Real‐world genomic landscape of colon and rectal cancer

Markus Schulze; XiaoZhe Wang; Jawad Hamad; Julia C F Quintanilha; Lincoln W Pasquina; Julia F Hopkins; Juergen Scheuenpflug; Zheng Feng

doi:10.1002/2211-5463.13957

. 2025 Jan 26;15(4):674–685. doi: 10.1002/2211-5463.13957

Real‐world genomic landscape of colon and rectal cancer

Markus Schulze ¹, XiaoZhe Wang ^2,^*, Jawad Hamad ³, Julia C F Quintanilha ⁴, Lincoln W Pasquina ⁴, Julia F Hopkins ⁴, Juergen Scheuenpflug ¹, Zheng Feng ^2,^*,^✉

PMCID: PMC11961397 PMID: 39865537

Abstract

MAPK signaling activation is an important driver event in colorectal cancer (CRC) tumorigenesis that informs therapy selection, but detection by liquid biopsy can be challenging. We analyze real‐world comprehensive genomic profiling (CGP) data to explore the landscape of alterations in BRAF or RAS in CRC patients (N = 51 982) and co‐occurrence with other biomarkers. A pathogenic RAS or BRAF alteration was found in 63.2% and 57.9% of colon and rectal cancer samples, respectively. In a subset of 140 patients with both tissue‐ and liquid‐based CGP, the sensitivity of liquid for results found by tissue was 100% when ctDNA tumor fraction was at least 1%, illustrating the utility of tissue and liquid biopsy in detecting driver alterations in CRC.

Keywords: BRAF, colorectal cancer, RAS, real‐world data

We confirmed enrichment of RTK alterations in patients with RAS/BRAF‐wt tumors and of PI3K pathway alterations in RAS/BRAF‐altered tumors with 51 982 real‐world tissue comprehensive genomic profiling (CGP) samples in CRC. In a subset of 140 patients with both tissue and liquid results, the sensitivity of liquid for results found by tissue was 100% when ctDNA tumor fraction was at least 1%.

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Abbreviations

BRAF: B‐Raf proto‐oncogene, serine/threonine kinase
CGP: comprehensive genomic profiling
CN: copy number event
CRC: colorectal cancer
ctDNA: circulating tumor DNA
Indel: insertion/deletion
KRAS: KRAS proto‐oncogene, GTPase
MAPK: mitogen‐activated protein kinase
MSI‐H: microsatellite instability high
MSI‐L: microsatellite instability low
MSS: microsatellite stable
mut/MB: mutations per megabase
NPV: negative predictive value
NRAS: NRAS proto‐oncogene, GTPase
PPA: positive percent agreement
RE: rearrangement
RWD: real‐world data
SNV: single‐nucleotide variant
TF: tumor fraction
TMB‐H: tumor mutational burden high
TMB‐L: tumor mutational burden low
wt: wild‐type

Molecular profiling of colorectal cancer (CRC), the third most common cancer worldwide [1], has revealed that activation of WNT, MAPK, and PI3K signaling as well as alterations in TGF‐beta and DNA damage response pathways are common features of CRC [2, 3].

MAPK signaling is frequently activated by alterations in KRAS, NRAS, or BRAF in CRC [4]. The RAS family consists of three closely related genes—KRAS, NRAS, and HRAS—with KRAS being the most commonly altered RAS family gene in CRC, followed by NRAS, whereas HRAS alteration is a very rare event [5, 6]. KRAS or NRAS alterations are important driver events in CRC tumorigenesis, a known predictive marker for targeted therapies and critical for treatment selection [7].

BRAF mutations can be subclassified into three distinct classes: Class 1 mutants that occur at position V600 signal as constitutively active monomers and are the most common class in CRC; class 2 mutants that form RAS‐independent RAF dimers; and class 3 mutants that have low or no kinase activity but act by amplifying upstream signals in the MAPK pathway [8].

Importantly, RAS and BRAF alterations are largely mutually exclusive [9], albeit class 3 mutations have been described to co‐occur more frequently with RAS alterations [3].

Class 1 mutants, on the other hand, are highly overrepresented in colorectal cancer with high microsatellite instability [7].

Tumors that do not harbor RAS/BRAF mutations show an overrepresentation of alterations that also induce activation of the MAPK pathway, for example, activating alterations in RTKs [10, 11].

Such alterations have been described in many different RTKs in CRC. Fusions of RTKs, that is, ALK, ROS1, RET, and NTRK1‐3 that occur at low prevalence in CRC are prominent examples. Other activating genetic events can affect EGFR, ERBB2, or FGFRs [12].

First‐line targeted therapy in combination with chemotherapy is the standard of care for most metastatic CRC patients with the type of targeted agent (e.g., anti‐EGFR, anti‐VEGF) depending on tumor location and molecular status (microsatellite instability, RAS, and BRAF status) [13].

Advances have been made to target the MAPK pathway in the clinic, for example, in KRAS G12C or BRAF V600‐mutated cancers [12]. KRAS G12V and G12D inhibitors are also being developed [14].

However, the optimal treatment combination strategies across multiple lines of treatment still remain unclear.

Therefore, comprehensive clinical trial design is needed to accommodate for the complex and evolving genomic landscape and to enable precision medicine in clinical trials. Real‐world data (RWD) is an important tool to achieve this goal, as it enables deep analysis of large cohorts of patients with a diverse treatment history [15, 16]. Here, analysis of RWD clinical genomics was conducted using the FoundationInsights™ web platform to explore the landscape of RAS, BRAF, and co‐occurring alterations in CRC.

Material and methods

The prevalence of gene alterations and their co‐occurrence with RAS/BRAF alterations in tumor tissue of CRC patients (N = 51 982) were investigated using the FoundationInsights™ web platform, which includes harmonized results from the FoundationOne® CDx [17], FoundationOne® Heme [18], and PD‐L1 assays from 2012 to March 2022. Data were collected from Foundation Medicine (FMI) solid (DNA‐based) and heme (DNA and RNA‐based) tests from 2012 to March 2022. Both assays are hybrid capture‐based next‐generation sequencing assays performed in a CLIA‐certified, CAP‐accredited laboratory (Foundation Medicine, Cambridge, MA, USA).

