Genomic Signatures in Luminal Breast Cancer

Impact of the genomic signature of 70-genes for breast cancer in the public system and in supplementary health care in a country of medium socioeconomic development

INTRODUCTION

The financial impact of breast cancer has been discussed due to its high incidence and the increased costs of systemic therapy and is even more relevant in countries with low and medium socioeconomic development.

OBJECTIVE

To evaluate the financial viability of using the MammaPrint™ (MP) genetic signature in a public and private system in a country with a medium socioeconomic development index.

MATERIAL AND METHOD

A pharmacoeconomic trial with a cost-benefit analysis evaluating the reduction in costs of chemotherapy, support drugs, and materials used during chemotherapy infusion in high-risk hormone receptor-positive (HR+) breast cancer patients submitted to analysis using the MammaPrint™ genetic signature.

RESULTS

The value of using MammaPrint™ in the Unified Health System (SUS) would bring an additional cost of US$ 1,334.56 per patient in the over-50 age group. In private medicine, the use of MammaPrint™ in the same population would result in cost savings ranging from US$ 2,422.53 to US$ 9,989.95 per patient.

CONCLUSION

The use of MP in RH + breast cancer patients with high clinical risk and low genomic risk in Brazil leads to significant savings in resources when applied to supplementary healthcare. In the SUS, reducing the costs of MP for large-scale use could make its application viable. These values need to be re-evaluated in each institution, using the methodology applied in the trial, adjusting according to costs, to obtain a result that reflects its reality.

Genomic signatures in breast cancer in a real-world setting: Experience in a Brazilian Northeastern Center

OBJECTIVE

We aim to evaluate the indication and use of genomic signatures in breast cancer patients and outcomes who in patients undergoing adjuvant chemotherapy or not.

METHODS

This is a retrospective study of breast cancer patients managed in a private oncology clinic in Teresina, from November 2014 to February 2021. All patients with an indication of genomic signature were included. Clinical and pathological variables, use of genomic signatures, treatment and follow-up were obtained. The nomogram to predict Oncotype DX results (University of Tennessee Medical Center) was also calculated. Clinical risk calculation was based on MINDACT, using the modified version of Adjuvant Online. The genetic signatures performed were: the Oncotype, MammaPrint and EndoPredict.

RESULTS

Fifty (50) female patients were included in the study. The mean age of the participants was 57.1 years. Among the patients receiving a genomic signature (26-52.0%), there was a change in treatment in 8 (30.7%) cases. Chemotherapy was indicated in four patients, It was contraindicated in another four patients. Treatment changed in 30.7% of the tested patients. Chemotherapy was indicated for those who would not receive it before. It was contraindicated in patients who would previously undergo chemotherapy.

Expression profiling of luminal B breast tumor in Indian women

PURPOSE

In this study, we aimed at profiling of luminal B breast cancer specific gene expression pattern in Indian women using mRNA-seq and validation based on TCGA expression data.

METHODS

RNA isolated from luminal B tumor and adjacent normal tissues was used for library construction and sequencing. Reference-based assemblies of these reads were used for differential gene expression analysis using DeSeq2. The DEGs were evaluated using TCGA expression data. Kaplan-Meier survival method was used to evaluate association between genes showing luminal B specific differential expression pattern and breast cancer prognosis and statistical significance was assessed using log-rank test. Alternate splicing analysis was done using rmats.

RESULTS

Differential expression analysis identified 2371 differentially expressed genes (DEGs) in luminal B breast tumors in comparison with adjacent normal tissues of Indian Women. Of them, 1692 DEGs were validated using TCGA luminal B paired samples. Integration of this data with the DEGs obtained by comparative analysis of unpaired luminal B with luminal A unpaired samples from TCGA resulted in 291 DEGs showing luminal B specific expression pattern. Further, 26 genes of prognostic value were identified. Differential splicing analysis between luminal B tumors and adjacent normal tissues in our cohort led to the identification of 687 genes showing significant differential alternate splicing events.

CONCLUSION

This study profiled gene expression pattern of luminal B tumors of Indian women and identified 26 key genes of prognostic value for luminal B breast cancer. This study also profiled differential alternate splicing and identified important alternate splicing events in luminal B breast cancer.

Development of a Machine Learning-Based Prognostic Model for Hormone Receptor-Positive Breast Cancer Using Nine-Gene Expression Signature

Background

Determining the prognosis of hormone receptor positive (HR+) breast cancer (BC), which accounts for 80% of all BCs, is critical in improving survival outcomes. Stratifying individuals at high risk of BC-related mortality and improving prognosis has been the focus of research for over a decade. However, these tools are not universal as they are limited to clinical factors. We hypothesized that a new framework for predicting prognosis in HR+ BC patients can develop using artificial intelligence.

