Category: Publication

Research supporting ClarityDX Prostate published in Nature Digital Medicine

Natalia Hotsaliuk2024-07-03T19:51:25+00:00

Published 26 June 2024

Development of an effective predictive screening tool for prostate cancer using the ClarityDX machine learning platform

M. Eric Hyndman, Robert J. Paproski, Adam Kinnaird, Adrian Fairey, Leonard Marks, Christian P. Pavlovich, Sean A. Fletcher, Roman Zachoval, Vanda Adamcova, Jiri Stejskal, Armen Aprikian, Christopher J. D. Wallis, Desmond Pink, Catalina Vasquez, Perrin H. Beatty & John D. Lewis

Abstract

The current prostate cancer (PCa) screen test, prostate-specific antigen (PSA), has a high sensitivity for PCa but low specificity for high-risk, clinically significant PCa (csPCa), resulting in overdiagnosis and overtreatment of non-csPCa. Early identification of csPCa while avoiding unnecessary biopsies in men with non-csPCa is challenging. We built an optimized machine learning platform (ClarityDX) and showed its utility in generating models predicting csPCa. Integrating the ClarityDX platform with blood-based biomarkers for clinically significant PCa and clinical biomarker data from a 3448-patient cohort, we developed a test to stratify patients’ risk of csPCa; called ClarityDX Prostate. When predicting high risk cancer in the validation cohort, ClarityDX Prostate showed 95% sensitivity, 35% specificity, 54% positive predictive value, and 91% negative predictive value, at a ≥ 25% threshold. Using ClarityDX Prostate at this threshold could avoid up to 35% of unnecessary prostate biopsies. ClarityDX Prostate showed higher accuracy for predicting the risk of csPCa than PSA alone and the tested model-based risk calculators. Using this test as a reflex test in men with elevated PSA levels may help patients and their healthcare providers decide if a prostate biopsy is necessary.

Full Article

Research supporting ClarityDX Prostate published in Nature Digital MedicineNatalia Hotsaliuk2024-07-03T19:51:25+00:00

EV-Fingerprint test predicts aggressive prostate cancer

perrin2023-09-02T22:04:14+00:00

Published 17 June 2023

Clinical analysis of EV- Fingerprint to predict grade group 3 and above prostate cancer and avoid prostate biopsy

Adrian Fairey, Robert J. Paproski, Desmond Pink, Deborah L. Sosnowski, Catalina Vasquez, Bryan Donnelly, Eric Hyndman, Armen Aprikian, Adam Kinnaird, Perrin H. Beatty, John D. Lewis

Abstract

There is an unmet clinical need for minimally invasive diagnostic tests to improve the detection of grade group (GG) ≥3 prostate cancer relative to prostate antigen-specific risk calculators. We determined the accuracy of the blood-based extracellular vesicle (EV) biomarker assay (EV Fingerprint test) at the point of a prostate biopsy decision to predict GG ≥3 from GG ≤2 and avoid unnecessary biopsies.

This study analyzed 415 men referred to urology clinics and scheduled for a prostate biopsy, were recruited to the APCaRI 01 prospective cohort study. The EV machine learning analysis platform was used to generate predictive EV models from microflow data. Logistic regression was then used to analyze the combined EV models and patient clinical data and generate the patients’ risk score for GG ≥3 prostate cancer.

The EV-Fingerprint test was evaluated using the area under the curve (AUC) in discrimination of GG ≥3 from GG ≤2 and benign disease on initial biopsy. EV-Fingerprint identified GG ≥3 cancer patients with high accuracy (0.81 AUC) at 95% sensitivity and 97% negative predictive value. Using a 7.85% probability cutoff, 95% of men with GG ≥3 would have been recommended a biopsy while avoiding 144 unnecessary biopsies (35%) and missing four GG ≥3 cancers (5%). Conversely, a 5% cutoff would have avoided 31 unnecessary biopsies (7%), missing no GG ≥3 cancers (0%).

EV-Fingerprint accurately predicted GG ≥3 prostate cancer and would have significantly reduced unnecessary prostate biopsies.

Full Article

EV-Fingerprint test predicts aggressive prostate cancerperrin2023-09-02T22:04:14+00:00

Cytometry Part A: Antibody titrations are critical for microflow cytometric analysis of extracellular vesicles

perrin2023-06-14T17:43:00+00:00

TECHNICAL NOTE

Desmond Pink, Arghya Basu, Michael Wong, Diana Pham, Juliana Valencia, Vivian Triana, Perrin H. Beatty, Aja M. Rieger, John D. Lewis

Optimization of flow cytometry assays for extracellular vesicles (EVs) often fail to include appropriate reagent titrations – the most critically antibody titration is either not performed or is incomplete. Using nonoptimal antibody concentration is one of the main sources of error leading to a lack of reproducible data. Antibody titration for the analysis of antigens on the surface of EVs is challenging for a variety of technical reasons. Using platelets as surrogates for cells and platelet-derived particles as surrogates for EV populations, we demonstrate our process for antibody titration, highlighting some of the key analysis parameters that may confound and surprise new researchers moving into the field of EV research. Additional care must be exercised to ensure instrument and reagent controls are utilized appropriately. Complete graphical analysis of positive and negative signal intensities, concentration, and separation or stain index data is highly beneficial when paired with visual analysis of the cytometry data. Using analytical flow cytometry procedures optimized for cells for EV analysis can lead to misleading and nonreproducible results.

