An Update on Breast Cancer Multigene Prognostic Tests-Emergent Clinical Biomarkers

A triple hormone receptor ER, AR, and VDR signature is a robust prognosis predictor in breast cancer

BACKGROUND

Despite evidence indicating the dominance of cell-of-origin signatures in molecular tumor patterns, translating these genome-wide patterns into actionable insights has been challenging. This study introduces breast cancer cell-of-origin signatures that offer significant prognostic value across all breast cancer subtypes and various clinical cohorts, compared to previously developed genomic signatures.

METHODS

We previously reported that triple hormone receptor (THR) co-expression patterns of androgen (AR), estrogen (ER), and vitamin D (VDR) receptors are maintained at the protein level in human breast cancers. Here, we developed corresponding mRNA signatures (THR-50 and THR-70) based on these patterns to categorize breast tumors by their THR expression levels. The THR mRNA signatures were evaluated across 56 breast cancer datasets (5040 patients) using Kaplan-Meier survival analysis, Cox proportional hazard regression, and unsupervised clustering.

RESULTS

The THR signatures effectively predict both overall and progression-free survival across all evaluated datasets, independent of subtype, grade, or treatment status, suggesting improvement over existing prognostic signatures. Furthermore, they delineate three distinct ER-positive breast cancer subtypes with significant survival in differences-expanding on the conventional two subtypes. Additionally, coupling THR-70 with an immune signature identifies a predominantly ER-negative breast cancer subgroup with a highly favorable prognosis, comparable to ER-positive cases, as well as an ER-negative subgroup with notably poor outcome, characterized by a 15-fold shorter survival.

CONCLUSIONS

The THR cell-of-origin signature introduces a novel dimension to breast cancer biology, potentially serving as a robust foundation for integrating additional prognostic biomarkers. These signatures offer utility as a prognostic index for stratifying existing breast cancer subtypes and for de novo classification of breast cancer cases. Moreover, THR signatures may also hold promise in predicting hormone treatment responses targeting AR and/or VDR.

Combining Molecular and Traditional Prognostic Factors: A Holistic Approach to Breast Cancer Prognostication

Breast cancer is a heterogeneous disease with various morphologies and molecular features, and it is the second leading cause of cancer death in women in developed countries. According to the literature, we currently lack both prognostic biomarkers and therapeutic targets. The most important prognostic factors are disease stage and Nottingham grade. We conducted a retrospective analysis involving 273 patients with BC who underwent neoadjuvant therapy before proceeding to curative surgical treatment between 1 January 2014 and 31 December 2023. Pathological procedures were conducted at the Department of Pathology, Emergency County Hospital of Targu Mureș, Romania. A statistical analysis was performed. Regarding the relationship between Nottingham grade and Ki67, grade I was associated with a Ki67 of less than 14. The relationship between tumor grade and luminal was similar (p = 0.0001): Grade I was associated with luminal A. Regarding TNM stage, it was statistically significantly correlated with TILs (p = 0.01) and RCB (p = 0.0001). Stages III and IV were associated with a high RCB and poor prognosis. Regarding the prognostic value, Nottingham grade 3 and TNM stages III and IV were correlated with low overall survival and disease-free survival, with poor prognosis, and, among the molecular variables, RCB played the most important prognostic role.

Bidirectional linkage of DNA barcodes for the multiplexed mapping of higher-order protein interactions in cells

Capturing the full complexity of the diverse hierarchical interactions in the protein interactome is challenging. Here we report a DNA-barcoding method for the multiplexed mapping of pairwise and higher-order protein interactions and their dynamics within cells. The method leverages antibodies conjugated with barcoded DNA strands that can bidirectionally hybridize and covalently link to linearize closely spaced interactions within individual 3D protein complexes, encoding and decoding the protein constituents and the interactions among them. By mapping protein interactions in cancer cells and normal cells, we found that tumour cells exhibit a larger diversity and abundance of protein complexes with higher-order interactions. In biopsies of human breast-cancer tissue, the method accurately identified the cancer subtype and revealed that higher-order protein interactions are associated with cancer aggressiveness.

Navigating the Intricacies of Tumor Heterogeneity: An Insight into Potential Prognostic Breast Cancer Biomarkers

Breast cancer is a heterogeneous disease with diverse histological and molecular subtypes. Luminal breast tumors are the most diagnosed subtype. In luminal breast cancer, hormone receptors (including ER, PR, HER2) play a diagnostic and prognostic role. Despite the effectiveness of endocrine therapy in luminal breast tumors, tumor recurrence and resistance occur, and this may highlight evolutionary strategies for survival driven by stemness. In this review we thus consider the association between estrogen signaling and stemness in mediating tumor processes. Many studies report stemness as one of the factors promoting tumor progression. Its association with estrogen signaling warrants further investigation and provides an opportunity for the identification of novel biomarkers which may be used for diagnostic, prognostic, and therapeutic purposes. Breast cancer stem cells have been characterized (CD44+ CD24-) and their role in promoting treatment resistance and tumor recurrence widely studied; however, the complexity of tumor progression which also involve microenvironmental factors suggests the existence of more varied cell phenotypes which mediate stemness and its role in tumor progression.

Biomarkers predictive of a response to extended endocrine therapy in breast cancer: a systematic review and meta-analysis

PURPOSE

Extension of adjuvant endocrine therapy beyond five years confers only modest survival benefit in breast cancer patients and carries risk of toxicities. This systematic review investigates the role of biomarker tests in predicting the clinical response to an extension of endocrine therapy.

METHODS

We searched Ovid MEDLINE, Ovid Embase, Global Index Medicus, and the Cochrane Central Register of Controlled Trials using an iterative approach to identify full-text articles related to breast cancer, endocrine therapy, and biomarkers.

RESULTS

Of the 1,217 unique reports identified, five studies were deemed eligible. Four investigated the Breast Cancer Index (BCI) assay in three distinct study populations. These studies consistently showed that BCI score was predictive of response to extended endocrine therapy among 1,946 combined patients, who were predominately non-Hispanic white and postmenopausal.

CONCLUSIONS

Evidence in the setting of predictive tests for extended endocrine therapy is sparse. Most relevant studies investigated the use of BCI, but these study populations were largely restricted to a single age, race, and ethnicity group. Future studies should evaluate a variety of biomarkers in diverse populations. Without sufficient evidence, physicians and patients face a difficult decision in balancing the benefits and risks of endocrine therapy extension.

Study of Drug Targets Associated With Oncogenesis and Cancer Cell Survival and the Therapeutic Activity of Engineered Ashwagandha Extract Having Differential Withanolide Constitutions

Ashwagandha (Withania somnifera) has gained worldwide popularity for a multitude of health benefits inclusive of cancer-preventive and curative effects. Despite numerous research data supporting the benefits of this wonder herb, the actual use of ashwagandha for cancer treatment in clinics is limited. The primary reason for this is the inconsistent therapeutic outcome due to highly variable composition and constitution of active ingredients in the plant extract impacting ashwagandha's pharmacology. We investigate here an engineered yield: an ashwagandha extract (Oncowithanib) that has a unique and fixed portion of active ingredients to achieve consistent and effective therapeutic activity. Using the MCF7 cell line, Oncowithanib was studied for its anti-neoplastic efficacy and drug targets associated with cell cycle regulation, translation machinery, and cell survival and apoptosis. Results demonstrate a dose-dependent decline in Oncowithanib-treated MCF7 cell viability and reduced colony-forming ability. Treated cells showed increased cell death as evidenced by enhancement of Caspase 3 enzyme activity and decreased expressions of cell proliferation markers such as Ki67 and Aurora Kinase A. Oncowithanib treatment was also found to be associated with expressional suppression of key cellular kinases such as RSK1, Akt1, and mTOR in MCF7 cells. Our findings indicate that Oncowithanib decreases MCF7 cell survival and propagation, and sheds light on common drug targets that might be good candidates for the development of cancer therapeutics. Further in-depth investigations are required to fully explore the potency and pharmacology of this novel extract. This study also highlights the importance of the standardization of herbal extracts to get consistent therapeutic activity for the disease indication.

