A differentiation-based phylogeny of cancer subtypes

Knocking down the expression of the molecular motors, myosin A, C and F genes in Toxoplasma gondii to decrease the parasite virulence

Toxoplasmosis is a serious parasitic infection and novel therapeutic options are highly demanded to effectively eliminate it. In current study, Toxoplasma gondii myosin A, C and F genes were knocked down using small interference RNA (siRNA) method and the parasite survival and virulence was evaluated in vitro and in vivo. The parasites were transfected with specific siRNA, virtually designed for myosin mRNAs, and co-cultured with human foreskin fibroblasts. The transfection rate and the viability of the transfected parasites were measured using flow cytometry and methyl thiazole tetrazolium (MTT) assays, respectively. Finally, the survival of BALB/c mice infected with siRNAs-transfected T. gondii was assessed. It was demonstrated that a transfection rate of 75.4% existed for siRNAs, resulting in 70% (P = 0.032), 80.6% (P = 0.017) and 85.5% (P = 0.013) gene suppression for myosin A, C and F in affected parasites, respectively, which was subsequently confirmed by Western blot analysis. Moreover, lower parasite viability was observed in those with knocked down myosin C with 80% (P = 0.0001), followed by 86.15% (P = 0.004) for myosin F and 92.3% (P = 0.083) for myosin A. Considerably higher mouse survival (about 40 h) was, also, demonstrated in mice challenged with myosin siRNA-transfected T. gondii, in comparison with control group challenged with wild-type parasites. In conclusion, myosin proteins knock down proposes a promising therapeutic strategy to combat toxoplasmosis.

Tumor cell heterogeneity and its transcriptional bases in pancreatic cancer: a tale of two cell types and their many variants

Pancreatic ductal adenocarcinoma (PDAC), one of the most highly lethal tumors, is characterized by complex histology, with a massive fibrotic stroma in which both pseudo-glandular structures and compact nests of abnormally differentiated tumor cells are embedded, in different proportions and with different mutual relationships in space. This complexity and the heterogeneity of the tumor component have hindered the development of a broadly accepted, clinically actionable classification of PDACs, either on a morphological or a molecular basis. Here, we discuss evidence suggesting that such heterogeneity can to a large extent, albeit not exclusively, be traced back to two main classes of PDAC cells that commonly coexist in the same tumor: cells that maintained their ability to differentiate toward endodermal, mucin-producing epithelia and epithelial cells unable to form glandular structures and instead characterized by various levels of squamous differentiation and the expression of mesenchymal lineage genes. The underlying gene regulatory networks and how they are controlled by distinct transcription factors, as well as the practical implications of these two different populations of tumor cells, are discussed.

Neural Network Deconvolution Method for Resolving Pathway-Level Progression of Tumor Clonal Expression Programs With Application to Breast Cancer Brain Metastases

Metastasis is the primary mechanism by which cancer results in mortality and there are currently no reliable treatment options once it occurs, making the metastatic process a critical target for new diagnostics and therapeutics. Treating metastasis before it appears is challenging, however, in part because metastases may be quite distinct genomically from the primary tumors from which they presumably emerged. Phylogenetic studies of cancer development have suggested that changes in tumor genomics over stages of progression often result from shifts in the abundance of clonal cellular populations, as late stages of progression may derive from or select for clonal populations rare in the primary tumor. The present study develops computational methods to infer clonal heterogeneity and dynamics across progression stages via deconvolution and clonal phylogeny reconstruction of pathway-level expression signatures in order to reconstruct how these processes might influence average changes in genomic signatures over progression. We show, via application to a study of gene expression in a collection of matched breast primary tumor and metastatic samples, that the method can infer coarse-grained substructure and stromal infiltration across the metastatic transition. The results suggest that genomic changes observed in metastasis, such as gain of the ErbB signaling pathway, are likely caused by early events in clonal evolution followed by expansion of minor clonal populations in metastasis, a finding that may have translational implications for early detection or prevention of metastasis.

