A256 DETERMINATION OF PROTEOMIC SIGNATURE OF RESPONSE TO NEOADJUVANT RADIO-CHEMOTHERAPY IN COLORECTAL CANCER PATIENTS
Bibliographic record
Abstract
Neoadjuvant radio-chemotherapy (NRCT) is current standard care for patients with colorectal cancer (CRC). Patients are often diagnosed late with locally advanced tumor completely invading wall of rectum (T3) or peripheral tissues or organs (T4), or if regional lymph node metastases are founded (N1/N2). In these cases, standard treatment is preoperative radiotherapy with concomitant chemotherapy. It reduces tumor infiltration and decreases tumor stage (down-staging), which is important because it increases a complete resection rate during surgery and improves loco-regional tumor control, patient survival and quality of life by preserving sphincter function, urinary and sexual organs. The Centre Hospitalier de l’Université de Sherbrooke (CHUS) processes approximately 50–80 new cases of CRC and undergoes this standard treatment protocol. While 60% of patients show positive response, including up to 17% with complete remission, a subset of patients with same tumor stage and having been treated with same technique do not respond favourably. Considering the severe secondary effects observed, there is a strong incentive to be able to predict outcome of the treatment. Clinical markers currently used are not able to predict individual response of patient that would allow personalization of treatment. Relevant biological factors to guide patient to customized adjuvant chemotherapy are missing to guide clinician in clinical decisions. Our aim is to investigate protein profile to identify prognostic biomarkers of response before and after NRCT by mass spectrometry. Tumor tissues obtained from biopsy and from surgery, before and after NRCT respectively, are used to identify an expression profile predictive of response. Paraffin-embedded samples are heated in detergents and xylene to remove paraffin and reverse formaldehyde crosslinking. Proteins are separated by SDS-PAGE and gel lane is subjected to in-gel trypsin digestion. Desalted peptides are analysed by mass spectrometry to determine expression of thousands of proteins. Experiments allowed us to determine proteomic signature representative of different clinical outcomes. We have validated method of extraction and protein identification and completed 37 human CRC samples, resulting in identification and quantification of over 3000 proteins, and we are currently processing a second batch of 115 samples received from pathology department. We identified several pathways and potential biomarkers that could be predictive of the outcome of treatment. Informations from this study generated large amount of data on proteins involved in processes of resistance to treatment. Using a novel proteomic approach, this multidisciplinary project will establish a personalized approach to optimize treatment of CRC. Merck
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".