Where indicated, the patient samples were subdivided into colon cancer (N = 42 800) and rectal cancer (N = 9182) in the FoundationInsights™ web platform. Patient age was restricted from age 18–89.

Alterations included single‐nucleotide variants (SNVs) or insertions/deletions (Indels), rearrangements (RE), and copy number events (CN). Tumor mutational burden (TMB) and microsatellite stability (MSI) status were also characterized as previously described [19]. Samples with ≥10 mutations/megabase were considered as TMB‐high (TMB‐H).

Briefly, MSI stability status repetitive loci were assessed for length of repeats and compared to an internal database to determine locus stability. A sample with an increased fraction of unstable loci is considered to have high or low microsatellite instability (MSI‐H, MSI‐L) or were designated microsatellite stable (MSS).

PD‐L1 22C3 was run according to manufacturer instructions in a CLIA‐certified and CAP‐accredited laboratory (Foundation Medicine, Inc) and scored with a tumor proportion score = # PD‐L1‐positive tumor cells/(total # of PD‐L1‐positive + PD‐L1‐negative tumor cells). All patient cases were tested with manufacturer‐recommended system level controls, H&E‐stained slide, negative reagent control slide, and PD‐L1 22C3 IHC slide. CNAs were determined using a comparative genomic hybridization‐like method as described [19]. Briefly, a genome‐wide log‐ratio profile of the sample is obtained, then segmented, and interpreted using allele frequencies of sequenced single‐nucleotide polymorphisms to estimate tumor purity and copy number at each segment.

Statistical analyses were performed with R v4.2.1 and custom scripts and the R‐packages data.table 1.14.6, stringr 1.5.0, gsubfn 0.7, and dplyr 1.0.10.

BRAF mutations (SNVs or Indels) observed in our cohort were assigned to class 1, 2, or 3 based on available literature [8, 20, 21, 22, 23, 24]. We did not include BRAF fusions in this analysis, albeit BRAF fusions containing the kinase domain are class 2 variants.

To evaluate concordance between tissue and liquid biopsy‐based profiling, patients with a confirmed diagnosis of colorectal cancer who underwent both tissue (FoundationOne CDx) and liquid (FoundationOne® Liquid CDx) CGP testing within an interval of up to 90 days between tissue and liquid collection were included. Positive percent agreement (PPA/sensitivity) and negative predictive value (NPV) for KRAS, NRAS, and BRAF V600E mutation detection were calculated using the tissue test as truth for comparison. PPA and NPV were calculated for all patients with tissue and liquid samples and for those patients with liquid sample with ctDNA tumor fraction (TF) ≥1% [25, 26, 27, 28, 29]. ctDNA TF calculation was based on a composite algorithm incorporating multiple factors including aneuploidy, variant allele frequency, and canonical alterations. Briefly, when significant aneuploidy is present, a copy number model is constructed based on a panel of >30 000 commonly heterozygous single‐nucleotide polymorphisms across the genome. When significant aneuploidy is not detectable, ctDNA TF is determined based on variant allele frequencies of short variants deemed likely to be of somatic origin as informed by fragmentomic information [28]. Bar plots for specific KRAS and NRAS mutations were generated using R software version 2023.03.0 + 386.

Approval for this study, including a waiver of informed consent and a HIPAA waiver of authorization, was obtained from the Western Institutional Review Board (Protocol No. 20152817).

Results

RAS/BRAF ‐alteration landscape in CRC

In colon cancer, 36.8% of patient samples were RAS/BRAF‐wt and 63.2% had a known or likely pathogenic RAS/BRAF alteration, respectively (Fig. 1A). Within the RAS/BRAF‐altered group, 76.8% harbored a KRAS alteration, 5.6% a NRAS alteration, 0.1% a HRAS alteration, and 15.2% a BRAF alteration.

As expected, RAS/BRAF alterations were co‐occurring rarely in 2.3% of samples (Fig. 1B).

A similar picture was seen in rectal cancer where 42.1% of samples were RAS/BRAF‐wt and 57.9% had a known or likely pathogenic RAS/BRAF alteration (Fig. 1C). Within the RAS/BRAF‐altered group, 82.8% harbored a KRAS alteration, 7.4% a NRAS alteration, 0.2% a HRAS alteration, and 7.5% a BRAF alteration. Again, RAS/BRAF alterations were co‐occurring rarely in 2.1% of samples (Fig. 1D).

Of all KRAS and NRAS alterations in CRC (SNVs and Indels, CN and RE regardless of annotation as pathogenic or presence in a hotspot), SNVs or Indels in exons 2, 3, and 4 constituted 96.0% and 94.3% of all alterations, respectively (Fig. 2A,B) the majority being present in one of the known amino acid hotspots (Fig. S1A,B). In contrast, of all HRAS‐SNVs and Indels (N = 295), only 48 (16.3%) were classified as likely pathogenic or pathogenic.

Fig. 2 — Classification of *KRAS and NRAS* alterations in colorectal cancer (CRC). (A) Percentage of SNVs or Indels in exons 2, 3, and 4, other SNVs or Indels, copy number (CN) alterations and rearrangements (RE) of all *KRAS* alterations (N = 26 970), regardless of annotation as pathogenic or presence in a hotspot. 96.0% of all alterations were SNVs or Indels in exons 2, 3, and 4, 0.9% were other SNVs or Indels, 0.1% were RE events, and 3.1% CN events. (B) Percentage of SNVs or Indels in exons 2, 3, and 4, other SNVs or Indels, copy number (CN) alterations and rearrangements (RE) of all *NRAS* alterations (N = 2396), regardless of annotation as pathogenic or presence in a hotspot. 94.3% of all alterations were SNVs or Indels in exons 2, 3, and 4, 1.9% were other SNVs or Indels, 0.4% were RE events, and 3.2% CN events. (C) Percentage of samples with the potentially targetable *KRAS* p.G12C, p.G12D, and p.G12V mutations. Relative to all samples (N = 51 982) the potentially targetable *KRAS* p.G12C, p.G12D, and p.G12V mutations were present in 3.6%, 14.9%, and 10.3% of samples, respectively.

