Application of Artificial Intelligence/Machine Vision & Learning for the Development of a Live Single-cell Phenotypic Biomarker Test to Predict Prostate Cancer Tumor Aggressiveness.

To assess the usefulness and applications of machine vision (MV) and machine learning (ML) techniques that have been used to develop a single cell-based phenotypic (live and fixed biomarkers) platform that correlates with tumor biological aggressiveness and risk stratification, 100 fresh prostate samples were acquired, and areas of prostate cancer were determined by post-surgery pathology reports logged by an independent pathologist. The prostate samples were dissociated into single-cell suspensions in the presence of an extracellular matrix formulation. These samples were analyzed via live-cell microscopy. Dynamic and fixed phenotypic biomarkers per cell were quantified using objective MV software and ML algorithms. The predictive nature of the ML algorithms was developed in two stages. First, random forest (RF) algorithms were developed using 70% of the samples. The developed algorithms were then tested for their predictive performance using the blinded test dataset that contained 30% of the samples in the second stage. Based on the ROC (receiver operating characteristic) curve analysis, thresholds were set to maximize both sensitivity and specificity. We determined the sensitivity and specificity of the assay by comparing the algorithm-generated predictions with adverse pathologic features in the radical prostatectomy (RP) specimens. Using MV and ML algorithms, the biomarkers predictive of adverse pathology at RP were ranked and a prostate cancer patient risk stratification test was developed that distinguishes patients based on surgical adverse pathology features. The ability to identify and track large numbers of individual cells over the length of the microscopy experimental monitoring cycles, in an automated way, created a large biomarker dataset of primary biomarkers. This biomarker dataset was then interrogated with ML algorithms used to correlate with post-surgical adverse pathology findings. Algorithms were generated that predicted adverse pathology with >0.85 sensitivity and specificity and an AUC (area under the curve) of >0.85. Phenotypic biomarkers provide cellular and molecular details that are informative for predicting post-surgical adverse pathologies when considering tumor biopsy samples. Artificial intelligence ML-based approaches for cancer risk stratification are emerging as important and powerful tools to compliment current measures of risk stratification. These techniques have capabilities to address tumor heterogeneity and the molecular complexity of prostate cancer. Specifically, the phenotypic test is a novel example of leveraging biomarkers and advances in MV and ML for developing a powerful prognostic and risk-stratification tool for prostate cancer patients.

Clinical Proof-of-concept of a Novel Platform Utilizing Biopsy-derived Live Single Cells, Phenotypic Biomarkers, and Machine Learning Toward a Precision Risk Stratification Test for Prostate Cancer Grade Groups 1 and 2 (Gleason 3 + 3 and 3 + 4).

OBJECTIVE: To examine the ability of a novel live primary-cell phenotypic (LPCP) test to predict postsurgical adverse pathology (P-SAP) features and risk stratify patients based on SAP features in a blinded study utilizing radical prostatectomy (RP) surgical specimens. METHODS: Two hundred fifty-one men undergoing RP were enrolled in a prospective, multicenter (10), and proof-of-concept study in the United States. Fresh prostate samples were taken from known areas of cancer in the operating room immediately after RP. Samples were shipped and tested at a central laboratory. Utilizing the LPCP test, a suite of phenotypic biomarkers was analyzed and quantified using objective machine vision software. Biomarkers were objectively ranked via machine learning-derived statistical algorithms (MLDSA) to predict postsurgical adverse pathological features. Sensitivity and specificity were determined by comparing blinded predictions and unblinded RP surgical pathology reports, training MLDSAs on 70% of biopsy cells and testing MLDSAs on the remaining 30% of biopsy cells across the tested patient population. RESULTS: The LPCP test predicted adverse pathologies post-RP with area under the curve (AUC) via receiver operating characteristics analysis of greater than 0.80 and distinguished between Prostate Cancer Grade Groups 1, 2, and 3/Gleason Scores 3 + 3, 3 + 4, and 4 + 3. Further, LPCP derived-biomarker scores predicted Gleason pattern, stage, and adverse pathology with high precision-AUCs>0.80. CONCLUSION: Using MLDSA-derived phenotypic biomarker scores, the LPCP test successfully risk stratified Prostate Cancer Grade Groups 1, 2, and 3 (Gleason 3 + 3 and 7) into distinct subgroups predicted to have surgical adverse pathologies or not with high performance (>0.85 AUC).

