Breast tumor subgroups reveal diverse clinical prognostic power

Analyzing the involvement of diverse cell death-related genes in diffuse large B-cell lymphoma using bioinformatics techniques

Diffuse large B-cell lymphoma (DLBCL) stands as the most prevalent subtype of non-Hodgkin's lymphoma and exhibits significant heterogeneity. Various forms of programmed cell death (PCD) have been established to have close associations with tumor onset and progression. To this end, this study has compiled 16 PCD-related genes. The investigation delved into genes linked with prognosis, constructing risk models through consecutive application of univariate Cox regression analysis and Lasso-Cox regression analysis. Furthermore, we employed RT-qPCR to validate the mRNA expression levels of certain diagnosis-related genes. Subsequently, the models underwent validation through KM survival curves and ROC curves, respectively. Additionally, nomogram models were formulated employing prognosis-related genes and risk scores. Lastly, disparities in immune cell infiltration abundance and the expression of immune checkpoint-associated genes between high- and low-risk groups, as classified by risk models, were explored. These findings contribute to a more comprehensive understanding of the role played by the 16 PCD-associated genes in DLBCL, shedding light on potential novel therapeutic strategies for the condition.

A systems biology approach and in vitro experiment indicated Rapamycin targets key cancer and cell cycle-related genes and miRNAs in triple-negative breast cancer cells

An anticancer drug known as Rapamycin acts by inhibiting the mammalian target of the Rapamycin pathway. This agent has recently been investigated for its potential therapeutic benefits in sensitizing drug-resistant breast cancer (BC) treatment. The molecular mechanism underlying these effects, however, is still a mystery. Using a systems biology method and in vitro experiment, this study sought to discover essential genes and microRNAs (miRNAs) targeted by Rapamycin in triple-negative BC (TNBC) cells to aid prospective new medications with less adverse effects in BC treatment. We developed the transcription factor-miRNA-gene and protein-protein interaction networks using the freely accessible microarray data sets. FANMOD and MCODE were utilized to identify critical regulatory motifs, clusters, and seeds. Then, functional enrichment analyses were conducted. Using topological analysis and motif detection, the most important genes and miRNAs were discovered. We used quantitative real-time polymerase chain reaction (qRT-PCR) to examine the effect of Rapamycin on the expression of the selected genes and miRNAs to verify our findings. We performed flow cytometry to investigate Rapamycin's impact on cell cycle and apoptosis. Furthermore, wound healing and migration assays were done. Three downregulated (PTGS2, EGFR, VEGFA) and three upregulated (c-MYC, MAPK1, PIK3R1) genes were chosen as candidates for additional experimental verification. There were also three upregulated miRNAs (miR-92a, miR-16, miR-20a) and three downregulated miRNAs (miR-146a, miR-145, miR-27a) among the six selected miRNAs. The qRT-PCR findings in MDA-MB-231 cells indicated that c-MYC, MAPK1, PIK3R1, miR-92a, miR-16, and miR-20a expression levels were considerably elevated following Rapamycin treatment, whereas PTGS2, EGFR, VEGFA, miR-146a, and miR-145 expression levels were dramatically lowered (p < 0.05). These genes are engaged in cancer pathways, transcriptional dysregulation in cancer, and cell cycle, according to the top pathway enrichment findings. Migration and wound healing abilities of the cells declined after Rapamycin treatment, and the number of apoptotic cells increased. We demonstrated that Rapamycin suppresses cell migration and metastasis in the TNBC cell line. In addition, our data indicated that Rapamycin induces apoptosis in this cell line. The discovered vital genes and miRNAs affected by Rapamycin are anticipated to have crucial roles in the pathogenesis of TNBC and its therapeutic resistance.

The Intricate Interplay between Cancer Stem Cells and Oncogenic miRNAs in Breast Cancer Progression and Metastasis

Complex signaling interactions between cancer cells and their microenvironments drive the clonal selection of cancer cells. Opposing forces of antitumor and tumorigenic potential regulate the survival of the fittest clones, while key genetic and epigenetic alterations in healthy cells force them to transform, overcome cell senescence, and proliferate in an uncontrolled manner. Both clinical samples and cancer cell lines provide researchers with an insight into the complex structure and hierarchy of cancer. Intratumor heterogeneity allows for multiple cancer cell subpopulations to simultaneously coexist within tumors. One category of these cancer cell subpopulations is cancer stem cells (CSCs), which possess stem-like characteristics and are not easily detectable. In the case of breast cancer, which is the most prevalent cancer type among females, such subpopulations of cells have been isolated and characterized via specific stem cell markers. These stem-like cells, known as breast cancer stem cells (BCSCs), have been linked to major events during tumorigenesis including invasion, metastasis and patient relapse following conventional therapies. Complex signaling circuitries seem to regulate the stemness and phenotypic plasticity of BCSCs along with their differentiation, evasion of immunosurveillance, invasiveness and metastatic potential. Within these complex circuitries, new key players begin to arise, with one of them being a category of small non-coding RNAs, known as miRNAs. Here, we review the importance of oncogenic miRNAs in the regulation of CSCs during breast cancer formation, promotion and metastasis, in order to highlight their anticipated usage as diagnostic and prognostic tools in the context of patient stratification and precision medicine.

Three-dimensional cell cultures as preclinical models to assess the biological activity of phytochemicals in breast cancer

The demand for the development of three-dimensional (3D) cell culture models in both/either drug screening and/or toxicology is gradually magnified. Natural Products derived from plants are known as phytochemicals and serve as resources for novel drugs and cancer therapy. Typical examples include taxol analogs (i.e., paclitaxel and docetaxel), vinca alkaloids (i.e., vincristine, vinblastine), and camptothecin analogs (topotecan, irinotecan). Breast cancer is the most frequent malignancy in women, with a 70% chance of patients being cured; however, metastatic disease is not considered curable using currently available chemotherapeutic options. In addition, phytochemicals present promising options for overcoming chemotherapy-related problems, such as drug resistance and toxic effects on non-target tissues. In the toxicological evaluation of these natural compounds, 3D cell culture models are a powerful tool for studying their effects on different tissues and organs in similar environments and behave as if they are in vivo conditions. Considering that 3D cell cultures represent a valuable platform for identifying the biological features of tumor cells as well as for screening natural products with antitumoral activity, the present review aims to summarize the most common 3D cell culture methods, focusing on multicellular tumor spheroids (MCTS) of breast cancer cell lines used in the discovery of phytochemicals with anticancer properties in the last ten years.

Modulating the Activity of Androgen Receptor for Treating Breast Cancer

The androgen receptor (AR) is a steroid hormone receptor widely detected in breast cancer. Evidence suggests that the AR might be a tumor suppressor in estrogen receptor alpha-positive (ERα+ve) breast cancer but a tumor promoter in estrogen receptor alpha-negative (ERα-ve) breast cancer. Modulating AR activity could be a potential strategy for treating breast cancer. For ERα+ve breast cancer, activation of the AR had been demonstrated to suppress the disease. In contrast, for ERα-ve breast cancer, blocking the AR could confer better prognosis to patients. These studies support the feasibility of utilizing AR modulators as anti-cancer drugs for different subtypes of breast cancer patients. Nevertheless, several issues still need to be addressed, such as the lack of standardization in the determination of AR positivity and the presence of AR splice variants. In future, the inclusion of the AR status in the breast cancer report at the time of diagnosis might help improve disease classification and treatment decision, thereby providing additional treatment strategies for breast cancer.

