T-cell large granular lymphocytic leukemia is a rare lymphoproliferative disorder characterized by the expansion of clonal CD3+CD8+ cytotoxic T lymphocytes (CTLs) and often associated with autoimmune disorders and immune-mediated cytopenias. We used next-generation exome sequencing to identify somatic mutations in CTLs from an index patient with large granular lymphocytic leukemia. Targeted resequencing was performed in a well-characterized cohort of 76 patients with this disorder, characterized by clonal T-cell–receptor rearrangements and increased numbers of large granular lymphocytes. Mutations in the signal transducer and activator of transcription 3 gene (STAT3) were found in 31 of 77 patients (40%) with large granular lymphocytic leukemia. Among these 31 patients, recurrent mutational hot spots included Y640F in 13 (17%), D661V in 7 (9%), D661Y in 7 (9%), and N647I in 3 (4%). All mutations were located in exon 21, encoding the Src homology 2 (SH2) domain, which mediates the dimerization and activation of STAT protein. The amino acid changes resulted in a more hydrophobic protein surface and were associated with phosphorylation of STAT3 and its localization in the nucleus. In vitro functional studies showed that the Y640F and D661V mutations increased the transcriptional activity of STAT3. In the affected patients, downstream target genes of the STAT3 pathway (IFNGR2, BCL2L1, and JAK2) were up-regulated. Patients with STAT3 mutations presented more often with neutropenia and rheumatoid arthritis than did patients without these mutations. The SH2 dimerization and activation domain of STAT3 is frequently mutated in patients with large granular lymphocytic leukemia; these findings suggest that aberrant STAT3 signaling underlies the pathogenesis of this disease. (Funded by the Academy of Finland and others.)

We present an individualized systems medicine (ISM) approach to optimize cancer drug therapies one patient at a time. ISM is based on (i) molecular profiling and ex vivo drug sensitivity and resistance testing (DSRT) of patients' cancer cells to 187 oncology drugs, (ii) clinical implementation of therapies predicted to be effective, and (iii) studying consecutive samples from the treated patients to understand the basis of resistance. Here, application of ISM to 28 samples from patients with acute myeloid leukemia (AML) uncovered five major taxonomic drug-response subtypes based on DSRT profiles, some with distinct genomic features (e.g., MLL gene fusions in subgroup IV and FLT3-ITD mutations in subgroup V). Therapy based on DSRT resulted in several clinical responses. After progression under DSRT-guided therapies, AML cells displayed significant clonal evolution and novel genomic changes potentially explaining resistance, whereas ex vivo DSRT data showed resistance to the clinically applied drugs and new vulnerabilities to previously ineffective drugs. Significance: Here, we demonstrate an ISM strategy to optimize safe and effective personalized cancer therapies for individual patients as well as to understand and predict disease evolution and the next line of therapy. This approach could facilitate systematic drug repositioning of approved targeted drugs as well as help to prioritize and de-risk emerging drugs for clinical testing. Cancer Discov; 3(12); 1416–29. ©2013 AACR. See related commentary by Hourigan and Karp, p. 1336 This article is highlighted in the In This Issue feature, p. 1317

Key Points: Somatic mutations were discovered for the first time in the SH2 domain of the STAT5b gene in LGL leukemia. The mutations are activating and lead to increased phosphorylation and transcriptional activity of STAT5b.

Background: Techniques enabling targeted re-sequencing of the protein coding sequences of the human genome on next generation sequencing instruments are of great interest. We conducted a systematic comparison of the solution-based exome capture kits provided by Agilent and Roche NimbleGen. A control DNA sample was captured with all four capture methods and prepared for Illumina GAII sequencing. Sequence data from additional samples prepared with the same protocols were also used in the comparison. Results: We developed a bioinformatics pipeline for quality control, short read alignment, variant identification and annotation of the sequence data. In our analysis, a larger percentage of the high quality reads from the NimbleGen captures than from the Agilent captures aligned to the capture target regions. High GC content of the target sequence was associated with poor capture success in all exome enrichment methods. Comparison of mean allele balances for heterozygous variants indicated a tendency to have more reference bases than variant bases in the heterozygous variant positions within the target regions in all methods. There was virtually no difference in the genotype concordance compared to genotypes derived from SNP arrays. A minimum of 11× coverage was required to make a heterozygote genotype call with 99% accuracy when compared to common SNPs on genome-wide association arrays. Conclusions: Libraries captured with NimbleGen kits aligned more accurately to the target regions. The updated NimbleGen kit most efficiently covered the exome with a minimum coverage of 20×, yet none of the kits captured all the Consensus Coding Sequence annotated exons.

Key Points: Germline activating STAT3 mutations were detected in 3 patients with autoimmunity, hypogammaglobulinemia, and mycobacterial disease. T-cell lymphoproliferation, deficiency of regulatory and helper 17 T cells, natural killer cells, dendritic cells, and eosinophils were common.

We generated ex vivo drug-response and multiomics profiling data for a prospective series of 252 samples from 186 patients with acute myeloid leukemia (AML). A functional precision medicine tumor board (FPMTB) integrated clinical, molecular, and functional data for application in clinical treatment decisions. Actionable drugs were found for 97% of patients with AML, and the recommendations were clinically implemented in 37 relapsed or refractory patients. We report a 59% objective response rate for the individually tailored therapies, including 13 complete responses, as well as bridging five patients with AML to allogeneic hematopoietic stem cell transplantation. Data integration across all cases enabled the identification of drug response biomarkers, such as the association of IL15 overexpression with resistance to FLT3 inhibitors. Integration of molecular profiling and large-scale drug response data across many patients will enable continuous improvement of the FPMTB recommendations, providing a paradigm for individualized implementation of functional precision cancer medicine. Significance: Oncogenomics data can guide clinical treatment decisions, but often such data are neither actionable nor predictive. Functional ex vivo drug testing contributes significant additional, clinically actionable therapeutic insights for individual patients with AML. Such data can be generated in four days, enabling rapid translation through FPMTB. See related commentary by Letai, p. 290. This article is highlighted in the In This Issue feature, p. 275