Methods and kits for assessing medical risk

Abstract

The present disclosure relates generally to methods and kits for making a clinical assessment, such as an early diagnostic or prognostic assessment, more particularly to methods and kits for identifying an infant at risk of developing bronchopulmonary dysplasia (BPD).

Description

METHODS AND KITS FOR ASSESSING MEDICAL RISK FIELD

[0001] The present disclosure relates generally to methods and kits for making a clinical assessment, such as an early diagnostic or prognostic assessment, more particularly to methods and kits for identifying an infant at risk of developing bronchopulmonary dysplasia (BPD). BACKGROUND OF THE INVENTION

[0002] Bronchopulmonary dysplasia (BPD) is the most common long-term respiratory complication of preterm birth. (6) BPD develops when lung alveolarization and vascularization are disrupted by antenatal (e.g., immaturity, presence of chorioamnionitis) and early postnatal (e.g. hyperoxia, ventilator-induced lung injury) factors. Currently, the diagnosis of BPD is defined by the need for respiratory support and / or oxygen supplementation at 36 weeks’ postmenstrual age (PMA). This assumes a predictable link between the different injury and inflammatory mechanisms in the lung and clinical care. Despite its importance, our ability to predict BPD and prevent or treat it appropriately is limited. (7) This is because our knowledge of the biological mediators underlying early lung injury is limited with few studies assessing biological samples from the first days of extremely preterm life. (8-10)

[0003] The most accurate tool for early prediction of BPD severity currently available, the National Institute of Child Health and Human Development (NICHD) ‘BPD Outcome Estimator’, relies on birth characteristics and clinical variables (gestational age, postnatal age, birth weight, sex, ethnicity, mode of respiratory support, and supplemental oxygen requirement) to predict BPD severity from postnatal day 1. (14) However, this approach has a number of limitations, including reported underestimation of the observed incidence of severe BPD, (15, 16) limited ethnicity inputs, (17) and failure to provide information about disease pathology or therapeutic interventions that may benefit individual infants. (18)

[0004] Hence, there remains an urgent need for identifying whether a subject is at risk of developing BPD. SUMMARY OF THE INVENTION

[0005] The present invention is predicated, at least in part, on the inventors’ surprising finding that differences in circulating proteins in preterm infants can be used to predict risk ofdeveloping BPD. The present inventors have also shown that protein biomarkers combined with three birth characteristics strongly predicted BPD development within the first 72 hours post-birth. Accordingly, in one aspect disclosed herein, there is provided a method of identifying an infant at risk of developing bronchopulmonary dysplasia (BPD), the method comprising: (a) obtaining a sample biomarker profile of a biological sample from an infant, wherein the sample biomarker profile evaluates at least one biomarker that is differentially expressed in a biological sample from an infant at risk of developing BPD when compared to a corresponding biological sample from an infant who is not at risk of developing BPD, wherein the biological sample is obtained from the infant at a time point from birth to about 76 hours post-birth, and wherein the biological sample is selected from the group consisting of whole blood, plasma and serum; (b) comparing the sample biomarker profile of step (a) to a reference biomarker profile, wherein the reference biomarker profile evaluates, for an individual biomarker in the sample biomarker profile, a corresponding biomarker; and (c) determining whether the infant is at risk of developing BPD based on the comparison of the sample biomarker profile and the reference biomarker profile.

[0006] In an embodiment, the sample biomarker profile evaluates at least one biomarker selected from the group consisting of IGLV3-27, EXTL2, TUBB, TUBB1, MMP9, MST1L, KRT2, AHSG, KRT17, COL18A1, AGT, ISLR, EFEMP1, NRCAM, CD5L, F12, COL1A2, FAP, APOD, ARPC1B, LDLR, CCT4, MSN, NRP1, SPTA1, PTGDS, DPYSL2, CTSD, ANXA1, PSMA6, VCL, CFHR2, CFB, CD14, CFH, CFI, MYH9, LRG1, APCS, F10, C8A, AMBP, PRG2, SAA4, LBP, APRT, RAN, ROBO4, SBSN, APOA4, KLKB1, F11, C4BPA, CD44, UGP2, PROS1 and ADAMTSL4.

[0007] In another aspect disclosed herein, there is provided a method of identifying an infant at risk of developing bronchopulmonary dysplasia (BPD), the method comprising: (a) obtaining a sample biomarker profile of a biological sample from an infant, wherein the sample biomarker profile evaluates at least one biomarker that is differentially expressed in a biological sample from an infant at risk of developing BPD when compared to a corresponding biological sample from an infant who is not at risk of developing BPD, wherein the at least one biomarker selected from the group consisting of IGLV3-27, EXTL2, TUBB, TUBB1, MMP9, MST1L, KRT2, AHSG, KRT17, COL18A1, AGT, ISLR, EFEMP1, NRCAM, CD5L, F12, COL1A2, FAP,APOD, ARPC1B, LDLR, CCT4, MSN, NRP1, SPTA1, PTGDS, DPYSL2, CTSD, ANXA1, PSMA6, VCL, CFHR2, CFB, CD14, CFH, CFI, MYH9, LRG1, APCS, F10, C8A, AMBP, PRG2, SAA4, LBP, ARPPRT, RAN, ROBO4, SBSN, APOA4, KLKB1, F11, C4BPA, CD44, UGP2, PROS1 and ADAMTSL4, and wherein the biological sample is selected from the group consisting of whole blood, plasma and serum; (b) comparing the sample biomarker profile of step (a) to a reference biomarker profile, wherein the reference biomarker profile evaluates, for an individual biomarker in the sample biomarker profile, a corresponding biomarker; and (c) determining whether the infant is at risk of developing BPD based on the comparison of the sample biomarker profile and the reference biomarker profile.

[0008] In an embodiment, the infant is a premature infant.

[0009] In an embodiment, the biological sample is obtained from the infant at a time point from birth to about 1-week post-birth.

[0010] In an embodiment, the method further comprises taking into account one or more birth risk factors of the infant.

[0011] In an embodiment, the birth risk factor is selected from one or more of birth weight or birth weight z-score, gestational age and sex.

[0012] In an embodiment, the biological sample is obtained from the infant at a time point from birth to about 1-week post-birth.

[0013] In an embodiment, the biological sample is obtained from the infant during the first week post-birth. In an embodiment, the biological sample is obtained from the infant at a time point from birth to about 12 hours post-birth. In an embodiment, the biological sample is obtained from the infant at a time point from about 2-6 hours post-birth. In an embodiment, the biological sample is obtained from the infant at about 4 hours post-birth. In an embodiment, the at least one biomarker is selected from the group consisting of IGLV3- 27, EXTL2, TUBB, TUBB1, MMP9, MST1L, KRT2, AHSG, KRT17, COL18A1, AGT, ISLR and EFEMP1. In an embodiment, the at least one biomarker is selected from the group consisting of IGLV3-27, EXTL2, TUBB, MMP9 and COL18A1. In an embodiment, the at least one biomarker is selected from the group consisting of IGLV3-27, TUBB, MST1L andKRT2. In an embodiment, the at least one biomarker is selected from the group consisting of AGT and AHSG.

[0014] In an embodiment, the biological sample is obtained from the infant at a time point from about 12-54 hours post-birth. In an embodiment, the biological sample is obtained from the infant at a time point from about 20-28 hours post-birth. In an embodiment, the biological sample is obtained from the infant at about 24 hours post-birth. In an embodiment, the at least one biomarker is selected from the group consisting of MMP9 NRCAM, CD5L, F12, COL1A2, FAP, APOD, ARPC1B, LDLR, CCT4, MSN, NRP1, SPTA1, PTGDS, DPYSL2, CTSD, ANXA1, RAN, AHSG and PSMA6. In an embodiment, the at least one biomarker is selected from the group consisting of CD5L, F12, FAP, CCT4 and MSN. In an embodiment, the at least one biomarker is selected from the group consisting of CCT4 and COL1A2. In an embodiment, the at least one biomarker is selected from the group consisting of NRCAM and AHSG.

[0015] In an embodiment, the biological sample is obtained from the infant at a time point from about 50-120 hours post-birth. In an embodiment, the biological sample is obtained from the infant at a time point from about 68-76 hours post-birth. In an embodiment, the biological sample is obtained from the infant at about 72 hours post-birth. In an embodiment, the at least one biomarker is selected from the group consisting of VCL, CFHR2, CFB, CD14, CFH, CFI, MYH9, F12, LRG1, APCS, F10, C8A, AMBP, PRG2, SAA4, LBP, APRT, RAN, ROBO4, SBSN, APOA4, KLKB1, F11, C4BPA, CD44, UGP2, PROS1 and ADAMTSL4. In an embodiment, the at least one biomarker is selected from the group consisting of VCL, CFB, CFI, MYH9 and LRG1. In an embodiment, the at least one biomarker is selected from the group consisting of CFB, MYH9, CFI, CD14, APCS, and C4BPA.

[0016] In an embodiment, the infant is less than 37 weeks gestational age. In an embodiment, the infant is less than 32 weeks gestational age. In an embodiment, the infant is less than 29 weeks gestational age.

[0017] In an embodiment, evaluation of the at least one biomarker comprises determining the level of at least one biomarker in plasma.

[0018] In an embodiment, the at least one biomarker is a protein or an RNA molecule.

[0019] In another aspect disclosed herein, there is provided a method of treating or preventing BPD in an infant, the method comprising identifying an infant at risk ofdeveloping BPD according to a method described herein; and administering a treatment for BPD to the infant identified as being risk of developing BPD.

[0020] In an embodiment, the treatment for BPD comprises administering to the identified infant a surfactant, oxygen therapy, ventilator therapy, a steroid, or inhaled nitric oxide.

[0021] The present disclosure also extends to a kit for identifying an infant at risk of developing bronchopulmonary dysplasia (BPD), the kit comprising one or more reagents for determining the level of at least one biomarker in a biological sample from an infant, wherein the at least one biomarker is selected from the group consisting of IGLV3-27, EXTL2, TUBB, TUBB1, MMP9, MST1L, KRT2, AHSG, KRT17, COL18A1, AGT, ISLR, EFEMP1, NRCAM, CD5L, F12, COL1A2, FAP, APOD, ARPC1B, LDLR, CCT4, MSN, NRP1, SPTA1, PTGDS, DPYSL2, CTSD, ANXA1, PSMA6, VCL, CFHR2, CFB, CD14, CFH, CFI, MYH9, LRG1, APCS, F10, C8A, AMBP, PRG2, SAA4, LBP, APRT, RAN, ROBO4, SBSN, APOA4, KLKB1, F11, C4BPA, CD44, UGP2, PROS1 and ADAMTSL4.

[0022] In an embodiment, the at least one biomarker is selected from the group consisting of IGLV3-27, EXTL2, TUBB, MMP9, COL18A1, CD5L, F12, FAP, CCT4M, MSN, VCL, CFB, CFI, MYH9, and LRG1. In another embodiment, the at least one biomarker is selected from the group consisting of IGLV3-27, EXTL2, VCL, CFB, MYH9, CCT4M and CD5L.

[0023] In another aspect disclosed herein, there is provided an array comprising one or more immobilized binding agents, wherein the one or more immobilized binding agents are capable of specifically binding to one or more biomarkers present in a biological sample, wherein the one or more biomarkers are selected from the group consisting of IGLV3-27, EXTL2, TUBB, TUBB1, MMP9, MST1L, KRT2, AHSG, KRT17, COL18A1, AGT, ISLR, EFEMP1, NRCAM, CD5L, F12, COL1A2, FAP, APOD, ARPC1B, LDLR, CCT4, MSN, NRP1, SPTA1, PTGDS, DPYSL2, CTSD, ANXA1, PSMA6, VCL, CFHR2, CFB, CD14, CFH, CFI, MYH9, LRG1, APCS, F10, C8A, AMBP, PRG2, SAA4, LBP, APRT, RAN, ROBO4, SBSN, APOA4, KLKB1, F11, C4BPA, CD44, UGP2, PROS1 and ADAMTSL4.

[0024] In an embodiment, the at least one biomarker is selected from the group consisting of IGLV3-27, EXTL2, TUBB, MMP9, COL18A1, CD5L, F12, FAP, CCT4M, MSN, VCL, CFB, CFI, MYH9, and LRG1. In another embodiment, the one or more biomarkers are selected from the group consisting of IGLV3-27, EXTL2, VCL, CFB, MYH9, CCT4M and CD5L.

[0025] In an embodiment, the one or more biomarkers are selected from the group consisting of IGLV3-27, EXTL2, TUBB, TUBB1, MMP9, MST1L, KRT2, AHSG, KRT17, COL18A1, AGT, ISLR and EFEMP1. In an embodiment, the one or more biomarkers are selected from the group consisting of MMP9 NRCAM, CD5L, F12, COL1A2, FAP, APOD, ARPC1B, LDLR, CCT4, MSN, NRP1, SPTA1, PTGDS, DPYSL2, CTSD, ANXA1, RAN, AHSG and PSMA6. In an embodiment, the one or more biomarkers are selected from the group consisting of VCL, CFHR2, CFB, CD14, CFH, CFI, MYH9, F12, LRG1, APCS, F10, C8A, AMBP, PRG2, SAA4, LBP, APRT, RAN, ROBO4, SBSN, APOA4, KLKB1, F11, C4BPA, CD44, UGP2, PROS1 and ADAMTSL4.

[0026] In an embodiment, the biological sample is plasma.

[0027] The present disclosure also extends to a method of stratifying an infant identified as being at risk of developing bronchopulmonary dysplasia (BPD) to a treatment regimen, the method comprising: (1) identifying whether an infant is at risk of developing BPD according to a method described herein; and (2) where the infant is determined from step (1) to be at risk of developing BPD, stratifying the infant to a treatment regimen for BPD.

[0028] The reference in this specification to any prior publication (or information derived from it), or to any matter which is known, is not, and should not be taken as an acknowledgement or admission or any form of suggestion that the prior publication (or information derived from it) or known matter forms part of the common general knowledge in the field of endeavour to which this specification relates. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 shows a STROBE diagram detailing the study recruitment.17 / 23 (74%) infants met the criteria for BPD. Prenatal and perinatal characteristics were similar between the BPD and no BPD groups except for lower weight z score in the BPD group (Table 1). The average time at which blood samples were collected was 3-, 25- and 74-hours post-birth.

[0030] Figure 2 shows the results of sequential window acquisition of all theoretical mass spectra (SWATH-MS) analysis of plasma obtained from preterm infants within the first 72 hours post-birth. (A) Venn diagram and (B) volcano plot of the differentially expressed proteins (DEPs) at 4-, 24- and 72-hours post-birth in a comparison of abundance in infants with (n=17) and without BPD (n=6); black circles represent proteins whose abundance was significantly altered, and light grey circles represent proteins whose abundance was not significantly altered. The 10 most altered proteins based on fold change are annotated.PANTHER (26) assigned (C) molecular function, (D) biological process and (E) protein class as applied to the DEP datasets are shown for each time point (clockwise representation in the pie charts). Figure 2F shows that biological processes assigned by gene ontology to the DEPs at each time point (time points are denoted as light grey (4 hours), mid grey (24 hours) and black (72 hours) shading). Figures 2G and 2H provide bar charts showing the number of DEPs within each sub-category assignment of selected biological process categories (time points are denoted as light grey (4 hours), mid grey (24 hours) and black (72 hours) shading).

[0031] Figure 3 shows the STRING bioinformatics analysis of proteins dysregulated in plasma obtained at (A) 4 hours, (B) 24 hours and (C) 72 hours post-birth from patients who are later diagnosed with BPD. (D) Summary of significant Reactome pathways (FDR < 0.05) containing at least 5 proteins / pathway. Thickness of lines connecting nodes in panels A-C indicates the strength of the supporting data. Node shading corresponds to pathway classification; black = haemostasis, mid grey = immune system, striped = developmental biology and white = not mapped to Reactome pathway. (E) Pathway mapping highlighting fold change in protein abundance of identified proteins within the coagulation and complement cascade pathways. Arrows indicate direction of fold change (BPD versus non- BPD) in abundance of significant plasma proteins. Proteins who abundance was not significantly altered are shown in grey boxes. Proteins that were not identified in the plasma identification dataset are shown in white.

[0032] Figure 4 shows heatmaps with clustering of BPD biomarker candidates identified at (A) 4-hours, (B) 24-hours and (C) 72-hours post-birth. Clustvis was used to create a heatmap for the DEPs across individuals in the study cohort. The columns correspond to individual participants while the rows correspond to proteins. Top panel contains annotation relating to participant observations from the time of blood sampling (FiO2level, intubation) and 36 weeks PMA (BPD diagnosis, duration of invasive ventilation). Rows are centred, unit variance scaling is applied to rows. Both rows and columns are clustered using Euclidean distance and complete linkage.

[0033] Figure 5 shows a flow chart detailing BPD diagnosis criteria used to assess infants aged 36+0and 36+6weeks’ PMA. IMV, invasive mechanical ventilation; CPAP, continuous positive airway pressure; HFNC, high flow nasal canula; BPD, bronchopulmonary dysplasia.

[0034] Figure 6 shows the abundance and relative abundance of protein biomarkers, as determined by SWATH-MS or SOMAscan®, in plasma samples taken from BPD and no BPD patients at 4, 24 and 72 hours post-birth. Each dot represents protein abundance in a single patient. DETAILED DESCRIPTION OF THE INVENTION

[0035] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the art to which the invention belongs. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, preferred methods and materials are described. For the purposes of the present invention, the following terms are defined below.

[0036] The articles "a" and "an" are used herein to refer to one or to more than one (i.e. to at least one) of the grammatical object of the article. By way of example, "a biomarker" means one biomarker or more than one biomarker, unless otherwise indicated.

[0037] Throughout this specification, unless the context requires otherwise, the words "comprise", "comprises" and "comprising" will be understood to imply the inclusion of a stated step or element or group of steps or elements but not the exclusion of any other step or element or group of steps or elements. Thus, use of the term "comprising" and the like indicates that the listed elements are required or mandatory, but that other elements are optional and may or may not be present.

[0038] The present invention is predicated, at least in part, on the inventors’ surprising finding that alterations in circulating proteins from 4 hours of age in preterm infants can be used to predict risk of developing BPD. The present inventors have also shown that protein biomarkers combined with three birth characteristics strongly predicted BPD development within the first 72 hours post-birth. Accordingly, in one aspect disclosed herein, there is provided a method of identifying an infant at risk of developing bronchopulmonary dysplasia (BPD), the method comprising: (a) obtaining a sample biomarker profile of a biological sample from an infant, wherein the sample biomarker profile evaluates at least one biomarker that is differentially expressed in a biological sample from an infant at risk of developing BPD when compared to a corresponding biological sample from an infant who is not at risk of developing BPD, wherein the biological sample is obtained from the infant at a timepoint from birth to about 76 hours post-birth, and wherein the biological sample is selected from the group consisting of whole blood, plasma and serum; (b) comparing the sample biomarker profile of step (a) to a reference biomarker profile, wherein the reference biomarker profile evaluates, for an individual biomarker in the sample biomarker profile, a corresponding biomarker; and (c) determining whether the infant is at risk of developing BPD based on the comparison of the sample biomarker profile and the reference biomarker profile.

[0039] In another aspect disclosed herein, there is provided a method of identifying an infant at risk of developing bronchopulmonary dysplasia (BPD), the method comprising: (a) obtaining a sample biomarker profile of a biological sample from an infant, wherein the sample biomarker profile evaluates at least one biomarker that is differentially expressed in a biological sample from an infant at risk of developing BPD when compared to a corresponding biological sample from an infant who is not at risk of developing BPD, wherein the at least one biomarker selected from the group consisting of IGLV3-27, EXTL2, TUBB, TUBB1, MMP9, MST1L, KRT2, AHSG, KRT17, COL18A1, AGT, ISLR, EFEMP1, NRCAM, CD5L, F12, COL1A2, FAP, APOD, ARPC1B, LDLR, CCT4, MSN, NRP1, SPTA1, PTGDS, DPYSL2, CTSD, ANXA1, PSMA6, VCL, CFHR2, CFB, CD14, CFH, CFI, MYH9, LRG1, APCS, F10, C8A, AMBP, PRG2, SAA4, LBP, ARPPRT, RAN, ROBO4, SBSN, APOA4, KLKB1, F11, C4BPA, CD44, UGP2, PROS1 and ADAMTSL4, and wherein the biological sample is selected from the group consisting of whole blood, plasma and serum; (b) comparing the sample biomarker profile of step (a) to a reference biomarker profile, wherein the reference biomarker profile evaluates, for an individual biomarker in the sample biomarker profile, a corresponding biomarker; and (c) determining whether the infant is at risk of developing BPD based on the comparison of the sample biomarker profile and the reference biomarker profile.

[0040] In an embodiment, the infant is a premature infant.

[0041] In an embodiment, the biological sample is obtained from the infant at a time point from birth to about 1 week post-birth.Bronchopulmonary dysplasia (BPD)

[0042] As reported by Sahni et al. (2023; Bronchopulmonary Dysplasia. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing), BPD is is one of the most common causes of morbidity and mortality in preterm infants. It is a chronic form of injury to the lungs caused by barotrauma and oxygen injury in preterm infants requiring mechanical ventilation. Despite significant advances in preterm infant care over the past few decades, including the development of surfactant and gentler modes of ventilation, the prevalence of this condition remains high.

[0043] The incidence of BPD can vary significantly between different birth centres, including because of different perinatal practices, management styles and lack of consistent definition of BPD. The highest observed rate of BPD is in infants born at a lower gestational age (GA) and lower birth weight, with data from the neonatal research network (Sahni and Mowes 2023, Bronchopulmonary Dysplasia. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing) showing the incidence of BPD in very low birth weight infants (birth weight less than 1.5 kilograms) to be ~40 to 68%, depending on the definition of BPD that was employed. This incidence was found to be inversely proportional to the GA of the infant. Other demographic risk factors associated with BPD include male sex, low birth weight, white race, impaired growth for GA and family history of asthma.

[0044] Injury from mechanical ventilation and reactive oxygen species to premature lungs in the presence of antenatal factors predisposing the lungs to BPD form the basis of pathogenesis of BPD in preterm neonates and ultimately results in aberrant tissue repair and arrest in lung development.

[0045] BPD occurs when lung development arrests in the late canalicular to saccular stages of lung development. The pathology in BPD characteristically demonstrates decreased septation and alveolar hypoplasia resulting in simplified large alveoli and reduced area available for gaseous exchange, dysregulated vasculature with smooth muscle hyperplasia in pulmonary vessels and abnormal alveolar capillaries, and the presence of increased interstitial fibroproliferation and alveolar septation. The clinical phenotype of infants with BPD can vary considerably. Some may be tachypneic and depending on the degree of pulmonary edema may have signs of respiratory distress like retractions, rales and increased work of breathing.

[0046] Evaluation of BPD typically involves assessment of blood gas, chest x-ray and evaluating the nutritional status of the patient. An arterial blood gas may reveal hypoxia, hypercarbia or acidosis. These patients remain on continuous pulse oximetry to help target adequate oxygen saturation. Many birth centres also utilize transcutaneous carbon dioxide monitoring to evaluate the ventilation of the infant. Chest radiographs may show decreased lung volumes, hyperinflation, areas of atelectasis, pulmonary edema, and pulmonary interstitial emphysema. High-resolution CT may demonstrate abnormalities not readily seen with routine chest radiography. Infants with moderate or severe BPD must be screened for pulmonary hypertension (PH) at 36 PMA using an echocardiogram. Some birth centres choose to screen all patients with BPD since the morbidity and mortality associated with the diagnosis of PH in BPD are quite high. The diagnosis of BPD is typically made clinically based on the GA, postmenstrual age (PMA), oxygen exposure, and oxygen requirement at 36 weeks PMA.

