or increased production of neoantigens recognized by the immune system.[7]

TMB in different cancers

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There is a large variation in TMB values across different cancer types as the number of somatic mutations can span from 0.01 to 400 mutations per megabase of genome.[1][10][11] It has been shown that melanoma, NSCLC and other squamous carcinomas have the highest levels of TMB in this order, while leukemias and pediatric tumors have the lowest levels of TMB and other cancers like breast, kidney, and ovary have intermediate TMB values.[10] There is also variation in TMB across different subtypes of different cancers.[10] 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.[1][10][11] For example, Fernandez et al. showed that TMB can range from 0.03 to 14.13 mutations per megabase (mean=1.23) in TCGA prostate cancer cohort while this range is from 0.04-99.68 mutations per megabase (mean=6.92) in TCGA bladder cancer cohort.[15] A recent study illustrated that different cut-offs are needed for different cancer types to find the patients who can benefit from ICI therapy.[1] 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.[10] Another factor that can affect TMB is whether the source of the sample is primary or metastatic tissue.[16] Most 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).[16] Scientists have shown that metastatic tumors usually have a higher TMB level compared to primary tumors and this can be due to monoclonal nature of metastatic lesions.[16]

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

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There are disparities between how TMB is calculated in clinical and research settings.[17] Broadly, whole genome sequencing, whole exome sequencing, and panel based approaches can be used to help to calculate TMB.[17] Studies of TMB from research perspectives typically incorporate 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.[17] Within panel based approaches, different strategies to calculate TMB have been adopted.[17] For instance, consider MSK-IMPACT developed by the Memorial Sloan Kettering Cancer Center and F1CDx developed by Foundation Medicine.[18][19] 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.[18] MSK-IMPACT calculates TMB with similar filtering criteria to those used in whole exome sequencing, considering both synonymous mutations and hotspot driver mutations.[19] Ensembles of targeted panels and whole exome sequencing panels have been recommended for optimal results.[20] As an approach that is potentially more expedient and cost effective than sequencing, TMB can be calculated directly from H&E stained pathology images using deep learning.[21]

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

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Overall, 5 primary factors have been identified to influence TMB calculations.[22]

Tumor Cell Content and Sequencing Coverage

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Greater tumor cell content and sequencing coverage play a key role in the quality of TMB data.[22] For instance, targeted panels may enable deeper sequencing compared to whole exome sequencing, enabling higher sensitivity, that have been shown to perform well even when tumor cell content is low (defined as <10%).[22] Targeted panels have shown to enable much greater coverage than in whole exome sequencing.[22] For example, one recent study reached a mean sequencing 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.[23]

Tissue Preprocessing

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Typically, tumor tissues are fixated in formalin to preserve tissue and cellular morphology in the formalin-fixed paraffin-embedded (FFPE) protocols.[24] 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.[24] 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.[22] Cytosine deamination is the major cause of baseline noise in Next Generation Sequencing, leading to the most prevalent sequence artifacts in FFPE (C:G > T:A).[22] This may generate artefacts that must be removed in the downstream pipeline.

Sequencing Strategy

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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.[22] 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.[22] 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.[24]

Bioinformatics Pipeline

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In most calculations of TMB, synonymous variants and germline variants are filtered out as they are unlikely to be directly involved in creating neoantigens.[22] However, some pipelines maintain synonymous variants.[24] To account for germline variants, ideally sequencing would have been performed on a matched non-tumor sample from each patient.[24] 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.[24] The choice of variant callers and other software in the downstream analyses may also affect how TMB is ultimately calculated.[24] TMB can be calculated directly from histopathology images using a multiscale deep learning pipeline, avoiding the need for sequencing and variant calling.[21]

Cut-offs

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Different studies have assigned different cut-offs to delineate between high and low TMB status.[22] 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.[24] 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.[24] However, in other cancer types, high TMB status has been classified as >20 mutations/Mb.[7]

Issues and future directions

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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.[10] TMB is also affected by these factors in addition to accessibility issues.[10] Biological factors like specimen type and cancer type as well as technical factors like sequencing technology can affect evaluation of TMB.[1] Thus, it is necessary to harmonize evaluation methods and there are still so many factors that can complicate this task.[1][10] For example, gene fusions and post-translational changes in proteins contribute to tumor behaviour and consequently response to therapy while these factors are not considered in TMB estimation.[10] In addition, currently all mutations have the same weight in TMB calculation, while they can have very different effects on proteins and pathways activity.[10] Furthermore, there is still no good answer to the question of how mutations in genes that are known to influence ICI therapy should be treated in TMB evaluation.[10] 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.[10]

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.[10] 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.[10] 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.[10][11]

