PI3Kα inhibitor CYH33 triggers antitumor immunity in murine breast cancer by activating CD8+T cells and promoting fatty acid metabolism.

BACKGROUND: The phosphatidylinositol 3-kinase (PI3K) is frequently hyperactivated in cancer and plays important roles in both malignant and immune cells. The effect of PI3Kα inhibitors on the tumor microenvironment (TME) remains largely unknown. Here, we investigated the modulation of the TME by a clinical PI3Kα-specific inhibitor CYH33. METHODS: The activity of CYH33 against a panel of murine tumors in the immune-competent context or athymic mice was detected. Single-cell RNA sequencing and multi-parameter flow cytometry were performed to determine the immune profiling of TME. The effect of CYH33 on immune cells was conducted with primary murine cells. RESULTS: CYH33 exhibited more potent antitumor activity in immune-competent context. CYH33 enhanced the infiltration and activation of CD8+T and CD4+T cells, while attenuating M2-like macrophages and regulatory CD4+T cells. Increase in memory T cells was confirmed by the induction of long-term immune memory on CYH33 treatment. Mechanistically, CYH33 relieved the suppressed expansion of CD8+T cells via preferential polarization of the macrophages to the M1 phenotype. CYH33 promoted fatty acid (FA) metabolism in the TME, while FA enhanced the activity of CD8+T cells in vitro. The combination of CYH33 with the FA synthase (FASN) inhibitor C75 synergistically inhibited tumor growth with enhanced host immunity. CONCLUSIONS: CYH33 induces immune activation and synergizes with FASN inhibitor to further promote the antitumor immunity, which gains novel insights into how PI3K inhibitors exert their activity by modulating TME and provides a rationale for the concurrent targeting of PI3K and FASN in breast cancer treatment.

Adaptive resistance to PI3Kα-selective inhibitor CYH33 is mediated by genomic and transcriptomic alterations in ESCC cells.

Phosphoinositide-3 kinase alpha-specific inhibitors (PI3Kαi) displayed promising potential for the treatment of esophageal squamous cell carcinoma (ESCC) with frequent activation in PI3K signaling. However, acquired resistance is likely to develop and limit the efficacy of PI3Kαi like other targeted therapies. To identify genomic adaptation to PI3Kαi, we applied whole-genome sequencing and detected gene mutation and amplification in four lines of ESCC cells established with adapted resistance to a novel PI3Kαi CYH33. Particularly, HRASG12S mutation was found in KYSE180C cells. Overexpression of HRASG12S in ESCC parental cells rendered resistance to CYH33. By contrast, down-regulation of HRASG12S restored the sensitivity of KYSE180C1 cells to CYH33, and combination of CYH33 and MEK162 displayed synergistic effect against KYSE180C1 cells and xenografts. Furthermore, elevated mTORC1, mitogen-activated protein kinase (MAPK), and c-Myc signaling pathways were found in resistant cells by RNA sequencing and combination of CYH33 and RAD001, MEK162, or OTX015 overcame the resistance to CYH33, which was accompanied with enhanced inhibition on S6, extracellular signal-regulated kinase 1 (ERK), or c-Myc, respectively. Overall, we characterized the adaptations to PI3Kαi in ESCC cells and identified combinatorial regimens that may circumvent resistance.

A CREB1/miR-433 reciprocal feedback loop modulates proliferation and metastasis in colorectal cancer.

Increasing evidence has indicated the prognostic value of miR-433 across a series of malignancy types. However, the underlying mechanisms involved in cancer progression haven't been sufficiently elucidated. In the present work, we found that miR-433 was downregulated in CRC tissues and cell lines. Ectopic expression of miR-433 obviously suppressed the proliferation, invasion and metastasis activity of CRC cells in vitro and in vivo. CREB1, CCAR1 and JNK1 were highly expressed and negatively correlated with miR-433 expression in CRC. CRC patients with higher expression of CREB1, CCAR1 or JNK1 presented a worse outcome relative to those with lower expression. CREB1 transactivated the expression of miR-433, and CREB1, CCAR1 and JNK1 simultaneously served as its targets, which in turn composed a feedback loop between CREB1 and miR-433. miR-433 blocked cell cycle progression and abolished EMT. Collectively, our study demonstrated the CREB1/miR-433 reciprocal feedback loop restrained the propagation, invasion and metastasis activities of CRC cells through abrogation of cell cycle progression and constraint of EMT.

