Blood based multimarker predictor of survival in pulmonary hypertension

Abstract

Disclosed are methods for identifying a severity and / or survival risk in a subject having or at risk of developing PAH, the method comprising the step of measuring ST2, IGFBP1, IGFBP2, IGFBP4, Endostatin, and Galectin-3 in a sample obtained from the subject, to identify the severity and / or survival risk associated with PAH in the subject. The methods also include treating the subject once the severity and / or survival risk of PAH is identified.

Description

[0001] BLOOD BASED MULTIMARKER PREDICTOR OF SURVIVAL IN PULMONARY HYPERTENSION FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT This invention was made with government support under grant HL135114 awarded by NIH. The government has certain rights in the invention. TECHNICAL FIELD The presently disclosed subject matter relates to the field of pulmonary arterial hypertension. More particularly, the presently disclosed subject matter provides methods and compositions useful in assessing and treating pulmonary arterial hypertension. BACKGROUND Pulmonary arterial hypertension (PAH) is a progressive, often fatal, vasculopathy causing right heart failure. The 2022 European Society of Cardiology and European Respiratory Society (ESC / ERS) emphasized the importance of risk assessment guiding aggressive therapy to improve PAH outcomes. Galiè et al., 2019; Humbert et al., 2022. Clinical risk scores include the REVEAL 2.0 score (Registry to Evaluate Early and Long- Term PAH Disease Management), and guidelines from the European Society for Cardiology and European Respiratory Society (ESC / ERS 2022). Benza et al., 2019; Benza et al., 2010; Humbert et al., 2010; and Galiè et al., 2016. These scores use clinical features (symptoms, New York Heart Association- Functional Class (NYHA-FC), six-minute walk distance (6MWD), hemodynamics (right atrial pressure, pulmonary vascular resistance, cardiac index), exercise testing (diffusing capacity for carbon monoxide, DLCO, peak oxygen consumption, (VO2), and B-Natriuretic peptide (BNP, or NT-proBNP) to estimate mortality risk. Galiè et al., 2019; Benza et al., 2019; Benza et al., 2018; Benza et al., 2012. While validated clinical models capture the spectrum of PAH disease, they require time consuming or invasive testing, which can limit their utility as interim screening tools. Blood biomarker-based risk models promise objective, minimally invasive assessment. To date, however, only NT-proBNP has gained widespread use for PAH risk assessment and is included in the REVEAL and ESC / ERS risk scores. Benza et al., 2010. NT-proBNP, a marker of myocardial wall stress, is an excellent prognostic marker at very high levels, typically greater than 1400 pg / mL; unfortunately, most PAH patients have moderately elevated NT-proBNP where interpretation is ambiguous. Fijalkowska et al., 2006. Recent work highlighted proteomics discovery for identification of new markers to predict risk. For example, Rhodes demonstrated the effective use of multiple biomarkers for risk discrimination, first in a nine protein, and then in a six protein score using an aptamer- based discovery approach. Rhodes et al., 2017; Rhodes et al., 2022. Likewise, promising single molecules show significance in PAH, many on clinically available assays, but have not yet found clinical use in addition to NT-proBNP. Candidate markers in PAH have been previously investigated, both in discovery and validation studies, including showing usefulness in addition to NT-proBNP. Simpson et al., 2020a; Griffiths et al., 2021; Yang et al., 2016; Yang et al., 2020; Nies et al., 2022. SUMMARY In some aspects, the presently disclosed subject matter provides a method of treating pulmonary arterial hypertension (PAH) in a subject diagnosed with PAH, the method comprising: (i) performing an immunoassay on a blood sample from the subject to generate a dataset comprising protein level data for biomarkers comprising Soluble Suppressor of Tumorigenicity (ST2), Insulin like growth factor binding protein 1 (IGFBP1), Insulin like growth factor binding protein 2 (IGFBP2), Insulin like growth factor binding protein 4 (IGFBP4), Endostatin, and Galectin-3; (ii) calculating a risk score that indicates probability of survival or risk of death or lung or heart-lung transplantation of the subject based on the dataset and clinical data comprising the subject’s age at PAH diagnosis, weight, and height; and (iii) administering or adjusting a PAH therapy to the subject. In some aspects, the presently disclosed subject matter provides a method of treating pulmonary arterial hypertension (PAH) in a subject diagnosed with PAH, the method comprising: (i) obtaining a subject’s dataset comprising protein level data for biomarkers comprising Soluble Suppressor of Tumorigenicity (ST2), Insulin like growth factor binding protein 1 (IGFBP1), Insulin like growth factor binding protein 2 (IGFBP2), Insulin like growth factor binding protein 4 (IGFBP4), Endostatin, and Galectin-3; (ii) calculating a risk score that indicates probability of survival or risk of death or lung or heart-lung transplantation of the subject based on the dataset and clinical data comprising the subject’s age at PAH diagnosis, weight, and height; and (iii) administering or adjusting a PAH therapy to the subject. In some aspects, calculating the risk score comprises using a predictive model to determine the risk score. In some aspects, the predictive model uses one or more statistical algorithms comprising one or more of Kaplan-Meier survival curve, Cox proportional hazard regressions, and / or clustering algorithms such as unsupervised K-means clustering algorithm. In some aspects, the blood sample is a peripheral blood serum sample. In some aspects, the dataset does not comprise protein level data for N-terminal pro b-type natriuretic peptide (NT-proBNP). In some aspects, the clinical data further comprises a ESC / ERS score or a REVEAL score. In some aspects, the immunoassay is a multiplex assay. In some aspects, the immunoassay comprises: obtaining the blood sample, wherein the blood sample comprises the biomarkers; contacting the blood sample with a plurality of distinct reagents; generating a plurality of distinct complexes between the reagents and the biomarkers; and detecting the distinct complexes to generate the dataset. In some aspects, the method further comprises monitoring a response of the subject to the PAH therapy by obtaining one or more additional risk scores for the subject after the the PAH therapy. In some aspects, the presently disclosed subject matter provides a method comprising the step of measuring ST2, IGFBP1, IGFBP2, IGFBP4, Endostatin, and Galectin-3 in a sample obtained from a subject. In certain aspects, the presently disclosed subject matter provides a method for identifying the severity of PAH in a subject having or suspected of having PAH, the method comprising the step of measuring ST2, IGFBP1, IGFBP2, IGFBP4, Endostatin, and Galectin-3 in a sample obtained from a subject. In other aspects, the presently disclosed subject matter provides a method for identifying the survival risk of a subject having or suspected of having PAH, the method comprising the step of measuring ST2, IGFBP1, IGFBP2, IGFBP4, Endostatin, and Galectin-3 in a sample obtained from a subject. In certain aspects, the method further comprises assessing a risk of an adverse event. In particular aspects, the adverse event is selected from death, lung transplant, heart transplant, and heart / lung transplant. In yet other aspects, the presently disclosed subject matter provides a method for identifying a subject as having or at risk of developing PAH, the method comprising the step of measuring ST2, IGFBP1, IGFBP2, IGFBP4, Endostatin, and Galectin-3 in a sample obtained from the subject, wherein an increased level of ST2, IGFBP1, IGFBP2, IGFBP4, Endostatin, and Galectin-3 relative to a control identifies the subject as having PAH. In even yet other aspects, the presently disclosed subject matter provides a method comprising the steps of: (a) detecting an increased level of ST2, IGFBP1, IGFBP2, IGFBP4, Endostatin, and Galectin-3 relative to a control in a sample obtained from a subject suspected of having PAH; and (b) treating the subject with a PAH therapy. In certain aspects, the presently disclosed methods further comprise measuring NT- proBNP. In certain aspects, the sample comprises a serum sample. In certain aspects, the measuring step is performed using an immunoassay. In particular aspects, the immunoassay comprises enzyme linked immunosorbent assay (ELISA). In certain aspects, the subject is suspected of having or has pulmonary arterial hypertension (PAH). In certain aspects, the presently disclosed methods further comprise one or more factors selected from an age of the subject at diagnosis, a weight of the subject at a time that the sample is obtained, and a height of the subject at a time that the sample is obtained. In certain aspects, the PAH therapy comprises one or more of endothelial receptor antagonists, prostacyclin pathway agonists, nitric oxide-cyclic guanosine monophosphate (NO-cGMP) enhancers, vasodilators, calcium channel blockers, anticoagulants, oxygen and diuretics. In particular aspects, the PAH therapy comprises administering prostacyclin or one or more analogs thereof. Certain aspects of the presently disclosed subject matter having been stated hereinabove, which are addressed in whole or in part by the presently disclosed subject matter, other aspects will become evident as the description proceeds when taken in connection with the accompanying Examples and Figures as best described herein below. 3 42150.601 BRIEF DESCRIPTION OF THE FIGURES The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee. Having thus described the presently disclosed subject matter in general terms, reference will now be made to the accompanying Figures, which are not necessarily drawn to scale, and wherein: FIG.1A and FIG.1B are flow charts of subject enrollment and eligibility for analysis; FIG.2A and FIG.2B shows biomarker based clusters in PAHB. FIG.2A is a plot of proteomic clusters. Colors represent cluster membership. FIG.2B is a scaled distribution of features by cluster. NT-proBNP included for reference; FIG.3A, FIG.3B, FIG.3C, FIG.3D, FIG.3E, and FIG.3F show Kaplan Meier Curve and forest plots of risk for event (death / transplant) based on cluster membership. FIG. 3A is a Kaplan Meier curve of event by cluster membership in PAHB training cohort. FIG. 3B is a Kaplan Meier curve of event by cluster membership in PAHB test cohort. FIG.3C is a Kaplan Meier curve of event by cluster membership in validation cohort. FIG.3D is a Forest plot of hazard ratio for event by cluster membership in PAHB training cohort. FIG. 3E is a Forest plot of hazard ratio for event by cluster membership in PAHB test cohort. FIG.3F is a Forest plot of hazard ratio for event by cluster membership in validation cohort; FIG.4A, FIG.4B, and FIG.4C show a ROC curve of Cluster membership (Blue), ESC / ERS risk (Green) and a combination of Cluster membership and ESC / ERS risk (Red) in the PAHB train (FIG.4A), PAHB test (FIG.4B), and validation cohorts (FIG.4C); FIG.5A, FIG.5B, and FIG.5C show a ROC curve of Cluster membership (Blue), REVEAL risk (Green) and a combination of Cluster membership and REVEAL risk (Red) in the PAHB train (FIG.5A), PAHB test (FIG.5B), and validation cohorts (FIG.5C); FIG.6A, FIG.6B, and FIG.6C show a ROC curve of Cluster membership (Blue), NT-proBNP (Green) and a combination of Cluster membership and NT-proBNP (Red) in the PAHB train (FIG.6A), PAHB test (FIG.6B), and validation cohorts (FIG.6C); FIG.7A, FIG.7B, FIG.7C, FIG.7D, FIG.7E, and FIG.7F show Kaplan Meier Curve and forest plots of risk for event (death / transplant) based on expanded cluster membership (adjustment for high NT-proBNP). FIG.7A is a Kaplan Meier curve of event by expanded cluster membership in PAHB training cohort. FIG.7B is a Kaplan Meier curve of event by expanded cluster membership in PAHB test cohort. FIG.7C is a Kaplan Meier curve of event by expanded cluster membership in validation cohort. FIG.7D is a Forest plot of hazard ratio for event by expanded cluster membership in PAHB training cohort. FIG.7E is a Forest plot of hazard ratio for event by expanded cluster membership in PAHB test cohort. FIG.7F is a Forest plot of hazard ratio for event by expanded cluster membership in validation cohort; FIG.8 is a ROC curve of expanded biomarker model in PAHB train (blue), PAHB test (green) and validation cohorts (red); 4 42150.601 FIG.9 is a scree plot of principal component analysis showing the percentage of variance explained by each principal component; FIG.10A and FIG.10B are plots of PC loads of principal component 1 and 2 for all variable (FIG.10A) and final biomarker model (FIG.10B); FIG.11A, FIG.11B, FIG.11C, FIG.11D, FIG.11E, and FIG.11F are Kaplan Meier Curve and forest plots of risk for event (death / transplant) based on ESC / ERS Risk. FIG. 11A is a Kaplan Meier curve of event by ESC / ERS risk in PAHB training cohort; FIG.11B is a Kaplan Meier curve of event by ESC / ERS risk in PAHB test cohort; FIG.11C is a Kaplan Meier curve of event by ESC / ERS risk in PAHB independent validation cohort; FIG.11D is a forest plot of hazard ratio for event by ESC / ERS risk in PAHB training cohort; FIG.11E is a forest plot of hazard ratio for event by ESC / ERS risk in PAHB test cohort; FIG.11F is a forest plot of hazard ratio for event by ESC / ERS risk in independent validation cohort; FIG.12A, FIG.12B, FIG.12C, FIG.12D, FIG.12E, and FIG.12F are Kaplan Meier Curves and forest plots of risks for event (death / transplant) based on REVEAL Risk. FIG. 12A is a Kaplan Meier curve of event by REVEAL risk in PAHB training cohort; FIG.12B is a Kaplan Meier curve of event by REVEAL risk in PAHB test cohort; FIG.12C is a Kaplan Meier curve of event by REVEAL risk in independent validation cohort; FIG.12D is a forest plot of hazard ratio for event by REVEAL risk in PAHB training cohort; FIG.12E is a forest plot of hazard ratio for event by REVEAL risk in PAHB test cohort; FIG.12F forest plot of hazard ratio for event by REVEAL risk in independent validation cohort; FIG.13A, FIG.13B, FIG.13C, FIG.13D, FIG.13E, and FIG.13F are Kaplan Meier Curves and forest plots of risk for event (death / transplant) based on NT-proBNP levels. FIG.13A is a Kaplan Meier curve of event by NT-proBNP levels in PAHB training cohort; FIG.13B is a Kaplan Meier curve of event by NT-proBNP levels in PAHB test cohort; FIG. 13C is a Kaplan Meier curve of event by NT-proBNP levels in independent validation cohort; FIG.13D is a forest plot of hazard ratio for event by NT-proBNP levels in PAHB training cohort; FIG.13E is a forest plot of hazard ratio for event by NT-proBNP levels in PAHB test cohort; FIG.13F forest plot of hazard ratio for event by NT-proBNP levels in independent validation cohort; FIG.14A is a Kaplan Meier Curve of risk for event (death / transplant) in children based on cluster membership; and FIG.14B is a ROC curve of risk models in children using European Respiratory Society (ESC / ERS) score, REVEAL score, NT-proBNP, Cluster model, and Cluster model combined with NT-proBNP. FIG.15 is a high-level block diagram of a computer (1500). FIG.15 illustrates a high-level block diagram of a computer (1500). Illustrated are at least one processor (1502) coupled to a chipset (1504). Also coupled to the chipset (1504) are a memory (1506), a storage device (1508), a keyboard (1510), a graphics adapter (1512), a pointing device (1514), and a network adapter (1516). A display (1518) is coupled to the graphics adapter (1512). In one embodiment, the functionality of the chipset (1504) is provided by a memory controller hub 1520) and an I / O controller hub (1522). In another embodiment, the memory 5 42150.601 (1506) is coupled directly to the processor (1502) instead of the chipset (1504). The storage device 1508 is any device capable of holding data, like a hard drive, compact disk read-only memory (CD-ROM), DVD, or a solid-state memory device. The memory (1506) holds instructions and data used by the processor (1502). The pointing device (1514) may be a mouse, track ball, or other type of pointing device, and is used in combination with the keyboard (1510) to input data into the computer system (1500). The graphics adapter (1512) displays images and other information on the display (1518). The network adapter (1516) couples the computer system (1500) to a local or wide area network. DETAILED DESCRIPTION The presently disclosed subject matter now will be described more fully hereinafter with reference to the accompanying Figures, in which some, but not all embodiments of the inventions are shown. Like numbers refer to like elements throughout. The presently disclosed subject matter may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will satisfy applicable legal requirements. Indeed, many modifications and other embodiments of the presently disclosed subject matter set forth herein will come to mind to one skilled in the art to which the presently disclosed subject matter pertains having the benefit of the teachings presented in the foregoing descriptions and the associated Figures. Therefore, it is to be understood that the presently disclosed subject matter is not to be limited to the specific embodiments disclosed and that modifications and other embodiments are intended to be included within the scope of the appended claims. 1. Abbreviations WSPH = World Society of Pulmonary Hypertension ERS = European Respiratory Society ESC = European Society of Cardiology IGFBP1 = Insulin like growth factor binding protein 1 IGFBP2 = Insulin like growth factor binding protein 2 IGFBP3 = Insulin like growth factor binding protein 3 IGFBP4 = Insulin like growth factor binding protein 4 ST2 = Soluble Suppressor of Tumorigenicity IL-6 = Interleukin-6 NT-proBNP= N-terminal pro hormone B-Natriuretic Peptide PAH = Pulmonary Arterial Hypertension IPAH= Idiopathic Pulmonary Arterial Hypertension APAH= Associated Pulmonary Arterial Hypertension FPAH= Familial Pulmonary Arterial Hypertension APAH=CTD = Associated Pulmonary Arterial Hypertension with connective tissue disease APAH-CHD = Associated Pulmonary Arterial Hypertension with congenital heart disease 6 42150.601 APAH-Drugs / Toxins = Associated Pulmonary Arterial Hypertension with drug or toxic exposure PVOD = Pulmonary veno-occlusive disease PAHB = PAH Biobank WHO- FC = World Health Organization Functional Class 6MWD = 6-Minute Walk Distance ELISA = Enzyme-Linked Immunosorbent Assay IQR = Interquartile Range RHC = Right Heart Catheterization RAP = Right Atrial Pressure mPAP = Mean Pulmonary Arterial Pressure PVR= Pulmonary Vascular Resistance PVRI= Pulmonary Vascular Resistance Index PCWP= Pulmonary Capillary Wedge Pressure CO= Cardiac output CI = Cardiac index WU = Woods Units IV / SQ PCA= Continuous infusion (IV or subcutaneous) prostacyclin analog In some aspects, the presently disclosed subject matter provides a method of treating pulmonary arterial hypertension (PAH) in a subject diagnosed with PAH, the method comprising: (i) performing an immunoassay on a blood sample from the subject to generate a dataset comprising protein level data for biomarkers comprising Soluble Suppressor of Tumorigenicity (ST2), Insulin like growth factor binding protein 1 (IGFBP1), Insulin like growth factor binding protein 2 (IGFBP2), Insulin like growth factor binding protein 4 (IGFBP4), Endostatin, and Galectin-3; (ii) calculating a risk score that indicates probability of survival or risk of death or lung or heart-lung transplantation of the subject based on the dataset and clinical data comprising the subject’s age at PAH diagnosis, weight, and height; and (iii) administering or adjusting a PAH therapy to the subject. In some aspects, the blood sample is a peripheral blood serum sample. In some aspects, the dataset does not comprise protein level data for N-terminal pro b-type natriuretic peptide (NT-proBNP). In some aspects, the dataset comprises protein level data for N- terminal pro b-type natriuretic peptide (NT-proBNP). In some aspects, the clinical data consists of the subject’s age at PAH diagnosis, weight, and height. In some aspects, the clinical data further comprises a ESC / ERS score or a REVEAL score. In some aspects, the presently disclosed subject matter provides a method of treating pulmonary arterial hypertension (PAH) in a subject diagnosed with PAH, the method comprising: (i) obtaining a subject’s dataset comprising protein level data for biomarkers comprising Soluble Suppressor of Tumorigenicity (ST2), Insulin like growth factor binding protein 1 (IGFBP1), Insulin like growth factor binding protein 2 (IGFBP2), Insulin like growth factor binding protein 4 (IGFBP4), Endostatin, and Galectin-3; (ii) calculating a risk score that indicates probability of survival or risk of death or lung or heart-lung 7 42150.601 transplantation of the subject based on the dataset and clinical data comprising the subject’s age at PAH diagnosis, weight, and height; and (iii) administering or adjusting a PAH therapy to the subject. In some aspects, the clinical data consists of the subject’s age at PAH diagnosis, weight, and height. In some aspects, the blood sample is a peripheral blood serum sample. In some aspects, the dataset does not comprise protein level data for N-terminal pro b-type natriuretic peptide (NT-proBNP). In some aspects, the dataset comprises protein level data for N- terminal pro b-type natriuretic peptide (NT-proBNP). In some aspects, the clinical data consists of the subject’s age at PAH diagnosis, weight, and height. In some aspects, the clinical data further comprises a ESC / ERS score or a REVEAL score. The quantity of one or more biomarkers of the present teachings can be indicated as a value. The value can be one or more numerical values resulting from the evaluation of a sample, and can be derived, e.g., by measuring level(s) of the biomarker(s) in a sample by an assay performed in a laboratory, or from dataset obtained from a provider such as a laboratory, or from a dataset stored on a server. Biomarker levels can be measured using any of several techniques known in the art. The present teachings encompass such techniques, and further include all subject fasting and / or temporal-based sampling procedures for measuring biomarkers. In some aspects, the levels of one or more analyte biomarkers or the levels of a specific panel of analyte biomarkers in a sample are compared to a reference standard (“reference standard” or “reference level”) in order to direct treatment decisions. The reference standard used for any embodiment disclosed herein may comprise average, mean, or median levels of the one or more analyte biomarkers or the levels of the specific panel of analyte biomarkers in a control population. The reference standard may further include an earlier time point for the same subject. For example, a reference standard may include a first time point, and the levels of the one or more analyte biomarkers can be examined again at second, third, fourth, fifth, sixth time points, etc. Any time point earlier than any particular time point can be considered a reference standard. The reference standard may additionally comprise cutoff values or any other statistical attribute of the control population, or earlier time points of the same subject, such as a standard deviation from the mean levels of the one or more analyte biomarkers or the levels of the specific panel of analyte biomarkers. In some embodiments, the control population may comprise healthy individuals or the same subject prior to the administration of any therapy. The actual measurement of levels of the biomarkers can be determined at the protein or nucleic acid level using any method known in the art. “Protein” detection comprises detection of full-length proteins, mature proteins, pre-proteins, polypeptides, isoforms, mutations, variants, post-translationally modified proteins and variants thereof, and can be detected in any suitable manner. Levels of biomarkers can be determined at the protein level, e.g., by measuring the serum levels of peptides encoded by the gene products described herein, or by measuring the enzymatic activities of these protein biomarkers. Such methods are well-known in the art and include, e.g., immunoassays based on antibodies to proteins encoded by the genes, aptamers or molecular imprints. Any biological material can be used for the detection / quantification of the protein or its activity. Alternatively, a suitable 8 42150.601 method can be selected to determine the activity of proteins encoded by the biomarker genes according to the activity of each protein analyzed. For biomarker proteins, polypeptides, isoforms, mutations, and variants thereof known to have enzymatic activity, the activities can be determined in vitro using enzyme assays known in the art. Such assays include, without limitation, protease assays, kinase assays, phosphatase assays, reductase assays, among many others. Modulation of the kinetics of enzyme activities can be determined by measuring the rate constant KM using known algorithms, such as the Hill plot, Michaelis- Menten equation, linear regression plots such as Lineweaver-Burk analysis, and Scatchard plot. Using sequence information provided by the public database entries for the biomarker, expression of the biomarker can be detected and measured using techniques well-known to those of skill in the art. For example, nucleic acid sequences in the sequence databases that correspond to nucleic acids of biomarkers can be used to construct primers and probes for detecting and / or measuring biomarker nucleic acids. These probes can be used in, e.g., Northern or Southern blot hybridization analyses, ribonuclease protection assays, and / or methods that quantitatively amplify specific nucleic acid sequences. As another example, sequences from sequence databases can be used to construct primers for specifically amplifying biomarker sequences in, e.g., amplification-based detection and quantitation methods such as reverse-transcription based polymerase chain reaction (RT- PCR) and PCR. When alterations in gene expression are associated with gene amplification, nucleotide deletions, polymorphisms, post-translational modifications and / or mutations, sequence comparisons in test and reference populations can be made by comparing relative amounts of the examined DNA sequences in the test and reference populations. As an example, Northern hybridization analysis using probes which specifically recognize one or more of these sequences can be used to determine gene expression. Alternatively, expression can be measured using RT-PCR; e.g., polynucleotide primers specific for the differentially expressed biomarker mRNA sequences reverse-transcribe the mRNA into DNA, which is then amplified in PCR and can be visualized and quantified. Biomarker RNA can also be quantified using, for example, other target amplification methods, such as TMA, SDA, and NASBA, or signal amplification methods (e.g., bDNA), and the like. Ribonuclease protection assays can also be used, using probes that specifically recognize one or more biomarker mRNA sequences, to determine gene expression. Alternatively, biomarker protein and nucleic acid metabolites can be measured. The term “metabolite” includes any chemical or biochemical product of a metabolic process, such as any compound produced by the processing, cleavage or consumption of a biological molecule (e.g., a protein, nucleic acid, carbohydrate, or lipid). Metabolites can be detected in a variety of ways known to one of skill in the art, including the refractive index spectroscopy (RI), ultra-violet spectroscopy (UV), fluorescence analysis, radiochemical analysis, near- infrared spectroscopy (near-IR), nuclear magnetic resonance spectroscopy (NMR), light scattering analysis (LS), mass spectrometry, pyrolysis mass spectrometry, nephelometry, dispersive Raman spectroscopy, gas chromatography combined with mass spectrometry, liquid chromatography combined with mass spectrometry, matrix-assisted laser desorption 9 42150.601 ionization-time of flight (MALDI-TOF) combined with mass spectrometry, ion spray spectroscopy combined with mass spectrometry, capillary electrophoresis, NMR and IR detection. See WO 04 / 056456 and WO 04 / 088309, each of which is hereby incorporated by reference in its entirety. In this regard, other biomarker analytes can be measured using the above-mentioned detection methods, or other methods known to the skilled artisan. For example, circulating calcium ions (Ca2+) can be detected in a sample using fluorescent dyes such as the Fluo series, Fura-2A, Rhod-2, among others. Other biomarker metabolites can be similarly detected using reagents that are specifically designed or tailored to detect such metabolites. In some embodiments, a biomarker is detected by contacting a subject sample with reagents, generating complexes of reagent and analyte, and detecting the complexes. Examples of “reagents” include but are not limited to nucleic acid primers and antibodies. In some embodiments of the present teachings an antibody binding assay is used to detect a biomarker; e.g., a sample from the subject is contacted with an antibody reagent that binds the biomarker analyte, a reaction product (or complex) comprising the antibody reagent and analyte is generated, and the presence (or absence) or amount of the complex is determined. The antibody reagent useful in detecting biomarker analytes can be monoclonal, polyclonal, chimeric, recombinant, or a fragment of the foregoing, as discussed in detail above, and the step of detecting the reaction product can be carried out with any suitable immunoassay. The sample from the subject is typically a biological fluid as described above, and can be the same sample of biological fluid as is used to conduct the method described above. In some aspects, the immunoassay is a multiplex assay. In some aspects, the immunoassay comprises: obtaining the blood sample, wherein the blood sample comprises the biomarkers; contacting the blood sample with a plurality of distinct reagents; generating a plurality of distinct complexes between the reagents and the biomarkers; and detecting the distinct complexes to generate the dataset. In some aspects, the immunoassay is a multiplex assay. A “multiplex assay” as used herein refers to an assay that simultaneously measures multiple analytes, e.g., protein analytes, in a single run or cycle of the assay. Immunoassays carried out in accordance with the present teachings can be homogeneous assays or heterogeneous assays. Immunoassays carried out in accordance with the present teachings can be multiplexed. In a homogeneous assay the immunological reaction can involve the specific antibody (e.g., anti-biomarker protein antibody), a labeled analyte, and the sample of interest. The label produces a signal, and the signal arising from the label becomes modified, directly or indirectly, upon binding of the labeled analyte to the antibody. Both the immunological reaction of binding, and detection of the extent of binding, can be carried out in a homogeneous solution. Immunochemical labels which can be employed include but are not limited to free radicals, radioisotopes, fluorescent dyes, enzymes, bacteriophages, and coenzymes. Immunoassays include competition assays. In a heterogeneous assay approach, the reagents can be the sample of interest, an antibody, and a reagent for producing a detectable signal. Samples as described above can be used. The antibody can be immobilized on a support, such as a bead (such as protein A 10 42150.601 and protein G agarose beads), plate or slide, and contacted with the sample suspected of containing the biomarker in liquid phase. The support is separated from the liquid phase, and either the support phase or the liquid phase is examined using methods known in the art for detecting signal. The signal is related to the presence of the analyte in the sample. Methods for producing a detectable signal include but are not limited to the use of radioactive labels, fluorescent labels, or enzyme labels. For example, if the antigen to be detected contains a second binding site, an antibody which binds to that site can be conjugated to a detectable (signal-generating) group and added to the liquid phase reaction solution before the separation step. The presence of the detectable group on the solid support indicates the presence of the biomarker in the test sample. Examples of suitable immunoassays include but are not limited to oligonucleotides, immunoblotting, immunoprecipitation, immunofluorescence methods, chemiluminescence methods, electrochemiluminescence (ECL), and / or enzyme-linked immunoassays (ELISA). Those skilled in the art will be familiar with numerous specific immunoassay formats and variations thereof which can be useful for carrying out the method disclosed herein. See, e.g., E. Maggio, Enzyme-Immunoassay (1980), CRC Press, Inc., Boca Raton, Fla. See also U.S. Pat. No.4,727,022 to C. Skold et al., titled “Novel Methods for Modulating Ligand-Receptor Interactions and their Application”; U.S. Pat. No.4,659,678 to GC Forrest et al., titled “Immunoassay of Antigens”; U.S. Pat. No.4,376,110 to GS David et al., titled “Immunometric Assays Using Monoclonal Antibodies”; U.S. Pat. No.4,275,149 to D. Litman et al., titled “Macromolecular Environment Control in Specific Receptor Assays”; U.S. Pat. No.4,233,402 to E. Maggio et al., titled “Reagents and Method Employing Channeling”; and, U.S. Pat. No.4,230,797 to R. Boguslaski et al., titled “Heterogenous Specific Binding Assay Employing a Coenzyme as Label.” Antibodies can be conjugated to a solid support suitable for a diagnostic assay (e.g., beads such as protein A or protein G agarose, microspheres, plates, slides or wells formed from materials such as latex or polystyrene) in accordance with known techniques, such as passive binding. Antibodies as described herein can likewise be conjugated to detectable labels or groups such as radiolabels (e.g., 35S, 125I, 131I), enzyme labels (e.g., horseradish peroxidase, alkaline phosphatase), and fluorescent labels (e.g., fluorescein, Alexa, green fluorescent protein, rhodamine) in accordance with known techniques. Antibodies may also be useful for detecting post-translational modifications of biomarkers. Examples of post-translational modifications include, but are not limited to tyrosine phosphorylation, threonine phosphorylation, serine phosphorylation, citrullination and glycosylation (e.g., O-GlcNAc). Such antibodies specifically detect the phosphorylated amino acids in a protein or proteins of interest, and can be used in the immunoblotting, immunofluorescence, and ELISA assays described herein. These antibodies are well-known to those skilled in the art, and commercially available. Post-translational modifications can also be determined using metastable ions in reflector matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF). See U. Wirth et al., Proteomics 2002, 2(10):1445-1451. 