Methods

A total of 2,338 HR+ human epidermal growth factor receptor 2 negative (HER2-) BC cases were analyzed from Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), The Cancer Genome Atlas (TCGA), and Gene Expression Omnibus (GEO) cohorts. Groups were then divided into high- and low-risk categories utilizing a recurrence prediction model (RPM). An RPM was created by extracting nine prognosis-related genes from over 18,000 genes using a logistic progression model.

Results

Risk classification by RPM was significantly stratified in both the discovery cohort and validation cohort. In the time-dependent area under the curve analysis, there was some variation depending on the cohort, but accuracy was found to decline significantly after about 10 years. Cell cycle related gene sets, MYC, and PI3K-AKT-mTOR signaling were enriched in high-risk tumors by the Gene Set Enrichment Analysis. High-risk tumors were associated with high levels of immune cells from the lymphoid and myeloid lineage and immune cytolytic activity, as well as low levels of stem cells and stromal cells. High-risk tumors were also associated with poor therapeutic effects of chemotherapy and endocrine therapy.

Conclusions

This model was able to stratify prognosis in multiple cohorts. This is because the model reflects major BC therapeutic target pathways and tumor immune microenvironment and, further is supported by the therapeutic effect of chemotherapy and endocrine therapy.

Analysis of the association of HER-2 low carcinomas and PAM50 assay in hormone receptor positive early-stage breast cancer

BACKGROUND

HER2-low has emerged as a new predictive biomarker in metastatic breast cancer. However, its prognostic value in early-stage carcinomas needs to be revisited. We aimed to evaluate the association of HER2-low carcinomas with PAM50 risk groups combined with clinicopathological variables in early breast cancer.

METHODS

We conducted a retrospective analysis of 332 patients with early-stage breast cancer that underwent PAM50 signature analysis between 2015 and 2021at Hospital Universitario 12 de Octubre (Madrid, Spain). Clinical and pathological variables were collected from medical records. After adjusting for potential confounders, we estimated Odds Ratio (OR) and 95% confidence interval for high-risk PAM50 subgroup, comparing HER2-low versus HER2-zero carcinomas by multivariable logistic regression. P values below 0.05 were deemed statistically significant.

RESULTS

192 (57%) patients were classified as HER2-low carcinomas. Median follow-up was 34 months. Adjusted OR for high-risk PAM50 when comparing HER2-low versus HER2-zero carcinomas was 1.31 (95% CI: 0.75-2.30, p = 0.33). The multivariable model detected significant associations for Ki-67% (≥20% vs. <20%: OR = 4.03, 95% CI: 2.15-7.56, p < 0.001), T staging category (T2/T3 vs. T1: OR = 3.44, 95% CI: 1.96-6.04, p < 0.001), progesterone receptor (PR ≥ 20% vs. <20%: OR = 0.44, 95% CI: 0.23-0.83, p = 0.01), nodal staging category (N+ vs. N0: OR = 3.8, 95% CI: 1.89-7.62, p < 0.001) and histological grade (grade 2 vs. 1: OR = 2.41, 95% CI: 1.01-5.73, p = 0.04; grade 3 vs 1: OR = 5.40, 95%CI: 1.98-14.60, p = 0.001).

CONCLUSIONS

In this early-stage breast cancer cohort, HER2-low was not associated with a high-risk PAM50 compared to HER2-zero carcinomas. Ki-67 ≥ 20%, T2/T3, histological grade 2/3, N+ and PR<20% were significantly associated to a high-risk PAM50.

Deregulated miRNA Expression in Triple-Negative Breast Cancer of Ancestral Genomic-Characterized Latina Patients

Among patients with triple-negative breast cancer (TNBC), several studies have suggested that deregulated microRNA (miRNA) expression may be associated with a more aggressive phenotype. Although tumor molecular signatures may be race- and/or ethnicity-specific, there is limited information on the molecular profiles in women with TNBC of Hispanic and Latin American ancestry. We simultaneously profiled TNBC biopsies for the genome-wide copy number and miRNA global expression from 28 Latina women and identified a panel of 28 miRNAs associated with copy number alterations (CNAs). Four selected miRNAs (miR-141-3p, miR-150-5p, miR-182-5p, and miR-661) were validated in a subset of tumor and adjacent non-tumor tissue samples, with miR-182-5p being the most discriminatory among tissue groups (AUC value > 0.8). MiR-141-3p up-regulation was associated with increased cancer recurrence; miR-661 down-regulation with larger tumor size; and down-regulation of miR-150-5p with larger tumor size, high p53 expression, increased cancer recurrence, presence of distant metastasis, and deceased status. This study reinforces the importance of integration analysis of CNAs and miRNAs in TNBC, allowing for the identification of interactions among molecular mechanisms. Additionally, this study emphasizes the significance of considering the patients ancestral background when examining TNBC, as it can influence the relationship between intrinsic tumor molecular characteristics and clinical manifestations of the disease.