Dissecting the novel partners of nuclear c-Raf and its role in all-trans retinoic acid (ATRA)-induced myeloblastic leukemia cells differentiation

All-trans retinoic acid (ATRA) is an anti-cancer differentiation therapy agent effective for acute promyelocytic leukemia (APL) but not acute myeloid leukemia (AML) in general. Using the HL-60 human non-APL AML model where ATRA causes nuclear enrichment of c-Raf that drives differentiation and G1/G0 cell cycle arrest, we now observe that c-Raf in the nucleus showed novel interactions with several prominent regulators of the cell cycle and cell differentiation. One is cyclin-dependent kinase 2 (Cdk2). ATRA treatment caused c-Raf to dissociate from Cdk2. This was associated with enhanced binding of Cdk2 with retinoic acid receptor α (RARα). Consistent with this novel Raf/CDK2/RARα axis contributing to differentiation, CD38 expression per cell, which is transcriptionally regulated by a retinoic acid response element (RARE), is enhanced. The RB tumor suppressor, a fundamental regulator of G1 cell cycle progression or arrest, was also targeted by c-Raf in the nucleus. RB and specifically the S608 phosphorylated form (pS608RB) complexed with c-Raf. ATRA treatment induced S608RB-hypophosphorylation associated with G1/G0 cell cycle arrest and release of c-Raf from RB. We also found that nuclear c-Raf interacted with SMARCD1, a pioneering component of the SWI/SNF chromatin remodeling complex. ATRA treatment diminished the amount of this protein bound to c-Raf. The data suggest that ATRA treatment to HL-60 human cells re-directed c-Raf from its historically pro-proliferation functions in the cytoplasm to pro-differentiation functions in the nucleus.

Robust and accurate deconvolution of tumor populations uncovers evolutionary mechanisms of breast cancer metastasis

MOTIVATION

Cancer develops and progresses through a clonal evolutionary process. Understanding progression to metastasis is of particular clinical importance, but is not easily analyzed by recent methods because it generally requires studying samples gathered years apart, for which modern single-cell sequencing is rarely an option. Revealing the clonal evolution mechanisms in the metastatic transition thus still depends on unmixing tumor subpopulations from bulk genomic data.

METHODS

We develop a novel toolkit called robust and accurate deconvolution (RAD) to deconvolve biologically meaningful tumor populations from multiple transcriptomic samples spanning the two progression states. RAD uses gene module compression to mitigate considerable noise in RNA, and a hybrid optimizer to achieve a robust and accurate solution. Finally, we apply a phylogenetic algorithm to infer how associated cell populations adapt across the metastatic transition via changes in expression programs and cell-type composition.

RESULTS

We validated the superior robustness and accuracy of RAD over alternative algorithms on a real dataset, and validated the effectiveness of gene module compression on both simulated and real bulk RNA data. We further applied the methods to a breast cancer metastasis dataset, and discovered common early events that promote tumor progression and migration to different metastatic sites, such as dysregulation of ECM-receptor, focal adhesion and PI3k-Akt pathways.

AVAILABILITY AND IMPLEMENTATION

The source code of the RAD package, models, experiments and technical details such as parameters, is available at https://github.com/CMUSchwartzLab/RAD.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

FOXA2 controls the cis-regulatory networks of pancreatic cancer cells in a differentiation grade-specific manner

Differentiation of normal and tumor cells is controlled by regulatory networks enforced by lineage-determining transcription factors (TFs). Among them, TFs such as FOXA1/2 bind naïve chromatin and induce its accessibility, thus establishing new gene regulatory networks. Pancreatic ductal adenocarcinoma (PDAC) is characterized by the coexistence of well- and poorly differentiated cells at all stages of disease. How the transcriptional networks determining such massive cellular heterogeneity are established remains to be determined. We found that FOXA2, a TF controlling pancreas specification, broadly contributed to the cis-regulatory networks of PDACs. Despite being expressed in both well- and poorly differentiated PDAC cells, FOXA2 displayed extensively different genomic distributions and controlled distinct gene expression programs. Grade-specific functions of FOXA2 depended on its partnership with TFs whose expression varied depending on the differentiation grade. These data suggest that FOXA2 contributes to the regulatory networks of heterogeneous PDAC cells via interactions with alternative partner TFs.