11 42150.601 In some aspects, calculating the risk score comprises using a predictive model to determine the risk score. In some aspects, the predictive model uses a clustering algorithm to predict death or lung or heart-lung transplantation. In some aspects, the predictive model can predict high probability or risk of death or lung or heart-lung transplantation in subjects with moderately elevated levels of NT-proBNP or ambiguous levels of NT-proBNP. In some aspects, an interpretation function is applied to transform the biomarker levels into a risk score, which provides a quantitative measure of risk in the subject. In some aspects, the predictive model uses one or more statistical algorithms comprising one or more of Kaplan-Meier survival curve, Cox proportional hazard regressions, and / or clustering algorithms such as unsupervised K-means clustering algorithm. In some aspects, the predictive model uses one or more statistical algorithms comprising one or more of Cox proportional hazard regressions, clustering algorithms, analysis of variants (ANOVA), Ada-boosting, Classification and Regression Trees (CART), boosted CART, Random Forest (RF), Recursive Partitioning Trees (RPART), Curds and Whey (CW), Curds and Whey-Lasso, principal component analysis (PCA), factor rotation analysis, Linear Discriminant Analysis (LDA), Eigengene Linear Discriminant Analysis (ELDA), quadratic discriminant analysis, Discriminant Function Analysis (DFA), Hidden Markov Models, kernel density estimation, kernel partial least squares algorithm, kernel matching pursuit algorithm, kernel Fisher's discriminate analysis algorithm, kernel principal components analysis algorithm; linear regression, Stepwise Regression, Forward-Backward Variable Stepwise Regression, Lasso shrinkage and selection, Elastic Net regularization and selection, Lasso and Elastic Net-regularized generalized linear model, Logistic Regression (LogReg), Kth-nearest neighbor (KNN), non-linear regression, classification, neural networks, partial least square, rules based classification, shrunken centroids (SC), sliced inverse regression, Standard for the Exchange of Product model data, Application Interpreted Constructs (StepAIC), super principal component (SPC) regression, Support Vector Machines (SVM), and Recursive Support Vector Machines (RSVM). In some embodiments, the predictive model utilizes information criteria. In some embodiments, the predictive model utilizes a calibration curve, area under receiver operator characteristic curve (ROC) analysis, Akaike Information Criterion (AIC), Harrell’s C-Index, Bayes Information Criterion (BIC), and / or cross-validation. Information criteria can be used to quantify the tradeoff between the inclusion of additional biomarkers or model improvement, and to minimize overfit. The resulting predictive models can be validated in other studies, or cross-validated in the study they were originally trained in, using such techniques as, for example, Leave-One-Out (LOO) and 10-Fold cross-validation (10-Fold CV). In some aspects, the method further comprises monitoring a response of the subject to the PAH therapy by obtaining one or more additional risk scores for the subject after the the PAH therapy. In some embodiments, the presently disclosed subject matter provides a method comprising the step of measuring ST2, IGFBP1, IGFBP2, IGFBP4, Endostatin, and Galectin-3 in a sample obtained from a subject. The actual measurement of levels of the 12 42150.601 biomarkers can be determined at the protein or nucleic acid level using any method known in the art. In certain embodiments, the presently disclosed subject matter provides a method for identifying the severity of PAH in a subject having or suspected of having PAH, the method comprising the step of measuring ST2, IGFBP1, IGFBP2, IGFBP4, Endostatin, and Galectin-3 in a sample obtained from a subject. The actual measurement of levels of the biomarkers can be determined at the protein or nucleic acid level using any method known in the art. In other embodiments, the presently disclosed subject matter provides a method for identifying the survival risk of a subject having or suspected of having PAH, the method comprising the step of measuring ST2, IGFBP1, IGFBP2, IGFBP4, Endostatin, and Galectin-3 in a sample obtained from a subject. The actual measurement of levels of the biomarkers can be determined at the protein or nucleic acid level using any method known in the art. In certain embodiments, the method further comprises assessing a risk of an adverse event. In particular embodiments, the adverse event is selected from death, lung transplant, heart transplant, and heart / lung transplant. In yet other embodiments, the presently disclosed subject matter provides a method for identifying a subject as having or at risk of developing PAH, the method comprising the step of measuring ST2, IGFBP1, IGFBP2, IGFBP4, Endostatin, and Galectin-3 in a sample obtained from the subject, wherein an increased level of ST2, IGFBP1, IGFBP2, IGFBP4, Endostatin, and Galectin-3 relative to a control identifies the subject as having PAH. The actual measurement of levels of the biomarkers can be determined at the protein or nucleic acid level using any method known in the art. In even yet other embodiments, the presently disclosed subject matter provides a method comprising the steps of: (a) detecting an increased level of ST2, IGFBP1, IGFBP2, IGFBP4, Endostatin, and Galectin-3 relative to a control in a sample obtained from a subject suspected of having PAH; and (b) treating the subject with a PAH therapy. The actual measurement of levels of the biomarkers can be determined at the protein or nucleic acid level using any method known in the art. In certain embodiments, the presently disclosed methods further comprise measuring NT-proBNP. In certain embodiments, the sample comprises a serum sample. In certain embodiments, the measuring step is performed using an immunoassay. In particular embodiments, the immunoassay comprises enzyme linked immunosorbent assay (ELISA). In certain embodiments, the subject is suspected of having or has pulmonary arterial hypertension (PAH). In certain embodiments, the presently disclosed methods further comprise one or more factors selected from an age of the subject at diagnosis, a weight of the subject at a time that the sample is obtained, and a height of the subject at a time that the sample is obtained. As used herein, a “predictive model,” which term may be used synonymously herein with “multivariate model” or simply a “model,” is a mathematical construct developed using 13 42150.601 a statistical algorithm or algorithms for classifying sets of data. The term “predicting” refers to generating a value for a datapoint without actually performing the clinical diagnostic procedures normally or otherwise required to produce that datapoint; “predicting” as used in this modeling context should not be understood solely to refer to the power of a model to predict a particular outcome. Predictive models can provide an interpretation function; e.g., a predictive model can be created by utilizing one or more statistical algorithms or methods to transform a dataset of observed data into a meaningful determination of a risk score or disease state of a subject. “Interpretation function,” as used herein, means the transformation of a set of observed data into a meaningful determination of particular interest. A set of data from particularly selected biomarkers, such as from the disclosed set of biomarkers, is input into an interpretation function according to the present teachings to derive a risk score. The interpretation function, in some embodiments, can be created from predictive or multivariate modeling based on statistical algorithms. Input to the interpretation function can comprise the results of testing two or more of the disclosed set of biomarkers, alone or in combination with clinical data, also described herein. A “dataset”, as used herein, is a set of numerical values. The dataset can comprise results from evaluation of a sample (or population of samples) under a desired condition. The dataset can be obtained, for example, by experimentally obtaining measures from a sample and constructing a dataset from these measurements; or alternatively, by obtaining a dataset from a service provider such as a laboratory, or from a database or a server on which the dataset has been stored. “Clinical data”, as used herein, includes all measures of the health status of a subject. Clinical data can include age, sex, smoking status, adiposity, body mass index (BMI), race / ethnicity, therapeutic regimen, disease duration, diastolic and systolic blood pressure, resting heart rate, height, weight, family history, other clinical data described herein and / or a clinical assessment described herein. A “clinical assessment,” or “clinical datapoint” or “clinical endpoint,” refers to a measure of disease activity or severity. A clinical assessment can include a score, a value, or a set of values that can be obtained from evaluation of a sample (or population of samples) from a subject or subjects under determined conditions. A clinical assessment can also be a questionnaire completed by a subject. A clinical assessment can also be predicted by biomarkers and / or other parameters. A “score” is a value or set of values selected so as to provide a quantitative measure of a variable or characteristic of a subject's condition, and / or to discriminate, differentiate or otherwise characterize a subject's condition. The value(s) comprising the score can be based on, for example, quantitative data resulting in a measured amount of one or more sample constituents obtained from the subject and clinical data. The score can be varied based on a set of values chosen by the practitioner. For example, a score can be set such that a value is given a range from 0-100, and a difference between two scores would be a value of at least one point. The practitioner can then assign risk based on the values. For example, in some embodiments a score of about 1 to 29 represents a low level of risk, a score of about 30 to 44 represents a moderate level of risk, and a score of about 45 to 100 represents a high level of 14 42150.601 risk. In some embodiments on a scale of 1-100 a score of ≤38 can represent a low or lower risk, and a score of >38 can represent a high or higher risk. In some embodiments on a scale of 1-100 a score of ≤30 can represent a low or lower risk, and a score of >30 can represent a high or higher risk. The cutoffs can vary. For example, in some embodiments a low score can be a score of <30, although for other utilities, a low score can be a score of <29 or <31. The risk score can also change based on the range of the score. For example, a score of 1 to 58 can represent a low level of risk when a range of 0-200 is utilized. Differences can be determined based on the range of score possibilities. For example, if using a score range of 0-100, a small difference in scores can be a difference of about 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 points; a moderate difference in scores can be a difference of about 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 points; and large differences can be a change in about 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 40, 45, or 50 points. Thus, by way of example, a practitioner can define a small difference in scores as about ≤6 points, a moderate difference in scores as about 7-20 points, and a large difference in scores as about >20 points. The difference can be expressed by any unit, for example, percentage points. For example, a practitioner can define a small difference as about ≤6 percentage points, moderate difference as about 7-20 percentage points, and a large difference as about >20 percentage points. “Statistically significant” herein means an observed alteration is greater than what would be expected to occur by chance alone (e.g., a “false positive”). Statistical significance can be determined by any of various methods well-known in the art. An example of a commonly used measure of statistical significance is the p-value. The p-value represents the probability of obtaining a given result equivalent to a particular datapoint, where the datapoint is the result of random chance alone. A result is often considered highly significant (not random chance) at a p-value less than or equal to 0.05. “Measuring” or “measurement” herein refers to determining the presence, absence, quantity, amount, or effective amount of a substance in a clinical or subject-derived sample, including the concentration levels of such substances, or evaluating the values or categorization of a subject's clinical parameters. The term “algorithm” encompasses any formula, model, mathematical equation, algorithmic, analytical or programmed process, or statistical technique or classification analysis that takes one or more inputs or parameters, whether continuous or categorical, and calculates an output value, index, index value or score. Examples of algorithms include but are not limited to ratios, sums, regression operators such as exponents or coefficients, biomarker value transformations and normalizations (including, without limitation, normalization schemes that are based on clinical parameters such as age, gender, ethnicity, etc.), rules and guidelines, statistical classification models, and neural networks trained on populations. Also of use in the context of biomarkers are linear and non-linear equations and statistical classification analyses to determine the relationship between (a) levels of biomarkers detected in a subject sample and (b) the level of the respective subject's disease state or activity. 15 42150.601 The term “computer” carries the meaning that is generally known in the art; that is, a machine for manipulating data according to a set of instructions. For illustration purposes only, FIG.15 is a high-level block diagram of a computer (1500). As is known in the art, a “computer” can have different and / or other components than those shown in FIG.2. In addition, the computer 1500 can lack certain illustrated components. Moreover, the storage device (1508) can be local and / or remote from the computer (1500) (such as embodied within a storage area network (SAN) or as a cloud storage). As is known in the art, the computer (1500) is adapted to execute computer program modules for providing functionality described herein. As used herein, the term “module” refers to computer program logic utilized to provide the specified functionality. Thus, a module can be implemented in a cloud, hardware, firmware, and / or software. In one embodiment, program modules are stored on the storage device (1508), loaded into the memory (1506), and executed by the processor (1502). The storage device can be a machine-readable storage medium. Embodiments of the entities described herein can include other and / or different modules than the ones described here. In addition, the functionality attributed to the modules can be performed by other or different modules in other embodiments. Moreover, this description occasionally omits the term “module” for purposes of clarity and convenience. In some aspect, the storage device, e.g. a machine-readable storage medium, can comprise, for example, a data storage material that is encoded with machine-readable data or data arrays. The data and machine-readable storage medium are capable of being used for a variety of purposes, when using a machine programmed with instructions for using said data. Such purposes include, without limitation, storing, accessing and manipulating information relating to the risk of a subject or population over time, or risk in response to treatment, or for drug discovery. Data comprising measurements of the biomarkers, algorithms, predictive models and multimarker models described herein, and / or the calculation or determination of a risk score can be implemented in computer programs that are executing on programmable computers, which comprise a processor, a data storage system, one or more input devices, one or more output devices, etc. Program code can be applied to the input data to perform the functions described herein, and to generate output information. This output information can then be applied to one or more output devices, according to methods well-known in the art. The computer can be, for example, a personal computer, a microcomputer, or a workstation of conventional design. The computer programs can be implemented in a high-level procedural or object- oriented programming language, to communicate with a computer system such as for example, the computer system illustrated in FIG.15. The programs can also be implemented in machine or assembly language. The programming language can also be a compiled or interpreted language. Each computer program can be stored on storage media or a device such as ROM, magnetic diskette, etc., and can be readable by a programmable computer for configuring and operating the computer when the storage media or device is read by the computer to perform the described procedures. Any health-related data management systems of the present teachings can be considered to be implemented as a computer-readable 16 42150.601 storage medium, configured with a computer program, where the storage medium causes a computer to operate in a specific manner to perform various functions, as described herein. The biomarkers disclosed herein can be used to generate a “subject biomarker profile” taken from subjects who have PAH. The subject biomarker profiles can then be compared to a reference biomarker profile, in order to diagnose or identify subjects with increased PAH risk or extremely high risk, high risk, moderately high risk, average risk or low risk of death or transplantation, even in patients with ambiguous levels of NT-proBNP or moderately elevated levels of NT-proBNP. The biomarker profiles, reference and subject, of embodiments of the present teachings can be contained in a machine-readable medium, such as analog tapes like those readable by a CD-ROM or USB flash media, among others. Such machine-readable media can also contain additional test results, such as clinical data and measurements of clinical assessments. The machine-readable media can also comprise subject information or clinical data; e.g., the subject's medical or family history. The machine-readable media can also contain information relating to other disease activity algorithms and computed scores or indices, such as those described herein. In some aspects, the presently disclosed subject matter provides a system for measuring biomarkers. In some aspects, the presently disclosed subject matter provides a system for performing an immunoassay. Tests for measuring risk according to various embodiments of the present teachings can be implemented on a variety of systems typically used for obtaining test results, such as results from immunological or nucleic acid detection assays. Such systems may comprise modules that automate sample preparation, that automate testing such as measuring biomarker levels, that facilitate testing of multiple samples, and / or are programmed to assay the same test or different tests on each sample. In some embodiments, the testing system comprises one or more of a sample preparation module, a clinical chemistry module, and an immunoassay module on one platform. Testing systems are typically designed such that they also comprise modules to collect, store, and track results, such as by connecting to and utilizing a database residing on hardware. Examples of these modules include physical and electronic data storage devices as are well- known in the art, such as a hard drive, flash memory, and magnetic tape. Test systems also generally comprise a module for reporting and / or visualizing results. Some examples of reporting modules include a visible display or graphical user interface, links to a database, a printer, etc. A number of testing systems are presently available that could be used to implement various embodiments of the present teachings. See, for example, testing systems described herein. Other testing systems known in the art include: the ARCHITECT series of integrated immunochemistry systems—high-throughput, automated, clinical chemistry analyzers (ARCHITECT is a registered trademark of Abbott Laboratories, Abbott Park, Ill. 60064). See C. Wilson et al., “Clinical Chemistry Analyzer Sub-System Level Performance,” American Association for Clinical Chemistry Annual Meeting, Chicago, Ill., Jul.23-27, 2006; and, H J Kisner, “Product development: the making of the Abbott ARCHITECT,” Clin. Lab. Manage. Rev.1997 Nov.-Dec., 11(6):419-21; A. Ognibene et al., “A new modular chemiluminescence immunoassay analyzer evaluated,” Clin. Chem. Lab. Med.2000 March, 38(3):251-60; J W Park et al., “Three-year experience in using total 17 42150.601 laboratory automation system,” Southeast Asian J. Trop. Med. Public Health 2002, 33 Suppl 2:68-73; D. Pauli et al., “The Abbott Architect c8000: analytical performance and productivity characteristics of a new analyzer applied to general chemistry testing,” Clin. Lab.2005, 51(1-2):31-41. In some aspects, the presently disclosed subject matter provides a system for calculating a risk score. In some embodiments, the system employs a module for applying a formula to an input comprising the measured levels of biomarkers in a panel, as described herein, and outputting a score. In some embodiments, the measured biomarker levels are test results, which serve as inputs to a computer that is programmed to apply the formula. The system may comprise other inputs in addition to or in combination with biomarker results in order to derive an output score; e.g., one or more clinical data, optionally combined with clinical assessments as described herein. In some embodiments the system can apply a formula to biomarker level inputs, and then output a risk score that can then be analyzed in conjunction with other inputs such as other clinical data, optionally combined with clinical assessments as described herein. In other embodiments, the system is designed to apply a formula to the biomarker and non-biomarker inputs (such as clinical data) together, and then report a composite output risk index. “Performance” in the context of the present teachings relates to the quality and overall usefulness of, e.g., a model, algorithm, or diagnostic or prognostic test. Factors to be considered in model or test performance include, but are not limited to, the clinical and analytical accuracy of the test, use characteristics such as stability of reagents and various components, ease of use of the model or test, health or economic value, and relative costs of various reagents and components of the test. Performing can mean the act of carrying out a function. As used herein, a “subject” means a human or animal. Usually the animal is a vertebrate such as a primate, rodent, domestic animal or game animal. Primates include chimpanzees, cynomologous monkeys, spider monkeys, and macaques, e.g., Rhesus. Rodents include mice, rats, wood chucks, ferrets, rabbits and hamsters. Domestic and game animals include cows, horses, pigs, deer, bison, buffalo, feline species, e.g., domestic cat, and canine species, e.g., dog, fox, wolf. The terms, “patient”, “individual” and “subject” are used interchangeably herein. In an embodiment, the subject is mammal. The mammal can be a human, non-human primate, mouse, rat, dog, cat, horse, or cow, but are not limited to these examples. In addition, the methods described herein can be used to treat domesticated animals and / or pets. In various embodiments, the subject is mouse or mice. In various embodiments, the subject is human. In certain embodiments, the subject is a pediatric subject. A pediatric subject can include a neonate (e.g., < 28 days), an infant (e.g., ≥ 28 days ≤ 1 yr), a child (e.g., > 1 yr ≤ 14 yr), an adolescent ( e.g., > 14 yr ≤ 19 yr), and a young adult, e.g., ≤ 19 yr. A subject can be one who has been previously diagnosed with or identified as suffering from or having a condition, disease, or disorder in need of treatment (e.g., PAH) or one or more complications related to the condition, disease, or disorder, and optionally, have already undergone treatment for the condition, disease, disorder, or the one or more 18 42150.601 complications related to the condition, disease, or disorder. Alternatively, a subject can also be one who has not been previously diagnosed as having a condition, disease, or disorder or one or more complications related to the condition, disease, or disorder. For example, a subject can be one who exhibits one or more risk factors for a condition, disease, or disorder, or one or more complications related to the condition, disease, or disorder, or a subject who does not exhibit risk factors. A “subject in need” of treatment for a particular condition, disease, or disorder can be a subject suspected of having that condition, disease, or disorder, diagnosed as having that condition, disease, or disorder, already treated or being treated for that condition, disease, or disorder, not treated for that condition, disease, or disorder, or at risk of developing that condition, disease, or disorder. In some embodiments, the subject is selected from the group consisting of a subject suspected of having a disease, a subject that has a disease, a subject diagnosed with a disease, a subject that has been treated for a disease, a subject that is being treated for a disease, and a subject that is at risk of developing a disease. In some embodiments, the subject is selected from the group consisting of a subject suspected of having PAH, a subject that has PAH, a subject diagnosed with PAH, a subject that has been treated for PAH, a subject that is being treated for PAH, and a subject that is at risk of developing PAH. By “at risk of” is intended to mean at increased risk of, compared to a normal subject, or compared to a control group, e.g., a patient population. Thus, a subject carrying a particular marker may have an increased risk for a specific condition, disease or disorder, and be identified as needing further testing. “Increased risk” or “elevated risk” mean any statistically significant increase in the probability, e.g., that the subject has the disorder. The risk is increased by at least 10%, at least 20%, and even at least 50% over the control group with which the comparison is being made. “Sample” is used herein in its broadest sense. The term “biological sample” as used herein denotes a sample taken or isolated from a biological organism. A sample or biological sample may comprise a bodily fluid including blood, serum, plasma, tears, aqueous and vitreous humor, spinal fluid; a soluble fraction of a cell or tissue preparation, or media in which cells were grown; or membrane isolated or extracted from a cell or tissue; polypeptides, or peptides in solution or bound to a substrate; a cell; a tissue, a tissue print, a fingerprint, skin or hair; fragments and derivatives thereof. Non-limiting examples of samples or biological samples include cheek swab; mucus; whole blood, blood, serum; plasma; urine; saliva, semen; lymph; fecal extract; sputum; other body fluid or biofluid; cell sample; and tissue sample etc. The term also includes a mixture of the above-mentioned samples or biological samples. The term “sample” also includes untreated or pretreated (or pre-processed) biological samples. In some embodiments, a sample or biological sample can comprise one or more cells from the subject. Subject samples or biological samples usually comprise derivatives of blood products, including blood, plasma and serum. In some embodiments, the sample is a biological sample. In some embodiments, the sample is blood. In some embodiments, the sample is plasma. In some embodiments, the sample is blood, plasma, serum, or urine. In certain embodiments, the sample is a serum sample. 19 42150.601 The terms “body fluid” or “bodily fluids” are liquids originating from inside the bodies of organisms. Bodily fluids include amniotic fluid, aqueous humour, vitreous humour, bile, blood (e.g., serum), breast milk, cerebrospinal fluid, cerumen (earwax), chyle, chyme, endolymph and perilymph, exudates, feces, female ejaculate, gastric acid, gastric juice, lymph, mucus (e.g., nasal drainage and phlegm), pericardial fluid, peritoneal fluid, pleural fluid, pus, rheum, saliva, sebum (skin oil), serous fluid, semen, sputum, synovial fluid, sweat, tears, urine, vaginal secretion, and vomit. Extracellular bodily fluids include intravascular fluid (blood plasma), interstitial fluids, lymphatic fluid and transcellular fluid. “Biological sample” also includes a mixture of the above-mentioned body fluids. “Biological samples” may be untreated or pretreated (or pre-processed) biological samples. Sample collection procedures and devices known in the art are suitable for use with various embodiment of the present invention. Examples of sample collection procedures and devices include but are not limited to: phlebotomy tubes (e.g., a vacutainer blood / specimen collection device for collection and / or storage of the blood / specimen), dried blood spots, Microvette CB300 Capillary Collection Device (Sarstedt), HemaXis blood collection devices (microfluidic technology, Hemaxis), Volumetric Absorptive Microsampling (such as CE-IVD Mitra microsampling device for accurate dried blood sampling (Neoteryx), HemaSpot™-HF Blood Collection Device, a tissue sample collection device; standard collection / storage device (e.g., a collection / storage device for collection and / or storage of a sample (e.g., blood, plasma, serum, urine, etc.); a dried blood spot sampling device. In some embodiments, the Volumetric Absorptive Microsampling (VAMS1M) samples can be stored and mailed, and an assay can be performed remotely. As used herein, the term “amino acid” refers to naturally occurring and synthetic amino acids, as well as amino acid analogs and amino acid mimetics that function in a manner similar to the naturally occurring amino acids. Naturally occurring amino acids are those encoded by the genetic code, as well as those amino acids that are later modified, e.g., hydroxyproline, -carboxyglutamate, and O-phosphoserine. Amino acid analogs refers to compounds that have the same basic chemical structure as a naturally occurring amino acid, i.e., an carbon that is bound to a hydrogen, a carboxyl group, an amino group, and an R group, e.g., homoserine, norleucine, methionine sulfoxide, methionine methyl sulfonium. Such analogs have modified R groups (e.g., norleucine) or modified peptide backbones, but retain the same basic chemical structure as a naturally occurring amino acid. Amino acid mimetics refers to chemical compounds that have a structure that is different from the general chemical structure of an amino acid, but that function s in a manner similar to a naturally occurring amino acid. Amino acids may be referred to herein by either their commonly known three letter symbols or by the one-letter symbols recommended by the IUPAC-IUB Biochemical Nomenclature Commission. Nucleotides, likewise, may be referred to by their commonly accepted single-letter codes. The term “peptide” as used herein refers to any compound containing at least two amino acid residues joined by an amide bond formed from the carboxyl group of one amino acid residue and the amino group of the adjacent amino acid residue. In some embodiments, peptide refers to a polymer of amino acid residues typically ranging in length from 2 to 20 42150.601 about 30, or to about 40, or to about 50, or to about 60, or to about 70 residues. In certain embodiments the peptide ranges in length from about 2, 3, 4, 5, 7, 9, 10, or 11 residues to about 60, 50, 45, 40, 45, 30, 25, 20, or 15 residues. In certain embodiments the peptide ranges in length from about 8, 9, 10, 11, or 12 residues to about 15, 20 or 25 residues. In some embodiments, the peptide ranges in length from 2 to about 12 residues, or 2 to about 20 residues, or 2 to about 30 residues, or 2 to about 40 residues, or 2 to about 50 residues, or 2 to about 60 residues, or 2 to about 70 residues. In certain embodiments the amino acid residues comprising the peptide are “L-form” amino acid residues, however, it is recognized that in various embodiments, “D” amino acids can be incorporated into the peptide. Peptides also include amino acid polymers in which one or more amino acid residues are an artificial chemical analogue of a corresponding naturally occurring amino acid, as well as to naturally occurring amino acid polymers. In addition, the term applies to amino acids joined by a peptide linkage or by other, “modified linkages” (e.g., where the peptide bond is replaced by an a-ester, a f3-ester, a thioamide, phosphonamide, carbamate, hydroxylate, and the like (see, e.g., Spatola, (1983) Chern. Biochem. Amino Acids and Proteins 7: 267-357), where the amide is replaced with a saturated amine (see, e.g., Skiles et al., U.S. Pat. No. 4,496,542, which is incorporated herein by reference, and Kaltenbronn et al., (1990) pp. 969-970 in Proc. Ί 1th American Peptide Symposium, ESCOM Science Publishers, The Netherlands, and the like)). A protein refers to any of a class of nitrogenous organic compounds that comprise large molecules composed of one or more long chains of amino acids and are an essential part of all living organisms. A protein may contain various modifications to the amino acid structure such as disulfide bond formation, phosphorylations and glycosylations. A linear chain of amino acid residues may be called a “polypeptide,” A protein contains at least one polypeptide. Short polypeptides, e.g., containing less than 20-30 residues, are sometimes referred to as “peptides.” “Antibody” refers to a polypeptide ligand substantially encoded by an immunoglobulin gene or immunoglobulin genes, or fragments thereof, which specifically binds and recognizes an epitope (e.g., an antigen). The recognized immunoglobulin genes include the kappa and lambda light chain constant region genes, the alpha, gamma, delta, epsilon and mu heavy chain constant region genes, and the myriad immunoglobulin variable region genes. Antibodies exist, e.g., as intact immunoglobulins or as a number of well characterized fragments produced by digestion with various peptidases. This includes, e.g., Fab’ and F(ab)’2fragments. The term “antibody,” as used herein, also includes antibody fragments either produced by the modification of whole antibodies or those synthesized de novo using recombinant DNA methodologies. It also includes polyclonal antibodies, monoclonal antibodies, chimeric antibodies, humanized antibodies, or single chain antibodies. “Fc” portion of an antibody refers to that portion of an immunoglobulin heavy chain that comprises one or more heavy chain constant region domains, CH1, CH2 and CH3, but does not include the heavy-chain variable region. The phrase “specifically (or selectively) binds” to an antibody or “specifically (or selectively) immunoreactive with,” when referring to a protein or peptide, refers to a binding 21 42150.601 reaction that is determinative of the presence of the protein in a heterogeneous population of proteins and other biologics. Thus, under designated immunoassay conditions, the specified antibodies bind to a particular protein at least two times the background and do not substantially bind in a significant amount to other proteins present in the sample. Specific binding to an antibody under such conditions may require an antibody that is selected for its specificity for a particular protein. A variety of immunoassay formats may be used to select antibodies specifically immunoreactive with a particular protein. For example, solid-phase ELISA immunoassays are routinely used to select antibodies specifically immunoreactive with a protein (see, e.g., Harlow & Lane, Antibodies, A Laboratory Manual (1988), for a description of immunoassay formats and conditions that can be used to determine specific immunoreactivity). The term “threshold” as used herein refers to the magnitude or intensity that must be exceeded for a certain reaction, phenomenon, result, or condition to occur or be considered relevant. The relevance can depend on context, e.g., it may refer to a positive, reactive or statistically significant relevance. By “binding assay” is meant a biochemical assay wherein the biomarkers are detected by binding to an agent, such as an antibody, through which the detection process is carried out. The detection process may involve fluorescent or radioactive labels, and the like. The assay may involve immobilization of the biomarker, or may take place in solution. “Immunoassay” is an assay that uses an antibody to specifically bind an antigen (e.g., a marker). The immunoassay is characterized by the use of specific binding properties of a particular antibody to isolate, target, and / or quantify the antigen. Non-limiting examples of immunoassays include ELISA (enzyme-linked immunosorbent assay), immunoprecipitation, SISCAPA (stable isotope standards and capture by anti-peptide antibodies), Western blot, etc. “Diagnostic” means identifying the presence or nature of a pathologic condition, disease, or disorder and includes identifying patients who are at risk of developing a specific condition, disease or disorder. Diagnostic methods differ in their sensitivity and specificity. The “sensitivity” of a diagnostic assay is the percentage of diseased individuals who test positive (percent of “true positives”). Diseased individuals not detected by the assay are “false negatives.” Subjects who are not diseased and who test negative in the assay, are termed “true negatives.” The “specificity” of a diagnostic assay is 1 minus the false positive rate, where the “false positive” rate is defined as the proportion of those without the disease who test positive. While a particular diagnostic method may not provide a definitive diagnosis of a condition, a disease, or a disorder, it suffices if the method provides a positive indication that aids in diagnosis. The terms “detection”, “detecting” and the like, may be used in the context of detecting biomarkers, detecting peptides, detecting proteins, or of detecting a condition, detecting a disease or a disorder (e.g., when positive assay results are obtained). In the latter context, “detecting” and “diagnosing” are considered synonymous. The terms “marker” or “biomarker” are used interchangeably herein, and in the context of the present invention refer to a protein or peptide (for example, protein or peptide 22 42150.601 associated with PAH or PAH as described herein) is differentially present in a sample taken from patients having a specific disease or disorder as compared to a control value, the control value consisting of, for example average or mean values in comparable samples taken from control subjects (e.g., a person with a negative diagnosis, normal or healthy subject). Biomarkers may be determined as specific peptides or proteins which may be detected by, for example, antibodies or mass spectroscopy. In some applications, for example, a mass spectroscopy or other profile of multiple antibodies may be used to determine multiple biomarkers, and differences between individual biomarkers and / or the partial or complete profile may be used for diagnosis. In some embodiments, the biomarkers may be detected by antibodies, mass spectrometry, or combinations thereof. A “test amount” of a marker refers to an amount of a marker present in a sample being tested. A test amount can be either in absolute amount (e.g., g / mi) or a relative amount (e.g., relative intensity of signals). A “diagnostic amount” of a marker refers to an amount of a marker in a subject’s sample that is consistent with a diagnosis of a particular disease or disorder. A diagnostic amount can be either in absolute amount (e.g., μg / ml) or a relative amount (e.g., relative intensity of signals). A “control amount” of a marker can be any amount or a range of amount which is to be compared against a test amount of a marker. For example, a control amount of a marker can be the amount of a marker in a person who does not suffer from the disease or disorder sought to be diagnosed, A control amount can be either in absolute amount (e.g., µg / ml) or a relative amount (e.g., relative intensity of signals). The term “differentially present” or “change in level” refers to differences in the quantity and / or the frequency of a marker present in a sample taken from patients having a specific disease or disorder as compared to a control subject. For example, a marker can be present at an elevated level or at a decreased level in samples of patients with the disease or disorder compared to a control value (e.g., determined from samples of control subjects). Alternatively, a marker can be detected at a higher frequency or at a lower frequency in samples of patients compared to samples of control subjects. A marker can be differentially present in terms of quantity, frequency or both as well as a ratio of differences between two or more specific modified amino acid residues and / or the protein itself. In one embodiment, an increase in the ratio of modified to unmodified proteins and peptides described herein is diagnostic of any one or more of the diseases described herein. A marker, compound, composition or substance is differentially present in a sample if the amount of the marker, compound, composition or substance in the sample is statistically significantly different from the amount of the marker, compound, composition or substance in another sample, or from a control value. For example, a compound is differentially present if it is present at least about 120%, at least about 130%, at least about 150%, at least about 180%, at least about 200%, at least about 300%, at least about 500%, at least about 700%, at least about 900%, or at least about 1000% greater or less than it is present in the other sample (e.g., control), or if it is detectable in one sample and not detectable in the other. 