Guideline compliance and outcomes of genetic testing in pancreatic cancer patients.

Abstract

688 Background: Guidelines recommend patients with pancreatic ductal adenocarcinoma (PDAC) undergo genetic testing for germline pathogenic variants (PV). The aim of this study was to evaluate compliance with recently updated guidelines (May 2019) and assess subsequent uptake and outcomes of genetic testing in PDAC patients. In addition, social and clinical factors associated with genetic testing were assessed. Methods: A retrospective chart review of patients diagnosed with PDAC between May 2018 and August 2020 was performed. Discussion and subsequent uptake of genetic testing was reviewed and compared between a 12-month period before (pre-guideline) and a 12-month period after (post-guideline) guidelines were updated, accounting for a three-month transition period. Univariate and multivariate logistic regression analysis was used to assess factors predictive of undergoing genetic testing. Results: In total, 534 patients with PDAC were identified; 321 (60.1%) in the pre-guideline period and 213 (39.9%) in the post-guideline period. The mean age at diagnosis was 68 years and 47% were female. Genetic testing was discussed in 34% (109/321) of pre-guideline and in 39% (84/213) of post-guideline patients ( P = .23). Of those, 82% (89/109) of patients in pre-guideline and 75% (63/84) in post-guideline groups underwent subsequent genetic testing ( P = .71). In 26 (17.1%) of 152 tested patients, a PV was identified, of which 17 (11.2%; 17/152) had a PDAC-associated PV. Age, cancer stage at diagnosis, length of survival, and having a first-degree relative with pancreatic cancer were significant predictors of genetic testing on multivariate analysis. Conclusions: Adherence to recently updated guidelines is poor and germline genetic testing in PDAC patients remains insufficient. Efforts to increase awareness of benefits of testing, which include personalized therapies for patients and cascade testing in family members for subsequent enrollment in pancreatic cancer surveillance programs, could improve future uptake.[Table: see text]

Early Diagnosis and Prognosis Prediction of Pancreatic Cancer Using Engineered Hybrid Core‐Shells in Laser Desorption/Ionization Mass Spectrometry

“…[ 37 ] Conversely, blood‐based molecular biomarkers, including nucleic acids, proteins, and metabolites, hold great promise for early PC diagnosis due to their ease of interpretation, convenience, and non‐invasiveness with high compliance rates (> 90%). [ 38 ] As compared to upstream molecules (nucleic acids and proteins, MW > 1 000 Da) with suboptimal diagnostic performance (e.g., specificity of 75% by CA19‐9), [ 39 ] small metabolites (MW < 1 000 Da) as downstream molecular biomarkers directly offer real‐time phenotypic information, [ 5b,33b,40 ] potentially leading to high‐performance blood tests. [ 41 ]…”

“…[37] Conversely, blood-based molecular biomarkers, including nucleic acids, proteins, and metabolites, hold great promise for early PC diagnosis due to their ease of interpretation, convenience, and non-invasiveness with high compliance rates (> 90%). [38] As compared to upstream molecules (nucleic acids and proteins, MW > 1 000 Da) with suboptimal diagnostic performance (e.g., specificity of 75% by CA19-9), [39] small metabolites (MW < 1 000 Da) as downstream molecular biomarkers directly offer real-time phenotypic information, [5b,33b,40] potentially leading to high-performance blood tests. [41] Previous studies of metabolic biomarkers for PC diagnosis mainly relied on liquid chromatography-MS (LC-MS) and gas chromatography-MS (GC-MS), [42] limited by slow analytical speed (up to hours per sample) and large sample consumption (sub-milliliter) due to the need for chromatographic separation and purification.…”

“…[49] In contrast, metabolic biomarkers as the downstream molecules, hold the potential to offer more timely feedback on the human body, [50] which have prognostic value in cancer but rarely explored in PC. [34,38] In this study, we developed an MPP score through the implementation of Cox regression analysis on the SMFs database, demonstrating desirable prognostic efficiency (p < 0.05 by log-rank test) of selected metabolites. Notably, the MPP score provided efficient prognosis prediction for PC patients with enhanced efficacy (AUC of 0.777, 95% CI of 0.707-0.838, p < 0.05, by Delong test), compared to CA19-9 (AUC of 0.699, 95% CI of 0.626-0.765), highlighting its potential for clinical application and thus improving cancer management.…”