Assessment of Pharmacological Interactions between SIRT2 Inhibitor AGK2 and Paclitaxel in Different Molecular Subtypes of Breast Cancer Cells

Breast carcinoma (BC) is the most commonly diagnosed type of cancer in women in the world. Although the advances in the treatment of BC patients are significant, numerous side effects, severe toxicity towards normal cells as well as the multidrug resistance (MDR) phenomenon restrict the effectiveness of the therapies used. Therefore, new active compounds which decrease the MDR, extend disease-free survival, thereby ameliorating the effectiveness of the current treatment regimens, are greatly needed. Histone deacetylase inhibitors (HDIs), including sirtuin inhibitors (SIRTi), are the epigenetic antitumor agents which induce a cytotoxic effect in different types of cancer cells, including BC cells. Currently, combined forms of therapy with two or even more chemotherapeutics are promising antineoplastic tools to obtain a better response to therapy and limit adverse effects. Thus, on the one hand, much more effective chemotherapeutics, e.g., sirtuin inhibitors (SIRTi), are in demand; on the other hand, combinations of accepted cytostatics are trialed. Thus, the aim of our research was to examine the combination effects of a renowned cytotoxic drug paclitaxel (PAX) and SIRT2 inhibitor AGK2 on the proliferation and viability of the T47D, MCF7, MDA-MB-231, MDA-MB-468, BT-549 and HCC1937 BC cells. Moreover, cell cycle arrest and apoptosis induction were explored. The type of pharmacological interactions between AGK2 and PAX in different molecular subtypes of BC cells was assessed using the advanced isobolographic method. Our findings demonstrated that the tested active agents singly inhibited viability and proliferation of BC cells as well as induced cell cycle arrest and apoptosis in the cell-dependent context. Additionally, AGK2 increased the antitumor effect of PAX in most BC cell lines. We observed that, depending on the BC cell lines, the combinations of tested drugs showed synergistic, additive or antagonistic pharmacological interaction. In conclusion, our studies demonstrated that the consolidated therapy with the use of AGK2 and PAX can be considered as a potential therapeutic regimen in the personalized cure of BC patients in the future.

Prognostic implications of N6-methyladenosine RNA regulators in breast cancer

The significance of N⁶-methyladenosine (m6A) RNA modifications in the progression of breast cancer (BC) has been recognised. However, their potential role and mechanism of action in the tumour microenvironment (TME) and immune response has not been demonstrated. Thus, the role of m6A regulators and their downstream target gene components in BC remain to be explored. In this study, we used a series of bioinformatics methods and experiments to conduct exploratory research on the possible role of m6A regulators in BC. First, two regulatory modes of immune activation and inactivation were determined by tumour classification. The TME, immune cell infiltration, and gene set variation analysis results confirmed the reliability of this pattern. The prognostic model of the m6A regulator was established by the least absolute shrinkage and selection operator and univariate and multivariate Cox analyses, with the two regulators most closely related to survival verified by real-time quantitative reverse transcription polymerase chain reaction. Next, the prognostic m6A regulator identified in the model was crossed with the differential copy number of variant genes in invasive BC (IBC), and it was determined that YTHDF1 was a hub regulator. Subsequently, single-cell analysis revealed the expression patterns of m6A regulators in different IBC cell populations and found that YTHDF1 had significantly higher expression in immune-related IBC cells. Therefore, we selected the intersection of the BC differential expression gene set and the differential expression gene set of a cell line with knocked-down YTHDF1 in literature to identify downstream target genes of YTHDF1, in which we found IFI6, EIR, and SPTBN1. A polymerase chain reaction was conducted to verify the results. Finally, we confirmed the role of YTHDF1 as a potential prognostic biomarker through pan-cancer analysis. Furthermore, our findings revealed that YTHDF1 can serve as a new molecular marker for BC immunotherapy.

MicroRNAs and Heat Shock Proteins in Breast Cancer Biology

Breast cancer has five major immune types; luminal A, luminal B, HER2, Basal-like, and normal-like. Cells produce a family of protein called heat shock proteins (Hsps) in response to exposure to thermal and other proteotoxic stresses play essential roles in cancer metabolism and this large family shows a diverse set of Hsp involvement in different breast cancer immune types. Recently, Hsp members categorized according to their immune type roles. Hsp family consists of several subtypes formed by molecular weight; Hsp70, Hsp90, Hsp100, Hsp40, Hsp60, and small molecule Hsps. Cancer cells employ Hsps as survival factors since most of these proteins prevent apoptosis. Several studies monitored Hsp roles in breast cancer cells and reported Hsp27 involvement in drug resistance, Hsp70 in tumor cell transformation-progression, and interaction with p53. Furthermore, the association of Hsp90 with steroid receptors and signaling proteins in patients with breast cancer directed research to focus on Hsp-based treatments. miRNAs are known to play key roles in all types of cancer that are upregulated or downregulated in cancer which respectively referred to as oncogenes (oncomirs) or tumor suppressors. Expression profiles of miRNAs may be used to classify, diagnose, and predict different cancer types. It is clear that miRNAs play regulatory roles in gene expression and this work reveals miRNA correlation to Hsp depending on specific breast cancer immune types. Deregulation of specific Hsp genes in breast cancer subtypes allows for identification of new targets for drug design and cancer treatment. Here, we performed miRNA network analysis by recruiting Hsp genes detected in breast cancer subtypes and reviewed some of the miRNAs related to aforementioned Hsp genes.

An Integrated Deep Network for Cancer Survival Prediction Using Omics Data

As a highly sophisticated disease that humanity faces, cancer is known to be associated with dysregulation of cellular mechanisms in different levels, which demands novel paradigms to capture informative features from different omics modalities in an integrated way. Successful stratification of patients with respect to their molecular profiles is a key step in precision medicine and in tailoring personalized treatment for critically ill patients. In this article, we use an integrated deep belief network to differentiate high-risk cancer patients from the low-risk ones in terms of the overall survival. Our study analyzes RNA, miRNA, and methylation molecular data modalities from both labeled and unlabeled samples to predict cancer survival and subsequently to provide risk stratification. To assess the robustness of our novel integrative analytics, we utilize datasets of three cancer types with 836 patients and show that our approach outperforms the most successful supervised and semi-supervised classification techniques applied to the same cancer prediction problems. In addition, despite the preconception that deep learning techniques require large size datasets for proper training, we have illustrated that our model can achieve better results for moderately sized cancer datasets.

Valproic Acid and Breast Cancer: State of the Art in 2021

Valproic acid (2-propylpentanoic acid, VPA) is a short-chain fatty acid, a member of the group of histone deacetylase inhibitors (HDIs). VPA has been successfully used in the treatment of epilepsy, bipolar disorders, and schizophrenia for over 50 years. Numerous in vitro and in vivo pre-clinical studies suggest that this well-known anticonvulsant drug significantly inhibits cancer cell proliferation by modulating multiple signaling pathways. Breast cancer (BC) is the most common malignancy affecting women worldwide. Despite significant progress in the treatment of BC, serious adverse effects, high toxicity to normal cells, and the occurrence of multi-drug resistance (MDR) still limit the effective therapy of BC patients. Thus, new agents which improve the effectiveness of currently used methods, decrease the emergence of MDR, and increase disease-free survival are highly needed. This review focuses on in vitro and in vivo experimental data on VPA, applied individually or in combination with other anti-cancer agents, in the treatment of different histological subtypes of BC.

