Study participantsDiscovery cohort

The large, publicly available, real-world dataset with clinical annotation and whole-genome sequencing (WGS) by the HMF was used as the discovery cohort17. All patients who received FTD/TPI as part of their standard-of-care treatment for mCRC were identified in May 2018 (Supplementary Table 2). These patients were included in 13 academic, teaching, and regional hospitals in the Netherlands. The study was approved by the Medical Ethical Committee of the University Medical Center Utrecht and was conducted in accordance with the Declaration of Helsinki (fourth edition). All patients provided written informed consent for the collection, analysis and pseudonymized sharing of paired tumor-normal WGS data and clinical characteristics for research purposes.

Real-world validation cohort

For validation, we retrospectively collected data of 1,012 patients with mCRC treated with FTD/TPI as part of standard of care between April 2016 and January 2022 at 36 academic, teaching and regional hospitals across Italy and the UK (Supplementary Tables 6 and 7). The data cutoff was April 2022. Tumor KRAS, NRAS and BRAF genotype was investigated locally as recommended by local guidelines. Fifty-two patients with unknown NRAS or BRAF status were excluded, resulting in a final cohort of 960 patients used for all analyses. For patients from the UK, the study built on a UK National Audit24 and data were handled in accordance with the Declaration of Helsinki. Formal ethical approval for data collection, analysis and pseudonymized sharing for research purposes was covered by UK Health Research Authority guidance (NHS Health Research Authority, Service Evaluation Clinical/Non-Financial Audit Usual Practice (in Public Health Including Health Protection)). For patients from Italy, data collection, analysis and pseudonymized sharing for research purposes was approved by the institutional review board of the Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Ca’ Granda Ospedale Maggiore Policlinico (Milano, Italy) and was conducted in accordance with the Declaration of Helsinki.

RECOURSE trial cohort

The RECOURSE trial design has been previously described in detail3. Briefly, the RECOURSE trial (NCT01607957) was an international double-blind, randomized, placebo-controlled, phase 3 trial comparing FTD/TPI plus best supportive care to placebo plus best supportive care. Heavily pretreated patients with refractory mCRC (n = 800) were randomly assigned in a 2:1 ratio to receive FTD/TPI or placebo. Within this process, patients were stratified based on KRAS status (mutant yes/no), time between first diagnosis of metastases and randomization (<18 versus ≥18 months) and geographical region (Japan or USA, Europe and Australia). The data cutoff was at 571 deaths, in accordance with the cutoff of the primary analysis. All patients in the study provided written informed consent, as stated in the original publication3.

Memorial Sloan Kettering Cancer Center CRC cohort

Somatic mutation data were downloaded from the cBioPortal for cancer genomics (http://cbioportal.org/msk-impact) on 8 August 2017. All samples with ‘GeneralTumorType’ = ‘Colorectal Cancer’ were included (Supplementary Table 1).

PDO cohort

PDOs were cultured from tumor biopsies of patients with mCRC, with approval of the Medical Ethical Committee of the Netherlands Cancer Institute. We used four KRASG12-mutated (SNS26: KRASG12S; TUM10: KRASG12V; TUM3: KRASG12A; TUM52: KRASG12C) and three KRASWT (TUM42, TUM50, TUM65) PDOs (Supplementary Table 9). The study was conducted in accordance with the Declaration of Helsinki. All patients provided written informed consent for organoid culture and collection, analysis and pseudonymized sharing of clinical characteristics for research purposes.

End points and study objectives

In the real-world discovery analysis, we searched for genome-wide somatic variants associated with OS and time on FTD/TPI treatment as end points. In the real-world validation analysis, the primary and secondary objectives were to assess the association of KRASG12 mutation status with OS and PFS, respectively, both in the population as a whole and in RAS/RAF mutation-based subpopulations. All end points used in the real-life analyses were measured from the start of FTD/TPI treatment and evaluated at participating institutions over the treatment course according to local practice. In our reanalysis of the RECOURSE trial, we tested OS benefit and PFS benefit of FTD/TPI versus placebo as the primary and secondary end points, respectively, in subgroups defined by codon-specific KRAS mutation status. This was in accordance with the hierarchy of end points prespecified in the RECOURSE trial protocol; these reanalyses were prespecified in a formal data request to the sponsor of the RECOURSE study before access to the data was granted.

