Evidence map›Paper›PMID 41030932›Full record

ArticlemedRxiv : the preprint server for health sciences2024

Predicting the Tumor Microenvironment Composition and Immunotherapy Response in Non-Small Cell Lung Cancer from Digital Histopathology Images.

Sushant Patkar, Alex Chen, Alina Basnet, Amber Bixby, Rahul Rajendran, Rachel Chernet, Susan Faso, Prashant A Kumar, Devashish Desai, Ola El-Zammar and 7 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2024. 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

5 · Who and what money

Authors and funding

17 authors.

Sushant PatkarArtificial Intelligence Resource (AIR), National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.ORCID 0000-0002-5877-0599
Alex ChenArtificial Intelligence Resource (AIR), National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.ORCID 0009-0003-5900-1892
Alina BasnetDepartment of Hematology and Oncology, SUNY Upstate Medical University, Syracuse, NY, USA.
Amber BixbyDepartment of Pathology and Laboratory Medicine, SUNY Upstate Medical University, Syracuse, NY, USA.
Rahul RajendranDepartment of Pathology and Laboratory Medicine, SUNY Upstate Medical University, Syracuse, NY, USA.
Rachel ChernetDepartment of Pathology and Laboratory Medicine, SUNY Upstate Medical University, Syracuse, NY, USA.
Susan FasoDepartment of Pathology and Laboratory Medicine, SUNY Upstate Medical University, Syracuse, NY, USA.
Prashant A KumarDepartment of Hematology and Oncology, SUNY Upstate Medical University, Syracuse, NY, USA.
Devashish DesaiDepartment of Hematology and Oncology, SUNY Upstate Medical University, Syracuse, NY, USA.
Ola El-ZammarDepartment of Pathology and Laboratory Medicine, SUNY Upstate Medical University, Syracuse, NY, USA.
Christopher CurtissDepartment of Pathology and Laboratory Medicine, SUNY Upstate Medical University, Syracuse, NY, USA.
Saverio J CarelloDepartment of Pathology and Laboratory Medicine, SUNY Upstate Medical University, Syracuse, NY, USA.
Michel NasrDepartment of Pathology and Laboratory Medicine, SUNY Upstate Medical University, Syracuse, NY, USA.
Peter ChoykeArtificial Intelligence Resource (AIR), National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Stephanie HarmonArtificial Intelligence Resource (AIR), National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Baris TurkbeyArtificial Intelligence Resource (AIR), National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Tamara JamaspishviliDepartment of Pathology and Laboratory Medicine, SUNY Upstate Medical University, Syracuse, NY, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Immune checkpoint inhibitors (ICI) have become integral to treatment of non-small cell lung cancer (NSCLC). However, reliable biomarkers predictive of immunotherapy efficacy are limited. Here, we introduce HistoTME, a novel weakly supervised deep learning approach to infer the tumor microenvironment (TME) composition directly from histopathology images of NSCLC patients. We show that HistoTME accurately predicts the expression of 30 distinct cell type-specific molecular signatures directly from whole slide images, achieving an average Pearson correlation of 0.5 with the ground truth on independent tumor cohorts. Furthermore, we find that HistoTME-predicted microenvironment signatures and their underlying interactions improve prognostication of lung cancer patients receiving immunotherapy, achieving an AUROC of 0.75[95% CI: 0.61-0.88] for predicting treatment responses following first-line ICI treatment, utilizing an external clinical cohort of 652 patients. Collectively, HistoTME presents an effective approach for interrogating the TME and predicting ICI response, complementing PD-L1 expression, and bringing us closer to personalized immuno-oncology.

Identifiers

PMID41030932
PMCPMC12478338

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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.