Glyoxalase 1 and protein kinase Cλ as potential therapeutic targets for late-stage breast cancer

Cancer cells upregulate the expression levels of glycolytic enzymes in order to reach the increased glycolysis required. One such upregulated glycolytic enzyme is glyoxalase 1 (GLO 1), which catalyzes the conversion of toxic methylglyoxal to nontoxic S-D-lactoylglutathione. Protein kinase Cλ (PKCλ) is also upregulated in various types of cancer and is involved in cancer progression. In the present study, the association between enhanced glycolysis and PKCλ in breast cancer was investigated. In human breast cancer, high GLO 1 expression was associated with high PKCλ expression at the protein (P<0.01) and mRNA levels (P<0.01). Furthermore, Wilcoxon and Cox regression model analysis revealed that patients with stage III-IV tumors with high GLO 1 and PKCλ expression had poor overall survival compared with patients expressing lower levels of these genes [P=0.040 (Gehan-Breslow generalized Wilcoxon test) and P=0.031 (hazard ratio, 2.36; 95% confidence interval, 1.08-5.16), respectively]. Treatment of MDA-MB-157 and MDA-MB-468 human basal-like breast cancer cells with TLSC702 (a GLO 1 inhibitor) and/or aurothiomalate (a PKCλ inhibitor) reduced both cell viability and tumor-sphere formation. These results suggested that GLO 1 and PKCλ were cooperatively involved in cancer progression and contributed to a poor prognosis in breast cancer. In conclusion, GLO 1 and PKCλ serve as potentially effective therapeutic targets for treatment of late-stage human breast cancer.

The tissue expression of MCT3, MCT8, and MCT9 genes in women with breast cancer

BACKGROUND

Breast cancer (BC) is a common malignancy with a high mortality rate. Malignant cell transformation is associated with metabolic changes. One group of proteins that are affected is the monocarboxylate transporters (MCTs-SLC16A). The MCTs comprise 14 members, and they play an important role in the growth, proliferation, and metabolism of cancer cells by transporting monocarboxylates such as lactate, pyruvate and thyroid hormones.

OBJECTIVE

We aimed to evaluate the expression of MCT3 (SLC16A8), MCT8 (SLC16A2) and MCT9 (SLC16A9) genes in breast cancer samples, comparing to normal adjacent tissues.

METHODS

Forty paired breast cancer tumor samples, the adjacent non-tumor and five healthy tissues were collected. Three cancer cell lines (MCF-7, MDA-MB-231, and SKBR3) were also analyzed. The expression of SLC16A8, SLC16A2 and SLC16A9 were assessed using quantitative real-time PCR. The relationship between gene expression with the pathological features of the tumors, and the hormone receptors status of the patient's tumors were also analyzed.

RESULTS

There was a significantly lower expression of the MCT3 gene in tumor samples compared to adjacent normal tissue and healthy samples (p value < 0.05). There was a significant difference in the expression of all three candidate genes between the BC tissues and normal tissues, and for the, tissues with different hormone receptor status and the molecular subtypes. Altered MCT8 and MCT9 gene expression was associated with a reduced survival CONCLUSION: MCT3 expression is significantly downregulated in breast cancer tissue. MCT3 may represent a novel therapeutic target in breast cancer patients, or in some hormone receptor subgroups.

Identification of Immune-Related Therapeutically Relevant Biomarkers in Breast Cancer and Breast Cancer Stem Cells by Transcriptome-Wide Analysis: A Clinical Prospective Study

Cancer stem cells (CSCs) represent a subset of tumor cells that are responsible for recurrence and metastasis of tumors. These cells are resistant to radiotherapy and chemotherapy. Immunotherapeutic strategies that target CSCs specifically have provided initial results; however, the mechanism of action of these strategies is unclear. The data were requested from The Cancer Genome Atlas and Genotype-Tissue Expression, followed with the survival analysis and weighted gene co-expression network analysis to detect survival and stemness related genes. Patients were divided into three groups based on their immune status by applying single sample GSEA (ssGSEA) with proven dependability by ESTIMATE analysis. The filtered key genes were analyzed using oncomine, GEPIA, HPA, qRT-PCR, and functional analysis. Patients in a group with a higher stemness and a lower immune infiltration showed a worse overall survival probability, stemness and immune infiltration characteristics of breast cancer progressed in a non-linear fashion. Thirteen key genes related to stemness and immunity were identified and the functional analysis indicated their crucial roles in cell proliferation and immune escape strategies. The qRT-PCR results showed that the expression of PIMREG and MTFR2 differed in different stages of patients. Our study revealed a promising potential for CSC-target immunotherapy in the early stage of cancer and a probable value for PIMREG and MTFR2 as biomarkers and targets for immunotherapy.

How Different Are the Molecular Mechanisms of Nodal and Distant Metastasis in Luminal A Breast Cancer?

Lymph node status is one of the best prognostic factors in breast cancer, however, its association with distant metastasis is not straightforward. Here we compare molecular mechanisms of nodal and distant metastasis in molecular subtypes of breast cancer, with major focus on luminal A patients. We analyze a new cohort of 706 patients (MMCI_706) as well as an independent cohort of 836 primary tumors with full gene expression information (SUPERTAM_HGU133A). We evaluate the risk of distant metastasis, analyze targetable molecular mechanisms in Gene Set Enrichment Analysis and identify relevant inhibitors. Lymph node positivity is generally associated with NF-κB and Src pathways and is related to high risk (OR: 5.062 and 2.401 in MMCI_706 and SUPERTAM_HGU133A, respectively, p < 0.05) of distant metastasis in luminal A patients. However, a part (≤15%) of lymph node negative tumors at the diagnosis develop the distant metastasis which is related to cell proliferation control and thrombolysis. Distant metastasis of lymph node positive patients is mostly associated with immune response. These pro-metastatic mechanisms further vary in other molecular subtypes. Our data indicate that the management of breast cancer and prevention of distant metastasis requires stratified approach based on targeted strategies.

Breast cancer prognosis signature: linking risk stratification to disease subtypes

Breast cancer is a very complex and heterogeneous disease with variable molecular mechanisms of carcinogenesis and clinical behaviors. The identification of prognostic risk factors may enable effective diagnosis and treatment of breast cancer. In particular, numerous gene-expression-based prognostic signatures were developed and some of them have already been applied into clinical trials and practice. In this study, we summarized several representative gene-expression-based signatures with significant prognostic value and separately assessed their ability of prognosis prediction in their originally targeted populations of breast cancer. Notably, many of the collected signatures were originally designed to predict the outcomes of estrogen receptor positive (ER+) patients or the whole breast cancer cohort; there are no typical signatures used for the prognostic prediction in a specific population of patients with the intrinsic subtype. We thus attempted to identify subtype-specific prognostic signatures via a computational framework for analyzing multi-omics profiles and patient survival. For both the discovery and an independent data set, we confirmed that subtype-specific signature is a strong and significant independent prognostic factor in the corresponding cohort. These results indicate that the subtype-specific prognostic signature has a much higher resolution in the risk stratification, which may lead to improved therapies and precision medicine for patients with breast cancer.

Typologies of Suicide: A Critical Literature Review

This review highlights proposed suicide typologies and identifies areas of future research. The current study is an illustrative, rather than exhaustive, qualitative review of theoretical and empirically derived typologies of suicide decedents. Theoretical and empirical typologies of suicide delineate between groups of suicide decedents based on individual, motivational, psychiatric, interpersonal, socio-demographic, and other variables. Certain core themes emerge across theoretical typologies including escape, aggression, intrapsychic pain, and relational concerns. Empirical typologies have identified unique patterns of life stressors, mental health history, health care utilization, and suicide method among suicide decedents. Future research should build on existing typological models of suicide to delineate when, and for whom, particular typologies of suicide may inform targeted prevention efforts. Researchers and clinicians should consider the characteristics and needs of particular high-risk groups when translating typological research into meaningful suicide prevention and intervention.

