Background/Objectives: Although the overall survival prognosis of patients in advanced stages of pancreatic ductal adenocarcinoma (PDAC) is poor, typically ranging from days to months from diagnosis, there are rare cases of patients remaining in therapy for longer periods of time. Early estimations of survival prognosis would allow rational decisions on complex therapy interventions, including radical surgery and robust systemic therapy regimens. Understandably, there is great interest in finding prognostic markers that can be used for patient stratification. We determined the role of various KRAS mutations in the prognosis of PDAC patients using biopsy samples and circulating tumor DNA. Methods: A total of 118 patients with PDAC, clinically confirmed by endoscopic ultrasound-guided fine-needle aspiration biopsy (EUS-FNB), were included in the study. DNA was extracted from cytological slides following a standard cytology evaluation to ensure adequacy (viability and quantity) and to mark the tumor cell fraction. Circulating tumor DNA (ctDNA) was extracted from plasma samples of 45 patients in stage IV of the disease. KRAS mutations in exons 12 and 13 were detected by denaturing capillary electrophoresis (DCE), revealing a minute presence of mutation-specific heteroduplexes. Kaplan-Meier survival curves were calculated for individual KRAS mutation types. Results:KRAS mutations were detected in 90% of tissue (106/118) and 44% of plasma (20/45) samples. All mutations were localized at exon 2, codon 12, with G12D (GGT > GAT) being the most frequent at 44% (47/106) and 65% (13/20), followed by other types including G12V (GGT > GTT) at 31% (33/106) and 10% (2/20), G12R (GGT > CGT) at 17% (18/106) and 10% (2/20), G12C (GGT/TGT) at 5% (5/106) and 0% (0/20) and G12S (GGT/AGT) at 1% (1/106) and 5% (1/20) in tissue and plasma samples, respectively. Two patients had two mutations simultaneously (G12V + G12S and G12D + G12S) in both types of samples (2%, 2/106 and 10%, 2/20 in tissue and plasma samples, respectively). The median survival of patients with the G12D mutation in tissues was less than half that of other patients (median survival 101 days, 95% CI: 80-600 vs. 228 days, 95% CI: 184-602), with a statistically significant overall difference in survival (p = 0.0080, log-rank test), and furthermore it was less than that of all combined patients with other mutation types (101 days, 95% CI: 80-600 vs. 210 days, 95% CI: 161-602, p = 0.0166). For plasma samples, the survival of patients with this mutation was six times shorter than that of patients without the G12D mutation (27 days, 95% CI: 8-334 vs. 161 days, 95% CI: 107-536, p = 0.0200). In contrast, patients with detected KRAS G12R in the tissue survived nearly twice as long as other patients in the aggregate (286 days, 95% CI: 70-602 vs. 162 days, 95% CI: 122-600, p = 0.0374) or patients with other KRAS mutations (286 days, 95% CI: 70-602 vs. 137 days, 95% CI: 107-600, p = 0.0257). Conclusions: Differentiation of specific KRAS mutations in EUS-FNB and ctDNA (above all, the crucial G12D and G12R) is feasible in routine management of PDAC patients and imperative for assessment of prognosis.

• Keywords
• EUS-FNB, KRAS, ctDNA, liquid biopsy, mutation type, pancreatic cancer, prognosis,
• MeSH
• Endoscopic Ultrasound-Guided Fine Needle Aspiration * MeSH
• Circulating Tumor DNA genetics   blood   MeSH
• Adult MeSH
• Carcinoma, Pancreatic Ductal * genetics   pathology   blood   MeSH
• Middle Aged MeSH
• Humans MeSH
• Mutation * MeSH
• Biomarkers, Tumor genetics   MeSH
• Pancreatic Neoplasms * genetics   pathology   mortality   MeSH
• Prognosis MeSH
• Proto-Oncogene Proteins p21(ras) * genetics   MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• Liquid Biopsy methods   MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Names of Substances
• Circulating Tumor DNA MeSH
• KRAS protein, human MeSH Browser
• Biomarkers, Tumor MeSH
• Proto-Oncogene Proteins p21(ras) * MeSH