23 42150.601 Alternatively, or additionally, a marker, compound, composition or substance is differentially present between samples if the frequency of detecting the marker, etc. in samples of patients suffering from a particular disease or disorder, is statistically significantly higher or lower than in the control samples or control values obtained from healthy individuals. For example, a biomarker is differentially present between the two sets of samples if it is detected at least about 10%, at least about 20%, at least about 30%, at least about 40%, at least about 50%, at least about 60%, at least about 70%, at least about 80%, at least about 90%, or at least about 100% more frequently or less frequently observed in one set of samples than the other set of samples. These exemplary values notwithstanding, it is expected that a skilled practitioner can determine cut-off points, etc., that represent a statistically significant difference to determine whether the marker is differentially present. The term “one or more of” refers to combinations of various protein biomarkers. The term encompasses 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15 ,16 ,17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40... N, where “N” is the total number of biomarker proteins in the particular embodiment. The term also encompasses, and is interchangeably used with, at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 15 ,16 ,17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, at least 30, at least 31, at least 32, at least 33, at least 34, at least 35, at least 36, at least 37, at least 38, at least 39, at least 40... N. It is understood that the recitation of biomarkers herein includes the phrase “one or more of” the biomarkers and, in particular, includes the “at least 1, at least 2, at least 3” and so forth language in each recited embodiment of a biomarker panel. “Detectable moiety” or a “label” refers to a composition detectable by spectroscopic, photochemical, biochemical, immunochemical, or chemical means. For example, useful labels include32P,35S, fluorescent dyes, electron-dense reagents, enzymes (e.g., as commonly used in an ELISA), biotin-streptavidin, digoxigenin, haptens and proteins for which antisera or monoclonal antibodies are available, or nucleic acid molecules with a sequence complementary to a target. The detectable moiety often generates a measurable signal, such as a radioactive, chromogenic, or fluorescent signal, that can be used to quantify the amount of bound detectable moiety in a sample. Quantitation of the signal is achieved by, e.g., scintillation counting, densitometry, flow cytometry, or direct analysis by mass spectrometry of intact protein or peptides. In some embodiments, the detectable moiety is a stable isotope. In some embodiments, the stable isotope is selected from the group consisting of15N,13C,18O and2H. In various embodiments the invention provides a method to identify protein biomarkers and patterns that are indicative of a disease. In various embodiments the invention provides a method to identify protein biomarkers and patterns that are indicative a disease is or may be present. In some embodiments these methods may provide objective rationale for further testing. In various embodiments the invention provides a method for the identification of a plurality of proteins from a sample, wherein each protein is correlated to one or more peptides, wherein each peptide is correlated to one or more transitions, 24 42150.601 wherein each transition comprises a Ql mass value. In various embodiments the invention provides a method for the identification of a plurality of proteins from a sample, wherein each protein is correlated to one or more peptides, wherein each peptide is correlated to one or more transitions, wherein each transition comprises a Ql mass value and a Q3 mass value. In various embodiments the invention provides a method for the identification of a plurality of proteins from a sample, wherein each protein is correlated to one or more peptides, wherein each peptide is correlated to one or more transitions, wherein each transition comprises a Q1 / Q3 mass value pair. As used herein, SRM stands for selected reaction monitoring. As used herein, MRM stands for multiple reaction monitoring. As used herein, PRM stands for parallel reaction monitoring. As used herein, SWATH stands for sequential window acquisition of all theoretical fragment ion spectra. As used herein, DIA stands for data-independent acquisition. As used herein, MS stands for mass spectrometry. As used herein, SIL stands for stable isotope-labeled. As used herein, “MS data” can be raw MS data obtained from a mass spectrometer and / or processed MS data in which peptides and their fragments (e.g., transitions and MS peaks) are already identified, analyzed and / or quantified. MS data can be Selective Reaction Monitoring (SRM) data, Multiple Reaction Monitoring (MRM) data, parallel reaction monitoring (PRM) data, Shotgun CID MS data, Original DIA MS Data, MSE MS data, p2CID MS Data, PAcIFIC MS Data, AIF MS Data, XDLA MS Data, SWATH MS data, or FT-ARM MS Data, or their combinations. In some embodiments of the present invention, based on SRM and / or MS, and / or PRM MS, allows for the detection and accurate quantification of specific peptides in complex mixtures. Selected Reaction Monitoring or Multiple Reaction Monitoring (SRM / MRM) mass spectrometry is a technology with the potential for reliable and comprehensive quantification of substances of low abundance in complex samples. SRM is performed on triple quadrupole-like instruments, in which increased selectivity is obtained through collision-induced dissociation. It is a non-scanning mass spectrometry technique, where two mass analyzers (Ql and Q3) are used as static mass filters, to monitor a particular fragment of a selected precursor. On triple quadrapole instruments, various ionization methods can be used including without limitation electrospray ionization, chemical ionization, electron ionization, atmospheric pressure chemical ionization, and matrix-assisted laser desorption ionization. Both the first mass analyzer and the collision cell are continuously exposed to ions from the source in a time dependent manner. Once the ions move into the third mass analyzer time dependence becomes a factor. On triple quadrupole instruments, the first quadrapole mass filter, Ql is the primary m / z selector after the sample leaves the ionization source. Any ions with mass-to-charge ratios other than the one selected for will not be allowed to infiltrate Ql. The collision cell, denoted as “q2”, located between the first quadrapole mass filter Ql and second quadrapole mass filter Q3, is where fragmentation of the sample occurs in the presence of an inert gas like argon, helium, or nitrogen. Upon exiting the collision cell, the fragmented ions then travel onto the second quadrapole mass 25 42150.601 filter Q3, where m / z selection can occur again. The specific pair of mass-over-charge (m / z) values associated to the precursor and fragment ions selected is referred to as a “transition”. The detector acts as a counting device for the ions matching the selected transition thereby returning an intensity distribution over time. MRM is when multiple SRM transitions are measured within the same experiment on the chromatographic time scale by rapidly switching between the different precursor / fragment pairs. Typically, the triple quadrupole instrument cycles through a series of transitions and records the signal of each transition as a function of the elution time. The method allows for additional selectivity by monitoring the chromatographic co-elution of multiple transitions for a given analyte. In addition to MRM, the choice of peptides can also be quantified through Parallel- Reaction Monitoring (PRM), Parallel reaction monitoring (PRM) is the application of SRM with parallel detection of all transitions in a single analysis using a high resolution mass spectrometer. PRM provides high selectivity, high sensitivity and high-throughput to quantify selected peptide (Ql), hence quantify proteins. Again, multiple peptides can be specifically selected for each protein. PRM methodology uses the quadrupole of a mass spectrometer to isolate a target precursor ion, fragments the targeted precursor ion in the collision cell, and then detects the resulting product ions in the Orbitrap mass analyzer. Quantification is carried out after data acquisition by extracting one or more fragment ions with 5-10 ppm mass windows. PRM uses a quadrupole time-of-flight (QTOF) or hybrid quadrupole-orbitrap (QOrbitrap) mass spectrometer to carry out the peptides / proteins quantitation. Examples of QTOF include but are not limited to: TripleTOF® 6600 or 5600 System (Sciex); X500R QTOF System (Sciex); 6500 Series Accurate-Mass Quadrupole Time-of-Flight (Q-TOF) (Agilent); or Xevo G2-XS QTof Quadrupole Time-of-Flight Mass Spectrometry (Waters). Examples of QObitrap include but are not limited to: Q Exactive™ Hybrid Quadrupole-Orbitrap Mass Spectrometer (the Thermo Scientific); or Orbitrap Fusion™ Tribrid™ (the Thermo Scientific). Non-limiting advantages of PRM include elimination of most interferences, provides more accuracy and attomole-level limits of detection and quantification, enables the confident confirmation of the peptide identity with spectral library matching, reduces assay development time since no target transitions need to be preselected, ensures UHPLC- compatible data acquisition speeds with spectrum multiplexing and advanced signal processing. SWATH MS is a data independent acquisition (DIA) method which aims to complement traditional mass spectrometry-based proteomics techniques such as shotgun and SRM methods. In essence, it allows a complete and permanent recording of all fragment ions of the detectable peptide precursors present in a biological sample. It thus combines the advantages of shotgun (high throughput) with those of SRM (high reproducibility and consistency). In some embodiments, the developed methods herein can be applied to the quantification of polypeptides(s) or protein(s) in biological sample(s). Any kind of biological samples comprising polypeptides or proteins can be the starting point and be analyzed by the methods herein. Indeed, any protein / peptide containing sample can be used 26 42150.601 for and analyzed by the methods produced here (e.g., tissues, cells). The methods herein can also be used with peptide mixtures obtained by digestion. Digestion of a polypeptide or protein includes any-kind of cleavage strategies such as enzymatic, chemical, physical or combinations thereof. In some embodiments, the analysis and / or comparison is performed on protein samples of wild-type or physiological / healthy origin against protein samples of mutant or pathological origin. As used herein, the terms “treat”, “treatment”, “treating”, or “amelioration” when used in reference to a disease, disorder or medical condition, refer to both therapeutic treatment and prophylactic or preventative measures, wherein the object is to reverse, alleviate, ameliorate, inhibit, lessen, slow down or stop the progression or severity of a symptom, a condition, a disease, or a disorder. The term “treating” includes reducing or alleviating at least one adverse effect or symptom of a condition, a disease, or a disorder. Treatment is generally “effective” if one or more symptoms or clinical markers are reduced. Alternatively, treatment is “effective” if the progression of a disease, disorder or medical condition is reduced or halted. That is, “treatment” includes not just the improvement of symptoms or markers, but also a cessation or at least slowing of progress or worsening of symptoms that would be expected in the absence of treatment. Also, “treatment” may mean to pursue or obtain beneficial results, or lower the chances of the individual developing the condition, disease, or disorder even if the treatment is ultimately unsuccessful. Those in need of treatment include those already with the condition, disease, or disorder as well as those prone to have the condition, disease, or disorder or those in whom the condition, disease, or disorder is to be prevented. Non-limiting examples of treatments or therapeutic treatments include pharmacological or biological therapies and / or interventional surgical treatments. A “therapeutic regimen,” “therapy” or “treatment(s),” as described herein, includes all clinical management of a subject and interventions, whether biological, chemical, physical, or a combination thereof, intended to sustain, ameliorate, improve, or otherwise alter the condition of a subject. These terms may be used synonymously herein. The term “preventative treatment” means maintaining or improving a healthy state or non-diseased state of a healthy subject or subject that does not have a disease. The term “preventative treatment” or “health surveillance “also means to prevent or to slow the appearance of symptoms associated with a condition, disease, or disorder. The term “preventative treatment” also means to prevent or slow a subject from obtaining a condition, disease, or disorder. As used herein, the term “administering,” refers to the placement an agent or a treatment as disclosed herein into a subject by a method or route which results in at least partial localization of the agent or treatment at a desired site. “Route of administration” may refer to any administration pathway known in the art, including but not limited to aerosol, nasal, via inhalation, oral, anal, intra-anal, peri-anal, transmucosal, transdermal, parenteral, enteral, topical or local. “Parenteral” refers to a route of administration that is generally associated with injection, including intratumoral, intracranial, intraventricular, intrathecal, 27 42150.601 epidural, intradural, intraorbital, infusion, intracapsular, intracardiac, intradermal, intramuscular, intraperitoneal, intrapulmonary, intraspinal, intrastemai, intrathecal, intrauterine, intravascular, intravenous, intraarterial, subarachnoid, subcapsular, subcutaneous, transmucosal, or transtracheal. Via the parenteral route, the compositions may be in the form of solutions or suspensions for infusion or for injection, or as lyophilized powders. Via the enteral route, the pharmaceutical compositions can be in the form of tablets, gel capsules, sugar-coated tablets, syrups, suspensions, solutions, powders, granules, emulsions, microspheres or nanospheres or lipid vesicles or polymer vesicles allowing controlled release. Via the topical route, the pharmaceutical compositions can be in the form of aerosol, lotion, cream, gel, ointment, suspensions, solutions or emulsions. In accordance with the present invention, “administering” can be self-administering. For example, it is considered as “administering” that a subject consumes a composition as disclosed herein. In certain embodiments, one or more tests are performed including, but not limited to, echocardiogram, chest X-ray, electrocardiogram, right heart catheterization and blood tests. Additional tests include computerized tomography (CT) scan, magnetic resonance imaging (MRI), pulmonary function test, polysomnogram, ventilation / perfusion (V / Q) scan and open biopsy. Treatment can include endothelial receptor antagonists, prostacyclin pathway agonists, nitric oxide-cyclic guanosine monophosphate (NO-cGMP) enhancers, vasodilators, calcium channel blockers, anticoagulants, and diuretics. In certain embodiments, endothelin receptor antagonists include, but are not limited to, ambrisentan (LETAIRIS®) (Gilead Sciences, Inc.), macitentan (OPSUMIT®) (Actelion Pharmaceuticals, Inc.), bosentan (TRACLEER®) (Actelion Pharmaceuticals, Inc.). In other embodiments, prostacyclin pathway agonists include, but are not limited to prostacyclin (epoprostenol), synthetic analogs of prostacyclin (intravenous treprostinil, subcutaneous treprostinil, inhaled treprostinil, and inhaled iloprost), and selexipag (UPTRAVI®) (Actelion Pharmaceuticals, Inc.). NO-cGMP enhancers include, but are not limited to PDE5 inhibitors (sildenafil (REVATIO® / VIAGRA®), tadalafil (ADCIRCA® / CIALIS®), and vardenafil (LEVITRA® / STAXYN®)) and soluble gyanylate cyclase stimulators (riociguat (ADEMPAS®) (Bayer). Vasodilators include, but are not limited to, epoprostenol (FLOLAN® / VELETRI®)), iloprost (VENTAVIS®), and treprostinil (TYVASO® / REMODULIN®, ORENITRAM®). Calcium channel blockers include, but are not limited to amlodipine (NORVASC), dilitiazem (CARDIZEM® / TIAZAC®), and nifedipine (PROCARDIA®). In particular embodiments, anticoagulants include, but are not limited to warfarin (COUMADIN® / JANTOVEN®). In particular embodiments, treatment comprises digoxin (LANOXIN®). In certain embodiments, treatment can comprise combination therapy including, but not limited to, tadalafil plus ambrisentan; sildenafil plus bosentan; bosentan added to either epoprostenol or treprostinil; treprostinil added to either bosentan or sildenafil; oral treprostinil added to an endothelin receptor antagonist and / or a phostphodiesterase-5 28 42150.601 inhibitor; sildenafil added to epoprostenol; sildenafil added to iloprost; and bosenan plus iloprost; riociguat added to sildenafil. A physician can determine whether / how the treatments can be administered. For example, in certain embodiments, PAH can be treat using a continuously infused prostacyclins or analogs thereof. Continuous IV or SQ infusions can include FLOLAN®, REMODULIN® and VELETRI®. In a specific embodiment, treatment comprises stem cell therapy. In further embodiments, treatment can comprise surgery including atrial septostomy and lung and heart transplants. In specific embodiments, the biomarker proteins of the present invention can be detected and / or measured by immunoassay. Immunoassay requires biospecific capture reagents / binding agent, such as antibodies, to capture the biomarkers. Many antibodies are available commercially. Antibodies also can be produced by methods well known in the art, e.g., by immunizing animals with the biomarkers. Biomarkers can be isolated from samples based on their binding characteristics. Alternatively, if the amino acid sequence of a polypeptide biomarker is known, the polypeptide can be synthesized and used to generate antibodies by methods well-known in the art. The present invention contemplates traditional immunoassays including, for example, sandwich immunoassays including ELISA or fluorescence-based immunoassays, immunoblots, Western Blots (WB), as well as other enzyme immunoassays. Nephelometry is an assay performed in liquid phase, in which antibodies are in solution. Binding of the antigen to the antibody results in changes in absorbance, which is measured. In a SELDI- based immunoassay, a biospecific capture reagent for the biomarker is attached to the surface of an MS probe, such as a pre-activated protein chip array. The biomarker is then specifically captured on the biochip through this reagent, and the captured biomarker is detected by mass spectrometry. In certain embodiments, the expression levels of the protein biomarkers employed herein are quantified by immunoassay, such as enzyme-linked immunoassay (ELISA) technology. In specific embodiments, the levels of expression of the biomarkers are determined by contacting the biological sample with antibodies, or antigen binding fragments thereof, that selectively bind to the biomarker; and detecting binding of the antibodies, or antigen binding fragments thereof, to the biomarkers. In certain embodiments, the binding agents employed in the disclosed methods and compositions are labeled with a detectable moiety. In other embodiments, a binding agent and a detection agent are used, in which the detection agent is labeled with a detectable moiety. For example, the level of a biomarker in a sample can be assayed by contacting the biological sample with an antibody, or antigen binding fragment thereof, that selectively binds to the target protein (referred to as a capture molecule or antibody or a binding agent), and detecting the binding of the antibody, or antigen-binding fragment thereof, to the protein. The detection can be performed using a second antibody to bind to the capture antibody complexed with its target biomarker. A target biomarker can be an entire protein, or a variant or modified form thereof. Kits for the detection of proteins as described herein 29 42150.601 can include pre-coated strip / plates, biotinylated secondary antibody, standards, controls, buffers, streptavidin-horse radish peroxidise (HRP), tetramethyl benzidine (TMB), stop reagents, and detailed instructions for carrying out the tests including performing standards. The present disclosure also provides methods for detecting protein in a sample obtained from a subject, wherein the levels of expression of the proteins in a biological sample are determined simultaneously. For example, in one embodiment, methods are provided that comprise: (a) contacting a biological sample obtained from the subject with a plurality of binding agents that each selectively bind to one or more biomarker proteins for a period of time sufficient to form binding agent-biomarker complexes; and (b) detecting binding of the binding agents to the one or more biomarker proteins. In further embodiments, detection thereby determines the levels of expression of the biomarkers in the biological sample; and the method can further comprise (c) comparing the levels of expression of the one or more biomarker proteins in the biological sample with predetermined threshold values, wherein levels of expression of at least one of the biomarker proteins above or below the predetermined threshold values indicates, for example, the subject has PAH, the severity of PAH, and / or is / will be responsive to PAH therapy. Examples of binding agents that can be effectively employed in such methods include, but are not limited to, antibodies or antigen-binding fragments thereof, aptamers, lectins and the like. Although antibodies are useful because of their extensive characterization, any other suitable agent (e.g., a peptide, an aptamer, or a small organic molecule) that specifically binds a biomarker of the present invention is optionally used in place of the antibody in the above described immunoassays. For example, an aptamer that specifically binds a biomarker and / or one or more of its breakdown products might be used. Aptamers are nucleic acid-based molecules that bind specific ligands. Methods for making aptamers with a particular binding specificity are known as detailed in U.S. Patents No.5,475,096; No. 5,670,637; No.5,696,249; No.5,270,163; No.5,707,796; No.5,595,877; No.5,660,985; No.5,567,588; No.5,683,867; No.5,637,459; and No.6,011,020. In specific embodiments, the assay performed on the biological sample can comprise contacting the biological sample with one or more capture agents (e.g., antibodies, peptides, aptamer, etc., combinations thereof) to form a biomarker:capture agent complex. The complexes can then be detected and / or quantified. A subject can then be identified as having PAH based on a comparison of the detected / quantified / measured levels of biomarkers to one or more reference controls as described herein. In one method, a first, or capture, binding agent, such as an antibody that specifically binds the protein biomarker of interest, is immobilized on a suitable solid phase substrate or carrier. The test biological sample is then contacted with the capture antibody and incubated for a desired period of time. After washing to remove unbound material, a second, detection, antibody that binds to a different, non-overlapping, epitope on the biomarker (or to the bound capture antibody) is then used to detect binding of the polypeptide biomarker to the capture antibody. The detection antibody is preferably conjugated, either directly or indirectly, to a detectable moiety. Examples of detectable moieties that can be employed in 30 42150.601 such methods include, but are not limited to, cheminescent and luminescent agents; fluorophores such as fluorescein, rhodamine and eosin; radioisotopes; colorimetric agents; and enzyme-substrate labels, such as biotin. In another embodiment, the assay is a competitive binding assay, wherein labeled protein biomarker is used in place of the labeled detection antibody, and the labeled biomarker and any unlabeled biomarker present in the test sample compete for binding to the capture antibody. The amount of biomarker bound to the capture antibody can be determined based on the proportion of labeled biomarker detected. Solid phase substrates, or carriers, that can be effectively employed in such assays are well known to those of skill in the art and include, for example, 96 well microtiter plates, glass, paper, and microporous membranes constructed, for example, of nitrocellulose, nylon, polyvinylidene difluoride, polyester, cellulose acetate, mixed cellulose esters and polycarbonate. Suitable microporous membranes include, for example, those described in US Patent Application Publication no. US 2010 / 0093557 A1. Methods for the automation of immunoassays are well known in the art and include, for example, those described in U.S. Patent Nos.5,885,530, 4,981,785, 6,159,750 and 5,358,691. The presence of several different protein biomarkers in a test sample can be detected simultaneously using a multiplex assay, such as a multiplex ELISA. Multiplex assays offer the advantages of high throughput, a small volume of sample being required, and the ability to detect different proteins across a board dynamic range of concentrations. In certain embodiments, such methods employ an array, wherein multiple binding agents (for example capture antibodies) specific for multiple biomarkers are immobilized on a substrate, such as a membrane, with each capture agent being positioned at a specific, pre- determined, location on the substrate. Methods for performing assays employing such arrays include those described, for example, in US Patent Application Publication nos. US2010 / 0093557A1 and US2010 / 0190656A1, the disclosures of which are hereby specifically incorporated by reference. Multiplex arrays in several different formats based on the utilization of, for example, flow cytometry, chemiluminescence or electron-chemiluminescence technology, can be used. Flow cytometric multiplex arrays, also known as bead-based multiplex arrays, include the Cytometric Bead Array (CBA) system from BD Biosciences (Bedford, Mass.) and multi- analyte profiling (xMAP®) technology from Luminex Corp. (Austin, Tex.), both of which employ bead sets which are distinguishable by flow cytometry. Each bead set is coated with a specific capture antibody. Fluorescence or streptavidin-labeled detection antibodies bind to specific capture antibody-biomarker complexes formed on the bead set. Multiple biomarkers can be recognized and measured by differences in the bead sets, with chromogenic or fluorogenic emissions being detected using flow cytometric analysis. In an alternative format, a multiplex ELISA from Quansys Biosciences (Logan, Utah) coats multiple specific capture antibodies at multiple spots (one antibody at one spot) in the same well on a 96-well microtiter plate. Chemiluminescence technology is then used to detect multiple biomarkers at the corresponding spots on the plate. 31 42150.601 In several embodiments, the protein biomarkers of the present invention may be detected by means of an electrochemical luminescent assay developed by Meso Scale Discovery (Gaithersburg, MD). Electrochemiluminescence detection uses labels that emit light when electrochemically stimulated. Background signals are minimal because the stimulation mechanism (electricity) is decoupled from the signal (light). Labels are stable, non-radioactive and offer a choice of convenient coupling chemistries. They emit light at approximately 620 nm, eliminating problems with color quenching. See U.S. Patents No. 7,497,997; No.7,491,540; No.7,288,410; No.7,036,946; No.7,052,861; No.6,977,722; No.6,919,173; No.6,673,533; No.6,413,783; No.6,362,011; No.6,319,670; No. 6,207,369; No.6,140,045; No.6,090,545; and No.5,866,434. See also U.S. Patent Applications Publication No.2009 / 0170121; No.2009 / 006339; No.2009 / 0065357; No. 2006 / 0172340; No.2006 / 0019319; No.2005 / 0142033; No.2005 / 0052646; No. 2004 / 0022677; No.2003 / 0124572; No.2003 / 0113713; No.2003 / 0003460; No. 2002 / 0137234; No.2002 / 0086335; and No.2001 / 0021534. The proteins of the present invention can be detected by other suitable methods. Detection paradigms that can be employed to this end include optical methods, electrochemical methods (voltammetry and amperometry techniques), atomic force microscopy, and radio frequency methods, e.g., multipolar resonance spectroscopy. Illustrative of optical methods, in addition to microscopy, both confocal and non-confocal, are detection of fluorescence, luminescence, chemiluminescence, absorbance, reflectance, transmittance, and birefringence or refractive index (e.g., surface plasmon resonance, ellipsometry, a resonant mirror method, a grating coupler waveguide method or interferometry). Furthermore, a sample may also be analyzed by means of a biochip. Biochips generally comprise solid substrates and have a generally planar surface, to which a capture reagent (also called an adsorbent or affinity reagent) is attached. Frequently, the surface of a biochip comprises a plurality of addressable locations, each of which has the capture reagent bound there. Protein biochips are biochips adapted for the capture of polypeptides. Many protein biochips are described in the art. These include, for example, protein biochips produced by Ciphergen Biosystems, Inc. (Fremont, CA.), Invitrogen Corp. (Carlsbad, CA), Affymetrix, Inc. (Fremong, CA), Zyomyx (Hayward, CA), R&D Systems, Inc. (Minneapolis, MN), Biacore (Uppsala, Sweden) and Procognia (Berkshire, UK). Examples of such protein biochips are described in the following patents or published patent applications: U.S. Patent No.6,537,749; U.S. Patent No.6,329,209; U.S. Patent No. 6,225,047; U.S. Patent No.5,242,828; PCT International Publication No. WO 00 / 56934; and PCT International Publication No. WO 03 / 048768. In a particular embodiment, the present invention comprises a microarray chip. More specifically, the chip comprises a small wafer that carries a collection of binding agents bound to its surface in an orderly pattern, each binding agent occupying a specific position on the chip. The set of binding agents specifically bind to each of the one or more one or more of the biomarkers described herein. In particular embodiments, a few micro- liters of blood serum or plasma are dropped on the chip array. protein biomarkers present in 32 42150.601 the tested specimen bind to the binding agents specifically recognized by them. Subtype and amount of bound mark is detected and quantified using, for example, a fluorescently-labeled secondary, subtype-specific antibody. In particular embodiments, an optical reader is used for bound biomarker detection and quantification. Thus, a system can comprise a chip array and an optical reader. In other embodiments, a chip is provided. In another embodiment, the present invention provides kits for detecting one or more proteins. The exact nature of the components configured in the inventive kit depends on its intended purpose. In one embodiment, the kit is configured particularly for human subjects. The materials or components assembled in the kit can be provided to the practitioner stored in any convenient and suitable ways that preserve their operability and utility. For example, the components can be in dissolved, dehydrated, or lyophilized form; they can be provided at room, refrigerated or frozen temperatures. The components are typically contained in suitable packaging material(s). As employed herein, the phrase “packaging material” refers to one or more physical structures used to house the contents of the kit, such as inventive compositions and the like. The packaging material is constructed by well- known methods, to provide a sterile, contaminant-free environment. As used herein, the term “package” refers to a suitable solid matrix or material such as glass, plastic, paper, foil, and the like, capable of holding the individual kit components. The packaging material generally has an external label which indicates the contents and / or purpose of the kit and / or its components. In various embodiments, the present invention provides a kit comprising: (a) one or more internal standards suitable for measurement of one or more proteins including any one or more of mass spectrometry, antibody method, antibodies, nucleic acid aptamer method, nucleic acid aptamers, immunoassay, ELISA, immunoprecipitation, SISCAPA, Western blot, or combinations thereof; and (b) reagents and instructions for sample processing, preparation and protein measurement / detection. The kit can further comprise (c) instructions for using the kit to measure proteins in a sample obtained from the subject. In particular embodiments, the kit comprises reagents necessary for processing of samples and performance of an immunoassay. In a specific embodiment, the immunoassay is an ELISA. Thus, in certain embodiments, the kit comprises a substrate for performing the assay (e.g., a 96-well polystyrene plate). The substrate can be coated with antibodies specific for an protein. In a further embodiment, the kit can comprise a detection antibody including, for example, a polyclonal antibody specific for an protein conjugated to a detectable moiety or label (e.g., horseradish peroxidase). The kit can also comprise a standard, e.g., a human IGFBP2 standard. The kit can also comprise one or more of a buffer diluent, calibrator diluent, wash buffer concentrate, color reagent, stop solution and plate sealers (e.g., adhesive strip). In particular embodiments, the kit may comprise a solid support, such as a chip, microtiter plate (e.g., a 96-well plate), bead, or resin having protein biomarker capture reagents attached thereon. The kit may further comprise a means for detecting the protein biomarkers, such as antibodies, and a secondary antibody-signal complex such as 33 42150.601 horseradish peroxidase (HRP)-conjugated goat anti-rabbit IgG antibody and tetramethyl benzidine (TMB) as a substrate for HRP. The kit may be provided as an immuno-chromatography strip comprising a membrane on which the antibodies are immobilized, and a means for detecting, e.g., gold particle bound antibodies, where the membrane, includes NC membrane and PVDF membrane. The kit may comprise a plastic plate on which a sample application pad, gold particle bound antibodies temporally immobilized on a glass fiber filter, a nitrocellulose membrane on which antibody bands and a secondary antibody band are immobilized and an absorbent pad are positioned in a serial manner, so as to keep continuous capillary flow of the sample. In certain embodiments, a subject can be diagnosed by adding a biological sample (e.g., blood) from the patient to the kit and detecting the relevant protein biomarkers conjugated with antibodies, specifically, by a method which comprises the steps of: (i) collecting blood from the patient; (ii) adding blood from patient to a diagnostic kit; and, (iii) detecting the protein biomarkers conjugated with antibodies. In other kit and diagnostic embodiments, blood will not be collected from the patient (i.e., it is already collected). Blood samples can be collected from subject of varying ages. Moreover, in other embodiments, the sample may comprise a serum, plasma sweat, tissue, urine or a clinical sample. The kit can also comprise a washing solution or instructions for making a washing solution, in which the combination of the capture reagents and the washing solution allows capture of the protein biomarkers on the solid support for subsequent detection by, e.g., antibodies or mass spectrometry. In a further embodiment, a kit can comprise instructions for suitable operational parameters in the form of a label or separate insert. For example, the instructions may inform a consumer about how to collect the sample, etc. In yet another embodiment, the kit can comprise one or more containers with protein biomarker samples, to be used as standard(s) for calibration or normalization. Detection of the markers described herein may be accomplished using a lateral flow assay. In particular embodiments, the protein biomarker proteins of the present invention can be captured and concentrated using nano particles. In a specific embodiment, the proteins can be captured and concentrated using Nanotrap® technology (Ceres Nanosciences, Inc. (Manassas, VA)). Briefly, the Nanotrap platform reduces pre-analytical variability by enabling biomarker enrichment, removal of high-abundance analytes, and by preventing degradation to highly labile analytes in an innovative, one-step collection workflow. Multiple analytes sequestered from a single sample can be concentrated and eluted into small volumes to effectively amplify, up to 100-fold or greater depending on the starting sample volume (Shafagati, 2014; Shafagati, 2013; Longo, et al., 2009), resulting in substantial improvements to downstream analytical sensitivity. In certain embodiments, the kit comprises reagents and components necessary for performing an electrochemiluminescent ELISA. Following long-standing patent law convention, the terms “a,” “an,” and “the” refer to “one or more” when used in this application, including the claims. Thus, for example, 34 42150.601 reference to “a subject” includes a plurality of subjects, unless the context clearly is to the contrary (e.g., a plurality of subjects), and so forth. Throughout this specification and the claims, the terms “comprise,” “comprises,” and “comprising” are used in a non-exclusive sense, except where the context requires otherwise. Likewise, the term “include” and its grammatical variants are intended to be non- limiting, such that recitation of items in a list is not to the exclusion of other like items that can be substituted or added to the listed items. For the purposes of this specification and appended claims, unless otherwise indicated, all numbers expressing amounts, sizes, dimensions, proportions, shapes, formulations, parameters, percentages, quantities, characteristics, and other numerical values used in the specification and claims, are to be understood as being modified in all instances by the term “about” even though the term “about” may not expressly appear with the value, amount or range. Accordingly, unless indicated to the contrary, the numerical parameters set forth in the following specification and attached claims are not and need not be exact, but may be approximate and / or larger or smaller as desired, reflecting tolerances, conversion factors, rounding off, measurement error and the like, and other factors known to those of skill in the art depending on the desired properties sought to be obtained by the presently disclosed subject matter. For example, the term “about,” when referring to a value can be meant to encompass variations of, in some embodiments, ± 100% in some embodiments ± 50%, in some embodiments ± 20%, in some embodiments ± 10%, in some embodiments ± 5%, in some embodiments ±1%, in some embodiments ± 0.5%, and in some embodiments ± 0.1% from the specified amount, as such variations are appropriate to perform the disclosed methods or employ the disclosed compositions. Further, the term “about” when used in connection with one or more numbers or numerical ranges, should be understood to refer to all such numbers, including all numbers in a range and modifies that range by extending the boundaries above and below the numerical values set forth. The recitation of numerical ranges by endpoints includes all numbers, e.g., whole integers, including fractions thereof, subsumed within that range (for example, the recitation of 1 to 5 includes 1, 2, 3, 4, and 5, as well as fractions thereof, e.g., 1.5, 2.25, 3.75, 4.1, and the like) and any range within that range. EXAMPLES The following Examples have been included to provide guidance to one of ordinary skill in the art for practicing representative embodiments of the presently disclosed subject matter. In light of the present disclosure and the general level of skill in the art, those of skill can appreciate that the following Examples are intended to be exemplary only and that numerous changes, modifications, and alterations can be employed without departing from the scope of the presently disclosed subject matter. The synthetic descriptions and specific examples that follow are only intended for the purposes of illustration, and are not to be construed as limiting in any manner to make compounds of the disclosure by other methods. EXAMPLE 1 35 42150.601 Multiple Biomarkers Are Equivalent To Clinical Pulmonary Arterial Hypertension Survival Risk Models 1.1 Overview Risk assessment in pulmonary arterial hypertension (PAH) is fundamental to guiding treatment and improved outcomes. Clinical models are excellent at identifying high-risk patients but leave uncertainty amongst moderate risk patients. Without wishing to be bound to any one particular theory, it was thought that a multiple blood biomarker model of PAH could improve risk discrimination over clinical models and be more convenient for patients. Using multiplex ELISA, we measured NT-proBNP, ST2, IL-6, Endostatin, Galectin- 3, HDGF, and IGF binding proteins (IGFBP1-7) in train (n=1623), test (n=696) and validation (n=237) cohorts. Clinical variables, biomarkers were evaluated by principal component analysis. NT-proBNP was not included to develop an NT-proBNP independent model. Unsupervised k-means clustering classified subjects into clusters. Transplant-free survival by cluster was examined using Kaplan-Meier and Cox proportional hazard regressions. Hazard by cluster was compared to NT-proBNP, REVEAL, and ESC / ERS Risk models alone, and combined clinical and biomarker models. The algorithm generated 5 clusters with risk discrimination using 6 biomarkers, weight, height, and age at PAH diagnosis. Adding the biomarker model to the ESC / ERS, and REVEAL scores improved the ESC / ERS and REVEAL scores. The best overall model was the biomarker model adjusted for NT-proBNP with the best C-statistic and AIC for the adjusted model compared to either the biomarker or NT-proBNP model alone. Based on this, a 6-category model was developed with the highest risk category being a combination of high-risk biomarker and highest NT-proBNP. A multi-biomarker model improved mortality risk prediction over current PAH clinical models, with best performance when combined with NT-proBNP. Clinical risk scores offer excellent predictive models but require multiple cumbersome tests; a blood biomarker model that performs equivalent to clinical models could enable frequent outpatient assessment of PAH patients supporting therapeutic decision-making and improving compliance. 1.2 Approach The presently disclosed subject matter used a machine learning approach to evaluate PAH-relevant proteins with clinical factors to develop a multi-biomarker PAH risk model. Each protein considered has been independently studied in PAH in addition to NT-proBNP, with plausible associations with PAH pathobiology. Combining well-described PAH markers that add to NT-proBNP in an easy to interpret prognostic model could have significant clinical utility. 1.3 Methods 1.3.1 Subject Enrollment This study was conducted in accordance with the Declaration of Helsinki and approved by the Johns Hopkins University Institutional Review Board (IRB) and the IRB at all enrolling centers with enrollees giving informed consent. Enrollees were diagnosed 36 42150.601

