The response to neoadjuvant chemoradiotherapy with 5-fluorouracil in locally advanced rectal cancer patients: a predictive proteomic signature
Bibliographic record
Abstract
BACKGROUND: Colorectal cancer is the third most common and the fourth most lethal cancer in the world. In the majority of cases, patients are diagnosed at an advanced stage or even metastatic, thus explaining the high mortality. The standard treatment for patients with locally advanced non-metastatic rectal cancer is neoadjuvant radio-chemotherapy (NRCT) with 5-fluorouracil (5-FU) followed by surgery, but the resistance rate to this treatment remains high with approximately 30% of non-responders. The lack of evidence available in clinical practice to predict NRCT resistance to 5-FU and to guide clinical practice therefore encourages the search for biomarkers of this resistance. METHODS: From twenty-three formalin-fixed paraffin-embedded (FFPE) biopsies performed before NRCT with 5-FU of locally advanced non-metastatic rectal cancer patients, we extracted and analysed the tumor proteome of these patients. From clinical data, we were able to classify the twenty-three patients in our cohort into three treatment response groups: non-responders (NR), partial responders (PR) and total responders (TR), and to compare the proteomes of these different groups. RESULTS: We have highlighted 384 differentially abundant proteins between NR and PR, 248 between NR and TR and 417 between PR and TR. Among these proteins, we have identified many differentially abundant proteins identified as having a role in cancer (IFIT1, FASTKD2, PIP4K2B, ARID1B, SLC25A33: overexpressed in TR; CALD1, CPA3, B3GALT5, CD177, RIPK1: overexpressed in NR). We have also identified that DPYD, the main degradation enzyme of 5-FU, was overexpressed in NR, as well as several ribosomal and mitochondrial proteins also overexpressed in NR. Data are available via ProteomeXchange with identifier PXD008440. CONCLUSIONS: From these retrospective study, we implemented a protein extraction protocol from FFPE biopsy to highlight protein differences between different response groups to RCTN with 5-FU in patients with locally advanced non-metastatic rectal cancer. These results will pave the way for a larger cohort for better sensitivity and specificity of the signature to guide decisions in the choice of treatment.
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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".