Ultra-sensitive CTC-based liquid biopsy for pancreatic cancer enabled by large blood volume analysis.

The limited sensitivity of circulating tumor cell (CTC) detection in pancreatic adenocarcinoma (PDAC) stems from their extremely low concentration in the whole circulating blood, necessitating enhanced detection methodologies. This study sought to amplify assay-sensitivity by employing diagnostic leukapheresis (DLA) to screen large blood volumes. Sixty patients were subjected to DLA, with a median processed blood volume of ~ 2.8 L and approximately 5% of the resulting DLA-product analyzed using CellSearch (CS). Notably, DLA significantly increased CS-CTC detection to 44% in M0-patients and 74% in M1-patients, yielding a 60-fold increase in CS-CTC enumeration. DLA also provided sufficient CS-CTCs for genomic profiling, thereby delivering additional genomic information compared to tissue biopsy samples. DLA CS-CTCs exhibited a pronounced negative prognostic impact on overall survival (OS), evidenced by a reduction in OS from 28.6 to 8.5 months (univariate: p = 0.002; multivariable: p = 0.043). Additionally, a marked enhancement in sensitivity was achieved (by around 3-4-times) compared to peripheral blood (PB) samples, with positive predictive values for OS being preserved at around 90%. Prognostic relevance of CS-CTCs in PDAC was further validated in PB-samples from 228 PDAC patients, consolidating the established association between CTC-presence and reduced OS (8.5 vs. 19.0 months, p < 0.001). In conclusion, DLA-derived CS-CTCs may serve as a viable tool for identifying high-risk PDAC-patients and aiding the optimization of multimodal treatment strategies. Moreover, DLA enables comprehensive diagnostic profiling by providing ample CTC material, reinforcing its utility as a reliable liquid-biopsy approach. This high-volume liquid-biopsy strategy presents a potential pathway for enhancing clinical management in this malignancy.

Molecular and morphological findings in a sample of oral surgery patients: What can we learn for multivariate concepts for age estimation?

It has already been proposed that a combined use of different molecular and morphological markers of aging in multivariate models may result in a greater accuracy of age estimation. However, such an approach can be complex and expensive, and not every combination may be useful. The significance and usefulness of combined analyses of D-aspartic acid in dentine, pentosidine in dentine, DNA methylation in buccal swabs at five genomic regions (PDE4C, RPA2, ELOVL2, DDO, and EDARADD), and third molar mineralization were tested by investigating a sample of 90 oral surgery patients. Machine learning models for age estimation were trained and evaluated, and the contribution of each parameter to multivariate models was tested by assessment of the predictor importance. For models based on D-aspartic acid, pentosidine, and the combination of both, mean absolute errors (MAEs) of 2.93, 3.41, and 2.68 years were calculated, respectively. The additional inclusion of the five DNAm markers did not improve the results. The sole DNAm-based model revealed a MAE of 4.14 years. In individuals under 28 years of age, the combination of the DNAm markers with the third molar mineralization stages reduced the MAE from 3.85 to 2.81 years. Our findings confirm that the combination of parameters in multivariate models may be very useful for age estimation. However, the inclusion of many parameters does not necessarily lead to better results. It is a task for future research to identify the best selection of parameters for the different requirements in forensic practice.