[0002] with WSPH Group 1 PAH, and older than 15-years, when proteomic markers, specifically NT-proBNP, reach stable adult levels. Li et al., 2018; Koerbin et al., 2012. All subjects were diagnosed by right heart catheterization with a mean pulmonary artery pressure (mPAP) >25mmHg, a PCWP <15 mmHg, and pulmonary vascular resistance (PVR) >3 WU, the definition of PAH at the time of study enrollment. Detailed enrollment, laboratory and statistical methods are in Section 2 herein below. 1.3.2 Pulmonary Arterial Hypertension Biobank (PAHB- training and test cohorts) Samples and clinical data were obtained from the National Biological Sample and Data Repository for PAH (PAHB N=2335), which includes data from 34 enrolling centers. Simpson et al., 2020a; Simpson et al., 2020b. Blood samples and clinical data, including demographics, medications, and comorbidities, are captured at enrollment. Cardiac catheterization data is captured at diagnosis. This group was split using random sampling into a training cohort (70%, N=1623) for model development and test cohort (30%, N=696) for initial validation (see FIG.1). 1.3.3 Validation Cohort A validation cohort (N=237) was enrolled from Johns Hopkins and Vanderbilt Universities Pulmonary Hypertension Programs. Damico et al., 2015. Samples and data were prospectively collected with censoring in 2018 (FIG.1). The most recent available hemodynamic, and functional data were used with concurrent blood specimens. 1.3.4 Laboratory Analysis Detailed laboratory methods were previously published. Simpson et al., 2020a; Yang et al., 2016; Simpson et al., 2020b. Briefly, a robotically spotted multiplex ELISA assay was developed to measure NT-proBNP, ST2, Endostatin, Galectin-3, and IL-6 concurrently with a separate ELISA developed to measure IGFBP1-3, and a separate ELISA each for IGFBP4 and HDGF (all on the Meso Scale Discovery platform, Gaithersburg, MD). Hatle et al., 1981. A total of 25 microliters of peripheral blood serum were necessary to measure all analytes. 1.3.5 Statistical Analysis Continuous variables are presented as means with standard deviation (SD) or median with interquartile range (IQR) based on normality of the data. Categorical variables are presented as N with percent. Descriptive information for the cohorts is shown Table 1A. 37 42150.601 Table 1A: Demographics of PAHB train, test, and Validation Cohorts at Enrollment PAHB-Train PAHB-Test Validation Demographics N = 1623 N = 696 N = 237 Age at 57 (46 - 67) 57 (45 - 67) 56 (45 - 67) Enrollment, Age at Diagnosis, 52 (41 - 63) 51 (39 - 63) 52 (41 - 64) Sex, n (%) 1,291 (80%) 555 (80%) 184 (83%) Weight (kg), 78 (65 - 93) 77 (64 - 91) 70 (61 - 86) Height (cm), 163 (158 - 170) 165 (159 - 170) 163 (157 - 170) BSA (m^2), 1.88 (1.71 - 2.09) 1.87 (1.69 - 2.07) 1.80 (1.60 - 2.00) Hispanic / LatinX, 156 (9.6%) 62 (8.9%) 1 (0.9%) n (%) White, n (%) 1,321 (81%) 555 (80%) 32 (28%) Black, n (%) 220 (14%) 93 (13%) 4 (3.5%) Other Race, n (%) 83 (5.1%) 49 (7.0%) 0 (0%) PHType, n (%) IPAH 618 (38%) 283 (41%) 96 (43%) HPAH* 145 (8.9%) 61 (8.8%) - FPAH* 65 (4.0%) 26 (3.7%) - APAH- 557 (34%) 219 (31%) 108 (48%) CTD APAH- 84 (5.2%) 38 (5.5%) 19 (8.5%) CHD APAH- 69 (4.3%) 36 (5.2%) - Drugs / Tox ins APAH- 33 (2.0%) 14 (2.0%) - HIV Portopulm 98 (6.0%) 34 (4.9%) - onary hypertensi on PVOD 7 (0.4%) 5 (0.7%) Other 12 (0.7%) 6 (0.9%) - APAH Biomarkers, median (IQR) NT-proBNP 678 (233 - 2,205) 684 (235 - 2080) 1062 (309 - (pg / mL) 2960) Endostatin 37.9 (28.1 - 52.1) 38.1 (27.8 - 51.4) 46.3 (28.5 - (ng / mL) 73.1) ST2 (ng / mL) 5.99 (3.91 - 9.30) 5.71 (3.89 - 9.45) 6.4 (4.6 - 11.0) IL-6 (pg / mL) 1.7 (0.5 - 3.4) 1.60 (0.42 - 3.29) 2.2 (1.5 - 4.4) HDGF (ng / mL) 0.8 (0.5 - 1.3) 0.8 (0.5 - 1.3) 0.7 (0.4 - 1.4) IGFBP1 (ng / mL) 32.2 (13.5 - 67.6) 28.9 (12.3 - 59.9) 33 (13 - 65) IGFBP2 (ng / mL) 266.1 (165.0 - 250.6 (158.1 - 258 (148 - 398) 405.4) 390.1) IGFBP3 (ng / mL) 2103.2 (1601.8 - 2080.4 (1656.6 - 2080 (1583 - 2648.5) 2721.5) 2821) IGFBP4 (ng / mL) 786.3 (547.2 - 809.6 (528.9 - 555 (388 - 771) 1166.2) 1153.8) Genetics, n (%) BMPR2 103 (6.3%) 38 (5.5%) - Mutation, n (%) 38 42150.601 Table 1A: Demographics of PAHB train, test, and Validation Cohorts at Enrollment PAHB-Train PAHB-Test Validation Other Pathogenic 40 (2.5%) 18 (2.6%) - Mutation, n (%) No identified 1,480 (91%) 640 (92%) - mutation, n (%) Outcomes Time from 44 (28 - 63) 45 (29 - 61) 49 (27 - 80) enrollment to censor (months), Primary Outcome, 370 (23%) 143 (21%) 95 (40%) n (%) Died, n 339 (21%) 138 (20%) 91 (39%) (%) Transplant 46 (2.8%) 11 (1.6%) 2 (1.6%) , n (%) REVEAL 2.0 7 (5 - 9) 7 (5 - 9) 6 (4 - 8) Score REVEAL 2.0 Risk, n (%) Low Risk 906 (56%) 405 (58%) 106 (69%) Average 214 (13%) 90 (13%) 24 (16%) Risk Moderatel 209 (13%) 93 (13%) 15 (9.7%) y High Risk High Risk 227 (14%) 85 (12%) 7 (4.5%) Extremely 67 (4.1%) 23 (3.3%) 2 (1.3%) High Risk ESC / ERS Risk, n (%) Low Risk 606 (37%) 260 (37%) 57 (37%) Intermedia 972 (60%) 416 (60%) 91 (59%) te Risk High Risk 45 (2.8%) 20 (2.9%) 7 (4.5%) All data presented as median (IQR) unless otherwise specified. *FPAH: Familial pulmonary arterial hypertension defined as multiple family members with disease regardless of mutation *HPAH: Heritable pulmonary arterial hypertension defined as any known pathogenic mutation, including those found after PAHB enrollment.7 Subjects had a significant family history without an identified genetic variant. Comorbid diagnoses at enrollment for the cohorts are shown in Table 1B and hemodynamic and functional variables are shown in Table 1C. Table 1B: Co-morbid diagnoses and medications at enrollment PAHB-Train PAHB- Validation Test Cardiovascular Disorders, N = 1623 N = 696 N = 237 n (%) Cardiovascular 275 (17) 133 (19) 19 (17) Disease (ischemia, cardiomyopathy) Congenital Heart 112 (6.9) 52 (7.5) 21 (18) Disease 39 42150.601 Table 1B: Co-morbid diagnoses and medications at enrollment PAHB-Train PAHB- Validation Test Pre-tricuspid 68 (4.2) 35 (5.0) - Shunt Post-tricuspid 47 (2.9) 19 (2.7) - Shunt Repaired 69 (4.3) 34 (4.9) - Shunt Respiratory Disorders, n (%) Restrictive Lung 157 (9.7) 63 (9.1) 3 (2.6) Disease Obstructive Lung 402 (25) 171 (25) 20 (17) Disease Sleep Apnea 454 (28) 183 (26) 18 (16) Other Systemic Disease, n (%) Diabetes 254 (16) 106 (15) 1 (0.9) Cancer 56 (3.5) 27 (3.9) 6 (5.2) HIV 33 (2.0) 15 (2.2) - Renal Insufficiency 103 (6.3) 46 (6.6) - Cirrhosis 115 (7.1) 33 (4.7) 1 (0.9) Thromboembolic 102 (6.3) 42 (6.0) 7 (6.1) Disease Thyroid Disease 335 (21) 151 (22) 24 (21) Anorexia 28 (1.7) 15 (2.2) - Medications, n (%) Phosphodiesterase 1,180 (73%) 484 (70%) 165 (74%) Inhibitor Endothelin Receptor 880 (54%) 378 (54%) 129 (58%) Antagonist Prostacyclin Analog 702 (43%) 303 (44%) 82 (37%) Calcium Channel 123 (7.6%) 48 (6.9%) 42 (19%) Blocker Soluble guanylate 40 (2.5%) 9 (1.3%) - cyclase stimulators All data presented as n (%) unless otherwise specified. Table 1C: Hemodynamic and functional variables PAHB-Train PAHB-Test Validation N N N Six Minute Walk 936 340 384 344 205 377 Distance (251 - 420) (250 - 417) (299 - 439) WHO Functional 1,189 494 128 Class, n / N (%) 1 76 / 1,189 31 / 494 16 / 128 (6.4%) (6.3%) (12%) 2 355 / 1,189 156 / 494 50 / 128 (30%) (32%) (39%) 3 666 / 1,189 261 / 494 62 / 128 (56%) (53%) (48%) 40 42150.601 4 92 / 1,189 46 / 494 - (7.7%) (9.3%) Heart Rate (bpm) 976 78 388 78 213 79 (69 - 89) (69 - 89) (70 - 87) Systolic Blood 983 124 391 126 153 122 Pressure (mmHg) (112 - 140) (113 - 139) (111 - 137) Diastolic Blood 983 77 391 76 153 71 Pressure (mmHg) (67 - 86) (66 - 86) (64 - 80) Mean arterial 993 92 395 92 153 89 pressure (mmHg) (83 - 104) (82 - 103) (80 - 97) Mean Right Atrial 1,593 8.0 678 8.0 151 21 Pressure (mmHg) (5.0 - 12.0) (5.0 - 12.0) (12 - 24) Mean Pulmonary 1,623 49 696 48 159 40 Artery Pressure (40 - 58) (39 - 58) (30 - 52) (mmHg) Mean Pulmonary 1,623 10.0 695 10.0 158 10.0 Capillary Wedge (7.0 - 13.0) (7.0 - 13.0) (8.0 - 13.0) Pressure (mmHg) Transpulmonary 1,622 39 691 38 158 30 Gradient (mmHg) (29 - 48) (29 - 48) (20 - 42) Pulmonary 1,603 8.5 680 8.8 154 6.2 Vascular (5.6 - 12.7) (5.6 - 12.9) (3.7 - 9.9) Resistance (WU) Pulmonary 1,587 4.5 674 4.6 154 3.49 Vascular (2.9 - 6.8) (2.9 - 7.0) (1.93 - 5.44) Resistance Index (WU*m2) Card iac Output 1,603 4.42 680 4.37 154 4.46 (L / min) (3.50 - 5.46) (3.45 - 5.45) (3.80 - 5.58) Cardiac Index 1,586 2.33 674 2.31 156 2.60 (L / min / m2) (1.87 - 2.87) (1.85 - 2.84) (2.17 - 3.14) *PAHB cardiac catheterization and functional data is at PAH diagnosis. Validation cardiac catheterization and functional data is at enrollment All data presented as median (IQR) unless otherwise specified Variables included biomarkers, demographic, genetic, PAH medications, hemodynamic and functional variables as well as the pairwise interactions of biomarkers and biomarkers with demographic variables, a total of 219 variables. Variables were initially evaluated by principal component analysis and chosen based on PC loads with more than 5% variability. This unsupervised method enabled identification and inclusion of variables that account for maximum variability in the dataset, without focusing on a particular outcome in the initial analysis. Unsupervised K-Means clustering was performed with K determined by calculating the sum of squared error (SSE) and using the gap statistic. Tibshirani et al., 2001. Combinations of variables based on PC loads were tried starting with biomarkers, then adding demographics, comorbidities, and genetics to understand the incremental added value of each variable. NT-proBNP was not included as a variable to identify models independent of NT-proBNP. Sensitivity testing using the PAHB training cohort generated 15 the cluster model with the best prognostic performance. The performance of the clusters to predict outcomes was then tested in the PAHB test and validation cohorts. The primary outcome was death or lung / heart-lung transplantation with censoring at the first event. 41 42150.601