[0047] The primary aim in the management of infants with BPD is to support them while lung growth occurs, limit further injury to the lungs, optimize lung function and detect complications associated with BPD. Various measures employed in the care of these infants include: (i) Surfactant Administration: Infants at risk of BPD can be treated with surfactant that is administered in liquid form via the endotracheal tube. (ii) Nutrition: Infants with BPD need an increased amount of energy to promote lung growth and repair. Their nutritional need may be up to 140 to 150 Kcal / Kg / day and protein intake from 3.5 to 4 g / kg / day. Breast milk is preferable to formula. Commercial milk fortifiers can be used to fortify the mother’s milk or donor breast milk to higher calories. Intramuscular supplementation of vitamin A started within a few days of birth and administered three times a week for 4 weeks has been shown to decrease the risk of BPD by 7%. (iii) Fluid restriction: Based on the severity of their lung disease, infants are restricted to a total fluid volume of 120 to 150 ml / kg / day. Restriction of fluid intake allows for improved pulmonary function by preventing pulmonary edema and improving gas exchange. Although frequently followed, there is insufficient data to support this practice. (iv) Minimize ventilator-associated lung injury: Non-invasive ventilation is the preferred mode of ventilation wherever feasible. If mechanical ventilation isnecessary, care should be taken to limit barotrauma and volutrauma. Early extubation has been shown to lower the rates of BPD. (v) Minimize oxygen associated injury: Exposure to hyperoxia is one of the most critical factors in the pathogenesis of BPD. There is significant controversy regarding SpO target ranges. One approach attempts to target SpO of 88-94% with a lower alarm limit of 88% and a higher alarm limit of 96%. (vi) Corticosteroids: Systemic corticosteroids have been used in BPD to improve lung function, reduce inflammation and reduce the need for mechanical ventilation. However, concerns about long term neurodevelopmental outcomes have led to recommendations to restrict their use to infants with severe BPD who remain ventilator dependent with high oxygen needs. Despite many clinical trials, it is unclear as to which corticosteroid provides the most benefit and at the optimal dose. Notwithstanding the variable evidence, low dose dexamethasone and hydrocortisone continue to be used in practice in the treatment of BPD. The current data does not demonstrate support for the routine use of inhaled corticosteroids to prevent BPD. (vii) Diuretics: Thiazides and loop diuretics are the most commonly used diuretics in the setting to improve short term pulmonary mechanics of BPD. These agents are most commonly used in infants who are ventilator dependent with increasing requirement of positive end-expiratory pressure despite fluid restriction. A systemic review of available literature does not show any improvement in long term clinical outcome in infants with established or developing BPD. (viii) Bronchodilators: administration of beta-2 agonists can decrease airway resistance and improve compliance. However, their routine use is not recommended in BPD as it has not been shown to improve long term outcomes. Their use should be restricted to manage acute episodes of bronchoconstriction in older infants who remain ventilator dependent.

[0048] Despite advances in treatment and care, BPD remains a chronic illness that persists beyond discharge from the hospital and into adulthood. Infants with BPD have a 50% chance of readmission to the hospital during their first year of life, with an increased risk of developing reactive airway disease, asthma, emphysema, and RSV bronchiolitis. BPD also affects their growth and neurodevelopmental outcome, noting that some premature infants with BPD are more likely to have delays in fine and gross motor skills, and language.Infants with BPD are also at high risk for cardiopulmonary sequelae, such as pulmonary hypertension, cor pulmonale, and systemic hypertension. Biomarkers

[0049] The term "biomarker" typically refers to a measurable characteristic that reflects the presence or nature (e.g., severity) of a physiological state, including an indicator of risk of developing a particular physiological state. For example, a biomarker may be present in a sample obtained from a subject before the onset of a physiological state, including a symptom thereof. Thus, the presence of the biomarker in a sample obtained from the subject is likely to be indicative of an increased risk that the subject will develop the physiological state or symptom thereof. Alternatively, or in addition, the biomarker may be normally present in a sample obtained from an individual before the onset of a physiological state, including a symptom thereof, but its expression may change (i.e., it is increased, upregulated, over- expressed; or decreased, downregulated, under-expressed) following the onset of a physiological state, including a symptom thereof. Thus, in this context, a change in the level of expression of the biomarker is indicative of an increased risk that the subject will develop the physiological state or symptom thereof. Alternatively, or in addition, a change in the level of expression of a biomarker may reflect a change in a particular physiological state, or symptom thereof, in a subject, thereby allowing the nature (e.g., severity) of the physiological state, or symptom thereof, to be tracked over a period of time. This approach may be useful in, for example, monitoring a treatment regimen for the purpose of assessing its effectiveness (or otherwise) in a subject. As herein described, reference to the level of expression of a biomarker includes the concentration of the biomarker (e.g., µg / mL, mg / mL, etc), the absolute amount (abundance) of the biomarker (e.g., µg, mg, MS spectral intensity, etc), or a gene expression product thereof (e.g., peptide, pro-peptide, metabolite thereof), as will be described in more detail below. Reference to the expression or level of expression of a biomarker may suitably be expressed by the level of activity of the biomarker. For example, where the biomarker is an enzyme, its expression or level of expression may be determined or measured by the level of activity of the enzyme on a suitable substrate.

[0050] In an embodiment, the sample biomarker profile evaluates at least one biomarker selected from the group consisting of IGLV3-27, EXTL2, TUBB, TUBB1, MMP9, MST1L, KRT2, AHSG, KRT17, COL18A1, AGT, ISLR, EFEMP1, NRCAM, CD5L, F12, COL1A2, FAP, APOD, ARPC1B, LDLR, CCT4, MSN, NRP1, SPTA1, PTGDS, DPYSL2, CTSD, ANXA1, PSMA6, VCL, CFHR2, CFB, CD14, CFH, CFI, MYH9, LRG1, APCS, F10, C8A,AMBP, PRG2, SAA4, LBP, APRT, RAN, ROBO4, SBSN, APOA4, KLKB1, F11, C4BPA, CD44, UGP2, PROS1 and ADAMTSL4.

[0051] In some embodiments, the sample biomarker profile evaluates at least 2, preferably at least 3, preferably at least 4, preferably at least 5, preferably at least 6, preferably at least 7, preferably at least 8, preferably at least 9, preferably at least 10, preferably at least 11, preferably at least 12, preferably at least 13, preferably at least 14, preferably at least 15, preferably at least 16, preferably at least 17, preferably at least 18, preferably at least 19, preferably at least 20, preferably at least 21, preferably at least 22, preferably at least 23, preferably at least 24, preferably at least 25, preferably at least 26, preferably at least 27, preferably at least 28, preferably at least 29, preferably at least 30, preferably at least 31, preferably at least 32, preferably at least 33, preferably at least 34, preferably at least 35, preferably at least 36, preferably at least 37, preferably at least 38, preferably at least 39, preferably at least 40, preferably at least 41, preferably at least 42, preferably at least 43, preferably at least 44, preferably at least 45, preferably at least 46, preferably at least 47, preferably at least 48, preferably at least 49, preferably at least 50, preferably at least 51, preferably at least 52, preferably at least 53, preferably at least 54, preferably at least 55, preferably at least 56, preferably at least 57, preferably at least 58, preferably at least 59, or preferably at least 60 biomarkers.

[0052] In some embodiments, the sample biomarker profile evaluates at least 2, preferably at least 3, preferably at least 4, preferably at least 5, preferably at least 6, preferably at least 7, preferably at least 8, preferably at least 9, preferably at least 10, preferably at least 11, preferably at least 12, preferably at least 13, preferably at least 14, preferably at least 15, preferably at least 16, preferably at least 17, preferably at least 18, preferably at least 19, preferably at least 20, preferably at least 21, preferably at least 22, preferably at least 23, preferably at least 24, preferably at least 25, preferably at least 26, preferably at least 27, preferably at least 28, preferably at least 29, preferably at least 30, preferably at least 31, preferably at least 32, preferably at least 33, preferably at least 34, preferably at least 35, preferably at least 36, preferably at least 37, preferably at least 38, preferably at least 39, preferably at least 40, preferably at least 41, preferably at least 42, preferably at least 43, preferably at least 44, preferably at least 45, preferably at least 46, preferably at least 47, preferably at least 48, preferably at least 49, preferably at least 50, preferably at least 51, preferably at least 52, preferably at least 53, preferably at least 54, preferably at least 55, preferably at least 56, preferably at least 57, preferably at least 58,preferably at least 59, or preferably 60 biomarkers selected from the group consisting of IGLV3-27, EXTL2, TUBB, TUBB1, MMP9, MST1L, KRT2, AHSG, KRT17, COL18A1, AGT, ISLR, EFEMP1, NRCAM, CD5L, F12, COL1A2, FAP, APOD, ARPC1B, LDLR, CCT4, MSN, NRP1, SPTA1, PTGDS, DPYSL2, CTSD, ANXA1, PSMA6, VCL, CFHR2, CFB, CD14, CFH, CFI, MYH9, LRG1, APCS, F10, C8A, AMBP, PRG2, SAA4, LBP, APRT, RAN, ROBO4, SBSN, APOA4, KLKB1, F11, C4BPA, CD44, UGP2, PROS1 and ADAMTSL4.

[0053] In an embodiment, evaluation of the at least one biomarker comprises determining the level of the at least one biomarker in a biological sample obtained from the infant at a time point from birth to about one week post-birth, optionally within about 6 days post-birth, optionally within about 5 days post-birth, optionally within about 4 days post-birth, optionally within about 3 days post-birth, optionally within about 2 days post-birth or optionally within about 1 day post-birth. In another embodiment, evaluation of the at least one biomarker comprises determining the level of the at least one biomarker in a biological sample obtained from the infant at a time point from birth to about 76 hours, optionally within about 30 minutes, optionally within about 1 hour, optionally within about 2 hours, optionally within about 3 hours, optionally within about 4 hours, optionally within about 5 hours, optionally within about 6 hours, optionally within about 7 hours, optionally within about 8 hours, optionally within about 9 hours, optionally within about 10 hours, optionally within about 11 hours, optionally within about 12 hours, optionally within about 13 hours, optionally within about 14 hours, optionally within about 15 hours, optionally within about 16 hours, optionally within about 17 hours, optionally within about 18 hours, optionally within about 19 hours, optionally within about 20 hours, optionally within about 21 hours, optionally within about 22 hours, optionally within about 23 hours, optionally within about 24 hours, optionally within about 25 hours, optionally within about 26 hours, optionally within about 27 hours, optionally within about 28 hours, optionally within about 29 hours, optionally within about 30 hours, optionally within about 31 hours, optionally within about 32 hours, optionally within about 33 hours, optionally within about 34 hours, optionally within about 35 hours, optionally within about 36 hours, optionally within about 37 hours, optionally within about 38 hours, optionally within about 39 hours, optionally within about 40 hours, optionally within about 41 hours, optionally within about 42 hours, optionally within about 43 hours, optionally within about 44 hours, optionally within about 45 hours, optionally within about 46 hours, optionally within about 47 hours, optionally within about48 hours, optionally within about 49 hours, optionally within about 50 hours, optionally within about 51 hour, optionally within about 52 hours, optionally within about 53 hours, optionally within about 54 hours, optionally within about 55 hours, optionally within about 56 hours, optionally within about 57 hours, optionally within about 58 hours, optionally within about 59 hours, optionally within about 60 hours, optionally within about 61 hours, optionally within about 62 hours, optionally within about 63 hours, optionally within about 64 hours, optionally within about 65 hours, optionally within about 66 hours, optionally within about 67 hours, optionally within about 68 hours, optionally within about 69 hours, optionally within about 70 hours, optionally within about 71 hours post-birth, optionally within about 72 hours post-birth, optionally within about 73 hours post-birth, optionally within about 74 hours post-birth, or optionally within about 75 hours post-birth.

[0054] As noted elsewhere herein, the present inventors have surprisingly found that biomarker alterations were temporally distinct. Thus, in an embodiment, the biological sample is obtained from the infant during the first week post-birth.

[0055] In an embodiment, the biological sample is obtained from the infant at a time point from birth to about 12 hours post-birth. In another embodiment, the biological sample is obtained from the infant at a time point from about 2-6 hours post-birth. For example, the biological sample is obtained from the infant at birth, optionally at about 0.5 hours, optionally at about 1 hour, optionally at about 1.5 hours, optionally at about 2 hours, optionally at about 2.5 hours, optionally at about 3 hours, optionally at about 3.5 hours, optionally at about 4 hours, optionally at about 4.5 hours, optionally at about 5 hours, optionally at about 5.5 hours, optionally at about 6 hours post-birth, optionally at about 6.5 hours post-birth, optionally at about 7 hours post-birth, optionally at about 7.5 hours post-birth, optionally at about 8 hours post-birth, optionally at about 8.5 hours post-birth, optionally at about 9 hours post-birth, optionally at about 9.5 hours post-birth, optionally at about 10 hours post-birth, optionally at about 10.5 hours post-birth, optionally at about 11 hours post-birth, optionally at about 11.5 hours post-birth or optionally at about 12 hours post-birth. In an embodiment, the biological sample is obtained from the infant at about 4 hours post-birth. In an embodiment where the biological sample is obtained from the infant at a time point from birth to about 12 hours post-birth, the at least one biomarker is selected from the group consisting of IGLV3- 27, EXTL2, TUBB, TUBB1, MMP9, MST1L, KRT2, AHSG, KRT17, COL18A1, AGT, ISLR and EFEMP1. In an embodiment, the sample biomarker profile evaluates at least 2, preferably at least 3, preferably at least 4, preferably at least 5, preferably at least 6,preferably at least 7, preferably at least 8, preferably at least 9, preferably at least 10, preferably at least 11, preferably at least 12, or preferably 13 biomarkers selected from the group consisting of IGLV3-27, EXTL2, TUBB, TUBB1, MMP9, MST1L, KRT2, AHSG, KRT17, COL18A1, AGT, ISLR and EFEMP1. In an embodiment where the biological sample is obtained from the infant at a time point from birth to about 12 hours post-birth, the at least one biomarker is selected from the group consisting of IGLV3-27, EXTL2, TUBB, MMP9 and COL18A1. In an embodiment, the sample biomarker profile evaluates at least 2, preferably at least 3, preferably at least 4, or preferably 5 biomarkers selected from the group consisting of IGLV3-27, EXTL2, TUBB, MMP9 and COL18A1.

[0056] In an embodiment, the biological sample is obtained from the infant at a time point from about 12 to about 54 hours post-birth. In an embodiment, the biological sample is obtained from the infant at a time point from about 20-28 hours post-birth. For example, the biological sample is obtained from the infant at about 12 hours, optionally at about 13 hours, optionally at about 14 hours, optionally at about 15 hours, optionally at about 16 hours, optionally at about 17 hours, optionally at about 18 hours, optionally at about 19 hours, optionally at about 20 hours, optionally at about 21 hours, optionally at about 22 hours, optionally at about 23 hours, optionally at about 24 hours, optionally at about 25 hours, optionally at about 26 hours, optionally at about 27 hours, optionally at about 28 hours post- birth, optionally at about 29 hours, optionally at about 30 hours, optionally at about 31 hours, optionally at about 32 hours, optionally at about 33 hours, optionally at about 34 hours, optionally at about 35 hours, optionally at about 36 hours, optionally at about 37 hours, optionally at about 38 hours, optionally at about 39 hours, optionally at about 40 hours, optionally at about 41 hours, optionally at about 42 hours, optionally at about 43 hours, optionally at about 44 hours, optionally at about 45 hours, optionally at about 46 hours, optionally at about 47 hours, optionally at about 48 hours, optionally at about 49 hours, optionally at about 50 hours, optionally at about 51 hours, optionally at about 52 hours, optionally at about 53 hours or optionally at about 54 hours. In an embodiment, the biological sample is obtained from the infant at about 24 hours post-birth. In an embodiment where the biological sample is obtained from the infant at a time point from about 20-28 hours post-birth, the at least one biomarker is selected from the group consisting of MMP9, NRCAM, CD5L, F12, COL1A2, FAP, APOD, ARPC1B, LDLR, CCT4, MSN, NRP1, SPTA1, PTGDS, DPYSL2, CTSD, ANXA1, RAN, AHSG and PSMA6. In an embodiment, the sample biomarker profile evaluates at least 2, preferably at least 3, preferably at least 4,preferably at least 5, preferably at least 6, preferably at least 7, preferably at least 8, preferably at least 9, preferably at least 10, preferably at least 11, preferably at least 12, more preferably 13, preferably at least 14, preferably at least 15, preferably at least 16, preferably at least 17, preferably at least 18, preferably at least 19, or preferably 20 biomarkers selected from the group consisting of MMP9, NRCAM, CD5L, F12, COL1A2, FAP, APOD, ARPC1B, LDLR, CCT4, MSN, NRP1, SPTA1, PTGDS, DPYSL2, CTSD, ANXA1, RAN, AHSG and PSMA6. In an embodiment where the biological sample is obtained from the infant at a time point from about 20-28 hours post-birth, the at least one biomarker is selected from the group consisting of CD5L, F12, FAP, CCT4 and MSN. In an embodiment, the sample biomarker profile evaluates at least 2, preferably at least 3, preferably at least 4, or preferably 5 biomarkers selected from the group consisting of CD5L, F12, FAP, CCT4 and MSN.

[0057] In an embodiment, the biological sample is obtained from the infant at a time point from about 50-120 hours post-birth. In another embodiment, the biological sample is obtained from the infant at a time point from about 68-76 hours post-birth. For example, the biological sample is obtained from the infant at about 50 hours, optionally at about 55 hours, optionally at about 60 hours, optionally at about 65 hours, optionally at about 68 hours, optionally at about 69 hours, optionally at about 70 hours, optionally at about 71 hours, optionally at about 72 hours, optionally at about 73 hours, optionally at about 74 hours, optionally at about 75 hours, optionally at about 76 hours post-birth, optionally at about 80 hours, optionally at about 85 hours, optionally at about 90 hours, optionally at about 95 hours, optionally at about 100 hours, optionally at about 105 hours, optionally at about 110 hours, optionally at about 115 hours, or optionally at about 120 hours. In an embodiment, the biological sample is obtained from the infant at about 72 hours post-birth. In an embodiment where the biological sample is obtained from the infant at a time point from about 68-76 hours post-birth, the at least one biomarker is selected from the group consisting of VCL, CFHR2, CFB, CD14, CFH, CFI, MYH9, F12, LRG1, APCS, F10, C8A, AMBP, PRG2, SAA4, LBP, APRT, RAN, ROBO4, SBSN, APOA4, KLKB1, F11, C4BPA, CD44, UGP2, PROS1 and ADAMTSL4. In an embodiment, the sample biomarker profile evaluates at least 2, preferably at least 3, preferably at least 4, preferably at least 5, preferably at least 6, preferably at least 7, preferably at least 8, preferably at least 9, preferably at least 10, preferably at least 11, preferably at least 12, more preferably 13, preferably at least 14, preferably at least 15, preferably at least 16, preferably at least 17, preferably at least 18,preferably at least 19, or preferably 20, preferably at least 21, preferably at least 22, more preferably 23, preferably at least 24, preferably at least 25, preferably at least 26, preferably at least 27, or preferably 28 biomarkers selected from the group consisting of VCL, CFHR2, CFB, CD14, CFH, CFI, MYH9, F12, LRG1, APCS, F10, C8A, AMBP, PRG2, SAA4, LBP, APRT, RAN, ROBO4, SBSN, APOA4, KLKB1, F11, C4BPA, CD44, UGP2, PROS1 and ADAMTSL4. In an embodiment where the biological sample is obtained from the infant at a time point from about 68-76 hours post-birth, the at least one biomarker is selected from the group consisting of VCL, CFB, CFI, MYH9 and LRG1. In an embodiment, the sample biomarker profile evaluates at least 2, preferably at least 3, preferably at least 4, or preferably 5 biomarkers selected from the group consisting of VCL, CFB, CFI, MYH9 and LRG1.

[0058] As noted elsewhere herein, the level of circulating IGLV3-27 protein in the blood of infants who developed BPD was unexpectedly found to be significantly lower when compared to the level of circulating IGLV3-27 protein in the blood of infants who did not go on to develop BPD. Thus, in an embodiment disclosed herein, the infant is determined to be at risk of BPD where the level of IGLV3-27 in the sample biomarker profile of the infant is lower as compared to the level of IGLV3-27 in the sample biomarker profile of an infant without BPD. In another embodiment, the infant is determined to be at risk of BPD where the level of IGLV3-27 in the sample biomarker profile of the infant is lower as compared to the level of IGLV3-27 in the sample biomarker profile of an infant without BPD where the sample biomarker profile is evaluated in a biological sample obtained from the infant at a time point from about 2 to 6 hours post-birth, preferably at about 4 hours post-birth.

[0059] As noted elsewhere herein, the level of circulating CD5L protein in the blood of infants who developed BPD was unexpectedly found to be significantly lower when compared to the level of circulating CD5L protein in the blood of infants who did not go on to develop BPD. Thus, in an embodiment disclosed herein, the infant is determined to be at risk of BPD where the level of CD5L in the sample biomarker profile of the infant is lower as compared to the level of CD5L in the sample biomarker profile of an infant without BPD. In another embodiment, the infant is determined to be at risk of BPD where the level of CD5L in the sample biomarker profile of the infant is lower as compared to the level of CD5L in the sample biomarker profile of an infant without BPD where the sample biomarker profile is evaluated in a biological sample obtained from the infant at a time point from about 20 to 28 hours post-birth, preferably at about 24 hours post-birth.

[0060] As noted elsewhere herein, the level of circulating VCL protein in the blood of infants who developed BPD was unexpectedly found to be significantly lower when compared to the level of circulating VCL protein in the blood of infants who did not go on to develop BPD. Thus, in an embodiment disclosed herein, the infant is determined to be at risk of BPD where the level of VCL in the sample biomarker profile of the infant is lower as compared to the level of VCL in the sample biomarker profile of an infant without BPD. In another embodiment, the infant is determined to be at risk of BPD where the level of VCL in the sample biomarker profile of the infant is lower as compared to the level of VCL in the sample biomarker profile of an infant without BPD where the sample biomarker profile is evaluated in a biological sample obtained from the infant at a time point from about 68 to 76 hours post-birth, preferably at about 72 hours post-birth.

[0061] As noted elsewhere herein, the level of circulating MYH9 protein in the blood of infants who developed BPD was unexpectedly found to be significantly lower when compared to the level of circulating MYH9 protein in the blood of infants who did not go on to develop BPD. Thus, in an embodiment disclosed herein, the infant is determined to be at risk of BPD where the level of MYH9 in the sample biomarker profile of the infant is lower as compared to the level of MYH9 in the sample biomarker profile of an infant without BPD. In another embodiment, the infant is determined to be at risk of BPD where the level of MYH9 in the sample biomarker profile of the infant is lower as compared to the level of MYH9 in the sample biomarker profile of an infant without BPD where the sample biomarker profile is evaluated in a biological sample obtained from the infant at a time point from about 68 to 76 hours post-birth, preferably at about 72 hours post-birth.

