Clinicogenomic landscape of pancreatic adenocarcinoma identifies KRAS mutant dosage as prognostic of overall survival

Abstract

Nearly all pancreatic adenocarcinomas (PDAC) are genomically characterized by KRAS exon 2 mutations. Most patients with PDAC present with advanced disease and are treated with cytotoxic therapy. Genomic biomarkers prognostic of disease outcomes have been challenging to identify. Herein leveraging a cohort of 2,336 patients spanning all disease stages, we characterize the genomic and clinical correlates of outcomes in PDAC. We show that a genomic subtype of KRAS wild-type tumors is associated with early disease onset, distinct somatic and germline features, and significantly better overall survival. Allelic imbalances at the KRAS locus are widespread. KRAS mutant allele dosage gains, observed in one in five (20%) KRAS-mutated diploid tumors, are correlated with advanced disease and demonstrate prognostic potential across disease stages. With the rapidly expanding landscape of KRAS targeting, our findings have potential implications for clinical practice and for understanding de novo and acquired resistance to RAS therapeutics.

Analyses of clinical and genomic data from a cohort of 2,336 patients with pancreatic adenocarcinoma find that KRAS dosage gains are a hallmark of disease progression and are predictive of poor outcomes across different disease stages.

Main

Pancreatic cancer is the third-highest cause of cancer-related mortality and has the lowest 5-year overall survival (OS) rate of all cancer types¹. For most patients with pancreatic adenocarcinoma (PDAC), the most common pancreatic cancer histology, chemotherapy remains the mainstay of therapy^2–5. More than 90% of PDACs exhibit activating mutations in KRAS hotspot residues, a majority of which have eluded targeted therapeutic approaches until recently⁶. KRAS wild-type (WT) tumors are enriched for actionable alterations in mitogen-activated protein kinase (MAPK) pathway genes such as BRAF, NTRK1, NTRK3 and NRG1 (refs. ^7–14).

Superior outcomes are observed in molecularly selected cohorts, such as patients with pathogenic variants in BRCA1, BRCA2 and PALB2, and with tumors harboring targetable alterations^2,3,15–18. Molecular profiling studies have also identified several prognostic features associated with poor outcomes in PDAC^19,20. In addition, gene expression-based stratification has identified two main subtypes of PDAC, classical and basal-like, that differ in molecular pathology, therapeutic vulnerabilities and outcomes^21–27.

More recently, allelic imbalance (unequal number of maternal and paternal copies) at the KRAS locus has been associated with poor outcomes in PDAC²⁸. Moreover, KRAS mutant allele gains promote aggressive phenotypes in mouse models of pancreatic cancer²⁹. Whole-genome doubling (WGD), a hallmark of advanced cancer and a negative prognostic factor for OS, is a key driver promoting allelic imbalances leading to KRAS mutant dosage gains^30,31. However, the prevalence of KRAS mutant allele dosage gains in PDAC and its association with disease progression remains poorly understood.

In this study, we leverage germline and somatic profiling of n = 2,336 patients to study the genomic and clinical correlates of outcomes for patients with PDAC. We incorporate clinical histories of n = 1,480 patients with long-term follow-up. We demonstrate that KRAS mutant dosage gains are a hallmark of disease progression and are prognostic of poor outcomes across all stages of PDAC.

Results

MSK-IMPACT PDAC study cohort

This study included n = 2,336 patients with PDAC whose tumors were prospectively sequenced as part of standard care at Memorial Sloan Kettering Cancer Center (MSK; Methods; Supplementary Tables 1 and 2). At diagnosis, 31% (n = 731) of patients had resectable tumors, 25% (n = 581) had borderline resectable/locally advanced tumors and 44% (n = 1,024) had metastatic disease (Table 1). Detailed clinical information including lines of treatments, time on treatment and best overall responses were manually curated for 63% (n = 1,480) of patients. The majority (61%) of the sequenced specimens were from primary PDAC, and 39% were from distant metastases. Median age at diagnosis was 67 years. Tumor specimens were sequenced to median depth of 606× using the FDA-authorized MSK-IMPACT clinical sequencing assay that encompasses up to 505 cancer genes³². Somatic substitutions, insertions, deletions, focal copy number amplifications, homozygous deletions and fusions in select genes were identified using a clinically validated pipeline and annotated using the FDA-recognized precision oncology knowledge base, OncoKB^33,34.

Table 1.

MSK-IMPACT PDAC study cohort characteristics

Characteristics	Resectable	BR/LA	Metastatic	Overall
	n = 731	n = 581	n = 1,024	n = 2,336
Age at diagnosis (years)	68 (24–91)a	66 (32–93)a	66 (30–89)a	67 (24–93)a
Sex
Male	382 (52%)b	293 (50%)b	539 (53%)b	1,214 (52%)b
Female	349 (48%)	288 (50%)	485 (47%)	1,122 (48%)
Genetic ancestry
European (EUR)	618 (85%)	443 (76%)	828 (81%)	1,889 (81%)
Ashkenazi Jewish (ASJ)	198 (27%)	111 (19%)	263 (26%)	572 (25%)
East Asian (EAS)	42 (5.7%)	27 (4.6%)	44 (4.3%)	113 (4.8%)
African (AFR)	11 (1.5%)	36 (6.2%)	35 (3.4%)	82 (3.5%)
South Asian (SAS)	8 (1.1%)	9 (1.5%)	22 (2.1%)	39 (1.7%)
Admixed/other	52 (7.1%)	66 (11%)	95 (9.3%)	213 (9.1%)
Sample type
Primary	651 (89%)	473 (81%)	300 (29%)	1,424 (61%)
Metastasis	80 (11%)	108 (19%)	724 (71%)	912 (39%)
MSI-H	4 (0.5%)	3 (0.5%)	3 (0.3%)	10 (0.4%)
TMB-H	10 (1.4%)	11 (1.9%)	14 (1.4%)	35 (1.5%)
Genomic subtype
KRASMUT	690 (94%)	557 (96%)	962 (94%)	2,209 (95%)
Other-MAPKMUT	26 (3.6%)	9 (1.5%)	41 (4.0%)	76 (3.3%)
MAPKWT	15 (2.1%)	15 (2.6%)	21 (2.1%)	51 (2.2%)
Resection surgery performed	719 (98%)	221 (38%)	11 (1.1%)	951 (41%)
OS (months)	31 (28, 34)c	19 (18, 20)c	11 (10, 12)c	18 (17, 19)c
Clinical curation	n = 502	n = 362	n = 616	n = 1,480
Tumor location
Head	356 (71%)	232 (64%)	260 (43%)	848 (58%)
Body/tail	145 (29%)	130 (36%)	339 (57%)	614 (42%)
Smoking history (former/current)	265 (53%)	176 (49%)	288 (47%)	729 (49%)
First-line systemic therapy for metastatic/recurrent disease	288 (57%)	227 (63%)	508 (82%)	1,023 (69%)
FOLFIRINOX	102 (35%)	39 (17%)	248 (49%)	389 (38%)
Gemcitabine/nab-paclitaxel	88 (31%)	107 (47%)	188 (37%)	383 (37%)
Other	98 (34%)	81 (36%)	72 (14%)	251 (25%)

^aMedian (range); ^bn (%); ^cmedian (95% CI).

BR/LA, borderline resectable/locally advanced.

