Patent Description:
Extracellular vesicles (EVs) hold great potential for diagnostics and prognostics in a variety of fields such as immunology, neurology, cardiology, and oncology. EVs include exosomes (<NUM>-<NUM>), microvesicles (<NUM>-<NUM>,<NUM>), apoptotic bodies (<NUM>-<NUM>,<NUM>), and very large oncosomes (<NUM>,<NUM>-<NUM>,<NUM>). Healthy and diseased cells continuously release EVs which contain many of the mRNA, miRNA, and protein markers from their cells of origin. EVs have been found in nearly all biological fluids including blood, urine, semen, and cerebrospinal fluid, making them promising targets for minimally-invasive diagnostic assays.

Multiple methods exist for EV characterization (<NPL>). Electron microscopy provides the highest resolution images of EVs but lacks high-throughput data acquisition, cannot easily measure many markers simultaneously, and may require time consuming and complicated data analysis since the raw data are images (<NPL>). Nanoparticle tracking analysis and tunable resistive pulse sensing allow rapid enumeration and sizing of particles but are not ideal for characterizing EV markers (<NPL>; <NPL>). Microflow cytometry (µFCM), also referred to as nanoscale or high sensitivity flow cytometry allows high-throughput characterization of the optical properties of particles, allowing quantification of particle size, concentration, and marker abundance for millions of EVs in minutes (Szatenek supra). These desirable characteristics make µFCM well-suited for highsensitivity EV-based clinical assays.

µFCM generates large amounts of data which complicates analysis. A typical <NUM>µL plasma sample that has been diluted <NUM>-fold can generate over <NUM>,<NUM>,<NUM> events each in a single minute of analysis with over a dozen optical properties. In other words, a single µL of plasma can have ><NUM> events. Other liquid biopsy types can have similar concentrations. The analysis of EVs by µFCM can examine at least <NUM> -<NUM>,<NUM> fold more sample events compared to Nanoparticle (NTA) or electron microscopy per sample analysis, providing a greater representative analysis of the whole sample. Traditional cell-based flow cytometry analysis typically involves generating bivariate scatter plots and quantifying event concentration within user-defined regions of interest (ROIs) over <NUM> quadrants since many cells have similar size and are characterized as marker positive or negative. Such methods are too simplistic for µFCM since EVs range in size and hence in marker abundance which necessitates the development of µFCM analysis tools that can rapidly process very large complex data sets.

When generating an EV-based diagnostic/prognostic assay, EVs must not only be characterized within biological samples but also analyzed for their ability to predict clinically meaningful conditions which can improve patient well-being and/or healthcare economics.

<NPL>, also discloses a prior art method of diagnosing a disease signature for a disease in a patient.

According to an aspect of the present invention, there is provided a method of diagnosing disease signature for a disease in a patient according to claim <NUM>. Further aspects of the invention are defined in the dependent claims.

These and other embodiments and features will be better understood with reference to the following description and drawings, in which:.

Described herein are embodiments illustrative of biomarkers for diagnosing disease, including clinically significant prostate cancer; methods of diagnosing disease, including clinically significant prostate cancer; and methods of developing disease prediction models and diagnostic tests using the same. It will be appreciated that the embodiments and examples described herein are for illustrative purposes intended for those skilled in the art and are not meant to be limiting in any way. All references to embodiments or examples throughout the disclosure should be considered a reference to an illustrative and non-limiting embodiment or an illustrative and non-limiting example.

It must also be noted that, as used in the specification and the appended claims, the singular forms "a," "an" and "the" include plural referents unless the context clearly dictates otherwise. For example, reference to an "antigen" or "antibody" is intended to include a plurality of antigen molecules or antibodies.

A method of diagnosing disease, such as clinically significant prostate cancer, in a patient is provided. For the purpose of the present discussion, "clinically significant prostate cancer" means a prostate cancer with a Gleason Group <NUM> or higher. The method involves the steps of: incubating a sample from the patient with one or more probes and/or antibodies which bind one or more biomarkers for the disease of interest; subjecting the sample to microflow cytometry; obtaining signal intensities for the one or more biomarkers and, optionally, obtaining one or more optical properties associated with the sample; processing the signal intensities and, if obtained, the one or more optical properties using custom algorithms to determine the concentration of different particle phenotypes in the patient sample; and diagnosing the patient with the disease based on the output of a machine learning algorithm using particle phenotype concentration data from patient samples as inputs for machine learning. In one embodiment, the disease may be cancer, in particular clinically significant prostate cancer, and the biomarkers correlating to cancer biomarkers, in particular clinically significant prostate cancer biomarkers.

