Tumor mutational burden

Tumour mutational burden (abbreviated as TMB) is a genetic characteristic of tumorous tissue that can be informative to cancer research and treatment.

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.

[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.

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.

[11] ICIs have been shown to improve patients' response and the survival rates as they help the immune system to target tumor cells.

[13] Researchers could also show a significant correlation between treatment response rate and TMB level in patients treated with anti-PD-1 or anit-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.

[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] 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.

[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.

[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.

[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] TMB can be calculated directly from histopathology images using a multiscale deep learning pipeline, avoiding the need for sequencing and variant calling.

[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] 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.

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.
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 )
Factors such as tumor cell content, tissue preprocessing, choice of sequencing technology, downstream bioinformatic pipelines, and TMB cutoffs can influence TMB calculations.