Validation of the 18-gene classifier as a prognostic biomarker of distant metastasis in breast cancer

FOXK2 amplification promotes breast cancer development and chemoresistance

Oncogene activation through DNA amplification or overexpression is a crucial driver of cancer initiation and progression. The FOXK2 gene, located on chromosome 17q25, encodes a transcription factor with a forkhead DNA-binding domain. Analysis of genomic datasets reveals that FOXK2 is frequently amplified and overexpressed in breast cancer, correlating with poor patient survival. Knockdown of FOXK2 significantly inhibited breast cancer cell proliferation, migration, anchorage-independent growth, and delayed tumor growth in a xenograft mouse model. Additionally, inhibiting FOXK2 sensitized breast cancer cells to chemotherapy. Co-overexpression of FOXK2 and mutant PI3KCA transformed non-tumorigenic MCF-10A cells, suggesting a role for FOXK2 in PI3KCA-driven tumorigenesis. CCNE2, PDK1, and ESR1 were identified as transcriptional targets of FOXK2 in MCF-7 cells. Small-molecule inhibitors of CCNE2/CDK2 (dinaciclib) and PDK1 (dichloroacetate) exhibited synergistic anti-tumor effects with PI3KCA inhibitor (alpelisib) in vitro. Inhibition of FOXK2 by dinaciclib synergistically enhanced the anti-tumor effects of alpelisib in a xenograft mouse model. Collectively, these findings highlight the oncogenic function of FOXK2 and suggest that FOXK2 and its downstream genes represent potential therapeutic targets in breast cancer.

RecurIndex-Guided postoperative radiotherapy with or without Avoidance of Irradiation of regional Nodes in 1-3 node-positive breast cancer (RIGAIN): a study protocol for a multicentre, open-label, randomised controlled prospective, phase III trial

INTRODUCTION

Postoperative radiotherapy in patients with breast cancer with one to three lymph node metastases, particularly within the pT1-2N1M0 cohort with a low clinical risk of local-regional recurrence (LRR), has incited a discourse surrounding personalised treatment strategies. Multigene testing for Recurrence Index (RecurIndex) model capably differentiates patients based on their level of LRR risk. This research aims to validate whether a more aggressive treatment approach can enhance clinical outcomes in N1 patients who possess a clinically low risk of LRR, yet a high RecurIndex-determined risk of LRR. Specifically, this entails postoperative whole breast irradiation combined with regional lymph node irradiation (RNI) following breast-conserving surgery or chest wall irradiation with RNI after mastectomy.

METHODS AND ANALYSIS

The RIGAIN (RecurIndex-Guided postoperative radiotherapy with or without Avoidance of Irradiation of regional Nodes in 1-3 node-positive breast cancer) Study is a multicentre, prospective, randomised, open-label, phase III clinical trial that is being conducted in China. In this study, patients with low clinical LRR risk but high RecurIndex-LRR risk are randomly assigned in a 1:1 ratio to the experimental group or the control group. In the experimental group, RNI is performed and the control group omits RNI. Efficacy and safety analyses will be conducted, enrolling a total of 540 patients (270 per group). The primary endpoint is invasive disease-free survival, and secondary endpoints include any first recurrence, LRR-free survival, distant metastasis-free survival, recurrence-free survival, overall survival, disease-free survival, breast cancer-specific mortality and assessment of patient quality of life. The study began in April 2023 and with a follow-up period of 60 months after the last participant completes radiation therapy.

ETHICS AND DISSEMINATION

The study was approved by the Ethics Committee of Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University (SYSKY-2022-097-02, V.3.1). It adheres to the Helsinki Declaration and Good Clinical Practice. Research findings will be submitted for publication in peer-reviewed journals.

TRIAL REGISTRATION NUMBER

NCT04069884.

MOXD1 is a lineage-specific gene and a tumor suppressor in neuroblastoma

Neuroblastoma is a childhood developmental cancer; however, its embryonic origins remain poorly understood. Moreover, in-depth studies of early tumor-driving events are limited because of the lack of appropriate models. Herein, we analyzed RNA sequencing data obtained from human neuroblastoma samples and found that loss of expression of trunk neural crest-enriched gene MOXD1 associates with advanced disease and worse outcome. Further, by using single-cell RNA sequencing data of human neuroblastoma cells and fetal adrenal glands and creating in vivo models of zebrafish, chick, and mouse, we show that MOXD1 is a determinate of tumor development. In addition, we found that MOXD1 expression is highly conserved and restricted to mesenchymal neuroblastoma cells and Schwann cell precursors during healthy development. Our findings identify MOXD1 as a lineage-restricted tumor-suppressor gene in neuroblastoma, potentiating further stratification of these tumors and development of novel therapeutic interventions.

A structured iterative division approach for non-sparse regression models and applications in biological data analysis

In this paper, we focus on the modeling problem of estimating data with non-sparse structures, specifically focusing on biological data that exhibit a high degree of relevant features. Various fields, such as biology and finance, face the challenge of non-sparse estimation. We address the problems using the proposed method, called structured iterative division. Structured iterative division effectively divides data into non-sparse and sparse structures and eliminates numerous irrelevant variables, significantly reducing the error while maintaining computational efficiency. Numerical and theoretical results demonstrate the competitive advantage of the proposed method on a wide range of problems, and the proposed method exhibits excellent statistical performance in numerical comparisons with several existing methods. We apply the proposed algorithm to two biology problems, gene microarray datasets, and chimeric protein datasets, to the prognostic risk of distant metastasis in breast cancer and Alzheimer's disease, respectively. Structured iterative division provides insights into gene identification and selection, and we also provide meaningful results in anticipating cancer risk and identifying key factors.