Genomic characteristics of PDACs

Overall, 95% (n = 2,209) of tumors harbored oncogenic alterations in KRAS (Fig. 1a). Notably, this includes 1% (n = 22) of tumors in which KRAS mutations were identified with sequencing read evidence below thresholds for clinical reporting (Methods). Expectedly, tumors WT for KRAS (KRAS^WT) were significantly enriched for oncogenic alterations in other MAPK pathway genes such as BRAF, NRAS, NF1, NTRK1, NTRK3, FGFR2, ERBB2, MAP2K1, ROS1, MET and RAF1 (collectively, 60% in KRAS^WT versus 7% in KRAS^MUT, P = 1.6 × 10⁻⁴⁷; Fig. 1a). Seven of 26 oncogenic BRAF alterations were in-frame deletions between amino acids N486 and P490, which were nearly absent in BRAF^MUT tumors of melanoma (2 of 749) and thyroid cancer (0 of 473)^14,35 (Extended Data Fig. 1a). Moreover, gain-of-function oncogenic fusions involving MAPK genes were nearly exclusive to KRAS^WT tumors (3.2% in KRAS^WT versus 0.04% in KRAS^MUT, P = 2 × 10⁻³⁵). Collectively, these other MAPK pathway-altered KRAS^WT (referred to as other-MAPK^MUT) tumors comprise 3% (n = 76) of tumors. Finally, 2% (n = 51) of tumors were WT for any MAPK pathway alteration (referred to as MAPK^WT). Hypothesizing that these MAPK^WT tumors may harbor occult MAPK alterations that eluded detection by genomic sequencing, we performed transcriptome sequencing on 11 tumors where sufficient quality material was available. We identified activating fusions involving MAPK pathway genes BRAF and NRG1 in two of 11 tumors (18%), suggesting that a substantial fraction of these tumors may be driven by alterations in non-MAPK pathway genes.

Fig. 1. Somatic alteration landscape in PDAC.

a, Oncoprint of somatic oncogenic alterations in selected genes (Methods) across the following three genomic groups: KRAS^MUT, other-MAPK^MUT and MAPK^WT. Tile plot on the left indicates gene-level alteration enrichment in other-MAPK^MUT and MAPK^WT subtypes compared to KRAS^MUT using two-sided Fisher exact test. An asterisk indicates that NRG1 was not included in the enrichment analysis as it was profiled only in a subset of samples (Methods). Other MAPK pathway genes include FGFR1, ERBB3, FGFR4, EGFR, RASA1, CBL, MAPK1, ALK, MAP2K2, ERRFI1, FLT3, JAK2, KIT, PDGFRA, RAC1, RET, RRAS2, SOS1 and SPRED1. b, TMB-H and MSI-H prevalence by genomic subtype among tumor samples with ≥30% purity (n = 1,126). c, Age at diagnosis by genomic subtype with statistical comparison by two-sided Wilcoxon rank sum test (n = 2,336). Boxes represent the 25th, 50th (median) and 75th percentiles. Whiskers represent the minimum and maximum values, no further than 1.5× the interquartile range from the respective quartiles, with points beyond this range plotted individually. d, Kaplan–Meier curves showing OS for three genomic subtypes (Methods). e, Forest plot of multivariable Cox proportional hazards model of OS in overall cohort (top, corresponding to d; n = 2,270) and among patients who did not receive targeted therapies (bottom; n = 2,187). Models were stratified by stage at diagnosis and adjusted for sex, age, ancestry, disease status, resection and interval between diagnosis and sample collection (full model shown in Extended Data Table 1). f, Distribution of stage at diagnosis by genomic subtypes (n = 2,336). g, Prevalence of oncogenic alterations in GNAS and CDKN2A/CDKN2B by stage at diagnosis among tumor samples with purity >30% (n = 1076). h, Genetic ancestry by stage at diagnosis (n = 2,336). i, Tumor location (body/tail versus head) by stage (n = 1,462). Statistical significance is displayed as nominal P value for significant results after multiple test correction by FDR by Wilcoxon rank sum test for c, two-sided chi-squared test for h and two-sided Fisher’s exact test for b, g and i. Error bars represent 95th percentile binomial CI around the mean for b, g and i, and 95th percentile confidence intervals of the HR estimate (colored squares) for e. mo, month.

Extended Data Fig. 1. Somatic alteration landscape in PDAC: additional insights.

a, Prevalence of BRAF alteration types in PDAC, melanoma and thyroid cancer. b, Oncoprint of somatic oncogenic alterations with tumor samples grouped by genomic subtype (as in Fig. 1a) and by primary or metastasis sample type. c, Age at diagnosis across genomic groups including all patients (left, n = 2,336) and excluding patients with pathogenic germline variants (right, n = 2,006). Nominal P values indicate statistical comparison by two-sided Wilcoxon rank sum test. Boxes represent the 25th, 50th (median) and 75th percentiles. Whiskers represent the minimum and maximum values, no further than 1.5× the interquartile range (IQR) from the respective upper and lower quartiles, with points beyond this range plotted individually. d, Gene-level alteration enrichment by genetic ancestry (n = 75 genes with sufficient sample size). e, Gene-level alteration enrichment by sex (n = 75 genes with sufficient sample size). Enrichment was calculated using a two-sided Fisher exact test with P values adjusted for multiple testing by FDR for assessment of significance for d and e.

Several significant differences in oncogenic alterations were identified among these three genomic subtypes of PDAC (KRAS^MUT, other-MAPK^MUT and MAPK^WT; Fig. 1a and Extended Data Fig. 1b). TP53 mutations were significantly more frequent in KRAS^MUT (78%) compared to other-MAPK^MUT (38%, P = 1.4 × 10⁻⁹) and MAPK^WT (45%, P = 1.7 × 10⁻⁵) tumors. Interestingly, the TP53 alteration rate among BRAF-mutated tumors was indistinguishable from that of KRAS^MUT tumors, supporting prior observations that BRAF mutations phenocopy KRAS mutations in pancreas cancers (78% in KRAS^MUT versus 73% in other-MAPK^MUT/BRAF^MUT, P = 0.6)³⁶. In contrast, GNAS, SMARCB1 and PIK3CA alterations were significantly enriched in MAPK^WT tumors compared to KRAS^MUT (16% versus 2%, P = 1.5 × 10⁻⁵; 8% versus <1%, P = 2.4 × 10⁻⁶; 10% versus 2%, P = 0.002, respectively). SMARCB1 loss in KRAS^WT tumors has previously been associated with the monomorphic anaplastic subtype of undifferentiated carcinomas with rhabdoid features³⁷. Here, three of four SMARCB1-altered tumors in MAPK^WT subtype presented with aggressive histologic features of either poor differentiation or high grade, although none exhibited undifferentiated rhabdoid features. FOXP1 and CREBBP alterations were nearly exclusive to other-MAPK^MUT (P = 5.3 × 10⁻⁵ and P = 1.3 × 10⁻⁵, respectively, compared to KRAS^MUT; 0% prevalence in MAPK^WT). ARID1A alterations were enriched in other-MAPK^MUT compared to KRAS^MUT tumors (21% versus 8% prevalence, P = 2.4 × 10⁻⁴). High tumor mutational burden (10 or more nonsynonymous mutations per megabase, TMB-H) and microsatellite instability-high (MSI-H) tumors were infrequent (1.5% and 0.4%, respectively) and were enriched in KRAS^WT tumors (TMB-H—6.1% versus 1.8% in KRAS^MUT tumors, P = 0.03; MSI-H—3.7% versus 0.3%, P = 0.006; Fig. 1b)¹¹.

We next explored the association between various clinical characteristics and genomic subtypes of PDAC. Age at diagnosis significantly varied by KRAS alteration status. Compared to KRAS^MUT tumors (median age = 67 years), patients in other-MAPK^MUT (64 years, P = 0.03) and MAPK^WT (58 years, P = 2 × 10⁻⁴) subtypes presented with a significantly earlier age at diagnosis (Fig. 1c and Extended Data Fig. 1c). Patients with MAPK^WT tumors also had a significantly different ancestry composition compared to patients with KRAS^MUT (two-sided chi-squared test, P = 0.004) or other-MAPK^MUT tumors (P = 0.04), marked by elevated rates of East Asian patients in other-MAPK^MUT and patients with African ancestry in MAPK^WT subtypes, indicating the possibility of unrecognized driver alterations in underrepresented patient populations (Fig. 1c). No significant differences were observed in gene-level alteration frequencies across sex or ancestry group (Extended Data Fig. 1d,e).

