Genomic Characteristics of Triple-Negative Breast Cancer Nominate Molecular Subtypes That Predict Chemotherapy Response

Advancements in triple-negative breast cancer sub-typing, diagnosis and treatment with assistance of artificial intelligence : a focused review

Triple negative breast cancer (TNBC) is most aggressive type of breast cancer with multiple invasive sub-types and leading cause of women's death worldwide. Lack of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER-2) causes it to spread rapidly making its treatment challenging due to unresponsiveness towards anti-HER and endocrine therapy. Hence, needing advanced therapeutic treatments and strategies in order to get better recovery from TNBC. Artificial intelligence (AI) has been emerged by giving its high inputs in the automated diagnosis as well as treatment of several diseases, particularly TNBC. AI based TNBC molecular sub-typing, diagnosis as well as therapeutic treatment has become successful now days. Therefore, present review has reviewed recent advancements in the role and assistance of AI particularly focusing on molecular sub-typing, diagnosis as well as treatment of TNBC. Meanwhile, advantages, certain limitations and future implications of AI assistance in the TNBC diagnosis and treatment are also discussed in order to fully understand readers regarding this issue.

Living Biobanks of Organoids: Valuable Resource for Translational Research

The emergence of organoids is considered a revolutionary model, changing the landscape of traditional translational research. These three-dimensional miniatures of human organs or tissues, cultivated from stem cells or biospecimens obtained from patients, faithfully replicate the structural and functional characteristics of specific target organs or tissues. In this extensive review, we explore the profound impact of organoids and assess the current state of living organoid biobanks, which are essential repositories for cryopreserving organoids derived from a variety of diseases. These resources hold significant value for translational research. We delve into the diverse origins of organoids, the underlying technologies, and their roles in recapitulating human development, disease modeling, as well as their potential applications in the pharmaceutical field. With a particular emphasis on biobanking organoids for prospective applications, we discuss how these advancements expedite the transition from bench to bedside translational research, thereby fostering personalized medicine and enriching our comprehension of human health.

Organoid models: the future companions of personalized drug development

High failure rates of the current drug development process are driving exemplary changes toward methodologies centered on human diseasein-vitromodeling. Organoids are self-organized tissue sub-units resembling their organ of origin and are widely acknowledged for their unique potential in recapitulating human physio-pathological mechanisms. They are transformative for human health by becoming the platform of choice to probe disease mechanisms and advance new therapies. Furthermore, the compounds' validation as therapeutics represents another point of the drug development pipeline where organoids may provide key understandings and help pharma organizations replace or reduce animal research. In this review, we focus on gastrointestinal organoid models, which are currently the most advanced organoid models in drug development. We focus on experimental validations of their value, and we propose avenues to enhance their use in drug discovery and development, as well as precision medicine and diagnostics.

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.

Ultrasound Radiomics Features to Identify Patients With Triple-Negative Breast Cancer: A Retrospective, Single-Center Study

OBJECTIVES

Patients with triple-negative breast cancer (TNBC) exhibit a fast tumor growth rate and poor survival outcomes. In this study, we aimed to develop and compare intelligent algorithms using ultrasound radiomics features in addition to clinical variables to identify patients with TNBC prior to histopathologic diagnosis.

METHODS

We used single-center, retrospective data of patients who underwent ultrasound before histopathologic verification and subsequent neoadjuvant systemic treatment (NAST). We developed a logistic regression with an elastic net penalty algorithm using pretreatment ultrasound radiomics features in addition to patient and tumor variables to identify patients with TNBC. Findings were compared to the histopathologic evaluation of the biopsy specimen. The main outcome measure was the area under the curve (AUC).