Automated Prognosis Marker Assessment in Breast Cancers Using BLEACH&STAIN Multiplexed Immunohistochemistry

Prognostic markers in routine clinical management of breast cancer are often assessed using RNA-based multi-gene panels that depend on fluctuating tumor purity. Multiplex fluorescence immunohistochemistry (mfIHC) holds the potential for an improved risk assessment. To enable automated prognosis marker detection (i.e., progesterone receptor [PR], estrogen receptor [ER], androgen receptor [AR], GATA3, TROP2, HER2, PD-L1, Ki67, TOP2A), a framework for automated breast cancer identification was developed and validated involving thirteen different artificial intelligence analysis steps and an algorithm for cell distance analysis using 11+1-marker-BLEACH&STAIN-mfIHC staining in 1404 invasive breast cancers of no special type (NST). The framework for automated breast cancer detection discriminated normal glands from malignant glands with an accuracy of 98.4%. This approach identified that five (PR, ER, AR, GATA3, PD-L1) of nine biomarkers were associated with prolonged overall survival (p ≤ 0.0095 each) and two of these (PR, AR) were found to be independent risk factors in multivariate analysis (p ≤ 0.0151 each). The combined assessment of PR-ER-AR-GATA3-PD-L1 as a five-marker prognosis score showed strong prognostic relevance (p < 0.0001) and was an independent risk factor in multivariate analysis (p = 0.0034). Automated breast cancer detection in combination with an artificial intelligence-based analysis of mfIHC enables a rapid and reliable analysis of multiple prognostic parameters. The strict limitation of the analysis to malignant cells excludes the impact of fluctuating tumor purity on assay precision.

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.

Efficacy of adjuvant CDK4/6 inhibitors in hormone receptor-positive breast cancer: a systematic review and meta-analysis

BACKGROUND

The combination of cyclin-dependent kinase 4/6 (CDK4/6) inhibitors and standard endocrine therapy (ET) in the adjuvant treatment of hormone receptor (HR)-positive/HER2-negative breast cancer (BC) has yielded conflicting results. We performed a pooled analysis of the adjuvant efficacy of CDK4/6 inhibitors by including data from the NATALEE trial, the most recent trial on this topic.

METHODS

We searched major databases and congress proceedings until 7 June 2023 to identify randomized controlled trials (RCT) comparing adjuvant CDK4/6 inhibitor plus ET combination versus ET in HR-positive/HER2-negative early-stage BC.

RESULTS

Four RCTs involving a total of 17,749 patients were included. According to the pooled analysis of these four studies, significant improvement in invasive disease-free survival (iDFS) was observed with the addition of CDK4/6 inhibitors to standard ET (HzR: 0.81, 95% CI 0.67-0.97). IDFS benefit was irrespective from menopausal status, Ki-67 index, tumor grade, and previous chemotherapy. CDK4/6 inhibitors plus ET had a significant improvement in iDFS in stage 3 whereas there was a trend toward better iDFS in stage 2 (HzR for stage 3: 0.67, 95% CI 0.58-0.78; HzR for stage 2: 0.74, 95% CI 0.55-1.01).

CONCLUSIONS

Addition of CDK4/6 inhibitors to standard ET in the adjuvant treatment of HR-positive/HER2-negative early-stage BC improves iDFS.

The Future of Precision Oncology

Our understanding of the molecular mechanisms underlying cancer development and evolution have evolved rapidly over recent years, and the variation from one patient to another is now widely recognized. Consequently, one-size-fits-all approaches to the treatment of cancer have been superseded by precision medicines that target specific disease characteristics, promising maximum clinical efficacy, minimal safety concerns, and reduced economic burden. While precision oncology has been very successful in the treatment of some tumors with specific characteristics, a large number of patients do not yet have access to precision medicines for their disease. The success of next-generation precision oncology depends on the discovery of new actionable disease characteristics, rapid, accurate, and comprehensive diagnosis of complex phenotypes within each patient, novel clinical trial designs with improved response rates, and worldwide access to novel targeted anticancer therapies for all patients. This review outlines some of the current technological trends, and highlights some of the complex multidisciplinary efforts that are underway to ensure that many more patients with cancer will be able to benefit from precision oncology in the near future.

Consensus guidelines for the management of HR-positive HER2/neu negative early breast cancer in India, SAARC region and other LMIC by DELPHI survey method

BACKGROUND

Precise prognostication is the key to optimum and effective treatment planning for early-stage hormone receptor (HR) positive, HER2/neu negative breast cancer patients. Differences in the breast cancer incidence and tumor anatomical features at diagnosis, pharmacogenomics data between Western and Indian women along with the vast diversity in the economic status and differences in insurance policies of these regions; suggest recommendations put forward for Western women might not be applicable to Indian/Asian women. Opinions from oncologists through a voting survey on various prognostic factors/tools to be considered for planning adjuvant therapy are consolidated in this report for the benefit of oncologists of the sub-continent, SAARC and Asia's LMIC (low and middle-income countries).

METHODS

A three-phase DELPHI survey was conducted to collect opinions on prognostic factors considered for planning adjuvant therapy in early-stage HR+/HER2/neu negative breast cancer patients. A panel of 25 oncologists with expertise in breast cancer participated in the survey conducted in 2021. The experts provided opinions as 'agree' or disagree' or 'not sure' in phases-1 and 2 which were conducted virtually; in the final phase-3, all the panel experts met in person and concluded the survey.

RESULTS

Opinions on 41 statements related to prognostic factors/tools and their implications in planning adjuvant endocrine/chemotherapy were collected. All the statements were supported by the latest data from the clinical trials (prospective/retrospective). The statements with opinions of consensus less than 66% were disseminated in phase-2, and later in phase-3 with supporting literature. In phase-3, all the opinions from panelists were consolidated and guidelines were framed.

CONCLUSIONS

This consensus guideline will assist oncologists of India, SAARC and LMIC countries in informed clinical decision-making on adjuvant treatment in early HR+/HER2/neu negative breast cancer patients.

Can chemotherapy be omitted for patients with N0 or N1 endocrine-sensitive breast cancer treated with gonadotropin-releasing hormone agonist and tamoxifen?

Purpose

Whether administering chemotherapy followed by tamoxifen plus a gonadotropin-releasing hormone (GnRH) agonist to treat patients with lower-risk hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-negative breast cancer provides a greater benefit than administering tamoxifen plus GnRH agonist alone remains unclear. This study aimed to compare the outcomes of propensity score-matched (PSM) patients who underwent these 2 types of treatment plans.

Methods

This retrospective study included patients treated at our institution between 2009 and 2019. Eligible patients had HR-positive, HER2-negative, invasive breast cancer who had undergone surgery. There were 579 patients with HR-positive, HER2-negative breast cancer who were treated with a GnRH agonist and tamoxifen; patients with pathologic N2 and those who received neoadjuvant chemotherapy were excluded. After 1:1 PSM of patients who underwent GnRH agonist treatment and tamoxifen with versus without chemotherapy, 122 patients from these 2 groups were analyzed. Survival rates were calculated using the Kaplan-Meier method and compared via the log-rank test.

Results

After PSM, there were no significant differences in several baseline characteristics between the 2 groups. After a median follow-up of 62.8 months, the patients in both groups demonstrated similar outcomes with no significant difference in disease-free survival (P = 0.596).

Conclusion

Patients derived no significant survival benefit from undergoing a chemotherapy regimen before receiving tamoxifen and GnRH agonist therapy compared to forgoing such chemotherapy.

Survival results according to Oncotype Dx recurrence score in patients with hormone receptor positive HER-2 negative early-stage breast cancer: first multicenter Oncotype Dx recurrence score survival data of Turkey

Background

The Oncotype Dx recurrence score (ODx-RS) guides the adjuvant chemotherapy decision-making process for patients with early-stage hormone receptor-positive, HER-2 receptor-negative breast cancer. This study aimed to evaluate survival and its correlation with ODx-RS in pT1-2, N0-N1mic patients treated with adjuvant therapy based on tumor board decisions.

Patients and methods

Estrogen-positive HER-2 negative early-stage breast cancer patients (pT1-2 N0, N1mic) with known ODx-RS, operated on between 2010 and 2014, were included in this study. The primary aim was to evaluate 5-year disease-free survival (DFS) rates according to ODX-RS.