Strengths and limitations of non-disclosive data analysis: a comparison of breast cancer survival classifiers using VisualSHIELD

Preserving data privacy is an important concern in the research use of patient data. The DataSHIELD suite enables privacy-aware advanced statistical analysis in a federated setting. Despite its many applications, it has a few open practical issues: the complexity of hosting a federated infrastructure, the performance penalty imposed by the privacy-preserving constraints, and the ease of use by non-technical users. In this work, we describe a case study in which we review different breast cancer classifiers and report our findings about the limits and advantages of such non-disclosive suite of tools in a realistic setting. Five independent gene expression datasets of breast cancer survival were downloaded from Gene Expression Omnibus (GEO) and pooled together through the federated infrastructure. Three previously published and two newly proposed 5-year cancer-free survival risk score classifiers were trained in a federated environment, and an additional reference classifier was trained with unconstrained data access. The performance of these six classifiers was systematically evaluated, and the results show that i) the published classifiers do not generalize well when applied to patient cohorts that differ from those used to develop them; ii) among the methods we tried, the classification using logistic regression worked better on average, closely followed by random forest; iii) the unconstrained version of the logistic regression classifier outperformed the federated version by 4% on average. Reproducibility of our experiments is ensured through the use of VisualSHIELD, an open-source tool that augments DataSHIELD with new functions, a standardized deployment procedure, and a simple graphical user interface.

DTX2 promotes glioma development via regulation of HLTF

BACKGROUND

Human Deltex 2 (DTX2) is a ubiquitin E3 ligase that functions as an oncogene and has been shown to participate in many human cancers. However, the role of DTX2 in glioma progression has remained obscure. In this study, we explore the mechanism underlying the function of DTX2 in glioma progression.

METHODS

The associations between DTX2 expression and clinical characteristics of glioma were determined by bioinformatic analysis of data from The Cancer Genome Atlas and Human Protein Atlas. The expression of DTX2 in glioma tissues was detected using immunohistochemistry and western blotting. Lentivirus-mediated gene knockdown and overexpression were used to determine the effects of DTX2 and helicase-like transcription element (HLTF) on glioma cell proliferation and migration with CCK-8, cell colony formation, transwell, and wound healing assays; flow cytometry in vitro; and animal models in vivo. The interaction of the DTX2 and HLTF proteins was verified by immunoprecipitation assay and confocal microscopy.

RESULTS

DTX2 was highly expressed in glioma samples, and this was correlated with worse overall survival. Silencing of DTX2 suppressed glioma cell viability, colony formation, and migration and induced cell apoptosis. In vitro ubiquitination assays confirmed that DTX2 could downregulate HLTF protein levels by increasing ubiquitination of the HLTF protein. We also observed that HLTF inhibited proliferation and migration of glioma cells. Subcutaneous xenografts with DTX2-overexpressing U87 cells showed significantly increased tumor volumes and weights.

CONCLUSIONS

We have identified DTX2/HLTF as a new axis in the development of glioma that could serve as a prognostic or therapeutic marker.

Prognostic analyses of genes associated with anoikis in breast cancer

Breast cancer (BRCA) is the most diagnosed cancer worldwide and is responsible for the highest cancer-associated mortality among women. It is evident that anoikis resistance contributes to tumour cell metastasis, and this is the primary cause of treatment failure for BRCA. However, anoikis-related gene (ARG) expression profiles and their prognostic value in BRCA remain unclear. In this study, a prognostic model of ARGs based on The Cancer Genome Atlas (TCGA) database was established using a least absolute shrinkage and selection operator analysis to evaluate the prognostic value of ARGs in BRCA. The risk factor graph demonstrated that the low-risk group had longer survival than the high-risk group, implying that the prognostic model had a good performance. We identified 11 ARGs that exhibited differential expression between the two risk groups in TCGA and Gene Expression Omnibus databases. Through Gene Ontology and Kyoto Encyclopaedia of Genes and Genomes enrichment analyses, we revealed that the screened ARGs were associated with tumour progression and metastasis. In addition, a protein-protein interaction network showed potential interactions among these ARGs. Furthermore, gene set enrichment analysis suggested that the Notch and Wnt signalling pathways were overexpressed in the high-risk group, and gene set variation analysis revealed that 38 hallmark genes differed between the two groups. Moreover, Kaplan-Meier survival curves and receiver operating characteristic curves were used to identify five ARGs (CD24, KRT15, MIA, NDRG1, TP63), and quantitative polymerase chain reaction was employed to assess the differential expression of these ARGs. Univariate and multivariate Cox regression analyses were then performed for the key ARGs, with the best prediction of 3 year survival. In conclusion, ARGs might play a crucial role in tumour progression and serve as indicators of prognosis in BRCA.

FOXK2 amplification and overexpression promotes breast cancer development and chemoresistance

Activation of oncogenes through DNA amplification/overexpression plays an important role in cancer initiation and progression. Chromosome 17 has many cancer-associated genetic anomalies. This cytogenetic anomaly is strongly associated with poor prognosis of breast cancer. FOXK2 gene is located on 17q25 and encodes a transcriptional factor with a forkhead DNA binding domain. By integrative analysis of public genomic datasets of breast cancers, we found that FOXK2 is frequently amplified and overexpressed in breast cancers. FOXK2 overexpression in breast cancer patients is associated with poor overall survival. FOXK2 knockdown significantly inhibits cell proliferation, invasion and metastasis, and anchorage-independent growth, as well as causes G0/G1 cell cycle arrest in breast cancer cells. Moreover, inhibition of FOXK2 expression sensitizes breast cancer cells to frontline anti-tumor chemotherapies. More importantly, co-overexpression of FOXK2 and PI3KCA with oncogenic mutations (E545K or H1047R) induces cellular transformation in non-tumorigenic MCF10A cells, suggesting that FOXK2 is an oncogene in breast cancer and is involved in PI3KCA-driven tumorigenesis. Our study identified CCNE2, PDK1, and Estrogen receptor alpha (ESR1) as direct transcriptional targets of FOXK2 in MCF-7 cells. Blocking CCNE2- and PDK1-mediated signaling by using small molecule inhibitors has synergistic anti-tumor effects in breast cancer cells. Furthermore, FOXK2 inhibition by gene knockdown or inhibitors for its transcriptional targets (CCNE2 and PDK1) in combination with PI3KCA inhibitor, Alpelisib, showed synergistic anti-tumor effects on breast cancer cells with PI3KCA oncogenic mutations. In summary, we provide compelling evidence that FOXK2 plays an oncogenic role in breast tumorigenesis and targeting FOXK2-mediated pathways may be a potential therapeutic strategy in breast cancer.

