Identification of new cancer stem cell markers and signaling pathways in HER‑2‑positive breast cancer by transcriptome sequencing

Modelling the disease: H2S-sensitivity and drug-resistance of triple negative breast cancer cells can be modulated by embedding in isotropic micro-environment

Three-dimensional (3D) cell culture systems provide more physiologically relevant information, representing more accurately the actual microenvironment where cells reside in tissues. However, the differences between the tissue culture plate (TCP) and 3D culture systems in terms of tumour cell growth, proliferation, migration, differentiation and response to the treatment have not been fully elucidated. Tumoroid microspheres containing the MDA-MB 231 breast cancer cell line were prepared using either tunable PEG-fibrinogen (PFs) or tunable PEG-silk fibroin (PSFs) hydrogels, respectively named MDAPFs and MDAPSFs. The cancer cells in the tumoroids showed changes both in globular morphology and at the protein expression level. A decrease of both Histone H3 acetylation and cyclin D1 expression in all 3D systems, compared to the 2D cell culture, was detected in parallel to changes of the matrix stiffness. The effects of a glutathionylated garlic extract (GSGa), a slow H2S-releasing donor, were investigated on both tumoroid systems. A pro-apoptotic effect of GSGa on tumour cell growth in 2D culture was observed as opposed to a pro-proliferative effect apparent in both MDAPFs and MDAPSFs. A dedicated ad hoc 3D cell migration chip was designed and optimized for studying tumour cell invasion in a gel-in-gel configuration. An anti-cell-invasion effect of the GSGa was observed in the 2D cell culture, whereas a pro-migratory effect in both MDAPFs and MDAPSFs was observed in the 3D cell migration chip assay. An increase of cyclin D1 expression after GSGa treatment was observed in agreement with an increase of the cell invasion index. Our results suggest that the "dimensionality" and the stiffness of the 3D cell culture milieu can change the response to both the gasotransmitter H2S and doxorubicin due to differences in both H2S diffusion and changes in protein expression. Moreover, we uncovered a direct relation between the cyclin D1 expression and the stiffness of the 3D cell culture milieu, suggesting the potential causal involvement of the cyclin D1 as a bio-marker for sensitivity of the tumour cells to their matrix stiffness. Therefore, our hydrogel-based tumoroids represent a valid tunable model for studying the physically induced transdifferentiation (PiT) of cancer cells and as a more reliable and predictive in vitro screening platform to investigate the effects of anti-tumour drugs.

SADLN: Self-attention based deep learning network of integrating multi-omics data for cancer subtype recognition

Integrating multi-omics data for cancer subtype recognition is an important task in bioinformatics. Recently, deep learning has been applied to recognize the subtype of cancers. However, existing studies almost integrate the multi-omics data simply by concatenation as the single data and then learn a latent low-dimensional representation through a deep learning model, which did not consider the distribution differently of omics data. Moreover, these methods ignore the relationship of samples. To tackle these problems, we proposed SADLN: A self-attention based deep learning network of integrating multi-omics data for cancer subtype recognition. SADLN combined encoder, self-attention, decoder, and discriminator into a unified framework, which can not only integrate multi-omics data but also adaptively model the sample's relationship for learning an accurately latent low-dimensional representation. With the integrated representation learned from the network, SADLN used Gaussian Mixture Model to identify cancer subtypes. Experiments on ten cancer datasets of TCGA demonstrated the advantages of SADLN compared to ten methods. The Self-Attention Based Deep Learning Network (SADLN) is an effective method of integrating multi-omics data for cancer subtype recognition.

Construction and Validation of a Protein-associated Prognostic Model for Gastrointestinal Cancer

Background Gastrointestinal cancer (GIC) is a prevalent and lethal malignant tumor. It is obligatory to investigate innovative biomarkers for the diagnosis and prognosis. Proteins play a crucial role in regulating the occurrence and progression of GIC. However, the prognostic value of proteins is unclear in GIC.

OBJECTIVE

This paper aims to identify the hub prognosis-related proteins (PAPs) and construct a prognosis model for GIC patients for clinical application.

METHODS

Protein expression data of GIC was obtained from The Cancer Proteome Atlas (TCPA) and downloaded the clinicopathological data from The Cancer Genome Atlas database (TCGA). Besides, hub proteins were filtrated via univariate and multivariate Cox regression analysis. Moreover, survival analysis and nomogram were used to predict overall survival (OS). We used the calibration curves to assess the consistency of predictive and actual survival rates. The consistency index (C-index) was used to evaluate the prognostic ability of the predictive model. Furthermore, functional enrichment analysis and protein co-expression of PAPs were used to explore their roles in GIC.