Live-cell phenotypic-biomarker microfluidic assay for the risk stratification of cancer patients via machine learning.

The risk stratification of prostate cancer and breast cancer tumours from patients relies on histopathology, selective genomic testing, or on other methods employing fixed formalin tissue samples. However, static biomarker measurements from bulk fixed-tissue samples provide limited accuracy and actionability. Here, we report the development of a live-primary-cell phenotypic-biomarker assay with single-cell resolution, and its validation with prostate cancer and breast cancer tissue samples for the prediction of post-surgical adverse pathology. The assay includes a collagen-I/fibronectin extracellular-matrix formulation, dynamic live-cell biomarkers, a microfluidic device, machine-vision analysis and machine-learning algorithms, and generates predictive scores of adverse pathology at the time of surgery. Predictive scores for the risk stratification of 59 prostate cancer patients and 47 breast cancer patients, with values for area under the curve in receiver-operating-characteristic curves surpassing 80%, support the validation of the assay and its potential clinical applicability for the risk stratification of cancer patients.

Rapid and Short-term Extracellular Matrix-mediated In Vitro Culturing of Tumor and Nontumor Human Primary Prostate Cells From Fresh Radical Prostatectomy Tissue.

OBJECTIVE: To culture prostate cells from fresh biopsy core samples from radical prostatectomy (RP) tissue. Further, given the genetic heterogeneity of prostate cells, the ability to culture single cells from primary prostate tissue may be of importance toward enabling single-cell characterization of primary prostate tissue via molecular and cellular phenotypic biomarkers. METHODS: A total of 260 consecutive tissue samples from RPs were collected between October 2014 and January 2016, transported at 4°C in serum-free media to an off-site central laboratory, dissociated, and cultured. A culture protocol, including a proprietary extracellular matrix formulation (ECMf), was developed that supports rapid and short-term single-cell culture of primary human prostate cells derived from fresh RP samples. RESULTS: A total of 251 samples, derived from RP samples, yielded primary human tumor and nontumor prostate cells. Cultured cells on ECMf exhibit (1) survival after transport from the operating room to the off-site centralized laboratory, (2) robust (>80%) adhesion and survival, and (3) expression of different cell-type-specific markers. Cells derived from samples of increasing Gleason score exhibited a greater number of focal adhesions and more focal adhesion activation as measured by phospho-focal adhesion kinase (Y397) immunofluorescence when patient-derived cells were cultured on ECMf. Increased Ki67 immunofluorescence levels were observed in cells derived from cancerous RP tissue when compared to noncancerous RP tissue. CONCLUSION: By utilizing a unique and defined extracellular matrix protein formulation, tumor and nontumor cells derived from primary human prostate tissue can be rapidly cultured and analyzed within 72 hours after harvesting from RP tissue.

Parabolic flight induces changes in gene expression patterns in Arabidopsis thaliana.

Our primary objective was to evaluate gene expression changes in Arabidopsis thaliana in response to parabolic flight as part of a comprehensive approach to the molecular biology of spaceflight-related adaptations. In addition, we wished to establish parabolic flight as a tractable operations platform for molecular biology studies. In a succession of experiments on NASA's KC-135 and C-9 parabolic aircraft, Arabidopsis plants were presented with replicated exposure to parabolic flight. Transcriptome profiling revealed that parabolic flight caused changes in gene expression patterns that stood the statistical tests of replication on three different flight days. The earliest response, after 20 parabolas, was characterized by a prominence of genes associated with signal transduction. After 40 parabolas, this prominence was largely replaced by genes associated with biotic and abiotic stimuli and stress. Among these responses, three metabolic processes stand out in particular: the induction of auxin metabolism and signaling, the differential expression of genes associated with calcium-mediated signaling, and the repression of genes associated with disease resistance and cell wall biochemistry. Many, but not all, of these responses are known to be involved in gravity sensing in plants. Changes in auxin-related gene expression were also recorded by reporter genes tuned to auxin signal pathways. These data demonstrate that the parabolic flight environment is appropriate for molecular biology research involving the transition to microgravity, in that with replication, proper controls, and analyses, gene expression changes can be observed in the time frames of typical parabolic flight experiments.

p31(comet) acts to ensure timely spindle checkpoint silencing subsequent to kinetochore attachment.

