A multiple breast cancer stem cell model to predict recurrence of T1-3, N0 breast cancer

The application of nanoparticles in theranostic systems targeting breast cancer stem cells: current progress and future challenges

Breast cancer (BC) is one of the diseases with the highest female mortality rates in the world and is closely related to breast cancer stem cells (BCSCs). Conventional breast cancer chemotherapy drugs target noncancer stem cells (non-CSCs), while cancer stem cells (CSCs) can still survive, which is an important reason for breast cancer drug resistance and local recurrence or distant metastasis. How to eradicate BCSCs while killing BCs is the key factor to improve the effect, and it is also an important scientific problem to be solved urgently. Therefore, targeted BCSC therapy has become a research hotspot. Interestingly, the emergence of nanotechnology provides a new idea for targeting BCSCs. This study summarizes the current application status of nanomaterials in targeting BCSCs, and attempts to construct a new type of lipid nanoparticle (LNP) that can target BCSCs through mRNA, providing a new idea for the treatment of BC.

Challenges in precision medicine in pancreatic cancer: A focus in cancer stem cells and microbiota

Pancreatic cancer adenocarcinoma (PDAC) is a lethal disease, with the lowest 5-years survival rate of all cancers due to late diagnosis. Despite the advance and success of precision oncology in gastrointestinal cancers, the frequency of molecular-informed therapy decisions in PDAC is currently neglectable. The reasons for this dismal situation are mainly the absence of effective early diagnostic biomarkers and therapy resistance. PDAC cancer stem cells (PDAC-SC), which are regarded as essential for tumor initiation, relapse and drug resistance, are highly dependent on their niche i.e. microanatomical structures of the tumor microenvironment. There is an altered microbiome in PDAC patients embedded within the highly desmoplastic tumor microenvironment, which is known to determine therapeutic responses and affecting survival in PDAC patients. We consider that understanding the communication network that exists between the microbiome and the PDAC-SC niche by co-culture of patient-derived organoids (PDOs) with TME microbiota would recapitulate the complexity of PDAC paving the way towards a precision oncology treatment-response prediction.

iTRAQ proteomics of sentinel lymph nodes for identification of extracellular matrix proteins to flag metastasis in early breast cancer

Patients with early breast cancer are affected by metastasis to axillary lymph nodes. Metastasis to these nodes is crucial for staging and quality of surgery. Sentinel Lymph Node Biopsy that is currently used to assess lymph node metastasis is not effective. This necessitates identification of biomarkers that can flag metastasis. Early stage breast cancer patients were recruited. Surgical resection of breast was followed by identification of sentinel lymph nodes. Fresh frozen section biopsy was used to assign metastatic and non-metastatic sentinel lymph nodes. Discovery phase included iTRAQ proteomics coupled with mass spectrometric analysis to identify differentially expressed proteins. Data is available via ProteomeXchange with identifier PXD027668. Validation was done by bioinformatic analysis and ELISA. There were 2398 unique protein groups and 109 differentially expressed proteins comparing metastatic and non-metastatic lymph nodes. Forty nine proteins were up-regulated, and sixty proteins that were down regulated in metastatic group. Bioinformatic analysis showed ECM-receptor interaction pathways to be implicated in lymph node metastasis. ELISA confirmed up-regulation of ECM proteins in metastatic lymph nodes. ECM proteins have requisite parameters to be developed as a diagnostic tool to assess status of sentinel lymph nodes to guide surgical intervention in early breast cancer.

Advancement of prognostic models in breast cancer: a narrative review

Objective

To provide a reference for clinical work and guide the decision-making of healthcare providers and end-users, we systematically reviewed the development, validation and classification of classical prognostic models for breast cancer.

Background

Patients suffering from breast cancer have different prognosis for its high heterogeneity. Accurate prognosis prediction and risk stratification for breast cancer are crucial for individualized treatment. There is a lack of systematic summary of breast cancer prognostic models.

Methods

We conducted a PubMed search with keywords "breast neoplasm", "prognostic model", "recurrence" and "metastasis", and screened the retrieved publications at three levels: title, abstract and full text. We identified the articles presented the development and/or validation of models based on clinicopathological factors, genomics, and machine learning (ML) methods to predict survival and/or benefits of adjuvant therapy in female breast cancer patients.

Conclusions

Combining prognostic-related variables with long-term clinical outcomes, researchers have developed a series of prognostic models based on clinicopathological parameters, genomic assays, and medical figures. The discrimination, calibration, overall performance, and clinical usefulness were validated by internal and/or external verifications. Clinicopathological models integrated the clinical parameters, including tumor size, histological grade, lymph node status, hormone receptor status to provide prognostic information for patients and doctors. Gene-expression assays deeply revealed the molecular heterogeneity of breast cancer, some of which have been cited by AJCC and National Comprehensive Cancer Network (NCCN) guidelines. In addition, the models based on the ML methods provided more detailed information for prognosis prediction by increasing the data dimension. Combined models incorporating clinical variables and genomics information are still required to be developed as the focus of further researches.

Cancer stem cells: Culprits in endocrine resistance and racial disparities in breast cancer outcomes

Breast cancer stem cells (BCSCs) promote endocrine therapy (ET) resistance, also known as endocrine resistance in hormone receptor (HR) positive breast cancer. Endocrine resistance occurs via mechanisms that are not yet fully understood. In vitro, in vivo and clinical data suggest that signaling cascades such as Notch, hypoxia inducible factor (HIF), and integrin/Akt promote BCSC-mediated endocrine resistance. Once HR positive breast cancer patients relapse on ET, targeted therapy agents such as cyclin dependent kinase inhibitors are frequently implemented, though secondary resistance remains a threat. Here, we discuss Notch, HIF, and integrin/Akt pathway regulation of BCSC activity and potential strategies to target these pathways to counteract endocrine resistance. We also discuss a plausible link between elevated BCSC-regulatory gene levels and reduced survival observed among African American women with basal-like breast cancer which lacks HR expression. Should future studies reveal a similar link for patients with luminal breast cancer, then the use of agents that impede BCSC activity could prove highly effective in improving clinical outcomes among African American breast cancer patients.