Degree and centrality-based approaches in network-based variable selection: Insights from the Singapore Longitudinal Aging Study

We describe a network-based method to obtain a subset of representative variables from clinical data of subjects of the second Singapore Longitudinal Aging Study (SLAS-2), while preserving to a good extent the predictive performance of the full set with regards to a multi-faceted index of successful aging, SAGE. To examine differences in predictive performance of high-degree nodes (“hubs”) and high-centrality ones (“cores”), we implement four subsetting strategies (two degree-based, two centrality-based) and obtain four surrogate sets of variables, which we use as input features for machine learning models to predict the SAGE index of subjects. All four models have variables belonging to the physical, cardiovascular, cognitive and immunological domains among their fifteen most important predictors. A fifth domain (leisure-time activities, LTA) is also present in some form. From a comparison of the surrogate sets’ size and predictive performance, a centrality-based approach (selection of the most central variable-nodes within each cluster) yielded the smallest-sized surrogate set, while having high prediction accuracy (measured by its model’s area-under-curve, AUC) in comparison to its analogous degree-based strategy (selection of the highest-degree nodes per cluster). Inclusion of the next most-central variables yielded negligible changes in predictive performance while more than doubling the surrogate set size. The centrality-based approach thus yields a surrogate set which offers a good balance between number of variables and prediction performance, and can act as a representative subset of the SLAS-2 clinical dataset.

ACTL6A interacts with p53 in acute promyelocytic leukemia cell lines to affect differentiation via the Sox2/Notch1 signaling pathway

Actin-like 6A (ACTL6A), a component of BAF chromatin remodeling complexes, is important for cell differentiation. Nevertheless, its role and mechanism in acute promyelocytic leukemia (APL) has not been reported. To identify the genes that may participate in the development of APL, we analyzed data from an APL cDNA microarray (GSE12662) in the NCBI database, and found that ACTL6A was up-regulated in APL patients. Subsequently, we investigated the function and mechanisms of ACTL6A in myeloid cell development. The expression of ACTL6A was gradually decreased during granulocytic differentiation in all-trans retinoic acid-treated NB4 and HL-60 cells, and phorbol myristate acetate-treated HL-60 cells. We also found that knockdown of ACTL6A promoted differentiation in NB4 and HL-60 cells, and decreased the levels of Sox2 and Notch1. Mechanistically, ACTL6A interacted with and was co-localized with Sox2 and p53. Meanwhile, CBL0137, an activator of p53, decreased the expression of ACTL6A and promoted differentiation in NB4 and HL-60 cells. These findings suggest that the inhibition of ACTL6A promotes differentiation via the Sox2 and Notch1 signaling pathways. Furthermore, the differentiation promoted by inhibiting ACTL6A could be regulated by p53 via its physical interaction with ACTL6A.

Mouse mammary tumour virus-like env nucleotide and p14 signal peptide are present in feline mammary carcinomas, but not in neoplastic or dysplastic canine mammary lesions

Mouse mammary tumour virus-like (MMTV-like) is suspected to be involved in human breast cancer and it has been hypothesized that companion animals might have a role in viral transmission. The aim of our study was to investigate the presence of MMTV-like nucleotide sequences and viral protein in a larger number of feline (FMCs) and canine mammary carcinomas (CMCs) by nested PCR and immunohistochemistry. Results showed that the presence of MMTV-like env sequence in FMCs was 7% (6/86), while all the CMCs and canine dysplastic lesions scored negative. All PCR-positive FMCs scored positive for the MMTV p14 signal peptide of the envelope precursor protein of the virus. In contrast, all PCR-negative FMCs and canine mammary lesions were also negative for immunohistochemistry analysis. Canine and feline normal mammary gland tissues scored negative for both PCR and MMTV-p14 protein. Multiple nucleotide alignment of MMTV-like env gene sequences isolated from cat showed 97% and 99% similarity with HMTV and MMTV, respectively, while the others two presented some polimorphisms. Particularly the sequences of one of these two tumors showed a polymorphism (c.7575 A> G), that causes a previously unreported amino acid substitution (Thr > Ala). In conclusion, the results of our study showed the presence of MMTV-like sequences and viral protein in some FMCs. Further studies are needed to understand whether this virus does play a role in the development of FMCs, if MMTV-like is an exogenous virus as these data suggest and, in such a case, how and from whom this virus was acquired.

