Daniel H. Hovelson

Purpose: NCI selected a network of Clinical Laboratory Improvement Amendments–certified laboratories performing routine next-generation sequencing (NGS) tumor testing to identify patients for the NCI Molecular Analysis for Therapy Choice (NCI-MATCH) trial. This large network provided a unique opportunity to compare variant detection and reporting between a wide range of testing platforms. Experimental Design: Twenty-eight NGS assays from 26 laboratories within the NCI-MATCH Network, including the NCI-MATCH central laboratory (CL) and 11 commercial and 14 academic designated laboratories (DL), were used for this study. DNA from eight cell lines and two clinical samples were sequenced. Pairwise comparisons in variant detection and reporting between each DL and CL were performed for single-nucleotide variant, insertion and deletion, and copy-number variant classes. Results: We observed high concordance in variant detection between CL and DL for single-nucleotide variants and insertions and deletions [average positive agreement (APA) > 95.4% for all pairwise comparisons] but lower concordance for variant reporting after analysis pipeline filtering. We observed much higher agreement between CL and assays using amplification as the target enrichment method (84.2% < APA ≤ 95.7%, average APA = 88.7%) than other assays using hybridization capture (69.7% < APA ≤ 93.8%, average APA = 77.4%) due to blacklisting of actionable variants in low complexity regions. For copy-number variant reporting, we observed high agreement (APA > 82%) except between CL and two assays (APA = 76.9% and 71.4%) due to differences in estimation of copy numbers. Notably, for all variants, differences in variant interpretation also contributed to reporting discrepancies. Conclusions: This study indicates that different NGS tumor profiling tests currently in widespread clinical use achieve high concordance between assays in variant detection. For variant reporting, observed discrepancies are mainly introduced during the bioinformatic analysis.

IRS is robust to self-reported race. A, Pie chart of self-reported race for all 24,463 patients in the SCMD with informative TMB and gene expression data needed to generate IRS regardless of treatment history (SCMD lock at the time of IRS development). The total number of patients in each racial group is shown. B, The percentage of TMB-H and IRS-H patients from the SCMD (n = 24,463 as in A stratified by self-reported race is plotted). Fisher exact test was used to test the differences in IRS-H (or TMB-H) between the White or Caucasian/European group and all other groups; groups where IRS-H (or TMB-H) was significantly greater (P < 0.05) versus the White or Caucasian group (potentially as a consequence of inappropriate filtering of germline variants in TMB determination for non-White or Caucasian groups) are indicated by *. Asian, Black or African American and Other groups were also considered together as non-European (blue). C, Further breakdown by TMB and IRS status and relevant tumor types. The percentage of IRS-H (bold hue) and TMB-H (light hue) stratified by White or Caucasian/European (red) and non-European (blue) self-reported race is plotted for the n = 6,138 total patients from A and B with one of the seven indicated primary tumor types (CRC = colorectal, EGC = esophagogastric, H&N = head and neck, Mel = melanoma, NSCLC = non–small cell lung carcinoma). The total number of European and non-European patients with each tumor type are indicated. D, Across eligible NCT03061305 patients treated with anti-PD-(L)1 monotherapy, we identified a validation cohort of all 575 patients not included in IRS discovery to assess the robustness of IRS (and the TMB component) to self-reported race. Anti-PD-(L)1 monotherapy rwPFS stratified by combined TMB and IRS status [TMB-H or IRS-H (TMB/IRS-H; black) vs. TMB-L and IRS-L (TMB/IRS-L; gray)] is shown (left) by unadjusted Kaplan–Meier analysis with the aHR [adjusted for age, sex assigned at birth, line of therapy, tumor type, anti-PD-(L)1 therapy type, inclusion in previous validation cohort, and self-reported race (non-European, unknown, or European)], 95% CI and P value for TMB/IRS status (TMB/IRS-H vs. TMB/IRS-L) shown. The number (n) of patients, events, and median rwPFS (with 95% CI) for each group are shown. E, Forest plot of rwPFS by IRS status in the cohort (all) and each self-reported racial group is shown. Significant associations are shown by filled in aHR estimates. F, Anti-PD-(L)1 monotherapy rwPFS stratified by TMB/IRS status is shown by unadjusted Kaplan–Meier analysis for the non-European subgroup (as in the overall cohort).