References

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Tumor mutational burden

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Tumor mutational burden (TMB) is defined as the total number of somatic per megabase of interrogated genomic in a tumor's DNA, reflecting the extent of genomic instability and potential for generating neoantigens that can elicit an . This quantitative measure serves as a key predictive for the efficacy of inhibitors (ICIs), such as anti-PD-1/ therapies, particularly in solid tumors where higher TMB levels correlate with improved response rates and survival outcomes. For instance, the U.S. (FDA) approved in 2020 for adult and pediatric patients with unresectable or metastatic solid tumors exhibiting a TMB of ≥10 per megabase, based on data from clinical trials like KEYNOTE-158. TMB is typically assessed through next-generation sequencing (NGS) of tumor tissue, with whole exome sequencing (WES) serving as the gold standard by covering approximately 30 megabases of coding regions, though targeted gene panels (e.g., 0.8–2.4 Mb in size, such as MSK-IMPACT or FoundationOne CDx) are more commonly used in clinical settings for their efficiency and cost-effectiveness. These panels focus on hundreds of cancer-related genes and calculate TMB by counting non-synonymous somatic variants, often excluding germline mutations, driver alterations, and sometimes synonymous changes to emphasize passenger mutations that drive immunogenicity. Blood-based TMB (bTMB), derived from circulating tumor DNA, offers a non-invasive alternative, showing concordance with tissue TMB and utility in monitoring treatment response, though it requires validation for broader adoption. Clinically, TMB varies significantly across tumor types, with higher burdens observed in cancers like melanoma (median ~71% high TMB cases), non-small cell lung cancer (44–50%), and microsatellite instability-high (MSI-H) tumors, while lower levels predominate in pediatric cancers, which have notably low TMB (typically <1 mut/Mb, with an average of ~9.6 total nonsynonymous mutations per tumor), and breast cancer. Approximately 13.2% of patients in the KEYNOTE-158 trial qualified as TMB-high, highlighting its relevance in stratifying patients for ICI therapy across pan-cancer settings. Despite these advances, challenges persist, including the lack of universal cutoffs (typically 10–20 mut/Mb but tumor-type dependent), variability in assay performance due to panel size and bioinformatics pipelines, and issues with tumor purity, intratumor heterogeneity, and sample quality that can lead to inconsistent reporting. As of 2025, emerging research continues to refine TMB cutoffs, questioning the one-size-fits-all approach of 10 mut/Mb for optimal immunotherapy prediction. Ongoing standardization efforts, such as those by Friends of Cancer Research and the Quality Assurance Initiative Pathology, aim to harmonize TMB assessment by recommending panels of at least 1 Mb and alignment with WES benchmarks to ensure reliable clinical decision-making.

Definition and Background

Core Concept of TMB

Tumor mutational burden (TMB) quantifies the density of somatic within a tumor's , specifically the total number of non-synonymous —such as missense, , and frameshift variants—per megabase of sequenced or , while excluding variants. This metric focuses on coding regions where can alter protein sequences, providing a measure of the tumor's genomic and potential for immune recognition. The biological foundation of TMB lies in its role as a proxy for neoantigen load, where somatic mutations generate novel peptides that are processed and presented on the tumor cell surface via (MHC) molecules. These neoantigens can be recognized by T-cells, thereby enhancing the tumor's and facilitating immune-mediated destruction. High TMB thus correlates with increased likelihood of effective antitumor immune responses, as more mutations diversify the neoantigen repertoire available for T-cell targeting. TMB is typically expressed in units of mutations per megabase (mut/Mb), allowing for standardized comparisons across samples. Measurements can differ based on the sequencing approach: whole exome sequencing (WES) provides a comprehensive assessment of the ~30-60 Mb coding genome, yielding a direct mut/Mb value, whereas targeted gene panels interrogate smaller subsets of genes and often require algorithmic normalization to estimate equivalent TMB scores. For instance, in non-small cell (NSCLC), median TMB values range from approximately 5-10 mut/Mb among smokers, reflecting tobacco-induced , compared to lower levels around 4 mut/Mb in never-smokers. High TMB has been linked to enhanced responses in , underscoring its relevance in precision oncology.

Historical Development

The concept of tumor mutational burden (TMB) emerged in the early 2010s through large-scale genomic initiatives like (TCGA), which began sequencing tumor exomes across multiple cancer types starting in 2006 and published pan-cancer analyses by 2013. These efforts revealed wide variations in loads among cancers, with initial observations linking higher mutation burdens to increased immune cell infiltration, such as CD8+ T cells, suggesting a potential role in antitumor immunity. By cataloging thousands of tumors, TCGA data laid the groundwork for understanding TMB as a quantitative measure of genomic instability, influencing subsequent research on neoantigen generation and immune recognition. Pivotal clinical studies in the mid-2010s established TMB's association with immunotherapy responses. In 2014, Snyder et al. analyzed exomes from melanoma patients treated with ipilimumab, a CTLA-4 inhibitor, finding that responders had significantly higher somatic mutation burdens compared to non-responders, correlating with neoantigen load and clinical benefit. This was followed in 2015 by Rizvi et al., who examined non-small cell lung cancer (NSCLC) tumors from patients receiving pembrolizumab, a PD-1 inhibitor, and reported that higher nonsynonymous mutation burdens (median 12.4 in responders vs. 6.7 in non-responders) predicted durable responses, independent of smoking history. These findings shifted TMB from a descriptive genomic metric to a candidate predictive biomarker for immune checkpoint blockade. Regulatory milestones accelerated TMB's clinical adoption. In June 2020, the U.S. (FDA) granted accelerated approval for as a tumor-agnostic in adult and pediatric patients with unresectable or metastatic solid tumors characterized by TMB-high status (≥10 mutations per megabase), based on data from the KEYNOTE-158 trial showing an objective response rate of 29.3% in this group. Concurrently, efforts by the Friends of TMB Harmonization Project, launched in 2017, addressed variability in TMB measurement across through multi-lab collaborations and reference standards, promoting standardization from a research tool to a reproducible clinical . By 2024, ongoing initiatives expanded TMB's utility in combination , with subgroup analyses in trials like KEYNOTE-942 showing benefits across TMB and statuses in . In 2023, a phase 3 trial (KEYNOTE-B15) was initiated to further evaluate mRNA-4157 plus in high-risk . As of 2025, research explores acquired high TMB post-targeted to enhance sensitivity.

