Targeted Treatment of Triple-Negative Breast Cancer

Co-targeting CDK 4/6 and C-MYC/STAT3/CCND1 axis and inhibition of tumorigenesis and epithelial-mesenchymal-transition in triple negative breast cancer by Pt(II) complexes bearing NH3 as trans-co-ligand

In search of potential anticancer agents, we synthesized SNO-donor salicylaldimine main ligand-based Pt(II) complexes bearing NH3 as co-ligand at trans-position (C1-C6). These complexes showed similarity in structure with transplatin as the two N donor atoms of the main ligand and NH3 co-ligand were coordinated to Pt in trans position to each other. Each complex with different substituents on the main ligand was characterized thoroughly by detailed spectroscopic and spectrophotometric methods. Four of these complexes were studied in solid state by single crystal X-ray analysis. The stability of reference complex C1 was measured in solution state in DMSO‑d6 or its mixture with D2O using 1H NMR methods. These complexes were further investigated for their anticancer activity in triple-negative-breast (TNBC) cells including MDA-MB-231, MDA-MB-468 and MDA-MB-436 cells. All these complexes showed satisfactory cytotoxic effect as revealed by the MTT results. Importantly, the highly active complex C4 anticancer effect was compared to the standard chemotherapeutic agents including cisplatin, oxaliplatin and 5-fluorouracil (5-FU). Functionally, C4 suppressed invasion, spheroids formation ability and clonogenic potential of cancer cells. C4 showed synergistic anticancer effect when used in combination with palbociclib, JQ1 and paclitaxel in TNBC cells. Mechanistically, C4 inhibited cyclin-dependent kinase (CDK)4/6 pathway and targeted the expressions of MYC/STAT3/CCND1/CNNE1 axis. Furthermore, C4 suppressed the EMT signaling pathway that suggested a role of C4 in the inhibition of TNBC metastasis. Our findings may pave further in detailed mechanistic study on these complexes as potential chemotherapeutic agents in different types of human cancers.

Nanodrug regulates ROS homeostasis via enhancing fatty acid oxidation and inhibiting autophagy to overcome tumor drug resistance

The treatment of drug-resistant tumors poses a significant challenge in the field of tumor therapy. Disrupting the homeostasis of reactive oxygen species (ROS) within tumor cells may represent a pivotal strategy for overcoming the prevalent issue of drug resistance. However, the restricted sustainability of ROS generation and the increased autophagy capacity exhibited by tumor cells hinder the application of ROS-based therapies. In this study, we developed liposome nanoparticles (Ato/CQ@L) for co-encapsulation of atorvastatin (Ato), an activator of AMP-activated protein kinase (AMPK), and chloroquine (CQ), an autophagy inhibitor. Upon internalization by tumor cells, Ato upregulated carnitine palmitoyltransferase 1(CPT1) concentration and promoted fatty acid oxidation (FAO) within the tumor cells. The process of FAO coupled with an abundance of fatty acid substrates, facilitates a sustained generation of ROS production. Concurrently, a positive feedback loop is established between escalated concentration of ROS and AMPK protein levels, resulting in a persistent elevation in ROS levels. In addition, CQ disrupted lysosomes, leading to an increased lysosomal pH and reducing autophagy in tumor cells. In both in vivo and in vitro experiments, the Ato/CQ@L treatment group exhibited a considerable enhancement in tumor cell apoptosis, validating the efficacy of this combined therapy. In summary, the combined therapy involving Ato and CQ addresses the inherent limitations of conventional ROS therapy, which include insufficient ROS production and increased autophagy. This approach holds significant potential as a treatment strategy for drug-resistant triple-negative breast cancer.

Antibody-Drug Conjugates: A New Therapeutic Approach for Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is an aggressive breast cancer subset associated with a worse prognosis and poor response to conventional chemotherapy. Despite recent advances in drug discovery, its management is still a challenge for clinicians, illuminating the unmet need to develop novel treatment approaches. Antibody-drug conjugates (ADC) are innovative oncology drugs that combine the specificity of monoclonal antibodies and the high efficacy of anticancer payloads, to deliver cytotoxic drugs selectively to cancer cells. Various ADCs were investigated for TNBC and have provided a promise for this aggressive women's cancer including the FDA-approved sacituzumab govitecan. In this chapter, we reviewed different ADCs studied for TNBC including their mechanisms of action, efficacy, and tolerability. Moreover, we have also discussed their therapeutic potential based on combinatorial approaches with other targeted therapies in early and metastatic TNBC.

Immune-based combinations for metastatic triple negative breast cancer in clinical trials: current knowledge and therapeutic prospects

INTRODUCTION

Immune checkpoint inhibitor (ICI) monotherapy appears to be effective in a small cohort of patients with metastatic triple negative breast cancer (mTNBC). This supports the exploration of strategies for increasing the efficacy of immunotherapy. To enhance overall response and clinical outcomes, several immune-based combinations are being investigated.

AREAS COVERED

The authors present a synopsis of current, state-of-art immune-based combinations in this setting and reflect on future possibilities. They shed light on recently presented and published clinical trials and ongoing studies. A literature search was conducted in October 2021; in addition, abstracts of international cancer meetings were reviewed.

EXPERT OPINION

Clinical trials suggest that ICI monotherapy could be beneficial in a minority of mTNBC patients; conversely, several immune-based combinations have reported notable results in recently presented or published studies. Some of these combination strategies have been approved for mTNBC - as in the case of chemoimmunotherapy in PD-L1 positive patients. Numerous trials are investigating novel ICI-based combinations and their results are eagerly awaited.

