Network Pharmacology Analysis on the Mechanism of Huangqi Sijunzi Decoction in Treating Cancer-Related Fatigue

Objective. This study aimed to determine the active ingredients of Huangqi Sijunzi Decoction (HQSJZD) and the targets in treating cancer-related fatigue (CRF) so as to investigate the treatment mechanism of HQSJZD for CRF. Methods. This study adopted the method of network pharmacology. The active ingredients and targets of HQSJZD were retrieved, and the targets of HQSJZD in treating CRF were obtained using a Venn diagram. Next, a protein-protein interaction (PPI) network was constructed using the String database. The core targets of HQSJZD in treating CRF were identified through topological analysis, and functional annotation analysis and pathway enrichment analysis were carried out. Subsequently, a compound-disease-target regulatory network was constructed using Cystoscape 3.8.0 software. Results. A total of 250 targets of HQSJZD ingredients, 1447 CRF-related genes, and 144 common targets were obtained. Through topological analysis, 61 core targets were screened. Bioinformatics annotation of these genes identified 2366 gene ontology (GO) terms and 172 enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Conclusion. The active ingredients in HQSJZD, that is, quercetin, luteolin, kaempferol, and naringenin, may act on AKT1, IL-6, VEGFA, MAPK3, CASP3, JUN, and EGFR to regulate the PI3K-Akt, TNF, and IL-17 signaling pathways, thereby suppressing inflammatory response, tumor gene expression, and tumor angiogenesis to treat CRF. This study investigated the pharmacological basis and mechanism of HQSJZD in the treatment of CRF using systematic pharmacology, which provides an important reference for further elucidation of the anti-CRF mechanism and clinical applications of HQSJZD, and also provides a method protocol for similar studies in the future.

High-dimensional covariance matrices tests for analyzing multi-tumor gene expression data

By collecting multiple sets per subject in microarray data, gene sets analysis requires characterize intra-subject variation using gene expression profiling. For each subject, the data can be written as a matrix with the different subsets of gene expressions (e.g. multiple tumor types) indexing the rows and the genes indexing the columns. To test the assumption of intra-subject (tumor) variation, we present and perform tests of multi-set sphericity and multi-set identity of covariance structures across subjects (tumor types). We demonstrate by both theoretical and empirical studies that the tests have good properties. We applied the proposed tests on The Cancer Genome Atlas (TCGA) and tested covariance structures for the gene expressions across several tumor types.

Optima: Optimal personalised treatment of early breast cancer using multi-parameter analysis, an international randomized trial of tumor gene expression test-directed chemotherapy treatment in a largely node-positive population.

TPS599 Background: Multi-parameter tumor gene expression assays (MPAs) are validated tools to assist adjuvant chemotherapy decisions for post-menopausal women with luminal-type node-negative breast cancer. Currently there is less certainty for women with 1-3 involved axillary lymph nodes and no information on MPA use for patients with higher level nodal involvement. Three RCTs with available data report chemotherapy benefit for premenopausal women; with limited use of ovarian function suppression (OFS) for non-chemotherapy treated participants, chemotherapy-induced menopause may explain these results. Methods: OPTIMA is an international academic, partially-blinded RCT of test-directed chemotherapy treatment with an adaptive design. Women and men aged 40 or older with resected luminal-type breast cancer may participate if they fulfil one of the following stage criteria: pN1-2; pN1mi with pT ≥20mm; pN0 with pT ≥30mm. Consenting patients are randomized between standard treatment with chemotherapy followed by endocrine therapy or to undergo Prosigna testing; those with high-Prosigna Score ( > 60) tumors receive standard treatment whilst those with low-score tumors are treated with endocrine therapy alone. Patients are informed only of their treatment; test details, and randomization for chemotherapy-treated patients are masked. Clinical choice of chemotherapy is declared at randomization from a menu of standard regimens. Endocrine therapy must be for at least 5 years. Women postmenopausal at trial entry should receive an AI; men, tamoxifen; and premenopausal women, either an AI or tamoxifen, and OFS for 3 or more years; OFS initiation may be deferred because of post-chemotherapy amenorrhea. OPTIMA aims to randomize 2250 patients in each arm to demonstrate non-inferiority of test directed treatment, defined as not more than 3% below the estimated 85% 5-year IDFS for the control arm with a one sided 5% significance level. Power is 81% assuming recruitment over 96-months from January 2017 and 12 months minimum follow-up. OPTIMA also has at least 80% power to demonstrate 3.5% non-inferiority of IDFS for patients with low Prosigna Score tumors (estimated 65% of participants). Cox proportional hazards models will be used to explore important prognostic factors including menopausal status. Additional secondary endpoints include DRFI. A cost-effectiveness analysis of protocol specified MPA driven treatment against standard clinical practice will be conducted. At 31/01/2021, 2004 patients had been randomized. The DMC reviewed the trial in December 2020 with knowledge of related trial results and suggested that the trial continues as planned. OPTIMA is registered as ISRCTN42400492 and funded by the UK NIHR Health Technology Assessment Programme, award number 10/34/501. Clinical trial information: ISRCTN42400492.

