Abstract A030: Blood-based screening panel for lung cancer based on clonal hematopoietic mutations in tumor infiltrating immune cells
Bibliographic record
Abstract
Abstract Early detection can significantly reduce mortality due to lung cancer. However, the high cost of the currently approved screening protocol has limited its uptake. Presented here is a blood-based screening panel based on clonal hematopoietic mutations. Mutations in tumor cells that inhibit immune destruction have been extensively studied. However, mutations in immune cells that may prevent an effective anti-tumor immune response remain relatively unstudied. Animal model studies suggest that clonal hematopoietic mutations in tumor infiltrating immune cells can modulate cancer progression, representing potential predictive biomarkers. The goal of this study was to determine if the clonal expansion of these mutations in blood samples could predict the occurrence of lung cancer. We identified a set of 98 potentially pathogenic clonal hematopoietic mutations in tumor infiltrating immune cells. A logistic regression machine learning model based on these mutations correctly classified lung cancer and non-cancer blood samples with 94.12% sensitivity (95% Confidence Interval: 92.20-96.04%) and 85.96% specificity (95% Confidence Interval: 82.98-88.95%) in a test set of 578 lung cancer and 545 non-cancer samples from 18 different cohorts. More importantly, 89.98% of the cancer cases were unambiguously predicted with probability of cancer >0.90 and 74.86% of the non-cancer cases with probability of cancer <0.10. These results suggest that it may be possible to develop an accurate blood-based lung cancer screening panel. Unlike most other “liquid biopsies” currently under development, the assay presented here is based on standard sequencing protocols and uses a relatively small number of rationally selected mutations as predictors. Citation Format: Ramu Anandakrishnan, Ryan Shahidi, Andrew Dai, Veneeth Antony. Blood-based screening panel for lung cancer based on clonal hematopoietic mutations in tumor infiltrating immune cells [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2023 Oct 1-4; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2023;11(12 Suppl):Abstract nr A030.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".