The Hallmarks of Predictive Oncology.

As the field of artificial intelligence evolves rapidly, these hallmarks are intended to capture fundamental, complementary concepts necessary for the progress and timely adoption of predictive modeling in precision oncology. Through these hallmarks, we hope to establish standards and guidelines that enable the symbiotic development of artificial intelligence and precision oncology.

Association of Genomic Prostate Score at positive margin with recurrence after radical prostatectomy.

Objectives: To evaluate the utility of the 17-gene Genomic Prostate Score® (GPS; MDxHealth, Irvine, CA, USA) performed on prostate cancer at the positive margin of the radical prostatectomy (RP) for its association with risk of subsequent biochemical recurrence (BCR).

Patients And Methods: We designed a case-cohort for the outcome of BCR, selecting 223 from a cohort of 813 RP patients treated at Johns Hopkins from 2008 to 2017 with positive margins and available clinical data; of these, 213 had available tissue and clinical data. RNA was isolated from formalin-fixed paraffin-embedded tumour tissue adjacent to the positive surgical margin and the GPS was evaluable in 203 of these patients with a score ranging from 0 to 100, with higher scores indicating higher risk.

Geometric parameters that affect the behavior of logic-gated CAR T cells.

Clinical applications of CAR-T cells are limited by the scarcity of tumor-specific targets and are often afflicted with the same on-target/off-tumor toxicities that plague other cancer treatments. A new promising strategy to enforce tumor selectivity is the use of logic-gated, two-receptor systems. One well-described application is termed Tmod™, which originally utilized a blocking inhibitory receptor directed towards HLA-I target antigens to create a protective NOT gate.

A Comprehensive Investigation of Active Learning Strategies for Conducting Anti-Cancer Drug Screening.

It is well-known that cancers of the same histology type can respond differently to a treatment. Thus, computational drug response prediction is of paramount importance for both preclinical drug screening studies and clinical treatment design. To build drug response prediction models, treatment response data need to be generated through screening experiments and used as input to train the prediction models.

Integration of Computational Docking into Anti-Cancer Drug Response Prediction Models.

Cancer is a heterogeneous disease in that tumors of the same histology type can respond differently to a treatment. Anti-cancer drug response prediction is of paramount importance for both drug development and patient treatment design. Although various computational methods and data have been used to develop drug response prediction models, it remains a challenging problem due to the complexities of cancer mechanisms and cancer-drug interactions.

A Descriptive Methodology for Studying the Ontogeny of Object Play and Breed Differences in Dogs ().

Play behavior is a prominent aspect of juvenile behavior for many animals, yet early development, especially play with objects, has received little attention. Our previous study on object play introduced our general methods, focusing on litter differences in the developmental trajectory of object play and toy preferences. Here, we present a detailed ethogram of more than 30 observed object play behaviors.

Data augmentation and multimodal learning for predicting drug response in patient-derived xenografts from gene expressions and histology images.

Patient-derived xenografts (PDXs) are an appealing platform for preclinical drug studies. A primary challenge in modeling drug response prediction (DRP) with PDXs and neural networks (NNs) is the limited number of drug response samples. We investigate multimodal neural network (MM-Net) and data augmentation for DRP in PDXs.

Deep learning methods for drug response prediction in cancer: Predominant and emerging trends.

Cancer claims millions of lives yearly worldwide. While many therapies have been made available in recent years, by in large cancer remains unsolved. Exploiting computational predictive models to study and treat cancer holds great promise in improving drug development and personalized design of treatment plans, ultimately suppressing tumors, alleviating suffering, and prolonging lives of patients.

Development and External Validation of a Machine Learning Model for Prediction of Lymph Node Metastasis in Patients with Prostate Cancer.

