Comprehensive pan-cancer genomic landscape of KRAS altered cancers and real-world outcomes in solid tumors

Abstract

Recent clinical development of KRAS inhibitors has heightened interest in the genomic landscape of KRAS-altered cancers. We performed a pan-cancer analysis of KRAS-altered samples from 426,706 adult patients with solid or hematologic malignancies using comprehensive genomic profiling; additional analyses included 62,369 liquid biopsy and 7241 pediatric samples. 23% of adult pan-cancer samples had KRAS alterations; 88% were mutations, most commonly G12D/G12V/G12C/G13D/G12R, and prevalence was similar in liquid biopsies. Co-alteration landscapes were largely similar across KRAS mutations but distinct from KRAS wild-type, though differences were observed in some tumor types for tumor mutational burden, PD-L1 expression, microsatellite instability, and other mutational signatures. Prognosis of KRAS-mutant versus other genomic cohorts of lung, pancreatic, and colorectal cancer were assessed using a real-world clinicogenomic database. As specific KRAS inhibitors and combination therapeutic strategies are being developed, genomic profiling to understand co-alterations and other biomarkers that may modulate response to targeted or immunotherapies will be imperative.

Fig. 1. Prevalence of KRAS alterations among adult patients with cancer.

a In the Foundation Medicine (FM) dataset of tissue or hematologic samples from 426,706 adult patients with cancer, KRAS was the most frequently altered oncogene with alterations in 23% of samples. A longtail of other top frequently altered oncogenes is shown; bar colors indicate alteration classes, SV: short variant mutation (e.g., substitutions, indels), CNA: copy number alteration, RE: rearrangement. b Prevalence in the FM dataset (left) and incidence estimates in the United States (right) of KRAS alterations in common adult tumor types (Supplemental Table 1). KRAS alterations are most prevalent in PDAC, appendix adenocarcinoma, small bowel and CRC tumor types. The highest incidence of KRAS alterations is estimated in CRC, non-Sq NSCLC and PDAC. c, d Number of cases in the FM database with KRAS mutations among (c) the 8 top indications with highest incidence of KRAS alterations and carcinoma of unknown primary (CUP) and d the 5 most common KRAS mutant isoforms.

Fig. 2. Prevalence of KRAS alteration subtypes by tumor type.

a Bar graph showing the prevalence of the most common types of KRAS alterations across indications. Disease subtypes are ordered based on the cumulative prevalence across the six highlighted KRAS alteration subtypes. Only tumor types with at least 250 total samples and a total KRAS alteration prevalence of at least 5% are shown. b Anatomic visualization of the prevalence of KRAS mutant isoforms. Percentage of total cases with KRAS mutations is shown for each tumor type and the corresponding ‘n’ for number of KRAS mutated cases. The four major tumor types assessed in this study as well as carcinoma of unknown primary (CUP) are highlighted in gray and represent the largest KRAS mutant populations. Major tumor types with relatively low prevalence of KRAS alterations such as breast, prostate, glioma, and melanoma are also shown. The body part background was custom designed by www.slideteam.net [slideteam.net].

Fig. 3. Co-occurrence of gene alterations among KRAS altered non-Sq NSCLC, PDAC, CRC and endometrial cancer.

a The prevalence of alterations was compared for KRAS altered and KRAS wild type (WT) non-Sq NSCLC (N = 62,836), PDAC (N = 19,386), CRC (N = 48,905), and endometrial (N = 14,375) tumor samples. For each tumor cohort, only genes altered in at least 50 cases and targeted across all the assay versions were included. For each gene, substitutions, short insertions/deletions, rearrangements, and copy number changes of known or likely functional significance detected using our assay were included. Driver genes highlighted in the National Comprehensive Cancer Network (NCCN) Guidelines as well as genes altered at a high prevalence (≥10%) are labeled for each volcano plot. Alterations in known driver oncogenes (labeled in green) tend to be mutually exclusive with KRAS alterations (left side of plots) in all four major tumor types studied (p ≤ 0.05; Odd’s ratio <1). b Oncoprints showing the frequency and mutual exclusivity of NCCN driver genes in KRAS altered (N = 22,794) vs KRAS WT (N = 40,042) non-Sq NSCLC. Fisher’s exact test was applied to assess patterns of co-occurrence and mutual exclusivity between KRAS and other genes alterations. P values were corrected with the Benjamini–Hochberg FDR method.

Fig. 4. Immunotherapy biomarkers and mutational signatures associated with KRASm isoforms in non-Sq NSCLC, PDAC, CRC, and endometrial cancers.

a Box plots showing the distribution of tumor mutational burden (TMB) in KRASm and KRAS WT tumors. TMB is higher in KRASm vs KRAS wild-type (WT) non-Sq NSCLC and endometrial cancer, and in particular for G12C and G12D subsets of non-Sq NSCLC and G13D in endometrial cancer. Each box plot displays the interquartile range (IQR), with the lower boundary representing the 25th percentile and the upper boundary representing 75th percentile. The line within the box displays the median and the whiskers extend to ±1.5 x IQR. b PD-L1 expression was relatively consistent across KRASm and WT subsets for the four major tumor types. In non-Sq NSCLC PD-L1 high expression was enriched in G12D/V/C and G13D subsets relative to WT and in endometrial tumors, any PD-L1 expression was enriched in G12C and G13D relative to WT. c Microsatellite instability (MSI) was low across non-Sq NSCLC and PDAC. In CRC, MSI-high was enriched in KRAS WT compared to KRASm subsets, whereas in endometrial tumors, G12D/V/C and G13D subsets had elevated MSI-high compared to WT. Each KRAS mutation isoform was compared against WT with p value thresholds: 0.0001: ****, 0.001: ***, 0.01: **, 0.05: *. d Six mutational signatures were assessed for KRASm isoforms. Tobacco signature was common across KRASm and WT non-Sq NSCLC and mismatch repair (MMR) was common across PDAC, CRC and endometrial tumors. Only a subset of cases were able to be assessed for mutational signatures and number of cases is shown below each bar.

Fig. 5. Real-world outcomes for patients with NSCLC, PDAC, and CRC carrying different oncodriver alterations.

Kaplan Meier curves for real world overall survival (rwOS) are shown with univariate (top) and multivariate (bottom) analysis tables for each disease subtype. Analysis was performed using the Flatiron Health-Foundation Medicine real-world clinicogenomic database. Patients with multiple driver alterations spanning >1 category were excluded. a In patients with advanced NSCLC harboring KRAS G12C mutant tumors have similar rwOS to other KRAS G12/13, KRAS non-G12/G13C, and driver negative patients. b In metastatic CRC, patients with KRAS G12C had similar rwOS compared to other KRAS mutant isoforms, BRAF V600E, and NRAS mutations, but worse rwOS compared to patients negative for KRAS and NRAS mutations and BRAF V600E (RAS/RAF negative). c In metastatic PDAC, rwOS was marginally inferior for KRAS G12C vs KRAS WT, although the differences were small and were not observed in the multivariable model.