Tumor Mutational Burden (TMB): Will It Be a Home Run?
By By Dr. Song Ling Poon, Senior Medical Science Liaison of ACT Genomics
Immunotherapies are the stars of today and continue to have more advancement on the horizon, especially the development of immune checkpoint inhibitors (ICIs) targeting PD-1/PD-L1 and CTLA-4. Nevertheless, up-to-date, the objective response rate of either PD-1 or PD-L1 inhibitors across different cancer types, unfortunately, ranges between 10 and 20%.
Therefore, how treatment is determined is also an on-going evolution with the emerging new biomarkers and metrics. When asked which factors will influence treatment decisions moving forward, the majority of the physicians take MSI status, apart from PD-L1 staining, as being ‘very influential’ – an understandable perception given the approvals for Keytruda and Opdivo in the US, based on MSI result in 2017 and 2018 respectively. Interestingly, physicians also anticipate that tumor mutational burden (TMB) to be a “potential influencing factor” to predict the response to checkpoint inhibitors.
TMB versus PD-L1
TMB is commonly defined as a measurement of somatic mutation within all coding regions in the tumor cell. It started to be suggested as a potential biomarker for ICI partly due to a retrospective analysis of the results from the Checkmate 026 trial where patients with high TMB showed higher response rates and longer progression-free survival with PD-1 blockade (i.e., nivolumab versus platinum-based doublet chemotherapy)1. Following, a post-hoc analysis in the nivolumab plus ipilimumab arm of the Checkmate 012 trial also showed longer PFS in TMB-high cases under checkpoint inhibitor therapy, especially if PD-L1 was present, but limited benefit in the TMB-low subgroup regardless of PD-L1 expression2. Conversely, by focusing on PD-L1 negative patients, the Checkmate 277 trial demonstrated a significant clinical benefit from the administration of nivolumab alone or in combination with ipilimumab for TMB-high cases, while patients with low TMB are truly refractory to checkpoint inhibitors3. Notably, in all aforementioned analyses, PD-L1 expression was not associated with TMB levels, indicating that the two biomarkers are largely independent. Unfortunately, the latest analysis of Checkmate 277 revealed no overall survival (OS) difference between TMB-high and TMB-low groups.
Issues to be Addressed
Similar to the challenges of using PD-L1 expression as a biomarker for patient selection, there are quite some unsolved aspects of TMB assessment and interpretation that need to be addressed before widespread adoption in the clinics. Conducting whole exome sequencing (WES) is considered the gold standard for TMB assessment. However, this is currently not feasible in clinical practice due to significant associated costs, long turn-around time, suboptimal, limited tissue samples, and inadequate infrastructure capacities required for data storage and analysis. Therefore, an alternative way is to use a targeted panel to extrapolate and estimate TMB. As such, multiple parameters that can influence the panel-based TMB measurement and related cutoff values need to be taken into consideration: (1) tumor type; (2) pre-analytics such as tumor cellularity, DNA quantity, and quality; (3) panel size and composition: how much of the exome needs to be sequenced to make an accurate prediction of a tumor’s total mutational burden; (4) read depth and coverage; (5) bioinformatics pipeline: limit of detection, the threshold for allele frequency, filter settings for germline events, deamination artifacts and sequencing artifacts.
Ongoing Efforts to Standardize TMB Calculation
Routine clinical TMB assessment is yet to be standardized and evidence-based stratification according to the mutational load is not yet sufficiently refined and tested. Noteworthy, there are efforts made to address the issue of standardized TMB estimation. In the United States, Friends of Cancer Research (FoCR) gathered stakeholders including industrial and diagnostic to conduct a three-step harmonization project4.
- Phase 1: In silico TMB analysis of TCGA datasets to uncover factors of variability between different assays used.
- Phase 2: A universal reference created using WES will be used to align TMB scores from targeted panels.
- Phase 3: A clinically meaningful cutoff will be determined in a retrospective analysis of samples with patient outcome data.
ACT Genomics is one of the diagnostic partners that joined this TMB Harmonization Project. Results from phase 1 in silico analysis showed that ACTOnco®+, ACT Genomics’ flagship panel which comprises 440 cancer-related genes, is capable of providing estimated TMB data that is highly correlated with the TMB measured with whole exome sequencing. On top of TMB data, ACTOnco®+ also provides genomic alterations of genes that are known to confer sensitivity or lack of benefit to ICI such as PTEN, STK11, JAK1/2, EGFR, MDM2/MDM4 and antigen processing and presentation genes. Importantly, this panel does not just interrogate the susceptibility of a tumour towards checkpoint inhibitor. It can also provide information towards targeted therapy, hormonal therapy, and chemotherapy.
The Way Forward
The search for a biomarker to predict the response to checkpoint inhibitors is an ongoing race. The ideal predictive model for checkpoint inhibitors may require multiple genomic aspects not just TMB, but also HLA genotype, specific genetic mutations, copy number, and the status of the tumor immune microenvironment to fully characterize the dynamic interactions between tumour cells with the immunomodulators. Developing such a predictive model will require a continuous process of model update and re-evaluation to tally the feasibility and reproducibility in the “real world” clinical setting. Nevertheless, in the meantime, TMB is, if nothing else, quietly making a home run to become a biomarker at the pan-tumor levels.
1. Carbone DP et al., N Engl J Med 2017; 376:2415-1
2. Hellmann MD et al., Cancer Cell 2018; 33:843-852.e4.
3. Hellmann MD et al., N Engl J Med 2018; 378:2093-104
4. Friends of Cancer Research (FoCR): Tumor Mutational Burden (TMB); Washington DC; 2018 May 10