Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity

The biology of circulating tumor cells

Metastasis is a biologically complex process consisting of numerous stochastic events which may tremendously differ across various cancer types. Circulating tumor cells (CTCs) are cells that are shed from primary tumors and metastatic deposits into the blood stream. CTCs bear a tremendous potential to improve our understanding of steps involved in the metastatic cascade, starting from intravasation of tumor cells into the circulation until the formation of clinically detectable metastasis. These efforts were propelled by novel high-resolution approaches to dissect the genomes and transcriptomes of CTCs. Furthermore, capturing of viable CTCs has paved the way for innovative culturing technologies to study fundamental characteristics of CTCs such as invasiveness, their kinetics and responses to selection barriers, such as given therapies. Hence the study of CTCs is not only instrumental as a basic research tool, but also allows the serial monitoring of tumor genotypes and may therefore provide predictive and prognostic biomarkers for clinicians. Here, we review how CTCs have contributed to significant insights into the metastatic process and how they may be utilized in clinical practice.

Tumor Evolution Inferred by Patterns of microRNA Expression through the Course of Disease, Therapy, and Recurrence in Breast Cancer

PURPOSE

Molecular evolution of tumors during progression, therapy, and metastasis is a major clinical challenge and the main reason for resistance to therapy. We hypothesized that microRNAs (miRNAs) that exhibit similar variation of expression through the course of disease in several patients have a significant function in the tumorigenic process.

EXPERIMENTAL DESIGN

Exploration of evolving disease by profiling 800 miRNA expression from serial samples of individual breast cancer patients at several time points: pretreatment, posttreatment, lymph nodes, and recurrence sites when available (58 unique samples from 19 patients). Using a dynamic approach for analysis, we identified expression modulation patterns and classified varying miRNAs into one of the eight possible temporal expression patterns.

RESULTS

The various patterns were found to be associated with different tumorigenic pathways. The dominant pattern identified an miRNA set that significantly differentiated between disease stages, and its pattern in each patient was also associated with response to therapy. These miRNAs were related to tumor proliferation and to the cell-cycle pathway, and their mRNA targets showed anticorrelated expression. Interestingly, the level of these miRNAs was lowest in matched recurrent samples from distant metastasis, indicating a gradual increase in proliferative potential through the course of disease. Finally, the average expression level of these miRNAs in the pretreatment biopsy was significantly different comparing patients experiencing recurrence to recurrence-free patients.

CONCLUSIONS

Serial tumor sampling combined with analysis of temporal expression patterns enabled to pinpoint significant signatures characterizing breast cancer progression, associated with response to therapy and with risk of recurrence. Clin Cancer Res; 22(14); 3651-62. ©2016 AACR.

Identification of neutral tumor evolution across cancer types

Despite extraordinary efforts to profile cancer genomes, interpreting the vast amount of genomic data in the light of cancer evolution remains challenging. Here we demonstrate that neutral tumor evolution results in a power-law distribution of the mutant allele frequencies reported by next-generation sequencing of tumor bulk samples. We find that the neutral power law fits with high precision 323 of 904 cancers from 14 types and from different cohorts. In malignancies identified as evolving neutrally, all clonal selection seemingly occurred before the onset of cancer growth and not in later-arising subclones, resulting in numerous passenger mutations that are responsible for intratumoral heterogeneity. Reanalyzing cancer sequencing data within the neutral framework allowed the measurement, in each patient, of both the in vivo mutation rate and the order and timing of mutations. This result provides a new way to interpret existing cancer genomic data and to discriminate between functional and non-functional intratumoral heterogeneity.

Cathepsin K induces platelet dysfunction and affects cell signaling in breast cancer - molecularly distinct behavior of cathepsin K in breast cancer

Background

Breast cancer comprises clinically and molecularly distinct tumor subgroups that differ in cell histology and biology and show divergent clinical phenotypes that impede phase III trials, such as those utilizing cathepsin K inhibitors. Here we correlate the epithelial-mesenchymal-like transition breast cancer cells and cathepsin K secretion with activation and aggregation of platelets. Cathepsin K is up-regulated in cancer cells that proteolyze extracellular matrix and contributes to invasiveness. Although proteolytically activated receptors (PARs) are activated by proteases, the direct interaction of cysteine cathepsins with PARs is poorly understood. In human platelets, PAR-1 and −4 are highly expressed, but PAR-3 shows low expression and unclear functions.

Methods

Platelet aggregation was monitored by measuring changes in turbidity. Platelets were immunoblotted with anti-phospho and total p38, Src-Tyr-416, FAK-Tyr-397, and TGFβ monoclonal antibody. Activation was measured in a flow cytometer and calcium mobilization in a confocal microscope. Mammary epithelial cells were prepared from the primary breast cancer samples of 15 women with Luminal-B subtype to produce primary cells.

Results

We demonstrate that platelets are aggregated by cathepsin K in a dose-dependent manner, but not by other cysteine cathepsins. PARs-3 and −4 were confirmed as the cathepsin K target by immunodetection and specific antagonists using a fibroblast cell line derived from PARs deficient mice. Moreover, through co-culture experiments, we show that platelets activated by cathepsin K mediated the up-regulation of SHH, PTHrP, OPN, and TGFβ in epithelial-mesenchymal-like cells from patients with Luminal B breast cancer.

