AI-powered multi-omic signature to predict treatment response of patients with colorectal cancer liver metastasis.
Bibliographic record
Abstract
252 Background: Colorectal cancer (CRC) is the third leading cause of cancer-related deaths in North America with over 50% of CRC patients developing liver metastases (LM) and 90% will die from metastatic disease. CRCLM are categorized into two main histological growth patterns (HGP) lesions: Desmoplastic (DHGP) and Replacement (RHGP). We have previously published that HGPs display distinct patterns of vascularization, local invasion, growth and response to treatment. Resected CRCLM patients with predominantly DHGP metastasis (~45%) receiving anti-VEGF therapy and chemotherapy have more than double the 5-year overall survival compared to patients with RHGP (~55%) who have received the same treatment. We are therefore treating patients that will not respond and should in fact be managed differently. Currently, there are no available biomarkers or stratification tools that predict colorectal cancer liver metastasis response to therapy. Methods: In collaboration with My Intelligent Machines (MIMs’), we used a proprietary GI2 (Genetic Interaction Graph Inference) algorithm inferring genetic interactions. We developed an AI based pipeline to develop a multi-modality signature, based on blood test analysis, to identify CRCLM patients who will 1) respond to Angiogenic Inhibitor-based therapies and 2) monitor the development of drug resistance (non-responders). Results: We obtained transcriptomic (tumor tissue) as well as proteomic data (liquid biopsy: Extracellular vesicle cargo), linked to clinical data from 40 chemonaïve patients, and fed the data into MIMs platform (BioMark). The software was able to identify differentially expressed genes common to the top differentially enriched proteins leading to a robust liquid biopsy biomarker signature distinguishing DHGP and RHGP. Furthermore, our data demonstrates a unique multi-modality signature providing biological insight for therapeutic failures and/or success. Conclusions: We will use these signatures to not only predict response to treatment but also decipher the molecular mechanisms driving these two phenotypes and identify unique targets. It is expected that this knowledge will lead to optimization of current treatment strategies, allowing for a precision therapy approach to the management of metastatic disease and cost saving for the health care system.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".