Molecular classification of solid tumours: towards pathway-driven therapeutics

Inhibition of WNT signaling by conjugated microRNA nano-carriers: A new therapeutic approach for treating triple-negative breast cancer a perspective review

Triple-Negative Breast Cancer is the most aggressive form and accounts the 15%-25% of all breast cancer. Receptors are absent in triple-negative breast cancer, which makes them unresponsive to the current hormonal therapies. The patients with TNBC are left with the option of cytotoxic chemotherapy. The Wnt pathways are connected to cancer, and when activated, they result in mammary hyperplasia and tumors. The tumor suppressor microRNAs can block tumor cell proliferation, invasion, and migration, lead to cancer cell death, and are also known to down-regulate the WNT signaling. Nanoparticles with microRNA have been seen to be more effective when compared with their single release. In this review, we have tried to understand how Wnt signaling plays a crucial role in TNBC, EMT, metastasis, anti-drug resistance, and regulation of Wnt by microRNA. The role of nano-carriers in delivering micro-RNA. The clinical biomarkers, including the present state-of-the-art, involve novel pathways of Wnt.

Functional Proteomic Profiling of Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is a subtype of breast cancer that comprises various disease entities, all of which share a set of common features: a lack of expression of the estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2, respectively. Because of their receptor status, conventional chemotherapy remains the main therapeutic option for TNBC patients. We employed a reverse phase protein array approach (RPPA), complemented by immunohistochemistry, to quantitatively profile the activation state of 84 actionable key signaling intermediates and phosphoproteins in a set of 44 TNBC samples. We performed supervised and unsupervised approaches to proteomic data analysis to identify groups of samples sharing common characteristics that could be amenable to existing therapies. We found the heterogenous activation of multiple pathways, with PI3 K/AKT/mTOR signaling being the most common event. Some specific individualized therapeutic possibilities include the expression of oncogenic KIT in association with cytokeratin 15 and Erk1/2 positive tumors, both of which may have clinical value.

Molecular Characterization of Testicular Germ Cell Tumors Using Tissue Microdissection

Testicular germ cell tumors are among the most common malignancies seen in children and young adults. Genomic studies have identified characteristic molecular profiles in testicular cancer, which are associated with histologic subtypes and may predict clinical behavior including treatment responses. Emerging molecular technologies analyzing tumor genomics, transcriptomics, and proteomics may now guide precision management of testicular tumors. Laser-assisted microdissection methods such as laser capture microdissection efficiently isolate selected tumor cells from routine pathology specimens, avoiding contamination from nontarget cell populations. Laser capture microdissection in combination with next generation sequencing makes precise high throughput genetic evaluation effective and efficient. The use of laser capture microdissection (LCM) for molecular testing may translate into great benefits for the clinical management of patients with testicular cancers. This review discusses application protocols for laser-assisted microdissection to investigate testicular germ cell tumors.

Molecular Signature for Lymphatic Invasion Associated with Survival of Epithelial Ovarian Cancer

Purpose

We aimed to develop molecular classifier that can predict lymphatic invasion and their clinical significance in epithelial ovarian cancer (EOC) patients.

Materials and Methods

We analyzed gene expression (mRNA, methylated DNA) in data from The Cancer Genome Atlas. To identify molecular signatures for lymphatic invasion, we found differentially expressed genes. The performance of classifier was validated by receiver operating characteristics analysis, logistic regression, linear discriminant analysis (LDA), and support vector machine (SVM). We assessed prognostic role of classifier using random survival forest (RSF) model and pathway deregulation score (PDS). For external validation,we analyzed microarray data from 26 EOC samples of Samsung Medical Center and curatedOvarianData database.

Results

We identified 21 mRNAs, and seven methylated DNAs from primary EOC tissues that predicted lymphatic invasion and created prognostic models. The classifier predicted lymphatic invasion well, which was validated by logistic regression, LDA, and SVM algorithm (C-index of 0.90, 0.71, and 0.74 for mRNA and C-index of 0.64, 0.68, and 0.69 for DNA methylation). Using RSF model, incorporating molecular data with clinical variables improved prediction of progression-free survival compared with using only clinical variables (p < 0.001 and p=0.008). Similarly, PDS enabled us to classify patients into high-risk and low-risk group, which resulted in survival difference in mRNA profiles (log-rank p-value=0.011). In external validation, gene signature was well correlated with prediction of lymphatic invasion and patients’ survival.

Conclusion

Molecular signature model predicting lymphatic invasion was well performed and also associated with survival of EOC patients.

Functional roles of long non-coding RNA in human breast cancer

The discovery of long noncoding RNA (LncRNA) changes our view of transcriptional and posttranscriptional regulation of gene expression. With application of new research techniques such as high-throughput sequencing, the biological functions of LncRNAs are gradually becoming to be understood. Multiple studies have shown that LncRNAs serve as carcinogenic factors or tumor suppressors in breast cancer with abnormal expression, prompts the question of whether they have potential value in predicting the stages and survival rate of breast cancer patients, and also as therapeutic targets. Focusing on the latest research data, this review mainly summarizes the tumorigenic mechanisms of certain LncRNAs in breast cancer, in order to provide a theoretical basis for finding safer, more effective treatment of breast cancer at the LncRNA molecular level.

Tumor-Specific Peptide, Selected from a Phage Peptide Library, Enhances Antitumor Activity of Lactaptin

A recombinant analogue of lactaptin (RL2), a new potential anticancer molecule, induces apoptosis in cultured tumor cells. The tumor suppression efficacy of RL2 was shown against mouse hepatoma-1 cells and MDA-MB-231 human breast adenocarcinoma cells. The RL2-based therapeutic drug lactaptin is distributed evenly throughout the organism, which reduces its antitumor efficacy. In the current study, we obtained a genetic construct that allows production of the recombinant fusion protein T3-RL2, consisting of RL2 and T3 peptide (YTYDPWLIFPAN), in E. coli cells. T3 peptide was selected from a phage peptide library as a result of two screenings: in vitro using MDA-MB-231 cell culture and in vivo using a mouse xenograft model of breast cancer MDA-MB-231. It was shown that the displayed peptide T3 provides binding and internalization of phage particles by MDA-MB-231 cells and their specific accumulation in MDA-MB-231 tumor tissue. In addition, based on the nucleotide sequences coding RL2 and the known tumor-targeting peptide iRGD, we obtained genetic constructs that provide synthesis of fusion proteins RL2-iRGD and RL-iRGD-His. We studied the cytotoxic activity of fusion proteins T3-RL2, RL2-iRGD and RL-iRGD-His in vitro using MDA-MB-231 and MCF-7 human adenocarcinoma cells. The in vitro results showed that the fusion proteins inhibit proliferation of both cell cultures, and their cytotoxic activity is higher than that of RL2. In vivo experiments on the study of the antitumor efficacy of the obtained fusion proteins demonstrated that T3-RL2 protein significantly inhibits MDA-MB-231 tumor growth in a xenograft model compared with RL2, while the antitumor effect of RL2-iRGD and RL-iRGD-His proteins is comparable to the effect of RL2.