[0003] The clinically based REVEAL 2.0 and ESC / ERS risk scores were calculated according to their respective algorithms (see Section 2, herein below). Benza et al., 2019; Benza et al., 2010; Galiè et al., 2016. Survival based on NT-proBNP was assessed as both a continuous and categorical variable based on centiles. Time to death or transplantation based on NT-proBNP, ESC / ERS and REVEAL scores, and the multi-marker model was assessed individually by Kaplan Meier analysis followed by Cox proportional hazard regressions. The performance of the multi-marker models (PAHB train, PAHB test, and validation) were compared to survival by NT-proBNP, the ESC / ERS risk and REVEAL 2.0 risk scores using the Akaike Information Criterion (AIC) and Harrell’s C-Index. A calibration curve was calculated for each model to assess model fit across risk strata. The multi-marker model was added to the ESC / ERS, REVEAL, and NT-proBNP survival models and compared to their respective baselines and to the multi-marker model alone using information criterion, Harrell’s C-Index and a calibration curve. Area under receiver operator characteristic curve (ROC) analysis was calculated for all models. All analyses were performed using R v.3.6.1. 1.4 Results Enrollment characteristics are shown in Table 1A-Table 1C. The PAHB training cohort (N=1623) and PAHB test cohort (N=696) were each 80% female. The validation cohort (N=237) was 83% female. The median age at enrollment was similar in all three cohorts. Most subjects in the PAHB train and test cohorts had IPAH (38%, 41%) or APAH (45.5%, 43.7%) with the majority of APAH associated with connective tissue disease in both groups (34%, 31%). The validation cohort included IPAH (43%), APAH- CTD (49%) and APAH-CHD (8%). Hemodynamics and functional data were consistent with moderate to severe PAH in all groups (Table 1C). In the PAHB training group, 370 subjects met the primary outcome (Table 1A, 23%) and 143 (21%) in the PAHB test group. In the validation cohort, 95 subjects met the primary outcome (40%). Univariate hazard ratios were calculated for each variable, the ESC / ERS criteria and REVEAL score in the PAHB training data set for comparability with prior studies (Table 4). Higher REVEAL and ESC / ERS risk were associated with an increased hazard for event. Functional class III-IV and female sex also was associated with an increased hazard for event. Collagen vascular disease, specifically Scleroderma, comorbid cardiovascular disease, restrictive or obstructive lung disease, and systemic disorders including diabetes, renal insufficiency, and cirrhosis were all associated with increased hazard for event. 42 42150.601 Table 4: Univariable Hazard Ratios of cluster membership, REVEAL and ERS / ESC risk, functional class, demographics, PAH subtype, genetics, and all comorbidities in PAHB Test Cohort Beta coefficient HR (95% CI for P Value HR) REVEAL Category Low Risk -1.4 0.25 (0.2-0.31) <0.0001 Average Risk 0.34 1.4 (1.1-1.9) 0.013 Moderately High Risk 0.53 1.7 (1.3-2.2) <0.0001 High Risk 1 2.8 (2.2-3.5) <0.0001 Extremely High Risk 1.2 3.4 (2.4-4.9) <0.0001 ERS / ESC Risk Category Low Risk -0.69 0.5 (0.41-0.6) <0.0001 Intermediate Risk 0.58 1.8 (1.5-2.1) <0.0001 High Risk 0.42 1.5 (1.1-2.2) 0.018 Functional Class Functional Class 1 / 2 -0.63 0.53 (0.41-0.7) <0.0001 Functional Class 3 0.37 1.5 (1.1-1.9) 0.003 Functional Class 4 0.54 1.7 (1.2-2.5) 0.004 Female Sex 0.42 1.5 (1.2-1.9) 0.0003 Enrollment Age (per year) 0.023 1 (1-1) <0.0001 Diagnosis Age (per year) 0.025 1 (1-1) <0.0001 Triple Medical Therapy 0.19 1.2 (0.97-1.5) 0.095 PAH Subtype IPAH -0.52 0.59 (0.48-0.74) <0.0001 FPAH -0.36 0.7 (0.38-1.3) 0.25 APAH Collagen Vascular <0.0001 Disease 0.47 1.6 (1.3-2) APAH Congenital Heart Disease -0.033 0.97 (0.65-1.4) 0.87 APAH Drug / Toxin Exposure -0.15 0.86 (0.5-1.5) 0.57 APAH HIV -0.19 0.83 (0.37-1.9) 0.65 Genetics BMPR2 Mutation -0.17 0.85 (0.55-1.3) 0.45 Other Pathogenic Mutation -0.085 0.92 (0.46-1.9) 0.81 Cardiovascular Disorders Cardiovascular Disease 0.42 1.5 (1.2-1.9) 0.00067 Pre-tricuspid Shunt -0.12 0.89 (0.53-1.5) 0.64 Post-tricuspid Shunt 0.12 1.1 (0.64-2) 0.68 Respiratory Disorders Restrictive Lung Disease 0.51 1.7 (1.2-2.2) 0.00066 Obstructive Lung Disease 0.26 1.3 (1-1.6) 0.023 Sleep Apnea 0.0092 1 (0.8-1.3) 0.94 Collagen Vascular Disease Scleroderma 0.69 2 (1.6-2.5) <0.0001 Rheumatoid Arthritis 0.4 1.5 (1-2.2) 0.05 Lupus -0.4 0.67 (0.41-1.1) 0.12 Other Collagen Vascular Disease 0.29 1.3 (1.1-1.7) 0.017 Endocrine Disorders Diabetes 0.28 1.3 (1-1.7) 0.036 Thyroid Disease 0.064 1.1 (0.83-1.4) 0.62 Other Systemic Disorders Cancer 0.2 1.2 (0.73-2.1) 0.44 HIV -0.19 0.83 (0.37-1.9) 0.65 Renal Insufficiency 0.94 2.6 (1.9-3.5) <0.0001 Cirrhosis 0.52 1.7 (1.2-2.3) 0.0021 Thromboembolic Disease 0.2 1.2 (0.82-1.8) 0.32 Anorexia 0.2 1.2 (0.73-2.1) 0.44 Exposures Appetite Suppressant Use -0.2 0.82 (0.58-1.1) 0.25 Recreational Drug User 0.12 1.1 (0.83-1.5) 0.45 History of Smoking 0.3 1.4 (1.1-1.7) 0.0037 5 1.4.1 Biomarker model 43 42150.601 The first six principal components each described more than 5% variability and together described 71.7% of variability in the data set (FIG.9). Variables with eigenvalues >1 were initially considered for model inclusion (Table 5). Combinations of variables based on PC loads were tested in the clustering model, starting with biomarkers, then adding demographics, comorbidities, and genetics to understand the incremental added value of each variable. The final group of variables for clustering was chosen to maximize explained variability with the minimum number of variables. Eigenvectors of variables in principle components 1 and 2 are shown in FIG.10 for (FIG.10A) all variables and (FIG.10B) variables in the final biomarker model. Notably, the variable interaction terms (i.e., ST2 * IGFBP2 or Endostatin * IGFBP2) performed better than single variables in the model with higher eigenvalues in each pc load (Table 5). Table 5: Variables with eigenvalues >1 in principal components 1-6. Principal components 1-6 were chosen based on percent variability >5%. Principal Component 1 Variable Eigenvalue Endostatin*IGFBP2 1.852039 IGFBP2*IGFBP4 1.756822 Endostatin*IGFBP1 1.703953 Diagnosis Age*IGFBP2.1 1.576181 Diagnosis Age*IGFBP2 1.576181 Enrollment Age*IGFBP2.1 1.572812 Enrollment Age*IGFBP2 1.572812 ST2*IGFBP2 1.560593 Endostatin*IGFBP4 1.550655 Gal3*IGFBP2 1.547968 IGFBP1*IGFBP4 1.546375 IGFBP1*IGFBP2 1.544933 Weight*IGFBP2 1.524344 IGFBP2 1.508014 IL-6*IGFBP2 1.501398 Height*IGFBP2 1.489396 Endostatin*ST2 1.437504 ST2*IGFBP4 1.415682 Diagnosis Age*Endostatin 1.407087 Enrollment Age*Endostatin 1.397076 BSA*IGFBP2 1.356606 ST2*IGFBP1 1.301464 Diagnosis Age*IGFBP4 1.284516 HDGF*IGFBP2 1.27436 HDGF*IGFBP2.1 1.27436 Enrollment Age*IGFBP4 1.274344 Diagnosis Age*IGFBP1 1.259449 Diagnosis Age*IGFBP1.1 1.259449 Endostatin*IL-6 1.251397 Endostatin 1.243864 Height*Endostatin 1.233404 Enrollment Age*IGFBP1 1.232283 Enrollment Age*IGFBP1.1 1.232283 Gal3*IGFBP1 1.224697 Weight*Endostatin 1.207321 Gal3*IGFBP4 1.196111 Endostatin*Gal3 1.180513 Endostatin*HDGF 1.165703 44 42150.601 Endostatin*HDGF.1 1.165703 IL-6*IGFBP4 1.1356 IGFBP4 1.092694 IL-6*IGFBP1 1.092097 Diagnosis Age*ST2 1.072634 Height*IGFBP4 1.070712 Enrollment Age*ST2 1.069785 IGFBP2*IGFBP3 1.063082 IGFBP2*IGFBP3.1 1.063082 Weight*IGFBP4 1.047791 Gal3*ST2 1.007288 Principal Component 2 Variable Eigenvalue Weight*IGFBP3 5.285058 Weight*IGFBP3.1 5.285058 IGFBP3 5.275483 IGFBP3.1 5.275483 IGFBP3.2 5.275483 Height*IGFBP3 5.213322 Height*IGFBP3.1 5.213322 BSA*IGFBP3 4.628827 BSA*IGFBP3.1 4.628827 Female*IGFBP3.1 3.909395 Female*IGFBP3 3.909395 Enrollment Age*IGFBP3 3.702585 Diagnosis Age*IGFBP3 3.453823 Endostatin*IGFBP3 2.548241 IL-6*IGFBP3 2.532215 IL-6*IGFBP3.1 2.532215 Gal3*IGFBP3 2.440695 PDE5*IGFBP3 2.259881 ST2*IGFBP3 2.178357 IGFBP3*IGFBP4 2.117215 IGFBP2*IGFBP3 2.001249 IGFBP2*IGFBP3.1 2.001249 IGFBP1*IGFBP3 1.757638 ERA*IGFBP3 1.050945 Principal Component 3 Variable Eigenvalue Weight*HDGF 5.791441 BSA*HDGF 5.599047 BSA*HDGF.1 5.599047 Height*HDGF 5.583112 HDGF 5.552781 HDGF*IL-6.1 4.606595 HDGF*IL-6 4.606595 Enrollment Age*HDGF 3.638939 Enrollment Age*HDGF.1 3.638939 Diagnosis Age*HDGF 3.358273 Diagnosis Age*HDGF.1 3.358273 Gal3*HDGF 2.300034 PDE5*HDGF 2.266384 Female*HDGF 2.236395 ST2*HDGF 2.178034 Endostatin*HDGF 2.133233 Endostatin*HDGF.1 2.133233 ERA*HDGF 1.237978 45 42150.601 BSA*IL-6 1.118478 HDGF*IGFBP2.1 1.101777 HDG F*IGFBP2 1.101777 Prostacyclin*HDGF 1.001886 Principal Component 4 Variable Eigenvalue BSA*Gal3 3.611491 BSA*Gal3.1 3.611491 Weight*Gal3 3.438605 Height*Gal3 3.242673 Diagnosis Age*Gal3 3.213693 Gal3 3.200099 Enrollment Age*Gal3 3.188527 Triple therapy*IGFBP1 2.867058 Triple therapy*IGFBP2 2.848147 Prostacyclin*IGFBP1 2.656744 Gal3*IL-6 2.568503 Triple therapy*HDGF 2.408211 Triple therapy*HDGF.1 2.408211 Prostacyclin*IGFBP2 2.274105 Triple therapy*ST2 2.001468 Endostatin*Gal3 1.995799 Triple therapy*IL-6 1.940049 Triple therapy*Endostatin 1.876099 Prostacyclin*HDGF 1.86941 Gal3*IGFBP4 1.671226 Prostacyclin*IGFBP3.1 1.547045 Prostacyclin*IGFBP3 1.547045 Female*Gal3 1.426307 Gal3*ST2 1.395266 ERA*IGFBP1 1.283031 IGFBP1 1.157424 HDGF*IGFBP1 1.144155 Prostacyclin*ST2 1.112007 Triple therapy*Gal3 1.106026 Height*IGFBP1 1.102033 ERA*IGFBP2 1.070012 Prostacyclin*IGFBP4 1.040309 Gal3*IGFBP3 1.015408 Weight*IGFBP1 1.010059 Principal Component 5 Variable Eigenvalue Triple therapy*Endostatin 4.624888 Triple therapy*Gal3 4.420684 Prostacyclin*Endostatin 4.182687 Triple therapy*ST2 3.700126 Triple therapy*IL-6 3.537446 Triple therapy*IGFBP2 3.355012 Prostacyclin*IGFBP4 3.323779 Triple therapy*IGFBP1 3.134267 Triple therapy*HDGF 3.105072 Triple therapy*HDGF.1 3.105072 Prostacyclin*ST2 2.935669 Prostacyclin*IL-6 2.913438 Prostacyclin*IGFBP3.1 2.111433 Prostacyclin*IGFBP3 2.111433 Prostacyclin*IGFBP2 2.069905 46 42150.601 Prostacyclin*HDGF 2.011062 Prostacyclin*IGFBP1 1.800225 BSA*Gal3 1.753329 BSA*Gal3.1 1.753329 Weight*Gal3 1.433161 Diagnosis Age*IGFBP1.1 1.283046 Diagnosis Age*IGFBP1 1.283046 Height*Gal3 1.275782 Gal3 1.238008 Enrollment Age*IGFBP1 1.231516 Enrollment Age*IGFBP1.1 1.231516 Endostatin*Gal3 1.155322 BSA*Endostatin 1.142294 Principal Component 6 Variable Eigenvalue Weight*IL-6 6.890482 Height*IL-6 6.775028 IL-6*Cat 6.715197 BSA*IL-6 6.305567 Enrollment Age*IL-6 4.167362 IL-6*IGFBP4 4.095226 Diagnosis Age*IL-6 3.870028 ST2*IL-6 3.608868 Gal3*HDGF 2.525951 IL-6*IGFBP3.1 2.51258 IL-6*IGFBP3 2.51258 Gal3 2.144656 ENDOSTATIN*IL-6 2.142426 IL-6*IGFBP2 2.120518 Height*Gal3 2.001018 IL-6*IGFBP1 1.849364 Weight*Gal3 1.84579 Enrollment Age*Gal3 1.845749 Female*Gal3 1.692706 Diagnosis Age*Gal3 1.691363 Prostacyclin*IL-6 1.665437 Gal3*IGFBP1 1.257186 Gal3*IGFBP2 1.188053 BSA*Gal3 1.033722 BSA*Gal3.1 1.033722 Female*HDGF 1.014299 Gal3*IGFBP3 1.007809 The final biomarker model included the variables ST2, IGFBP1, IGFBP2, IGFBP4, Endostatin, Galectin-3, age at diagnosis, weight, and height (at the time of blood draw) with interaction terms of the included variables used in the final model. Other demographics, genetics, hemodynamics, and comorbidities did not improve the model beyond those 9 variables. The biomarker model consisted of 5 distinct clusters (FIG.2A). NT-proBNP, shown in FIG.2B for comparison, was extremely elevated in Cluster A, but more variable 10 in Cluster B-E in the train, test and validation cohorts (FIG.2B, Table 2A-Table 2C). Similarly, Cluster A had significantly higher biomarker levels than other clusters (FIG. 2B, scaled for comparison) which had more variability between clusters. Median and 47 42150.601 interquartile range of all biomarkers, age at diagnosis, weight, and height are shown in Table 2A-Table 2C for all cohorts.

