Integrative analysis of the inter-tumoral heterogeneity of triple-negative breast cancer

Multimodal data integration in early-stage breast cancer

The use of biomarkers in breast cancer has significantly improved patient outcomes through targeted therapies, such as hormone therapy anti-Her2 therapy and CDK4/6 or PARP inhibitors. However, existing knowledge does not fully encompass the diverse nature of breast cancer, particularly in triple-negative tumors. The integration of multi-omics and multimodal data has the potential to provide new insights into biological processes, to improve breast cancer patient stratification, enhance prognosis and response prediction, and identify new biomarkers. This review presents a comprehensive overview of the state-of-the-art multimodal (including molecular and image) data integration algorithms developed and with applicability to breast cancer stratification, prognosis, or biomarker identification. We examined the primary challenges and opportunities of these multimodal data integration algorithms, including their advantages, limitations, and critical considerations for future research. We aimed to describe models that are not only academically and preclinically relevant, but also applicable to clinical settings.

Clustering individuals using INMTD: a novel versatile multi-view embedding framework integrating omics and imaging data

MOTIVATION

Combining omics and images can lead to a more comprehensive clustering of individuals than classic single-view approaches. Among the various approaches for multi-view clustering, nonnegative matrix tri-factorization (NMTF) and nonnegative Tucker decomposition (NTD) are advantageous in learning low-rank embeddings with promising interpretability. Besides, there is a need to handle unwanted drivers of clusterings (i.e. confounders).

RESULTS

In this work, we introduce a novel multi-view clustering method based on NMTF and NTD, named INMTD, which integrates omics and 3D imaging data to derive unconfounded subgroups of individuals. According to the adjusted Rand index, INMTD outperformed other clustering methods on a synthetic dataset with known clusters. In the application to real-life facial-genomic data, INMTD generated biologically relevant embeddings for individuals, genetics, and facial morphology. By removing confounded embedding vectors, we derived an unconfounded clustering with better internal and external quality; the genetic and facial annotations of each derived subgroup highlighted distinctive characteristics. In conclusion, INMTD can effectively integrate omics data and 3D images for unconfounded clustering with biologically meaningful interpretation.

AVAILABILITY AND IMPLEMENTATION

INMTD is freely available at https://github.com/ZuqiLi/INMTD.

Mesenchymal-like immune-altered is the fourth robust triple-negative breast cancer molecular subtype

BACKGROUND

Robust molecular subtyping of triple-negative breast cancer (TNBC) is a prerequisite for the success of precision medicine. Today, there is a clear consensus on three TNBC molecular subtypes: luminal androgen receptor (LAR), basal-like immune-activated (BLIA), and basal-like immune-suppressed (BLIS). However, the debate about the robustness of other subtypes is still open.

METHODS

An unprecedented number (n = 1942) of TNBC patient data was collected. Microarray- and RNAseq-based cohorts were independently investigated. Unsupervised analyses were conducted using k-means consensus clustering. Clusters of patients were then functionally annotated using different approaches. Prediction of response to chemotherapy and targeted therapies, immune checkpoint blockade, and radiotherapy were also screened for each TNBC subtype.

RESULTS

Four TNBC subtypes were identified in the cohort: LAR (19.36%); mesenchymal stem-like (MSL/MES) (17.35%); BLIA (31.06%); and BLIS (32.23%). Regarding the MSL/MES subtype, we suggest renaming it to mesenchymal-like immune-altered (MLIA) to emphasize its specific histological background and nature of immune response. Treatment response prediction results show, among other things, that despite immune activation, immune checkpoint blockade is probably less or completely ineffective in MLIA, possibly caused by mesenchymal background and/or an enrichment in dysfunctional cytotoxic T lymphocytes. TNBC subtyping results were included in the bc-GenExMiner v5.0 webtool ( http://bcgenex.ico.unicancer.fr ).

CONCLUSION

The mesenchymal TNBC subtype is characterized by an exhausted and altered immune response, and resistance to immune checkpoint inhibitors. Consensus for molecular classification of TNBC subtyping and prediction of cancer treatment responses helps usher in the era of precision medicine for TNBC patients.

Integrative analysis of cancer multimodality data identifying COPS5 as a novel biomarker of diffuse large B-cell lymphoma

Preventing, diagnosing, and treating diseases requires accurate clinical biomarkers, which remains challenging. Recently, advanced computational approaches have accelerated the discovery of promising biomarkers from high-dimensional multimodal data. Although machine-learning methods have greatly contributed to the research fields, handling data sparseness, which is not unusual in research settings, is still an issue as it leads to limited interpretability and performance in the presence of missing information. Here, we propose a novel pipeline integrating joint non-negative matrix factorization (JNMF), identifying key features within sparse high-dimensional heterogeneous data, and a biological pathway analysis, interpreting the functionality of features by detecting activated signaling pathways. By applying our pipeline to large-scale public cancer datasets, we identified sets of genomic features relevant to specific cancer types as common pattern modules (CPMs) of JNMF. We further detected COPS5 as a potential upstream regulator of pathways associated with diffuse large B-cell lymphoma (DLBCL). COPS5 exhibited co-overexpression with MYC, TP53, and BCL2, known DLBCL marker genes, and its high expression was correlated with a lower survival probability of DLBCL patients. Using the CRISPR-Cas9 system, we confirmed the tumor growth effect of COPS5, which suggests it as a novel prognostic biomarker for DLBCL. Our results highlight that integrating multiple high-dimensional data and effectively decomposing them to interpretable dimensions unravels hidden biological importance, which enhances the discovery of clinical biomarkers.

Unraveling Biomarker Signatures in Triple-Negative Breast Cancer: A Systematic Review for Targeted Approaches

Triple-negative breast cancer (TNBC) is one of the most aggressive subtypes of breast cancer, marked by poor outcomes and dismal prognosis. Due to the absence of targetable receptors, chemotherapy still represents the main therapeutic option. Therefore, current research is now focusing on understanding the specific molecular pathways implicated in TNBC, in order to identify novel biomarker signatures and develop targeted therapies able to improve its clinical management. With the aim of identifying novel molecular features characterizing TNBC, elucidating the mechanisms by which these molecular biomarkers are implicated in the tumor development and progression, and assessing the impact on cancerous cells following their inhibition or modulation, we conducted a literature search from the earliest works to December 2023 on PubMed, Scopus, and Web Of Science. A total of 146 studies were selected. The results obtained demonstrated that TNBC is characterized by a heterogeneous molecular profile. Several biomarkers have proven not only to be characteristic of TNBC but also to serve as potential effective therapeutic targets, holding the promise of a new era of personalized treatments able to improve its prognosis. The pre-clinical findings that have emerged from our systematic review set the stage for further investigation in forthcoming clinical trials.

