A robust, effective and clinically verifiable biomarker in multiple cancer types.
TMB Harmonization Project Overview (https://www.focr.org/tmb)
Immunotherapy has been approved as a first-line of treatment for several cancers, and may also be an effective treatment for patients with certain cancers that are resistant to prior treatment. However, immunotherapy doesn’t always work for every patient. Thus selection of patients on independent predictor of response such as TMB (Tumor Mutational Burden) becomes crucial across various cancer types.
TMB has incredible potential to be the key that unlocks the door for personalized therapy in oncology. The TMB of a tumor sample is calculated by the number of non-synonymous somatic mutations (single nucleotide variants and small insertions/deletions) per megabase in coding regions. Earlier studies have found a remarkable association between TMB values and the production of neo-antigen peptides and it is illustrated to be highly correlated with tumor immune response. TMB represents both the stability level of the tumor genome and heterogeneity of the tumor microenvironment. The potential of TMB as a pan-cancer predictive signature was an incredibly exciting prospect, and bringing it into routine clinical practice so the right patient at the right time can benefit from immunotherapy has always been the dogma of precision medicine. However, methods to calculate TMB varied greatly in scientific research and clinical practice.
In clinical diagnostic setup, different testing methodologies and reporting strategies add confusion to trial enrollment, create chaos to clinical practice and in the worst case lead to the wrong conclusion at the point of patient care.
For TMB in research, the most common method used is WES (Whole Exome Sequencing), which sequences only the coding regions of the genome, with average coverage of 120X to 150X. WES allows comprehensive measurement of TMB and is considered to be the gold standard. Initial studies showing a correlation between TMB and enhanced response to immunotherapy were based on WES datasets for TMB quantification. Despite the proven utility of WES in measuring TMB and predicting response to PD-1/PD-L1 blockade, it has many limitations. WES is expensive, time consuming, and labor intensive, and, therefore, difficult to incorporate into clinical practice.
In clinical application, targeted enrichment panels (gene panels) of various genomic sizes are routinely used to estimate TMB. Although only a small number of genes (about 300–500) are covered, gene panel sequences can identify rare somatic mutations because of the higher sequencing depth compared to WES. However, differences in panel size, gene coverage, library construction, sequencing, in addition to the underlying bioinformatics pipelines, are known drivers of variability in TMB estimates across laboratories.
Variability of TMB estimates across different panels and lack of robust predictive cutoffs, were prime hindrance to adopt TMB as a biomarker in clinical practice. This led to the ‘ TMB Harmonization Consortium’, which began in late 2017 to address these challenges head on, anticipating the success of TMB as a predictive biomarker for immunotherapy across multiple tumor types.
The main challenge for accurate panel-based TMB quantification was the ability to extrapolate the global mutational burden from the narrow sequencing space targeted by a gene panel. This was overcome by determining the concordance between panel-based and WES-based TMB, which is considered the reference for TMB quantification. Calibration methods using samples derived from The Cancer Genome Atlas (TCGA), which became a viable approach to aligning TMB scores. Further, variation across panels using patient formalin-fixed paraffin-embedded (FFPE) tissue samples were analysed. This empirical analysis compared panel TMB results to an agreed universal reference standard, consisting of a collection of human tumor-derived reference cell lines that span a clinically meaningful TMB dynamic range. Lastly in a retrospective clinical study, samples from patients treated with immunotherapy were evaluated to determine optimal TMB cut-offs for treatment decisions.
Several gene panels are being optimized to estimate TMB at reduced sequencing costs, and emerging evidence supports the feasibility of TMB quantification from liquid biopsies. Even an accurate TMB value is an imperfect predictor of immunotherapy response and further studies are needed to enhance its value as clinically useful immunotherapy biomarker. Integration of TMB with other potential immunotherapy biomarkers represents a promising way to refine prediction of immunotherapy responders.
Image source: https://www.focr.org/tmb