Abstract B034: Decoding the Leukocyte Effect: How Cell Retention Shapes ML Outcomes in Platelet RNA-based Cancer Detection
Bibliographic record
Abstract
Abstract Introduction: Liquid biopsy offers a promising, minimally invasive approach for cancer detection by providing valuable insights into tumor biology. Among the various sources used in liquid biopsy, platelets and their RNA stand out as a unique diagnostic tool, reflecting the body’s systemic response to cancer. However, during laboratory platelet extraction known as platelet washing, white blood cells (WBCs) retention can occur, potentially confounding platelet-derived RNA sequencing data. Materials and methods: To address this challenge, we developed a method to quantify WBC enrichment in platelet RNA-seq datasets. Using the largest publicly available dataset (GEO GSE183635), which includes 2,351 samples from cancer patients, healthy donors, and individuals with benign, non-cancer conditions, we identified 3 sample clusters based on leukocyte marker levels. This allowed us to differentiate two entirely distinct sets of samples, with the lowest and the highest WBC retention, containing 377 matched samples in each set. We then assessed how the leukocyte presence influences the performance of machine learning models for cancer classification for the low and high leukocyte subgroup. Results: Across the full test set (n=453), our model achieved an area under the curve (AUC) of 0.94. Interestingly, in the low-leukocyte subset (n=229), the AUC slightly decreased to 0.93, whereas in the high-leukocyte subset (n=224), it increased to 0.95. Discussion: These results suggest that while leukocyte retention may slightly enhance classification performance, platelets alone provide substantial diagnostic value. Overall, our findings highlight the strong potential of platelet-based liquid biopsy and reveal how understanding leukocyte retention can further refine cancer detection strategies. Citation Format: Michał Sieczczynski, Krzysztof Pastuszak, Anna J. Zaczek, Matthew T. Rondina, Sjors GJG. in 't Veld, Myron G. Best, Anna Supernat. Decoding the Leukocyte Effect: How Cell Retention Shapes ML Outcomes in Platelet RNA-based Cancer Detection [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr B034.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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.
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".