[0004] Table 2A: Demographics, Biomarkers, Genetics, Outcomes and Co-Morbidities by Cluster Membership in PAHB Training Cohort Cluster Cluster E Demographics A Cluster B Cluster C Cluster D N = 219 N = 348 N = 241 N = 3 N = 471 (Median, IQR) 44 Age at 65 61 62 54 49 Enrollment (58- 72) (52- 69) (51- 69) (45- 64) (40- 59) Age at 62 56 56 49 44 Diagnosis (54- 69) (46- 66) (45- 66) (41- 60) (33- 55) Sex, n (%) 169 (77%) 248 (71%) 202 (84%) 248 (72%) 424 (90%) Weight (kg) 75 80 75 82 77 (66- 88) (66- 95) (63- 89) (68- 98) (64- 93) Height (cm) 163 165 163 165 163 (158- 170) (159- 172) (157- 168) (160- 173) (157- 168) Hispanic / Lati 9 (4.1%) 24 (6.9%) 18 (7.5%) 41 (12%) 64 (14%) nX, n (%) White / Black / n(%)177 / 38 / 4 2361 / 70 / 40 Other,97 / 41 / 10 211 / 21 / 9 275 / 50 / 20PAH Subtype, n (%) IPAH 78 (36%) 103 (30%) 88 (37%) 134 (39%) 215 (46%) HPAH 8 (3.7%) 29 (8.3%) 20 (8.3%) 31 (9.0%) 57 (12%) FPAH 1 (0.5%) 7 (2.0%) 10 (4.1%) 15 (4.4%) 32 (6.8%) APAH- 103 (47%) 150 (43%) 94 (39%) 98 (28%) 112 (24%) CTD APAH- 7 (3.2%) 12 (3.4%) 8 (3.3%) 21 (6.1%) 36 (7.6%) CHD Drugs / To 7 (3.2%) 14 (4.0%) 10 (4.1%) 23 (6.7%) 15 (3.2%) xins HIV 5 (2.3%) 5 (1.4%) 5 (2.1%) 7 (2.0%) 11 (2.3%) Portopul 10 (4.6%) 33 (9.5%) 14 (5.8%) 28 (8.1%) 13 (2.8%) monary hypertens ion PVOD 1 (0.5%) 1 (0.3%) 0 (0%) 0 (0%) 5 (1.1%) Other 0 (0%) 1 (0.3%) 2 (0.8%) 2 (0.6%) 7 (1.5%) APAH Biomarkers NT-proBNP 5,982 1,446 822 564 233 (pg / mL) (108-69,473) (25- 24,487) (11 - 31,640) (15 - 11,881) (0.1 - 12,050) Endostatin 71 42.3 42.2 32.8 29 (ng / mL) (32 - 418) (2.7 - 148.3) (3.9 - 115) (6.7 - 104.8) (0.0 - 101) ST2 (ng / mL) 14 9.8 4.4 6.8 3.4 (5.3 - 312) (5.6 - 117.0) (0.3 - 6.9) (4.3 - 45.0) (0.0 - 6.4) IL-6 (pg / mL) 3.3 2.4 1.5 1.8 1.0 (0.0 - 2,619) (0.0 - 1,643.9) (0.0 - 116) (0.0 - 63.5) (0.0 - 135) HDGF 0.9 (0.0 - 28) 0.9 (0.0 - 21.8) 0.7 (0.0 - 5.1) 0.8 (0.0 - 9.5) 0.7 (0.0 - 18) (ng / mL) IGFBP1 86.5 44 48.9 18.6 14.5 (ng / mL) (15.5 - 1,234) (5.6 - 348) (6.2 - 376) (1.1 - 419) (0.9 - 427) IGFBP2 593 347 380 178 154 (ng / mL) (285 - 3,561) (151 - 1,305) (214 - 1,261) (34.2 - 334) (34 - 448) IGFBP4 1,582 913 899 687 569 (ng / mL) (1.0 - 6,257.) (1.0 - 4,002) (1.0 - 4,434) (1.0 - 3,478) (1.0 - 2,611) Genetics BMPR2 5 (2.3%) 21 (6.0%) 14 (5.8%) 24 (7.0%) 39 (8.3%) Mutation, n (%) Other 5 (2.3%) 9 (2.6%) 6 (2.5%) 5 (1.5%) 15 (3.2%) Pathogenic Mutation, n (%) Outcomes Time from 27 37 47 48 55 enrollment to (13 - 39) (22 - 51) (31 - 66) (33 - 64) (40 - 68) 49 42150.601 Table 2A: Demographics, Biomarkers, Genetics, Outcomes and Co-Morbidities by Cluster Membership in PAHB Training Cohort Cluster A Cluster B Cluste Cluster E Demographics r C Cluster D N = 219 N = 348 N = 241 N = 3 N = 471 (Median, IQR) 44 censor (months) Primary 106 (48%) 116 (33%) 57 (24%) 53 (15%) 38 (8.1%) Outcome, n (%) Died, n 98 (45%) 109 (31%) 49 (20%) 50 (15%) 33 (7.0%) ( % ) Transpla 15 (6.8%) 12 (3.4%) 8 (3.3%) 3 (0.9%) 8 (1.7%) nt, n (%) REVEAL 2.0 9 (8 - 10) 8 (6 - 10) 8 (6 - 9) 7 (5 - 8) 5 (4 - 7) Score REVEAL 2.0 Risk, n (%) Low Risk 46 (21%) 130 (37%) 120 (50%) 215 (62%) 395 (84%) Average 34 (16%) 57 (16%) 38 (16%) 52 (15%) 33 (7.0%) Risk Moderate 45 (21%) 62 (18%) 36 (15%) 37 (11%) 29 (6.2%) ly High Risk High 67 (31%) 77 (22%) 37 (15%) 33 (9.6%) 13 (2.8%) Risk Extremel 27 (12%) 22 (6.3%) 10 (4.1%) 7 (2.0%) 1 (0.2%) y High Risk ERS / ESC Risk, n (%) Low Risk 57 (26%) 125 (36%) 113 (47%) 167 (49%) 276 (59%) Intermedi 146 (67%) 203 (58%) 116 (48%) 160 (47%) 185 (39%) ate Risk High 16 (7.3%) 20 (5.7%) 12 (5.0%) 17 (4.9%) 10 (2.1%) Risk Hemodynamics and Functional Status, median (IQR) Heart Rate 77 78 78 77 78 (bpm) (70 - 89) (70 - 89) (70 - 89) (68 - 87) (69 - 88) Mean right 10 (6 - 15) 9 (6 - 13) 8 (5 - 12) 8 (5 - 12) 7 (5 - 10) atrial pressure (mmHg) Mean 47 48 48 49 50 Pulmonary (39 - 56) (40 - 57) (39 - 56) (39 - 58) (40 - 60) artery pressure (mmHg) Mean 10 (7 - 13) 10.0 (7 - 14) 10 (8 - 13) 10 (7 - 13) 10 (7 - 13) pulmonary capillary wedge pressure (mmHg) Pulmonary 8.7 8.3 8.1 8.5 9.0 Vascular (6.1 -12.7) (5.2 - 12.6) (5.6 -12.5) (5.3 - 11.8) (6.0 -13.1) Resistance (WU) Transpulmon 37 38 37 39 41 ary Gradient (28 - 47) (29 - 45) (28 - 46) (29 - 49) (30 - 49) (mmHg) Cardiac 2.23 2.35 2.40 2.25 2.34 Index (1.83-2.86) (1.82 - 2.92) (1.95-2.85) (1.90 -2.88) (1.89-2.87) (L / min / m2) 50 42150.601 Table 2A: Demographics, Biomarkers, Genetics, Outcomes and Co-Morbidities by Cluster Membership in PAHB Training Cohort graphics Cluster A Clu Cluster E Demo ster B Cluster C Cluster D N = 219 N = 348 N = N = 471 (Median, IQR) 241 N = 344 WHO Functional5.5 / 17 / 64 / 13 6.5 / 23 / 61 / 9.3 5.4 / 4.9 / 38 / 52 / 5.2Class32 / 54 / 9 9.5 / 33 / 53 / 5.3(%I / II / III / IV) Six Minute 275 305 318 360 389 Walk (180 -351) (212 - 385) (254 -397) (274 - 426) (320 -460) Distance (m) Cardiovascular Disorders, n (%) Congenital 11 (5.0) 18 (5.2) 10 (4.1) 29 (8.4) 44 (9.3) Heart Disease Pre- 5 (2.3) 9 (2.6) 5 (2.1) 18 (5.2) 31 (6.6) Tricuspid Shunt Post- 6 (2.7) 10 (2.9) 5 (2.1) 11 (3.2) 15 (3.2) Tricuspid Shunt Cardiovascul 52 (24) 65 (19) 37 (15) 53 (15) 68 (14) ar Disease, n (%) Respiratory Disorders, n (%) Restrictive 28 (13) 50 (14) 24 (10.0) 23 (6.7) 32 (6.8) Lung Disease Obstructive 60 (27) 106 (30) 60 (25) 81 (24) 95 (20) Lung Disease Sleep Apnea 61 (28) 112 (32) 61 (25) 109 (32) 111 (24) Collagen Vascular Disease, n (%) Scleroderma 73 (33) 88 (25) 59 (24) 46 (13) 33 (7.0) Rheumatoid 16 (7.3) 21 (6.0) 15 (6.2) 19 (5.5) 16 (3.4) Arthritis Lupus 12 (5.5) 23 (6.6) 7 (2.9) 19 (5.5) 32 (6.8) Other 52 (24) 79 (23) 51 (21) 49 (14) 69 (15) Collagen Vascular Disease Other Systemic Disease, n (%) Diabetes 46 (21) 66 (19) 33 (14) 63 (18) 46 (9.8) Cancer 9 (4.1) 15 (4.3) 5 (2.1) 15 (4.4) 12 (2.5) HIV 5 (2.3) 5 (1.4) 5 (2.1) 7 (2.0) 11 (2.3) Renal 43 (20) 25 (7.2) 15 (6.2) 13 (3.8) 7 (1.5) Insufficiency Cirrhosis 13 (5.9) 39 (11) 14 (5.8) 35 (10) 14 (3.0) Thromboemb 23 (11) 35 (10) 11 (4.6) 16 (4.7) 17 (3.6) olic Disease Thyroid 58 (26) 77 (22) 45 (19) 70 (20) 85 (18) Disease Anorexia 6 (2.7) 8 (2.3) 6 (2.5) 6 (1.7) 2 (0.4) Medications, n (%) Phosphodiest 159 (73%) 255 (73%) 172 (71%) 250 (73%) 344 (73%) erase Inhibitor Endothelin 104 (47%) 172 (49%) 145 (60%) 184 (53%) 275 (58%) Receptor Antagonist Prostacyclin 122 (56%) 183 (53%) 108 (45%) 129 (38%) 160 (34%) Analog 51 42150.601 Table 2A: Demographics, Biomarkers, Genetics, Outcomes and Co-Morbidities by Cluster Membership in PAHB Training Cohort Cluste Cluster E Demographics r A Cluster B Cluster C Cluster D N = 219 N = 348 N = 241 N N = 471 (Median, IQR) = 344 Calcium 4 (1.8%) 15 (4.3%) 11 (4.6%) 30 (8.7%) 63 (13%) Channel Blocker Soluble 3 (1.4%) 16 (4.6%) 4 (1.7%) 6 (1.7%) 11 (2.3%) guanylate cyclase stimulators All data presented as Median (IQR) or n (%) unless otherwise noted *Indicates difference between groups with p<0.05 after Bonferroni correction for multiple testing 52 42150.601 Table 2B: Demographics, Biomarkers, Genetics, Outcomes and Co-Morbidities by Cluster Membership in PAHB Test Cohort Cluster A Cluster B Cluster C Cluster D Cluster E Demographics N = 88 N = 124 N = 162 N = 113 N = 209 Age at 66 59 55 61 50 Enrollment (56 - 76) (51 - 67) (43 - 65) (51 - 70) (37 - 62) Age at Diagnosis 63 55 49 57 44 (49 - 71) (47 - 64) (37 - 62) (44 - 68) (32 - 55) Sex, n (%) 71 (81%) 89 (72%) 119 (73%) 95 (84%) 181 (87%) Weight (kg) 76 80 80 73 76 (64 - 97) (68 - 91) (64 - 94) (61 - 86) (64 - 91) Height (cm) 163 166 165 165 163 (157 - 169) (160 - 173) (160 - 173) (158 - 170) (159 - 168) Hispanic / LatinX, 1 (1.1%) 10 (8.1%) 13 (8.0%) 11 (9.7%) 27 (13%) n (%) White / Black / Oth 68 / 15 / 5 103 / 15 / 6 126 / 27 / 9 95163 / 26 / 20 er, n (%) / 9 / 9PAH Subtype, n (%) IPAH 39 (44%) 47 (38%) 52 (32%) 39 (35%) 106 (51%) HPAH 3 (3.4%) 10 (8.1%) 19 (12%) 7 (6.2%) 22 (11%) FPAH 8 (2.2%) 11 (2.8%) 26 (3.9%) 48 (5.3%) APAH-CTD 32 (36%) 44 (35%) 52 (32%) 46 (41%) 45 (22%) APAH-CHD 2 (2.3%) 4 (3.2%) 10 (6.2%) 4 (3.5%) 18 (8.6%) Drugs / Toxins 5 (5.7%) 4 (3.2%) 13 (8.0%) 6 (5.3%) 8 (3.8%) HIV 2 (2.3%) 2 (1.6%) 5 (3.1%) 2 (1.8%) 3 (1.4%) Portopulmon 4 (4.5%) 11 (8.9%) 7 (4.3%) 7 (6.2%) 5 (2.4%) ary hypertension PVOD 0 (0%) 1 (0.8%) 1 (0.6%) 2 (1.8%) 1 (0.5%) Other APAH 1 (1.1%) 1 (0.8%) 3 (1.9%) 0 (0%) 1 (0.5%) Biomarkers NT-proBNP 4,925 1,462 669 890 276 (pg / mL) (1,522- 9,738) (467 - 3,850) (223 - 1,510) (411 - 1,736) (102 - 547) Endostatin 71 43 35 42 28 (ng / mL) (56 - 87) (35 - 55) (28 - 43.5) (33 - 55) (22 - 36) ST2 (ng / mL) 16 10 7.2 4.5 3.4 (11 - 29) (7.8 - 14) (5.9 - 8.8) (3.8 - 5.3) (2.6 - 4) IL-6 (pg / mL) 3.2 2.9 1.5 1.7 1.0 (1.1 - 5.4) (1.2 - 5) (0.0 - 3.1) (0.4 - 2.8) (0.3 - 1.9) HDGF (ng / mL) 1.0 0.8 0.8 0.9 0.7 (0.7 - 1.4) (0.5 - 1.3) (0.5 - 1.3) (0.4 - 1.4) (0.4 - 1.2) IGFBP1 (ng / mL) 86 47 18 49.3 14 (50 - 132.7) (27 - 72) (8.9 - 33) (27.0 - 78.5) (6.6 - 27) IGFBP2 (ng / mL) 582 346 172.9 372 155 (446 - 812) (273 - 455) (126 - 223) (317 - 445) (110 - 210) IGFBP4 (ng / mL) 1,846 924 638 888 624 (1,324 -2,343) (677 - 1,244) (435 - 879) (692 - 1,187) (435 - 855) Genetics BMPR2 1 (1.1%) 6 (4.8%) 14 (8.6%) 3 (2.7%) 14 (6.7%) Mutation, n (%) Other Pathogenic 0 (0%) 5 (4.0%) 2 (1.2%) 4 (3.5%) 7 (3.3%) Mutation, n (%) Outcomes Time from 28 40 40 49 53 enrollment to (17 - 46) (27 - 56) (27 - 58) (33 - 64) (40 - 68) censor (months) Primary 45 (51%) 41 (33%) 26 (16%) 16 (14%) 15 (7.2%) Outcome, n (%) Died, n (%) 45 (51%) 39 (31%) 25 (15%) 16 (14%) 13 (6.2%) Transplant, n 1 (1.1%) 3 (2.4%) 2 (1.2%) 2 (1.8%) 3 (1.4%) (%) REVEAL 2.0 9 (8 - 10) 8 (6 - 9) 6 (5 - 9) 7 (6 - 9) 5 (4 - 7) Score 53 42150.601 Table 2B: Demographics, Biomarkers, Genetics, Outcomes and Co-Morbidities by Cluster Membership in PAHB Test Cohort Cluster A Cluster B Cluster C Cluster D Cluster E Demographics N = 88 N = 124 N = 162 N = 113 N = 209 REVEAL 2.0 Risk, n (%) Low Risk 13 (15%) 51 (41%) 106 (65%) 65 (58%) 170 (81%) Average Risk 16 (18%) 19 (15%) 15 (9.3%) 19 (17%) 21 (10%) Moderately 20 (23%) 23 (19%) 24 (15%) 13 (12%) 13 (6.2%) High Risk High Risk 30 (34%) 25 (20%) 11 (6.8%) 14 (12%) 5 (2.4%) Extremely 9 (10%) 6 (4.8%) 6 (3.7%) 2 (1.8%) 0 (0%) High Risk ERS / ESC Risk, n (%) Low Risk 17 (19%) 54 (44%) 71 (44%) 51 (45%) 124 (59%) Intermediate 62 (70%) 64 (52%) 84 (52%) 53 (47%) 79 (38%) Risk High Risk 9 (10%) 6 (4.8%) 7 (4.3%) 9 (8.0%) 6 (2.9%) Hemodynamics, median (IQR) Heart Rate (bpm) 80 80 80 76 76 (72 - 89) (71 - 88) (69 - 90) (68 - 85) (67 - 89) Mean right atrial 11 (8 - 16) 8 (5 - 13) 8 (6- 12) 7 (5 - 11) 7 (5 - 11) pressure (mmHg) Mean Pulmonary 49 51 48 44 49 artery pressure (39 - 55) (41 - 59) (38 - 60) (35 - 56) (40 - 57) (mmHg) Mean pulmonary 10.0 10.0 10.0 10.0 10.0 capillary wedge (8.0 - 13.0) (7.0 - 13.0) (8.0 - 13.0) (7.0 - 13.0) (7.0 - 12.0) pressure (mmHg) Pulmonary 10.0 9.1 8.5 8.0 8.8 Vascular (6.4 - 13.7) (5.8 - 14.3) (5.2 - 13.4) (4.8 - 11.9) (6.1 - 12.2) Resistance (WU) Transpulmonary 38 41 38 36 39 Gradient (mmHg) (30 - 44) (30 - 49) (28 - 48) (25 - 46) (30 - 48) Cardiac Index 2.02 2.29 2.27 2.56 2.39 (L / min / m2) (1.69 - 2.55) (1.76 - 2.80) (1.85 - 2.74) (1.97 - 2.99) (1.90 -2.93) WHO Functional Class 1.4 / 20 / 66 / 13 7.6 / 27 / 52 / 13 8.8 / 31 / 53 / 7.9 6.2 / 38 / 48 / 7.8 5.9 / 38 / 49 / 7.2 (%I / II / III / IV) Six Minute Walk 242 313 360 337 381 Distance (m) (203 - 309) (197 - 395) (271 - 427) (242 - 387) (318 - 470) Cardiovascular Disorders, n (%) Congenital Heart 1(1.1) 7 (5.29 (14) Disease6) 10 (6.2) 5 (4.4)Pre-Tricuspid 1(1.1) 4 (3.2) 4 (2.4)22 (11) Shunt2 (1.8)Post- Tricuspid 0 (0) 3 (2.4) 6 (3.7) 3 (2.7) 6 (2.9) Shunt Cardiovascular 25 (28) 24 (1925 (12) Disease, n (%)) 31 (19) 28 (25)Respiratory Disorders, n (%) Restrictive Lung 13 (15) 14 (11) 11 (6.8) 11 (9.7) 14 (6.7) Disease Obstructive Lung 28 (32) 38 (31) 36 (22) 27 (24) 42 (20) Disease Sleep Apnea 31 (35) 41 (33) 38 (23) 23 (20) 50 (24) Collagen Vascular Disease, n (%) Scleroderma 25 (28) 28 (23) 28 (17) 28 (25) 12 (5.7) Rheumatoid 5 (5.7) 9 (7.3) 11 (6.8) 5 (4.4) 7 (3.3) Arthritis 54 42150.601 Table 2B: Demographics, Biomarkers, Genetics, Outcomes and Co-Morbidities by Cluster Membership in PAHB Test Cohort Cluster A Cluster B Cluster C Cluster D Cluster E Demographics N = 88 N = 124 N = 162 N = 113 N = 209 Lupus 4 (4.5) 9 (7.3) 16 (9.9) 8 (7.1) 15 (7.2) Other Collagen 19 (22) 24 (19) 38 (23) 31 (27) 25 (12) Vascular Disease Other Systemic Disease, n (%) Diabetes 19 (22) 23 (19) 29 (18) 10 (8.8) 25 (12) Cancer 5 (5.7) 5 (4.0) 8 (4.9) 4 (3.5) 5 (2.4) HIV 2 (2.3) 3 (2.4) 5 (3.1) 2 (1.8) 3 (1.4) Renal Insufficiency67 (19)* 18 (4.5)* 43 (6.5)* 22 (2.4)*Cirrhosis 4 (4.5) 12 (9.7) 7 (4.3) 7 (6.2) 3 (1.4) Thromboembolic 7 (8.0) 8 (6.5) 6 (3.7) 12 (11) 9 (4.3) Disease Thyroid Disease 21 (24) 27 (22) 34 (21) 28 (25) 41 (20) Anorexia 11 (3.1)* 10 (2.5) 13 (2.0) 12 (1.3)* Medications, n (%) Phosphodiesteras 64 (73%) 89 (72%) 118 (73%) 77 (68%) 136 (65%) e Inhibitor Endothelin 50 (57%) 64 (52%) 90 (56%) 66 (58%) 108 (52%) Receptor Antagonist Prostacyclin 52 (59%) 71 (57%) 71 (44%) 40 (35%) 69 (33%) Analog Calcium Channel 2 (2.3%) 6 (4.8%) 10 (6.2%) 8 (7.1%) 22 (11%) Blocker Soluble guanylate 1 (1.1%) 1 (0.8%) 2 (1.2%) 0 (0%) 5 (2.4%) cyclase stimulators All data presented as Median (IQR) or n (%) unless otherwise noted *Indicates difference between groups with p<0.05 after Bonferroni correction for multiple testing Table 2C: Demographics, Biomarkers, Genetics, Outcomes and Co-Morbidities by Cluster Membership in Validation Demographics Cluster A Cluster B Cluster C Cluster D Cluster E N = 48 N = 53 N = 31 N = 118 N = Age at 64 (54 - 71) 58 (52 - 72) 55 (46 - 65) 63 (56 - 69) 50 (39 - 62) Enrollment Age at 55 (48 - 63) 56 (47 - 66) 47 (38 - 64) 61 (50 - 66) 43 (32 - 59) Diagnosis Sex, n (%) 25 (89%) 34 (81%) 33 (75%) 30 (83%) 64 (85%) Weight (kg) 66 (56 - 79) 65 (59 - 82) 78 (65 - 91) 66 (56 - 80) 73 (64 - 88) Height (cm) 165 163 163 163 163 (161 - 173) (157 - 171) (160 - 170) (157 - 171) (155 - 168) PAH Subtype, n (%) APAH- 1 (3.6%) 5 (12%) 7 (16%) 1 (2.8%) 5 (6.7%) CHD APAH- 16 (57%) 24 (57%) 21 (48%) 21 (58%) 26 (35%) CTD IPAH 11 (39%) 13 (31%) 16 (36%) 14 (39%) 44 (59%) Biomarkers NT-proBNP 7,787 1,951 893 1,063 324 (pg / mL) (6,128 - 13,216) (1,282 - 6,868) (308 - 1,983) (508 - 2,863) (151 - 737) Endostatin 110 54 48 39 29.6 (ng / mL) (92 - 141) (36 - 68) (34.6 - 63.5) (31.0 - 70) (21 - 46) ST2 (ng / mL) 19.9 12.1 7.7 5.2 4.0 (15.5 - 28) (9.5 - 15.6) (6.5 - 10.0) (4.6 - 5.7) (3 - 5) IL-6 (pg / mL) 6.2 3.0 2.0 1.8 1.5 (3.8 - 10.4) (1.5 - 5.1) (1.5 - 3.6) (1.5 - 2.8) (1.1 - 3.1) 55 42150.601 Table 2C: Demographics, Biomarkers, Genetics, Outcomes and Co-Morbidities by Cluster Membership in Validation Demographics Cluster A Cluster B Cluster C Cluster D Cluster E N = 48 N = 53 N = 31 N = 118 N = HDGF (ng / mL) 0.5 0.9 0.7 0.8 0.6 (0.4 - 1.0) (0.4 - 1.3) (0.6 - 1.2) (0.4 - 1.6) (0.3 - 1.3) IGFBP1 98.7 54.8 16.8 39.4 15 (ng / mL) (52 - 258) (32.6 - 71) (8.7 - 39) (32 - 64) (6.8 - 32) IGFBP2 641.5 372 173 382 138 (ng / mL) (494 - 930) (295 - 479) (132 - 226) (315 - 449) (92 - 177) IGFBP4 1,271 650 529 701 392 (ng / mL) (909 - 1,652) (496 - 801) (409 - 633) (541 - 777) (314 - 506) Outcomes Time from 16 (6 - 38) 29 (14 - 65) 55 (37 - 81) 56 (42 - 76) 63 (36 - 84) enrollment to censor (months) Primary 23 (82%) 26 (62%) 16 (36%) 10 (28%) 14 (19%) Outcome, n (%) Died, n (%) 23 (82%) 24 (57%) 16 (36%) 10 (28%) 12 (16%) Transplant, 0 (0%) 2 (8.3%) 0 (0%) 0 (0%) 0 (0%) n (%) REVEAL 2.0 9 (8 - 9) 8 (7 - 9) 6 (5 - 8) 7 (5 - 7) 4 (3 - 6) Score REVEAL 2.0 Risk, n (%) Low Risk 4 (14%) 17 (40%) 30 (68%) 28 (78%) 70 (93%) Average 9 (32%) 10 (24%) 5 (11%) 5 (14%) 1 (1.3%) Ris k Moderately 10 (36%) 11 (26%) 3 (6.8%) 3 (8.3%) 3 (4.0%) High Risk High Risk 5 (18%) 4 (9.5%) 5 (11%) 0 (0%) 1 (1.3%) Extremely 0 (0%) 0 (0%) 1 (2.3%) 0 (0%) 0 (0%) High Risk ERS / ESC Risk, n (%) Low Risk 0 (0%) 4 (21%) 9 (36%) 4 (20%) 27 (51%) Intermediat 2 (33%) 10 (53%) 12 (48%) 13 (65%) 24 (45%) e Risk High Risk 4 (67%) 5 (26%) 4 (16%) 3 (15%) 2 (3.8%) Hemodynamics, median (IQR) Heart Rate 82 (78 - 86) 81 (72 - 94) 82 (68 - 89) 76 (69 - 82) 73 (68 - 86) (bpm) Mean right 8.0 8.0 8.0 7.5 7.0 atrial pressure (6.0 - 12.0) (4.0 - 10.0) (4.2 - 12.0) (5.0 - 9.2) (4.0 - 8.0) (mmHg) Mean 48 (42 - 55) 46 (39 - 54) 45 (33 - 57) 36 (31 - 48) 34 (27 - 44) Pulmonary artery pressure (mmHg) Mean 9.0 10.0 10.0 10.0 10.5 pulmonary (6.0 - 13.0) (6.5 - 13.5) (9.0 - 12.0) (8.0 - 13.0) (7.2 - 12.0) capillary wedge pressure (mmHg) Pulmonary 10.2 8.1 6.2 5.7 4.5 Vascular (7.0 - 14.0) (5.9 - 12.0) (3.8 - 9.6) (4.1 - 9.1) (3.1 - 8.9) Resistance (WU) Transpulmonar 42 (32 - 46) 37 (28 - 44) 30 (21 - 44) 27 (22 - 38) 25 (17 - 36) y Gradient (mmHg) Cardiac Index 2.25 2.53 2.57 2.64 2.69 (L / min / m2) (2.14 - 2.62) (2.08 - 2.95) (2.18 - 3.30) (2.32 - 3.38) (2.27 - 3.07) 56 42150.601 Table 2C: Demographics, Biomarkers, Genetics, Outcomes and Co-Morbidities by Cluster Membership in Validation Demographics Cluster A Cluster B Cluster C Cluster D Cluster E N = 48 N = 53 N = 31 N = 118 N = WHO Functional0 / 0 / 100 / 0 11 / 50 / 39 / 0 8 / 24 / 68 / 0 26 / 13 / 53 / 34 / 0Class21 / 53 / 0(%I / II / III / IV) Six Minute 314 329 350 365 424 Walk Distance (238 - 362) (278 - 405) (285 - 422) (301 - 393) (353 - 474) (m) Cardiovascular Disorders, n (%) Cardiovascular 4 (17) 6 (25) 5 (20) 0 (0) 4 (14) Disease Congenital 1 (4.2) 6 (25) 7 (28) 2 (12) 5 (18) Heart Disease Respiratory Disorders, n (%) Restrictive 0 (0) 1 (4.2) 2 (8.0) 0 (0) 0 (0) Lung Disease Obstructive 2 (8.3) 5 (21) 7 (28) 4 (25) 2 (7.1) Lung Disease Sleep Apnea 5 (21) 3 (12) 4 (16) 2 (12) 4 (14) Collagen Vascular Disease, n (%) Scleroderma 9 (33) 20 (53) 15 (42) 18 (58) 23 (45) Rheumatoid 0 (0) 2 (8.3) 2 (8.0) 0 (0) 0 (0) Arthritis Lupus 0 (0) 0 (0) 2 (8.0) 2 (12) 1 (3.6) Other Collagen 1 (4.2) 0 (0) 1 (4.0) 1 (6.2) 1 (3.6) Vascular Disease Other Systemic Disorders, n (%) Diabetes 0 (0) 1 (4.2) 0 (0) 0 (0) 0 (0) Cancer 3 (12) 0 (0) 3 (12) 0 (0) 0 (0) HIV 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) Cirrhosis 0 (0) 1 (4.2) 0 (0) 0 (0) 0 (0) Thromboembol 3 (12) 3 (12) 1 (4.0) 0 (0) 0 (0) ic Disease Thyroid 5 (21) 4 (17) 4 (16) 4 (25) 7 (25) Disease Medications, n (%) Phosphodiester 18 (64%) 32 (76%) 36 (82%) 27 (75%) 54 (72%) ase Inhibitor Endothelin 14 (50%) 23 (55%) 31 (70%) 21 (58%) 41 (55%) Receptor Antagonist Prostacyclin 18 (64%) 17 (40%) 16 (36%) 10 (28%) 21 (28%) Analog Calcium 5 (18%) 7 (17%) 9 (20%) 4 (11%) 17 (23%) Channel Blocker All data presented as Median (IQR) or n (%) unless otherwise noted *Indicates difference between groups with p<0.05 after Bonferroni correction for multiple testing Cluster A subjects in all cohorts had more high-risk features including older age at enrollment, higher REVEAL 2.0 Risk scores, a higher proportion of subjects with ESC / ERS intermediate or high risk, NYHA class III-IV symptoms, shorter 6-minute walk 5 distance, and slightly lower cardiac index (Table 2A-C). Clusters B-E were more clinically similar; particularly Cluster B, C and D where ages at enrollment, REVEAL 2.0 57 42150.601 scores, subjects in each ESC / ERS risk category, NYHA functional class and 6-minute walk distances were not significantly different (Table 2A-C). Right atrial pressure was slightly higher in Cluster A, but other hemodynamics were relatively similar across clusters in the train and test cohorts (Table 2A-B). Cluster A had worse hemodynamics in the validation cohort (Table 2C). FIG.3 shows outcomes by cluster in the PAHB training (FIG.3A, FIG.3D), the PAHB test (FIG.3B, FIG.3E) and validation (FIG.3C, FIG.3F) cohorts by both Kaplan Meier and Cox proportional hazards models. Transplant free survival was distinctly different in each cluster, with good separation in the middle groups (Clusters B, C, D). The same analysis was repeated based on the ESC / ERS risk categories (FIG.11), the REVEAL risk score (FIG.12) and NT-proBNP divided by quintiles (FIG.13). The top quintile of NT- proBNP had poor outcomes, but less separation in the moderate to low range (FIG.13). 1.4.2 Comparison to Current Clinical Models The biomarker model was compared to the REVEAL risk category and ESC / ERS risk (FIG.12 and FIG.13) with model fit assessed by fitting Cox proportional hazards models and comparing the information criterion (AIC) and C-Index (Table 3) and by ROC curve analysis (FIG.4, FIG.5, FIG.6). In all three cohorts, the biomarker model had better C-Statistic and lower AIC than the ESC / ERS score, and addition of the biomarker model to the ESC / ERS score improved performance of that score (FIG.4, Table 3). The biomarker model had a similar C-Statistic to the REVEAL score, but slightly better AIC. The combination of the REVEAL score and the biomarker model performed better than either one alone (FIG.5, Table 3). Finally, the biomarker model performed similarly to NT- proBNP with similar C-Statistic, but the combination of the biomarker model and NT- proBNP had the highest C-Statistic and lowest AIC in all cohorts (FIG.6, FIG.7, Table 3). Table 3. Cox Multivariable hazard ratios (most severe group) for Cluster-Based model, REVEAL Score and ERS / ESC score in PAHB and Validation cohorts Model Hazard Ratio C-statistic (95% AIC (95% CI, p) CI) Highest risk category PAHB Training Cohort A PAHB Biomarker model 10.5 (7.2-15.2, 0.73 (0.70 – 5018 <0.001) 0.75) B PAHB ERS / ESC Risk 3.4 (2.0 – 5.7, 0.64 (0.61 – 5184 <0.001) 0.66) C PAHB REVEAL Risk Category 7.1 (4.8 – 10.4, 0.74 (0.71 – 5052 <0.001) 0.77) D PAHB NT-proBNP Quintiles 10.5 (6.9 – 15.9, 0.74 (0.72 – 4998 <0.001) 0.77) E PAHB Biomarker model + ERS / ESC 8.9 (6.1 – 13.1, 0.73 (0.70 – 5008 Risk <0.001) 0.75) F PAHB Biomarker model + REVEAL 5.7 (3.8 – 8.6, 0.76 (0.73 – 4962 Risk Category <0.001) 0.78) G PAHB Biomarker model + NT- 4.3 (2.8 – 6.6, 0.76 (0.74 – 4942 proBNP <0.001) 0.79) PAHB Test Cohort H PAHB Biomarker model 11.5 (6.4 – 20.7, 0.74 (0.70 – 1709 <0.001) 0.78) 58 42150.601