Results

A total of 203 eligible patients were included in the study, with a median age of 48 (range 26-75) and median follow-up of 84 (range 23-138) months. ROC curve analysis for all patients revealed a recurrence cut-off age of 45 years, prompting evaluation by grouping patients as ≤45 years vs. >45 years. No significant difference in five-year DFS rates was observed between the endocrine-only (ET) and chemo-endocrine (CE) groups. However, among the ET group, DFS was higher in patients over 45 years compared to those aged ≤45 years. When stratifying by ODx-RS as 0-17 and ≥18, DFS was significantly higher in the former group within the ET group. However, such differences were not seen in the CE group. In the ET group, an ODx-RS ≥18 and menopausal status were identified as independent factors affecting survival, with only an ODx-RS ≥18 impacting DFS in patients aged ≤45 years. The ROC curve analysis for this subgroup found the ODx-RS cut-off to be 18.

Conclusion

This first multicenter Oncotype Dx survival analysis in Turkey demonstrates the importance of Oncotype Dx recurrence score and age in determining treatment strategies for early-stage breast cancer patients. As a different aproach to the literature, our findings suggest that the addition of chemotherapy to endocrine therapy in young patients (≤45 years) with Oncotype Dx recurrence scores of ≥18 improves DFS.

CITED1 as a marker of favourable outcome in anti-endocrine treated, estrogen-receptor positive, lymph-node negative breast cancer

OBJECTIVE

To investigate CITED1 as a potential biomarker of anti-endocrine response and breast cancer recurrence, given its previously determined role in mediating estrogen-dependant transcription. The study is a continuation of earlier work establishing the role of CITED1 in mammary gland development.

RESULTS

CITED1 mRNA is associated with estrogen-receptor positivity and selectively expressed in the GOBO dataset of cell lines and tumours representing the luminal-molecular subtype. In patients treated with tamoxifen, higher CITED1 correlated with better outcome, suggesting a role in anti-estrogen response. The effect was particularly evident in the subset of estrogen-receptor positive, lymph-node negative (ER+/LN-) patients although noticeable divergence of the groups was apparent only after five years. Tissue microarray (TMA) analysis further validated the association of CITED1 protein, by immunohistochemistry, with favourable outcome in ER+, tamoxifen-treated patients. Although we also found a favourable response to anti-endocrine treatment in a larger TCGA dataset, the tamoxifen-specific effect was not replicated. Finally, MCF7 cells overexpressing CITED1 showed selective amplification of AREG but not TGFα suggesting that maintenance of specific ERα-CITED1 mediated transcription is important for the long-term response to anti-endocrine therapy. These findings together confirm the proposed mechanism of action of CITED1 and support its potential use as a prognostic biomarker.

The usefulness of CanAssist Breast over Ki67 in breast cancer recurrence risk assessment

BACKGROUND

Assessment of Ki67 by immunohistochemistry (IHC) has limited utility in clinical practice owing to analytical validity issues. According to International Ki67 Working Group (IKWG) guidelines, treatment should be guided by a prognostic test in patients expressing intermediate Ki67 range, >5%-<30%. The objective of the study is to compare the prognostic performance of CanAssist Breast (CAB) with that of Ki67 across various Ki67 prognostic groups.

METHODS

The cohort had 1701 patients. Various risk groups were compared for the distant relapse-free interval (DRFi) derived from Kaplan-Meier survival analysis. As per IKWG, patients are categorized into three risk groups: low-risk (<5%), intermediate risk (>5%-<30%), and high-risk (>30%). CAB generates two risk groups, low and high risk based on a predefined cutoff.

RESULTS

In the total cohort, 76% of the patients were low risk (LR) by CAB as against 46% by Ki67 with a similar DRFi of 94%. In the node-negative sub-cohort, 87% were LR by CAB with a DRFi of 97% against 49% by Ki67 with a DRFi of 96%. In subgroups of patients with T1 or N1 or G2 tumors, Ki67-based risk stratification was not significant while it was significant by CAB. In the intermediate Ki67 (>5%-<30%) category up to 89% (N0 sub-cohort) were LR by CAB and the percentage of LR patients was 25% (p < 0.0001) higher compared to NPI or mAOL. In the low Ki67 (≤5%) group, up to 19% were segregated as high-risk by CAB with 86% DRFi suggesting the requirement of chemotherapy in these low Ki67 patients.

CONCLUSION

CAB provided superior prognostic information in various Ki67 subgroups, especially in the intermediate Ki67 group.

hist2RNA: An Efficient Deep Learning Architecture to Predict Gene Expression from Breast Cancer Histopathology Images

Gene expression can be used to subtype breast cancer with improved prediction of risk of recurrence and treatment responsiveness over that obtained using routine immunohistochemistry (IHC). However, in the clinic, molecular profiling is primarily used for ER+ breast cancer, which is costly, tissue destructive, requires specialised platforms, and takes several weeks to obtain a result. Deep learning algorithms can effectively extract morphological patterns in digital histopathology images to predict molecular phenotypes quickly and cost-effectively. We propose a new, computationally efficient approach called hist2RNA inspired by bulk RNA sequencing techniques to predict the expression of 138 genes (incorporated from 6 commercially available molecular profiling tests), including luminal PAM50 subtype, from hematoxylin and eosin (H&E)-stained whole slide images (WSIs). The training phase involves the aggregation of extracted features for each patient from a pretrained model to predict gene expression at the patient level using annotated H&E images from The Cancer Genome Atlas (TCGA, n = 335). We demonstrate successful gene prediction on a held-out test set (n = 160, corr = 0.82 across patients, corr = 0.29 across genes) and perform exploratory analysis on an external tissue microarray (TMA) dataset (n = 498) with known IHC and survival information. Our model is able to predict gene expression and luminal PAM50 subtype (Luminal A versus Luminal B) on the TMA dataset with prognostic significance for overall survival in univariate analysis (c-index = 0.56, hazard ratio = 2.16 (95% CI 1.12-3.06), p < 5 × 10-3), and independent significance in multivariate analysis incorporating standard clinicopathological variables (c-index = 0.65, hazard ratio = 1.87 (95% CI 1.30-2.68), p < 5 × 10-3). The proposed strategy achieves superior performance while requiring less training time, resulting in less energy consumption and computational cost compared to patch-based models. Additionally, hist2RNA predicts gene expression that has potential to determine luminal molecular subtypes which correlates with overall survival, without the need for expensive molecular testing.

Chromatin Remodeling Enzyme Cluster Predicts Prognosis and Clinical Benefit of Therapeutic Strategy in Breast Cancer

The treatment provided for breast cancer depends on the expression of hormone receptors, human epidermal growth factor receptor-2 (HER2), and cancer staging. Surgical intervention, along with chemotherapy or radiation therapy, is the mainstay of treatment. Currently, precision medicine has led to personalized treatment using reliable biomarkers for the heterogeneity of breast cancer. Recent studies have shown that epigenetic modifications contribute to tumorigenesis through alterations in the expression of tumor suppressor genes. Our aim was to investigate the role of epigenetic modifications in genes involved in breast cancer. A total of 486 patients from The Cancer Genome Atlas Pan-cancer BRCA project were enrolled in our study. Hierarchical agglomerative clustering analysis further divided the 31 candidate genes into 2 clusters according to the optimal number. Kaplan–Meier plots showed worse progression-free survival (PFS) in the high-risk group of gene cluster 1 (GC1). In addition, the high-risk group showed worse PFS in GC1 with lymph node invasion, which also presented a trend of better PFS when chemotherapy was combined with radiotherapy than when chemotherapy was administered alone. In conclusion, we developed a novel panel using hierarchical clustering that high-risk groups of GC1 may be promising predictive biomarkers in the clinical treatment of patients with breast cancer.

Esr1 but Not CYP19A1 Overexpression in Mammary Epithelial Cells during Reproductive Senescence Induces Pregnancy-Like Proliferative Mammary Disease Responsive to Anti-Hormonals

Molecular-level analyses of breast carcinogenesis benefit from vivo disease models. Estrogen receptor 1 (Esr1) and cytochrome P450 family 19 subfamily A member 1 (CYP19A1) overexpression targeted to mammary epithelial cells in genetically engineered mouse models induces largely similar rates of proliferative mammary disease in prereproductive senescent mice. Herein, with natural reproductive senescence, Esr1 overexpression compared with CYP19A1 overexpression resulted in significantly higher rates of preneoplasia and cancer. Before reproductive senescence, Esr1, but not CYP19A1, overexpressing mice are tamoxifen resistant. However, during reproductive senescence, Esr1 mice exhibited responsiveness. Both Esr1 and CYP19A1 are responsive to letrozole before and after reproductive senescence. Gene Set Enrichment Analyses of RNA-sequencing data sets showed that higher disease rates in Esr1 mice were accompanied by significantly higher expression of cell proliferation genes, including members of prognostic platforms for women with early-stage hormone receptor-positive disease. Tamoxifen and letrozole exposure induced down-regulation of these genes and resolved differences between the two models. Both Esr1 and CYP19A1 overexpression induced abnormal developmental patterns of pregnancy-like gene expression. This resolved with progression through reproductive senescence in CYP19A1 mice, but was more persistent in Esr1 mice, resolving only with tamoxifen and letrozole exposure. In summary, genetically engineered mouse models of Esr1 and CYP19A1 overexpression revealed a diversion of disease processes resulting from the two distinct molecular pathophysiological mammary gland-targeted intrusions into estrogen signaling during reproductive senescence.

