Genome Analysis of Osteosarcoma Progression Samples Identifies FGFR1 Overexpression as a Potential Treatment Target and CHM as a Candidate Tumor Suppressor Gene

Large Scale Comparative Deconvolution Analysis of the Canine and Human Osteosarcoma Tumor Microenvironment Uncovers Conserved Clinically Relevant Subtypes

Osteosarcoma is a relatively rare but aggressive cancer of the bones with a shortage of effective biomarkers. Although less common in humans, Osteosarcomas are fairly common in adult pet dogs and have been shown to share many similarities with their human analogs. In this work, we analyze bulk transcriptomic data of 213 primary and 100 metastatic Osteosarcoma samples from 210 pet dogs enrolled in nation-wide clinical trials to uncover three Tumor Microenvironment (TME)-based subtypes: Immune Enriched (IE), Immune Enriched Dense Extra-Cellular Matrix-like (IE-ECM) and Immune Desert (ID) with distinct cell type compositions, oncogenic pathway activity and chromosomal instability. Furthermore, leveraging bulk transcriptomic data of canine primary tumors and their matched metastases from different sites, we characterize how the Osteosarcoma TME evolves from primary to metastatic disease in a standard of care clinical setting and assess its overall impact on clinical outcomes of canines. Most importantly, we find that TME-based subtypes of canine Osteosarcomas are conserved in humans and predictive of progression free survival outcomes of human patients, independently of known prognostic biomarkers such as presence of metastatic disease at diagnosis and percent necrosis following chemotherapy. In summary, these results demonstrate the power of using canines to model the human Osteosarcoma TME and discover novel biomarkers for clinical translation.

Integration of single-cell sequencing and bulk expression data reveals chemokine signaling pathway in proliferating cells is associated with the survival outcome of osteosarcoma

BACKGROUND

Osteosarcoma, as the most common primary bone malignancy, is urgent to be well-studied on the biomarkers and therapeutic targets to improve the five-year survival rate. Transcriptomic analysis using single-cell RNA or bulk RNA sequencing has been developed to detect biomarkers in various cancer types.

METHODS AND RESULTS

We applied Scissor to combine single-cell RNA-seq data and bulk transcriptome data of osteosarcoma, providing cell-level information and sample phenotypes to identify the survival-associated cell subpopulations. By investigating the differences between the survival-associated cell subpopulations, we identified CCL21, CCL22, CCL24, CXCL11, CXCL12, CXCL13, GNAI2, and RAC2 in the proliferating cells that are significantly associated with osteosarcoma patient outcome. Then we assigned the risk score for each sample based on the cell proportion-normalized gene expression and validated it in the public dataset.

CONCLUSIONS

This study provides the clinical insight that chemokine signaling pathway genes (CCL21, CCL22, CCL24, CXCL11, CXCL12, CXCL13, GNAI2, and RAC2) in proliferating cells might be the potential biomarkers for treatment of osteosarcoma.

The Clinical Significance and Potential Molecular Mechanism of Upregulated CDC28 Protein Kinase Regulatory Subunit 1B in Osteosarcoma

BACKGROUND

CDC28 Protein Kinase Regulatory Subunit 1B (CKS1B) is a member of cyclin-dependent kinase subfamily and the relationship between CKS1B and osteosarcoma (OS) remains to be explored.

METHODS

80 OS and 41 nontumor tissue samples were arranged to conduct immunohistochemistry (IHC) to evaluate CKS1B expression between OS and nontumor samples. The standard mean deviation (SMD) was calculated based on in-house IHC and tissue microarrays and exterior high-throughput datasets for further verification of CKS1B expression in OS. The effect of CKS1B expression on clinicopathological and overall survival of OS patients was measured through public high-throughput datasets, and analysis of immune infiltration and single-cell RNA-seq was applied to ascertain molecular mechanism of CKS1B in OS.

RESULTS

A total of 197 OS samples and 83 nontumor samples (including tissue and cell line) were obtained from in-house IHC, microarrays, and exterior high-throughput datasets. The analysis of integrated expression status demonstrated upregulation of CKS1B in OS (SMD = 1.38, 95% CI [0.52-2.25]) and the significant power of CKS1B expression in distinguishing OS samples from nontumor samples (Area under the Curve (AUC) = 0.89, 95% CI [0.86-0.91]). Clinicopathological and prognosis analysis indicated no remarkable significance but inference of immune infiltration and single-cell RNA-seq prompted that OS patients with overexpressed CKS1B were more likely to suffer OS metastasis while MYC Protooncogene may be the upstream regulon of CKS1B in proliferating osteoblastic OS cells.

CONCLUSIONS

In this study, sufficient evidence was provided for upregulation of CKS1B in OS. The advanced effect of CKS1B on OS progression indicates a foreground of CKS1B as a biomarker for OS.

