Using microarray-based subtyping methods for breast cancer in the era of high-throughput RNA sequencing

Molecular subtyping improves breast cancer diagnosis in the Copenhagen Breast Cancer Genomics Study

BACKGROUNDIntrinsic molecular subtypes define distinct biological breast cancers and can be used to further improve diagnosis and risk allocation.METHODSThe Copenhagen Breast Cancer Genomics Study (CBCGS) prospectively included women diagnosed with breast cancer at Rigshospitalet from 2014 to 2021. Eligible patients were females with a primary invasive breast cancer (T1c, if N0M0; otherwise, any T, any N, or any M stage) and no prior malignancy. All patients underwent molecular profiling with the CIT256 and PAM50 molecular profile.RESULTSIn the study period, 2,816 patients were included in the CBCGS. Molecular subtyping showed an increase in nonluminal (molecular-apocrine, luminal C, and Basal-like) as compared with luminal (luminal A, luminal B, and Normal-like) subtypes with increasing stage from I to IV. Across all stages, we found a significant difference in survival among subtypes; 91% of patients with LumA were alive at 5 years compared with 91% for LumB, 84% for LumC, 82% for mApo, and 80% for Basal-like. We identified 442 tumors (16%) that were discordant in subtype between CIT256 and IHC. Discordant subtype proved to be a risk factor of death among patients with IHC luminal breast cancer (hazard ratio [HR], 2.08; 95% CI, 1.51-2.86) in a multivariable Cox regression analysis. Discordance occurred more often among patients with N3, stage IV, or grade III disease.CONCLUSIONOur findings indicate that molecular subtypes are a predominant classification for survival. Assessment is particularly crucial for patients with IHC luminal breast cancer with known high-risk factors, since they are at an increased risk of harboring an aggressive molecular subtype.

Multigene profiles to guide the use of neoadjuvant chemotherapy for breast cancer: a Copenhagen Breast Cancer Genomics Study

Estrogen receptor (ER) and human epidermal growth factor 2 (HER2) expression guide the use of neoadjuvant chemotherapy (NACT) in patients with early breast cancer. We evaluate the independent predictive value of adding a multigene profile (CIT256 and PAM50) to immunohistochemical (IHC) profile regarding pathological complete response (pCR) and conversion of positive to negative axillary lymph node status. The cohort includes 458 patients who had genomic profiling performed as standard of care. Using logistic regression, higher pCR and node conversion rates among patients with Non-luminal subtypes are shown, and importantly the predictive value is independent of IHC profile. In patients with ER-positive and HER2-negative breast cancer an odds ratio of 9.78 (95% CI 2.60;36.8), P < 0.001 is found for pCR among CIT256 Non-luminal vs. Luminal subtypes. The results suggest a role for integrated use of up-front multigene subtyping for selection of a neoadjuvant approach in ER-positive HER2-negative breast cancer.

An mRNA characterization model predicting survival in patients with invasive breast cancer based on The Cancer Genome Atlas database

BACKGROUND

Invasive breast cancer is a highly heterogeneous tumor, although there have been many prediction methods for invasive breast cancer risk prediction, the prediction effect is not satisfactory. There is an urgent need to develop a more accurate method to predict the prognosis of patients with invasive breast cancer.

OBJECTIVE

To identify potential mRNAs and construct risk prediction models for invasive breast cancer based on bioinformaticsMETHODS: In this study, we investigated the differences in mRNA expression profiles between invasive breast cancer and normal breast samples, and constructed a risk model for the prediction of prognosis of invasive breast cancer with univariate and multivariate Cox analyses.

RESULTS

We constructed a risk model comprising 8 mRNAs (PAX7, ZIC2, APOA5, TP53AIP1,MYBPH, USP41, DACT2, and POU3F2) for the prediction of invasive breast cancer prognosis. We used the 8-mRNA risk prediction model to divide 1076 samples into high-risk groups and low-risk groups, the Kaplan-Meier curve showed that the high-risk group was closely related to the poor prognosis of overall survival in patients with invasive breast cancer. The receiver operating characteristic curve revealed an area under the curve of 0.773 for the 8 mRNA model at 3-year overall survival, indicating that this model showed good specificity and sensitivity for prediction of prognosis of invasive breast cancer.

CONCLUSIONS

The study provides an effective bioinformatic analysis for the better understanding of the molecular pathogenesis and prognosis risk assessment of invasive breast cancer.

Identification of Platform-Independent Diagnostic Biomarker Panel for Hepatocellular Carcinoma Using Large-Scale Transcriptomics Data

The high mortality rate of hepatocellular carcinoma (HCC) is primarily due to its late diagnosis. In the past, numerous attempts have been made to design genetic biomarkers for the identification of HCC; unfortunately, most of the studies are based on small datasets obtained from a specific platform or lack reasonable validation performance on the external datasets. In order to identify a universal expression-based diagnostic biomarker panel for HCC that can be applicable across multiple platforms, we have employed large-scale transcriptomic profiling datasets containing a total of 2,316 HCC and 1,665 non-tumorous tissue samples. These samples were obtained from 30 studies generated by mainly four types of profiling techniques (Affymetrix, Illumina, Agilent, and High-throughput sequencing), which are implemented in a wide range of platforms. Firstly, we scrutinized overlapping 26 genes that are differentially expressed in numerous datasets. Subsequently, we identified a panel of three genes (FCN3, CLEC1B, and PRC1) as HCC biomarker using different feature selection techniques. Three-genes-based HCC biomarker identified HCC samples in training/validation datasets with an accuracy between 93 and 98%, Area Under Receiver Operating Characteristic curve (AUROC) in a range of 0.97 to 1.0. A reasonable performance, i.e., AUROC 0.91–0.96 achieved on validation dataset containing peripheral blood mononuclear cells, concurred their non-invasive utility. Furthermore, the prognostic potential of these genes was evaluated on TCGA-LIHC and GSE14520 cohorts using univariate survival analysis. This analysis revealed that these genes are prognostic indicators for various types of the survivals of HCC patients (e.g., Overall Survival, Progression-Free Survival, Disease-Free Survival). These genes significantly stratified high-risk and low-risk HCC patients (p-value <0.05). In conclusion, we identified a universal platform-independent three-genes-based biomarker that can predict HCC patients with high precision and also possess significant prognostic potential. Eventually, we developed a web server HCCpred based on the above study to facilitate scientific community (http://webs.iiitd.edu.in/raghava/hccpred/).