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.

Tumor mutational burden quantification from targeted gene panels: major advancements and challenges

“…Besides the accuracy of panel-based TMB quantification, it is critical to assess its capability to discriminate between immunotherapy responders and nonresponders, as previously observed for WES-based TMB. Several exploratory analyses demonstrated that panelbased TMB, as simulated in silico by downsampling a WES dataset to only include genes targeted by the Foun-dationOne gene panel, associates with immunotherapy response [8,26] or with signatures of immune checkpoint activation [38]. Comparable results were observed in similar in silico analyses for other gene panels, such as the Trusight170 [39,40] or MSK-IMPACT [26] (Additional file 4: Table S4).…”

“…Moreover, the adopted cutoffs sometimes differ across different studies on the same gene panel (Table 1). Up to now, the TMB cutoff of 10 mutations per Mb, measured by the FoundationOne gene panel and found to best discriminate between responders and non-responders to immunotherapy in NSCLC patients, is the only one which has been validated in a separate further study [28,50,51]; this cutoff was also observed, but not yet validated, in melanoma [38] and in metastatic urothelial carcinoma [15] (Table 1). Interestingly, these cancer types present a TMB distribution similar to that of NSCLC [52].…”

“…Moreover, we still need to standardize the conversion of the reference WES-based TMB values to panel-based TMB, since the lower sequencing coverage and higher sequencing depth of gene panels, as compared to WES, may lead to decreased accuracy of TMB values and increased sensitivity in variant calling. For cross-panels or panel-to-WES TMB conversion, an in silico approach was proposed, where TMB distributions derived from two different technologies were interpolated and aligned and TMB cutoffs were mapped across distributions [38]. However, a consensus on a standard method to convert TMB values is still missing.…”

Biomarkers for Response to Immune Checkpoint Blockade

“…Similarly, it is not clear what the right TMB threshold should be to optimally identify potential responders, or www.annualreviews.org • Biomarkers for Response to ICB 335 if TMB functions as a predictor of response only in some cancer classes. A pan-cancer analysis of TCGA (The Cancer Genome Atlas) data has demonstrated that an optimal TMB threshold that identifies tumors with evidence of lymphocytic infiltration and immune checkpoint gene expression may only be present in some cancer classes (Panda et al 2017). Moreover, different cancer types may have different TMB cutoffs to optimally predict response (Panda et al 2017, Samstein et al 2019.…”

The Mutational Landscape of the SCAN-B Real-World Primary Breast Cancer Transcriptome

“…Questions remain however, as TMB is influenced by many biological and technical factors such as ploidy, tumor heterogeneity and clonality (ASCO SITC 2019, Abstract 27), sample tumor cell content, sequencing depth, and variant filtering. Which cutoff to use for stratifying patients into TMB groups is also still emerging [109,110], and specifically in RNA-seq has to our knowledge not been addressed. Due to this, and to account for different expression profiles per tumor, we decided to use the median amount of non-synonymous mutations per MB of transcriptome across the cohort (0.08 mutations) to stratify patients into TMB high and low groups and use it to study OS in different conventional treatment and biomarker subgroups.…”