Identification of a five-lncRNA signature for the diagnosis and prognosis of gastric cancer

Gastric cancer (GC) is one of the most aggressive malignancies and has a poor prognosis. Identifying novel diagnostic and prognostic markers is of great importance for the management and treatment of GC. Long non-coding RNAs (lncRNAs), which are involved in multiple processes during the development and progression of cancer, may act as potential biomarkers of GC. Here, by performing data mining using four microarray data sets of GC downloaded from the Gene Expression Omnibus (GEO) database with different classifiers and risk score analyses, we identified a five-lncRNA signature (AK001094, AK024171, AK093735, BC003519 and NR_003573) displaying both diagnostic and prognostic values for GC. The results of the Kaplan-Meier survival analysis and log-rank test showed that the risk score based on this five-lncRNA signature was closely associated with overall survival time (p = 0.0001). Further analysis revealed that the risk score is an independent predictor of prognosis. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) analysis of 30 pairs of GC tissue samples confirmed that the five lncRNAs were dysregulated in GC, and receiver operating characteristic (ROC) curves showed the high diagnostic ability of combining the five lncRNAs, with an area under the curve (AUC) of 0.95 ± 0.025. The five lncRNAs involved in several cancer-related pathways were identified using gene set enrichment analysis (GSEA). These findings indicate that the five-lncRNA signature may have a good clinical applicability for determining the diagnosis and predicting the prognosis of GC.

Identification of mutated driver pathways in cancer using a multi-objective optimization model

New-generation high-throughput technologies, including next-generation sequencing technology, have been extensively applied to solve biological problems. As a result, large cancer genomics projects such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium are producing large amount of rich and diverse data in multiple cancer types. The identification of mutated driver genes and driver pathways from these data is a significant challenge. Genome aberrations in cancer cells can be divided into two types: random 'passenger mutation' and functional 'driver mutation'. In this paper, we introduced a Multi-objective Optimization model based on a Genetic Algorithm (MOGA) to solve the maximum weight submatrix problem, which can be employed to identify driver genes and driver pathways promoting cancer proliferation. The maximum weight submatrix problem defined to find mutated driver pathways is based on two specific properties, i.e., high coverage and high exclusivity. The multi-objective optimization model can adjust the trade-off between high coverage and high exclusivity. We proposed an integrative model by combining gene expression data and mutation data to improve the performance of the MOGA algorithm in a biological context.

Significance of EGFR signaling pathway genetic alterations in radically resected non-small cell lung cancers from a Polish cohort. One institutional study

PURPOSE

We evaluated the distribution and clinical impact of EGFR, KRAS and HER2 copy number gains and EGFR, KRAS and BRAF activating mutations in resected non-small cell lung cancers (NSCLCs) from 151 Polish patients.

MATERIALS AND METHODS

Quantitative PCR and DNA sequencing were used for copy number evaluation and mutational analysis, respectively.

RESULTS

An increased EGFR CN was found in 21.2% of the tumors, more commonly of the non-squamous histology (P=0.029), larger in size (P=0.004) and those obtained from women (P=0.040). HER2 copy gain was observed in 21.8% of the patients, more frequently with lymph node metastases (P=0.048) and stage IIIA disease (P=0.061). KRAS gain was found in 29.3% of the tumors, and was not associated with patients' clinicopathological features. No BRAF mutations were found. EGFR and KRAS mutation frequency and associations with clinicopathological characteristics did not differ significantly from those previously described for the NSCLC patients of Caucasian ethnicity. Strong associations existed between most of the analyzed alterations. In the multivariate model, EGFR mutations constituted an independent prognostic factor of the disease recurrence in adenocarcinoma patients (HR 7.20; 95%CI 1.31-39.48; P=0.023), while an increased EGFR copy number tended to indicate a shorter overall survival (HR 4.85; 95%CI 0.92-25.58; P=0.062).

CONCLUSIONS

EGFR pathway genes alterations are frequent in NSCLCs from Polish patients and have a prognostic potential for patients' clinical outcome after a curative tumor resection. Gene CN evaluation by quantitative PCR provides comparable results and enables assay standardization, yet the optimal scoring system needs to be developed.

Assessing the clinical value of targeted massively parallel sequencing in a longitudinal, prospective population-based study of cancer patients

Introduction:

Recent discoveries in cancer research have revealed a plethora of clinically actionable mutations that provide therapeutic, prognostic and predictive benefit to patients. The feasibility of screening mutations as part of the routine clinical care of patients remains relatively unexplored as the demonstration of massively parallel sequencing (MPS) of tumours in the general population is required to assess its value towards the health-care system.

Methods:

Cancer 2015 study is a large-scale, prospective, multisite cohort of newly diagnosed cancer patients from Victoria, Australia with 1094 patients recruited. MPS was performed using the Illumina TruSeq Amplicon Cancer Panel.

Results:

Overall, 854 patients were successfully sequenced for 48 common cancer genes. Accurate determination of clinically relevant mutations was possible including in less characterised cancer types; however, technical limitations including formalin-induced sequencing artefacts were uncovered. Applying strict filtering criteria, clinically relevant mutations were identified in 63% of patients, with 26% of patients displaying a mutation with therapeutic implications. A subset of patients was validated for canonical mutations using the Agena Bioscience MassARRAY system with 100% concordance. Whereas the prevalence of mutations was consistent with other institutionally based series for some tumour streams (breast carcinoma and colorectal adenocarcinoma), others were different (lung adenocarcinoma and head and neck squamous cell carcinoma), which has significant implications for health economic modelling of particular targeted agents. Actionable mutations in tumours not usually thought to harbour such genetic changes were also identified.

Conclusions:

Reliable delivery of a diagnostic assay able to screen for a range of actionable mutations in this cohort was achieved, opening unexpected avenues for investigation and treatment of cancer patients.