Expression of FGD3 gene as prognostic factor in young breast cancer patients

The FGD3 gene works as a cell migration inhibitor and seems to be a promising indicator of outcome in some human cancers including breast. In this study, we analysed for the first time the prognostic role of FGD3 in young breast cancer patients. We studied the relationship between traditional prognostic factors, FGD3 expression and outcome in ≤40 years breast cancer patients. We found that lower FGD3 expression decreased the probability of disease-free survival (p = 0.042) and overall survival (p = 0.007). In a multivariate analysis for overall survival AJCC stage (p = 0.005) and FGD3 expression (p = 0.03) resulted independent prognostic factors. Low FGD3 expression increased the risk of death from disease (HR 5.73, p = 0.03). Moreover, low FGD3 expression was associated with more widespread lymph node involvement (p = 0.04) and a lower FGD3 staining intensity was found in positive-lymph-node patients vs negative (p = 0.003) and in patients with ≥10 involved lymph nodes vs <10 (p = 0.05). Our results suggest FGD3 to be a significant independent prognostic factor in young breast cancer patients in terms of disease-free survival and overall survival. A lower expression increased the risk of recurrence and death from disease and was associated with widespread lymph node metastases.

Stratification of Breast Cancer by Integrating Gene Expression Data and Clinical Variables

Breast cancer is a heterogeneous disease. Although gene expression profiling has led to the definition of several subtypes of breast cancer, the precise discovery of the subtypes remains a challenge. Clinical data is another promising source. In this study, clinical variables are utilized and integrated to gene expressions for the stratification of breast cancer. We adopt two phases: gene selection and clustering, where the integration is in the gene selection phase; only genes whose expressions are most relevant to each clinical variable and least redundant among themselves are selected for further clustering. In practice, we simply utilize maximum relevance minimum redundancy (mRMR) for gene selection and k-means for clustering. We compare the results of our method with those of two commonly used only expression-based breast cancer stratification methods: prediction analysis of microarray 50 (PAM50) and highest variability (HV). The result is that our method outperforms them in identifying subtypes significantly associated with five-year survival and recurrence time. Specifically, our method identified recurrence-associated breast cancer subtypes that were not identified by PAM50 and HV. Additionally, our analysis discovered three survival-associated luminal-A subgroups and two survival-associated luminal-B subgroups. The study indicates that screening clinically relevant gene expressions yields improved breast cancer stratification.

Improvement of cancer subtype prediction by incorporating transcriptome expression data and heterogeneous biological networks

BACKGROUND

Identification of cancer subtypes is of great importance to facilitate cancer diagnosis and therapy. A number of methods have been proposed to integrate multi-sources data to identify cancer subtypes in recent years. However, few of them consider the regulatory associations between genome features and the contribution weights of different data-views in data integration. It is widely accepted that the regulatory associations between features play important roles in cancer subtype studies. In addition, different data-views may have different contributions in data integration for cancer subtype prediction.

RESULTS

In this paper, we propose a method, CSPRV, to improve the cancer subtype prediction by incorporating multi-sources transcriptome expression data and heterogeneous biological networks. We extract multiple expression features of each genome element based on the regulatory associations in the heterogeneous biological networks and use a generalized matrix correlation method (RV₂) to predict the similarities between samples in each view of expression data. We fuse the similarity information in multiple data-views according to different integration weights. Based on the integrated similarities between samples, we cluster samples into different subtype groups. Comprehensive experiments on TCGA cancer datasets demonstrate that the proposed method can identify more clinically meaningful cancer subtypes comparing with most existing methods.

CONCLUSIONS

The consideration of regulatory associations between biological features and data-views contribution is important to improve the understanding of cancer subtypes. The proposed method provides an open framework to incorporate transcriptome expression data and biological regulation network to predict cancer subtypes.

Computational Methods for Subtyping of Tumors and Their Applications for Deciphering Tumor Heterogeneity

With the rapid development of deep sequencing technologies, many programs are generating multi-platform genomic profiles (e.g., somatic mutation, DNA methylation, and gene expression) for a large number of tumors. This activity has provided unique opportunities and challenges to stratify tumors and decipher tumor heterogeneity. In this chapter, we summarize several computational methods to address the challenge of tumor stratification with different types of genomic data. We further introduce their applications in emerging large-scale genomic data to show their effectiveness in deciphering tumor heterogeneity and clinical relevance.

Integrating genomic data and pathological images to effectively predict breast cancer clinical outcome

BACKGROUND AND OBJECTIVE

Breast cancer is a leading cause of death from cancer for females. The high mortality rate of breast cancer is largely due to the complexity among invasive breast cancer and its significantly varied clinical outcomes. Therefore, improving the accuracy of breast cancer survival prediction has important significance and becomes one of the major research areas. Nowadays many computational models have been proposed for breast cancer survival prediction, however, most of them generate the predictive models by employing only the genomic data information and few of them consider the complementary information from pathological images.

METHODS

In our study, we introduce a novel method called GPMKL based on multiple kernel learning (MKL), which efficiently employs heterogeneous information containing genomic data (gene expression, copy number alteration, gene methylation, protein expression) and pathological images. With above heterogeneous features, GPMKL is proposed to execute feature fusion which is embedded in breast cancer classification.

RESULTS

Performance analysis of the GPMKL model indicates that the pathological image information plays a critical part in accurately predicting the survival time of breast cancer patients. Furthermore, the proposed method is compared with other existing breast cancer survival prediction methods, and the results demonstrate that the proposed framework with pathological images performs remarkably better than the existing survival prediction methods.

CONCLUSIONS

All results performed in our study suggest that the usefulness and superiority of GPMKL in predicting human breast cancer survival.

A Similarity Regression Fusion Model for Integrating Multi-Omics Data to Identify Cancer Subtypes

The identification of cancer subtypes is crucial to cancer diagnosis and treatments. A number of methods have been proposed to identify cancer subtypes by integrating multi-omics data in recent years. However, the existing methods rarely consider the biases of similarity between samples and weights of different omics data in integration. More accurate and flexible integration approaches need to be developed to comprehensively investigate cancer subtypes. In this paper, we propose a simple and flexible similarity fusion model for integrating multi-omics data to identify cancer subtypes. We consider the similarity biases between samples in each omics data and predict corrected similarities between samples using a generalized linear model. We integrate the corrected similarity information from multi-omics data according to different data-view weights. Based on the integrative similarity information, we cluster patient samples into different subtype groups. Comprehensive experiments demonstrate that the proposed approach obtains more significant results than the state-of-the-art integrative methods. In conclusion, our approach provides an effective and flexible tool to investigate subtypes in cancer by integrating multi-omics data.

Radiogenomics analysis identifies correlations of digital mammography with clinical molecular signatures in breast cancer

In breast cancer, well-known gene expression subtypes have been related to a specific clinical outcome. However, their impact on the breast tissue phenotype has been poorly studied. Here, we investigate the association of imaging data of tumors to gene expression signatures from 71 patients with breast cancer that underwent pre-treatment digital mammograms and tumor biopsies. From digital mammograms, a semi-automated radiogenomics analysis generated 1,078 features describing the shape, signal distribution, and texture of tumors along their contralateral image used as control. From tumor biopsy, we estimated the OncotypeDX and PAM50 recurrence scores using gene expression microarrays. Then, we used multivariate analysis under stringent cross-validation to train models predicting recurrence scores. Few univariate features reached Spearman correlation coefficients above 0.4. Nevertheless, multivariate analysis yielded significantly correlated models for both signatures (correlation of OncotypeDX = 0.49 ± 0.07 and PAM50 = 0.32 ± 0.10 in stringent cross-validation and OncotypeDX = 0.83 and PAM50 = 0.78 for a unique model). Equivalent models trained from the unaffected contralateral breast were not correlated suggesting that the image signatures were tumor-specific and that overfitting was not a considerable issue. We also noted that models were improved by combining clinical information (triple negative status and progesterone receptor). The models used mostly wavelets and fractal features suggesting their importance to capture tumor information. Our results suggest that molecular-based recurrence risk and breast cancer subtypes have observable radiographic phenotypes. To our knowledge, this is the first study associating mammographic information to gene expression recurrence signatures.