RESULTS

We included 1161 patients, 813 in the development set and 348 in the validation set. Median age was 50.1 years and 24.4% (283 of 1161) had TNBC. The integrative model using radiomics and clinical information showed significantly better performance in identifying TNBC compared to the radiomics model (AUC: 0.71, 95% confidence interval [CI]: 0.65-0.76 versus 0.64, 95% CI: 0.57-0.71, P = .004). The five most important variables were cN status, shape surface volume ratio (SA:V), gray level co-occurrence matrix (GLCM) correlation, gray level dependence matrix (GLDM) dependence nonuniformity normalized, and age. Patients with TNBC were more often categorized as BI-RADS 4 than BI-RADS 5 compared to non-TNBC patients (P = .002).

CONCLUSION

A machine learning algorithm showed promising potential to identify patients with TNBC using ultrasound radiomics features and clinical information prior to histopathologic evaluation.

More Than Meets the Eye: A Case of Breast Cancer Switching from Being Luminal-Androgen-Receptor-Positive to Being Hormone-Receptor-Positive

Triple-negative breast cancer (TNBC) represents about 15% of all breast cancers and is usually characterized by aggressive clinical behavior and a poor prognosis. Four TNBC subgroups have been previously defined with different molecular profiles: (i) luminal androgen receptor (LAR), (ii) mesenchymal (MES), (iii) basal-like immunosuppressed (BLIS) and (iv) basal-like immune-activated (BLIA). Among these, LAR is characterized by the expression of the androgen receptor (AR), and exhibits genomic characteristics that resemble luminal breast cancers, with a still undefined prognosis and clinical behavior. Here, we report a case of a woman affected by recurring LAR TNBC, which underwent phenotypic changes throughout its natural history. After the initial diagnosis of LAR breast cancer, the patient experienced local recurrence with strong expression of the estrogen receptor. Due to this finding, she started treatment with a CDK4/6-inhibitor and an aromatase inhibitor, followed by oral vinorelbine, both with dismal outcomes. Then, she received everolimus and exemestane, which determined temporary disease stabilization. An extensive NGS analysis of tumor tissue showed PIK3CA and HER2 mutations. Our case is consistent with previous reports of LAR breast cancer and underlines the potential utility of re-biopsy and molecular testing in breast cancer (BC), especially in rare subtypes.

ER Negative Breast Cancer and miRNA: There Is More to Decipher Than What the Pathologist Can See!

Breast cancer (BC), the most prevalent cancer in women, is a heterogenous disease. Despite advancements in BC diagnosis, prognosis, and therapeutics, survival rates have drastically decreased in the metastatic setting. Therefore, BC still remains a medical challenge. The evolution of high-throughput technology has highlighted gaps in the classification system of BCs. Of particular interest is the notorious triple negative BC, which was recounted as being heterogenous itself and it overlaps with distinct subtypes, namely molecular apocrine (MA) and luminal androgen (LAR) BCs. These subtypes are, even today, still misdiagnosed and poorly treated. As such, researchers and clinicians have been looking for ways through which to refine BC classification in order to properly understand the initiation, development, progression, and the responses to the treatment of BCs. One tool is biomarkers and, specifically, microRNA (miRNA), which are highly reported as associated with BC carcinogenesis. In this review, the diverse roles of miRNA in estrogen receptor negative (ER-) and androgen receptor positive (AR+) BC are depicted. While highlighting their oncogenic and tumor suppressor functions in tumor progression, we will discuss their diagnostic, prognostic, and predictive biomarker potentials, as well as their drug sensitivity/resistance activity. The association of several miRNAs in the KEGG-reported pathways that are related to ER-BC carcinogenesis is presented. The identification and verification of accurate miRNA panels is a cornerstone for tackling BC classification setbacks, as is also the deciphering of the carcinogenesis regulators of ER - AR + BC.

