PURPOSE: Approximately 20% of patients with TP53-mutant myelodysplastic syndromes (MDS) achieve complete remission (CR) with hypomethylating agents. Eprenetapopt (APR-246) is a novel, first-in-class, small molecule that restores wild-type p53 functions in TP53-mutant cells. METHODS: This was a phase Ib/II study to determine the safety, recommended phase II dose, and efficacy of eprenetapopt administered in combination with azacitidine in patients with TP53-mutant MDS or acute myeloid leukemia (AML) with 20%-30% marrow blasts (ClinicalTrials.gov identifier: NCT03072043 ). RESULTS: Fifty-five patients (40 MDS, 11 AML, and four MDS/myeloproliferative neoplasms) with at least one TP53 mutation were treated. The overall response rate was 71% with 44% achieving CR. Of patients with MDS, 73% (n = 29) responded with 50% (n = 20) achieving CR and 58% (23/40) a cytogenetic response. The overall response rate and CR rate for patients with AML was 64% (n = 7) and 36% (n = 4), respectively. Patients with only TP53 mutations by next-generation sequencing had higher rates of CR (69% v 25%; P = .006). Responding patients had significant reductions in TP53 variant allele frequency and p53 expression by immunohistochemistry, with 21 (38%) achieving complete molecular remission (variant allele frequency < 5%). Median overall survival was 10.8 months with significant improvement in responding versus nonresponding patients by landmark analysis (14.6 v 7.5 months; P = .0005). Overall, 19/55 (35%) patients underwent allogeneic hematopoietic stem-cell transplant, with a median overall survival of 14.7 months. Adverse events were similar to those reported for azacitidine or eprenetapopt monotherapy, with the most common grade ≥ 3 adverse events being febrile neutropenia (33%), leukopenia (29%), and neutropenia (29%). CONCLUSION: Combination treatment with eprenetapopt and azacitidine is well-tolerated yielding high rates of clinical response and molecular remissions in patients with TP53-mutant MDS and oligoblastic AML.

Identification of clinically relevant tumor subtypes and omics signatures is an important task in cancer translational research for precision medicine. Large-scale genomic profiling studies such as The Cancer Genome Atlas (TCGA) Research Network have generated vast amounts of genomic, transcriptomic, epigenomic, and proteomic data. While these studies have provided great resources for researchers to discover clinically relevant tumor subtypes and driver molecular alterations, there are few computationally efficient methods and tools for integrative clustering analysis of these multi-type omics data. Therefore, the aim of this article is to develop a fully Bayesian latent variable method (called iClusterBayes) that can jointly model omics data of continuous and discrete data types for identification of tumor subtypes and relevant omics features. Specifically, the proposed method uses a few latent variables to capture the inherent structure of multiple omics data sets to achieve joint dimension reduction. As a result, the tumor samples can be clustered in the latent variable space and relevant omics features that drive the sample clustering are identified through Bayesian variable selection. This method significantly improve on the existing integrative clustering method iClusterPlus in terms of statistical inference and computational speed. By analyzing TCGA and simulated data sets, we demonstrate the excellent performance of the proposed method in revealing clinically meaningful tumor subtypes and driver omics features.

Muscle-invasive bladder cancers (MIBCs) cause approximately 150 000 deaths per year worldwide. Survival for MIBC patients is heterogeneous, with no clinically validated molecular markers that predict clinical outcome. Non-MIBCs (NMIBCs) generally have favorable outcome; however, a portion progress to MIBC. Hence, development of a prognostic tool that can guide decision-making is crucial for improving clinical management of bladder urothelial carcinomas. Tumor grade is defined by pathologic evaluation of tumor cell differentiation, and it often associates with clinical outcome. The current study extrapolates this conventional wisdom and combines it with molecular profiling. We developed an 18-gene signature that molecularly defines urothelial cellular differentiation, thus classifying MIBCs and NMIBCs into two subgroups: basal and differentiated. We evaluated the prognostic capability of this "tumor differentiation signature" and three other existing gene signatures including the The Cancer Genome Atlas (TCGA; 2707 genes), MD Anderson Cancer Center (MDA; 2252 genes/2697 probes), and University of North Carolina at Chapel Hill (UNC; 47 genes) using five gene expression data sets derived from MIBC and NMIBC patients. All statistical tests were two-sided. The tumor differentiation signature demonstrated consistency and statistical robustness toward stratifying MIBC patients into different overall survival outcomes (TCGA cohort 1, P = .03; MDA discovery, P = .009; MDA validation, P = .01), while the other signatures were not as consistent. In addition, we analyzed the progression (Ta/T1 progressing to ≥T2) probability of NMIBCs. NMIBC patients with a basal tumor differentiation signature associated with worse progression outcome (P = .008). Gene functional term enrichment and gene set enrichment analyses revealed that genes involved in the biologic process of immune response and inflammatory response are among the most elevated within basal bladder cancers, implicating them as candidates for immune checkpoint therapies. These results provide definitive evidence that a biology-prioritizing clustering methodology generates meaningful insights into patient stratification and reveals targetable molecular pathways to impact future therapeutic approach.