Distinguishing three subtypes of hematopoietic cells based on gene expression profiles using a support vector machine.

Hematopoiesis is a complicated process involving a series of biological sub-processes that lead to the formation of various blood components. A widely accepted model of early hematopoiesis proceeds from long-term hematopoietic stem cells (LT-HSCs) to multipotent progenitors (MPPs) and then to lineage-committed progenitors. However, the molecular mechanisms of early hematopoiesis have not been fully characterized. In this study, we applied a computational strategy to identify the gene expression signatures distinguishing three types of closely related hematopoietic cells collected in recent studies: (1) hematopoietic stem cell/multipotent progenitor cells; (2) LT-HSCs; and (3) hematopoietic progenitor cells. Each cell in these cell types was represented by its gene expression profile among a total number of 20,475 genes. The expression features were analyzed by a Monte-Carlo Feature Selection (MCFS) method, resulting in a feature list. Then, the incremental feature selection (IFS) and a support vector machine (SVM) optimized with a sequential minimum optimization (SMO) algorithm were employed to access the optimal classifier with the highest Matthews correlation coefficient (MCC) value of 0.889, in which 6698 features were used to represent cells. In addition, through an updated program of MCFS method, seventeen decision rules can be obtained, which can classify the three cell types with an overall accuracy of 0.812. Using a literature review, both the rules and the top features used for building the optimal classifier were confirmed to be commonly used or potential biological markers for distinguishing the three cell types of HSPCs. This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang.

Identifying and analyzing different cancer subtypes using RNA-seq data of blood platelets.

Detection and diagnosis of cancer are especially important for early prevention and effective treatments. Traditional methods of cancer detection are usually time-consuming and expensive. Liquid biopsy, a newly proposed noninvasive detection approach, can promote the accuracy and decrease the cost of detection according to a personalized expression profile. However, few studies have been performed to analyze this type of data, which can promote more effective methods for detection of different cancer subtypes. In this study, we applied some reliable machine learning algorithms to analyze data retrieved from patients who had one of six cancer subtypes (breast cancer, colorectal cancer, glioblastoma, hepatobiliary cancer, lung cancer and pancreatic cancer) as well as healthy persons. Quantitative gene expression profiles were used to encode each sample. Then, they were analyzed by the maximum relevance minimum redundancy method. Two feature lists were obtained in which genes were ranked rigorously. The incremental feature selection method was applied to the mRMR feature list to extract the optimal feature subset, which can be used in the support vector machine algorithm to determine the best performance for the detection of cancer subtypes and healthy controls. The ten-fold cross-validation for the constructed optimal classification model yielded an overall accuracy of 0.751. On the other hand, we extracted the top eighteen features (genes), including TTN, RHOH, RPS20, TRBC2, in another feature list, the MaxRel feature list, and performed a detailed analysis of them. The results indicated that these genes could be important biomarkers for discriminating different cancer subtypes and healthy controls.

Identifying Novel Candidate Genes Related to Apoptosis from a Protein-Protein Interaction Network.

Apoptosis is the process of programmed cell death (PCD) that occurs in multicellular organisms. This process of normal cell death is required to maintain the balance of homeostasis. In addition, some diseases, such as obesity, cancer, and neurodegenerative diseases, can be cured through apoptosis, which produces few side effects. An effective comprehension of the mechanisms underlying apoptosis will be helpful to prevent and treat some diseases. The identification of genes related to apoptosis is essential to uncover its underlying mechanisms. In this study, a computational method was proposed to identify novel candidate genes related to apoptosis. First, protein-protein interaction information was used to construct a weighted graph. Second, a shortest path algorithm was applied to the graph to search for new candidate genes. Finally, the obtained genes were filtered by a permutation test. As a result, 26 genes were obtained, and we discuss their likelihood of being novel apoptosis-related genes by collecting evidence from published literature.