Compliance to genomic test recommendations to guide adjuvant chemotherapy decision-making in the case of hormone receptor-positive, human epidermal growth factor receptor 2-negative breast cancer, in real-life settings

BACKGROUND

Genomic tests are a useful tool for adjuvant chemotherapy decision-making in the case of hormone receptor-positive (HR+), and human epidermal growth factor receptor 2-negative (HER2-) breast cancer with intermediate prognostic factors. Real-life data on the use of tests can help identify the target population for testing.

METHODS

French multicentric study (8 centers) including patients, all candidates for adjuvant chemotherapy for HR-positive, HER2-negative early breast cancer. We describe the percentage of tests performed outside recommendations, according to the year of testing. We calculated a ratio defined as the number of tests required to avoid chemotherapy for one patient, and according to patient and cancer characteristics. We then performed a cost-saving analysis using medical cost data over a period of 1 year from diagnosis, calculated from a previous study. Finally, we calculated the threshold of the ratio (number of tests required to avoid chemotherapy for one patient) below which the use of genomic tests was cost-saving.

RESULTS

A total of 2331 patients underwent a Prosigna test. The ratio (performed test/avoided chemotherapy) was 2.8 [95% CI: 2.7-2.9] in the whole population. In the group following recommendations for test indication, the ratio was 2.3 [95% CI: 2.2-2.4]. In the case of non-abidance by recommendations, the ratio was 3 [95% CI: 2.8-3.2]. Chemotherapy was avoided in 841 patients (36%) following the results of the Prosigna test. The direct medical costs saved over 1 year of care were 3,878,798€ and 1,718,472€ in the group of patients following test recommendations. We calculated that the ratio (performed test/avoided chemotherapy) needed to be under 6.9 for testing to prove cost-saving.

CONCLUSION

The use of genomic testing proved cost-saving in this large multicentric real-life analysis, even in certain cases when the test was performed outside recommendations.

Advances in PET/CT Imaging for Breast Cancer

One out of eight women will be affected by breast cancer during her lifetime. Imaging plays a key role in breast cancer detection and management, providing physicians with information about tumor location, heterogeneity, and dissemination. In this review, we describe the latest advances in PET/CT imaging of breast cancer, including novel applications of 18F-FDG PET/CT and the development and testing of new agents for primary and metastatic breast tumor imaging and therapy. Ultimately, these radiopharmaceuticals may guide personalized approaches to optimize treatment based on the patient's specific tumor profile, and may become a new standard of care. In addition, they may enhance the assessment of treatment efficacy and lead to improved outcomes for patients with a breast cancer diagnosis.

Identification of Novel Diagnostic and Prognostic Gene Signature Biomarkers for Breast Cancer Using Artificial Intelligence and Machine Learning Assisted Transcriptomics Analysis

BACKGROUND

Breast cancer (BC) is one of the most common female cancers. Clinical and histopathological information is collectively used for diagnosis, but is often not precise. We applied machine learning (ML) methods to identify the valuable gene signature model based on differentially expressed genes (DEGs) for BC diagnosis and prognosis.

METHODS

A cohort of 701 samples from 11 GEO BC microarray datasets was used for the identification of significant DEGs. Seven ML methods, including RFECV-LR, RFECV-SVM, LR-L1, SVC-L1, RF, and Extra-Trees were applied for gene reduction and the construction of a diagnostic model for cancer classification. Kaplan-Meier survival analysis was performed for prognostic signature construction. The potential biomarkers were confirmed via qRT-PCR and validated by another set of ML methods including GBDT, XGBoost, AdaBoost, KNN, and MLP.

RESULTS

We identified 355 DEGs and predicted BC-associated pathways, including kinetochore metaphase signaling, PTEN, senescence, and phagosome-formation pathways. A hub of 28 DEGs and a novel diagnostic nine-gene signature (COL10A, S100P, ADAMTS5, WISP1, COMP, CXCL10, LYVE1, COL11A1, and INHBA) were identified using stringent filter conditions. Similarly, a novel prognostic model consisting of eight-gene signatures (CCNE2, NUSAP1, TPX2, S100P, ITM2A, LIFR, TNXA, and ZBTB16) was also identified using disease-free survival and overall survival analysis. Gene signatures were validated by another set of ML methods. Finally, qRT-PCR results confirmed the expression of the identified gene signatures in BC.

