Jillian F. Wise

Tissue stiffening is central to disease progression and aggressiveness in pancreatic adenocarcinoma (PDAC). Here we report how the biomechanical characteristics of the cell culture conditions impact the amount, as well as the cargo, of extracellular vesicles (EVs) produced by pancreatic cancer cells. Cells grown on softer matrices (0.5 kPa) release upwards of 40 times more EVs than those grown on stiffer surfaces (8 kPa) and their cargo is more enriched in protein‐coding RNAs related with mitochondrial and cytoskeleton pathways. Moreover, cells grown in a 3D conformation release more EVs than those grown in 2D, with the repeat RNA content being maintained across all different cell culture conditions. Our study provides evidence to support that the mechanical microenvironment of cells influences EV generation, with distinct consequences for coding and non‐coding RNA packaged within the EVs.

Background: Carcinoma of unknown primary (CUP) is a subset of metastatic cancers in which the primary tissue source of the cancer cells remains unidentified. CUP is the eighth most common malignancy worldwide, accounting for up to 5% of all malignancies. Representing an exceptionally aggressive metastatic cancer, the median survival is approximately 3 to 6 months. The tissue in which cancer arises plays a key role in our understanding of sensitivities to various forms of cell death. Thus, the lack of knowledge on the tissue of origin (TOO) makes it difficult to devise tailored and effective treatments for patients with CUP. Developing quick and clinically implementable methods to identify the TOO of the primary site is crucial in treating patients with CUP. Noncoding RNAs may hold potential for origin identification and provide a robust route to clinical implementation due to their resistance against chemical degradation. Objective: This study aims to investigate the potential of microRNAs, a subset of noncoding RNAs, as highly accurate biomarkers for detecting the TOO through data-driven, machine learning approaches for metastatic cancers. Methods: We used microRNA expression data from The Cancer Genome Atlas data set and assessed various machine learning approaches, from simple classifiers to deep learning approaches. As a test of our classifiers, we evaluated the accuracy on a separate set of 194 primary tumor samples from the Sequence Read Archive. We used permutation feature importance to determine the potential microRNA biomarkers and assessed them with principal component analysis and t-distributed stochastic neighbor embedding visualizations. Results: Our results show that it is possible to design robust classifiers to detect the TOO for metastatic samples on The Cancer Genome Atlas data set, with an accuracy of up to 97% (351/362), which may be used in situations of CUP. Our findings show that deep learning techniques enhance prediction accuracy. We progressed from an initial accuracy prediction of 62.5% (226/362) with decision trees to 93.2% (337/362) with logistic regression, finally achieving 97% (351/362) accuracy using deep learning on metastatic samples. On the Sequence Read Archive validation set, a lower accuracy of 41.2% (77/188) was achieved by the decision tree, while deep learning achieved a higher accuracy of 80.4% (151/188). Notably, our feature importance analysis showed the top 3 most important features for predicting TOO to be microRNA-10b, microRNA-205, and microRNA-196b, which aligns with previous work. Conclusions: Our findings highlight the potential of using machine learning techniques to devise accurate tests for detecting TOO for CUP. Since microRNAs are carried throughout the body via extracellular vesicles secreted from cells, they may serve as key biomarkers for liquid biopsy due to their presence in blood plasma. Our work serves as a foundation toward developing blood-based cancer detection tests based on the presence of microRNA.

Comparison gene expression and H3K27ac between MGH1518 serial models

Frequency of amplified oncogenes across PDX panel, similarity of ecMYC/L/N structures, and regression analysis of average drug response versus MYC paralog copy number, expression and amplicon type.

Acquired ecMYCN amplification in MGH1578 serial models and lineage oncogene expression compared with ecDNA status across the PDX panel.

Calculation of delta-AUC and comparison of EC with EP in PDX models.

Frequency of amplified oncogenes across PDX panel, similarity of ecMYC/L/N structures, and regression analysis of average drug response versus MYC paralog copy number, expression and amplicon type.

Reconstruction of ecDNAs containing MYC paralogs and MYCL FISH in MGH1501-1A metaphase chromosomes.

Levels of ecMYC in untreated and treated MGH1518-3A xenografts