Genomic Landscapes and Hallmarks of Mutant RAS in Human Cancers

Abstract

The RAS family of small GTPases represents the most commonly activated oncogenes in human cancers. To better understand the prevalence of somatic RAS mutations and the compendium of genes that are coaltered in RAS-mutant tumors, we analyzed targeted next-generation sequencing data of 607,863 mutations from 66,372 tumors in 51 cancer types in the AACR Project GENIE Registry. Bayesian hierarchical models were implemented to estimate the cancer-specific prevalence of RAS and non-RAS somatic mutations, to evaluate co-occurrence and mutual exclusivity, and to model the effects of tumor mutation burden and mutational signatures on comutation patterns. These analyses revealed differential RAS prevalence and comutations with non-RAS genes in a cancer lineage-dependent and context-dependent manner, with differences across age, sex, and ethnic groups. Allele-specific RAS co-mutational patterns included an enrichment in NTRK3 and chromatin-regulating gene mutations in KRAS G12C-mutant non-small cell lung cancer. Integrated multiomic analyses of 10,217 tumors from The Cancer Genome Atlas (TCGA) revealed distinct genotype-driven gene expression programs pointing to differential recruitment of cancer hallmarks as well as phenotypic differences and immune surveillance states in the tumor microenvironment of RAS-mutant tumors. The distinct genomic tracks discovered in RAS-mutant tumors reflected differential clinical outcomes in TCGA cohort and in an independent cohort of patients with KRAS G12C-mutant non-small cell lung cancer that received immunotherapy-containing regimens. The RAS genetic architecture points to cancer lineage-specific therapeutic vulnerabilities that can be leveraged for rationally combining RAS-mutant allele-directed therapies with targeted therapies and immunotherapy.

Significance: The complex genomic landscape of RAS-mutant tumors is reflective of selection processes in a cancer lineage-specific and context-dependent manner, highlighting differential therapeutic vulnerabilities that can be clinically translated.

Figure 1.

Prevalence of KRAS codon 12, 13, and 61 and G12C mutations is tumor type and context dependent. A, Prevalence of KRAS mutations at codons 12, 13, and 61; the empirical estimate of the KRAS prevalence is shown as solid gray points, while the posterior median prevalence is shown as clear blue circles. KRAS codon 12 sequence alterations were dominant in PAC (_65.367.2_69.1%), ampullary cancer (_37.046.3_55.9%), appendiceal cancer (_37.642.9_48.1%), small bowel cancer (_25.431.4_37.8%), colorectal carcinoma (_28.930.0_31.1%), NSCLC (_24.225.0_25.9%), CUP (_14.115.7_17.4%), uterine cancer (_11.012.3_13.8%), ovarian cancer (_7.58.6_9.8%), hepatobiliary cancer (_6.98.2_9.8%), cervical cancer (_4.97.2_10.1%), neuroendocrine tumors (_4.36.5_9.3%), germ cell tumors (_4.35.7_7.4%), and bladder cancer (_3.34.2_5.2%), while KRAS codon 13 and 61 mutations were found at lower frequencies in these cancers. KRAS codon 13 sequence alterations were detected in _7.27.9_8.5% of colorectal carcinoma, _3.15.6_9.2% of small bowel cancer, _1.33.8_8.4% of ampullary cancer, _2.13.8_6.1% of appendiceal cancer, _1.32.8_5.3% of B-lymphoblastic leukemia/lymphoma, 1.₇2.3_2.9% of uterine cancer and _1.82.1_2.3% of NSCLC, whereas KRAS codon 61 mutations were detected in _4.75.5_6.4% of PAC, _1.13.4_0.8% of ampullary cancer, _0.92.3_4.8% of small bowel cancer, _1.51.8_2.2% of colorectal carcinoma, _1.51.8_2.0% of NSCLC, and _0.91.5_2.3% of thyroid cancer. KRAS G12C mutations were detected in ₁₁12₁₃% of NSCLC, _1.53.5_6.3% of small bowel cancer, _0.83.2_7.1% of ampullary cancer, _2.73.1_3.6% of colorectal carcinoma, _2.33.0_3.8% of CUP, _1.12.5_4.3% of appendiceal cancer, _0.71.1_1.6% of uterine cancer, and less than 1% of PAC, hepatobiliary cancer, small cell lung cancer, neuroendocrine tumors, and bladder cancer. B, Posterior median prevalence estimates for mutant KRAS alleles at codons 12, 13, and 61 varied based on the tumor type interrogated. As a representative example, NSCLC predominantly harbored KRAS G12C mutations in contrast to gastrointestinal tumors and pancreatic cancer that predominantly harbored KRAS G12D and G12V mutations. Similarly, posterior median prevalence estimates for mutant NRAS alleles at codons 12, 13, and 61 showed an enrichment in NRAS Q61R and Q61K mutations in melanoma and thyroid cancer. Error bars, 95% credible intervals. CRC, colorectal cancer; BLL, B-lymphoblastic leukemia; MDS/MPN, myelodysplastic syndrome/myeloproliferative neoplasm; SCLC, small cell lung cancer; CNS, central nervous system tumor.