CONCLUSION

The ML approach helped construct novel diagnostic and prognostic models based on the expression profiling of BC. The identified nine-gene signature and eight-gene signatures showed excellent potential in BC diagnosis and prognosis, respectively.

BRK confers tamoxifen-resistance in breast cancer via regulation of tyrosine phosphorylation of CDK1

Tamoxifen (Tam) has been the first-line therapy for estrogen receptor-positive breast cancer since its FDA-approval in 1998. Tam-resistance, however, presents a challenge and the mechanisms that drive it have yet to be fully elucidated. The non-receptor tyrosine kinase BRK/PTK6 is a promising candidate as previous research has shown that BRK knockdown resensitizes Tam-resistant breast cancer cells to the drug. However, the specific mechanisms that drive its importance to resistance remain to be investigated. Here, we investigate the role and mechanism of action of BRK in Tam-resistant (TamR), ER+, and T47D breast cancer cells using phosphopeptide enrichment and high throughput phopshoproteomics analysis. We conducted BRK-specific shRNA knockdown in TamR T47D cells and compared phosphopeptides identified in these cells with their Tam-resistant counterpart and parental, Tam-sensitive cells (Par). A total of 6492 STY phosphosites were identified. Of these sites, 3739 high-confidence pST sites and 118 high-confidence pY sites were analyzed for significant changes in phosphorylation levels to identify pathways that were differentially regulated in TamR versus Par and to investigate changes in these pathways when BRK is knocked down in TamR. We observed and validated increased CDK1 phosphorylation at Y15 in TamR cells compared to BRK-depleted TamR cells. Our data suggest that BRK is a potential Y15-directed CDK1 regulatory kinase in Tam-resistant breast cancer.

Potential Role of Circulating miRNAs for Breast Cancer Management in the Neoadjuvant Setting: A Road to Pave

Recently, circulating microRNAs (miRNAs) have emerged as potential non-invasive biomarkers for breast cancer (BC) management. In the context of BC patients undergoing neoadjuvant chemotherapy (NAC), the possibility of obtaining repeated, non-invasive biological samples from patients before, during, and after treatment is incredibly convenient and provides the opportunity to investigate circulating miRNAs as diagnostic, predictive, and prognostic tools. The present review aims to summarize major findings in this setting, thus highlighting their potential applicability in daily clinical practice and their possible limitations. In all the contexts (diagnostic, predictive, and prognostic), circulating miR-21-5p and miR-34a-5p have emerged as the most promising non-invasive biomarkers for BC patients undergoing NAC. Specifically, their high baseline level could discriminate between BC patients and healthy controls. On the other hand, in predictive and prognostic investigations, low circulating miR-21-5p and miR-34a-5p levels may identify patients with better outcomes, in terms of both treatment response and invasive disease-free survival. However, the findings in this field have been very heterogeneous. Indeed, pre-analytical and analytical variables, as well as factors related to patients, may explain the inconsistency among different study results. Thus, further clinical trials, with more precise patient inclusion criteria and more standardized methodological approaches, are definitely needed to better define the potential role of these promising non-invasive biomarkers.

Omics approaches to understanding the efficacy and safety of disease-modifying treatments in multiple sclerosis

From the perspective of precision medicine, the challenge for the future is to improve the accuracy of diagnosis, prognosis, and prediction of therapeutic responses through the identification of biomarkers. In this framework, the omics sciences (genomics, transcriptomics, proteomics, and metabolomics) and their combined use represent innovative approaches for the exploration of the complexity and heterogeneity of multiple sclerosis (MS). This review examines the evidence currently available on the application of omics sciences to MS, analyses the methods, their limitations, the samples used, and their characteristics, with a particular focus on biomarkers associated with the disease state, exposure to disease-modifying treatments (DMTs), and drug efficacies and safety profiles.

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.