Biomarkers and personalized medicine: current status and further perspectives with special focus on dermatology

Biomarkers are of increasingly high importance in medicine, particularly in the realm of 'personalized medicine'. They are valuable for predicting prognosis and dose selection. Moreover, they may be helpful in detecting therapeutic and adverse responses and in patient stratification based on efficacy or safety prediction. Thus, biomarkers are essential tools for the selection of appropriate patients for treatment with certain drugs to and enable personalized medicine, that is 'providing the right treatment to the right patient, at the right dose at the right time'. Currently, there are six drugs approved for dermatological indications with recommended or mandatory biomarker testing. Most of them are used to treat melanoma and human immunodeficiency virus infection. In contrast to the few fully validated biomarkers, many exploratory biomarkers and biomarker candidates have potential applications. Prognostic biomarkers are of particular significance for malignant conditions. Similarly, diagnostic biomarkers are important in autoimmune diseases. Disease severity biomarkers are helpful tools in the treatment for inflammatory skin diseases. Identification, qualification and implementation of the different kinds of biomarkers are challenging and frequently necessitate collaborative efforts. This is particularly true for stratification biomarkers that require a companion diagnostic marker that is co-developed with a certain drug. In this article general definitions and requirements for biomarkers as well as for the impact of biomarkers in dermatology are reviewed and opportunities and challenges are discussed.

Impact of suppression of tumorigenicity 14 (ST14)/serine protease 14 (Prss14) expression analysis on the prognosis and management of estrogen receptor negative breast cancer

To elucidate the role of a type II transmembrane serine protease, ST14/Prss14, during breast cancer progression, we utilized publically accessible databases including TCGA, GEO, NCI-60, and CCLE. Survival of breast cancer patients with high ST14/Prss14 expression is significantly poor in estrogen receptor (ER) negative populations regardless of the ratios of ST14/Prss14 to its inhibitors, SPINT1 or SPINT2. In a clustering of 1085 selected EMT signature genes, ST14/Prss14 is located in the same cluster with CDH3, and closer to post-EMT markers, CDH2, VIM, and FN1 than to the pre-EMT marker, CDH1. Coexpression analyses of known ST14/Prss14 substrates and transcription factors revealed context dependent action. In cell lines, paradoxically, ST14/Prss14 expression is higher in the ER positive group and located closer to CDH1 in clustering. This apparent contradiction is not likely due to ST14/Prss14 expression in a cancer microenvironment, nor due to negative regulation by ER. Genes consistently coexpressed with ST14/Prss14 include transcription factors, ELF5, GRHL1, VGLL1, suggesting currently unknown mechanisms for regulation. Here, we report that ST14/Prss14 is an emerging therapeutic target for breast cancer where HER2 is not applicable. In addition we suggest that careful conclusions should be drawn not exclusively from the cell line studies for target development.

High Expression of FGD3, a Putative Regulator of Cell Morphology and Motility, Is Prognostic of Favorable Outcome in Multiple Cancers

Purpose

Identification of single-gene biomarkers that are prognostic of outcome can shed new insights on the molecular mechanisms that drive breast cancer and other cancers.

Methods

Exploratory analysis of 20,464 single-gene messenger RNAs (mRNAs) in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) discovery cohort indicates that low expression of FGD3 mRNA is prognostic for poor outcome. Prognostic significance of faciogenital dysplasia 3 (FGD3), SUSD3, and other single-gene proliferation markers was evaluated in breast cancer and The Cancer Genome Atlas (TCGA) cohorts.

Results

A meta-analysis of Cox regression of FGD3 mRNA as a continuous variable for overall survival of estrogen receptor (ER)–positive samples in METABRIC discovery, METABRIC validation, TCGA breast cancer, and Combination Chemotherapy in Treating Women With Breast Cancer (E2197) cohorts resulted in a combined hazard ratio (HR) of 0.69 (95% CI, 0.63 to 0.75), indicating better outcome with high expression. In the ER-negative samples, the combined meta-analysis HR was 0.72 (95% CI, 0.63 to 0.82), suggesting that FGD3 is prognostic regardless of ER status. The potential of FGD3 as a biomarker for freedom from recurrence was evaluated in the Breast International Group 1-98 (BIG 1-98; Letrozole or Tamoxifen in Treating Postmenopausal Women With Breast Cancer) study (HR, 0.85; 95% CI, 0.76 to 0.93) for breast cancer–free interval. In the Hungarian Academy of Science (HAS) breast cancer cohort, splitting on the median had an HR of 0.49 (95% CI, 0.42 to 0.58) for recurrence-free survival. A comparison of the Stouffer P value in five ER-positive cohorts showed that FGD3 (P = 3.8^E-14) outperformed MKI67 (P = 1.06^E-8) and AURKA (P = 2.61^E-5). A comparison of the Stouffer P value in four ER-negative cohorts showed that FGD3 (P = 3.88^E-5) outperformed MKI67 (P = .477) and AURKA (P = .820).

Conclusion

FGD3 was previously shown to inhibit cell migration. FGD3 mRNA is regulated by ESR1 and is associated with favorable outcome in six distinct breast cancer cohorts and four TCGA cancer cohorts. This suggests that FGD3 is an important clinical biomarker.

Neutrophil/Lymphocyte and Platelet/Lymphocyte Ratios are Not Different among Breast Cancer Subtypes

Background:

Breast cancer is a heterogeneous complex of diseases comprising different subtypes that have different treatment responses and clinical outcomes. Systemic inflammation is known to be associated with poor prognosis in many types of cancer. The neutrophil / lymphocyte ratio (NLR) and platelet / lymphocyte ratio (PLR) are factors used as indicators of inflammation. In this study, we evaluated NLR and PLR ratios in breast cancer subtypes.

Methods:

A total of 255 breast cancer patients were evaluated retrospectively. Patients were classified into three subtypes: estrogen receptor (ER)- or progesterone receptor (PR)-positive tumors were classified as luminal tumors; human epidermal growth factor receptor-2 (HER2)-overexpressed and ER-negative tumors were classified as HER2-positive tumors; and ER, PR, and HER2-negative tumors were classified as triple-negative tumors. The NLR and PLR were calculated.

Results:

The median NLR and PLR were 3 (0.37–37,1) and 137 (37.1–421.3), respectively. 66.7% of the patients were luminal type, 19.2% were HER2 positive, and 14.1% were triple negative. NLR was not associated with grade (p: 0.412), lymphovascular invasion (p: 0.326), tumor size (p: 0.232) and metastatic lymph node involvement (p: 0.406). PLR was higher in the patients with lymph node metastasis than in those without lymph node metastasis (p: 0.03). The NLR was 2 in the luminal group, 1.8 in the HER2-positive group, and 1.9 in the triple-negative group, but the differences were not significant(p: 0.051). PLR was 141 in the luminal group, 136 in the HER2-positive group, and 130 in the triple-negative group, but the differences were not significant.