ELOVL5 and IGFBP6 genes modulate sensitivity of breast cancer cells to ferroptosis

Introduction: Relapse of breast cancer is one of the key obstacles to successful treatment. Previously we have shown that low expression of ELOVL5 and IGFBP6 genes in breast cancer tissue corresponded to poor prognosis. ELOVL5 participates directly in the elongation of polyunsaturated fatty acids (PUFAs) that are considered to play an important role in cancer cell metabolism. Thus, in this work we studied the changes in lipid metabolism in breast cancer cells with reduced expression of either ELOVL5 or IGFBP6 gene. Methods: MDA-MB-231 cells with a stable knockdown of either ELOVL5 or IGFBP6 gene were used in this study. Transcriptomic and proteomic analysis as well as RT-PCR were utilized to assess gene expression. Content of individual fatty acids in the cells was measured with HPLC-MS. HPLC was used for analysis of the kinetics of PUFAs uptake. Cell viability was measured with MTS assay. Flow cytometry was used to measure activation of apoptosis. Fluorescent microscopy was utilized to assess accumulation of ROS and formation of lipid droplets. Glutathione peroxidase activity was measured with a colorimetric assay. Results: We found that the knockdown of IGFBP6 gene led to significant changes in the profile of fatty acids in the cells and in the expression of many genes associated with lipid metabolism. As some PUFAs are known to inhibit proliferation and cause death of cancer cells, we also tested the response of the cells to single PUFAs and to combinations of docosahexaenoic acid (DHA, a n-3 PUFA) with standard chemotherapeutic drugs. Our data suggest that external PUFAs cause cell death by activation of ferroptosis, an iron-dependent mechanism of cell death with excessive lipid peroxidation. Moreover, both knockdowns increased cells' sensitivity to ferroptosis, probably due to a significant decrease in the activity of the antioxidant enzyme GPX4. Addition of DHA to commonly used chemotherapeutic drugs enhanced their effect significantly, especially for the cells with low expression of IGFBP6 gene. Discussion: The results of this study suggest that addition of PUFAs to the treatment regimen for the patients with low expression of IGFBP6 and ELOVL5 genes can be potentially beneficial and is worth testing in a clinically relevant setting.

RecurIndex assay as an aid for adjuvant chemotherapy decisions in HR-positive HER2-negative breast cancer patients

Background

Adjuvant chemotherapy is a major adjuvant treatment modality for hormonal receptor (HR)-positive and HER2-negative early breast cancer, but only 2%-20% of patients derive practical benefits. How to balance its potential benefits and risks becomes a challenging clinical problem. The purpose of this study was to assess whether RecurIndex assay could serve as an aid for adjuvant chemotherapy decisions in Chinese patients with HR-positive HER2-negative early breast cancer.

Methods

The tissue samples of pT1-2N0 HR-positive HER2-negative breast cancer from multiple centers were detected using RecurIndex assay, based on which the patients were assigned into low- and high-risk groups. The survival outcomes of low- and high-risk patients including those with and without adjuvant chemotherapy were compared, and the risk factors for recurrence and metastasis were identified.

Results

Totally 445 patients were eligible for analysis. By contrast to high-risk patients, low-risk patients represented better 7-year recurrence-free survival (RFS), distant recurrence-free survival (DRFS) and local recurrence-free survival (LRFS) rates. For low-risk patients, no significant differences were shown between those with and without adjuvant chemotherapy in 7-year RFS, DRFS and LRFS rates. These differences were also inapparent between high-risk patients with and without adjuvant chemotherapy. The multivariate model revealed high-risk patients had a significantly elevated risk of recurrence and metastasis than those at low risk.

Conclusion

HR-positive HER2-negative early breast cancer patients at low risk stratified by RecurIndex assay might be exempt from adjuvant chemotherapy. Whether adjuvant chemotherapy may derive survival benefits for high-risk patients still needs larger cohorts to verify.

Prognostic Comparison between Oncotype DX® and a 23-Gene Classifier, RecurIndex®, on the Taiwan Breast Cancer Population

The applicability of the Oncotype DX^® (Genomic Health, Inc., Redwood City, CA, USA) recurrence score (RS) in Asian populations is unclear. A 23-gene classifier, RecurIndex^® (Amwise Diagnostics, Pte. Ltd., Singapore), has been developed based on the gene expression profiles of early-stage breast cancer patients of ethnic Han Chinese population in Taiwan. This study aimed to compare the performance of the Oncotype DX^® RS with the RecurIndex^® recurrence index (RI) for predicting relapse-free survival. Therefore, we calculated both the RI and RS for 110 early stage breast cancer patients, with the cut-off value for high-risk recurrence set at 26 and 29 for the RS and the RI, respectively. With relapse-free interval (RFI) as the primary endpoint, the concordance between RS and RI was 78.2% (Kappa value = 0.297). For a median follow-up interval of 27 months, there was a statistically significant difference in RFI between the high- and low-risk groups defined by the RI (p = 0.04) but not between risk groups defined by the RS (p = 0.66). In conclusion, whereas there was high concordance between the RecurIndex^® RI and the Oncotype DX RS, the current data showed that the RI had a better discrimination for recurrence risk than the RS. Subsequent studies with larger sample sizes will be needed to confirm the superiority of the RI over the RS in the Asian population.