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is associated with a very poor prognosis, with near-identical incidence and mortality. According to the World Health Organization Globocan Database, the estimated number of new cases worldwide will rise by 70% between 2020 and 2040. There are no effective screening methods available so far, even for high-risk individuals. The prognosis of PDAC, even at its early stages, is still mostly unsatisfactory. Impaired glucose metabolism is present in about 3/4 of PDAC cases. METHODS: Available literature on pancreatic cancer and diabetes mellitus was reviewed using a PubMed database. Data from a national oncology registry (on PDAC) and information from a registry of healthcare providers (on diabetes mellitus and a number of abdominal ultrasound investigations) were obtained. RESULTS: New-onset diabetes mellitus in subjects older than 60 years should be an incentive for a prompt and detailed investigation to exclude PDAC. Type 2 diabetes mellitus, diabetes mellitus associated with chronic non-malignant diseases of the exocrine pancreas, and PDAC-associated type 3c diabetes mellitus are the most frequent types. Proper differentiation of particular types of new-onset diabetes mellitus is a starting point for a population-based program. An algorithm for subsequent steps of the workup was proposed. CONCLUSIONS: The structured, well-differentiated, and elaborately designed approach to the elderly with a new onset of diabetes mellitus could improve the current situation in diagnostics and subsequent poor outcomes of therapy of PDAC.

• Keywords
• age over 60 years, diagnostic algorithm, new-onset diabetes mellitus, pancreatic ductal adenocarcinoma,
• Publication type
• Journal Article MeSH
• Review MeSH

Background: Observation of anticancer therapy effect by monitoring of minimal residual disease (MRD) is becoming an important tool in management of non-small cell lung cancer (NSCLC). The approach is based on periodic detection and quantification of tumor-specific somatic DNA mutation in circulating tumor DNA (ctDNA) extracted from patient plasma. For such repetitive testing, complex liquid-biopsy techniques relying on ultra-deep NGS sequencing are impractical. There are other, cost-effective, methods for ctDNA analysis, typically based on quantitative PCR or digital PCR, which are applicable for detecting specific individual mutations in hotspots. While such methods are routinely used in NSCLC therapy prediction, however, extension to cover broader spectrum of mutations (e.g., in tumor suppressor genes) is required for universal longitudinal MRD monitoring. Methods: For a set of tissue samples from 81 NSCLC patients we have applied a denaturing capillary electrophoresis (DCE) for initial detection of somatic mutations within 8 predesigned PCR amplicons covering oncogenes and tumor suppressor genes. Mutation-negative samples were then subjected to a large panel NGS sequencing. For each patient mutation found in tissue was then traced over time in ctDNA by DCE. Results: In total we have detected a somatic mutation in tissue of 63 patients. For those we have then prospectively analyzed ctDNA from collected plasma samples over a period of up to 2 years. The dynamics of ctDNA during the initial chemotherapy therapy cycles as well as in the long-term follow-up matched the clinically observed response. Conclusion: Detection and quantification of tumor-specific mutations in ctDNA represents a viable complement to MRD monitoring during therapy of NSCLC patients. The presented approach relying on initial tissue mutation detection by DCE combined with NGS and a subsequent ctDNA mutation testing by DCE only represents a cost-effective approach for its routine implementation.

• Keywords
• KRAS mutations, NSCLC, TP53 mutations, capillary electrophoresis, ctDNA, liquid biopsy, minimal residual disease,
• MeSH
• Circulating Tumor DNA * genetics   MeSH
• DNA, Neoplasm genetics   MeSH
• Electrophoresis, Capillary MeSH
• Humans MeSH
• Mutation genetics   MeSH
• Biomarkers, Tumor genetics   MeSH
• Lung Neoplasms * drug therapy   MeSH
• Carcinoma, Non-Small-Cell Lung * genetics   therapy   MeSH
• Neoplasm, Residual MeSH
• High-Throughput Nucleotide Sequencing methods   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Names of Substances
• Circulating Tumor DNA * MeSH
• DNA, Neoplasm MeSH
• Biomarkers, Tumor MeSH