Single cell transcriptome profiling of retinal ganglion cells identifies cellular subtypes

Retinal ganglion cells (RGCs) convey the major output of information collected from the eye to the brain. Thirty subtypes of RGCs have been identified to date. Here, we analyze 6225 RGCs (average of 5000 genes per cell) from right and left eyes by single-cell RNA-seq and classify them into 40 subtypes using clustering algorithms. We identify additional subtypes and markers, as well as transcription factors predicted to cooperate in specifying RGC subtypes. Zic1, a marker of the right eye-enriched subtype, is validated by immunostaining in situ. Runx1 and Fst, the markers of other subtypes, are validated in purified RGCs by fluorescent in situ hybridization (FISH) and immunostaining. We show the extent of gene expression variability needed for subtype segregation, and we show a hierarchy in diversification from a cell-type population to subtypes. Finally, we present a website for comparing the gene expression of RGC subtypes.

The evolution of tumour phylogenetics: principles and practice

Rapid advances in high-throughput sequencing and a growing realization of the importance of evolutionary theory to cancer genomics have led to a proliferation of phylogenetic studies of tumour progression. These studies have yielded not only new insights but also a plethora of experimental approaches, sometimes reaching conflicting or poorly supported conclusions. Here, we consider this body of work in light of the key computational principles underpinning phylogenetic inference, with the goal of providing practical guidance on the design and analysis of scientifically rigorous tumour phylogeny studies. We survey the range of methods and tools available to the researcher, their key applications, and the various unsolved problems, closing with a perspective on the prospects and broader implications of this field.

Distance in cancer gene expression from stem cells predicts patient survival

The degree of histologic cellular differentiation of a cancer has been associated with prognosis but is subjectively assessed. We hypothesized that information about tumor differentiation of individual cancers could be derived objectively from cancer gene expression data, and would allow creation of a cancer phylogenetic framework that would correlate with clinical, histologic and molecular characteristics of the cancers, as well as predict prognosis. Here we utilized mRNA expression data from 4,413 patient samples with 7 diverse cancer histologies to explore the utility of ordering samples by their distance in gene expression from that of stem cells. A differentiation baseline was obtained by including expression data of human embryonic stem cells (hESC) and human mesenchymal stem cells (hMSC) for solid tumors, and of hESC and CD34+ cells for liquid tumors. We found that the correlation distance (the degree of similarity) between the gene expression profile of a tumor sample and that of stem cells orients cancers in a clinically coherent fashion. For all histologies analyzed (including carcinomas, sarcomas, and hematologic malignancies), patients with cancers with gene expression patterns most similar to that of stem cells had poorer overall survival. We also found that the genes in all undifferentiated cancers of diverse histologies that were most differentially expressed were associated with up-regulation of specific oncogenes and down-regulation of specific tumor suppressor genes. Thus, a stem cell-oriented phylogeny of cancers allows for the derivation of a novel cancer gene expression signature found in all undifferentiated forms of diverse cancer histologies, that is competitive in predicting overall survival in cancer patients compared to previously published prediction models, and is coherent in that gene expression was associated with up-regulation of specific oncogenes and down-regulation of specific tumor suppressor genes associated with regulation of the multicellular state.

PhyloOncology: Understanding cancer through phylogenetic analysis

Despite decades of research and an enormity of resultant data, cancer remains a significant public health problem. New tools and fresh perspectives are needed to obtain fundamental insights, to develop better prognostic and predictive tools, and to identify improved therapeutic interventions. With increasingly common genome-scale data, one suite of algorithms and concepts with potential to shed light on cancer biology is phylogenetics, a scientific discipline used in diverse fields. From grouping subsets of cancer samples to tracing subclonal evolution during cancer progression and metastasis, the use of phylogenetics is a powerful systems biology approach. Well-developed phylogenetic applications provide fast, robust approaches to analyze high-dimensional, heterogeneous cancer data sets. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby.

Classifying the Progression of Ductal Carcinoma from Single-Cell Sampled Data via Integer Linear Programming: A Case Study

Ductal Carcinoma In Situ (DCIS) is a precursor lesion of Invasive Ductal Carcinoma (IDC) of the breast. Investigating its temporal progression could provide fundamental new insights for the development of better diagnostic tools to predict which cases of DCIS will progress to IDC. We investigate the problem of reconstructing a plausible progression from single-cell sampled data of an individual with synchronous DCIS and IDC. Specifically, by using a number of assumptions derived from the observation of cellular atypia occurring in IDC, we design a possible predictive model using integer linear programming (ILP). Computational experiments carried out on a preexisting data set of 13 patients with simultaneous DCIS and IDC show that the corresponding predicted progression models are classifiable into categories having specific evolutionary characteristics. The approach provides new insights into mechanisms of clonal progression in breast cancers and helps illustrate the power of the ILP approach for similar problems in reconstructing tumor evolution scenarios under complex sets of constraints.

