Abstract P2-10-34: Development and validation of ClinicoMolecular Triad Classification (CMTC), a platform for breast cancer (BC) prognostic and predictive gene signature portfolios.
Bibliographic record
Abstract
Abstract Background: Numerous gene signatures have claimed prognostic significance in BCs. Each of these gene signatures was designed to answer a specific clinical or biological question, often by dichotomizing the targeted populations into a good and a bad risk group. None of these gene signatures on its own has sufficient degree of complexity to fully characterize this very heterogenous group of diseases, and hence lacks the flexibility to personalize treatments. To exploit the full potential of the genomic approach, we developed an 803-gene molecular classification, termed ClinicoMolecular Triad Classification (CMTC) that categorized BCs into 3 clinical treatment groups (triad) that can serve as a basic framework to guide management. CMTC also provide a detailed “portfolio” of 14 other gene signatures and 19 oncogenic pathways to allow further customization of the treatments. The ability to get CMTC portfolio results at the time of initial diagnosis offers the unique advantage of early treatment planning, including the use of pre-operative chemotherapy to improve breast conservation in selected patients. This study aimed to validate the CMTC classification using an independent BC cohort. Study design/ results: RNA from fine needle aspirates were collected in a prospective BC cohort (n = 340) between 2008 and 2010 at Princess Margaret Hospital and Mount Sinai Hospital, Toronto, we included all newly diagnosed BC patients going for surgery who consented to join the study. DNA microarray analyses were carried out using genome-wide Illumina Human Ref-8 version 3 Beadarrays, which contained >24K oligonucleotide probes. After excluding tumors with low RNA yield (n = 8, success rate 97%), non-invasive cancers (n = 27), insufficient follow-up data (n = 21), CMTC divided the remaining 284 BCs into 3 similar sized groups (triad). At a median follow-up of 32 months (range 6.3–52 months), the short-term recurrence was significantly worse (p = 0.0048) in the poor prognostic groups. This result was similar to using an independent external validation cohort (n = 2100) with long-term follow-up reported before, CMTC outperformed all other gene signatures in predicting prognosis and treatment response. Discussion/conclusion: This prospective validation cohort study demonstrated reproducibility of CMTC in classifying BCs into the three major treatment groups and its prognostic significance. CMTC can be used as a platform to personalize treatments: CMTC-1 BCs (ER+, low proliferation) in general can be treated with surgery and tamoxifen alone. CMTC-2 tumours (ER+, high proliferation) will require additional treatments, including chemotherapy, in addition to tamoxifen; other biologics can be prescribed based on the activities of additional oncogenic pathways. Neo-adjuvant chemotherapy should be considered for CMTC-3 tumours (triple negative and HER2+) with addition of trastuzumab in those that show activation of the HER2 pathway. CMTC portfolio is being further developed into a genomic platform to guide personalized BC treatments.. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P2-10-34.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".