Evidence map›Paper›PMID 40227722›Full record

ArticleCancers2025

Mutational Profile of Blood and Tumor Tissue and Biomarkers of Response to PD-1 Inhibitors in Patients with Cutaneous Squamous Cell Carcinoma.

Mark J Chang, Daniel B Stamos, Cetin Urtis, Nathan L Bowers, Lauren M Schmalz, Logan J Deyo, Martin F Porebski, Abdur Rahman Jabir, Paul M Bunch, Thomas W Lycan and 7 more

Abstract read
In one paragraph

Article in Cancers, 2025. 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
–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

2 citing papers in PubMed.

  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

17 authors.

Mark J ChangDepartment of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA.
Daniel B StamosDepartment of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA.
Cetin UrtisCenter for Cancer Genomics and Precision Oncology, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA.
Nathan L BowersKnoxville Institute of Dermatology, Knoxville, TN 37919, USA.
Lauren M SchmalzCenter for Cancer Genomics and Precision Oncology, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA.
Logan J DeyoDepartment of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA.
Martin F PorebskiDepartment of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA.
Abdur Rahman JabirDepartment of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA.ORCID 0000-0003-2166-1513
Paul M BunchWake Forest Baptist Comprehensive Cancer Center, Winston-Salem, NC 27157, USA.ORCID 0000-0001-6129-7299
Thomas W LycanDepartment of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA.ORCID 0000-0001-9475-1558
Laura Buchanan DoerflerDepartment of Dermatology, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA.
Hafiz S PatwaWake Forest Baptist Comprehensive Cancer Center, Winston-Salem, NC 27157, USA.
Joshua D WaltonenWake Forest Baptist Comprehensive Cancer Center, Winston-Salem, NC 27157, USA.
Christopher A SullivanWake Forest Baptist Comprehensive Cancer Center, Winston-Salem, NC 27157, USA.
J Dale BrowneWake Forest Baptist Comprehensive Cancer Center, Winston-Salem, NC 27157, USA.
Wei ZhangCenter for Cancer Genomics and Precision Oncology, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA.ORCID 0000-0002-2235-1851
Mercedes PorosnicuDepartment of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA.

Funding

Tumor Tissue CoreP30CA012197 · NCI · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI Ruben A. Mesa · 1985 to 2026
$55.4M
Model-based Prediction of Redox-Modulated Responses to Cancer TreatmentsU01CA215848 · NCI · GEORGIA INSTITUTE OF TECHNOLOGY · PI FURDUI, CRISTINA MARIA, KEMP, MELISSA LAMBETH · 2017 to 2021
$3.6M
CTSA K12 Program at Wake ForestK12TR004931 · NCATS · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI Nicholette D. Allred · 2024 to 2026
$2.3M
NCATS NIH HHS K12 TR004931NCI NIH HHS P30CA012197NCI NIH HHS U01 CA215848
6 · The paper itself

Abstract

BACKGROUND/

objectivesCutaneous squamous cell carcinoma (cSCC) harbors one of the most mutated genomes. There are limited data on the genomic profile and its predictive potential for response to immunotherapy with PD-1 inhibitors in cSCC.

methodsThis study retrospectively reviewed cSCC patients treated with PD-1 inhibitor monotherapy at a single institution. Clinical characteristics, treatment outcomes, PD-L1 expression, tumor mutation burden (TMB), and genomic profile in tumor and blood were analyzed. Logistic regression and a support vector classifier were used to validate identified biomarkers of significance.

resultsTwenty-five patients were evaluable for response and had genomics tested in tumor and/or blood. Of the total, 80% of patients achieved an objective response: 40% complete response (CR), 32% partial response (PR) for more than 6 months, and 8% stable disease (SD) for more than 1 year; 20% of patients progressed on treatment. With a median follow-up of 21 months, progression-free survival (PFS) was 28 months in responders vs. 3 months in non-responders (

conclusionsPD-1 inhibitor monotherapy produces an impressive response. Eight gene mutations were significantly more frequent in non-responders. PD-L1 and TMB were inconclusive in predicting treatment response to anti-PD-L1.

Indexed as

biomarkerscheckpoint inhibitorctDNAcutaneous squamous cell carcinoma (cSCC)genomicsimmunotherapyPD-1 inhibitorPD-L1tDNAtumor mutational burden (TMB)

Identifiers

PMID40227722
PMCPMC11987913

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Registered trials

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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.