Non-coding RNAs: biological functions and applications

Analyses of the international human genome sequencing results in 2004 converged to a consensual number of ~20,000 protein-coding genes, spanning over <2% of the total genomic sequence. Therefore, the developmental and physiological complexity of human beings remains unaccounted if viewed only in terms of the number of protein-coding genes; the epigenetic influences involving chromatin remodelling and RNA interference and alternative precursor messenger RNA splicing of functional protein-coding transcripts as well as post-translational modifications of proteins increase the diversity and the functionality of the proteome and likely explain the increased complexity. In addition, there has been an explosion of research addressing possible functional roles for the other 98% of the human genome that does not encode proteins. In fact, >90% of the human genome is likely to be transcribed yielding a complex network of overlapping transcripts that include tens of thousands of long RNAs with little or no protein forming capacity; they are collectively called non-coding RNA. This review highlights the fundamental concepts of biological roles of non-coding RNA and their importance in regulation of cellular physiology under disease conditions like cancer.

Simulated annealing based algorithm for identifying mutated driver pathways in cancer

With the development of next-generation DNA sequencing technologies, large-scale cancer genomics projects can be implemented to help researchers to identify driver genes, driver mutations, and driver pathways, which promote cancer proliferation in large numbers of cancer patients. Hence, one of the remaining challenges is to distinguish functional mutations vital for cancer development, and filter out the unfunctional and random "passenger mutations." In this study, we introduce a modified method to solve the so-called maximum weight submatrix problem which is used to identify mutated driver pathways in cancer. The problem is based on two combinatorial properties, that is, coverage and exclusivity. Particularly, we enhance an integrative model which combines gene mutation and expression data. The experimental results on simulated data show that, compared with the other methods, our method is more efficient. Finally, we apply the proposed method on two real biological datasets. The results show that our proposed method is also applicable in real practice.

Global analysis of miRNA gene clusters and gene families reveals dynamic and coordinated expression

To further understand the potential expression relationships of miRNAs in miRNA gene clusters and gene families, a global analysis was performed in 4 paired tumor (breast cancer) and adjacent normal tissue samples using deep sequencing datasets. The compositions of miRNA gene clusters and families are not random, and clustered and homologous miRNAs may have close relationships with overlapped miRNA species. Members in the miRNA group always had various expression levels, and even some showed larger expression divergence. Despite the dynamic expression as well as individual difference, these miRNAs always indicated consistent or similar deregulation patterns. The consistent deregulation expression may contribute to dynamic and coordinated interaction between different miRNAs in regulatory network. Further, we found that those clustered or homologous miRNAs that were also identified as sense and antisense miRNAs showed larger expression divergence. miRNA gene clusters and families indicated important biological roles, and the specific distribution and expression further enrich and ensure the flexible and robust regulatory network.

Multi-layered cancer chromosomal instability phenotype

Whole-chromosomal instability (W-CIN) – unequal chromosome distribution during cell division – is a characteristic feature of a majority of cancer cells distinguishing them from their normal counterparts. The precise molecular mechanisms that may cause mis-segregation of chromosomes in tumor cells just recently became more evident. The consequences of W-CIN are numerous and play a critical role in carcinogenesis. W-CIN mediates evolution of cancer cell population under selective pressure and can facilitate the accumulation of genetic changes that promote malignancy. It has both tumor-promoting and tumor-suppressive effects, and their balance could be beneficial or detrimental for carcinogenesis. The characterization of W-CIN as a complex multi-layered adaptive phenotype highlights the intra- and extracellular adaptations to the consequences of genome reshuffling. It also provides a framework for targeting aggressive chromosomally unstable cancers.

Integrated genomic analysis of triple-negative breast cancers reveals novel microRNAs associated with clinical and molecular phenotypes and sheds light on the pathways they control

Background

This study focuses on the analysis of miRNAs expression data in a cohort of 181 well characterised breast cancer samples composed primarily of triple-negative (ER/PR/HER2-negative) tumours with associated genome-wide DNA and mRNA data, extensive patient follow-up and pathological information.

Results

We identified 7 miRNAs associated with prognosis in the triple-negative tumours and an additional 7 when the analysis was extended to the set of all ER-negative cases. miRNAs linked to an unfavourable prognosis were associated with a broad spectrum of motility mechanisms involved in the invasion of stromal tissues, such as cell-adhesion, growth factor-mediated signalling pathways, interaction with the extracellular matrix and cytoskeleton remodelling. When we compared different intrinsic molecular subtypes we found 46 miRNAs that were specifically expressed in one or more intrinsic subtypes. Integrated genomic analyses indicated these miRNAs to be influenced by DNA genomic aberrations and to have an overall influence on the expression levels of their predicted targets. Among others, our analyses highlighted the role of miR-17-92 and miR-106b-25, two polycistronic miRNA clusters with known oncogenic functions. We showed that their basal-like subtype specific up-regulation is influenced by increased DNA copy number and contributes to the transcriptional phenotype as well as the activation of oncogenic pathways in basal-like tumours.

Conclusions

This study analyses previously unreported miRNA, mRNA and DNA data and integrates these with pathological and clinical information, from a well-annotated cohort of breast cancers enriched for triple-negative subtypes. It provides a conceptual framework, as well as integrative methods and system-level results and contributes to elucidate the role of miRNAs as biomarkers and modulators of oncogenic processes in these types of tumours.

The role of chromosome missegregation in cancer development: a theoretical approach using agent-based modelling

Many cancers are aneuploid. However, the precise role that chromosomal instability plays in the development of cancer and in the response of tumours to treatment is still hotly debated. Here, to explore this question from a theoretical standpoint we have developed an agent-based model of tissue homeostasis in which to test the likely effects of whole chromosome mis-segregation during cancer development. In stochastic simulations, chromosome mis-segregation events at cell division lead to the generation of a diverse population of aneuploid clones that over time exhibit hyperplastic growth. Significantly, the course of cancer evolution depends on genetic linkage, as the structure of chromosomes lost or gained through mis-segregation events and the level of genetic instability function in tandem to determine the trajectory of cancer evolution. As a result, simulated cancers differ in their level of genetic stability and in their growth rates. We used this system to investigate the consequences of these differences in tumour heterogeneity for anti-cancer therapies based on surgery and anti-mitotic drugs that selectively target proliferating cells. As expected, simulated treatments induce a transient delay in tumour growth, and reveal a significant difference in the efficacy of different therapy regimes in treating genetically stable and unstable tumours. These data support clinical observations in which a poor prognosis is correlated with a high level of chromosome mis-segregation. However, stochastic simulations run in parallel also exhibit a wide range of behaviours, and the response of individual simulations (equivalent to single tumours) to anti-cancer therapy prove extremely variable. The model therefore highlights the difficulties of predicting the outcome of a given anti-cancer treatment, even in cases in which it is possible to determine the genotype of the entire set of cells within the developing tumour.

