Evidence map›Paper›PMID 37943166›Full record

ArticleCancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology2024

Effects of KRAS Genetic Interactions on Outcomes in Cancers of the Lung, Pancreas, and Colorectum.

Isabella N Grabski, John V Heymach, Kenneth L Kehl, Scott Kopetz, Ken S Lau, Gregory J Riely, Deborah Schrag, Rona Yaeger, Rafael A Irizarry, Kevin M Haigis

Open access · bronzeAbstract read
In one paragraph

Article in Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.9field-weighted citation impact, top 20% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 5 institutions in 1 country.

Isabella N GrabskiDepartment of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts.ORCID 0000-0002-0616-5469
John V HeymachDepartment of Thoracic and Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.ORCID 0000-0001-9068-8942
Kenneth L KehlDivision of Population Sciences, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts.ORCID 0000-0001-5339-9797
Scott KopetzDepartment of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.ORCID 0000-0001-9647-3416
Ken S LauDepartment of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, Tennessee.ORCID 0000-0001-8438-0319
Gregory J RielyDepartment of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York.ORCID 0000-0001-5357-4313
Deborah SchragDepartment of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York.ORCID 0000-0002-4334-5717
Rona YaegerDepartment of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York.ORCID 0000-0002-7233-5454
Rafael A IrizarryDepartment of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts.ORCID 0000-0002-3944-4309
Kevin M HaigisDepartment of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts.ORCID 0000-0003-1922-4509
Memorial Sloan Kettering Cancer Center · USDana-Farber Cancer Institute · USHarvard University · USThe University of Texas MD Anderson Cancer Center · USVanderbilt University · US

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Hongbiao Carl Lekaye · 1985 to 2026
$347.4M
Data Analysis Tools for Emerging High-Throughput TechnologiesR35GM131802 · NIGMS · DANA-FARBER CANCER INST · PI Rafael Angel Irizarry · 2019 to 2026
$4.1M
Overcoming bias and unwanted variability in next generation sequencingR01HG005220 · NHGRI · JOHNS HOPKINS UNIVERSITY · PI IRIZARRY, RAFAEL ANGEL · 2010 to 2018
$3.6M
Mouse models of Kras-mutant colorectal cancerR01CA246653 · NCI · DANA-FARBER CANCER INST · PI HAIGIS, KEVIN · 2020 to 2024
$3.1M
Tissue-specific genetic interactions in cancerR01CA232372 · NCI · DANA-FARBER CANCER INST · PI HAIGIS, KEVIN · 2018 to 2022
$2.9M
Basic and Translational studies of Ras-mutant colorectal cancerR01CA178017 · NCI · BETH ISRAEL DEACONESS MEDICAL CENTER · PI HAIGIS, KEVIN · 2014 to 2018
$1.8M
NCI NIH HHS P30 CA008748NCI NIH HHS R01 CA178017NCI NIH HHS R01 CA232372NCI NIH HHS R01 CA246653NHGRI NIH HHS R01 HG005220NIGMS NIH HHS R35 GM131802
6 · The paper itself

Abstract

backgroundKRAS is among the most commonly mutated oncogenes in cancer, and previous studies have shown associations with survival in many cancer contexts. Evidence from both clinical observations and mouse experiments further suggests that these associations are allele- and tissue-specific. These findings motivate using clinical data to understand gene interactions and clinical covariates within different alleles and tissues.

methodsWe analyze genomic and clinical data from the AACR Project GENIE Biopharma Collaborative for samples from lung, colorectal, and pancreatic cancers. For each of these cancer types, we report epidemiological associations for different KRAS alleles, apply principal component analysis (PCA) to discover groups of genes co-mutated with KRAS, and identify distinct clusters of patient profiles with implications for survival.

resultsKRAS mutations were associated with inferior survival in lung, colon, and pancreas, although the specific mutations implicated varied by disease. Tissue- and allele-specific associations with smoking, sex, age, and race were found. Tissue-specific genetic interactions with KRAS were identified by PCA, which were clustered to produce five, four, and two patient profiles in lung, colon, and pancreas. Membership in these profiles was associated with survival in all three cancer types.

conclusionsKRAS mutations have tissue- and allele-specific associations with inferior survival, clinical covariates, and genetic interactions. IMPACT: Our results provide greater insight into the tissue- and allele-specific associations with KRAS mutations and identify clusters of patients that are associated with survival and clinical attributes from combinations of genetic interactions with KRAS mutations.

Indexed as

Lung NeoplasmsPancreatic NeoplasmsAnimalsHumansLungMutationPancreasProto-Oncogene Proteins p21(ras)KRAS protein, humanProto-Oncogene Proteins p21(ras)

Identifiers

PMID37943166
PMCPMC10841605
OpenAlexW4388525576

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.