To evaluate differences in OS across the three genomic subtypes, we used a multivariable Cox proportional hazards model stratified by clinical stage accounting for age, resection status, disease status, sex and genetic ancestry (Methods). We observed that patients with other-MAPK^MUT and MAPK^WT tumors had significantly longer OS compared to patients with KRAS^MUT tumors (KRAS^MUT versus MAPK^WT—adjusted hazard ratio (HR_adj) = 0.69, CI = 0.48–0.98, P = 0.041; KRAS^MUT versus other-MAPK^MUT—HR_adj = 0.69, CI = 0.51–0.93, P = 0.014; Fig. 1d,e, top, and Extended Data Table 1). Improved outcomes among KRAS^WT patients have been attributed to higher prevalence of targetable alterations in this group¹⁴. After excluding patients who received targeted therapies (n = 83), we noted that OS was indistinguishable between patients with KRAS^MUT and other-MAPK^MUT tumors (HR_adj = 0.95, P = 0.7), whereas patients with MAPK^WT tumors had significantly better OS (HR_adj = 0.68, CI = 0.47–0.97, P = 0.035; Fig. 1e, bottom, and Extended Data Table 1). This suggests that the well-recognized OS advantage among patients with KRAS^WT tumors extends beyond those with targetable alterations.

Extended Data Table 1.

Overall survival by genomic group

OS by genomic group

OS by genomic group among all patients in the cohort considered for survival analysis (Methods, n = 2,270; left), and among patients who did not receive targeted treatment for actionable alterations (n = 2,187, right). Multivariable Cox proportional hazards model of OS, stratified by stage at diagnosis. Displayed P values are nominal two-sided P values associated with the observed hazard ratios from the multivariable regression models. HR, hazard ratio; CI, confidence interval. Bold numbers indicate P values <0.05.

Somatic and clinical characteristics across clinical stages

The stage at diagnosis was similar across genomic subtypes (Fig. 1f). Among other genes, GNAS, which is associated with IPMN precursor lesions with improved outcomes over PanIN-derived PDACs, was significantly enriched in resectable tumors (7.9%) while CDKN2A/CDKN2B alteration rate increased with disease progression ranging from 44% in resectable disease to 60% in metastatic tumors (P = 6.9 × 10⁻⁵; Fig. 1g and Extended Data Fig. 2a)^38,39. No pathway-level differences in alteration rates were observed across disease stages (Extended Data Fig. 2b). Patients with African ancestry presented with more advanced disease (87%) compared to other ancestries (67% in European and 63% in East Asian; Fig. 1h). The location of the tumor in the pancreas was also strongly associated with stage—resectable tumors most commonly arose in the head (71%), while metastatic tumors frequently arose in the body/tail (57%; Fig. 1i and Extended Data Fig. 2d).

Extended Data Fig. 2. Stage differences in pathway and gene alteration patterns, age and additional clinical features.

a,b, Pathway- (a) and gene-level alteration (b) prevalence by clinical stage at diagnosis. Analysis limited to high-purity tumor samples (n = 1,076) and genes or pathways altered in at least 3 tumors in at least one stage. c, Age at diagnosis across the clinical stages at diagnosis including all patients (left, n = 2,336) and excluding patients with pathogenic germline variants (right, n = 2,006). Boxes represent the 25th, 50th (median) and 75th percentiles. Whiskers represent the minimum and maximum values, no further than 1.5× the interquartile range (IQR) from the respective upper and lower quartiles, with points beyond this range plotted individually. Groups were compared using two-sided Wilcoxon rank sum tests; nominal P value displayed. d, Tumor location, tobacco exposure and personal history of pancreatitis, hypertension, cancer, autoimmune disease, coronary artery disease and diabetes by clinical stage at diagnosis (n = 1,480). Error bars represent 95th percentile binomial CIs around the mean for a, b and d. Enrichment was calculated using a two-sided Fisher exact test for a, b and d with nominal P values displayed. P values were adjusted for multiple testing by FDR for assessment of significance for a and b (colored by P_adj < 0.05).

Germline mutations and concomitant somatic alterations

Ten percent of all patients harbored germline pathogenic mutations in high- and moderate-penetrance genes, including BRCA2 (n = 86, 3.7%), BRCA1 (n = 41, 1.8%), ATM (n = 41, 1.8%) and PALB2 (n = 11, 0.5%; Fig. 2a). Lynch syndrome with germline mutations in mismatch repair genes MLH1, MSH2, MSH6 and PMS2 was identified in 17 patients (0.7%), of which six (35%) presented with MSI inferred from sequencing^40,41. Although pathogenic germline variants in BRCA1, BRCA2 and CHEK2 were more frequent among those with Ashkenazi Jewish (ASJ) ancestry, ATM and PALB2 germline variants were more common among those without ASJ ancestry (Extended Data Fig. 3a). Patients with metastatic disease presented with slightly elevated rates of germline pathogenic variants compared to those with earlier stage disease (12.3% versus 9.6%, P = 0.01), driven primarily by an enrichment in BRCA1/BRCA2 alterations in tumors from patients with metastatic disease (Extended Data Fig. 3b). Germline pathogenic variants were more frequent among MAPK^WT (25%) compared to other-MAPK^MUT (17%, P = 0.05) and KRAS^MUT (10%, P = 0.001). This elevated rate was underpinned by higher prevalence of pathogenic germline mutations in ATM (gATM) in MAPK^WT patients compared to KRAS^MUT (18% versus 1.4%, P = 2 × 10⁻⁶; Fig. 2a). Germline ATM mutations also co-occurred with somatic GNAS mutations, possibly attributed to IPMN-derived PDAC^42,43. Notably, this increased gATM burden in MAPK^WT tumors was specific to mutations of germline origin as the rate of somatic ATM mutations (sATM) in MAPK^WT tumors was not different from the other genomic subtypes (P > 0.05; Fig. 2a)⁴³.

Fig. 2. Germline alteration landscape in PDAC.

a, Oncoprint of pathogenic germline variants and somatic oncogenic alterations by genomic subtype. Displayed sBRCA2, sBRCA1 and sATM alterations are exclusively in sporadic tumors without a pathogenic germline variant in these genes. An asterisk indicates that MMR includes MSH2, MSH6, MLH1 and PMS2. ‘Other’ includes all other pathogenic variants in high- and moderate-penetrance genes—BRIP1, CDKN2A, CHEK2, FLCN, HOXB13, MITF, NBN, NF1, RAD51D, SDHA, SMARCA4, STK11, TP53 and TSC1. Tiles at left show gene-level enrichment in other-MAPK^MUT and MAPK^WT subtypes compared to KRAS^MUT by two-sided Fisher’s exact test. b, Rates of loss of heterozygosity (Methods) at BRCA2, BRCA1, ATM, PALB2, MMR and other loci (as in a) in n = 1,946 patients with germline pathogenic variants, somatic mutations in sporadic cancers and patients WT for any alteration in corresponding genes (comparisons by two-sided Fisher's exact test). Error bars represent 95th percentile binomial confidence intervals around the mean. c, Pattern of germline and somatic ATM and TP53 alterations, with monoallelic or biallelic zygosity status indicated. d, Pattern of germline and somatic BRCA1 and TP53 alterations, as in c.

Extended Data Fig. 3. Pathogenic germline variant prevalence by ancestry and stage.

a, Distribution of genetically inferred ancestry across the overall cohort (left) and by germline mutated genes. Asterisks denote enrichment of ASJ-EUR ancestry by Fisher’s exact test (q < 0.05). b, Distribution of clinical stage at diagnosis across the overall cohort (left) and by germline mutated genes. Asterisks denote chi-squared test (q < 0.05).

Strong selection for biallelic inactivation through copy number loss of heterozygosity (LOH) was observed in gBRCA1, gBRCA2, gPALB2 and gATM carriers (Fig. 2b; Methods). The rate of LOH was higher in sporadic tumors with somatic mutations in BRCA2 and ATM compared to tumors without mutations in these genes, but lower compared to LOH rates observed in germline carriers^18,44–46. As previously reported, gATM and sTP53 mutations were mutually exclusive (P = 4 × 10⁻¹¹), and sTP53 alterations and gBRCA1 mutations co-occurred (P = 4 × 10⁻⁷)^44,47. Interestingly, taking zygosity into account, irrespective of origin of mutation (somatic or germline), tumors with biallelic loss of ATM were notably depleted for sTP53 mutations (Fig. 2c)⁴⁴. In contrast, both monoallelic and biallelic BRCA1 mutations showed equal co-occurrence with sTP53 mutations (Fig. 2d).