[0062] As noted elsewhere herein, the level of circulating EXTL2, TUBB, MMP9 and COL18A1 proteins in the blood of infants who developed BPD were each unexpectedly found to be significantly higher when compared to the level of the corresponding biomarker protein in the blood of infants who did not go on to develop BPD. Thus, in an embodiment disclosed herein, the infant is determined to be at risk of BPD where the level of at least one of EXTL2, TUBB, MMP9 and COL18A1 in the sample biomarker profile of the infant is higher as compared to the level of the corresponding biomarker in a sample biomarker profile of an infant without BPD. In another embodiment, the infant is determined to be at risk of BPD where the level of at least one of EXTL2, TUBB, MMP9 and COL18A1 in the sample biomarker profile of the infant is higher as compared to the level of the corresponding biomarker in the sample biomarker profile of an infant without BPD where the samplebiomarker profile is evaluated in a biological sample obtained from the infant at a time point from about 2 to 6 hours post-birth, preferably at about 4 hours post-birth.

[0063] As noted elsewhere herein, the level of circulating F12, FAP, CCT4 and MSN proteins in the blood of infants who developed BPD were each unexpectedly found to be significantly higher when compared to the level of the corresponding biomarker in the blood of infants who did not go on to develop BPD. Thus, in an embodiment disclosed herein, the infant is determined to be at risk of BPD where the level of at least one of F12, FAP, CCT4 and MSN in the sample biomarker profile of the infant is higher as compared to the level of the corresponding biomarker in a sample biomarker profile of an infant without BPD. In another embodiment, the infant is determined to be at risk of BPD where the level of at least one of F12, FAP, CCT4 and MSN in the sample biomarker profile of the infant is higher as compared to the level of the corresponding biomarker in the sample biomarker profile of an infant without BPD where the sample biomarker profile is evaluated in a biological sample obtained from the infant at a time point from about 20 to 28 hours post-birth, preferably at about 24 hours post-birth.

[0064] As noted elsewhere herein, the level of circulating CFB, CFI and LRG1 proteins in the blood of infants who developed BPD were each unexpectedly found to be significantly higher when compared to the level of the corresponding biomarker in the blood of infants who did not go on to develop BPD. Thus, in an embodiment disclosed herein, the infant is determined to be at risk of BPD where the level of at least one of CFB, CFI, and LRG1 in the sample biomarker profile of the infant is higher as compared to the level of the corresponding biomarker in a sample biomarker profile of an infant without BPD. In another embodiment, the infant is determined to be at risk of BPD where the level of at least one of CFB, CFI, and LRG1 in the sample biomarker profile of the infant is higher as compared to the level of the corresponding biomarker in the sample biomarker profile of an infant without BPD where the sample biomarker profile is evaluated in a biological sample obtained from the infant at a time point from about 68 to 76 hours post-birth, preferably at about 72 hours post-birth.

[0065] In an embodiment, the sample biomarker profile evaluates at least 2 biomarkers selected from the group consisting of TUBB1, MST1L, AHSG, KRT17, MMP9, KRT2 and COL18A1. In another embodiment, the sample biomarker profile evaluates at least 2 biomarkers selected from the group consisting of TUBB1, MST1L, AHSG, KRT17, MMP9, KRT2 and COL18A1 in a biological sample obtained from the infant at a time point from about 2 to 6 hours post-birth, preferably at about 4 hours post-birth.

[0066] In an embodiment, the sample biomarker profile evaluates at least 2 biomarkers selected from the group consisting of MMP9, NRCAM, COL1A2, APOD, LDLR, NRP1, SPTA1, PTGDS, DPYSL2, CTSD, ANXA1, RAN, CCT4, MSN, FAP, AHSG, PSMA6 and ARPC1B. In another embodiment, the sample biomarker profile evaluates at least 2 biomarkers selected from the group consisting of MMP9, NRCAM, COL1A2, APOD, LDLR, NRP1, SPTA1, PTGDS, DPYSL2, CTSD, ANXA1, RAN, CCT4, MSN, FAP, AHSG, PSMA6 and ARPC1B in a biological sample obtained from the infant at a time point from about 20 to 28 hours post-birth, preferably at about 24 hours post-birth.

[0067] In an embodiment, the sample biomarker profile evaluates at least 2 biomarkers selected from the group consisting of CD14, CFH, F12, APCS, F10, C8A, AMBP, PRG2, SAA4, RAN, ROBO4, APOA4, KLKB1, F11, C4BPA, UGP2, PROS1, CD44, MYH9, LBP, APRT, ADAMTSL4 and SBSN. In another embodiment, the sample biomarker profile evaluates at least 2 biomarkers selected from the group consisting of CD14, CFH, F12, APCS, F10, C8A, AMBP, PRG2, SAA4, RAN, ROBO4, APOA4, KLKB1, F11, C4BPA, UGP2, PROS1, CD44, MYH9, LBP, APRT, ADAMTSL4 and SBSN in a biological sample obtained from the infant at a time point from about 68 to 76 hours post-birth, preferably at about 72 hours post-birth.

[0068] In an embodiment, the sample biomarker profile evaluates at least 2 biomarkers selected from the group consisting of TUBB1, MST1L, AHSG, KRT17, MMP9, KRT2, COL18A1, NRCAM, COL1A2, APOD, LDLR, NRP1, SPTA1, PTGDS, DPYSL2, CTSD, ANXA1, RAN, CCT4, MSN, FAP, PSMA6, ARPC1B, CD14, CFH, F12, APCS, F10, C8A, AMBP, PRG2, SAA4, ROBO4, APOA4, KLKB1, F11, C4BPA, UGP2, PROS1, CD44, MYH9, LBP, APRT, ADAMTSL4 and SBSN. In another embodiment, the sample biomarker profile evaluates at least 2 biomarkers selected from the group consisting of TUBB1, MST1L, AHSG, KRT17, MMP9, KRT2, COL18A1, NRCAM, COL1A2, APOD, LDLR, NRP1, SPTA1, PTGDS, DPYSL2, CTSD, ANXA1, RAN, CCT4, MSN, FAP, PSMA6, ARPC1B, CD14, CFH, F12, APCS, F10, C8A, AMBP, PRG2, SAA4, ROBO4, APOA4, KLKB1, F11, C4BPA, UGP2, PROS1, CD44, MYH9, LBP, APRT, ADAMTSL4 and SBSN in a biological sample obtained from the infant at a time point from about 2 to 76 hours post-birth.

[0069] It is to be understood that each of the at least 2 biomarkers selected from the group consisting of IGLV3-27, EXTL2, TUBB, TUBB1, MMP9, MST1L, KRT2, AHSG, KRT17, COL18A1, AGT, ISLR, EFEMP1, NRCAM, CD5L, COL1A2, FAP, APOD, ARPC1B,LDLR, CCT4, MSN, NRP1, SPTA1, PTGDS, DPYSL2, CTSD, ANXA1, PSMA6, VCL, CFHR2, CFB, CD14, CFH, CFI, MYH9, F12, LRG1, APCS, F10, C8A, AMBP, PRG2, SAA4, LBP, APRT, RAN, ROBO4, SBSN, APOA4, KLKB1, F11, C4BPA, CD44, UGP2, PROS1 and ADAMTSL4 may be evaluated in a biological sample obtained from the infant at two or more different time points, including at two or more time points from about 2 to 76 hours post-birth.

[0070] In one embodiment, the sample biomarker profile evaluates a first biomarker in a biological sample obtained from the infant at a time point from about 2 to 6 hours post birth, preferably at about 4 hours post-birth; and a second biomarker in a biological sample obtained from the infant at a time point from about 20 to 28 hours post birth, preferably at about 24 hours post-birth. In another embodiment, the sample biomarker profile evaluates a first biomarker in a biological sample obtained from the infant at a time point from about 2 to 6 hours post birth, preferably at about 4 hours post-birth; and a second biomarker in a biological sample obtained from the infant at a time point from about 68 to 76 hours post birth, preferably at about 72 hours post-birth. In another embodiment, the sample biomarker profile evaluates a first biomarker in a biological sample obtained from the infant at a time point from about 20 to 26 hours post birth, preferably at about 24 hours post-birth; and a second biomarker in a biological sample obtained from the infant at a time point from about 68 to 76 hours post birth, preferably at about 72 hours post-birth.

[0071] In another embodiment, the sample biomarker profile evaluates a first biomarker selected from the group consisting of TUBB1, MST1L, AHSG, KRT17, MMP9, KRT2, COL18A1, NRCAM, COLA2, APOD, LDLR, NRP1, SPTA1, PTGDS, DPYSL2, CTSD, ANXA1, RAN, CCT4, MSN, FAP, PSMA6, ARPC1B, CD14, CFH, F12, APCS, F10, C8A, AMBP, PRG2, SAA4, ROBO4, APOA4, KLKB1, F11, C4BPA, UGP2, PROS1, CD44, MYH9, LBP, APRT, ADAMTSL4 and SBSN in a biological sample obtained from the infant at a time point from about 2 to 6 hours post birth, preferably at about 4 hours post-birth; and a second biomarker selected from the group consisting of TUBB1, MST1L, AHSG, KRT17, MMP9, KRT2, COL18A1, NRCAM, COLA2, APOD, LDLR, NRP1, SPTA1, PTGDS, DPYSL2, CTSD, ANXA1, RAN, CCT4, MSN, FAP, PSMA6, ARPC1B, CD14, CFH, F12, APCS, F10, C8A, AMBP, PRG2, SAA4, ROBO4, APOA4, KLKB1, F11, C4BPA, UGP2, PROS1, CD44, MYH9, LBP, APRT, ADAMTSL4 and SBSN in a biological sample obtained from the infant at a time point from about 20 to 28 hours post birth, preferably at about 24 hours post-birth, wherein the first biomarker is different to the second biomarker.

[0072] In another embodiment, the sample biomarker profile evaluates a first biomarker selected from the group consisting of IGLV3-27, EXTL2, TUBB, TUBB1, MMP9, MST1L, KRT2, AHSG, KRT17, COL18A1, AGT, ISLR and EFEMP1 in a biological sample obtained from the infant at a time point from about 2 to 6 hours post birth, preferably at about 4 hours post-birth; and a second biomarker selected from the group consisting of MMP9 NRCAM, CD5L, F12, COL1A2, FAP, APOD, ARPC1B, LDLR, CCT4, MSN, NRP1, SPTA1, PTGDS, DPYSL2, CTSD, ANXA1, RAN, AHSG and PSMA6 in a biological sample obtained from the infant at a time point from about 20 to 28 hours post birth, preferably at about 24 hours post-birth, wherein the first biomarker is different to the second biomarker.

[0073] In another embodiment, the sample biomarker profile evaluates a first biomarker selected from the group consisting of IGLV3-27, EXTL2, TUBB, MMP9 and COL18A1 in a biological sample obtained from the infant at a time point from about 2 to 6 hours post birth, preferably at about 4 hours post-birth; and a second biomarker selected from the group consisting of CD5l, F12, FAP, CCT4 and MSN in a biological sample obtained from the infant at a time point from about 20 to 28 hours post birth, preferably at about 24 hours post- birth, wherein the first biomarker is different to the second biomarker.

[0074] In another embodiment, the sample biomarker profile evaluates a first biomarker selected from the group consisting of TUBB1, MST1L, AHSG, KRT17, MMP9, KRT2, COL18A1, NRCAM, COLA2, APOD, LDLR, NRP1, SPTA1, PTGDS, DPYSL2, CTSD, ANXA1, RAN, CCT4, MSN, FAP, PSMA6, ARPC1B, CD14, CFH, F12, APCS, F10, C8A, AMBP, PRG2, SAA4, ROBO4, APOA4, KLKB1, F11, C4BPA, UGP2, PROS1, CD44, MYH9, LBP, APRT, ADAMTSL4 and SBSN in a biological sample obtained from the infant at a time point from about 2 to 6 hours post birth, preferably at about 4 hours post-birth; and a second biomarker selected from the group consisting of TUBB1, MST1L, AHSG, KRT17, MMP9, KRT2, COL18A1, NRCAM, COLA2, APOD, LDLR, NRP1, SPTA1, PTGDS, DPYSL2, CTSD, ANXA1, RAN, CCT4, MSN, FAP, PSMA6, ARPC1B, CD14, CFH, F12, APCS, F10, C8A, AMBP, PRG2, SAA4, ROBO4, APOA4, KLKB1, F11, C4BPA, UGP2, PROS1, CD44, MYH9, LBP, APRT, ADAMTSL4 and SBSN in a biological sample obtained from the infant at a time point from about 68 to 76 hours post birth, preferably at about 72 hours post-birth, wherein the first biomarker is different to the second biomarker.

[0075] In another embodiment, the sample biomarker profile evaluates a first biomarker selected from the group consisting of IGLV3-27, EXTL2, TUBB, TUBB1, MMP9, MST1L, KRT2, AHSG, KRT17, COL18A1, AGT, ISLR and EFEMP1 in a biological sample obtainedfrom the infant at a time point from about 2 to 6 hours post birth, preferably at about 4 hours post-birth; and a second biomarker selected from the group consisting of VCL, CFHR2, CFB, CD14, CFH, CFI, MYH9, F12, LRG1, APCS, F10, C8A, AMBP, PRG2, SAA4, LBP, APRT, RAN, ROBO4, SBSN, APOA4, KLKB1, F11, C4BPA, CD44, UGP2, PROS1 and ADAMTSL4 in a biological sample obtained from the infant at a time point from about 68 to 76 hours post birth, preferably at about 72 hours post-birth, wherein the first biomarker is different to the second biomarker.

[0076] In another embodiment, the sample biomarker profile evaluates a first biomarker selected from the group consisting of IGLV3-27, EXTL2, TUBB, MMP9 and COL18A1 in a biological sample obtained from the infant at a time point from about 2 to 6 hours post birth, preferably at about 4 hours post-birth; and a second biomarker selected from the group consisting of VCL, CFB, CFI, MYH9 and LRG1 in a biological sample obtained from the infant at a time point from about 68 to 76 hours post birth, preferably at about 72 hours post- birth, wherein the first biomarker is different to the second biomarker.

[0077] In another embodiment, the sample biomarker profile evaluates a first biomarker selected from the group consisting of TUBB1, MST1L, AHSG, KRT17, MMP9, KRT2, COL18A1, NRCAM, COLA2, APOD, LDLR, NRP1, SPTA1, PTGDS, DPYSL2, CTSD, ANXA1, RAN, CCT4, MSN, FAP, PSMA6, ARPC1B, CD14, CFH, F12, APCS, F10, C8A, AMBP, PRG2, SAA4, ROBO4, APOA4, KLKB1, F11, C4BPA, UGP2, PROS1, CD44, MYH9, LBP, APRT, ADAMTSL4 and SBSN in a biological sample obtained from the infant at a time point from about 20 to 26 hours post birth, preferably at about 24 hours post-birth; and a second biomarker selected from the group consisting of TUBB1, MST1L, AHSG, KRT17, MMP9, KRT2, COL18A1, NRCAM, COLA2, APOD, LDLR, NRP1, SPTA1, PTGDS, DPYSL2, CTSD, ANXA1, RAN, CCT4, MSN, FAP, PSMA6, ARPC1B, CD14, CFH, F12, APCS, F10, C8A, AMBP, PRG2, SAA4, ROBO4, APOA4, KLKB1, F11, C4BPA, UGP2, PROS1, CD44, MYH9, LBP, APRT, ADAMTSL4 and SBSN in a biological sample obtained from the infant at a time point from about 68 to 76 hours post birth, preferably at about 72 hours post-birth, wherein the first biomarker is different to the second biomarker.

[0078] In another embodiment, the sample biomarker profile evaluates a first biomarker selected from the group consisting of MMP9 NRCAM, CD5L, F12, COL1A2, FAP, APOD, ARPC1B, LDLR, CCT4, MSN, NRP1, SPTA1, PTGDS, DPYSL2, CTSD, ANXA1, RAN, AHSG and PSMA6 in a biological sample obtained from the infant at a time point from about20 to 26 hours post birth, preferably at about 24 hours post-birth; and a second biomarker selected from the group consisting of VCL, CFHR2, CFB, CD14, CFH, CFI, MYH9, F12, LRG1, APCS, F10, C8A, AMBP, PRG2, SAA4, LBP, APRT, RAN, ROBO4, SBSN, APOA4, KLKB1, F11, C4BPA, CD44, UGP2, PROS1 and ADAMTSL4 in a biological sample obtained from the infant at a time point from about 68 to 76 hours post birth, preferably at about 72 hours post-birth, wherein the first biomarker is different to the second biomarker.

[0079] In another embodiment, the sample biomarker profile evaluates a first biomarker selected from the group consisting of CD5l, F12, FAP, CCT4 and MSN in a biological sample obtained from the infant at a time point from about 20 to 26 hours post birth, preferably at about 24 hours post-birth; and a second biomarker selected from the group consisting of VCL, CFB, CFI, MYH9 and LRG1 in a biological sample obtained from the infant at a time point from about 68 to 76 hours post birth, preferably at about 72 hours post- birth, wherein the first biomarker is different to the second biomarker.

[0080] The present disclosure also extends to sample biomarker profiles that evaluate at least 2 biomarkers, including at least 2 biomarkers selected from the group consisting of IGLV3-27, EXTL2, TUBB, TUBB1, MMP9, MST1L, KRT2, AHSG, KRT17, COL18A1, AGT, ISLR, EFEMP1, NRCAM, CD5L, COL1A2, FAP, APOD, ARPC1B, LDLR, CCT4, MSN, NRP1, SPTA1, PTGDS, DPYSL2, CTSD, ANXA1, RAN, PSMA6, VCL, CFHR2, CFB, CD14, CFH, CFI, MYH9, F12, LRG1, APCS, F10, C8A, AMBP, PRG2, SAA4, LBP, APRT, ROBO4, SBSN, APOA4, KLKB1, F11, C4BPA, CD44, UGP2, PROS1 and ADAMTSL4, in a biological sample obtained from the infant at a single time point, including a single time point from about 2 to 76 hours post-birth.

[0081] The present disclosure also extends to sample biomarker profiles that evaluate at least 2 biomarkers, including at least 2 biomarkers selected from the group consisting of TUBB1, MST1L, AHSG, KRT17, MMP9, KRT2, COL18A1, NRCAM, COLA2, APOD, LDLR, NRP1, SPTA1, PTGDS, DPYSL2, CTSD, ANXA1, RAN, CCT4, MSN, FAP, PSMA6, ARPC1B, CD14, CFH, F12, APCS, F10, C8A, AMBP, PRG2, SAA4, ROBO4, APOA4, KLKB1, F11, C4BPA, UGP2, PROS1, CD44, MYH9, LBP, APRT, ADAMTSL4 and SBSN, in a biological sample obtained from the infant at a single time point, including a single time point from about 2 to 76 hours post-birth.

[0082] The present disclosure also extends to sample biomarker profiles that evaluate at least 2 biomarkers, including at least 2 biomarkers selected from the group consisting of IGLV3-27, EXTL2, TUBB, MMP9, COL18A1, CD5l, F12, FAP, CCT4, MSN, VCL, CFB, CFI, MYH9 and LRG1, in a biological sample obtained from the infant at a single time point, including a single time point from about 2 to 76 hours post-birth. The sample biomarker profile may evaluate the at least one biomarker in a single biological sample obtained from the infant, or in two or more biological samples obtained from the infant. Where the sample biomarker profile evaluates the at least one biomarker in two or more biological samples obtained from the infant, the two or more biological samples may be obtained from the infant at a single time point, or from two or more time points, from about 2 to 76 hours post-birth. In an embodiment, the sample biomarker profile evaluates the at least one biomarker in a biological sample obtained from the infant at a time point from about 2 to 6 hours post birth, preferably at about 4 hours post-birth. In another embodiment, the sample biomarker profile evaluates the at least one biomarker in a biological sample obtained from the infant at a time point from about 68 to 76 hours post birth, preferably at about 72 hours post-birth. In another embodiment, the sample biomarker profile evaluates the at least one biomarker in a biological sample obtained from the infant at a time point from about 20 to 26 hours post birth, preferably at about 24 hours post-birth.

[0083] In an embodiment, the sample biomarker profile evaluates the at least 2 biomarkers in a biological sample obtained from the infant at a time point from about 2 to 6 hours post birth, preferably at about 4 hours post-birth. In another embodiment, the sample biomarker profile evaluates the at least 2 biomarkers in a biological sample obtained from the infant at a time point from about 68 to 76 hours post birth, preferably at about 72 hours post- birth. In another embodiment, the sample biomarker profile evaluates the at least 2 biomarkers in a biological sample obtained from the infant at a time point from about 20 to 26 hours post birth, preferably at about 24 hours post-birth.

[0084] In an embodiment, the sample biomarker profile evaluates at least 2 biomarkers selected from the group consisting of TUBB1, MST1L, AHSG, KRT17, MMP9, KRT2, COL18A1, NRCAM, COLA2, APOD, LDLR, NRP1, SPTA1, PTGDS, DPYSL2, CTSD, ANXA1, RAN, CCT4, MSN, FAP, PSMA6, ARPC1B, CD14, CFH, F12, APCS, F10, C8A, AMBP, PRG2, SAA4, ROBO4, APOA4, KLKB1, F11, C4BPA, UGP2, PROS1, CD44, MYH9, LBP, APRT, ADAMTSL4 and SBSN, in a biological sample obtained from theinfant at a time point from about 2 to 6 hours post birth, preferably at about 4 hours post- birth.

[0085] In an embodiment, the at least 2 biomarkers are selected from the group consisting of TUBB1, MST1L, AHSG, KRT17, MMP9, KRT2 and COL18A1, in a biological sample obtained from the infant at a time point from about 2 to 6 hours post birth, preferably at about 4 hours post-birth. In an embodiment, the sample biomarker profile evaluates 2 biomarkers selected from the group consisting of TUBB1 and MMP9, TUBB1 and MST1L, TUBB1 and AHSG, TUBB1 and KRT17, TUBB1 and KRT2, TUBB1 and COL18A1, MST1L and COL18A1, AHSG and KRT17, AHSG and COL18A1, and KRT17 and COL18A1, in a biological sample obtained from the infant at a time point from about 2 to 6 hours post birth, preferably at about 4 hours post-birth.

[0086] In an embodiment, the sample biomarker profile evaluates at least 2 biomarkers selected from the group consisting of TUBB1, MST1L, AHSG, KRT17, MMP9, KRT2, COL18A1, NRCAM, COLA2, APOD, LDLR, NRP1, SPTA1, PTGDS, DPYSL2, CTSD, ANXA1, RAN, CCT4, MSN, FAP, PSMA6, ARPC1B, CD14, CFH, F12, APCS, F10, C8A, AMBP, PRG2, SAA4, ROBO4, APOA4, KLKB1, F11, C4BPA, UGP2, PROS1, CD44, MYH9, LBP, APRT, ADAMTSL4 and SBSN, in a biological sample obtained from the infant at a time point from about 20 to 28 hours post birth, preferably at about 24 hours post- birth.

