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Gene Expression Classifier Reveals Prognostic Osteosarcoma Microenvironment Molecular Subtypes.

ABSTRACT: Osteosarcoma (OSA) is the most common bone malignancy and displays high heterogeneity of molecular phenotypes. This study aimed to characterize the molecular features of OSA by developing a classification system based on the gene expression profile of the tumor microenvironment. Integrative analysis was performed using specimens and clinical information for OSA patients from the TARGET program. Using a matrix factorization method, we identified two molecular subtypes significantly associated with prognosis, S1 (infiltration type) and S2 (escape type). Both subtypes displayed unique features of functional significance features and cellular infiltration characteristics. We determined that immune and stromal infiltrates were abundant in subtype S1 compare to that in subtype S2. Furthermore, higher expression of immune checkpoint PDCD1LG2 and HAVCR2 was associated with improved prognosis, while a preferable chemotherapeutic response was associated with FAP-positive fibroblasts in subtype S1. Alternatively, subtype S2 is characterized by a lack of effective cytotoxic responses and loss of major histocompatibility complex class I molecule expression. A gene classifier was ultimately generated to enable OSA classification and the results were confirmed using the GSE21257 validation set. Correlations between the percentage of fibroblasts and/or fibrosis and CD8⁺ cells, and their clinical responses to chemotherapy were assessed and verified based on 47 OSA primary tumors. This study established a new OSA classification system for stratifying OSA patient risk, thereby further defining the genetic diversity of OSA and allowing for improved efficiency of personalized therapy.

SUBMITTER: Song YJ

PROVIDER: S-EPMC8093635 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Although osteosarcoma (OS) is a rare cancer, it is the most common primary malignant bone tumor in children and adolescents. BRCAness is a phenotypical trait in tumors with a defect in homologous recombination repair, resembling tumors with inactivation of BRCA1/2, rendering these tumors sensitive to poly (ADP)-ribose polymerase inhibitors (PARPi). Recently, OS was shown to exhibit molecular features of BRCAness. Our goal was to develop a method complementing existing genomic methods to aid clinical decision making on administering PARPi in OS patients. OS samples with DNA-methylation data were divided to BRCAness-positive and negative groups based on the degree of their genomic instability (n = 41). Methylation probes were ranked according to decreasing variance difference between two groups. The top 2000 probes were selected for training and cross-validation of the random forest algorithm. Two-thirds of available OS RNA-Seq samples (n = 17) from the top and bottom of the sample list ranked according to genome instability score were subjected to differential expression and, subsequently, to gene set enrichment analysis (GSEA). The combined accuracy of trained random forest was 85% and the average area under the ROC curve (AUC) was 0.95. There were 449 upregulated and 1,079 downregulated genes in the BRCAness-positive group (fdr < 0.05). GSEA of upregulated genes detected enrichment of DNA replication and mismatch repair and homologous recombination signatures (FWER < 0.05). Validation of the BRCAness classifier with an independent OS set (n = 20) collected later in the course of study showed AUC of 0.87 with an accuracy of 90%. GSEA signatures computed for this test set were matching the ones observed in the training set enrichment analysis. In conclusion, we developed a new classifier based on DNA-methylation patterns that detects BRCAness in OS samples with high accuracy. GSEA identified genome instability signatures. Machine-learning and gene expression approaches add new epigenomic and transcriptomic aspects to already established genomic methods for evaluation of BRCAness in osteosarcoma and can be extended to cancers characterized by genome instability.

Project description:BackgroundColon cancer (CC) pathological staging fails to accurately predict recurrence, and to date, no gene expression signature has proven reliable for prognosis stratification in clinical practice, perhaps because CC is a heterogeneous disease. The aim of this study was to establish a comprehensive molecular classification of CC based on mRNA expression profile analyses.Methods and findingsFresh-frozen primary tumor samples from a large multicenter cohort of 750 patients with stage I to IV CC who underwent surgery between 1987 and 2007 in seven centers were characterized for common DNA alterations, including BRAF, KRAS, and TP53 mutations, CpG island methylator phenotype, mismatch repair status, and chromosomal instability status, and were screened with whole genome and transcriptome arrays. 566 samples fulfilled RNA quality requirements. Unsupervised consensus hierarchical clustering applied to gene expression data from a discovery subset of 443 CC samples identified six molecular subtypes. These subtypes were associated with distinct clinicopathological characteristics, molecular alterations, specific enrichments of supervised gene expression signatures (stem cell phenotype-like, normal-like, serrated CC phenotype-like), and deregulated signaling pathways. Based on their main biological characteristics, we distinguished a deficient mismatch repair subtype, a KRAS mutant subtype, a cancer stem cell subtype, and three chromosomal instability subtypes, including one associated with down-regulated immune pathways, one with up-regulation of the Wnt pathway, and one displaying a normal-like gene expression profile. The classification was validated in the remaining 123 samples plus an independent set of 1,058 CC samples, including eight public datasets. Furthermore, prognosis was analyzed in the subset of stage II-III CC samples. The subtypes C4 and C6, but not the subtypes C1, C2, C3, and C5, were independently associated with shorter relapse-free survival, even after adjusting for age, sex, stage, and the emerging prognostic classifier Oncotype DX Colon Cancer Assay recurrence score (hazard ratio 1.5, 95% CI 1.1-2.1, p?=?0.0097). However, a limitation of this study is that information on tumor grade and number of nodes examined was not available.ConclusionsWe describe the first, to our knowledge, robust transcriptome-based classification of CC that improves the current disease stratification based on clinicopathological variables and common DNA markers. The biological relevance of these subtypes is illustrated by significant differences in prognosis. This analysis provides possibilities for improving prognostic models and therapeutic strategies. In conclusion, we report a new classification of CC into six molecular subtypes that arise through distinct biological pathways.

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Gene Expression Classifier Reveals Prognostic Osteosarcoma Microenvironment Molecular Subtypes.

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