Metabolic Patterns of High-Invasive and Low-Invasive Oral Squamous Cell Carcinoma Cells Using Quantitative Metabolomics and 13C-Glucose Tracing

Most current metabolomics studies of oral squamous cell carcinoma (OSCC) are mainly focused on identifying potential biomarkers for early screening and diagnosis, while few studies have investigated the metabolic profiles promoting metastasis. In this study, we aimed to explore the altered metabolic pathways associated with metastasis of OSCC. Here, we identified four OSCC cell models (CAL27, HN6, HSC-3, SAS) that possess different invasive heterogeneity via the transwell invasion assay and divided them into high-invasive (HN6, SAS) and low-invasive (CAL27, HSC-3) cells. Quantitative analysis and stable isotope tracing using [U-13C6] glucose were performed to detect the altered metabolites in high-invasive OSCC cells, low-invasive OSCC cells and normal human oral keratinocytes (HOK). The metabolic changes in the high-invasive and low-invasive cells included elevated glycolysis, increased fatty acid metabolism and an impaired TCA cycle compared with HOK. Moreover, pathway analysis demonstrated significant differences in fatty acid biosynthesis; arachidonic acid (AA) metabolism; and glycine, serine and threonine metabolism between the high-invasive and low-invasive cells. Furthermore, the high-invasive cells displayed a significant increase in the percentages of 13C-glycine, 13C-palmitate, 13C-stearic acid, 13C-oleic acid, 13C-AA and estimated FADS1/2 activities compared with the low-invasive cells. Overall, this exploratory study suggested that the metabolic differences related to the metastatic phenotypes of OSCC cells were concentrated in glycine metabolism, de novo fatty acid synthesis and polyunsaturated fatty acid (PUFA) metabolism, providing a comprehensive understanding of the metabolic alterations and a basis for studying related molecular mechanisms in metastatic OSCC cells.

Gankyrin inhibits ferroptosis through the p53/SLC7A11/GPX4 axis in triple-negative breast cancer cells

Gankyrin is found in high levels in triple-negative breast cancer (TNBC) and has been established to form a complex with the E3 ubiquitin ligase MDM2 and p53, resulting in the degradation of p53 in hepatocarcinoma cells. Therefore, this study sought to determine whether gankyrin could inhibit ferroptosis through this mechanism in TNBC cells. The expression of gankyrin was investigated in relation to the prognosis of TNBC using bioinformatics. Co-immunoprecipitation and GST pull-down assays were then conducted to determine the presence of a gankyrin and MDM2 complex. RT-qPCR and immunoblotting were used to examine molecules related to ferroptosis, such as gankyrin, p53, MDM2, SLC7A11, and GPX4. Additionally, cell death was evaluated using flow cytometry detection of 7-AAD and a lactate dehydrogenase release assay, as well as lipid peroxide C11-BODIPY. Results showed that the expression of gankyrin is significantly higher in TNBC tissues and cell lines, and is associated with a poor prognosis for patients. Subsequent studies revealed that inhibiting gankyrin activity triggered ferroptosis in TNBC cells. Additionally, silencing gankyrin caused an increase in the expression of the p53 protein, without altering its mRNA expression. Co-immunoprecipitation and GST pull-down experiments indicated that gankyrin and MDM2 form a complex. In mouse embryonic fibroblasts lacking both MDM2 and p53, this gankyrin/MDM2 complex was observed to ubiquitinate p53, thus raising the expression of molecules inhibited by ferroptosis, such as SLC7A11 and GPX4. Furthermore, silencing gankyrin in TNBC cells disrupted the formation of the gankyrin/MDM2 complex, hindered the degradation of p53, increased SLC7A11 expression, impeded cysteine uptake, and decreased GPX4 production. Our findings suggest that TNBC cells are able to prevent cell ferroptosis through the gankyrin/p53/SLC7A11/GPX4 signaling pathway, indicating that gankyrin may be a useful biomarker for predicting TNBC prognosis or a potential therapeutic target.

Phenotypic heterogeneity drives differential disease outcome in a mouse model of triple negative breast cancer

The triple negative breast cancer (TNBC) subtype is one of the most aggressive forms of breast cancer that has poor clinical outcome and is an unmet clinical challenge. Accumulating evidence suggests that intratumoral heterogeneity or the presence of phenotypically distinct cell populations within a tumor play a crucial role in chemoresistance, tumor progression and metastasis. An increased understanding of the molecular regulators of intratumoral heterogeneity is crucial to the development of effective therapeutic strategies in TNBC. To this end, we used an unbiased approach to identify a molecular mediator of intratumoral heterogeneity in breast cancer by isolating two tumor cell populations (T1 and T2) from the 4T1 TNBC model. Phenotypic characterization revealed that the cells are different in terms of their morphology, proliferation and self-renewal ability in vitro as well as primary tumor formation and metastatic potential in vivo. Bioinformatic analysis followed by Kaplan Meier survival analysis in TNBC patients identified Metastasis associated colon cancer 1 (Macc1) as one of the top candidate genes mediating the aggressive phenotype in the T1 tumor cells. The role of Macc1 in regulating the proliferative phenotype was validated and taken forward in a therapeutic context with Lovastatin, a small molecule transcriptional inhibitor of Macc1 to target the T1 cell population. This study increases our understanding of the molecular underpinnings of intratumoral heterogeneity in breast cancer that is critical to improve the treatment of women currently living with the highly aggressive TNBC subtype.

Similarity network fusion to identify phenotypes of small-for-gestational-age fetuses

Fetal growth restriction (FGR) affects 5-10% of pregnancies, is the largest contributor to fetal death, and can have long-term consequences for the child. Implementation of a standard clinical classification system is hampered by the multiphenotypic spectrum of small fetuses with substantial differences in perinatal risks. Machine learning and multiomics data can potentially revolutionize clinical decision-making in FGR by identifying new phenotypes. Herein, we describe a cluster analysis of FGR based on an unbiased machine-learning method. Our results confirm the existence of two subtypes of human FGR with distinct molecular and clinical features based on multiomic analysis. In addition, we demonstrated that clusters generated by machine learning significantly outperform single data subtype analysis and biologically support the current clinical classification in predicting adverse maternal and neonatal outcomes. Our approach can aid in the refinement of clinical classification systems for FGR supported by molecular and clinical signatures.

Integrated Multi-Omic Analysis Reveals Immunosuppressive Phenotype Associated with Poor Outcomes in High-Grade Serous Ovarian Cancer

High-grade serous ovarian cancer (HGSOC) is characterized by a complex genomic landscape, with both genetic and epigenetic diversity contributing to its pathogenesis, disease course, and response to treatment. To better understand the association between genomic features and response to treatment among 370 patients with newly diagnosed HGSOC, we utilized multi-omic data and semi-biased clustering of HGSOC specimens profiled by TCGA. A Cox regression model was deployed to select model input features based on the influence on disease recurrence. Among the features most significantly correlated with recurrence were the promotor-associated probes for the NFRKB and DPT genes and the TREML1 gene. Using 1467 transcriptomic and methylomic features as input to consensus clustering, we identified four distinct tumor clusters-three of which had noteworthy differences in treatment response and time to disease recurrence. Each cluster had unique divergence in differential analyses and distinctly enriched pathways therein. Differences in predicted stromal and immune cell-type composition were also observed, with an immune-suppressive phenotype specific to one cluster, which associated with short time to disease recurrence. Our model features were additionally used as a neural network input layer to validate the previously defined clusters with high prediction accuracy (91.3%). Overall, our approach highlights an integrated data utilization workflow from tumor-derived samples, which can be used to uncover novel drivers of clinical outcomes.

