Dual inhibition of enhancer of zeste homolog 1/2 overactivates WNT signaling to deplete cancer stem cells in multiple myeloma.

Multiple myeloma (MM) is an incurable hematological malignancy caused by accumulation of abnormal clonal plasma cells. Despite the recent development of novel therapies, relapse of MM eventually occurs as a result of a remaining population of drug-resistant myeloma stem cells. Side population (SP) cells show cancer stem cell-like characteristics in MM; thus, targeting these cells is a promising strategy to completely cure this malignancy. Herein, we showed that SP cells expressed higher levels of enhancer of zeste homolog (EZH) 1 and EZH2, which encode the catalytic subunits of Polycomb repressive complex 2 (PRC2), than non-SP cells, suggesting that EZH1 as well as EZH2 contributes to the stemness maintenance of the MM cells and that targeting both EZH1/2 is potentially a significant therapeutic approach for eradicating myeloma stem cells. A novel orally bioavailable EZH1/2 dual inhibitor, OR-S1, effectively eradicated SP cells and had a greater antitumor effect than a selective EZH2 inhibitor in vitro and in vivo, including a unique patient-derived xenograft model. Moreover, long-term continuous dosing of OR-S1 completely cured mice bearing orthotopic xenografts. Additionally, PRC2 directly regulated WNT signaling in MM, and overactivation of this signaling induced by dual inhibition of EZH1/2 eradicated myeloma stem cells and negatively affected tumorigenesis, suggesting that repression of WNT signaling by PRC2 plays an important role in stemness maintenance of MM cells. Our results show the role of EZH1/2 in the maintenance of myeloma stem cells and provide a preclinical rationale for therapeutic application of OR-S1, leading to significant advances in the treatment of MM.

Japan PGx Data Science Consortium Database: SNPs and HLA genotype data from 2994 Japanese healthy individuals for pharmacogenomics studies.

Japan Pharmacogenomics Data Science Consortium (JPDSC) has assembled a database for conducting pharmacogenomics (PGx) studies in Japanese subjects. The database contains the genotypes of 2.5 million single-nucleotide polymorphisms (SNPs) and 5 human leukocyte antigen loci from 2994 Japanese healthy volunteers, as well as 121 kinds of clinical information, including self-reports, physiological data, hematological data and biochemical data. In this article, the reliability of our data was evaluated by principal component analysis (PCA) and association analysis for hematological and biochemical traits by using genome-wide SNP data. PCA of the SNPs showed that all the samples were collected from the Japanese population and that the samples were separated into two major clusters by birthplace, Okinawa and other than Okinawa, as had been previously reported. Among 87 SNPs that have been reported to be associated with 18 hematological and biochemical traits in genome-wide association studies (GWAS), the associations of 56 SNPs were replicated using our data base. Statistical power simulations showed that the sample size of the JPDSC control database is large enough to detect genetic markers having a relatively strong association even when the case sample size is small. The JPDSC database will be useful as control data for conducting PGx studies to explore genetic markers to improve the safety and efficacy of drugs either during clinical development or in post-marketing.

Deep learning generates custom-made logistic regression models for explaining how breast cancer subtypes are classified.

Differentiating the intrinsic subtypes of breast cancer is crucial for deciding the best treatment strategy. Deep learning can predict the subtypes from genetic information more accurately than conventional statistical methods, but to date, deep learning has not been directly utilized to examine which genes are associated with which subtypes. To clarify the mechanisms embedded in the intrinsic subtypes, we developed an explainable deep learning model called a point-wise linear (PWL) model that generates a custom-made logistic regression for each patient. Logistic regression, which is familiar to both physicians and medical informatics researchers, allows us to analyze the importance of the feature variables, and the PWL model harnesses these practical abilities of logistic regression. In this study, we show that analyzing breast cancer subtypes is clinically beneficial for patients and one of the best ways to validate the capability of the PWL model. First, we trained the PWL model with RNA-seq data to predict PAM50 intrinsic subtypes and applied it to the 41/50 genes of PAM50 through the subtype prediction task. Second, we developed a deep enrichment analysis method to reveal the relationships between the PAM50 subtypes and the copy numbers of breast cancer. Our findings showed that the PWL model utilized genes relevant to the cell cycle-related pathways. These preliminary successes in breast cancer subtype analysis demonstrate the potential of our analysis strategy to clarify the mechanisms underlying breast cancer and improve overall clinical outcomes.

Highly potent, orally active novel small-molecule HPK1 inhibitor DS21150768 induces anti-tumor responses in multiple syngeneic tumor mouse models.

Augmenting T-cell activity is a promising approach to enhance the efficacy of cancer immunotherapy treatment. Hematopoietic progenitor kinase 1 (HPK1) is predominantly expressed in immune cells and negatively regulates T-cell receptor signaling. It is reported that inhibition of the kinase function of HPK1 results in tumor growth suppression by enhancing cancer immunity. Thus, developing HPK1 inhibitors has attracted considerable attention as a future cancer immunotherapy approach. However, despite recent progress in HPK1 biology and pharmacology, various challenges still remain, such as developing HPK1 inhibitors with favorable pharmacological profiles and identifying tumor characteristics that can be applied to define susceptibility to HPK1 inhibition. Here, we present the identification and pharmacological evaluation of DS21150768, a potent small-molecule HPK1 inhibitor with a novel chemical scaffold. DS21150768 shows remarkable inhibition of HPK1 kinase activity, and in vitro studies demonstrated its potent activity to enhance T-cell function. DS21150768 is orally bioavailable and shows sustained plasma exposure, which leads to enhanced cytokine responses in vivo. We conducted a comparison of the anti-tumor efficacy of DS21150768 alone or in combination with anti-PD-1 antibody in 12 different mouse cancer cell models, and observed that the treatments suppressed tumor growth in multiple models. Furthermore, Gene Set Enrichment Analysis demonstrated significant enrichment of immune-related gene signatures in the tumor models responsive to DS21150768 treatment. Our results provide a path forward for the future development of HPK1 inhibitors and fundamental insights into biomarkers of HPK1-targeted therapy.