A method of identifying a disease signature is also provided. The method involves the steps of: incubating samples from a healthy subjects and samples from subjects with a known disease with one or more probes which bind to one or more biomarkers for a disease; subjecting the samples to microflow cytometry; obtaining signal intensities for the one or more biomarkers and, optionally, obtaining one or more optical properties associated with each sample; log transforming the signal intensities from the one or more biomarkers and, if present, the one or more optical properties to produce transformed signal intensities; binning particles with similar transformed signal intensities in regions of interest (ROI); determining the concentration of particles in each ROI (which is calculated by dividing the total particle counts in each ROI by the sample volume analyzed during data acquisition), comparing the particle concentration data in each ROI between the samples from healthy subjects and samples from the subjects with a known disease; determining receiver operator characteristic (ROC) area under the curve (AUC) values for each ROI from each combination of markers; and selecting a combination of biomarkers that provides the highest AUC values to obtain the disease signature for the disease.

Samples that are useful in the present invention include, but are not limited to, biological samples, such as blood (or components thereof), semen, milk, etc. In the present invention, extracellular vesicles do not need to be isolated and purified, as is required in other methods. Instead, serum or plasma can be isolated from blood as per standard clinical diagnostic procedures and used, without further purification and processing, in the methods described herein.

The samples are incubated with probes associated with the biomarkers for the disease of interest or the disease being diagnosed, or a particular type of small particle like in this case an EV. Probes can include, but are not limited to, whole antibodies or antibody components such as F(ab), F(ab')<NUM> or F(ab') fragments, minibodies, etc. against specific antigens, or peptides against specific targets. Probes can also include various dyes that permit identification of particular components in the sample. For example, incubation with lipophilic dyes which stain membrane bound small particles can aid in the segregation of protein aggregates from lipid bound particles in a sample. Typically probes will have a directly conjugated secondary component, such as a fluorescent conjugate, that aids in the detection of the probe bound target.

To detect PSMA positive EVs, the sample can be incubated with the PSMA specific monoclonal antibody J591 (available through BZL Biologics, LLC) which has been directly conjugated with a dye such as DyLight <NUM>. Alternatively a non-conjugated probe (e.g. PSMA specific monoclonal antibody J591), after incubation with sample, may be further incubated with a secondary agent to identify the primary probe used in the assay. For example, incubation with the Qdot565-conjugated donkey anti-mouse IgG antibody, which then permits detection in the µFCM assay. Typically, the biomarker probes will be specific to a biological molecule that is only or primarily expressed in cells or tissues affected by the disease of interest. However, the biomarkers can be specific for a particular cell type. Moreover, more than one biomarker can be used to identify more than one feature of the disease of interest and/or cell type.

Incubation of samples with biomarker probes may be done as a single sample + single probe format, or as a single sample + multiple probe formats. The format of the incubation may provide different answers as each biomarker probe may provide different information on EV populations in a sample. For example, a probe may indicate lipid bound versus non-lipid bound events, or an epithelial versus non-epithelial particle origin. In other cases a probe may indicate disease presence, disease presence and aggressiveness. Multiple probes in an incubation can have similar indications of particle origin, disease presence, and disease aggressiveness. Thus the combination of probes may have significant implications for detection of a disease phenotype.

Particle size and enumeration can be estimated by light scatter. The light scatter characteristics combined with the fluorescence intensity described above can provide a unique phenotype for each particle. These particle phenotypes can be used singular or combined with multiple biomarkers can provided a unique disease signature for the disease of interest.

The samples are then subjected to µFCM, using a commercially available machine, such as, but not limited to, the Apogee A50 microflow cytometer or the CytoFLEX or DxFlex Flow Cytometer. Raw data obtained from the µFCM analysis can be extracted using algorithms, written in MATLAB, R, or Python, and organized as individual particles as rows and light scatter and fluorescence intensities as columns. The time each particle was recorded can be represented in a separate column.

