Prognostic value of CD73 expression in resected colorectal cancer liver metastasis.
Bibliographic record
Abstract
3584 Background: As approximately 80% of patients recur and die of their cancer after undergoing curative-intent resection of colorectal cancer liver metastasis (CRLM) combined with systemic chemotherapy, novel prognostic biomarkers and therapeutic targets are needed to improve outcomes. We investigated in CRLMs whether the expression of the cell-surface enzyme CD73, rate limiting for the degradation of extracellular AMP into immune-suppressive adenosine, could define a subset of patients with distinct prognosis. Methods: A tissue microarray-based analysis of 391 CRLMs resected in 214 patients (2011-2014) and followed prospectively until 10/2017. Each CRLM was arrayed with six 0.6 mm intratumoral punch biopsies, with automated quantification of CD73, cytokeratins 8/18 (CK) and DAPI expression measured by multiplex immunofluorescence. We tested associations between CD73 and pathological variables, time to recurrence (TTR) and disease specific survival (DSS). Results: The mean patient age was 62.7 years, 78.5% received pre-operative chemotherapy (mean of 6 cycles), and a median of 2 CRLMs/patient were resected. The median TTR and DSS of patients was 15.4 and 56.7 months, respectively. CD73 expression was similar whether patients received or not pre-operative chemotherapy, but was higher in patients with larger CRLMs ( > 5cm vs. ≤5cm, p = 0.01), poor pathologic response to pre-operative chemotherapy (Rubbia-Brandt TRG score 4-5 vs. 1-2-3, p = 0.01), and more necrosis ( > 25% vs. ≤25% surface necrosis, p = 0.004). In patients who received pre-operative chemotherapy, those with CD73high CRLMs (n = 62, 38.3%) had significantly shorter median TTR (11 vs. 18 months, p < 0.001) and DSS (47 vs. 58 months, p = 0.007) than CD73low patients. Pathologic response to chemotherapy was only associated with TTR, not DSS. By multivariate analysis, CD73high CRLMs was a poor prognostic factor independent of clinicopathological features for both TTR and DSS. Conclusions: Our results support that CD73 expression in CRLMs may identify a subset of patients with poor prognosis after exposure to systemic chemotherapy, providing insights to guide now-designed trials targeting CD73 with antibodies or the adenosine A2A receptor with inhibitors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".