Artificial intelligence: opportunities and challenges in the clinical applications of triple-negative breast cancer

Triple-negative breast cancer (TNBC) accounts for 15-20% of all invasive breast cancer subtypes. Owing to its clinical characteristics, such as the lack of effective therapeutic targets, high invasiveness, and high recurrence rate, TNBC is difficult to treat and has a poor prognosis. Currently, with the accumulation of large amounts of medical data and the development of computing technology, artificial intelligence (AI), particularly machine learning, has been applied to various aspects of TNBC research, including early screening, diagnosis, identification of molecular subtypes, personalised treatment, and prediction of prognosis and treatment response. In this review, we discussed the general principles of artificial intelligence, summarised its main applications in the diagnosis and treatment of TNBC, and provided new ideas and theoretical basis for the clinical diagnosis and treatment of TNBC.

Tumor organoid biobank-new platform for medical research

Organoids are a new type of 3D model for tumor research, which makes up for the shortcomings of cell lines and xenograft models, and promotes the development of personalized precision medicine. Long-term culture, expansion and storage of organoids provide the necessary conditions for the establishment of biobanks. Biobanks standardize the collection and preservation of normal or pathological specimens, as well as related clinical information. The tumor organoid biobank has a good quality control system, which is conducive to the clinical transformation and large-scale application of tumor organoids, such as disease modeling, new drug development and high-throughput drug screening. This article summarized the common tumor types of patient-derived organoid (PDO) biobanks and the necessary information for biobank construction, such as the number of organoids, morphology, success rate of culture and resuscitation, pathological types. In our results, we found that patient-derived tumor organoid (PDTO) biobanks were being established more and more, with the Netherlands, the United States, and China establishing the most. Biobanks of colorectal, pancreas, breast, glioma, and bladder cancers were established more, which reflected the relative maturity of culture techniques for these tumors. In addition, we provided insights on the precautions and future development direction of PDTO biobank building.

Leveraging diverse cell-death patterns to predict the prognosis and drug sensitivity of triple-negative breast cancer patients after surgery

BACKGROUND

Postoperative progression and chemotherapy resistance is the major cause of treatment failure in patients with triple-negative breast cancer (TNBC). Currently, there is a lack of an ideal predictive model for the progression and drug sensitivity of postoperative TNBC patients. Diverse programmed cell death (PCD) patterns play an important role in tumor progression, which has the potential to be a prognostic and drug sensitivity indicator for TNBC after surgery.

MATERIALS AND METHODS

Twelve PCD patterns (apoptosis, necroptosis, pyroptosis, ferroptosis, cuproptosis, entotic cell death, netotic cell death, parthanatos, lysosome-dependent cell death, autophagy-dependent cell death, alkaliptosis, and oxeiptosis) were analyzed for model construction. Bulk transcriptome, single-cell transcriptome, genomics, and clinical information were collected from TCGA-BRCA, METABRIC, GSE58812, GSE21653, GSE176078, GSE75688, and KM-plotter cohorts to validate the model.

RESULTS

The machine learning algorithm established a cell death index (CDI) with a 12-gene signature. Validated in five independent datasets, TNBC patients with high CDI had a worse prognosis after surgery. Two molecular subtypes of TNBC with distinct vital biological processes were identified by an unsupervised clustering model. A nomogram with high predictive performance was constructed by incorporating CDI with clinical features. Furthermore, CDI was associated with immune checkpoint genes and key tumor microenvironment components by integrated analysis of bulk and single-cell transcriptome. TNBC patients with high CDI are resistant to standard adjuvant chemotherapy regimens (docetaxel, oxaliplatin, etc.); however, they might be sensitive to palbociclib (an FDA-approved drug for luminal breast cancer).

CONCLUSION

Generally, we established a novel CDI model by comprehensively analyzing diverse cell death patterns, which can accurately predict clinical prognosis and drug sensitivity of TNBC after surgery. A user-friendly website was created to facilitate the application of this prediction model (https://tnbc.shinyapps.io/CDI_Model/).

