Abstract A036: Plasma extracellular RNA for the detection of pancreatic cancer
Bibliographic record
Abstract
Abstract Pancreatic ductal adenocarcinoma (PDA) is expected to become the second leading cause of cancer-related death by 2030. Patients are often diagnosed at the metastatic stage, at which point treatments are limited and have non-uniform efficacy in patients. Advances in transcriptomic subtyping of tumor tissue can segregate patients into two broad subtypes, Classical and Basal-like, which are predictive of survival and response to first-line chemotherapy. However, tumor tissue is often difficult or impossible to obtain, particularly in patients with advanced disease, hindering the clinical implementation of transcriptomic subtyping. Profiling the extracellular RNA (exRNA) content in plasma provides an opportunity for a non-invasive readout of the tumor transcriptome. Here, we aim to establish the feasibility of liquid biopsy—particularly, plasma exRNA-profiling—for the diagnosis and subtyping of PDA. Extracellular vesicle (EV) encapsulated exRNA was isolated from 1mL of frozen plasma collected between 2017-2020 from a pilot cohort of 8 patients with stage III-IV PDA and processed alongside plasma from 6 healthy volunteers. UMI-tagged libraries were constructed from low-input exRNA to generate sequencing data. Gene body coverage analysis indicated that exRNAs consisted of intact full-length transcripts. Of these transcripts, we found that there was a median of 34,975 genes encoded by exRNAs, deriving mostly from protein-coding regions. Most genes captured by exRNA sequencing had low expression. Comparing between groups, we detected a significantly greater number of genes and novel splicing junctions in PDA patients. Of the cancer-specific transcripts, differential expression analysis revealed significant upregulation of membrane organelle and RNA-processing related pathways. PDA exRNA profiles did not completely recapitulate the bulk tumor transcriptome, suggesting specific mechanisms for EV encapsulation of RNAs and exRNA release. To classify tumor subtypes by plasma exRNA sequencing, we identified an exRNA-specific gene panel from bulk tumor transcriptome signatures. Using this gene panel, tumor subtype was correctly inferred from 6/7 PDA patients. A combined gene panel derived from all tumor-associated exRNA signatures distinguished 7/8 PDA patients from healthy controls. In conclusion, plasma exRNA recapitulated elements of the tumor transcriptome and has utility in PDA diagnosis. The establishment of diagnostic markers will be further investigated in the expanded study cohort comprised of 91 unique stage III-IV PDA donors with plasma collected at baseline, 150 stage III-IV PDA donors with plasma banked at timepoints throughout chemotherapy treatment, and 100 age- and sex-matched non-PDA controls donors. Leveraging the study cohort, future work will also investigate plasma exRNA signatures as prognostic markers to predict treatment outcomes and monitor treatment responses in patients with advanced PDA. Citation Format: Linsey Gong, Ling Xi, Yuanchang Fang, Karen Ng, Gun Ho Jang, Amy X. Zhang, Julie M. Wilson, Stephanie Ramotar, Jennifer J. Knox, Grainne M. O'Kane, Steven Gallinger, Hansen H. He, Faiyaz Notta. Plasma extracellular RNA for the detection of pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr A036.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".