The Vicious Cycle of Melanoma-Microglia Crosstalk: Inter-Melanoma Variations in the Brain-Metastasis-Promoting IL-6/JAK/STAT3 Signaling Pathway

Previous studies from our lab demonstrated that the crosstalk between brain-metastasizing melanoma cells and microglia, the macrophage-like cells of the central nervous system, fuels progression to metastasis. In the present study, an in-depth investigation of melanoma-microglia interactions elucidated a pro-metastatic molecular mechanism that drives a vicious melanoma-brain-metastasis cycle. We employed RNA-Sequencing, HTG miRNA whole transcriptome assay, and reverse phase protein arrays (RPPA) to analyze the impact of melanoma-microglia interactions on sustainability and progression of four different human brain-metastasizing melanoma cell lines. Microglia cells exposed to melanoma-derived IL-6 exhibited upregulated levels of STAT3 phosphorylation and SOCS3 expression, which, in turn, promoted melanoma cell viability and metastatic potential. IL-6/STAT3 pathway inhibitors diminished the pro-metastatic functions of microglia and reduced melanoma progression. SOCS3 overexpression in microglia cells evoked microglial support in melanoma brain metastasis by increasing melanoma cell migration and proliferation. Different melanomas exhibited heterogeneity in their microglia-activating capacity as well as in their response to microglia-derived signals. In spite of this reality and based on the results of the present study, we concluded that the activation of the IL-6/STAT3/SOCS3 pathway in microglia is a major mechanism by which reciprocal melanoma-microglia signaling engineers the interacting microglia to reinforce the progression of melanoma brain metastasis. This mechanism may operate differently in different melanomas.

DNA methylation differences in noncoding regions in ER negative breast tumors between Black and White women

Introduction

Incidence of estrogen receptor (ER)-negative breast cancer, an aggressive tumor subtype associated with worse prognosis, is higher among African American/Black women than other US racial and ethnic groups. The reasons for this disparity remain poorly understood but may be partially explained by differences in the epigenetic landscape.

Methods

We previously conducted genome-wide DNA methylation profiling of ER- breast tumors from Black and White women and identified a large number of differentially methylated loci (DML) by race. Our initial analysis focused on DML mapping to protein-coding genes. In this study, motivated by increasing appreciation for the biological importance of the non-protein coding genome, we focused on 96 DMLs mapping to intergenic and noncoding RNA regions, using paired Illumina Infinium Human Methylation 450K array and RNA-seq data to assess the relationship between CpG methylation and RNA expression of genes located up to 1Mb away from the CpG site.

Results

Twenty-three (23) DMLs were significantly correlated with the expression of 36 genes (FDR<0.05), with some DMLs associated with the expression of single gene and others associated with more than one gene. One DML (cg20401567), hypermethylated in ER- tumors from Black versus White women, mapped to a putative enhancer/super-enhancer element located 1.3 Kb downstream of HOXB2. Increased methylation at this CpG correlated with decreased expression of HOXB2 (Rho=-0.74, FDR<0.001) and other HOXB/HOXB-AS genes. Analysis of an independent set of 207 ER- breast cancers from TCGA similarly confirmed hypermethylation at cg20401567 and reduced HOXB2 expression in tumors from Black versus White women (Rho=-0.75, FDR<0.001).

Discussion

Our findings indicate that epigenetic differences in ER- tumors between Black and White women are linked to altered gene expression and may hold functional significance in breast cancer pathogenesis.

Combating breast cancer progression through combination therapy with hypomethylating agent and glucocorticoid

Breast cancer is the leading cause of cancer-related death in women. Among breast cancer types, triple-negative breast cancer (TNBC) accounts for 15% of all breast cancers with aggressive tumor behavior. By using bioinformatic approaches, we observed that the microRNA-708 promoter is highly methylated in breast carcinomas, and this methylation is linked to a poor prognosis. Moreover, microRNA-708 expression correlates with better clinical outcomes in TNBC patients. Combination treatment with the hypomethylating agent decitabine and synthetic glucocorticoid significantly increased the expression of microRNA-708, reactivated DNMT-suppressed pathways, and decreased the expression of multiple metastasis-promoting genes such as matrix metalloproteinases (MMPs) and IL-1β, leading to the suppression of breast cancer cell proliferation, migration, and invasion, as well as reduced tumor growth and distant metastasis in the TNBC xenograft mouse model. Overall, our study reveals a therapeutic opportunity in which a combined regimen of decitabine with glucocorticoid may have therapeutic potential in treating TNBC patients.

Fluorouracil exacerbates alpha-crystallin B chain-mediated cell migration in triple-negative breast cancer cell lines

Among triple-negative breast cancer (TNBC) subtypes, the basal-like 2 (BL2) subtype shows the lowest survival rate and the highest risk of metastasis after treatment with chemotherapy. Research has shown that αB-crystallin (CRYAB) is more highly expressed in the basal-like subtypes than in the other subtypes and is associated with brain metastasis in TNBC patients. We therefore hypothesized that αB-crystallin is associated with increased cell motility in the BL2 subtype after treatment with chemotherapy. Here, we evaluated the effect of fluorouracil (5-FU), a typical chemotherapy for the treatment of TNBC, on cell motility by utilizing a cell line with high αB-crystallin expression (HCC1806). A wound healing assay revealed that 5-FU significantly increased cell motility in HCC1806 cells, but not in MDA-MB-231 cells, which have low αB-crystallin expression. Also, cell motility was not increased by 5-FU treatment in HCC1806 cells harboring stealth siRNA targeting CRYAB. In addition, the cell motility of MDA-MB-231 cells overexpressing αB-crystallin was significantly higher than that of MDA-MB-231 cells harboring a control vector. Thus, 5-FU increased cell motility in cell lines with high, but not low, αB-crystallin expression. These results suggest that 5-FU-induced cell migration is mediated by αB-crystallin in the BL2 subtype of TNBC.

TrkA expression directs the anti-neoplastic activity of MLK3 inhibitors in triple-negative breast cancer

Mixed Lineage Kinase 3 (MLK3) is a viable target for neoplastic diseases; however, it is unclear whether its activators or inhibitors can act as anti-neoplastic agents. We reported that the MLK3 kinase activity was higher in triple-negative (TNBC) than in hormone receptor-positive human breast tumors, where estrogen inhibited MLK3 kinase activity and provided a survival advantage to ER⁺ breast cancer cells. Herein, we show that in TNBC, the higher MLK3 kinase activity paradoxically promotes cancer cell survival. Knockdown of MLK3 or MLK3 inhibitors, CEP-1347 and URMC-099, attenuated tumorigenesis of TNBC cell line and Patient-Derived (PDX) xenografts. The MLK3 kinase inhibitors decreased both the expression and activation of MLK3, PAK1, and NF-kB protein and caused cell death in TNBC breast xenografts. RNA-seq analysis identified several genes downregulated by MLK3 inhibition, and the NGF/TrkA MAPK pathway was significantly enriched in tumors sensitive to growth inhibition by MLK3 inhibitors. The TNBC cell line unresponsive to kinase inhibitor had substantially lower TrkA, and overexpression of TrkA restored the sensitivity to MLK3 inhibition. These results suggest that the functions of MLK3 in breast cancer cells depend on downstream targets in TNBC tumors expressing TrkA, and MLK3 kinase inhibition may provide a novel targeted therapy.

