Identification of Five Cytotoxicity-Related Genes Involved in the Progression of Triple-Negative Breast Cancer

Background: Breast cancer is one of the deadly tumors in women, and its incidence continues to increase. This study aimed to identify novel therapeutic molecules using RNA sequencing (RNA-seq) data of breast cancer from our hospital.Methods: 30 pairs of human breast cancer tissue and matched normal tissue were collected and RNA sequenced in our hospital. Differentially expressed genes (DEGs) were calculated with raw data by the R package “edgeR”, and functionally annotated using R package “clusterProfiler”. Tumor-infiltrating immune cells (TIICs) were estimated using a website tool TIMER 2.0. Effects of key genes on therapeutic efficacy were analyzed using RNA-seq data and drug sensitivity data from two databases: the Cancer Cell Line Encyclopedia (CCLE) and the Cancer Therapeutics Response Portal (CTRP).Results: There were 2,953 DEGs between cancerous and matched normal tissue, as well as 975 DEGs between primary breast cancer and metastatic breast cancer. These genes were primarily enriched in PI3K-Akt signaling pathway, calcium signaling pathway, cAMP signaling pathway, and cell cycle. Notably, CD8+ T cell, M0 macrophage, M1 macrophage, regulatory T cell and follicular helper T cell were significantly elevated in cancerous tissue as compared with matched normal tissue. Eventually, we found five genes (GALNTL5, MLIP, HMCN2, LRRN4CL, and DUOX2) were markedly corelated with CD8+ T cell infiltration and cytotoxicity, and associated with therapeutic response.Conclusion: We found five key genes associated with tumor progression, CD8+ T cell and therapeutic efficacy. The findings would provide potential molecular targets for the treatment of breast cancer.

Biopsy bacterial signature can predict patient tissue malignancy

Considerable recent research has indicated the presence of bacteria in a variety of human tumours and matched normal tissue. Rather than focusing on further identification of bacteria within tumour samples, we reversed the hypothesis to query if establishing the bacterial profile of a tissue biopsy could reveal its histology / malignancy status. The aim of the present study was therefore to differentiate between malignant and non-malignant fresh breast biopsy specimens, collected specifically for this purpose, based on bacterial sequence data alone. Fresh tissue biopsies were obtained from breast cancer patients and subjected to 16S rRNA gene sequencing. Progressive microbiological and bioinformatic contamination control practices were imparted at all points of specimen handling and bioinformatic manipulation. Differences in breast tumour and matched normal tissues were probed using a variety of statistical and machine-learning-based strategies. Breast tumour and matched normal tissue microbiome profiles proved sufficiently different to indicate that a classification strategy using bacterial biomarkers could be effective. Leave-one-out cross-validation of the predictive model confirmed the ability to identify malignant breast tissue from its bacterial signature with 84.78% accuracy, with a corresponding area under the receiver operating characteristic curve of 0.888. This study provides proof-of-concept data, from fit-for-purpose study material, on the potential to use the bacterial signature of tissue biopsies to identify their malignancy status.

Genomic Expression Profiling and Bioinformatics Analysis of Chronic Recurrent Multifocal Osteomyelitis

Objective. Chronic nonbacterial osteomyelitis (CNO) is an autoinflammatory bone disorder. Its most severe form is referred to as chronic recurrent multifocal osteomyelitis (CRMO). Currently, the exact molecular pathophysiology of CNO/CRMO remains unknown. No uniform diagnostic standard and treatment protocol were available for this disease. The aim of this study was to identify the differentially expressed genes (DEGs) in CRMO tissues compared to normal control tissues to investigate the mechanisms of CRMO. Materials. Microarray data from the GSE133378 (12 CRMO and 148 matched normal tissue samples) data sets were downloaded from the Gene Expression Omnibus (GEO) database. DEGs were identified using the limma package in the R software. Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and protein-protein interaction (PPI) network analysis were performed to further investigate the function of the identified DEGs. Results. This study identified a total of 1299 differentially expressed mRNAs, including1177 upregulated genes and 122 downregulated genes, between CRMO and matched normal tissue samples. GO analyses showed that DEGs were enriched in immune-related terms. KEGG pathway enrichment analyses showed that the DEGs were mainly related to oxidative phosphorylation, ribosome, and Parkinson disease. Eight modules were extracted from the gene expression network, including one module constituted with immune-related genes and one module constituted with ribosomal-related genes. Conclusion. Oxidative phosphorylation, ribosome, and Parkinson disease pathways were significantly associated with CRMO. The immune-related genes including IRF5, OAS3, and HLA-A, as well as numerous ribosomal-related genes, might be implicated in the pathogenesis of CRMO. The identification of these genes may contribute to the development of early diagnostic tools, prognostic markers, or therapeutic targets in CRMO.

