Noninvasive Early Identification of Therapeutic Benefit from Immune Checkpoint Inhibition

Abstract

Although treatment of non-small cell lung cancer (NSCLC) with immune checkpoint inhibitors (ICIs) can produce remarkably durable responses, most patients develop early disease progression. Furthermore, initial response assessment by conventional imaging is often unable to identify which patients will achieve durable clinical benefit (DCB). Here, we demonstrate that pre-treatment circulating tumor DNA (ctDNA) and peripheral CD8 T cell levels are independently associated with DCB. We further show that ctDNA dynamics after a single infusion can aid in identification of patients who will achieve DCB. Integrating these determinants, we developed and validated an entirely noninvasive multiparameter assay (DIREct-On, Durable Immunotherapy Response Estimation by immune profiling and ctDNA-On-treatment) that robustly predicts which patients will achieve DCB with higher accuracy than any individual feature. Taken together, these results demonstrate that integrated ctDNA and circulating immune cell profiling can provide accurate, noninvasive, and early forecasting of ultimate outcomes for NSCLC patients receiving ICIs.

Keywords: circulating tumor DNA; immune checkpoint inhibition; immunotherapy; liquid biopsy; non-small cell lung cancer; response classification.

Conflict of interest statement

Declaration of Interests J.J.C. reports paid consultancy from Lexent Bio Inc. A.A.C. reports speaker honoraria and travel support from Roche Sequencing Solutions (RSS), Varian, and Foundation Medicine; a research grant from RSS; and has served as a paid consultant for Oscar Health. T.M. is a co-founder of Imvaq. J.W.N. reports research support from Genentech (GNE)/Roche, Merck, Novartis, Boehringer Ingelheim, Exelixis, Takeda Pharmaceuticals, Nektar Therapeutics, Adaptimmune, and GSK, and has served in a consulting or advisory role for AstraZeneca (AZ), GNE/Roche, Exelixis Inc., Jounce Therapeutics, Takeda Pharmaceuticals, and Eli Lilly. H.A.W. has received honoraria from Novartis and AZ and has participated on the advisory boards of Xcovery, Janssen, and Mirati. S.K.P. reports grant support from EpicentRx, Forty Seven, Bayer, and Boehringer Ingelheim and serves in a consulting or advisory role for AZ, AbbVie, G1 Therapeutics, and Pfizer. M. Das reports grant support from AbbVie, United Therapeutics, Varian, and Celgene and serves in a consulting role for AZ and Bristol-Myers Squibb (BMS). A.M.N. has patent filings related to expression deconvolution and cancer biomarkers and has served as a consultant for Roche, Merck, and CiberMed. M.D.H. reports paid consultancy from BMS, Merck, GNE, AZ/MedImmune, Nektar, Syndax, Janssen, Mirati Therapeutics, Shattuck Labs, and Blueprint Medicines; travel/honoraria from BMS and AZ; research funding from BMS; and a patent has been filed by MSK related to the use of tumor mutation burden to predict response to immunotherapy (PCT/US2015/062208), which has received licensing fees from Personal Genome Diagnostics. A.A.A. reports ownership interest in CiberMed and FortySeven, patent filings related to cancer biomarkers, and paid consultancy from GNE, Roche, Chugai, Gilead, and Celgene. M. Diehn reports research funding from Varian, ownership interest in CiberMed, patent filings related to cancer biomarkers, paid consultancy from Roche, AZ, and BioNTech, and travel/honoraria from Reflexion. M.Diehn, A.A.A., B.Y.N., and M.S.E. are co-inventors on a provisional patent application filed by Stanford University relating to this manuscript.

Copyright © 2020 Elsevier Inc. All rights reserved.

Figures

Figure 1:. Association of radiologic response and durable clinical benefit in NSCLC.

A) Rate of DCB or NDB in advanced NSCLC patients receiving PD-(L)1 blockade-based ICI achieving partial response (PR), stable disease (SD), or progressive disease (PD) at the first scan by RECIST v1.1 criteria. B) Study schematic. Pre-treatment blood from NSCLC patients receiving PD-(L)1 blockade-based ICI was collected and fractionated for ctDNA analysis and RNA-seq for immune profiling. Pre-treatment tumor biopsies were used to assess PD-L1 expression. Early on-treatment blood was collected within 4 weeks for ctDNA monitoring. Tumor and immune features were tested for their association with ultimate outcomes. See also Figure S1.

Figure 2:. Pre-treatment ctDNA-normalized tumor mutation burden predicts response to ICI.