An Analysis Regarding the Prognostic Significance of MAVS and Its Underlying Biological Mechanism in Ovarian Cancer

The present study evaluates the value of mitochondrial antiviral signaling (MAVS) expression as a potential diagnostic biomarker and therapeutic target for ovarian cancer (OC) and analyses the underlying biological mechanism in this pathology. First, the association between MAVS expression determined by immunohistochemical (IHC) and clinical characteristics was systematically investigated. Overexpression of MAVS was associated with advanced clinical factors and poor survival of OC patients. Second, bioinformatics analyses, namely, gene expression, mutation analysis, gene set variation analysis (GSVA), gene set enrichment analysis (GSEA), and weighted gene co-expression network analysis (WGCNA), were performed to evaluate the potential biological functions of MAVS in OC. The results showed that MAVS may play a critical role in immune cell infiltration. CIBERSORT was applied to assess the infiltration of immune cells in OC. CD8+ T cells, γδT cells, and eosinophils had significantly negative correlations with MAVS expression. Finally, sensitivity analysis found that patients with high MAVS expression were predicted to be significantly less responsive to cisplatin and paclitaxel. In conclusion, these findings suggested that MAVS influences biological behavior by regulating the immune response and that it can be used as a predictive marker for poor prognosis in OC.

Filtering of the Gene Signature as the Predictors of Cisplatin-Resistance in Ovarian Cancer

BACKGROUND

Gene expression profiling and prediction of drug responses based on the molecular signature indicate new molecular biomarkers which help to find the most effective drugs according to the tumor characteristics.

OBJECTIVES

In this study two independent datasets, GSE28646 and GSE15372 were subjected to meta-analysis based on Affymetrix microarrays.

MATERIAL AND METHODS

In-silico methods were used to determine differentially expressed genes (DEGs) in the previously reported sensitive and resistant A2780 cell lines to Cisplatin. Gene Fuzzy Scoring (GFS) and Principle Component Analysis (PCA) were then used to eliminate batch effects and reduce data dimension, respectively. Moreover, SVM method was performed to classify sensitive and resistant data samples. Furthermore, Wilcoxon Rank sum test was performed to determine DEGs. Following the selection of drug resistance markers, several networks including transcription factor-target regulatory network and miRNA-target network were constructed and Differential correlation analysis was performed on these networks.

RESULTS

The trained SVM successfully classified sensitive and resistant data samples. Moreover, Performing DiffCorr analysis on the sensitive and resistant samples resulted in detection of 27 and 25 significant (with correlation ≥|0.9|) pairs of genes that respectively correspond to newly constructed correlations and loss of correlations in the resistant samples.

CONCLUSIONS

Our results indicated the functional genes and networks in Cisplatin resistance of ovarian cancer cells and support the importance of differential expression studies in ovarian cancer chemotherapeutic agent responsiveness.

Genomics and Therapeutic Vulnerabilities of Primary Bone Tumors

Osteosarcoma, Ewing sarcoma and chondrosarcoma are rare diseases but the most common primary tumors of bone. The genes directly involved in the sarcomagenesis, tumor progression and treatment responsiveness are not completely defined for these tumors, and the powerful discovery of genetic analysis is highly warranted in the view of improving the therapy and cure of patients. The review summarizes recent advances concerning the molecular and genetic background of these three neoplasms and, of their most common variants, highlights the putative therapeutic targets and the clinical trials that are presently active, and notes the fundamental issues that remain unanswered. In the era of personalized medicine, the rarity of sarcomas may not be the major obstacle, provided that each patient is studied extensively according to a road map that combines emerging genomic and functional approaches toward the selection of novel therapeutic strategies.

Receptor Tyrosine Kinases in Osteosarcoma: 2019 Update

The primary conclusions of our 2014 contribution to this series were as follows: Multiple receptor tyrosine kinases (RTKs) likely contribute to aggressive phenotypes in osteosarcoma and, therefore, inhibition of multiple RTKs is likely necessary for successful clinical outcomes. Inhibition of multiple RTKs may also be useful to overcome resistance to inhibitors of individual RTKs as well as resistance to conventional chemotherapies. Different combinations of RTKs are likely important in individual patients. AXL, EPHB2, FGFR2, IGF1R, and RET were identified as promising therapeutic targets by our in vitro phosphoproteomic/siRNA screen of 42 RTKs in the highly metastatic LM7 and 143B human osteosarcoma cell lines. This chapter is intended to provide an update on these topics as well as the large number of osteosarcoma clinical studies of inhibitors of multiple tyrosine kinases (multi-TKIs) that were recently published.

Genetic testing for high-grade osteosarcoma: a guide for future tailored treatments?

INTRODUCTION

Genetic characterization of osteosarcoma has evolved during the last decade, thanks to the integrated application of conventional and new candidate-driven and genome-wide technologies. Areas covered: This review provides an overview of the state of art in genetic testing applied to osteosarcoma, with particular regard to novel candidate genetic biomarkers that can be analyzed in tumor tissue and blood samples, which might be used to predict toxicity and prognosis, detect disease relapse, and improve patients' selection criteria for tailoring treatment. Expert commentary: Genetic testing based on modern technologies is expected to indicate new osteosarcoma-related prognostic markers and driver genes, which may highlight novel therapeutic targets and patients stratification biomarkers. The definition of tailored or targeted treatment approaches may improve outcome of patients with localized tumors and, even more, of those with metastatic disease, for whom progress in cure probability is highly warranted.

