Comprehensive transcriptome analysis identifies novel molecular subtypes and subtype-specific RNAs of triple-negative breast cancer

Harnessing Tumor-Infiltrating Lymphocytes in Triple-Negative Breast Cancer: Opportunities and Barriers to Clinical Integration

Triple-negative breast cancer (TNBC) continues to present a therapeutic challenge due to the fact that by definition, these cancer cells lack the expression of targetable receptors. Current treatment options include cytotoxic chemotherapy, antibody-drug conjugates (ADC), and the PD-1 checkpoint inhibitor, pembrolizumab. Due to high rates of recurrence, current guidelines for early-stage TNBC recommend either multi-agent chemotherapy or chemo-immunotherapy in all patients other than those with node-negative tumors < 0.5 cm. This approach can lead to significant long-term effects for TNBC survivors, driving a growing interest in de-escalating therapy where appropriate. Tumor infiltrating lymphocytes (TILs) represent a promising prognostic and predictive biomarker for TNBC. These diverse immune cells are present in the tumor microenvironment and within the tumor itself, and multiple retrospective studies have demonstrated that a higher number of TILs in early-stage TNBC portends a favorable prognosis. Research has also explored the potential of TIL scores to predict the response to immunotherapy. However, several barriers to the widespread use of TILs in clinical practice remain, including logistical and technical challenges with the scoring of TILs and lack of prospective trials to validate the trends seen in retrospective studies. This review will present the current understanding of the role of TILs in TNBC and discuss the future directions of TIL research.

Quantitative proteomics analysis of triple-negative breast cancers

Triple-negative breast cancer (TNBC) accounts for approximately 15% of all Breast Cancer (BC) cases with poorer prognosis and clinical outcomes compared to other BC subtypes due to greater tumor heterogeneity and few therapeutically targetable oncogenic drivers. To reveal actionable pathways for anti-cancer treatment, we use a proteomic approach to quantitatively compare the abundances of 6306 proteins across 55 formalin-fixed and paraffin-embedded (FFPE) TNBC tumors. We identified four major TNBC clusters by unsupervised clustering analysis of protein abundances. Analyses of clinicopathological characteristics revealed associations between the proteomic profiles and clinical phenotypes exhibited by each subtype. We validate the findings by inferring immune and stromal cell type composition from genome-wide DNA methylation profiles. Finally, quantitative proteomics on TNBC cell lines was conducted to identify in vitro models for each subtype. Collectively, our data provide subtype-specific insights into molecular drivers, clinicopathological phenotypes, tumor microenvironment (TME) compositions, and potential pharmacologic vulnerabilities for further investigations.

MVNMF: Multiview nonnegative matrix factorization for radio-multigenomic analysis in breast cancer prognosis

Radiogenomic research provides a deeper understanding of breast cancer biology by investigating the correlations between imaging phenotypes and genetic data. However, current radiogenomic research primarily focuses on the correlation between imaging phenotypes and single-genomic data (e.g., gene expression data), overlooking the potential of multi-genomics data to unveil more nuances in cancer characterization. To this end, we propose a multiview nonnegative matrix factorization (MVNMF) method for the radio-multigenomic analysis that identifies dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) features associated with multi-genomics data, including DNA copy number alterations, mutations, and mRNAs, each of which is independently predictive of cancer outcomes. MVNMF incorporates subspace learning and multiview regularization into a unified model to simultaneously select features and explore correlations. Subspace learning is utilized to identify representative radiomic features crucial for tumor analysis, while multiview regularization enables the learning of the correlation between the identified radiomic features and multi-genomics data. Experimental results showed that, for overall survival prediction in breast cancer, MVNMF classified patients into two distinct groups characterized by significant differences in survival (p = 0.0012). Furthermore, it achieved better performance with a C-index of 0.698 compared to the method without considering any genomics data (C-index = 0.528). MVNMF is an effective framework for identifying radiomic features linked to multi-genomics data, which improves its predictive power and provides a better understanding of the biological mechanisms underlying observed phenotypes. MVNMF offers a novel framework for prognostic prediction in breast cancer, with the potential to catalyze further radiogenomic/radio-multigenomic studies.

Triple-negative breast cancer molecular subtypes and potential detection targets for biological therapy indications

Triple-negative breast cancer (TNBC) is a highly aggressive subtype of breast cancer associated with poor prognosis. While chemotherapy remains the conventional treatment approach, its efficacy is limited and often accompanied by significant toxicity. Advances in precision-targeted therapies have expanded treatment options for TNBC, including immunotherapy, poly (ADP-ribose) polymerase inhibitors, androgen receptor inhibitors, cell cycle-dependent kinase inhibitors, and signaling pathway inhibitors. However, the heterogeneous nature of TNBC contributes to variations in treatment outcomes, underscoring the importance of identifying intrinsic molecular subtypes for personalized therapy. Additionally, due to patient-specific variability, the therapeutic response to targeted treatments is inconsistent. This highlights the need to strategize patients based on potential therapeutic targets for targeted drugs to optimize treatment strategies. This review summarizes the classification strategies and immunohistochemical (IHC) biomarkers for TNBC subtypes, along with potential targets for identifying indications for targeted drug therapy. These insights aim to support the development of personalized treatment approaches for TNBC patients.

Population-specific patterns in assessing molecular subtypes of young black females with triple-negative breast cancer

We determined triple-negative breast cancer (TNBC) subtypes, genetic ancestry, and immune features in a cohort of self-reported Black females with TNBC diagnosed at or below age 50. Among 104 tumors, 34.6% were basal-like 1 (BL1), 17.3% basal-like 2 (BL2), 9.6% luminal androgen receptor (LAR), 26.9% mesenchymal (M), and 11.5% unsubtyped (UNS). Subtypes resembled those seen in Europeans or East Asians, with less LAR (9.6% vs. 14.6-24.4%) and more UNS (11.5% vs. 0-7.5%). "High" proportion of West African ancestry was associated with more LAR (14.9% vs. 4.9%) and less M (25.5% vs. 34.2%). M demonstrated reduced immune activity and was marginally associated with worse overall survival in a multivariate model including stage, West African ancestry, BMI, and TILs, meriting future research. Our study is the largest to date of TNBC subtypes in young Black females. These results reinforce TNBC subtypes' application across populations and potential use as a prognostic biomarker.

Druggable Molecular Networks in BRCA1/BRCA2-Mutated Breast Cancer

Mutations in the tumor suppressor genes BRCA1 and BRCA2 are associated with the triple-negative breast cancer phenotype, particularly aggressive and hard-to-treat tumors lacking estrogen, progesterone, and human epidermal growth factor receptor 2. This research aimed to understand the metabolic and genetic links behind BRCA1 and BRCA2 mutations and investigate their relationship with effective therapies. Using the Cytoscape software, two networks were generated through a bibliographic analysis of articles retrieved from the PubMed-NCBI database. We identified 98 genes deregulated by BRCA mutations, and 24 were modulated by therapies. In particular, BIRC5, SIRT1, MYC, EZH2, and CSN2 are influenced by BRCA1, while BCL2, BAX, and BRIP1 are influenced by BRCA2 mutation. Moreover, the study evaluated the efficacy of several promising therapies, targeting only BRCA1/BRCA2-mutated cells. In this context, CDDO-Imidazolide was shown to increase ROS levels and induce DNA damage. Similarly, resveratrol decreased the expression of the anti-apoptotic gene BIRC5 while it increased SIRT1 both in vitro and in vivo. Other specific drugs were found to induce apoptosis selectively in BRCA-mutated cells or block cell growth when the mutation occurs, i.e., 3-deazaneplanocin A, genistein or daidzein, and PARP inhibitors. Finally, over-representation analysis on the genes highlights ferroptosis and proteoglycan pathways as potential drug targets for more effective treatments.

Androgen receptor expression distribution characteristics in young female breast cancer patients in China: a study of clinicopathological features

Background

The expression of androgen receptor (AR) in breast cancer has potential implications for predicting clinical outcomes, especially amongst young female patients. Numerous studies have reported that the co-expression of AR with hormone receptors (HRs) is correlated with a favorable prognosis in breast cancer. However, research on the frequency and distribution of AR expression in Chinese breast cancer patients is limited. This study aims to investigate the relationship between AR expression and the expression of progesterone receptor (PR), estrogen receptor (ER), P53, human epidermal growth factor receptor 2 (HER2), and epidermal growth factor receptor (EGFR) in breast cancer patients, and the distribution of molecular subtypes of breast cancer. Further, we aim to explore the pattern of AR expression and its correlation with clinicopathological features and prognosis among young female patients in China.