Isoform level expression profiles provide better cancer signatures than gene level expression profiles

BACKGROUND

The majority of mammalian genes generate multiple transcript variants and protein isoforms through alternative transcription and/or alternative splicing, and the dynamic changes at the transcript/isoform level between non-oncogenic and cancer cells remain largely unexplored. We hypothesized that isoform level expression profiles would be better than gene level expression profiles at discriminating between non-oncogenic and cancer cellsgene level.

METHODS

We analyzed 160 Affymetrix exon-array datasets, comprising cell lines of non-oncogenic or oncogenic tissue origins. We obtained the transcript-level and gene level expression estimates, and used unsupervised and supervised clustering algorithms to study the profile similarity between the samples at both gene and isoform levels.

RESULTS

Hierarchical clustering, based on isoform level expressions, effectively grouped the non-oncogenic and oncogenic cell lines with a virtually perfect homogeneity-grouping rate (97.5%), regardless of the tissue origin of the cell lines. However, gene levelthis rate was much lower, being 75% at best based on the gene level expressions. Statistical analyses of the difference between cancer and non-oncogenic samples identified the existence of numerous genes with differentially expressed isoforms, which otherwise were not significant at the gene level. We also found that canonical pathways of protein ubiquitination, purine metabolism, and breast-cancer regulation by stathmin1 were significantly enriched among genes thatshow differential expression at isoform level but not at gene level.

CONCLUSIONS

In summary, cancer cell lines, regardless of their tissue of origin, can be effectively discriminated from non-cancer cell lines at isoform level, but not at gene level. This study suggests the existence of an isoform signature, rather than a gene signature, which could be used to distinguish cancer cells from normal cells.

The long non-coding RNAs, a new cancer diagnostic and therapeutic gold mine

The conventional view of gene regulation in biology has centered around protein-coding genes via the central dogma of DNA-->mRNA-->protein. The discovery of thousands of long non-coding RNAs (lncRNAs) has certainly changed our view of the complexity of mammalian genomes and transcriptomes, as well as many other aspects of biology including transcriptional and posttranscriptional regulation of gene expression. Accumulating reports of misregulated lncRNA expression across numerous cancer types suggest that aberrant lncRNA expression may be a major contributor to tumorigenesis. Here, we summarize recent data about the biological characteristics of lncRNAs in cancer pathways. These include examples with a wide range of molecular mechanisms involved in gene regulation. We also consider the medical implications, and discuss how lncRNAs can be used for cancer diagnosis and prognosis, and serve as potential therapeutic targets. As more examples of regulation by lncRNA are uncovered, one might predict that the large transcripts will eventually rival small RNAs and proteins in their versatility as regulators of genetic information.

Proteomic analysis of frozen tissue samples using laser capture microdissection

The discovery of effective cancer biomarkers is essential for the development of both advanced molecular diagnostics and new therapies/medications. Finding and exploiting useful clinical biomarkers for cancer patients is fundamentally linked to improving outcomes. Towards these aims, the heterogeneous nature of tumors represents a significant problem. Thus, methods establishing an effective functional linkage between laser capture microdissection (LCM) and mass spectrometry (MS) provides for an enhanced molecular profiling of homogenous, specifically targeted cell populations from solid tumors. Utilizing frozen tissue avoids molecular degradation and bias that can be induced by other preservation techniques. Since clinical samples are often of a small quantity, tissue losses must be minimized. Therefore, all steps are carried out in the same single tube. Proteins are identified through peptide sequencing and subsequent matching against a specific proteomic database. Using such an approach enhances clinical biomarker discovery in the following ways. First, LCM allows for the complexity of a solid tumor to be reduced. Second, MS provides for the profiling of proteins, which are the ultimate bio-effectors. Third, by selecting for tumor proper or microenvironment-specific cells from clinical samples, the heterogeneity of individual solid tumors is directly addressed. Finally, since proteins are the targets of most pharmaceuticals, the enriched protein data streams can then be further analyzed for potential biomarkers, drug targets, pathway elucidation, as well as an enhanced understanding of the various pathologic processes under study. Within this context, the following method illustrates in detail a synergy between LCM and MS for an enhanced molecular profiling of solid tumors and clinical biomarker discovery.

Establishment, characterization and chemosensitivity of three mismatch repair deficient cell lines from sporadic and inherited colorectal carcinomas

Background

Colorectal cancer (CRC) represents a morphologic and molecular heterogenic disease. This heterogeneity substantially impairs drug effectiveness and prognosis. The subtype of mismatch repair deficient (MMR-D) CRCs, accounting for about 15% of all cases, shows particular differential responses up to resistance towards currently approved cytostatic drugs. Pre-clinical in vitro models representing molecular features of MMR-D tumors are thus mandatory for identifying biomarkers that finally help to predict responses towards new cytostatic drugs. Here, we describe the successful establishment and characterization of three patient-derived MMR-D cell lines (HROC24, HROC87, and HROC113) along with their corresponding xenografts.

Methodology

MMR-D cell lines (HROC24, HROC87, and HROC113) were established from a total of ten clinicopathological well-defined MMR-D cases (120 CRC cases in total). Cells were comprehensively characterized by phenotype, morphology, growth kinetics, invasiveness, and molecular profile. Additionally, response to clinically relevant chemotherapeutics was examined in vitro and in vivo.

Principal Findings

Two MMR-D lines showing CIMP-H derived from sporadic CRC (HROC24: K-ras^wt, B-raf^mut, HROC87: K-ras^wt, B-raf^mut), whereas the HROC113 cell line (K-ras^mut, B-raf^wt) was HNPCC-associated. A diploid DNA-status could be verified by flow cytometry and SNP Array analysis. All cell lines were characterized as epithelial (EpCAM⁺) tumor cells, showing surface tumor marker expression (CEACAM⁺). MHC-class II was inducible by Interferon-γ stimulation. Growth kinetics as well as invasive potential was quite heterogeneous between individual lines. Besides, MMR-D cell lines exhibited distinct responsiveness towards chemotherapeutics, even when comparing in vitro and in vivo sensitivity.