The spindle assembly checkpoint links the onset of anaphase to completion of chromosome-microtubule attachment and is mediated by the binding of Mad and Bub proteins to kinetochores of unattached or maloriented chromosomes. Mad2 and BubR1 traffic between kinetochores and the cytosol, thereby transmitting a "wait anaphase" signal to the anaphase-promoting complex. It is generally assumed that this signal dissipates automatically upon kinetochore-microtubule binding, but it has been shown that under conditions of nocodazole-induced arrest p31(comet), a Mad2-binding protein, is required for mitotic progression. In this article we investigate the localization and function of p31(comet) during normal, unperturbed mitosis in human and marsupial cells. We find that, like Mad2, p31(comet) traffics on and off kinetochores and is also present in the cytosol. Cells depleted of p31(comet) arrest in metaphase with mature bipolar kinetochore-microtubule attachments, a satisfied checkpoint, and high cyclin B levels. Thus p31(comet) is required for timely mitotic exit. We propose that p31(comet) is an essential component of the machinery that silences the checkpoint during each cell cycle.

APC16 is a conserved subunit of the anaphase-promoting complex/cyclosome.

Error-free chromosome segregation depends on timely activation of the multi-subunit E3 ubiquitin ligase APC/C. Activation of the APC/C initiates chromosome segregation and mitotic exit by targeting critical cell-cycle regulators for destruction. The APC/C is the principle target of the mitotic checkpoint, which prevents segregation while chromosomes are unattached to spindle microtubules. We now report the identification and characterization of APC16, a conserved subunit of the APC/C. APC16 was found in association with tandem-affinity-purified mitotic checkpoint complex protein complexes. APC16 is a bona fide subunit of human APC/C: it is present in APC/C complexes throughout the cell cycle, the phenotype of APC16-depleted cells copies depletion of other APC/C subunits, and APC16 is important for APC/C activity towards mitotic substrates. APC16 sequence homologues can be identified in metazoans, but not fungi, by four conserved primary sequence stretches. We provide evidence that the C. elegans gene K10D2.4 and the D. rerio gene zgc:110659 are functional equivalents of human APC16. Our findings show that APC/C is composed of previously undescribed subunits, and raise the question of why metazoan APC/C is molecularly different from unicellular APC/C.

Small-molecule kinase inhibitors provide insight into Mps1 cell cycle function.

Mps1, a dual-specificity kinase, is required for the proper functioning of the spindle assembly checkpoint and for the maintenance of chromosomal stability. As Mps1 function has been implicated in numerous phases of the cell cycle, the development of a potent, selective small-molecule inhibitor of Mps1 should facilitate dissection of Mps1-related biology. We describe the cellular effects and Mps1 cocrystal structures of new, selective small-molecule inhibitors of Mps1. Consistent with RNAi studies, chemical inhibition of Mps1 leads to defects in Mad1 and Mad2 establishment at unattached kinetochores, decreased Aurora B kinase activity, premature mitotic exit and gross aneuploidy, without any evidence of centrosome duplication defects. However, in U2OS cells having extra centrosomes (an abnormality found in some cancers), Mps1 inhibition increases the frequency of multipolar mitoses. Lastly, Mps1 inhibitor treatment resulted in a decrease in cancer cell viability.

Divalent cation effects on interactions between multiple Arabidopsis 14-3-3 isoforms and phosphopeptide targets.