Methods

Formalin-fixed paraffin-embedded tissue samples from 321 young female breast cancer patients were collected from the Third Hospital of Nanchang. Immunohistochemistry was used to assess the expression of AR, ER, PR, HER2, and Ki67. A statistical analysis was conducted to explore the correlation between the expression of AR and these molecular markers, as well as their distribution across different molecular subtypes of breast cancer, and their prognostic significance.

Results

A total of 321 breast cancer patients were included in this study. Significant correlations were found between the positive expression of AR and the high expression of PR and ER (P<0.001). The rate of P53 positivity was significantly higher in the AR-positive patients than the AR-negative patients (P=0.01). Additionally, HER2 expression was significantly higher in the AR-positive patients than the AR-negative patients (P<0.001). Notably, the rate of EGFR positivity was significantly lower in the AR-positive patients compared to AR-negative patients (P<0.001). In relation to the molecular subtypes, AR positivity was significantly associated with the luminal A subtype (P<0.001), while the triple-negative breast cancer (TNBC)/basal-like subtype was more common in the AR-negative patients.

Conclusions

This study revealed that in young female breast cancer patients in China, AR-positive breast cancer was significantly associated with the high expression of HRs, increased P53 expression and reduced EGFR expression. The expression status of AR can serve as a biomarker to predict therapeutic responses but could also influence the classification of molecular subtypes and the selection of treatment strategies.

Divide and Conquer-Targeted Therapy for Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is the most aggressive and malignant type of breast cancer with limited treatment options and poor prognosis. One of the most significant impediments in TNBC treatment is the high heterogeneity of this disease, as highlighted by the detection of several molecular subtypes of TNBC. Each subtype is driven by distinct mutations and pathway aberrations, giving rise to specific molecular characteristics closely connected to clinical behavior, outcomes, and drug sensitivity. This review summarizes the knowledge regarding TNBC molecular subtypes and how it can be harnessed to devise tailored treatment strategies instead of blindly using targeted drugs. We provide an overview of novel targeted agents and key insights about new treatment modalities with an emphasis on the androgen receptor signaling pathway, cancer stem cell-associated pathways, phosphatidylinositol 3-kinase (PI3K)/AKT pathway, growth factor signaling, and immunotherapy.

Advancements in clinical research and emerging therapies for triple-negative breast cancer treatment

Triple-negative breast cancer (TNBC), defined by the lack of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor 2 (HER2) expression, is acknowledged as the most aggressive form of breast cancer (BC), comprising 15%-20% of all primary cases. Despite the prevalence of TNBC, effective and well-tolerated targeted therapies remain limited, with chemotherapy continuing to be the mainstay of treatment. However, the horizon is brightened by recent advancements in immunotherapy and antibody-drug conjugates (ADCs), which have garnered the U.S. Food and Drug Administration (FDA) approval for various stages of TNBC. Poly (ADP-ribose) polymerase inhibitors (PARPi), particularly for TNBC with BRCA mutations, present a promising avenue, albeit with the challenge of resistance that must be addressed. The success of phosphoinositide-3 kinase (PI3K) pathway inhibitors in hormone receptor (HR)-positive BC suggests potential applicability in TNBC, spurring optimism within the research community. This review endeavors to offer a comprehensive synthesis of both established and cutting-edge targeted therapies for TNBC. We delve into the specifics of PARPi, androgen receptor (AR) inhibitors, Cancer stem cells (CSCs), PI3K/Protein Kinase B (AKT)/mammalian target of rapamycin (mTOR), the transforming growth factor-beta (TGF-β), Ntoch, Wnt/β-catenin, hedgehog (Hh) pathway inhibitors, Epigenetic target-mediated drug delivery, ADCs, immune checkpoint inhibitors (ICIs)and novel immunotherapeutic solutions, contextualizing TNBC within current treatment paradigms. By elucidating the mechanisms of these drugs and their prospective clinical applications, we aim to shed light on the challenges and underscore the beacon of hope that translational research and innovative therapies represent for the oncology field.

The SRC-family serves as a therapeutic target in triple negative breast cancer with acquired resistance to chemotherapy

BACKGROUND

Resistance to chemotherapy, combined with heterogeneity among resistant tumors, represents a significant challenge in the clinical management of triple negative breast cancer (TNBC). By dissecting molecular pathways associated with treatment resistance, we sought to define patient sub-groups and actionable targets for next-line treatment.

METHODS

Bulk RNA sequencing and reverse phase protein array profiling were performed on isogenic patient-derived xenografts (PDX) representing paclitaxel-sensitive and -resistant tumors. Pathways identified as upregulated in the resistant model were further explored as targets in PDX explants. Their clinical relevance was assessed in two distinct patient cohorts (NeoAva and MET500).

RESULTS

Increased activity in signaling pathways involving SRC-family kinases (SFKs)- and MAPK/ERK was found in treatment resistant PDX, with targeted inhibitors being significantly more potent in resistant tumors. Up-regulation of SFKs- and MAPK/ERK-pathways was also detected in a sub-group of chemoresistant patients after neoadjuvant treatment. Furthermore, High SFK expression (of either SRC, FYN and/or YES1) was detected in metastatic lesions of TNBC patients with fast progressing disease (median disease-free interval 27 vs 105 months).

CONCLUSIONS

Upregulation of SFK-signaling is found in a subset of chemoresistant tumors and is persistent in metastatic lesions. Based on pre-clinical results, these patients may respond favorably to treatment targeting SFKs.

Improving efficacy of TNBC immunotherapy: based on analysis and subtyping of immune microenvironment

Triple-negative breast cancer (TNBC) is a highly aggressive type of breast cancer that encompasses several distinct subtypes. Recent advances in immunotherapy offer a promising future for the treatment of these highly heterogeneous and readily metastatic tumors. Despite advancements, the efficacy of immunotherapy remains limited as shown by unimproved efficacy of PD-L1 biomarker and limited patient benefit. To enhance the effectiveness of TNBC immunotherapy, we conducted investigation on the microenvironment, and corresponding therapeutic interventions of TNBC and recommended further investigation into the identification of additional biomarkers that can facilitate the subtyping of TNBC for more targeted therapeutic approaches. TNBC is a highly aggressive subtype with dismal long-term survival due to the lack of opportunities for traditional endocrine and targeted therapies. Recent advances in immunotherapy have shown promise, but response rates can be limited due to the heterogeneous tumor microenvironments and developed therapy resistance, especially in metastatic cases. In this review, we will investigate the tumor microenvironment of TNBC and corresponding therapeutic interventions. We will summarize current subtyping strategies and available biomarkers for TNBC immunotherapy, with a particular emphasis on the need for further research to identify additional prognostic markers and refine tailored therapies for specific TNBC subtypes. These efforts aim to improve treatment sensitivity and ultimately enhance survival outcomes for advanced-stage TNBC patients.

A 10-Gene Signature to Predict the Prognosis of Early-Stage Triple-Negative Breast Cancer

PURPOSE

Triple-negative breast cancer (TNBC) is a particularly challenging subtype of breast cancer, with a poorer prognosis compared to other subtypes. Unfortunately, unlike luminal-type cancers, there is no validated biomarker to predict the prognosis of patients with early-stage TNBC. Accurate biomarkers are needed to establish effective therapeutic strategies.

MATERIALS AND METHODS

In this study, we analyzed gene expression profiles of tumor samples from 184 TNBC patients (training cohort, n=76; validation cohort, n=108) using RNA sequencing.

RESULTS

By combining weighted gene expression, we identified a 10-gene signature (DGKH, GADD45B, KLF7, LYST, NR6A1, PYCARD, ROBO1, SLC22A20P, SLC24A3, and SLC45A4) that stratified patients by risk score with high sensitivity (92.31%), specificity (92.06%), and accuracy (92.11%) for invasive disease-free survival. The 10-gene signature was validated in a separate institution cohort and supported by meta-analysis for biological relevance to well-known driving pathways in TNBC. Furthermore, the 10-gene signature was the only independent factor for invasive disease-free survival in multivariate analysis when compared to other potential biomarkers of TNBC molecular subtypes and T-cell receptor β diversity. 10-gene signature also further categorized patients classified as molecular subtypes according to risk scores.

