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Disease Area Cancer, Diagnostics & prognostics

Load Versus Response

Why do some cancer patients respond better than others to the blanket application of immunotherapy? Tumor mutational load appears to play a key role, but studies have so far been limited to single types of cancer, so questions remain about resistance mechanisms across the disease spectrum. Now, a paper published in Nature Genetics helps broaden our understanding – at least when it comes to immune checkpoint inhibitor (ICI) drugs (1).

“The idea that tumor mutational burden (TMB) would be associated with a response to immunotherapy across multiple cancers – I think many in the field would have said that’s likely to be true,” says Luc Morris, one of the authors of the study. “But, until our study, it wasn’t clear that you could generalize that idea across more than a few cancer types.”

To find more evidence of TMB’s role, the team needed access to tumor genomic data from participants across several different cancer groups, and so a large collaborative study was initiated at the Memorial Sloan Kettering Cancer Centre (MSKCC) in New York. “This was real team science,” says Morris. “All disease groups brought their expertise to help us analyze the patient-level data properly and produce the highest quality clinical data.”

Using next-generation sequencing, the team assessed the genomic data of over 1,500 patients treated with ICI, and over 5,000 non-ICI-treated patients. The results were clear; higher TMB (the highest 20 percent in each histology) was associated with better overall patient survival, but the definition of “high” varied significantly between cancers. “TMB alone does have predictive value,” says Morris. “But we know other factors will be important – including the inflamed state of the tumor microenvironment, levels of T-cell infiltration and exhaustion in the microenvironment, HLA genotype, copy number alterations, specific genetic alterations, and others.”

That’s the next big step the field needs to make – to integrate better precision into how we triage into these therapies, as opposed to alternative options.

How could this information be applied as a clinical biomarker? Genome sequencing for patients typically uses targeted panels, and so Morris was keen to answer another question: “Could we get useful, predictive TMB data from panel sequencing, when we only cover a small percentage of the exome? Our study suggests the answer is yes.” What about the issue of mutational fidelity across cancer types? “We also need to study these cancers separately – there is no universal definition of TMB – what’s high in one cancer type might be low in another,” admits Morris.

Morris believes the future is promising. Combining high-throughput analysis of TMB with specific mutational profiles for different cancer types may provide a novel approach to determining which patients may benefit from immunotherapy. “That’s the next big step the field needs to make – to integrate better precision into how we triage into these therapies, as opposed to alternative options,” says Morris.

With his collaborators at the MSKCC –including Robert Samstein, Timothy Chan and David Solit – Morris is looking to sequence more patient tumors to refine the predictive power of current models. “The goal is to gain a 360 degree picture – using tissue both before and after therapy – to see adaptive resistance mechanisms in action.”

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  1. R M Samstein et al., “Tumor mutational load predicts survival after immunotherapy across multiple cancer types.” Nat. Genet. [Epub ahead of print] (2019) PMID: 30643254

About the Author

Jonathan James

As an assistant editor for The Translational Scientist, I can combine two of my passions; translational science research and science communication. Having thrown myself into various editing and other science communication gigs whilst at University I came to realise the importance of good quality content that delivers in an exciting and engaging way.

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