The minimum and maximum cut-offs for light scatter/fluorescence intensity for each particle phenotype can be determined through optimization experiments, which involve using a range of different cut-offs for a range of different light scatter/fluorescence intensities and identifying the cut-offs that provide the highest receiver operator characteristic under the curve from previously acquired patient data.

The number of particles in each particle phenotype can be determined using custom processing scripts which groups particles with similar light scatter and marker intensity. Particle phenotype concentrations are calculated based on particle phenotype counts, the length of time the sample was run, the sample flow rate of the µFCM, and the dilution factor of the sample. If the patient has more than one FCM data file (i.e. multiple replicates), particle phenotype concentrations can be averaged across all replicate µFCM date files.

It is also possible to calculate particle phenotype concentrations in samples using a Dynamic Fluorescence Thresholding algorithm by identifying the biomarker positivity status for each particle in each patient. To determine if particles for each patient sample are positive for a biomarker, a kernel density estimation (KDE) function is applied to the histogram plots of a single probe signal from a single patient sample. The fluorescence value F1 is identified as the region on the X-axis (fluorescence intensity) of the histogram plot that intersects with the highest region on the Y-axis (particle density) on the KDE plot for the biomarker negative particle population, which is the largest peak near the left side of the KDE plot. Next, the slopes of the KDE curve are calculated for many different higher signal intensities from F1. A second fluorescence value, F2, is identified from where the slope is most negative, which is half way down the right side of the negative particle population. The fluorescence intensity value that separates biomarker positive and negative particles (Fs) is equal to F1 + (<NUM> * (F2 - F1)) + F3 where F3 is a small arbitrary fluorescence intensity value that is added to help ensure biomarker negative particles are not classified as biomarker positive particles. Particles with fluorescence intensities above or below Fs are positive or negative for the biomarker, respectively. Once all particles for each patient are classified as positive / negative for each biomarker, the log transformed light scatter signals, used to estimate particle size, are binned into an arbitrary number of groups. Finally, particle phenotypes are created based on all possible combinations of biomarker status (negative / positive) as well as light scatter bin.

From the data collected above, a data set for machine learning is constructed. A table can be created with particle phenotype concentrations for all patients. In one iteration, rows can represent patients and columns represent particle phenotype concentration. However, it will be clear to a person skilled in the art that the data can be represented in an opposite manner or in some other tabular form.

Clinically relevant data can be added as additional columns, or rows depending on how the data set is created, to the table. This data can be used as additional features for machine learning (e.g., does PSA with the µFCM data provide better predictions of who has clinically significant prostate cancer?) or it may be used as labels that the machine learning algorithms need to predict (e.g., identification of which patients have clinically significant prostate cancer).

Once the data set is created, an optimized machine learning model capable of predicting clinical status from µFCM with or without clinical data is generated. Software used for machine learning can include, but is not limited to, R, MATLAB, KNIME, and python. Machine learning models can include single decision tree, support vector machines, k-nearest neighbor, linear regression, logistic regression, discriminant analysis, random forest, neural networks, and XGBoost. The algorithm providing the highest ROC AUC for predicting a clinical condition can be further optimized. All machine learning algorithms are analyzed using <NUM>-fold cross-validation which involves splitting the data into <NUM> separate groups. A model can be created using <NUM> of the <NUM> groups and model accuracy can be determined against the held-out group. The groups are shuffled and the process is repeated <NUM> more times so that every patient is used once in the held-out group. This ensures model accuracy is determined on data that was not used to create the model.

Machine learning algorithm optimization includes identifying which µFCM / clinical features should be kept / removed before model creation using recursive-feature elimination. This algorithm identifies the most important features from a model using all data (e.g., XGBoost feature importance using the xgb. importance function in R). Multiple data sets are created which include the top <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, or <NUM>% most important features and the data set which provides the highest ROC AUC using <NUM>-fold cross-validation contains the features which will be kept for the final machine learning model. Other feature selection algorithms including genetic algorithms and simulated annealing can also be used at this step.