[IRF6 gene mutation analysis in a van Der Woude syndrome family in Henan province].

PURPOSE: To investigate IRF6 gene mutation in a van Der Woude syndrome (VWS) family in Henan province. METHODS: PCR and DNA sequencing was employed to detect the mutation of IRF6.Secondary construction transformation analysis was performed using PIX-Protein Identification software. RESULTS: A CGC>TGC(r.279c-->t) transversion of IRF6 was identified in condon 6, showing complete segregation with the disease phenotypes and was resulting in changes of the secondary constructure of IRF6. CONCLUSION: VWS is caused by mutations in IRF6 gene, and IRF6 is closely related to the development of lip, palate and tooth.

Mutation analysis of 20 SARS virus genome sequences: evidence for negative selection in replicase ORF1b and spike gene.

AIM: Recently, more SARS-CoV virus genome sequences are released to the GenBank database. The aim of this study is to reveal the evolution forces of SARS-CoV virus by analyzing the nucleotide mutations in these sequences. METHODS: We obtained 20 SARS-CoV virus genome sequences from NCBI database, and calculated the ratio of non-synonymous nucleotide substitution per non-synonymous site (Ka) and synonymous nucleotide substitution per synonymous site (Ks) for SARS-CoV virus genes. RESULTS: The Ka/Ks ratios for replicase polyprotein ORF1a, ORF1b, and spike protein gene are 1.09 (P=0.6501), 0.38 (P=0.0074), 0.65 (P=0.0685) respectively. CONCLUSION: SARS-CoV virus replicase polyprotein ORF1b is undergoing negative selection; negative selection force is also probably operating on spike protein gene. These results provide basis for future developing a new drug and vaccine against SARS.

Linkage analysis of the candidate genes of familial restless legs syndrome.

This research was performed to investigate the relationship between 16 candidate genes responsible for dopaminergic transmission or iron metabolism and familial restless legs syndrome. Genotyping was performed in a Han restless legs syndrome family using the technique of fluorescence-based genescan with the microsatellite markers selected in chromosomal regions flanking the candidate genes. Classical linkage analysis was conducted under the autosomal dominant genetic mode. Results showed that all of the LOD scores at recombination fraction 0.00 are smaller than -2.00, which indicated that these loci were not linked to familial restless legs syndrome. No linkage was found between the candidate genes and RLS in this family. Familial restless legs syndrome may be caused by another gene related to dopaminergic transmission and iron metabolism or there is new mechanism involved in this disease.

A high-resolution genetic and physical map of a mouse coat abnormality locus (Uncv).

More than a hundred of loci (genes) affect the development of mouse and human hair. A locus of Uncv (uncovered) has been confirmed to be involved in hairlessness for homozygote and sparse hair for heterozygote. Except hairlessness(or called uncovered coat), the homozygote was also accompanied by growth retard and puberty delay. Identification of the mutation in the gene will be important for understanding the related diseases in human. Although the uncovered locus (Uncv) has been mapped to the mouse distal chromosome 11(Chr11), the high-resolution genetic map and physical map of the locus has not been created. In this study, 2074 F2 mouse populations from backcross [BALB/c (Uncv/Uncv) x C3H (+/+)]x BALB/c (Uncv/Uncv) and [BALB/c (Uncv/Uncv) x C57BL/6 (+/+)] x BALB/c(Uncv/ Uncv) were genotyped using 16 polymorphic markers with an approximately 20 cM interval on mouse distal Chr11. By genetic linkage analysis, Uncv locus was mapped to an approximately 1.4 cM interval between markers D11Mit337 and D11Mit338 with the following order: proximal D11Mit338-D11Mit203 (Uncv)-D11Mit103 -D11Mit337 distal on mouse Chr11. And then, a contig of 35 BACs representing the Uncv-containing region was constructed. The contig covered 800-1000 kb region flanked by 189K10-SP6 and D11Mit103. Together, we have constructed the high-resolution genetic map and detailed physical map of the Uncv region. This will facilitate the identification of the Uncv loci.