Alzheimer’s disease (AD) is the most common form of neurodegenerative dementia and its timely diagnosis remains a major challenge in biomarker discovery. In the present study, we analyzed publicly available high-throughput low-sample -omics datasets from studies in AD blood, by the AutoML technology Just Add Data Bio (JADBIO), to construct accurate predictive models for use as diagnostic biosignatures. Considering data from AD patients and age–sex matched cognitively healthy individuals, we produced three best performing diagnostic biosignatures specific for the presence of AD: A. A 506-feature transcriptomic dataset from 48 AD and 22 controls led to a miRNA-based biosignature via Support Vector Machines with three miRNA predictors (AUC 0.975 (0.906, 1.000)), B. A 38,327-feature transcriptomic dataset from 134 AD and 100 controls led to six mRNA-based statistically equivalent signatures via Classification Random Forests with 25 mRNA predictors (AUC 0.846 (0.778, 0.905)) and C. A 9483-feature proteomic dataset from 25 AD and 37 controls led to a protein-based biosignature via Ridge Logistic Regression with seven protein predictors (AUC 0.921 (0.849, 0.972)). These performance metrics were also validated through the JADBIO pipeline confirming stability. In conclusion, using the automated machine learning tool JADBIO, we produced accurate predictive biosignatures extrapolating available low sample -omics data. These results offer options for minimally invasive blood-based diagnostic tests for AD, awaiting clinical validation based on respective laboratory assays. They also highlight the value of AutoML in biomarker discovery.

Objective We prospectively recorded clinical and laboratory parameters from patients with metastatic non-small cell lung cancer (NSCLC) treated with 2nd line PD-1/PD-L1 inhibitors in order to address their effect on treatment outcomes. Materials and methods Clinicopathological information (age, performance status, smoking, body mass index, histology, organs with metastases), use and duration of proton pump inhibitors, steroids and antibiotics (ATB) and laboratory values [neutrophil/lymphocyte ratio, LDH, albumin] were prospectively collected. Steroid administration was defined as the use of > 10 mg prednisone equivalent for ≥ 10 days. Prolonged ATB administration was defined as ATB ≥ 14 days 30 days before or within the first 3 months of treatment. JADBio, a machine learning pipeline was applied for further multivariate analysis. Results Data from 66 pts with non-oncogenic driven metastatic NSCLC were analyzed; 15.2% experienced partial response (PR), 34.8% stable disease (SD) and 50% progressive disease (PD). Median overall survival (OS) was 6.77 months. ATB administration did not affect patient OS [HR = 1.35 (CI: 0.761–2.406, p = 0.304)], however, prolonged ATBs [HR = 2.95 (CI: 1.62–5.36, p = 0.0001)] and the presence of bone metastases [HR = 1.89 (CI: 1.02–3.51, p = 0.049)] independently predicted for shorter survival. Prolonged ATB administration, bone metastases, liver metastases and BMI < 25 kg/m2 were selected by JADbio as the important features that were associated with increased probability of developing disease progression as response to treatment. The resulting algorithm that was created was able to predict the probability of disease stabilization (PR or SD) in a single individual with an AUC = 0.806 [95% CI:0.714–0.889]. Conclusions Our results demonstrate an adverse effect of prolonged ATBs on response and survival and underscore their importance along with the presence of bone metastases, liver metastases and low BMI in the individual prediction of outcomes in patients treated with immunotherapy.

e21609 Background: We prospectively recorded common clinical and laboratory parameters of pts with metastatic NSCLC treated with 2nd line ICIs to evaluate their potential value in a clinical outcome prediction model. Methods: Data on patient (age, PS, BMI) and disease characteristics (histology, sites/number of metastases), smoking status, use/duration of co-medications [proton pump inhibitors (PPIs) inhalational/p.os steroids, antibiotics (ATB)], laboratory values [neutrophil/lymphocyte ratio (NLR), LDH] and response to previous therapy were prospectively collected. Pts were categorized as having steroids if they had received steroids > 10mg for ≥10d (starting from 15d before or within the first 3 months of treatment). Prolonged ATB administration was defined as ATBs ≥14d (30d before or within the first 3 months). JAD Bio (www.jadbio.com), an Automated Machine Learning service that analyzes biological data with emphasis on feature selection was used to create predictive models. Results: 66 pts were evaluated; median follow up time was 6.37 months. 15.2% of patients had PR, 34.8% SD and 50% PD. Median PFS and OS were 3.5 and 6.77 months, respectively. Steroids had negative impact on disease stabilization (PR+SD) rates (p = 0.042) and PFS (p = 0.013) but not OS (p = 0.051). ATBs negatively affected RR (p = 0.046) only, whereas, prolonged ATB exposure was associated with lower RR (p = 0.007), PFS (p = 0.0001) and OS (p = 0.001). In multivariate analysis, steroids [HR = 2.54 (CI:1.23-5.29, p = 0.012)], prolonged ATBs [HR = 3.46 (CI:1.72-6.95, p = 0.0001)], liver HR = 2.92 (CI:1.45-5.78, p = 0.003) and bone HR = 2.06 (CI:1.041-4.10, p = 0.038) metastases independently predicted for shorter PFS. Only prolonged ATBs [HR = 2.52 (CI: 1.39-4.54, p = 0.002)] and bone metastases [HR = 2.26 (CI: 1.23-4.17, p = 0.009)] independently predicted for shorter OS. Analysis of the investigated parameters by JAD Bio predicted disease stabilization with an accuracy of 71%. Importantly, PDL-1 status was not included due to high rates of missing data. Conclusions: Our results corroborate previous evidence on the detrimental role of prolonged ATB administration on ICIs efficacy, possibly related to perturbation of the gut microbiota. Modeling, including other significant parameters from larger patient cohorts, could result in a robust estimation of outcome.

Molecular gene-expression datasets consist of samples with tens of thousands of measured quantities (i.e., high dimensional data). However, lower-dimensional representations that retain the useful biological information do exist. We present a novel algorithm for such dimensionality reduction called Pathway Activity Score Learning (PASL). The major novelty of PASL is that the constructed features directly correspond to known molecular pathways (genesets in general) and can be interpreted as pathway activity scores. Hence, unlike PCA and similar methods, PASL’s latent space has a fairly straightforward biological interpretation. PASL is shown to outperform in predictive performance the state-of-the-art method (PLIER) on two collections of breast cancer and leukemia gene expression datasets. PASL is also trained on a large corpus of 50000 gene expression samples to construct a universal dictionary of features across different tissues and pathologies. The dictionary validated on 35643 held-out samples for reconstruction error. It is then applied on 165 held-out datasets spanning a diverse range of diseases. The AutoML tool JADBio is employed to show that the predictive information in the PASL-created feature space is retained after the transformation. The code is available at https://github.com/mensxmachina/PASL.

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Platinum-based chemotherapy (CT) is a standard treatment for lung cancer, however a variety of chemoresistance mechanisms can impair its efficacy. MicroRNAs (miRNAs) represent potential biomarkers for the prediction of treatment efficacy in nonsmall cell lung cancer (NSCLC). We herein used a bioinformatics approach to identify differentially expressed (DE) miRNAs associated with response to platinum-based CT in NSCLC. We identified 6 miRNAs targeting signaling molecules participating in biological pathways involved in cancer and drug resistance. In summary, we developed a 6-miRNA signature that potentially predicts the response to cisplatin in NSCLC and warrants further validation in clinical samples.