Background: Pelvic lymph node dissection (PLND) is the gold standard for diagnosis of lymph node involvement (LNI) in patients with prostate cancer. The Roach formula, Memorial Sloan Kettering Cancer Center (MSKCC) calculator, and Briganti 2012 nomogram are elegant and simple traditional tools used to estimate the risk of LNI and select patients for PLND.

Objective: To determine whether machine learning (ML) can improve patient selection and outperform currently available tools for predicting LNI using similar readily available clinicopathologic variables.

ERG Status at the Margin Is Associated With Biochemical Recurrence After Radical Prostatectomy With Positive Surgical Margins.

Positive surgical margins at radical prostatectomy are associated with an increased risk of biochemical recurrence (BCR). However, there is considerable variability in outcomes, suggesting that molecular biomarkers-when assessed specifically at the margin tumor tissue-may be useful to stratify prognosis in this group. We used a case-cohort design for the outcome of BCR, selecting 215 patients from a cohort of 813 patients undergoing prostatectomy treated at the Johns Hopkins from 2008 to 2017 with positive margins and available clinical data.

IsoPSA Performance Characteristics are Unaffected by 5-Alpha Reductase Inhibitors or Alpha-Blockers: Results From the IsoPSA Validation Study.

Objective: To. determine the impact of 5-α reductase inhibitors or α-blockers on IsoPSA performance for the detection of actionable prostate cancer.

Materials And Methods: This is a secondary analysis of data from an institutional review board approved, prospective, multicenter(8-sites) study evaluating IsoPSA in men ≥ 50 years of age with a total PSA ≥ 4 ng/mL with planned prostate biopsy who met previously described inclusion and exclusion criteria.

Urinary PSA and Serum PSA for Aggressive Prostate Cancer Detection.

Serum PSA, together with digital rectal examination and imaging of the prostate gland, have remained the gold standard in urological practices for the management of and intervention for prostate cancer. Based on these adopted practices, the limitations of serum PSA in identifying aggressive prostate cancer has led us to evaluate whether urinary PSA levels might have any clinical utility in prostate cancer diagnosis. Utilizing the Access Hybritech PSA assay, we evaluated a total of n = 437 urine specimens from post-DRE prostate cancer patients.

AI-accelerated protein-ligand docking for SARS-CoV-2 is 100-fold faster with no significant change in detection.

Protein-ligand docking is a computational method for identifying drug leads. The method is capable of narrowing a vast library of compounds down to a tractable size for downstream simulation or experimental testing and is widely used in drug discovery. While there has been progress in accelerating scoring of compounds with artificial intelligence, few works have bridged these successes back to the virtual screening community in terms of utility and forward-looking development.

Urinary marker panels for aggressive prostate cancer detection.

Majority of patients with indolent prostate cancer (PCa) can be managed with active surveillance. Therefore, finding biomarkers for classifying patients between indolent and aggressive PCa is essential. In this study, we investigated urinary marker panels composed of urinary glycopeptides and/or urinary prostate-specific antigen (PSA) for their clinical utility in distinguishing non-aggressive (Grade Group 1) from aggressive (Grade Group ≥ 2) PCa.

Cell Cycle Progression Score, but Not Phosphatase and Tensin Homolog Loss, Is an Independent Prognostic Factor for Metastasis in Intermediate- and High-risk Prostate Cancer in Men Treated With and Without Salvage Radiotherapy.

Purpose: The prognostic value for metastasis of the cell-cycle progression score and phosphatase and tensin homolog haven't been evaluated jointly in contemporary men with exclusively intermediate- or high-risk prostate cancer. We evaluated associations of cell-cycle progression and phosphatase and tensin homolog with metastasis-free survival in contemporary intermediate/high-risk prostate cancer patients overall, and intermediate/high-risk men receiving salvage radiotherapy.

Materials And Methods: In a case-cohort of 209 prostatectomy patients with intermediate/high-risk prostate cancer, and a cohort of 172 such men who received salvage radiotherapy, cell-cycle progression score was calculated from RNA expression, and phosphatase and tensin homolog was analyzed by immunohistochemistry.