BitPhylogeny: a probabilistic framework for reconstructing intra-tumor phylogenies

Cancer has long been understood as a somatic evolutionary process, but many details of tumor progression remain elusive. Here, we present BitPhylogenyBitPhylogeny, a probabilistic framework to reconstruct intra-tumor evolutionary pathways. Using a full Bayesian approach, we jointly estimate the number and composition of clones in the sample as well as the most likely tree connecting them. We validate our approach in the controlled setting of a simulation study and compare it against several competing methods. In two case studies, we demonstrate how BitPhylogeny BitPhylogeny reconstructs tumor phylogenies from methylation patterns in colon cancer and from single-cell exomes in myeloproliferative neoplasm.

Most human non-GCIMP glioblastoma subtypes evolve from a common proneural-like precursor glioma

To understand the relationships between the non-GCIMP glioblastoma (GBM) subgroups, we performed mathematical modeling to predict the temporal sequence of driver events during tumorigenesis. The most common order of evolutionary events is 1) chromosome (chr) 7 gain and chr10 loss, followed by 2) CDKN2A loss and/or TP53 mutation, and 3) alterations canonical for specific subtypes. We then developed a computational methodology to identify drivers of broad copy number changes, identifying PDGFA (chr7) and PTEN (chr10) as driving initial nondisjunction events. These predictions were validated using mouse modeling, showing that PDGFA is sufficient to induce proneural-like gliomas and that additional NF1 loss converts proneural to the mesenchymal subtype. Our findings suggest that most non-GCIMP mesenchymal GBMs arise as, and evolve from, a proneural-like precursor.

A mixed integer linear programming model to reconstruct phylogenies from single nucleotide polymorphism haplotypes under the maximum parsimony criterion

Background

Phylogeny estimation from aligned haplotype sequences has attracted more and more attention in the recent years due to its importance in analysis of many fine-scale genetic data. Its application fields range from medical research, to drug discovery, to epidemiology, to population dynamics. The literature on molecular phylogenetics proposes a number of criteria for selecting a phylogeny from among plausible alternatives. Usually, such criteria can be expressed by means of objective functions, and the phylogenies that optimize them are referred to as optimal. One of the most important estimation criteria is the parsimony which states that the optimal phylogeny T^∗for a set H of n haplotype sequences over a common set of variable loci is the one that satisfies the following requirements: (i) it has the shortest length and (ii) it is such that, for each pair of distinct haplotypes hi,hj∈H, the sum of the edge weights belonging to the path from h_i to h_j in T^∗ is not smaller than the observed number of changes between h_i and h_j. Finding the most parsimonious phylogeny for H involves solving an optimization problem, called the Most Parsimonious Phylogeny Estimation Problem (MPPEP), which is NP-hard in many of its versions.

Results

In this article we investigate a recent version of the MPPEP that arises when input data consist of single nucleotide polymorphism haplotypes extracted from a population of individuals on a common genomic region. Specifically, we explore the prospects for improving on the implicit enumeration strategy of implicit enumeration strategy used in previous work using a novel problem formulation and a series of strengthening valid inequalities and preliminary symmetry breaking constraints to more precisely bound the solution space and accelerate implicit enumeration of possible optimal phylogenies. We present the basic formulation and then introduce a series of provable valid constraints to reduce the solution space. We then prove that these constraints can often lead to significant reductions in the gap between the optimal solution and its non-integral linear programming bound relative to the prior art as well as often substantially faster processing of moderately hard problem instances.

Conclusion

We provide an indication of the conditions under which such an optimal enumeration approach is likely to be feasible, suggesting that these strategies are usable for relatively large numbers of taxa, although with stricter limits on numbers of variable sites. The work thus provides methodology suitable for provably optimal solution of some harder instances that resist all prior approaches.