Conclusion:

We could not find any significant differences for NLR and PLR according to breast cancer subtypes.

Potential Diagnostic and Therapeutic Applications of Oligonucleotide Aptamers in Breast Cancer

Breast cancer is one of the most common causes of cancer related deaths in women. Currently, with the development of early detection, increased social awareness and kinds of treatment options, survival rate has improved in nearly every type of breast cancer patients. However, about one third patients still have increased chances of recurrence within five years and the five-year relative survival rate in patients with metastasis is less than 30%. Breast cancer contains multiple subtypes. Each subtype could cause distinct clinical outcomes and systemic interventions. Thereby, new targeted therapies are of particular importance to solve this major clinical problem. Aptamers, often termed “chemical antibodies”, are functionally similar to antibodies and have demonstrated their superiority of recognizing target with high selectivity, affinity and stability. With these intrinsic properties, aptamers have been widely studied in cancer biology and some are in clinical trials. In this review, we will firstly discuss about the global impacts and mechanisms of breast cancer, then briefly highlight applications of aptamers that have been developed for breast cancer and finally summarize various challenges in clinical translation of aptamers.

Network based stratification of major cancers by integrating somatic mutation and gene expression data

The stratification of cancer into subtypes that are significantly associated with clinical outcomes is beneficial for targeted prognosis and treatment. In this study, we integrated somatic mutation and gene expression data to identify clusters of patients. In contrast to previous studies, we constructed cancer-type-specific significant co-expression networks (SCNs) rather than using a fixed gene network across all cancers, such as the network-based stratification (NBS) method, which ignores cancer heterogeneity. For each type of cancer, the gene expression data were used to construct the SCN network, while the gene somatic mutation data were mapped onto the network, propagated, and used for further clustering. For the clustering, we adopted an improved network-regularized non-negative matrix factorization (netNMF) (netNMF_HC) for a more precise classification. We applied our method to various datasets, including ovarian cancer (OV), lung adenocarcinoma (LUAD) and uterine corpus endometrial carcinoma (UCEC) cohorts derived from the TCGA (The Cancer Genome Atlas) project. Based on the results, we evaluated the performance of our method to identify survival-relevant subtypes and further compared it to the NBS method, which adopts priori networks and netNMF algorithm. The proposed algorithm outperformed the NBS method in identifying informative cancer subtypes that were significantly associated with clinical outcomes in most cancer types we studied. In particular, our method identified survival-associated UCEC subtypes that were not identified by the NBS method. Our analysis indicated valid subtyping of patient could be applied by mutation data with cancer-type-specific SCNs and netNMF_HC for individual cancers because of specific cancer co-expression patterns and more precise clustering.

Basal-like breast cancer: molecular profiles, clinical features and survival outcomes

BACKGROUND

Basal-like constitutes an important molecular subtype of breast cancer characterised by an aggressive behaviour and a limited therapy response. The outcome of patients within this subtype is, however, divergent. Some individuals show an increased risk of dying in the first five years, and others a long-term survival of over ten years after the diagnosis. In this study, we aim at identifying markers associated with basal-like patients' survival and characterising subgroups with distinct disease outcome.

METHODS

We explored the genomic and transcriptomic profiles of 351 basal-like samples from the METABRIC and ROCK data sets. Two selection methods, labelled Differential and Survival filters, were employed to determine genes/probes that are differentially expressed in tumour and control samples, and are associated with overall survival. These probes were further used to define molecular subgroups, which vary at the microRNA level and in DNA copy number.

RESULTS

We identified the expression signature of 80 probes that distinguishes between two basal-like subgroups with distinct clinical features and survival outcomes. Genes included in this list have been mainly linked to cancer immune response, epithelial-mesenchymal transition and cell cycle. In particular, high levels of CXCR6, HCST, C3AR1 and FPR3 were found in Basal I; whereas HJURP, RRP12 and DNMT3B appeared over-expressed in Basal II. These genes exhibited the highest betweenness centrality and node degree values and play a key role in the basal-like breast cancer differentiation. Further molecular analysis revealed 17 miRNAs correlated to the subgroups, including hsa-miR-342-5p, -150, -155, -200c and -17. Additionally, increased percentages of gains/amplifications were detected on chromosomes 1q, 3q, 8q, 10p and 17q, and losses/deletions on 4q, 5q, 8p and X, associated with reduced survival.

CONCLUSIONS

The proposed signature supports the existence of at least two subgroups of basal-like breast cancers with distinct disease outcome. The identification of patients at a low risk may impact the clinical decisions-making by reducing the prescription of high-dose chemotherapy and, consequently, avoiding adverse effects. The recognition of other aggressive features within this subtype may be also critical for improving individual care and for delineating more effective therapies for patients at high risk.

Identification of Genetic and Epigenetic Variants Associated with Breast Cancer Prognosis by Integrative Bioinformatics Analysis

INTRODUCTION

Breast cancer being a multifaceted disease constitutes a wide spectrum of histological and molecular variability in tumors. However, the task for the identification of these variances is complicated by the interplay between inherited genetic and epigenetic aberrations. Therefore, this study provides an extrapolate outlook to the sinister partnership between DNA methylation and single-nucleotide polymorphisms (SNPs) in relevance to the identification of prognostic markers in breast cancer. The effect of these SNPs on methylation is defined as methylation quantitative trait loci (meQTL).

MATERIALS AND METHODS

We developed a novel method to identify prognostic gene signatures for breast cancer by integrating genomic and epigenomic data. This is based on the hypothesis that multiple sources of evidence pointing to the same gene or pathway are likely to lead to reduced false positives. We also apply random resampling to reduce overfitting noise by dividing samples into training and testing data sets. Specifically, the common samples between Illumina 450 DNA methylation, Affymetrix SNP array, and clinical data sets obtained from the Cancer Genome Atlas (TCGA) for breast invasive carcinoma (BRCA) were randomly divided into training and test models. An intensive statistical analysis based on log-rank test and Cox proportional hazard model has established a significant association between differential methylation and the stratification of breast cancer patients into high- and low-risk groups, respectively.

RESULTS

The comprehensive assessment based on the conjoint effect of CpG–SNP pair has guided in delaminating the breast cancer patients into the high- and low-risk groups. In particular, the most significant association was found with respect to cg05370838–rs2230576, cg00956490–rs940453, and cg11340537–rs2640785 CpG–SNP pairs. These CpG–SNP pairs were strongly associated with differential expression of ADAM8, CREB5, and EXPH5 genes, respectively. Besides, the exclusive effect of SNPs such as rs10101376, rs140679, and rs1538146 also hold significant prognostic determinant.

CONCLUSIONS

Thus, the analysis based on DNA methylation and SNPs have resulted in the identification of novel susceptible loci that hold prognostic relevance in breast cancer.

MicroRNAs as biomarkers for early breast cancer diagnosis, prognosis and therapy prediction

Breast cancer is a major health problem that affects one in eight women worldwide. As such, detecting breast cancer at an early stage anticipates better disease outcome and prolonged patient survival. Extensive research has shown that microRNA (miRNA) are dysregulated at all stages of breast cancer. miRNA are a class of small noncoding RNA molecules that can modulate gene expression and are easily accessible and quantifiable. This review highlights miRNA as diagnostic, prognostic and therapy predictive biomarkers for early breast cancer with an emphasis on the latter. It also examines the challenges that lie ahead in their use as biomarkers. Noteworthy, this review addresses miRNAs reported in patients with early breast cancer prior to chemotherapy, radiotherapy, surgical procedures or distant metastasis (unless indicated otherwise). In this context, miRNA that are mentioned in this review were significantly modulated using more than one statistical test and/or validated by at least two studies. A standardized protocol for miRNA assessment is proposed starting from sample collection to data analysis that ensures comparative analysis of data and reproducibility of results.