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.

Integrative Bioinformatics Analysis Reveals CHEK1 and UBE2C as Luminal A Breast Cancer Subtype Biomarkers

In light of the limited number of targetable oncogenic drivers in breast cancer (BRCA), it is important to identify effective and druggable gene targets for the treatment of this devastating disease. Herein, the GSE102484 dataset containing expression profiling data from 683 BRCA patients was re-analyzed using weighted gene co-expression network analysis (WGCNA). The yellow module with the highest correlation to BRCA progression was screened out, followed by functional enrichment analysis and establishment of a protein–protein interaction (PPI) network. After further validation through survival analysis and expression evaluation, CHEK1 and UBE2C were finally identified as hub genes related to the progression of BRCA, especially the luminal A breast cancer subtype. Notably, both hub genes were found to be dysregulated in multiple types of immune cells and closely correlated with tumor infiltration, as revealed by Tumor Immune Estimation Resource (TIMER) along with other bioinformatic tools. Construction of transcription factors (TF)-hub gene network further confirmed the existence of 11 TFs which could regulate both hub genes simultaneously. Our present study may facilitate the invention of targeted therapeutic drugs and provide novel insights into the understanding of the mechanism beneath the progression of BRCA.

Environmental cues from neural crest derivatives act as metastatic triggers in an embryonic neuroblastoma model

Embryonic malignant transformation is concomitant to organogenesis, often affecting multipotent and migratory progenitors. While lineage relationships between malignant cells and their physiological counterparts are extensively investigated, the contribution of exogenous embryonic signals is not fully known. Neuroblastoma (NB) is a childhood malignancy of the peripheral nervous system arising from the embryonic trunk neural crest (NC) and characterized by heterogeneous and interconvertible tumor cell identities. Here, using experimental models mimicking the embryonic context coupled to proteomic and transcriptomic analyses, we show that signals released by embryonic sympathetic ganglia, including Olfactomedin-1, induce NB cells to shift from a noradrenergic to mesenchymal identity, and to activate a gene program promoting NB metastatic onset and dissemination. From this gene program, we extract a core signature specifically shared by metastatic cancers with NC origin. This reveals non-cell autonomous embryonic contributions regulating the plasticity of NB identities and setting pro-dissemination gene programs common to NC-derived cancers.

An inducible CRISPR/Cas9 screen identifies DTX2 as a transcriptional regulator of human telomerase

Most tumor cells reactivate telomerase to ensure unlimited proliferation, whereas the expression of human telomerase reverse transcriptase (hTERT) is tightly regulated and rate-limiting for telomerase activity maintenance. Several general transcription factors (TFs) have been found in regulating hTERT transcription; however, a systematic study is lacking. Here we performed an inducible CRISPR/Cas9 KO screen using an hTERT core promoter-driven reporter. We identified numerous positive regulators including an E3 ligase DTX2. In telomerase-positive cancer cells, DTX2 depletion downregulated hTERT transcription and telomerase activity, contributing to progressive telomere shortening, growth arrest, and increased apoptosis. Utilizing BioID, we characterized multiple TFs as DTX2 proximal proteins, among which NFIC functioned corporately with DTX2 in promoting hTERT transcription. Further analysis demonstrated that DTX2 mediated K63-linked ubiquitination of NFIC, which facilitated NFIC binding to the hTERT promoter and enhanced hTERT expression. These findings highlight a new hTERT regulatory pathway that may be exploited for potential cancer therapeutics.

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An inducible CRISPR/Cas9 screen identifies regulators for hTERT transcription

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DTX2 deficiency leads to telomere shortening and cell growth arrest

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DTX2 mediates ubiquitination on NFIC, stabilizing NFIC binding on hTERT promoter

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DTX2-NFIC functions corporately to promote hTERT transcription and tumorigenesis

Cost-Effectiveness Analysis from a Societal Perspective of Recurrence Index for Distant Recurrence (RecurIndex) in Women with Hormone Receptor-Positive and HER2-Negative Early-Stage Breast Cancer

Purpose

A clinical-genomic prognostic multigene panel (RI-DR assay, RecurIndex^®), predicting the risk level of distant recurrence (DR) in early-stage breast cancer (EBC) patients with an Asian background, has been validated as a valuable tool for identifying high-risk patients to develop distant recurrence (metastasis). Although the clinical benefit of adjuvant chemotherapy from the assay’s prediction is already proved, its affordability remains uncertain. This study is the first time in which the long-term cost-effectiveness of the RI-DR assay is evaluated.

Patients and Methods

A lifetime Markov decision-analytic model was developed from a societal perspective to estimate the life-years gained (LYGs), quality-adjusted life-years (QALYs), medical costs, and incremental cost-effectiveness ratios (ICERs), comparing EBC women with and without RI-DR genomic testing. A decision tree was used to classify patients in one of the fifteen end nodes (by order, each arm was stratified by a patient being tested or not with the RI-DR assay, being treated or not with adjuvant chemotherapy and had no, minor, major, or fatal toxicity after adjuvant chemotherapy). Health utilities, costs, transition probabilities, and survival data were extracted from the scientific literature. Deterministic sensitivity analysis (DSA) and probabilistic sensitivity analysis (PSA) were performed on variables to assess the robustness of the model. A willingness-to-pay (WTP) threshold of 790,000 NT$ per QALY gained was considered as a cost-effectiveness criterion.