Overexpression of Estrogen Receptor α in Mammary Glands of Aging Mice Is Associated with a Proliferative Risk Signature and Generation of Estrogen Receptor α-Positive Mammary Adenocarcinomas

Age is a risk factor for human estrogen receptor-positive breast cancer, with highest prevalence following menopause. While transcriptome risk profiling is available for human breast cancers, it is not yet developed for prognostication for primary or secondary breast cancer development utilizing at-risk breast tissue. Both estrogen receptor α (ER) and aromatase overexpression have been linked to human breast cancer. Herein, conditional genetically engineered mouse models of estrogen receptor 1 (Esr1) and cytochrome P450 family 19 subfamily A member 1 (CYP19A1) were used to show that induction of Esr1 overexpression just before or with reproductive senescence and maintained through age 30 months resulted in significantly higher prevalence of estrogen receptor-positive adenocarcinomas than CYP19A1 overexpression. All adenocarcinomas tested showed high percentages of ER+ cells. Mammary cancer development was preceded by a persistent proliferative transcriptome risk signature initiated within 1 week of transgene induction that showed parallels to the Prosigna/Prediction Analysis of Microarray 50 human prognostic signature for early-stage human ER+ breast cancer. CYP19A1 mice also developed ER+ mammary cancers, but histology was more divided between adenocarcinoma and adenosquamous, with one ER- adenocarcinoma. Results demonstrate that, like humans, generation of ER+ adenocarcinoma in mice was facilitated by aging mice past the age of reproductive senescence. Esr1 overexpression was associated with a proliferative estrogen pathway-linked signature that preceded appearance of ER+ mammary adenocarcinomas.

Construction of a fatty acid metabolism-related gene signature for predicting prognosis and immune response in breast cancer

Background: Breast cancer has the highest incidence among malignant tumors in women, and its prevalence ranks first in global cancer morbidity. Aim: This study aimed to explore the feasibility of a prognostic model for patients with breast cancer based on the differential expression of genes related to fatty acid metabolism. Methods: The mRNA expression matrix of breast cancer and paracancer tissues was downloaded from The Cancer Genome Atlas database. The differentially expressed genes related to fatty acid metabolism were screened in R language. The TRRUST database was used to predict transcriptional regulators related to hub genes and construct an mRNA-transcription factor interaction network. A consensus clustering approach was used to identify different fatty acid regulatory patterns. In combination with patient survival data, Lasso and multivariate Cox proportional risk regression models were used to establish polygenic prognostic models based on fatty acid metabolism. The median risk score was used to categorize patients into high- and low-risk groups. Kaplan-Meier survival curves were used to analyze the survival differences between both groups. The Cox regression analysis included risk score and clinicopathological factors to determine whether risk score was an independent risk factor. Models based on genes associated with fatty acid metabolism were evaluated using receiver operating characteristic curves. A comparison was made between risk score levels and the fatty acid metabolism-associated genes in different subtypes of breast cancer. The differential gene sets of the Kyoto Encyclopedia of Genes and Genomes for screening high- and low-risk populations were compared using a gene set enrichment analysis. Furthermore, we utilized CIBERSORT to examine the abundance of immune cells in breast cancer in different clustering models. Results: High expression levels of ALDH1A1 and UBE2L6 prevented breast cancer, whereas high RDH16 expression levels increased its risk. Our comprehensive assessment of the association between prognostic risk scoring models and tumor microenvironment characteristics showed significant differences in the abundance of various immune cells between high- and low-risk breast cancer patients. Conclusions: By assessing fatty acid metabolism patterns, we gained a better understanding of the infiltration characteristics of the tumor microenvironment. Our findings are valuable for prognosis prediction and treatment of patients with breast cancer based on their clinicopathological characteristics.

RNA interference: Promising approach for breast cancer diagnosis and treatment

Today, cancer is one of the main health-related challenges, and in the meantime, breast cancer (BC) is one of the most common cancers among women, with an alarming number of incidences and deaths every year. For this reason, the discovery of novel and more effective approaches for the diagnosis, treatment, and monitoring of the disease are very important. In this regard, scientists are looking for diagnostic molecules to achieve the above-mentioned goals with higher accuracy and specificity. RNA interference (RNAi) is a posttranslational regulatory process mediated by microRNA intervention and small interfering RNAs. After transcription and edition, these two noncoding RNAs are integrated and activated with the RNA-induced silencing complex (RISC) and AGO2 to connect the target mRNA by their complementary sequence and suppress their translation, thus reducing the expression of their target genes. These two RNAi categories show different patterns in different BC types and stages compared to healthy cells, and hence, these molecules have high diagnostic, monitoring, and therapeutic potentials. This article aims to review the RNAi pathway and diagnostic and therapeutic potentials with a special focus on BC.

Comparing Genetic Risk and Clinical Risk Classification in Luminal-like Breast Cancer Patients Using a 23-Gene Classifier

Background: A 23-gene classifier has been developed based on gene expression profiles of Taiwanese luminal-like breast cancer. We aim to stratify risk of relapse and identify patients who may benefit from adjuvant chemotherapy based on genetic model among distinct clinical risk groups. Methods: There were 248 luminal (hormone receptor-positive and human epidermal growth factor receptor II-negative) breast cancer patients with 23-gene classifier results. Using the modified Adjuvant! Online definition, clinical high/low-risk groups were tabulated with the genetic model. The primary endpoint was a recurrence-free interval (RFI) at 5 years. Results: There was a significant difference between the high/low-risk groups defined by the 23-gene classifier for the 5-year prognosis of recurrence (16 recurrences in high-risk and 3 recurrences in low-risk; log-rank test: p < 0.0001). Among the clinically high-risk group, the 5-year RFI of high risk defined by the 23-gene classifier was significantly higher than that of the low-risk group (15 recurrences in high-risk and 2 recurrences in low-risk; log-rank test: p < 0.0001). Conclusion: This study showed that 23-gene classifier can be used to stratify clinically high-risk patients into distinct survival patterns based on genomic risks and displays the potentiality to guide adjuvant chemotherapy. The 23-gene classifier can provide a better estimation of breast cancer prognosis which can help physicians make a better treatment decision.

Efficacy of chemotherapy in patients with HR+/HER2-Invasive lobular breast cancer

INTRODUCTION

Invasive Lobular Breast Cancer (ILC) harbors unique clinicopathologic features. Data on optimal treatment modalities focusing on ILC remain scarce. We aim to investigate the benefit of chemotherapy in early-stage hormone receptor-positive (HR+) and human epidermal growth factor receptor-2 negative (HER2-) ILC.

METHODS

Female patients with early HR+/HER2- ILC (stages I-III) who underwent surgery were selected from the National Cancer Database (2010-2016) and grouped into four treatment cohorts: surgery only(S), chemotherapy alone (CT), endocrine therapy alone (ET), and combined chemotherapy followed by endocrine therapy (CET). Descriptive and bi-variate statistics summarized baseline characteristics and compared them across cohorts. A secondary analysis accounting for OncotypeDX (ODX) information was performed, stratifying for low (<26) and high (≥26) ODX. Kaplan-Meier (KM) and Cox proportional hazard models evaluated the relationship between treatment modality and overall survival (OS), stratifying for ODX scoring.