Relative to all samples the potentially targetable KRAS p.G12C, p.G12D, and p.G12V mutations were present in 3.6%, 14.9%, and 10.3% of samples, respectively (Fig. 2C).

Agreement between circulating tumor DNA‐based and tissue‐based CGP for detection of KRAS , NRAS, and BRAF V600E mutations

We next sought to assess agreement in detection of KRAS, NRAS, and BRAF V600E mutations between tissue and liquid‐based (ctDNA) CGP. A subset of the 140 specimens described above were identified to have liquid biopsy‐based CGP results from samples collected within 90 days of the tissue biopsy. Among these 140 pairs, the positive percent agreement (PPA) for KRAS, NRAS, and BRAF V600E mutation detection in liquid was 78.5% and 90.0%, respectively, while the negative predictive value (NPV) was 77.3% and 99.2%, respectively (Fig. 3A).

Fig. 3 — Agreement in detection of *KRAS*, *NRAS*, and *BRAF* V600E mutations between tissue‐ and liquid‐based comprehensive genomic profiling (CGP). (A) Positive percent agreement (PPA) and negative predictive value (NPV) for all pairs (N = 140). (B) NPV and PPA for patients with tumor fraction (TF) ≥1% (N = 96). (C) *KRAS*‐specific mutations detected in tissue‐based CGP, all liquid‐based CGP, and liquid‐based CGP with TF ≥1%. (D) *NRAS*‐specific mutations detected in tissue‐based CGP, all liquid‐based CGP, and liquid‐based CGP with TF ≥1%.

Ninety‐six of these patients (69%) had a high amount of tumor shed as quantified by a ctDNA tumor fraction (TF) ≥1% in the liquid biopsy. Among these patients, both PPA and NPV for KRAS, NRAS, and BRAF V600E detection in liquid was 100% (Fig. 3B).

The most prevalent KRAS alterations were found at similar frequencies among the paired tissue/liquid cohort, including G12D (tissue: 30%; liquid: 26%) and G12V (tissue: 30%; liquid: 32%). The most prevalent NRAS mutation detected in tissue and liquid was NRAS Q61K (36% in tissue and in liquid whole cohort, and 42% in the subcohort with TF ≥1%) (Fig. 3C,D).

Frequency of frequently altered genes and immuno‐oncology biomarkers in colon and rectal cancer

In the larger cohort of tissue samples, the genes overlapping between colon and rectal cancer among the top 10 genes with highest alteration frequency were located in relevant signaling pathways, including the Wnt pathway (APC), RAS and PI3K pathways (KRAS, PIK3CA), TGFB (SMAD4), and NOTCH signaling pathway (SOX9), in addition to tumor suppressor genes such as TP53 and FBXW7 (Fig. S2A,C).

The five most frequently altered potentially targetable genes in colon and rectal cancer were PIK3CA (19.6% and 14.2%), PTEN (8.7% and 5.9%), ERBB2 (4.7% and 6.0%), EGFR (2.5% and 2.0%), and FGFR1 (2.0% and 2.7%). NTRK alterations (NTRK1/2/3) were present in 0.9% and 0.5%, ROS1 alterations in 0.4% and 0.2%, RET alterations in 0.6% and 0.4%, and ALK alterations in 0.5% and 0.4% of samples in colon and rectal cancer, respectively (Fig. S2B,D). In colon cancer, alterations in the PI3K pathway genes PIK3CA, PTEN, AKT1, and AKT2 were significantly overrepresented in the RAS/BRAF‐altered subgroup in comparison to the RAS/BRAF‐wt subgroup (P < 0.01, Fisher's exact test with Benjamini–Hochberg correction), only AKT3 alterations were not significantly overrepresented (P > 0.05, Fisher's exact test with Benjamini–Hochberg correction). Alterations of the RTKs ALK, ROS1, NTRK1, RET, EGFR, ERBB2, FGFR1, and FGFR2 were significantly overrepresented in the RAS/BRAF‐wt group (P < 0.01, Fisher's exact test with Benjamini–Hochberg correction). NTRK2, NTRK3, FGFR3, and FGFR4 did not show a significant overrepresentation (P > 0.05, Fisher's exact test with Benjamini–Hochberg correction, Fig. 4A).

Fig. 4 — Quantification of potentially targetable alterations by *RAS*/*BRAF* status in colon and rectal cancer. (A, B) Many alterations in genes of the *PI3K* pathway were significantly overrepresented in the *RAS*/*BRAF*‐altered subgroup (N = 27 047) in comparison to the *RAS*/*BRAF*‐wt subgroup (N = 15 753) (*PIK3CA*, *PTEN*, *AKT1*, *AKT2*), whereas alterations in many RTKs (*NTRK1*, *RET*, *EGFR*, *ERBB2*, *FGFR1*, *FGFR2*) were significantly overrepresented in the *RAS*/*BRAF*‐wt subgroup in colon cancer (A). Similar differences were observed in rectal cancer, where *PIK3CA* and *PTEN* alterations were significantly overrepresented in the *RAS*/*BRAF*‐altered subgroup (N = 5320) and *EGFR*, *ERBB2*, *FGFR1*, and *FGFR3* were significantly overrepresented in the *RAS*/*BRAF*‐wt subgroup (N = 3862) (B). Statistical comparison was performed with Fisher's exact test, shown are Benjamini–Hochberg adjusted P‐values, *P < 0.05, **P < 0.01, ***P < 0.001. (C) Percentage of *RAS*/*BRAF*‐wt (N = 15 753), *BRAF* class 1 (N = 3474), *KRAS* p.G12C (N = 1546), *KRAS* p.G12D (N = 6477), or *KRAS* p.G12V‐mutated (N = 4360) samples that showed a co‐alteration in one of the seven most commonly altered potentially targetable genes (*EGFR*, *ERBB2*, *FGFR1*, *PIK3CA*, *PTEN*, *AKT1*, *AKT2*) in colon cancer. *BRAF* class 1 mutated samples showed a relatively high prevalence of *PTEN* and *AKT1* alterations. (D) Percentage of *RAS*/*BRAF*‐wt (N = 3862), *BRAF* class 1 (N = 223), *KRAS* p.G12C (N = 313), *KRAS* p.G12D (N = 1273), or *KRAS* p.G12V‐mutated (N = 971) samples that showed a co‐alteration in one of the seven most commonly altered potentially targetable genes (*EGFR*, *ERBB2*, *FGFR1*, *PIK3CA*, *PTEN*, *AKT1*, *AKT2*) in rectal cancer. *BRAF* class 1 mutated samples showed a relatively high prevalence of *PTEN*, *AKT1*, and *AKT2* alterations.