Exploring Real-World HER2-Low Data in Early-Stage Triple-Negative Breast Cancer: Insights and Implications

Purpose

This study aimed to compare the clinical behavior, clinicopathological and sociodemographic characteristics of patients with early-stage triple-negative breast cancer (TNBC) who belong to the HER2-low and HER2-zero subgroups.

Patients and Methods

This study involved a thorough search in the internal database of a single Brazilian institution to identify women with TNBC who underwent neoadjuvant chemotherapy (NACT) followed by curative surgery within the period from January 2010 to December 2014. HER2 analysis through immunohistochemistry (IHC) and, if required, amplification by in situ hybridization, was conducted using core biopsy samples. The study assesses outcomes of residual cancer burden (RCB), event-free survival (EFS), and overall survival (OS).

Results

A total of 170 cases were analyzed, with a mean age of 51.4 years (standard deviation, SD 11.2). The HER2 status was categorized as IHC 0, 1+, or 2+ in 80 (47.1%), 73 (42.9%), and 17 (10%) patients, respectively. No significant differences were observed in the prevalence of clinical pathological characteristics among the subgroups. The absence of significant results for clinicopathological and demographic features hindered the multivariate analysis of HER2 subgroups. Similarly, no significant differences were found in the RCB, EFS, and OS outcomes between HER2 subgroups.

Conclusion

The findings of this study suggest that, in early-stage TNBC, the clinical behavior and survival outcomes of the HER2-low subgroup may not differ significantly from those of the HER2-zero subgroup.

Advances in Single-Cell Sequencing Technology and Its Applications in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer and is mainly treated with chemotherapy-based combination therapy. In recent years, with the increasing development of global precision medicine, single-cell sequencing (SCS) has become one of the most promising technologies in the field of biotechnology. Moreover, the related application of this technology in TNBC has been applied and developed. By using SCS to study the heterogeneity of TNBC tumor cells, metastasis, drug resistance mechanisms, mutations, and cloning; it can further guide clinical chemotherapy, targeted therapy, and immunotherapy. To further reflect the importance of SCS in TNBC, this paper elaborated on and summarized the research and application progress of SCS in TNBC.

Brain Neural Progenitors are New Predictive Biomarkers for Breast Cancer Hormonotherapy

Heterogeneity of the tumor microenvironment (TME) is one of the major causes of treatment resistance in breast cancer. Among TME components, nervous system role in clinical outcome has been underestimated. Identifying neuronal signatures associated with treatment response will help to characterize neuronal influence on tumor progression and identify new treatment targets. The search for hormonotherapy-predictive biomarkers was implemented by supervised machine learning (ML) analysis on merged transcriptomics datasets from public databases. ML-derived genes were investigated by pathway enrichment analysis, and potential gene signatures were curated by removing the variables that were not strictly nervous system specific. The predictive and prognostic abilities of the generated signatures were examined by Cox models, in the initial cohort and seven external cohorts. Generated signature performances were compared with 14 other published signatures, in both the initial and external cohorts. Underlying biological mechanisms were explored using deconvolution tools (CIBERSORTx and xCell). Our pipeline generated two nervous system-related signatures of 24 genes and 97 genes (NervSign24 and NervSign97). These signatures were prognostic and hormonotherapy-predictive, but not chemotherapy-predictive. When comparing their predictive performance with 14 published risk signatures in six hormonotherapy-treated cohorts, NervSign97 and NervSign24 were the two best performers. Pathway enrichment score and deconvolution analysis identified brain neural progenitor presence and perineural invasion as nervous system-related mechanisms positively associated with NervSign97 and poor clinical prognosis in hormonotherapy-treated patients. Transcriptomic profiling has identified two nervous system-related signatures that were validated in clinical samples as hormonotherapy-predictive signatures, meriting further exploration of neuronal component involvement in tumor progression.

Significance

The development of personalized and precision medicine is the future of cancer therapy. With only two gene expression signatures approved by FDA for breast cancer, we are in need of new ones that can reliably stratify patients for optimal treatment. This study provides two hormonotherapy-predictive and prognostic signatures that are related to nervous system in TME. It highlights tumor neuronal components as potential new targets for breast cancer therapy.

Unsupervised Multi-Omics Data Integration Methods: A Comprehensive Review

Through the developments of Omics technologies and dissemination of large-scale datasets, such as those from The Cancer Genome Atlas, Alzheimer’s Disease Neuroimaging Initiative, and Genotype-Tissue Expression, it is becoming increasingly possible to study complex biological processes and disease mechanisms more holistically. However, to obtain a comprehensive view of these complex systems, it is crucial to integrate data across various Omics modalities, and also leverage external knowledge available in biological databases. This review aims to provide an overview of multi-Omics data integration methods with different statistical approaches, focusing on unsupervised learning tasks, including disease onset prediction, biomarker discovery, disease subtyping, module discovery, and network/pathway analysis. We also briefly review feature selection methods, multi-Omics data sets, and resources/tools that constitute critical components for carrying out the integration.

Subnetwork representation learning for discovering network biomarkers in predicting lymph node metastasis in early oral cancer

Cervical lymph node metastasis is the leading cause of poor prognosis in oral tongue squamous cell carcinoma and also occurs in the early stages. The current clinical diagnosis depends on a physical examination that is not enough to determine whether micrometastasis remains. The transcriptome profiling technique has shown great potential for predicting micrometastasis by capturing the dynamic activation state of genes. However, there are several technical challenges in using transcriptome data to model patient conditions: (1) An Insufficient number of samples compared to the number of genes, (2) Complex dependence between genes that govern the cancer phenotype, and (3) Heterogeneity between patients between cohorts that differ geographically and racially. We developed a computational framework to learn the subnetwork representation of the transcriptome to discover network biomarkers and determine the potential of metastasis in early oral tongue squamous cell carcinoma. Our method achieved high accuracy in predicting the potential of metastasis in two geographically and racially different groups of patients. The robustness of the model and the reproducibility of the discovered network biomarkers show great potential as a tool to diagnose lymph node metastasis in early oral cancer.

Multiomics subtyping for clinically prognostic cancer subtypes and personalized therapy: A systematic review and meta-analysis

PURPOSE

Multiomics cancer subtyping is becoming increasingly popular for directing state-of-the-art therapeutics. However, these methods have never been systematically assessed for their ability to capture cancer prognosis for identified subtypes, which is essential to effectively treat patients.

METHODS

We systematically searched PubMed, The Cancer Genome Atlas, and Pan-Cancer Atlas for multiomics cancer subtyping studies from 2010 through 2019. Studies comprising at least 50 patients and examining survival were included. Pooled Cox and logistic mixed-effects models were used to compare the ability of multiomics subtyping methods to identify clinically prognostic subtypes, and a structural equation model was used to examine causal paths underlying subtyping method and mortality.

