Molecular Cluster Mining of Adrenocortical Carcinoma via Multi-Omics Data Analysis Aids Precise Clinical Therapy

Abstract

Adrenocortical carcinoma (ACC) is a malignancy of the endocrine system. We collected clinical and pathological features, genomic mutations, DNA methylation profiles, and mRNA, lncRNA, microRNA, and somatic mutations in ACC patients from the TCGA, GSE19750, GSE33371, and GSE49278 cohorts. Based on the MOVICS algorithm, the patients were divided into ACC1-3 subtypes by comprehensive multi-omics data analysis. We found that immune-related pathways were more activated, and drug metabolism pathways were enriched in ACC1 subtype patients. Furthermore, ACC1 patients were sensitive to PD-1 immunotherapy and had the lowest sensitivity to chemotherapeutic drugs. Patients with the ACC2 subtype had the worst survival prognosis and the highest tumor-mutation rate. Meanwhile, cell-cycle-related pathways, amino-acid-synthesis pathways, and immunosuppressive cells were enriched in ACC2 patients. Steroid and cholesterol biosynthetic pathways were enriched in patients with the ACC3 subtype. DNA-repair-related pathways were enriched in subtypes ACC2 and ACC3. The sensitivity of the ACC2 subtype to cisplatin, doxorubicin, gemcitabine, and etoposide was better than that of the other two subtypes. For 5-fluorouracil, there was no significant difference in sensitivity to paclitaxel between the three groups. A comprehensive analysis of multi-omics data will provide new clues for the prognosis and treatment of patients with ACC.

Neutrophils of MDSCs in ovarian cancer are negatively related to Immunotherapy and prognosis: a multi-omics-based consensus clustering

“…In an effort to facilitate the concatenation-, modeland transformation-based integration methods, a dozen of studies have successfully explored available tools for multi-omics integration and employed for different cancer types [44][45][46][47][48][49]. MOVICS package provides a unified interface for 10 state-of-the-art multi-omics clustering aforementioned algorithms, currently supports up to 6 omics data for joint clustering, and has already been used extensively in colon, gastric, and Adrenocortical cancer [50][51][52].…”

Pathogenic Insights into DNA Mismatch Repair (MMR) Genes–Proteins and Microsatellite Instability: Focus on Adrenocortical Carcinoma and Beyond

“…For instance, a single-center experience with pembrolizumab in terms of a phase II clinical trial was based on 16 subjects with ACC (50% females; 63% with hormonally active profile), and 93% of them were microsatellite stable (1/16 with positive MSI); the drug showed only a modest benefit regardless the MSI status [114]. A pan-cancer and multi-omics analysis in adults with ACC, in order to classify the patients for immunotherapy, has had heterogeneous results, and the integration of the MMR system in this larger and challenging picture is still an open issue [115][116][117]. The evaluation of MMR/MSI status as part of the current assessment for both pediatric and adult patients with ACC represents one step to the future.…”

Machine learning combining multi-omics data and network algorithms identifies adrenocortical carcinoma prognostic biomarkers

“…For example, it has been shown to be effective in early blood biomarker characterization for ovarian cancer detection (Xiao et al, 2022); as well as in tumor subtype classification (Pucher et al, 2019) and target discovery for successful centronuclear myopathy treatment (Djeddi et al, 2021). In the context of ACC, this approach has been recently shown to be useful for patient stratification (Guan et al, 2022).…”