Evidence map›Paper›PMID 40527927›Full record

ArticleNPJ precision oncology2025

Tissue-based genomic profiling of 300,000 tumors highlights the detection of variants with low allele fraction.

Saumya D Sisoudiya, Hanna Tukachinsky, Rachel B Keller-Evans, Alexa B Schrock, Richard S P Huang, Ole Gjoerup, Michael J Pishvaian, Rachna Shroff, Ethan S Sokol, Lucas Dennis and 2 more

Abstract read
In one paragraph

Article in NPJ precision oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Article
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  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. HighCancers · 2025
    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

12 authors.

Saumya D SisoudiyaFoundation Medicine Inc., Cambridge, MA, USA.
Hanna TukachinskyFoundation Medicine Inc., Cambridge, MA, USA.
Rachel B Keller-EvansFoundation Medicine Inc., Cambridge, MA, USA.
Alexa B SchrockFoundation Medicine Inc., Cambridge, MA, USA.
Richard S P HuangFoundation Medicine Inc., Cambridge, MA, USA.
Ole GjoerupFoundation Medicine Inc., Cambridge, MA, USA.
Michael J PishvaianJohns Hopkins University School of Medicine, Washington, DC, USA.
Rachna ShroffUniversity of Arizona Cancer Center, Tucson, AZ, USA.
Ethan S SokolFoundation Medicine Inc., Cambridge, MA, USA.
Lucas DennisFoundation Medicine Inc., Cambridge, MA, USA.
Garrett M FramptonFoundation Medicine Inc., Cambridge, MA, USA.
Smruthy SivakumarFoundation Medicine Inc., Cambridge, MA, USA. ssivakumar@foundationmedicine.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tumor tissues obtained in the clinical setting typically have low purity and display treatment-associated genetic heterogeneity, contributing to variants at low variant allele fractions (VAF). We present a pan-cancer landscape and the therapeutic impact of somatic variants detected at low VAF (≤10%) from tumor tissues in 331,503 patients, across 78 tumor types, that received an FDA-approved comprehensive genomic profiling (CGP) test targeting ~324 genes during routine clinical care. 29% of patients had at least one variant detected at VAF ≤10% and 16% at VAF ≤5%. Among the frequently diagnosed tumors, several cases showed low VAF variants: pancreatic (37%), non-small cell lung cancer (35%), colorectal (29%) and prostate (24%). Treatment resistance-associated alterations had lower median VAF than driver alterations, although variants with VAF ≤5% comprised both driver and resistance alterations. This highlights the importance of CGP in detecting low VAF variants, to better inform clinical actionability and guide personalized treatment for patients with cancer.

Identifiers

PMID40527927
PMCPMC12174337

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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.