Transcriptional Network Architecture of Breast Cancer Molecular Subtypes

Breast cancer heterogeneity is evident at the clinical, histological and molecular level. High throughput technologies allowed the identification of intrinsic subtypes that capture transcriptional differences among tumors. A remaining question is whether said differences are associated to a particular transcriptional program which involves different connections between the same molecules. In other words, whether particular transcriptional network architectures can be linked to specific phenotypes. In this work we infer, construct and analyze transcriptional networks from whole-genome gene expression microarrays, by using an information theory approach. We use 493 samples of primary breast cancer tissue classified in four molecular subtypes: Luminal A, Luminal B, Basal and HER2-enriched. For comparison, a network for non-tumoral mammary tissue (61 samples) is also inferred and analyzed. Transcriptional networks present particular architectures in each breast cancer subtype as well as in the non-tumor breast tissue. We find substantial differences between the non-tumor network and those networks inferred from cancer samples, in both structure and gene composition. More importantly, we find specific network architectural features associated to each breast cancer subtype. Based on breast cancer networks' centrality, we identify genes previously associated to the disease, either, generally (i.e., CNR2) or to a particular subtype (such as LCK). Similarly, we identify LUZP4, a gene barely explored in breast cancer, playing a role in transcriptional networks with subtype-specific relevance. With this approach we observe architectural differences between cancer and non-cancer at network level, as well as differences between cancer subtype networks which might be associated with breast cancer heterogeneity. The centrality measures of these networks allow us to identify genes with potential biomedical implications to breast cancer.

Over-expression of EPS15 is a favorable prognostic factor in breast cancer

As a crucial player in terminating growth factor signaling, EPS15 plays important roles in many malignancies including breast cancer. To explore the potential association of EPS15 with the clinical outcome of breast cancer, we conducted gene expression survival analysis using six independent datasets, checked its expression quantitative loci and their associated genes, and explored the networking of these genes with EPS15. Our results show that over-expression of EPS15 is significantly associated with a favorable clinical outcome of breast cancer, especially in tumors harbouring a positive estrogen receptor status. 21 unique SNPs were found to be associated with EPS15 expression. Among the neighboring genes of these SNPs, five (MTUS1, DOCK5, MSRA, SLIT3 and SKAP1) are genetically connected with EPS15 and its physical partners. These genes including EPS15 also show significant concurrent expressions, and four exhibit distinct relevance regarding patient survival. High expressions of EPS15 and MSRA show a distinct combinatorial favorable survival, suggesting the clinical relevance of their co-activation. In summary, over-expression of EPS15 is a potential favorable prognostic marker in breast cancer, which can be used clinically alone or together with other genes such as MSRA to avail therapeutic decision-making.

H3K4 acetylation, H3K9 acetylation and H3K27 methylation in breast tumor molecular subtypes

Aim: Here, we investigated how the St Gallen breast molecular subtypes displayed distinct histone H3 profiles. Patients & methods: 192 breast tumors divided into five St Gallen molecular subtypes (luminal A, luminal B HER2-, luminal B HER2+, HER2+ and basal-like) were evaluated for their histone H3 modifications on gene promoters. Results: ANOVA analysis allowed to identify specific H3 signatures according to three groups of genes: hormonal receptor genes (ERS1, ERS2, PGR), genes modifying histones (EZH2, P300, SRC3) and tumor suppressor gene (BRCA1). A similar profile inside high-risk cancers (luminal B [HER2+], HER2+ and basal-like) compared with low-risk cancers including luminal A and luminal B (HER2-) were demonstrated. Conclusion: The H3 modifications might contribute to clarify the differences between breast cancer subtypes.

Extensive Transcriptomic and Genomic Analysis Provides New Insights about Luminal Breast Cancers

Despite constituting approximately two thirds of all breast cancers, the luminal A and B tumours are poorly classified at both clinical and molecular levels. There are contradictory reports on the nature of these subtypes: some define them as intrinsic entities, others as a continuum. With the aim of addressing these uncertainties and identifying molecular signatures of patients at risk, we conducted a comprehensive transcriptomic and genomic analysis of 2,425 luminal breast cancer samples. Our results indicate that the separation between the molecular luminal A and B subtypes—per definition—is not associated with intrinsic characteristics evident in the differentiation between other subtypes. Moreover, t-SNE and MST-kNN clustering approaches based on 10,000 probes, associated with luminal tumour initiation and/or development, revealed the close connections between luminal A and B tumours, with no evidence of a clear boundary between them. Thus, we considered all luminal tumours as a single heterogeneous group for analysis purposes. We first stratified luminal tumours into two distinct groups by their HER2 gene cluster co-expression: HER2-amplified luminal and ordinary-luminal. The former group is associated with distinct transcriptomic and genomic profiles, and poor prognosis; it comprises approximately 8% of all luminal cases. For the remaining ordinary-luminal tumours we further identified the molecular signature correlated with disease outcomes, exhibiting an approximately continuous gene expression range from low to high risk. Thus, we employed four virtual quantiles to segregate the groups of patients. The clinico-pathological characteristics and ratios of genomic aberrations are concordant with the variations in gene expression profiles, hinting at a progressive staging. The comparison with the current separation into luminal A and B subtypes revealed a substantially improved survival stratification. Concluding, we suggest a review of the definition of luminal A and B subtypes. A proposition for a revisited delineation is provided in this study.

A microscopic landscape of the invasive breast cancer genome

Histologic grade is one of the most important microscopic features used to predict the prognosis of invasive breast cancer and may serve as a marker for studying cancer driving genomic abnormalities in vivo. We analyzed whole genome sequencing data from 680 cases of TCGA invasive ductal carcinomas of the breast and correlated them to corresponding pathology information. Ten genetic abnormalities were found to be statistically associated with histologic grade, including three most prevalent cancer driver events, TP53 and PIK3CA mutations and MYC amplification. A distinct genetic interaction among these genomic abnormalities was revealed as measured by the histologic grading score. While TP53 mutation and MYC amplification were synergistic in promoting tumor progression, PIK3CA mutation was found to have alleviated the oncogenic effect of either the TP53 mutation or MYC amplification, and was associated with a significant reduction in mitotic activity in TP53 mutated and/or MYC amplified breast cancer. Furthermore, we discovered that different types of genetic abnormalities (mutation versus amplification) within the same cancer driver gene (PIK3CA or GATA3) were associated with opposite histologic changes in invasive breast cancer. In conclusion, our study suggests that histologic grade may serve as a biomarker to define cancer driving genetic events in vivo.

Mutated Pathways as a Guide to Adjuvant Therapy Treatments for Breast Cancer

Adjuvant therapy following breast cancer surgery generally consists of either a course of chemotherapy, if the cancer lacks hormone receptors, or a course of hormonal therapy, otherwise. Here, we report a correlation between adjuvant strategy and mutated pathway patterns. In particular, we find that for breast cancer patients, pathways enriched in nonsynonymous mutations in the chemotherapy group are distinct from those of the hormonal therapy group. We apply a recently developed method that identifies collaborative pathway groups for hormone and chemotherapy patients. A collaborative group of pathways is one in which each member is altered in the same-generally large-number of samples. In particular, we find the following: (i) a chemotherapy group consisting of three pathways and a hormone therapy group consisting of 20, the members of the two groups being mutually exclusive; (ii) each group is highly enriched in breast cancer drivers; and (iii) the pathway groups are correlates of subtype-based therapeutic recommendations. These results suggest that patient profiling using these pathway groups can potentially enable the development of personalized treatment plans that may be more accurate and specific than those currently available.