[0005] Ta odel, Mode AIC I 1775 J 1716 K 1707 L 1699 M 1678 N 1677 Valid O 811 P 832 Q 828 R 805 . . S Validation Biomarker model + 6.9 (3.1 – 15.3, 0.77 (0.72 – 809 ERS / ESC Risk <0.001) 0.82) T Validation Biomarker model + 8.2 (3.7 – 18.1, 0.79 (0.74 – 852 REVEAL Risk Category <0.001) 0.85) U Validation Biomarker model + NT- 3.8 (1.6 – 9.0, 0.80 (0.75 – 798 proBNP 0.002) 0.85) Given the performance of the combination of NT-proBNP and the biomarker model, a new combined model was generated mimicking a clinical algorithm where a high NT- proBNP would suggest high risk and the biomarker model would give additional information. Subjects with an NT-proBNP >1400 pg / dL, based on previously described cutoffs for NT-proBNP, were increased by one severity level in the biomarker model. This generated a total of 6 categories with the top category, Cluster A’, being a combination of those with the highest risk by NT-proBNP and the highest risk based on the biomarker model. This model had good performance with an AUROC of 0.745 in the PAHB training cohort and 0.75 and 0.78 in the PAHB test cohort and validation cohorts, respectively (FIG. 8). The combined biomarker and NT-proBNP model had improved performance over any other model. The biomarker model was re-evaluated after excluding all subjects with a high NT-proBNP to identify those whose risk was not captured by NT-proBNP alone. In the PAHB training cohort, an additional 90 subjects with events (205 total events not identified 15 by NT-proBNP) were identified in the highest risk clusters. Likewise, in the PAHB test cohort an additional 30 subjects with events (out of 79 events not identified by NT-proBNP) were identified in the highest risk clusters. In the validation cohort, an additional 17 subjects with events (out of 52) were identified by the highest risk clusters. 1.5 Discussion 59 42150.601 Understanding risk in pulmonary hypertension is vital for optimizing treatment and is a seminal component of the 2022 ESC / ERS guidelines. Humbert et al., 2022. Practical components of risk assessment include ease of data collection, use of existing variables, reliability and objectiveness of data, and interpretability of results. Biomarkers offer the opportunity to non-invasively and objectively assess risk. Only NT-proBNP / BNP, however, has gained clinical use. Other multi-component risk scores, including the ESC / ERS risk guidelines and the REVEAL score have good performance, but require multiple labor and time intensive / invasive tests making them difficult to calculate on a frequent or remote basis. Most importantly, scores must have a method of weighting or combining disparate or conflicting variables to give the clinician a more complete understanding of multifactorial risk. This Example demonstrates that a panel of six easily accessible blood-based biomarkers with three non-invasive demographic measures is equivalent to current mortality risk prediction reliant on invasive and subjective clinical measures. When added to NT- proBNP, this model outperforms clinical risk models, and NT-proBNP alone, identifying high risk subjects who were not identified by NT-proBNP. The strengths of this model include easy data collection via a single blood test and standard demographic details, and the interpretability of the results with risk clusters that differentiate between patients with similar biomarker levels. As with many prediction tools, the underlying methods are complex utilizing combinatorial effects of the biomarkers, but the output of this score is easily interpretable risk categories, including the addition of NT-proBNP above a well described cutoff to generate discrete risk categories. Very few biomarkers have been tested in large, multi-center diverse cohorts, and most have been evaluated individually rather than in tandem or with inclusion of interaction effects. The agnostic methods used to identify important variables in this model allowed us to use the fewest relevant variables and allowed for inclusion of variable interaction terms to enhance the performance of the model. The clustering algorithm uses continuous variables, retaining granularity, and improving separation within the moderate risk groups. The markers used in this study are not novel, and many have been repeatedly described and validated in pulmonary hypertension including the well described markers ST2, Endostatin, and Galectin-3. Simpson et al., 2020b; Damico et al., 2015; Geenen et al., 2019; Simpson et al., 2021; Scelsi et al., 2020; Barman et al., 2018. In this Example, however, we demonstrate that the combination of these well- described markers is more effective at predicting adverse outcomes than a single marker alone. Even after excluding subjects identified by a high NT-proBNP, a substantial number of subjects with events were identified by the biomarker model. While there were differences in age, comorbidities, hemodynamics, and medication use between the highest risk and lowest risk clusters, subjects were remarkably similar between highest-risk Cluster A and the moderate risk Clusters B, C, and D. Thus, the biomarker model differentiated risk groups based on biomarker profiles, despite the presence or absence of other risk factors, therapies, and hemodynamics at diagnosis. 60 42150.601 An interesting finding was the performance of NT-proBNP alone compared to validated clinical scores (which include NT-proBNP) and the biomarker model. NT-proBNP is a cardiometabolic peptide that is released by the heart due to increased wall stress and stretch and is a superior predictor of heart failure. Lewis et al., 2020; Fu et al., 2018. Haddad recently demonstrated the centrality of NT-proBNP amongst other clinical risk predictors in a network model of right heart failure. Haddad et al., 2022. Our findings are similar, showing that NT-proBNP is an excellent predictor of outcomes in pulmonary hypertension, especially at high concentrations which suggest worsening right ventricular performance. We have previously shown that single biomarkers add to NT-proBNP risk prediction, and in this study show that a multimarker model adds risk discrimination in the moderate range capturing subjects at high risk with moderate NT-proBNP. The present study’s strengths include a large, diverse multi-center cohort for the training and test dataset. The PAHB is a prevalent cohort of ambulatory subjects on PAH- specific therapies representing multiple PAH subtypes and enrolled from multiple centers across the United States. Thus, this blood-based biomarker model was trained on and is intended for heterogeneous PAH cohorts on medical therapy. The additional validation group is less diverse but includes higher risk subjects; the model still shows good performance in this group. The choice of markers was unsupervised looking at many variables including demographics, biomarkers, hemodynamics, functional variables, and comorbidities, with the final variables chosen to maximize variability. Finally, the unsupervised variable selection and biomarker model allows variables to be weighted and subjects sorted despite having otherwise similar biomarker values. Limitations of this study include its cross-sectional design, which precludes longitudinal assessment. The validation cohort differed from the training and test cohorts with a higher proportion of higher risk APAH-CTD subjects. Nonetheless, the model performed well across all three cohorts suggesting good reproducibility. There is evidence of a possible survivor bias affecting enrollment of subjects with HPAH in the PAHB, with more BMPR2 positive subjects in Cluster E. This observation was surprising given the severe phenotype and rapid disease progression in BMPR2 positive patients. Alternatively, BMPR2 mutation carriers may be treated aggressively. The ESC / ERS risk criteria and the REVEAL score didn’t perform as well in this study, although the 1 year predicted survival for both scores were comparable to what has been described by those scores. Benza et al., 2019; Benza et al., 2010; Galiè et al., 2016. Performance may be related missing variables, particularly in the ESC / ERS risk criteria where missing variables included VO2 and VE / VCO2, right atrial area, and pericardial effusion. These missing variables highlight the difficulty with clinical models requiring multiple, complex elements for accurate prediction. Conversely, these biomarkers are measured on a single blood sample with standard demographic variables while performing equivalent to clinical models. This non-invasive blood biomarker model offers the potential for ambulatory assessment of PAH using a simple blood test and demographic information. Useful 61 42150.601 biomarkers integrate easy measurement, additional discrimination, safety, cost-effectiveness, and the potential to change management. Wang et al., 2011. This multi-marker tool requires a small volume of peripheral blood and has equivalent or better model performance compared to invasive tests, making it inherently safer and more cost-effective. We have not yet studied this prospectively to evaluate potential to inform management. This model is the first completely non-invasive risk tool for pulmonary vascular disease, developed and tested in a large and diverse cohort, with validation and using well-described proteins. Such a tool permits frequent risk assessment, timely therapeutic adjustments and potentially improving outcomes. Non-invasive risk assessment is an unmet need in pulmonary vascular disease; this proteomic model provides a first of its kind multi-marker risk tool with potential to improve pulmonary hypertension treatment and outcomes. EXAMPLE 2 Supplemental Methods 2.1 Cohorts 2.1.1 PAH Biobank (PAHB) Samples and clinical data were obtained from the National Institutes of Health and National Heart, Lung, and Blood Institute funded PAH Biobank (www.pahbiobank.org), which includes data aggregated from 34 enrollment centers across North America. Specimen collection was approved by the IRBs at each center, and informed consent was obtained for all subjects prior to their enrollment. Eligible enrollees are patients with World Symposium on Pulmonary Hypertension (WSPH) Group 1 PAH. The model was developed on subjects with all types of group 1 PAH. All subjects underwent right heart cardiac catheterization at diagnosis and had a mean pulmonary artery pressure (mPAP) >25mmHg, a pulmonary capillary wedge pressure (PCWP) <15 mmHg or left ventricular end diastolic pressure (LVEDP) <15mmhg, and pulmonary vascular resistance (PVR) >3 WU. This was the definition of PAH at the time of PAH Biobank enrollment. Subjects with a PCWP or LVEDP>15mmHg but <18mmHg were enrolled if they also had a PVR>3 WU and were not diagnosed with any other WSPH pulmonary hypertension subtype. Familial PAH (FPAH) was defined as a subject having at least two affected family members. Heritable PAH (HPAH) was defined as subjects with FPAH or subjects with an identified pathogenic variant, some of which were identified after enrollment into the PAH Biobank. Clinical data is extracted from the electronic medical records of each patient, and de-identified data is managed by the PAH Biobank. Diagnostic data including right heart catheterization, symptoms, and functional measures (NYHA functional class, six-minute walk distance) were recorded from the time of diagnosis. Age, sex, exposures, medications, and comorbid conditions were recorded from the time of enrollment, concurrent with blood specimen. Patients provide whole blood specimens via venipuncture at enrollment, which are stored as serum in secure freezers at Cincinnati Children’s Hospital Medical Center. 62 42150.601 Subjects were included in the study if they were older than 15 years of age at PAH diagnosis, had complete demographic data (Enrollment and diagnosis age, sex, PAH subtype, comorbid diagnoses), and an adequate blood sample. The age cutoff of 15 years was chosen based on the age at which proteomic markers, specifically NT-proBNP reach stable adult levels. Co-morbid diagnoses were collected at the time of enrollment. Rare comorbid diagnoses (<20 subjects) were grouped with physiologically similar diagnoses, (for example, subtypes of restrictive or obstructive lung disease were grouped together as either restrictive or obstructive lung disease, different subtypes of coronary artery disease were grouped as coronary artery disease). There were a total of 2335 subjects enrolled from the PAHB. 2.1.2 Validation Cohort An independent validation cohort was developed with enrollment from the Johns Hopkins Pulmonary Hypertension Program and the Vanderbilt University Pulmonary Hypertension Program (Total N=250). 2.1.3 Johns Hopkins Pulmonary Hypertension Program (JHPH) The Johns Hopkins Pulmonary Hypertension (JHPH) cohort is an independent cohort of adult patients that was enrolled through the JHPH Program (n = 123). Patients 18 years of age or older who were diagnosed with PAH by right heart catheterization were included. PAH subtypes were classified on the basis of guidelines defined by Sixth World Symposium on Pulmonary Hypertension. The diagnosis of connective tissue disease (CTD) was based on meeting either the American College of Rheumatology criteria, the presence of at least three of five features of the CREST syndrome (calcinosis, Raynaud’s phenomenon, esophageal dysmotility, sclerodactyly, and telangiectasia), or definite Raynaud’s phenomenon and the presence of a specific systemic sclerosis–related autoantibody. Pulmonary fibrosis was excluded in this cohort. The JHPH has maintained a registry since 2005 and sample collection started in 2007. Patients were followed prospectively until the data was right censored for analysis in 2019. Survival was determined by review of the electronic record and search of the Social Security Death Index. Median follow-up was 4.2 years with an interqaurtile range of 4 months to 9.9 years. 2.1.4 Vanderbilt University Pulmonary Hypertension program The Vanderbilt University Pulmonary Hypertension program (N=127) is a nationally accredited pulmonary hypertension clinical and research program that has been enrolling and following subjects since 1994. Patients aged 18 years or older, diagnosed with PAH by right heart catheterization, and with available blood samples were enrolled. Subjects were followed longitudinally, with 3 blood samples available and concurrent clinical information. The most recent blood sample and concurrent clinical data was used for this study. Subjects had pulmonary arterial hypertension, specifically idiopathic (IPAH), collagen vascular disease subtype (APAH-CTD), or congenital heart disease subtype (APAH-CHD). Hemodynamic, echocardiographic, and functional data was collected concurrent with blood sample. Data was right censored at the time of assay 63 42150.601 performance. The median follow-up for this study was 8.3 years with an interquartile range of 2.3 years to 12.9 years, although subjects continue to be followed. 2.2 Laboratory Analysis Blood samples were collected at the time of enrollment. A custom multiplex ELISA assay was developed to measure ST2, Endostatin, IL-6, Galectin-3 and NT- proBNP simultaneously using robotically spotted capture antibodies on the 96-well plate format (Meso Scale Discovery, Gaithersburg, MD). A separate multiplex ELISA assay was developed to measure IGFBP1-3 and 7. IGFBP4 and HDGF was measured on separate robotically spotted plates (Meso Scale Discovery, Gaithersburg, MD). Capture antibody-spotted plates were blocked with 5% BSA-PBS complemented with 0.05% TWEEN (PBS-T) and incubated at room temperature on an orbital shaker (500 rpm) for 60 minutes. Calibrators for Endostatin (R&D Systems DIY1098, part 841455), IL-6 (Cat# C0049-2, MSD), NT-proBNP (MSD C01XX-1), Galectin-3 (R&D 840355), and ST-2 (R&D 840760) were produced using commercially provided diluent (MSD R51BB-3). The detection antibody cocktail for Endostatin (R&D Systems DIY1098, part 841456), IL-6 (Cat# D21AK, 50X, MSD) NT-proBNP (MSD D21JK-1), and ST-2 (R&D 840354) was prepared in commercially provided diluent (MSD R51BA-5) and supplemented with 0.5 µg / mL SA-Sulfo-tag (MSD, R32AD-5). Multiplex ELISAs for IGFBP1-3 and IGFBP7 were developed on the MSD platform (Meso Scale Diagnostics, MD) using Duoset reagents from R&D Systems. The capture antibodies for IGFBP1-3 and IGFBP7 (DY871, DY804, R&D Systems, MN) were spotted on MSD plate, calibrators and biotin labeled detection antibodies from the same Duosets were used to optimize the performance of individual ELISA. The quality of the performance of the ELISAs were equal or better than the ones that R&D Duosets described. After incubation and washing, the plate was read in an MSD Sector Imager 2400. Inter-assay reliability by percent coefficient of variation across 30 plates were: Endostatin 2.8%, IL-66.5%, ST22.3%, Galectin-39.4%, HDGF 3.3%, NT-proBNP 3.0%, IGFBP1 4.1%, IGFBP22.6%, IGFBP47.7%. A total of 25 microliters of serum were necessary to measure all analytes on this multiplex assay. 2.3 Data preparation All variables were evaluated for outliers, or biologically implausible values and referred to the enrolling data bank (PAHB Biobank, JHPH, Vanderbilt). The PAH Biobank used the definition of familial PAH as a subject with multiple family members with disease or identified mutation. The PAH Biobank also performed genetic testing on all enrollees, identifying pathogenic mutations in subjects previously clinically classified as having idiopathic PAH. For this study, subjects were identified as having familial PAH (FPAH) based on the original classification. A second classifier was added for heritable PAH (HPAH) if they had an identified mutation including those with a pathogenic mutation identified after enrollment. Subjects with an EIF2AK4 mutation were identified as having pulmonary veno-occlusive disease (PVOD) and also were considered separately from HPAH given the significant differences in clinical phenotype. 2.3.1 Rank ordering of biomarkers 64 42150.601 Biomarker distribution was significantly right skewed, with persistent tails after log transformation. Thus, biomarkers were transformed based on rank percentile (1-100). IL-6 was undetectable in about 20% of the cohort and thus was divided into quintiles for analysis. 2.3.2 Missing Data Core variables to be used in clustering were identified and assessed for missingness with a threshold of 10% missingness. Further variables including age at enrollment, sex, and PAH type were identified as key variables. Observations with high degree of missingness, or missingness in key variables were referred to the enrolling centers for clarification. Logical inconsistencies in missing variables were checked, (i.e., PVR missing but PCWP, mPAP and CO present, mPAP missing but SPAP and DPAP present). Free text notes in the data set were searched for further missing information (i.e., notes about type of connective tissue disease, or prior repair of specified cardiac lesion). Six-minute walk distance, pulmonary artery compliance, stroke volume, heart rate, systolic / diastolic blood pressure, and mean arterial pressure were missing over 40% of observations and were excluded from clustering. Hemodynamics taken with vasodilator testing or during exercise had significant missingness and thus were excluded from clustering. Missing variables were filled in using multiple imputation if they met the threshold level of less than 10% missingness. Missingness was assessed using the VIM Package. Missing values were imputed using the MICE Package. All clinical variables used for clustering were winsorized at the 1% and 99% and then normalized (scaled and centered) prior to analysis. Biomarkers were not winsorized as there were no outliers after rank ordering. 2.3.3 Calculation of REVEAL risk scores and REVEAL risk categories: A REVEAL registry risk score was calculated based on the original REVEAL registry algorithm with updated cutoffs from the REVEAL 2.0 algorithm (REVEAL 2.0 score). Benza et al., 2019. The REVEAL 2.0 score is a multi-variable weighted mortality risk prediction score that is well validated; variables include age, sex, PAH type, RAP, PVR, heart rate, systolic blood pressure, functional class, six-minute walk distance (6MWD), diffusion capacity for carbon monoxide (DLCO), presence of a pericardial effusion, and BNP or NT-proBNP, with updated cutoffs used to calculate the REVEAL 2.0 score. The REVEAL 2.0 score also includes additional variables, all cause hospitalization and renal function (eGFR) which were not available and thus not included in calculations. Humbert et al., 2022. Calculation of REVEAL 2.0 scores includes 12 possible parameters, with points added or subtracted from a baseline score of 6 based on clinical variables. While the REVEAL 2.0 score includes 12 possible parameters, not all are expected to be always available; the REVEAL score maintains validity if at least 7 parameters are available for calculation. Humbert et al., 2022; Benza et al., 2019. Thus, subjects’ scores were calculated based on available parameters if at least 7 parameters were available. REVEAL scores were divided into 5 REVEAL risk categories based on the original REVEAL registry algorithm. Benza et al., 2018. 65 42150.601 Classification of scores into risk categories was as follows: category 1, low risk, included scores 1-7; category 2, average risk, for a score of 8; category 3, moderately high risk, for a score of 9; category 4, high risk, for a score of 10-11; and category 5, very high risk, for a score greater than 12. Subjects were divided to generate REVEAL 2.0 risk categories based on REVEAL 2.0 scores. Humbert et al., 2022. Kaplan Meier analysis and Cox proportional hazards models were used to compare outcomes across REVEAL Risk groups. 2.3.4 Calculation of ERS / ESC Risk The ERS / ESC Risk score was calculated according the guidelines from the European Respiratory Society and European Society of Cardiology (ERS / ESC) guidelines. Humbert et al., 2010. These guidelines categorize patients into low, intermediate and high risk based on the presence / absence of clinical variables that are either risk factors or protective. The guidelines define risk based on one-year expected mortality with low risk<5% expected mortality, intermediate risk 5-10% expected mortality, and high risk >10% expected mortality. Variables include clinical signs of heart failure, progression of symptoms, syncope, and WHO functional class. Other variables include 6- minute walk distance, peak VO2 and VE / VCO2 slope on cardiopulmonary exercise testing, NT-proBNP, right atrial area and presence of a pericardial effusion on either echocardiogram or cardiac MRI, and right atrial pressure, cardiac index and SvO2 >65% on cardiac catheterization. Subjects in both the PAHB and the validation cohorts were classified based on available variables. Kaplan Meier analysis and Cox proportional hazards models were used to compare outcomes across ERS / ESC Risk groups. 2.3.5 Risk based on NT-proBNP Risk for adverse outcome (death / transplant) was assessed based on NT-proBNP alone, as a continuous variable, and based on centile, decile and then quintile, collapsing percentile groups with similar risk. Survival based on NT-proBNP was evaluated by Kaplan Meier analysis and cox proportional hazard ratio. 2.4 Statistical Analysis Continuous variables are presented as means with standard deviation (SD) or median with interquartile range (IQR) based on normality of the data. Categorical variables are presented as N with percent. Descriptive information for the cohorts is shown Table 1A. Comorbid diagnoses at enrollment for the cohorts are shown in Table 1B. Features included all biomarkers, demographic variables (age at enrollment, age at PAH diagnosis, sex, height, weight, BSA), PAH medications, pathogenic mutations, hemodynamic variables, and functional class. Pairwise interaction of each biomarker (HDGF x Endostatin, NTproBNP x ST2, and the like), and the pairwise interaction of biomarkers with clinical variables were added as features. A total of 320 variables were considered. Notably, NT-proBNP was not included in the feature selection. Principal component analysis was performed with features taken from the top principal component based on scree plot. Clustering using selected features was performed using K-Means with K determined using the silhouette method and the gap statistic method. Li et al., 2018. Sensitivity testing was performed to identify the best fit model in the PAHB. 66 42150.601 Time to death or lung / heart-lung transplantation by each cluster was performed by Kaplan Meier analysis. Cox proportional hazard ratios were calculated and compared for each cluster. The cluster models were compared to one another, and to baseline survival in the cohort based on PAH subtype, NYHA functional class, NT-proBNP quintile, REVEAL 2.0 risk category, and ERS / ESC risk category. Survival models based on cluster membership, REVEAL Risk score and ERS / ESC Risk scores were compared using information criterion (AIC) and Harrell’s C-Index (concordance index). REVEAL and ESC / ERS scores were adjusted based on cluster membership by adding or subtracting points based on cluster. Subjects in the highest risk cluster received additional points added to the REVEAL 2.0 score, while those in the lowest risk cluster had points subtracted from the REVEAL 2.0 score. Similarly, points were added to the ESC / ERS score if a subject was in the highest risk cluster and points were subtracted for the lowest cluster. REVEAL categories were re-calculated based on the adjusted score and ESC / ERS categories were recalculated based on the adjusted score with adjustment for the additional variable. Adjusted REVEAL and ESC / ERS scores were compared to the baseline REVEAL and ESC / ERS scores and to the cluster model alone using information criterion (AIC) and Harrell’s C-Index (concordance index). An adjusted cluster model was made adding NT-proBNP to the model. Cutoffs of NT-proBNP were used knowing that NT-proBNP is a poor prognostic marker when >1500 pg / mL and a good prognostic marker when <300 pg / mL. Subjects were moved up by one cluster if NT-proBNP was >1500 pg / mL, or down one cluster if NT-proBNP was <300 pg / mL. Survival was again evaluated using Kaplan Meier and cox proportional hazard ratios and compared to the original cluster model using information criterion (AIC) and Harrell’s C-Index. EXAMPLE 3 A Non-Invasive Proteomic Model Predicts Pediatric Pulmonary Hypertension Survival 3.1 Background Pulmonary arterial hypertension (PAH) is a progressive often fatal disease. Risk assessment is fundamental to treatment and improved outcomes. Adult risk models (European Respiratory Society (ESC / ERS) and REVEAL scores) and rely on invasive (cardiac catheterization) and subjective features. There is a pressing need for non-invasive risk assessment in pediatric PAH. 3.2 Overview The Example investigates whether a machine learning derived proteomic model will accurately predict pediatric PAH survival. 3.3 Methods Using multiplex ELISA, we measured ST2, IL-6, Endostatin, Galectin-3, and IGF binding proteins (IGFBP1-7) in adult and pediatric cohorts (PAH Biobank). The model was trained on the larger adult cohort (N=2335) and used to develop a pediatric model. NT- proBNP was excluded to develop an NT-proBNP independent model. Unsupervised k- 67 42150.601