CONCLUSION

Our novel findings may help address the prognostic challenges in TNBC and the 10-gene signature could serve as a novel biomarker for risk-based patient care.

Developing combination therapies with biologics in triple-negative breast cancer

INTRODUCTION

Novel compounds have entered the triple-negative breast cancer (TNBC) treatment algorithm, namely immune checkpoints inhibitors (ICIs), PARP inhibitors and antibody-drug conjugates (ADCs). The optimization of treatment efficacy can be enhanced with the use of combination treatments, and the incorporation of novel compounds. In this review, we discuss the combination treatments under development for the treatment of TNBC.

AREAS COVERED

The development of new drugs occurring in recent years has boosted the research for novel combinations to target TNBC heterogeneity and improve outcomes. ICIs, ADCs, tyrosine kinase inhibitors (TKIs), and PARP inhibitors have emerged as leading players in this new landscape, while other compounds like novel intracellular pathways inhibitors or cancer vaccines are drawing more and more interest. The future of TNBC is outlined in combination approaches, and based on new cancer targets, including many chemotherapy-free treatments.

EXPERT OPINION

A large number of TNBC therapies have either proved clinically ineffective or weighted by unacceptable safety profiles. Others, however, have provided promising results and are currently in late-stage clinical trials, while a few have actually changed clinical practice in recent years. As novel, more and more selective drugs come up, combination strategies focusing the concept of synergy are fully warranted for the future.

Present and Future of Immunotherapy for Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) lacks the expression of estrogen receptors (ERs), human epidermal growth factor receptor 2 (HER2), and progesterone receptors (PRs). TNBC has the poorest prognosis among breast cancer subtypes and is more likely to respond to immunotherapy due to its higher expression of PD-L1 and a greater percentage of tumor-infiltrating lymphocytes. Immunotherapy has revolutionized TNBC treatment, especially with the FDA's approval of pembrolizumab (Keytruda) combined with chemotherapy for advanced cases, opening new avenues for treating this deadly disease. Although immunotherapy can significantly improve patient outcomes in a subset of patients, achieving the desired response rate for all remains an unmet clinical goal. Strategies that enhance responses to immune checkpoint blockade, including combining immunotherapy with chemotherapy, molecularly targeted therapy, or radiotherapy, may improve response rates and clinical outcomes. In this review, we provide a short background on TNBC and immunotherapy and explore the different types of immunotherapy strategies that are currently being evaluated in TNBC. Additionally, we review why combination strategies may be beneficial, provide an overview of the combination strategies, and discuss the novel immunotherapeutic opportunities that may be approved in the near future for TNBC.

Mesenchymal-like immune-altered is the fourth robust triple-negative breast cancer molecular subtype

BACKGROUND

Robust molecular subtyping of triple-negative breast cancer (TNBC) is a prerequisite for the success of precision medicine. Today, there is a clear consensus on three TNBC molecular subtypes: luminal androgen receptor (LAR), basal-like immune-activated (BLIA), and basal-like immune-suppressed (BLIS). However, the debate about the robustness of other subtypes is still open.

METHODS

An unprecedented number (n = 1942) of TNBC patient data was collected. Microarray- and RNAseq-based cohorts were independently investigated. Unsupervised analyses were conducted using k-means consensus clustering. Clusters of patients were then functionally annotated using different approaches. Prediction of response to chemotherapy and targeted therapies, immune checkpoint blockade, and radiotherapy were also screened for each TNBC subtype.

RESULTS

Four TNBC subtypes were identified in the cohort: LAR (19.36%); mesenchymal stem-like (MSL/MES) (17.35%); BLIA (31.06%); and BLIS (32.23%). Regarding the MSL/MES subtype, we suggest renaming it to mesenchymal-like immune-altered (MLIA) to emphasize its specific histological background and nature of immune response. Treatment response prediction results show, among other things, that despite immune activation, immune checkpoint blockade is probably less or completely ineffective in MLIA, possibly caused by mesenchymal background and/or an enrichment in dysfunctional cytotoxic T lymphocytes. TNBC subtyping results were included in the bc-GenExMiner v5.0 webtool ( http://bcgenex.ico.unicancer.fr ).

CONCLUSION

The mesenchymal TNBC subtype is characterized by an exhausted and altered immune response, and resistance to immune checkpoint inhibitors. Consensus for molecular classification of TNBC subtyping and prediction of cancer treatment responses helps usher in the era of precision medicine for TNBC patients.

Triple-Negative Breast Cancer: Molecular Particularities Still a Challenge

Worldwide, breast cancer (BC) is one of the most common cancers in women and is responsible for the highest number of cancer-related deaths among women, with a special clinical behavior and therapy response. Triple-negative breast cancer (TNBC) is seen as a highly invasive BC, characterized by a short survival, higher mortality, recurrence, and metastasis when it is compared to the other BC subtypes. The molecular subtyping of TNBC based on mRNA expression levels does not accurately reflect protein expression levels, which impacts targeted therapy effectiveness and prognostic predictions. Most TNBC cases exhibit a high frequency of homologous recombination (HR) DNA repair deficiency (HRD) signatures and are associated with a complex genomic profile. Biomarker research in TNBC includes investigating genetic mutations, gene expression patterns, immune system-related markers, and other factors that can provide valuable information for diagnosis, treatment selection, and patient outcomes. Additionally, these biomarkers are often crucial in the development of personalized and precision medicine approaches, where treatments are customized to each patient's unique characteristics. This ongoing research is essential for improving the management and outcomes of TNBC, which is a challenging and heterogeneous form of breast cancer. The findings of this research have practical implications for refining treatment strategies, particularly in selecting appropriate systemic therapies and integrating traditional treatment modalities like surgery and radiotherapy into comprehensive care plans for TNBC patients.

Management of triple-negative breast cancer by natural compounds through different mechanistic pathways

Triple-negative breast cancer (TNBC) is the most severe form of breast cancer, characterized by the loss of estrogen, progesterone, and human epidermal growth factor receptors. It is caused by various genetic and epigenetic factors, resulting in poor prognosis. Epigenetic changes, such as DNA methylation and histone modification, are the leading mechanisms responsible for TNBC progression and metastasis. This review comprehensively covers the various subtypes of TNBC and their epigenetic causes. In addition, the genetic association of TNBC with all significant genes and signaling pathways linked to the progression of this form of cancer has been enlisted. Furthermore, the possible uses of natural compounds through different mechanistic pathways have also been discussed in detail for the successful management of TNBC.

Bifunctionality and Antitumor Efficacy of ZG-126, a Vitamin D Receptor Agonist/Histone Deacetylase Inhibitor Hybrid Molecule

Analogues of hormonal vitamin D, 1,25-dihydroxyvitamin D (1,25D), signal through the nuclear vitamin D receptor (VDR). They have potential in combination therapies with other anticancer agents such as histone deacetylase inhibitors (HDACi's). Here, we characterize the ZG series of hybrid compounds that combine HDACi within the backbone of a VDR agonist. All display improved solubility, with ZG-126 being the most robustly bifunctional molecule in multiple cell lines. ZG-126 is well tolerated and strongly induces VDR target gene expression in vivo at therapeutic doses. Its antitumor efficacy is superior to 1,25D and the HDACi SAHA, separately or together, in mouse models of melanoma and triple-negative breast cancer (TNBC). Notably, ZG-126 treatment reduces metastases almost 4-fold in an aggressive TNBC model. ZG-126 also reduces total macrophage infiltration and the proportion of immunosuppressive M2-polarized macrophages in TNBC tumors by 2-fold. ZG-126 thus represents a bifunctional and efficacious anticancer agent with improved physicochemical properties.

Breast Cancer Stem Cells and Tumor Heterogeneity: Characteristics and Therapeutic Strategies

Breast cancer is one of the most frequently detected malignancies worldwide. It is responsible for more than 15% of all death cases caused by cancer in women. Breast cancer is a heterogeneous disease representing various histological types, molecular characteristics, and clinical profiles. However, all breast cancers are organized in a hierarchy of heterogeneous cell populations, with a small proportion of cancer stem cells (breast cancer stem cells (BCSCs)) playing a putative role in cancer progression, and they are responsible for therapeutic failure. In different molecular subtypes of breast cancer, they present different characteristics, with specific marker profiles, prognoses, and treatments. Recent efforts have focused on tackling the Wnt, Notch, Hedgehog, PI3K/Akt/mTOR, and HER2 signaling pathways. Developing diagnostics and therapeutic strategies enables more efficient elimination of the tumor mass together with the stem cell population. Thus, the knowledge about appropriate therapeutic methods targeting both "normal" breast cancer cells and breast cancer stem cell subpopulations is crucial for success in cancer elimination.

