Identification and prediction of difficult-to-treat rheumatoid arthritis patients in structured and unstructured routine care data: results from a hackathon.

BACKGROUND: The new concept of difficult-to-treat rheumatoid arthritis (D2T RA) refers to RA patients who remain symptomatic after several lines of treatment, resulting in a high patient and economic burden. During a hackathon, we aimed to identify and predict D2T RA patients in structured and unstructured routine care data. METHODS: Routine care data of 1873 RA patients were extracted from the Utrecht Patient Oriented Database. Data from a previous cross-sectional study, in which 152 RA patients were clinically classified as either D2T or non-D2T, served as a validation set. Machine learning techniques, text mining, and feature importance analyses were performed to identify and predict D2T RA patients based on structured and unstructured routine care data. RESULTS: We identified 123 potentially new D2T RA patients by applying the D2T RA definition in structured and unstructured routine care data. Additionally, we developed a D2T RA identification model derived from a feature importance analysis of all available structured data (AUC-ROC 0.88 (95% CI 0.82-0.94)), and we demonstrated the potential of longitudinal hematological data to differentiate D2T from non-D2T RA patients using supervised dimension reduction. Lastly, using data up to the time of starting the first biological treatment, we predicted future development of D2TRA (AUC-ROC 0.73 (95% CI 0.71-0.75)). CONCLUSIONS: During this hackathon, we have demonstrated the potential of different techniques for the identification and prediction of D2T RA patients in structured as well as unstructured routine care data. The results are promising and should be optimized and validated in future research.

Molecular data show conserved DNA locations distinguishing lung cancer subtypes and regulation of immune genes.

INTRODUCTION: Non-small-cell lung cancer exhibits a range of transcriptional and epigenetic patterns that not only define distinct phenotypes, but may also govern immune related genes, which have a major impact on survival. METHODS: We used open-source RNA expression and DNA methylation data of the Cancer Genome Atlas with matched non-cancerous tissue to evaluate whether these pretreatment molecular patterns also influenced genes related to the immune system and overall survival. RESULTS: The distinction between lung adenocarcinoma and squamous cell carcinoma are determined by 1083 conserved methylation loci and RNA expression of 203 genes which differ for >80 % of patients between the two subtypes. Using the RNA expression profiles of 6 genes, more than 95 % of patients could be correctly classified as having either adeno or squamous cell lung cancer. Comparing tumor tissue with matched normal tissue, no differences in RNA expression were found for costimulatory and co-inhibitory genes, nor genes involved in cytokine release. However, genes involved in antigen presentation had a lower expression and a wider distribution in tumor tissue. DISCUSSION: Only a small number of genes, influenced by DNA methylation, determine the lung cancer subtype. The antigen presentation of cancer cells is dysfunctional, while other T cell immune functions appear to remain intact.