Multivariate assessment of SPARC expression in resected pancreatic ductal adenocarcinoma to identify subgroups that are sensitive to adjuvant gemcitabine.
Bibliographic record
Abstract
297 Background: Secreted Protein, Acid, Cysteine-Rich (SPARC) has recently been postulated as a therapeutic target in pancreatic ductal adenocarcinoma (PDAC). The clinical trial findings investigating SPARC expression and nab-paclitaxel sensitivity have been discordant. This study aims to develop an integrated component based approach to the quantification of SPARC in PDAC to identify discrete predictive subgroups in a cohort of resected patients treated with an gemcitabine (GEM) or subjected to post-surgical observation. Methods: Immunohistochemical quantification of SPARC was performed on the epithelial and stromal compartments of resected PDAC on 219 patient samples on a tissue-microarray. The staining was assessed by the generation of H-Scores. The resultant scores were subjected to unsupervised hierarchical clustering. The maximum number of clusters was determined through an a priori decision that no cluster could be composed of less than 15% of the cohort. Univariable disease specific survival (DSS) analysis was performed with the Kaplan-Meier method to examine the cluster specific survival profiles with regard to gemcitabine sensitivity. Results: Mean age was 67 [38-88] with 56% being male. Most of the cohort had advanced disease with pT3 = 95% and pN1 = 72%. Lymphovascular and perinueural invasion were found in 58% and 93% of the cohort respectively. Clusters ranging in size from 35 to 76 cases were derived and represented the four-biomarker combinations of Low/Low, Low/High, High/High, and High/Low for the epithelial and stromal components respectively. None of the clinico-pathologic variables were significantly enriched in the clusters. Assessment of the predictive ability of the 4 clusters demonstrated that only one cluster (High/High) representing 76 (35%) patients in this cohort was sensitive to adjuvant GEM (p = 0.0067). Conclusions: This study shows that there is enhanced value in a combinatorial approach to the examination of SPARC in the stromal and epithelial components of PDAC where we have discovered that co-expression in both the epithelial and stromal components is significantly associated with sensitivity to adjuvant GEM.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".