Network Analysis Identifies Regulators of Basal-Like Breast Cancer Reprogramming and Endocrine Therapy Vulnerability

Network-based approach elucidates critical genes in BRCA subtypes and chemotherapy response in triple negative breast cancer

Breast cancers (BRCA) exhibit substantial transcriptional heterogeneity, posing a significant clinical challenge. The global transcriptional changes in a disease context, however, are likely mediated by few key genes which reflect disease etiology better than the differentially expressed genes (DEGs). We apply our network-based tool PathExt to 1,059 BRCA tumors across 4 subtypes to identify key mediator genes in each subtype. Compared to conventional differential expression analysis, PathExt-identified genes exhibit greater concordance across tumors, revealing shared and subtype-specific biological processes; better recapitulate BRCA-associated genes in multiple benchmarks, and are more essential in BRCA subtype-specific cell lines. Single-cell transcriptomic analysis reveals a subtype-specific distribution of PathExt-identified genes in multiple cell types from the tumor microenvironment. Application of PathExt to a TNBC chemotherapy response dataset identified subtype-specific key genes and biological processes associated with resistance. We described putative drugs that target key genes potentially mediating drug resistance.

Decoding the principle of cell-fate determination for its reverse control

Understanding and manipulating cell fate determination is pivotal in biology. Cell fate is determined by intricate and nonlinear interactions among molecules, making mathematical model-based quantitative analysis indispensable for its elucidation. Nevertheless, obtaining the essential dynamic experimental data for model development has been a significant obstacle. However, recent advancements in large-scale omics data technology are providing the necessary foundation for developing such models. Based on accumulated experimental evidence, we can postulate that cell fate is governed by a limited number of core regulatory circuits. Following this concept, we present a conceptual control framework that leverages single-cell RNA-seq data for dynamic molecular regulatory network modeling, aiming to identify and manipulate core regulatory circuits and their master regulators to drive desired cellular state transitions. We illustrate the proposed framework by applying it to the reversion of lung cancer cell states, although it is more broadly applicable to understanding and controlling a wide range of cell-fate determination processes.

The metabolism-related lncRNA signature predicts the prognosis of breast cancer patients

Long non-coding RNAs (lncRNAs) involved in metabolism are recognized as significant factors in breast cancer (BC) progression. We constructed a novel prognostic signature for BC using metabolism-related lncRNAs and investigated their underlying mechanisms. The training and validation cohorts were established from BC patients acquired from two public sources: The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The prognostic signature of metabolism-related lncRNAs was constructed using the least absolute shrinkage and selection operator (LASSO) cox regression analysis. We developed and validated a new prognostic risk model for BC using the signature of metabolism-related lncRNAs (SIRLNT, SIAH2-AS1, MIR205HG, USP30-AS1, MIR200CHG, TFAP2A-AS1, AP005131.2, AL031316.1, C6orf99). The risk score obtained from this signature was proven to be an independent prognostic factor for BC patients, resulting in a poor overall survival (OS) for individuals in the high-risk group. The area under the curve (AUC) for OS at three and five years were 0.67 and 0.65 in the TCGA cohort, and 0.697 and 0.68 in the GEO validation cohort, respectively. The prognostic signature demonstrated a robust association with the immunological state of BC patients. Conventional chemotherapeutics, such as docetaxel and paclitaxel, showed greater efficacy in BC patients classified as high-risk. A nomogram with a c-index of 0.764 was developed to forecast the survival time of BC patients, considering their risk score and age. The silencing of C6orf99 markedly decreased the proliferation, migration, and invasion capacities in MCF-7 cells. Our study identified a signature of metabolism-related lncRNAs that predicts outcomes in BC patients and could assist in tailoring personalized prevention and treatment plans.