TAP: a targeted clinical genomics pipeline for detecting transcript variants using RNA-seq data

BACKGROUND

RNA-seq is a powerful and cost-effective technology for molecular diagnostics of cancer and other diseases, and it can reach its full potential when coupled with validated clinical-grade informatics tools. Despite recent advances in long-read sequencing, transcriptome assembly of short reads remains a useful and cost-effective methodology for unveiling transcript-level rearrangements and novel isoforms. One of the major concerns for adopting the proven de novo assembly approach for RNA-seq data in clinical settings has been the analysis turnaround time. To address this concern, we have developed a targeted approach to expedite assembly and analysis of RNA-seq data.

RESULTS

Here we present our Targeted Assembly Pipeline (TAP), which consists of four stages: 1) alignment-free gene-level classification of RNA-seq reads using BioBloomTools, 2) de novo assembly of individual targets using Trans-ABySS, 3) alignment of assembled contigs to the reference genome and transcriptome with GMAP and BWA and 4) structural and splicing variant detection using PAVFinder. We show that PAVFinder is a robust gene fusion detection tool when compared to established methods such as Tophat-Fusion and deFuse on simulated data of 448 events. Using the Leucegene acute myeloid leukemia (AML) RNA-seq data and a set of 580 COSMIC target genes, TAP identified a wide range of hallmark molecular anomalies including gene fusions, tandem duplications, insertions and deletions in agreement with published literature results. Moreover, also in this dataset, TAP captured AML-specific splicing variants such as skipped exons and novel splice sites reported in studies elsewhere. Running time of TAP on 100-150 million read pairs and a 580-gene set is one to 2 hours on a 48-core machine.

CONCLUSIONS

We demonstrated that TAP is a fast and robust RNA-seq variant detection pipeline that is potentially amenable to clinical applications. TAP is available at http://www.bcgsc.ca/platform/bioinfo/software/pavfinder.

Protein Tyrosine Phosphatase Receptor Type G (PTPRG) Controls Fibroblast Growth Factor Receptor (FGFR) 1 Activity and Influences Sensitivity to FGFR Kinase Inhibitors

Recently, FGFR1 was found to be overexpressed in osteosarcoma and represents an important target for precision medicine. However, because targeted cancer therapy based on FGFR inhibitors has so far been less efficient than expected, a detailed understanding of the target is important. We have here applied proximity-dependent biotin labeling combined with label-free quantitative mass spectrometry to identify determinants of FGFR1 activity in an osteosarcoma cell line. Many known FGFR interactors were identified (e.g. FRS2, PLCG1, RSK2, SRC), but the data also suggested novel determinants. A strong hit in our screen was the tyrosine phosphatase PTPRG. We show that PTPRG and FGFR1 interact and colocalize at the plasma membrane where PTPRG directly dephosphorylates activated FGFR1. We further show that osteosarcoma cell lines depleted for PTPRG display increased FGFR activity and are hypersensitive to stimulation by FGF1. In addition, PTPRG depletion elevated cell growth and negatively affected the efficacy of FGFR kinase inhibitors. Thus, PTPRG may have future clinical relevance by being a predictor of outcome after FGFR inhibitor treatment.

Upregulation of ATG4A promotes osteosarcoma cell epithelial-to-mesenchymal transition through the Notch signaling pathway

Osteosarcoma is a malignant tumor in children and adolescents. Previous studies showed that ATG4A is an autophagy-related gene involved in cancers. In this study, we aimed to identify the biological role of ATG4A in osteosarcoma. The expression levels of ATG4A were analyzed in osteosarcoma tissues by using reverse transcription-quantitative polymerase chain reaction (qRT-PCR) and western blotting. ATG4A was knocked-down or overexpressed in SAOS2 and HOS cell lines by transfection. Cell counting kit-8 (CCK-8) and clone formation assay were used to assess the effects of ATG4A on cell proliferation. Wound healing and Transwell assays were performed to evaluate the effects of ATG4A on cell migration and invasion, respectively. Epithelial-mesenchymal transition (EMT) markers and Notch signaling pathway targeting molecules were examined by western blotting. The results indicated that ATG4A was up-regulated in osteosarcoma tissues. In SAOS2 cells, knockdown of ATG4A inhibited the proliferation, migration and invasion, up-regulated the expression of E-cadherin and down-regulated the expression of vimentin, Notch1 and Hes1. In HOS cells, overexpression of ATG4A promoted the proliferation, migration and invasion, up-regulated the expression of vimentin, Notch1 and Hes1 and down-regulated the expression of E-cadherin. In conclusion, these findings demonstrate that ATG4A is up-regulated in osteosarcoma tissues. In osteosarcoma cells, ATG4A promotes the EMT process partly by the Notch signaling pathway. These results suggest that ATG4A might represent a potential therapeutic target for patients with osteosarcoma.