[0087] In an embodiment, the at least 2 biomarkers are selected from the group consisting of MMP9, NRCAM, COL1A2, APOD, LDLR, NRP1, SPTA1, PTGDS, DPYSL2, CTSD, ANXA1, RAN, CCT4, MSN, FAP, AHSG, PSMA6, and ARPC1B, in a biological sample obtained from the infant at a time point from about 20 to 28 hours post birth, preferably at about 24 hours post-birth. In an embodiment, the sample biomarker profile evaluates 2 biomarkers selected from the group consisting of MMP9 and NRCAM, MMP9 and CCT4, MMP9 and MSN, NRCAM and FAP, NRCAM and MSN, NRCAM and AHSG, NRCAM and PSMA6, COL1A2 and FAP, COL1A2 and ARPC1B, COL1A2 and CCT4, COL1A2 and MSN, COL1A2 and PTGDS, COL1A2 and DPYSL2, APOD and ARPC1B, APOD and LDLR, APOD and CCT4, APOD and MSN, APOD and PTGDS, APOD and CTSD, APOD and AHSG, APOD and PSMA6, LDLR and CCT4, LDLR and PTGDS, LDLR and DYPSL2, NRP1 and SPTA1, NRP1 and PTGDS, NRP1 and DPYSL2, NRP1 and CTSD, NRP1 and PSMA6, SPTA1 and AHSG, PTGDS and DPYSL2, PTGDS and ANXA1, PTGDS and AHSG, DPYSL2 and RAN, CTSD and ANXA1, ANXA1 and PSMA6, and RAN andPSMA6, in a biological sample obtained from the infant at a time point from about 20 to 28 hours post birth, preferably at about 24 hours post-birth.

[0088] In an embodiment, the sample biomarker profile evaluates at least 2 biomarkers selected from the group consisting of TUBB1, MST1L, AHSG, KRT17, MMP9, KRT2, COL18A1, NRCAM, COLA2, APOD, LDLR, NRP1, SPTA1, PTGDS, DPYSL2, CTSD, ANXA1, RAN, CCT4, MSN, FAP, PSMA6, ARPC1B, CD14, CFH, F12, APCS, F10, C8A, AMBP, PRG2, SAA4, ROBO4, APOA4, KLKB1, F11, C4BPA, UGP2, PROS1, CD44, MYH9, LBP, APRT, ADAMTSL4 and SBSN, in a biological sample obtained from the infant at a time point from about 68 to 76 hours post birth, preferably at about 72 hours post- birth.

[0089] In an embodiment, the at least 2 biomarkers are selected from the group consisting of CD14, CFH, F12, APCS, F10, C8A, AMBP, PRG2, SAA4, RAN, ROBO4, APOA4, KLKB1, F11, C4BPA, UGP2, PROS1, CD44, MYH9, LBP, APRT, ADAMTSL4 and SBSN, in a biological sample obtained from the infant at a time point from about 68 to 76 hours post birth, preferably at about 72 hours post-birth.. In an embodiment, the sample biomarker profile evaluates 2 biomarkers selected from the group consisting of CD14 and PRG2, CD14 and CD44, CFH and MYH9, CFH and APCS, CFH and PRG2, CFH and LBP, CFH and APRT, CFH and ROBO4, CFH and ADAMTSL4, F12 and APCS, F12 and PRG2, F12 and LBP, F12 and APRT, APCS and SAA4, F10 and PRG2, C8A and LBP, C8A and APRT, C8A and ROBO4, C8A and ADAMTSL4, AMBP and SAA4, AMBP and PRG2, AMBP and LBP, AMBP and APRT, AMBP and ROBO4, AMBP and ADAMTSL4, PRG2 and LBP, PRG2 and APRT, PRG2 and APOA4, PRG2 and KLKB1, PRG2 and F11, PRG2 and UGP2, SAA4 and LBP, SAA4 and APRT, SAA4 and ROBO4, RAN and SBSN, RAN and KLKB1, RAN and F11, RAN and C4BPA, RAN and CD44, RAN and PROS1, RAN and ADAMTSL4, ROBO4 and UGP2, APOA4 and ADAMTSL4, KLKB1 and ADAMTSL4, F11 and ADAMTSL4, C4BPA and ADAMTSL4, UGP2 and PROS1, and PROS1 and ADAMTSL4, in a biological sample obtained from the infant at a time point from about 68 to 76 hours post birth, preferably at about 72 hours post-birth.

[0090] In an embodiment, the sample biomarker profile evaluates at least 2 biomarkers selected from the group consisting of TUBB1, MST1L, AHSG, KRT17, MMP9, KRT2, COL18A1, NRCAM, COLA2, APOD, LDLR, NRP1, SPTA1, PTGDS, DPYSL2, CTSD, ANXA1, RAN, CCT4, MSN, FAP, PSMA6, ARPC1B, CD14, CFH, F12, APCS, F10, C8A, AMBP, PRG2, SAA4, ROBO4, APOA4, KLKB1, F11, C4BPA, UGP2, PROS1, CD44,MYH9, LBP, APRT, ADAMTSL4 and SBSN, in a biological sample obtained from the infant at a time point from about 2 to 6 hours post birth, preferably at about 4 hours post- birth. In an embodiment, the sample biomarker profile evaluates at least 2 biomarkers selected from the group consisting of TUBB1, MST1L, AHSG, KRT17, MMP9, KRT2, COL18A1, NRCAM, COLA2, APOD, LDLR, NRP1, SPTA1, PTGDS, DPYSL2, CTSD, ANXA1, RAN, CCT4, MSN, FAP, PSMA6, ARPC1B, CD14, CFH, F12, APCS, F10, C8A, AMBP, PRG2, SAA4, ROBO4, APOA4, KLKB1, F11, C4BPA, UGP2, PROS1, CD44, MYH9, LBP, APRT, ADAMTSL4 and SBSN, in a biological sample obtained from the infant at a time point from about 20 to 28 hours post birth, preferably at about 24 hours post- birth. In an embodiment, the sample biomarker profile evaluates at least 2 biomarkers selected from the group consisting of TUBB1, MST1L, AHSG, KRT17, MMP9, KRT2, COL18A1, NRCAM, COLA2, APOD, LDLR, NRP1, SPTA1, PTGDS, DPYSL2, CTSD, ANXA1, RAN, CCT4, MSN, FAP, PSMA6, ARPC1B, CD14, CFH, F12, APCS, F10, C8A, AMBP, PRG2, SAA4, ROBO4, APOA4, KLKB1, F11, C4BPA, UGP2, PROS1, CD44, MYH9, LBP, APRT, ADAMTSL4 and SBSN, in a biological sample obtained from the infant at a time point from about 68 to 76 hours post birth, preferably at about 72 hours post- birth.

[0091] In an embodiment, the sample biomarker profile evaluates 2 biomarkers in a biological sample obtained from the infant at a time point from about 2 to 6 hours post birth, preferably at about 4 hours post-birth, wherein the 2 biomarkers are selected from the "4 hour" biomarker pairs shown in Table 4, as disclosed herein. In an embodiment, the sample biomarker profile evaluates 2 biomarkers in a biological sample obtained from the infant at a time point from about 20 to 26 hours post birth, preferably at about 24 hours post-birth, wherein the 2 biomarkers are selected from the "24 hour" biomarker pairs shown in Table 4, as disclosed herein. In an embodiment, the sample biomarker profile evaluates 2 biomarkers in a biological sample obtained from the infant at a time point from about 68 to 76 hours post birth, preferably at about 72 hours post-birth, wherein the 2 biomarkers are selected from the "72 hour" biomarker pairs shown in Table 4, as disclosed herein.

[0092] In another embodiment, the sample biomarker profile evaluates a first biomarker selected from the group consisting of IGLV3-27, EXTL2, TUBB, TUBB1, MMP9, MST1L, KRT2, AHSG, KRT17, COL18A1, AGT, ISLR and EFEMP1; a second biomarker selected from the group consisting of MMP9 NRCAM, CD5L, F12, COL1A2, FAP, APOD, ARPC1B, LDLR, CCT4, MSN, NRP1, SPTA1, PTGDS, DPYSL2, CTSD, ANXA1, RAN,AHSG and PSMA6; and a third biomarker selected from the group consisting of VCL, CFHR2, CFB, CD14, CFH, CFI, MYH9, F12, LRG1, APCS, F10, C8A, AMBP, PRG2, SAA4, LBP, APRT, RAN, ROBO4, SBSN, APOA4, KLKB1, F11, C4BPA, CD44, UGP2, PROS1 and ADAMTSL4, wherein the at least 3 biomarkers are evaluated in a biological sample obtained from the infant at a time point from birth to about 76 hours post birth.

[0093] In another embodiment, the sample biomarker profile evaluates a first biomarker selected from the group consisting of IGLV3-27, EXTL2, TUBB, MMP9 and COL18A1; a second biomarker selected from the group consisting of CD5l, F12, FAP, CCT4 and MSN; and a third biomarker selected from the group consisting of VCL, CFB, CFI, MYH9 and LRG1 wherein the at least 3 biomarkers are evaluated in a biological sample obtained from the infant at a time point from birth to about 76 hours post birth.

[0094] In an embodiment, the sample biomarker profile evaluates at least biomarker selected from the group consisting of AHSG, CFB and CFI. In another embodiment, the sample biomarker profile evaluates at least biomarker selected from the group consisting of AHSG, AGT, CCT4, ANXA1, CFB, MYH9, CFI, CD14 and C4BPA. Birth risk factors

[0095] As noted elsewhere herein, the present inventors have surprisingly identified circulating biomarkers that, when combined with one or more birth risk factors (also referred to herein as birth parameters or birth measures), may predict BPD as early as 4 hours post- birth. Thus, in an embodiment, the method further comprises taking into account one or more birth risk factors of the infant. Suitable birth risk factors will be familiar to persons skilled in the art, illustrative examples of which include birth weight, gestational age and / or sex. Thus, in an embodiment, the birth risk factor is selected from birth weight (including raw birth weight (e.g., grams or kilograms) or birth weight z-score), gestational age and sex. In an embodiment, the birth risk factor is selected from the group consisting of birth weight, gestational age, sex and a combination of any of the foregoing. In an embodiment, the birth risk factor is birth weight, gestational age and sex. Biological samples

[0096] In an embodiment, evaluation of the at least one biomarker comprises determining the level of the at least one biomarker. In an embodiment, evaluation of the at least one biomarker comprises determining the abundance of the at least one biomarker. In anembodiment, evaluation of the at least one biomarker comprises determining the concentration of the at least one biomarker.

[0097] Evaluation of the at least one biomarker may comprise determining the level of the at least one biomarker in a whole blood sample from the subject. However, it would be understood by persons skilled in the art that the at least one biomarker may be suitably evaluated or determined (e.g., measured) in a component of whole blood, such as a serum or plasma sample from the subject. Thus, in an embodiment, evaluation of the at least one biomarker comprises determining the level of the at least one biomarker in whole blood, serum or plasma. In an embodiment, evaluation of the at least one biomarker comprises determining the level of the at least one biomarker in plasma or whole blood. In an embodiment, evaluation of the at least one biomarker comprises determining the level of the at least one biomarker in plasma. In an embodiment, evaluation of the at least one biomarker comprises determining the level of the at least one biomarker in whole blood. Where the evaluation of the at least one biomarker comprises determining the level of the at least one biomarker in whole blood, the method will suitably comprise determining the level of the at least one biomarker using an assay, device or test that has the necessary sensitivity to measure the level of the at least one biomarker in whole blood, including in a sample of whole blood. In some embodiments, the at least one biomarker would suitably be selected from candidate biomarkers whose level or abundance in whole blood is highest when compared to the other candidate biomarkers described herein. However, the methods described herein may suitably evaluate the at least one biomarker whose level or abundance in whole blood is comparatively low, as long as the method employed has the requisite sensitivity to measure the at least one biomarker. In an embodiment, where evaluation of the at least one biomarker comprises determining the level of the at least one biomarker in whole blood, the at least one biomarker is selected from the group consisting of AHSG, CFB and CFI. In another embodiment, where evaluation of the at least one biomarker comprises determining the level of the at least one biomarker in whole blood, the at least one biomarker is selected from the group consisting of AHSG, AGT, CCT4, ANXA1, CFB, MYH9, CFI, CD14 and C4BPA.

[0098] The term "reference biomarker" is used herein to denote a biomarker that has been identified as being associated with, or associated with a risk of, BPD. For example, a reference biomarker can have a different abundance for a sample population of reference individuals at risk of BPD as compared to individuals who are not at risk of, do not go on todevelop, BPD. A reference biomarker profile provides a compositional analysis (e.g., concentration, number ratio or mole percentage (%) of the biomarker) in which one or more, two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, twelve or more, fifteen or more, twenty or more, fifty or more, one-hundred or more or a greater number of biomarkers are evaluated.

[0099] In some embodiments, it may be desirable to measure the at least one biomarker at the protein level. As an illustrative example, the at least one biomarker is EXTL2 protein, as measured, for example, in a blood, plasma or serum sample of a subject. However, it will be understood that, in some instances, the biomarker can be a gene expression product, such as a transcript (e.g., mRNA). Methods of measuring expression products such as proteins and transcripts are known to persons skilled in the art, with some illustrative examples described below.

[0100] In an embodiment, a biomarker can be a gene expression product, including a polynucleotide or polypeptide. The term "gene" as used herein refers to any and all discrete coding regions of the cell’s genome, as well as associated non-coding and regulatory regions. The term "gene" is also intended to mean the open reading frame encoding specific polypeptides, introns, and adjacent 5' and 3' non-coding nucleotide sequences involved in the regulation of expression. In this regard, the gene may further comprise control signals such as promoters, enhancers, termination and / or polyadenylation signals that are naturally associated with a given gene, or heterologous control signals. The DNA sequences may be cDNA or genomic DNA or a fragment thereof. The gene may be introduced into an appropriate vector for extrachromosomal maintenance or for integration into the host.

[0101] The term "nucleic acid" or "polynucleotide" as used herein designates mRNA, RNA, cRNA, cDNA or DNA. The term typically refers to a polymeric form of nucleotides of at least 10 bases in length, either ribonucleotides or deoxynucleotides or a modified form of either type of nucleotide. The term includes single and double stranded forms of DNA or RNA. "Protein," "polypeptide" and "peptide" are also used interchangeably herein to refer to a polymer of amino acid residues and to variants and synthetic analogues of the same.

[0102] In some embodiments, evaluation of the biomarker comprises determining the level of the at least one biomarker. As used herein the terms "level" and "amount" are used interchangeably herein to refer to a quantitative amount (e.g., weight or moles or number), a semi-quantitative amount, a relative amount (e.g., weight % or mole % within class or aratio), a concentration, and the like. Thus, these terms encompasses absolute or relative amounts or concentrations of biomarkers in a sample, including ratios of levels of biomarkers, and odds ratios of levels or ratios of odds ratios. Biomarker levels in cohorts of subjects may be represented as mean levels and standard deviations as shown in some of the Tables and Figures herein.

[0103] Biomarkers may be quantified or detected using any suitable technique, including, but not limited to, nucleic acid- and protein-based assays. In illustrative nucleic acid-based assays, nucleic acid is isolated from cells contained in a biological sample according to standard methodologies (Sambrook, et al., 1989, supra; and Ausubel et al., 1994, supra). The nucleic acid is typically fractionated (e.g., poly A+RNA) or whole cell RNA. Where RNA is used as the subject of detection, it may be desired to convert the RNA to a complementary DNA. In some embodiments, the nucleic acid is amplified by a template- dependent nucleic acid amplification technique. A number of template dependent processes are available to amplify the biomarker sequences present in a given template sample. An exemplary nucleic acid amplification technique is the polymerase chain reaction (referred to as PCR), which is described in detail in U.S. Pat. Nos.4,683,195, 4,683,202 and 4,800,159, Ausubel et al. (supra), and in Innis et al., ("PCR Protocols", Academic Press, Inc., San Diego Calif., 1990). Briefly, in PCR, two primer sequences are prepared that are complementary to regions on opposite complementary strands of the biomarker sequence. An excess of deoxynucleotide triphosphates are added to a reaction mixture along with a DNA polymerase, e.g., Taq polymerase. If a cognate biomarker sequence is present in a sample, the primers will bind to the biomarker and the polymerase will cause the primers to be extended along the biomarker sequence by adding on nucleotides. By raising and lowering the temperature of the reaction mixture, the extended primers will dissociate from the biomarker to form reaction products, excess primers will bind to the biomarker and to the reaction products and the process is repeated. A reverse transcriptase PCR amplification procedure may be performed in order to quantify the amount of mRNA amplified. Methods of reverse transcribing RNA into cDNA are well known and described in Sambrook et al., 1989, supra. Alternative methods for reverse transcription utilize thermostable, RNA-dependent DNA polymerases. These methods are described in WO 90 / 07641. Polymerase chain reaction methodologies are well known in the art.

[0104] In certain embodiments, the template-dependent amplification involves quantification of transcripts in real-time. For example, RNA or DNA may be quantified usingthe Real-Time PCR technique (Higuchi, 1992, et al., Biotechnology 10: 413-417). By determining the concentration of the amplified products of the target DNA in PCR reactions that have completed the same number of cycles and are in their linear ranges, it is possible to determine the relative concentrations of the specific target sequence in the original DNA mixture. If the DNA mixtures are cDNAs synthesized from RNAs isolated from different tissues or cells, the relative abundance of the specific mRNA from which the target sequence was derived can be determined for the respective tissues or cells. This direct proportionality between the concentration of the PCR products and the relative mRNA abundance is only true in the linear range of the PCR reaction. The final concentration of the target DNA in the plateau portion of the curve is determined by the availability of reagents in the reaction mix and is independent of the original concentration of target DNA. In specific embodiments, multiplexed, tandem PCR (MT-PCR) is employed, which uses a two-step process for gene expression profiling from small quantities of RNA or DNA, as described for example in US Pat. Appl. Pub. No. 20070190540. In the first step, RNA is converted into cDNA and amplified using multiplexed gene specific primers. In the second step each individual gene is quantitated by real time PCR.

[0105] In certain embodiments, target nucleic acids are quantified using blotting techniques, which are well known to those of skill in the art. Southern blotting involves the use of DNA as a target, whereas Northern blotting involves the use of RNA as a target. Each provides different types of information, although cDNA blotting is analogous, in many aspects, to blotting of RNA species. Briefly, a probe is used to target a DNA or RNA species that has been immobilized on a suitable matrix, often a filter of nitrocellulose. The different species should be spatially separated to facilitate analysis. This often is accomplished by gel electrophoresis of nucleic acid species followed by "blotting" on to the filter. Subsequently, the blotted target is incubated with a probe (usually labelled) under conditions that promote denaturation and rehybridisation. Because the probe is designed to base pair with the target, the probe will bind a portion of the target sequence under renaturing conditions. Unbound probe is then removed, and detection is accomplished as described above. Following detection / quantification, one may compare the results seen in a given subject with a control reaction or a statistically significant reference group or population of control subjects as defined herein. In this way, it is possible to correlate the amount of a biomarker nucleic acid detected with the likelihood that a subject is at risk of developing BPD.

[0106] Also contemplated are biochip-based technologies such as those described by Hacia et al. (1996, Nature Genetics 14: 441-447) and Shoemaker et al. (1996, Nature Genetics 14: 450-456). Briefly, these techniques involve quantitative methods for analysing large numbers of genes rapidly and accurately. By tagging genes with oligonucleotides or using fixed probe arrays, one can employ biochip technology to segregate target molecules as high-density arrays and screen these molecules on the basis of hybridization. See also Pease et al. (1994, Proc. Natl. Acad. Sci. U.S.A.91: 5022-5026); Fodor et al. (1991, Science 251: 767-773). Briefly, nucleic acid probes to biomarker polynucleotides are made and attached to biochips to be used in screening and diagnostic methods, as outlined herein. The nucleic acid probes attached to the biochip are designed to be substantially complementary to specific expressed biomarker nucleic acids, i.e., the target sequence (either the target sequence of the sample or to other probe sequences, for example in sandwich assays), such that hybridization of the target sequence and the probes of the present invention occur. This complementarity need not be perfect; there may be any number of base pair mismatches, which will interfere with hybridization between the target sequence and the nucleic acid probes of the present invention. However, if the number of mismatches is so great that no hybridization can occur under even the least stringent of hybridization conditions, the sequence is not a complementary target sequence. In certain embodiments, more than one probe per sequence is used, with either overlapping probes or probes to different sections of the target being used. That is, two, three, four or more probes, with three being desirable, are used to build in a redundancy for a particular target. The probes can be overlapping (i.e. have some sequence in common), or separate.

[0107] In an illustrative biochip analysis, oligonucleotide probes on the biochip are exposed to or contacted with a nucleic acid sample suspected of containing one or more biomarker polynucleotides under conditions favouring specific hybridization. Sample extracts of DNA or RNA, either single or double-stranded, may be prepared from fluid suspensions of biological materials, or by grinding biological materials, or following a cell lysis step which includes, but is not limited to, lysis effected by treatment with SDS (or other detergents), osmotic shock, guanidinium isothiocyanate and lysozyme. Suitable DNA, which may be used in the method of the invention, includes cDNA. Such DNA may be prepared by any one of a number of commonly used protocols as for example described in Ausubel, et al., 1994, supra, and Sambrook, et al., et al., 1989, supra.

[0108] Suitable RNA, which may be used in the method of the invention, includes messenger RNA, complementary RNA transcribed from DNA (cRNA) or genomic or subgenomic RNA. Such RNA may be prepared using standard protocols as for example described in the relevant sections of Ausubel, et al.1994, supra and Sambrook, et al.1989, supra).

[0109] cDNA may be fragmented, for example, by sonication or by treatment with restriction endonucleases. Suitably, cDNA is fragmented such that resultant DNA fragments are of a length greater than the length of the immobilized oligonucleotide probe(s) but small enough to allow rapid access thereto under suitable hybridization conditions. Alternatively, fragments of cDNA may be selected and amplified using a suitable nucleotide amplification technique, as described for example above, involving appropriate random or specific primers.

[0110] Usually the target biomarker polynucleotides are detectably labelled so that their hybridization to individual probes can be determined. The target polynucleotides are typically detectably labelled with a reporter molecule illustrative examples of which include chromogens, catalysts, enzymes, fluorochromes, chemiluminescent molecules, bioluminescent molecules, lanthanide ions (e.g., Eu34), a radioisotope and a direct visual label. In the case of a direct visual label, use may be made of a colloidal metallic or non- metallic particle, a dye particle, an enzyme or a substrate, an organic polymer, a latex particle, a liposome, or other vesicle containing a signal producing substance and the like. Illustrative labels of this type include large colloids, for example, metal colloids such as those from gold, selenium, silver, tin and titanium oxide. In some embodiments, in which an enzyme is used as a direct visual label, biotinylated bases are incorporated into a target polynucleotide.

[0111] The hybrid-forming step can be performed under suitable conditions for hybridizing oligonucleotide probes to test nucleic acid including DNA or RNA. In this regard, reference may be made, for example, to NUCLEIC ACID HYBRIDIZATION, A PRACTICAL APPROACH (Homes and Higgins, eds.) (IRL press, Washington D.C., 1985). In general, whether hybridization takes place is influenced by the length of the oligonucleotide probe and the polynucleotide sequence under test, the pH, the temperature, the concentration of mono- and divalent cations, the proportion of G and C nucleotides in the hybrid-forming region, the viscosity of the medium and the possible presence of denaturants. Such variables also influence the time required for hybridization. The preferred conditionswill therefore depend upon the particular application. Such empirical conditions, however, can be routinely determined without undue experimentation.

