Abstract A67: Improving tissue allocation for research in pediatric solid tumors
Bibliographic record
Abstract
Abstract Introduction: In pediatric cancer, there is an urgent need for research that can identify and validate new therapeutic modalities for pediatric cancers. Diagnostic biopsy samples remain the ideal tissue samples for research and must be collected from surplus biopsy material, which is often extremely limited. Here we describe our efforts to optimize tissue allocation for clinical care and research as a joint effort between Department of Pathology at Boston Children’s Hospital and the Pediatric Solid Tumor Program at the Dana-Farber/Boston Children’s Cancer and Blood Disorders Center. Methods: Oncologists, pathologists, surgeons, interventional radiologists, pathology technical staff, and clinical research coordinators participated in the workflow design. The group agreed to develop a formalized procedure to address these five steps: 1) patient identification and consent, 2) prioritization of research objectives, 3) advance communication of tissue requests to the pathology staff, 4) tissue preparation, and 5) tissue distribution. It was unanimously agreed that all tissue must flow through the pathology department. On or before the day of surgery, clinical research teams sent the pathologist a patient-specific electronic communication indicating research consent and detailing the prioritized disease-specific research requests, including the procedure planned, all tissue types and tissue volumes requested, and details of any special preparation needed, such as avoiding decalcification in bone tumors. The communication was optimized to be clear but brief, with the goal of minimizing the impact to the pathologist’s work load. Pathologists were surveyed about the change in process. Results: Over a five-year period (2013-2018), 662 pediatric DFCI/BCH solid tumor patients have consented to one or more trials that request FFPE, frozen, or fresh tissue. Tumor types represent a spectrum of cases, with many rare and singular diagnoses. Of 1,768 research tissue requests, 1,121 (63%) were fulfilled. Clinical study requests from resection specimens were the most likely to be fulfilled (95% of 390 requests fulfilled), while basic research requests from core biopsies were the least likely to be fulfilled (26% of 255 requests fulfilled). In an anonymous survey, 7 of 7 pathologists report that the process had improved since the introduction of the electronic communication. Conclusions: A collaborative and informed model for tissue allocation is successful in distributing tissue for clinical studies and basic research projects. Our workflows and policies have gained pathologist approval and streamlined our processes. As clinical and research programs evolve, a thoughtful tissue allocation process will facilitate ongoing research. Citation Format: R. Seth Pinches, Catherine Clinton, Abigail Ward, Stephanie C Meyer, Alyaa Al-Ibraheemi, Suzanne Forrest, Gianna R. Strand, Hillary Detert, Anne Piche-Schulman, Kristen Gil, Tamara Restrepo, Rosemarie Tavares-Proulx, Jeffrey Goldsmith, Raja Shaikh, Christopher Weldon, Sanda Alexandrescu, Allison F. O’Neill, Monica Hollowell, Marian H. Harris, Katherine A. Janeway, Brian D. Crompton, Alanna Church. Improving tissue allocation for research in pediatric solid tumors [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr A67.
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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 | 0.057 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".