Vα24-invariant natural killer T cells (NKTs) localize to tumors and have inherent antitumor properties, making them attractive chimeric antigen receptor (CAR) carriers for redirected cancer immunotherapy. However, clinical application of CAR-NKTs has been impeded, as mechanisms responsible for NKT expansion and the in vivo persistence of these cells are unknown. Here, we demonstrated that antigen-induced expansion of primary NKTs in vitro associates with the accumulation of a CD62L⁺ subset and exhaustion of CD62L^– cells. Only CD62L⁺ NKTs survived and proliferated in response to secondary stimulation. When transferred to immune-deficient NSG mice, CD62L⁺ NKTs persisted 5 times longer than CD62L^– NKTs. Moreover, CD62L⁺ cells transduced with a CD19-specific CAR achieved sustained tumor regression in a B cell lymphoma model. Proliferating CD62L⁺ cells downregulated or maintained CD62L expression when activated via T cell receptor alone or in combination with costimulatory receptors. We generated HLA^null K562 cell clones that were engineered to express CD1d and costimulatory ligands. Clone B-8-2 (HLA^nullCD1d^medCD86^high4-1BBL^medOX40L^high) induced the highest rates of NKT expansion and CD62L expression. B-8-2–expanded CAR-NKTs exhibited prolonged in vivo persistence and superior therapeutic activities in models of lymphoma and neuroblastoma. Therefore, we have identified CD62L as a marker of a distinct NKT subset endowed with high proliferative potential and have developed artificial antigen-presenting cells that generate CD62L-enriched NKTs for effective cancer immunotherapy.

High resolution microarrays and second-generation sequencing platforms are powerful tools to investigate genome-wide alterations in DNA copy number, methylation and gene expression associated with a disease. An integrated genomic profiling approach measures multiple omics data types simultaneously in the same set of biological samples. Such approach renders an integrated data resolution that would not be available with any single data type. In this study, we use penalized latent variable regression methods for joint modeling of multiple omics data types to identify common latent variables that can be used to cluster patient samples into biologically and clinically relevant disease subtypes. We consider lasso [J. Roy. Statist. Soc. Ser. B 58 (1996) 267–288], elastic net [J. R. Stat. Soc. Ser. B Stat. Methodol. 67 (2005) 301–320] and fused lasso [J. R. Stat. Soc. Ser. B Stat. Methodol. 67 (2005) 91–108] methods to induce sparsity in the coefficient vectors, revealing important genomic features that have significant contributions to the latent variables. An iterative ridge regression is used to compute the sparse coefficient vectors. In model selection, a uniform design [Monographs on Statistics and Applied Probability (1994) Chapman & Hall] is used to seek “experimental” points that scattered uniformly across the search domain for efficient sampling of tuning parameter combinations. We compared our method to sparse singular value decomposition (SVD) and penalized Gaussian mixture model (GMM) using both real and simulated data sets. The proposed method is applied to integrate genomic, epigenomic and transcriptomic data for subtype analysis in breast and lung cancer data sets.

Nutrient excess, a major driver of obesity, diminishes hypothalamic responses to exogenously administered leptin, a critical hormone of energy balance. Here, we aimed to identify a physiological signal that arises from excess caloric intake and negatively controls hypothalamic leptin action. We found that deficiency of the gastric inhibitory polypeptide receptor (Gipr) for the gut-derived incretin hormone GIP protected against diet-induced neural leptin resistance. Furthermore, a centrally administered antibody that neutralizes GIPR had remarkable antiobesity effects in diet-induced obese mice, including reduced body weight and adiposity, and a decreased hypothalamic level of SOCS3, an inhibitor of leptin actions. In contrast, centrally administered GIP diminished hypothalamic sensitivity to leptin and increased hypothalamic levels of Socs3. Finally, we show that GIP increased the active form of the small GTPase Rap1 in the brain and that its activation was required for the central actions of GIP. Altogether, our results identify GIPR/Rap1 signaling in the brain as a molecular pathway linking overnutrition to the control of neural leptin actions.