Figure 2.

Prevalence of mutant RAS alleles at codons 12, 13, or 61 stratified by age, race, and sex reveals differential host-dependent frequencies. A–F, Prevalence of KRAS-mutant alleles varies by age group, race, and sex for patients with cancer of unknown primary (A), NSCLC (B), pancreatic adenocarcinoma (C), colorectal cancer (D), melanoma (E), and ovarian cancer (F). G–I, Cancer-specific estimates of RAS prevalence obtained from a single hierarchical model (y-axis) were compared with the overall prevalence from 51 separate cancer-specific models, where the effects of age (G), race (H), and sex (I) were modeled hierarchically (x-axis). For several cancers including NSCLC, the prevalence of RAS mutations in demographic subgroups differed significantly from the overall prevalence estimates. Specifically, when compared with the overall population prevalence estimates, RAS mutations were less prevalent in patients younger than 40 years with melanoma (_7.311.8_15.7% lower) and CUP (_7.312.5_16.5% lower), and patients younger than 50 years with NSCLC (_7.611.0_14.2% lower) and PAC (_7.714.6_21.7% lower). RAS mutations were more prevalent in patients younger than 40 years with ovarian cancer (_8.915.8_23.5% higher) and B-lymphoblastic leukemia/lymphoma (_0.28.2_16.7% higher) compared with the overall population. A negative difference in prevalence indicates higher prevalence in the subset compared with the overall population prevalence. BLL, B-lymphoblastic leukemia; CRC, colorectal cancer.

Figure 3.

Co-occurrence of RAS hotspot mutations with sequence and structural non-RAS mutations is cancer lineage specific. A, Volcano plots of the posterior median log OR (x-axis) versus negative log₁₀P value (y-axis) for the association between RAS mutations and non-RAS variants. Mutations in non-RAS genes with log OR greater than 0 co-occur with RAS mutations as opposed to non-RAS mutations with an interaction coefficient less than 0, which are comutated with RAS at a rate lower than expected or are mutually exclusive. The further to the right on the x axis the closest to true co-occurrence and the further to the left of the axis the closest to true mutual exclusivity, with the statistical significance of the difference from 0 (which indicates independence) plotted on the y-axis. B, For statistically significant associations, 95% posterior credible intervals of the log OR are indicated by error bars. For genes associated with RAS mutations in multiple cancers, multiple vertically offset error bars are displayed. C and D, Volcano plots and posterior credible intervals for the association between RAS mutations and inactivating non-RAS mutations. E, Volcano plots of the posterior median log OR (x-axis) versus negative log₁₀P (y-axis) for the association between RAS mutations with deep deletions, high copy amplifications, and gene fusions. F, 95% posterior credible intervals of the log OR for RAS/non-RAS fusions. Despite the small number of fusions included in the analyses that precluded firm statistical conclusions, fusions involving ALK (EML4-ALK) and RET (RET-KIF5B) genes showed a pattern of mutual exclusivity with RAS mutations. CRC, colorectal cancer.

Figure 4.

Differential comutation patterns in KRAS- and NRAS-mutant tumors point to RAS/non-RAS gene dependencies based on host features and cancer lineage. Differential RAS-mutant allele comutation patterns per tumor type and by age, race, and sex for KRAS/ATM in NSCLC (A), KRAS/RBM10 in NSCLC (B), KRAS/NTRK3 in NSCLC (C), KRAS/STK11 in NSCLC (D), KRAS/KEAP1 in NSCLC (E), KRAS/EGFR in NSCLC (F), KRAS/PIK3CA in colorectal carcinoma (G), KRAS/FBXW7 in colorectal carcinoma (H), KRAS/RNF43 in colorectal carcinoma (I), KRAS/SF3B1 in colorectal carcinoma (J), KRAS/BRAF in colorectal carcinoma (K), KRAS/TP53 in colorectal carcinoma (L), KRAS/TP53 in PAC (M), KRAS/SMAD4 in PAC (N), KRAS/CDKN2A in PAC (O), KRAS/DAX in PAC (P), KRAS/MEN1 in PAC (Q), KRAS/CASP8 in colorectal carcinoma (R), NRAS/BRAF in melanoma (S), NRAS/TERT in melanoma (T), KRAS/NF1 in melanoma (U), KRAS/PTEN in uterine cancer (V), KRAS/TP53 in uterine cancer (W), and NRAS/BRAF in thyroid cancer (X). Log OR is plotted for each stratified RAS/non-RAS comutation. Red, co-occurrence; blue, mutual exclusivity or occurrence less frequently than expected under independence. CRC, colorectal cancer.

Figure 5.