Results

The incremental cost per QALY gained under base-case assumptions of the model was 173,842 NT$. Findings on the variation in model input parameters were robust and confirmed that every key variable was cost-effective for the benefit of RI-DR testing.

Conclusion

The clinical-genomic RI-DR assay is cost-effective in guiding adjuvant chemotherapy decisions compared to current clinical practice guidelines.

The Clinical Research and Latest Application of Genomic Assays in Early-Stage Breast Cancer

Breast cancer is a kind of malignant tumor that seriously endangers women's life and health. Once diagnosed, most patients will receive a combination of treatments to achieve a cure. However, breast cancer is a heterogeneous disease. Even with the same clinical stage and pathological features, its response to treatment and postoperative recurrence risk may still be completely different. With the advent of genomic assay, some patients with early-stage breast cancer who originally needed treatment can still achieve long-term disease-free survival without adjuvant chemotherapy, so as to achieve personalized and accurate treatment mode to a certain extent. In this paper, we reviewed the 5 most widely used and studied genomic panel technologies in breast cancer, namely Oncotype DX, MammaPrint, RecurIndex, PAM50, and EndoPredict, according to accessibility and availability. Based on the results of the completed or ongoing clinical studies, we summarized the origin, applicable population, and clinical efficacy of each detection method, and discussed the potential development prospect of detection technology in the future.

Disparities in Access to Systemic Treatment for Breast Cancer in Thailand and Major Asian Territories

Purpose

Methods

Results

Conclusion

Using Breast Cancer Gene Expression Signatures in Clinical Practice: Unsolved Issues, Ongoing Trials and Future Perspectives

The development of gene expression signatures since the early 2000's has offered standardized assays to evaluate the prognosis of early breast cancer. Five signatures are currently commercially available and recommended by several international guidelines to individualize adjuvant chemotherapy decisions in hormone receptors-positive/HER2-negative early breast cancer. However, many questions remain unanswered about their predictive ability, reproducibility and external validity in specific populations. They also represent a new hope to tailor (neo)adjuvant systemic treatment, adjuvant radiation therapy, hormone therapy duration and to identify a subset of patients who might benefit from CDK4/6 inhibitor adjuvant treatment. This review will highlight these particular issues, address the remaining questions and discuss the ongoing and future trials.

Is it beneficial for patients with pT1-2N1M0 breast cancer to receive postmastectomy radiotherapy? An analysis based on RecurIndex assay

The benefit of postmastectomy radiotherapy (PMRT) for pT1-2N1M0 breast cancer patients currently remains controversial. This study was conducted to investigate whether pT1-2N1M0 breast cancer patients could benefit from PMRT based on RecurIndex assay. The clinical data of 213 pT1-2N1M0 breast cancer patients were retrospectively analyzed. Through RecurIndex assay, 81 cases were assessed as the low risk, and 132 as the high risk. Compared to low-risk patients, high-risk patients especially those not receiving PMRT had a significantly increased risk of recurrence and metastasis, and worse 7-year local-regional recurrence-free interval (LRFI), distance recurrence-free interval (DRFI) and recurrence-free survival (RFS) rates. PMRT-based subgroup analysis indicated no significant differences between the low-risk patients with and without PMRT in 7-year LRFI, DRFI, RFS and overall survival (OS) rates, but apparent differences were all shown between the high-risk patients with and without PMRT in 7-year LRFI, DRFI, RFS and OS rates. Overall, for pT1-2N1M0 breast cancer patients at low risk of recurrence and metastasis stratified by RecurIndex assay, there may be a phenomenon of no PMRT benefits, while for those at high risk, use of PMRT may produce survival benefits.

Identification of genes and pathways related to breast cancer metastasis in an integrated cohort

BACKGROUND

Breast cancer is the most common malignant disease in women. Metastasis is the most common cause of death from this cancer. Screening genes related to breast cancer metastasis may help elucidate the mechanisms governing metastasis and identify molecular targets for antimetastatic therapy. The development of advanced algorithms enables us to perform cross-study analysis to improve the robustness of the results.

MATERIALS AND METHODS

Ten data sets meeting our criteria for differential expression analyses were obtained from the Gene Expression Omnibus (GEO) database. Among these data sets, five based on the same platform were formed into a large cohort using the XPN algorithm. Differentially expressed genes (DEGs) associated with breast cancer metastasis were identified using the differential expression via distance synthesis (DEDS) algorithm. A cross-platform method was employed to verify these DEGs in all ten selected data sets. The top 50 validated DEGs are represented with heat maps. Based on the validated DEGs, Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Protein interaction (PPI) networks were constructed to further illustrate the direct and indirect associations among the DEGs. Survival analysis was performed to explore whether these genes can affect breast cancer patient prognosis.

RESULTS

A total of 817 DEGs were identified using the DEDS algorithm. Of these DEGs, 450 genes were validated by the second algorithm. Enriched KEGG pathway terms demonstrated that these 450 DEGs may be involved in the cell cycle and oocyte meiosis in addition to their functions in ECM-receptor interaction and protein digestion and absorption. PPI network analysis for the proteins encoded by the DEGs indicated that these genes may be primarily involved in the cell cycle and extracellular matrix. In particular, several genes played roles in multiple signalling pathways and were related to patient survival. These genes were also observed to be targetable in the CTD2 database.

CONCLUSIONS

Our study analysed multiple cross-platform data sets using two different algorithms, helping elucidate the molecular mechanisms and identify several potential therapeutic targets of metastatic breast cancer. In addition, several genes exhibited promise for applications in targeted therapy against metastasis in future research.