A) Outcomes of PD-L1 blockade treated patients from the POPLAR/OAK Cohort (Gandara et al., 2018) (POPLAR/OAK ICI Cohort) stratified by high bTMB/MB (≥14) and low bTMB/MB (<14). P-value calculated by two-sided Fisher’s Exact Test (DCB n = 139; NDB n = 290). B) Pre-treatment ctDNA concentration (haploid genome equivalents per mL of plasma, hGE/mL) and C) ctDNA-normalized bTMB (norm. bTMB) in POPLAR/OAK ICI Cohort. P-values were calculating using a Wilcoxon test. D) Area under the curve (AUC) for individual parameters in immunotherapy patients generated by leave-one-out cross-validation (LOOCV) ROC analysis. E) Probability of PFS for high norm. bTMB (median = 4.14 mo.) and low norm. bTMB (median = 2.16 mo.) PD-L1 blockade patients stratified by the LOOCV-identified optimal cut-point in the POPLAR OAK ICI Cohort (n = 429). F) Outcomes of chemotherapy treated patients from the POPLAR/OAK Cohort (Gandara et al., 2018) (POPLAR/OAK Chemo Cohort) stratified by high bTMB/MB (≥14) and low bTMB/MB (<14). P-value calculated by two-sided Fisher’s Exact Test (DCB n=118; NDB n = 306). G) Pre-treatment ctDNA concentration and H) norm. bTMB (POPLAR/OAK Chemo Cohort) in chemotherapy patients. P-values were calculated using a Wilcoxon test. I) Probability of PFS for high norm. bTMB (median = 3.25 mo.) and low norm. bTMB (median = 4.00 mo.) chemotherapy patients stratified by the LOOCV-identified optimal cut-point in the POPLAR/OAK Chemo Cohort (n = 424). J) Probability of PFS for high norm. bTMB (median = 16.49 mo.) and low norm. bTMB (median = 1.92 mo.) patients who received single-agent PD-(L)1 blockade in this study, exclusive of the DIREct Validation Cohort (n = 37) stratified by the cut-point of norm. bTMB identified in the POPLAR/OAK ICI Cohort. See also Figure S2.

Figure 3:. Pre-treatment circulating immune profiles and early on-treatment ctDNA dynamics predict outcomes to PD-(L)1 blockade-based ICI.

A) Comparison of pre-treatment circulating immune cell populations by CIBERSORTx restricted to those with >1% median frequency in patients achieving DCB (n = 20) vs. NBD (n = 17). Shown are AUCs and 95% confidence intervals (CIs) generated by bootstrapping for classifying DCB versus NDB by each cell population. Shown on the top is the median relative abundance of each cell type in these patients. B) Pre-treatment relative CD8 T cell fraction in circulation of ICI DCB (n = 20) and NDB (n = 17) with available CIBERSORTx immune profiling. P-value was calculating using a Wilcoxon test. C) Bootstrapping-generated accuracy of outcome classification using the indicated pre-treatment tumor, ctDNA, and/or immune parameters in cases with all data types available (n = 27). D) Early on-treatment (≤4 weeks) ctDNA concentration normalized to pre-treatment ctDNA concentration (median = 2.4 weeks, n = 46, DCB = 27, NDB = 19). Colors indicate the ultimate clinical outcome. The dashed line indicates 50% of pre-treatment ctDNA concentration (ctDNA molecular response). E) Early on-treatment ctDNA concentration at the first timepoint after the first cycle of ICI normalized to pre-treatment ctDNA concentration, stratified by ultimate clinical outcome. Colors indicate if the sample was collected ≤2 or 2–3.3 weeks after treatment initiation. The dashed line indicates ctDNA molecular response. F) Probability of PFS in patients with at least a 0.5-fold ctDNA drop from baseline (median = 22.4 mo.) and those without at least a 0.5-fold ctDNA drop (median = 2.30 mo.) within 4 weeks of treatment start. See also Figure S3.

Figure 4:. Multiparameter Bayesian frameworks for noninvasive outcome classification.

A) Schematic depicting the discovery and validation approach for generating and testing the DIREct models. B) Patient characteristics. Each column represents an individual patient. Tumor histology, smoking status, best overall response, tumor PD-L1 expression, and PD-(L)1 blockade-based ICI therapy type (PD-(L)1 blockade alone; PD-(L)1 blockade with either CTLA-4 or chemotherapy) are indicated. PFS is shown in months, where asterisks signify ongoing responses. TMB is presented as the number of nonsynonymous mutations per megabase of the coding exome captured, measured in the blood (See Figure S2A–B). Mutations in the most recurrently mutated genes in TCGA NSCLC cases that also overlapped the NSCLC-focused CAPP-Seq selector in our cohort are shown at the bottom. See also Figure S4.

Figure 5:. DIREct-On enables fully noninvasive outcome classification.