Bioinformatics analysis

All genomics data of the discovery cohort was publicly available and provided by the HMF under the approved data request DR-015. WGS (median depths approximately 100 and approximately 40 for tumor and normal, respectively) and bioinformatics analysis of the discovery cohort were performed by the HMF as described previously17, with an optimized pipeline based on open-source tools freely available on GitHub (https://github.com/hartwigmedical/pipeline5). Somatic genomic drivers were identified as an integrated functionality of PURPLE v.2.43 (ref. 17). Briefly, somatic mutations were considered drivers if they fulfilled one of the following criteria: (1) mutations in oncogenes located at—or within five bases of—known hotspots; (2) inframe indels in oncogenes with repeat count <8 repeats; (3) biallelic (that is, the WT allele is lost) nonsense, splice or indel variants in tumor suppressor genes (TSGs); and (4) mutations in oncogenes or TSGs with a sample-specific driver likelihood >80%, as calculated by PURPLE as described previously17. For this manuscript, we only considered TSG mutations to be drivers if (1) they were biallelic or (2) in the case of multiple mutations in the gene for which the summed variant ploidies exceeded the gene ploidy within the sample −0.5 (for example, the classical APC two-hit hypothesis). Amplifications were considered to be drivers if (1) they affected an oncogene with pan-cancer evidence for recurrent amplification17 and (2) this oncogene had a copy number exceeding three times the sample ploidy. Deletions were considered to be drivers if (1) they affected TSGs with pan-cancer evidence for recurrent deletion17 and (2) they were homozygous (absolute gene copy number <0.5).

Statistical analysis

Median time on treatment, OS and PFS were calculated using the Kaplan–Meier method. OS and time on treatment were compared between biomarker positive versus negative patients in the discovery cohort using the exact log-rank test. In this analysis, multiple-hypothesis correction was performed using the Benjamini–Hochberg procedure. HRs and corresponding 95% CIs, and Wald test-based two-sided P values, were estimated from Cox regression models. The proportional-hazards assumption was tested using the methodology developed by Grambsch and Therneau25, with a significance threshold of P = 0.05; categorical covariates were modeled as stratification factors (rather than covariates) where appropriate to prevent assumption violations. ‘Unadjusted’ Cox regression analyses of the real-world validation cohort were performed in a univariate manner. ‘Adjusted’ Cox regression analyses of the real-world validation cohort were stratified for ECOG performance status (0–1 versus ≥2) and adjusted for seven additional covariates: time since diagnosis of first metastases (<18 versus ≥18 months); geographical region (UK versus Italy); age (<65 versus ≥65 years); sex; sidedness (left versus right); previous surgery (yes versus no); and peritoneal disease at the start of FTD/TPI treatment (yes versus no). For the RECOURSE trial-based analyses, ‘unadjusted’ Cox regression was stratified for two stratification factors of the trial: time since diagnosis of first metastases (<18 versus ≥18 months) and geographical region (Japan versus the USA, Europe and Australia). The third stratification factor of the trial, KRAS mutation status, was omitted because of high collinearity with our variables of interest (codon-specific KRAS status). For ‘adjusted’ RECOURSE trial-based analyses, we used stratified, multivariate Cox regression to adjust for eight prognostic factors on top of the two stratification factors used in the unadjusted analyses. These included: age (<65 versus ≥65 years); sex; ECOG performance status (0 versus 1); primary site of the disease (colon versus rectum); disease refractory to fluoropyrimidine as part of the last previous regimen (yes versus no); previous use of regorafenib; number of previous regimens (2, 3 or ≥4); and number of metastatic sites (1–2 versus ≥3). The rationale behind the selection of covariates for multivariate Cox regression is specified below. No subsequent covariate selection was performed; hence, all eight covariates plus the two stratification factors were included in all multivariate models of RECOURSE trial data. None of these variables were predictive of FTD/TPI benefit in the RECOURSE trial (P > 0.20 for all variables)3. Dose–response curves were fitted using Prism v.9.0.0 (GraphPad Software) on log2-transformed FTD concentration values versus viability; the resulting fitted curves were then used to calculate IC50 values. Baseline characteristics were compared by Fisher’s exact test for categorical variables with two levels and by chi-squared test in the case of more than two levels. All reported P values are two-sided. In the main text and figures, all P values smaller than 0.001 were reported as <0.001. RECOURSE trial data-based survival analyses were performed on the intention-to-treat population and were prespecified in a formal data request before access to the data was granted.