Mutant allelic imbalance at KRAS locus

We next evaluated the extent to which KRAS^MUT tumors harbor copy number allelic imbalance (unequal number of maternal and paternal alleles) at KRAS locus and mechanism by which allelic imbalance occurs²⁸ (Fig. 3a). To increase sensitivity and specificity to infer allelic state, we restricted our analysis to n = 1,157 KRAS^MUT tumors with sufficiently high-quality copy number fits (Methods). Of these 1,157 tumors, 42% presented with allelic imbalance at KRAS locus including focal or arm-level amplifications (4%, n = 48), shallow gains (16%, n = 186), copy-neutral LOH (CNLOH; 5%, n = 62), LOH (11%, n = 129) and losses after WGD (5%, n = 56; Fig. 3b). Selection for KRAS mutant allele was widespread with 93% of all imbalance events preferentially gaining or retaining the mutant allele. One in five (19%) KRAS^MUT tumors harbored WGD, and the rate of allelic imbalance was substantially higher in WGD tumors (75%) versus non-WGD tumors (30%; Fig. 3c). Consequently, KRAS^MUT allele gain was more than twice as common in WGD tumors (43% with three or more mutant copies) compared to non-WGD tumors (20% with two or more mutant copies; Fig. 3d). Moreover, the magnitude of mutant-allele gains also varied by WGD status. Tumors with two or more mutant-allele gains (that is, ≥4 mutant-alleles in WGD and ≥3 mutant-alleles in non-WGD tumors) were substantially more frequent in tumors with WGD (20% versus 6% in non-WGD tumors).

Fig. 3. KRAS mutant allele dosage gains and their prognostic implications.

a, Schematic of copy number states as related to mutant copy gain and retention of the WT allele in cases of allelic imbalance. The ‘Loss after WGD’ state indicates any copy number losses of the minor allele following WGD but excludes complete losses of the minor allele which are considered as CNLOH (Methods). b, Overall prevalence of copy number states in KRAS^MUT tumors. c, Prevalence of copy number state with allele selection by WGD status. d, Estimated number of gained mutant KRAS copies by WGD status. Tumors in which the WT allele was gained, or the mutant allele was lost are not shown here (n = 32, ‘WT selection’ in c). e, Kaplan–Meier curves of OS stratified by the number of mutant KRAS copies in diploid (non-WGD) KRAS^MUT tumors excluding tumors with gain of WT allele (n = 865; Extended Data Table 2). f, Prevalence of KRAS copy number states (left) and mutant copy gain (two or more mutant copies; right) by clinical stage at diagnosis (n = 874). Statistical comparisons show pairwise two-sided Fisher's exact tests. g, Kaplan–Meier curves of OS stratified by the number of mutant KRAS copies as in e, within each clinical stage at diagnosis (Extended Data Table 2). h, Kaplan–Meier curves of OS stratified by copy number state in diploid (non-WGD) KRAS^MUT tumors excluding tumors with gain of WT allele (n = 865; Extended Data Table 3). i, Forest plots of multivariable Cox proportional hazards model of OS by KRAS copy number state as in h, within each clinical stage at diagnosis (n = 865; Extended Data Table 3). Error bars represent 95th percentile binomial CI in f, and 95th percentile CI of the HR in i. Displayed P values in e, g–i are two-sided nominal P values from multivariable Cox proportional hazards models that include age, sex, ancestry and time from diagnosis to sample collection as covariates. Models for e and h are stratified by clinical stage at diagnosis (Extended Data Tables 3 and 4). R, resectable; M, metastatic.

KRAS mutant dosage and prognostic effect

KRAS^MUT PDAC tumors with a gain of mutant-allele present with aggressive phenotypes in mice and have worse OS in patients^28,29. Herein we sought to evaluate the prognostic effect of dosage gains of KRAS^MUT on OS across disease stages. To mitigate the confounding effects of WGD, a notable negative predictor of OS, we limited our analysis to non-WGD tumors (n = 934; Methods)³⁰. In a multivariable Cox model of OS stratified by stage at diagnosis and adjusted for sex, age at diagnosis, genetic ancestry, and time from diagnosis to sample collection, we observed that patients with tumors with any gain of KRAS^MUT allele had significantly lower OS compared to those with tumors with one mutant allele (P = 3.5 × 10⁻⁷, HR_adj = 1.7, CI = 1.4–2.0; Fig. 3e and Extended Data Table 2). Although no significant difference in OS was observed between patients with tumors harboring two mutant copies (n = 130) and those with 3 or more mutant copies (n = 49) when stratifying by stage, a larger sample size may be required to identify incremental effects of additional dosage gains on prognosis.

Extended Data Table 2.

Overall survival by KRAS mutant allele dosage

OS by KRAS mutant allele dosage

OS by KRAS mutant allele dosage among patients with KRAS^MUT tumors evaluable for KRAS allele-specific copy number considered for survival analysis (Methods, n = 865). Multivariable Cox proportional hazards model of OS, stratified by stage at diagnosis and within each stage. Displayed P values are nominal two-sided P values associated with the observed hazard ratios from the multivariable regression models. Bold numbers indicate P values <0.05.

KRAS^MUT allele dosage gains were more frequent in metastatic tumors (29%) compared to tumors from patients with locally advanced (14%) or resectable (8%) disease and were strongly correlated with advanced disease (P = 5.7 × 10⁻¹¹, chi-squared trend test; Fig. 3f). This supports prior observations in mouse models with KRAS G12D dosage gains, which demonstrated amplified RAS transcriptional programs and exhibited rapid disease progression²⁹. Notably, we observe the poor prognostic effect of mutant allele dosage across disease stages (Fig. 3g). Among patients with resectable disease, the median OS was significantly lower in patients with tumors harboring dosage gains of the mutant-allele compared to those whose tumors had one mutant copy (23 months versus 32 months; HR_adj = 2.16, CI = 1.1–4.3, P = 0.03). Similarly, among patients with metastatic disease, KRAS^MUT dosage gains were associated with 5 months shorter OS compared to patients whose tumors had a single KRAS^MUT copy (8.5 months versus 13 months; HR_adj = 1.63, CI = 1.3–2.1, P = 4.9 × 10⁻⁵; Extended Data Table 2).

The WT KRAS allele in KRAS^MUT tumors has previously been shown to act as a tumor suppressor^14,48,49. Tumors adapt during disease progression by losing the WT allele or acquiring additional copies of the mutant allele^50,51. We therefore hypothesized that patients with KRAS^MUT tumors with either gain of mutant or with loss of WT should have substantially worse prognosis compared to patients with balanced KRAS^MUT tumors. However, we observed that only the KRAS^MUT allele dosage is a notable predictor of poor prognosis, independent of loss or retention of the WT allele. Among patients with KRAS^MUT tumors that have a single copy of the mutant allele, OS was indistinguishable between patients with tumors that retained the WT allele and those that lost the WT allele (Fig. 3h and Extended Data Table 3). Both groups of patients also demonstrated significantly improved OS compared to patients with KRAS^MUT tumors with mutant-allele gains, irrespective of WT allele status. However, interestingly, among patients with tumors with KRAS^MUT dosage gains, patients with tumors with loss of WT had significantly worse OS compared to patients with tumors with WT retained (HR_adj = 1.64, 95% confidence interval (CI) = 1.1–2.5, P = 0.016 for CNLOH with gain-of-mutant as reference). This effect was most prominent among metastatic tumors (Fig. 3i and Extended Data Table 3). Taken together, our findings indicate a synergistic effect of losing the WT allele in tumors with KRAS^MUT dosage gains in promoting worse disease outcomes.

Extended Data Table 3.

OS by KRAS copy number state

OS by KRAS copy number state among patients with KRAS^MUT tumors and determinate KRAS allele-specific copy number considered for survival analysis (n = 865). Multivariable Cox proportional hazards model of OS, stratified by stage at diagnosis and within each stage. Displayed P values are nominal two-sided P values associated with the observed hazard ratios from the multivariable regression models. Bold numbers indicate P values <0.05.

