Evidence map›Paper›PMID 38328153›Full record

ArticlebioRxiv : the preprint server for biology2024

Integrated cancer cell-specific single-cell RNA-seq datasets of immune checkpoint blockade-treated patients.

Mahnoor N Gondal, Marcin Cieslik, Arul M Chinnaiyan

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 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

3 authors.

Mahnoor N GondalDepartment of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI USA.ORCID 0000-0002-9419-6758
Marcin CieslikDepartment of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI USA.
Arul M ChinnaiyanDepartment of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI USA.ORCID 0000-0001-9282-3415

Funding

Tissue/InformaticsP50CA186786 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Ganesh S Palapattu · 2014 to 2026
$27.6M
Exploring Precision Oncology: From Gene Fusions to lncRNAsR35CA231996 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI CHINNAIYAN, ARUL M · 2018 to 2024
$6.4M
Michigan-VUMC Biomarker Characterization CenterU2CCA271854 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Jeffrey John Tosoian · 2022 to 2026
$5.4M
NCI NIH HHS P50 CA186786NCI NIH HHS R35 CA231996NCI NIH HHS U2C CA271854
6 · The paper itself

Abstract

Immune checkpoint blockade (ICB) therapies have emerged as a promising avenue for the treatment of various cancers. Despite their success, the efficacy of these treatments is variable across patients and cancer types. Numerous single-cell RNA-sequencing (scRNA-seq) studies have been conducted to unravel cell-specific responses to ICB treatment. However, these studies are limited in their sample sizes and require advanced coding skills for exploration. Here, we have compiled eight scRNA-seq datasets from nine cancer types, encompassing 174 patients, and 90,270 cancer cells. This compilation forms a unique resource tailored for investigating how cancer cells respond to ICB treatment across cancer types. We meticulously curated, quality-checked, pre-processed, and analyzed the data, ensuring easy access for researchers. Moreover, we designed a user-friendly interface for seamless exploration. By sharing the code and data for creating these interfaces, we aim to assist fellow researchers. These resources offer valuable support to those interested in leveraging and exploring single-cell datasets across diverse cancer types, facilitating a comprehensive understanding of ICB responses.

Identifiers

PMID38328153
PMCPMC10849474

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LicenceCC BY
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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.