Candidate biomarker selection for the discovery cohort

The procedure for the selection of candidate biomarkers was as follows. Somatic genomic driver alterations (mutations and copy number alterations) were included as candidate biomarkers at increasingly specific ‘levels’: (1) gene-level biomarkers, for example, ‘APC alteration’, which could either be by mutation or copy number alteration; (2) variant class-level biomarkers, for example, ‘APC mutation’ or ‘APC deletion’; (3) codon-level biomarkers, for example, ‘APC codon 1450 mutation’; and (4) amino acid change-specific biomarkers, for example, ‘APC p.Thr562Met mutation’. In cases where biomarkers of different levels showed complete redundancy, only the most specific level was included. For example, all KRAS alterations in the cohort were mutations leading to complete redundancy between ‘KRAS alteration’ and ‘KRAS mutation’. Hence, KRAS mutation was selected as the most specific level and included as a candidate biomarker, whereas KRAS alteration was excluded. All candidate biomarkers occurring in at least five patients in the discovery cohort were tested for association with treatment outcomes. Supplementary Table 3 provides a comprehensive overview of the frequencies of all candidate biomarkers identified in our cohort.

Variable selection for multivariate Cox regressionReal-world validation cohort

We selected eight variables for multivariate (adjusted) Cox proportional-hazards modeling of the real-world validation cohort. In this process, we aimed to harmonize the selection as much as possible to the variables used in the Cox regression modeling of the RECOURSE trial-based data (see below), with some alterations.

The ECOG performance status (0–1 versus ≥2) was used as a stratification factor because this variable violated the proportional-hazards assumption when modeled as a covariate. Furthermore, we adjusted for seven additional covariates: time since diagnosis of first metastases (<18 versus ≥18 months); geographical region (UK versus Italy); age (<65 versus ≥65 years); sex; sidedness (left versus right); previous surgery (yes versus no); and peritoneal disease at the start of FTD/TPI treatment (yes versus no). Sidedness was used instead of primary site of the disease (colon versus rectum, as used in the RECOURSE trial-based analyses), because sidedness was most strongly associated with OS and only one of these two variables could be included due to high collinearity. Due to data unavailability for the real-world validation cohort, we were unable to factor in if the disease was refractory to fluoropyrimidine as part of the last previous regimen, previous use of regorafenib, the number of previous regimens and the number of metastatic sites in the analyses. In RECOURSE trial-based analyses, none of these factors were predictive and only the latter variable was prognostic for OS. Instead, based on significant (univariate) associations with OS in the real-world validation cohort, we decided to add the two variables ‘previous surgery’ and ‘peritoneal disease at the start of FTD/TPI treatment’ to our selection of covariates, although these data were unavailable for the RECOURSE trial dataset.

RECOURSE trial-based analyses

We selected ten variables for multivariate (adjusted) Cox proportional-hazards modeling of RECOURSE trial data.

