Evidence map›Paper›PMID 41512871›Full record

ArticleCancer cell2026

The ADAPT learning cancer treatment system: ARPA-H's initiative to revolutionize cancer therapy.

Andrea H Bild, Michelle C Sangar, Jasmine A McQuerry, Trey Ideker, Scott Kopetz, Lisa Carey, Aritro Nath, Daniel Marcus, Allison Regier, Naim Rashid and 10 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancer cell, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

20 authors.

Andrea H BildAdvanced Research Projects Agency for Health (ARPA-H), U.S. Department of Health and Human Services, Washington, DC, USA; Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, Duarte, CA, USA. Electronic address: andrea.bild@arpa-h.gov.
Michelle C SangarECS Federal, LLC, in support of the Advanced Research Projects Agency for Health (ARPA-H), U.S. Department of Health and Human Services, Washington, DC, USA.
Jasmine A McQuerryECS Federal, LLC, in support of the Advanced Research Projects Agency for Health (ARPA-H), U.S. Department of Health and Human Services, Washington, DC, USA.
Trey IdekerDepartment of Medicine, University of California, San Diego, La Jolla, CA, USA; Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA; Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA.
Scott KopetzDepartment of Gastrointestinal Medical Oncology, Division of Cancer Medicine, MD Anderson Cancer Center, Houston, TX, USA.
Lisa CareyLineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Aritro NathDepartment of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, Duarte, CA, USA.
Daniel MarcusMallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.
Allison RegierDNAnexus, Mountain View, CA, USA.
Naim RashidLineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Regina BarzilayComputer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA; Jameel Clinic, Massachusetts Institute of Technology, Cambridge, MA, USA.
Eric WinerDepartment of Internal Medicine, Yale School of Medicine, New Haven, CT, USA; Yale Cancer Center, Smilow Cancer Hospital, New Haven, CT, USA.
Ravi SalgiaDepartment of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, Duarte, CA, USA.
Jyoti MalhotraDepartment of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, Duarte, CA, USA.
Andrew GentlesDepartment of Pathology, Stanford University, Stanford, CA, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA; Department of Medicine, Stanford University, Palo Alto, CA, USA.
Kenneth BuetowSchool of Life Sciences, Arizona State University, Tempe, AZ, USA; Center for Evolution and Medicine, Arizona State University, Tempe, AZ, USA; Computational Sciences and Informatics Program for Complex Adaptive Systems, Arizona State University, Tempe, AZ, USA.
Faisal MahmoodDepartment of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
David W MarkmanMarkman Group, LLC, in support of the Advanced Research Projects Agency for Health (ARPA-H), U.S. Department of Health and Human Services, Washington, DC, USA.
James A EddyAvantiqor, LLC, in support of the Advanced Research Projects Agency for Health (ARPA-H), U.S. Department of Health and Human Services, Washington, DC, USA.
ADAPT Consortium

Funding

SToP Cancer SPORE: Developmental Research ProgramP50CA257911 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Jen Jen Yeh · 2022 to 2026
$12.9M
Integrating tumor and stroma to understand and predict treatment responseU01CA274298 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Naim Ur Rashid, Susan Tsai · 2022 to 2026
$4.7M
Targeting regenerative reprogramming in colorectal cancerK08CA279499 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Salvador Alonso Martinez · 2023 to 2026
$1.2M
NCI NIH HHS K08 CA279499NCI NIH HHS P50 CA257911NCI NIH HHS U01 CA274298
6 · The paper itself

Abstract

ADAPT is a nationwide initiative to transform cancer care by detecting and responding to tumor evolution in real time. Integrating multimodal data, interpretable AI, and an evolutionary clinical trial platform, ADAPT predicts emerging resistance traits and guides treatment adjustments as tumors change. A unified national infrastructure enables continuous learning across patients, linking discovery directly to care. By making therapy responsive to tumor changes, ADAPT delivers a scalable model designed to improve outcomes in precision oncology.

Indexed as

NeoplasmsPrecision MedicineHumans

Identifiers

PMID41512871
PMCPMC12912820

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.