Clinical Significance

Biomarker Role in Immunotherapy

Tumor mutational burden (TMB) serves as a predictive for response to inhibitors (ICIs), with the U.S. (FDA) granting accelerated approval in June 2020 for in adult and pediatric patients with unresectable or metastatic tumors exhibiting high TMB (≥10 mutations per megabase, mut/Mb), as determined by an FDA-approved companion diagnostic test. This tumor-agnostic approval highlights TMB's role independent of programmed death-ligand 1 () expression, enabling broader application across cancer types where ICI efficacy was previously limited by status. Mechanistically, elevated TMB correlates with a higher neoantigen burden, as somatic mutations generate novel peptides presented on molecules, thereby enhancing T-cell recognition and infiltration into the . This increased promotes (TILs), particularly cytotoxic T cells, which synergize with ICIs such as anti-PD-1 and anti-PD-L1 therapies to reinvigorate exhausted immune responses and improve antitumor activity. Consequently, tumors with high TMB exhibit greater sensitivity to checkpoint blockade compared to those with low TMB, where neoantigen scarcity limits immune activation. Meta-analyses of patients treated with ICIs substantiate TMB's predictive value, demonstrating that high TMB cohorts achieve significantly better (PFS) than low TMB groups, with pooled hazard ratios (HRs) ranging from 0.45 to 0.54. For instance, one analysis of over 4,500 patients across multiple studies reported an HR of 0.45 (95% CI: 0.36–0.56) for PFS in high versus low TMB, underscoring TMB's association with prolonged ICI benefit. Another in non-small cell lung cancer confirmed an HR of 0.54 (95% CI: 0.46–0.63), reinforcing TMB's utility in stratifying ICI responders. TMB's predictive strength varies by cancer type, proving more robust in non-smoker-associated malignancies such as instability-high (MSI-H) colorectal cancer, where high TMB aligns closely with enhanced ICI responses due to shared defect-driven hypermutation. In contrast, its predictive value is attenuated in smoker-linked cancers like lung adenocarcinoma, where environmental mutagens confound TMB's direct correlation with neoantigen and ICI . This specificity highlights TMB's contextual limitations as a pan-cancer , emphasizing the need for integrated assessments in diverse tumor etiologies.

Predictive Value for Treatment Response

Tumor mutational burden (TMB) serves as a predictive for response to , particularly inhibitors, with high TMB levels associated with improved objective response rates (ORR), (PFS), and overall survival (OS) in various clinical trials. In the phase 2 KEYNOTE-158 trial evaluating in patients with advanced tumors, those with high TMB (≥10 mutations per megabase) demonstrated an ORR of 29% compared to 10% in patients with low TMB, highlighting TMB's utility across tumor types independent of status. This trial's results supported the FDA approval of for TMB-high tumors, underscoring the biomarker’s role in identifying responders to PD-1 inhibition. Recent updates from trials in non-small cell lung cancer (NSCLC) further affirm TMB's predictive value. In the phase 3 227 trial, patients with high TMB treated with nivolumab plus showed durable OS benefits, with 5-year OS rates exceeding those in arms, particularly in TMB-high subgroups. Analyses from the CA209-7AL trial in unresectable stage III NSCLC revealed that high TMB patients receiving consolidative nivolumab after achieved significantly longer PFS (not reached versus 15.2 months in low TMB; p=0.042). These findings extend TMB's relevance to perioperative and consolidative settings, where high TMB predicted enhanced treatment efficacy. High TMB correlates with higher rates of complete and partial responses, as well as extended duration of response (DOR). For instance, in patients treated with PD-1 inhibitors, high TMB subgroups exhibited ORRs up to 45%, reflecting increased neoantigen load and immune activation. Across trials like KEYNOTE-158, DOR in TMB-high patients was notably prolonged, often not reached at median follow-up, compared to 12-18 months in low TMB cohorts, representing an extension of 6-12 months or more in responsive cases. This benefit is most pronounced with PD-1 inhibitors such as and nivolumab, where high TMB independently predicts superior outcomes, whereas responses to CTLA-4 inhibitors like alone are weaker and less TMB-dependent. Subgroup analyses emphasize TMB's value beyond microsatellite instability-high tumors. In microsatellite-stable (MSS) tumors with high TMB, yields significant benefits, with ORRs comparable to MSI-high cases and improved PFS in PD-1-treated patients. For rare cancers like small cell (SCLC), a 2025 real-world analysis across multiple cancers including SCLC showed a non-significant trend toward better overall survival with high TMB (HR 0.89, 95% CI 0.44-1.09) on inhibition, supporting further evaluation of TMB in metastatic SCLC . These insights support TMB-high/MSS identification for expanded access in underrepresented tumor types.