Achillea fragrantissima (Forssk.) Sch.Bip Flower Dichloromethane Extract Exerts Anti-Proliferative and Pro-Apoptotic Properties in Human Triple-Negative Breast Cancer (MDA-MB-231) Cells: In Vitro and In Silico Studies

The aggressive triple-negative breast cancer (TNBC) is a challenging disease due to the absence of tailored therapy. The search for new therapies involves intensive research focusing on natural sources. Achillea fragrantissima (A. fragrantissima) is a traditional medicine from the Middle East region. Various solvent extracts from different A. fragrantissima plant parts, including flowers, leaves, and roots, were tested on TNBC MDA-MB-231 cells. Using liquid chromatography, the fingerprinting revealed rich and diverse compositions for A. fragrantissima plant parts using polar to non-polar solvent extracts indicating possible differences in bioactivities. Using the CellTiter-Glo™ viability assay, the half-maximal inhibitory concentration (IC₅₀) values were determined for each extract and ranged from 32.4 to 161.7 µg/mL. The A. fragrantissima flower dichloromethane extract had the lowest mean IC₅₀ value and was chosen for further investigation. Upon treatment with increasing A. fragrantissima flower dichloromethane extract concentrations, the MDA-MB-231 cells displayed, in a dose-dependent manner, enhanced morphological and biochemical hallmarks of apoptosis, including cell shrinkage, phosphatidylserine exposure, caspase activity, and mitochondrial outer membrane permeabilization, assessed using phase-contrast microscopy, fluorescence-activated single-cell sorting analysis, Image-iT™ live caspase, and mitochondrial transition pore opening activity, respectively. Anticancer target prediction and molecular docking studies revealed the inhibitory activity of a few A. fragrantissima flower dichloromethane extract-derived metabolites against carbonic anhydrase IX, an enzyme reported for its anti-apoptotic properties. In conclusion, these findings suggest promising therapeutic values of the A. fragrantissima flower dichloromethane extract against TNBC development.

PSAT1 gene as a biomarker for targeting triple negative breast cancer in presence of Rapamycin

The Triple-Negative Breast Cancers (TNBCs) poor survival outcomes are related to not responding to hormonal therapy or targeting HER2 protein. The aim of this study was to introduce a drug among the conventional chemotherapy drugs based on the transcripts of the TNBC patients. The transcriptome of MDA-MB-468 cell line as a TNBC model was analyzed in the presence of 17 common drugs in fifteen GEO datasets. Then, decreased gene lists in the presence of each drug were compared in different subtypes of breast cancer based on TCGA data. The effect of selected drug on survival and apoptosis rates of MDA-MB-468 and MCF-7 cells (as ER+/PR + model) were compared. The outcomes showed that the expression of the decreased gene by Rapamycin among other drugs increased in TNBC subtype compared to the other two subtypes including triple-positive and ER+/PR+. The expression of PSAT1 in TNBC group was significant higher than normal and other subtypes (p ≤ 0.0001), and was introduced as a biomarker in TNBC that its expression was down-regulated by Rapamycin based on in silico studies. In the presence of 3000 µg/mL Rapamycin, there was a sharp decrease in MDA-MB-468 cell viability (p ≤ 0.0001) and a significant increase in apoptosis compared to MCF-7 cells treated with the same concentration of the drug. Moreover, RT-qPCR revealed PSAT1 expression more decreased under Rapamycin-treatment in TNBC cells compared to ER + PR + cells. The results of this study show that Rapamycin is a suitable drug for targeting TNBC based on its transcriptome and biomarker.Supplemental data for this article is available online at.

SMARCA4 Depletion Induces Cisplatin Resistance by Activating YAP1-Mediated Epithelial-to-Mesenchymal Transition in Triple-Negative Breast Cancer

Simple Summary

SMARCA4 mutations were over-representative in cisplatin resistance and metastatic triple-negative breast cancer (TNBC). Additionally, SMARCA4 inactivation induced the mesenchymal-like subtype TNBC. The epithelial-to-mesenchymal transition and Hippo-YAP/TAZ pathways were activated in SMARCA4 inactivation samples of both SMARCA4 knockout cell lines and TNBC patients. In SMARCA4 knockout cells, the YAP1 inhibitor verteporfin suppressed YAP1 target genes. This study depicts the clinical importance of SMARCA4 depletion in TNBC and suggests YAP/TAZ as a novel target for cisplatin-resistant patients.