After feature selection, the tunable parameters of the machine learning algorithm can be optimized through grid searching. This involves providing multiple values for each tunable algorithm parameter (e.g., XGBoost parameters such as "nrounds": <NUM>, <NUM>, and <NUM> as well as "max_depth": <NUM>, <NUM>, and <NUM>) and testing every combination of possible parameter values. The set of parameter values providing the highest ROC AUC using <NUM>-fold cross-validation is used for the final machine learning model.

The final machine learning model optimization involves ensembling many (typically <NUM>:<NUM>) models together by averaging the predictions from all models. All models will use the optimized features and parameters described above, but each model will use a slightly different cohort of patients (e.g., randomly selected <NUM>% of patients) for model creation. This causes each model to be unique and the average of all models' predictions will provide a more accurate and stable prediction of clinical status then using a single model with the full data set. The final optimized ensembled model is saved on a computer for future use.

The final machine learning model can be used to predict clinical status of new patients. New patient data which includes particle phenotype concentrations with or without clinical data can be used as input for the final machine learning model to predict the probability that a patient has a specific clinical condition.

Using the method described above, patients with previously diagnosed clinically significant prostate cancer were studied to determine the particle phenotypes/biomarkers most commonly associated with the disease. These particle phenotypes/biomarkers are shown in Table <NUM> with additional proof of concept in <FIG>, <FIG>, <FIG>, <FIG> and <FIG>.

Due to the various sizes of different EVs, the goal was to separate the µFCM data into many different ROIs, where each ROI represents the concentration of different EVs, and use machine learning on the ROI data to predict clinical conditions (<FIG>). Before creating such models, it was important to first identify which clinical conditions the µFCM data can best predict. Automated analysis scripts were used to create AUC maps of the µFCM data for predicting <NUM> different clinical conditions which were relevant to the PSMA and ghrelin probes.

When averaging the highest <NUM>% of AUCs within the LALS-PSMA, LALS-ghrelin, and PSMA-ghrelin AUC maps, predicting PCa grade group <NUM> and <NUM>+ provided the highest averaged AUCs (<FIG>). Interestingly, all three bivariate AUCs maps provided top <NUM>% AUCs above <NUM> for predicting these high grade PCa with LALS-PSMA having AUCs above <NUM> for predicting grade group <NUM> PCa. The LALS-PSMA AUC maps displayed an interesting pattern shift when comparing the different PCa grade groups (<FIG>). When estimating particle size using LALS, prediction of grade group <NUM>+ displayed relatively smaller PSMA-positive particles with AUCs above <NUM>, meaning particle concentration in these ROIs in general is higher in patients with grade group <NUM> + PCa, whereas larger PSMA-positive particles mostly displayed AUCs below <NUM>, meaning particle concentration in these ROIs in general is lower in patients with grade group <NUM>+ PCa. The AUC maps for higher grade groups demonstrated a progressive inversion of this phenotype with grade group <NUM> PCa having AUCs ><NUM> for larger PSMA-positive particles and AUCs approximately <NUM> for many smaller PSMA-positive particles. This phenotype inversion became quite noticeable with grade group <NUM>+ AUC maps. Previous clinical trials have shown that grade group <NUM> PCa patients receiving radical prostatectomy had a <NUM> year recurrence-free progression of under <NUM>, which was significantly lower than ><NUM> for those patients with grade group <NUM> PCa (<NUM>). This suggests that most men with grade group <NUM> PCa have metastatic disease at diagnosis since surgical removal of the primary tumor does not cure the patients of PCa. Without being limited by theory, the greater abundance of larger PSMA-positive particles in higher grade PCa patients may be partly due to circulating metastatic cells since larger EVs (><NUM>) from localized tumor cells would have difficulty intravasating into blood vessels.

Due to ghrelin's role in energy and glucose metabolism (<NPL>), AUC maps were created for predicting diabetes. A range of different sized ghrelin-positive particles displayed AUCs near <NUM>, suggesting that diabetic men have EVs with elevated levels of ghrelin receptors (<FIG>).

Using the LALS-PSMA data, correlation maps were created for PSA, tumor stage, and weight (<FIG>). Relatively large particles slightly positive for PSMA demonstrated the highest positive correlation with PSA whereas large particles with strong PSMA positivity correlated best with tumor stage. Such correlations are not surprising since <NUM>) prostate PSMA expression has been shown to correlate with PSA at diagnosis (<NPL>), and <NUM>) higher grade tumors are more likely to spread, explaining the similarity between the higher grade AUC maps and the tumor stage correlation map.