RESULTS

Finally, a prognosis model was conducted based on ten PAPs (CYCLIND1, DVL3, NCADHERIN, SYK, ANNEXIN VII, CD20, CMET, RB, TFRC, and PREX1). The risk score calculated by the model was an independent prognostic predictor. Compared with the high-risk subgroup, the low-risk subgroup had better OS. In the TCGA cohort, the area under the curve value of the receiver operating characteristic curve of the prognostic model was 0.692. The expression of proteins and risk score had a significant association with the clinicopathological characteristics of GIC. Besides, a nomogram based on GIC clinicopathological features and risk scores could properly predict the OS of individual GIC patients. The C-index is 0.71 in the TCGA cohort and 0.73 in the GEO cohort.

CONCLUSION

The results indicate that the risk score is an independent prognostic biomarker and is related to the malignant clinical features of GIC patients. Besides, several PAPs associated with the survival and clinicopathological characteristics of GIC might be potential biomarkers for GIC diagnosis and treatment.

Cholesterol Synthesis Is Important for Breast Cancer Cell Tumor Sphere Formation and Invasion

Breast cancer has a high risk of recurrence and distant metastasis after remission. Controlling distant metastasis is important for reducing breast cancer mortality, but accomplishing this goal remains elusive. In this study, we investigated the molecular pathways underlying metastasis using cells that mimic the breast cancer distant metastasis process. HCC1143 breast cancer cells were cultured under two-dimensional (2D)-adherent, tumor sphere (TS), and reattached (ReA) culture conditions to mimic primary tumors, circulating tumor cells, and metastasized tumors, respectively. ReA cells demonstrated increased TS formation and enhanced invasion capacity compared to the original 2D-cultured parental cells. In addition, ReA cells had a higher frequency of ESA⁺CD44⁺CD24⁻ population, which represents a stem-cell-like cell population. RNA sequencing identified the cholesterol synthesis pathway as one of the most significantly increased pathways in TS and ReA cells compared to parental cells, which was verified by measuring intracellular cholesterol levels. Furthermore, the pharmacological inhibition of the cholesterol synthesis pathway decreased the ability of cancer cells to form TSs and invade. Our results suggest that the cholesterol synthesis pathway plays an important role in the distant metastasis of breast cancer cells by augmenting TS formation and invasion capacity.

‘Breast Cancer Resistance Likelihood and Personalized Treatment Through Integrated Multiomics’

In recent times, enormous progress has been made in improving the diagnosis and therapeutic strategies for breast carcinoma, yet it remains the most prevalent cancer and second highest contributor to cancer-related deaths in women. Breast cancer (BC) affects one in eight females globally. In 2018 alone, 1.4 million cases were identified worldwide in postmenopausal women and 645,000 cases in premenopausal females, and this burden is constantly increasing. This shows that still a lot of efforts are required to discover therapeutic remedies for this disease. One of the major clinical complications associated with the treatment of breast carcinoma is the development of therapeutic resistance. Multidrug resistance (MDR) and consequent relapse on therapy are prevalent issues related to breast carcinoma; it is due to our incomplete understanding of the molecular mechanisms of breast carcinoma disease. Therefore, elucidating the molecular mechanisms involved in drug resistance is critical. For management of breast carcinoma, the treatment decision not only depends on the assessment of prognosis factors but also on the evaluation of pathological and clinical factors. Integrated data assessments of these multiple factors of breast carcinoma through multiomics can provide significant insight and hope for making therapeutic decisions. This omics approach is particularly helpful since it identifies the biomarkers of disease progression and treatment progress by collective characterization and quantification of pools of biological molecules within and among the cancerous cells. The scrupulous understanding of cancer and its treatment at the molecular level led to the concept of a personalized approach, which is one of the most significant advancements in modern oncology. Likewise, there are certain genetic and non-genetic tests available for BC which can help in personalized therapy. Genetically inherited risks can be screened for personal predisposition to BC, and genetic changes or variations (mutations) can also be identified to decide on the best treatment. Ultimately, further understanding of BC at the molecular level (multiomics) will define more precise choices in personalized medicine. In this review, we have summarized therapeutic resistance associated with BC and the techniques used for its management.

