Evidence map›Paper›PMID 42239045›Full record

ArticlebioRxiv : the preprint server for biology2026

A community machine learning challenge to predict the effects of gene perturbations on T cell differentiation for cancer immunotherapy.

Jiaqi Zhang, Marc A Schwartz, Mohammed Mutaher, Oluwatomisin Olajide, Yuri Pritykin, Orr Ashenberg, Nir Hacohen, Caroline Uhler

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 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.

Jiaqi ZhangEric and Wendy Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, 02142, MA, USA.ORCID 0000-0001-9039-6843
Marc A SchwartzBroad Institute of MIT and Harvard, Cambridge, MA, 02142 USA.
Mohammed MutaherBroad Institute of MIT and Harvard, Cambridge, MA, 02142 USA.
Oluwatomisin OlajideBroad Institute of MIT and Harvard, Cambridge, MA, 02142 USA.
Yuri PritykinDepartment of Computer Science and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540, USA.
Orr AshenbergEric and Wendy Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, 02142, MA, USA.
Nir HacohenBroad Institute of MIT and Harvard, Cambridge, MA, 02142 USA.
Caroline UhlerEric and Wendy Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, 02142, MA, USA.

Funding

Spatial and temporal resolution to dissect cellular circuits controlling intestinal physiology, immunity, and inflammatory pathologiesRC2DK135492 · NIDDK · BROAD INSTITUTE, INC. · PI Caroline Uhler, Ramnik J Xavier · 2023 to 2026
$7.9M
Regulatory genomics of T cells in mouse and humanDP2AI171161 · NIAID · PRINCETON UNIVERSITY · PI Yury Pritykin · 2022 to 2026
$2.4M
Causal Representation Learning for the Spatial Analysis of Transcriptomic and Imaging Data in Tissue ContextsDP2AT012345 · NCCIH · BROAD INSTITUTE, INC. · PI UHLER, CAROLINE · 2022 to 2025
$2.3M
NCCIH NIH HHS DP2 AT012345NIAID NIH HHS DP2 AI171161NIDDK NIH HHS RC2 DK135492
6 · The paper itself

Abstract

Perturbations of genes with functional importance in T cells could be used to change the distribution of CD8 T cell states to enhance anti-tumor functions for cancer immunotherapies. We launched a world-wide computational challenge to predict the effects of gene perturbations and to devise objective functions for prioritizing gene perturbations that lead to desired T-cell state distributions. We supported the challenge by generating a single-cell Perturb-seq dataset profiling the effect of knocking out 73 individual expert-defined genes in T cells transferred into a mouse melanoma model. We compared the top algorithms developed by participants, and found that performance was primarily determined by the prior data used for gene feature representation, with perturbational data derived features, proving most effective. Experimental validation of the top 61 genes nominated by the algorithms revealed that perturbation of

Identifiers

PMID42239045
PMCPMC13228547

What OpenQuestion holds

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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.