A similar pattern was detectable in rectal cancer, where PIK3CA and PTEN alterations were significantly overrepresented in the RAS/BRAF‐altered subgroup and EGFR, ERBB2, FGFR1, and FGFR3 alterations were significantly overrepresented in the RAS/BRAF‐wt subgroup (Fig. 4B). Further analysis focusing on BRAF class 1 as well as KRAS p.G12C, p.G12D, and p.G12V and the seven most common potentially druggable alterations revealed that both PTEN, AKT1, and AKT2 alterations were enriched in BRAF class 1 mutated samples, whereas alteration frequencies of EGFR, ERBB2, and FGFR1 were low in all four subgroups in comparison to RAS/BRAF‐wt samples in colon and rectal cancer (Fig. 4C,D).

The prevalence of immuno‐oncology biomarkers, such as MSI, TMB, and CD274 (PD‐L1), was also explored.

In colon and rectal cancer, 6.1% and 1.7% of samples were MSI‐H, respectively (Fig. S3A,B). 9.6% and 4.6% of samples were TMB‐H (Fig. S4A,B).

Of note, 98.9% of all MSI‐H samples were also TMB‐H by the definition used in this study. In addition, we quantified the prevalence of POLE alterations, as germline and somatic POLE alterations are a known cause of a high mutational load in CRC [30]. 0.6% and 0.4% of samples were POLE‐altered in colon and rectal cancer, respectively (Fig. S2B,D). MSI‐H or TMB‐H samples were significantly more abundant in the RAS/BRAF‐altered subset in both colon and rectal cancer: In the RAS/BRAF‐wt subset, 4.5% were MSI‐H and 7.8% TMB‐H, whereas in the RAS/BRAF‐altered subset 7.1% were MSI‐H and 10.6% TMB‐H in colon cancer (P < 0.001, Fisher's exact test, Figs S3C, S4C).

In the RAS/BRAF‐wt subset, 1.3% were MSI‐H and 4.1% TMB‐H, whereas in the RAS/BRAF‐altered subset 2.0% were MSI‐H and 5.0% TMB‐H in rectal cancer (P < 0.05, Fisher's exact test, Figs S3D, S4D). 0.3% of samples were CD274‐altered in both colon and rectal cancer, respectively (Fig. S2B,D). CD274 was not significantly enriched between RAS/BRAF‐wt and RAS/BRAF‐altered samples.

Most alterations—81.6%—classified as (likely) pathogenic in CD274 were copy number alterations (Fig. 5A). All likely pathogenic or pathogenic CN events in CD274 were amplifications. The subset of samples with a CD274 amplification for which PD‐L1 staining data was available showed a significant enrichment of PD‐L1‐positive samples in the group with amplification (P < 0.001, Fisher's exact test, Fig. 5B).

MSI‐H samples are only more abundant in BRAF class 1 and HRAS‐mutated CRC samples

Importantly, this effect was driven by BRAF class 1 mutated samples that showed a significant overrepresentation (adjusted P < 0.001, Fisher's exact test with Benjamini–Hochberg correction) of MSI‐H samples in comparison to RAS/BRAF‐wt samples in all CRC cases. Among the BRAF class 1 mutated samples, the percentage of MSI‐H samples was 30.3%, whereas only 3.9% of RAS/BRAF‐wt samples were MSI‐H. In addition, samples with a (likely) pathogenic HRAS‐SNV or Indel showed a significant overrepresentation of MSI‐H samples (adjusted P < 0.001, Fisher's exact test with Benjamini–Hochberg correction), the percentage of MSI‐H samples was 59.1%. BRAF class 3 (2.2% MSI‐H), NRAS (1.5% MSI‐H), and KRAS (3.2% MSI‐H) mutated samples showed an underrepresentation of MSI‐H samples. There was no significant difference between RAS/BRAF‐wt and BRAF class 2 (2.5% MSI‐H) mutated samples (P > 0.05, Fisher's exact test with Benjamini–Hochberg correction, Fig. 6A). In line with this finding, BRAF class 1 mutated samples showed a higher proportion of alterations in MLH1, MSH6, and PMS2—but not MSH2—in comparison to RAS/BRAF‐wt, BRAF class 2, or BRAF class 3 mutated samples. Samples with a HRAS‐SNV or Indel showed an even higher proportion of alterations in MLH1, MSH6, PMS2, and MSH2—regardless of the classification of the HRAS variant as likely pathogenic/pathogenic or ambiguous/unknown (Fig. 6C). The results for TMB were similar, with the exception that neither BRAF class 2 (7.3% TMB‐H) nor 3 (6.5% TMB‐H) mutated samples showed a significant difference in comparison to RAS/BRAF‐wt (7.1% TMB‐H) samples (P > 0.05, Fisher's exact test with Benjamini–Hochberg correction). Among the BRAF class 1 mutated samples, the percentage of TMB‐H samples was 33.9%, in the samples with HRAS mutation 68.8% and in KRAS‐ or NRAS‐altered samples 6.7% and 4.8% percent, respectively (Fig. 6B).

