Evidence map›Paper›PMID 42864412›Full record

ArticleNature medicine2026

A health-system-scale, episode-resolved multimodal atlas of cancer cachexia.

Venise Jan Castillon, Amy X Xie, Sonia Boscenco, Ethan Tse, Neil Ruthen, Jamie Wang, Ziad Bakouny, Michele Waters, Chenlian Fu, Christopher Fong and 12 more

Abstract read
PubMed Publisher
In one paragraph

Article in Nature medicine, 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

22 authors.

Venise Jan Castillon *Computational Oncology Service, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Amy X Xie *Computational Oncology Service, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.ORCID http://orcid.org/0000-0002-1441-7849
Sonia BoscencoComputational Oncology Service, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Ethan TseComputational Oncology Service, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Neil RuthenComputational Oncology Service, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.ORCID http://orcid.org/0000-0002-4409-9503
Jamie WangComputational Oncology Service, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.ORCID http://orcid.org/0000-0001-8725-5601
Ziad BakounyComputational Oncology Service, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.ORCID http://orcid.org/0000-0003-1906-5704
Michele WatersComputational Oncology Service, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.ORCID http://orcid.org/0000-0002-5416-5172
Chenlian FuComputational Oncology Service, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Christopher FongComputational Oncology Service, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Mustafa SalehDana-Farber Cancer Institute, Boston, MA, USA.ORCID http://orcid.org/0000-0003-2757-113X
Tarek BaroudDana-Farber Cancer Institute, Boston, MA, USA.
Wungki ParkDepartment of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.ORCID http://orcid.org/0000-0002-8006-3102
Urvi A ShahDepartment of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.ORCID http://orcid.org/0000-0001-8419-1091
A Ari HakimiDepartment of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.ORCID http://orcid.org/0000-0002-0930-8824
Puneeth IyengarDepartment of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.ORCID http://orcid.org/0000-0003-3740-7915
Justin JeeComputational Oncology Service, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.ORCID http://orcid.org/0000-0001-9783-0139
Eileen WhiteRutgers Cancer Institute, Rutgers University, New Brunswick, NJ, USA.ORCID http://orcid.org/0000-0003-2961-3065
Alexander GusevDana-Farber Cancer Institute, Boston, MA, USA.ORCID http://orcid.org/0000-0002-7980-4620
Tobias JanowitzCold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.ORCID http://orcid.org/0000-0002-7820-3727
Marcus D GoncalvesHolman Division of Endocrinology, Diabetes & Metabolism, Department of Medicine and Radiation Oncology, NYU Langone Health, New York, NY, USA.ORCID http://orcid.org/0000-0002-0784-9248
Ed ReznikComputational Oncology Service, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA. reznike@mskcc.org.ORCID http://orcid.org/0000-0002-6511-5947

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancer cachexia is a wasting syndrome with outsized impact on morbidity and mortality, yet its drivers and systemic consequences remain poorly defined. In this study, we repurposed longitudinal clinicogenomic data from 52,236 patients with cancer profiled by MSK-IMPACT (2010-2024) at Memorial Sloan Kettering Cancer Center to define episode-resolved trajectories of consensus-defined cachectic weight loss and linked these events to outcomes, serology and tumor genotypes. Cachectic episodes clustered near periods of disease progression and were associated with inferior outcomes in nearly all cancer types. Across diverse malignancies, cachectic weight loss episodes exhibited a consistent serologic signature characterized by lower albumin and hemoglobin and higher levels of liver enzymes. Numerous somatic tumor genotypes, including TP53 mutation across several diseases, were also associated with more frequent onset of cachectic episodes. Motivated by these observations, we developed a multivariate model to predict impending risk of cachectic weight loss in patients with lung, colorectal and prostate cancer, validated in an independent, external cohort from Dana-Farber Cancer Institute (n = 5,785). Routine clinicogenomic data are, therefore, a scalable resource for discovery in cancer cachexia.

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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.