Convergence of RAS/non-RAS comutations in cancer hallmarks and signaling pathways, accounting for TMB and mutational spectra. A, KRAS- and NRAS-mutant alleles were mutually exclusive with mutations in the RAS/RAF/MAPK pathway, while they co-occurred with mutations in the NRF2 pathway in NSCLC and colorectal carcinoma, PI3K/AKT pathway in colorectal carcinoma, cell-cycle progression in PAC, and chromatin regulation and DNA damage response in NSCLC. B, Triple mutations in KRAS G12C-mutant NSCLC, uterine, PAC, colorectal carcinoma, and CUP tumors. C, Concordance between RAS/non-RAS gene associations between the multicancer model and a TMB-stratified model. Vertical error bars indicate the IQR of the overall association across TMB strata. Highlighted are RAS/non-RAS associations that were statistically significant in the multicancer model but have an overall association in the TMB-stratified model near 0, suggesting that the associations identified in the multicancer model were confounded by TMB. D, Examples of comutations confounded by TMB include RAS codon 12, 13, and 61 mutations and APC mutations in colorectal carcinoma, KRAS codon 12, 13, and 61 mutations in colorectal carcinoma, KRAS codon 12, 13, and 61 mutations and KEAP1 and STK11 mutations in NSCLC. While KRAS/APC and TP53 co-occurrence was influenced by higher TMB in colorectal carcinoma, an inverse association was noted in NSCLC, where KRAS/STK11 and KEAP1 comutations were positively selected in TMB-low tumors. CRC, colorectal cancer.

Figure 6.

Differential expression profiles driven by KRAS G12C comutation status in LUADs. Comutation-driven GSEA leveraging transcriptomic profiles from RNA sequencing revealed marked differences in gene expression programs depending on KRAS G12C comutations. A, Normalized enrichment scores from GSEA in KRAS G12C-mutant LUADs harboring different comutations were used as an input for UMAP dimensionality reduction, which revealed convergence of gene sets in distinct clusters related to immune/inflammatory response, metabolism, sustained/mitogenic signaling, oxidative phosphorylation, apoptosis, DNA maintenance, replication and repair, and cell-cycle progression. B and C,KRAS G12C/KEAP1 comutant LUADs showed a downregulation of gene sets related to inflammatory responses, while showing an enrichment in metabolism, oxidative phosphorylation reactive oxygen species pathway gene sets. The continuous significance score (Signif) indicates the −log₁₀(P_adj) * sign(fold-change) from the GSEA. Red, upregulation; blue, downregulation. D and E, A prominent upregulation of inflammatory response related gene expression programs was noted in the TME of KRAS G12C/TP53 comutant LUADs, together with gene sets related to cell-cycle progression and E2F-driven proliferation. Quantile-quantile plots were generated to visually compare the ranks of genes in the pathway to ranks that were sampled from a discrete uniform distribution. Adjusted P values for gene set differential expression are provided for comparison of KRAS G12C/non–RAS-mutant LUAD to KRAS G12C-mutant LUAD. HM, Hallmark; KG, Kyoto Encyclopedia of Genes and Genomes, KEGG.

Figure 7.

Survival analyses for differentially comutated KRAS G12C-mutant NSCLC in a cohort of patients treated with chemotherapy and immunotherapy-containing regimens. A, Patients with NSCLC harboring KRAS G12C and STK11 comutations had a shorter overall survival with immunotherapy (n = 24 vs. n = 82, median survival 5.65 vs. 23.52 months, log-rank P = 0.003, HR: 2.31; 95% confidence interval, CI: 1.31–4.07). B, Conversely, co-occurrence of KRAS G12C and TP53 hotspot alterations was associated with longer overall survival with immunotherapy immunotherapy-containing regimens (n = 56 vs. n = 76, median survival 23.52 vs. 10.09 months, log-rank P = 0.05, HR: 0.63, 95% CI: 0.39–1.01). C,KRAS G12C/AMER1 comutations conferred a worse prognosis for patients with metastatic NSCLC treated with immunotherapy-containing regimens (n = 4 vs. n = 128, median survival of 4.55 vs. 12.85 months, log-rank P = 0.029, HR: 2.93, 95% CI: 1.06–8.07). D, Patients with KRAS G12C/PIK3CA-mutant NSCLC (n = 5) had a significantly shorter overall survival compared with patients with KRAS G12C-mutant tumors who received first-line immunotherapy (n = 25, 4.4 months vs. not reached, log-rank P = 0.028, HR: 3.48, 95% CI: 1.07–11.34). IO, immunotherapy.

Figure 8.

RAS comutations involve cancer hallmarks in a cancer lineage–dependent manner. RAS comutation patterns point to dependencies of mutant RAS on oncogenic signaling under cancer hallmark pathways. Master regulators of different cancer hallmarks are comutated or are mutually exclusive with RAS-activating mutation depending on the tumor tissue origin. A representative example is that of the cell-cycle regulators CDKN2A and CDKN2B that are comutated with RAS in pancreatic cancer and melanoma, while less frequently comutated in NSCLC. Similarly, cancer hallmarks are differentially recruited in RAS-mutant tumors depending on the tumor tissue lineage. TP53 alterations co-occurred with RAS mutations in pancreatic cancer but were comutated less frequently than expected in RAS-mutant uterine, ovarian cancer, colorectal cancer, and NSCLC. This context-dependent genomic radial of RAS-mutant tumors may point to potential targets for combination therapeutic interventions.