Fig. 6 — Quantification of microsatellite instability and tumor mutational burden in CRC by *BRAF* mutation class or *RAS* mutation. (A) Microsatellite instability high (MSI‐H) samples were highly and significantly overrepresented in samples that showed a *BRAF* class 1 SNV (N = 3588) in comparison to *RAS*/*BRAF*‐wt samples (no likely pathogenic/pathogenic *RAS*/*BRAF* alteration, N = 18 866). Samples that showed a *BRAF* class 3 SNV (N = 696), or a likely pathogenic/pathogenic SNV or Indel in *NRAS* (N = 2054) or *KRAS* (N = 24 198) showed a moderate, but significant underrepresentation of MSI‐H samples. The small group of samples with a likely pathogenic/pathogenic SNV or Indel in *HRAS* (N = 44) showed the highest proportion of MSI‐H samples. The difference between samples that contained a *BRAF* class 2 SNV (N = 284) or Indel and *RAS*/*BRAF*‐wt samples was not significant. Statistical comparison was performed with Fisher's exact test, shown are Benjamini–Hochberg adjusted P‐values, *P < 0.05, **P < 0.01, ***P < 0.001. Only significant differences are indicated. (B) Tumor mutational burden high (TMB‐H) samples were highly and significantly overrepresented in samples that showed a *BRAF* class 1 SNV (N = 3697) in comparison to *RAS*/*BRAF*‐wt (no likely pathogenic/pathogenic *RAS*/*BRAF* alteration, N = 19 615) samples. Samples that showed a likely pathogenic/pathogenic SNV or Indel in *NRAS* (N = 2131) showed a moderate, but significant underrepresentation of TMB‐H samples. The difference between samples that contained a *KRAS* (N = 25 361), *BRAF* class 2 (N = 289), or 3 SNV or Indel (N = 725) and *RAS*/*BRAF*‐wt samples was not significant. The small group of samples with a likely pathogenic/pathogenic SNV or Indel in *HRAS* (N = 48) showed the highest proportion of TMB‐H samples. Statistical comparison was performed with Fisher's exact test, shown are Benjamini–Hochberg adjusted P‐values, *P < 0.05, **P < 0.01, ***P < 0.001. Only significant differences are indicated. (C) Quantification of *MLH1*, *MSH2*, *MSH6*, and *PMS2* alterations by *BRAF*‐mutation class or samples with *HRAS*‐SNV/Indel. *BRAF* class 1 mutated samples (N = 3697) showed a higher proportion of alterations in *MLH1*, *MSH6*, and *PMS2*—but not *MSH2*—in comparison to *RAS*/*BRAF*‐wt (N = 19 615), *BRAF* class 2 (N = 289), or *BRAF* class 3 (N = 725) mutated samples, whereas *HRAS*‐mutated samples (likely pathogenic/pathogenic—N = 48, unknown/ambiguous—N = 247) showed a higher proportion of alterations in all four genes. *MLH1* alterations were present in 2.1% of *BRAF* class 1 mutated samples, 1.3% of *RAS*/*BRAF*‐wt, 0.3% of *BRAF* class 2, 0.6% of *BRAF* class 3 mutated samples 14.6% of samples with a (likely) pathogenic *HRAS* mutation, and 7.7% of samples with a *HRAS* mutation classified as unknown or ambiguous. *MSH2* alterations were present in 1.1% of *BRAF* class 1 mutated samples, in 1.2% of *RAS*/*BRAF*‐wt, 1.4% of *BRAF* class 2, 0.8% of *BRAF* class 3 mutated samples, 14.6% of samples with a (likely) pathogenic *HRAS* mutation, and 9.7% of samples with a *HRAS* mutation classified as unknown or ambiguous. *MSH6* alterations were present in 8.3% of *BRAF* class 1 mutated samples, in 1.7% of *RAS*/*BRAF*‐wt, 1.0% of *BRAF* class 2, 2.1% of *BRAF* class 3 mutated samples, 22.9% of samples with a (likely) pathogenic *HRAS* mutation, and 25.5% of samples with a *HRAS* mutation classified as unknown or ambiguous. *PMS2* alterations were present in 2.2% of *BRAF* class 1 mutated samples, in 0.8% of *RAS*/*BRAF*‐wt, 1.0% of *BRAF* class 2, 0.1% of *BRAF* class 3 mutated samples, 6.3% of samples with a (likely) pathogenic *HRAS* mutation, and 4.5% of samples with a *HRAS* mutation classified as unknown or ambiguous. (D) Characterization of *RAS* co‐alterations with *BRAF* mutations based on the 10 most common *BRAF*‐SNVs in CRC. Whereas *RAS* co‐alterations were rare in samples with *BRAF* V600E mutation (class 1, N = 3690), both samples with *BRAF* class 2 (G469A—N = 65, K601E—N = 48, G469R—N = 41, G469V—N = 32) mutations as well as class 3 mutations (D594G—N = 311, D594N—N = 99, G466E—N = 54, N581S—N = 52, N581I—N = 47, G466V—N = 52) frequently co‐occurred with *RAS*‐SNVs or Indels (restricted to likely pathogenic or pathogenic mutations).

Both BRAF class 2 and 3 mutations co‐occur frequently with RAS ‐SNVs or Indels in CRC

Finally, we quantified how many samples show BRAF class 1, 2, or 3 mutations and a co‐occurring RAS‐SNV or Indel, based on the 10 most common BRAF mutations (with information regarding their class assignment available) in our cohort. Interestingly, while BRAF V600E co‐occurred with RAS‐SNVs or Indels rarely (1.4% of samples), BRAF class 2 (G469A—23.1%, K601E—31.3%, G469R—34.1%, G469V—43.8%) mutations as well as class 3 mutations (D594G—28.0%, D594N—32.3%, G466E—61.1%, N581S—28.8%, N581I—34.0%, G466V—51.9%) frequently co‐occurred with RAS‐SNVs or Indels (restricted to likely pathogenic or pathogenic) (Fig. 6D).

Discussion

Real‐world data (RWD)‐based clinical genomic profiling has provided important insights for precision medicine due to the ability to access data from large cohort sizes, for example, for mutations which were not sufficiently represented in clinical trials or which are rare in general [15, 16, 31, 32].

In this study, RWD confirmed that the vast majority of RAS alterations in CRC are SNVs or Indels in exons 2, 3, and 4 and occur in the known hotspots by both tissue and liquid‐based CGP in KRAS and NRAS. Agreement between tissue‐based and liquid‐based CGP for detection of RAS and BRAF alterations results in PPA and NPV of 100% when liquid biopsy specimens containing ctDNA TF ≥1% were selected. The concentration of ctDNA itself is known to depend on tumor stage as well as biological differences in ctDNA release between tumors [33, 34].