Prognostic Implications

Tumor mutational burden (TMB) serves as an intrinsic prognostic in cancer patients, particularly in cohorts not receiving , where high TMB frequently correlates with poorer overall survival due to underlying tumor aggressiveness. In a systematic pan-cancer evaluation of (TCGA) data encompassing 6,035 patients across 20 cancer types, high TMB was significantly associated with worse overall survival in 8 cancers, including (hazard ratio [HR] 2.47, 95% CI 1.02–5.98, P=0.045), (HR 6.10, 95% CI 1.91–19.46, P=0.002), colorectal adenocarcinoma, esophageal carcinoma, kidney renal clear cell carcinoma, liver , , and pancreatic adenocarcinoma. Conversely, high TMB predicted better survival in 6 cancer types, such as bladder urothelial carcinoma and kidney renal papillary cell carcinoma, while showing no significant impact in the remaining 6. This divergent pattern underscores TMB's context-specific prognostic utility for risk stratification independent of treatment. In immunotherapy-naïve patients with advanced malignancies, TMB exhibits a nonlinear relationship with , where intermediate levels (>5 and <20 mutations/Mb) confer the worst outcomes compared to low (≤5 mutations/Mb) or high (≥20 mutations/Mb) burdens. Among 1,415 such patients, those with intermediate TMB had a median overall of 174 weeks (HR 1.29, 95% CI 1.07–1.54, P<0.01) versus 238 weeks for low TMB, while high TMB yielded 195 weeks (HR 0.98, P=0.90), suggesting that moderate mutational loads may reflect suboptimal immune equilibrium without therapeutic intervention. Although high TMB often signals favorable responses in immunotherapy contexts, its prognostic implications in chemotherapy-only or untreated settings are generally adverse, as evidenced by mixed signals in pan-cancer analyses where elevated TMB aligns with reduced in over half of evaluated cohorts. The adverse prognostic role of high TMB in non-immunotherapy scenarios stems from its reflection of genomic instability, driven by defects in DNA repair pathways such as mismatch repair and nucleotide excision repair, which foster tumor proliferation, chromosomal aberrations, and heightened metastasis risk. This instability correlates with aggressive tumor biology, including increased cell cycle activity and structural variants that promote invasive growth, thereby worsening patient outcomes independent of immune checkpoint modulation. For instance, high TMB also enables neoantigen generation that could enhance immune surveillance, but in untreated settings, the dominant effect is unchecked genomic chaos leading to rapid disease progression. Specific examples highlight TMB's variable prognostic weight across histologies. In gliomas, elevated TMB is linked to shorter overall survival, with higher burdens associating with advanced grade, older age, and structural mutations that exacerbate poor outcomes in this immunologically "cold" tumor type. In contrast, breast cancer demonstrates largely neutral TMB-prognosis associations in non-immunotherapy cohorts, where high TMB does not independently influence survival unless integrated with factors like immune infiltration or specific gene signatures, reflecting the hormone-driven stability of many breast tumors.

Variation Across Cancer Types

Cancers with High TMB

Tumor mutational burden (TMB) is considered high when exceeding 10 mutations per megabase (mut/Mb), a threshold commonly used for classification in clinical and research settings. Approximately 13% of solid tumors across various cohorts meet this criterion, highlighting a subset of malignancies with elevated genomic instability that may influence therapeutic strategies. Among cancers frequently exhibiting high TMB, cutaneous melanoma stands out with median values of 10-14 mut/Mb, primarily driven by ultraviolet (UV) radiation exposure that induces characteristic C>T transitions. Non-small cell lung cancer (NSCLC) in smokers also shows elevated TMB, with medians around 10-12 mut/Mb attributable to mutagens causing G>T transversions. instability-high (MSI-high) colorectal cancers, resulting from deficiency, often display markedly higher TMB levels exceeding 50 mut/Mb, reflecting hypermutation due to unrepaired replication errors. Endometrial cancers harboring POLE proofreading domain mutations represent another example, where these hereditary alterations lead to ultrahigh TMB often surpassing 100 mut/Mb through polymerase infidelity. Elevated TMB in these cancers arises from diverse etiologies, including environmental factors like UV light in and in NSCLC, endogenous processes such as cytidine deaminase hyperactivity contributing to clustered mutations in multiple tumor types, and hereditary defects like POLE mutations in endometrial carcinoma. Virus-negative , distinct from polyomavirus-positive cases, exhibits high TMB (often >20 mut/Mb) linked to UV signatures, with 2025 analyses confirming its association with aggressive disease and potential responsiveness. Clinically, high-TMB cancers demonstrate improved outcomes with inhibitors (ICIs), with objective response rates of approximately 20-30% in pan-solid tumor cohorts treated with anti-PD-1/ therapies, underscoring TMB's role as a predictive .

Cancers with Low TMB

Tumor mutational burden (TMB) is generally low in the majority of adult solid tumors, reflecting limited exposure to environmental mutagens and proficient mechanisms across these cancer types. Pediatric tumors characteristically display very low TMB, often below 1 mut/Mb, as evidenced by pan-cancer analyses reporting medians as low as 0.09 mut/Mb, attributed to their embryonal origins and reduced lifetime accumulation of somatic mutations compared to adult cancers. Similarly, IDH-mutant gliomas maintain low TMB levels, typically ranging from 1 to 3 mut/Mb, with the vast majority below 2 mut/Mb, due to intact mismatch repair pathways and limited hypermutator phenotypes in these tumors. Prostate cancers also feature low TMB, averaging 2 to 4 mut/Mb and driven primarily by signaling rather than mutagenic processes, further supported by high-TMB subsets being rare (approximately 2–5% of cases). The inherently low TMB in these cancers stems from multiple factors, including minimal exposure to exogenous mutagens such as UV radiation or tobacco smoke, which contrasts with high-TMB tumors like melanomas or lung cancers. Efficient mechanisms, such as wild-type BRCA1/2 status and proficient mismatch repair, prevent mutation accumulation in low-TMB cancers like and IDH-mutant gliomas. Additionally, suppressive tumor microenvironments in these immunologically "cold" tumors, characterized by low T-cell infiltration, further limit neoantigen presentation and mutational evolution. Clinically, low-TMB cancers exhibit poorer responses to inhibitors (ICIs), with objective response rates often below 10%, as demonstrated in pan-tumor cohorts where low-TMB groups showed only 6-9% efficacy compared to higher rates in TMB-elevated subsets. In low-TMB , 2024 analyses have highlighted alternative biomarkers, such as T-cell inflamed signatures, which correlate with improved and potential ICI benefit independent of TMB, emphasizing the need for multifaceted immune profiling in therapy selection. These characteristics underscore the challenges in applying TMB-based immunotherapy to low-TMB cancers, prompting exploration of targeted therapies tailored to their molecular drivers.

