Alliance A071401: Phase II Trial of Focal Adhesion Kinase Inhibition in Meningiomas With Somatic <i>NF2</i> Mutations

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Abstract

PURPOSE

Patients with progressive or recurrent meningiomas have limited systemic therapy options. Focal adhesion kinase (FAK) inhibition has a synthetic lethal relationship with NF2 loss. Given the predominance of NF2 mutations in meningiomas, we evaluated the efficacy of GSK2256098, a FAK inhibitor, as part of the first genomically driven phase II study in recurrent or progressive grade 1-3 meningiomas.

PATIENTS AND METHODS

Eligible patients whose tumors screened positively for NF2 mutations were treated with GSK2256098, 750 mg orally twice daily, until progressive disease. Efficacy was evaluated using two coprimary end points: progression-free survival at 6 months (PFS6) and response rate by Macdonald criteria, where PFS6 was evaluated separately within grade-based subgroups: grade 1 versus 2/3 meningiomas. Per study design, the FAK inhibitor would be considered promising in this patient population if either end point met the corresponding decision criteria for efficacy.

RESULTS

Of 322 patients screened for all mutation cohorts of the study, 36 eligible and evaluable patients with NF2 mutations were enrolled and treated: 12 grade 1 and 24 grade 2/3 patients. Across all grades, one patient had a partial response and 24 had stable disease as their best response to treatment. In grade 1 patients, the observed PFS6 rate was 83% (10/12 patients; 95% CI, 52 to 98). In grade 2/3 patients, the observed PFS6 rate was 33% (8/24 patients; 95% CI, 16 to 55). The study met the PFS6 efficacy end point both for the grade 1 and the grade 2/3 cohorts. Treatment was well tolerated; seven patients had a maximum grade 3 adverse event that was at least possibly related to treatment with no grade 4 or 5 events.

CONCLUSION

GSK2256098 was well tolerated and resulted in an improved PFS6 rate in patients with recurrent or progressive NF2-mutated meningiomas, compared with historical controls. The criteria for promising activity were met, and FAK inhibition warrants further evaluation for this patient population.

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Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.

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Geraldine Auwera, Mauricio Carneiro, Christopher Hartl … (2013)

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