Clinical Advances in Triple Negative Breast Cancer Treatment: Focus on Poly (L-lactide-coglycolide) Nanoparticles

Triple negative breast cancer (TNBC) is the most aggressive type of breast cancer and is associated with high probability of metastasis and poor prognosis. Chemotherapeutics and surgery remain the most common options for TNBC patients; however, chemotherapeutic resistance and relapse of tumors limit the progression free survival and patient life span. This review provides an overview of recent chemotherapeutics that are in clinical trial, and the combination of drugs that are being investigated to overcome the drug resistance and to improve patient survival in different molecular subtypes of TNBCs. Nanotherapeutics have emerged as a promising platform for TNBC treatment and aim to improve the selectivity and solubility of drugs, reduce systemic side effects, and overcome multi-drug resistance. The study explores the role of nanoparticles for TNBC treatment and summarizes the types of nanoparticles that are in clinical trials. Poly(L-lactide-co-glycolide) (PLGA) is the most studied polymeric carrier for drug delivery and for TNBC treatment in research and in clinics. This review is about providing recent advancements in PLGA nanotherapeutic formulations and their application to help treat TNBC. Some background on current chemotherapies and pathway inhibitors is provided so that the readers are aware of what is currently considered for TNBC. Some of the pathway inhibitors may also be of importance for nanotherapeutics development. SIGNIFICANCE STATEMENT: This minireview summarizes the progress on chemotherapeutics and nanoparticle delivery for treatment of TNBC and specifically highlights the lead compounds that are in clinical trials.

Triple Negative Breast Cancer: Molecular Subtype-Specific Immune Landscapes with Therapeutic Implications

Triple Negative Breast Cancer (TNBC) is characterized by distinct molecular subtypes with unique biological and clinical features. This systematic review aimed to identify articles examining the differences in the tumor immune microenvironment (TIME) across different TNBC molecular subtypes. Six studies meeting inclusion criteria were analyzed, utilizing gene expression profiling and bioinformatic analyses to classify TNBC samples into molecular subtypes, as well as immunohistochemistry and cell deconvolution methods to characterize the TIME. Results revealed significant heterogeneity in immune cell composition among TNBC subtypes, with the immunomodulatory (IM) subtype demonstrating robust immune infiltration, composed mainly of adaptive immune cells along with an increased density of CTLA-4+ and PD-1+ TILs, high PD-L1 tumor cell expression, and upregulation of FOXP3+ Tregs. A more immunosuppressive TIME with a predominance of innate immune cells and lower levels of tumor-infiltrating lymphocytes (TILs) was observed in luminal androgen receptor (LAR) tumors. In mesenchymal stem-like (MSL) tumors, the TIME was mainly composed of innate immune cells, with a high number of M2 tumor-associated macrophages (TAMs), while the BL and M tumors displayed poor adaptive and innate immune responses, indicating an "immune-cold" phenotype. Differential activation of signaling pathways, genomic diversity, and metabolic reprogramming were identified as contributors to TIME heterogeneity. Understanding this interplay is crucial for tailoring therapeutic strategies, especially regarding immunotherapy.

Genomic characterization reveals distinct mutational landscapes and therapeutic implications between different molecular subtypes of triple-negative breast cancer

Triple-negative breast cancer (TNBC) has high heterogeneity, poor prognosis, and limited treatment success. Recently, an immunohistochemistry-based surrogate classification for the "Fudan University Shanghai Cancer Center (FUSCC) subtyping" has been developed and is considered more suitable for clinical application. Seventy-one paraffin-embedded sections of surgically resected TNBC were classified into four molecular subtypes using the IHC-based surrogate classification. Genomic analysis was performed by targeted next-generation sequencing and the specificity of the subtypes was explored by bioinformatics, including survival analysis, multivariate Cox regression, pathway enrichment, Pyclone analysis, mutational signature analysis and PHIAL analysis. AKT1 and BRCA1 mutations were identified as independent prognostic factors in TNBC. TNBC molecular subtypes encompass distinct genomic landscapes that show specific heterogeneities. The luminal androgen receptor (LAR) subtype was associated with mutations in PIK3CA and PI3K pathways, which are potentially sensitive to PI3K pathway inhibitors. The basal-like immune-suppressed (BLIS) subtype was characterized by high genomic instability and the specific possession of signature 19 while patients in the immunomodulatory (IM) subtype belonged to the PD-L1 ≥ 1% subgroup with enrichment in Notch signaling, suggesting a possible benefit of immune checkpoint inhibitors and Notch inhibitors. Moreover, mesenchymal-like (MES) tumors displayed enrichment in the receptor tyrosine kinase (RTK)-RAS pathway and potential sensitivity to RTK pathway inhibitors. The findings suggest potential treatment targets and prognostic factors, indicating the possibility of TNBC stratified therapy in the future.

Triple-Negative Breast Cancer Subclassified by Immunohistochemistry: Correlation with Clinical and Pathological Outcomes in Patients Receiving Neoadjuvant Chemotherapy

The intrinsic subtype of triple-negative breast cancer (TNBC) is based on genomic evaluation. In this study, we report the survival and pathological complete response (pCR) rates of TNBC patients subtyped by IHC and treated with neoadjuvant chemotherapy (NACT). A retrospective cohort of 187 TNBC patients who received NACT between 2008 and 2017 was used, and IHC subtyping was performed on biopsy specimens before chemotherapy. The subtyping revealed predominantly basal-like tumors (IHC-BL, 61%), followed by basal-like immune-suppressed tumors (IHC-BLIS, 31%), mesenchymal tumors (12.5%), luminal androgen receptor tumors (IHC-LAR, 12%), and basal-like immune-activated tumors (IHC-BLIA, 10.9%). The pCR rate varied among subtypes, with IHC-BLIA showing the highest (30.0%) and IHC-LAR showing the lowest (4.5%). IHC-BLIS led in recurrence sites. Overall and disease-free survival analyses did not show significant differences among subtypes, although IHC-BLIA demonstrated a trend toward better survival, and IHC-mesenchymal, worse. Patients who achieved pCR exhibited significantly better disease-free survival and overall survival than non-responders. This study underscores the potential of IHC-based subtyping in TNBC management, highlighting distinct response patterns to neoadjuvant chemotherapy and potential implications for treatment strategies. Further research is warranted to validate these findings and explore tailored therapeutic approaches for specific TNBC subtypes.

Deep learning framework for comprehensive molecular and prognostic stratifications of triple-negative breast cancer

Triple-negative breast cancer (TNBC) is the most challenging breast cancer subtype. Molecular stratification and target therapy bring clinical benefit for TNBC patients, but it is difficult to implement comprehensive molecular testing in clinical practice. Here, using our multi-omics TNBC cohort (N = 425), a deep learning-based framework was devised and validated for comprehensive predictions of molecular features, subtypes and prognosis from pathological whole slide images. The framework first incorporated a neural network to decompose the tissue on WSIs, followed by a second one which was trained based on certain tissue types for predicting different targets. Multi-omics molecular features were analyzed including somatic mutations, copy number alterations, germline mutations, biological pathway activities, metabolomics features and immunotherapy biomarkers. It was shown that the molecular features with therapeutic implications can be predicted including the somatic PIK3CA mutation, germline BRCA2 mutation and PD-L1 protein expression (area under the curve [AUC]: 0.78, 0.79 and 0.74 respectively). The molecular subtypes of TNBC can be identified (AUC: 0.84, 0.85, 0.93 and 0.73 for the basal-like immune-suppressed, immunomodulatory, luminal androgen receptor, and mesenchymal-like subtypes respectively) and their distinctive morphological patterns were revealed, which provided novel insights into the heterogeneity of TNBC. A neural network integrating image features and clinical covariates stratified patients into groups with different survival outcomes (log-rank P < 0.001). Our prediction framework and neural network models were externally validated on the TNBC cases from TCGA (N = 143) and appeared robust to the changes in patient population. For potential clinical translation, we built a novel online platform, where we modularized and deployed our framework along with the validated models. It can realize real-time one-stop prediction for new cases. In summary, using only pathological WSIs, our proposed framework can enable comprehensive stratifications of TNBC patients and provide valuable information for therapeutic decision-making. It had the potential to be clinically implemented and promote the personalized management of TNBC.