We observed, in accordance with earlier observations, enrichment of RTK alterations in the RAS/BRAF‐wt and of PI3K pathway alterations in the RAS/BRAF‐altered subgroup [10, 11, 35, 36]. PTEN, AKT1, and AKT2 alterations showed a high prevalence in BRAF class 1 mutated samples.

Consistent with previous studies in metastatic CRC [21], both TMB‐H and MSI‐H samples were significantly more abundant in the RAS/BRAF‐altered cohort, an effect that is driven by BRAF class 1 mutated samples. This confirms co‐targeting of BRAF class 1 mutants with both AKT inhibition and/or immune checkpoint inhibition as attractive therapeutic concept in this subgroup. The optimal treatment sequence for this subgroup will require additional precision medicine trials focusing on the treatment sequence and response toward immune checkpoint inhibition and/or AKT inhibition. Our large cohort size enabled us to characterize BRAF class 2 and 3 mutated samples as well as HRAS‐mutated samples separately. Samples with BRAF class 2 and class 3 mutation did not show any enrichment of MSI‐H samples in agreement with earlier reports [21, 37]. Interestingly, the very small group of samples with HRAS‐SNVs or Indels showed a distinct behavior with a strong enrichment in MSI‐H samples and a co‐alteration pattern of genes involved in mismatch repair.

While the frequencies of concomitant RAS mutations are similar to earlier studies in non‐BRAF V600E‐mutated CRC [38], we found that KRAS and NRAS co‐alterations were frequent in both BRAF class 2 and 3 mutated samples, and not confined to class 3 mutated samples [3, 37]. A reason for this may be the treatment history of the patients, as, for example, treatment with anti‐EGFR antibodies is known to enrich MAPK pathway alterations [39].

The strength of our study is the large cohort size (N = 51 982) that enables generation of meaningful data from an unprecedented number of cases with rare alterations. In summary, RWD mining from both tissue and liquid biopsy‐based CGP can provide valuable insights and has the potential to play an important role in informing key clinical decision‐making such as comprehensive clinical study design including patient selection and stratification criteria.

Conflict of interest

MS and JS are employees of Merck Healthcare KGaA, Darmstadt, Germany. XW and ZF are employees of EMD Serono Research and Development Institute, Inc., Billerica, MA, USA, an affiliate of Merck KGaA, Darmstadt, Germany. JH is an employee of Merck Serono Middle East FZ‐Ltd., Dubai, United Arab Emirates, an affiliate of Merck KGaA, Darmstadt, Germany. JCFQ and LWP are employees of Foundation Medicine Inc, Cambridge, MA, USA, a wholly owned subsidiary of Roche, and have equity interest in Roche. JFH was employed by Foundation Medicine and owned stock in Roche Holding AG while contributing to the manuscript.

Author contributions

ZF and JS: Conception and design. MS, LWP, and JCFQ: Analysis and interpretation of data. MS, XW, JH, JCFQ, LWP, JFH, JS, and ZF: Writing, review, and/or revision of the manuscript. JFH: Administrative, technical, or material support. ZF: Study supervision.

Supporting information

Fig. S1. Overview of KRAS and NRAS mutations by hotspot in CRC.

FEB4-15-674-s001.tif^{(428.9KB, tif)}

Fig. S2. Prevalence of common and potentially targetable alterations in colon and rectal cancer.

FEB4-15-674-s004.tif^{(879.3KB, tif)}

Fig. S3. Quantification of microsatellite‐instability in colon and rectal cancer by RAS/BRAF‐status.

FEB4-15-674-s002.tif^{(470.1KB, tif)}

Fig. S4. Quantification of tumor mutational burden in colon and rectal cancer by RAS/BRAF‐status.

FEB4-15-674-s003.tif^{(425.6KB, tif)}

Acknowledgements

This study (including acquisition of data; analysis and interpretation of data; study supervision, conception, and design; development of methodology; and administrative, technical, or material support) was sponsored by Merck Healthcare KGaA (CrossRef Funder ID: 10.13039/100009945). Writing and editorial support was funded by Merck Healthcare KGaA (CrossRef Funder ID: 10.13039/100009945). We thank Chinedu Nworu (Foundation Medicine Inc, Cambridge, MA, USA) for his support. We thank ClinicalThinking, Inc, Hamilton, NJ, USA, for their support with publication management, which was funded by Merck Healthcare KGaA, Darmstadt, Germany, in accordance with Good Publication Practice (GPP) guidelines (https://www.ismpp.org/gpp‐2022).

Data accessibility

Data used for the analysis of tissue alterations have been retrieved via the commercially available FoundationInsights web platform. Only the final results as presented in this manuscript are available. Patient‐level liquid biopsy data are proprietary to Foundation Medicine, Inc.

References

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Fig. S1. Overview of KRAS and NRAS mutations by hotspot in CRC.

FEB4-15-674-s001.tif^{(428.9KB, tif)}

Fig. S2. Prevalence of common and potentially targetable alterations in colon and rectal cancer.

FEB4-15-674-s004.tif^{(879.3KB, tif)}

Fig. S3. Quantification of microsatellite‐instability in colon and rectal cancer by RAS/BRAF‐status.

FEB4-15-674-s002.tif^{(470.1KB, tif)}

Fig. S4. Quantification of tumor mutational burden in colon and rectal cancer by RAS/BRAF‐status.