Comprehensive molecular and genomic characterization of pancreatic tropism in metastatic renal cell carcinoma.

633 Background: Patients with metastatic renal cell carcinoma (mRCC) involving the pancreas have been shown to exhibit a relatively indolent course, yet the biologic explanation is unclear. We sought to characterize the genomic landscape of patients with mRCC harboring pancreatic metastases to identify molecular drivers of pancreatic tropism. Methods: mRCC patients harboring pancreatic metastases from UTSW and Cleveland Clinic were identified. Clinicopathologic data and oncologic outcomes were analyzed. Samples were obtained from primary tumors, metastatic sites (including pancreatic or other distant metastases), and matched normal tissue. Whole exome (WES) and RNA sequencing of tumors was conducted. Patient-derived xenograft (PDX) models were generated from a subset of patients, and the engrafted tumors were analyzed. Results: 31 mRCC patients with pancreatic metastases were included with 54 tumor samples derived from the primary tumor or thrombus (24), pancreatic metastasis (21), or other metastatic sites (9). Median follow-up was 101 months. Clinicopathologic characteristics were similar between the two institutional cohorts, and all but one patient were favorable or intermediate IMDC risk. All patients had clear cell histology. 8 patients (26%) were metastatic at diagnosis, and median time to metastasis in the remaining patients was 74 months (IQR 32-120). Overall (OS) and cancer-specific (CSS) survival did not vary by IMDC risk group. Morphologically, tumors largely displayed low-grade acinar patterns. WES with matched normal tissue and RNAseq were completed with adequate quality for 48 and 30 samples, respectively. 14 PDX lines were generated, of which 5 (36%) engrafted stably (≥2 passages). WES from 2 tumorgraft specimens revealed preservation of specific mutations in the corresponding human samples. Conclusions: mRCC patients with pancreatic metastases exhibit remarkably favorable survival outcomes. The relatively indolent biology of these tumors is reflected histologically and genomically and can be recapitulated in PDX models. Understanding tumor heterogeneity may help refine prognostic models for mRCC and hold implications for improved personalization of therapy.

WINTHER: An international study to select rational therapeutics based on the analysis of matched tumor and normal biopsies in subjects with advanced malignancies.

TPS11625 Background: Today, personalized cancer medicine implies matching the patient’s tumor genomic characteristics with molecularly and immune targeted agents. Although there are an increasing number of DNA aberrations that can now be matched to a cognate therapy, some patients do not display such druggable oncogene drivers. Methods: WINTHER is an open non-randomized study involving 6 cancer centers in France, Spain, Israel, Canada and USA applying genomic and also transcriptomic assays to guide treatment decisions. The novelty of the WINTHER approach lies in the use of tumor and matched normal tissue biopsies together and an algorithm for predicting efficacy of therapies. The aim is to provide a rational therapeutic choice for all of the patients enrolled in the study whether or not they harbor actionable DNA alterations. The study endpoint is the comparison of the progression-free-survival (PFS) under the WINTHER selected therapy to the PFS of the last therapeutic line. Patients included have refractory metastatic cancer of any histological type, with at least one prior therapeutic regimen and performance status of 0 to 1. Patients who have received a matched treatment based on a molecular anomaly as their immediate prior therapy were excluded. After consent, patients undergo a tumor and histologically-matched normal tissue biopsy. Extracted DNA and RNA of both tumor and normal from frozen tissues at the local center under common standard operating procedures are sent to centralized laboratories for omics investigations. DNA is investigated at Foundation Medicine Inc. and RNA at Gustave Roussy using Agilent technology. For RNA, the WINTHER algorithm is applied on the differential RNA expression data between tumor and normal tissues and establishes the list of drugs with the presumed higher score of efficacy for each patient. Patients with actionable genomic events enter in ARM A, and patients without any druggable anomaly of the DNA enter in ARM B and are treated using the WINTHER algorithm RNA-based treatment decision tool. To date, the trial has recruited 303 patients. Clinical trial information: NCT01856296.