Gene expression profiling in prognosis of distant recurrence in HR-positive and HER2-negative breast cancer patients

Biomarkers in Cancer Detection, Diagnosis, and Prognosis

Biomarkers are vital in healthcare as they provide valuable insights into disease diagnosis, prognosis, treatment response, and personalized medicine. They serve as objective indicators, enabling early detection and intervention, leading to improved patient outcomes and reduced costs. Biomarkers also guide treatment decisions by predicting disease outcomes and facilitating individualized treatment plans. They play a role in monitoring disease progression, adjusting treatments, and detecting early signs of recurrence. Furthermore, biomarkers enhance drug development and clinical trials by identifying suitable patients and accelerating the approval process. In this review paper, we described a variety of biomarkers applicable for cancer detection and diagnosis, such as imaging-based diagnosis (CT, SPECT, MRI, and PET), blood-based biomarkers (proteins, genes, mRNA, and peptides), cell imaging-based diagnosis (needle biopsy and CTC), tissue imaging-based diagnosis (IHC), and genetic-based biomarkers (RNAseq, scRNAseq, and spatial transcriptomics).

Breast cancer gene expression signatures: development and clinical significance-a narrative review

Background and Objective

Breast cancer gene expression signatures are developing rapidly and are expected to better understand the intrinsic features of the tumor, and also to optimize the treatment strategy in clinical practice. This review is to summarize the controversy and consensus in clinical practice of gene expression signatures, and to provide our perspective on these issues as well as recommendation for future direction.

Methods

We reviewed English publications in PubMed related to breast cancer gene expression signatures from 2002 to 2022.

Key Content and Findings

Five mature commercial gene expression signatures: Oncotype, MammaPrint, Prosigna/PAM50, EndoPredict and Breast Cancer Index (BCI) are available to provide the prognostic and predictive assessment. Although they could help to evaluate the risk of recurrence and to predict the benefits of certain treatments, their applications remain challenging. Treatment decisions should be determined by a combination of related clinical pathological factors in clinical practice.

Conclusions

Gene expression signatures could assist in the determination of the adjuvant therapy of early-stage breast cancer. The prospective randomized clinical trials showed that chemotherapy may be exempted in low-risk patients. More sufficient data are expected for the application in radiotherapy, extended endocrine therapy, and neoadjuvant treatment. The treatment cannot be determined by a single factor but by comprehensive assessments of clinicopathological factors, test purpose, and cost-effectiveness. Patients will benefit from personalized treatments with the publication of further evidence.

The beneficial role of Asian-based RecurIndex test in the prognostic prediction in Chinese male breast cancer patients

RecurIndex, a multigene profiling assay, can predict the risk of local recurrence and distant metastasis in female breast cancer (FBC), but its role in male breast cancer (MBC) remains unclear. In this study, the clinicopathological data of 43 consecutive MBC patients undergoing surgeries between 2009 and 2018 were retrospectively analysed. Their paraffin-embedded tissue sections were examined by RecurIndex test which comprised 2 models: recurrence index for local recurrence (RI-LR) and recurrence index for distant recurrence (RI-DR). Of 43 patients, there were 26 low-risk and 17 high-risk patients assessed by RI-LR, while 17 low-risk and 26 high-risk patients by RI-DR. For RI-LR, tumor N stage showed statistically significant (P < 0.001) between low- and high-risk patients; for RI-DR, differences were pronounced in tumor grade (P = 0.033), T stage (P = 0.043) and N stage (P = 0.003). In terms of clinical outcomes, the overall survival (OS) of low- and high-risk patients stratified by RI-LR showed no statistically significant differences (P = 0.460), while high-risk patients identified by RI-DR had a significantly worse distant recurrence-free survival (DRFS) (P = 0.035), progression-free survival (PFS) (P = 0.019) and OS (P = 0.044) than low-risk patients. Overall, RI-DR can effectively predict the DRFS, PFS and OS of MBC patients and identify those at low risk of recurrence, which may serve as a potential prognostic tool for MBC.

Development of a Gene-Based Prediction Model for Recurrence of Colorectal Cancer Using an Ensemble Learning Algorithm

It is difficult to determine which patients with stage I and II colorectal cancer are at high risk of recurrence, qualifying them to undergo adjuvant chemotherapy. In this study, we aimed to determine a gene signature using gene expression data that could successfully identify high risk of recurrence among stage I and II colorectal cancer patients. First, a synthetic minority oversampling technique was used to address the problem of imbalanced data due to rare recurrence events. We then applied a sequential workflow of three methods (significance analysis of microarrays, logistic regression, and recursive feature elimination) to identify genes differentially expressed between patients with and without recurrence. To stabilize the prediction algorithm, we repeated the above processes on 10 subsets by bagging the training data set and then used support vector machine methods to construct the prediction models. The final predictions were determined by majority voting. The 10 models, using 51 differentially expressed genes, successfully predicted a high risk of recurrence within 3 years in the training data set, with a sensitivity of 91.18%. For the validation data sets, the sensitivity of the prediction with samples from two other countries was 80.00% and 91.67%. These prediction models can potentially function as a tool to decide if adjuvant chemotherapy should be administered after surgery for patients with stage I and II colorectal cancer.