Evidence map›Paper›PMID 42120871›Full record

Articlenpj aging2026

Aging dictates tumor-specific genomic alterations across cancer types.

Isabella N Grabski, Jaime L Schneider, Sara Hanahan, Arvind Ravi, Justin F Gainor, Kevin M Haigis, Rafael A Irizarry, Marcia C Haigis

Abstract read
In one paragraph

Article in npj aging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Isabella N Grabski *Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA.
Jaime L Schneider *Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical, School, Boston, MA, USA. jaime_schneider@dfci.harvard.edu.
Sara HanahanDepartment of Cell Biology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.
Arvind RaviBroad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA.
Justin F GainorMassachusetts General Hospital Cancer Center and Department of Medicine, Harvard Medical, School, Boston, MA, USA.
Kevin M HaigisDepartment of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA. kevin_haigis@dfci.harvard.edu.
Rafael A IrizarryDepartment of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA. rafael_irizarry@dfci.harvard.edu.
Marcia C HaigisDepartment of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical, School, Boston, MA, USA. marcia_haigis@hms.harvard.edu.

Funding

Dana Farber/Harvard Cancer Consortium Career Development Program in Clinical OncologyK12CA087723 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI David Tsai Ting · 2002 to 2026
$17.9M
Molecular mechanisms of Nutrient sensing in cancerR01CA213062 · NCI · HARVARD MEDICAL SCHOOL · PI HAIGIS, MARCIA · 2017 to 2021
$1.9M
NCI NIH HHS K12 CA087723NCI NIH HHS R01 CA213062
6 · The paper itself

Abstract

Cancer risk increases exponentially with age. As the world's population grows older, the absolute number of cancer diagnoses and cancer-related deaths continues to rise globally, despite declines in age-adjusted cancer mortality in many high-income countries. However, there is still little known about how molecular features of tumors differ according to patient age and how these genomic alterations may impact the treatment of patients of advanced age compared to younger patients who are diagnosed with malignancy. In this study, we survey the largest clinically- and genomically-annotated database available and identify age-associated mutations across distinct cancer types, defined as genes whose mutation rates in cross-sectional cancer populations differ by age. By interrogating paired clinical and genomic data from the AACR Project GENIE database across brain, breast, colorectum, lung, pancreas, and skin cancers, we identified somatic mutations in tumors with distinct age-dependent patterns. We also uncovered age-specific associations in the co-mutation patterns among these genes, demonstrating relationships among mutations that strengthen or weaken with age, as well as connections between age-specific mutation patterns and environmental covariates. Finally, in an exploratory analysis, we observed age-associated trends between PTPRT mutations in lung cancer and clinical response to immune checkpoint inhibitors, highlighting the possibility that certain genomic alterations may differentially associate with outcomes across patient age groups.

Identifiers

PMID42120871
PMCPMC13388700

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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.