KRAS mutant allele-specific differences

Over 98% of all codon substitutions in KRAS were at G12 (91%) and Q61 (7%) residues (Fig. 4a). Among KRAS^MUT tumors, G12D (41%) was the most abundant hotspot mutation followed by G12V (32%), and G12R (16%). Fourteen tumors (0.6%) harbored multiple KRAS hotspot mutations (Extended Data Fig. 4a,b). No significant differences were observed in KRAS variant prevalence by stage at diagnosis, sex, genetic ancestry or age at diagnosis (Fig. 4a and Extended Data Fig. 4c). Patients with G12R-mutant tumors compared to those with G12D-mutant tumors were less likely to have a smoking history (40% versus 55%, P = 2 × 10⁻⁴; Extended Data Fig. 4c).

Fig. 4. Differential genomic and prognostic features of KRAS variants.

a, Top, prevalence of the most common KRAS variants among all KRAS^MUT tumors. Bottom, disease stage composition, distributions of allelic imbalances and KRAS^MUT allele dosage gains across patients with different KRAS variants. Allelic imbalances and dosage gains are shown only for non-WGD tumors as indicated. Displayed nominal P value denotes statistical comparison of distribution of gene-level copy number state by two-sided chi-squared test. Tumors with multiple driver KRAS mutations are not shown here (n = 14; Extended Data Fig. 4a,b). Error bars for KRAS^MUT CN represent 95th percentile binomial CIs around the mean. b, Kaplan–Meier curve of OS among KRAS G12D, G12V and G12R variants. Displayed P values are nominal two-sided P values from a multivariable Cox proportional hazards model stratified by stage at diagnosis and accounting for KRAS mutant allele copy number, sex, age, ancestry, disease status and interval from diagnosis to sample collection (Extended Data Table 4). c, Oncoprint showing the prevalence of co-occurring TP53, CDKN2A/CDKN2B, SMAD4, ARID1A, AKT2, and RB1 mutations and TGFβ, SWI/SNF, PI3K and RTK–Ras signaling pathway alterations among KRAS G12D, G12R and G12V tumors. Tiles on the left indicate pairwise enrichment testing of co-occurring alterations between KRAS G12D versus G12R and G12V mutant tumors by two-sided Fisher's exact test.

Extended Data Fig. 4. Differential genomic and prognostic features of KRAS variants: additional insights.

a, Bubble chart of co-occurring KRAS mutations. Number and bubble size indicate the prevalence of a given combination, and number in parentheses is the prevalence of the mutation in the overall cohort. b, Purity-adjusted variant allele frequency (VAF) of KRAS mutations by tumor, ordered by difference in VAF. Please note the 14 tumors shown here are excluded from Fig. 4. c, Prevalence of sex, genetic ancestry, age at diagnosis and smoking status by KRAS variant. Dotted line for age shows overall median. P value denotes two-sided Fisher’s exact test. Boxes for age represent the 25th, 50th (median) and 75th percentiles. Whiskers represent the minimum and maximum values, no further than 1.5× the interquartile range (IQR) from the respective upper and lower quartiles, with points beyond this range plotted individually. d, Prevalence of WGD by KRAS variant (n = 1,150); prevalence of KRAS allelic imbalance and KRAS allele selection state by KRAS variant among diploid tumors (n = 927). e,f, Progression-free survival (PFS) differences between first-line standard-of-care treatments across the different KRAS variant groups. e, Kaplan–Meier curves for PFS differences between FOLFIRINOX (5-FU) and gemcitabine for KRAS G12D, G12V and G12R. P values represent statistical comparison of univariate Kaplan–Meier curves by log-rank test. f, Multivariable Cox proportional hazards model for e. P values are nominal two-sided P values from the Cox regression model. Error bars represent 95th percentile binomial CIs around the mean for c and d.

In a multivariable Cox model stratified by stage at diagnosis and accounting for mutant KRAS gain, we evaluated the differences in OS among patients with tumors harboring the most common KRAS variants (Extended Data Table 4). Patients with KRAS G12R-mutant tumors had significantly better OS compared with G12D-driven cancers (HR_adj = 0.78, CI = 0.67–0.92, P = 0.003; Fig. 4b). Among patients with de novo metastatic disease, those with KRAS G12D, G12V or G12R mutations did not show significant differences in progression-free survival between those who received first-line FOLFIRINOX compared to gemcitabine-based therapy (Extended Data Fig. 4e,f). Despite the noted differences in functional effects of different KRAS alleles, the rates of genome doubling and mutant-allele dosage gains were similar across alleles (Fig. 4a and Extended Data Fig. 4d)⁵². However, in comparing G12D and G12R tumors, SMAD4 alterations were significantly more common in G12R tumors (30% in G12R versus 21% in G12D, P = 0.001), while ARID1A alterations were more frequent in G12D tumors (10% in G12D versus 5% in G12R, P = 0.002; Fig. 4c)⁵³.

Extended Data Table 4.

OS by KRAS variant

OS by KRAS variant among patients with KRAS^MUT tumors with a single KRAS variant considered for survival analysis (Methods, n = 2,130). Multivariable Cox proportional hazards model of OS, stratified by stage at diagnosis. Displayed P values are nominal two-sided P values associated with the observed hazard ratios from the multivariable regression models. Bold numbers indicate P values <0.05.

Clinical actionability in PDAC

Approximately 10% of patients with PDAC harbored standard-of-care biomarkers of response to targeted therapies as defined by OncoKB level 1 (for example, MSI-H, TMB-H and oncogenic MAPK pathway alterations in NTRK1, NTRK3 and RET) and level 2 (KRAS G12C, BRCA1/BRCA2, PALB2; OncoKB version 4.12, December 2023)³⁴. Most strikingly, an additional 78% of patients harbored biomarkers with compelling clinical evidence of response to specific drugs (OncoKB level 3A), nearly all of which (98%) were attributed to KRAS G12D/V/R/A/S mutations for which favorable responses to RAS inhibitors have been observed in recent phase I trials (Fig. 5a)^6,34. The clinical actionability varied across the genomic subtypes with other-MAPK^MUT patients, expectedly, harboring the highest fraction (18%) of OncoKB level 1 alterations (Extended Data Fig. 5). For patients with metastatic PDAC who were fit for systemic therapy, chemotherapy with either a 5-fluorouracil (5-FU) or gemcitabine-based backbone was administered (Table 1 and Fig. 5b)⁵. Treatment selection reflected changes in standard-of-care guidelines over the course of the study⁵⁴.

Fig. 5. Clinically actionable alterations and treatment landscape.

a, Highest OncoKB level of evidence by patient, with actionable (levels 1, 2 and 3A) alterations labeled. b, Alluvial diagram of treatment sequence for all 1,480 patients with treatment annotation by clinical stage at diagnosis showing prevalence of neoadjuvant therapy, resection surgery, adjuvant therapy and up to 5 rounds of systemic therapy. Different lines of treatment are shown on the x axis. c, Clinicogenomic characterization of n = 29 patients who were metastatic at presentation and received PARP-inhibitor therapy as part of systemic treatment. Cumulative times on PARP-inhibitor therapy and platinum therapy are shown as bar charts, along with demographic information (age at diagnosis, sex and genetic ancestry), germline and somatic alterations in HRD genes with associated zygosity, and somatic alterations in other commonly altered genes or genes of interest. The patient marked with a plus (+) received PARP-inhibitor therapy to target a germline RAD50 mutation (not shown). d, OS for n = 304 patients who were metastatic at presentation and received either 5-FU or gemcitabine-based first-line treatments (top left). Top-right, bottom-left and bottom-right, Kaplan–Meier curves of OS by alteration status of indicated genes. P values are nominal two-sided P values from a multivariable Cox model that accounts for sex, age, ancestry and interval from diagnosis to sample collection for each gene (Methods; Extended Data Table 5).

Extended Data Fig. 5. OncoKB clinical actionability across the three genomic subtypes of PDAC.

Prevalence of actionable alterations by OncoKB levels of actionability across KRAS^MUT, other-MAPK^MUT and MAPK^WT tumors.

