Evidence map›Paper›PMID 42373221›Full record

ArticleCancer genomics & proteomics

Integrative Genomic and Clinical Profiling of Colorectal Cancer Liver Metastases to Guide Personalized Surgery and Liver Transplantation.

Dimitrios Moris, Brian Nguyen, Alexander Kroemer, Ioannis A Ziogas, Anastasios D Giannou, Thomas M Fishbein, Yuri S Genyk, Piyush Gupta

Abstract read
In one paragraph

Article in Cancer genomics & proteomics. 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.

Dimitrios MorisMedStar Georgetown Transplant Institute, Washington, DC, U.S.A.; dimmoris@yahoo.com.
Brian NguyenMedStar Georgetown Transplant Institute, Washington, DC, U.S.A.
Alexander KroemerMedStar Georgetown Transplant Institute, Washington, DC, U.S.A.
Ioannis A ZiogasDepartment of Surgery, Division of Transplant Surgery, University of Colorado Anschutz Medical Center and Children's Hospital Colorado, Aurora, CO, U.S.A.
Anastasios D GiannouDepartment of Surgery, University of British Columbia, Vancouver, Canada.
Thomas M FishbeinMedStar Georgetown Transplant Institute, Washington, DC, U.S.A.
Yuri S GenykMedStar Georgetown Transplant Institute, Washington, DC, U.S.A.
Piyush GuptaMedStar Georgetown Transplant Institute, Washington, DC, U.S.A.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND/

aimColorectal cancer liver metastases (CRLM) remain a leading cause of cancer-related mortality. Although hepatic resection is the only established curative option, recurrence exceeds 60%, underscoring substantial biologic heterogeneity. Liver transplantation (LT) has re-emerged for highly selected patients with unresectable CRLM, but optimal biologic selection criteria remain undefined. This study integrates genomic and clinical data to develop a biologically grounded framework for surgical and transplant decision-making. MATERIALS AND

methodsThe Memorial Sloan Kettering 2017 metastatic colorectal cancer cohort was analyzed using cBioPortal-formatted clinical, genomic, and survival data. The study included patients with liver-only metastatic presentation. Genomic variables comprised

resultsAmong 503 patients, molecular-risk groups demonstrated distinct survival (5-year OS: 74.5%, 56.6%, and 49.8%;

conclusionMolecular-risk stratification and integrated modeling identify clinically meaningful prognostic groups in CRLM. These findings support incorporation of genomic profiling into precision surgical and transplant evaluation, while emphasizing the need for prospective validation.

Indexed as

Biomarkers, TumorColorectal NeoplasmsGenomicsLiver NeoplasmsLiver TransplantationPrecision MedicineAgedFemaleHumansMaleMiddle AgedMutationBiomarkers, Tumorcolorectal liver metastasesdecision-makingGenomicsmachine learningmultiomics

Identifiers

PMID42373221
PMCPMC13321708

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.