Abstract

The role of SMARCA4, an ATPase subunit of the SWI/SNF chromatin remodeling complex, in genomic organization is well studied in various cancer types. However, its oncogenic role and therapeutic implications are relatively unknown in triple-negative breast cancer (TNBC). We investigated the clinical implication and downstream regulation induced by SMARCA4 inactivation using large-scale genome and transcriptome profiles. Additionally, SMARCA4 was knocked out in MDA-MB-468 and MDA-MB-231 using CRISPR/Cas9 to identify gene regulation and a targetable pathway. First, we observed an increase in SMARCA4 mutations in cisplatin resistance and metastasis in TNBC patients. Its inactivation was associated with the mesenchymal-like (MSL) subtype. Gene expression analysis showed that the epithelial-to-mesenchymal transition (EMT) pathway was activated in SMARCA4-deficient patients. Next, the Hippo pathway was activated in the SMARCA4 inactivation group, as evidenced by the higher CTNNB1, TGF-β, and YAP1 oncogene signature scores. In SMARCA4 knockout cells, EMT was upregulated, and the cell line transcriptome changed from the SL to the MSL subtype. SMARCA4 knockout cells showed cisplatin resistance and Hippo-YAP/TAZ target gene activation. The YAP1 inhibitor verteporfin suppressed the expression of YAP1 target genes, and decreased cell viability and invasiveness on SMARCA4 knockout cells. SMARCA4 inactivation in TNBC endowed the resistance to cisplatin via EMT activation. The YAP1 inhibitor could become a novel strategy for patients with SMARCA4-inactivated TNBC.

Organoid Models for Cancer Research-From Bed to Bench Side and Back

Organoids are a new 3D ex vivo culture system that have been applied in various fields of biomedical research. First isolated from the murine small intestine, they have since been established from a wide range of organs and tissues, both in healthy and diseased states. Organoids genetically, functionally and phenotypically retain the characteristics of their tissue of origin even after multiple passages, making them a valuable tool in studying various physiologic and pathophysiologic processes. The finding that organoids can also be established from tumor tissue or can be engineered to recapitulate tumor tissue has dramatically increased their use in cancer research. In this review, we discuss the potential of organoids to close the gap between preclinical in vitro and in vivo models as well as clinical trials in cancer research focusing on drug investigation and development.

An Immune Model to Predict Prognosis of Breast Cancer Patients Receiving Neoadjuvant Chemotherapy Based on Support Vector Machine

Tumor microenvironment has been increasingly proved to be crucial during the development of breast cancer. The theory about the conversion of cold and hot tumor attracted the attention to the influences of traditional therapeutic strategies on immune system. Various genetic models have been constructed, although the relation between immune system and local microenvironment still remains unclear. In this study, we tested and collected the immune index of 262 breast cancer patients before and after neoadjuvant chemotherapy. Five indexes were selected and analyzed to form the prediction model, including the ratio values between after and before neoadjuvant chemotherapy of CD4⁺/CD8⁺ T cell ratio; lymphosum of T, B, and natural killer (NK) cells; CD3⁺CD8⁺ cytotoxic T cell percent; CD16⁺CD56⁺ NK cell absolute value; and CD3⁺CD4⁺ helper T cell percent. Interestingly, these characters are both the ratio value of immune status after neoadjuvant chemotherapy to the baseline. Then the prediction model was constructed by support vector machine (accuracy rate = 75.71%, area under curve = 0.793). Beyond the prognostic effect and prediction significance, the study instead emphasized the importance of immune status in traditional systemic therapies. The result provided new evidence that the dynamic change of immune status during neoadjuvant chemotherapy should be paid more attention.

