The BC Generations Project as a tumor tissue resource for cancer research.
Bibliographic record
Abstract
e22512 Background: Population-based cohort studies can be a resource for tumor specimens, annotated with demographic, lifestyle, and health history data, that support innovative studies of cancer. These data are increasingly important for clinical studies as they can be useful predictors of prognosis and treatment response (1,2) Our aim was to establish and test a process for accessing tumor samples, held at pathology laboratories around British Columbia (BC), for participants of the BC Generations Project (BCGP). Methods: Through the BC Cancer Registry, pathology reports linked with each BCGP solid cancer case were manually reviewed to extract information (e.g., location of pathology laboratory, sample type (i.e., biopsy or excision), and accession number) needed to request tumor samples. From among the successfully abstracted pathology reports, we randomly selected a total of 200 breast, lung, bladder, and pancreatic cancer cases for which to attempt retrieval of formalin-fixed, paraffin-embedded (FFPE) blocks from pathology laboratories across BC. We selected these cancer sites as they capture a representative range of cancer characteristics (e.g., tumor sizes) with which to evaluate the effectiveness of our access process. Results: We successfully identified pathology reports for 1,100 (93%) of the 1,180 incident solid cancer cases diagnosed in BCGP as of 2019 and we successfully retrieved 183 (92%) of the 200 FFPE that we requested from pathology laboratories. No important differences in retrieval rates by cancer site, sample location (Greater Vancouver vs. Outside Greater Vancouver), sample type (biopsy vs. excision) or year of diagnosis were identified. Conclusions: This linkage exercise demonstrates the tremendous potential for BCGP to serve as a population-based resource for highly annotated tumor tissue samples. A text mining solution recently implemented by the Registry will allow us to automate the process for data abstraction and should capture pathology reports for 100% of all newly diagnosed BCGP cancer cases moving forward. This will further enhance the utility of BCGP as a high-quality tumor tissue research resource. References: Lugo D, Pulido AL, Mihos CG, Issa O, Cusnir M, Horvath SA, Lin J, Santana O. The effects of physical activity on cancer prevention, treatment and prognosis: A review of the literature. Complement Ther Med 2019; 44:9–13. Peppone LJ, Mustian KM, Morrow GR, Dozier AM, Ossip DJ, Janelsins MC, Sprod LK, McIntosh S. The Effect of Cigarette Smoking on Cancer Treatment–Related Side Effects. The Oncologist 2011; 16:1784–92.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".