Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Feb 3;8(1):27.
doi: 10.1038/s41698-024-00505-0.

Impact of KRAS mutations and co-mutations on clinical outcomes in pancreatic ductal adenocarcinoma

Abdelrahman Yousef  1 , Mahmoud Yousef  1 , Saikat Chowdhury  1 , Kawther Abdilleh  2 , Mark Knafl  3 , Paul Edelkamp  4 , Kristin Alfaro-Munoz  1 , Ray Chacko  1 , Jennifer Peterson  1 , Brandon G Smaglo  1 , Robert A Wolff  1 , Shubham Pant  1 , Michael S Lee  1 , Jason Willis  1 , Michael Overman  1 , Sudheer Doss  2 , Lynn Matrisian  2 , Mark W Hurd  5 , Rebecca Snyder  6 , Matthew H G Katz  6 , Huamin Wang  7 , Anirban Maitra  5   7 , John Paul Shen  1 , Dan Zhao  8
Affiliations

Impact of KRAS mutations and co-mutations on clinical outcomes in pancreatic ductal adenocarcinoma

Abdelrahman Yousef et al. NPJ Precis Oncol. .

Abstract

The relevance of KRAS mutation alleles to clinical outcome remains inconclusive in pancreatic adenocarcinoma (PDAC). We conducted a retrospective study of 803 patients with PDAC (42% with metastatic disease) at MD Anderson Cancer Center. Overall survival (OS) analysis demonstrated that KRAS mutation status and subtypes were prognostic (p < 0.001). Relative to patients with KRAS wildtype tumors (median OS 38 months), patients with KRASG12R had a similar OS (median 34 months), while patients with KRASQ61 and KRASG12D mutated tumors had shorter OS (median 20 months [HR: 1.9, 95% CI 1.2-3.0, p = 0.006] and 22 months [HR: 1.7, 95% CI 1.3-2.3, p < 0.001], respectively). There was enrichment of KRASG12D mutation in metastatic tumors (34% vs 24%, OR: 1.7, 95% CI 1.2-2.4, p = 0.001) and enrichment of KRASG12R in well and moderately differentiated tumors (14% vs 9%, OR: 1.7, 95% CI 1.05-2.99, p = 0.04). Similar findings were observed in the external validation cohort (PanCAN's Know Your Tumor® dataset, n = 408).

PubMed Disclaimer

Conflict of interest statement

D.Z. has clinical trial contracts with Mirati and CARsgen and served as a member of the advisory board for Affini-T.K.A., S.D., and L.M. are employees of the Pancreatic Cancer Action Network, a nonprofit cancer advocacy organization that receives donations from private and commercial entities and may benefit indirectly financially and non-financially from this publication. S.P. reports research funding from Mirati Therapeutics, Lilly, Xencor, Novartis, Rgenix, Bristol-Myers Squibb, Astellas Pharma, Framewave, 4D Pharma, Boehringer Ingelheim, NGM Biopharmaceuticals, Janssen, Arcus Biosciences, Elicio Therapeutics, Bionote, Ipsen, Zymeworks, Pfizer, ImmunoMET, Immuneering, Amal Therapeutics. S.P. is a member of the advisory board for Zymeworks, Ipsen, Novartis, Janssen, Boehringer Ingelheim, AskGene Pharma, BPGbio, Jazz Pharmaceuticals, AstraZeneca, US WorldMeds, Nihon Medi-Physics Co., Ltd., Alligator Bioscience. M.L. consults for Pfizer, Delcath, Janssen, BioNTech, G1 Therapeutics, Imvax, and Bayer and received research funding from institutions from EpimAb BioTherapeutics, Merck, Erasca, Boehringer Ingelheim, Arcus Biosciences, Repare Therapeutics, Trisalus Life Sciences, and Xilis. A.M. is a consultant for Tezcat Biotechnologies and is a co-inventor on a patent that has been licensed from Johns Hopkins University by Thrive Earlier Detection (an Exact Sciences company). A.M. serves on the scientific and medical advisory board for Pancreatic Cancer Action Network and Pancreatic Cancer UK. J.P.S. receives grants, research support, or collaborates with Celsius Therapeutics, BostonGene, Caris Life Sciences, Natera, Xilis, Palantir, and Genentech. J.P.S. reports consulting and stock ownership with Engine Biosciences, NaDeNo Nanoscience.