This selection included all factors prespecified in the RECOURSE trial study protocol, except KRAS status and ethnicity, totaling eight prespecified factors: time since diagnosis of first metastases (<18 versus ≥18 months (stratification factor of the study); geographical region (Japan versus the USA, Europe and Australia; stratification factor of the study); age (<65 versus ≥65 years); sex; ECOG performance status (0 versus 1); primary site of the disease (colon versus rectum); number of previous regimens (2, 3 or ≥4); and number of metastatic sites (1–2 versus ≥3). KRAS status was excluded because of collinearity with our variables of interest (KRASG12 mutation, KRASG13 mutation, KRASWT). Ethnicity was excluded for two reasons. First, the sponsor of the RECOURSE trial could not share the original ethnicity data for privacy reasons because the number of Black participants (nine patients) was below a predefined threshold put in place to prevent patient reidentification. For this reason, the ethnicity item has been modified to a quasi-identifier of ‘Asian’ versus ‘Other’ (White or Black). In the RECOURSE trial, the original ethnicity variable was not significantly prognostic or predictive for OS3. Second, the modified ethnicity variable showed high collinearity (and hence redundancy) with the included factor ‘geographical region’ because 266 out of 266 (100%) of participants from the ‘Asia’ region had the ‘Asian’ ethnicity and 522 out of 534 (98%) of participants from the USA, Europe and Australia regions had the ‘Other’ (which included Black and White) ethnicity.

Next, we included two additional factors in our multivariate models: (1) disease refractory to fluoropyrimidine as part of the last previous regimen; and (2) previous use of regorafenib. These factors were not prespecified in the RECOURSE trial protocol for multivariate analyses but were used for the subgroup analyses reported by Mayer et al.3. We decided to include these pretreatment-related factors in our multivariate models because patients with KRAS mutant tumors showed significant differences regarding their pretreatment profiles as compared to patients with KRASWT tumors. Patients with KRAS mutant tumors were more often refractory to fluoropyrimidine as part of their last previous regimen and were less heavily pretreated than patients with KRASWT (Table 2).

BRAF mutation status was not included in our selection because this information was missing for 676 out of 800 (85%) patients. Subgroup analysis of the population with BRAF mutant tumors was not possible because BRAF mutations were detected in only eight patients.

Organoid and cell line cultures and drug assays

PDOs were cultured, expanded and assayed as described previously22. FTD (catalog no. S1778, Selleckchem) and 5-FU (catalog no. S1209, Selleckchem) were reconstituted in DMSO (catalog no. D2650, Sigma-Aldrich) at a stock concentration of 50 mM. PDOs were exposed to a two-step, eightfold dilution of FTD (range = 0.781–200 μM) for 11 d or to 5-FU for 6 d in a two-step, eightfold dilution (range = 0.781–200 μM). Culture medium and FTD were refreshed every 3–4 d. The isogenic cell lines were assayed with FTD and 5-FU in a similar fashion, shortening the assay duration to 3 d. The used concentrations were adjusted to include more data points of lower concentrations (range = 0.1 nM, 0.5 nM, 1 μM, 2 μM, 5 μM, 10 μM, 20 μM, 50 μM, 200 μM, 500 μM). The readout was performed using the MTT Assay Kit for Cell Proliferation (catalog no. ab211091, Abcam); culture medium was replaced with 100 μl of a 1:1 mix of MTT reagent with serum-free RPMI 1640 (catalog no. 21875034, Gibco), which was replaced by 150 μl MTT solvent after incubation, according to the manufacturer’s protocol. Then, absorbance was measured at OD590 nM on an Infinite 200 Pro plate reader (Tecan Life Sciences).