Conclusions

These newly established and well-characterized, low-passage MMR-D cell lines provide a useful tool for future investigations on the biological characteristics of MMR-D CRCs, both of sporadic and hereditary origin. Additionally, matched patient-derived immune cells allow for comparative genetic studies.

Insertion/deletion polymorphisms are convenient and reliable markers to assess chromosomal instability in human tumors

Chromosomal instability (CIN) is frequently associated with a poor outcome in human carcinomas. The genomes of the main human malignancies are well defined as hundreds of tumors have been characterized by arrays. Targeting the appropriate chromosomes with set of markers appears as a realistic approach for CIN assessment. We decided to test the reliability of different insertion/deletion (InDel) polymorphisms to detect allelic loss in a subset of previously characterized hepatocellular carcinomas (HCC). To this aim 3 kinds of markers, L1 insertion (n=1), Alu insertions (n=4) and Marshfield InDel (MID, n=8) markers, were tested on a series of 68 paired HCC/non-tumor liver samples that were previously characterized for loss of heterozygosity (LOH). All markers were analyzed on agarose gels and some were tested with the high resolution melting (HRM) technique. Heterozygosity of the tested markers was high with a mean of 0.489 and a range of 0.265-0.525. Using 6 markers for chromosome 8p, the sensitivity of the method was high. LOH was detected in all samples known to be affected (n=34) whereas retention was found in 29/30 samples (specificity of 96.6%). Finally, the HRM analysis applied to 2 MID markers provided consistent profiles enabling closed-tube determination of chromosomes 17p and 18q status. Overall, our work suggests that different types of InDel markers are suitable for CIN detection in human tumors and may provide convenient and useful information for basic or translational research as well as for future applications in clinical practice.

Epigenetics advancing personalized nanomedicine in cancer therapy

Personalized medicine aims to deliver the right drug to a right patient at the right time. It offers unique opportunities to integrate new technologies and concepts to disease prognosis, diagnosis and therapeutics. While selective personalized therapies are conceptually impressive, the majority of cancer therapies have dismal outcome. Such therapeutic failure could result from no response, drug resistance, disease relapse or severe side effect from improper drug delivery. Nanomedicine, the application of nanotechnology in medicine, has a potential to advance the identification of diagnostic and prognostic biomarkers and the delivery of right drug to disease sites. Epigenetic aberrations dynamically contribute to cancer pathogenesis. Given the individualized traits of epigenetic biomarkers, epigenetic considerations would significantly refine personalized nanomedicine. This review aims to dissect the interface of personalized medicine with nanomedicine and epigenetics. I will outline the progress and highlight challenges and areas that can be further explored perfecting the personalized health care.

Efficient methods for identifying mutated driver pathways in cancer

MOTIVATION

The first step for clinical diagnostics, prognostics and targeted therapeutics of cancer is to comprehensively understand its molecular mechanisms. Large-scale cancer genomics projects are providing a large volume of data about genomic, epigenomic and gene expression aberrations in multiple cancer types. One of the remaining challenges is to identify driver mutations, driver genes and driver pathways promoting cancer proliferation and filter out the unfunctional and passenger ones.

RESULTS

In this study, we propose two methods to solve the so-called maximum weight submatrix problem, which is designed to de novo identify mutated driver pathways from mutation data in cancer. The first one is an exact method that can be helpful for assessing other approximate or/and heuristic algorithms. The second one is a stochastic and flexible method that can be employed to incorporate other types of information to improve the first method. Particularly, we propose an integrative model to combine mutation and expression data. We first apply our methods onto simulated data to show their efficiency. We further apply the proposed methods onto several real biological datasets, such as the mutation profiles of 74 head and neck squamous cell carcinomas samples, 90 glioblastoma tumor samples and 313 ovarian carcinoma samples. The gene expression profiles were also considered for the later two data. The results show that our integrative model can identify more biologically relevant gene sets. We have implemented all these methods and made a package called mutated driver pathway finder, which can be easily used for other researchers.

AVAILABILITY

A MATLAB package of MDPFinder is available at http://zhangroup.aporc.org/ShiHuaZhang.

CONTACT

zsh@amss.ac.cn.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identification of a chrXq27.3 microRNA cluster associated with early relapse in advanced stage ovarian cancer patients

A major challenge in advanced-stage epithelial ovarian cancer (EOC) is prediction of chemoresistant relapse. Our aim was to identify a microRNA (miRNA) signature associated with early relapse in advanced-stage EOC patients. miRNA expression was assessed by microarray profiling in training (n = 55) and test (n = 30) sets selected on the basis of time to relapse (TTR), followed by internal quantitative reverse transcriptase-PCR validation on a set of 45 consecutive cases unselected for clinical response and external in silico validation on publicly available datasets. Thirty-two differentially expressed miRNAs in early vs. late relapsing patients were identified in the training set. In the test set, 8 of these, belonging to a cluster located on chrXq27.3, were down-modulated in early relapsing patients. Hierarchical clustering of the internal validation set according to chrXq27.3 miRNA expression associated low miRNA expression with shorter TTR (log-rank P=0.00074, HR 2.44). The cluster was an independent prognostic factor in both internal and external validation sets. Forced expression of chrXq27.3-cluster selected miRNAs in human EOC cellular models was associated to reduction of cell proliferation and increased sensitivity to cisplatin. The role of down-modulation of the chrXq27.3 miRNA cluster in early relapse of advanced-stage EOC patients and its association to a reduced sensitivity to chemotherapeutic treatments warrant further investigation.

