PURPOSE: Molecular tumor boards (MTBs) provide physicians with a treatment recommendation for complex tumor-specific genomic alterations. National and international consensus to reach a recommendation is lacking. In this article, we analyze the effectiveness of an MTB decision-making methodology for patients with non–small-cell lung cancer (NSCLC) with rare or complex mutational profiles as implemented in the University Medical Center Groningen (UMCG). METHODS: The UMCG-MTB comprises (pulmonary) oncologists, pathologists, clinical scientists in molecular pathology, and structural biologists. Recommendations are based on reported actionability of variants and molecular interpretation of pathways affected by the variant and supported by molecular modeling. A retrospective analysis of 110 NSCLC cases (representing 106 patients) with suggested treatment of complex genomic alterations and corresponding treatment outcomes for targeted therapy was performed. RESULTS: The MTB recommended targeted therapy for 59 of 110 NSCLC cases with complex molecular profiles: 24 within a clinical trial, 15 in accordance with guidelines (on label) and 20 off label. All but 16 recommendations involved patients with an EGFR or ALK mutation. Treatment outcome was analyzed for patients with available follow-up (10 on label and 16 off label). Adherence to the MTB recommendation (21 of 26; 81%) resulted in an objective response rate of 67% (14 of 21), with a median progression-free survival of 6.3 months (interquartile range, 3.2-10.6 months) and an overall survival of 10.4 months (interquartile range, 6.3-14.6 months). CONCLUSION: Targeted therapy recommendations resulting from the UMCG-MTB workflow for complex molecular profiles were highly adhered to and resulted in a positive clinical response in the majority of patients with metastatic NSCLC.

Non-small cell lung cancer (NSCLC) patients with Anaplastic Lymphoma Kinase (ALK) gene fusions respond well to ALK inhibitors but commonly develop on-target resistance mutations. The objective of this study is to collect clinical evidence for subsequent treatment with ALK inhibitors. Local experience with on-target ALK resistance mutations and review of the literature identified 387 patients with ALK inhibitor resistance mutations. Clinical benefit of mutation-inhibitor combinations was assessed based on reported response, progression-free survival and duration of treatment. Furthermore, this clinical evidence was compared to previously reported in vitro sensitivity of mutations to the inhibitors. Of the pooled population of 387 patients in this analysis, 239 (62%) received at least 1 additional line of ALK inhibition after developing on-target resistance to ALK inhibitor therapy. Clinical benefit was reported for 177 (68%) patients, but differed for each mutation-inhibitor combination. Agreement between in vitro predicted sensitivity of 6 published models and observed clinical benefit ranged from 69% to 89%. The observed clinical evidence for highest probability of response in the context of specific on-target ALK inhibitor resistance mutations is presented. Molecular diagnostics performed on tissue samples that are refractive to ALK inhibitor therapy can reveal new options for targeted therapy for NSCLC patients. Our comprehensive overview of clinical evidence of drug actionability of ALK on-target resistance mechanisms may serve as a practical guide to select the most optimal drug for individual patients.

Introduction: Malaria is one of the most prevalent human infections worldwide with over 40% of the world’s population living in malaria-endemic areas. In the absence of an effective vaccine, emergence of drug-resistant strains requires urgent drug development. Current methods applied to drug target validation, a crucial step in drug discovery, possess limitations in malaria. These constraints require the development of techniques capable of simplifying the validation of Plasmodial targets. Areas covered: The authors review the current state of the art in techniques used to validate drug targets in malaria, including our contribution – the protein interference assay (PIA) – as an additional tool in rapid in vivo target validation. Expert opinion: Each technique in this review has advantages and disadvantages, implying that future validation efforts should not focus on a single approach, but integrate multiple approaches. PIA is a significant addition to the current toolset of antimalarial validation. Validation of aspartate metabolism as a druggable pathway provided proof of concept of how oligomeric interfaces can be exploited to control specific activity in vivo. PIA has the potential to be applied not only to other enzymes/pathways of the malaria parasite but could, in principle, be extrapolated to other infectious diseases.

The parallel advances of different scientific fields provide a contemporary scenario where collaboration is not a differential, but actually a requirement. In this context, crystallography has had a major contribution on the medical sciences, providing a “face” for targets of diseases that previously were known solely by name or sequence. Worldwide, cancer still leads the number of annual deaths, with 9.6 million associated deaths, with a major contribution from lung cancer and its 1.7 million deaths. Since the relationship between cancer and kinases was unraveled, these proteins have been extensively explored and became associated with drugs that later attained blockbuster status. Crystallographic structures of kinases related to lung cancer and their developed and marketed drugs provided insight on their conformation in the absence or presence of small molecules. Notwithstanding, these structures were also of service once the initially highly successful drugs started to lose their effectiveness in the emergence of mutations. This review focuses on a subclassification of lung cancer, non-small cell lung cancer (NSCLC), and major oncogenic driver mutations in kinases, and how crystallographic structures can be used, not only to provide awareness of the function and inhibition of these mutations, but also how these structures can be used in further computational studies aiming at addressing these novel mutations in the field of personalized medicine.