Clinical validation of IsoPSA, a single parameter, structure-focused assay for improved detection of prostate cancer: A prospective, multicenter study.

Background: IsoPSA is a blood-based test that assesses prostate cancer (CaP) risk by partitioning and detecting cancer-specific structural isoforms of prostate specific antigen (PSA) with an aqueous 2- phase system.

Objective: To validate the diagnostic performance of IsoPSA for High-Grade CaP and Any CaP risk on biopsy in men age ≥ 50 with total PSA ≥ 4 ng/ml.

Design, Setting, And Participants: Prospective, multicenter study of 888 men scheduled for prostate biopsy at 8 academic and community sites between August 2015 and August 2020.

Protocol for high-throughput cloning, expression, purification, and evaluation of bispecific antibodies.

Bispecific antibodies are a powerful new class of therapeutics, but their development often requires enormous amounts of time and resources. Here, we describe a high-throughput protocol for cloning, expressing, purifying, and evaluating bispecific antibodies. This protocol enables the rapid screening of large panels of bispecific molecules to identify top candidates for further development.

Predicting high-grade prostate cancer at initial biopsy: clinical performance of the ExoDx (EPI) Prostate Intelliscore test in three independent prospective studies.

Background: The ability to discriminate indolent from clinically significant prostate cancer (PC) at the initial biopsy remains a challenge. The ExoDx Prostate (IntelliScore) (EPI) test is a noninvasive liquid biopsy that quantifies three RNA targets in urine exosomes. The EPI test stratifies patients for risk of high-grade prostate cancer (HGPC; ≥ Grade Group 2 [GG] PC) in men ≥ 50 years with equivocal prostate-specific antigen (PSA) (2-10 ng/mL).

A cross-study analysis of drug response prediction in cancer cell lines.

To enable personalized cancer treatment, machine learning models have been developed to predict drug response as a function of tumor and drug features. However, most algorithm development efforts have relied on cross-validation within a single study to assess model accuracy. While an essential first step, cross-validation within a biological data set typically provides an overly optimistic estimate of the prediction performance on independent test sets.

Prospective Multicenter Comparison of Open and Robotic Radical Prostatectomy: The PROST-QA/RP2 Consortium.

Purpose: Our goal was to evaluate the comparative effectiveness of robot-assisted laparoscopic prostatectomy (RALP) and open radical prostatectomy (ORP) in a multicenter study.

Materials And Methods: We evaluated men with localized prostate cancer at 11 high-volume academic medical centers in the United States from the PROST-QA (2003-2006) and the PROST-QA/RP2 cohorts (2010-2013) with a pre-specified goal of comparing RALP (549) and ORP (545). We measured longitudinal patient-reported health-related quality of life (HRQOL) at pre-treatment and at 2, 6, 12, and 24 months, and pathological and perioperative outcomes/complications.

Development of Parallel Reaction Monitoring Assays for the Detection of Aggressive Prostate Cancer Using Urinary Glycoproteins.

Recently, we have found that two urinary glycoproteins, prostatic acid phosphatase (ACPP) and clusterin (CLU), combined with serum prostate-specific antigen (PSA) can serve as a three-signature panel for detecting aggressive prostate cancer (PCa) based on a quantitative glycoproteomic study. To facilitate the translation of candidates into clinically applicable tests, robust and accurate targeted parallel reaction monitoring (PRM) assays that can be widely adopted in multiple labs were developed in this study. The developed PRM assays for the urinary glycopeptides, FLN*ESYK from ACPP and EDALN*ETR from CLU, demonstrated good repeatability and a sufficient working range covering three to four orders of magnitude, and their performance in differentiating aggressive PCa was assessed by the quantitative analysis of urine specimens collected from 69 nonaggressive (Gleason score = 6) and 73 aggressive (Gleason ≥ 8) PCa patients.