The Metastatic Prostate Cancer project (MPCproject): Translational genomics through direct patient engagement.
Bibliographic record
Abstract
279 Background: While there has been substantial advancement in the genomic understanding of metastatic prostate cancer (MPC), there is still much to be discovered. Additional progress is dependent upon obtaining a large amount of clinically-annotated genomic data. Therefore, we piloted a direct-to-patient nationwide research initiative where patients can contribute their medical records and biospecimens to accelerate research ( mpcproject.org ). Methods: In collaboration with patients and advocacy groups, we have developed a website ( mpcproject.org ). Participants are asked to complete a 17-question survey about their experiences with prostate cancer and an electronic informed consent. All participants receive a saliva kit for germline DNA and blood kit for circulating tumor DNA (ctDNA). Additionally, medical records are collected and archived tissue samples are requested if available. Ultra low pass whole genome sequencing (ULP-WGS) and whole exome sequencing (WES) are performed on the whole blood samples. WES is performed on saliva samples. Genomic, clinical, and patient-reported data will be shared widely with the research community. Aggregate study results will be reported to patients. Results: As of October 2017, 12 pilot patients aged 47-74 from 7 states, provided informed consent. 7 saliva kits, 4 blood kits, and 2 medical records were received. 4 patients were diagnosed with de novo metastatic disease, 8 reported a family history of breast and/or prostate cancer, 6 reported a secondary malignancy. All blood kits were submitted for ULP-WGS and WES. Updated genomic, clinical, and patient-reported data will be presented. Conclusions: We have provided preliminary evidence that partnering directly with MPC patients enabled the remote collection of saliva and blood samples, medical records, and patient-reported data. At the conclusion of the pilot phase, the MPC Project will open enrollment for all men with metastatic and advanced prostate cancer in the US and Canada.
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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 | 0.027 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.038 | 0.007 |
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".