Given the results of the AUC/correlation maps, the µFCM data was used to predict clinically significant PCa which were defined as grade group <NUM>+ since these patients demonstrate significantly worse outcome than grade group <NUM> and lower PCa patients.

µFCM data was analyzed by manual gating to provide a benchmark of conventional analysis. Creating manual gates around specific particle populations is a non-trivial task since different particle populations exist on different patient scatter plots with some slight shifts in population locations (<FIG>). For simplicity, gates were created that grouped all marker-positive particles. When compared to non-clinically significant PCa, only the concentration of ghrelin-positive particles was significantly higher in clinically significant PCa by <NUM>-fold (p < <NUM>, <FIG>). The AUCs of PSMA-, ghrelin-, and PSMA/ghrelin-positive particle concentrations for predicting clinically significant PCa were all below <NUM> (<FIG>). These low AUCs may be explained by the AUC maps which show the gates encompassing particles with AUCs above and below <NUM> (<FIG>).

viSNE plots of both clinically significant and non-clinically significant particles together uncovered more particle populations than were visible with conventional scatter plots (<FIG>). Particles were clustered using K-means, expectation maximization Gaussian mixture model, and fast search/density peaks algorithms, and the last algorithm was the only one which could maintain large clusters with irregular shapes (<FIG> and <FIG>). Two clusters achieved ><NUM> cluster purity for clinically significant PCa, suggesting that these particle populations are in higher levels within clinically significant PCa patients (<FIG>). Although these results appear promising to exploit clinically, the non-reproducible nature of viSNE requires all data to be analyzed simultaneously. Since viSNE can only handle up to <NUM>,<NUM> events, ><NUM>% of particles in the <NUM> patient cohort would be removed from analysis.

In order to optimize the prediction of clinically significant PCa from µFCM data, particle concentrations from ROIs were used as training data for <NUM> different machine learning algorithms. For LALS-PSMA, LALS-ghrelin, and PSMA-ghrelin data sets, XGBoost provided the highest AUCs at <NUM>, <NUM>, and <NUM> (<FIG>). All subsequent analysis used the PSMA-ghrelin data set with XGBoost.

As expected for a decision tree-based model, monotonic transformations of the µFCM data did not improve XGBoost model performance (<FIG>). The XGBoost variable gain map, which displays the most important ROIs for XGBoost model accuracy, illustrated that many different particle populations are important for the XGBoost model (Fig. 10a). The ROIs with relatively high variable gain mostly overlapped with regions on the AUC map that were well above and below <NUM>, suggesting that particle populations which had higher and lower concentrations in clinically significant PCa patients were important for the model (Fig. 10b).

Changing the binning strategy to above or below <NUM> caused AUCs to decrease, suggesting that this level of resolution is preferred for predicting clinically significant PCa. Creating increasingly larger ensembles of XGBoost models increased model performance (<FIG>). Compared to single XGBoost models, an ensemble of <NUM> models provided a <NUM>% improvement in AUC and reduced model variability by <NUM>%. Larger XGBoost ensembles could be made for greater model performance although such small benefits in accuracy would also have greater processing/memory requirements. Grid searching XGBoost parameters and recursive feature elimination increased XGBoost AUCs by <NUM>% and <NUM>%, respectively (<FIG>). Combining grid searching, feature selection, and ensembling significantly increased the XGBoost AUC by <NUM>% (p < <NUM>), suggesting an additive interaction between model optimization techniques. Citrus and manual gating analysis of the PSMA-ghrelin data set provided significantly lower AUCs, <NUM> and <NUM>, respectively, compared to our optimized XGBoost model at <NUM>. (p < <NUM>). The present optimized XGBoost model also outperformed PSA which was the only clinical features which significantly differed between clinically significant and non-clinically significant PCa patients (p = <NUM>, Table <NUM>).

To compare the present optimized model with SOC for predicting clinically significant PCa, logistic regression models were created using SOC with or without our µFCM-based XGBoost model predictions. A waterfall plot of patient predictions from the SOC and µFCM model provided <NUM>% sensitivity and <NUM>% specificity when using a cutoff probability of <NUM> (<FIG> and Table <NUM>). Adding SOC to µFCM predictions slightly increased the AUC to <NUM> which was significantly greater than the <NUM> AUC from SOC alone (p < <NUM>), demonstrating the clinical value of the µFCM-based XGBoost model (<FIG>).

