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Record W4362521465 · doi:10.1371/journal.pone.0269324

InterMEL: An international biorepository and clinical database to uncover predictors of survival in early-stage melanoma

2023· article· en· W4362521465 on OpenAlexaff
Irene Orlow, Keimya Sadeghi, Sharon N. Edmiston, Jessica M. Kenney, Cecilia Lezcano, James S. Wilmott, Anne Ε. Cust, Richard A. Scolyer, Graham J. Mann, Tim K. Lee, Hazel Burke, Valerie Jakrot, Ping Shang, Peter M. Ferguson, Tawny W. Boyce, Jennifer S. Ko, Peter Ngo, Pauline Funchain, Judy R. Rees, Kelli O’Connell, Honglin Hao, Eloise Parrish, Kathleen Conway, Paul B. Googe, David W. Ollila, Stergios J. Moschos, Eva Hernando, Douglas Hanniford, Diana Argibay, Christopher I. Amos, Jeffrey E. Lee, Iman Osman, Li Luo, Pei Fen Kuan, Arshi Aurora, Bonnie E. Gould Rothberg, Marcus Bosenberg, Meg R. Gerstenblith, Cheryl L. Thompson, Paul N. Bogner, Ivan Gorlov, Sheri L. Holmen, Elise K. Brunsgaard, Yvonne M. Saenger, Ronglai Shen, Venkatraman Seshan, Eduardo Nagore, Marc S. Ernstoff, Klaus J. Busam, Colin B. Begg, Nancy E. Thomas, Marianne Berwick

Bibliographic record

VenuePLoS ONE · 2023
Typearticle
Languageen
FieldMedicine
TopicCutaneous Melanoma Detection and Management
Canadian institutionsBC Cancer Agency
FundersNational Institute of General Medical SciencesUniversity of Texas MD Anderson Cancer CenterNational Institutes of HealthRobert E. Leet and Clara Guthrie Patterson TrustCycle for SurvivalUniversity of SydneyHuntsman Cancer InstituteFaculty of Medicine and Health, University of SydneyMarie-Josée and Henry R. Kravis Center for Molecular OncologyMemorial Sloan-Kettering Cancer CenterAustralian Research CouncilChar and Chuck Fowler Family FoundationMedical Research CouncilRoswell Park Alliance Foundation, Roswell Park Cancer InstituteNational Cancer InstituteMelanoma Research AllianceCleveland FoundationMelanoma Institute AustraliaNational Health and Medical Research CouncilFoundation for the National Institutes of Health
KeywordsBiorepositoryDNA methylationMelanomaOncologyStage (stratigraphy)MedicineMethylationBiologyPathologyBioinformaticsCancer researchInternal medicineGene expressionDNAGeneGenetics

Abstract

fetched live from OpenAlex

INTRODUCTION: We are conducting a multicenter study to identify classifiers predictive of disease-specific survival in patients with primary melanomas. Here we delineate the unique aspects, challenges, and best practices for optimizing a study of generally small-sized pigmented tumor samples including primary melanomas of at least 1.05mm from AJTCC TNM stage IIA-IIID patients. We also evaluated tissue-derived predictors of extracted nucleic acids' quality and success in downstream testing. This ongoing study will target 1,000 melanomas within the international InterMEL consortium. METHODS: Following a pre-established protocol, participating centers ship formalin-fixed paraffin embedded (FFPE) tissue sections to Memorial Sloan Kettering Cancer Center for the centralized handling, dermatopathology review and histology-guided coextraction of RNA and DNA. Samples are distributed for evaluation of somatic mutations using next gen sequencing (NGS) with the MSK-IMPACTTM assay, methylation-profiling (Infinium MethylationEPIC arrays), and miRNA expression (Nanostring nCounter Human v3 miRNA Expression Assay). RESULTS: Sufficient material was obtained for screening of miRNA expression in 683/685 (99%) eligible melanomas, methylation in 467 (68%), and somatic mutations in 560 (82%). In 446/685 (65%) cases, aliquots of RNA/DNA were sufficient for testing with all three platforms. Among samples evaluated by the time of this analysis, the mean NGS coverage was 249x, 59 (18.6%) samples had coverage below 100x, and 41/414 (10%) failed methylation QC due to low intensity probes or insufficient Meta-Mixed Interquartile (BMIQ)- and single sample (ss)- Noob normalizations. Six of 683 RNAs (1%) failed Nanostring QC due to the low proportion of probes above the minimum threshold. Age of the FFPE tissue blocks (p<0.001) and time elapsed from sectioning to co-extraction (p = 0.002) were associated with methylation screening failures. Melanin reduced the ability to amplify fragments of 200bp or greater (absent/lightly pigmented vs heavily pigmented, p<0.003). Conversely, heavily pigmented tumors rendered greater amounts of RNA (p<0.001), and of RNA above 200 nucleotides (p<0.001). CONCLUSION: Our experience with many archival tissues demonstrates that with careful management of tissue processing and quality control it is possible to conduct multi-omic studies in a complex multi-institutional setting for investigations involving minute quantities of FFPE tumors, as in studies of early-stage melanoma. The study describes, for the first time, the optimal strategy for obtaining archival and limited tumor tissue, the characteristics of the nucleic acids co-extracted from a unique cell lysate, and success rate in downstream applications. In addition, our findings provide an estimate of the anticipated attrition that will guide other large multicenter research and consortia.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.318

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.063
GPT teacher head0.313
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2023
Admission routes1
Has abstractyes

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