1898 - Genomic correlates of response to anti-PDL1 Atezolizumab in non-small-cell lung cancer OAK and POPLAR trials

Background

Limitations of integrating biological knowledgebases with genomics impedes the development of predictive correlates that would help in personalization of immunotherapy. Non-small cell lung cancer (NSCLC) patients treated with atezolizumab, an antiprogrammed death-ligand 1 (PDL1) antibody, have better overall survival when compared to patients receiving docetaxel chemotherapy in Poplar (Lancet, 2016) and Oak (Lancet, 2017). We hypothesized that patterns in the mutations of immune signatures would correlate with the immunotherapeutic effect of atezolizumab in NSCLC.

Methods

Sequencing data from Poplar (n = 277 patients) and Oak (n = 725 patients) trials was analyzed for understanding genomic alterations in relation to patient outcomes. Signatures from publicly-available knowledgebases were integrated with the genomics and clinicopathological data into an algorithm for identifying patterns correlative of immunotherapeutic response.

Results

Patients benefitting from atezolizumab were more likely to have mutations in oncoimmunity-related genes which were significantly overlapping between the two trials. These overlapping genes were used to develop a de novo signature comprising of CDKN1A, ERRFI1, JAK2, NOTCH2, ACVR1B, NFKBIA, GNA13, MERTK, BTG1, CDKN1B, FOXP1, PDK1, ETV6, MLL2, SMAD3, DICER1 and BRCA2. Mutations in any of the genes within the signature identified patient subpopulations with higher immunotherapeutic response to Atezolizumab in both Oak (HR = 0.3, P = 1.8e-6 versus HR = 0.76, P = 5.5e-3 for entire cohort) and Poplar (HR = 0.18 and P = 3.6e-2 versus HR = 0.71, P = 0.023 for all patients) trials. Prediction efficiency of the signature was significantly better than established biomarkers such as PDL1 staining (Nature, 2014) in both Oak (HR: 0.58 and P = 1.8e-6) and Poplar (HR: 0.58 and P = 0.04) trials indicating that genomic correlates could add significant value to existing modalities in predicting immunotherapy response.

Conclusions

In summary, integration of biological knowledgebases with genomics suggests that a rheostat of mutational burden in non-overlapping genesets is associated with immunotherapeutic effect and identifies novel genomic correlates for response to Atezolizumab.

Clinical trial identification

NCT02008227; NCT01903993.

Editorial acknowledgement

Legal entity responsible for the study

Roche.

Funding

Roche.

Disclosure

D.R. Gandara: Research grant: Bristol-Myers Squibb, Roche-Genentech, Novartis, Merck; Consultancy: AstraZeneca, Celgene, CellMax Life, FujiFilm, Roche-Genentech, Guardant Health, Inviata, IO Biotech, Lilly, Liquid Genomics, Merck, Samsung Bioepis, Pfizer. All other authors have declared no conflicts of interest.