Effect of the Expression of ELOVL5 and IGFBP6 Genes on the Metastatic Potential of Breast Cancer Cells

Breast cancer (BC) is the leading cause of death from malignant neoplasms among women worldwide, and metastatic BC presents the biggest problems for treatment. Previously, it was shown that lower expression of ELOVL5 and IGFBP6 genes is associated with a higher risk of the formation of distant metastases in BC. In this work, we studied the change in phenotypical traits, as well as in the transcriptomic and proteomic profiles of BC cells as a result of the stable knockdown of ELOVL5 and IGFBP6 genes. The knockdown of ELOVL5 and IGFBP6 genes was found to lead to a strong increase in the expression of the matrix metalloproteinase (MMP) MMP1. These results were in good agreement with the correlation analysis of gene expression in tumor samples from patients and were additionally confirmed by zymography. The knockdown of ELOVL5 and IGFBP6 genes was also discovered to change the expression of a group of genes involved in the formation of intercellular contacts. In particular, the expression of the CDH11 gene was markedly reduced, which also complies with the correlation analysis. The spheroid formation assay showed that intercellular adhesion decreased as a result of the knockdown of the ELOVL5 and IGFBP6 genes. Thus, the obtained data indicate that malignant breast tumors with reduced expression of the ELOVL5 and IGFBP6 genes can metastasize with a higher probability due to a more efficient invasion of tumor cells.

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.

A Clinicogenetic Prognostic Classifier for Prediction of Recurrence and Survival in Asian Breast Cancer Patients

Background

Several prognostic factors affect the recurrence of breast cancer in patients who undergo mastectomy. Assays of the expression profiles of multiple genes increase the probability of overexpression of certain genes and thus can potentially characterize the risk of metastasis.

Methods

We propose a 20-gene classifier for predicting patients with high/low risk of recurrence within 5 years. Gene expression levels from a quantitative PCR assay were used to screen 473 luminal breast cancer patients treated at Taiwan Hospital (positive for estrogen and progesterone receptors, negative for human epidermal growth factor receptor 2). Gene expression scores, along with clinical information (age, tumor stage, and nodal stage), were evaluated for risk prediction. The classifier could correctly predict patients with and without relapse (logistic regression, P<0.05).

Results

A Cox proportional hazards regression analysis showed that the 20-gene panel was prognostic with hazard ratios of 5.63 (95% confidence interval 2.77-11.5, univariate) and 5.56 (2.62-11.8, multivariate) for the “genetic” model, and of 8.02 (3.52-18.3, univariate) and 19.8 (5.96-65.87, multivariate) for the “clinicogenetic” model during a 5-year follow-up.

Conclusions

The proposed 20-gene classifier can successfully separate the patients into two risk groups, and the two risk group had significantly different relapse rate and prognosis. This 20-gene classifier can provide better estimation of prognosis, which can help physicians to make better personalized treatment plans.

A 23-gene prognostic classifier for prediction of recurrence and survival for Asian breast cancer patients

We report a 23- gene-classifier profiled from Asian women, with the primary purpose of assessing its clinical utility towards improved risk stratification for relapse for breast cancer patients from Asian cohorts within 10 years’ following mastectomy. Four hundred and twenty-two breast cancer patients underwent mastectomy and were used to train the classifier on a logistic regression model. A subset of 197 patients were chosen to be entered into the follow-up studies post mastectomy who were examined to determine the patterns of recurrence and survival analysis based on gene expression of the gene classifier, age at diagnosis, tumor stage and lymph node status, over a 5 and 10 years follow-up period. Metastasis to lymph node (N2-N3) with N0 as the reference (N2 vs. N0 hazard ratio: 2.02 (1.05–8.70), N3 vs. N0 hazard ratio: 4.32 (1.41–13.22) for 5 years) and gene expression of the 23-gene panel (P=0.06, 5 years and 0.02, 10 years, log-rank test) were found to have significant discriminatory effects on the risk of relapse (HR (95%CI):2.50 (0.95–6.50)). Furthermore, survival curves for subgroup analysis with N0-N1 and T1-T2 predicted patients with higher risk scores. The study provides robust evidence of the effectiveness of the 23-gene-classifier and could be used to determine the risk of relapse event (locoregional and distant recurrence) in Asian patients, leading to a meaningful reduction in chemotherapy recommendations.

Identifying Biomarkers to Predict the Progression and Prognosis of Breast Cancer by Weighted Gene Co-expression Network Analysis

Breast cancer (BC) is the leading cause of cancer death among women worldwide. The molecular mechanisms of its pathogenesis are still to be investigated. In our study, differentially expressed genes (DEGs) were screened between BC and normal tissues. Based on the DEGs, a weighted gene co-expression network analysis (WGCNA) was performed in 683 BC samples, and eight co-expressed gene modules were identified. In addition, by relating the eight co-expressed modules to clinical information, we found the blue module and pathological stage had a significant correlation (r = 0.24, p = 1e–10). Validated by multiple independent datasets, using one-way ANOVA, survival analysis and expression level revalidation, we finally screened 12 hub genes that can predict BC progression and prognosis. Functional annotation analysis indicated that the hub genes were enriched in cell division and cell cycle regulation. Importantly, higher expression of the 12 hub genes indicated poor overall survival, recurrence-free survival, and disease-free survival in BC patients. In addition, the expression of the 12 hub genes showed a significantly positive correlation with the expression of cell proliferation marker Ki-67 in BC. In summary, our study has identified 12 hub genes associated with the progression and prognosis of BC; these hub genes might lead to poor outcomes by regulating the cell division and cell cycle. These hub genes may serve as a biomarker and help to distinguish different pathological stages for BC patients.