DRE, digital rectal exam; SOC, standard of care; CI, <NUM>% confidence interval; ROC AUC, receiver operator characteristic area under the curve; PPV, positive predictive value; NPV, negative predictive value;.

Upon further analysis of the <NUM> patient cohort, it was observed that men with enlarged prostates (><NUM> cc) were significantly less likely to have PCa, meaning that compared to men with normal sized prostates, a greater percentage of men with enlarged prostates underwent unnecessary biopsies. Based on current clinical practice, men primarily receive prostate biopsies due to high PSA levels and/or abnormal DRE. The fraction of patients with abnormal DRE was similar between men with normal and enlarged prostates (<FIG>) while PSA levels were significantly higher in men with enlarged prostates (p < <NUM>, <FIG>), suggesting that elevated PSA was responsible for the increased number of unnecessary biopsies. Normalizing PSA levels using PSA density (PSA divided by prostate volume) may not be ideal since PSA density was significantly lower in men with enlarged prostate (<FIG>). For men with enlarged prostates, the SOC + µFCM probability scores for clinically significant PCa were significantly different between non-clinically significant and clinically significant PCa patients (p < <NUM>, <FIG>), and using the previously define probability cutoff threshold in Table <NUM>, <NUM>% and <NUM>% of patients with clinically significant and non-clinically significant PCa would be recommended for biopsy, respectively, eliminating approximately half of unnecessary biopsies while still maintain <NUM>% sensitivity for detecting clinically significant PCa (<FIG>).

Pre-biopsy plasma samples were acquired from the Alberta Prostate Cancer Research Initiative (APCaRI) biorepository. The inclusion criteria were adult men without prior prostate cancer diagnosis who were: (<NUM>) referred to urology clinics in Alberta for prostate concerns and were being scheduled for a prostate biopsy; and (<NUM>) undergoing transurethral prostate surgery for diagnosis or treatment of prostate abnormalities. All patients provided written informed consent, and the study was approved by the scientific ethics committees at the Prostate Cancer Centre (Calgary, Alberta, Canada) and the Northern Alberta Urology Centre (Edmonton, Alberta, Canada). Patients were enrolled between June <NUM> and September <NUM>. Transrectal ultrasound guided prostate biopsies were performed with a median of <NUM> cores per patient and evaluated according to each hospital's SOPs. Test results were not provided to the clinical sites for patient care. Laboratory personnel who acquired patient samples and ran tests with them were blinded for patient characteristics. Blood was collected and processed to collect plasma as per institutional SOPs and time from arm to -<NUM> freezer was <NUM> hours or less. In particular, blood samples were collected in clinical grade vacutainers. For plasma preparation, samples underwent a <NUM> step centrifugation process. First a standard <NUM> xg for <NUM> minutes to provide separation of plasma from other blood components followed by a second <NUM> xg x <NUM> minutes centrifugation to pellet platelets. Blood collected in serum tubes is first allowed to clot for <NUM>-<NUM> minutes and then a single <NUM> xg x <NUM> minutes centrifugation step is performed.

Frozen plasma samples were thawed, centrifuged at <NUM>,<NUM> x g for <NUM> minutes to remove large debris and platelet particles, and incubated with <NUM>µg/mL J591 antibody and <NUM>/<NUM> final dilution of secondary Qdot565-conjugated donkey anti-mouse IgG antibody. Samples were also incubated with <NUM> Ghrelin Cy5 probe containing the first <NUM> amino acids of ghrelin. Thirty minutes after probe incubation, samples were diluted <NUM>-fold in double filtered (<NUM>) phosphate buffered saline and analyzed with the Apogee A50 microflow cytometer using a flow rate of <NUM>µL/minute. Samples were run for up to <NUM> minutes or until <NUM>,<NUM>,<NUM> events were recorded, whichever came first. Plasma from each patient was run in triplicate. Conventional manual gating analysis of µFCM data was performed using Histogram version <NUM>. <NUM> software (Apogee Flow Systems).