Treatments Targeting the Androgen Receptor and Its Splice Variants in Breast Cancer

Breast cancer is a major cause of death worldwide. The complexity of endocrine regulation in breast cancer may allow the cancer cells to escape from a particular treatment and result in resistant and aggressive disease. These breast cancers usually have fewer treatment options. Targeted therapies for cancer patients may offer fewer adverse side effects because of specificity compared to conventional chemotherapy. Signaling pathways of nuclear receptors, such as the estrogen receptor (ER), have been intensively studied and used as therapeutic targets. Recently, the role of the androgen receptor (AR) in breast cancer is gaining greater attention as a therapeutic target and as a prognostic biomarker. The expression of constitutively active truncated AR splice variants in breast cancer is a possible mechanism contributing to treatment resistance. Therefore, targeting both the full-length AR and AR variants, either through the activation or suppression of AR function, depending on the status of the ER, progesterone receptor, or human epidermal growth factor receptor 2, may provide additional treatment options. Studies targeting AR in combination with other treatment strategies are ongoing in clinical trials. The determination of the status of nuclear receptors to classify and identify patient subgroups will facilitate optimized and targeted combination therapies.

Undifferentially Expressed CXXC5 as a Transcriptionally Regulatory Biomarker of Breast Cancer

This work hypothesizes that some genes undergo radically changed transcription regulations (TRs) in breast cancer (BC), but don't show differential expressions for unknown reasons. The TR of a gene is quantitatively formulated by a regression model between the expression of this gene and multiple transcription factors (TFs). The difference between the predicted and real expression levels of a gene in a query sample is defined as the mqTrans value of this gene, which quantitatively reflects its regulatory changes. This work systematically screens the undifferentially expressed genes with differentially expressed mqTrans values in 1036 samples across five datasets and three ethnic groups. This study calls the 25 genes satisfying the above hypothesis in at least four datasets as dark biomarkers, and the strong dark biomarker gene CXXC5 (CXXC Finger Protein 5) is even supported by all the five independent BC datasets. Although CXXC5 does not show differential expressions in BC, its transcription regulations show quantitative associations with BCs in diversified cohorts. The overlapping long noncoding RNAs (lncRNAs) may have contributed their transcripts to the expression miscalculations of dark biomarkers. The mqTrans analysis serves as a complementary view of the transcriptome-based detections of biomarkers that are ignored by many existing studies.

Unveiling the molecular structure and role of RBBP4/7: implications for epigenetic regulation and cancer research

Retinoblastoma-binding protein (RBBP) family is a class of proteins that can interact with tumor suppressor retinoblastoma protein (pRb). RBBP4 and RBBP7 are the only pair of homologous proteins in this family, serving as scaffold proteins whose main function is to offer a platform to indirectly connect two proteins. This characteristic allows them to extensively participate in the binding of various proteins and epigenetic complexes, indirectly influencing the function of effector proteins. As a result, they are often highlighted in organism activities involving active epigenetic modifications, such as embryonic development and cancer activation. In this review, we summarize the structural characteristics of RBBP4/7, the complexes they are involved in, their roles in embryonic development and cancer, as well as potential future research directions, which we hope to inspire the field of epigenetic research in the future.

MicroRNAs as Molecular Biomarkers for the Characterization of Basal-like Breast Tumor Subtype

Breast cancer is a heterogeneous disease highlighted by the presence of multiple tumor variants and the basal-like breast cancer (BLBC) is considered to be the most aggressive variant with limited therapeutics and a poor prognosis. Though the absence of detectable protein and hormonal receptors as biomarkers hinders early detection, the integration of genomic and transcriptomic profiling led to the identification of additional variants in BLBC. The high-throughput analysis of tissue-specific micro-ribonucleic acids (microRNAs/miRNAs) that are deemed to have a significant role in the development of breast cancer also displayed distinct expression profiles in each subtype of breast cancer and thus emerged to be a robust approach for the precise characterization of the BLBC subtypes. The classification schematic of breast cancer is still a fluid entity that continues to evolve alongside technological advancement, and the transcriptomic profiling of tissue-specific microRNAs is projected to aid in the substratification and diagnosis of the BLBC tumor subtype. In this review, we summarize the current knowledge on breast tumor classification, aim to collect comprehensive evidence based on the microRNA expression profiles, and explore their potential as prospective biomarkers of BLBC.

