Evidence map›Paper›PMID 39303028›Full record

ArticleScience advances2024

Prediction of immunotherapy response using mutations to cancer protein assemblies.

JungHo Kong, Xiaoyu Zhao, Akshat Singhal, Sungjoon Park, Robin Bachelder, Jeanne Shen, Haiyu Zhang, Jimin Moon, Changho Ahn, Chan-Young Ock and 2 more

Abstract read
In one paragraph

Article in Science advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Mutation-informed gene pairs to predict melanoma metastasis.Cell communication and signaling : CCS · 2026
    Article
  5. Interferon Epsilon Loss Is Elusive 9p21 Link to Immune-Cold Tumors, Resistant to Immune Checkpoint Therapy, and Endogenous CXCL9/10 Induction.Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer · 2025
    Review
  6. Review
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.

JungHo KongDepartment of Medicine and Moores Cancer Center, School of Medicine, University of California San Diego, San Diego, CA, USA.
Xiaoyu ZhaoDepartment of Medicine and Moores Cancer Center, School of Medicine, University of California San Diego, San Diego, CA, USA.ORCID 0000-0002-6081-2196
Akshat SinghalDepartment of Computer Science and Engineering, University of California San Diego, San Diego, CA, USA.ORCID 0000-0001-5371-526X
Sungjoon ParkDepartment of Medicine and Moores Cancer Center, School of Medicine, University of California San Diego, San Diego, CA, USA.
Robin BachelderDepartment of Medicine and Moores Cancer Center, School of Medicine, University of California San Diego, San Diego, CA, USA.
Jeanne ShenDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USA.ORCID 0000-0002-1519-0308
Haiyu ZhangDepartment of Pathology, Stanford University School of Medicine, Stanford, CA, USA.ORCID 0000-0002-2317-3380
Jimin MoonLunit Incorporated, Seoul, South Korea.ORCID 0009-0009-7923-948X
Changho AhnLunit Incorporated, Seoul, South Korea.ORCID 0000-0002-0702-0608
Chan-Young OckLunit Incorporated, Seoul, South Korea.ORCID 0000-0002-6755-9728
Hannah CarterDepartment of Medicine and Moores Cancer Center, School of Medicine, University of California San Diego, San Diego, CA, USA.ORCID 0000-0002-1729-2463
Trey IdekerDepartment of Medicine and Moores Cancer Center, School of Medicine, University of California San Diego, San Diego, CA, USA.ORCID 0000-0002-1708-8454

Funding

The Cancer Cell Map Initiative v2.0U54CA274502 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Trey Ideker, Nevan J Krogan · 2022 to 2026
$14.2M
UCSD Cancer Center Training Program in Drug DevelopmentT32CA121938 · NCI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Michael Bouvet, Dwayne G. Stupack · 2006 to 2026
$10.3M
A Systems Approach to Mapping the DNA Damage ResponseR01ES014811 · NIEHS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI IDEKER, TREY · 2005 to 2021
$8.6M
Multi-modal machine learning to guide adjuvant therapy in surgically resectable colorectal cancerR01CA270437 · NCI · STANFORD UNIVERSITY · PI Jeanne Shen · 2023 to 2026
$2.5M
Comprehensive identification of germline-somatic interactionsR01CA269919 · NCI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Ludmil B Alexandrov, Hannah Kathryn Carter · 2022 to 2026
$2.2M
NCI NIH HHS R01 CA269919NCI NIH HHS R01 CA270437NCI NIH HHS T32 CA121938NCI NIH HHS U54 CA274502NIEHS NIH HHS R01 ES014811
6 · The paper itself

Abstract

While immune checkpoint inhibitors have revolutionized cancer therapy, many patients exhibit poor outcomes. Here, we show immunotherapy responses in bladder and non-small cell lung cancers are effectively predicted by factoring tumor mutation burden (TMB) into burdens on specific protein assemblies. This approach identifies 13 protein assemblies for which the assembly-level mutation burden (AMB) predicts treatment outcomes, which can be combined to powerfully separate responders from nonresponders in multiple cohorts (e.g., 76% versus 37% bladder cancer 1-year survival). These results are corroborated by (i) engineered disruptions in the predictive assemblies, which modulate immunotherapy response in mice, and (ii) histochemistry showing that predicted responders have elevated inflammation. The 13 assemblies have diverse roles in DNA damage checkpoints, oxidative stress, or Janus kinase/signal transducers and activators of transcription signaling and include unexpected genes (e.g., PIK3CG and FOXP1) for which mutation affects treatment response. This study provides a roadmap for using tumor cell biology to factor mutational effects on immune response.

Indexed as

ImmunotherapyMutationAnimalsCarcinoma, Non-Small-Cell LungHumansImmune Checkpoint InhibitorsLung NeoplasmsMiceNeoplasm ProteinsNeoplasmsTreatment OutcomeUrinary Bladder NeoplasmsImmune Checkpoint InhibitorsNeoplasm Proteins

Identifiers

PMID39303028
PMCPMC11414719

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

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.