Update on the Role of NFκB in Promoting Aggressive Phenotypes of Estrogen Receptor-Positive Breast Cancer

The majority of breast cancers are diagnosed as estrogen receptor-positive (ER+) and respond well to ER-targeted endocrine therapy. Despite the initial treatability of ER+ breast cancer, this subtype still accounts for the majority of deaths. This is partly due to the changing molecular characteristics of tumors as they progress to aggressive, metastatic, and frequently therapy resistant disease. In these advanced tumors, targeting ER alone is often less effective, as other signaling pathways become active, and ER takes on a redundant or divergent role. One signaling pathway whose crosstalk with ER has been widely studied is the nuclear factor kappa B (NFκB) signaling pathway. NFκB is frequently implicated in ER+ tumor progression to an aggressive disease state. Although ER and NFκB frequently co-repress each other, it has emerged that the 2 pathways can positively converge to play a role in promoting endocrine resistance, metastasis, and disease relapse. This will be reviewed here, paying particular attention to new developments in the field. Ultimately, finding targeted therapies that remain effective as tumors progress remains one of the biggest challenges for the successful treatment of ER+ breast cancer. Although early attempts to therapeutically block NFκB activity frequently resulted in systemic toxicity, there are some effective options. The drugs parthenolide and dimethyl fumarate have both been shown to effectively inhibit NFκB, reducing tumor aggressiveness and reversing endocrine therapy resistance. This highlights the need to revisit targeting NFκB in the clinic to potentially improve outcome for patients with ER+ breast cancer.

Risk factors of distant metastasis after surgery among different breast cancer subtypes: a hospital-based study in Indonesia

Background

More than one third of breast cancer patients including those that are diagnosed in early stages will develop distant metastasis. Patterns of distant metastasis and the associated risks according to the molecular subtypes are not completely revealed particularly in populations of patients with delayed diagnosis and advanced stages.

Methods

Breast cancer patients (n = 1304) admitted to our institute (2014–2017) were evaluated to identify the metastatic patterns and the associated risks. Metastatic breast cancers at diagnosis were found in 245 patients (18.7%), and 1059 patients were then grouped into non-metastatic and metastatic groups after a median follow-up of 3.8 years.

Results

Infiltration of the tumor to the skin and chest wall prevailed as the most powerful predictor for distant metastasis (OR 2.115, 95% CI 1.544–2.898) particularly in the luminal A-like subtype (OR 2.685, 95% CI 1.649–4.371). Nodal involvement was also significantly associated with the risk of distant metastasis (OR 1.855, 95% CI 1.319–2.611), and the risk was higher in the Luminal A-like subtype (OR 2.572, 95% CI 1.547–4.278). Luminal A-like subtype had a significant higher risk of bone metastasis (OR 1.601, 95% CI 1.106–2.358). In respect to treatment, a combination of anthracyclines and taxanes-based chemotherapy was significantly associated with lower distant organ spread in comparison with anthracycline-based chemotherapy (OR 0.510, 95% CI 0.355–0.766) and the effect was stronger in Luminal A-like subtype (OR 0.417, 95% CI 0.226–0.769). Classification into Luminal and non-Luminal subtypes revealed significant higher risks of bone metastasis in the Luminal subtype (OR 1.793, 95% CI 1.209–2.660) and pulmonary metastasis in non-Luminal breast cancer (OR 1.445, 95% CI 1.003–2.083).

Conclusion

In addition to guiding the treatment plan, a comprehensive analysis of clinicopathological variables including the molecular subtypes could assist in the determination of distant metastasis risks of breast cancer patients. Our study offers new perspectives concerning the risks of distant metastasis in breast cancer subtypes in order to plan intensive surveillance or escalation of treatment particularly in a setting where patients are predominantly diagnosed in late stages.

Cancer Stem Cell Plasticity - A Deadly Deal

Intratumoral heterogeneity is a major ongoing challenge in the effective therapeutic targeting of cancer. Accumulating evidence suggests that a fraction of cells within a tumor termed Cancer Stem Cells (CSCs) are primarily responsible for this diversity resulting in therapeutic resistance and metastasis. Adding to this complexity, recent studies have shown that there can be different subpopulations of CSCs with varying biochemical and biophysical traits resulting in varied dissemination and drug-resistance potential. Moreover, cancer cells can exhibit a high level of plasticity or the ability to dynamically switch between CSC and non-CSC states or among different subsets of CSCs. In addition, CSCs also display extensive metabolic plasticity. The molecular mechanisms underlying these different interconnected axes of plasticity has been under extensive investigation and the trans-differentiation process of Epithelial to Mesenchymal transition (EMT) has been identified as a major contributing factor. Besides genetic and epigenetic factors, CSC plasticity is also shaped by non-cell-autonomous effects such as the tumor microenvironment (TME). In this review, we discuss the latest developments in decoding mechanisms and implications of CSC plasticity in tumor progression at biochemical and biophysical levels, and the latest in silico approaches being taken for characterizing cancer cell plasticity. These efforts can help improve existing therapeutic approaches by taking into consideration the contribution of cellular plasticity/heterogeneity in enabling drug resistance.