[0112] After the hybrid-forming step, the probes are washed to remove any unbound nucleic acid with a hybridization buffer. This washing step leaves only bound target polynucleotides. The probes are then examined to identify which probes have hybridized to a target polynucleotide.

[0113] The hybridization reactions are then detected to determine which of the probes has hybridized to a corresponding target sequence. Depending on the nature of the reporter molecule associated with a target polynucleotide, a signal may be instrumentally detected by irradiating a fluorescent label with light and detecting fluorescence in a fluorimeter; by providing for an enzyme system to produce a dye which could be detected using a spectrophotometer; or detection of a dye particle or a coloured colloidal metallic or non metallic particle using a reflectometer; in the case of using a radioactive label or chemiluminescent molecule employing a radiation counter or autoradiography. Accordingly, a detection means may be adapted to detect or scan light associated with the label which light may include fluorescent, luminescent, focused beam or laser light. In such a case, a charge couple device (CCD) or a photocell can be used to scan for emission of light from a probe:target polynucleotide hybrid from each location in the micro-array and record the data directly in a digital computer. In some cases, electronic detection of the signal may not be necessary. For example, with enzymatically generated colour spots associated with nucleic acid array format, visual examination of the array will allow interpretation of the pattern on the array. In the case of a nucleic acid array, the detection means is suitably interfaced with pattern recognition software to convert the pattern of signals from the array into a plain language genetic profile. In certain embodiments, oligonucleotide probes specific for different biomarker polynucleotides are in the form of a nucleic acid array and detection of a signal generated from a reporter molecule on the array is performed using a ‘chip reader’. A detection system that can be used by a ‘chip reader’ is described for example by Pirrung et al (U.S. Patent No. 5,143,854). The chip reader will typically also incorporate some signal processing to determine whether the signal at a particular array position or feature is a true positive or maybe a spurious signal. Exemplary chip readers are described for example by Fodor et al (U.S. Patent No., 5,925,525). Alternatively, when the array is made using a mixture of individually addressable kinds of labelled microbeads, the reaction may be detected using flow cytometry.

[0114] In other embodiments, biomarker protein levels are assayed using protein-based assays known in the art. For example, when a biomarker protein is an enzyme, the protein can be quantified based upon its catalytic activity or based upon the number of molecules of the protein contained in a sample.

[0115] Antibody-based techniques may also be employed to determine the level of a biomarker in a sample, non-limiting examples of which include immunoassays, such as the enzyme-linked immunosorbent assay (ELISA) and the radioimmunoassay (RIA).

[0116] In some embodiments, protein-capture arrays that permit simultaneous detection and / or quantification of a large number of proteins are employed. For example, low-density protein arrays on filter membranes, such as the universal protein array system (Ge, 2000 Nucleic Acids Res. 28(2):e3) allow imaging of arrayed antigens using standard ELISA techniques and a scanning charge-coupled device (CCD) detector. Immuno-sensor arrays have also been developed that enable the simultaneous detection of clinical analytes. It is now possible using protein arrays to profile protein expression in bodily fluids, such as in sera of healthy subjects or patients, as well as in subjects pre- and post- treatment.

[0117] Exemplary protein capture arrays include arrays comprising spatially addressed antigen-binding molecules, commonly referred to as antibody arrays, which can facilitate extensive parallel analysis of numerous proteins defining a proteome or subproteome. Antibody arrays have been shown to have the required properties of specificity and acceptable background, and some are available commercially (e.g., BD Biosciences, Clontech, BioRad and Sigma). Other illustrative examples of suitable arrays, including the OLink and SOMAscan platforms, as described by Raffield et al. (Proteomics, 2020; 20(12)e1900278), the entire contents of which is incorporated herein by reference. Various methods for the preparation of antibody arrays have been reported (see, e.g., Lopez et al., 2003 J. Chromatogr. B 787:19-27; Cahill, 2000 Trends in Biotechnology 7:47-51; U.S. Pat. App. Pub. 2002 / 0055186; U.S. Pat. App. Pub. 2003 / 0003599; PCT publication WO 03 / 062444; PCT publication WO 03 / 077851; PCT publication WO 02 / 59601; PCT publication WO 02 / 39120; PCT publication WO 01 / 79849; PCT publication WO 99 / 39210). The antigen-binding molecules of such arrays may recognise at least a subset of proteins expressed by a cell or population of cells, illustrative examples of which include growth factor receptors, hormone receptors, neurotransmitter receptors, catecholamine receptors, amino acid derivative receptors, cytokine receptors, extracellular matrix receptors, antibodies, lectins, cytokines, serpins, proteases, kinases, phosphatases, ras-like GTPases, hydrolases,steroid hormone receptors, transcription factors, heat-shock transcription factors, DNA- binding proteins, zinc-finger proteins, leucine-zipper proteins, homeodomain proteins, intracellular signal transduction modulators and effectors, apoptosis-related factors, DNA synthesis factors, DNA repair factors, DNA recombination factors and cell-surface antigens.

[0118] Individual spatially distinct protein-capture agents are typically attached to a support surface, which is generally planar or contoured. Common physical supports include glass slides, silicon, microwells, nitrocellulose or PVDF membranes, and magnetic and other microbeads.

[0119] Particles in suspension can also be used as the basis of arrays, providing they are coded for identification; systems include colour coding for microbeads (e.g., available from Quanterix (e.g., Simoa Bead-based assay), Luminex, Bio-Rad and Nanomics Biosystems) and semiconductor nanocrystals (e.g., QDotsTM, available from Quantum Dots), and barcoding for beads (UltraPlexTM, available from Smartbeads) and multimetal microrods (NanobarcodesTMparticles, available from Surromed). Beads can also be assembled into planar arrays on semiconductor chips (e.g., available from LEAPS technology and BioArray Solutions). Where particles are used, individual protein-capture agents are typically attached to an individual particle to provide the spatial definition or separation of the array. The particles may then be assayed separately, but in parallel, in a compartmentalized way, for example in the wells of a microtitre plate or in separate test tubes.

[0120] In an illustrative example, a protein sample, which is optionally fragmented to form peptide fragments (see, e.g., U.S. Pat. App. Pub. 2002 / 0055186) is delivered to a protein-capture array under conditions suitable for protein or peptide binding, and the array is washed to remove unbound or non-specifically bound components of the sample from the array. Next, the presence or amount of protein or peptide bound to each feature of the array is detected using a suitable detection system. The amount of protein bound to a feature of the array may be determined relative to the amount of a second protein bound to a second feature of the array. In certain embodiments, the amount of the second protein in the sample is already known or known to be invariant.

[0121] In another illustrative example of a protein-capture array is Luminex-based multiplex assay, which is a bead-based multiplexing assay, where beads are internally dyed with fluorescent dyes to produce a specific spectral address. Biomolecules (such as an oligo or antibody) can be conjugated to the surface of beads to capture analytes of interest. Flowcytometric or other suitable imaging technologies known to persons skilled in the art can then be used for characterization of the beads, as well as for detection of analyte presence. The Luminex technology enables are large number of proteins, genes or other gene expression products (e.g., 100 or more, 200 or more, 300 or more, 400 or more) to be detected using very small sample volume (e.g., in a 96 or 384-well plate). In some embodiments, the protein- capture array is Bio-Plex Luminex-100 Station (Bio-Rad) as described previously.

[0122] Other methods of determining the level of a biomarker protein in a sample include mass spectrometry (MS). Suitable MS-based methods will be familiar to persons skilled in the art, illustrative examples of which include multiple reaction-monitoring (MRM) mass spectrometry (as described by Lieber and Zimmerman, 2013, Biochem.52(22):3797- 3806 and by Cohen Freue and Zimmerman, 2012, Circ. Cardiovasc. Genet., 5:738), the entire contents of which are incorporated herein by reference), liquid chromatography coupled to tandem mass spectrometry (LC‐MS / MS; as described by Aebersold and Mann (2016, Nature, 537: 347–355; the entire contents of which is incorporated herein by reference) and sequential window acquisition of all theoretical mass spectra (SWATH‐MS; as described by Ludwig et al. (Mol. Syst. Biol, 2018, 14(8)e8126) and Gillet et al. (2012; Mol. Cell Proteomics 11: O111.016717), the entire contents of which are incorporated herein by reference).

[0123] In some embodiments, the level of a biomarker is normalized against a housekeeping biomarker. The term "housekeeping biomarker" refers to a biomarker or group of biomarkers (e.g., polynucleotides and / or polypeptides), which are typically found at a constant level in the cell type(s) being analyzed and across the conditions being assessed. In some embodiments, the housekeeping biomarker is a "housekeeping gene." A "housekeeping gene" refers herein to a gene or group of genes which encode proteins whose activities are essential for the maintenance of cell function and which are typically found at a constant level in the cell type(s) being analyzed and across the conditions being assessed. Biomarker Profiles

[0124] As used herein, the terms "profile" and "biomarker profile" are used interchangeably herein to denote any set of data that represents the distinctive features or characteristics associated with a condition of interest, such as with a particular prediction, diagnosis and / or prognosis of a specified condition as taught herein. The term generally encompasses quantification of one or more biomarkers, inter alia, nucleic acid profiles, such as, for example, gene expression profiles (e.g., sets of gene expression data that representsmRNA levels of one or more genes associated with a condition of interest), as well as protein, polypeptide or peptide profiles, such as, for example, protein expression profiles (e.g., sets of protein expression data that represents the levels of one or more proteins associated with a condition of interest), and any combination thereof. In one embodiment, the terms "profile" or "biomarker profile" are used herein to denote any set of data that represents the distinctive features or characteristics associated with BPD or with a risk of developing BPD.

[0125] The term "reference biomarker profile" is used herein to denote a pattern of expression of at least one biomarker for a sample population of reference individuals who may be individuals at risk of, or have, BPD. In some embodiments, the "reference biomarker profile" is used herein to denote a pattern of expression of at least one biomarker for a sample population of reference individuals at risk of BPD. In some embodiments, the "reference biomarker profile" is used herein to denote a pattern of expression of at least one biomarker for a sample population of reference individuals not at risk of BPD. A reference biomarker profile may be identified based on reference data measured for individuals in the sample population. Reference data typically include the measurement of at least one biomarker. The measurement may include information regarding the activity, such as its concentration and / or abundance, of any expression product or measurable molecule (e.g., protein), as is described elsewhere herein. The reference data may also include other additional relevant information, such as clinical data, including, but not limited to, age, the presence, absence, degree, severity or progression of a symptom associated with BPD, amino acid or nucleotide related genomics information associated with BPD, and the like and this is not intended to be limiting, as will be apparent from the description herein.

[0126] The reference data may be acquired in any appropriate manner, such as obtaining gene expression product data from a plurality of subjects, selected to include individuals with BPD. The terms "expression" or "gene expression" refer to production of RNA message or translation of RNA message into proteins or polypeptides, or both. Detection of either types of gene expression in use of any of the methods described herein is encompassed by the present invention. Gene expression product data are collected, for example, by obtaining a biological sample, such as a blood sample from the subject, and performing a quantification, semi-quantification or qualification process, such as sequence-specific nucleic acid amplification, including PCR (Polymerase Chain Reaction) or the like, in order to assess the expression, and in particular, the level or abundance of one or more reference biomarker.Quantified values indicative of the relative activity can then be stored as part of the reference data.

[0127] Biomarker profiles may be created in a number of ways and may be the combination of measurable biomarkers or aspects of biomarkers using methods such as ratios, or other more complex association methods or algorithms (e.g., rule-based methods), as discussed for example in more detail herein. A biomarker profile evaluates at least one biomarker. However, in other embodiments, the biomarker profile evaluates at least 2 biomarkers (e.g., 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 11 or more, 12 or more, 13 or more, 14 or more, 15 or more, 16 or more, 17 or more, 18 or more, 19 or more, 20 or more, 25 or more, 30 or more, 40 or more, 50 or more, 60 or more, 70 or more, 80 or more, 90 or more, 100 or more, 200 or more). Where the biomarker profile comprises two or more measurements, the measurements can correspond to the same or different biomarkers. For example, distinct reference profiles may represent the degree of risk (e.g., an abnormally elevated risk) of BPD, as compared to no or normal risk of BPD. In another example, distinct reference profiles may represent predictions of differing degrees of risk of BPD.

[0128] A reference biomarker profile or a sample biomarker profile can be quantitative, semi-quantitative and / or qualitative. For example, the biomarker profile can evaluate the presence or absence of at least one biomarker, can evaluate the presence of at least one biomarker above or below a particular threshold, and / or can evaluate the relative or absolute amount of at least one biomarker.

[0129] In some embodiments, the subject’s risk of BPD is determined by comparing the biomarker profile in a sample obtained from the subject (i.e., the sample biomarker profile) with a reference biomarker profile in a healthy population of subjects or in a population of subjects who do not develop BPD. Alternatively, the subject’s risk of BPD is determined by comparing the biomarker profile in a sample obtained from the subject (i.e., the sample biomarker profile) with a reference biomarker profile from a population of subjects who present with BPD or who go on to develop BPD. For example, a subject’s risk of BPD can be determined by comparing the level of expression of a biomarker in a sample obtained from the subject with a level that is representative of a mean or median level of the expression of the biomarker(s) in a population of individuals who present with, or go on to develop, BPD.

[0130] In some embodiments, the expression of the at least one biomarker in a sample population of reference individuals, as broadly defined herein, is used to generate a biomarker profile; namely, of subjects at risk of BPD (the reference group) and of healthy controls or subjects without BPD (the control group). For instance, a particular biomarker may be less abundant in the reference group as compared to the control group. The data may be represented as an overall signature score or the profile may be represented as a barcode, heat-map, z-score, receiver-operator characteristics (ROC) curve or other graphical representation known to persons skilled in the art to facilitate the determination of a test subject's risk of BPD. The abundance of the corresponding biomarker in a test subject may be represented in the same way, thereby providing a sample biomarker profile, such that a comparison of the sample profile with the reference profile may be undertaken to determine the test subject's risk of BPD.

[0131] It will be appreciated that, once collected, the reference data can be stored in a database allowing them to be subsequently retrieved, for example, by a processing system for subsequent use in accordance with the methods described herein. The processing system may also store an indication of the identity of each of the reference biomarkers as a reference biomarker collection or panel where there are two or more reference biomarkers.

[0132] In illustrative examples, the biomarker profile evaluates at least one biomarker (e.g., 1, 2, 3, 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, 30, 35, 40, 45, 50, 60 or more).

[0133] In an embodiment, the level of a biomarker in the reference biomarker profile is at least 101%, 102%, 103%, 104%, 105%, 106%, 107% 108%, 109%, 110%, 120%, 130%, 140%, 150%, 160%, 170%, 180%, 190%, 200%, 300%, 400%, 500%, 600%, 700%, 800%, 900% or 1000% (i.e. an increased or higher level) of the level of a corresponding biomarker in the control group.

[0134] In other embodiments, the level of a biomarker in the reference biomarker profile is at least 99%, 98%, 97%, 96%, 95%, 94%, 93% 92%, 91%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20% or at least 10%, (i.e. a decreased or lower level) of the level of a corresponding biomarker in the control group. Corresponding biomarker

[0135] By "corresponding biomarker" is meant a biomarker that is structurally and / or functionally similar or identical to a reference biomarker. Representative correspondingbiomarkers include expression products of allelic variants (same locus), homologs (different locus), and orthologs (different organism) of reference biomarker genes. Nucleic acid variants of reference biomarker genes and encoded biomarker polynucleotide expression products can contain nucleotide substitutions, deletions, inversions and / or insertions. Variation can occur in either or both the coding and non-coding regions. The variations can produce both conservative and non-conservative amino acid substitutions (as compared in the encoded product). For nucleotide sequences, conservative variants include those sequences that, because of the degeneracy of the genetic code, encode the amino acid sequence of a reference biomarker.

[0136] Variants of a particular biomarker gene or polynucleotide may have at least about 40%, 45%, 50%, 51%, 52%, 53%, 54%, 55%, 56%, 57%, 58%, 59% 60%, 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69% 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or more sequence identity to that particular nucleotide sequence as determined by sequence alignment programs known in the art using default parameters.

[0137] Corresponding biomarkers also include amino acid sequences that display substantial sequence similarity or identity to the amino acid sequence of a reference biomarker polypeptide. An amino acid sequence that corresponds to a reference amino acid sequence may display at least about 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 97, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99% or even up to 100% sequence similarity or identity to a reference amino acid sequence as determined by sequence alignment programs known in the art using default parameters.

[0138] In some embodiments, calculations of sequence similarity or sequence identity between sequences can be performed. For example, to determine the percent identity of two amino acid sequences, or of two nucleic acid sequences, the sequences are aligned for optimal comparison purposes (e.g., gaps can be introduced in one or both of a first and a second amino acid or nucleic acid sequence for optimal alignment and non-homologous sequences can be disregarded for comparison purposes). In some embodiments, the length of a reference sequence aligned for comparison purposes is at least 30%, usually at least 40%, more usually at least 50%, 60%, and even more usually at least 70%, 80%, 90%, 100% of the length of the reference sequence. The amino acid residues or nucleotides at corresponding amino acid positions or nucleotide positions are then compared. When a position in the firstsequence is occupied by the same amino acid residue or nucleotide at the corresponding position in the second sequence, then the molecules are identical at that position. For amino acid sequence comparison, when a position in the first sequence is occupied by the same or similar amino acid residue (i.e., conservative substitution) at the corresponding position in the second sequence, then the molecules are similar at that position.

[0139] The percent identity between the two sequences is a function of the number of identical amino acid residues or nucleotides shared by the sequences at individual positions, considering the number of gaps, and the length of each gap, which need to be introduced for optimal alignment of the two sequences. By contrast, the percent similarity between two amino acid sequences is a function of the number of identical and similar amino acid residues shared by the sequences at individual positions, considering the number of gaps, and the length of each gap, which need to be introduced for optimal alignment of the two sequences.

[0140] The comparison of sequences and determination of percent identity or percent similarity between sequences can be accomplished using a mathematical algorithm. In certain embodiments, the percent identity or similarity between amino acid sequences is determined using the Needleman and Wünsch, (1970, J. Mol. Biol.48: 444-453) algorithm which has been incorporated into the GAP program in the GCG software package (available at http: / / www.gcg.com), using either a Blossum 62 matrix or a PAM250 matrix, and a gap weight of 16, 14, 12, 10, 8, 6, or 4 and a length weight of 1, 2, 3, 4, 5, or 6. In specific embodiments, the percent identity between nucleotide sequences is determined using the GAP program in the GCG software package (available at http: / / www.gcg.com), using a NWSgapdna.CMP matrix and a gap weight of 40, 50, 60, 70, or 80 and a length weight of 1, 2, 3, 4, 5, or 6. An non-limiting set of parameters (and the one that should be used unless otherwise specified) includes a Blossum 62 scoring matrix with a gap penalty of 12, a gap extend penalty of 4, and a frameshift gap penalty of 5.

[0141] In some embodiments, the percent identity or similarity between amino acid or nucleotide sequences can be determined using the algorithm of E. Meyers and W. Miller (1989, Cabios, 4: 11-17) which has been incorporated into the ALIGN program (version 2.0), using a PAM120 weight residue table, a gap length penalty of 12 and a gap penalty of 4.

[0142] The nucleic acid and protein sequences described herein can be used as a "query sequence" to perform a search against public databases to, for example, identify other family members or related sequences. Such searches can be performed using the NBLAST andXBLAST programs (version 2.0) of Altschul, et al., (1990, J. Mol. Biol, 215: 403-10). BLAST nucleotide searches can be performed with the NBLAST program, score = 100, wordlength = 12 to obtain nucleotide sequences homologous to 53010 nucleic acid molecules of the invention. BLAST protein searches can be performed with the XBLAST program, score = 50, wordlength = 3 to obtain amino acid sequences homologous to 53010 protein molecules of the invention. To obtain gapped alignments for comparison purposes, Gapped BLAST can be utilized as described in Altschul et al., (1997, Nucleic Acids Res, 25: 3389- 3402). When utilizing BLAST and Gapped BLAST programs, the default parameters of the respective programs (e.g., XBLAST and NBLAST) can be used.

[0143] Corresponding biomarker polynucleotides also include nucleic acid sequences that hybridize to reference biomarker polynucleotides, or to their complements, under stringency conditions described below. As used herein, the term "hybridizes under low stringency, medium stringency, high stringency, or very high stringency conditions" describes conditions for hybridization and optionally washing. "Hybridization" is used herein to denote the pairing of complementary nucleotide sequences to produce a DNA-DNA hybrid or a DNA-RNA hybrid. Complementary base sequences are those sequences that are related by the base-pairing rules. In DNA, A pairs with T and C pairs with G. In RNA, U pairs with A and C pairs with G. In this regard, the terms "match" and "mismatch" as used herein refer to the hybridization potential of paired nucleotides in complementary nucleic acid strands. Matched nucleotides hybridize efficiently, such as the classical A-T and G-C base pair mentioned above. Mismatches are other combinations of nucleotides that do not hybridize efficiently.

[0144] Guidance for performing hybridization reactions can be found in Ausubel et al., (1998, supra), Sections 6.3.1-6.3.6. Aqueous and non-aqueous methods are described in that reference and either can be used. Reference herein to low stringency conditions include and encompass from at least about 1% v / v to at least about 15% v / v formamide and from at least about 1 M to at least about 2 M salt for hybridization at 42 °C, and at least about 1 M to at least about 2 M salt for washing at 42 °C. Low stringency conditions also may include 1% Bovine Serum Albumin (BSA), 1 mM EDTA, 0.5 M NaHPO4(pH 7.2), 7% SDS for hybridization at 65 °C, and (i) 2 × SSC, 0.1% SDS; or (ii) 0.5% BSA, 1 mM EDTA, 40 mM NaHPO4(pH 7.2), 5% SDS for washing at room temperature. One embodiment of low stringency conditions includes hybridization in 6 × sodium chloride / sodium citrate (SSC) at about 45 °C, followed by two washes in 0.2 × SSC, 0.1% SDS at least at 50 °C (thetemperature of the washes can be increased to 55 °C for low stringency conditions). Medium stringency conditions include and encompass from at least about 16% v / v to at least about 30% v / v formamide and from at least about 0.5 M to at least about 0.9 M salt for hybridization at 42 °C, and at least about 0.1 M to at least about 0.2 M salt for washing at 55 °C. Medium stringency conditions also may include 1% Bovine Serum Albumin (BSA), 1 mM EDTA, 0.5 M NaHPO4(pH 7.2), 7% SDS for hybridization at 65 °C, and (i) 2 × SSC, 0.1% SDS; or (ii) 0.5% BSA, 1 mM EDTA, 40 mM NaHPO4(pH 7.2), 5% SDS for washing at 60-65 °C. One embodiment of medium stringency conditions includes hybridizing in 6 × SSC at about 45 °C, followed by one or more washes in 0.2 × SSC, 0.1% SDS at 60 °C. High stringency conditions include and encompass from at least about 31% v / v to at least about 50% v / v formamide and from about 0.01 M to about 0.15 M salt for hybridization at 42 °C, and about 0.01 M to about 0.02 M salt for washing at 55 °C. High stringency conditions also may include 1% BSA, 1 mM EDTA, 0.5 M NaHPO4(pH 7.2), 7% SDS for hybridization at 65 °C, and (i) 0.2 × SSC, 0.1% SDS; or (ii) 0.5% BSA, 1 mM EDTA, 40 mM NaHPO4(pH 7.2), 1% SDS for washing at a temperature in excess of 65 °C. One embodiment of high stringency conditions includes hybridizing in 6 × SSC at about 45°C, followed by one or more washes in 0.2 × SSC, 0.1% SDS at 65 °C.

