Serial multiomic analysis in advanced breast cancer: A novel platform to inform therapeutic decisions.
Bibliographic record
Abstract
e13001 Background: We designed and implemented a platform wherein patients with metastatic treatment-refractory cancer would receive individually tailored treatment informed by multi-omic data and iteratively adapted in real-time. Here we describe the workflow of this platform and quantify operational characteristics for a metastatic breast cancer cohort. Methods: Participants with metastatic breast cancer progressing after standard-of-care therapy were enrolled. Sites demonstrating disease progression were targeted for biopsy. Resultant tissue was analyzed by a next-generation sequencing (NGS) solid tumor panel, whole exome sequencing, whole transcriptomic sequencing, multiplex protein analysis of a panel of cancer-relevant proteins and phosphoproteins, and focused immunohistochemistry (IHC) and fluorescence in-situ hybridization (FISH) analysis. Results: Between 1/1/2017 to 12/31/2021, 74 participants were consented and 55 were enrolled. Median age at enrollment was 54 years, most were ECOG 0-1, and received a median of 2 lines of therapy in the metastatic setting. There were 95 biopsies collected from 55 individual participants and analytics were successfully generated in 94% of the cases. At the provider’s request, an integrated clinical, molecular, and cancer biology tumor board (TB) was convened for 25 participants. A total of 21 participants received matched therapy based on clinical parameters coupled to a consideration of biologically informed therapeutic vulnerabilities and 6 had a PFS2/PFS1 > 1.3. Twenty-one participants had a median of 2 repeat progression biopsies (range 2-5) and new alterations with potential therapeutic implications were identified. Repeat biopsies could have led to a new treatment in 11 of the participants, though only 3 received a therapy recommended by the tumor board owing to either rapid disease progression, clinical deterioration, or lack of access to drug. Conclusions: We demonstrated the practical feasibility and initial operating characteristics of a platform designed to comprehensively understand and integrate the clinical and molecular characteristics of a cohort with metastatic progressive breast cancer as they evolve on therapy and to use this information to deliver iteratively tailored therapy in real time. Further, clinical responses were observed in a subset of participants. These findings suggest an opportunity to incorporate this platform earlier in a disease course or to design adaptive clinical trials with access to therapies aimed at targeting new biological alteration.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".