Patient µFCM fcs files were analyzed using a custom MATLAB (version R2017a) script. Within each fcs file, signal intensities for all channels were log transformed and particles with similar optical properties were binned using <NUM>-bins per optical property unless stated otherwise. Three different bivariate histograms of particle concentration were created: <NUM>) large angle light scatter (LALS) and PSMA stain intensity, <NUM>) LALS and ghrelin probe stain intensity, and <NUM>) PSMA and ghrelin probe stain intensity. Each bivariate histogram contained <NUM> ROls (32x32 bins). Particle concentration in each ROI was averaged over the three replicates per patient.

The µFCM data was used to predict binary clinical features (e.g., patients with or without diabetes, normal or abnormal digital rectal exam) and correlate with ordinal or interval clinical features (e.g., tumor stage or PSA, respectively) using a custom MATLAB script. To minimize the code needed for automated analysis, an excel instruction file was created which described how the µFCM data should be analyzed for each clinical feature. Within the instruction file, each clinical feature was a separate column and each row contained specific information or instructions. Specific information included the location of the clinical feature within the database, the type of data for each clinical feature (binary or ordinal/interval), and the value which represents missing data for that clinical feature. Instructions primarily involved how the clinical feature should be transformed which included thresholding values when binarizing features, deriving the PCa grade groups from Gleason scores, and determining age from dates of birth. Patients missing data for the clinical feature were removed from analysis for that clinical feature.

Once clinical feature data was retrieved from the database for all patients and transformed, µFCM particle concentration data for each ROI was used to predict or correlate with clinical features. For binary clinical features, receiver operator characteristic (ROC) area under the curve (AUC) values were determined for each ROI and AUC maps were generated for each bivariate data set including LALS-PSMA, LALS-ghrelin, and PSMA-ghrelin. For ordinal/interval clinical features, Pearson correlation coefficients were determined for each ROI and correlation maps were generated for each bivariate data set. The highest <NUM>% of AUC values in each AUC map were averaged and these values were compared across clinical features.

viSNE plots were created using Cyt version <NUM> software run on MATLAB (<NUM>). Each patient's triplicate fcs files were concatenated into one fcs file. Two new fcs files were created: one using events from patients with grade group <NUM> and lower PCa (non-clinically significant PCa), and the other using events from patients with grade group <NUM> and higher PCa (clinically significant PCa). These two fcs files had a total of approximately <NUM>,<NUM> events with an equal number of events from each patient within their group. With Cyt software, <NUM>,<NUM> events from both of these two fcs files were randomly subsampled and merged to create <NUM>,<NUM> events which were visualized with viSNE using the bh-SNE transformation using LALS, PSMA, and ghrelin channels and clustered with the k-means and expectation maximization Gaussian mixture model algorithms. The viSNE results were exported from Cyt and also clustered using the fast search / density peaks algorithm using the DensityClust function for Matlab (<NPL>). Event pair Euclidean distances were determined using the pdist2 function. For setting delta and rho parameters using the paraSet function, the percent neighbor variable was set to <NUM>% and a Gaussian kernel was used. Cluster centers were selected using delta values between <NUM> and <NUM> as well as rho values between <NUM> and <NUM>. For all clustering algorithms, <NUM> clusters were created over the <NUM>,<NUM> events. Cluster purity for clinically significant PCa was defined as the number of clinically significant PCa events divided by the total number events within each cluster. Only clusters with at least <NUM> particles (<NUM>% of total particles) were analyzed.

MATLAB's classification learner app was used to test <NUM> different machine learning algorithms to predict clinically significant PCa using particle concentration µFCM data. These algorithms included individual/bagged/boosted decision trees, linear/quadratic/cubic/Gaussian support vector machines, logistic regression, linear/quadratic/subspace discriminant analysis, and k-nearest neighbors. XGBoost was also tested using the 'xgboost' package in R (version <NUM>. All machine learning algorithms used default settings and <NUM>-fold cross-validation repeated at least <NUM> times with patient randomization between repeats.

