Characterizing the genomic landscape of locally advanced pancreatic cancer.
Bibliographic record
Abstract
697 Background: Locally advanced pancreatic cancer (LAPC) accounts for approximately 30% of pancreatic ductal adenocarcinomas (PDAC). Optimal management for LAPC is controversial and combination treatments are extrapolated from treatment guidelines for metastatic disease. The biological underpinnings of LAPC remain unclear, although the loss of tumor suppressor SMAD4 has been associated with metastatic disease. Here we characterize the genomic landscape of LAPC in a large series of prospectively sequenced PDAC. Methods: Clinical, genomic and survival data were obtained from the COMPASS trial (NCT02750657), a prospective multi-institutional study that included patients with treatment-naïve advanced PDAC, with predominantly metastatic cases due to ease of biopsy. Fresh tumor tissue was acquired by percutaneous core biopsy for real-time whole genome sequencing (WGS) and RNA sequencing (RNAseq). Laser capture micro-dissection was performed for all cases. Response to therapy was assessed every 8 weeks, and patients were followed prospectively. Results: Of 268 patients (268 with available WGS and 253 with RNAseq), 37 (14%) had LAPC. Baseline epidemiological variables were similar, with no differences between sex, age, smoking status, or history of diabetes. Patients with LAPC had a lower BMI (median 22 vs 24, p=0.005) and lower baseline CA19-9 (median 488 vs 2551, p=0.002) than metastatic cases. All patients with LAPC had a low (<2 points) Gustave Roussy Immune Score, previously shown to be prognostic in our dataset. Driver alternations were similar in LAPC and metastatic cases with no differences in SMAD4 loss between the two groups; however, major imbalances in mutant KRAS were absent in the LAPC group. The burden of SNVs, indels and SVs was significantly lower in LAPC (Table). All LAPC cases demonstrated a classical RNA subtype, compared to 23% in metastatic cases ( p=0.0008). Median OS measured 12.5 and 8.3 months for LAPC and metastatic cases, respectively (HR 0.65, 95% CI 0.48-0.89, p=0.003). In addition, when measuring CD8+ T cell infiltration by IHC using median cut-off values, primary site biopsies had a higher CD8+ T cell infiltrate compared to metastatic sites (77% vs 56% CD8-high, respectively, p=0.01). Conclusions: These integrated genomic, RNA subtyping and early immunophenotyping results suggest that LAPC demonstrate less genomic instability and classical programming. Further study will delineate how these genomic differences, along with better clinical features, may influence treatment decision-making and design of future clinical trials. Clinical trial information: NCT02750657 . [Table: see text]
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".