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Stratifying osteosarcoma: minimizing and maximizing therapy.

ABSTRACT: Patients who are newly diagnosed with osteosarcoma face a daunting year of medical and surgical therapy, often filled with hospitalizations and changes in lifestyle. Fortunately, the majority of patients endure this struggle to become long-term survivors. However, follow-up studies of cancer survivors are revealing the sequelae of this curative therapy. Just as disturbingly, there remains a large subset of patients for whom conventional therapy is inadequate and who succumb to disease. In this review, we propose that therapeutic strategies for osteosarcoma patients must rely on stratification of patients into risk categories, in order to minimize therapy for some, while expanding treatment for others. We then focus on two molecular targets for the treatment of patients with high-risk osteosarcoma.

SUBMITTER: Niswander LM

PROVIDER: S-EPMC7316378 | biostudies-literature | 2010 Jul

REPOSITORIES: biostudies-literature

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Stratifying osteosarcoma: minimizing and maximizing therapy.

Niswander Lisa M LM Kim Su Young SY

Current oncology reports 20100701 4

Patients who are newly diagnosed with osteosarcoma face a daunting year of medical and surgical therapy, often filled with hospitalizations and changes in lifestyle. Fortunately, the majority of patients endure this struggle to become long-term survivors. However, follow-up studies of cancer survivors are revealing the sequelae of this curative therapy. Just as disturbingly, there remains a large subset of patients for whom conventional therapy is inadequate and who succumb to disease. In this r ...[more]

PMID: 20473649

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Project description:BackgroundThe use of gene signatures can potentially be of considerable value in the field of clinical diagnosis. However, gene signatures defined with different methods can be quite various even when applied the same disease and the same endpoint. Previous studies have shown that the correct selection of subsets of genes from microarray data is key for the accurate classification of disease phenotypes, and a number of methods have been proposed for the purpose. However, these methods refine the subsets by only considering each single feature, and they do not confirm the association between the genes identified in each gene signature and the phenotype of the disease. We proposed an innovative new method termed Minimize Feature's Size (MFS) based on multiple level similarity analyses and association between the genes and disease for breast cancer endpoints by comparing classifier models generated from the second phase of MicroArray Quality Control (MAQC-II), trying to develop effective meta-analysis strategies to transform the MAQC-II signatures into a robust and reliable set of biomarker for clinical applications.ResultsWe analyzed the similarity of the multiple gene signatures in an endpoint and between the two endpoints of breast cancer at probe and gene levels, the results indicate that disease-related genes can be preferably selected as the components of gene signature, and that the gene signatures for the two endpoints could be interchangeable. The minimized signatures were built at probe level by using MFS for each endpoint. By applying the approach, we generated a much smaller set of gene signature with the similar predictive power compared with those gene signatures from MAQC-II.ConclusionsOur results indicate that gene signatures of both large and small sizes could perform equally well in clinical applications. Besides, consistency and biological significances can be detected among different gene signatures, reflecting the studying endpoints. New classifiers built with MFS exhibit improved performance with both internal and external validation, suggesting that MFS method generally reduces redundancies for features within gene signatures and improves the performance of the model. Consequently, our strategy will be beneficial for the microarray-based clinical applications.

Project description:BackgroundOsteosarcoma (OS) poses significant challenges in treatment and lacks reliable prognostic markers. Epigenetic alterations play a crucial role in disease progression. This study aimed to develop an accurate prognostic signature for OS using epigenetic modification genes (EMGs).MethodsThe Therapeutically Applicable Research to Generate Effective Treatments (TARGET)-OS cohort was analyzed. Univariate Cox analysis identified survival-associated EMGs. Based on least absolute shrinkage and selection operator (LASSO) regression and multivariate analysis, a 6-gene prognostic signature termed the epigenetic modification-related prognostic signature (EMRPS) was derived in the testing cohort. Kaplan-Meier and receiver operating characteristic (ROC) curve analysis confirmed predictive accuracy through internal and external validation (GEO accession GSE21257). A prognostic nomogram incorporating EMRPS and clinical features was constructed. Transcriptomic analysis including differential gene expression, Gene Ontology (GO), gene set enrichment analysis (GSEA), and immune infiltration analysis was conducted to explore mechanisms linking EMRPS to OS prognosis. Additionally, EMRPS impact on drug sensitivity was predicted.ResultsA 6-gene EMRPS comprising DDX24, DNAJC1, HDAC4, SIRT7, SP140 and UHRF2 was successfully developed. The high-risk group showed significantly shorter survival, consistently observed in both internal and external validation. EMRPS demonstrated high predictive efficacy for 1-, 3-, and 5-year overall survival, with area under curve (AUC) >0.85 in training and ~0.7 in testing. The nomogram integrating age, gender, metastasis status, and EMRPS exhibited high predictive performance based on concordance index analysis. Mechanistic analysis indicated the low-risk group had increased immune infiltration and activity with higher immune checkpoint expression, reflecting an immune-activated tumor microenvironment (TME) suitable for immunotherapy. Drug sensitivity analysis revealed the low-risk group had increased sensitivity to cisplatin, a first-line OS chemotherapy.ConclusionsOur study successfully established an efficient EMRPS and nomogram, highlighting their potential as novel prognostic markers and indicators for selecting appropriate immunotherapy and chemotherapy candidates in OS treatment.

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Stratifying osteosarcoma: minimizing and maximizing therapy.

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Stratifying osteosarcoma: minimizing and maximizing therapy.

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