Potential of Dietary HDAC2i in Breast Cancer Patients Receiving PD-1/PD-L1 Inhibitors

Breast cancer (BC) is a lethal malignancy with high morbidity and mortality but lacks effective treatments thus far. Despite the introduction of immune checkpoint inhibitors (ICIs) (including PD-1/PD-L1 inhibitors), durable and optimal clinical benefits still remain elusive for a considerable number of BC patients. To break through such a dilemma, novel ICI-based combination therapy has been explored for enhancing the therapeutic effect. Recent evidence has just pointed out that the HDAC2 inhibitor (HDAC2i), which has been proven to exhibit an anti-cancer effect, can act as a sensitizer for ICIs therapy. Simultaneously, dietary intervention, as a crucial supportive therapy, has been reported to provide ingredients containing HDAC2 inhibitory activity. Thus, the novel integration of dietary intervention with ICIs therapy may offer promising possibilities for improving treatment outcomes. In this study, we first conducted the differential expression and prognostic analyses of HDAC2 and BC patients using the GENT2 and Kaplan-Meier plotter platform. Then, we summarized the potential diet candidates for such an integrated therapeutic strategy. This article not only provides a whole new therapeutic strategy for an HDAC2i-containing diet combined with PD-1/PD-L1 inhibitors for BC treatment, but also aims to ignite enthusiasm for exploring this field.

Vimentin-positive invasive breast carcinoma of no special type: A breast carcinoma with lethal biological characteristics

Vimentin is a stable mesenchymal immunohistochemical marker and is widely recognized as a major marker of mesenchymal tumors. The purpose of the present study was to investigate if the vimentin expression status might serve as a significant predictor of outcomes in patients with invasive breast carcinoma of no special type (IBC-NST) and to investigate, by comprehensive RNA sequencing analyses, the mechanisms involved in the heightened malignant potential of vimentin-positive IBC-NSTs. This study, conducted using the data of 855 patients with IBC-NST, clearly identified vimentin expression status as a very important independent biological parameter for accurately predicting the outcomes in patients with IBC-NST. RNA sequence analyses clearly demonstrated significant upregulation of coding RNAs known to be closely associated with cell proliferation or cellular senescence, and significant downregulation of coding RNAs known to be closely associated with transmembrane transport in vimentin-positive IBC-NSTs. We conclude that vimentin-positive IBC-NSTs show heightened malignant biological characteristics, possibly attributable to the upregulation of RNAs closely associated with proliferative activity and cellular senescence, and downregulation of RNAs closely associated with transmembrane transport in IBC-NSTs.

Normalizing Input-Output Relationships of Cancer Networks for Reversion Therapy

Accumulated genetic alterations in cancer cells distort cellular stimulus-response (or input-output) relationships, resulting in uncontrolled proliferation. However, the complex molecular interaction network within a cell implicates a possibility of restoring such distorted input-output relationships by rewiring the signal flow through controlling hidden molecular switches. Here, a system framework of analyzing cellular input-output relationships in consideration of various genetic alterations and identifying possible molecular switches that can normalize the distorted relationships based on Boolean network modeling and dynamics analysis is presented. Such reversion is demonstrated by the analysis of a number of cancer molecular networks together with a focused case study on bladder cancer with in vitro experiments and patient survival data analysis. The origin of reversibility from an evolutionary point of view based on the redundancy and robustness intrinsically embedded in complex molecular regulatory networks is further discussed.