We next evaluated molecular correlates of response to poly-ADP ribose polymerase inhibitors (PARPi). In our clinically curated cohort, n = 29 patients with stage IV disease at diagnosis received PARPi therapy at any time during their treatment. Of these, 25 patients had germline (n = 23) or somatic (n = 2) mutations in BRCA2 (n = 18), BRCA1 (n = 6) and PALB2 (n = 1; Fig. 5c). Twenty-eight of 29 patients had KRAS^MUT tumors, 4 of which also had mutant allele dosage gains. Overall, 38% (11 of 29) of patients received PARPi therapy for more than six months (median 17 months; range = 8–56). Ten of 11 (91%) patients had BRCA2^MUT; the remaining patient had a BRCA1^WT/BRCA2^WT and MAPK^WT tumor with a BAP1 loss-of-function fusion. While all ten BRCA2-mutant tumors deriving benefit from PARPi (defined as >6 months on treatment) had biallelic inactivation, no benefit was observed among six other BRCA2-mutant tumors with biallelic losses, suggesting that biallelic loss of BRCA1/BRCA2 is an important but insufficient biomarker alone of response to PARPi⁵⁵.

We next sought to identify the genomic correlates of OS in KRAS^MUT patients with de novo metastatic disease who received first-line standard-of-care chemotherapy (n = 304, median OS = 10.5 months; Methods; Fig. 5d). We evaluated associations between genes altered in at least 3% of patients (n = 13 genes) and OS using a multivariable Cox regression model accounting for sex, age, ancestry and interval from diagnosis to biospecimen collection (Methods; Extended Data Table 5). Only patients with BRCA2 alterations (including germline carriers) showed modestly improved outcomes (HR_adj = 0.66, 95% CI = 0.44–0.98, P = 0.038), in line with the established response to platinum therapy^16,18,56 (Fig. 5d). After correcting for multiple hypothesis testing, alterations in two genes remained significant. Both were associated with a significantly shorter OS—RNF43 (HR_adj = 2.79, 95% CI = 1.40–5.56, P_adj = 0.047; median OS = 6.6 months RNF43^MUT versus 10.8 months RNF43^WT) and AKT2 amplifications (HR_adj = 2.03, 95% CI = 1.26, 3.29, P_adj = 0.048; median OS = 8.6 months in AKT2^MUT versus 10.8 months in AKT2^WT). Although not commonly altered in PDAC (<5% in this cohort), RNF43 loss-of-function activity has been shown to be KRAS dependent⁵⁷. While KRAS mutant gains were more frequent in RNF43^MUT tumors, this did not reach statistical significance in our cohort (67% of RNF43^MUT tumors had KRAS mutant gains versus 30% in RNF43^WT, P = 0.2). Together, these results suggest that prognosis in patients with PDAC is most linked to KRAS mutation and dosage in the majority of patients, and that somatic alterations in other genes do not explain the wide variation in response to standard therapies.

Extended Data Table 5.

OS by gene-level alterations

OS by genomic alterations in n = 13 genes among patients with metastatic disease at diagnosis with KRAS^MUT tumors who received first-line chemotherapy considered for survival analysis, limited to high-purity samples (Methods, n = 304). Multivariable Cox proportional hazards model of OS, stratified by stage at diagnosis. Displayed P values are nominal two-sided P values associated with the observed hazard ratios from the multivariable regression models. Bold numbers indicate P values <0.05.

Discussion

Herein we study the detailed clinicogenomic profiles of n = 2,336 patients to characterize prognostic biomarkers guiding clinical outcomes in PDAC. Compared to prior studies, our cohort has several unique strengths including many molecularly profiled metastatic tumors, detailed clinical histories for two-thirds of patients, and matched tumor and normal sequencing that allowed for incorporation of germline results and robust inference of allele-specific copy number states at the mutated loci. Collectively, these strengths enabled improved classification of PDAC into genomic subtypes and the demonstration of prognostic potential of mutant allele dosage gains in KRAS^MUT tumors across all disease stages.

Our findings have several clinical implications. The predictive role of KRAS mutant dosage gains in impacting antitumor response and therapeutic outcomes remains unknown, although we expect it to be important. As many RAS-targeted therapies are advancing in clinical trials, it is imperative to conduct analyses to evaluate whether KRAS mutant dosage has a role in mediating efficacy as well as depth and duration of response to treatment. Findings from such studies may provide impetus for future trials to stratify patients by KRAS mutant dosage in evaluating therapeutic outcomes. For example, randomized clinical trials that evaluate patients with KRAS mutant dosage gains in both the intervention as well as control arms are key to evaluate response to RAS inhibitors as well as current standard-of-care therapies. Second, shallow gains of KRAS mutant alleles, including single-copy gains, which are associated with poor outcomes and can potentially guide care, are currently not reported by any clinical sequencing assays. There is an imminent need for evaluation, standardization and incorporation of these low-level gains into reports for clinical interpretation. Third, acknowledging the challenge of small biopsy specimens in the diagnosis and management of patients with PDAC, it is essential to understand whether circulating tumor DNA can provide a noninvasive means of assessing KRAS mutant dosage and whether sequential analyses could provide a dynamic and real-time assessment to inform response and resistance.

Although PDACs are genomically grouped by KRAS status, our data argue that the KRAS^WT tumors comprise two distinct molecular groups stratified by whether or not they harbor oncogenic alterations in other MAPK pathway genes. The other-MAPK^MUT and MAPK^WT subtypes comprise 3% and 2% of all PDACs, respectively, and reveal distinct etiologic and molecular factors underpinning tumorigenesis. However, further molecular and biochemical studies that measure the activity of Ras–ERK signaling as well as the upregulation of downstream components including MYC or YAP are needed to establish MAPK independence among these MAPK^WT tumors⁵⁸.

In this large clinicogenomically characterized cohort of patients with PDAC, several important limitations need highlighting. Patients were evaluated and treated at a major referral center for PDAC with inherent biases of fitness, age and other characteristics. With current trends of increasing incidence of early-onset PDAC, especially in younger women, these characteristics are important to note^59,60. We report a high proportion of patients undergoing resection of primary pancreatic cancer, 41%, which is higher than the broader population of all patients with PDAC (Table 1). Relatedly, only 15% of our cohort was non-European, highlighting the need for these results to be evaluated in subgroups with greater ethnic and racially diverse populations. This study primarily focuses on genomic features derived from MSK-IMPACT. Additional transcriptomic assessments are needed to fully characterize pathway-level activity and to identify the relationship between genomic biomarkers described herein and previously described transcriptomic subtypes (classical and basal) that have been associated with prognosis and for which predictive implications are under evaluation in clinical trials (NCT04469556)²⁵. Given the prognostic potential of both KRAS dosage and the basal-like transcriptomic subtype, a comprehensive analysis of subtyping is needed to determine whether these are indicative of the same or independent pathways of tumor progression.

Comprehensive analysis of germline and somatic alterations is critically important in PDAC and both have therapeutic implications. Additionally, integration with demographic and clinical information is required to link understanding of underlying tumor biology with treatment implications and outcomes. We identify distinct molecular and clinical features of KRAS^MUT, other-MAPK^MUT and MAPK^WT genomic subtypes and further stratify KRAS^MUT tumors by allele-specific copy number. Collectively, these findings demonstrate that increased KRAS mutant allele dosage is an important negative prognostic feature that will need to be integrated into clinical practice as we move into the era of RAS-directed therapeutics for PDAC.

Methods

The research was reviewed and overseen by the Institutional Review Board at Memorial Sloan Kettering Cancer Center.