Targeting purine synthesis in ASS1-expressing tumors enhances the response to immune checkpoint inhibitors

Argininosuccinate synthase (ASS1) downregulation in different tumors has been shown to support cell proliferation and yet, in several common cancer subsets ASS1 expression associates with poor patient prognosis. Here we demonstrate that ASS1 expression under glucose deprivation is induced by c-MYC, providing survival benefit by increasing nitric oxide synthesis and activating the gluconeogenic enzymes pyruvate carboxylase and phosphoenolpyruvate carboxykinase by S-nitrosylation. The resulting increased flux through gluconeogenesis enhances serine, glycine and subsequently purine synthesis. Notably, high ASS1-expressing breast cancer mice do not respond to immune checkpoint inhibitors and patients with breast cancer with high ASS1 have more metastases. We further find that inhibiting purine synthesis increases pyrimidine to purine ratio, elevates expression of the immunoproteasome and significantly enhances the response of autologous primary CD8⁺ T cells to anti-PD-1. These results suggest that treating patients with high-ASS1 cancers with purine synthesis inhibition is beneficial and may also sensitize them to immune checkpoint inhibition therapy.

Potent Anticancer Effect of the Natural Steroidal Saponin Gracillin Is Produced by Inhibiting Glycolysis and Oxidative Phosphorylation-Mediated Bioenergetics

Metabolic rewiring to utilize aerobic glycolysis is a hallmark of cancer. However, recent findings suggest the role of mitochondria in energy generation in cancer cells and the metabolic switch to oxidative phosphorylation (OXPHOS) in response to the blockade of glycolysis. We previously demonstrated that the antitumor effect of gracillin occurs through the inhibition of mitochondrial complex II-mediated energy production. Here, we investigated the potential of gracillin as an anticancer agent targeting both glycolysis and OXPHOS in breast and lung cancer cells. Along with the reduction in adenosine triphosphate (ATP) production, gracillin markedly suppresses the production of several glycolysis-associated metabolites. A docking analysis and enzyme assay suggested phosphoglycerate kinase 1 (PGK1) is a potential target for the antiglycolytic effect of gracillin. Gracillin reduced the viability and colony formation ability of breast cancer cells by inducing apoptosis. Gracillin displayed efficacious antitumor effects in mice bearing breast cancer cell line or breast cancer patient-derived tumor xenografts with no overt changes in body weight. An analysis of publicly available datasets further suggested that PGK1 expression is associated with metastasis status and poor prognosis in patients with breast cancer. These results suggest that gracillin is a natural anticancer agent that inhibits both glycolysis and mitochondria-mediated bioenergetics.

A new clinical-genomic model to predict 10-year recurrence risk in primary operable breast cancer patients

This study aimed to validate the long-term prognostic value of a new clinical-genomic model, Distant Genetic Model-Clinical Variable Model 6 (DGM-CM6), developed in Asia as a prognostic panel for all subtypes of breast cancer. We included 752 operable stage I–III breast cancer patients representing all subtypes treated from 2005 to 2014 as the validation cohort. The median follow-up was 95.8 months. The low- and high-risk patients classified by DGM-CM6 (RI-DR) had significant differences in 10-year distant recurrence-free interval (DRFI) (94.1% vs. 85.0%, P < 0.0001) and relapse-free survival (RFS) (90.0% vs. 80.5%, P = 0.0003). External validation using EMTAB-365 dataset showed similar observation (P < 0.0001). DGM-CM6 was an independent prognostic factor by multivariate analysis with hazard ratios of 3.1 (1.6–6.0) for RFS (P = 0.0009) and 3.8 (1.6–9.0) for DRFI (P = 0.0028). Comparing the C-index of DGM-CM6 and PAM50-ROR scores, the former performed better than the latter in predicting long-term DRFI and RFS, especially in N0, ER/PR-positive, and HER2-negative patients.

A preliminary report of head-to-head comparison of 18-gene-based clinical-genomic model and oncotype DX 21-gene assay for predicting recurrence of early-stage breast cancer

Background

The information of Oncotype DX applied in Asian breast cancer patients is limited. A recurrence index for distant recurrence (RI-DR) has been developed for early-stage breast cancer (EBC) from tumor samples in Chinese patients. In this study, we compared the prognostic performance of the Oncotype DX (ODx) recurrence score (RS) with the RI-DR for any recurrence risk type.

Materials and methods

One hundred thirty-eight (138) patients with hormone receptor-positive and human epidermal growth factor receptor 2-negative EBC who were previously tested with ODx were included for testing with the RI-DR. The cutoff score to partition the low- and high-risk patients was 26 for RS and 36 for RI-DR. The primary endpoint was recurrence-free survival (RFS).

Results

The concordance between the RI-DR and RS was 83% in N0 patients and 81% in node-positive patients when the RS score cutoff was set at 26. With a median follow-up interval of 36.8 months, the 4-year RFS for the high- and low-risk groups categorized by the RS were 61.9% and 95.0%, respectively (hazard ratio: 10.6, 95.0% confidence interval [CI]: 1.8–62.9). The 4-year RFS in the high- and low-risk groups categorized by the RI-DR were 72.6% and 98.5%, respectively (hazard ratio: 18.9, 95% CI: 1.8–138.8).

Conclusion

This paper illustrated the performance of RI-DR and ODx RS in breast cancer women in Taiwan. There was high concordance between the RI-DR and RS. The RI-DR is not inferior to the RS in predicting RFS in EBC patients. This study will fill the gap between the current and best practice in Chinese patients.