LncRNAs exhibit subtype-specific expression, survival associations, and cancer-promoting effects in breast cancer

Long non-coding RNAs (lncRNAs) play important roles in cancer progression, influencing processes such as invasion, metastasis, and drug resistance. Their reported cell type-dependent expression patterns suggest the potential for specialized functions in specific contexts. In breast cancer, lncRNA expression has been associated with different subtypes, highlighting their relevance in disease heterogeneity. However, our understanding of lncRNA function within breast cancer subtypes remains limited, warranting further investigation. We conducted a comprehensive analysis using the TANRIC dataset derived from the TCGA-BRCA cohort, profiling the expression, patient survival associations and immune cell type correlations of 12,727 lncRNAs across subtypes. Our findings revealed subtype-specific associations of lncRNAs with patient survival, tumor infiltrating lymphocytes and other immune cells. Targeting of lncRNAs exhibiting subtype-specific survival associations and expression in a panel of breast cancer cells demonstrated a selective reduction in cell proliferation within their associated subtype, supporting subtype-specific functions of certain lncRNAs. Characterization of HER2 + -specific lncRNA LINC01269 and TNBC-specific lncRNA AL078604.2 showed nuclear localization and altered expression of hundreds of genes enriched in cancer-promoting processes, including apoptosis, cell proliferation and immune cell regulation. This work emphasizes the importance of considering the heterogeneity of breast cancer subtypes and the need for subtype-specific analyses to fully uncover the relevance and potential impact of lncRNAs. Collectively, these findings demonstrate the contribution of lncRNAs to the distinct molecular, prognostic, and cellular composition of breast cancer subtypes.

Basal-epithelial subpopulations underlie and predict chemotherapy resistance in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype, characterized by extensive intratumoral heterogeneity, high metastasis, and chemoresistance, leading to poor clinical outcomes. Despite progress, the mechanistic basis of these aggressive behaviors remains poorly understood. Using single-cell and spatial transcriptome analysis, here we discovered basal epithelial subpopulations located within the stroma that exhibit chemoresistance characteristics. The subpopulations are defined by distinct signature genes that show a frequent gain in copy number and exhibit an activated epithelial-to-mesenchymal transition program. A subset of these genes can accurately predict chemotherapy response and are associated with poor prognosis. Interestingly, among these genes, elevated ITGB1 participates in enhancing intercellular signaling while ACTN1 confers a survival advantage to foster chemoresistance. Furthermore, by subjecting the transcriptional signatures to drug repurposing analysis, we find that chemoresistant tumors may benefit from distinct inhibitors in treatment-naive versus post-NAC patients. These findings shed light on the mechanistic basis of chemoresistance while providing the best-in-class biomarker to predict chemotherapy response and alternate therapeutic avenues for improved management of TNBC patients resistant to chemotherapy.

Unraveling Biomarker Signatures in Triple-Negative Breast Cancer: A Systematic Review for Targeted Approaches

Triple-negative breast cancer (TNBC) is one of the most aggressive subtypes of breast cancer, marked by poor outcomes and dismal prognosis. Due to the absence of targetable receptors, chemotherapy still represents the main therapeutic option. Therefore, current research is now focusing on understanding the specific molecular pathways implicated in TNBC, in order to identify novel biomarker signatures and develop targeted therapies able to improve its clinical management. With the aim of identifying novel molecular features characterizing TNBC, elucidating the mechanisms by which these molecular biomarkers are implicated in the tumor development and progression, and assessing the impact on cancerous cells following their inhibition or modulation, we conducted a literature search from the earliest works to December 2023 on PubMed, Scopus, and Web Of Science. A total of 146 studies were selected. The results obtained demonstrated that TNBC is characterized by a heterogeneous molecular profile. Several biomarkers have proven not only to be characteristic of TNBC but also to serve as potential effective therapeutic targets, holding the promise of a new era of personalized treatments able to improve its prognosis. The pre-clinical findings that have emerged from our systematic review set the stage for further investigation in forthcoming clinical trials.

Rational and trial design of FASCINATE-N: a prospective, randomized, precision-based umbrella trial

Background

With our growing insight into the molecular heterogeneity and biological characteristics of breast cancer, individualized treatment is the future of cancer treatment. In this prospective Fudan University Shanghai Cancer Center Breast Cancer Precision Platform Series study - neoadjuvant therapy (FASCINATE-N) trial, we classify breast cancer patients using multiomic characteristics into different subtypes to evaluate the efficacy of precision-based targeted therapies compared to standard neoadjuvant chemotherapy.

Methods and design

The FASCINATE-N trial is a prospective, randomized, precision-based umbrella trial that plans to enroll 716 women with early breast cancer. After enrollment, patients will first be divided into three groups: hormone receptor (HR)+/human epidermal growth factor receptor 2 (HER2)-, HER2+, and HR-/HER2-. The HR+/HER2- patients are further stratified using fusion and clustering of similarity network fusion (SNF) algorithm into four subtypes; HER2+ patients are divided into HR+/HER2+ and HR-/HER2+ subtypes; and HR-/HER2- patients are stratified using the Fudan University Shanghai Cancer Center classification. For the assignment of drugs to patients, Bayesian methods of adaptive randomization will be used. The primary endpoint is pathological complete response rate; secondary endpoints include 3-year invasive disease-free survival, overall response rate, and toxicities according to common terminology criteria for adverse events (CTCAE) scale version 4.0 and the ratio of patients with complete cell cycle arrest (Ki67 < 2.7%) in HR+/HER2+ breast cancer.

Discussion

The goal of our trial is to test the efficacy of our subtyping-based treatment in a neoadjuvant setting and to conduct a pilot study into the efficacy of targeted therapies within each precision-based subtype. The precision-based treatment arm can be updated with the refinement of our subtyping method, the discovery of new targets, and the development of novel targeted drugs. Our trial offers a unique opportunity to provide patients with individualized neoadjuvant therapy and test promising novel treatments that may further benefit patients.

Trial registration

ClinicalTrials.gov identifier: NCT05582499 (https://classic.clinicaltrials.gov/ct2/show/NCT05582499).

Targeting Receptor Tyrosine Kinases as a Novel Strategy for the Treatment of Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) comprises a group of aggressive and heterogeneous breast carcinoma. Chemotherapy is the mainstay for the treatment of triple-negative tumors. Nevertheless, the success of chemotherapeutic treatments is limited by their toxicity and development of acquired resistance leading to therapeutic failure and tumor relapse. Hence, there is an urgent need to explore novel targeted therapies for TNBC. Receptor tyrosine kinases (RTKs) are a family of transmembrane receptors that are key regulators of intracellular signaling pathways controlling cell proliferation, differentiation, survival, and motility. Aberrant activity and/or expression of several types of RTKs have been strongly connected to tumorigenesis. RTKs are frequently overexpressed and/or deregulated in triple-negative breast tumors and are further associated with tumor progression and reduced survival in patients. Therefore, targeting RTKs could be an appealing therapeutic strategy for the treatment of TNBC. This review summarizes the current evidence regarding the antitumor activity of RTK inhibitors in preclinical models of TNBC. The review also provides insights into the clinical trials evaluating the use of RTK inhibitors for the treatment of patients with TNBC.

Triple Negative Breast Cancers: An Obsolete Entity?

Triple negative breast cancer is defined on the basis of what it is not. It has served as a useful umbrella entity for management of patients with breast cancer for the last couple of decades. However, during this period a number of novel therapies have become available. These therapies have been documented to be useful in subsets of TNBCs that can be identified on the basis of distinct biologic alterations. Herein we revisit the categorization and usage of the TNBC as an entity to assess its utility in view of the currently available therapies.