Interrelationship between microsatellite instability and microRNA in gastrointestinal cancer

There is an increasing understanding of the roles that microsatellite instability (MSI) plays in Lynch syndrome (by mutations) and sporadic (by mainly epigenetic changes) gastrointestinal (GI) and other cancers. Deficient DNA mismatch repair (MMR) results in the strong mutator phenotype known as MSI, which is the hallmark of cancers arising within Lynch syndrome. MSI is characterized by length alterations within simple repeated sequences called microsatellites. Lynch syndrome occurs primarily because of germline mutations in one of the MMR genes, mainly MLH1 or MSH2, less frequently MSH6, and rarely PMS2. MSI is also observed in about 15% of sporadic colorectal, gastric, and endometrial cancers and in lower frequencies in a minority of other cancers where it is often associated with the hypermethylation of the MLH1 gene. miRNAs are small noncoding RNAs that regulate gene expression at the posttranscriptional level and are critical in many biological processes and cellular pathways. There is accumulating evidence to support the notion that the interrelationship between MSI and miRNA plays a key role in the pathogenesis of GI cancer. As a possible new mechanism underlying MSI, overexpression of miR-155 has been shown to downregulate expression of MLH1, MSH2, and MSH6. Thus, a subset of MSI-positive (MSI+) cancers without known MMR defects may result from miR-155 overexpression. Target genes of frameshift mutation for MSI are involved in various cellular functions, such as DNA repair, cell signaling, and apoptosis. A novel class of target genes that included not only epigenetic modifier genes, such as HDAC2, but also miRNA processing machinery genes, including TARBP2 and XPO5, were found to be mutated in MSI+ GI cancers. Thus, a subset of MSI+ colorectal cancers (CRCs) has been proposed to exhibit a mutated miRNA machinery phenotype. Genetic, epigenetic, and transcriptomic differences exist between MSI+ and MSI− cancers. Molecular signatures of miRNA expression apparently have the potential to distinguish between MSI+ and MSI− CRCs. In this review, we summarize recent advances in the MSI pathogenesis of GI cancer, with the focus on its relationship with miRNA as well as on the potential to use MSI and related alterations as biomarkers and novel therapeutic targets.

DNA methylation profiles in precancerous tissue and cancers: carcinogenetic risk estimation and prognostication based on DNA methylation status

Alterations in DNA methylation, which are associated with DNA methyltransferase abnormalities and result in silencing of tumor-related genes and chromosomal instability, are involved even in precancerous changes in various organs. DNA methylation alterations also account for the histological heterogeneity and clinicopathological diversity of human cancers. Therefore, we have analyzed DNA methylation on a genome-wide scale in clinical tissue samples. Our approach using the bacterial artificial chromosome array-based methylated CpG island amplification method has revealed that DNA methylation alterations correlated with the future development of more malignant cancers are already accumulated at the precancerous stage in the kidney, liver and urinary tract. DNA methylation profiles at precancerous stages are basically inherited by the corresponding cancers developing in individual patients. Such DNA methylation alterations may confer vulnerability to further genetic and epigenetic alterations, generate more malignant cancers, and thus determine patient outcome. On the basis of bacterial artificial chromosome array-based methylated CpG island amplification data, indicators for carcinogenetic risk estimation have been established using liver tissue specimens from patients with hepatitis virus infection, chronic hepatitis and liver cirrhosis or histologically normal urothelia, and for prognostication using biopsy or surgically resected specimens from patients with renal cell carcinoma, hepatocellular carcinoma and urothelial carcinoma. Such genome-wide DNA methylation profiling has now firmly established the clinical relevance of translational epigenetics.

Primary colorectal cancers and their subsequent hepatic metastases are genetically different: implications for selection of patients for targeted treatment

PURPOSE

In the era of DNA-guided personalized cancer treatment, it is essential to conduct predictive analysis on the tissue that matters. Here, we analyzed genetic differences between primary colorectal adenocarcinomas (CRC) and their respective hepatic metastasis.

EXPERIMENTAL DESIGN

The primary CRC and the subsequent hepatic metastasis of 21 patients with CRC were analyzed using targeted deep-sequencing of DNA isolated from formalin-fixed, paraffin-embedded archived material.

RESULTS

We have interrogated the genetic constitution of a designed "Cancer Mini-Genome" consisting of all exons of 1,264 genes associated with pathways relevant to cancer. In total, 6,696 known and 1,305 novel variations were identified in 1,174 and 667 genes, respectively, including 817 variants that potentially altered protein function. On average, 83 (SD = 69) potentially function-impairing variations were gained in the metastasis and 70 (SD = 48) variations were lost, showing that the primary tumor and hepatic metastasis are genetically significantly different. Besides novel and known variations in genes such as KRAS, BRAF, KDR, FLT1, PTEN, and PI3KCA, aberrations in the up/downstream genes of EGFR/PI3K/VEGF-pathways and other pathways (mTOR, TGFβ, etc.) were also detected, potentially influencing therapeutic responsiveness. Chemotherapy between removal of the primary tumor and the metastasis (N = 11) did not further increase the amount of genetic variation.

CONCLUSION

Our study indicates that the genetic characteristics of the hepatic metastases are different from those of the primary CRC tumor. As a consequence, the choice of treatment in studies investigating targeted therapies should ideally be based on the genetic properties of the metastasis rather than on those of the primary tumor.

Biomarker robustness reveals the PDGF network as driving disease outcome in ovarian cancer patients in multiple studies

Background

Ovarian cancer causes more deaths than any other gynecological cancer. Identifying the molecular mechanisms that drive disease progress in ovarian cancer is a critical step in providing therapeutics, improving diagnostics, and affiliating clinical behavior with disease etiology. Identification of molecular interactions that stratify prognosis is key in facilitating a clinical-molecular perspective.

Results

The Cancer Genome Atlas has recently made available the molecular characteristics of more than 500 patients. We used the TCGA multi-analysis study, and two additional datasets and a set of computational algorithms that we developed. The computational algorithms are based on methods that identify network alterations and quantify network behavior through gene expression.

We identify a network biomarker that significantly stratifies survival rates in ovarian cancer patients. Interestingly, expression levels of single or sets of genes do not explain the prognostic stratification. The discovered biomarker is composed of the network around the PDGF pathway. The biomarker enables prognosis stratification.

Conclusion

The work presented here demonstrates, through the power of gene-expression networks, the criticality of the PDGF network in driving disease course. In uncovering the specific interactions within the network, that drive the phenotype, we catalyze targeted treatment, facilitate prognosis and offer a novel perspective into hidden disease heterogeneity.

The breast cancer genome--a key for better oncology

Molecular classification has added important knowledge to breast cancer biology, but has yet to be implemented as a clinical standard. Full sequencing of breast cancer genomes could potentially refine classification and give a more complete picture of the mutational profile of cancer and thus aid therapy decisions. Future treatment guidelines must be based on the knowledge derived from histopathological sub-classification of tumors, but with added information from genomic signatures when properly clinically validated. The objective of this article is to give some background on molecular classification, the potential of next generation sequencing, and to outline how this information could be implemented in the clinic.