SMAC, a computational system to link literature, biomedical and expression data

High-throughput technologies have produced a large amount of experimental and biomedical data creating an urgent need for comprehensive and automated mining approaches. To meet this need, we developed SMAC (SMart Automatic Classification method): a tool to extract, prioritise, integrate and analyse biomedical and molecular data according to user-defined terms. The robust ranking step performed on Medical Subject Headings (MeSH) ensures that papers are prioritised based on specific user requirements. SMAC then retrieves any related molecular data from the Gene Expression Omnibus and performs a wide range of bioinformatics analyses to extract biological insights. These features make SMAC a robust tool to explore the literature around any biomedical topic. SMAC can easily be customised/expanded and is distributed as a Docker container (https://hub.docker.com/r/hfx320/smac) ready-to-use on Windows, Mac and Linux OS. SMAC’s functionalities have already been adapted and integrated into the Breast Cancer Now Tissue Bank bioinformatics platform and the Pancreatic Expression Database.

Identification of Hub Genes Using Co-Expression Network Analysis in Breast Cancer as a Tool to Predict Different Stages

Background

Breast cancer has a high mortality rate and is the most common cancer of women worldwide. Our gene co-expression network analysis identified the genes closely related to the pathological stage of breast cancer.

Material/Methods

We performed weighted gene co-expression network analysis (WGCNA) from the Gene Expression Omnibus (GEO) database, and performed pathway enrichment analysis on genes from significant modules.

Results

A non-metastatic sample (374) of breast cancer from GSE102484 was used to construct the gene co-expression network. All 49 hub genes have been shown to be upregulated, and 19 of the 49 hub genes are significantly upregulated in breast cancer tissue. The roles of the genes CASC5, CKAP2L, FAM83D, KIF18B, KIF23, SKA1, GINS1, CDCA5, and MCM6 in breast cancer are unclear, so in order to better reveal the staging of breast cancer markers, it is necessary to study those hub genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes indicated that 49 hub genes were enriched to sister chromatid cohesion, spindle midzone, microtubule motor activity, cell cycle, and something else. Additionally, there is an independent data set – GSE20685 – for module preservation analysis, survival analysis, and gene validation.

Conclusions

This study identified 49 hub genes that were associated with pathologic stage of breast cancer, 19 of which were significantly upregulated in breast cancer. Risk stratification, therapeutic decision making, and prognosis predication might be improved by our study results. This study provides new insights into biomarkers of breast cancer, which might influence the future direction of breast cancer research.

Comparison of long-term results between radiotherapy after breast-conserving surgery and postmastectomy radiotherapy in stage T1-2N1M0 breast cancer

Purpose

Postoperative radiotherapy (RT) can improve survival for T1-2N1 breast cancer. However, there exists a concern whether BCS plus RT has the same or a superior therapeutic effect as that of mastectomy. In this study, we aimed to compare the long-term results between RT after BCS and postmastectomy RT in stage T1-2N1M0 breast cancer.

Patients and Methods

Totally 1816 pathological stage T1-2N1M0 breast cancer patients were analyzed. The propensity score matching (PSM) method was used to select 196 pairs of patients between BCS and mastectomy receiving postoperative RT. Five-year locoregional relapse (LRR), locoregional relapse-free survival (LRFS), distant metastasis (DM), distant metastasis-free survival (DMFS), disease-free survival (DFS), breast cancer-specific survival (BCSS) were analyzed as endpoints.

Results

In the whole group, significant differences were observed in all endpoints (P<0.05) between the no-RT and RT groups. For patients receiving mastectomy, DM, DMFS, DFS and BCSS rates had no differences between the two groups. For patients without RT in the multivariable analysis, the molecular subtype was associated with each endpoint (P<0.05). Age, primary tumor site, tumor size, and LVI status were significantly associated with DM. The analysis of 196 pairs of patients selected by PSM showed that BCS plus RT resulted in a significantly lower 5-year DM rate (P=0.015) and superior survival in terms of the 5-year DMFS (P=0.046), DFS (P=0.049) and BCSS (P=0.024) compared with mastectomy.

Conclusions

Postoperative radiotherapy remarkably improved survival in T1-2N1M0 breast cancer but not in the mastectomy subgroup, except for LRR and LRFS. Patients with BCS plus RT had better survival compared with those with postmastectomy radiation in terms of DM, DMFS, DFS and BCSS.

Identification of five hub genes as monitoring biomarkers for breast cancer metastasis in silico

BACKGROUND

Breast cancer is one of the most common endocrine cancers among females worldwide. Distant metastasis of breast cancer is causing an increasing number of breast cancer-related deaths. However, the potential mechanisms of metastasis and candidate biomarkers remain to be further explored.

RESULTS

The gene expression profiles of GSE102484 were downloaded from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was used to screen for the most potent gene modules associated with the metastatic risk of breast cancer, and a total of 12 modules were identified based on the analysis. In the most significant module (R2 = 0.68), 21 network hub genes (MM > 0.90) were retained for further analyses. Next, protein-protein interaction (PPI) networks were used to further explore the biomarkers with the most interactions in gene modules. According to the PPI networks, five hub genes (TPX2, KIF2C, CDCA8, BUB1B, and CCNA2) were identified as key genes associated with breast cancer progression. Furthermore, the prognostic value and differential expression of these genes were validated based on data from The Cancer Genome Atlas (TCGA) and Kaplan-Meier (KM) Plotter. Receiver operating characteristic (ROC) curve analysis revealed that the mRNA expression levels of these five hub genes showed excellent diagnostic value for breast cancer and adjacent tissues. Moreover, these five hub genes were significantly associated with worse distant metastasis-free survival (DMFS) in the patient cohort based on KM Plotter.

CONCLUSION

Five hub genes (TPX2, KIF2C, CDCA8, BUB1B, and CCNA2) associated with the risk of distant metastasis were extracted for further research, which might be used as biomarkers to predict distant metastasis of breast cancer.