Study cohort

Between January 2014 and September 2021, 2,671 tumor and matched normal DNA samples from 2,566 patients with PDAC were subjected to molecular testing using MSK-IMPACT, an FDA-authorized clinical next-generation sequencing panel. Patients provided consent for tumor profiling under an institutional Memorial Sloan Kettering Cancer Center (MSK) IRB-approved research prospective protocol, ‘Tumor Genomic Profiling in Patients Evaluated for Targeted Cancer Therapy’ (NCT01775072). Patients were not compensated financially for participation in the study. Tumor samples with low sequence coverage (<100×) or no detectable somatic alterations likely due to low tumor content quality were excluded (112 samples from 99 patients). Upon further review, 62 patients were excluded for having a diagnosis other than PDAC, and an additional 69 patients were excluded for having incomplete medical records. From the remaining 2,336 patients, one representative sample from each patient was selected for further analysis based on several criteria including higher tumor purity, higher sequence coverage, sample passing of allele-specific copy number (FACETS) quality control criteria (https://github.com/taylor-lab/facets-preview/) and the specific IMPACT panel size on which the specimen was sequenced. Of these 2,336 patients, 1,480 patients who had at least one year of clinical follow-up at our center between January 2014 and March 2021 underwent comprehensive clinical annotation after manual curation of medical health records. Genetic ancestry was inferred from IMPACT as described previously⁶⁰. Comprehensive demographic and clinical information are included in Table 1. Self-reported sex information was collected from patient medical records. No sex-based criteria were used to select patients in this study. Gender information was not considered in the study.

Tumor sequencing and mutation assessment

Tumor samples were sequenced using the MSK-IMPACT targeted sequencing panel as described previously³³. Briefly, tumor type and purity were assessed by pathology from H&E-stained slides of tumor samples. Genomic DNA from formalin-fixed, paraffin-embedded (FFPE) tumor and matched normal (peripheral blood) samples was extracted, and targeted sequencing was performed using custom DNA probes against all exons and selected introns of a given panel of cancer genes. Tumors were sequenced using four different generations of MSK-IMPACT panels containing 341 genes (n = 17 samples), 410 genes (n = 438), 468 genes (n = 1,536) and 505 genes (n = 345). The median sequencing depth of tumors was 606× (25th percentile: 469×, 75th percentile: 749×). The median purity of the tumors, estimated by FACETS, was 31%. For tumors without FACETS estimated purity, the pathologist estimated tumor purity was used to determine if the sample meets sufficient quality criteria to be included in the cohort (see above). Somatic mutations, copy number alterations and structural rearrangements in select genes were identified using a previously described pipeline validated for use in a Clinical Laboratory Improvement Amendments (CLIA)-compliant laboratory³³. All somatic alterations were annotated for clinical actionability using OncoKB version. 4.12, December 2023³⁴. All somatic alterations that were identified as ‘oncogenic’ or ‘likely oncogenic’ by OncoKB were considered as drivers and used exclusively in all analyses (annotated as [Gene]^MUT, regardless of alteration type) unless otherwise noted. Genes were included in the oncoprint in Fig. 1a if they met any of the following criteria: mutated in >5% of patients, MAPK pathway, MYC pathway, clinically actionable genes mutated in ≥1% of patients, or significantly enriched in either other-MAPK^MUT or MAPK^WT tumors. Pathway gene lists were defined by the oncogenic signaling pathways described in ref. ⁶¹. Germline variant discovery and pathogenicity assessment was performed as previously described^62,63. MSI was determined by MSIsensor, with tumors identified with an MSIsensor score of 10 or higher classified as microsatellite instable (MSI-H, microsatellite instability high)^64,65. A select subset of the cohort (n = 90 patients) received additional clinical testing for fusions using the custom RNA-seq panel (MSK-Fusion) that utilizes Archer Multiplex PCR technology⁶⁶. All tumors wild-type for KRAS mutations were evaluated for read evidence at subdetection thresholds. In these tumors, we genotyped known KRAS hotspot mutations using a custom tool (https://github.com/mskcc/GetBaseCountsMultiSample) and identified reads with mapping quality scores of at least 20. All mapping reads were manually reviewed using IGV (http://software.broadinstitute.org/software/igv/) to identify high-quality reads. A read is considered high quality if there are no mismatches and if the evidence supporting the mutated base is not in the leading or trailing ten bases of the sequencing read.

RNA sequencing for fusion detection

We performed RNA sequencing on 11 tumor specimens derived from FFPE tissue blocks. Fusions were called using FusionCatcher v1.20 (https://github.com/ndaniel/fusioncatcher) and Arriba v2.1.0 (https://github.com/suhrig/arriba). Results from each caller were merged into a single output file according to matching gene IDs and breakpoints. Noncoding fusions from FusionCatcher were excluded from the final output of the pipeline. All fusion calls were annotated by OncoKB. Two tumors harbored activating fusions involving MAPK pathway genes. In one sample, the ATP1B1–NRG1 rearrangement fused the third exon of ATP1B1 to the second exon of NRG1. In another sample, the fourteenth exon of GIT2 was fused to the fourth exon of BRAF. In both events, the entire kinase domain of the 3′ gene was retained.

Allele-specific copy number analysis

Allele-specific copy number was inferred using the FACETS algorithm (v0.5.14)⁶⁷. Briefly, FACETS was run on each tumor sample in a two-step mode as previously described²⁸. The first step aims to identify the normalized tumor to normal sequencing coverage ratio corresponding to the diploid state. Using this, the second step aims to identify the focal gains and losses. FACETS-derived purity and ploidy estimate for the tumor sample and the allele-specific integer copy number for each locus was inferred. To identify and exclude tumors with poor quality copy number fits, we applied a series of quality control criteria (https://github.com/taylor-lab/facets-preview/) that include degree of evidence supporting the diploid state, the fraction of the genome with homozygous deletions, fraction of the genome that is estimated to be subclonal, hypersegmentation, concordance between integer copy number estimate and the allelic configuration and whether FACETS was able to estimate purity (C.B., D.M., Ino de Bruijn, Mingxuan Zhang, Michael V. Gormally et al., in revision). Overall, 1,555 of 2,322 tumor samples for which we were able to generate FACETS profiles had sufficiently high-quality copy number fits evaluable for further analysis. WGD status is inferred as previously described (https://github.com/mskcc/facets-suite)³⁰. Briefly, a tumor is deemed to have undergone genome doubling if more than 50% of the autosomal genome has a major copy number of 2 or higher. In our cohort, 339 of 1,555 tumors were WGD-positive. Clonality of somatic mutations was determined as described previously⁶⁸. Briefly, we first infer the cancer cell fraction (CCF) of mutation using the variant allele frequency of the somatic mutation, the integer copy number at the locus, the read coverage and the FACETS estimated tumor purity. A mutation is deemed to be clonal if either the CCF estimate is 80% or higher, or if the CCF estimate is 70% or higher and the upper bound of the 95th percentile CI of the CCF estimate is greater than 90% (https://github.com/mskcc/facets-suite). LOH at specific loci harboring germline/somatic mutations or WT alleles was assessed using allele-specific copy number inference from FACETS. A locus is considered to have LOH if the lcn is 0.

KRAS allelic imbalance and mutant allele dosage gains

Of the 1,555 tumors with sufficiently high-quality copy number profiles, 1,157 were KRAS^MUT with determinable integer copy number. We then assigned each of these tumors to six different allelic copy number states based on the total copy number (tcn) and lower copy number (lcn) at the KRAS locus as well as the WGD status (Fig. 3a). In non-WGD tumors, diploid tumors with tcn:lcn of 2:1 were considered as ‘Balanced’ (heterozygous). Tumors with complete loss of one inherited allele (lcn of 0) were considered as ‘LOH’ if the tcn is 1, and ‘CNLOH’ if tcn is >1. Tumors were considered to harbor KRAS ‘amplifications’ if our clinical pipeline identified focal KRAS amplifications or the tcn in FACETS was ≥5 in the absence of WGD, or ≥6 with WGD. The tumors that retained both inherited maternal and paternal alleles but have gained additional copies of one or both inherited alleles were considered as ‘Gains’. Among WGD-positive tumors, we accounted for the higher balanced state (tcn:lcn of 4:2) in ascribing allelic copy number states associated with losses and gains. WGD-positive tumors that acquired single-copy losses (irrespective of whether the loss targeted the WT or the mutant allele) were considered as ‘Loss after WGD’. To be considered as a ‘Gain’ in WGD-positive tumors, the tcn is required to be 5 or higher.