Aberration in DNA methylation in B-cell lymphomas has a complex origin and increases with disease severity

Despite mounting evidence that epigenetic abnormalities play a key role in cancer biology, their contributions to the malignant phenotype remain poorly understood. Here we studied genome-wide DNA methylation in normal B-cell populations and subtypes of B-cell non-Hodgkin lymphoma: follicular lymphoma and diffuse large B-cell lymphomas. These lymphomas display striking and progressive intra-tumor heterogeneity and also inter-patient heterogeneity in their cytosine methylation patterns. Epigenetic heterogeneity is initiated in normal germinal center B-cells, increases markedly with disease aggressiveness, and is associated with unfavorable clinical outcome. Moreover, patterns of abnormal methylation vary depending upon chromosomal regions, gene density and the status of neighboring genes. DNA methylation abnormalities arise via two distinct processes: i) lymphomagenic transcriptional regulators perturb promoter DNA methylation in a target gene-specific manner, and ii) aberrant epigenetic states tend to spread to neighboring promoters in the absence of CTCF insulator binding sites.

Follicular lymphomas and diffuse large B-cell lymphomas are the most common non-Hodgkin lymphomas. Although these diseases share many mutant alleles, the underlying cause of the different phenotypes remains unclear. We show that direct comparison of DNA methylation patterning provides insights about gene deregulation during lymphomagenesis and explains the nature of the different clinical behavior.

Applying ecological and evolutionary theory to cancer: a long and winding road

Since the mid 1970s, cancer has been described as a process of Darwinian evolution, with somatic cellular selection and evolution being the fundamental processes leading to malignancy and its many manifestations (neoangiogenesis, evasion of the immune system, metastasis, and resistance to therapies). Historically, little attention has been placed on applications of evolutionary biology to understanding and controlling neoplastic progression and to prevent therapeutic failures. This is now beginning to change, and there is a growing international interest in the interface between cancer and evolutionary biology. The objective of this introduction is first to describe the basic ideas and concepts linking evolutionary biology to cancer. We then present four major fronts where the evolutionary perspective is most developed, namely laboratory and clinical models, mathematical models, databases, and techniques and assays. Finally, we discuss several of the most promising challenges and future prospects in this interdisciplinary research direction in the war against cancer.

Solid tumor differentiation therapy - is it possible?

Genetic and epigenetic events within a cell which promote a block in normal development or differentiation coupled with unregulated proliferation are hallmarks of neoplastic transformation. Differentiation therapy involves the use of agents with the ability to induce differentiation in cells that have lost this ability, i.e. cancer cells. The promise of differentiation-based therapy as a viable treatment modality is perhaps best characterized by the addition of retinoids in the treatment of acute promyelocytic leukemia (APML) revolutionizing the management of APML and dramatically improving survival. However, interest and application of differentiationbased therapy for the treatment of solid malignancies have lagged due to deficiencies in our understanding of differentiation pathways in solid malignancies. Over the past decade, a differentiation-based developmental model for solid tumors has emerged providing insights into the biology of various solid tumors as well as identification of targetable pathways capable of re-activating blocked terminal differentiation programs. Furthermore, a variety of agents including retinoids, histone deacetylase inhibitors (HDACI), PPARγ agonists, and others, currently in use for a variety of malignancies, have been shown to induce differentiation in solid tumors. Herein we discuss the relevancy of differentiation-based therapies in solid tumors, using soft tissue sarcomas (STS) as a biologic and clinical model, and review the preclinical data to support its role as a promising modality of therapy for the treatment of solid tumors.

A mouse mammary tumor virus env-like exogenous sequence is strictly related to progression of human sporadic breast carcinoma

A viral etiology of human breast cancer (HBC) has been postulated for decades since the identification of mouse mammary tumor virus (MMTV). The detection of MMTV env-like exogenous sequences (MMTVels) in 30% to 40% of invasive HBCs increased attention to this hypothesis. Looking for MMTVels during cancer progression may contribute to a better understanding of their role in HBC. Herein, we analyzed HBC preinvasive lesions for the presence of MMTVels. Samples were obtained by laser microdissection of FFPE tissues: 20 usual-type ductal hyperplasias, 22 atypical ductal hyperplasias (ADHs), 49 ductal carcinomas in situ (DCISs), 20 infiltrating ductal carcinomas (IDCs), and 26 normal epithelial cells collateral to a DCIS or an IDC. Controls included reductive mammoplastic tissue, thyroid and colon carcinoma, and blood samples from healthy donors. MMTVels were detected by fluorescence-nested PCR. DNA samples from the tissues of nine patients were analyzed by real-time quantitative PCR, revealing a different viral load correlated with stage of progression. Furthermore, as never previously described, the presence of MMTVels was investigated by chromogenic in situ hybridization. MMTVels were found in 19% of normal epithelial cells collateral to a DCIS or an IDC, 27% of ADHs, 82% of DCISs, and 35% of IDCs. No MMTVels were found in the control samples. Quantitative PCR and chromogenic in situ hybridization confirmed these results. These data could contribute to our understanding of the role of MMTVels in HBC.