PREDICT underestimates survival of patients with HER2-positive early-stage breast cancer

The prognostic performance of PREDICT in patients with HER2-positive early breast cancer (EBC) treated in the modern era with effective chemotherapy and anti-HER2 targeted therapies is unclear. Therefore, we investigated its prognostic performance using data extracted from ALTTO, a phase III trial evaluating adjuvant lapatinib ± trastuzumab vs. trastuzumab alone in patients with HER2-positive EBC. Our analysis included 2794 patients. After a median follow-up of 6.0 years (IQR, 5.8-6.7), 182 deaths were observed. Overall, PREDICT underestimated 5-year OS by 6.7% (95% CI, 5.8-7.6): observed 5-year OS was 94.7% vs. predicted 88.0%. The underestimation was consistent across all subgroups, including those according to the type of anti HER2-therapy. The highest absolute differences were observed for patients with hormone receptor negative-disease, nodal involvement, and large tumor size (13.0%, 15.8%, and 15.3%, respectively). AUC under the ROC curve was 73.7% (95% CI 69.7-77.8) in the overall population, ranging between 61.7% and 77.7% across the analyzed subgroups. In conclusion, our analysis showed that PREDICT highly underestimated OS in HER2-positive EBC. Hence, it should be used with caution to give prognostic estimation to HER2-positive EBC patients treated in the modern era with effective chemotherapy and anti-HER2 targeted therapies.

An iron metabolism and immune related gene signature for the prediction of clinical outcome and molecular characteristics of triple-negative breast cancer

BACKGROUND

An imbalance of intracellular iron metabolism can lead to the occurrence of ferroptosis. Ferroptosis can be a factor in the remodeling of the immune microenvironment and can affect the efficacy of cancer immunotherapy. How to combine ferroptosis-promoting modalities with immunotherapy to suppress triple-negative breast cancer (TNBC) has become an issue of great interest in cancer therapy. However, potential biomarkers related to iron metabolism and immune regulation in TNBC remain poorly understand.

METHODS

We constructed an optimal prognostic TNBC-IMRGs (iron metabolism and immune-related genes) signature using least absolute shrinkage and selection operator (LASSO) cox regression. Survival analysis and ROC curves were analyzed to identify the predictive value in a training cohort and external validation cohorts. The correlations of gene signature with ferroptosis regulators and immune infiltration are also discussed. Finally, we combined the gene signature with the clinical model to construct a combined model, which was further evaluated using a calibration curve and decision curve analysis (DCA).

RESULTS

Compared with the high-risk group, TNBC patients with low-risk scores had a remarkably better prognosis in both the training set and external validation sets. Both the IMRGs signature and combined model had a high predictive capacity, 1/3/5- year AUC: 0.866, 0.869, 0.754, and 1/3/5-yaer AUC: 0.942, 0.934, 0.846, respectively. The calibration curve and DCA also indicate a good predictive performance of the combined model. Gene set enrichment analysis (GSEA) suggests that the high-risk group is mainly enriched in metabolic processes, while the low-risk group is mostly clustered in immune related pathways. Multiple algorithms and single sample GSEA further show that the low-risk score is associated with a high tumor immune infiltration level. Differences in expression of ferroptosis regulators are also observed among different risk groups.

CONCLUSIONS

The IMRGs signature based on a combination of iron metabolism and immune factors may contribute to evaluating prognosis, understanding molecular characteristics and selecting treatment options in TNBC.

Oncotype DX Recurrence Score in premenopausal women

In the past 20 years, clinicians have shifted away from relying solely on clinicopathologic indicators toward increasing use of multigene expression assays in guiding treatment decisions regarding adjuvant chemotherapy for early-stage hormone receptor (HR)-positive, HER2-negative breast cancer. Oncotype DX Recurrence Score (RS) is one of the most widely used multigene assays when considering indications for adjuvant chemotherapy, and guidelines have recently incorporated its use in women with early HR-positive HER2-negative breast cancer and up to three positive lymph nodes. While multiple retrospective and prospective clinical studies have demonstrated that most women with a low- to mid-range RS (0-25) can safely forgo chemotherapy, premenopausal women remain an important subgroup for which recommendations based on RS are ill-defined. The majority of patients included in clinical trials and retrospective analyses validating the use of RS have been postmenopausal women. In the subgroup of premenopausal women with HR-positive HER2-negative breast cancer, studies indicate that traditional clinicopathologic methods for assessing risk continue to be powerful tools when combined with RS to predict benefit from chemotherapy. This suggests that there is an element of uncaptured risk inherent to the premenopausal state that evades characterization by RS alone. This review describes the evidence that has supported the recommendation of RS in clinical guidelines, specifically focusing on data for its current use in premenopausal women. We review available data regarding the impact of the menstrual cycle on hormonally regulated gene expression, which may drive variations in the RS. Further research on the reliability and interpretation of the RS in the premenopausal subgroup is necessary and represents a gap in knowledge of how the RS should be applied in premenopausal women.

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.