The machine learning algorithm with the highest AUC was then optimized by <NUM>) comparing <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, and <NUM> bins when processing the µFCM data, <NUM>) creating ensembles of <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, and <NUM> models using the same machine learning algorithm but randomly selecting different subsets of patients as training data and averaging model predictions, <NUM>) selecting the best subset of µFCM ROIs using recursive feature elimination with the R 'caret' package, and <NUM>) grid searching algorithm parameters (XGBoost: nrounds = <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>; max_depth = <NUM>, <NUM>, <NUM>, <NUM>; eta = <NUM>, <NUM>; gamma = <NUM>; colsample_bytree = <NUM>; min_child_weight = <NUM>; subsample = <NUM>). The binning/ensembling/features/parameters that provided the highest AUCs were used together to create a final model for predicting clinically significant PCa. This model was compared to manual gating analysis using Histogram software and Citrus with default settings using R. Citrus predicts clinical conditions from flow cytometry data by using hierarchical clustering and lasso-regularized logistic regression and nearest shrunken centroid methods (<NPL>).

To incorporating standard of care (SOC) clinical features, including PSA, age, DRE, family history of PCa, previous negative biopsy, and race (black = <NUM>, other races = <NUM>), with the final µFCM model probability predictions, a logistic regression model was created using all of these features. This model was compared to a similar logistic regression model without using µFCM data.

Unless stated otherwise, bar/dot plots with error bars represent mean ± standard error of the mean. When comparing <NUM> groups, unpaired two-tailed t-tests were used for interval data and Fisher's exact tests were used for contingency tables. One-way ANOVA was used for comparing <NUM> or more groups using Tukey's multiple comparison test. ROC curves were compared by DeLong's method using the 'pROC' package in R. When possible, ROC cut-off values were determined using ~<NUM>% sensitivity and the resulting specificity and positive/negative predictive values were determined using GraphPad Prism version <NUM> software.

As shown in <FIG>, for assay optimization, <NUM>,<NUM> HeLa-GFP cells in medium were incubated and exposed to various amounts of microbubbles and ultrasound. Medium was analyzed for fluorescent Evs before and after sonication. For testing assay sensitivity, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, and <NUM>,<NUM> PC3 prostate cancer cells expressing palmitoylated GFP were mixed with <NUM>,<NUM>,<NUM> HT1080 cells (background) and were sonicated with microbubbles. Medium was analyzed by microflow cytometry pre/post sonication.

The assay has single cancer cell detection with higher theoretical SNR than conventional flow cytometry. Ultrasound ≥<NUM> Mpa pressure generates maximal EVs. Ultrasoundmediated EV release linearly increases with ultrasound cycles and microbubble concentration.

As shown in <FIG>, light scatter signals were recorded from calibration beads under different voltages (<NUM> - <NUM> V in +<NUM> V). Light scatter histograms were created and linearly shifted to match beads run at <NUM> V. Each bead histogram peak was shifted to match the same peak of beads run at <NUM> V (non-linear shifting).

Plasma samples from <NUM> patients were stained with prostate-specific membrane antigen. Data was processed with fixed <NUM> x <NUM> binning (LALS vs PSMA) or using an algorithm that identified particles positive or negative for PSMA using dynamic fluorescence thresholding and separated groups further based on degree of PSMA positivity. XGBoost models were created to predict patients with aggressive prostate cancer (Gleason <NUM>+<NUM> and higher). Models were used on the same data with modified fluorescence (x <NUM> - <NUM>) and AUCs were calculated.

Claim 1:
A method of diagnosing disease signature for a disease in a patient, the method comprising the steps of:
incubating a sample comprising extracellular vesicles (EVs) from the patient with one or more probes that bind biomarkers for the disease of interest bound to the EVs;
subjecting the sample to microflow cytometry to identify the EVs;
obtaining signal intensities for the one or more biomarkers and, obtaining one or more optical properties associated with the sample;
processing the signal intensities and, if obtained, the one or more optical properties to calculate concentrations of different particle phenotypes in the sample; wherein the processing comprises log transforming the signal intensities to produce transformed signal intensities; and binning particles with similar transformed signal intensities into regions of interest (ROI) for each optical property where each ROI is considered a different particle phenotype; and
calculating particle phenotype concentrations in samples using a Dynamic Fluorescence Thresholding algorithm by identifying the biomarker positivity status for each particle in each patient;
using these concentrations of particle phenotypes as inputs for machine learning algorithms to determine the probability of patients having clinically significant prostate cancer.