FEB4-15-674-s003.tif^{(425.6KB, tif)}

Data Availability Statement

[feb413957-bib-0001] 1. Morgan E, Arnold M, Gini A, Lorenzoni V, Cabasag CJ, Laversanne M, Vignat J, Ferlay J, Murphy N and Bray F (2023) Global burden of colorectal cancer in 2020 and 2040: incidence and mortality estimates from GLOBOCAN. Gut 72, 338–344. [DOI] [PubMed] [Google Scholar]

[feb413957-bib-0002] 2. Cancer Genome Atlas, N (2012) Comprehensive molecular characterization of human colon and rectal cancer. Nature 487, 330–337. [DOI] [PMC free article] [PubMed] [Google Scholar]

[feb413957-bib-0003] 3. Yaeger R, Chatila WK, Lipsyc MD, Hechtman JF, Cercek A, Sanchez‐Vega F, Jayakumaran G, Middha S, Zehir A, Donoghue MTA et al. (2018) Clinical sequencing defines the genomic landscape of metastatic colorectal cancer. Cancer Cell 33, e3. [DOI] [PMC free article] [PubMed] [Google Scholar]

[feb413957-bib-0004] 4. Yaeger R and Corcoran RB (2019) Targeting alterations in the RAF‐MEK pathway. Cancer Discov 9, 329–341. [DOI] [PMC free article] [PubMed] [Google Scholar]

[feb413957-bib-0005] 5. Prior IA, Lewis PD and Mattos C (2012) A comprehensive survey of Ras mutations in cancer. Cancer Res 72, 2457–2467. [DOI] [PMC free article] [PubMed] [Google Scholar]

[feb413957-bib-0006] 6. Serebriiskii IG, Connelly C, Frampton G, Newberg J, Cooke M, Miller V, Ali S, Ross JS, Handorf E, Arora S et al. (2019) Comprehensive characterization of RAS mutations in colon and rectal cancers in old and young patients. Nat Commun 10, 3722. [DOI] [PMC free article] [PubMed] [Google Scholar]

[feb413957-bib-0007] 7. Pescott CP, Boutmy E, Batech M, Ronga P and Lamy FX (2021) Real‐world healthcare resource utilization and costs of weekly versus every‐2‐week cetuximab in metastatic colorectal cancer. J Comp Eff Res 10, 353–364. [DOI] [PubMed] [Google Scholar]

[feb413957-bib-0008] 8. Ciombor KK, Strickler JH, Bekaii‐Saab TS and Yaeger R (2022) BRAF‐mutated advanced colorectal cancer: a rapidly changing therapeutic landscape. J Clin Oncol 40, 2706–2715. [DOI] [PMC free article] [PubMed] [Google Scholar]

[feb413957-bib-0009] 9. Bellio H, Fumet JD and Ghiringhelli F (2021) Targeting BRAF and RAS in colorectal cancer. Cancers 13, 2201. [DOI] [PMC free article] [PubMed] [Google Scholar]

[feb413957-bib-0010] 10. Ross JS, Fakih M, Ali SM, Elvin JA, Schrock AB, Suh J, Vergilio JA, Ramkissoon S, Severson E, Daniel S et al. (2018) Targeting HER2 in colorectal cancer: the landscape of amplification and short variant mutations in ERBB2 and ERBB3. Cancer 124, 1358–1373. [DOI] [PMC free article] [PubMed] [Google Scholar]

[feb413957-bib-0011] 11. Pagani F, Randon G, Guarini V, Raimondi A, Prisciandaro M, Lobefaro R, Di Bartolomeo M, Sozzi G, de Braud F, Gasparini P et al. (2019) The landscape of actionable gene fusions in colorectal cancer. Int J Mol Sci 20, 5319. [DOI] [PMC free article] [PubMed] [Google Scholar]

[feb413957-bib-0012] 12. Di Nicolantonio F, Vitiello PP, Marsoni S, Siena S, Tabernero J, Trusolino L, Bernards R and Bardelli A (2021) Precision oncology in metastatic colorectal cancer ‐ from biology to medicine. Nat Rev Clin Oncol 18, 506–525. [DOI] [PubMed] [Google Scholar]

[feb413957-bib-0013] 13. Morris VK, Kennedy EB, Baxter NN, Benson AB 3rd, Cercek A, Cho M, Ciombor KK, Cremolini C, Davis A, Deming DA et al. (2023) Treatment of metastatic colorectal cancer: ASCO guideline. J Clin Oncol 41, 678–700. [DOI] [PMC free article] [PubMed] [Google Scholar]

[feb413957-bib-0014] 14. Tria SM, Burge ME and Whitehall VLJ (2023) The therapeutic landscape for KRAS‐mutated colorectal cancers. Cancers 15, 2375. [DOI] [PMC free article] [PubMed] [Google Scholar]

[feb413957-bib-0015] 15. Feng Z, Pignon JC, Sharaf R, Romanov N, Doudement J, Albacker LA, Kurata N, Smith NR, Matsushita N and Scheuenpflug J (2022) Precision oncology driven real world clinical genomics data mining of MET alterations, TMB, and PDL‐1 to empower indication agnostic patient enrollment and combination strategies [abstract]. Cancer Res 82, 5737. [Google Scholar]

[feb413957-bib-0016] 16. Wang D, Elenbaas B, Murugesan K, Albacker LA, Doudement J, Scheuenpflug J, Locatelli G and Feng Z (2021) Comprehensive molecular profiling of DNA damage response (DDR) deficiencies in advanced solid tumors using a real‐world genomic database [abstract]. Cancer Res 81, 2062. [Google Scholar]

[feb413957-bib-0017] 17. Frampton GM, Fichtenholtz A, Otto GA, Wang K, Downing SR, He J, Schnall‐Levin M, White J, Sanford EM, An P et al. (2013) Development and validation of a clinical cancer genomic profiling test based on massively parallel DNA sequencing. Nat Biotechnol 31, 1023–1031. [DOI] [PMC free article] [PubMed] [Google Scholar]

[feb413957-bib-0018] 18. He J, Abdel‐Wahab O, Nahas MK, Wang K, Rampal RK, Intlekofer AM, Patel J, Krivstov A, Frampton GM, Young LE et al. (2016) Integrated genomic DNA/RNA profiling of hematologic malignancies in the clinical setting. Blood 127, 3004–3014. [DOI] [PMC free article] [PubMed] [Google Scholar]

[feb413957-bib-0019] 19. Milbury CA, Creeden J, Yip WK, Smith DL, Pattani V, Maxwell K, Sawchyn B, Gjoerup O, Meng W, Skoletsky J et al. (2022) Clinical and analytical validation of FoundationOne(R)CDx, a comprehensive genomic profiling assay for solid tumors. PLoS One 17, e0264138. [DOI] [PMC free article] [PubMed] [Google Scholar]