RESULTS

A total of 31 studies comprising 10,848 unique patients across 32 cancers were analyzed. Latent-variable subtyping was significantly associated with overall survival (adjusted hazard ratio, 2.81; 95% CI, 1.16-6.83; P = .023) and vital status (1 year adjusted odds ratio, 4.71; 95% CI, 1.34-16.49; P = .015; 5 year adjusted odds ratio, 7.69; 95% CI, 1.83-32.29; P = .005); latent-variable-identified subtypes had greater associations with mortality across models (adjusted hazard ratio, 1.19; 95% CI, 1.01-1.42; P = .050). Our structural equation model confirmed the path from subtyping method through multiomics subtype (βˆ = 0.66; P = .048) on survival (βˆ = 0.37; P = .008).

CONCLUSION

Multiomics methods have different abilities to define clinically prognostic cancer subtypes, which should be considered before administration of personalized therapy; preliminary evidence suggests that latent-variable methods better identify clinically prognostic biomarkers and subtypes.

NSG-Pro mouse model for uncovering resistance mechanisms and unique vulnerabilities in human luminal breast cancers

Most breast cancer deaths are caused by estrogen receptor-α–positive (ER+) disease. Preclinical progress is hampered by a shortage of therapy-naïve ER+ tumor models that recapitulate metastatic progression and clinically relevant therapy resistance. Human prolactin (hPRL) is a risk factor for primary and metastatic ER+ breast cancer. Because mouse prolactin fails to activate hPRL receptors, we developed a prolactin-humanized Nod-SCID-IL2Rγ (NSG) mouse (NSG-Pro) with physiological hPRL levels. Here, we show that NSG-Pro mice facilitate establishment of therapy-naïve, estrogen-dependent PDX tumors that progress to lethal metastatic disease. Preclinical trials provide first-in-mouse efficacy of pharmacological hPRL suppression on residual ER+ human breast cancer metastases and document divergent biology and drug responsiveness of tumors grown in NSG-Pro versus NSG mice. Oncogenomic analyses of PDX lines in NSG-Pro mice revealed clinically relevant therapy-resistance mechanisms and unexpected, potently actionable vulnerabilities such as DNA-repair aberrations. The NSG-Pro mouse unlocks previously inaccessible precision medicine approaches for ER+ breast cancers.

Outcome-Oriented Deep Temporal Phenotyping of Disease Progression

Chronic diseases evolve slowly throughout a patient's lifetime creating heterogeneous progression patterns that make clinical outcomes remarkably varied across individual patients. A tool capable of identifying temporal phenotypes based on the patients different progression patterns and clinical outcomes would allow clinicians to better forecast disease progression by recognizing a group of similar past patients, and to better design treatment guidelines that are tailored to specific phenotypes. To build such a tool, we propose a deep learning approach, which we refer to as outcome-oriented deep temporal phenotyping (ODTP), to identify temporal phenotypes of disease progression considering what type of clinical outcomes will occur and when based on the longitudinal observations. More specifically, we model clinical outcomes throughout a patient's longitudinal observations via time-to-event (TTE) processes whose conditional intensity functions are estimated as non-linear functions using a recurrent neural network. Temporal phenotyping of disease progression is carried out by our novel loss function that is specifically designed to learn discrete latent representations that best characterize the underlying TTE processes. The key insight here is that learning such discrete representations groups progression patterns considering the similarity in expected clinical outcomes, and thus naturally provides outcome-oriented temporal phenotypes. We demonstrate the power of ODTP by applying it to a real-world heterogeneous cohort of 11 779 stage III breast cancer patients from the U.K. National Cancer Registration and Analysis Service. The experiments show that ODTP identifies temporal phenotypes that are strongly associated with the future clinical outcomes and achieves significant gain on the homogeneity and heterogeneity measures over existing methods. Furthermore, we are able to identify the key driving factors that lead to transitions between phenotypes which can be translated into actionable information to support better clinical decision-making.

Triple negative breast cancer and non-small cell lung cancer: Clinical challenges and nano-formulation approaches

Triple negative breast cancer (TNBC) and non-small cell lung cancer (NSCLC) are amongst the most aggressive forms of solid tumors. TNBC is highlighted by absence of genetic components of progesterone receptor, HER2/neu and estrogen receptor in breast cancer. NSCLC is characterized by integration of malignant carcinoma into respiratory system. Both cancers are associated with poor median and overall survival rates with low progression free survival with high incidences of relapse. These cancers are characterized by tumor heterogeneity, genetic mutations, generation of cancer-stem cells, immune-resistance and chemoresistance. Further, these neoplasms have been reported for tumor cross-talk into second primary cancers for each other. Current chemotherapeutic regimens include usage of multiple agents in tandem to affect tumor cells through multiple mechanisms with various such combinations being clinically tested. However, lack of controlled delivery and effective temporospatial presence of chemotherapeutics has resulted in suboptimal therapeutic response. Consequently, passive targeted albumin bound paclitaxel and PEGylated liposomal doxorubicin have been clinically used and tested with newer drugs for improved therapeutic efficacy in these cancers. Active targeting of nanocarriers against surface overexpressed proteins in both neoplasms have been explored. However, use of single agent nanoparticulate formulations against both cancers have failed to elicit desired outcomes. This review aims to identify clinical unmet need in these cancers while establishing a correlation with tested nano-formulation approaches and issues with preclinical to clinical translation. Lipid and polymer-based drug-drug and drug-gene combinatorial nanocarriers delivering multiple chemotherapeutics simultaneously to desired site of action have been detailed. Finally, emerging opportunities such as pharmacological targets (immune check point and epigentic modulators) as well as gene-based modulation (siRNA/CRISPR/Cas9) and the nano-formulation challenges for effective treatment of both cancers have been explored.

Revealing nuclear receptor hub modules from Basal-like breast cancer expression networks

Nuclear receptors are a class of transcriptional factors. Together with their co-regulators, they regulate development, homeostasis, and metabolism in a ligand-dependent manner. Their ability to respond to environmental stimuli rapidly makes them versatile cellular components. Their coordinated activities regulate essential pathways in normal physiology and in disease. Due to their complexity, the challenge remains in understanding their direct associations in cancer development. Basal-like breast cancer is an aggressive form of breast cancer that often lacks ER, PR and Her2. The absence of these receptors limits the treatment for patients to the non-selective cytotoxic and cytostatic drugs. To identify potential drug targets it is essential to identify the most important nuclear receptor association network motifs in Basal-like subtype progression. This research aimed to reveal the transcriptional network patterns, in the hope to capture the underlying molecular state driving Basal-like oncogenesis. In this work, we illustrate a multidisciplinary approach of integrating an unsupervised machine learning clustering method with network modelling to reveal unique transcriptional patterns (network motifs) underlying Basal-like breast cancer. The unsupervised clustering method provides a natural stratification of breast cancer patients, revealing the underlying heterogeneity in Basal-like. Identification of gene correlation networks (GCNs) from Basal-like patients in both the TCGA and METABRIC databases revealed three critical transcriptional regulatory constellations that are enriched in Basal-like. These represent critical NR components implicated in Basal-like breast cancer transcription. This approach is easily adaptable and applicable to reveal critical signalling relationships in other diseases.