Full Article

Cytometry Part A: Antibody titrations are critical for microflow cytometric analysis of extracellular vesiclesperrin2023-06-14T17:43:00+00:00

Molecular Oncology: Building predictive disease models using extracellular vesicle microscale flow cytometry and machine learning

perrin2023-01-09T20:17:59+00:00

We developed the extracellular vesicle machine learning analysis platform (EVMAP) to improve the prediction of diseases such as cancer. The platform combines extracellular vesicle analysis using microscale cytometry with a machine learning approach to generate predictive models. In this work, we utilized EVMAP to generate a blood test to predict high-grade prostate cancer in men that was significantly more accurate than the prostate-specific antigen test. This platform could be applied to many different diseases.

Full Article

Molecular Oncology: Building predictive disease models using extracellular vesicle microscale flow cytometry and machine learningperrin2023-01-09T20:17:59+00:00

BMJ Open Cohort Profile Article: the Alberta Prostate Cancer Research Initiative (APCaRI) Registry and Biorepository facilitates technology translation to the clinic through the use of linked, longitudinal clinical and patient-reported data and biospecimens from men in Alberta, Canada

Forward Momentum2022-01-04T18:52:15+00:00

The APCaRI Registry and Biorepository, established in 2014, facilitates the collection of clinical and patient-reported data plus biospecimens, to measure prostate cancer outcomes and support the development and clinical translation of innovative technologies. The ultimate goal is to better diagnose and predict outcomes for patients with prostate cancer.

AUTHOR: CATALINA VASQUEZ, MICHAEL KOLINSKY, RUME DJEBAH, MAXWELL UHLICH, BRYAN DONNELLY, ADRIAN S FAIREY, ERIC HYNDMAN, NAWAID USMANI, JACKSON WU, PETER VENNER, DEAN RUETHER, GERALD TODD, MICHAEL CHETNER, R TRAFFORD CRUMP, PERRIN H BEATTY, JOHN D LEWIS

Full Article

BMJ Open Cohort Profile Article: the Alberta Prostate Cancer Research Initiative (APCaRI) Registry and Biorepository facilitates technology translation to the clinic through the use of linked, longitudinal clinical and patient-reported data and biospecimens from men in Alberta, CanadaForward Momentum2022-01-04T18:52:15+00:00

Cowpea mosaic virus nanoparticles for cancer imaging and therapy

Forward Momentum2022-01-04T18:55:06+00:00

Nanoparticle platforms are attractive for theranostic applications due to their multifunctionality and multivalency. Some of the most promising nano-scale scaffolds have been co-opted from nature, such as the cowpea mosaic virus (CPMV). What makes CPMV so promising? They are non-infectious and nontoxic to humans and safe for use in intravital imaging and drug delivery. Click on the link to read more.

AUTHOR: PERRIN BEATTY AND JOHN LEWIS

Full Article

Cowpea mosaic virus nanoparticles for cancer imaging and therapyForward Momentum2022-01-04T18:55:06+00:00

PROSPeCT: A Predictive Research Online System for Prostate Cancer Tasks

Forward Momentum2022-01-04T18:55:55+00:00

We are pleased to present PROSPeCT, a user-friendly online clinical information system that offers an efficient way to query APCaRIs (www.APCaRI.ca) robust and expanding patient database to generate relevant and accurate results. Read the details in our article published in the Journal of Clinical Oncology – Clinical Cancer Informatics. Thank you to the Alberta Cancer Foundation and many others for their support.

AUTHOR: AUTHOR: MARIA CUTUMISU, CATALINA VASQUEZ, MAXWELL UHLICH, PERRIN H. BEATTY, HOMEIRA HAMAYELI-MEHRABANI, RUME DJEBAH, ALBERT MURTHA, RUSSELL GREINER, AND JOHN D. LEWIS

Full Article

PROSPeCT: A Predictive Research Online System for Prostate Cancer TasksForward Momentum2022-01-04T18:55:55+00:00

Intravital imaging tumor screen used to identify novel metastasis-blocking therapeutic targets

Forward Momentum2022-01-04T18:56:47+00:00

This is a micro-review of an original research article from the Lewis research group that identified multiple novel metastasis-blocking targets, by using a whole genome screen and intravital imaging approach, that could be used as therapeutic targets to inhibit solid tumour cell motility.

AUTHOR: KONSTANTIN STOLETOV, LIAN WILLETTS, PERRIN H. BEATTY, AND JOHN D. LEWIS

Full Article

Intravital imaging tumor screen used to identify novel metastasis-blocking therapeutic targetsForward Momentum2022-01-04T18:56:47+00:00

Follow Us

Contact Us