[feb413957-bib-0020] 20. Bracht JWP, Karachaliou N, Bivona T, Lanman RB, Faull I, Nagy RJ, Drozdowskyj A, Berenguer J, Fernandez‐Bruno M, Molina‐Vila MA et al. (2019) BRAF mutations classes I, II, and III in NSCLC patients included in the SLLIP trial: the need for a new pre‐clinical treatment rationale. Cancers 11, 1381. [DOI] [PMC free article] [PubMed] [Google Scholar]

[feb413957-bib-0021] 21. Schirripa M, Biason P, Lonardi S, Pella N, Pino MS, Urbano F, Antoniotti C, Cremolini C, Corallo S, Pietrantonio F et al. (2019) Class 1, 2, and 3 BRAF‐mutated metastatic colorectal cancer: a detailed clinical, pathologic, and molecular characterization. Clin Cancer Res 25, 3954–3961. [DOI] [PubMed] [Google Scholar]

[feb413957-bib-0022] 22. Dankner M, Rose AAN, Rajkumar S, Siegel PM and Watson IR (2018) Classifying BRAF alterations in cancer: new rational therapeutic strategies for actionable mutations. Oncogene 37, 3183–3199. [DOI] [PubMed] [Google Scholar]

[feb413957-bib-0023] 23. Yao Z, Yaeger R, Rodrik‐Outmezguine VS, Tao A, Torres NM, Chang MT, Drosten M, Zhao H, Cecchi F, Hembrough T et al. (2017) Tumours with class 3 BRAF mutants are sensitive to the inhibition of activated RAS. Nature 548, 234–238. [DOI] [PMC free article] [PubMed] [Google Scholar]

[feb413957-bib-0024] 24. Lokhandwala PM, Tseng LH, Rodriguez E, Zheng G, Pallavajjalla A, Gocke CD, Eshleman JR and Lin MT (2019) Clinical mutational profiling and categorization of BRAF mutations in melanomas using next generation sequencing. BMC Cancer 19, 665. [DOI] [PMC free article] [PubMed] [Google Scholar]

[feb413957-bib-0025] 25. Li M, Fendler B, Yang L, Madison R, Decker B, Gjoerup O, McGregor K, Aiyer A, Hughes J, Hegde P et al. (2021) Utility of plasma tumor fraction (TF) to inform sensitivity of FoundationOne liquid CDx (F1LCDx) [abstract]. Cancer Res 81, 2231. [Google Scholar]

[feb413957-bib-0026] 26. Rolfo CD, Madison R, Pasquina LW, Brown DW, Huang Y, Hughes JD, Oxnard GR and Husain H (2023) Utility of ctDNA tumor fraction to inform negative liquid biopsy (LBx) results and need for tissue reflex in advanced non‐small cell lung cancer (aNSCLC) [abstract]. J Clin Oncol 41, 9076. [Google Scholar]

[feb413957-bib-0027] 27. Rolfo CD, Madison RW, Pasquina LW, Brown DW, Huang Y, Hughes JD, Graf RP, Oxnard GR and Husain H (2024) Measurement of ctDNA tumor fraction identifies informative negative liquid biopsy results and informs value of tissue confirmation. Clin Cancer Res 30, 2452–2460. [DOI] [PMC free article] [PubMed] [Google Scholar]

[feb413957-bib-0028] 28. Chiang AC, Madison RW, Huang Y, Fine A, Jin DX, Oxnard GR, Hughes J, June Assaf Z, Cao Y, Antic V et al. (2024) Validation of a tumor‐naïve circulating tumor DNA (ctDNA) response monitoring panel in advanced non‐small cell lung cancer (aNSCLC) [abstract]. Cancer Res 84, 971. [Google Scholar]

[feb413957-bib-0029] 29. Woodhouse R, Li M, Hughes J, Delfosse D, Skoletsky J, Ma P, Meng W, Dewal N, Milbury C, Clark T et al. (2020) Clinical and analytical validation of FoundationOne liquid CDx, a novel 324‐gene cfDNA‐based comprehensive genomic profiling assay for cancers of solid tumor origin. PLoS One 15, e0237802. [DOI] [PMC free article] [PubMed] [Google Scholar]

[feb413957-bib-0030] 30. Briggs S and Tomlinson I (2013) Germline and somatic polymerase epsilon and delta mutations define a new class of hypermutated colorectal and endometrial cancers. J Pathol 230, 148–153. [DOI] [PMC free article] [PubMed] [Google Scholar]

[feb413957-bib-0031] 31. Rugo HS, Raskina K, Schrock AB, Madison RW, Graf RP, Sokol ES, Sivakumar S, Lee JK, Fisher V, Oxnard GR et al. (2023) Biology and targetability of the extended Spectrum of PIK3CA mutations detected in breast carcinoma. Clin Cancer Res 29, 1056–1067. [DOI] [PMC free article] [PubMed] [Google Scholar]

[feb413957-bib-0032] 32. Westphalen CB, Krebs MG, Le Tourneau C, Sokol ES, Maund SL, Wilson TR, Jin DX, Newberg JY, Fabrizio D, Veronese L et al. (2021) Genomic context of NTRK1/2/3 fusion‐positive tumours from a large real‐world population. NPJ Precis Oncol 5, 69. [DOI] [PMC free article] [PubMed] [Google Scholar]

[feb413957-bib-0033] 33. Bettegowda C, Sausen M, Leary RJ, Kinde I, Wang Y, Agrawal N, Bartlett BR, Wang H, Luber B, Alani RM et al. (2014) Detection of circulating tumor DNA in early‐ and late‐stage human malignancies. Sci Transl Med 6, 224ra24. [DOI] [PMC free article] [PubMed] [Google Scholar]

[feb413957-bib-0034] 34. Dasari A, Morris VK, Allegra CJ, Atreya C, Benson AB 3rd, Boland P, Chung K, Copur MS, Corcoran RB, Deming DA et al. (2020) ctDNA applications and integration in colorectal cancer: an NCI colon and Rectal‐anal task forces whitepaper. Nat Rev Clin Oncol 17, 757–770. [DOI] [PMC free article] [PubMed] [Google Scholar]

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PERMALINK

Real‐world genomic landscape of colon and rectal cancer

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Jawad Hamad

Julia C F Quintanilha

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