Prognostic Value of Immunohistochemistry-based Subtyping Before and After Neoadjuvant Chemotherapy in Patients with Triple-negative Breast Cancer

To assess the predictive and prognostic value of a subtyping method based on immunohistochemistry in patients with triple-negative breast cancer (TNBC) treated with neoadjuvant chemotherapy (NAC). This study included patients with TNBC treated with anthracycline- and taxane-based NAC and curative surgery. Immunohistochemical (IHC) subtyping was performed using core needle biopsy specimens before NAC (pre-NAC) and residual tumors after NAC (post-NAC). Logistic regression was performed to identify predictive biomarkers of pathological complete response (pCR). Invasive disease-free survival (iDFS), distant disease-free survival (DDFS), and overall survival (OS) were assessed using the log-rank test and Cox proportional hazards regression. A total of 230 patients were followed up for a median of 59 months. Clinical lymph node status and the pre-NAC subtype were independent predictors of pCR (P=0.006 and 0.005, respectively). The pre-NAC subtype was an independent prognostic factor for long-term survival (iDFS: P < 0.001, DDFS: P=0.010, and OS: P=0.044). Among patients with residual disease (RD) after NAC, approximately 45% of tumors changed their IHC subtype. Furthermore, the post-NAC subtype, but not the pre-NAC subtype, was strongly associated with the survival of patients with RD (iDFS: P < 0.001, DDFS: P=0.005, and OS: P=0.006). The IHC subtype predicted response to NAC and long-term survival in patients with early TNBC. In patients with RD, almost 45% of the tumors changed subtype after NAC. The IHC subtype should be considered when planning additional therapies pre- and post-NAC.

A ten long noncoding RNA-based prognostic risk model construction and mechanism study in the basal-like immune-suppressed subtype of triple-negative breast cancer

Background

According to the Fudan University Shanghai Cancer Center (FUSCC) system, triple-negative breast cancer (TNBC) is divided into four stable subtypes: (I) luminal androgen receptor, (II) immunomodulatory, (III) basal-like immune-suppressed (BLIS), and (IV) mesenchymal-like. However, the treatment outcomes of the corresponding targeted therapies are unsatisfactory, especially for the BLIS subtype. Therefore, we aimed to identify the key long noncoding RNAs (lncRNAs) to construct a prognostic model for BLIS subtype and discover potential targets to explore potential therapeutic strategies in this study.

Methods

The FUSCC cohort was used to establish a prognostic risk model via least absolute shrinkage and selection operator (LASSO) and Cox regression analysis. The Cancer Genome Atlas (TCGA) cohort was then used to evaluate and verify the model. To understand the functional aspects of the model, functional, immune landscape, mutation, and drug sensitivity analyses were performed between high- and low-risk groups.

Results

Ten prognostic-related lncRNAs identified, including C5ORF66-AS2, DIO3OS, FZD10-DT, LINC00393, LNC-ERI1-32, LNC-FOXO1-2, LNC-SPARCL1-1, HCG23, LNC-MMD-4 and LNC-TMEM106C-6, were selected for risk score system construction. The results showed that the model constructed could divide the patients with BLIS subtype into two groups of high and low risk, and patients with higher risk scores had shorter recurrence-free survival. In addition, drug sensitivity analysis identified 3 compounds, including BMS-754807, cytochalasin b, and linifanib, that could have a potential therapeutic effect on patients with the BLIS subtype.

Conclusions

The risk prognosis model showed good prognostic value for the BLIS subtype patients, and the ten lncRNAs may be potential therapeutic targets.

A Review of AI-Based Radiomics and Computational Pathology Approaches in Triple-Negative Breast Cancer: Current Applications and Perspectives

Breast cancer is one of the most common and deadly cancers worldwide. Approximately, 20% of all breast cancers are characterized as triple negative (TNBC). TNBC typically is associated with a poorer prognosis relative to other breast cancer subtypes. Due to its aggressiveness and lack of response to hormonal therapy, conventional cytotoxic chemotherapy is the usual treatment; however, this treatment is not always effective, and an important percentage of patients develop recurrence. More recently, immunotherapy has started to be used on some populations with TNBC showing promising results. Unfortunately, immunotherapy is only applicable to a minority of patients and responses in metastatic TNBC have overall been modest in comparison to other cancer types. This situation evidences the need for developing effective biomarkers that help to stratify and personalize patient management. Thanks to recent advances in artificial intelligence (AI), there has been an increasing interest in its use for medical applications aiming at supporting clinical decision making. Several works have used AI in combination with diagnostic medical imaging, more specifically radiology and digitized histopathological tissue samples, aiming to extract disease-specific information that is difficult to quantify by the human eye. These works have demonstrated that analysis of such images in the context of TNBC has great potential for (1) risk-stratifying patients to identify those patients who are more likely to experience disease recurrence or die from the disease and (2) predicting pathologic complete response. In this manuscript, we present an overview on AI and its integration with radiology and histopathological images for developing prognostic and predictive approaches for TNBC. We present state of the art approaches in the literature and discuss the opportunities and challenges with developing AI algorithms regarding further development and clinical deployment, including identifying those patients who may benefit from certain treatments (e.g., adjuvant chemotherapy) from those who may not and thereby should be directed toward other therapies, discovering potential differences between populations, and identifying disease subtypes.

Artificial intelligence in breast imaging: potentials and challenges

Breast cancer, which is the most common type of malignant tumor among humans, is a leading cause of death in females. Standard treatment strategies, including neoadjuvant chemotherapy, surgery, postoperative chemotherapy, targeted therapy, endocrine therapy, and radiotherapy, are tailored for individual patients. Such personalized therapies have tremendously reduced the threat of breast cancer in females. Furthermore, early imaging screening plays an important role in reducing the treatment cycle and improving breast cancer prognosis. The recent innovative revolution in artificial intelligence (AI) has aided radiologists in the early and accurate diagnosis of breast cancer. In this review, we introduce the necessity of incorporating AI into breast imaging and the applications of AI in mammography, ultrasonography, magnetic resonance imaging, and positron emission tomography/computed tomography based on published articles since 1994. Moreover, the challenges of AI in breast imaging are discussed.

Prognosis difference between HER2-low and HER2-zero breast cancer patients: a systematic review and meta-analysis

BACKGROUND

HER2-low breast cancer (BC) is proposed to be a special population of patients with an immunohistochemistry (IHC) score of 1 + or 2 + and non-amplified in situ hybridization (ISH) results. The role and prognostic impact of HER2-low BC is still controversial. This meta-analysis aims to explore the prognostic difference between of HER2-low and HER2-zero characteristic in BC patients.

METHODS

A meta-analysis was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and eligible studies were search in PubMed, Web of Science and EMBASE databases. Quality assessment of included studies were performed by Quality in Prognostic Studies (QUIPS) tool. Hazard ratios (HRs) and corresponding 95% confidence interval (CI) for overall survival (OS) and disease-free survival (DFS) were pooled in a meta-analysis. Furthermore, subgroup analysis, sensitivity analysis, and analysis for publication bias were conducted.

RESULTS

Eighteen studies comprising a total of 93,317 patients were included for meta-analysis. BC patients with HER2-low characteristic have longer OS (HRs 0.87, 95% CI 0.81-0.93, p < 0.0001) and DFS (HRs 0.82, 95% CI 0.73-0.93, p = 0.001) compared to those with HER2-zero characteristic. Subgroup analysis indicate that the source of heterogeneity may come from the hormone receptor (HR) status group. Although, the publication bias was detected, sensitivity analysis and the trim-and-fill method analysis demonstrated the stability and reliability of the results.

CONCLUSION

HER2-low BC patients have longer OS and DFS compared to HER2-zero BC patients, and its prognostic value is consistent among different HR status patients. Whether HER2-low breast cancer is an independent subtype of breast cancer is still a subject of ongoing research, and more studies are needed to fully understand the molecular and clinical features of this subtype.

Development of a CD8+ T cell-based molecular classification for predicting prognosis and heterogeneity in triple-negative breast cancer by integrated analysis of single-cell and bulk RNA-sequencing

Background

Triple-negative breast cancer (TNBC), although the most intractable subtype, is characterized by abundant immunogenicity, which enhances responsiveness to immunotherapeutic measures.

Methods

First, we identified CD8+ T cell core genes (TRCG) based on single-cell sequence and traditional transcriptome sequencing and then used this data to develop a first-of-its-kind classification system based on CD8+ T cells in patients with TNBC. Next, TRCG-related patterns were systematically analyzed, and their correlation with genomic features, immune activity (microenvironment associated with immune infiltration), and clinicopathological characteristics were assessed in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), the Cancer Genome Atlas (TCGA), GSE103091, GSE96058 databases. Additionally, a CD8+ T cell-related prognostic signature (TRPS) was developed to quantify a patient-specific TRCG pattern. What's more, the genes-related TRPS was validated by polymerase chain reaction (PCR) experiment.

