Association of differential expression of immunoregulatory molecules and presence of targetable mutations may inform rational design of clinical trials

Abstract

Background: Immune checkpoint inhibitors (ICIs) and genomic biomarker-driven targeted therapies have revolutionized the modern oncologic treatment arsenal. The next step has been to combine targeted agents and ICIs. In doing so, some combination regimens may be more logical than others.

Patients and methods: Whole-exome and whole-transcriptome sequencing were performed on 2739 unselected later-stage clinical cases from 24 solid tumor subtypes in the NantHealth database, and data were also curated from 5746 similarly sequenced patients across 28 solid tumor subtypes in The Cancer Genome Atlas (TCGA). Significant differential expression of 10 immunoregulatory molecules [IRMs (genes)] was analyzed for association with mutant versus wild-type genes.

Results: Twenty-three significant associations between currently actionable variants and RNA-expressed checkpoint genes were identified in the TCGA cases; 10 were validated in the external cohort of 2739 clinical cases from NantHealth (P values were adjusted using Benjamini-Hochberg multiple hypothesis correction to reduce false-discovery rate). Within the same 5746 TCGA profiles, 2740 TCGA patients were identified as having one or more potentially oncogenic single-nucleotide variant (SNV) mutation within an established 50-gene hotspot panel. Of the 50 genes, SNVs within 15 were found to be significantly associated with differential expression of at least one IRM after adjusting for tissue enrichment; six were confirmed significant associations in an independent set of 2739 clinical cases from NantHealth.

Conclusions: Logically combining ICIs with targeted therapies may offer unique treatment strategies for patients with cancer. The presence of specific mutations impacts the expression of IRMs, an observation of potential importance for selecting combinations of gene- and immune-targeted therapeutics.

Keywords: checkpoint molecules; genomic medicine; immunoregulatory molecules; next-generation sequencing; oncogenes; precision oncology; targetable mutations.

Figure 1

(A) Immune checkpoint expression changes in the presence of sensitizing mutations. Twenty-three significant associations were found between defined targetable mutations and differential checkpoint expression from the 5746 patients in The Cancer Genome Atlas (TCGA) database. In the top panel, red dots indicate statistically significant upregulation of the immune regulatory molecule (IRM), while blue dots indicate significant downregulation in TCGA. X-axis represents the association between specific gene mutation and IRM. Y-axis is the level of expression of the checkpoint based on the specific gene mutation. For example, BRAF V600E mutations are significantly associated with having upregulation of PDL1 in RNA (far left-hand side) [as denoted with the red dots signifying upregulation of the IRM in RNA (see the upper part of the panel for dots)]. Further, BRAF V600E mutations are significantly associated with downregulation of IDO1 in RNA [far right-hand side, as denoted with the blue dots signifying downregulation of the IRM in RNA (see the upper part of the panel for dots)]. The asterisks (n = 10) below represent confirmed significant associations in an independent set of 2739 clinical cases from NantHealth. P values were adjusted using Benjamini–Hochberg multiple hypothesis correction to reduce false-discovery rate. (B) Immune checkpoint expression changes in the presence of hotspot mutations. Fifteen associations were found between potentially pathogenic mutations and differential checkpoint expression after adjusting for tissue enrichment of the various genes. In the top panel, red dots indicate statistically significant upregulation of the IRM, while blue dots indicate significant downregulation in TCGA. Within 5746 TCGA profiles, 2740 TCGA solid-tumor samples had one or more mutation [single-nucleotide variant (SNV)] within an established hotspot gene-panel—AmpliSeq 50-gene HotSpot v2 panel. Of the 50 studied driver genes, SNVs within 15 were found to be significantly associated with differential expression of at least one IRM. For example, CDKN2A mutations, regardless of tumor type, are significantly associated with having upregulation of CTLA4 in RNA (far left-hand side) [as denoted with the red dots signifying upregulation of the IRM in RNA (see upper part of the panel for dots)]. Further, APC mutations are significantly associated with downregulation of TIM3 in RNA (far right-hand side) [as denoted with the blue dots signifying downregulation of the IRM in RNA (see upper part of the panel for dots)]. (See also Supplementary Table S4, available at https://doi.org/10.1016/j.esmoop.2022.100396.) Asterisks (n = 6) were confirmed significant associations in an independent set of 2739 clinical cases from NantHealth. P value was adjusted based on the multiple hypothesis correction (false-discovery rate).