[0145] In certain embodiments, a corresponding biomarker polynucleotide is one that hybridizes to a disclosed nucleotide sequence under very high stringency conditions. One embodiment of very high stringency conditions includes hybridizing 0.5 M sodium phosphate, 7% SDS at 65 °C, followed by one or more washes at 0.2 × SSC, 1% SDS at 65 °C.

[0146] Other stringency conditions are well known in the art and a skilled addressee will recognize that various factors can be manipulated to optimize the specificity of the hybridization. Optimization of the stringency of the final washes can serve to ensure a high degree of hybridization. For detailed examples, see Ausubel et al., supra at pages 2.10.1 to 2.10.16 and Sambrook et al. (1989, supra) at sections 1.101 to 1.104. Risk or likelihood of developing BPD

[0147] The term "risk" is used to denote a subject's likelihood, based on the sample biomarker profile as determined for that subject, of developing BPD (or not) on the basis of the reference biomarker profile, as herein described. Accordingly, the terms "risk" and "likelihood" are used interchangeably herein, unless otherwise stated.

[0148] It would be apparent to persons skilled in the art that the risk that a subject will develop BPD will vary, for example, from being at low or decreased risk of developing BPD to being at high or increased risk of developing BPD. By "low or decreased risk" is meant that the subject is less likely to develop BPD as compared to a subject determined to be a "high or increased risk" subject. Conversely, a "high or increased risk" subject is a subject who is more likely to develop BPD as compared to a subject who is not at risk or a "low risk" subject.

[0149] Likelihood is suitably based on mathematical modeling. An increased likelihood, for example, may be relative or absolute and may be expressed qualitatively or quantitatively. For instance, an increased risk may be expressed as simply determining the subject's level of a given biomarker and placing the test subject in an "increased risk" category, based upon the corresponding reference biomarker profile as determined, for example, from previous population studies. Alternatively, a numerical expression of the test subject's increased risk may be determined based upon biomarker level analysis.

[0150] As used herein, the term "probability" refers to the probability of class membership for a sample as determined by a given mathematical model and is construed to be equivalent likelihood in this context.

[0151] In some embodiments, likelihood is assessed by comparing the level or abundance of at least one biomarker to one or more preselected level, also referred to herein as a threshold or reference level. Thresholds may be selected that provide an acceptable ability to predict risk, treatment success, etc. In illustrative examples, receiver operating characteristic (ROC) curves are calculated by plotting the value of a variable versus its relative frequency in two populations in which a first population is considered at risk of developing BPD and a second population that is not considered to be at risk, or have a low risk, of developing BPD (called, arbitrarily, for example, "control subjects").

[0152] In some embodiments, the subject is considered at risk of developing BPD where the at least one biomarker in the sample biomarker profile for the subject is downregulated as compared to the level of the corresponding biomarker in a control subject.

[0153] In some embodiments, the subject is considered at risk of developing BPD where the level of the at least one biomarker in the sample biomarker profile for the subject is higher as compared to the level of the corresponding biomarker in a control subject.

[0154] For any particular biomarker, a distribution of biomarker levels for subjects who are at risk or not at risk of developing BPD may overlap. Under such conditions, a test may not absolutely distinguish a subject who is at risk of developing BPD from a subject who is not at risk of developing BPD with absolute (i.e., 100%) accuracy, and the area of overlap indicates where the test cannot distinguish the two subjects. A threshold can be selected, above which (or below which, depending on how a biomarker changes with risk) the test is considered to be "positive" and below which the test is considered to be "negative." The area under the ROC curve (AUC) provides the C-statistic, which is a measure of the probability that the perceived measurement will allow correct identification of risk (see, e.g., Hanley et al., Radiology 143: 29-36 (1982)).

[0155] In some embodiments, a positive likelihood ratio, negative likelihood ratio, odds ratio, and / or AUC or receiver operating characteristic (ROC) values are used as a measure of a method’s ability to predict risk of developing BPD. As used herein, the term "likelihood ratio" is the probability that a given test result would be observed in a subject with a likelihood of such risk, divided by the probability that that same result would be observed in a subject without a likelihood of such risk. Thus, a positive likelihood ratio is the probability of a positive result observed in subjects with the specified risk divided by the probability of a positive results in subjects without the specified risk. A negative likelihood ratio is the probability of a negative result in subjects without the specified risk divided by the probability of a negative result in subjects with specified risk. The term "odds ratio," as used herein, refers to the ratio of the odds of an event occurring in one group (e.g., a control group) to the odds of it occurring in another group (e.g., the BPD risk group), or to a data-based estimate of that ratio. The term "area under the curve" or "AUC" refers to the area under the curve of a receiver operating characteristic (ROC) curve, both of which are well known in the art. AUC measures are useful for comparing the accuracy of a classifier across the complete data range. Classifiers with a greater AUC have a greater capacity to classify unknowns correctly between two groups of interest (e.g., a control group and a BPD risk group). ROC curves are useful for plotting the performance of a particular feature (e.g., any of the biomarkers described herein and / or any item of additional biomedical information) in distinguishing or discriminating between two populations (e.g., cases having a condition and controls without the condition). Typically, the feature data across the entire population (e.g., the cases and controls) are sorted in ascending order based on the value of a single feature. Then, for each value for that feature, the true positive and false positive rates for the data arecalculated. The sensitivity is determined by counting the number of cases above the value for that feature and then dividing by the total number of cases. The specificity is determined by counting the number of controls below the value for that feature and then dividing by the total number of controls. Although this definition refers to scenarios in which a feature is elevated in cases compared to controls, this definition also applies to scenarios in which a feature is lower in cases compared to the controls (in such a scenario, samples below the value for that feature would be counted). ROC curves can be generated for a single feature as well as for other single outputs, for example, a combination of two or more features can be mathematically combined (e.g., added, subtracted, multiplied, etc.) to produce a single value, and this single value can be plotted in a ROC curve. Additionally, any combination of multiple features, in which the combination derives a single output value, can be plotted in a ROC curve. These combinations of features may comprise a test. The ROC curve is the plot of the sensitivity of a test against the specificity of the test, where sensitivity is traditionally presented on the vertical axis and specificity is traditionally presented on the horizontal axis. Thus, "AUC ROC values" are equal to the probability that a classifier will rank a randomly chosen positive instance higher than a randomly chosen negative one. An AUC ROC value may be an alternative to the Mann-Whitney U test, which tests for the median difference between scores obtained in the two groups considered if the groups are of continuous data, or to the Wilcoxon test of ranks.

[0156] In some embodiments, at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) biomarker or a panel of biomarkers is selected to discriminate between subjects with or without risk of developing BPD with at least about 50%, preferably at least about 55%, preferably at least about 60%, preferably at least about 65%, preferably at least about 70%, preferably at least about 75%, preferably at least about 80%, preferably at least about 85%, preferably at least about 90%, preferably at least about 95% accuracy, preferably at least about 96% accuracy, preferably at least about 97% accuracy, preferably at least about 98% accuracy, or more preferable at least about 99% accuracy, or having a C-statistic of at least about 0.50, preferably at least about 0.55, preferably at least about 0.60, preferably at least about 0.65, preferably at least about 0.70, preferably at least about 0.75, preferably at least about 0.80, preferably at least about 0.85, preferably at least about 0.90, preferably at least about 0.95, preferably at least about 0.96, preferably at least about 0.97, preferably at least about 0.98, or more preferably at least about 0.96.

[0157] In the case of a positive likelihood ratio, a value of 1 indicates that a positive result is equally likely among subjects in both the "BPD risk" and "control" groups; a value greater than 1 indicates that a positive result is more likely in the BPD risk group; and a value less than 1 indicates that a positive result is more likely in the control group. In this context, "BPD risk group" is meant to refer to a population of reference individuals considered to be at risk of developing BPD and a "control group" is meant to refer to a group of subjects considered not to be at risk of developing BPD. In the case of a negative likelihood ratio, a value of 1 indicates that a negative result is equally likely among subjects in both the "BPD risk" and "control" groups; a value greater than 1 indicates that a negative result is more likely in the "BPD risk" group; and a value less than 1 indicates that a negative result is more likely in the "control" group. In the case of an odds ratio, a value of 1 indicates that a positive result is equally likely among subjects in both the "BPD risk" and "control" groups; a value greater than 1 indicates that a positive result is more likely in the "BPD risk" group; and a value less than 1 indicates that a positive result is more likely in the "control" group. In the case of an AUC ROC value, this is computed by numerical integration of the ROC curve. The range of this value can be 0.5 to 1.0. A value of 0.5 indicates that a classifier (e.g., a biomarker profile) is no better than a 50% chance to classify unknowns correctly between two groups of interest, while 1.0 indicates the relatively best prognostic accuracy. In certain embodiments, biomarkers and / or biomarker panels are selected to exhibit a positive or negative likelihood ratio of at least about 1.5 or more or about 0.67 or less, at least about 2 or more or about 0.5 or less, at least about 5 or more or about 0.2 or less, at least about 10 or more or about 0.1 or less, or at least about 20 or more or about 0.05 or less.

[0158] In certain embodiments, the at least one biomarker is selected to exhibit an odds ratio of at least about 2 or more or about 0.5 or less, preferably at least about 3 or more or about 0.33 or less, preferably at least about 4 or more or about 0.25 or less, preferably at least about 5 or more or about 0.2 or less, or more preferably at least about 10 or more or about 0.1 or less.

[0159] In certain embodiments, the at least one biomarker is selected to exhibit an AUC ROC value of greater than 0.5, preferably at least 0.6, more preferably 0.7, still more preferably at least 0.8, even more preferably at least 0.9, and most preferably at least 0.95.

[0160] In some cases, multiple thresholds may be determined in so-called "tertile," "quartile," or "quintile" analyses. In these methods, the "BPD risk" and "control" groups are considered together as a single population, and are divided into 3, 4, or 5 (or more) "bins"having equal numbers of individuals. The boundary between two of these "bins" may be considered "thresholds." The degree of risk can then be assigned based on which "bin" a test subject falls into.

[0161] In other embodiments, particular thresholds for the reference biomarker(s) measured are not relied upon to determine if the biomarker level(s) obtained from a subject are correlated to risk of developing BPD. For example, a temporal change in the biomarker(s) can be used to rule in or out such risk. Alternatively, biomarker(s) are correlated to such risk by the presence or absence of one or more biomarkers in a particular assay format. In the case of biomarker profiles, the present invention may utilize an evaluation of the entire profile of biomarkers to provide a single result value (e.g., a "panel response" value expressed either as a numeric score or as a percentage risk). In such embodiments, an increase, decrease, or other change (e.g., slope over time) in a certain subset of biomarkers may be sufficient to indicate risk of developing BPD in a subject, while an increase, decrease, or other change in a different subset of biomarkers may be sufficient to indicate the same risk in another subject.

[0162] In certain embodiments, a panel of biomarkers is selected to assist in distinguishing between the "BPD risk" and "control" groups with at least about 70%, preferably at least about 80%, preferably at least about 85%, preferably at least about 90% or more preferably at least about 95% sensitivity, suitably in combination with at least about 70%, preferably at least about 80%, preferably at least about 85%, preferably at least about 90% or more preferably at least about 95% specificity. In some embodiments, both the sensitivity and specificity are at least about 75%, preferably at least about 80%, preferably at least about 85%, preferably at least about 90% or more preferably at least about 95%.

[0163] The phrases "assessing the likelihood" and "determining the likelihood," as used herein, refer to methods by which the skilled artisan can predict a subject's risk of developing BPD. The skilled artisan will understand that this phrase includes within its scope an increased probability that the subject will develop BPD; that is, such risk is more likely to be present or absent in a subject. For example, the probability that an individual identified as being at risk of developing BPD may be expressed as a "positive predictive value" or "PPV." Positive predictive value can be calculated as the number of true positives divided by the sum of the true positives and false positives. PPV is determined by the characteristics of the predictive methods of the present invention as well as the prevalence of the condition in the population analysed. The statistical algorithms can be selected such that the positive predictive value in a population considered to be at risk of developing BPD is in the range of70% to 99% and can be, for example, at least about 70%, preferably at least about 75%, preferably at least about 76%, preferably at least about 77%, preferably at least about 78%, preferably at least about 79%, preferably at least about 80%, preferably at least about 81%, preferably at least about 82%, preferably at least about 83%, preferably at least about 84%, preferably at least about 85%, preferably at least about 86%, preferably at least about 87%, preferably at least about 88%, preferably at least about 89%, preferably at least about 90%, preferably at least about 91%, preferably at least about 92%, preferably at least about 93%, preferably at least about 94%, preferably at least about 95%, preferably at least about 96%, preferably at least about 97%, preferably at least about 98%, or more preferably at least about 99%.

[0164] In other examples, the probability that a subject is identified as not being at risk of developing BPD may be expressed as a "negative predictive value" or "NPV." Negative predictive value can be calculated as the number of true negatives divided by the sum of the true negatives and false negatives. Negative predictive value is determined by the characteristics of the diagnostic or prognostic method, system, or code as well as the prevalence of risk in the population analyzed. The statistical methods and models can be selected such that the negative predictive value in a population considered at risk of developing BPD is in the range of about 70% to about 99% and can be, for example, at least about 70%, preferably at least about 75%, preferably at least about 76%, preferably at least about 77%, preferably at least about 78%, preferably at least about 79%, preferably at least about 80%, preferably at least about 81%, preferably at least about 82%, preferably at least about 83%, preferably at least about 84%, preferably at least about 85%, preferably at least about 86%, preferably at least about 87%, preferably at least about 88%, preferably at least about 89%, preferably at least about 90%, preferably at least about 91%, preferably at least about 92%, preferably at least about 93%, preferably at least about 94%, preferably at least about 95%, preferably at least about 96%, preferably at least about 97%, preferably at least about 98%, or more preferably at least about 99%.

[0165] In some embodiments, a subject is determined as being at significant risk of developing BPD. By "significant risk" is meant that the subject has a reasonable probability (e.g., 0.6, 0.7, 0.8, 0.9 or more) of developing BPD.

[0166] The methods of the present invention, as broadly described herein, also permit the generation of high-density data sets that can be evaluated using informatics approaches. High data density informatics analytical methods are known and software is available to those inthe art, e.g., cluster analysis (Pirouette, Informetrix), class prediction (SIMCA-P, Umetrics), principal components analysis of a computationally modeled dataset (SIMCA-P, Umetrics), 2D cluster analysis (GeneLinker Platinum, Improved Outcomes Software), and metabolic pathway analysis (biotech.icmb.utexas.edu). The choice of software packages offers specific tools for questions of interest (Kennedy et al., Solving Data Mining Problems Through Pattern Recognition. Indianapolis: Prentice Hall PTR, 1997; Golub et al., (2999) Science 286:531-7; Eriksson et al., Multi and Megavariate Analysis Principles and Applications: Umetrics, Umea, 2001). In general, any suitable mathematic analyses can be used to evaluate at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, et.) biomarker in a biomarker profile with respect to determining the likelihood that a subject is at risk of developing BPD. For example, methods such as multivariate analysis of variance, multivariate regression, and / or multiple regression can be used to determine relationships between dependent variables (e.g., clinical measures) and independent variables (e.g., levels of biomarkers). Clustering, including both hierarchical and non-hierarchical methods, as well as nonmetric Dimensional Scaling can be used to determine associations or relationships among variables and among changes in those variables.

[0167] In some embodiments, a biomarker profile is used to assign a risk score which describes a mathematical equation for evaluation or prediction of risk.

[0168] In addition, principal component analysis is a common way of reducing the dimension of studies, and can be used to interpret the variance-covariance structure of a data set. Principal components may be used in such applications as multiple regression and cluster analysis. Factor analysis is used to describe the covariance by constructing "hidden" variables from the observed variables. Factor analysis may be considered an extension of principal component analysis, where principal component analysis is used as parameter estimation along with the maximum likelihood method. Furthermore, simple hypothesis such as equality of two vectors of means can be tested using Hotelling’s T squared statistic.

[0169] In some embodiments, the data sets corresponding to biomarker profiles are used to create a diagnostic or predictive rule or model based on the application of a statistical and machine learning algorithm. Such an algorithm uses relationships between a biomarker profile and risk of developing BPD observed in control subjects or typically cohorts of control subjects (sometimes referred to as training data), which provides combined control or reference biomarker profiles for comparison with biomarker profiles of a subject. The dataare used to infer relationships that are then used to predict the status of a subject and the presence or absence of risk of developing BPD.

[0170] Persons skilled in the art of data analysis will recognize that many different forms of inferring relationships in the training data may be used without materially changing the present invention. Subject

[0171] The terms "subject," "individual" and "patient" are used interchangeably herein and typically refer to any infant. In an embodiment, the infant is a premature infant. A subject is typically considered to be a premature infant if the infant was born before 37 weeks' gestation. In an embodiment, the premature infant is less than about 37 weeks gestational age (e.g., 36, 35, 34, 33, 32, 31, 30, 29, 28, 27, 26, 25, 24, 23 or 22 weeks gestational age). In an embodiment, the premature infant is less than about 37 weeks gestational age. In an embodiment, the premature infant is less than about 36 weeks gestational age. In an embodiment, the premature infant is less than about 35 weeks gestational age. In an embodiment, the premature infant is less than about 34 weeks gestational age. In an embodiment, the premature infant is less than about 33 weeks gestational age. In an embodiment, the premature infant is less than about 32 weeks gestational age. In an embodiment, the premature infant is less than about 31 weeks gestational age. In an embodiment, the premature infant is less than about 30 weeks gestational age. In an embodiment, the premature infant is less than about 29 weeks gestational age. In an embodiment, the premature infant is less than about 28 weeks gestational age. In an embodiment, the premature infant is less than about 27 weeks gestational age. In an embodiment, the premature infant is less than about 26 weeks gestational age. In an embodiment, the premature infant is less than about 25 weeks gestational age. In an embodiment, the premature infant is less than about 24 weeks gestational age. In an embodiment, the premature infant is less than about 23 weeks gestational age. In an embodiment, the premature infant is less than about 22 weeks gestational age. Sample

[0172] The biomarker profiles disclosed herein are evaluated (detected or measured) in a biological sample. A biological sample may include a sample that may be extracted, untreated, treated, diluted or concentrated from a subject. In some embodiments, thebiological sample has not been extracted from the subject, such as when the sample biomarker profile can be obtained by evaluating the level of the at least one biomarker in situ. In preferred embodiments, the biological sample is a sample obtained from the subject that is reasonably expected to comprise the at least one biomarker of interest. Non-limiting examples of biological samples include, but are not limited to, tissue, bodily fluid (for example, blood, serum, plasma, saliva, urine, tears, peritoneal fluid, ascitic fluid, vaginal secretion, breast fluid, breast milk, lymph fluid, cerebrospinal fluid or mucosa secretion), umbilical cord blood, chorionic villi, amniotic fluid, an embryo, embryonic tissues, lymph fluid, cerebrospinal fluid, mucosa secretion, or other body exudate, fecal matter, an individual cell or extract of the such sources that contain the protein or nucleic acid of the same, and subcellular structures such as mitochondria, obtained using protocols well established within the art. In certain embodiments, the biological sample contains blood, especially peripheral blood, or a fraction or extract thereof, such as serum or plasma.

[0173] In some embodiments disclosed herein, the biological sample is a whole blood sample. In some embodiments, the biological sample is a serum sample. In some embodiments, the biological sample is a plasma sample.

[0174] The biological sample may be processed and analyzed for the purpose of determining the sample biomarker profile, in accordance with the present invention, almost immediately following collection (i.e., as a fresh sample), or it may be stored for subsequent analysis. If storage of the biological sample is desired or required, it would be understood by persons skilled in the art that it should ideally be stored under conditions that preserve the integrity of the biomarker of interest within the sample (e.g., at -80°C).

[0175] By "obtained" is meant to come into possession. Biological or reference samples so obtained include, for example, nucleic acid extracts or polypeptide extracts isolated or derived from a particular source. For instance, the extract may be isolated directly from a biological fluid or tissue of a subject. Stratifying a subject to a treatment regimen

[0176] As noted elsewhere herein, the present disclosure extends to a method of treating or preventing BPD in an infant, the method comprising identifying an infant at risk of developing BPD according to a method described herein; and administering a treatment for BPD to the infant identified as being risk of developing BPD.

[0177] In yet another aspect disclosed herein, there is provided a method of stratifying an infant identified as being at risk of developing bronchopulmonary dysplasia (BPD) to a treatment regimen, the method comprising: (1) identifying whether an infant is at risk of developing BPD according to a method described herein; and (2) where the infant is determined from step (1) to be at risk of developing BPD, stratifying the infant to a treatment regimen for BPD.

[0178] The present disclosure also extends to the management of risk of developing BPD in a subject. The management of said risk can include the use of therapeutic agents or treatment regimens for treating BPD, or a symptom thereof. Suitable treatment regimens will be familiar to persons skilled in the art, illustrative examples of which may include improved nutrition (e.g., up to 140 to 150 Kcal / Kg / day and protein intake from 3.5 to 4 g / kg / day), fluid restriction (e.g., to a total fluid volume of 120 to 150 ml / kg / day), non-invasive ventilation to limit barotrauma and volutrauma, minimized hypoxia (e.g., a SpO target range of 88-94% with a lower alarm limit of 88% and a higher alarm limit of 96%), the use of corticosteroids, diuretics (e.g., thiazides and loop diuretics) and / or bronchodilators.

[0179] The term "treating" as used herein, unless otherwise indicated, means alleviating, inhibiting the progress of, or preventing, either partially or completely, the pathophysiology of BPD, or a symptom thereof. The term "treatment" as used herein, unless otherwise indicated, refers to the act of treating.

[0180] Following diagnosis, the treatment regimen to be adopted or prescribed may depend on several factors, including the age, weight and general health of the subject. Another determinative factor may be the degree of risk of developing BPD as determined by the sample biomarker profile in accordance with the methods described herein. For instance, where the subject is determined to be at high risk of developing BPD, a different mode of ventilation support may be applied, such as high frequency oscillating ventilation, as compared to a subject who is determined to be at low risk of developing BPD. The treatment regimen may also depend on existing clinical parameters relevant to the BPD, such as birth weight, gestational age, and / or sex.

[0181] Thus, the present disclosure contemplates exposing the subject to a treatment regimen if the subject is determined to be at risk of developing BPD in accordance with the methods described herein. Non-limiting examples of such treatment regimens are describedelsewhere herein. In some embodiments, the subject is exposed to a combination of two or more additional treatment regimens (e.g., 2, 3 or more, 4 or more, 5 or more, 6 or more). Kits

[0182] The present disclosure also extends to a kit for identifying an infant at risk of developing bronchopulmonary dysplasia (BPD), the kit comprising one or more reagents for determining the level of at least one biomarker in a biological sample from an infant, wherein the at least one biomarker is selected from the group consisting of IGLV3-27, EXTL2, TUBB, TUBB1, MMP9, MST1L, KRT2, AHSG, KRT17, COL18A1, AGT, ISLR, EFEMP1, NRCAM, CD5L, COL1A2, FAP, APOD, ARPC1B, LDLR, CCT4, MSN, NRP1, SPTA1, PTGDS, DPYSL2, CTSD, ANXA1, PSMA6, VCL, CFHR2, CFB, CD14, CFH, CFI, MYH9, F12, LRG1, APCS, F10, C8A, AMBP, PRG2, SAA4, LBP, APRT, RAN, ROBO4, SBSN, APOA4, KLKB1, F11, C4BPA, CD44, UGP2, PROS1 and ADAMTSL4.