RESULTS

N = 15,271 patients were included. The CET cohort (29.8%) was more likely to be younger and have no co-morbidities, advanced tumor stage or high ODX score (≥26). No significant difference in OS comparing ET to CET (HR:1.08, 95%CI:0.93-1.26, p = 0.31) was observed, adjusting for confounders. N = 5,561 patients had ODX results available. No significant difference in 5-year OS was observed comparing the ET to CET cohorts, both in patients an ODX score <26 (HR:1.10; 95%CI:0.69-1.76, p = 0.69) and ODX score ≥26 (HR:1.18; 95%CI:0.51-2.75, p = 0.69).

CONCLUSION

Chemotherapy demonstrated no added survival benefit in HR+/HER2- ILC, even in tumors with ODX ≥26. Prospective trials identifying potential subgroups of patients with ILC who could benefit from chemotherapy are needed.

Combined Focused Next-Generation Sequencing Assays to Guide Precision Oncology in Solid Tumors: A Retrospective Analysis from an Institutional Molecular Tumor Board

BACKGROUND

Increasing knowledge of cancer biology and an expanding spectrum of molecularly targeted therapies provide the basis for precision oncology. Despite extensive gene diagnostics, previous reports indicate that less than 10% of patients benefit from this concept.

METHODS

We retrospectively analyzed all patients referred to our center's Molecular Tumor Board (MTB) from 2018 to 2021. Molecular testing by next-generation sequencing (NGS) included a 67-gene panel for the detection of short-sequence variants and copy-number alterations, a 53- or 137-gene fusion panel and an ultra-low-coverage whole-genome sequencing for the detection of additional copy-number alterations outside the panel's target regions. Immunohistochemistry for microsatellite instability and PD-L1 expression complemented NGS.

RESULTS

A total of 109 patients were referred to the MTB. In all, 78 patients received therapeutic proposals (70 based on NGS) and 33 were treated accordingly. Evaluable patients treated with MTB-recommended therapy (n = 30) had significantly longer progression-free survival than patients treated with other therapies (n = 17) (4.3 vs. 1.9 months, p = 0.0094). Seven patients treated with off-label regimens experienced major clinical benefits.

CONCLUSION

The combined focused sequencing assays detected targetable alterations in the majority of patients. Patient benefits appeared to lie in the same range as with large-scale sequencing approaches.

ZNF92, an unexplored transcription factor with remarkably distinct breast cancer over-expression associated with prognosis and cell-of-origin

Tumor phenotype is shaped both by transforming genomic alterations and the normal cell-of-origin. We identified a cell-of-origin associated prognostic gene expression signature, ET-9, that correlates with remarkably shorter overall and relapse free breast cancer survival, 8.7 and 6.2 years respectively. The genes associated with the ET-9 signature are regulated by histone deacetylase 7 (HDAC7) partly through ZNF92, a previously unexplored transcription factor with a single PubMed citation since its cloning in 1990s. Remarkably, ZNF92 is distinctively over-expressed in breast cancer compared to other tumor types, on a par with the breast cancer specificity of the estrogen receptor. Importantly, ET-9 signature appears to be independent of proliferation, and correlates with outcome in lymph-node positive, HER2+, post-chemotherapy and triple-negative breast cancers. These features distinguish ET-9 from existing breast cancer prognostic signatures that are generally related to proliferation and correlate with outcome in lymph-node negative, ER-positive, HER2-negative breast cancers. Our results suggest that ET-9 could be also utilized as a predictive signature to select patients for HDAC inhibitor treatment.

Emerging Roles of Long Noncoding RNAs in Breast Cancer Epigenetics and Epitranscriptomics

Breast carcinogenesis is a multistep process that involves both genetic and epigenetic changes. Epigenetics refers to reversible changes in gene expression that are not accompanied by changes in gene sequence. In breast cancer (BC), dysregulated epigenetic changes, such as DNA methylation and histone modifications, are accompanied by epitranscriptomic changes, in particular adenine to inosine modifications within RNA molecules. Factors that trigger these phenomena are largely unknown, but there is evidence for widespread participation of long noncoding RNAs (lncRNAs) that already have been linked to virtually any aspect of BC biology, making them promising biomarkers and therapeutic targets in BC patients. Here, we provide a systematic review of known and possible roles of lncRNAs in epigenetic and epitranscriptomic processes, along with methods and tools to study them, followed by a brief overview of current challenges regarding the use of lncRNAs in medical applications.

Pathological Diagnosis, Work-Up and Reporting of Breast Cancer 1st Central-Eastern European Professional Consensus Statement on Breast Cancer

This text is based on the recommendations accepted by the 4th Hungarian Consensus Conference on Breast Cancer, modified on the basis of the international consultation and conference within the frames of the Central-Eastern European Academy of Oncology. The recommendations cover non-operative, intraoperative and postoperative diagnostics, determination of prognostic and predictive markers and the content of cytology and histology reports. Furthermore, they address some specific issues such as the current status of multigene molecular markers, the role of pathologists in clinical trials and prerequisites for their involvement, and some remarks about the future.

Breast cancer in the era of precision medicine

Breast cancer is a heterogeneous disorder with different molecular subtypes and biological characteristics for which there are diverse therapeutic approaches and clinical outcomes specific to any molecular subtype. It is a global health concern due to a lack of efficient therapy regimens that might be used for all disease subtypes. Therefore, treatment customization for each patient depending on molecular characteristics should be considered. Precision medicine for breast cancer is an approach to diagnosis, treatment, and prevention of the disease that takes into consideration the patient's genetic makeup. Precision medicine provides the promise of highly individualized treatment, in which each individual breast cancer patient receives the most appropriate diagnostics and targeted therapies based on the genetic profile of cancer. The knowledge about the molecular features and development of breast cancer treatment approaches has increased, which led to the development of new targeted therapeutics. Tumor genomic profiling is the standard of care for breast cancer that could contribute to taking steps to better management of malignancies. It holds great promise for accurate prognostication, prediction of response to common systemic therapies, and individualized monitoring of the disease. The emergence of targeted treatment has significantly enhanced the survival of patients with breast cancer and contributed to reducing the economic costs of the health system. In this review, we summarized the therapeutic approaches associated with the molecular classification of breast cancer to help the best treatment selection specific to the target patient.

CmP Signaling Network Leads to Identification of Prognostic Biomarkers for Triple-Negative Breast Cancer in Caucasian Women

Objective: Triple-negative breast cancer (TNBC) constitutes ∼15% of all diagnosed invasive breast cancer cases with limited options for treatment since immunotherapies that target ER, PR, and HER2 receptors are ineffective. Progesterone (PRG) can induce its effects through either classic, nonclassic, or combined responses by binding to classic nuclear PRG receptors (nPRs) or nonclassic membrane PRG receptors (mPRs). Under PRG-induced actions, we previously demonstrated that the CCM signaling complex (CSC) can couple both nPRs and mPRs into a CmPn signaling network, which plays an important role during nPR(+) breast cancer tumorigenesis. We recently defined the novel CmP signaling network in African American women (AAW)-derived TNBC cells, which overlapped with our previously defined CmPn network in nPR(+) breast cancer cells. Methods: Under mPR-specific steroid actions, we measured alterations to key tumorigenic pathways in Caucasian American women (CAW)- derived TNBC cells, with RNAseq/proteomic and systems biology approaches. Exemption from ethics approval from IRB: This study only utilized cultured NBC cell lines with publicly available TNBC clinical data sets. Results: Our results demonstrated that TNBCs in CAW share similar altered signaling pathways, as TNBCs in AAW, under mPR-specific steroid actions, demonstrating the overall aggressive nature of TNBCs, regardless of racial differences. Furthermore, in this report, we have deconvoluted the CmP signalosome, using systems biology approaches and CAW-TNBC clinical data, to identify 21 new CAW-TNBC-specific prognostic biomarkers that reinforce the definitive role of CSC and mPR signaling during CAW-TNBC tumorigenesis. Conclusion: This new set of potential prognostic biomarkers may revolutionize molecular mechanisms and currently known concepts of tumorigenesis in CAW-TNBCs, leading to hopeful new therapeutic strategies.

