Abstract 5534: Building the organization framework for biopsy-driven translational research: The Quebec Clinical Research Organization in Cancer (Q-CROC) experience
Bibliographic record
Abstract
Abstract Introduction: The success of personalized medicine in oncology relies on translational research efforts to identify biomarkers that will influence clinical management. The discovery and validation of biomarkers is a concerted effort requiring an organizational framework that is often underestimated. The Quebec Clinical Research Organization in Cancer (Q-CROC) consortium is a multi-disciplinary and multi-institutional group of scientists and clinicians devoted to integrating and enhancing translational and clinical research capacity in Quebec. We describe here the organizational framework driving a multicenter, prospective study to identify biomarkers of clinical resistance to first-line therapy in metastatic colorectal cancer (NCT00984048, Q-CROC-01). Results: The Q-CROC consortium has put in place an organizational infrastructure to support the activities and operations of its translational projects. We identified and addressed several critical issues during the course of the Q-CROC-01 translational project that were also common to our subsequent biomarker-driven trial in lymphoma (Q-CROC-02, NCT01238692) and breast cancer (Q-CROC-03, NCT01276899). Examples of these issues include: (i) feasibility and burden of tissue collection at participating sites, (ii) limiting pre-analytical variability in blood and tissue specimens for functional downstream applications, (iii) verification of tumor content on biopsy specimens, (iv) tracking sample flow, (v) integration of clinical data with discovery platforms, and (vi) engaging participation throughout all steps of the project. In part to address the above issues, we established five operational Cores: clinical, biobank, biospecimen processing, bioanalytical and bioinformatic. A further challenge was the integration between these Cores, who for the most part operated in silos. We observed that a critical element to unify all components of the consortium was a scientific project management team, consisting of dedicated individuals regularly interacting with each Core to ensure that objectives were aligned and deliverables were met. This academic framework for translational research may be comparable to that of multicenter clinical trials undertaken by industry, but some challenges, including financial and time constraints, data sharing and IP agreements, and engagement of its members, may be more palpable in the academic setting. Conclusion: Infrastructure science is underestimated and under-reported in translational cancer research and is crucial to the success of any large-scale biomarker discovery effort. Our experience with three multi-institutional biomarker-driven trials is that progress hinges upon the availability of an infrastructure that is not only the sum of its parts but that provides a concrete link between each component. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 5534. doi:1538-7445.AM2012-5534
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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 | 0.213 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.023 | 0.008 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".