[0183] In an embodiment, the at least one biomarker is selected from the group consisting of IGLV3-27, EXTL2, TUBB, MMP9, COL18A1, CD5L, F12, FAP, CCT4, MSN, VCL, CFB, CFI, MYH9 and LRG1. In another embodiment, the at least one biomarker is selected from the group consisting of IGLV3-27, EXTL2, VCL, CFB, MYH9, CCT4M and CD5L.

[0184] The kits may suitably contain reagents for obtaining a sample biomarker profile in accordance with the methods as herein described. Kits for carrying out the methods of the present invention may include, in suitable container means, (i) a reagent for detecting the at least one biomarker, (ii) a probe that comprises an antibody or nucleic acid sequence that specifically binds to the at least one biomarker, (iii) a label for detecting the presence of the probe and (iv) instructions for how to measure the level of expression of the at least one biomarker. The container means of the kits will generally include at least one vial, test tube, flask, bottle, syringe and / or other container into which a first antibody specific for the at least one biomarker or a first nucleic acid specific for the at least one biomarker may be placed and / or suitably aliquoted. In some embodiments, the container means of the kit may be provided in a lateral flow format, wherein a first antibody specific for the at least one biomarker or a first nucleic acid specific for the at least one biomarker is fixed on a substrate that is contained within the lateral flow device. Where a second and / or third and / or additional component is provided, the kit will also generally contain a second, third and / or other additional container into which this component may be placed. Alternatively, a container may contain a mixture of more than one reagent, each reagent specifically binding adifferent biomarker in accordance with the present invention, when required. The kits of the present invention will also typically include means for containing the reagents (e.g., nucleic acids, polypeptides etc.) in close confinement for commercial sale. Such containers may include injection and / or blow-moulded plastic containers into which the desired vials are retained.

[0185] The kits may further comprise positive and negative controls, including a reference biomarker profile, as well as instructions for the use of kit components contained therein, in accordance with the methods of the present invention.

[0186] All the essential materials and reagents required for detecting and quantifying biomarker expression products may be assembled together in a kit, which is encompassed by the present invention. The kits may also optionally include appropriate reagents for detection of labels, positive and negative controls, washing solutions, blotting membranes, microtiter plate dilution buffers and the like. For example, a nucleic acid-based detection kit may include (i) a biomarker polynucleotide (which may be used as a positive control), (ii) a primer or probe that specifically hybridizes to a biomarker polynucleotide. Also included may be enzymes suitable for amplifying nucleic acids including various polymerases (Reverse Transcriptase, Taq, Sequenase^DNA ligase etc. depending on the nucleic acid amplification technique employed), deoxynucleotides and buffers to provide the necessary reaction mixture for amplification. Such kits also generally will comprise, in suitable means, distinct containers for each individual reagent and enzyme as well as for each primer or probe. Alternatively, a protein-based detection kit may include (i) a biomarker polypeptide (which may be used as a positive control), (ii) an antibody that binds specifically to a biomarker polypeptide. The kit can also feature various devices (e.g., one or more) and reagents (e.g., one or more) for performing one of the assays described herein; and / or printed instructions for using the kit to quantify the expression of a biomarker gene.

[0187] The present disclosure also extends to devices for identifying an infant at risk of developing bronchopulmonary dysplasia (BPD), the device comprising one or more reagents for determining the level of at least one biomarker in a biological sample from an infant, wherein the at least one biomarker is selected from the group consisting of IGLV3-27, EXTL2, TUBB, TUBB1, MMP9, MST1L, KRT2, AHSG, KRT17, COL18A1, AGT, ISLR, EFEMP1, NRCAM, CD5L, COL1A2, FAP, APOD, ARPC1B, LDLR, CCT4, MSN, NRP1, SPTA1, PTGDS, DPYSL2, CTSD, ANXA1, PSMA6, VCL, CFHR2, CFB, CD14, CFH, CFI, MYH9, F12, LRG1, APCS, F10, C8A, AMBP, PRG2, SAA4, LBP, APRT, RAN, ROBO4,SBSN, APOA4, KLKB1, F11, C4BPA, CD44, UGP2, PROS1 and ADAMTSL4. In an embodiment, the at least one biomarker is selected from the group consisting of IGLV3-27, EXTL2, TUBB, MMP9, COL18A1, CD5L, F12, FAP, CCT4, MSN, VCL, CFB, CFI, MYH9 and LRG1. In another embodiment, the at least one biomarker is selected from the group consisting of IGLV3-27, EXTL2, VCL, CFB, MYH9, CCT4M and CD5L. Suitable devices will be familiar to persons skilled in the art, illustrative examples of which include lateral flow devices, such as those described, for example, in Koczula and Gallotta (Essays Biochem. 2016 30; 60(1):111-20) and Di Nardo et al. "Ten Years of Lateral Flow Immunoassay Technique Applications: Trends, Challenges and Future Perspectives". Sensors (Basel).2021; 21(15):5185). Other illustrative examples of suitable rapid, point-of-care devices include clinical chemistry analysers, such as the Samsung LABGEOPT10, and blood gas analysers.

[0188] In another aspect disclosed herein, there is provided an array comprising one or more immobilized binding agents, wherein the one or more immobilized binding agents are capable of specifically binding to one or more biomarkers present in a biological sample, wherein the one or more biomarkers are selected from the group consisting of IGLV3-27, EXTL2, TUBB, TUBB1, MMP9, MST1L, KRT2, AHSG, KRT17, COL18A1, AGT, ISLR, EFEMP1, NRCAM, CD5L, COL1A2, FAP, APOD, ARPC1B, LDLR, CCT4, MSN, NRP1, SPTA1, PTGDS, DPYSL2, CTSD, ANXA1, RAN, PSMA6, VCL, CFHR2, CFB, CD14, CFH, CFI, MYH9, F12, LRG1, APCS, F10, C8A, AMBP, PRG2, SAA4, LBP, APRT, ROBO4, SBSN, APOA4, KLKB1, F11, C4BPA, CD44, UGP2, PROS1 and ADAMTSL4.

[0189] In an embodiment, the one or more biomarkers are selected from the group consisting of IGLV3-27, EXTL2, TUBB, MMP9, COL18A1, CD5L, F12, FAP, CCT4, MSN, VCL, CFB, CFI, MYH9 and LRG1. In another embodiment, the one or more biomarkers are selected from the group consisting of IGLV3-27, EXTL2, VCL, CFB, MYH9, CCT4M and CD5L.

[0190] In an embodiment, the one or more biomarkers are selected from the group consisting of IGLV3-27, EXTL2, TUBB, TUBB1, MMP9, MST1L, KRT2, AHSG, KRT17, COL18A1, AGT, ISLR and EFEMP1. In an embodiment, the one or more biomarkers are selected from the group consisting of MMP9 NRCAM, CD5L, F12, COL1A2, FAP, APOD, ARPC1B, LDLR, CCT4, MSN, NRP1, SPTA1, PTGDS, DPYSL2, CTSD, ANXA1, RAN, AHSG and PSMA6. In an embodiment, the one or more biomarkers are selected from the group consisting of VCL, CFHR2, CFB, CD14, CFH, CFI, MYH9, F12, LRG1, APCS, F10,C8A, AMBP, PRG2, SAA4, LBP, APRT, RAN, ROBO4, SBSN, APOA4, KLKB1, F11, C4BPA, CD44, UGP2, PROS1 and ADAMTSL4.

[0191] In an embodiment, the one or more biomarkers are selected from the group consisting of AHSG, CFB and CFI. In another embodiment, the one or more biomarkers are selected from the group consisting of AHSG, AGT, CCT4, ANXA1, CFB, MYH9, CFI, CD14 and C4BPA.

[0192] It will be appreciated that the above described terms and associated definitions are used for the purpose of explanation only and are not intended to be limiting.

[0193] In order that the invention may be readily understood and put into practical effect, particular preferred embodiments will now be described by way of the following non-limiting examples. EXAMPLES

[0194] The aim of this study was to (1) identify biomarkers associated with the development of BPD in an infant, (2) elucidate some of the biological mechanisms underlying early preterm lung injury, and (3) assess the diagnostic accuracy of these proteins, including when combined with the three strongest known clinical predictors of BPD risk: gestational age, birth weight and sex. MATERIALS AND METHODS A. Study site and patient enrolment criteria

[0195] This proof of concept, observational, discovery study was conducted at the Newborn Intensive Care Unit of The Royal Women’s Hospital, Melbourne, Australia. An initial study examining changes in four protein biomarkers was approved by the Human Research Ethics Committee of The Royal Women’s Hospital (HREC 49838). An approved amendment to the original protocol allowed for proteome analysis of residual plasma samples if additional consent from parents was acquired and the infant had not died prior to 36 weeks PMA. Infants were eligible for inclusion if they were born <29 weeks’ gestation, likely to need respiratory support in the first 72 hours post-birth and informed parental consent (either prospectively or deferred) was obtained within 24 hours of birth. Infants were excluded if the clinical team requested the parents not be approached for the study in the timeframe of eligibility, or the infant had a major congenital anomaly or pulmonary hypoplasia. Infantsmanaged via a deferred consent pathway were withdrawn from the study and data not analysed if parents declined participation. B. Blood collection

[0196] We aimed to collect blood samples from infants as soon as possible after admission at 24- and 72-hours post-birth (+ / - 4 hours) in EDTA-treated tubes, centrifuged at 1,500xg for 10 minutes and the plasma stored at -80°C in 15µl aliquots prior to full batch transfer to the Australian Proteomics Analysis Facility (Sydney, Australia) for mass spectrometry assessment. C. Demographic and clinical data

[0197] Birth weight, sex, gestational age and maternal information (prenatal steroid administration, chorioamnionitis diagnosis and mode of birth) were recorded.

[0198] Information relating to acute illness severity was collected at all time points. Respiratory variables collected at the time of blood sampling included fraction of inspired oxygen (FiO2), peripheral oxygen saturation (SpO2), mode of ventilation and positive end- expiratory pressure (PEEP) with continuous positive airway pressure level classified as PEEP for analysis. In addition, the mean FiO2in the preceding 6 hours (24- and 72-hours post-birth sampling points) or since birth (4-hour post-birth sampling point) was also recorded. S / F ratio (SpO2 / FiO2)(19, 20) was calculated. Arterial or capillary blood gas results including partial pressure of carbon dioxide (pCO2) and arterial oxygen tension (PaO2; if available) were collected. Where arterial blood gas results were available measures of acute lung injury were calculated as follows: alveolar-arterial oxygen diffusion gradient, (21) P / FP ratio (PaO2 / FiO2x PEEP) (22) and arterial / Alveolar (a / A) oxygenation ratio. (23)

[0199] Infants were assessed for BPD between 36+0and 36+6weeks PMA using the Walsh criteria.(24) Infants with an effective FiO2less than 0.3 whilst receiving low flow oxygen therapy also underwent a modified Walsh air reduction trial (Figure 5). (25) The total duration of invasive ventilation at BPD assessment was recorded. D. Sequential Window Acquisition of All Theoretical Mass Spectra (SWATH-MS) assessment of plasma proteins

[0200] Plasma sample preparation: Briefly, 10 μL of plasma sample was diluted in 190 μL of 50 mM ammonium bicarbonate solution before reducing with 5 mM dithiothreitol (DTT) and alkylating with 10mM iodoacetamide (IAA). 33 µg of each sample was thendigested trypsin (1:25 ratio) for 16 hours at 37 °C. The digested sample was acidified prior to information dependent acquisition (IDA) and SWATH-MS analysis.

[0201] IDA MS: A 6600 TripleTOF mass spectrometer (AB Sciex) coupled to an Ekspert™ nanoLC 425 system (Eksigent) was employed for both IDA and SWATH-MS analysis. 10 μl of a randomly chosen sample was used for 1D-IDA. For 2D-IDA, 11 μl of each sample were pooled and fractionated by High pH-reversed phase-high-performance liquid chromatography (HpH-RP-HPLC). Following HpH-RP-HPLC separation, 17 fractions were dried and resuspended, with 10 μl of each fraction then injected for 2D IDA.10 µl of each sample was used for SWATH-MS analysis. SWATH-MS were acquired in random order with one blank run after each sample. The same sample used in 1D-IDA was used as a technical replicate in SWATH-MS.

[0202] SWATH-MS data processing: Protein identification from IDA data was performed with ProteinPilot (v5.0; AB Sciex) based on the Homo sapien SwissProt database containing 20, 420 proteins. Carbamidomethylation of Cysteine residues was selected as a fixed modification. An Unused Score cut-off was set to 1.3 (95% confidence for identification) and global protein False Discovery Rate (FDR) of 1%. The ion library used in SWATH extraction and quantitation was constructed from 1D and 2D-IDAs and contained 829 proteins. SWATH data were extracted using PeakView (v2.2) with peptides (max 100 peptides per protein) with confidence ≥99% and FDR ≤ 1% included in the quantitation and shared and modified peptides excluded. A local peptide assay library was derived from the 1D IDA and contained 163 identified proteins. E. SOMAscan® assessment of plasma proteins

[0203] A custom SOMAscan® assay was developed with the following protein targets; EXTL2, TUBB1, MMP9, AHSG, KRT17, COL18A1, AGT, ISLR, EFEMP1, NRCAM, CD5L, FAP, APOD, ARPC1B, LDLR, MSN, NRP1, SPTA1, PTGDS, DPYSL2, CTSD, ANXA1, AHSG, PSMA6, VCL, CFHR2, CFB, CD14, CFH, CFI, MYH9, F12, LRG1, APCS, F10, C8A, AMBP, PRG2, SAA4, LBP, APRT, RAN, ROBO4, SBSN, APOA4, KLKB1, F11, C4BPA, CD44, UGP2, and PROS1.

[0204] Plasma sample preparation: Briefly, 55 μL of EDTA-treated plasma sample was treated according to the following steps: (i) Analyte / primer beads-binding SOMAmers (consisting of a synthetic fluorophore-labeled SOMAmer bound via a photocleavable linker to a biotin moiety) underwent equilibration; (ii) Analyte / SOMAmer complexes wereimmobili zed using a streptavidin-substituted matrix; (iii) UV light was used to cleave, and thus analyte–SOMAmer complexes were released into solution; (iv) Subsequent immobilization of analyte–SOMAmer complexes via analyte-borne biotinylation onto a streptavidin matrix; (v) Elution of complexes allowing release of SOMAmers that act as analyte quantification surrogates; (vi) Hybridization to SOMAmer-complementary oligonucleotides to enable quantification. Standards were used for calibration as described previously in O’Sullivan et al., Nature Methods, 2010, v7, p.876-882.

[0205] SOMAscan® data processing: Post data extraction, data quality was visually confirmed via density plots of the relative fluorescence unit (RFU) and global normalization of the data performed. Correlation analysis was performed for each biomarker comparing intensity reporter value (SWATH-MS) against RFU (SOMAscan®) to provide an idea of the correspondence between the two protein detection methodologies. E. Sample Size

[0206] We aimed to study a convenience sample of 30 infants based upon the maximum permitted sample population from our primary four protein biomarker study. F. Statistical Analysis

[0207] Demographic and Clinical data: GraphPad Prism 9v.3.0. (San Diego, CA) was used for statistical analysis of demographic and clinical data and p<0.05 considered significant. Difference in categorical variables were tested using Fischer’s exact test. Normality of numerical data was assessed using Shapiro-Wilk test. Parametric data is shown as mean ± standard deviation and statistical differences were tested using the unpaired t-test. Non-parametric data is shown as median (interquartile range) and the Mann-Whitney test used to assess statistical differences.

[0208] Determination of differential protein expression (DEP): To identify significant alterations in protein abundance at each time point between infants who were diagnosed with BPD at 36 weeks’ PMA (BPD group) and those who were not (no BPD group) the log transformed protein abundance of each identified protein was compared using Welch’s t-test (GraphPad v9.0). P values of <0.05 were considered significant. G. Bioinformatic Analysis

[0209] Molecular function, biological process and protein class composition of identified DEPs were assigned using the PANTHER tool. (26) Mapping of protein networks andenrichment analysis of Reactome pathways associated with DEPs was performed using the STRING database (v11.5). (27) H. Identification of BPD Biomarker Candidates

[0210] In the initial study using the SWATH-MS, candidate biomarkers proteins were determined to be DEPs in whom an initial C-index evaluation (unadjusted for optimism) was statistically significant (unadjusted likelihood ratio test <0.05). To visualise how patients were clustered by abundance of candidate biomarker proteins, unsupervised hierarchical clustering was performed and heatmaps generated using the ClustVis online tool. (28) I. BPD Risk Prediction Analysis

[0211] BPD risk models were formulated using the SWATH-MS data. In this approach, binary logistic regression models were constructed with traditional risk factors (birth weight, gestational age, sex) with or without protein biomarker candidates (identified above at each timepoint, 4-, 24-, 72-hours post-birth) and BPD as the outcome. To simplify reporting respiratory acuity was binomially stratified during data analysis and defined as need for invasive mechanical ventilation at Day 10 after birth and need for an FiO2>0.25

[0212] Model performance measures: Two overall measures of performance were used to assess the BPD prediction capability of currently used clinical risk factors (birth weight z- score, gestational age, sex) with or without candidate protein biomarkers. The choice of clinical risk factors was predicated, at least in part, on an intention to use known BPD predictive measures that would not be impacted by postnatal clinical care. The first was Nagelkerke’s R2 that captures aspects of discrimination (i.e. the ability to distinguish high- from low-risk subjects) and calibration (i.e. how well predicted risk agrees with actual risk) (29). The second measure of performance, the C-index provides a measure of discrimination and is a generalisation of the area under a receiver operating characteristic curve. (30)

[0213] Internal validation and bootstrapping to correct for over-optimism: We internally validated the performance of every predictive model with bootstrapping (1000 per model). Simulation studies have demonstrated that this approach (compared to others, e.g. cross- validation), provides less biased and more stable estimates of optimism-corrected performance. (31) Optimism refers to the inherent bias toward overestimated performance metrics (e.g., C-index) in the internal derivation dataset.(32) Measures of optimism for given performance metrics are estimated from the average of many models derived from bootstrap samples which are then used to subtract from (adjust) the original performance metrics. Thisinternal validation approach moderates our expectations from the predictive model by providing more realistic model performance estimates.

[0214] In follow-up studies examining changes in protein abundance identified using the SOMAscan® assay, receiver operating curves (ROC) were used to understand the specificity and sensitivity of protein biomarker candidates identified within the SWATH-MS dataset. Example 1 - STROBE diagram detailing study recruitment

[0215] Due to restrictions during the COVID-19 pandemic, a total of 28 infants were recruited. Consent to study the plasma proteome was obtained in 23 infants (Figure 1).

[0216] Table 1, below, provides a comparison of demographic data of the enrolled infants. Table 1:

[0217] BPD bronchopulmonary dysplasia. *Includes incomplete, complete<7days and complete>7 days. Data are number of infants (percentage) or mean ± standard deviation. P value referring to comparison testing via †Fisher’s exact test or unpaired t-test.

[0218] Measures of acute illness severity were similar between the BPD and No BPD groups over the first 72 hours post-birth, with the exception of a BPD-associated increase in pCO2(p=0.051; [Mann-Whitney test]) and reduction in the P / FP ratio (p = 0.09 at 4-hours post-birth, and an increase in mean FiO2over the previous 6 hours (p = 0.005) and reductionin the S / F ratio at 72 hours post-birth (Table 2). Measures of acute illness severity were similar between the BPD and no BPD groups over the first 72 hours post-birth. pCO2was within the Unit’s range of 40-55 mmHg, except for the No-BPD infants at 4 and 72 hours who had lower pCO2. In both groups, the level of oxygen support reduced with time (Table 2).Example 2 - SWATH-MS identification of the plasma proteome

[0220] SWATH-MS identified a total of 453 plasma proteins. 71 proteins were differentially expressed (DEPs) between the BPD and no BPD groups and the number of DEPs increased with time (Figure 2A). Matrix metallopeptidase 9 (MMP9) was the only DEP identified at all time-points. Volcano plots were used to visualize patterns of differential expression at each time point (Figure 2B). At all time-points, more DEPs were increased in abundance than decreased. PANTHER (26) was used to assign the molecular function, biological process and protein class of DEPs at each time point (Figure 2C-E). Amongst the molecular functions identified as over-represented amongst DEPs; binding, catalytic activity and molecular function regulator were identified at all time points, however structural molecule activity was only observed at 4-hours post-birth, and proteins with molecular transducer activity only identified at 24- and 72-hours post-birth (Figure 2C). There were fewer biological process associations at 4-hours compared with the other time-points, with biological adhesion, localization and locomotion processes proteins only identified at 24- and 72-hours (Figure 2D). Similarly, DEPs at 4-hours exhibited the least diversity in protein classes (7 protein classes), when compared with 24-hours (12 protein classes) and 72-hours (9 protein classes) with a 3-fold increase in the defence / immunity protein class observed between 4- and 72-hours and a concurrent 3-fold reduction in cytoskeletal class DEPs over the same period (Figure 2D).

[0221] Protein group clustering is shown in Figure 3(A-C) for each time point, with enriched Reactome pathways highlighted and a summary of temporal pathway enrichments detailed in Figure 3D. At all timepoints, the STRING network had significantly more interactions than expected, indicating that the DEP set was biologically connected as a group. Several pathways were enriched only at a single time-point and these included; Metabolism of proteins (4-hours), axon guidance and signal transduction (24-hours) and formation of fibrin clot, the innate immune system and regulation of the complement cascade (72-hours). There were no common pathways identified between 24 hours and either 4 or 72-hours. Derangements in haemostasis overall and platelet degranulation specifically were identified at both 4- and 72-hours, with manual annotation identifying 28 DEPs from across the time points which mapped to the coordinated coagulation and complement cascades (Figure 3E).Example 3 - BPD prediction modelling

[0222] The overall performance estimates (optimism-adjusted C-index, Nagelkerke’s R2) in the full prediction model (birth measures + DEP) together with the difference in performance estimates ( ΔC-adj ΔN-adj) to indicate performance improvement when the protein biomarker is added to the base model (gestational age, weight, sex) are shown for all potential biomarkers in Table 3 below, with the top 5 proteins ranked by optimism adjusted C-index, as shown in Table 4 below. Of the 71 DEPs identified across all time-points, 85% of DEPs at 4 hours, 70% at 24 hours and 68% at 72 hours were confirmed as potential BPD biomarkers. The addition of protein abundance to the base model increased the C-index from 0.66 (base model only) to 0.69-0.83 (base model + DEP) indicating high variability in the ability of the DEP to meaningfully improve prediction of BPD from the base model. For example, whilst adding ‘IGLV3-27’ to the base model increased the C-index increased from 0.66 to 0.80, Nagelkerke’s R2 by 49.5% and the overall model fit (LRT p<0.001) the addition of ‘ISLR’ to the base model only improved the C-index by 0.03, reduced Nagelkerke’s R2 by 2.9% and did not improve overall model fit (LRT p=0.127).

