Abstract 693: Deep learning analysis of circulating tumor DNA identified a pan-tumor molecular subtype with enhanced response to durvalumab (anti-PDL1)

• Published in: Cancer research (Chicago, Ill.) Ill.), 2019-07, Vol.79 (13_Supplement), p.693-693
• Main Authors: Wu, Song, Sridhar, Sriram, Si, Han, Kuziora, Michael, Englert, Judson, Abdullah, Shaad E., Dennis, Philip, Xiao, Feng, Gao, Guozhi, Ranade, Koustubh, Higgs, Brandon W.
• Format: Article
• Language: English
• ISSN: 0008-5472; 1538-7445
• DOI: 10.1158/1538-7445.AM2019-693

Tumor mutation burden (TMB) has emerged as a promising predictive biomarker for anti-PD1/L1; however, whether somatic mutations in specific genes can sensitize to immunotherapy is poorly understood. We used deep learning to identify molecular subtypes from mutations detectable in circulating tumor DNA (ctDNA) and associated these with outcomes with anti-PDL1 (durvalumab) and durvalumab + anti-CTLA4 (tremelimumab) therapy. Patient blood samples from 4 nonrandomized phase 2 trials evaluating durvalumab (NCT01693562, 1L+ solid tumors; NCT02087423, 3L+ NSCLC) or durvalumab+ tremelimumab (NCT02000947, 2L+ NSCLC; NCT02261220, 2L+ solid tumors) were sequenced with the Guardant 360 assay of 73 genes. CtDNA mutations were examined pre-treatment in a discovery cohort of 818 patients composed of >15 tumor types and validated in a cohort of 94 patients composed of >5 tumor types. Deep learning neuron network was computed using Tensorflow to identify 4 unique molecular subgroups. Kaplan-Meier analysis was calculated and objective response rates (ORR) were assessed with RECISIST v1.1. The subgroups consisted of: DS1 (N = 215/818, 26%) enriched in RB1 mutations (17%) and low TMB, DS2 (N = 237/818, 29%) enriched in ARID1A (33%), NFE2L2 (8%), and cKIT (13%) mutations, DS3 (N = 251/818, 31%) enriched in EGFR, ERBB2, BRAF and STK11 mutations, and DS4 (N = 115/818, 14%) enriched in TERT, FGFR3, and PIK3CA mutations. In the discovery cohort, the DS2 subgroup had most improved outcomes (mOS=24.6 months, mPFS=3.7 months, ORR=27%) compared to the other 3 subgroups. DS1 subgroup had the worst outcomes (mOS=6.3 months, mPFS=1.6 months, ORR=10%), while the biomarker evaluable population (BEP, n=818) had ORR=17%. In the validation cohort, DS2 also had the most improved outcomes (mOS=not reached, mPFS=13.2 months, and ORR=64%), with the BEP (n=94) having an ORR=37%. PD-L1 was not correlated with any specific molecular subgroup, while an IFNg mRNA signature trended with correlation with subgroup DS2. Deep learning analysis of ctDNA identified a molecular subgroup with enhanced response to anti-PDL1 treatment in multiple tumor types. Citation Format: Song Wu, Sriram Sridhar, Han Si, Michael Kuziora, Judson Englert, Shaad E. Abdullah, Philip Dennis, Feng Xiao, Guozhi Gao, Koustubh Ranade, Brandon W. Higgs. Deep learning analysis of circulating tumor DNA identified a pan-tumor molecular subtype with enhanced response to durvalumab (anti-PDL1) [abstract]. In: Proceedings of the American As