ALLSTAR: Inference of ReliAble CausaL RuLes between Somatic MuTAtions and CanceR Phenotypes.

MOTIVATION: Recent advances in DNA sequencing technologies have allowed the detailed characterization of genomes in large cohorts of tumors, highlighting their extreme heterogeneity, with no two tumors sharing the same complement of somatic mutations. Such heterogeneity hinders our ability to identify somatic mutations important for the disease, including mutations that determine clinically relevant phenotypes (e.g., cancer subtypes). Several tools have been developed to identify somatic mutations related to cancer phenotypes. However, such tools identify correlations between somatic mutations and cancer phenotypes, with no guarantee of highlighting causal relations. RESULTS: We describe ALLSTAR, a novel tool to infer reliable causal relations between somatic mutations and cancer phenotypes. ALLSTAR identifies reliable causal rules highlighting combinations of somatic mutations with the highest impact in terms of average effect on the phenotype. While we prove that the underlying computational problem is NP-hard, we develop a branch-and-bound approach that employs protein-protein interaction networks and novel bounds for pruning the search space, while properly correcting for multiple hypothesis testing. Our extensive experimental evaluation on synthetic data shows that our tool is able to identify reliable causal relations in large cancer cohorts. Moreover, the reliable causal rules identified by our tool in cancer data show that our approach identifies several somatic mutations known to be relevant for cancer phenotypes as well as novel biologically meaningful relations. AVAILABILITY AND IMPLEMENTATION: Code, data, and scripts to reproduce the experiments available at https://github.com/VandinLab/ALLSTAR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Which extra-renal flare is 'difficult to treat' in systemic lupus erythematosus? A one-year longitudinal study comparing traditional and machine learning approaches.

OBJECTIVES: To describe phenotypes and outcomes of extra-renal flares in SLE, to identify clusters of extra-renal flares based on baseline features, and to develop a machine learning (ML) tool capable of predicting 'difficult to treat' (D2T) flares. METHODS: Extra-renal flares that occurred in our cohort over the last five years with at least one year of follow-up were included. Baseline clinical variables were described and flares assigned to clusters. Attainment of remission and low disease activity state (LLDAS) at 12 months were compared. Flares were then considered 'D2T' in case of non-attainment of LLDAS at 6 and 12 months. Baseline features were used to train a ML model able to predict future D2T-flares, at admission. Traditional approaches were then compared with informatic techniques. RESULTS: Among 420 SLE patients of the cohort, 114 flares occurred between 2015 and 2021; 79 extra-renal flares, predominantly mucocutaneous (24.1%) and musculoskeletal (45.6%), were considered. After 12 months, 79.4% and 49.4% were in LLDAS and in remission, respectively, while 17 flares were classified as D2T (21.5%); D2T flares received a higher cumulative and daily dose of glucocorticoids. Among the clusters, cluster 'D' (mild-moderate flares with mucocutaneous manifestations in patients with history of skin involvement) was associated with the lowest rate of remission. Among clinical data, not being on LLDAS at 3 months was the unique independent predictor of D2T flares. CONCLUSIONS: Our clusterization well separates extra-renal flares according to their baseline features and may propose a new identification standard. D2T flares, especially refractory skin manifestations, are frequent in SLE and represent an unmet need in the management of the disease as they are associated with higher glucocorticoid (GC) dosage and risk of damage accrual. Our ML model could help in the early identification of D2T flares, flagging them to elevate the attention threshold at admission.

Comorbidities in the Spondyloarthritis GISEA Cohort: an average treatment effect analysis on patients treated with bDMARDs.

OBJECTIVES: We aimed to investigate the effectiveness of tumour necrosis factor inhibitors (TNFi), anti-interleukin-17 or interleukin-12/23 monoclonal antibodies (anti-IL) on comorbidities in a cohort of patients with spondyloarthritis (SpA), using an average treatment effect (ATE) analysis. METHODS: SpA patients from the multicentre Italian GISEA Registry were divided into groups according to pharmacological exposure: no treatment (G0), TNFi (G1) and non-responders to TNFi switched to anti-IL (G2). In each group, we recorded the prevalence and incidence of infectious, cardiopulmonary, endocrinological, gastrointestinal, oncologic, renal and neurologic comorbidities. Each comorbidity was then fitted for ATE and baseline features were evaluated for importance. RESULTS: The main findings of this study comprising 4458 SpA patients relate to cancer, other gastrointestinal diseases (OGID) and fibromyalgia. ATE showed no increased risk of solid cancer in G1 (0.42 95% CI 0.20-0.85) and G2 (0.26 95% CI 0.08-0.71) vs. G0, with significantly higher incidence in G0 (14.07/1000 patient-years, p=0.0001). Conversely, a significantly higher risk of OGID and fibromyalgia was found in G1 (1.56 95% CI 1.06-2.33; 1.69 95% CI 1.05-2.68, respectively) and G2 (1.91 95% CI 1.05-3.24; 2.13 95% CI 1.14-3.41, respectively) vs. G0. No treatment risk reduction was observed in haematological malignancies, cardiovascular events and endocrinological comorbidities. CONCLUSIONS: Overall, our study confirms the safety of TNFi and anti-IL in SpA patients, albeit with some caveats pertaining to solid cancers, OGID and fibromyalgia. Furthermore, taking into consideration causality with observational data may yield more reliable and relevant clinical information.

Multi-Design Differential Expression Profiling of COVID-19 Lung Autopsy Specimens Reveals Significantly Deregulated Inflammatory Pathways and SFTPC Impaired Transcription.

The transcriptomic profiling of lung damage associated with SARS-CoV-2 infection may lead to the development of effective therapies to prevent COVID-19-related deaths. We selected a series of 21 autoptic lung samples, 14 of which had positive nasopharyngeal swabs for SARS-CoV-2 and a clinical diagnosis of COVID-19-related death; their pulmonary viral load was quantified with a specific probe for SARS-CoV-2. The remaining seven cases had no documented respiratory disease and were used as controls. RNA from formalin-fixed paraffin-embedded (FFPE) tissue samples was extracted to perform gene expression profiling by means of targeted (Nanostring) and comprehensive RNA-Seq. Two differential expression designs were carried out leading to relevant results in terms of deregulation. SARS-CoV-2 positive specimens presented a significant overexpression in genes of the type I interferon signaling pathway (IFIT1, OAS1, ISG15 and RSAD2), complement activation (C2 and CFB), macrophage polarization (PKM, SIGLEC1, CD163 and MS4A4A) and Cathepsin C (CTSC). CD163, Siglec-1 and Cathepsin C overexpression was validated by immunohistochemistry. SFTPC, the encoding gene for pulmonary-associated surfactant protein C, emerged as a key identifier of COVID-19 patients with high viral load. This study successfully recognized SARS-CoV-2 specific immune signatures in lung samples and highlighted new potential therapeutic targets. A better understanding of the immunopathogenic mechanisms of SARS-CoV-2 induced lung damage is required to develop effective individualized pharmacological strategies.