SBIR-STTR Award

We are proposing a new approach for prostate cancer pathology using 3D open-top light-sheet (OTLS) microscopy and optical clearing. Approximately 20% of patients with an initial negative prostate biopsy procedure are found to have prostate cancer on a subsequent biopsy procedure. Previous studies have shown that examination of 100% of biopsy tissue improved prostate cancer detection rate, but these studies used labor-intensive manual sectioning techniques that are practical for a clinical laboratory. We hypothesize that imaging 100% of prostate biopsy tissue using OTLS microscopy, rather than 1-2% of the biopsy using current pathology technology, can detect occult carcinoma that would have otherwise been missed in a subset of patients. Furthermore, we intend to show that 3D OTLS can be seamlessly integrated into the current clinical laboratory workflow. The specific aims of this proposal are: 1) Validate that our OTLS methods do not interfere with the current pathology practice of formalin-fixed paraffin-embedded (FFPE) tissue sections, hematoxylin and eosin (H&E) histology, and immunohistochemistry (IHC), and 2) To show that 3D pathology can detect occult cancer missed by traditional microscopy, we will compare diagnosis of "pseudo-H&E" 3D OTLS images to traditional H&E pathology. We will use archived FFPE biopsy blocks from patients with prostate cancer who had a negative initial biopsy result. Through the studies in this proposal, we will validate the non-interference of our innovative 3D OTLS methods with standard pathology practice, a necessary prerequisite for adoption within the pathology community and for regulatory considerations. We will also provide preliminary evidence to support the clinical value of 3D pathology. This project addresses the IMAT goal of developing substantially improved cancer detection and risk assessment technologies.

Public Health Relevance Statement:
The diagnosis of prostate cancer relies on centuries-old pathology technology, which has flaws that lead to undetected carcinoma in biopsy specimens. This project aims to develop innovative open-top light-sheet microscopy methods for prostate cancer pathology in two ways: 1) to demonstrate non-interference with the current standard of care methods, and 2) to provide preliminary evidence supporting the clinical value of the technology. The objectives of this proposal are consistent with the goals of the NCI IMAT SBIR program, which aims to support the early-stage development and validation of highly innovative technologies relevant to cancer diagnosis in the context of commercial use. Terms:

Prostate cancer is a curable disease when treated early before metastases occur. Current diagnostic methods struggle to accurately predict which patients will develop metastases, resulting in the development of advanced, incurable disease in some patients who initially present with localized disease. We propose to tailor the development of our 3D spatial biology platform towards predicting which prostate cancer patients will develop metastases. Our 3D spatial biology platform is the basis for multiple prominent publications, has an international user base, and has demonstrated compatibility with current laboratory workflows during our Phase I award studies. The specific aims of this proposal are: 1) Develop an accelerated and automated sample preparation process, 2) Develop machine learning models to segment tissue structures and extract 3D features for use in predictive models, and 3) Create a multiparameter model to predict metastatic risk from prostate biopsy samples. The studies in this proposal will accelerate the workflow to a clinically feasible turnaround time, build a predictive model, and demonstrate the benefits of our technology in a defined prostate cancer patient subpopulation. Our partners in this proposal (CorePlus) can rapidly deploy the developed technologies in a clinical laboratory with regulatory accreditation and an existing referral base. Successful completion of this project will position Alpenglow Biosciences towards an advanced stage in commercialization with high potential for success. This project addresses the IMAT goal of developing substantially improved cancer detection and risk assessment technologies.