Insight into the heterogeneity of breast cancer through next-generation sequencing

Rapid and sophisticated improvements in molecular analysis have allowed us to sequence whole human genomes as well as cancer genomes, and the findings suggest that we may be approaching the ability to individualize the diagnosis and treatment of cancer. This paradigmatic shift in approach will require clinicians and researchers to overcome several challenges including the huge spectrum of tumor types within a given cancer, as well as the cell-to-cell variations observed within tumors. This review discusses how next-generation sequencing of breast cancer genomes already reveals insight into tumor heterogeneity and how it can contribute to future breast cancer classification and management.

Inferring causal genomic alterations in breast cancer using gene expression data

BACKGROUND

One of the primary objectives in cancer research is to identify causal genomic alterations, such as somatic copy number variation (CNV) and somatic mutations, during tumor development. Many valuable studies lack genomic data to detect CNV; therefore, methods that are able to infer CNVs from gene expression data would help maximize the value of these studies.

RESULTS

We developed a framework for identifying recurrent regions of CNV and distinguishing the cancer driver genes from the passenger genes in the regions. By inferring CNV regions across many datasets we were able to identify 109 recurrent amplified/deleted CNV regions. Many of these regions are enriched for genes involved in many important processes associated with tumorigenesis and cancer progression. Genes in these recurrent CNV regions were then examined in the context of gene regulatory networks to prioritize putative cancer driver genes. The cancer driver genes uncovered by the framework include not only well-known oncogenes but also a number of novel cancer susceptibility genes validated via siRNA experiments.

CONCLUSIONS

To our knowledge, this is the first effort to systematically identify and validate drivers for expression based CNV regions in breast cancer. The framework where the wavelet analysis of copy number alteration based on expression coupled with the gene regulatory network analysis, provides a blueprint for leveraging genomic data to identify key regulatory components and gene targets. This integrative approach can be applied to many other large-scale gene expression studies and other novel types of cancer data such as next-generation sequencing based expression (RNA-Seq) as well as CNV data.

MR evaluation of response to targeted treatment in cancer cells

The development of molecular technologies, together with progressive sophistication of molecular imaging methods, has allowed the further elucidation of the multiple mutations and dysregulatory effects of pathways leading to oncogenesis. Acting against these pathways by specifically targeted agents represents a major challenge for current research efforts in oncology. As conventional anatomically based pharmacological endpoints may be inadequate to monitor the tumor response to these targeted treatments, the identification and use of more appropriate, noninvasive pharmacodynamic biomarkers appear to be crucial to optimize the design, dosage and schedule of these novel therapeutic approaches. An aberrant choline phospholipid metabolism and enhanced flux of glucose derivatives through glycolysis, which sustain the redirection of mitochondrial ATP to glucose phosphorylation, are two major hallmarks of cancer cells. This review focuses on the changes detected in these pathways by MRS in response to targeted treatments. The progress and limitations of our present understanding of the mechanisms underlying MRS-detected phosphocholine accumulation in cancer cells are discussed in the light of gene and protein expression and the activation of different enzymes involved in phosphatidylcholine biosynthesis and catabolism. Examples of alterations induced in the MRS choline profile of cells exposed to different agents or to tumor environmental factors are presented. Current studies aimed at the identification in cancer cells of MRS-detected pharmacodynamic markers of therapies targeted against specific conditional or constitutive cell receptor stimulation are then reviewed. Finally, the perspectives of present efforts addressed to identify enzymes of the phosphatidylcholine cycle as possible novel targets for anticancer therapy are summarized.

Functional gene expression profile underlying methotrexate-induced senescence in human colon cancer cells

Cellular functions accompanying establishment of premature senescence in methotrexate-treated human colon cancer C85 cells are deciphered in the present study from validated competitive expression microarray data, analyzed with the use of Ingenuity Pathways Analysis (IPA) software. The nitrosative/oxidative stress, inferred from upregulated expression of inducible nitric oxide synthase (iNOS) and mitochondrial dysfunction-associated genes, including monoamine oxidases MAOA and MAOB, β-amyloid precursor protein (APP) and presenilin 1 (PSEN1), is identified as the main determinant of signaling pathways operating during senescence establishment. Activation of p53-signaling pathway is found associated with both apoptotic and autophagic components contributing to this process. Activation of nuclear factor κB (NF-κB), resulting from interferon γ (IFNγ), integrin, interleukin 1β (IL-1β), IL-4, IL-13, IL-22, Toll-like receptors (TLRs) 1, 2 and 3, growth factors and tumor necrosis factor (TNF) superfamily members signaling, is found to underpin inflammatory properties of senescent C85 cells. Upregulation of p21-activated kinases (PAK2 and PAK6), several Rho molecules and myosin regulatory light chains MYL12A and MYL12B, indicates acquisition of motility by those cells. Mitogen-activated protein kinase p38 MAPK β, extracellular signal-regulated kinases ERK2 and ERK5, protein kinase B AKT1, as well as calcium, are identified as factors coordinating signaling pathways in senescent C85 cells.

Profiling solid tumor heterogeneity by LCM and biological MS of fresh-frozen tissue sections

The heterogeneous nature of solid tumors represents a common problem in mass spectrometry (MS)-based analysis of fresh-frozen tissue specimens. Here, we describe a method that relies on synergy between laser capture microdissection (LCM) and MS for enhanced molecular profiling of solid tumors. This method involves dissection of homogeneous histologic cell types from thin fresh-frozen tissue sections via LCM, coupled with liquid chromatography (LC)-MS analysis. Such an approach enables an in-depth molecular profiling of captured cells. This is a bottom-up proteomic approach, where proteins are identified through peptide sequencing and matching against a specific proteomic database. Sample losses are minimized, since lysis, solubilization, and digestion are carried out directly on LCM caps in buffered methanol using a single tube, thus reducing sample loss between these steps. The rationale for the LCM-MS coupling is that once the optimal method parameters are established for a solid tumor of interest, homogeneous histologic tumor/tissue cells (i.e., tumor proper, stroma, etc.) can be effectively studied for potential biomarkers, drug targets, pathway analysis, as well as enhanced understanding of the pathological process under study.