Figures

Fig. 1
Fig. 1. Study flowchart diagram.
The flowchart shows cohort patient selection. Abbreviations include MD Anderson (MD Anderson Cancer Center).
Fig. 2
Fig. 2. Overall survival (OS) with KRAS mutations and mutation subtypes.
a KM OS curves of all patients, and stage IV patients only b with KRAS-mutated PDAC c Univariate analysis of OS with KRAS mutation subtypes and d Frequencies of different KRAS mutations in patients with KRAS-mutant PDAC (n = 578).
Fig. 3
Fig. 3. OS with stage and histopathological grade and KRAS mutations.
a KM OS curves for tumor stage of our cohort. b KM OS curves for tumor histopathological grade of our cohort. c Bar plot showing enrichment of KRASG12D mutation in metastatic disease. d Bar plot showing enrichment of KRASG12R in well and moderately differentiated tumors.
Fig. 4
Fig. 4. Allele-specific co-mutations with KRAS in the MDA cohort.
a Oncoplot showing the distribution of different KRAS mutational subtypes with the different genes in our cohort. b Heatmap showing the co-mutation landscape of the different KRAS mutation subtypes with the different genes and their frequencies. c Bar plot showing the most frequently mutated genes in our cohort.
Fig. 5
Fig. 5. Co-mutation with KRAS and OS analysis of MDA cohort.
a Co-mutation analysis of the MD Anderson cohort. Associations between prevalent driver mutations were assessed using Fisher’s exact method and a significant FDR-corrected p indicated by asterixis (*FDR-corrected p < 0.1). b Forest plot showing HR for death (from a univariable analysis) for driver mutations in our cohort, wildtype of each gene was used as reference. c KM OS analysis in patients with metastatic PDAC stratified by molecular subtype.
Fig. 6
Fig. 6. Allele-specific co-mutations with KRAS of KYT cohort.
a Oncoplot showing the somatic mutation distribution across the KYT cohort. b Heatmap showing the co-mutation landscape of the different KRAS mutation subtypes with the different genes and their frequencies in KYT cohort.
Fig. 7
Fig. 7. Co-mutation analysis of KYT Cohort with OS.
a Co-mutation analysis of KYT cohort, associations between prevalent driver mutations were assessed using the Fisher’s exact method and a significant FDR-corrected p indicated by asterixis (*FDR-corrected p < 0.1). b Bar plot showing the difference in median overall survival between different KRAS mutation subtypes. * indicate p < 0.05 using log-rank test for survival. ^Wildtype: indicate no pathogenic mutations were detected.
See this image and copyright information in PMC

Update of

References

    1. Mizrahi JD, Surana R, Valle JW, Shroff RT. Pancreatic cancer. Lancet. 2020;395:2008–2020. doi: 10.1016/S0140-6736(20)30974-0. - DOI - PubMed
    1. Rahib L, Wehner MR, Matrisian LM, Nead KT. Estimated projection of US cancer incidence and death to 2040. JAMA Network Open. 2021;4:e214708–e214708. doi: 10.1001/jamanetworkopen.2021.4708. - DOI - PMC - PubMed
    1. Conroy T, et al. FOLFIRINOX versus gemcitabine for metastatic pancreatic cancer. N. Engl. J. Med. 2011;364:1817–1825. doi: 10.1056/NEJMoa1011923. - DOI - PubMed
    1. Tempero MA, et al. Pancreatic adenocarcinoma, version 2.2021, NCCN clinical practice guidelines in oncology. J. Natl. Compr. Canc. Netw. 2021;19:439–457. doi: 10.6004/jnccn.2021.0017. - DOI - PubMed
    1. Von Hoff DD, et al. Increased survival in pancreatic cancer with nab-paclitaxel plus gemcitabine. N. Engl. J. Med. 2013;369:1691–1703. doi: 10.1056/NEJMoa1304369. - DOI - PMC - PubMed