Metastatic breast cancer cells in the bone marrow microenvironment: novel insights into oncoprotection

Among all cancers, malignancies of the breast are the second leading cause of cancer death in the United States after carcinoma of the lung. One of the major factors considered when assessing the prognosis of breast cancer patients is whether the tumor has metastasized to distant organs. Although the exact phenotype of the malignant cells responsible for metastasis and dormancy is still unknown, growing evidence has revealed that they may have stem cell-like properties that may account for resistance to chemotherapy and radiation. One process that has been attributed to primary tumor metastasis is the epithelial-to-mesenchymal transition. In this review, we specifically discuss breast cancer dissemination to the bone marrow and factors that ultimately serve to shelter and promote tumor growth, including the complex relationship between mesenchymal stem cells (MSCs) and various aspects of the immune system, carcinoma-associated fibroblasts, and the diverse components of the tumor microenvironment. A better understanding of the journey from the primary tumor site to the bone marrow and subsequently the oncoprotective role of MSCs and other factors within that microenvironment can potentially lead to development of novel therapeutic targets.

Quantitative interpretation of a genetic model of carcinogenesis using computer simulations

The genetic model of tumorigenesis by Vogelstein et al. (V theory) and the molecular definition of cancer hallmarks by Hanahan and Weinberg (W theory) represent two of the most comprehensive and systemic understandings of cancer. Here, we develop a mathematical model that quantitatively interprets these seminal cancer theories, starting from a set of equations describing the short life cycle of an individual cell in uterine epithelium during tissue regeneration. The process of malignant transformation of an individual cell is followed and the tissue (or tumor) is described as a composite of individual cells in order to quantitatively account for intra-tumor heterogeneity. Our model describes normal tissue regeneration, malignant transformation, cancer incidence including dormant/transient tumors, and tumor evolution. Further, a novel mechanism for the initiation of metastasis resulting from substantial cell death is proposed. Finally, model simulations suggest two different mechanisms of metastatic inefficiency for aggressive and less aggressive cancer cells. Our work suggests that cellular de-differentiation is one major oncogenic pathway, a hypothesis based on a numerical description of a cell's differentiation status that can effectively and mathematically interpret some major concepts in V/W theories such as progressive transformation of normal cells, tumor evolution, and cancer hallmarks. Our model is a mathematical interpretation of cancer phenotypes that complements the well developed V/W theories based upon description of causal biological and molecular events. It is possible that further developments incorporating patient- and tissue-specific variables may build an even more comprehensive model to explain clinical observations and provide some novel insights for understanding cancer.

A differentiation-based microRNA signature identifies leiomyosarcoma as a mesenchymal stem cell-related malignancy

Smooth muscle (SM) is a spontaneously contractile tissue that provides physical support and function to organs such as the uterus. Uterine smooth muscle-related neoplasia comprise common well-differentiated benign lesions called leiomyomas (ULM), and rare, highly aggressive and pleomorphic tumors named leiomyosarcomas (ULMS). MicroRNAs (miRNAs) are small non-coding RNAs that play essential roles in normal cellular development and tissue homeostasis that can be used to accurately subclassify different tumor types. Here, we demonstrate that miRNAs are required for full smooth muscle cell (SMC) differentiation of bone marrow-derived human mesenchymal stem cells (hMSCs). We also report a miRNA signature associated with this process. Moreover, we show that this signature, along with miRNA profiles for ULMS and ULM, are able to subclassify tumors of smooth muscle origin along SM differentiation. Using multiple computational analyses, we determined that ULMS are more similar to hMSCs as opposed to ULM, which are linked with more mature SMCs and myometrium. Furthermore, a comparison of the SM differentiation and ULMS miRNA signatures identified miRNAs strictly associated with SM maturation or transformation, as well as those modulated in both processes indicating a possible dual role. These results support separate origins and/or divergent transformation pathways for ULM and ULMS, resulting in drastically different states of differentiation. In summary, this work expands on our knowledge of the regulation of SM differentiation and sarcoma pathogenesis.