Johns Hopkins University
United States
A multidisciplinary BCC for ovarian cancer early detection: translating discoveries to clinical use with a by-design approach
(Overall) High-grade serous ovarian carcinoma (HGSOC) is the most common histological subtype of epithelial ovarian cancer. The overarching goal of the proposed Biomarker Characterization Center (BCC) is to apply a bydesign approach based on biology of HGSOC pathogenesis and unmet clinical needs to identify, verify and prioritize, and validate biomarkers, and to develop them into an in vitro diagnostic multivariate index assay (IVDMIA) with the intended use to capture HGSOC in high-risk women at the early stages including i) precursors, ii) confinement to the ovary/fallopian tube or iii) low-volume diseases in high-risk women (BRCA1/2 carriers). The biomarkers that we propose to discover and validate in this proposal are intended for early detection but not necessarily for screening in general population. The BCC’s capability in advanced data generation technologies, multiplexed target assay development, and bioinformatics/data science will serve as resources for the EDRN. Based on the success of our current EDRN projects, this BCC will continue our ongoing biomarker development studies including the validation of candidate biomarkers that we have identified through the current BDL. We propose the following specific aims: 1. To optimize and use novel specimen collection and processing technologies, and an iterative and cumulative process that takes advantage of our newly gained knowledge of the biology in ovarian cancer pathogenesis. BDL 2. To optimize and apply innovative bioinformatics, data sciences, and AI/ML tools that incorporate existing knowledge and data to improve discovery of low frequency biomarkers that with their functionally shared pathways/networks could collectively deliver an improved sensitivity while retaining a high specificity. BDL 3. To further develop and optimize the process for efficient multiplex targeted assay development with respect to analytical performance, throughput, and specimen volume requirement for a broad spectrum of candidate biomarkers using a “fit for purpose” approach. BRL 4. To optimize and apply a by-design approach to translating discoveries into clinical tests. Its application had been critical in the development of two FDA cleared tests by JHU team members for the preoperative assessment of ovarian malignancy risk. BDL/BRL 5. To provide expertise and analytical and data science capabilities to the entire EDRN community. The multi-disciplinary team that we have assembled (molecular cancer biology, pathology, clinical chemistry, mass spectrometry, biostatistics, data science, bioengineering), the unique, novel yet biologically and statistically sound approaches, and our long-standing experience in biomarker research and translating discoveries to FDA cleared clinical tests all together ensure the success of this proposed BCC.
Publications
- Chien YW, Wang Y, Huang P, Lawson BC, Kolin DL, Chui MH, Vang R, Numan TA, Soong TR, Wang BG, Smith SA, Chen CL, Stone R, Douville C, Wang TL, Shih IM. Morphologic and Molecular Heterogeneity of High-grade Serous Carcinoma Precursor Lesions. The American journal of surgical pathology. 2024 Apr 1;48(4):475-486. Epub 2024 Feb 1. PMID: 38298022
- Wang Y, Huang P, Wang BG, Murdock T, Cope L, Hsu FC, Wang TL, Shih IM. Spatial Transcriptomic Analysis of Ovarian Cancer Precursors Reveals Reactivation of IGFBP2 during Pathogenesis. Cancer research. 2022 Dec 16;82(24):4528-4541. PMID: 36206311
- Fu Y, Lu Y, Wang Y, Zhang B, Zhang Z, Yu G, Liu C, Clarke R, Herrington DM, Wang Y. DDN3.0: determining significant rewiring of biological network structure with differential dependency networks. Bioinformatics (Oxford, England). 2024 Jun 3;40. (6). PMID: 38902940
- Wu CT, Du D, Chen L, Dai R, Liu C, Yu G, Bhardwaj S, Parker SJ, Zhang Z, Clarke R, Herrington DM, Wang Y. CAM3.0: determining cell type composition and expression from bulk tissues with fully unsupervised deconvolution. Bioinformatics (Oxford, England). 2024 Mar 4;40. (3). PMID: 38407991
- Du D, Bhardwaj S, Lu Y, Wang Y, Parker SJ, Zhang Z, Van Eyk JE, Yu G, Clarke R, Herrington DM, Wang Y. Embracing the informative missingness and silent gene in analyzing biologically diverse samples. Scientific reports. 2024 Nov 16;14(1):28265. PMID: 39550430