Abstract 2273: Unsupervised clustering of multi-omics molecular layers reveals consensus molecular subtypes showing potential therapeutic opportunities for pancreatic cancer
Bibliographic record
Abstract
Abstract Introduction: Pancreatic cancer is a lethal disease showing dismal prognosis and therapeutic resistance. Previous molecular subtypes from genome or transcriptome did not show clinical relevance regarding precision strategy for optimal therapeutic options followed by precise patient classification. This study aims to uncover consensus molecular subtypes from cancer-specific multi-omics data showing clinically relevant therapeutic opportunities. Methods: We performed comprehensive analyses using the dataset from the cancer dependency map (DepMap) project, including cancer-specific molecular characterization with multi-omics data, genome-wide loss-of-function screening using the CRISPR-Cas9 system, and cancer drug sensitivity. The subtype-specific molecular signatures were validated in independent translational cohorts (TCGA-PAAD; n=150, ICGC-PACA-AU; n=461, ICGC-PACA-CA; n=317). Results: Integrative profiling of multi-omics molecular layers (Mutational signature, Copy number alteration, Transcriptome, MicroRNA, Chromatic profile, Proteome, and Metabolome) from pancreatic cancer cell lines (n=59) from the Cancer Cell Line Encyclopedia (CCLE) revealed a total of three cancer-specific molecular subtypes showing distinct tumor biology through all omics layer as well as clinical relevance with unique molecular dependency. Major molecular features of each subtype were reproducible in the validation cohorts. Subtype-specific molecular biomarkers, including mutational signature and metabolites, were identified. Finally, the target drug with subtype-specific genetic dependency was analyzed to provide precision strategy according to distinct subtypes’ molecular characterization. Conclusions: Integrative profiling from multi-omics molecular layers revealed precision strategies based on cancer-specific molecular subtypes of pancreatic cancer in terms of tumor classification and discriminative therapeutic opportunities. Prospective translational studies companion with clinical trials based on cancer-specific molecular subtypes is mandatory to establish the precision strategy for managing pancreatic cancer. Citation Format: Sung Hwan Lee, Jiyeon Park. Unsupervised clustering of multi-omics molecular layers reveals consensus molecular subtypes showing potential therapeutic opportunities for pancreatic cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2273.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".