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Record W4386317625 · doi:10.1093/dote/doad052.161

348. IDENTIFYING NOVEL MOLECULAR SUBTYPES OF ESOPHAGEAL ADENOCARCINOMA USING LASER CAPTURE MICRODISSECTED RNA-SEQ SAMPLES

2023· article· en· W4386317625 on OpenAlexaff
Gavin W. Wilson, James Cotton, Frances Allison, Yvonne Bach, Jonathan Allen, Gail Darling, Elena Elimova, Sangeetha Kalimuthu, Jonathan Yeung

Bibliographic record

VenueDiseases of the Esophagus · 2023
Typearticle
Languageen
FieldMedicine
TopicEsophageal Cancer Research and Treatment
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsLaser capture microdissectionMedicineMicrodissectionEsophagusGene expressionBiopsyPathologyAdenocarcinomaRNA-SeqGeneGene expression profilingImmunohistochemistryBarrett's esophagusCohortCancer researchTranscriptomeInternal medicineCancerBiologyGenetics

Abstract

fetched live from OpenAlex

Abstract Background Treatment options for esophageal adenocarcinoma (EAC) are limited by a lack of disease stratification methods. In other cancers, gene expression profiling has been successfully used to identify prognostically-relevant and treatment-susceptible molecular subtypes. Previous efforts to subtype EAC have been hindered by low biopsy and resection tumour cellularity. We hypothesize that laser-capture microdissection (LCM) tumour cell enrichment will permit the classification EAC tumours into multiple molecular subtypes that will be important for future therapeutic strategies. Methods Treatment naïve patient samples (N = 52) were collected from primary biopsies (N = 37), resections (N = 10) and metastatic biopsies (N = 5). Samples were laser-capture microdissected to enrich tumour cells followed by total RNA-seq. Gene expression was quantified using salmon and non-negative matrix factorization with 10 components was used to identify gene expression programs. The components identified were validated on a publicly available normal tissue cohort (N = 13 esophagus, gastric, intestinal, Barrett’s samples) and the TCGA EAC cohort (N = 80). We applied NMF with 4 components to the normal tissue (K = cohort) to identify normal tissue gene signatures to validate our RNA-seq data. Results We verified that our RNA-seq samples were depleted for normal tissue using the four NMF signatures from normal tissue cohort and, as expected, our samples were depleted for gene expression from the gastric and esophagus tissues compared to TCGA. Next, we explored the gene expression programs from our tumour samples and identified seven tumour-intrinsic components and three tumour microenvironmental components. The top 50 genes from each tumour intrinsic components were used with consensus cluster plus to identify K = 6 subtypes (Figure 1). Interestingly, two of these subtypes would be difficult to separate from normal tissue contamination without the use of LCM. Conclusion We have successfully used LCM to deplete normal tissue gene expression from our esophageal adenocarcinoma cohort and identified six molecular subtypes. We are currently evaluating the treatment and clinical implications of these subtypes and aiming to accumulate an additional (N = 50) EAC RNA-seq samples.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.039
GPT teacher head0.318
Teacher spread0.279 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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