Multianalyte Prognostic Signature Including Circulating Tumor DNA and Circulating Tumor Cells in Patients With Advanced Pancreatic Adenocarcinoma.

PURPOSE: Pancreatic ductal adenocarcinoma (PDAC) is associated with a poor prognosis. Multianalyte signatures, including liquid biopsy and traditional clinical variables, have shown promise for improving prognostication in other solid tumors but have not yet been rigorously assessed for PDAC. MATERIALS AND METHODS: We performed a prospective cohort study of patients with newly diagnosed locally advanced pancreatic cancer (LAPC) or metastatic PDAC (mPDAC) who were planned to undergo systemic therapy. We collected peripheral blood before systemic therapy and assessed circulating tumor cells (CTCs), cell-free DNA concentration (cfDNA), and circulating tumor KRAS (ctKRAS)-variant allele fraction (VAF). Association of variables with overall survival (OS) was assessed in univariate and multivariate survival analysis, and comparisons were made between models containing liquid biopsy variables combined with traditional clinical prognostic variables versus models containing traditional clinical prognostic variables alone. RESULTS: One hundred four patients, 40 with LAPC and 64 with mPDAC, were enrolled. CTCs, cfDNA concentration, and ctKRAS VAF were all significantly higher in patients with mPDAC than patients with LAPC. ctKRAS VAF (cube root; 0.05 unit increments; hazard ratio, 1.11; 95% CI, 1.03 to 1.21; P = .01), and CTCs ≥ 1/mL (hazard ratio, 2.22; 95% CI, 1.34 to 3.69; P = .002) were significantly associated with worse OS in multivariate analysis while cfDNA concentration was not. A model selected by backward selection containing traditional clinical variables plus liquid biopsy variables had better discrimination of OS compared with a model containing traditional clinical variables alone (optimism-corrected Harrell's C-statistic 0.725 v 0.681). CONCLUSION: A multianalyte prognostic signature containing CTCs, ctKRAS, and cfDNA concentration outperformed a model containing traditional clinical variables alone suggesting that CTCs, ctKRAS, and cfDNA provide prognostic information complementary to traditional clinical variables in advanced PDAC.

Phase II Study of Maintenance Rucaparib in Patients With Platinum-Sensitive Advanced Pancreatic Cancer and a Pathogenic Germline or Somatic Variant in BRCA1, BRCA2, or PALB2.

PURPOSE: Olaparib, a poly (ADP-ribose) polymerase (PARP) inhibitor (PARPi), is approved as maintenance therapy for patients with advanced pancreatic cancer (PC) and a germline BRCA1 or BRCA2 pathogenic variant (PV). This investigator-initiated, single-arm phase II study assessed the role of the PARPi rucaparib as maintenance therapy in advanced PC with germline or somatic PV in BRCA1, BRCA2, or PALB2. PATIENTS AND METHODS: Eligible patients had advanced PC; germline (g) or somatic (s) PVs in BRCA1, BRCA2, or PALB2, and received at least 16 weeks of platinum-based chemotherapy without evidence of platinum resistance. Chemotherapy was discontinued and patients received rucaparib 600 mg orally twice a day until progression. The primary end point was the progression-free survival (PFS) rate at 6 months (PFS6). Secondary end points included safety, ORR, disease control rate, duration of response, and overall survival. RESULTS: Of 46 enrolled patients, 42 were evaluable (27 gBRCA2, seven gBRCA1, six gPALB2, and two sBRCA2). PFS6 was 59.5% (95% CI, 44.6 to 74.4), median PFS was 13.1 months (95% CI, 4.4 to 21.8), and median overall survival was 23.5 months (95% CI, 20 to 27). The PFS at 12 months was 54.8%. ORR of the 36 patients with measurable disease was 41.7% (3 complete responses; 12 partial responses; 95% CI, 25.5 to 59.2), and disease control rate was 66.7% (95% CI, 49.0 to 81.4). Median duration of response was 17.3 months (95% CI, 8.8 to 25.8). Responses occurred in patients with gBRCA2 (41%, 11 out of 27), gPALB2 (50%, 3 out of 6), and sBRCA2 (50%, 1 out of 2). No new safety signals were noted. CONCLUSION: Maintenance rucaparib is a safe and effective therapy for platinum-sensitive, advanced PC with a PV in BRCA1, BRCA2, or PALB2. The finding of efficacy in patients with gPALB2 and sBRCA2 PVs expands the population likely to benefit from PARPi beyond gBRCA1/2 PV carriers.

Combining Machine Learning and Nanofluidic Technology To Diagnose Pancreatic Cancer Using Exosomes.

Circulating exosomes contain a wealth of proteomic and genetic information, presenting an enormous opportunity in cancer diagnostics. While microfluidic approaches have been used to successfully isolate cells from complex samples, scaling these approaches for exosome isolation has been limited by the low throughput and susceptibility to clogging of nanofluidics. Moreover, the analysis of exosomal biomarkers is confounded by substantial heterogeneity between patients and within a tumor itself. To address these challenges, we developed a multichannel nanofluidic system to analyze crude clinical samples. Using this platform, we isolated exosomes from healthy and diseased murine and clinical cohorts, profiled the RNA cargo inside of these exosomes, and applied a machine learning algorithm to generate predictive panels that could identify samples derived from heterogeneous cancer-bearing individuals. Using this approach, we classified cancer and precancer mice from healthy controls, as well as pancreatic cancer patients from healthy controls, in blinded studies.

A magnetic micropore chip for rapid (<1 hour) unbiased circulating tumor cell isolation and in situ RNA analysis.

The use of microtechnology for the highly selective isolation and sensitive detection of circulating tumor cells has shown enormous promise. One challenge for this technology is that the small feature sizes - which are the key to this technology's performance - can result in low sample throughput and susceptibility to clogging. Additionally, conventional molecular analysis of CTCs often requires cells to be taken off-chip for sample preparation and purification before analysis, leading to the loss of rare cells. To address these challenges, we have developed a microchip platform that combines fast, magnetic micropore based negative immunomagnetic selection (>10 mL h-1) with rapid on-chip in situ RNA profiling (>100× faster than conventional RNA labeling). This integrated chip can isolate both rare circulating cells and cell clusters directly from whole blood and allow individual cells to be profiled for multiple RNA cancer biomarkers, achieving sample-to-answer in less than 1 hour for 10 mL of whole blood. To demonstrate the power of this approach, we applied our device to the circulating tumor cell based diagnosis of pancreatic cancer. We used a genetically engineered lineage-labeled mouse model of pancreatic cancer (KPCY) to validate the performance of our chip. We show that in a cohort of patient samples (N = 25) that this device can detect and perform in situ RNA analysis on circulating tumor cells in patients with pancreatic cancer, even in those with extremely sparse CTCs (<1 CTC mL-1 of whole blood).