Measurement Methods

Sequencing Approaches

Tumor mutational burden (TMB) assessment relies on genomic sequencing to detect somatic mutations in tumor DNA, with various approaches balancing comprehensiveness, cost, and clinical feasibility. The primary methods include whole exome sequencing (WES), targeted next-generation sequencing (NGS) panels, and (WGS), each leveraging high-throughput platforms to quantify mutations per megabase in coding regions. These techniques typically require paired tumor-normal samples to distinguish somatic from variants, though tumor-only approaches are increasingly used with computational adjustments. Whole exome sequencing (WES) serves as the gold standard for TMB measurement due to its unbiased capture of protein-coding variants across approximately 2% of the , focusing on the ~20,000 genes and 30-60 megabases of exonic sequence. To ensure accurate detection of low-frequency subclonal in heterogeneous tumors, WES typically demands high sequencing depth of 100-300x coverage for both tumor and matched normal samples. This method provides a of somatic without , making it for research benchmarks, though its broader genomic scope increases volume and processing demands compared to targeted alternatives. In clinical settings, targeted NGS panels have become the standard for TMB evaluation, interrogating 300-500 cancer-relevant genes to assess mutations within a focused genomic space of 1-2 megabases, which is then extrapolated to estimate whole-exome equivalents. Prominent examples include the MSK-IMPACT panel, which sequences 505 genes at depths exceeding 500x, and FoundationOne CDx, a 324-gene approved by the FDA for companion diagnostics, both demonstrating strong concordance (r > 0.9) with WES-derived TMB values in validation studies. These panels prioritize actionable alterations alongside TMB, enabling efficient integration into routine workflows while minimizing off-target noise from non-coding regions. Whole genome sequencing (WGS) offers the most comprehensive TMB assessment by capturing all somatic mutations across the entire 3-billion-base-pair , including non-coding variants that may influence tumor or immune recognition. However, its resource-intensive nature—requiring terabytes of , extended computational runtimes, and costs 10-100 times higher than WES—limits routine adoption to specialized research cohorts, such as those exploring pan-cancer mutational landscapes. WGS excels in detecting indels, copy number variations, and structural rearrangements that contribute to overall mutational load but is less practical for high-throughput clinical TMB screening. Most TMB sequencing employs short-read platforms like Illumina's NovaSeq systems, which generate 100-300 base-pair reads with >99.9% accuracy, supporting the high-depth requirements of WES and targeted panels through sequencing. Emerging long-read technologies, such as PacBio's HiFi sequencing, are gaining traction for resolving complex structural variants and repetitive regions that short-read methods struggle with, potentially enhancing TMB accuracy in structurally heterogeneous tumors by 2025. Additionally, liquid approaches using (ctDNA) are seeing increased adoption by late 2025, with NGS-based assays like those from Guardant Health enabling non-invasive TMB monitoring from blood samples, though sensitivity remains lower (detecting down to approximately 0.1–0.5% frequencies) compared to tissue-based methods.

Calculation Formulas and Pipelines

Tumor mutational burden (TMB) is fundamentally calculated as the number of somatic non-synonymous per megabase (Mb) of the , excluding synonymous , intronic variants, and alterations unless otherwise specified in the protocol. For whole-exome sequencing (WES), the size is typically standardized at 38 Mb to enable consistent normalization across samples. The core formula can be expressed as: TMB=∑somatic non-synonymous mutations38\text{TMB} = \frac{\sum \text{somatic non-synonymous mutations}}{38} where the denominator represents the size in Mb, yielding mutations per Mb (mut/Mb). The computational pipeline for deriving TMB from sequencing data involves several sequential steps to ensure accuracy and reproducibility. Initial read alignment is performed using tools like the Genome Analysis Toolkit (GATK) to map tumor and matched normal sequences to a , followed by preprocessing to mark duplicates and perform base quality score recalibration. Variant calling then identifies somatic single-nucleotide variants (SNVs) and insertions/deletions (indels), commonly employing MuTect2, which models tumor-specific noise and compares against the matched normal to distinguish somatic from events. filtering is applied using resources like the Genome Aggregation Database (gnomAD) to remove common polymorphisms, retaining only high-confidence somatic calls. of variants occurs next, often with the Variant Effect Predictor (VEP) tool, to classify mutations as non-synonymous (e.g., missense, ) and filter out non-coding or synonymous changes. Finally, burden normalization computes TMB by dividing the count of qualifying somatic mutations by the effectively covered size in Mb, accounting for sequencing depth and panel-specific regions if not using WES. Adjustments to the standard TMB calculation may incorporate distinctions between and mutations, as well as clonal versus subclonal variants, to refine estimates for clinical relevance. While TMB traditionally includes both (neutral) and (oncogenic) mutations to reflect overall neoantigen potential, some pipelines exclude known drivers or apply weights to emphasize passengers, which constitute the majority of somatic events. For clonality, subclonal mutations (present in tumor subpopulations) are often downweighted or excluded relative to clonal ones, as they may dilute predictive signals for response. Tumor purity adjustments are critical, particularly in heterogeneous samples, using an extended formula such as: TMBadjusted=∑somatic mutationscovered Mb×purity estimate\text{TMB}_{\text{adjusted}} = \frac{\sum \text{somatic mutations}}{\text{covered Mb} \times \text{purity estimate}} where purity is derived from tools like FACETS or ABSOLUTE to correct for stromal contamination. Recent standards emphasize pipeline reproducibility, with the 2024 Association for Molecular Pathology (AMP), American Society of Clinical Oncology (ASCO), College of American Pathologists (CAP), and Society for Immunotherapy of Cancer (SITC) consensus recommendations advocating detailed documentation of variant calling thresholds, filtering criteria, and normalization methods to minimize inter-assay variability. Custom scripts, often integrated with GATK workflows, handle Mb normalization, while open-source tools like TMBcalc provide end-to-end pipelines for pan-cancer TMB computation, ensuring alignment with these guidelines.

