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Combined MEK‐BRAF inhibition is a well‐established treatment strategy in BRAF‐mutated cancer, most prominently in malignant melanoma with durable responses being achieved through this targeted therapy. However, a subset of patients face primary unresponsiveness despite presence of the activating mutation at position V600E, and others acquire resistance under treatment. Underlying resistance mechanisms are largely unknown, and diagnostic tests to predict tumor response to BRAF‐MEK inhibitor treatment are unavailable.
Multiple myeloma represents the second most common hematologic malignancy, and point mutations in BRAF are detectable in about 10% of patients. Targeted inhibition has been successfully applied, with mixed responses observed in a substantial subset of patients mirroring the widespread spatial heterogeneity in this genomically complex disease. Central nervous system (CNS) involvement is an extremely rare, extramedullary form of multiple myeloma that can be diagnosed in less than 1% of patients. It is considered an ultimate high‐risk feature, associated with unfavorable cytogenetics, and, even with intense treatment applied, survival is short, reaching less than 12 months in most cases. Here we not only describe the first patient with an extramedullary CNS relapse responding to targeted dabrafenib and trametinib treatment, we furthermore provide evidence that a point mutation within the capicua transcriptional repressor (CIC) gene mediated the acquired resistance in this patient.
Background: Natural language processing (NLP) is a powerful tool supporting the generation of Real-World Evidence (RWE). There is no NLP system that enables the extensive querying of parameters specific to multiple myeloma (MM) out of unstructured medical reports. We therefore created a MM-specific ontology to accelerate the information extraction (IE) out of unstructured text. Methods: Our MM ontology consists of extensive MM-specific and hierarchically structured attributes and values. We implemented “A Rule-based Information Extraction System” (ARIES) that uses this ontology. We evaluated ARIES on 200 randomly selected medical reports of patients diagnosed with MM. Results: Our system achieved a high F1-Score of 0.92 on the evaluation dataset with a precision of 0.87 and recall of 0.98. Conclusions: Our rule-based IE system enables the comprehensive querying of medical reports. The IE accelerates the extraction of data and enables clinicians to faster generate RWE on hematological issues. RWE helps clinicians to make decisions in an evidence-based manner. Our tool easily accelerates the integration of research evidence into everyday clinical practice.