Identification of gene signature for treatment response to guide precision oncology in clear-cell renal cell carcinoma

Autor: Alan I. So, Ninadh M. D'Costa, Yen-Yi Lin, César Monjarás-Ávila, Davide P. Cinà, Faraz Hach, Christian K. Kollmannsberger, Raunak Shrestha, Robert H. Bell, Claudia Chavez-Munoz, Hossein Asghari
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Oncology
Male
Kidney Disease
medicine.medical_treatment
Datasets as Topic
lcsh:Medicine
Angiogenesis Inhibitors
Disease
Medical Oncology
Targeted therapy
Cohort Studies
Antineoplastic Agents
Immunological
Renal cell carcinoma
Cluster Analysis
Precision Medicine
lcsh:Science
Cancer
screening and diagnosis
Multidisciplinary
Tumor
Middle Aged
Prognosis
Kidney Neoplasms
Detection
Immunological
Female
Biotechnology
4.2 Evaluation of markers and technologies
medicine.medical_specialty
Stromal cell
Clinical Decision-Making
Antineoplastic Agents
Predictive markers
Article
Immune system
Clinical Research
Internal medicine
Biomarkers
Tumor
medicine
Genetics
Humans
Carcinoma
Renal Cell
business.industry
Gene Expression Profiling
Patient Selection
Carcinoma
lcsh:R
Renal Cell
Gene signature
medicine.disease
4.1 Discovery and preclinical testing of markers and technologies
Clinical trial
Clear cell renal cell carcinoma
Good Health and Well Being
Feasibility Studies
lcsh:Q
business
Transcriptome
Biomarkers
Zdroj: Scientific Reports, Vol 10, Iss 1, Pp 1-9 (2020)
Scientific reports, vol 10, iss 1
Scientific Reports
ISSN: 2045-2322
DOI: 10.1038/s41598-020-58804-y
Popis: Clear-cell renal cell carcinoma (ccRCC) is a common therapy resistant disease with aberrant angiogenic and immunosuppressive features. Patients with metastatic disease are treated with targeted therapies based on clinical features: low-risk patients are usually treated with anti-angiogenic drugs and intermediate/high-risk patients with immune therapy. However, there are no biomarkers available to guide treatment choice for these patients. A recently published phase II clinical trial observed a correlation between ccRCC patients’ clustering and their response to targeted therapy. However, the clustering of these groups was not distinct. Here, we analyzed the gene expression profile of 469 ccRCC patients, using featured selection technique, and have developed a refined 66-gene signature for improved sub-classification of patients. Moreover, we have identified a novel comprehensive expression profile to distinguish between migratory stromal and immune cells. Furthermore, the proposed 66-gene signature was validated using a different cohort of 64 ccRCC patients. These findings are foundational for the development of reliable biomarkers that may guide treatment decision-making and improve therapy response in ccRCC patients.
Databáze: OpenAIRE
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