Influencing Factors and Standardization

Biological and Technical Variables

Tumor purity, defined as the proportion of cancer cells within a tumor sample relative to non-cancerous components such as stromal or inflammatory cells, significantly influences tumor mutational burden (TMB) estimation. Low tumor purity dilutes the signal from somatic mutations, leading to underestimation of TMB, as non-tumor cells contribute fewer mutations and reduce the variant allele frequency (VAF) of true tumor variants. Guidelines recommend a minimum tumor purity of 20% for reliable tissue-based panel sequencing assays to ensure sufficient sensitivity in detecting mutations above a typical 5% VAF threshold. Computational tools like ABSOLUTE can estimate tumor purity from sequencing data by integrating copy number alterations and allele frequencies, enabling purity-adjusted TMB calculations that improve accuracy in samples with 15-40% purity. Infiltration by immune or tumor microenvironment (TME) cells further complicates estimates, as lower purity correlates with reduced TMB sensitivity and potential false negatives in immunotherapy response prediction. Tumor heterogeneity, both spatial and temporal, introduces variability in TMB measurements by causing differences in mutation profiles across tumor regions or over time. Spatial heterogeneity arises from subclonal evolution, where distinct tumor areas harbor varying mutation burdens, leading to discordant TMB values between biopsy sites; for instance, in pulmonary adenocarcinoma, intratumor regions can show marked differences in somatic mutations that affect panel-based TMB assessments. Temporal heterogeneity, driven by treatment effects or disease progression, can alter TMB as new mutations emerge or subclones expand, as observed in gastroesophageal adenocarcinoma where baseline and post-treatment samples exhibited heterogeneous TMB and PD-L1 expression. This variability underscores the need for multi-region sampling to capture representative TMB, though single biopsies may underestimate overall burden due to sampling bias. Sequencing coverage, or depth, is a critical technical variable affecting TMB accuracy, as insufficient depth fails to detect low-frequency while uneven coverage biases detection toward mutation hotspots. A minimum coverage of 100x is generally required for reliable variant calling in TMB assays, with 250x or higher recommended for subclonal or low-VAF to minimize false negatives; however, TMB estimates remain sensitive to depth variations, with lower depths leading to inconsistent counts above typical thresholds like 5% VAF. High-depth sequencing, such as >750x in targeted panels, enhances stability but increases costs, particularly for heterogeneous tumors where uniform coverage across the is challenging. Preprocessing factors, including sample fixation and DNA quality, can introduce artifacts that skew TMB results. Formalin-fixed paraffin-embedded (FFPE) tissues, commonly used in clinical settings, cause DNA damage via deamination, resulting in artifactual C>T transitions that inflate TMB by mimicking somatic mutations. Fresh frozen samples are preferred to avoid these artifacts, as they yield higher-quality DNA with fewer false positives; FFPE-derived DNA requires quality metrics like fragment lengths exceeding 150 to ensure reliable library preparation and sequencing. Microbial in sequencing reads, often from reagents or environmental sources, can further bias TMB by introducing non-human variants or diluting tumor signal if not filtered, necessitating computational pipelines to maintain estimate integrity.

