Follicular lymphoma (FL) is an indolent but largely incurable disease. Some patients suffer histological transformation to a more aggressive subtype with poorer prognosis. This study aimed to improve our understanding of the genetics underlying FL histological transformation, and to identify genetic drivers or promoters of the transformation by elucidating the differences between FL samples from patients who did and did not transform. We conducted targeted massive parallel sequencing of 22 pre-transformed FL/transformed diffuse large B-cell lymphoma pairs and 20 diagnostic samples from non-transformed FL patients. Additionally, 22 matched samples from 11 transformed FL patients (pre-transformed FL and diffuse large B-cell lymphoma) and 9 non-transformed FLs were studied for copy number variation using SNP arrays. We identified recurrently mutated genes that were enriched at transformation, most notably LRP1B , GNA13 and POU2AF1 , which have roles in B-cell differentiation, GC architecture and migration. Mutations in POU2AF1 might be associated with lower levels of expression, were more frequent in transformed FLs, and seemed to be specific to transformed- compared with de novo- diffuse large B-cell lymphomas. Pre-transformed FLs carried more mutations per sample and had greater subclonal heterogeneity than non-transformed FLs. Finally, we identified four mutated genes in FL samples that differed between patients who did and did not transform: NOTCH2 , DTX1 , UBE2A and HIST1H1E . The presence of mutations in these genes was associated with shorter time to transformation when mutated in the FL biopsies. This information might be useful for identifying patients at higher risk of transformation.

Lung transplant recipients constitute a high-risk group for developing lung cancer. Among our patients, lung cancer was predominantly diagnosed in the native lung and at an advanced stage. The primary tumour was the main cause of death in most of these patients.

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If Electronic Health Records contain a large amount of information about the patient's condition and response to treatment, which can potentially revolutionize the clinical practice, such information is seldom considered due to the complexity of its extraction and analysis. We here report on a first integration of an NLP framework for the analysis of clinical records of lung cancer patients making use of a telephone assistance service of a major Spanish hospital. We specifically show how some relevant data, about patient demographics and health condition, can be extracted; and how some relevant analyses can be performed, aimed at improving the usefulness of the service. We thus demonstrate that the use of EHR texts, and their integration inside a data analysis framework, is technically feasible and worth of further study.

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