Biology subjects

Farnell, D.

Publications and source records attributed to Farnell, D..

3 recordsLinked to original sources

bioRxiv2021

Modeling High-grade serous ovarian carcinoma using a combination of in vivo electroporation and CRISPR/Cas9 mediated genome editing

Ovarian cancer remains the most lethal gynecological cancer today. High-grade serous ovarian carcinoma (HGSC) is the most common and lethal type of ovarian cancer and is most frequently diagnosed at advanced stages. Here, we developed a novel strategy to generate somatic ovarian cancer mouse models using a combination of in vivo electroporation and CRISPR/Cas9 mediated genome editing. We mutated tumor suppressor genes associated with HGSC in two different combinations; Brca1, Tp53, Pten with/without Lkb1 and successfully generated HGSC, however, with different latencies and pathophysiology. By utilizing Cre lineage tracing in our system, we visualized peritoneal micrometastases in an immune-competent environment. Because our strategy is flexible in selecting mutation combinations and targeting areas, it would be useful for generating ovarian cancer mouse models. Significance StatementHigh grade serous ovarian cancer (HGSC) is the most common ovarian malignancy but our knowledge of early tumorigenesis is still quite limited due to late diagnosis of patients. We developed a new strategy of generating mouse ovarian cancer models using a combination of in vivo electroporation and CRISPR-mediated genome editing. We demonstrated that a combination of three tumor suppressor gene mutations (Brca1, Tp53 and Pten) is sufficient to develop HGSCs. Interesting, an additional mutation in Lkb1 drastically changed tumor latency, penetrance and pathophysiology through changing its cell-of-origin. Our strategy is highly flexible in selection of mutation combinations and targeting areas in immune competent mice and useful to study early tumorigenesis of ovarian cancer.

cancer biology↗

bioRxiv2020

Synthesis of diagnostic quality cancer pathology images

Deep learning-based computer vision methods have recently made remarkable breakthroughs in the analysis and classification of cancer pathology images. However, there has been relatively little investigation of the utility of deep neural networks to synthesize medical images. In this study, we evaluated the efficacy of generative adversarial networks (GANs) to synthesize high resolution pathology images of ten histological types of cancer, including five cancer types from The Cancer Genome Atlas (TCGA) and the five major histological subtypes of ovarian carcinoma. The quality of these images was assessed using a comprehensive survey of board-certified pathologists (n = 9) and pathology trainees (n = 6). Our results show that the real and synthetic images are classified by histotype with comparable accuracies, and the synthetic images are visually indistinguishable from real images. Furthermore, we trained deep convolutional neural networks (CNNs) to diagnose the different cancer types and determined that the synthetic images perform as well as additional real images when used to supplement a small training set. These findings have important applications in proficiency testing of medical practitioners and quality assurance in clinical laboratories. Furthermore, training of computer-aided diagnostic systems can benefit from synthetic images where labeled datasets are limited (e.g., rare cancers). We have created a publicly available website where clinicians and researchers can attempt questions from the image survey at http://gan.aimlab.ca/.

bioinformatics↗

bioRxiv2019

Arginine depletion through ADI-PEG20 to treat argininosuccinate synthase deficient ovarian cancer, including small cell carcinoma of the ovary, hypercalcemic type

PurposeMany rare ovarian cancer subtypes such as small cell carcinoma of the ovary, hypercalcemic type (SCCOHT) have poor prognosis due to their aggressive nature and resistance to standard platinum and taxane based chemotherapy. The development of effective therapeutics has been hindered by the rarity of such tumors. We sought to identify targetable vulnerabilities in rare ovarian cancer subtypes. Experimental DesignWe compared the global proteomic landscape of six cases each of endometrioid ovarian cancer (ENOC), clear cell ovarian cancer (CCOC), and SCCOHT to the most common subtype high grade serous ovarian cancer (HGSC) to identify potential therapeutic targets. Immunohistochemistry of tissue microarrays were used as validation of ASS1 deficiency. The efficacy of arginine-depriving therapeutic ADI-PEG20 was assessed in vitro using cell lines and patient derived xenograft mouse models representing SCCOHT. ResultsGlobal proteomic analysis identified low ASS1 expression in ENOC, CCOC, and SCCOHT compared to HGSC. Low ASS1 levels were validated through IHC in a large patient cohort. The lowest levels of ASS1 were observed in SCCOHT, where ASS1 was absent in 2/15 cases, and expressed in less than 5% of the tumor cells in 8/15 cases. ASS1 deficient ovarian cancer cells were sensitive to ADI-PEG20 treatment regardless of subtype in vitro. Furthermore, in two cell line mouse xenograft models and one patient derived mouse xenograft model of SCCOHT, once a week treatment of ADI-PEG20 (30mg/kg and 15mg/kg) inhibited tumor growth in vivo. ConclusionsPreclinical in vitro and in vivo studies identified ADI-PEG20 as a potential therapy for patients with rare ovarian cancers including SCCOHT. Translational relevanceMany rare ovarian cancers lack effective management strategies and are resistant to the standard platinum- and taxane-based chemotherapy. Thus, for a rare ovarian cancer subtype like small cell carcinoma of the ovary, hypercalcemic type (SCCOHT) - an aggressive malignancy affecting young women in their twenties, effective targeted therapeutics are urgently needed. We used global proteomics to identify a deficiency in arginosuccinate synthase (ASS1) as a common feature among some rare ovarian cancer subtypes. Using in-vitro and in-vivo models, we demonstrated that the arginine-depriving investigational agent ADI-PEG20 effectively inhibited cell growth in ASS1 deficient ovarian cancers including SCCOHT, establishing it as a potential therapeutic agent for rare ovarian cancer subtypes deficient in ASS1. Further clinical investigation is warranted.

cancer biology↗