Fogg, K. | Search bioRxiv

bioRxiv2024

Empowering High Throughput Screening of 3D Models: Automated Dispensing of Cervical and Endometrial Cancer Cells

PurposeCervical and endometrial cancers pose significant challenges in womens healthcare due to their high mortality rates and limited treatment options. High throughput screening (HTS) of cervical and endometrial cancer in vitro models offer a promising avenue for drug repurposing and broadening patient treatment options. Traditional two-dimensional (2D) cell-based screenings have limited capabilities to capture crucial multicellular interactions, that are improved upon in three dimensional (3D) multicellular tissue engineered models. However, manual fabrication of the 3D platforms is both time consuming and subject to variability. Thus, the goal of this study was to utilize automated cell dispensing to fabricate 3D cell-based HTS platforms using the HP D100 Single Cell Dispenser to dispense cervical and endometrial cancer cells. MethodsWe evaluated the effects of automated dispensing of the cancer cell lines by tuning the dispensing protocol to align with cell size measured in solution and the minimum cell number for acceptable cell viability and proliferation. We modified our previously reported coculture models of cervical and endometrial cancer to be in a 384 well plate format and measured microvessel length and cancer cell invasion. ResultsAutomatically and manually dispensed cells were directly compared revealing minimal differences between the dispensing methods. These findings suggest that automated dispensing of cancer cells minimally affects cell behavior and can be deployed to decrease in vitro model fabrication time. ConclusionsBy streamlining the manufacturing process, automated dispensing holds promise for enhancing efficiency and scalability of 3D in vitro HTS platforms, ultimately contributing to advancement in cancer research and treatment.

bioengineering↗

bioRxiv2022

Characterizing the extracellular matrix transcriptome of cervical, endometrial, anduterine cancers

Increasingly, the matrisome, a set of proteins that form the core of the extracellular matrix (ECM) or are closely associated with it, has been demonstrated to play a key role in tumor progression. However, in the context of gynecological cancers, the matrisome has not been well characterized. A holistic, yet targeted, exploration of the tumor microenvironment is critical for better understanding the progression of gynecological cancers, identifying key biomarkers for cancer progression, establishing the role of gene expression in patient survival, and for assisting in the development of new targeted therapies. In this work, we explored the matrisome gene expression profiles of cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), uterine corpus endometrial carcinoma (UCEC), and uterine carcinosarcoma (UCS) using publicly available RNA-seq data from The Cancer Genome Atlas (TCGA) and The Genotype-Tissue Expression (GTEx) portal. We hypothesized that the matrisomal expression patterns of CESC, UCEC, and UCS would be highly distinct with respect to genes which are differentially expressed and hold inferential significance with respect to tumor progression, patient survival, or both. Through a combination of statistical and machine learning analysis techniques, we identified sets of genes and gene networks which characterized each of the gynecological cancer cohorts. Our findings demonstrate that the matrisome is critical for characterizing gynecological cancers and transcriptomic mechanisms of cancer progression and outcome. Furthermore, while the goal of pan-cancer transcriptional analyses is often to highlight the shared attributes of these cancer types, we demonstrate that they are highly distinct diseases which require separate analysis, modeling, and treatment approaches. In future studies, matrisome genes and gene ontology terms that were identified as critical for predicting patient survival or cancer stage can be evaluated as potential drug targets and incorporated into in vitro models of disease.

cancer biology↗