[0223] Using protein abundance of the 13-potential BPD biomarkers identified at 4 hours, two patient clusters were observed in unsupervised clustering (Figure 4A). Cluster 1 contained two participants from the BPD group with FiO2>0.25 at the time of blood sampling. Cluster 2 included the remaining patients with BPD and a subgroup of patients who both did not have BPD and received invasive ventilation were mechanically ventilated for less than 10 days. In the prediction analysis, the range of optimism-adjusted C-index at 4 hours was 0.69-0.84. The top-5 proteins included the immune protein IGLV3-27, EXTL2 which is required for heparan-sulfate biosynthesis, the microtubule structural protein TUBB, the extracellular matrix protein COL18A1 and the tissue remodelling protein MMP9.

[0224] At 24 hours post-birth the 20-protein panel grouped participants into those with BPD and those without BPD in unsupervised hierarchical clustering analysis (Figure 4B). The predictive performance of the 24-hours protein panel was lower than that of the 4-hour panel (0.65-0.78). The 5 proteins with the highest predictive performance were the chaperone protein CCT4, tissue remodelling and wound healing protein FAP, coagulation factor F12, immunity and actin cytoskeleton protein MSN and the lipid synthesis regulator CD5L.

[0225] Unsupervised clustering indicated that the 28-protein panel identified at 72-hours could differentiate between patients with and without a BPD diagnosis. All patients within the no BPD cluster received invasive ventilation for less than 10 days (Figure 4C). The range of adjusted C-index values was greatest at 72 hours; 0.42-0.83. The 5 best predictors of BPD outcome were complement proteins CFB and CFI, cytoskeletal protein VCL, cell adhesion molecule MYH9 and LRG1 which is involved in wound healing. Table 3: Biomarker prediction at 4-, 24- and 72-hours post-birth. Full model includes birth risk factors (gestational age, gender, sex) plus protein. Base model includes only birth risk factors - the optimism-adjusted C-index and Nagelkerke’s R2for the base model were 0.668 and 0.068, respectively.

[0226] C=C-index; C-adj=optimism-adjusted C-index; N=Nagelkerke’s R2value; N- adj=optimism-adjusted Nagelkerke’s R2value; ΔC-adj=difference in optimism-adjusted C- indexes between base and full model; ΔN-adj=difference in optimism-adjusted Nagelkerke’s R2values between base and full model; LRT p=Likelihood Ratio Test p-value comparing full vs base model. Table 4: Biomarker prediction at 4-, 24- and 72-hours post-birth. Full model includes birth risk factors (gestational age, weight, sex) plus protein. Base model includes only birth risk factors - the optimism-adjusted C-index and Nagelkerke’s R2for the base model were 0.668 and 0.068, respectively. Differences in performance estimates ( ΔC-adj ΔN-adj) and a likelihood ratio test (LRT) indicate potential predictive performance improvements between models with / without protein biomarkers.

[0227] DEP=Differentially expressed protein; BPD=Bronchopulmonary dysplasia; FC=Fold change; C=C-index; C-adj=optimism-adjusted C-index; N=Nagelkerke’s R2value; N-adj=optimism-adjusted Nagelkerke’s R2value; ΔC-adj=difference in optimism-adjusted C-indexes between base and full model; ΔN-adj=difference in optimism-adjusted Nagelkerke’s R2values between base and full model; LRT p=Likelihood Ratio Test p-value comparing full vs base model. Table 5: Combinations of biomarkers evaluated in plasma samples obtained from premature infants at 4 hours, 24 hours or 72 hours that were shown to improve predictive power for BPD by >0.05.Example 4 - Comparison of protein abundance of candidate biomarkers from SWATH- MS and SOMAscan® assays

[0228] Biomarker expression was initially detected using Sequential Window Acquisition of all Theoretical Mass Spectra (SWATH-MS), a data-independent acquisition (DIA) method that uses mass spectrometry to perform quantitative analysis of proteins with quantitative accuracy and consistency. Proteins were designated potential biomarkers if they differed in abundance between the BPD and no BPD groups and, if combined with birth characteristics, could predict BPD within the first 72 hours of birth (defined by p<0.05 in likelihood ratio testing). After identification of potential biomarkers within the SWATH-MS dataset, protein abundance of the candidate biomarkers was measured in remnant study plasma samples using SOMAscan® assay technology as an alternative protein detection method, including to confirm if the protein biomarkers maintained predictive ability when proteins were detected via a fluorescence-based protein-detection assay. Whilst both SWATH-MS and SOMAscan® are capable of detecting protein abundance in a sample, SOMAscan® relies on the specificity of antibodies for target antigens (i.e., the biomarker candidate), whereas mass spectrometry directly measures the mass of molecules and their fragments that makes up a protein. This makes SOMAscan® generally less sensitive (requiring higher detection limits) than SWATH-MS / mass spectrometry. In any event, proteins detected with both systems have the highest potential for further confirmation, insofar as such biomarkers may be detected by less sensitive, rapid detection system (SOMAscan®) systems, as well as by more sensitive systems (SWATH-MS).

[0229] Table 6 below details a comparison of changes in protein abundance and statistical significance between the SWATH-MS and SOMAscan® datasets. In addition,correlation analysis was performed to directly compare the two protein detection methodologies.

[0230] As shown in Table 6, at the 4 hour time-point, two biomarkers showed strong predictive performance across both SWATH-MS and SOMAscan® datasets (ANGT / AGT, FETUA / AHSG), and 4 biomarkers showed strong predictive performance across only a single dataset (LV327 / IGLV3-27, TBB5 / TUBB, MSTL, K22E / KRT2).

[0231] At the 24 hour time-point, two biomarkers showed strong predictive performace across both SWATH-MS and SOMAscan® datasets (NRCAM, FETUA / AHSG) and two biomarkers showed strong predictive performance across only a single dataset (TCPD / CCT4, CO1A2 / COL1A2).

[0232] At the 72 hour time-point, six biomarkers showed strong predictive performance across both SWATH-MS and SOMAscan® datasets (CFAB / CFB, MYH9, CFAI / CFI, CD14, SAMP / APCS, C4BPA).

[0233] Figure 6 shows the abundance and relative abundance of protein biomarkers, as determined by SWATH-MS or SOMAscan®, in plasma samples taken from BPD and no BPD patients at 4, 24 and 72 hours post-birth.

[0234] The biomarker potential of the following proteins was confirmed by both SWATH-MS and SOMAscan®: AGT, AHSG, NRCAM, ANXA1, AHSG, CFB, CFI, MYH9, CD14, APCS, C4BPA, and SAA4.

[0235] Difference in abundance was not shown with SOMAscan® for the following proteins: EXTL2, COL18A1, MMP9, KRT17, EFEMP1, ISLR, FAP, F12, MSN, CD5L, ARPC1B, DPYSL2, LDLR, PSMA6, APOD, RAN, NRP1, CTSD, PTGDS, SPTA1, VCL, LRG1, CFHR2, SBSN, APOA4, PRG2, UGPA, CD44, C8A, RAN.

[0236] The protein biomarkers IGLV3-27, TUBB, MST1L, KRT2, COL1A2, CCT4, and ADAMTSL4 were not available for testing with SOMAscan®. Example 5 - ROC analysis of a subset of candidate biomarkers and birth characteristics

[0237] Multiple logistic regression analysis was performed to assess if the addition of birth characteristics to protein biomarker candidate improved predictive outcome in both the SWATH-MS and SOMAscan® datasets.

[0238] Two models were tested: BPD~protein+GA+BW z-score+sex and BPD~protein+GA+BW z-score.

[0239] As shown in Table 7, below, all candidate biomarkers improved the ability to predict BPD when compared to the birth characteristic prediction model. Best predictive ability for ANGT / AGT (4 hours) and CO1A2 / COLA2 (24 hours) was in the protein only model. For all other potential biomarkers, prediction was highest when combined with birth weight z-score, gestational age and + / - sex, depending on protein.Discussion

[0241] Reducing the trajectory of life-long chronic lung disease in infants born extremely preterm, requires identification of the biological mediators which characterise early lung injury responses. This study demonstrates, for the first time, a protein signature of BPD risk within the first 72 hours post-birth and is the only study to detail alterations in the profile of circulating proteins over multiple time points, providing a comprehensive map of early pathological changes to date. This study further implicates a broader wound healing response which is temporally specific and includes hemostasis disturbances via platelet degranulation as early as 4-hours post-birth and altered axon guidance molecules (AGMs) at 24-hours post-birth. Identification of AGMs was unexpected.

[0242] This study also sought to assess the ability of the identified biomarkers to improve BPD prediction, including in combination with three important birth measures of risk - gestational age, birth weight z-score and sex. The robust prediction modelling employed in this study included two estimate methods: (i) the C-index to assess the ability of the models to discriminate between groups and (ii) Nagelkerke’s R-squared value which provides an assessment of the overall model performance. In both methods, optimism-adjusted bootstrapping was used given that the original C-index estimates in the combinatorial model for IGLV3-27 and EXTL2 suggested 100% predicative accuracy when combined with birth measures, a highly improbable outcome in view of the relatively small study cohort. The estimated accuracy was reduced but remained high, at 80% (IGLV3-27) and 84% (EXTL2), following C-index adjustment.

[0243] The best protein biomarkers candidates were IGLV3-27 and EXTL2 identified at 4 hours post-birth. These proteins exhibited the largest fold change in expression (-1.57 and 2.04 respectively) and a predictive ability of 80% and 84% when combined with birth measures. The identification of aberrant EXTL2 levels within the first hours post-birth suggests that early targeting of EXTL2 or mechanisms to maintain normal EXTL2 levels would be beneficial.

[0244] The results from this study illustrate an early perinatal landscape that includes not only inflammation but also periods of disturbed hemotasis and tissue remodelling in the hours following preterm birth. Protein alterations were temporally distinct, suggesting that appropriate lung protective interventions given at the right time may beneficially improve BPD rates. Further, biomarkers were identified that can advantageously predict BPD as early as 4 hours post-birth. Identification of lung injury within the first hours of respiratory support commencing may also significantly enhance the success of prognostic and therapeutic strategies applied within the early antenatal period.Table 8: Description of biomarkersTable 9: Categorisation of candidate biomarkers by biological role / pathwaysBIBLIOGRAPHY 6. Siffel C, Kistler KD, Lewis JFM, Sarda SP. Global incidence of bronchopulmonary dysplasia among extremely preterm infants: a systematic literature review. J Matern Fetal Neonatal Med.2021;34(11):1721-31. 7. Bhandari V, Sahni M. Can biomarkers be used to predict bronchopulmonary dysplasia? J Pediatr (Rio J).2021;97(3):253-5. 8. Bhargava M, Becker TL, Viken KJ, Jagtap PD, Dey S, Steinbach MS, et al. Proteomic profiles in acute respiratory distress syndrome differentiates survivors from non-survivors. PLoS One.2014;9(10):e109713. 9. Collaco JM, McGrath-Morrow SA, Griffiths M, Chavez-Valdez R, Parkinson C, Zhu J, et al. Perinatal Inflammatory Biomarkers and Respiratory Disease in Preterm Infants. J Pediatr.2022;246:34-9 e3. 10. D'Angio CT, Ambalavanan N, Carlo WA, McDonald SA, Skogstrand K, Hougaard DM, et al. Blood Cytokine Profiles Associated with Distinct Patterns of Bronchopulmonary Dysplasia among Extremely Low Birth Weight Infants. J Pediatr.2016;174:45-51 e5. 14. Laughon MM, Langer JC, Bose CL, Smith PB, Ambalavanan N, Kennedy KA, et al. Prediction of bronchopulmonary dysplasia by postnatal age in extremely premature infants. Am J Respir Crit Care Med.2011;183(12):1715-22. 15. Baker EK, Davis PG. Bronchopulmonary dysplasia outcome estimator in current neonatal practice. Acta Paediatr.2021;110(1):166-7. 16. Truog WE, Nyp MF, Taylor J, Gratny LL, Escobar H, Manimtim WM, et al. Infants born at <29 weeks: pulmonary outcomes from a hybrid perinatal system. J Perinatol.2014;34(1):59-63. 17. Vesoulis ZA, McPherson CC, Whitehead HV. Racial disparities in calculated risk for bronchopulmonary dysplasia: a dataset. Data Brief.2020;30:105674. 18. Thebaud B, Goss KN, Laughon M, Whitsett JA, Abman SH, Steinhorn RH, et al. Bronchopulmonary dysplasia. Nat Rev Dis Primers.2019;5(1):78.19. Bilan N, Dastranji A, Ghalehgolab Behbahani A. Comparison of the spo2 / fio2 ratio and the pao2 / fio2 ratio in patients with acute lung injury or acute respiratory distress syndrome. J Cardiovasc Thorac Res.2015;7(1):28-31. 20. Radicioni M, Leonardi A, Lanciotti L, Rinaldi VE, Bini V, Camerini PG. How to improve CPAP failure prediction in preterm infants with RDS: a pilot study. Eur J Pediatr.2021;180(3):709-16. 23. Dimitriou G, Fouzas S, Giannakopoulos I, Papadopoulos VG, Decavalas G, Mantagos S. Prediction of respiratory failure in late-preterm infants with respiratory distress at birth. Eur J Pediatr.2011;170(1):45-50. 24. Walsh MC, Wilson-Costello D, Zadell A, Newman N, Fanaroff A. Safety, reliability, and validity of a physiologic definition of bronchopulmonary dysplasia. J Perinatol.2003;23(6):451-6. 25. Walsh MC, Yao Q, Gettner P, Hale E, Collins M, Hensman A, et al. Impact of a physiologic definition on bronchopulmonary dysplasia rates. Pediatrics. 2004;114(5):1305-11. 26. Thomas PD, Campbell MJ, Kejariwal A, Mi H, Karlak B, Daverman R, et al. PANTHER: a library of protein families and subfamilies indexed by function. Genome Res.2003;13(9):2129-41. 27. Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, et al. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res.2015;43(Database issue):D447-52. 28. Metsalu T, Vilo J. ClustVis: a web tool for visualizing clustering of multivariate data using Principal Component Analysis and heatmap. Nucleic Acids Res. 2015;43(W1):W566-70. 29. Steyerberg EW, Vickers AJ, Cook NR, Gerds T, Gonen M, Obuchowski N, et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology.2010;21(1):128-38. 30. Steyerberg EW. Clinical Prediction Models: A practical Approach to Development, Validation, and Updating.1 ed. New York: Springer New York; 2009. 31. Steyerberg EW, Harrell FE, Jr., Borsboom GJ, Eijkemans MJ, Vergouwe Y, Habbema JD. Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. J Clin Epidemiol.2001;54(8):774-81.

Claims

CLAIMS:

1. A method of identifying an infant at risk of developing bronchopulmonary dysplasia (BPD), the method comprising: a. obtaining a sample biomarker profile of a biological sample from an infant, wherein the sample biomarker profile evaluates at least one biomarker that is differentially expressed in a biological sample from an infant at risk of developing BPD when compared to a corresponding biological sample from an infant who is not at risk of developing BPD, wherein the biological sample is obtained from the infant at a time point from birth to about 76 hours post-birth, and wherein the biological sample is selected from the group consisting of whole blood, plasma and serum; b. comparing the sample biomarker profile of step (a) to a reference biomarker profile, wherein the reference biomarker profile evaluates, for an individual biomarker in the sample biomarker profile, a corresponding biomarker; and c. determining whether the infant is at risk of developing BPD based on the comparison of the sample biomarker profile and the reference biomarker profile.

2. The method of claim 1, wherein the sample biomarker profile evaluates at least one biomarker selected from the group consisting of IGLV3-27, EXTL2, TUBB, TUBB1, MMP9, MST1L, KRT2, AHSG, KRT17, COL18A1, AGT, ISLR, EFEMP1, NRCAM, CD5L, COL1A2, FAP, APOD, ARPC1B, LDLR, CCT4, MSN, NRP1, SPTA1, PTGDS, DPYSL2, CTSD, ANXA1, PSMA6, VCL, CFHR2, CFB, CD14, CFH, CFI, MYH9, F12, LRG1, APCS, F10, C8A, AMBP, PRG2, SAA4, LBP, APRT, RAN, R0B04, SBSN, AP0A4, KLKB1, Fll, C4BPA, CD44, UGP2, PROS1 and ADAMTSL4.

3. The method of claim 1 or claim 2, further comprising taking into account one or more birth risk factors of the infant.

4. The method of claim 3, wherein the birth risk factor is selected from one or more of birth weight, gestational age and sex.

5. The method of any one of claims 1 to 4, wherein the biological sample is obtained from the infant at a time point from about 2-6 hours post-birth.

6. The method of claim 5, wherein the biological sample is obtained from the infant at about 4 hours post-birth.

7. The method of claim 5 or claim 6, wherein the at least one biomarker is selected from the group consisting of IGLV3-27, EXTL2, TUBB, TUBB1, MMP9, MST1L, KRT2, AHSG, KRT17, COL18A1, AGT, ISLR and EFEMP1.

8. The method of claim 7, wherein the at least one biomarker is selected from the group consisting of IGLV3-27, EXTL2, TUBB, MMP9 and COL18A1.

9. The method of claim 5 or claim 6, wherein the at least one biomarker is selected from the group consisting of ANGT and AHSG.

10. The method of any one of claims 1 to 4, wherein the biological sample is obtained from the infant at a time point from about 20-28 hours post-birth.

11. The method of claim 10, wherein the biological sample is obtained from the infant at about 24 hours post-birth.

12. The method of claim 10 or claim 11, wherein the at least one biomarker is selected from the group consisting of MMP9, NRCAM, CD5L, F12, COL1A2, FAP, APOD, ARPC1B, LDLR, CCT4, MSN, NRP1, SPTA1, PTGDS, DPYSL2, CTSD, ANXA1, RAN, AHSG and PSMA6.

13. The method of claim 12, wherein the at least one biomarker is selected from the group consisting of CD5L, F12, FAP, CCT4 and MSN.

14. The method of claim 12, wherein the at least one biomarker is selected from the group consisting of NRCAM and AHSG.

15. The method of any one of claims 1 to 4, wherein the biological sample is obtained from the infant at a time point from about 68-76 hours post-birth.

16. The method of claim 15, wherein the biological sample is obtained from the infant at about 72 hours post-birth.

17. The method of claim 15 or claim 16, wherein the at least one biomarker is selected from the group consisting of VCL, CFHR2, CFB, CD14, CFH, CFI, MYH9, F12, LRG1, APCS, F10, C8A, AMBP, PRG2, SAA4, LBP, APRT, RAN, ROBO4, SBSN, AP0A4, KLKB1, Fl 1, C4BPA, CD44, UGP2, PROS1 and ADAMTSL4.

18. The method of claim 17, wherein the at least one biomarker is selected from the group consisting of VCL, CFB, CFI, MYH9 and LRG1.

19. The method of claim 15 or claim 16, wherein the at least one biomarker is selected from the group consisting of CFB, MYH9, CFI, CD14, APCS, and C4BPA.

20. The method of any one of claims 1 to 19, wherein the infant is less than 37 weeks gestational age.

21. The method of claim 20, wherein the infant is less than 32 weeks gestational age.

22. The method of claim 20 or claim 21, wherein the infant is less than 29 weeks gestational age.

23. The method of any one of claims 1 to 22, wherein evaluation of the at least one biomarker comprises determining the level of at least one biomarker in plasma.

24. The method of any one of claims 1 to 23, wherein the at least one biomarker is a protein or an RNA molecule.

25. A method of treating or preventing BPD in an infant, the method comprising identifying an infant at risk of developing BPD according to the method of any one of claims 1 to 24; and administering a treatment for BPD to the infant identified as being risk of developing BPD.

26. The method of claim 25, wherein the treatment for BPD comprises administering to the identified infant a surfactant, oxygen therapy, ventilator therapy, a steroid, or inhaled nitric oxide.

27. A kit for identifying an infant at risk of developing bronchopulmonary dysplasia (BPD), the kit comprising one or more reagents for determining the level of at least one biomarker in a biological sample from an infant, wherein the at least one biomarker is selected from the group consisting of IGLV3-27, EXTL2, TUBB, TUBB1, MMP9, MST1L, KRT2, AHSG, KRT17, COL18A1, AGT, ISLR, EFEMP1, NRCAM, CD5L, COL1A2, FAP, APOD, ARPC1B, LDLR, CCT4, MSN, NRP1, SPTA1, PTGDS, DPYSL2, CTSD, ANXA1, RAN, PSMA6, VCL, CFHR2, CFB, CD14, CFH, CFI, MYH9, F12, LRG1, APCS, F10, C8A, AMBP, PRG2, SAA4, LBP, APRT, ROBO4, SBSN, APOA4, KLKB1, Fl 1, C4BPA, CD44, UGP2, PROS1 and ADAMTSL4.

28. The kit of claim 27, wherein the at least one biomarker is selected from the group consisting of IGLV3-27, EXTL2, VCL, CFB, MYH9, CCT4M and CD5L.

29. An array comprising one or more immobilized binding agents, wherein the one or more Immobilized binding agents are capable of specifically binding to one or more biomarkers present in a biological sample, wherein the one or more biomarkers are selected from the group consisting of IGLV3-27, EXTL2, TUBB, TUBB1, MMP9, MST1L, KRT2, AHSG, KRT17, COL18A1, AGT, ISLR, EFEMP1, NRCAM,CD5L, F12, C0L1A2, FAP, APOD, ARPC1B, LDLR, CCT4, MSN, NRP1, SPTA1, PTGDS, DPYSL2, CTSD, ANXA1, RAN, PSMA6, VCL, CFHR2, CFB, CD14, CFH, CFI, MYH9, LRG1, APCS, F10, C8A, AMBP, PRG2, SAA4, LBP, APRT, ROBO4, SBSN, APOA4, KLKB1, Fll, C4BPA, CD44, UGP2, PROS1 and ADAMTSL4.

30. The array of claim 29, wherein the one or more biomarkers are selected from the group consisting of IGLV3-27, EXTL2, VCL, CFB, MYH9, CCT4M and CD5L.

31. The kit of claim 27 or claim 28, or the array of claim 29 or claim 30, wherein the one or more biomarkers are selected from the group consisting of IGLV3-27, EXTL2, TUBB, TUBB1, MMP9, MST1L, KRT2, AHSG, KRT17, COL18A1, AGT, ISLR and EFEMP1.

32. The kit of claim 27 or claim 28. or the array of claim 29 or claim 30, wherein the one or more biomarkers are selected from the group consisting of MMP9 NRCAM, CD5L, F12, COL1A2, FAP, APOD, ARPC1B, LDLR, CCT4, MSN, NRP1, SPTA1, PTGDS, DPYSL2, CTSD, ANXA1, RAN, AHSG and PSMA6.

33. The kit of claim 27 or claim 28, or the array of claim 29 or claim 30, wherein the one or more biomarkers are selected from the group consisting of VCL, CFHR2, CFB, CD14, CFH, CFI, MYH9, F12, LRG1, APCS, F10, C8A, AMBP, PRG2, SAA4, LBP, APRT, RAN, ROBO4, SBSN, APOA4, KLKB1, Fll, C4BPA, CD44, UGP2, PROS1 and ADAMTSL4.

34. The kit or array of any one of claims 27 to 33, wherein the biological sample is plasma.

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