Results

This study, for the first time, distinguished two subsets in patients with TNBC based on the TRCG. The immune microenvironment and prognostic stratification between these have distinct heterogeneity. Furthermore, this study constructed a novel scoring system named TRPS, which we show to be a robust prognostic marker for TNBC that is related to the intensity of immune infiltration and immunotherapy. Moreover, the levels of genes related the TRPS were validated by quantitative Real-Time PCR.

Conclusions

Consequently, this study unraveled an association between the TRCG and the tumor microenvironment in TNBC. TRPS model represents an effective tool for survival prediction and treatment guidance in TNBC that can also help identify individual variations in TME and stratify patients who are sensitive to anticancer immunotherapy.

Long non-coding RNA AC099850.4 correlates with advanced disease state and predicts worse prognosis in triple-negative breast cancer

Our understanding of the function of long non-coding RNAs (lncRNAs) in health and disease states has evolved over the past decades due to the many advances in genome research. In the current study, we characterized the lncRNA transcriptome enriched in triple-negative breast cancer (TNBC, n = 42) and estrogen receptor (ER+, n = 42) breast cancer compared to normal breast tissue (n = 56). Given the aggressive nature of TNBC, our data revealed selective enrichment of 57 lncRNAs in TNBC. Among those, AC099850.4 lncRNA was chosen for further investigation where it exhibited elevated expression, which was further confirmed in a second TNBC cohort (n = 360) where its expression correlated with a worse prognosis. Network analysis of AC099850.4high TNBC highlighted enrichment in functional categories indicative of cell cycle activation and mitosis. Ingenuity pathway analysis on the differentially expressed genes in AC099850.4high TNBC revealed the activation of the canonical kinetochore metaphase signaling pathway, pyridoxal 5'-phosphate salvage pathway, and salvage pathways of pyrimidine ribonucleotides. Additionally, upstream regulator analysis predicted the activation of several upstream regulator networks including CKAP2L, FOXM1, RABL6, PCLAF, and MITF, while upstream regulator networks of TP53, NUPR1, TRPS1, and CDKN1A were suppressed. Interestingly, elevated expression of AC099850.4 correlated with worse short-term relapse-free survival (log-rank p = 0.01). Taken together, our data are the first to reveal AC099850.4 as an unfavorable prognostic marker in TNBC, associated with more aggressive clinicopathological features, and suggest its potential utilization as a prognostic biomarker and therapeutic target in TNBC.

Recent advances in targeted strategies for triple-negative breast cancer

Triple-negative breast cancer (TNBC), a highly aggressive subtype of breast cancer, negatively expresses estrogen receptor, progesterone receptor, and the human epidermal growth factor receptor 2 (HER2). Although chemotherapy is the main form of treatment for patients with TNBC, the effectiveness of chemotherapy for TNBC is still limited. The search for more effective therapies is urgent. Multiple targeted therapeutic strategies have emerged according to the specific molecules and signaling pathways expressed in TNBC. These include PI3K/AKT/mTOR inhibitors, epidermal growth factor receptor inhibitors, Notch inhibitors, poly ADP-ribose polymerase inhibitors, and antibody-drug conjugates. Moreover, immune checkpoint inhibitors, for example, pembrolizumab, atezolizumab, and durvalumab, are widely explored in the clinic. We summarize recent advances in targeted therapy and immunotherapy in TNBC, with the aim of serving as a reference for the development of individualized treatment of patients with TNBC in the future.

Advances in the Management of Early-Stage Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is a subtype of breast cancer with both inter- and intratumor heterogeneity, thought to result in a more aggressive course and worse outcomes. Neoadjuvant therapy (NAT) has become the preferred treatment modality of early-stage TNBC as it allows for the downstaging of tumors in the breast and axilla, monitoring early treatment response, and most importantly, provides important prognostic information that is essential to determining post-surgical therapies to improve outcomes. It focuses on combinations of systemic drugs to optimize pathologic complete response (pCR). Excellent response to NAT has allowed surgical de-escalation in ideal candidates. Further, treatment algorithms guide the systemic management of patients based on their pCR status following surgery. The expanding knowledge of molecular pathways, genomic sequencing, and the immunological profile of TNBC has led to the use of immune checkpoint inhibitors and targeted agents, including PARP inhibitors, further revolutionizing the therapeutic landscape of this clinical entity. However, subgroups most likely to benefit from these novel approaches in TNBC remain elusive and are being extensively studied. In this review, we describe current practices and promising therapeutic options on the horizon for TNBC, surgical advances, and future trends in molecular determinants of response to therapy in early-stage TNBC.

Genomic characteristics of triple negative apocrine carcinoma: a comparison to triple negative breast cancer

Apocrine carcinoma is a rare breast cancer subtype. As such, the genomic characteristics of apocrine carcinoma with triple negative immunohistochemical results (TNAC), which has been treated as triple negative breast cancer (TNBC), have not been revealed. In this study, we evaluated the genomic characteristics of TNAC compared to TNBC with low Ki-67 (LK-TNBC). In the genetic analysis of 73 TNACs and 32 LK-TNBCs, the most frequently mutated driver gene in TNAC was TP53 (16/56, 28.6%), followed by PIK3CA (9/56, 16.1%), ZNF717 (8/56, 14.3%), and PIK3R1 (6/56, 10.71%). Mutational signature analysis showed enrichment of defective DNA mismatch repair (MMR)-related signatures (SBS6 and SBS21) and the SBS5 signature in TNAC, whereas an APOBEC activity-associated mutational signature (SBS13) was more prominent in LK-TNBC (Student's t test, p < 0.05). In intrinsic subtyping, 38.4% of TNACs were classified as luminal A, 27.4% as luminal B, 26.0% as HER2-enriched (HER2-E), 2.7% as basal, and 5.5% as normal-like. The basal subtype was the most dominant subtype (43.8%) in LK-TNBC (p < 0.001), followed by luminal B (21.9%), HER2-E (21.9%), and luminal A (12.5%). In the survival analysis, TNAC had a five-year disease-free survival (DFS) rate of 92.2% compared to 59.1% for LK-TNBC (P = 0.001) and a five-year overall survival (OS) rate of 95.3% compared to 74.6% for LK-TNBC (P = 0.0099). TNAC has different genetic characteristics and better survival outcomes than LK-TNBC. In particular, normal-like and luminal A subtypes in TNAC have much better DFS and OS than other intrinsic subtypes. Our findings are expected to impact medical practice for patients diagnosed with TNAC.

Next-Generation Sequencing and Triple-Negative Breast Cancer: Insights and Applications

The poor survival of triple-negative breast cancer (TNBC) is due to its aggressive behavior, large heterogeneity, and high risk of recurrence. A comprehensive molecular investigation of this type of breast cancer using high-throughput next-generation sequencing (NGS) methods may help to elucidate its potential progression and discover biomarkers related to patient survival. In this review, the NGS applications in TNBC research are described. Many NGS studies point to TP53 mutations, immunocheckpoint response genes, and aberrations in the PIK3CA and DNA repair pathways as recurrent pathogenic alterations in TNBC. Beyond their diagnostic and predictive/prognostic value, these findings suggest potential personalized treatments in PD -L1-positive TNBC or in TNBC with a homologous recombination deficit. Moreover, the comprehensive sequencing of large genomes with NGS has enabled the identification of novel markers with clinical value in TNBC, such as AURKA, MYC, and JARID2 mutations. In addition, NGS investigations to explore ethnicity-specific alterations have pointed to EZH2 overexpression, BRCA1 alterations, and a BRCA2-delaAAGA mutation as possible molecular signatures of African and African American TNBC. Finally, the development of long-read sequencing methods and their combination with optimized short-read techniques promise to improve the efficiency of NGS approaches for future massive clinical use.