[0006] means clustering classified subjects into clusters. Transplant-free survival by cluster was examined using Kaplan-Meier and Cox proportional hazard regressions. Model performance was compared using AUROC to NT-proBNP, the ESC / ERS and REVEAL models. 3.4 Results The algorithm generated 3 clusters using 6 biomarkers, weight, height, and age at PAH diagnosis (FIG.14A). The multimarker model performed better than the ESC / ERS and REVEAL scores and equivalently to NT-proBNP (FIG.14B). The best model was the multimarker model adjusted for NT-proBNP with an AUROC of 0.82 (FIG.14B). Hemodynamic and functional measures were similar across groups in the biomarker model despit diff rin r i l Table Vari C, Age 6 - 17.5) Six 2 - 483) WH 43%) WH 27%) WH %) Mea - 9.0) Mea n Pulmonary Artery Pressure, 55 (52 - 65) 53 (35 - 71) 56 (41 - 63) mmHg Pulmonary Vascular Resistance, WU 10 (8 - 11) 21 (11 - 49) 15 (8 - 29) Cardiac Index, L / min 2.92 (2.39 - 3.06) 3.73 (2.98 - 4.85) 3.52 (2.81 - 4.53) Hazard Ratio for Death / Transplant 14.6 (4.5 – 48) 2.5 (0.8 – 8.1) 1 (Reference) 3.5 Summary This multi marker model assessed pediatric PAH severity and prognosis with improved accuracy compared to current clinical models. A simple blood test may enable frequent assessment of pediatric PAH patients in an outpatient setting to support therapeutic decision-making without invasive and high-risk procedures. REFERENCES All publications, patent applications, patents, and other references mentioned in the specification are indicative of the level of those skilled in the art to which the presently disclosed subject matter pertains. All publications, patent applications, patents, and other references are herein incorporated by reference to the same extent as if each individual 25 publication, patent application, patent, and other reference was specifically and individually indicated to be incorporated by reference. It will be understood that, although a number of patent applications, patents, and other references are referred to herein, such reference does not constitute an admission that any of these documents form part of the common general knowledge in the art. 68 42150.601 Galiè N, Channick RN, Frantz RP, Grünig E, Jing ZC, Moiseeva O, et al. Risk stratification and medical therapy of pulmonary arterial hypertension. European Respiratory Journal.2019;53(1):1801889. 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Claims

THAT WHICH IS CLAIMED:1 . A method comprising the step of measuring ST2, IGFBP I , IGFBP2, IGFBP4, Endostatin, and Gal ectin-3 in a sample obtained from a subject.

2. A method for identifying the severity of PAH in a subject having or suspected of having PAH, the method comprising the step of measuring ST2, IGFBP 1, IGFBP2, IGFBP4, Endostatin, and Galectin-3 in a sample obtained from a subject.

3. A method for identifying the survival risk of a subject having or suspected of having PAH, the method comprising the step of measuring ST2, IGFBP 1, IGFBP2, IGFBP4, Endostatin, and Galectin-3 in a sample obtained from a subject.

4. The method of claim 3, wherein the survival risk is associated with an adverse event selected from death, lung transplant, heart transplant, and heart / lung transplant.

5. A method for identifying a subject as having or at risk of developing PAH, the method comprising the step of measuring ST2, IGFBP1, IGFBP2, IGFBP4, Endostatin, and Galectin-3 in a sample obtained from the subject, wherein an increased level of ST2, IGFBP 1, IGFBP2, IGFBP4, Endostatin, and Galectin-3 relative to a control identifies the subject as having PAH.

6. A method comprising the steps of: (a) detecting an increased level of ST2, IGFBP 1, IGFBP2, IGFBP4, Endostatin, and Galectin-3 relative to a control in a sample obtained from a subject suspected of having PAH; and (b) treating the subject with a PAH therapy.

7. The method of claim 6, wherein the PAH therapy comprises one or more of endothelial receptor antagonists, prostacyclin pathway agonists, nitric oxide-cyclic guanosine monophosphate (NO-cGMP) enhancers, vasodilators, calcium channel blockers, anticoagulants, oxygen and diuretics.

8. The method of claim 6, wherein the PAH therapy comprises administering prostacyclin or one or more analogs thereof.

9. The method of any one of claim 1 to claim 8, further comprising measuring NT-proBNP.

10. The method of any one of claim 1 to claim 9, further comprising one or more factors selected from an age of the subject at diagnosis, a weight of the subject at a time that the sample is obtained, and a height of the subject at a time that the sample is obtained.

11. The method of any one of claim 1 to claim 10, wherein the sample comprises a serum sample.

12. The method of any one of claim 1 to claim 11, wherein the measuring step is performed using an immunoassay.

13. The method of claim 12, wherein the immunoassay comprises enzyme linked immunosorbent assay (ELISA).

14. A method of treating pulmonary arterial hypertension (PAH) in a subject diagnosed with PAH, the method comprising: (i) performing an immunoassay on a blood sample from the subject to generate a dataset comprising protein level data for biomarkers comprising Soluble Suppressor of Tumorigenicity (ST2), Insulin like growth factor binding protein 1 (IGFBP1), Insulin like growth factor binding protein 2 (IGFBP2), Insulin like growth factor binding protein 4 (IGFBP4), Endostatin, and Galectin-3; (ii) calculating a risk score that indicates probability of survival or risk of death or lung or heart-lung transplantation of the subject based on the dataset and clinical data comprising the subject’s age at PAH diagnosis, weight, and height; and (iii) administering or adjusting a PAH therapy to the subject.

15. The method of claim 14, wherein calculating the risk score comprises using a predictive model to determine the risk score.

16. The method of claim 15, wherein the predictive model uses one or more statistical algorithms comprising one or more of Kaplan-Meier survival curve, Cox proportional hazard regressions, and / or clustering algorithms such as unsupervised K-means clustering algorithm.

17. The method of claim 15, wherein the predictive model uses a clustering algorithm to predict death or lung or heart-lung transplantation or transplant-free survival.

18. The method of claim 15, wherein the predictive model can predict high probability or risk of death or lung or heart-lung transplantation in subjects with moderately elevated levels of NT-proBNP or ambiguous levels of NT-proBNP.

19. The method of claim 14, wherein the blood sample is a peripheral blood serum sample.

20. The method of claim 14, wherein the dataset does not comprise protein level data for N-terminal pro b-type natriuretic peptide (NT-proBNP).

21. The method of claim 14, wherein the clinical data further comprises a ESC / ERS score or a REVEAL score.

22. The method of claim 14, wherein the dataset comprises protein level data for N- terminal pro b-type natriuretic peptide (NT-proBNP).

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