Current Triple-Negative Breast Cancer Subtypes: Dissecting the Most Aggressive Form of Breast Cancer

Triple-negative breast cancer (TNBC) is a highly heterogeneous disease defined by the absence of estrogen receptor (ER) and progesterone receptor (PR) expression, and human epidermal growth factor receptor 2 (HER2) overexpression that lacks targeted treatments, leading to dismal clinical outcomes. Thus, better stratification systems that reflect intrinsic and clinically useful differences between TNBC tumors will sharpen the treatment approaches and improve clinical outcomes. The lack of a rational classification system for TNBC also impacts current and emerging therapeutic alternatives. In the past years, several new methodologies to stratify TNBC have arisen thanks to the implementation of microarray technology, high-throughput sequencing, and bioinformatic methods, exponentially increasing the amount of genomic, epigenomic, transcriptomic, and proteomic information available. Thus, new TNBC subtypes are being characterized with the promise to advance the treatment of this challenging disease. However, the diverse nature of the molecular data, the poor integration between the various methods, and the lack of cost-effective methods for systematic classification have hampered the widespread implementation of these promising developments. However, the advent of artificial intelligence applied to translational oncology promises to bring light into definitive TNBC subtypes. This review provides a comprehensive summary of the available classification strategies. It includes evaluating the overlap between the molecular, immunohistochemical, and clinical characteristics between these approaches and a perspective about the increasing applications of artificial intelligence to identify definitive and clinically relevant TNBC subtypes.

Atovaquone Suppresses the Growth of Metastatic Triple-Negative Breast Tumors in Lungs and Brain by Inhibiting Integrin/FAK Signaling Axis

Triple-negative breast cancer (TNBC) is considered to be the most aggressive and malignant neoplasm and is highly metastatic in nature. In the current study, we investigated the anti-metastatic potential of atovaquone, a protozoal drug prescribed for Pneumocystis pneumonia. We showed that atovaquone induced apoptosis and reduced the survival of several aggressive metastatic TNBC cell lines including metastatic patient-derived cells by reducing the expression of integrin α6, integrin β4, FAK, Src, and Vimentin. In order to study the efficacy of atovaquone in suppressing metastasized breast tumor cells in brain and lungs, we performed three in vivo experiments. We demonstrated that oral administration of 50 mg/kg of atovaquone suppressed MDA-MB-231 breast tumor growth by 90% in lungs in an intravenous metastatic tumor model. Anti-metastatic effect of atovaquone was further determined by intracardiac injection of 4T1-luc breast tumor cells into the left ventricle of mouse heart. Our results showed that atovaquone treatment suppressed the growth of metastatic tumors in lungs, liver and brain by 70%, 50% and 30% respectively. In an intracranial model, the growth of HCC1806-luc brain tumors in atovaquone treated mice was about 55% less than that of control. Taken together, our results indicate the anti-metastatic effects of atovaquone in vitro and in vivo in various breast tumor metastasis models.

Mutant p53 suppresses innate immune signaling to promote tumorigenesis

Mutant p53 (mtp53) proteins can exert cancer-promoting gain-of-function activities. We report a mechanism by which mtp53 suppresses both cell-autonomous and non-cell-autonomous signaling to promote cancer cell survival and evasion of tumor immune surveillance. Mtp53 interferes with the function of the cytoplasmic DNA sensing machinery, cGAS-STING-TBK1-IRF3, that activates the innate immune response. Mtp53, but not wild-type p53, binds to TANK-binding protein kinase 1 (TBK1) and prevents the formation of a trimeric complex between TBK1, STING, and IRF3, which is required for activation, nuclear translocation, and transcriptional activity of IRF3. Inactivation of innate immune signaling by mtp53 alters cytokine production, resulting in immune evasion. Restoring TBK1 signaling is sufficient to bypass mtp53 and lead to restored immune cell function and cancer cell eradication. This work is of translational interest because therapeutic approaches that restore TBK1 function could potentially reactivate immune surveillance and eliminate mtp53 tumors.

Recent Advances in Integrative Multi-Omics Research in Breast and Ovarian Cancer

The underlying molecular heterogeneity of cancer is responsible for the dynamic clinical landscape of this disease. The combination of genomic and proteomic alterations, including both inherited and acquired mutations, promotes tumor diversity and accounts for variable disease progression, therapeutic response, and clinical outcome. Recent advances in high-throughput proteogenomic profiling of tumor samples have resulted in the identification of novel oncogenic drivers, tumor suppressors, and signaling networks; biomarkers for the prediction of drug sensitivity and disease progression; and have contributed to the development of novel and more effective treatment strategies. In this review, we will focus on the impact of historical and recent advances in single platform and integrative proteogenomic studies in breast and ovarian cancer, which constitute two of the most lethal forms of cancer for women, and discuss the molecular similarities of these diseases, the impact of these findings on our understanding of tumor biology as well as the clinical applicability of these discoveries.

Triple-Negative Breast Cancer: Assessing the Role of Immunohistochemical Biomarkers on Neoadjuvant Treatment

OBJECTIVE

This study aimed to investigate the influence of immunohistochemical (IHC) biomarkers in the response to neoadjuvant chemotherapy (NACT) and survival outcomes in the subset of locally advanced triple-negative breast cancer (TNBC).

MATERIALS AND METHODS

The epidermal growth factor receptor (EGFR), androgen receptor (AR), cytokeratins (CK5/6, CK14 and CK17), Ki67 and p53 immunohistochemistry were evaluated on 171 cases of TNBC submitted to NACT and subsequently to surgery. Intensity and percentage of the expression of these biomarkers were combined to formulate a specific score, that was correlated with prognostic features and assessed for survival outcomes.

RESULTS

Most patients had advanced clinical-stage tumors (stage III: 83.6%; cT3/T4: 85.9%; cN1-3: 71.3%). The predominant histological subtype was high-grade (67.3%) and invasive ductal carcinoma (93.6%). The residual cancer burden (RCB) 0-1 corresponded to 28.7% of cases and low-risk lymph node ratio (LNR) represented 77.2%. High Ki67 expression only showed a significant correlation with grade 3 tumors (p = 0.0157). CK5/6 was observed in 16% (27/169), CK14 was positive in 10.1% (17/169), CK17 in 91.1% (153/168), p53 in 52.6% (70/133), EGFR in 92.9% (157/169 cases), AR in 13% (22/169) and Ki67 index was scored ≥40% in 57.9% (95/165). No IHC biomarker significantly impacted response or survival. Regarding the analysis of the outcomes of event-free survival (EFS) and overall survival (OS), clinical stage (p = 0.014 and p = 0.042, respectively), RCB (p < 0.0001 and p <0.0001, respectively) and LNR (p <0.0001 and p <0.0001, respectively) showed significant association.

CONCLUSION

No IHC biomarker evaluated showed a significant association with a response or survival outcomes in TNBC patients. Clinical stage, LNR and RCB stood out for strongly influencing survival.