Cutoff Determination and Harmonization

Determining appropriate cutoffs for tumor mutational burden (TMB) is essential for identifying patients likely to benefit from , with the U.S. (FDA) establishing a threshold of ≥10 mutations per megabase (mut/Mb) for high TMB in solid tumors based on the KEYNOTE-158 trial, which supported approval of for this group. Cancer-specific cutoffs often vary to account for inherent differences in mutational landscapes; for instance, studies frequently employ a higher threshold of 20 mut/Mb to classify high TMB, reflecting its elevated baseline mutation rates compared to other cancers. Additionally, percentile-based approaches, such as designating the top 20% of TMB values within a specific cancer type as high, provide a relative framework that adapts to population-level data and enhances prognostic relevance across diverse cohorts. Efforts to harmonize TMB measurements address discrepancies arising from differences, with the Friends of Cancer Research (FOCR) TMB Harmonization Project, culminating in key findings by 2024, demonstrating that inter-laboratory comparisons exhibit 20-30% variability due to panel size, gene coverage, and bioinformatics pipelines. Phase II of this initiative developed calibration models to align targeted next-generation sequencing panels with whole-exome sequencing (WES) references, promoting consistent TMB estimation and reducing classification discordance for high versus low TMB. Validation of TMB assays relies on proficiency testing programs, such as those offered by the (), which evaluate laboratory performance using standardized samples to ensure reliable quantification across clinical settings. Reference materials, including those developed through FOCR collaborations, facilitate calibration by providing benchmarks for detection, minimizing technical biases in TMB reporting. Recent guidelines from the Association for Molecular Pathology (AMP), CAP, and Society for Immunotherapy of Cancer integrate WES as a gold standard with panel-based assays, demonstrating high correlation (often >0.85) between WES and panel-based TMB measurements when appropriate calibration is applied, thereby supporting broader clinical adoption and comparability.

Challenges and Limitations

Assay and Interpretation Issues

One major challenge in TMB assays is , particularly the discordance between targeted next-generation sequencing (NGS) panels and whole-exome sequencing (WES), which can reach up to 40% due to differences in coverage and variant detection sensitivity. Batch effects in NGS further compromise , as variations in library preparation, sequencing depth, and bioinformatics pipelines can lead to inconsistent TMB estimates across runs or laboratories. Recent 2024 studies have highlighted that formalin-fixed paraffin-embedded (FFPE) artifacts, such as deamination-induced C>T transitions, contribute to false-high TMB calls, necessitating stringent variant (VAF) thresholds to mitigate these errors. Interpretation of TMB results is prone to that can distort clinical . Overcounting mutations in hypermutated regions, often driven by positive selection in cancer genes captured by targeted panels, leads to systematic overestimation of TMB compared to genome-wide assessments. Ignoring tumor exacerbates this, as spatial and temporal heterogeneity—resulting from clonal dynamics and neoantigen loss—causes TMB variability across tumor sites or over time, potentially misrepresenting . Liquid biopsy approaches, reliant on (ctDNA), frequently underestimate TMB due to variable shedding rates, with high TMB rarely detectable below 1% ctDNA fraction, limiting their utility in low-shedding tumors. Validation gaps persist in TMB assays, underscored by the scarcity of prospective trials demonstrating consistent predictive value across diverse cohorts. Inter-assay variability compounds this, arising from variations in panel size, germline filtering, and synonymous mutation inclusion, as seen between platforms like FoundationOne CDx and MSK-IMPACT. Emerging issues involve AI integration in variant calling, where biases toward high-confidence calls can amplify errors in heterogeneous samples. 2025 reports indicate AI models are susceptible to in low-coverage data, reducing accuracy in TMB quantification for suboptimal sequencing inputs like those from liquid biopsies. These challenges highlight the need for harmonized protocols to enhance assay reliability, as explored in ongoing efforts.

Clinical and Regulatory Hurdles

Despite its promise as a predictive for response, tumor mutational burden (TMB) exhibits inconsistent pan-cancer performance, limiting its clinical utility in routine practice. For instance, while high TMB correlates with improved outcomes in certain immunogenic tumors like non-small cell lung cancer and , it fails to reliably predict responses in others, such as those with low TMB but alternative responsiveness mechanisms, including microsatellite instability-high (MSI-H) colorectal cancers where neoantigen load drives efficacy independently of TMB levels. Moreover, retrospective analyses underpinning key approvals, such as the KEYNOTE-158 trial for , introduce selection biases that overestimate TMB's predictive value, as post-hoc assessments may not capture real-world variability in patient cohorts. These limitations highlight evidence gaps, with response rates in high-TMB cohorts (≥20 mutations/Mb) varying across studies, often around 30-60%. Regulatory hurdles further impede TMB's integration into clinical guidelines. The U.S. (FDA) granted accelerated approval in 2020 for in adults and pediatric patients with unresectable or metastatic TMB-high (≥10 mutations/Mb) solid tumors that have progressed following prior treatments and lack satisfactory alternatives, based on the KEYNOTE-158 basket trial demonstrating a 29% objective response rate. However, this remains an accelerated approval pending confirmatory trials, and controversies persist regarding the trial's reliance on a single companion diagnostic assay (FoundationOne CDx), raising concerns over generalizability. In contrast, the (EMA) has not approved or any agent specifically for TMB-high indications, leading to regulatory variations across countries where TMB testing lacks harmonized endorsement and reimbursement pathways differ significantly. These discrepancies complicate global adoption, as evidenced by ongoing debates in 2024 about TMB's agnostic status versus the need for tumor-specific validations. Adoption barriers exacerbate these challenges, primarily driven by high testing costs and limited accessibility. Next-generation sequencing-based TMB assessments typically range from $3,000 to $5,000 per test in the U.S. as of 2025, often exceeding $5,000 when bundled with comprehensive genomic profiling, straining healthcare budgets and deterring widespread use. In low-resource settings, inadequate and mechanisms further restrict access, with surveys indicating that up to 59% of barriers to advanced sequencing stem from funding issues. Additionally, the requirement for FDA-approved companion diagnostics like FoundationOne CDx mandates specialized labs, creating logistical hurdles in diverse clinical environments. Ethical concerns around over-testing have intensified in , as inconsistent TMB cutoffs and assay variability may lead to unnecessary in non-responders, raising issues of overtreatment, financial burden, and potential harm without clear prospective evidence. Debates continue on whether TMB should serve as a sole or be incorporated into composite scores with expression or to mitigate these risks.