Statistical control of structural networks with limited interventions to minimize cellular phenotypic diversity represented by point attractors

The underlying genetic networks of cells give rise to diverse behaviors known as phenotypes. Control of this cellular phenotypic diversity (CPD) may reveal key targets that govern differentiation during development or drug resistance in cancer. This work establishes an approach to control CPD that encompasses practical constraints, including model limitations, the number of simultaneous control targets, which targets are viable for control, and the granularity of control. Cellular networks are often limited to the structure of interactions, due to the practical difficulty of modeling interaction dynamics. However, these dynamics are essential to CPD. In response, our statistical control approach infers the CPD directly from the structure of a network, by considering an ensemble average function over all possible Boolean dynamics for each node in the network. These ensemble average functions are combined with an acyclic form of the network to infer the number of point attractors. Our approach is applied to several known biological models and shown to outperform existing approaches. Statistical control of CPD offers a new avenue to contend with systemic processes such as differentiation and cancer, despite practical limitations in the field.

Critical transition and reversion of tumorigenesis

Cancer is caused by the accumulation of genetic alterations and therefore has been historically considered to be irreversible. Intriguingly, several studies have reported that cancer cells can be reversed to be normal cells under certain circumstances. Despite these experimental observations, conceptual and theoretical frameworks that explain these phenomena and enable their exploration in a systematic way are lacking. In this review, we provide an overview of cancer reversion studies and describe recent advancements in systems biological approaches based on attractor landscape analysis. We suggest that the critical transition in tumorigenesis is an important clue for achieving cancer reversion. During tumorigenesis, a critical transition may occur at a tipping point, where cells undergo abrupt changes and reach a new equilibrium state that is determined by complex intracellular regulatory events. We introduce a conceptual framework based on attractor landscapes through which we can investigate the critical transition in tumorigenesis and induce its reversion by combining intracellular molecular perturbation and extracellular signaling controls. Finally, we present a cancer reversion therapy approach that may be a paradigm-changing alternative to current cancer cell-killing therapies.

Newly Developed Targeted Therapies Against the Androgen Receptor in Triple-Negative Breast Cancer: A Review

Among different types of breast cancers (BC), triple-negative BC (TNBC) amounts to 15% to 20% of breast malignancies. Three principal characteristics of TNBC cells are (i) extreme aggressiveness, (ii) absence of hormones, and (iii) growth factor receptors. Due to the lack or poor expression of the estrogen receptor, human epidermal growth factor receptor 2, and progesterone receptor, TNBC is resistant to hormones and endocrine therapies. Consequently, chemotherapy is currently used as the primary approach against TNBC. Expression of androgen receptor (AR) in carcinoma cells has been observed in a subset of patients with TNBC; therefore, inhibiting androgen signaling pathways holds promise for TNBC targeting. The new AR inhibitors have opened up new therapy possibilities for BC patients carrying AR-positive TNBC cells. Our group provides a comprehensive review of the structure and function of the AR and clinical evidence for targeting the cell's nuclear receptor in TNBC. We updated AR agonists, inhibitors, and antagonists. We also presented a new era of genetic manipulating CRISPR/Cas9 and nanotechnology as state-of-the-art approaches against AR to promote the efficiency of targeted therapy in TNBC. SIGNIFICANCE STATEMENT: The lack of effective treatment for triple-negative breast cancer is a health challenge. The main disadvantages of existing treatments are their side effects, due to their nonspecific targeting. Molecular targeting of cellular receptors, such as androgen receptors, increased expression in malignant tissues, significantly improving the survival rate of breast cancer patients.

The molecular subtypes of triple negative breast cancer were defined and a ligand-receptor pair score model was constructed by comprehensive analysis of ligand-receptor pairs

Background

Intercellular communication mediated by ligand-receptor interactions in tumor microenvironment (TME) has a profound impact on tumor progression. This study aimed to explore the molecular subtypes mediated by ligand-receptor (LR) pairs in triple negative breast cancer (TNBC), identify the most important LR pairs to construct a prognostic risk model, and study their effect on TNBC immunotherapy.