Immunotherapy for the Breast Cancer treatment: Current Evidence and Therapeutic Options

Breast cancer (BC) stands at the first position among all forms of malignancies found in women globally. The available therapeutic approaches for breast cancer includes chemotherapy, radiation therapy, hormonal therapy and finally surgery. Despite the conventional therapies, in recent years the advance immunology based therapeutics emerge a potential in breast cancer treatment, including immune checkpoint blockades, vaccines and in combination with other treatment strategies. Although, commonly used treatments like trastuzumab/pertuzumab for human epidermal growth factor receptor 2 (Her2) positive and hormone therapy for estrogen receptor (ER) positive and/or progesterone receptor (PR) positive BC are specific but triple negative breast cancer (TNBC) cases remain a great challenge for treatment measures. Immune checkpoint inhibitors (anti-PD-1/ anti-CTLA-4) and anti-cancer vaccines (NeuVax, Muc-1, AVX901, INO-1400 and CEA), either alone or in combination with other therapies have created new paradigm in therapeutic world. In this review, we highlighted the current immunotherapeutic aspects and their ongoing trials towards the better treatment regimen for BC.

CRISPR/Cas9 based genome editing for targeted transcriptional control in triple-negative breast cancer

Breast cancer (BC) is the most common type of cancer in women at the global level and the highest mortality rate has been observed with triple-negative breast cancer (TNBC). Accumulation of genetic lesions an aberrant gene expression and protein degradation are considered to underlie the onset of tumorigenesis and metastasis. Therefore, the challenge to identify the genes and molecules that could be potentially used as potent biomarkers for personalized medicine against TNBC with minimal or no associated side effects. Discovery of the clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR associated protein 9 (Cas9) arrangement and an increasing repertoire of its new variants has provided a much-needed fillip towards editing TNBC genomes. In this review, we discuss the CRISPR/Cas9 genome editing, CRISPR Technology for diagnosis of (Triple-negative breast cancer) TNBC, Drug Resistance, and potential applications of CRISPR/Cas9 and its variants in deciphering or engineering intricate molecular and epigenetic mechanisms associated with TNBC. Furthermore, we have also explored the TNBC and CRISPR/Cas9 genome editing potential for repairing, genetic modifications in TNBC.

Clinical Implications of Androgen-Positive Triple-Negative Breast Cancer

This review summarizes the recent findings of a vast array of studies conducted on androgen receptor-positive triple-negative breast cancer (AR-positive TNBC) to provide a better understanding of this specific breast cancer subgroup. AR expression is correlated with higher age, lower histological grade, lower proliferation index Ki-67, spiculated masses, and calcifications on mammography. Studies investigating the correlation between AR expression and lymph node metastasis are highly discordant. In addition, results regarding prognosis are highly contradictory. AR antagonists are a promising novel therapeutic approach in AR-positive TNBC. However, AR signaling pathways should be more investigated in order to understand the influence of AR expression on TNBC more thoroughly.

Organoid of ovarian cancer: genomic analysis and drug screening

Ovarian cancer is the most common malignant tumors of the female reproductive system, and its standard treatments are cytoreductive surgery and platinum-based adjuvant chemotherapy. Great advances have been achieved in novel treatment strategies, including targeted therapy and immunotherapy. However, ovarian cancer has the highest mortality rate among gynecological tumors due to therapeutic resistance and the gap between preclinical data and actual clinical efficacy. Organoids are a 3D culture model that markedly affects gene analysis, drug screening, and drug sensitivity determination of tumors, especially when used in targeted therapy and immunotherapy. In addition, organoid can lead to advances in the preclinical research of ovarian cancer due to its convenient cultivation, good genetic stability, and high homology with primary tumors.