Future Directions

Integration with Other Biomarkers

Tumor mutational burden (TMB) is increasingly integrated with other biomarkers to enhance its predictive utility for response in , addressing limitations such as variability across tumor types and incomplete correlation with clinical outcomes. Combining TMB with expression has shown improved prognostic accuracy; for instance, a composite score incorporating TMB, PD-L1 on immune cells, and CD39 expression achieved area under the curve (AUC) values of 0.649 for 12-month overall survival and 0.674 for 24-month survival in muscle-invasive patients treated with PD-L1 blockade, outperforming individual markers. Similarly, in advanced urothelial , high TMB (≥175 mutations per ) combined with PD-L1 combined positive score (CPS) ≥10 identified subgroups with superior and overall survival benefits from monotherapy or with compared to chemotherapy alone. TMB also overlaps significantly with (MSI), with approximately 80-100% of MSI-high cases in exhibiting high TMB (≥10 mutations per megabase), allowing MSI status to serve as a complementary indicator for immunotherapy eligibility in TMB-assessed tumors. Neoantigen prediction tools further refine TMB's role by estimating immunogenic peptide loads from mutations; tools like pVAC-Seq and NetMHCpan-4.1 use whole- sequencing data to prioritize neoantigens based on TMB-derived variants, correlating higher predicted neoantigen burdens with improved responses to inhibitors in non-small cell and . Composite scores that incorporate TMB with immune-related metrics provide a more holistic assessment of tumor . The Tumor Immune Dysfunction and Exclusion () score, derived from transcriptomic signatures of T-cell dysfunction and exclusion, is often combined with TMB to predict resistance; for example, in subtypes, TIDE-integrated analyses with TMB and neoantigen load identified immune evasion patterns, with lower TIDE scores in high-TMB groups associating with better efficacy. Recent multi-omics panels, such as those leveraging on genomic and histopathological data, integrate TMB with tumor-infiltrating lymphocyte (TIL) density; in , a 20-gene TMB estimation model correlated with increased + TIL density (R=0.891 for neoantigen burden), enabling prognostic stratification independent of traditional TMB cutoffs. These 2025-era approaches, including lasso-based models from TCGA cohorts, emphasize TMB's synergy with TIL metrics to forecast . The primary benefits of these integrations include reduced false negatives among low-TMB patients who may still respond to due to other favorable immune features. Clonal TMB variants, when combined with neoantigen predictions, enhance specificity (0.8-0.9) for identifying true responders while minimizing misclassification in heterogeneous tumors like urothelial cancer. Clinical examples from 2024 meta-analyses in head and neck demonstrate this, where high TMB predicted superior overall response rates ( 2.62) and survival ( 0.53) to inhibitors across 1,200 patients, with integrations potentially capturing low-TMB subsets via or MSI overlays to broaden treatment access. As of October 2025, studies presented at ESMO highlighted AI-powered (e.g., Lunit AI) predicting outcomes in colorectal, , and cancers, complementing TMB by analyzing H&E slides for immune features. Professional frameworks endorse TMB within multi-biomarker panels rather than as a standalone metric. The European Society for Medical Oncology (ESMO) guidelines recommend reporting TMB alongside other genomic biomarkers like MSI and deficiency in solid tumor assays, specifying validated thresholds (e.g., ≥10 mutations per megabase) and graphical visualizations to guide blockade decisions. This approach ensures TMB contributes to personalized strategies without over-reliance on isolated thresholds.

Ongoing Research and Innovations

Recent phase III clinical trials in 2025 are investigating tumor mutational burden (TMB)-guided inhibitors (ICIs) in rare tumors, such as the ongoing evaluation of in advanced miscellaneous rare solid tumors. Additionally, studies like the envafolimab monotherapy trial in high-TMB advanced solid tumors highlight TMB's role in selecting patients across diverse histologies, including rarer subtypes. Longitudinal TMB tracking via has emerged as a key focus for monitoring resistance, with 2025 research demonstrating that early on-treatment TMB dynamics in predict response and clonal evolution in head and neck . Innovations in are advancing the understanding of intratumor TMB heterogeneity, enabling detection of low-frequency somatic variants and resistant subclones that bulk sequencing misses. Complementing this, models are predicting TMB directly from hematoxylin and eosin-stained slides, achieving area under the curve accuracies of 0.910–0.934 in lung and colorectal cancers, which could streamline non-invasive assessments without genomic sequencing. Emerging research explores -tumor TMB interactions, where variants in genes like , FANCL, and MSH6 significantly elevate somatic TMB levels across pan-cancer cohorts, potentially enhancing neoantigen load and ICI efficacy. The gut microbiome's influence on mutational burden is also under investigation, with microbial interactions inducing mismatch repair deficiency signatures that increase rates in colorectal tumors. From 2024 to 2025, efforts to standardize liquid biopsy for TMB have intensified through consortia like Friends of Cancer Research, developing guidelines for validation, bioinformatics pipelines, and orthogonal confirmation to enable reliable dynamic monitoring. Looking ahead, global consortia, such as the Worldwide Innovative Network (WIN) and Friends of Cancer Research, are prioritizing diverse population data to address ancestry-driven TMB variations, aiming to recalibrate thresholds for equitable . As of October 2025, ESMO research linked thymic to response, suggesting it as a complementary to TMB for patient selection.

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