Public Health Relevance Statement:
NARRATIVE The diagnosis of prostate cancer relies on centuries-old pathology technology, which has flaws that lead to poor prediction of metastatic risk in biopsy specimens. This project aims to develop innovative methods for prostate cancer pathology in two ways: 1) to accelerate and automate the preparation of prostate biopsies for 3D imaging, and 2) to develop machine learning algorithms and deploy them in a study to demonstrate the clinical value of the technology. The objectives of this proposal are consistent with the goals of the NCI IMAT SBIR program, which aims to support the early-stage development and validation of highly innovative technologies relevant to cancer diagnosis in the context of commercial use. Terms: <3-D; 3-D Imaging; 3-Dimensional; 3D; 3D imaging; AI algorithm; AI system; Acceleration; Accreditation; Address; Adoption; Advanced Development; Antibodies; Archives; Artificial Intelligence; Automation; Award; Biologic Sciences; Biological Sciences; Biology; Biopsy; Biopsy Sample; Biopsy Specimen; Bioscience; Body Tissues; Cancer Cause; Cancer Detection; Cancer Etiology; Cancer Patient; Cancers; Carcinoma; Cell Body; Cells; Cessation of life; Characteristics; Clinical; Clinical Data; Color Perception; Coloring Agents; Computational toolkit; Computer Reasoning; Data Set; Death; Development; Diagnosis; Diagnostic; Diagnostic Method; Diagnostic Procedure; Diagnostic Technique; Disease; Disorder; Drugs; Dyes; Early identification; Effectiveness; Epithelial cancer; Future; Glass; Goals; Grant; H and E; Hematoxylin and Eosin; Hematoxylin and Eosin Staining Method; Hour; Human; Image; Imaging Device; Imaging Instrument; Imaging Tool; Immune; Immunes; Indolent; International; Laboratories; Life Sciences; Localized Disease; Machine Intelligence; Machine Learning; Malignant Epithelial Neoplasms; Malignant Epithelial Tumors; Malignant Neoplasms; Malignant Tumor; Malignant Tumor of the Prostate; Malignant neoplasm of prostate; Malignant prostatic tumor; Medication; Metastasis; Metastasize; Metastatic Lesion; Metastatic Mass; Metastatic Neoplasm; Metastatic Tumor; Methods; Modeling; Modern Man; Molecular Computations; Morbidity; Morbidity - disease rate; Nature; Neoplasm Metastasis; Nerve; Nomograms; Pathologist; Pathology; Patient outcome; Patient-Centered Outcomes; Patient-Focused Outcomes; Patients; Pharmaceutical Preparations; Phase; Pilot s; Position; Positioning Attribute; Preparation; Prevalence; Process; Prostate; Prostate CA; Prostate Cancer; Prostate Gland; Prostate carcinoma; Prostate malignancy; Prostatic Cancer; Prostatic Gland; Prostatic carcinoma; Protocol; Protocols documentation; Publications; Risk; SBIR; Sampling; Scientific Publication; Secondary Neoplasm; Secondary Tumor; Slide; Small Business Innovation Research; Small Business Innovation Research Grant; Speed; Staining method; Stains; Standardization; Structure; Techniques; Technology; Technology Assessment; Three-Dimensional Imaging; Time; Tissues; Training; Validation; accredited; androgen independent prostate cancer; androgen indifferent prostate cancer; androgen insensitive prostate cancer; androgen resistance in prostate cancer; androgen resistant prostate cancer; artificial intelligence algorithm; base; bases; cancer diagnosis; cancer metastasis; cancer risk; carcinoma prostatic cancer; castration resistant CaP; castration resistant PCa; castration resistant prostate cancer; commercialization; computational toolbox; computational tools; computational toolset; computer based prediction; computerized tools; curative intervention; curative therapeutic; curative therapy; curative treatments; developmental; drug/agent; efficacy validation; epithelial carcinoma; head-to-head analysis; head-to-head comparison; high risk; hormone refractory prostate cancer; image construction; image generation; image reconstruction; imaging; improved; innovate; innovation; innovative; innovative technologies; machine based learning; machine learned algorithm; machine learning algorithm; machine learning based algorithm; machine learning based model; machine learning model; machine learning prediction algorithm; malignancy; men; molecular diagnostics; mortality; neoplasm/cancer; new drug treatments; new drugs; new pharmacological therapeutic; new therapeutics; new therapy; next generation therapeutics; novel drug treatments; novel drugs; novel pharmaco-therapeutic; novel pharmacological therapeutic; novel therapeutics; novel therapy; patient oriented outcomes; patient subclass; patient subcluster; patient subgroups; patient subpopulations; patient subsets; patient subtypes; pilot study; predictive modeling; preparations; programs; prostate biopsy; prostate cancer resistant to androgen; statistics; success; three dimensional; tissue preparation; tool; tumor cell metastasis; validate efficacy; validations