Identifying a Clinically Applicable Mutational Burden Threshold as a Potential Biomarker of Response to Immune Checkpoint Therapy in Solid Tumors

Abstract

Purpose: An association between mutational burden and response to immune checkpoint therapy has been documented in several cancer types. The potential for such a mutational burden threshold to predict response to immune checkpoint therapy was evaluated in several clinical datasets, where mutational burden was measured either by whole-exome sequencing (WXS) or using commercially available sequencing panels.

Methods: WXS and RNA-seq data of 33 solid cancer types from TCGA were analyzed to determine whether a robust immune checkpoint activating mutation (iCAM) burden threshold associated with evidence of immune checkpoint activation exists in these cancers that may serve as a biomarker for response to immune checkpoint blockade therapy.

Results: We find that a robust iCAM threshold, associated with signatures of immune checkpoint activation, exists in 8 of 33 solid cancers: melanoma, lung adenocarcinoma, colon adenocarcinoma, endometrial cancer, stomach adenocarcinoma, cervical cancer, ER+HER2- breast cancer, and bladder-urothelial cancer. Tumors with mutational burden higher than the threshold (iCAM+) also had clear histologic evidence of lymphocytic infiltration. In published datasets of melanoma, lung adenocarcinoma and colon cancer, patients with iCAM+ tumors had significantly better response to immune checkpoint therapy compared to those with iCAM- tumors. ROC analysis using TCGA predictions as gold standard showed that iCAM+ tumors are accurately identifiable using clinical sequencing assays, such as FoundationOne or StrandAdvantage. Using the FoundationOne derived threshold, analysis of 113 melanoma tumors, showed that iCAM+ patients have significantly better response to immune checkpoint therapy. iCAM+ and iCAM- tumors have distinct mutation patterns and different immune microenvironments.

Conclusion: In 8 solid cancers, a mutational burden threshold exists that may predict response to immune checkpoint blockade. This threshold is identifiable using available clinical sequencing assays.

Fig 1.

A robust immune checkpoint–activating mutation (iCAM) threshold associated with evidence of immune checkpoint activation can be identified in eight cancer types in The Cancer Genome Atlas (TCGA). (A) Distributions of nonsynonymous mutational burden in eight cancer types (in log₁₀ scale) separate the samples into iCAM-positive (blue) and iCAM-negative (gold) subsets. (B) Immune checkpoint activation criterion (Data Supplement) significantly higher in iCAM-positive compared with iCAM-negative is shown in gold for the eight cancer types. (C) Results from blinded pathologic quantification of lymphocyte infiltration in high-resolution histologic images from TCGA of 15 iCAM-positive and 15 iCAM-negative tumors in each of the eight cancer types. iCAM-positive samples (blue) and iCAM-negative samples (gold); the shape widths are proportional to number of samples. BLCA, bladder cancer; CESC, cervical cancer; COAD, colon adenocarcinoma; CTLA-4; cytotoxic T-lymphocyte antigen-4; ER, estrogen receptor; HER2, human epidermal growth factor receptor 2; LUAD, lung adenocarcinoma; PD-1; programmed death-1; PD-L1, programmed death ligand-1; PD-L2, programmed death ligand-2; SKCM, skin cutaneous melanoma; STAD, stomach adenocarcinoma; UCEC, endometrial cancer.

Fig 2.

The immune checkpoint–activating mutation (iCAM) threshold is associated with clinical outcomes in patients treated with immune checkpoint blockade in published datasets. (A) Comparison of response in patients with skin melanoma to cytotoxic T-lymphocyte antigen-4 (CTLA-4) targeted therapy by iCAM status in two independent datasets.^, (B) Comparison of response in patients with lung adenocarcinoma and colorectal cancer to programmed death-1 targeted therapy by iCAM status. CB, clinical benefit; CR, complete response; DCB, durable clinical benefit; PD, progressive disease; PR, partial response.

Fig 3.

Immune checkpoint–activating mutation (iCAM)–positive tumors can be identified with high accuracy using routine clinical assays. Receiver operating characteristic curves for iCAM status determination of The Cancer Genome Atlas (TCGA) samples using only the exons used in FoundationOne (blue) and StrandAdvantage (gold) assays. Whole-exome sequencing TCGA classification was used as the gold standard. The area under the receiver operating characteristic curve for these two assays, respectively, was as follows: melanoma (0.98, 0.94), lung (0.94, 0.86), colon (0.99, 0.96), endometrial (0.98, 0.95), stomach (0.99, 0.98), cervical (0.88, 0.78), estrogen receptor (ER)–positive/human epidermal growth factor receptor 2 (HER2)–negative breast (0.85, 0.78), and bladder (0.90, 0.86). BLCA, bladder cancer; CESC, cervical cancer; COAD, colon adenocarcinoma; LUAD, lung adenocarcinoma; SKCM, skin cutaneous melanoma; STAD, stomach adenocarcinoma; UCEC, endometrial cancer.

Fig 4.

Prospective validation of immune checkpoint–activating mutation (iCAM) threshold for melanoma using FoundationOne assay results predicts outcome of immune checkpoint blockade in an independent cohort of patients with melanoma. Shown are comparisons of response rates, progression-free survival (PFS), and overall survival in 113 patients with metastatic melanoma treated with single-agent programmed death-1 (PD-1)–targeted therapy, stratified by iCAM status using the FoundationOne assay. CR, objective complete response; PD, progressive disease; PR, objective partial response; SD, stable disease (includes mixed response).

Fig 5.

Selected significantly mutated genes (MutSigCV, false discovery rate < 0.1) in immune checkpoint–activating mutation (iCAM)–positive and iCAM-negative tumors. Columns are samples and rows are genes (white, gene not significantly mutated in that class; gold, gene significantly mutated in that class but not mutated in that sample; blue and gray, gene significantly mutated in iCAM-positive [blue]/iCAM-negative [gray] class and mutated in that sample).

Fig 6.

Mutation and immune signatures of immune checkpoint–activating mutation (iCAM)–positive versus iCAM-negative tumors. (A) Comparison of the fractional contribution of mutation signatures from Catalogue of Somatic Mutations in Cancer for etiologies associated with iCAM-positive versus iCAM-negative status in the eight cancer types. (B) Other immune signatures associated with iCAM status (natural killer [NK] fraction in leukocytes, M1 fraction in macrophages, and regulatory T cell (T-regs) fraction in T cells in iCAM-positive and iCAM-negative tumors). The numbers of iCAM-negative and iCAM-positive tumors, respectively, in (B) are as follows: SKCM (71, 211), LUAD (218, 203), COAD (81, 36), UCEC (39, 37), STAD (187, 55), CESC (84, 49), estrogen receptor (ER)–positive/human epidermal growth factor receptor 2 (HER2)–negative (391, 80), and BLCA (34, 134). Only tumors with a CIBERSORT P value < .05 were included in (B). APOBEC, apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like; BLCA, bladder cancer; CESC, cervical cancer; COAD, colon adenocarcinoma; Deamination, deamination of 5-methylcytosine; LUAD, lung adenocarcinoma; MMR, mismatch repair defect; POLE, polymerase epsilon proof-reading defect; SKCM, skin cutaneous melanoma; STAD, stomach adenocarcinoma; UCEC, endometrial cancer; UV, ultraviolet.