Histone deacetylase inhibitors in the treatment of hematological malignancies and solid tumors

The human genome is epigenetically organized through a series of modifications to the histone proteins that interact with the DNA. In cancer, many of the proteins that regulate these modifications can be altered in both function and expression. One example of this is the family of histone deacetylases (HDACs), which as their name implies remove acetyl groups from the histone proteins, allowing for more condensed nucleosomal structure. HDACs have increased expression in cancer and are also believed to promote carcinogenesis through the acetylation and interaction with key transcriptional regulators. Given this, small molecule histone deacetylases inhibitors have been identified and developed, which not only inhibit HDACs, but can also lead to growth arrest, differentiation, and/or apoptosis in tumors both in vitro and in vivo. Here, we will discuss some of the recent developments in clinical trials utilizing HDACs inhibitors for the treatment of both hematological malignancies as well as solid tumors.

Microsatellite instability in colorectal cancer

Approximately 20 percent of right-sided colon cancers and 5 percent of left-sided colon and rectal cancers have a deficient DNA mismatch repair system. This results in the widespread accumulation of mutations to nucleotide repeats, some of which occur within the coding regions of cancer-related genes such as TGFβRII and BAX. A standardized definition for microsatellite instability (MSI) based on the presence of deletions to mononucleotide repeats is gaining widespread acceptance in both research and the clinic. Colorectal cancer (CRC) with MSI are characterized histologically by an abundance of tumor-infiltrating lymphocytes, poor differentiation and a signet ring or mucinous phenotype. In younger patients these tumors usually develop along the chromosomal instability pathway, in which case the mismatch repair genes are inactivated by germline mutation, somatic mutation and loss of heterozygosity. In older patients MSI CRC usually develops against a background of widespread hypermethylation that includes methylation-induced silencing of the mismatch repair gene MLH1. The overall biological and clinical phenotype of MSI CRC that arise in these two pathways is likely to be different and may account for some of the discordant results reported in the literature relating to the clinical properties of these tumors. The available evidence indicates that MSI is unlikely to be a clinically useful marker for the prognostic stratification of early-stage CRC. The predictive value of MSI for response to 5-fluorouracil-based chemotherapy remains controversial, while for other agents the predictive value is difficult to assess because they are used in combination regimens. The MSI phenotype is being actively investigated for novel therapeutic approaches based on the principle of synthetic lethality. Finally, the MSI status of CRC is an extremely useful marker for population-based screening programs that aim to identify individuals and families with the hereditary cancer condition known as Lynch syndrome.

Large non-coding RNAs: missing links in cancer?

Cellular homeostasis is achieved by the proper balance of regulatory networks that if disrupted can lead to cellular transformation. These cell circuits are fine-tuned and maintained by the coordinated function of proteins and non-coding RNAs (ncRNAs). In addition to the well-characterized protein coding and microRNAs constituents, large ncRNAs are also emerging as important regulatory molecules in tumor-suppressor and oncogenic pathways. Recent studies have revealed mechanistic insight of large ncRNAs regulating key cancer pathways at a transcriptional, post-transcriptional and epigenetic level. Here we synthesize these latest advances within the context of their mechanistic roles in regulating and maintaining cellular equilibrium. We posit that similar to protein-coding genes, large ncRNAs are a newly emerging class of oncogenic and tumor-suppressor genes. Our growing knowledge of the role of large ncRNAs in cellular transformation is pointing towards their potential use as biomarkers and targets for novel therapeutic approaches in the future.

Computational method for estimating DNA copy numbers in normal samples, cancer cell lines, and solid tumors using array comparative genomic hybridization

Genomic copy number variations are a typical feature of cancer. These variations may influence cancer outcomes as well as effectiveness of treatment. There are many computational methods developed to detect regions with deletions and amplifications without estimating actual copy numbers (CN) in these regions. We have developed a computational method capable of detecting regions with deletions and amplifications as well as estimating actual copy numbers in these regions. The method is based on determining how signal intensity from different probes is related to CN, taking into account changes in the total genome size, and incorporating into analysis contamination of the solid tumors with benign tissue. Hidden Markov Model is used to obtain the most likely CN solution. The method has been implemented for Affymetrix 500K GeneChip arrays and Agilent 244K oligonucleotide arrays. The results of CN analysis for normal cell lines, cancer cell lines, and tumor samples are presented. The method is capable of detecting copy number alterations in tumor samples with up to 80% contamination with benign tissue. Analysis of 178 cancer cell lines reveals multiple regions of common homozygous deletions and strong amplifications encompassing known tumor suppressor genes and oncogenes as well as novel cancer related genes.

Gene expression: protein interaction systems network modeling identifies transformation-associated molecules and pathways in ovarian cancer

Multiple, dissimilar genetic defects in cancers of the same origin contribute to heterogeneity in tumor phenotypes and therapeutic responses of patients, yet the associated molecular mechanisms remain elusive. Here, we show at the systems level that serous ovarian carcinoma is marked by the activation of interconnected modules associated with a specific gene set that was derived from three independent tumor-specific gene expression data sets. Network prediction algorithms combined with preestablished protein interaction networks and known functionalities affirmed the importance of genes associated with ovarian cancer as predictive biomarkers, besides "discovering" novel ones purely on the basis of interconnectivity, whose precise involvement remains to be investigated. Copy number alterations and aberrant epigenetic regulation were identified and validated as significant influences on gene expression. More importantly, three functional modules centering on c-Myc activation, altered retinoblastoma signaling, and p53/cell cycle/DNA damage repair pathways have been identified for their involvement in transformation-associated events. Further studies will assign significance to and aid the design of a panel of specific markers predictive of individual- and tumor-specific pathways. In the parlance of this emerging field, such networks of gene-hub interactions may define personalized therapeutic decisions.

Triple-negative breast cancer: present challenges and new perspectives

Triple-negative breast cancers (TNBC), characterized by absence of estrogen receptor (ER), progesterone receptor (PR) and lack of overexpression of human epidermal growth factor receptor 2 (HER2), are typically associated with poor prognosis, due to aggressive tumor phenotype(s), only partial response to chemotherapy and present lack of clinically established targeted therapies. Advances in the design of individualized strategies for treatment of TNBC patients require further elucidation, by combined 'omics' approaches, of the molecular mechanisms underlying TNBC phenotypic heterogeneity, and the still poorly understood association of TNBC with BRCA1 mutations. An overview is here presented on TNBC profiling in terms of expression signatures, within the functional genomic breast tumor classification, and ongoing efforts toward identification of new therapy targets and bioimaging markers. Due to the complexity of aberrant molecular patterns involved in expression, pathological progression and biological/clinical heterogeneity, the search for novel TNBC biomarkers and therapy targets requires collection of multi-dimensional data sets, use of robust multivariate data analysis techniques and development of innovative systems biology approaches.