Intron retention is a robust marker of intertumoral heterogeneity in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer with a 5-year survival rate of <8%. Unsupervised clustering of 76 PDAC patients based on intron retention (IR) events resulted in two clusters of tumors (IR-1 and IR-2). While gene expression-based clusters are not predictive of patient outcome in this cohort, the clusters we developed based on intron retention were associated with differences in progression-free interval. IR levels are lower and clinical outcome is worse in IR-1 compared with IR-2. Oncogenes were significantly enriched in the set of 262 differentially retained introns between the two IR clusters. Higher IR levels in IR-2 correlate with higher gene expression, consistent with detention of intron-containing transcripts in the nucleus in IR-2. Out of 258 genes encoding RNA-binding proteins (RBP) that were differentially expressed between IR-1 and IR-2, the motifs for seven RBPs were significantly enriched in the 262-intron set, and the expression of 25 RBPs were highly correlated with retention levels of 139 introns. Network analysis suggested that retention of introns in IR-2 could result from disruption of an RBP protein-protein interaction network previously linked to efficient intron removal. Finally, IR-based clusters developed for the majority of the 20 cancer types surveyed had two clusters with asymmetrical distributions of IR events like PDAC, with one cluster containing mostly intron loss events. Taken together, our findings suggest IR may be an important biomarker for subclassifying tumors.

Modulatory Role of microRNAs in Triple Negative Breast Cancer with Basal-Like Phenotype

Development of new research, classification, and therapeutic options are urgently required due to the fact that TNBC is a heterogeneous malignancy. The expression of high molecular weight cytokeratins identifies a biologically and clinically distinct subgroup of TNBCs with a basal-like phenotype, representing about 75% of TNBCs, while the remaining 25% includes all other intrinsic subtypes. The triple negative phenotype in basal-like breast cancer (BLBC) makes it unresponsive to endocrine therapy, i.e., tamoxifen, aromatase inhibitors, and/or anti-HER2-targeted therapies; for this reason, only chemotherapy can be considered an approach available for systemic treatment even if it shows poor prognosis. Therefore, treatment for these subgroups of patients is a strong challenge for oncologists due to disease heterogeneity and the absence of unambiguous molecular targets. Dysregulation of the cellular miRNAome has been related to huge cellular process deregulations underlying human malignancy. Consequently, epigenetics is a field of great promise in cancer research. Increasing evidence suggests that specific miRNA clusters/signatures might be of clinical utility in TNBCs with basal-like phenotype. The epigenetic mechanisms behind tumorigenesis enable progress in the treatment, diagnosis, and prevention of cancer. This review intends to summarize the epigenetic findings related to miRNAome in TNBCs with basal-like phenotype.

A Machine Learning-Assisted Nanoparticle-Printed Biochip for Real-Time Single Cancer Cell Analysis

Cancers are a complex conglomerate of heterogeneous cell populations with varying genotypes and phenotypes. The intercellular heterogeneity within the same tumor and intratumor heterogeneity within various tumors are the leading causes of resistance to cancer therapies and varied outcomes in different patients. Therefore, performing single-cell analysis is essential to identify and classify cancer cell types and study cellular heterogeneity. Here, the development of a machine learning-assisted nanoparticle-printed biochip for single-cell analysis is reported. The biochip is integrated by combining powerful machine learning techniques with easily accessible inkjet printing and microfluidics technology. The biochip is easily prototype-able, miniaturized, and cost-effective, potentially capable of differentiating a variety of cell types in a label-free manner. n-feature classifiers are established and their performance metrics are evaluated. The biochip's utility to discriminate noncancerous cells from cancerous cells at the single-cell level is demonstrated. The biochip's utility in classifying cancer sub-type cells is also demonstrated. It is envisioned that such a chip has potential applications in single-cell studies, tumor heterogeneity studies, and perhaps in point-of-care cancer diagnostics-especially in developing countries where the cost, limited infrastructures, and limited access to medical technologies are of the utmost importance.

Integrative Network Fusion: A Multi-Omics Approach in Molecular Profiling

Recent technological advances and international efforts, such as The Cancer Genome Atlas (TCGA), have made available several pan-cancer datasets encompassing multiple omics layers with detailed clinical information in large collection of samples. The need has thus arisen for the development of computational methods aimed at improving cancer subtyping and biomarker identification from multi-modal data. Here we apply the Integrative Network Fusion (INF) pipeline, which combines multiple omics layers exploiting Similarity Network Fusion (SNF) within a machine learning predictive framework. INF includes a feature ranking scheme (rSNF) on SNF-integrated features, used by a classifier over juxtaposed multi-omics features (juXT). In particular, we show instances of INF implementing Random Forest (RF) and linear Support Vector Machine (LSVM) as the classifier, and two baseline RF and LSVM models are also trained on juXT. A compact RF model, called rSNFi, trained on the intersection of top-ranked biomarkers from the two approaches juXT and rSNF is finally derived. All the classifiers are run in a 10x5-fold cross-validation schema to warrant reproducibility, following the guidelines for an unbiased Data Analysis Plan by the US FDA-led initiatives MAQC/SEQC. INF is demonstrated on four classification tasks on three multi-modal TCGA oncogenomics datasets. Gene expression, protein expression and copy number variants are used to predict estrogen receptor status (BRCA-ER, N = 381) and breast invasive carcinoma subtypes (BRCA-subtypes, N = 305), while gene expression, miRNA expression and methylation data is used as predictor layers for acute myeloid leukemia and renal clear cell carcinoma survival (AML-OS, N = 157; KIRC-OS, N = 181). In test, INF achieved similar Matthews Correlation Coefficient (MCC) values and 97% to 83% smaller feature sizes (FS), compared with juXT for BRCA-ER (MCC: 0.83 vs. 0.80; FS: 56 vs. 1801) and BRCA-subtypes (0.84 vs. 0.80; 302 vs. 1801), improving KIRC-OS performance (0.38 vs. 0.31; 111 vs. 2319). INF predictions are generally more accurate in test than one-dimensional omics models, with smaller signatures too, where transcriptomics consistently play the leading role. Overall, the INF framework effectively integrates multiple data levels in oncogenomics classification tasks, improving over the performance of single layers alone and naive juxtaposition, and provides compact signature sizes.