Isogenic cell line construction: Colo320 KRAS G12D knock in

Single-guide RNA oligonucleotide sequences were designed on Chop-Chop (http://chopchop.cbu.uib.no/#). CRISPR–Cas9 CRISPR RNA (crRNA) (5′-CUUGUGGUAGUUGGAGCUGG-3′) and trans-activating crRNA (tracrRNA) (catalog no. 1072532), Cas9 Nuclease V3 (catalog no. 1081058) and HDR Donor Oligo (5′-ATTCTGAATTAGCTGTATCGTCAAGGCACTCTTGCCTACGCCGTCAGCTCCCACTACCACAAGTTTATATTCAGTCATTTTCAGC-3′) were purchased from Integrated DNA Technologies. Briefly, guide RNA (gRNA) complexes were formed as described previously26 by combining equal amounts of crRNA (160 μM in stock) and tracrRNA (160 μM in stock) in Duplex Buffer (cat no. 11-01-03-01, Integrated DNA Technologies) and heating the oligonucleotides to 95 °C, followed by slowly cooling to room temperature. Cas9 nuclease was then added (the molar ratio of crRNA:Cas9 nuclease was 1:0.5) to the gRNA complexes, followed by 15-min incubation at room temperature. The Cas9 ribonucleoprotein (ctRNP) complexes were then stored on ice until use. DNA HDR templates were prepared by diluting the HDR Donor Oligo stock to 10 μM in nuclease-free water. Electroporation was performed by using the 4D-Nucleofector X Unit (catalog no. AAF-1003X, Lonza) according to the manufacturer’s instructions. For each sample, 2 × 105 cells were resuspended in Ingenio Electroporation Solution (catalog no. MIR 50111, Mirus Bio). Per reaction, 2.5 μM ctRNP and 0.5 μM HDR template were added to the cell suspension. We next pipetted 20 μl of each sample into individual wells of 16-well Nucleocuvette Strips (catalog no. AXP-1004, Lonza) and ran the program CM-137. After electroporation, cells were sorted by FACS and single cells were cultured in 96-well plates for up to two weeks. For single-cell clones, the presence of the KRASG12D mutation was then confirmed by Sanger sequencing.

Western blot analysis

Western blot analysis was performed on the isogenic cell lines and PDOs treated with FTD or 5-FU, at different concentrations, for 24 h (cell lines) or 48 h (PDOs). For PDOs (but not for the isogenic cell lines), the extracellular matrix was removed by incubating with 2 mg ml−1 type II dispase (catalog no. D4693, Sigma-Aldrich) for 10 min at 37 °C. Cells were washed with PBS and lysed in RIPA Lysis and Extraction Buffer (catalog no. 89901, Thermo Fisher Scientific), supplemented with Phosphatase Inhibitor Cocktail (catalog no. 78420, Thermo Fisher Scientific) and Halt Protease Inhibitor Cocktail (catalog no. 87786, Thermo Fisher Scientific). Protein concentration was determined using Coomassie Brilliant Blue G-250 (catalog no. 1610803, Bio-Rad Laboratories). Protein samples were run on NuPAGE 4–12% Bis-Tris Gels (catalog no. NP0323BOX, Thermo Fisher Scientific), transfer was performed using the iBlot 2 Gel Transfer Device (catalog no. IB21001, Thermo Fisher Scientific) and compatible iBlot Transfer Stack; nitrocellulose (catalog no. IB301002, Thermo Fisher Scientific) membranes were used. Membranes were blocked in 5% BSA (catalog no. 10735094001, Sigma-Aldrich) in PBS plus 0.2% Tween-20 (catalog no. P1379-1L, Sigma-Aldrich) for 1 h, then incubated with primary antibodies in 5% BSA in PBS and Tween-20. As primary antibodies, we used anti-phospho-Histone H2A.X (Ser139, catalog no. 05-636, Sigma-Aldrich) and anti-HSP 90α/β (catalog no. sc-13119, Santa Cruz Biotechnology), which were diluted 1:1,000 in PBS with 5% BSA. The secondary antibody (anti-mouse IgG, HRP-linked antibody, catalog no. 7076, Cell Signaling Technology) was diluted 1:1,000 in 5% BSA in PBS and Tween-20. The blots were incubated with Clarity Max Western ECL Substrate (catalog no. 1705062, Bio-Rad Laboratories) and the luminescence signal was imaged using the ChemiDoc Imaging System (catalog no. 17001401, Bio-Rad Laboratories).

Colony formation assay

Cells were seeded into six-well plates (1.5–2 × 104 cells per well) and cultured in the presence of drugs at the indicated concentrations. For each cell line, cells cultured using different conditions were fixed in methanol (catalog no. 32213, Honeywell) and stained with 0.1% crystal violet solution (catalog no. V5265, Sigma-Aldrich).

Role of the funding source

The funders of the study had no role in study design, data collection, data analysis, data interpretation or writing of the article. Authors had full access to all the data and had the final responsibility to submit for publication.

Statistical methods and associated software

The statistical methods and associated packages used in this study are summarized in Table 3.

Table 3 Statistical methods and associated softwareReporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.