Towards overtreatment-free immunotherapy: Using genomic scars to select treatment beneficiaries in lung cancer

In advanced non-small cell lung cancer (NSCLC), response to immunotherapy is difficult to predict from pre-treatment information. Given the toxicity of immunotherapy and its financial burden on the healthcare system, we set out to identify patients for whom treatment is effective using mutational signatures from DNA mutations in pre-treatment tissue. Analysis of single base substitutions, doublet base substitutions, indels, and copy number alteration signatures in the discovery set (m =101 patients) linked tobacco smoking signature (SBS4) and thiopurine chemotherapy exposure-associated signature (SBS87) to durable benefit. Combining both signatures in a machine learning model separated patients with a progression free survival hazard ratio of 0.40+0.28 −0.17 on the cross validated discovery set and 0.24+0.31
−0.14 on an independent external validation set (m = 56). This paper demonstrates that the fingerprints of mutagenesis, codified through mutational signatures, can be used to select advanced NSCLC patients who may benefit from immunotherapy, thus reducing unnecessary patient burden.