Dataset Information

Identifying prostate cancer and its clinical risk in asymptomatic men using machine learning of high dimensional peripheral blood flow cytometric natural killer cell subset phenotyping data.

ABSTRACT: We demonstrate that prostate cancer can be identified by flow cytometric profiling of blood immune cell subsets. Herein, we profiled natural killer (NK) cell subsets in the blood of 72 asymptomatic men with Prostate-Specific Antigen (PSA) levels < 20 ng ml-1, of whom 31 had benign disease (no cancer) and 41 had prostate cancer. Statistical and computational methods identified a panel of eight phenotypic features ([Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text]) that, when incorporated into an Ensemble machine learning prediction model, distinguished between the presence of benign prostate disease and prostate cancer. The machine learning model was then adapted to predict the D'Amico Risk Classification using data from 54 patients with prostate cancer and was shown to accurately differentiate between the presence of low-/intermediate-risk disease and high-risk disease without the need for additional clinical data. This simple blood test has the potential to transform prostate cancer diagnostics.

SUBMITTER: Hood SP

PROVIDER: S-EPMC7386909 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Identifying prostate cancer and its clinical risk in asymptomatic men using machine learning of high dimensional peripheral blood flow cytometric natural killer cell subset phenotyping data.

Hood Simon P SP Cosma Georgina G Foulds Gemma A GA Johnson Catherine C Reeder Stephen S McArdle Stéphanie E SE Khan Masood A MA Pockley A Graham AG

eLife 20200728

We demonstrate that prostate cancer can be identified by flow cytometric profiling of blood immune cell subsets. Herein, we profiled natural killer (NK) cell subsets in the blood of 72 asymptomatic men with Prostate-Specific Antigen (PSA) levels < 20 ng ml<sup>-1</sup>, of whom 31 had benign disease (no cancer) and 41 had prostate cancer. Statistical and computational methods identified a panel of eight phenotypic features ([Formula: see text], [Formula: see text], [Formula: see text], [Formula: ...[more]

PMID: 32717179

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Project description:Detecting the presence of prostate cancer (PCa) and distinguishing low- or intermediate-risk disease from high-risk disease early, and without the need for potentially unnecessary invasive biopsies remains a significant clinical challenge. The aim of this study is to determine whether the T and B cell phenotypic features which we have previously identified as being able to distinguish between benign prostate disease and PCa in asymptomatic men having Prostate-Specific Antigen (PSA) levels < 20 ng/ml can also be used to detect the presence and clinical risk of PCa in a larger cohort of patients whose PSA levels ranged between 3 and 2617 ng/ml. The peripheral blood of 130 asymptomatic men having elevated Prostate-Specific Antigen (PSA) levels was immune profiled using multiparametric whole blood flow cytometry. Of these men, 42 were subsequently diagnosed as having benign prostate disease and 88 as having PCa on biopsy-based evidence. We built a bidirectional Long Short-Term Memory Deep Neural Network (biLSTM) model for detecting the presence of PCa in men which combined the previously-identified phenotypic features (CD8+CD45RA-CD27-CD28- (CD8+ Effector Memory cells), CD4+CD45RA-CD27-CD28- (CD4+ Effector Memory cells), CD4+CD45RA+CD27-CD28- (CD4+ Terminally Differentiated Effector Memory Cells re-expressing CD45RA), CD3-CD19+ (B cells), CD3+CD56+CD8+CD4+ (NKT cells) with Age. The performance of the PCa presence 'detection' model was: Acc: 86.79 ( ± 0.10), Sensitivity: 82.78% (± 0.15); Specificity: 95.83% (± 0.11) on the test set (test set that was not used during training and validation); AUC: 89.31% (± 0.07), ORP-FPR: 7.50% (± 0.20), ORP-TPR: 84.44% (± 0.14). A second biLSTM 'risk' model combined the immunophenotypic features with PSA to predict whether a patient with PCa has high-risk disease (defined by the D'Amico Risk Classification) achieved the following: Acc: 94.90% (± 6.29), Sensitivity: 92% (± 21.39); Specificity: 96.11 (± 0.00); AUC: 94.06% (± 10.69), ORP-FPR: 3.89% (± 0.00), ORP-TPR: 92% (± 21.39). The ORP-FPR for predicting the presence of PCa when combining FC+PSA was lower than that of PSA alone. This study demonstrates that AI approaches based on peripheral blood phenotyping profiles can distinguish between benign prostate disease and PCa and predict clinical risk in asymptomatic men having elevated PSA levels.

Project description:Our laboratory has recently demonstrated that natural killer (NK) cells are capable of eradicating orthotopically implanted mouse GL26 and rat CNS-1 malignant gliomas soon after intracranial engraftment if the cancer cells are rendered deficient in their expression of the β-galactoside-binding lectin galectin-1 (gal-1). More recent work now shows that a population of Gr-1+/CD11b+ myeloid cells is critical to this effect. To better understand the mechanisms by which NK and myeloid cells cooperate to confer gal-1-deficient tumor rejection we have developed a comprehensive protocol for the isolation and analysis of glioma-infiltrating peripheral blood mononuclear cells (PBMC). The method is demonstrated here by comparing PBMC infiltration into the tumor microenvironment of gal-1-expressing GL26 gliomas with those rendered gal-1-deficient via shRNA knockdown. The protocol begins with a description of how to culture and prepare GL26 cells for inoculation into the syngeneic C57BL/6J mouse brain. It then explains the steps involved in the isolation and flow cytometric analysis of glioma-infiltrating PBMCs from the early brain tumor microenvironment. The method is adaptable to a number of in vivo experimental designs in which temporal data on immune infiltration into the brain is required. The method is sensitive and highly reproducible, as glioma-infiltrating PBMCs can be isolated from intracranial tumors as soon as 24 hr post-tumor engraftment with similar cell counts observed from time point matched tumors throughout independent experiments. A single experimentalist can perform the method from brain harvesting to flow cytometric analysis of glioma-infiltrating PBMCs in roughly 4-6 hr depending on the number of samples to be analyzed. Alternative glioma models and/or cell-specific detection antibodies may also be used at the experimentalists' discretion to assess the infiltration of several other immune cell types of interest without the need for alterations to the overall procedure.

Dataset Information

Identifying prostate cancer and its clinical risk in asymptomatic men using machine learning of high dimensional peripheral blood flow cytometric natural killer cell subset phenotyping data.

Publications

Identifying prostate cancer and its clinical risk in asymptomatic men using machine learning of high dimensional peripheral blood flow cytometric natural killer cell subset phenotyping data.

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