Identification of a prognosis‑associated signature associated with energy metabolism in triple‑negative breast cancer

At present, a large number of exciting results have been found regarding energy metabolism within the triple-negative breast cancer (TNBC) field. Apart from aerobic glycolysis, a number of other catabolic pathways have also been demonstrated to participate in energy generation. However, the prognostic value of energy metabolism for TNBC currently remains unclear. In the present study, the association between gene expression profiles of energy metabolism and outcomes in patients with TNBC was examined using datasets obtained from the Gene Expression Omnibus and The Cancer Genome Atlas. In total, four robust TNBC subtypes were identified on the basis of negative matrix factorization clustering and gene expression patterns, which exhibited distinct immunological, molecular and prognostic (disease-free survival) features. The differentially expressed genes were subsequently identified from the subgroup that demonstrated the poorest prognosis compared with the remaining 3 subgroups, where their biological functions were assessed further by means of gene ontology enrichment analysis. Any signatures found to be associated with energy metabolism were then established using the Cox proportional hazards model to assess patient prognosis. According to results of Kaplan-Meier analysis, the constructed signature consisting of eight genes that were associated with energy metabolism distinguished patient outcomes into low- and high-risk groups. In addition, this signature, which was found to be markedly associated with the clinical characteristics of the patients, served as an independent factor in predicting TNBC patient prognosis. According to gene set enrichment analysis, the gene sets related to the high-risk group participated in the MAPK signal transduction pathway, focal adhesion and extracellular matrix receptor interaction, whilst those related to the low-risk group were revealed to be mainly associated with mismatch repair and propanoate metabolism. Findings from the present study shed new light on the role of energy metabolism within TNBC, where the eight-gene signature associated with energy metabolism constructed can be utilized as a new prognostic marker for predicting survival in patients with TNBC.

Integrative analysis of genomic amplification-dependent expression and loss-of-function screen identifies ASAP1 as a driver gene in triple-negative breast cancer progression

The genetically heterogeneous triple-negative breast cancer (TNBC) continues to be an intractable disease, due to lack of effective targeted therapies. Gene amplification is a major event in tumorigenesis. Genes with amplification-dependent expression are being explored as therapeutic targets for cancer treatment. In this study, we have applied Analytical Multi-scale Identification of Recurring Events analysis and transcript quantification in the TNBC genome across 222 TNBC tumors and identified 138 candidate genes with positive correlation in copy number gain (CNG) and gene expression. siRNA-based loss-of-function screen of the candidate genes has validated EGFR, MYC, ASAP1, IRF2BP2, and CCT5 genes as drivers promoting proliferation in different TNBC cells. MYC, ASAP1, IRF2BP2, and CCT5 display frequent CNG and concurrent expression over 2173 breast cancer tumors (cBioPortal dataset). More frequently are MYC and ASAP1 amplified in TNBC tumors (>30%, n = 320). In particular, high expression of ASAP1, the ADP-ribosylation factor GTPase-activating protein, is significantly related to poor metastatic relapse-free survival of TNBC patients (n = 257, bc-GenExMiner). Furthermore, we have revealed that silencing of ASAP1 modulates numerous cytokine and apoptosis signaling components, such as IL1B, TRAF1, AIFM2, and MAP3K11 that are clinically relevant to survival outcomes of TNBC patients. ASAP1 has been reported to promote invasion and metastasis in various cancer cells. Our findings that ASAP1 is an amplification-dependent TNBC driver gene promoting TNBC cell proliferation, functioning upstream apoptosis components, and correlating to clinical outcomes of TNBC patients, support ASAP1 as a potential actionable target for TNBC treatment.

Network Diffusion Promotes the Integrative Analysis of Multiple Omics

The development of integrative methods is one of the main challenges in bioinformatics. Network-based methods for the analysis of multiple gene-centered datasets take into account known and/or inferred relations between genes. In the last decades, the mathematical machinery of network diffusion—also referred to as network propagation—has been exploited in several network-based pipelines, thanks to its ability of amplifying association between genes that lie in network proximity. Indeed, network diffusion provides a quantitative estimation of network proximity between genes associated with one or more different data types, from simple binary vectors to real vectors. Therefore, this powerful data transformation method has also been increasingly used in integrative analyses of multiple collections of biological scores and/or one or more interaction networks. We present an overview of the state of the art of bioinformatics pipelines that use network diffusion processes for the integrative analysis of omics data. We discuss the fundamental ways in which network diffusion is exploited, open issues and potential developments in the field. Current trends suggest that network diffusion is a tool of broad utility in omics data analysis. It is reasonable to think that it will continue to be used and further refined as new data types arise (e.g. single cell datasets) and the identification of system-level patterns will be considered more and more important in omics data analysis.

Molecular determinants of drug response in TNBC cell lines

PURPOSE

There is a need for biomarkers of drug efficacy for targeted therapies in triple-negative breast cancer (TNBC). As a step toward this, we identify multi-omic molecular determinants of anti-TNBC efficacy in cell lines for a panel of oncology drugs.

METHODS

Using 23 TNBC cell lines, drug sensitivity scores (DSS3) were determined using a panel of investigational drugs and drugs approved for other indications. Molecular readouts were generated for each cell line using RNA sequencing, RNA targeted panels, DNA sequencing, and functional proteomics. DSS3 values were correlated with molecular readouts using a FDR-corrected significance cutoff of p* < 0.05 and yielded molecular determinant panels that predict anti-TNBC efficacy.

RESULTS

Six molecular determinant panels were obtained from 12 drugs we prioritized based on their efficacy. Determinant panels were largely devoid of DNA mutations of the targeted pathway. Molecular determinants were obtained by correlating DSS3 with molecular readouts. We found that co-inhibiting molecular correlate pathways leads to robust synergy across many cell lines.

CONCLUSIONS

These findings demonstrate an integrated method to identify biomarkers of drug efficacy in TNBC where DNA predictions correlate poorly with drug response. Our work outlines a framework for the identification of novel molecular determinants and optimal companion drugs for combination therapy based on these correlates.

Cell-Free microRNAs as Potential Oral Cancer Biomarkers: From Diagnosis to Therapy

Oral cavity cancer is the most frequent malignancy of the head and neck. Unfortunately, despite educational interventions for prevention and early diagnosis, oral cancer patients are often diagnosed in advanced stages associated with poor prognosis and life expectancy. Therefore, there is an urgent need to find noninvasive biomarkers to improve early detection of this tumor. Liquid biopsy has emerged as a valuable tool in medical oncology which provides new horizons for improving clinical decision making. Notably, cell-free microRNAs (miRNAs), a class of short non-coding RNAs, are emerging as novel noninvasive cancer biomarkers. Here, we provide an overview of the potential clinical application of cell-free miRNAs as diagnostic, prognostic, and therapeutic biomarkers in oral cancer.

Genetic and Genomic Advances in Breast Cancer Diagnosis and Treatment

Advances in genetic testing for people at high risk for cancer and in targeted gene therapy for breast cancer are rapidly emerging, including newly developed key hormone receptor-targeted therapies and individualized molecular fusion identification and treatment options. These advances are contributing to a new era in cancer treatment modalities and care delivery. As more innovative and advanced treatment options emerge, women's health outcomes and survival rates may improve. Nursing professionals in primary care and women's health specialties must be aware of the latest options for testing, referrals, and treatment modalities.

Targeting triple negative breast cancer heterogeneity with chalcones: a molecular insight

Triple negative breast cancers (TNBCs) are aggressive heterogeneous cancers with not yet determined conventional targeted medication. Therefore, identification of new alternatives or improved treatment options to combat this deadly disease is highly needed. On the other hand, various derived products with chalcone scaffold were historically considered excellent candidates for the development of anticancer drugs. Chalcones unique chemical structure and their substantial biological activities in cancer cells make them an extremely attractive target for the treatment of several human carcinomas including TNBCs. This review highlights the promising therapeutic role of chalcones in TNBC management.