Methods

LR pairs subclasses of TNBC were categorized by consensus clustering based on LR Pairs in METABRIC dataset. Least absolute shrinkage and selection operator (LASSO) Cox regression and stepwise Akaike information criterion (stepAIC) were conducted to build a LR pairs score model. The relationship between LR pairs score and immune cell infiltration, stromal score and immune score associated with TME was analyzed, and the prediction of drug therapy and immunotherapy efficacy by LR pairs score was evaluated.

Results

According to the expression pattern of 145 TNBC prognostic LR pairs, the samples were divided into three subclasses with different survival outcomes, copy number variation (CNV), TME immune cell infiltration, stromal score and immune score. The LR pairs score model constructed in the METABRIC dataset was composed of four LR pairs, and its predictive significance for TNBC prognosis was verified in GSE58812 and GSE21653 cohorts. In addition, LR pairs score was negatively correlated with several immune pathways regulating immunity and immune score, and related to the sensitivity of anti-neoplastic drugs and the effect of anti-PD-L1 therapy.

Conclusion

Our study confirmed the impact of LR pairs on the molecular heterogeneity of TNBC, characterized three LR pairs subtypes with different survival outcomes and TME patterns, and proposed a LR pairs score system with predictive significance for TNBC prognosis and anti-PD-L1 therapeutic effect, which provides a potential evaluation scheme for TNBC management.

Deciphering clinical significance of BCL11A isoforms and protein expression roles in triple-negative breast cancer subtype

Purpose

Triple negative breast cancer (TNBC) is an aggressive clinical tumor, accounting for about 25% of breast cancer (BC) related deaths. Chemotherapy is the only therapeutic option to treat TNBC, hence a detailed understanding of the biology and its categorization is required. To investigate the clinical relevance of BCL11A in TNBC subtype, we focused on gene and protein expression and its mutational status in a large cohort of this molecular subtype.

Methods

Gene expression profiling of BCL11A and its isoforms (BCL11A-XL, BCL11A-L and BCL11A-S) has been determined in Luminal A, Luminal B, HER2-enriched and TNBC subtypes. BCL11A protein expression has been analyzed by immunohistochemistry (IHC) and its mutational status by Sanger sequencing.

Results

In our study, BCL11A was significantly overexpressed in TNBC both at transcriptional and translational levels compared to other BC molecular subtypes. A total of 404 TNBCs were selected and examined showing a high prevalence of BCL11A-XL (37.3%) and BCL11A-L (31.4%) isoform expression in TNBC, associated with a 26% of BCL11A protein expression levels. BCL11A protein expression predicts scarce LIV (HR = 0.52; 95% CI, 0.29–0.92, P = 0.03) and AR downregulation (HR = 0.37; 95% CI, 0.16–0.88; P = 0.02), as well as a higher proliferative index in TNBC cells. BCL11A-L expression is associated with more aggressive TNBC histological types, such as medullary and metaplastic carcinoma.

Conclusion

Our finding showed that BCL11A protein expression acts as an unfavorable prognostic factor in TNBC patients, especially in non luminal TNBCs subgroups. These results may yield a better treatment strategy by providing a new parameter for TNBC classification.

NETISCE: a network-based tool for cell fate reprogramming

The search for effective therapeutic targets in fields like regenerative medicine and cancer research has generated interest in cell fate reprogramming. This cellular reprogramming paradigm can drive cells to a desired target state from any initial state. However, methods for identifying reprogramming targets remain limited for biological systems that lack large sets of experimental data or a dynamical characterization. We present NETISCE, a novel computational tool for identifying cell fate reprogramming targets in static networks. In combination with machine learning algorithms, NETISCE estimates the attractor landscape and predicts reprogramming targets using signal flow analysis and feedback vertex set control, respectively. Through validations in studies of cell fate reprogramming from developmental, stem cell, and cancer biology, we show that NETISCE can predict previously identified cell fate reprogramming targets and identify potentially novel combinations of targets. NETISCE extends cell fate reprogramming studies to larger-scale biological networks without the need for full model parameterization and can be implemented by experimental and computational biologists to identify parts of a biological system relevant to the desired reprogramming task.