Efficacy of Rac and Cdc42 Inhibitor MBQ-167 in Triple-negative Breast Cancer

Triple-negative breast cancer (TNBC) is an aggressive form of breast cancer, with a high predisposition for locally invasive and metastatic cancer. With the objective to reduce cancer metastasis, we developed small molecule inhibitors to target the drivers of metastasis, the Rho GTPases Rac and Cdc42. Of these, MBQ-167 inhibits both Rac and Cdc42 with IC50s of 103 and 78 nmol/L, respectively; and consequently, inhibits p21-activated kinase (PAK) signaling, metastatic cancer cell proliferation, migration, and mammosphere growth; induces cell-cycle arrest and apoptosis; and decreases HER2-type mammary fatpad tumor growth and metastasis (Humphries-Bickley and colleagues, 2017). Herein, we used nuclear magnetic resonance to show that MBQ-167 directly interacts with Rac1 to displace specific amino acids, and consequently inhibits Rac.GTP loading and viability in TNBC cell lines. Phosphokinome arrays in the MDA-MB-231 human TNBC cells show that phosphorylation status of kinases independent of the Rac/Cdc42/PAK pathway are not significantly changed following 200 nmol/L MBQ-167 treatment. Western blotting shows that initial increases in phospho-c-Jun and phospho-CREB in response to MBQ-167 are not sustained with prolonged exposure, as also confirmed by a decrease in their transcriptional targets. MBQ-167 inhibits tumor growth, and spontaneous and experimental metastasis in immunocompromised (human TNBC) and immunocompetent (mouse TNBC) models. Moreover, per oral administration of MBQ-167 at 100 mg/kg body weight is not toxic to immunocompetent BALB/c mice and has a half-life of 4.6 hours in plasma. These results highlight the specificity, potency, and bioavailability of MBQ-167, and support its clinical potential as a TNBC therapeutic.

Biomarkers for breast cancer immunotherapy: PD-L1, TILs, and beyond

INTRODUCTION

Immune checkpoint inhibitors (ICIs) have recently entered into the therapeutic scenario of metastatic breast cancer. However, only a proportion of patients benefit from ICIs and immune-based combinations, so the identification of reliable predictors of response remains an unmet need.

AREAS COVERED

We discuss potential predictors of response to ICIs in breast cancer, including PD-L1 expression, tumor-infiltrating lymphocytes (TILs), tumor mutational burden (TMB), and several other biomarkers and suggest future directions of research in this setting. A literature search was conducted in October 2021 of Pubmed/Medline, Cochrane library and Scopus databases; in addition, abstract of international cancer meetings were reviewed.

EXPERT OPINION

In terms of predictors of response to immunotherapy in TNBC patients, several biomarkers are being evaluated. Valuable data on predictive biomarkers have recently emerged, including host-related factors, immune-related cells, and protein and genetic markers. Data supporting immunotherapy in the metastatic triple-negative breast cancer setting are not concordant, but there have been some positive phase III trials including IMpassion130 and KEYNOTE-355. Phase II and III (neo)adjuvant trials are supportive of this therapeutic strategy. Further investigations are warranted in this challenging area.

Multi-Parametric MRI-Based Radiomics Models for Predicting Molecular Subtype and Androgen Receptor Expression in Breast Cancer

Objective

To investigate whether radiomics features extracted from multi-parametric MRI combining machine learning approach can predict molecular subtype and androgen receptor (AR) expression of breast cancer in a non-invasive way.

Materials and Methods

Patients diagnosed with clinical T2–4 stage breast cancer from March 2016 to July 2020 were retrospectively enrolled. The molecular subtypes and AR expression in pre-treatment biopsy specimens were assessed. A total of 4,198 radiomics features were extracted from the pre-biopsy multi-parametric MRI (including dynamic contrast-enhancement T1-weighted images, fat-suppressed T2-weighted images, and apparent diffusion coefficient map) of each patient. We applied several feature selection strategies including the least absolute shrinkage and selection operator (LASSO), and recursive feature elimination (RFE), the maximum relevance minimum redundancy (mRMR), Boruta and Pearson correlation analysis, to select the most optimal features. We then built 120 diagnostic models using distinct classification algorithms and feature sets divided by MRI sequences and selection strategies to predict molecular subtype and AR expression of breast cancer in the testing dataset of leave-one-out cross-validation (LOOCV). The performances of binary classification models were assessed via the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). And the performances of multiclass classification models were assessed via AUC, overall accuracy, precision, recall rate, and F1-score.

Results

A total of 162 patients (mean age, 46.91 ± 10.08 years) were enrolled in this study; 30 were low-AR expression and 132 were high-AR expression. HR+/HER2− cancers were diagnosed in 56 cases (34.6%), HER2+ cancers in 81 cases (50.0%), and TNBC in 25 patients (15.4%). There was no significant difference in clinicopathologic characteristics between low-AR and high-AR groups (P > 0.05), except the menopausal status, ER, PR, HER2, and Ki-67 index (P = 0.043, <0.001, <0.001, 0.015, and 0.006, respectively). No significant difference in clinicopathologic characteristics was observed among three molecular subtypes except the AR status and Ki-67 (P = <0.001 and 0.012, respectively). The Multilayer Perceptron (MLP) showed the best performance in discriminating AR expression, with an AUC of 0.907 and an accuracy of 85.8% in the testing dataset. The highest performances were obtained for discriminating TNBC vs. non-TNBC (AUC: 0.965, accuracy: 92.6%), HER2+ vs. HER2− (AUC: 0.840, accuracy: 79.0%), and HR+/HER2− vs. others (AUC: 0.860, accuracy: 82.1%) using MLP as well. The micro-AUC of MLP multiclass classification model was 0.896, and the overall accuracy was 0.735.

Conclusions

Multi-parametric MRI-based radiomics combining with machine learning approaches provide a promising method to predict the molecular subtype and AR expression of breast cancer non-invasively.