Development of Cyclic Peptides Targeting the Epidermal Growth Factor Receptor in Mesenchymal Triple-Negative Breast Cancer Subtype

Triple-negative breast cancer (TNBC) is an aggressive malignancy characterized by the lack of expression of estrogen and progesterone receptors and amplification of human epidermal growth factor receptor 2 (HER2). Being the Epidermal Growth Factor Receptor (EGFR) highly expressed in mesenchymal TNBC and correlated with aggressive growth behavior, it represents an ideal target for anticancer drugs. Here, we have applied the phage display for selecting two highly specific peptide ligands for targeting the EGFR overexpressed in MDA-MB-231 cells, a human TNBC cell line. Molecular docking predicted the peptide-binding affinities and sites in the extracellular domain of EGFR. The binding of the FITC-conjugated peptides to human and murine TNBC cells was validated by flow cytometry. Confocal microscopy confirmed the peptide binding specificity to EGFR-positive MDA-MB-231 tumor xenograft tissues and their co-localization with the membrane EGFR. Further, the peptide stimulation did not affect the cell cycle of TNBC cells, which is of interest for their utility for tumor targeting. Our data indicate that these novel peptides are highly specific ligands for the EGFR overexpressed in TNBC cells, and thus they could be used in conjugation with nanoparticles for tumor-targeted delivery of anticancer drugs.

Facts and Perspectives: Implications of tumor glycolysis on immunotherapy response in triple negative breast cancer

Triple negative breast cancer (TNBC) is an aggressive disease that is difficult to treat and portends a poor prognosis in many patients. Recent efforts to implement immune checkpoint inhibitors into the treatment landscape of TNBC have led to improved outcomes in a subset of patients both in the early stage and metastatic settings. However, a large portion of patients with TNBC remain resistant to immune checkpoint inhibitors and have limited treatment options beyond cytotoxic chemotherapy. The interplay between the anti-tumor immune response and tumor metabolism contributes to immunotherapy response in the preclinical setting, and likely in the clinical setting as well. Specifically, tumor glycolysis and lactate production influence the tumor immune microenvironment through creation of metabolic competition with infiltrating immune cells, which impacts response to immune checkpoint blockade. In this review, we will focus on how glucose metabolism within TNBC tumors influences the response to immune checkpoint blockade and potential ways of harnessing this information to improve clinical outcomes.

Breast cancer heterogeneity and its implication in personalized precision therapy

Breast cancer heterogeneity determines cancer progression, treatment effects, and prognosis. However, the precise mechanism for this heterogeneity remains unknown owing to its complexity. Here, we summarize the origins of breast cancer heterogeneity and its influence on disease progression, recurrence, and therapeutic resistance. We review the possible mechanisms of heterogeneity and the research methods used to analyze it. We also highlight the importance of cell interactions for the origins of breast cancer heterogeneity, which can be further categorized into cooperative and competitive interactions. Finally, we provide new insights into precise individual treatments based on heterogeneity.

A Clinical Cognizance of Molecular and Pathological Diagnostic Approach of TNBC

Genetic, transcriptional, and clinical heterogeneity of disease has remained to be a prominent obstacle to the development of a targeted therapeutic approach against TNBC. So far, based on tumor size, lymph node status, and histologic features TNBC subtypes were stratified. Insights into inter and intratumoral heterogeneity of TNBC were gained by next-generation sequencing, genomic, transcriptomic, proteomic, and clinicopathological characterization. To depict tumor response to neoadjuvant chemotherapy, radiological characterization may also a play significant role. Biomarkers for subtyping TNBC were highly needed to depict the survival outcome. This chapter discussed the available and possible molecular and pathological diagnostic approaches to TNBC. Furthermore, the integration of morphological and genomic data may emerge as a promising approach for the identification of new therapeutic and prognostic markers to predict the likely outcome of the disease. This chapter aims to highlight the molecular and pathological diagnostic approaches to depict both metastatic and non-metastatic TNBC.&nbsp;<br>

Pathological changes in GPCR signal organisation: Opportunities for targeted therapies for triple negative breast cancer

Triple negative breast cancer (TNBC) has the poorest prognosis compared to other breast cancer subtypes, due to a historical lack of targeted therapies and high rates of relapse. Greater insight into the components of signalling pathways in TNBC tumour cells has led to the clinical evaluation, and in some cases approval, of targeted therapies. In the last decade, G protein-coupled receptors, such as the β₂-adrenoceptor, have emerged as potential new therapeutic targets. Here, we describe how the β₂-adrenoceptor accelerates TNBC progression in response to stress, and the unique signalling pathway activated by the β₂-adrenoceptor to drive the invasion of an aggressive TNBC tumour cell. We highlight evidence that supports an altered organisation of GPCRs in tumour cells, and suggests that activation of the same GPCR in a different cellular location can control unique cell responses. Finally, we speculate how the relocation of GPCRs to the "wrong" place in tumour cells presents opportunities to develop targeted anti-cancer GPCR drugs with greater efficacy and minimal adverse effects.

Integrative network analysis reveals subtype-specific long non-coding RNA regulatory mechanisms in head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSC) is one of most common malignancies with high mortality worldwide. Importantly, the molecular heterogeneity of HNSC complicates the clinical diagnosis and treatment, leading to poor overall survival outcomes. To dissect the complex heterogeneity, recent studies have reported multiple molecular subtyping systems. For instance, HNSC can be subdivided to four distinct molecular subtypes: atypical, basal, classical, and mesenchymal, of which the mesenchymal subtype is characterized by upregulated epithelial-mesenchymal transition (EMT) and associated with poorer survival outcomes. Despite a wealth of studies into the complex molecular heterogeneity, the regulatory mechanism specific to this aggressive subtype remain largely unclear. Herein, we developed a network-based bioinformatics framework that integrates lncRNA and mRNA expression profiles to elucidate the subtype-specific regulatory mechanisms. Applying the framework to HNSC, we identified a clinically relevant lncRNA LNCOG as a key master regulator mediating EMT underlying the mesenchymal subtype. Five genes with strong prognostic values, namely ANXA5, ITGA5, CCBE1, P4HA2, and EPHX3, were predicted to be the putative targets of LNCOG and subsequently validated in other independent datasets. By integrative analysis of the miRNA expression profiles, we found that LNCOG may act as a ceRNA to sponge miR-148a-3p thereby upregulating ITGA5 to promote HNSC progression. Furthermore, our drug sensitivity analysis demonstrated that the five putative targets of LNCOG were also predictive of the sensitivities of multiple FDA-approved drugs. In summary, our bioinformatics framework facilitates the dissection of cancer subtype-specific lncRNA regulatory mechanisms, providing potential novel biomarkers for more optimized treatment of HNSC.

Identification of a minimum number of genes to predict triple-negative breast cancer subgroups from gene expression profiles

BACKGROUND

Triple-negative breast cancer (TNBC) is a very heterogeneous disease. Several gene expression and mutation profiling approaches were used to classify it, and all converged to the identification of distinct molecular subtypes, with some overlapping across different approaches. However, a standardised tool to routinely classify TNBC in the clinics and guide personalised treatment is lacking. We aimed at defining a specific gene signature for each of the six TNBC subtypes proposed by Lehman et al. in 2011 (basal-like 1 (BL1); basal-like 2 (BL2); mesenchymal (M); immunomodulatory (IM); mesenchymal stem-like (MSL); and luminal androgen receptor (LAR)), to be able to accurately predict them.

METHODS

Lehman's TNBCtype subtyping tool was applied to RNA-sequencing data from 482 TNBC (GSE164458), and a minimal subtype-specific gene signature was defined by combining two class comparison techniques with seven attribute selection methods. Several machine learning algorithms for subtype prediction were used, and the best classifier was applied on microarray data from 72 Italian TNBC and on the TNBC subset of the BRCA-TCGA data set.

RESULTS

We identified two signatures with the 120 and 81 top up- and downregulated genes that define the six TNBC subtypes, with prediction accuracy ranging from 88.6 to 89.4%, and even improving after removal of the least important genes. Network analysis was used to identify highly interconnected genes within each subgroup. Two druggable matrix metalloproteinases were found in the BL1 and BL2 subsets, and several druggable targets were complementary to androgen receptor or aromatase in the LAR subset. Several secondary drug-target interactions were found among the upregulated genes in the M, IM and MSL subsets.

CONCLUSIONS

Our study took full advantage of available TNBC data sets to stratify samples and genes into distinct subtypes, according to gene expression profiles. The development of a data mining approach to acquire a large amount of information from several data sets has allowed us to identify a well-determined minimal number of genes that may help in the recognition of TNBC subtypes. These genes, most of which have been previously found to be associated with breast cancer, have the potential to become novel diagnostic markers and/or therapeutic targets for specific TNBC subsets.