Transcriptome profiles of stem-like cells from primary breast cancers allow identification of ITGA7 as a predictive marker of chemotherapy response

BACKGROUND

Breast cancer stem cells (BCSCs) are drivers of therapy-resistance, therefore are responsible for poor survival. Molecular signatures of BCSCs from primary cancers remain undefined. Here, we identify the consistent transcriptome of primary BCSCs shared across breast cancer subtypes, and we examine the clinical relevance of ITGA7, one of the genes differentially expressed in BCSCs.

METHODS

Primary BCSCs were assessed using immunohistochemistry and fluorescently labelled using Aldefluor (n = 17). Transcriptomes of fluorescently sorted BCSCs and matched non-stem cancer cells were determined using RNA-seq (n = 6). ITGA7 expression was examined in breast cancers using immunohistochemistry (n = 305), and its functional role was tested using siRNA in breast cancer cells.

RESULTS

Proportions of BCSCs varied from 0 to 9.4%. 38 genes were significantly differentially expressed in BCSCs; genes were enriched for functions in vessel morphogenesis, motility, and metabolism. ITGA7 was found to be significantly downregulated in BCSCs, and low expression significantly correlated with reduced survival in patients treated with chemotherapy, and with chemoresistance in breast cancer cells in vitro.

CONCLUSIONS

This study is the first to define the molecular profile of BCSCs from a range of primary breast cancers. ITGA7 acts as a predictive marker for chemotherapy response, in accordance with its downregulation in BCSCs.

Cancer Stem-Like Phenotype of Mitochondria Dysfunctional Hep3B Hepatocellular Carcinoma Cell Line

Mitochondria are major organelles that play various roles in cells, and mitochondrial dysfunction is the main cause of numerous diseases. Mitochondrial dysfunction also occurs in many cancer cells, and these changes are known to affect malignancy. The mitochondria of normal embryonic stem cells (ESCs) exist in an undifferentiated state and do not function properly. We hypothesized that mitochondrial dysfunction in cancer cells caused by the depletion of mitochondrial DNA might be similar to the mitochondrial state of ESCs. We generated mitochondria dysfunctional (ρ0) cells from the Hep3B hepatocellular carcinoma cell line and tested whether these ρ0 cells show cancer stem-like properties, such as self-renewal, chemotherapy resistance, and angiogenesis. Compared with Hep3B cells, the characteristics of each cancer stem-like cell were increased in Hep3B/ρ0 cells. The Hep3B/ρ0 cells formed a continuous and large sphere from a single cell. Additionally, the Hep3B/ρ0 cells showed resistance to the anticancer drug doxorubicin because of the increased expression of ATP-binding cassette Subfamily B Member 1. The Hep3B/ρ0 conditioned medium induced more and thicker blood vessels and increased the mobility and invasiveness of the blood vessel cells. Therefore, our data suggest that mitochondrial dysfunction can transform cancer cells into cancer stem-like cells.

Molecular pathology underlying the robustness of cancer stem cells

Intratumoral heterogeneity is tightly associated with the failure of anticancer treatment modalities including conventional chemotherapy, radiation therapy, and molecularly targeted therapy. Such heterogeneity is generated in an evolutionary manner not only as a result of genetic alterations but also by the presence of cancer stem cells (CSCs). CSCs are proposed to exist at the top of a tumor cell hierarchy and are undifferentiated tumor cells that manifest enhanced tumorigenic and metastatic potential, self-renewal capacity, and therapeutic resistance. Properties that contribute to the robustness of CSCs include the abilities to withstand redox stress, to rapidly repair damaged DNA, to adapt to a hyperinflammatory or hyponutritious tumor microenvironment, and to expel anticancer drugs by the action of ATP-binding cassette transporters as well as plasticity with regard to the transition between dormant CSC and transit-amplifying progenitor cell phenotypes. In addition, CSCs manifest the phenomenon of metabolic reprogramming, which is essential for maintenance of their self-renewal potential and their ability to adapt to changes in the tumor microenvironment. Elucidation of the molecular underpinnings of these biological features of CSCs is key to the development of novel anticancer therapies. In this review, we highlight the pathological relevance of CSCs in terms of their hallmarks and identification, the properties of their niche—both in primary tumors and at potential sites of metastasis—and their resistance to oxidative stress dependent on system xc (−).

