Two approaches to making use of all 500 genes are being discussed.
- One use would be to make very complex in silico models of tumor cell biology. Companies like Cellworks and Darwin Health are exploring this. I think of this as a growing industry for "digital genomics."
- Another use is finding something about those 500 sequenced genes that can be assessed and applied clinically -- but requiring the exponential jump in gene data and unrelated to the small number of approved drug-gene pairings.
- An approach here is Total Mutational Burden (TMB), and Foundation Medicine has just published a large new study.
Total mutational burden is being cited as a potential biomarker for the effectiveness of immuno-oncology checkpoint drugs. Chalmers et al. report the results from 100,000 patient studies conducted in a wide range of tumors from the FMI archives (Genome Medicine, Open Access, here.) FMI finds that:
- They confirmed the previous understanding that lung cancer and melanoma are most likely to have rampant mutations, but found that rampant mutations also occurred at lower rates in many cancers.
- You get most of the information for 0.5MB DNA; an exome confirms the high or low mutation burden you already low, whereas accurate assessment falls off below 0.5MB.
- PMS2 mutations tend to be associated with rampant mutations, as is aging.
The authors also used Cancer Genome Atlas (TCGA) data. Coverage at Genomeweb, here.
Update May 9: FMI emphasized the growing role and value of TMB in its May 9 investor call, here.
Update October 6: For a UCSD paper on TMB clinical impact (Khagi et al.) here.
For AACR news with a re-analysis of Opdivo data using TMB, BiopharmaDive, here.
For an earlier FMI paper on 63,220 tumors, see Hartmaier et al., Genome Med 9:16, February 2017, here.