Supplementary Figure S6 shows three group Immunotherapy Response Score classification of the anti-PD-(L)1 monotherapy validation cohort

Supplementary Table S3 shows unadjusted restricted mean survival time analysis of for real-world PFS and overall survival in the monotherapy validation cohort

Supplementary Figure S3 shows how Immunotherapy Response Score adds to tumor mutation burden and microsatellite instability status for anti-PD-(L)1 monotherapy clinical benefit

Supplementary Figure S8. shows overlap weighting propensity score analysis of the chemotherapy, anti-PD-(L)1, and chemotherapy + anti-PD-(L)1 validation cohort

Supplementary Table S2 shows the adjusted Cox proportional hazards models for real-world progression free survival and overall survival in the monotherapy validation cohort

Confirmation of the added utility of the IRS versus clinical PD-L1 IHC and TMB alone. A, Normalized PD-L1 (CD274) expression (and the other IRS expression components) by the qTP platform used to generate IRS were validated versus qRT-PCR in a validation cohort of 96 FFPE tumor tissue samples tested by clinical CGP and in parallel qTP. The Pearson correlation and linear range of each component is shown (Supplementary Fig. S1). B and C, The Pearson correlation of normalized PD-L1 expression by qTP [log2 normalized reads per million (nRPM) units] versus clinical PD-L1 IHC score (in submitted pathology reports) was determined in two cohorts of clinical FFPE tumor tissues [regardless of TMB availability and anti-PD-(L)1 treatment]. B, PD-L1 expression by qTP (log2 normalized units) versus PD-L1 IHC by TPS [using the 22C3 antibody clone (log2 TPS) in 276 clinically tested FFPE NSCLC tumors with available TPS is plotted]. The linear fit, Pearson correlation (r), and P value are shown. C, PD-L1 expression by qTP versus PD-L1 IHC by CPS using the 22C3 antibody clone (log2 CPS) in 221 clinically tested FFPE tumors (23 tumor types; most frequently EGC) with available TPS is plotted. The linear fit, Pearson correlation (r), and P value are shown. D, Using B and C, we identified a cohort of all 189 eligible NCT03061305 patients with IRS and PD-(L)1 IHC in accompanying pathology reports who were treated with anti-PD-(L)1 therapy (± chemotherapy). The association of biomarkers with anti-PD-(L)1 rwPFS was determined by Cox proportional hazards modeling [adjusting for age, sex assigned at birth, line of therapy, tumor type, therapy type (monotherapy vs. chemotherapy combination), and inclusion in IRS discovery status]. PD-L1 IHC score (continuous; log2) was included in the baseline model (Model 1), with the aHR, 95% CI, number (n) of patients and events, and P value shown for the biomarker term by forest plot. TMB status (-H vs. -L; pink) and IRS status (-H vs. -L; light blue) were separately added to this model (Models 2 and 3, respectively). The significance of each biomarker term is shown and the P value of the LRT comparing the full (Model 2 or 3) versus reduced (Model 1) model is shown. Model 4 includes PD-L1 IHC, TMB, and IRS. Significant biomarker terms are shown by filled in aHR estimates. E, Anti-PD-(L)1 rwPFS stratified by IRS group is shown by unadjusted Kaplan–Meier analysis, with the aHR from Model 4 in D shown. See Supplementary Table S7 for full subgroup analysis.

Supplementary Figure S4 shows a validation of the predictive nature of Immunotherapy Response Score

Supplementary Figure S8. shows overlap weighting propensity score analysis of the chemotherapy, anti-PD-(L)1, and chemotherapy + anti-PD-(L)1 validation cohort