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Intratumoral heterogeneity driven by CSCs is responsible for therapeutic resistance.

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CTCs survive in the distant organs and achieve colonization, causing metastasis.

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E/M hybrid cancer cells due to partial EMT exhibit the highest metastatic potential.

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The CSC niche regulates stemness in metastatic disease as well as in primary tumor.

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Activation of system xc(-) by CD44 variant in CSCs is a promising therapeutic target.

Cancer stem cells and macrophages: molecular connections and future perspectives against cancer

Cancer stem cells (CSCs) have been considered the key drivers of cancer initiation and progression due to their unlimited self-renewal capacity and their ability to induce tumor formation. Macrophages, particularly tumor-associated macrophages (TAMs), establish a tumor microenvironment to protect and induce CSCs development and dissemination. Many studies in the past decade have been performed to understand the molecular mediators of CSCs and TAMs, and several studies have elucidated the complex crosstalk that occurs between these two cell types. The aim of this review is to define the complex crosstalk between these two cell types and to highlight potential future anti-cancer strategies.

Cancer Stem Cell (CSC) Inhibitors in Oncology-A Promise for a Better Therapeutic Outcome: State of the Art and Future Perspectives

Cancer stem cells (CSCs), a subpopulation of cancer cells endowed with self-renewal, tumorigenicity, pluripotency, chemoresistance, differentiation, invasive ability, and plasticity, reside in specialized tumor niches and are responsible for tumor maintenance, metastasis, therapy resistance, and tumor relapse. The new-age "hierarchical or CSC" model of tumor heterogeneity is based on the concept of eradicating CSCs to prevent tumor relapse and therapy resistance. Small-molecular entities and biologics acting on various stemness signaling pathways, surface markers, efflux transporters, or components of complex tumor microenvironment are under intense investigation as potential anti-CSC agents. In addition, smart nanotherapeutic tools have proved their utility in achieving CSC targeting. Several CSC inhibitors in clinical development have shown promise, either as mono- or combination therapy, in refractory and difficult-to-treat cancers. Clinical investigations with CSC marker follow-up as a measure of clinical efficacy are needed to turn the "hype" into the "hope" these new-age oncology therapeutics have to offer.

Overlapping variants in the blood, tissues and cell lines for patients with intracranial meningiomas are predominant in stem cell-related genes

OBJECTIVE

Bulk tissue genomic analysis of meningiomas identified common somatic mutations, however, it often excluded blood-related variants. In contrast, genomic characterisation of primary cell lines that can provide critical information regarding growth and proliferation, have been rare. In our work, we identified the variants that are present in the blood, tissues and corresponding cell lines that are likely to be predictive, tumorigenic and progressive.

METHOD

Whole-exome sequencing was used to identify variants and distinguish related pathways that exist in 42 blood, tissues and corresponding cell lines (BTCs) samples for patients with intracranial meningiomas. Conventional sequencing was used for the confirmation of variants. Integrative analysis of the gene expression for the corresponding samples was utilised for further interpretations.

RESULTS

In total, 926 BTC variants were detected, implicating 845 genes. A pathway analysis of all BTC genes with damaging variants indicated the 'cell morphogenesis involved in differentiation' stem cell-related pathway to be the most frequently affected pathway. Concordantly, five stem cell-related genes, GPRIN2, ALDH3B2, ASPN, THSD7A and SIGLEC6, showed BTC variants in at least five of the patients. Variants that were heterozygous in the blood and homozygous in the tissues or the corresponding cell lines were rare (average: 1.3 ± 0.3%), and included variants in the RUNX2 and CCDC114 genes. An analysis comparing the variants detected only in tumours with aggressive features indicated a total of 240 BTC genes, implicating the 'homophilic cell adhesion via plasma membrane adhesion molecules' pathway, and identifying the stem cell-related transcription coactivator NCOA3/AIB1/SRC3 as the most frequent BTC gene. Further analysis of the possible impact of the poly-Q mutation present in the NCOA3 gene indicated associated deregulation of 15 genes, including the up-regulation of the stem cell related SEMA3D gene and the angiogenesis related VEGFA gene.

CONCLUSION

Stem cell-related pathways and genes showed high prevalence in the BTC variants, and novel variants in stem cell-related genes were identified for meningioma. These variants can potentially be used as predictive, tumorigenic and progressive biomarkers for meningioma.