Characterization of transcriptome diversity and in vitro behavior of primary human high-risk breast cells

Biology and transcriptomes of non-cancerous human mammary epithelial cells at risk for breast cancer development were explored following primary isolation utilizing conditional reprogramming cell technology from mastectomy tissue ipsilateral to invasive breast cancer. Cultures demonstrated consistent categorizable behaviors. Relative viability and mammosphere formation differed between samples but were stable across three different mammary-specific media. E2F cell cycle target genes expression levels were positively correlated with viability and advancing age was inversely associated. Estrogen growth response was associated with Tissue necrosis factor signaling and Interferon alpha response gene enrichment. Neoadjuvant chemotherapy exposure significantly altered transcriptomes, shifting them towards expression of genes linked to mammary stem cell formation. Breast cancer prognostic signature sets include genes that in normal development are limited to specific stages of pregnancy or the menstrual cycle. Sample transcriptomes were queried for stage specific gene expression patterns. All cancer samples and a portion of high-risk samples showed overlapping stages reflective of abnormal gene expression patterns, while other high-risk samples exhibited more stage specific patterns. In conclusion, at-risk cells preserve behavioral and transcriptome diversity that could reflect different risk profiles. It is possible that prognostic platforms analogous to those used for breast cancer could be developed for high-risk mammary cells.

CmP signaling network unveils novel biomarkers for triple negative breast cancer in African American women

Breast cancer is the most diagnosed cancer worldwide and remains the second leading cause of cancer death. While breast cancer mortality has steadily declined over the past decades through medical advances, an alarming disparity in breast cancer mortality has emerged between African American women (AAW) and Caucasian American women (CAW). New evidence suggests more aggressive behavior of triple-negative breast cancer (TNBC) in AAW may contribute to racial differences in tumor biology and mortality. Progesterone (PRG) can exert its cellular effects through either its classic, non-classic, or combined responses through binding to either classic nuclear PRG receptors (nPRs) or non-classic membrane PRG receptors (mPRs), warranting both pathways equally important in PRG-mediated signaling. In our previous report, we demonstrated that the CCM signaling complex (CSC) consisting of CCM1, CCM2, and CCM3 can couple both nPRs and mPRs signaling cascades to form a CSC-mPRs-PRG-nPRs (CmPn) signaling network in nPR positive(+) breast cancer cells. In this report, we furthered our research by establishing the CSC-mPRs-PRG (CmP) signaling network in nPR(-) breast cancer cells, demonstrating that a common core mechanism exists, regardless of nPR(+⁣/⁣-) status. This is the first report stating that inducible expression patterns exist between CCMs and major mPRs in TNBC cells. Furthermore, we firstly show mPRs in TNBC cells are localized in the nucleus and participate in nucleocytoplasmic shuttling in a coordinately synchronized fashion with CCMs under steroid actions, following the same cellular distribution as other well-defined steroid hormone receptors. Finally, for the first time, we deconvoluted the CmP signalosome by using systems biology and TNBC clinical data, which helped us understand key factors within the CmP network and identify 6 specific biomarkers with potential clinical applications associated with AAW-TNBC tumorigenesis. These novel biomarkers could have immediate clinical implications to dramatically improve health disparities among AAW-TNBCs.

Characterization of Mitochondrial Proteome and Function in Luminal A and Basal-like Breast Cancer Subtypes Reveals Alteration in Mitochondrial Dynamics and Bioenergetics Relevant to Their Diagnosis

Breast cancer (BC) is the most prevalent cancer and the one with the highest mortality among women worldwide. Although the molecular classification of BC has been a helpful tool for diagnosing and predicting the treatment of BC, developments are still being made to improve the diagnosis and find new therapeutic targets. Mitochondrial dysfunction is a crucial feature of cancer, which can be associated with cancer aggressiveness. Although the importance of mitochondrial dynamics in cancer is well recognized, its involvement in the mitochondrial function and bioenergetics context in BC molecular subtypes has been scantly explored. In this study, we combined mitochondrial function and bioenergetics experiments in MCF7 and MDA-MB-231 cell lines with statistical and bioinformatics analyses of the mitochondrial proteome of luminal A and basal-like tumors. We demonstrate that basal-like tumors exhibit a vicious cycle between mitochondrial fusion and fission; impaired but not completely inactive mitochondrial function; and the Warburg effect, associated with decreased oxidative phosphorylation (OXPHOS) complexes I and III. Together with the results obtained in the cell lines and the mitochondrial proteome analysis, two mitochondrial signatures were proposed: one signature reflecting alterations in mitochondrial functions and a second signature exclusively of OXPHOS, which allow us to distinguish between luminal A and basal-like tumors.

A Novel Prognostic Four-Gene Signature of Breast Cancer Identified by Integrated Bioinformatics Analysis

Molecular analysis facilitates the prediction of overall survival (OS) of breast cancer and decision-making of the treatment plan. The current study was designed to identify new prognostic genes for breast cancer and construct an effective prognostic signature with integrated bioinformatics analysis. Differentially expressed genes in breast cancer samples from The Cancer Genome Atlas (TCGA) dataset were filtered by univariate Cox regression analysis. The prognostic model was optimized by the Akaike information criterion and further validated using the TCGA dataset (n = 1014) and Gene Expression Omnibus (GEO) dataset (n = 307). The correlation between the risk score and clinical information was assessed by univariate and multivariate Cox regression analyses. Functional pathways in relation to high-risk and low-risk groups were analyzed using gene set enrichment analysis (GSEA). Four prognostic genes (EXOC6, GPC6, PCK2, and NFATC2) were screened and used to construct a prognostic model, which showed robust performance in classifying the high-risk and low-risk groups. The risk score was significantly related to clinical features and OS. We identified 19 functional pathways significantly associated with the risk score. This study constructed a new prognostic model with a high prediction performance for breast cancer. The four-gene prognostic signature could serve as an effective tool to predict prognosis and assist the management of breast cancer patients.

Gene Expression Profiling in Early Breast Cancer-Patient Stratification Based on Molecular and Tumor Microenvironment Features

Patients with early-stage hormone receptor-positive, human epidermal growth factor receptor 2-negative (HER2−) breast cancer (BC) are typically treated with surgery, followed by adjuvant systemic endocrine therapy with or without adjuvant chemotherapy and radiation therapy. Current guidelines regarding the use of adjuvant systemic therapy depend on clinical and pathological factors, such as the morphological assessment of tumor subtype; histological grade; tumor size; lymphovascular invasion; and lymph node status combined with estrogen receptor, progesterone receptor, and HER2 biomarker profiles assessed using immunohistochemistry and in situ hybridization. Additionally, the prognostic and predictive value of tumor-infiltrating lymphocytes and their composition is emerging as a key marker in triple negative (TNBC) and HER2-enriched molecular breast tumor subtypes. However, all these factors do not necessarily reflect the molecular heterogeneity and complexity of breast cancer. In the last two decades, gene expression signatures or profiling (GEP) tests have been developed to predict the risk of disease recurrence and estimate the potential benefit of receiving adjuvant systemic chemotherapy in patients with luminal breast cancer. GEPs have been utilized to help physicians to refine decision-making process, complementing clinicopathological parameters, and can now be used to classify the risk of recurrence and tailoring personalized treatments. Several clinical trials using GEPs validate the increasing value of such assays in different clinical settings, addressing relevant clinical endpoints. Finally, the recent approval of immune checkpoint inhibitors in TNBC and the increasing use of immunotherapy in different molecular BC populations highlight the opportunity to refine current GEPs by including a variety of immune-related genes that may help to improve predicting drug response and finetune prognosis.

Investigating Deep Learning Based Breast Cancer Subtyping Using Pan-Cancer and Multi-Omic Data

Breast Cancer comprises multiple subtypes implicated in prognosis. Existing stratification methods rely on the expression quantification of small gene sets. Next Generation Sequencing promises large amounts of omic data in the next years. In this scenario, we explore the potential of machine learning and, particularly, deep learning for breast cancer subtyping. Due to the paucity of publicly available data, we leverage on pan-cancer and non-cancer data to design semi-supervised settings. We make use of multi-omic data, including microRNA expressions and copy number alterations, and we provide an in-depth investigation of several supervised and semi-supervised architectures. Obtained accuracy results show simpler models to perform at least as well as the deep semi-supervised approaches on our task over gene expression data. When multi-omic data types are combined together, performance of deep models shows little (if any) improvement in accuracy, indicating the need for further analysis on larger datasets of multi-omic data as and when they become available. From a biological perspective, our linear model mostly confirms known gene-subtype annotations. Conversely, deep approaches model non-linear relationships, which is reflected in a more varied and still unexplored set of representative omic features that may prove useful for breast cancer subtyping.