A Pan-Cancer Census of Dominant Tumor Immune Archetypes

SUMMARYCancers display significant heterogeneity with respect to tissue of origin, driver mutations and other features of the surrounding tissue. It is likely that persistent tumors differentially engage inherent patterns–here ‘Archetypes’–of the immune system, to both benefit from a tumor immune microenvironment (TIME) and to disengage tumor-targeting. To discover dominant immune system archetypes, the Immunoprofiler Initiative (IPI) processed 364 individual tumors across 12 cancer types using standardized protocols. Computational clustering of flow cytometry and transcriptomic data obtained from cell sub compartments uncovered archetypes that exist across indications. These Immune composition-based archetypes differentiate tumors based upon unique immune and tumor gene-expression patterns. Archetypes discovered this way also tie closely to well-established classifications of tumor biology. The IPI resource provides a template for understanding cancer immunity as a collection of dominant patterns of immune infiltration and provides a rational path forward to learn how to modulate these patterns to improve therapy.

Single Cell Analysis Can Define Distinct Evolution of Tumor Sites in Follicular Lymphoma

Tumor heterogeneity complicates biomarker development and fosters drug resistance in solid malignancies. In lymphoma, our knowledge of site-to-site heterogeneity and its clinical implications is still limited. Here, we profiled two nodal, synchronously-acquired tumor samples from ten follicular lymphoma patients using single cell RNA, B cell receptor (BCR) and T cell receptor sequencing, and flow cytometry. By following the rapidly mutating tumor immunoglobulin genes, we discovered that BCR subclones were shared between the two tumor sites in some patients, but in many patients the disease had evolved separately with limited tumor cell migration between the sites. Patients exhibiting divergent BCR evolution also exhibited divergent tumor gene expression and cell surface protein profiles. While the overall composition of the tumor microenvironment did not differ significantly between sites, we did detect a specific correlation between site-to-site tumor heterogeneity and T follicular helper (Tfh) cell abundance. We further observed enrichment of particular ligand-receptor pairs between tumor and Tfh cells, including CD40 and CD40LG, and a significant correlation between tumor CD40 expression and Tfh proliferation. Our study may explain discordant responses to systemic therapies, underscores the difficulty of capturing a patient's disease with a single biopsy, and furthers our understanding of tumor-immune networks in follicular lymphoma.

Gene expression and survival analysis in cancer research using online open access platforms: a comparative analysis

The development of genomics and transcriptomics and the potential associated with sharing data related with cancer, led to a growing understanding of cancer biology and to the identification of new biomarkers. Analysis of tumor gene expression and associated patient survival rate enables the dissection of the impact of certain genes in cancer patient ́s survival. For that purpose, it is essential to choose user-friendly platforms, where it is easy to analyze, compare and collect information for a certain set of genes. The goal of this article is to compare the content and utility of five open access online platforms for tumor gene expression and patient survival analysis from TCGA datasets – cBioPortal, USCS Xena, GEPIA, UALCAN and ONCOLNC. We explore these platforms from the point of view of a lay user, assessing their applicability to study differences in gene expression in tumor vs normal tissues, or according to cancer stage, and the impact of such expression patterns on patient survival. Although all five platforms are very intuitive and access to the data is easy, they vary in the information available, results visualization, and statistical tests performed. Therefore, the choice of a platform must take into account the study goals. For some purposes, a combination of platforms may be required.

N6-methyladenine modification in noncoding RNAs and its function in cancer

Non-coding RNAs are the main component of the extensive transcription results of the mammalian genome. They are not transcribed into proteins but play critical roles in regulating multiple biological processes and affecting cancer progression. m6A modification is one of the most abundant internal RNA modification of mammalian cells, and it involves almost all aspects of RNA metabolism. Recent research revealed tight correlations between m6A modification and ncRNAs and indicated the interaction between m6A and ncRNAs act a pivotal part in the development of cancer. The correlation between m6A modification and ncRNAs provides a new perspective for exploring the potential regulatory mechanism of tumor gene expression, and suggest that m6A modification and ncRNAs may be important prognostic markers and therapeutic targets for multiple cancers. In this review, we summarize the potential regulatory mechanisms between m6A methylation and ncRNAs, highlighting how their relationship affects biological functions in cancer.