Oscillations in cellular divalent cation concentrations are key events that can trigger signal transduction cascades. Common cellular divalent cations, such as calcium and magnesium, interact with 14-3-3 proteins. The metal ion interaction causes a conformational change in the 14-3-3 proteins, which is manifested as an increase in hydrophobicity. In this study, the effect of divalent cations on the interaction between 14-3-3 proteins and target peptides was investigated using surface plasmon resonance and isothermal titration calorimetry. The binding between ten recombinant Arabidopsis 14-3-3 isoforms and two synthetic target peptides was observed in the presence of various physiologically relevant concentrations of calcium or magnesium, from 1 microM to 1 mM or from 1 microM to 5 mM, respectively. The synthetic target peptides were based on sequences from Arabidopsis nitrate reductase (NR2) and the plasma membrane proton pump (AHA2) representing fundamentally different target classes. Isoforms representing every branch of the Arabidopsis 14-3-3 phylogenetic tree were tested. The general result for all cases is that an increased concentration of divalent cations in solution causes an increase in the concentration of 14-3-3 protein interacting with the respective phosphopeptide.

Hypobaric biology: Arabidopsis gene expression at low atmospheric pressure.

As a step in developing an understanding of plant adaptation to low atmospheric pressures, we have identified genes central to the initial response of Arabidopsis to hypobaria. Exposure of plants to an atmosphere of 10 kPa compared with the sea-level pressure of 101 kPa resulted in the significant differential expression of more than 200 genes between the two treatments. Less than one-half of the genes induced by hypobaria are similarly affected by hypoxia, suggesting that response to hypobaria is unique and is more complex than an adaptation to the reduced partial pressure of oxygen inherent to hypobaric environments. In addition, the suites of genes induced by hypobaria confirm that water movement is a paramount issue at low atmospheric pressures, because many of gene products intersect abscisic acid-related, drought-induced pathways. A motivational constituent of these experiments is the need to address the National Aeronautics and Space Administration's plans to include plants as integral components of advanced life support systems. The design of bioregenerative life support systems seeks to maximize productivity within structures engineered to minimize mass and resource consumption. Currently, there are severe limitations to producing Earth-orbital, lunar, or Martian plant growth facilities that contain Earth-normal atmospheric pressures within light, transparent structures. However, some engineering limitations can be offset by growing plants in reduced atmospheric pressures. Characterization of the hypobaric response can therefore provide data to guide systems engineering development for bioregenerative life support, as well as lead to fundamental insights into aspects of desiccation metabolism and the means by which plants monitor water relations.

The 14-3-3s.

Multiple members of the 14-3-3 protein family have been found in all eukaryotes so far investigated, yet they are apparently absent from prokaryotes. The major native forms of 14-3-3s are homo- and hetero-dimers, the biological functions of which are to interact physically with specific client proteins and thereby effect a change in the client. As a result, 14-3-3s are involved in a vast array of processes such as the response to stress, cell-cycle control, and apoptosis, serving as adapters, activators, and repressors. There are currently 133 full-length sequences available in GenBank for this highly conserved protein family. A phylogenetic tree based on the conserved middle core region of the protein sequences shows that, in plants, the 14-3-3 family can be divided into two clearly defined groups. The core region encodes an amphipathic groove that binds the multitude of client proteins that have conserved 14-3-3-recognition sequences. The amino and carboxyl termini of 14-3-3 proteins are much more divergent than the core region and may interact with isoform-specific client proteins and/or confer specialized subcellular and tissue localization.

Remote sensing of gene expression in Planta: transgenic plants as monitors of exogenous stress perception in extraterrestrial environments.

Transgenic arabidopsis plants containing the alcohol dehydrogenase (Adh) gene promoter fused to the green fluorescent protein (GFP) reporter gene were developed as biological sensors for monitoring physiological responses to unique environments. Plants were monitored in vivo during exposure to hypoxia, high salt, cold, and abcissic acid in experiments designed to characterize the utility and responses of the Adh/GFP biosensors. Plants in the presence of environmental stimuli that induced the Adh promoter responded by expressing GFP, which in turn generated a detectable fluorescent signal. The GFP signal degraded when the inducing stimulus was removed. Digital imaging of the Adh/GFP plants exposed to each of the exogenous stresses demonstrated that the stress-induced gene expression could be followed in real time. The experimental results established the feasibility of using a digital monitoring system for collecting gene expression data in real time from Transgenic Arabidopsis Gene Expression System (TAGES) biosensor plants during space exploration experiments.