Evidence map›Paper›PMID 32405608›Full record

ArticleJCO precision oncology2020

Model Combining Tumor Molecular and Clinicopathologic Risk Factors Predicts Sentinel Lymph Node Metastasis in Primary Cutaneous Melanoma.

Domenico Bellomo, Suzette M Arias-Mejias, Chandru Ramana, Joel B Heim, Enrica Quattrocchi, Sindhuja Sominidi-Damodaran, Alina G Bridges, Julia S Lehman, Tina J Hieken, James W Jakub and 11 more

Registry-linked trialAbstract read
In one paragraph

Article in JCO precision oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06566404 (Gene Expression Profiles and ctDNA for Risk Stratification in Patients With Melanoma Eligible for Lymph Node Sentinel Biopsy), which is not on this map. Cited by 55 papers.

0numbers the graph read from it
0cells of the map it votes in
55citing 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.

NCT06566404 not yet recruitingnot on this mapstarted 2024, after this paper: background citation

Gene Expression Profiles and ctDNA for Risk Stratification in Patients With Melanoma Eligible for Lymph Node Sentinel Biopsy (CORRESPOND): an Observational Prospective Study.

TypeobservationalSponsorEuropean Institute of OncologyRan2024 to 2028Enrolled200ConditionsMelanoma
3 · Its place in the literature

Who cites it

55 citing papers in PubMed.

  1. Article
  2. Review
  3. A deep learning-clinical nomogram hybrid for predicting sentinel lymph node metastasis in melanoma.Journal of the European Academy of Dermatology and Venereology : JEADV · 2026
    Article
  4. Article
  5. Precision medicine in cutaneous melanoma-A comprehensive review.Journal of the European Academy of Dermatology and Venereology : JEADV · 2026
    Review
  6. Current Surgical Management for Acral Melanoma.Current treatment options in oncology · 2025
    Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Metabolic insights into tumor lymph node metastasis in melanoma.Cellular oncology (Dordrecht, Netherlands) · 2024
    Review
  18. Review
  19. Article
  20. 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

21 authors.

Domenico BellomoSkylineDx B.V., Rotterdam, NL.
Suzette M Arias-MejiasMayo Clinic, Rochester, MN, USA.
Chandru RamanaMayo Clinic, Rochester, MN, USA.
Joel B HeimMayo Clinic, Rochester, MN, USA.
Enrica QuattrocchiMayo Clinic, Rochester, MN, USA.
Sindhuja Sominidi-DamodaranMayo Clinic, Rochester, MN, USA.
Alina G BridgesMayo Clinic, Rochester, MN, USA.
Julia S LehmanMayo Clinic, Rochester, MN, USA.
Tina J HiekenMayo Clinic, Rochester, MN, USA.
James W JakubMayo Clinic, Rochester, MN, USA.
Mark R PittelkowMayo Clinic, Scottsdale, AZ, USA.
David J DiCaudoMayo Clinic, Scottsdale, AZ, USA.
Barbara A PockajMayo Clinic, Scottsdale, AZ, USA.
Jason C SluzevichMayo Clinic, Jacksonville, FL, USA.
Mark A CappelMayo Clinic, Jacksonville, FL, USA.
Sanjay P BagariaMayo Clinic, Jacksonville, FL, USA.
Charles PerniciaroGulf Coast Dermatopathology Laboratory, Tampa, FL, USA.
Félicia J Tjien-FoohSkylineDx B.V., Rotterdam, NL.
Martin H van VlietSkylineDx B.V., Rotterdam, NL.
Jvalini DwarkasingSkylineDx B.V., Rotterdam, NL.
Alexander MevesMayo Clinic, Rochester, MN, USA.

Funding

Mayo Clinic Center for Translational Science ActivitiesUL1TR000135 · NCATS · MAYO CLINIC ROCHESTER · PI KHOSLA, SUNDEEP · 2012 to 2015
$41.2M
Precision Medicine by Targeting Cell Adhesion in MelanomaK08CA215105 · NCI · MAYO CLINIC ROCHESTER · PI MEVES, ALEXANDER · 2017 to 2021
$854k
NCATS NIH HHS UL1 TR000135NCI NIH HHS K08 CA215105
6 · The paper itself

Abstract

purposeMore than 80% of patients who undergo sentinel lymph node (SLN) biopsy have no nodal metastasis. Here we describe a model that combines clinicopathologic and molecular variables to identify patients with thin and intermediate thickness melanomas who may forgo the SLN biopsy procedure due to their low risk of nodal metastasis. PATIENTS AND

methodsGenes with functional roles in melanoma metastasis were discovered by analysis of next generation sequencing data and case control studies. We then used PCR to quantify gene expression in diagnostic biopsy tissue across a prospectively designed archival cohort of 754 consecutive thin and intermediate thickness primary cutaneous melanomas. Outcome of interest was SLN biopsy metastasis within 90 days of melanoma diagnosis. A penalized maximum likelihood estimation algorithm was used to train logistic regression models in a repeated cross validation scheme to predict the presence of SLN metastasis from molecular, clinical and histologic variables.

resultsExpression of genes with roles in epithelial-to-mesenchymal transition (glia derived nexin, growth differentiation factor 15, integrin β3, interleukin 8, lysyl oxidase homolog 4, TGFβ receptor type 1 and tissue-type plasminogen activator) and melanosome function (melanoma antigen recognized by T cells 1) were associated with SLN metastasis. The predictive ability of a model that only considered clinicopathologic or gene expression variables was outperformed by a model which included molecular variables in combination with the clinicopathologic predictors Breslow thickness and patient age; AUC, 0.82; 95% CI, 0.78-0.86; SLN biopsy reduction rate of 42% at a negative predictive value of 96%.

conclusionA combined model including clinicopathologic and gene expression variables improved the identification of melanoma patients who may forgo the SLN biopsy procedure due to their low risk of nodal metastasis.

Identifiers

PMID32405608
PMCPMC7220172

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Registered trials

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.