A) Proportion of patients expected to achieve DCB (blue) or NDB (orange) by the DIREct-Pre model stratified by clinical outcome determined by RECIST in the DIREct Discovery Cohort (n = 34). B) Probability of PFS for high DIREct-Pre score (median = 8.2 mo.) and low DIREct-Pre score (median = 2.0 mo.) patients in the DIREct Discovery Cohort, using the optimal cut-point identified by LOOCV analysis (n = 34). C) Probability of PFS for high DIREct-Pre score (median = 8.4 mo.) and low DIREct-Pre score (median = 2.6 mo.) patients in the DIREct Validation Cohort using the cut-point defined in the DIREct Discovery Cohort (n = 38). D) Hazard ratio (top) or accuracy (bottom) for low scores (below LOOCV-generated cut-point) of each model with the indicated parameters only considering patients with all parameters available in the DIREct Discovery Cohort (n = 26). Error bars represent the 95% CIs generated by bootstrapping. NS = not significant, ** = P < 0.01. E) Proportion of patients expected to achieve DCB (blue) or NDB (orange) by DIREct-On stratified by clinical outcome determined by RECIST in the DIREct Discovery Cohort (n = 34). F) Probability of PFS for high DIREct-On score (median = 16.5 mo.) and low DIREct-On score (median = 1.9 mo.) patients in the DIREct Discovery Cohort, using the optimal cut-point identified by LOOCV analysis (n = 34). G) Probability of PFS for high DIREct-On score (median = 8.5 mo.) and low DIREct-On score (median = 2.1 mo.) patients in the DIREct Validation Cohort using the cut-point defined in the DIREct Discovery Cohort (n = 38). H) Accuracy for each individual parameter and DIREct-On in the combined DIREct Discovery and Validation Cohorts for those cases with all data types available, using the cut-points identified the DIREct Discovery Cohort (n = 58). Error bars represent 95% CIs generated by bootstrapping. ** = P < 0.01, *** = P < 0.001. I) Net reclassification improvement of DIREct-On compared to each individual feature (top) and DIREct-On compared to Bayesian models with each feature that comprises DIREct-On removed (bottom). Errors bars represent 95% CIs generated by bootstrapping. * = P < 0.01, ** = P < 0.01. See also Figure S5.

Figure 6:. Clinical course for patients in the DIREct Discovery and Validation Cohorts.

Swimmers chart for patients with high (left) or low (right) DIREct-on scores. Chart depicts timing of on-treatment blood draw for DIREct-On results (triangles), RECIST v1.1 status at the first scan (squares without outline) and the scan demonstrating the best overall response (squares with outline). In cases where the first scan was also the best overall response scan only the first scan is shown. Progression events or time of censoring is shown (red circle = progression, open circle = no progression at last follow-up). Last infusion date is depicted as a vertical line (black = treatment finished, pink = treatment ongoing). Patients belonging to the DIREct Discovery (purple) or Validation Cohort (tan) are indicated by diamonds. See also Figure S6.

Figure 7:. DIREct-On enables early forecasting of ultimate outcomes.

A) Probability of PFS for high DIREct-On score (median = 11.69 mo.) patients, actual DCB patients measured by RECIST (median = 11.69 mo.), low DIREct-On score (median = 1.94 mo.) patients, and actual NDB patients measured by RECIST (median = 1.94 mo.) in the combined DIREct Discovery and Validation cohorts (n = 72). B) Probability of PFS from start of therapy stratified by DIREct-On score (solid line = expected DCB, dashed lines = expected NDB) in patients in the DIREct Discovery and Validation Cohorts treated with PD-1/PD-L1 single-agent blockade (purple), PD-1 and CTLA-4 combination therapy (orange), or the combination of PD-1 and chemotherapy (green). C) DIREct-On score in the combined DIREct Discovery and Validation Cohorts (indicated by shape and color) stratified by response measured by RECIST and DCB versus NDB. The horizontal line indicates the threshold identified in the discovery cohort to best classify DCB versus NDB. D) Vignette for patient with high DIREct-On score and stable disease at the first scan. E) Vignette for patient with low DIREct-On score and stable disease at the first scan. F) Probability of PFS for high DIREct-On score (median = 10.26 mo.) and low DIREct-On score (median = 3.62 mo.), in those patients with RECIST stable disease at the first available scan in the combined DIREct Discovery and Validation Cohorts (n = 18). G) Potential application of DIREct-On to personalize immunotherapy in front-line treatment of advanced NSCLC. Patients could begin by receiving single agent PD-(L)1 blockade for one cycle and could then either remain on PD-(L)1 blockade if DIREct-On forecasts durable response or undergo treatment adaptation or escalation if DIREct-On forecasts lack of durable benefit. See also Figure S7.