Relevance of biopsy-derived pancreatic organoids in the development of efficient transcriptomic signatures to predict adjuvant chemosensitivity in pancreatic cancer

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Most patient with pancreatic cancer are treated by chemotherapy.

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Treatments selection are not personalized on the tumor characteristics.

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Signatures predicting chemotherapy efficiency are essential for personalizing treatments.

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An RNA signature of gemcitabine-sensitivity is developed leveraged on the dissimilarities between 2D and 3D in vitro models.

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Combining different in vitro models can help in defining clinically efficient transcriptomic signatures.

Pancreatic ductal adenocarcinoma (PDAC) patients are frequently treated by chemotherapy. Even if personalized therapy based on molecular analysis can be performed for some tumors, PDAC regimens selection is still mainly based on patients' performance status and expected efficacy. Therefore, the establishment of molecular predictors of chemotherapeutic efficacy could potentially improve prognosis by tailoring treatments. We have recently developed an RNA-based signature that predicts the efficacy of adjuvant gemcitabine using 38 PDAC primary cell cultures. While demonstrated its efficiency, a significant association with the classical/basal-like PDAC spectrum was observed. We hypothesized that this flaw was due to the basal-like biased phenotype of cellular models used in our strategy. To overcome this limitation, we generated a prospective cohort of 27 consecutive biopsied derived pancreatic organoids (BDPO) and include them in the signature identification strategy. As BDPO's do not have the same biased phenotype as primary cell cultures we expect they can compensate one with each other and cover a broader range of molecular phenotypes. We then obtained an improved signature predicting gemcitabine sensibility that was validated in a cohort of 300 resected PDAC patients that have or have not received adjuvant gemcitabine. We demonstrated a significant association between the improved signature and the overall and disease-free survival in patients predicted as sensitive and treated with adjuvant gemcitabine. We propose then that including BDPO along primary cell cultures represent a powerful strategy that helps to overcome primary cell cultures limitations producing unbiased RNA-based signatures predictive of adjuvant treatments in PDAC.

Effect of non-sentinel metastasis on adjuvant treatment decisions and survival in Z0011 eligible non-screened detected breast cancer population

BACKGROUND

The Z0011 trial results have shown that axillary lymph node dissection (ALND) can be avoided in cT1-2 patients undergoing breast conservation surgery with 1-2 metastatic sentinel lymph nodes (SLNs). We compared the clinicopathological characteristics of the Z0011 eligible non-screen detected breast cancer patients' cohort with the Z0011 trial study population. Additionally, we have explored the effect of non-sentinel metastasis on adjuvant treatment decisions and survival.

METHODS

The details of early breast cancer (EBC) patients fulfilling Z0011 eligibility criteria were retrieved from a prospectively maintained database (2013-2017) and electronic medical records. We used Statistical Package for the Social Sciences 25 and Stata V15 for the data analysis.

RESULTS

128/194 (66%) sentinel lymph node biopsy positive patients had fulfilled the Z0011 inclusion criteria. Compared to the Z0011 study, our cohort patients were younger, with more aggressive disease (higher T2, Grade 3), had a higher rate of macrometastasis (82.8% versus 58.8%) and non-SLN metastasis (48% versus 27%). The information gained by ALND had changed decisions for chemotherapy in 3% and no change of radiotherapy in Z0011 eligible patients. Further nodal positivity in completion ALND was not significantly associated with overall survival (p = 0.86) and disease-free survival (p = 0.5).

CONCLUSION

Z0011 eligible Indian EBC patients are significantly different from the Z0011 study population, with younger age of presentation, higher grade, a higher rate of both SLN macro metastasis and non-SLN positivity. The impact of non-sentinel metastasis on adjuvant treatment decisions and survival is minimal.

Are There Limits in Explainability of Prognostic Biomarkers? Scrutinizing Biological Utility of Established Signatures

Prognostic biomarkers can have an important role in the clinical practice because they allow stratification of patients in terms of predicting the outcome of a disorder. Obstacles for developing such markers include lack of robustness when using different data sets and limited concordance among similar signatures. In this paper, we highlight a new problem that relates to the biological meaning of already established prognostic gene expression signatures. Specifically, it is commonly assumed that prognostic markers provide sensible biological information and molecular explanations about the underlying disorder. However, recent studies on prognostic biomarkers investigating 80 established signatures of breast and prostate cancer demonstrated that this is not the case. We will show that this surprising result is related to the distinction between causal models and predictive models and the obfuscating usage of these models in the biomedical literature. Furthermore, we suggest a falsification procedure for studies aiming to establish a prognostic signature to safeguard against false expectations with respect to biological utility.

High mitochondrial content is associated with breast cancer aggressiveness

Mitochondria are relevant for cancer initiation and progression. Antibodies against mitochondrially encoded cytochrome c oxidase II (MTCO2), targeting a mitochondria specific epitope, can be used to quantitate the mitochondria content of tumor cells. The present study evaluated the impact of the cellular mitochondrial content on the prognosis of patients with breast cancer using immunohistochemical analysis on 2,197 arrayed breast cancer specimens. Results were compared with histological tumor parameters, patient overall survival, tumor cell proliferation using Ki67 labeling index (Ki67LI) and various other molecular features. Tumor cells exhibited stronger MTCO2 expression than normal breast epithelial cells. MTCO2 immunostaining was largely absent in normal breast epithelium, but was observed in 71.9% of 1,797 analyzable cancer specimens, including 34.6% tumors with weak expression, 22.3% with moderate expression and 15.0% with strong expression. High MTCO2 expression was significantly associated with advanced tumor stage, high Bloom-Richardson-Elston/Nottingham (BRE) grade, nodal metastasis and shorter overall survival (P<0.0001 each). In multivariate analysis, MTCO2 expression did not provide prognostic information independent of BRE grade, pathological tumor and pathological lymph node status. Additionally, significant associations were observed for high MTCO2 expression and various molecular features, including high Ki67LI, amplifications of HER2, MYC, CCND1 and MDM2, deletions of PTEN, 8p21 and 9p, low estrogen receptor expression (P<0.0001 each) and progesterone receptor expression (P<0.0001). The present study demonstrated that high MTCO2 expression was strongly associated with a poor prognosis and unfavorable phenotypical and molecular tumor features in patients with breast cancer. This suggests that the mitochondrial content may have a pivotal role in breast cancer progression.

Evaluation of multiple transcriptomic gene risk signatures in male breast cancer

Male breast cancer (BCa) is a rare disease accounting for less than 1% of all breast cancers and 1% of all cancers in males. The clinical management is largely extrapolated from female BCa. Several multigene assays are increasingly used to guide clinical treatment decisions in female BCa, however, there are limited data on the utility of these tests in male BCa. Here we present the gene expression results of 381 M0, ER+ve, HER2-ve male BCa patients enrolled in the Part 1 (retrospective analysis) of the International Male Breast Cancer Program. Using a custom NanoString™ panel comprised of the genes from the commercial risk tests Prosigna®, OncotypeDX®, and MammaPrint®, risk scores and intrinsic subtyping data were generated to recapitulate the commercial tests as described by us previously. We also examined the prognostic value of other risk scores such as the Genomic Grade Index (GGI), IHC4-mRNA and our prognostic 95-gene signature. In this sample set of male BCa, we demonstrated prognostic utility on univariate analysis. Across all signatures, patients whose samples were identified as low-risk experienced better outcomes than intermediate-risk, with those classed as high risk experiencing the poorest outcomes. As seen with female BCa, the concordance between tests was poor, with C-index values ranging from 40.3% to 78.2% and Kappa values ranging from 0.17 to 0.58. To our knowledge, this is the largest study of male breast cancers assayed to generate risk scores of the current commercial and academic risk tests demonstrating comparable clinical utility to female BCa.

