Pulmonary Arterial Hypertension Biomarker: NOTCH3 Serum Levels
Study populations and sample collection
Cross-sectional cohorts
Between 2010 and 2021,serum samples where prospectively collected from 341 individuals with IPAH and 376 without PH (Table 1) being treated at the University of California, San Diego (UCSD) (San Diego cohort: 100 IPAH, 200 non-PH), the University of Arizona (Phoenix cohort: 140 IPAH, 125 non-PH) and the Massachusetts General Hospital (Boston cohort: 101 IPAH, 51 non-PH) (Table 1). A sample size of 341 individuals with IPAH and 376 individuals without PH was calculated to provide 90% power to detect a minimum effect size of 0.27 for the difference in serum NOTCH3-ECD levels between the two groups with a two-sided α = 0.05. The San diego and Phoenix IPAH patient samples were collected on an outpatient basis, whereas the Boston patients with IPAH had serum collected during ICU hospitalization for IPAH. Informed consent was obtained for all individuals from each cohort. Patient sex was resolute by assigned sex according to each patient’s hospital records. All IPAH blood samples were collected within 1 month of RHC or ECHO. Control individuals in the San Diego and Phoenix cohorts were healthy paid volunteers, whereas the Boston control cohort comprised ICU patients without PH, being treated for other non-lung-related diseases (51 nonintubated patients comprising 37 multi-trauma orthopedic patients without lung contusion, acute respiratory distress syndrome or pulmonary embolism; 10 individuals recovering from elective large abdominal, vascular or orthopedic operations; 3 patients with a closed head injury; and 1 patient with amyotrophic lateral sclerosis). All individuals,including controls,underwent RHC as part of this study. All IPAH patients tested negative for HIV and active hepatitis viral infection, as well as anti-nuclear, anti-centromere, anti-mitochondrial, anti-double-stranded DNA, anti-topoisomerase 1, anti-Ro and anti-La antibodies. No patient with IPAH was undergoing evaluation for liver transplantation or had received a previous liver transplant. Patients with IPAH did not undergo genetic testing at the time of diagnosis and none had a family history of PAH (WHO group 1) or other PH (WHO groups 2-5). The determination of IPAH was made by an integrated assessment by an experienced PH pulmonologist or cardiologist, with further adjudication as needed by a committee of PH physicians. Inclusion and exclusion criteria for the diagnosis of IPAH is given in Supplementary table 1.
Patients with subtypes of WHO group 1 PAH associated with previously diagnosed heritable mutations, methamphetamine, scleroderma, HIV, congenital heart defects, portal hypertension or pulmonary veno-occlusive disease were not included in the primary analysis, although serum samples from these patients were collected from UCSD and PH centers in the United States and United Kingdom for secondary comparative analysis. Serum samples were also obtained from individuals with non-PH vasculitides that affect the lung and also individuals with malignancies expressing NOTCH3 for additional secondary comparative analysis. Informed consent was obtained for all individuals at participating institutions for the above patient groups. Sam
### Cross-reactivity testing
ELISA specificity for the anti-NOTCH3-ECD antibody was tested by adding recombinant human NOTCH1-ECD (beta Life Sciences, cat. no. BLPSN-3544), NOTCH2-ECD (Beta Life Sciences, cat. no. BLPSN-3547) and NOTCH4-ECD (Beta Life Sciences, cat. no.BLPSN-3549) peptides diluted in phosphate-buffered saline to concentrations of 0.78-100 ng ml−1. Cross-reactivity was determined quantitatively by optical density compared to blank controls. Experiments were performed in triplicate on 3 days separately, using different assay lots.
### Immunoprecipitation and western blotting
Immunoprecipitation and western blotting were performed as previously described59. The antibodies used for western blotting were: human anti-NOTCH3-ECD antibody (Sigma-Aldrich, clone 2G8, cat.no. MABF937, 1:1,000), goat polyclonal anti-rat secondary antibody (Thermo Fisher Scientific, cat. no. 31470, 1:5,000), rabbit polyclonal anti-transferrin antibody (Thermo Fisher Scientific, cat. no. PA527306, 1:1,000) and horseradish peroxidase-conjugated goat polyclonal anti-rabbit immunoglobulin G antibody (Thermo Fisher Scientific, cat.no. 31460, 1:5,000). For immunoprecipitation experiments, primary antibodies were used at a concentration of 6 μg of antibody per 1 mg of total protein. Experiments were performed in triplicate on 3 days separately.
### Statistical analysis
#### Diagnostic analyses
The primary objective was to assess the ability of serum NOTCH3-ECD to differentiate between individuals with IPAH and individuals without PH. logistic regression was used to generate ROCs to assess the ability of serum NOTCH3-ECD levels to predict the presence of IPAH in three geographically separate cohorts (San diego, Phoenix and Boston) and one combined cohort.
Specifically, the optimal cutoff of serum NOTCH3-ECD to maximize diagnostic sensitivity and specificity to differentiate between individuals with IPAH and individuals without PH was determined by calculating the optimal Youden’s Index and F1 sThe REVEAL 2.035 and COMPERA 2.036 calculators were included in each individual machine learning model, with and without NOTCH3-ECD levels. There were a maximum of 13 categorical variables (demographics: men, age >60 years, eGFR < 60 ml min−1 1.73 m−2 or renal insufficiency, NYHA class, systolic blood pressure ≥110 mm Hg or <110 mm Hg, heart rate ≤96 beats per min or >96 beats per min, all-cause hospitalizations ≤6 months, 6MWD categories in respect of each calculator, NT-proBNP or BNP values respective to each calculator, pericardial effusion on echocardiogram, percentage predicted DLCO ≤ 40, mRAP > 20 mm hg within 1 year and PVR < 5 Wood units on RHC) and one continuous variable (serum NOTCH3-ECD) included for each model with respect to the corresponding mortality calculator. Data were formatted into a binary framework for each of the categorical variables by converting them into dummy variables. To avoid perfect multicollinearity, the first of the dummy variables was dropped from the model. The final dataset was divided into training (80%) and testing (20%) data subsets.
The models were constructed using the randomForest package in R and hyperparameters, including the number of trees, mtry (number of predictors to sample at each split) and min_n (number of observations needed to split nodes). Then, the models were optimized as described previously