No abstract

Duvelisib (IPI-145) is an oral inhibitor of phosphatidylinositol 3-kinase (PI3K)-δ/γ isoforms currently in clinical development. PI3K-δ/γ inhibition may directly inhibit malignant T-cell growth, making duvelisib a promising candidate for patients with peripheral (PTCL) or cutaneous (CTCL) T-cell lymphoma. Inhibition of either isoform may also contribute to clinical responses by modulating nonmalignant immune cells. We investigated these dual effects in a TCL cohort from a phase 1, open-label study of duvelisib in patients with relapsed or refractory PTCL (n = 16) and CTCL (n = 19), along with in vitro and in vivo models of TCL. The overall response rates in patients with PTCL and CTCL were 50.0% and 31.6%, respectively ( = .32). There were 3 complete responses, all among patients with PTCL. Activity was seen across a wide spectrum of subtypes. The most frequently observed grade 3 and 4 adverse events were transaminase increases (40% alanine aminotransferase, 17% aspartate aminotransferase), maculopapular rash (17%), and neutropenia (17%). Responders and nonresponders had markedly different changes in serum cytokine profiles induced by duvelisib. In vitro, duvelisib potently killed 3 of 4 TCL lines with constitutive phospho-AKT (pAKT) vs 0 of 7 lines lacking pAKT ( = .024) and exceeded cell killing by the PI3K-δ-specific inhibitor idelalisib. Administration of duvelisib to mice engrafted with a PTCL patient-derived xenograft resulted in a shift among tumor-associated macrophages from the immunosuppressive M2-like phenotype to the inflammatory M1-like phenotype. In summary, duvelisib demonstrated promising clinical activity and an acceptable safety profile in relapsed/refractory TCL, as well as preclinical evidence of both tumor cell-autonomous and immune-mediated effects. This trial was registered at www.clinicaltrials.gov as #NCT01476657.

Profiling post-translational modifications represents an alternative dimension to gene expression data in characterizing cellular processes. Many cellular responses to drugs are mediated by changes in cellular phosphosignaling. We sought to develop a common platform on which phosphosignaling responses could be profiled across thousands of samples, and created a targeted MS assay that profiles a reduced-representation set of phosphopeptides that we show to be strong indicators of responses to chemical perturbagens.To develop the assay, we investigated the coordinate regulation of phosphosites in samples derived from three cell lines treated with 26 different bioactive small molecules. Phosphopeptide analytes were selected from these discovery studies by clustering and picking 1 to 2 proxy members from each cluster. A quantitative, targeted parallel reaction monitoring assay was developed to directly measure 96 reduced-representation probes. Sample processing for proteolytic digestion, protein quantification, peptide desalting, and phosphopeptide enrichment have been fully automated, making possible the simultaneous processing of 96 samples in only 3 days, with a plate phosphopeptide enrichment variance of 12%. This highly reproducible process allowed ϳ95% of the reduced-representation phosphopeptide probes to be detected in ϳ200 samples.The performance of the assay was evaluated by measuring the probes in new samples generated under treatment conditions from discovery experiments, recapitulating the observations of deeper experiments using a fraction of the analytical effort. We measured these probes in new experiments varying the treatments, cell types, and timepoints to demonstrate generalizability. We demonstrated that the assay is sensitive to disruptions in common signaling pathways (e.g. MAPK, PI3K/mTOR, and CDK). The high-throughput, reduced-representation phosphoproteomics assay provides a platform for the comparison of perturbations across a range of biological conditions, suitable for profiling thousands of samples. We believe the assay will prove highly useful for classification of known and novel drug and genetic mechanisms through comparison of phosphoproteomic signatures.

SUMMARYAlthough the value of proteomics has been demonstrated, cost and scale are typically prohibitive, and gene expression profiling remains dominant for characterizing cellular responses to perturbations. However, high-throughput sentinel assays provide an opportunity for proteomics to contribute at a meaningful scale. We present a systematic library resource (90 drugs 3 6 cell lines) of proteomic signatures that measure changes in the reduced-representation phosphoproteome (P100) and changes in epigenetic marks on histones (GCP). A majority of these drugs elicited reproducible signatures, but notable cell line- and assay-specific differences were observed. Using the “connectivity” framework, we compared signatures across cell types and integrated data across assays, including a transcriptional assay (L1000). Consistent connectivity among cell types revealed cellular responses that transcended lineage, and consistent connectivity among assays revealed unexpected associations between drugs. We further leveraged the resource against public data to formulate hypotheses for treatment of multiple myeloma and acute lymphocytic leukemia. This resource is publicly available at https://clue.io/proteomics.

Data-Independent Acquisition (DIA) is a technique that promises to comprehensively detect and quantify all peptides above an instrument's limit of detection. Several software tools to analyze DIA data have been developed in recent years. However, several challenges still remain, like confidently identifying peptides, defining integration boundaries, dealing with interference for selected transitions, and scoring and filtering of peptide signals in order to control false discovery rates. In practice, a visual inspection of the signals is still required, which is impractical with large datasets. Avant-garde is a new tool to refine DIA (and PRM) by removing interfered transitions, adjusting integration boundaries and scoring peaks to control the FDR. Unlike other tools where MS runs are scored independently from each other, Avant-garde uses a novel data-driven scoring strategy. DIA signals are refined by learning from the data itself, using all measurements in all samples together to achieve the best optimization. We evaluated the performances of Avant-garde with a calibrated sample using spiked-in standards in a complex background, a phospho-enriched dataset (Abelin et al , 2016), and two complex hybrid proteome samples for benchmarking DIA software tools. The results clearly showed that Avant-garde is capable of improving the selectivity, accuracy, and reproducibility of the quantification results in very complex biological matrices. We have further shown that it can evaluate the suitability of a peak to be used for quantification reaching the same levels of selectivity, accuracy, and reproducibility obtained with manual validation.