Enzymes in the Cholesterol Synthesis Pathway: Interactomics in the Cancer Context

A global protein interactome ensures the maintenance of regulatory, signaling and structural processes in cells, but at the same time, aberrations in the repertoire of protein-protein interactions usually cause a disease onset. Many metabolic enzymes catalyze multistage transformation of cholesterol precursors in the cholesterol biosynthesis pathway. Cancer-associated deregulation of these enzymes through various molecular mechanisms results in pathological cholesterol accumulation (its precursors) which can be disease risk factors. This work is aimed at systematization and bioinformatic analysis of the available interactomics data on seventeen enzymes in the cholesterol pathway, encoded by HMGCR, MVK, PMVK, MVD, FDPS, FDFT1, SQLE, LSS, DHCR24, CYP51A1, TM7SF2, MSMO1, NSDHL, HSD17B7, EBP, SC5D, DHCR7 genes. The spectrum of 165 unique and 21 common protein partners that physically interact with target enzymes was selected from several interatomic resources. Among them there were 47 modifying proteins from different protein kinases/phosphatases and ubiquitin-protein ligases/deubiquitinases families. A literature search, enrichment and gene co-expression analysis showed that about a quarter of the identified protein partners was associated with cancer hallmarks and over-represented in cancer pathways. Our results allow to update the current fundamental view on protein-protein interactions and regulatory aspects of the cholesterol synthesis enzymes and annotate of their sub-interactomes in term of possible involvement in cancers that will contribute to prioritization of protein targets for future drug development.

Triple Negative Breast Cancer in an Appalachian Region: Exponential Tumor Grade Increase with Age of Diagnosis

INTRODUCTION

Triple negative breast cancer is an aggressive breast cancer with decreased five-year survival, increased risk for recurrence, and higher risk for metastases. Unlike other breast cancers, it has no targeted treatment and has heterogeneous genetics which make classification and treatment difficult.

PURPOSE

The purpose of our research was to compare triple negative breast cancer to non-triple negative breast cancer to identify key epidemiologic factors that might lead to improved basic science directives for biomarkers, treatments, and classification.

METHODS

The state cancer registry was used to provide the first West Virginia state-wide population evaluation of triple negative breast cancer.

RESULTS

The research reveals novel results that tumor grade increases exponentially with the age at diagnosis.

IMPLICATIONS

This creates an epidemiologic foundation for future research to define whether the disease, access to care, biology of aging, or some other factor cause this significant finding. In addition, results reveal decreased use of testing that could be increased to improve biomarker identification, targeted treatments, and classification of triple negative breast cancer.

Prediction of Late Recurrence and Distant Metastasis in Early-stage Breast Cancer: Overview of Current and Emerging Biomarkers

Recently, a significant number of breast cancer (BC) patients have been diagnosed at an early stage. It is therefore critical to accurately predict the risk of recurrence and distant metastasis for better management of BC in this setting. Clinicopathologic patterns, particularly lymph node status, tumor size, and hormonal receptor status are routinely used to identify women at increased risk of recurrence. However, these factors have limitations regarding their predictive ability for late metastasis risk in patients with early BC. Emerging molecular signatures using gene expression-based approaches have improved the prognostic and predictive accuracy for this indication. However, the use of their based-scores for risk assessment has provided contradictory findings. Therefore, developing and using newly emerged alternative predictive and prognostic biomarkers for identifying patients at high- and low-risk is of great importance. The present review discusses some serum biomarkers and multigene profiling scores for predicting late recurrence and distant metastasis in early-stage BC based on recently published studies and clinical trials.

Comparative survival analysis of multiparametric tests-when molecular tests disagree-A TEAM Pathology study

Multiparametric assays for risk stratification are widely used in the management of both node negative and node positive hormone receptor positive invasive breast cancer. Recent data from multiple sources suggests that different tests may provide different risk estimates at the individual patient level. The TEAM pathology study consists of 3284 postmenopausal ER+ve breast cancers treated with endocrine therapy Using genes comprising the following multi-parametric tests OncotypeDx®, Prosigna™ and MammaPrint® signatures were trained to recapitulate true assay results. Patients were then classified into risk groups and survival assessed. Whilst likelihood χ2 ratios suggested limited value for combining tests, Kaplan-Meier and LogRank tests within risk groups suggested combinations of tests provided statistically significant stratification of potential clinical value. Paradoxically whilst Prosigna-trained results stratified Oncotype-trained subgroups across low and intermediate risk categories, only intermediate risk Prosigna-trained cases were further stratified by Oncotype-trained results. Both Oncotype-trained and Prosigna-trained results further stratified MammaPrint-trained low risk cases, and MammaPrint-trained results also stratified Oncotype-trained low and intermediate risk groups but not Prosigna-trained results. Comparisons between existing multiparametric tests are challenging, and evidence on discordance between tests in risk stratification presents further dilemmas. Detailed analysis of the TEAM pathology study suggests a complex inter-relationship between test results in the same patient cohorts which requires careful evaluation regarding test utility. Further prognostic improvement appears both desirable and achievable.

Multi-Gene Testing Overview with a Clinical Perspective in Metastatic Triple-Negative Breast Cancer

Next-generation sequencing (NGS) is the technology of choice for the routine screening of tumor samples in clinical practice. In this setting, the targeted sequencing of a restricted number of clinically relevant genes represents the most practical option when looking for genetic variants associated with cancer, as well as for the choice of targeted treatments. In this review, we analyze available NGS platforms and clinical applications of multi-gene testing in breast cancer, with a focus on metastatic triple-negative breast cancer (mTNBC). We make an overview of the clinical utility of multi-gene testing in mTNBC, and then, as immunotherapy is emerging as a possible targeted therapy for mTNBC, we also briefly report on the results of the latest clinical trials involving immune checkpoint inhibitors (ICIs) and TNBC, where NGS could play a role for the potential predictive utility of homologous recombination repair deficiency (HRD) and tumor mutational burden (TMB).

The Signal Transducer IL6ST (gp130) as a Predictive and Prognostic Biomarker in Breast Cancer

Novel biomarkers are needed to continue to improve breast cancer clinical management and outcome. IL6-like cytokines, whose pleiotropic functions include roles in many hallmarks of malignancy, rely on the signal transducer IL6ST (gp130) for all their signalling. To date, 10 separate independent studies based on the analysis of clinical breast cancer samples have identified IL6ST as a predictor. Consistent findings suggest that IL6ST is a positive prognostic factor and is associated with ER status. Interestingly, these studies include 4 multigene signatures (EndoPredict, EER4, IRSN-23 and 42GC) that incorporate IL6ST to predict risk of recurrence or outcome from endocrine or chemotherapy. Here we review the existing evidence on the promising predictive and prognostic value of IL6ST. We also discuss how this potential could be further translated into clinical practice beyond the EndoPredict tool, which is already available in the clinic. The most promising route to further exploit IL6ST's promising predicting power will likely be through additional hybrid multifactor signatures that allow for more robust stratification of ER+ breast tumours into discrete groups with distinct outcomes, thus enabling greater refinement of the treatment-selection process.

INSTIGO Trial: Evaluation of a Plasma Protein Profile as a Predictive Biomarker for Metastatic Relapse of Triple Negative Breast Cancer

Background

Triple negative breast cancer (TNBC) accounts for 10-20% of breast cancers but has no specific therapy. While TNBC may be more sensitive to chemotherapy than other types of breast cancer, it has a poor prognosis. Most TNBC relapses occur during the five years following treatment, however predictive biomarkers of metastatic relapse are still lacking. High tumour-infiltrating lymphocytes (TILs) levels before and after neo-adjuvant chemotherapy (NAC) are associated with lower relapse risk and longer survival but TILs assessment is highly error-prone and still not introduced into the clinic. Therefore, having reliable biomarker of relapse, but easier to assess, remains essential for TNBC management. Searching for such biomarkers among serum/plasma proteins, circulating tumoral DNA (ctDNA) and blood cells appear relevant.

Methods

This single-centre and prospective study aims to discover predictive biomarkers of TNBC relapse and particularly focuses on plasma proteins. Blood samples will be taken at diagnosis, on the day of first-line or post-NAC surgery, on the day of radiotherapy start, then 6 months and one year after radiotherapy. A blood sample will be taken at the time of metastatic relapse diagnosis. Blood samples will be used for circulating protein quantification, blood cell counts and circulating tumour DNA quantification. A tumour RNA signature, based on the analysis of the RNA expression of 6 genes, will also be tested from the initial biopsy taken routinely. In NAC patients, TILs quantity will be assessed on TNBC pre-treatment biopsy and surgical specimen.

Ethics and Dissemination

INSTIGO belongs to category 2 interventional research on humans. This study has been approved by the SUD-EST IV ethics committee and is conducted in accordance with the Declaration of Helsinki and General Data Protection Regulation (GDPR). Study findings will be published in peer-reviewed medical journals.

Clinical Trial Registration

ClinicalTrials.gov, identifier NCT04438681.