Evidence map›Paper›PMID 41379552›Full record

ArticleCancer research2026

Transcriptomic Plasticity Is a Hallmark of Metastatic Pancreatic Cancer.

Alejandro Jiménez-Sánchez, Sitara Persad, Akimasa Hayashi, Shigeaki Umeda, Roshan Sharma, Yubin Xie, Arnav Mehta, Wungki Park, Ignas Masilionis, Tinyi Chu and 9 more

Abstract read
In one paragraph

Article in Cancer research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. 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

19 authors.

Alejandro Jiménez-Sánchez *Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, New York.ORCID 0000-0003-1879-7057
Sitara Persad *Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, New York.ORCID 0009-0009-0422-3280
Akimasa Hayashi *The David M Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, New York.ORCID 0000-0002-7424-8127
Shigeaki UmedaThe David M Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, New York.ORCID 0000-0001-5693-1515
Roshan SharmaComputational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, New York.ORCID 0000-0001-9886-009X
Yubin XieComputational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, New York.ORCID 0000-0003-2542-2544
Arnav MehtaMassachusetts General Hospital Cancer Center , Harvard Medical School (HMS), Boston, Massachusetts.ORCID 0000-0002-0313-2848
Wungki ParkThe David M Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, New York.ORCID 0000-0002-8006-3102
Ignas MasilionisComputational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, New York.ORCID 0000-0001-7937-9014
Tinyi ChuComputational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, New York.ORCID 0000-0003-3369-6692
Feiyang ZhuDepartment of Computer Science, Fu Foundation School of Engineering & Applied Science, Columbia University, New York, New York.ORCID 0009-0007-7487-3348
Jungeui HongThe David M Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, New York.ORCID 0009-0009-5998-2102
Ronan ChaligneComputational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, New York.ORCID 0000-0003-4332-3291
Eileen M O'ReillyThe David M Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, New York.ORCID 0000-0002-8076-9199
Linas MazutisComputational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, New York.ORCID 0000-0002-5552-6427
Tal NawyComputational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, New York.ORCID 0000-0003-3720-3699
Itsik Pe'erDepartment of Computer Science, Fu Foundation School of Engineering & Applied Science, Columbia University, New York, New York.ORCID 0000-0002-6128-7231
Christine A Iacobuzio-DonahueThe David M Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, New York.ORCID 0000-0002-4672-3023
Dana Pe'erComputational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, New York.ORCID 0000-0002-9259-8817

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI SELWYN M VICKERS · 1985 to 2026
$347.4M
The Center for Tumor-Immune Systems Biology at MSKCCU54CA274492 · NCI · SLOAN-KETTERING INST CAN RESEARCH · PI Dana Pe'er · 2022 to 2026
$16.3M
Total Neoadjuvant Therapy (TNT) for Borderline Resectable and Locally Advanced Pancreatic AdenocarcinomaP50CA257881 · NCI · SLOAN-KETTERING INST CAN RESEARCH · PI Christine A Iacobuzio-Donahue · 2022 to 2026
$14.3M
Transition to Metastatic State: Lung Cancer, Pancreatic Cancer and Brain MetastasisU2CCA233284 · NCI · SLOAN-KETTERING INST CAN RESEARCH · PI PE'ER, DANA · 2018 to 2022
$12.9M
Interrogating the Evolutionary Dynamics of Cancer for Clinical Benefit and ActionabilityR35CA220508 · NCI · SLOAN-KETTERING INST CAN RESEARCH · PI IACOBUZIO-DONAHUE, CHRISTINE A · 2018 to 2024
$6.9M
American Association for Cancer Research (AACR) AACR-CRUK Transatlantic FellowshipHoward Hughes Medical Institute (HHMI)National Institutes of Health (NIH) P30 CA08748National Institutes of Health (NIH) P50 CA25788National Institutes of Health (NIH) R35 CA220508-03NCI NIH HHS P30 CA008748NCI NIH HHS P50 CA257881NCI NIH HHS R35 CA220508NCI NIH HHS U2C CA233284NCI NIH HHS U54 CA274492
6 · The paper itself

Abstract

Metastasis is the leading cause of cancer deaths. To develop strategies for intercepting metastatic progression, a better understanding of how tumor cells adapt to vastly different organ contexts is needed. To investigate this question, a single-cell transcriptomic atlas of primary tumors and diverse metastatic samples (liver, omentum, peritoneum, stomach wall, lymph node, and diaphragm) from a patient with pancreatic ductal adenocarcinoma who underwent rapid autopsy was generated. Using unsupervised archetype analysis, both shared and site-specific gene programs were identified, including lipid metabolism and gastrointestinal programs prevalent in peritoneal and stomach wall lesions, respectively. We developed phylogenetic inference from copy-number alterations in single-cell sequencing observations (PICASSO) as a probabilistic approach for inferring clonal phylogeny from single-cell and matched whole-exome sequencing data. Comparison of PICASSO-generated clonal structure with phenotypic signatures revealed that pancreatic cancer cells adapted to local environments with minimal contribution from clonal genotype. Our results suggest a paradigm whereby strong environmental effects are imposed on highly plastic cancer cells during metastatic dissemination. SIGNIFICANCE: Single-cell transcriptional profiling of primary tumor and metastases from rapid autopsy samples of an individual with pancreatic cancer, combined with probabilistic clonal inference by PICASSO, reveals substantial transcriptomic plasticity in metastatic cells. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI .

Indexed as

Carcinoma, Pancreatic DuctalPancreatic NeoplasmsTranscriptomeCell PlasticityDNA Copy Number VariationsExome SequencingGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMaleNeoplasm MetastasisSingle-Cell Analysis

Identifiers

PMID41379552
PMCPMC13044532

What OpenQuestion holds

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LicenceCC BY
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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.