The expected number of KRAS mutant alleles is estimated using the observed variant allele frequency, tumor purity and total copy number at the KRAS locus as previously described^28,69. We considered tumors with a higher number of mutant copies than WT copies to have undergone mutant allele selection. These include tumors identified as having ‘LOH’ (tcn:lcn of 1:0 for non-WGD tumors and 2:0 for WGD-positive tumors), ‘CNLOH’ (complete loss of wild-type with either ≥2 mutant copies in non-WGD tumors, or, ≥3 mutant copies in WGD-positive tumors), ‘Gain of mutant’ (with higher number of mutant copies than wild-type copies), and finally, among WGD-positive tumors, ‘WGD+Loss of WT’ tumors in which a copy number losses after WGD event targeted the wild-type allele (Fig. 3c). Tumors in which selection following copy number gains/losses resulted in preferential retention of the KRAS WT allele were rare (n = 32, ‘WT selection’, Fig. 3c) and were not considered further.

Statistics and reproducibility

Enrichment testing was performed using two-sided Fisher’s exact tests with multiple test correction by the FDR method where appropriate. For gene-level enrichment analyses, we evaluated all cancer genes that are captured on all versions of the MSK-IMPACT panels (n = 341 genes) and were altered in at least 3 samples in the dataset. Unless otherwise noted, enrichment tests were limited to alterations detected by MSK-IMPACT and annotated as drivers (oncogenic or likely oncogenic) by OncoKB. Error bars on all figures showing counts/prevalence data represent 95% CIs of the binomial probability. Two-sided chi-squared tests were used for testing differences in prevalence of nonbinary variables such as ancestry and clinical stage. Two-sided Wilcoxon rank sum tests were used for comparison of numeric variables such as age. In boxplots, the center line represents the median, lower and upper hinges show the 25th and 75th percentiles, and lower and upper whiskers show the minimum and maximum values no further than 1.5× the interquartile range, respectively, with outlier points beyond this range plotted individually. Analyses were conducted using R version 4.3.0 with the rstatix (v0.7.2), stats (v4.3.0) and binom (v1.1.1.1) packages and visualized using ggplot2 (v3.4.4). Oncoprints were generated using cBioPortal for Cancer Genomics^70–72.

Survival

OS was calculated as the interval from date of IMPACT sample collection to date of death or last follow-up. Patients with no follow-up after IMPACT sample collection were censored on day 1 (n = 9). Patients with missing date of IMPACT specimen collection (n = 2) or those with IMPACT specimen collection before January 2014 (n = 64) were excluded from survival analyses. All P values and adjusted HRs associated with survival analyses are from multivariable Cox proportional hazards models accounting for covariates including age, sex, ancestry and interval from diagnosis to sample collection. Full models are shown in Extended Data Tables. Covariates were selected based on statistically significant univariate associations with OS. MSI type and TMB were not found to have a statistically significant association with OS in a univariate model and therefore were not included as covariates in multivariable models. Models were either stratified by clinical stage at diagnosis or evaluated within each stage to account for known differences in OS by stage. Median OS values were calculated using the univariate Kaplan–Meier estimator. Analyses were conducted using R version 4.3.0 with the tidyverse (v2.0.0), survival (v3.5-5) and gtsummary (v1.7.2) packages.

For stage and survival analyses associated with KRAS copy number (Fig. 3), resectable patients who had experienced a recurrence by the time of sample collection were excluded (n = 95) to limit variation in disease progression within the stage. For OS comparison by binary gene alteration status (Fig. 5d), genes were considered if they had a somatic alteration prevalence of at least 3% of metastatic patients who had received standard-of-care chemotherapy with either 5-FU to gemcitabine backbones who could be evaluated for OS (n = 473 patients, 13 genes). Samples were then limited to those with high purity (30% or higher) to reduce the bias of false negatives in the WT group (n = 304 patients). Chemotherapy backbone (5-FU versus gemcitabine/nab-paclitaxel) was not significantly associated with OS and was therefore not included as a covariate (P > 0.05). Multivariable Cox proportional hazards models were constructed for each gene (Extended Data Table 5), and P values of the genomic alterations in each model were adjusted by FDR with n = 13.

Reporting summary

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Online content

Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests and statements of data and code availability are available at 10.1038/s41591-024-03362-3.

Author contributions

A.M.V., M.A.P., N.S., M.F.B., C.A.I.-D., C.B. and E.M.O. conceived the study. A.M.V, M.A.P., J.F.C., S.N., D. Muldoon, D.C., M.C., N.S., M.F.B., C.B. and E.M.O. designed and performed data analysis. J.F.C. and M.C. provided statistical expertise and analyzed clinical outcomes data. M.M., D. Mandelker and Z.K.S. provided germline variant pathogenicity assessment. A.E. provided project management. A.Z., C.F., B.N., A.R.B, D. Mandelker, E.V., Z.K.S. and M.F.B. assisted with prospective genomic and clinical data collection and sample annotation. Z.K.S., N.S. and M.F.B. supported consent infrastructure. O.B. and E.V. assisted with sample acquisition for additional analysis. A.M.V., F.B., D.P.K., W.P., K.H.Y., M.A.S., W.R.J, A.C.W. and E.M.O. provided samples. A.M.V., M.A.P., C.B. and E.M.O. wrote the manuscript with input from all authors. All authors reviewed and approved the manuscript. C.B. and E.M.O. contributed equally as senior authors.

Peer review

Peer review information

Nature Medicine thanks Mariano Ponz-Sarvise, Channing Der and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Anna Maria Ranzoni, in collaboration with the Nature Medicine team.

Data availability

Genomic and associated clinical data for all patients and tumor samples included in this study have been deposited in cBioPortal for Cancer Genomics and are publicly accessible and downloadable at https://www.cbioportal.org/study/summary?id=pdac_msk_2024. Raw tumor and normal sequencing data from MSK-IMPACT, as well as all data associated with germline variants, are considered protected information and access is available under restricted access subject to additional institutional approvals. These data may be requested for appropriate use from the corresponding authors by email (bandlamc@mskcc.org, oreillye@mskcc.org); requests will be reviewed within 4 weeks. Data will be shared for a span of 2 years within 2 weeks of execution of a data transfer agreement with MSK, which will retain all title and rights to the data and results from their use.

Code availability

Algorithms and R packages used are open-source and described in the Methods. The FACETS algorithm for allele-specific copy number is available on GitHub at https://github.com/taylor-lab/facets-preview/. The OncoKB knowledge base used for annotation is available at https://www.oncokb.org/ and through the API on GitHub at https://github.com/oncokb/oncokb-annotator.

Competing interests

M.A.P. declares stock ownership in Amgen. A.M.V. declares consulting activity from AstraZeneca (spouse), Eli Lilly (spouse) and Paige AI (spouse), and intellectual property rights (SOPHiA Genetics) (spouse). B.N. is a current employee of Eli Lilly and Company. D. Mandelker declares consulting fees from AstraZeneca. F.B. receives research support from BMS. D.P.K. receives funding from the Thompson Family Foundation and Applebaum Foundation and is a consultant at Merck, BMS, BeiGene, Lilly, Abbvie, Incyte, Janssen, Listen and TG Therapeutics. A.R.B. declares stock ownership in Johnson & Johnson and intellectual property rights in SOPHiA Genetics. W.P. receives research funding from Merck, Astellas, Miracogen, Amgen and Revolution Medicines, is a consultancy/advisory board member for Astellas, EXACT Therapeutics, Innovent Biologics and Regeneron and has received honoraria for CME: American Physician Institute, Integrity. M.F.B. declares consulting activity from AstraZeneca, Eli Lilly and Paige AI and intellectual property rights (SOPHiA Genetics). E.M.O. receives research funding from Agenus, Amgen, Genentech/Roche, BioNTech, AstraZeneca, Arcus, Elicio, Parker Institute, NIH/NCI and Digestive Care, consulting/DSMB role at Arcus, Ability Pharma, Alligator, Agenus, BioNTech, Ipsen, Merck, Moma Therapeutics, Novartis, Syros, Leap Therapeutics, Astellas, BMS, Fibrogen, Revolution Medicine, Merus, Moma Therapeutics and Tango; Agios (spouse), Genentech-Roche (spouse), Eisai (spouse) Servier (spouse). The remaining authors declare no competing interests.

Extended data

is available for this paper at 10.1038/s41591-024-03362-3.

Supplementary information

The online version contains supplementary material available at 10.1038/s41591-024-03362-3.