RIP1K and RIP3K provoked by shikonin induce cell cycle arrest in the triple negative breast cancer cell line, MDA-MB-468: necroptosis as a desperate programmed suicide pathway

Resistance to cell death and reprogramming of metabolism are important in neoplastic cells. Increased resistance to apoptosis and recurrence of tumors are the major roadblocks to effective treatment of triple negative breast cancer. It has been thought that execution of necroptosis involves ROS generation and mitochondrial dysfunction in malignant cells. In this study, the effect of shikonin, an active substance from the dried root of Lithospermum erythrorhizon, on the induction of necroptosis or apoptosis, via RIP1K-RIP3K expressions has been examined in the triple negative breast cancer cell line. The expression levels of RIP1K and RIP3K, caspase-3 and caspase-8 activities, the levels of ROS, and mitochondrial membrane potential have been studied in the shikonin-treated MDA-MB-468 cell line. An increase in the ROS levels and a reduction in mitochondrial membrane potential have been observed in the shikonin-treated cells. Cell death has mainly occurred through necroptosis with a significant increase in the RIP1K and RIP3K expressions, and characteristic morphological changes have been observed. In the presence of Nec-1, caspase-3 mediating apoptosis has occurred in the shikonin-treated cells. The current findings have revealed that shikonin provoked mitochondrial ROS production in the triple negative breast cancer cell line, which works as a double-edged executioner's ax in the execution of necroptosis or apoptosis. The main route of cell death induced by shikonin is RIP1K-RIP3K-mediated necroptosis, but in the presence of Nec-1, apoptosis has prevailed. The present results shed a new light on the possible treatment of drug-resistant triple negative breast cancer.

Tumor characterization and stratification by integrated molecular profiles reveals essential pan-cancer features

BACKGROUND

Identification of tumor heterogeneity and genomic similarities across different cancer types is essential to the design of effective stratified treatments and for the discovery of treatments that can be extended to different types of tumors. However, systematic investigations on comprehensive molecular profiles have not been fully explored to achieve this goal.

RESULTS

Here, we performed a network-based integrative pan-cancer genomic analysis on >3000 samples from 12 cancer types to uncover novel stratifications among tumors. Our study not only revealed recurrently reported cross-cancer similarities, but also identified novel ones. The macro-scale stratification demonstrates strong clinical relevance and reveals consistent risk tendency among cancer types. The micro-scale stratification shows essential pan-cancer heterogeneity with subgroup-specific gene network characteristics and biological functions.

CONCLUSIONS

In summary, our comprehensive network-based pan-cancer stratification provides valuable information about inter- and intra- cancer stratification for patient clinical assessments and therapeutic strategies.

Identification of Novel Breast Cancer Subtype-Specific Biomarkers by Integrating Genomics Analysis of DNA Copy Number Aberrations and miRNA-mRNA Dual Expression Profiling

Breast cancer is a heterogeneous disease with well-defined molecular subtypes. Currently, comparative genomic hybridization arrays (aCGH) techniques have been developed rapidly, and recent evidences in studies of breast cancer suggest that tumors within gene expression subtypes share similar DNA copy number aberrations (CNA) which can be used to further subdivide subtypes. Moreover, subtype-specific miRNA expression profiles are also proposed as novel signatures for breast cancer classification. The identification of mRNA or miRNA expression-based breast cancer subtypes is considered an instructive means of prognosis. Here, we conducted an integrated analysis based on copy number aberrations data and miRNA-mRNA dual expression profiling data to identify breast cancer subtype-specific biomarkers. Interestingly, we found a group of genes residing in subtype-specific CNA regions that also display the corresponding changes in mRNAs levels and their target miRNAs' expression. Among them, the predicted direct correlation of BRCA1-miR-143-miR-145 pairs was selected for experimental validation. The study results indicated that BRCA1 positively regulates miR-143-miR-145 expression and miR-143-miR-145 can serve as promising novel biomarkers for breast cancer subtyping. In our integrated genomics analysis and experimental validation, a new frame to predict candidate biomarkers of breast cancer subtype is provided and offers assistance in order to understand the potential disease etiology of the breast cancer subtypes.

MicroRNA-10b and minichromosome maintenance complex component 5 gene as prognostic biomarkers in breast cancer

The aim of this study is to identify micro-ribonucleic acid (microRNA) and its target, in addition to their relationship to the outcome in breast cancer (BC). To achieve this aim, we investigated microRNA-10b (miR-10b) and minichromosome maintenance complex component 5 (MCM5 mRNA) expression in 230 breast tissue samples by real-time PCR and semiquantitative conventional RT-PCR, respectively. Relapse-free survival (RFS) associated with miRNA-10b and MCM5 mRNA were tested by Kaplan-Meier survival analysis. The impact of miRNA-10b andMCM5 mRNA expression on the survival was evaluated by Cox proportional hazard regression model. The expression of miRNA-10b and MCM5 mRNA was positive in 86.4 and 79.7 % breast cancer patients, respectively. The overall concordance rate between miRNA-10b and MCM5 RNA was 90.4 %. The median follow-up period was 50 months. The survival analysis showed that high levels of both miR-10b and MCM5 were associated with short relapse free survival of BC. We identified MCM5 mRNA expression changes consistent with the miRNA-10b target regulation. Thus, we could consider miRNA-10b and MCM5 mRNA as prognostic markers and potential therapeutic targets in breast cancer to be applied to other patient data sets.

Discovery of co-occurring driver pathways in cancer

Background

It has been widely realized that pathways rather than individual genes govern the course of carcinogenesis. Therefore, discovering driver pathways is becoming an important step to understand the molecular mechanisms underlying cancer and design efficient treatments for cancer patients. Previous studies have focused mainly on observation of the alterations in cancer genomes at the individual gene or single pathway level. However, a great deal of evidence has indicated that multiple pathways often function cooperatively in carcinogenesis and other key biological processes.

Results

In this study, an exact mathematical programming method was proposed to de novo identify co-occurring mutated driver pathways (CoMDP) in carcinogenesis without any prior information beyond mutation profiles. Two possible properties of mutations that occurred in cooperative pathways were exploited to achieve this: (1) each individual pathway has high coverage and high exclusivity; and (2) the mutations between the pair of pathways showed statistically significant co-occurrence. The efficiency of CoMDP was validated first by testing on simulated data and comparing it with a previous method. Then CoMDP was applied to several real biological data including glioblastoma, lung adenocarcinoma, and ovarian carcinoma datasets. The discovered co-occurring driver pathways were here found to be involved in several key biological processes, such as cell survival and protein synthesis. Moreover, CoMDP was modified to (1) identify an extra pathway co-occurring with a known pathway and (2) detect multiple significant co-occurring driver pathways for carcinogenesis.

Conclusions

The present method can be used to identify gene sets with more biological relevance than the ones currently used for the discovery of single driver pathways.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-271) contains supplementary material, which is available to authorized users.

Toward a systematic understanding of cancers: a survey of the pan-cancer study

Studies on molecular aberrations of cancer patients have increased unprecedentedly in scale and accessibility, allowing large-scale integrative cross-cancer analysis. Pan-cancer study is becoming a valuable paradigm for cancer genomics. Here, we review recent advances in this field and highlight the potential challenges and directions especially from the computational angle.