Conclusions

Cathepsin K induces platelet dysfunction and affects signaling in breast cancer cells.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-016-2203-7) contains supplementary material, which is available to authorized users.

Imaging tumour cell heterogeneity following cell transplantation into optically clear immune-deficient zebrafish

Cancers contain a wide diversity of cell types that are defined by differentiation states, genetic mutations and altered epigenetic programmes that impart functional diversity to individual cells. Elevated tumour cell heterogeneity is linked with progression, therapy resistance and relapse. Yet, imaging of tumour cell heterogeneity and the hallmarks of cancer has been a technical and biological challenge. Here we develop optically clear immune-compromised rag2^E450fs(casper) zebrafish for optimized cell transplantation and direct visualization of fluorescently labelled cancer cells at single-cell resolution. Tumour engraftment permits dynamic imaging of neovascularization, niche partitioning of tumour-propagating cells in embryonal rhabdomyosarcoma, emergence of clonal dominance in T-cell acute lymphoblastic leukaemia and tumour evolution resulting in elevated growth and metastasis in BRAF^V600E-driven melanoma. Cell transplantation approaches using optically clear immune-compromised zebrafish provide unique opportunities to uncover biology underlying cancer and to dynamically visualize cancer processes at single-cell resolution in vivo.

Direct visualisation of heterogeneous cell populations in live animals has been challenging. Here, the authors optimize cell transplantation into optically clear immune-deficient zebrafish, and use intravital imaging to track and to assess functional diversity of individual cancer cells in vivo.

Next-Generation Pathology

The field of pathology is rapidly transforming from a semiquantitative and empirical science toward a big data discipline. Large data sets from across multiple omics fields may now be extracted from a patient's tissue sample. Tissue is, however, complex, heterogeneous, and prone to artifact. A reductionist view of tissue and disease progression, which does not take this complexity into account, may lead to single biomarkers failing in clinical trials. The integration of standardized multi-omics big data and the retention of valuable information on spatial heterogeneity are imperative to model complex disease mechanisms. Mathematical modeling through systems pathology approaches is the ideal medium to distill the significant information from these large, multi-parametric, and hierarchical data sets. Systems pathology may also predict the dynamical response of disease progression or response to therapy regimens from a static tissue sample. Next-generation pathology will incorporate big data with systems medicine in order to personalize clinical practice for both prognostic and predictive patient care.

How to be good at being bad: centrosome amplification and mitotic propensity drive intratumoral heterogeneity

Cancer is truly an iconic disease--a tour de force whose multiple formidable strengths can be attributed to the bewildering heterogeneity that a tumor can manifest both spatially and temporally. A Darwinian evolutionary process is believed to undergird, at least in part, the generation of this heterogeneity that contributes to poor clinical outcomes. Risk assessment in clinical oncology is currently based on a small number of clinicopathologic factors (like stage, histological grade, receptor status, and serum tumor markers) and offers limited accuracy in predicting disease course as evidenced by the prognostic heterogeneity that persists in risk segments produced by present-day models. We posit that this insufficiency stems from the exclusion of key risk contributors from such models, especially the omission of certain factors implicated in generating intratumoral heterogeneity. The extent of centrosome amplification and the mitotic propensity inherent in a tumor are two such vital factors whose contributions to poor prognosis are presently overlooked in risk prognostication. Supernumerary centrosomes occur widely in tumors and are potent drivers of chromosomal instability that fosters intratumoral heterogeneity. The mitotic propensity of a proliferating population of tumor cells reflects the cell cycling kinetics of that population. Since frequent passage through improperly regulated mitotic divisions accelerates production of diverse genotypes, the mitotic propensity inherent in a tumor serves as a powerful beacon of risk. In this review, we highlight how centrosome amplification and error-prone mitoses contribute to poor clinical outcomes and urge the need to develop these cancer-specific traits as much-needed clinically-facile prognostic biomarkers with immense potential value for individualized cancer treatment in the clinic.

Pan-cancer analysis of the extent and consequences of intratumor heterogeneity

Intratumor heterogeneity (ITH) drives neoplastic progression and therapeutic resistance. We used the bioinformatics tools 'expanding ploidy and allele frequency on nested subpopulations' (EXPANDS) and PyClone to detect clones that are present at a ≥10% frequency in 1,165 exome sequences from tumors in The Cancer Genome Atlas. 86% of tumors across 12 cancer types had at least two clones. ITH in the morphology of nuclei was associated with genetic ITH (Spearman's correlation coefficient, ρ = 0.24-0.41; P < 0.001). Mutation of a driver gene that typically appears in smaller clones was a survival risk factor (hazard ratio (HR) = 2.15, 95% confidence interval (CI): 1.71-2.69). The risk of mortality also increased when >2 clones coexisted in the same tumor sample (HR = 1.49, 95% CI: 1.20-1.87). In two independent data sets, copy-number alterations affecting either <25% or >75% of a tumor's genome predicted reduced risk (HR = 0.15, 95% CI: 0.08-0.29). Mortality risk also declined when >4 clones coexisted in the sample, suggesting a trade-off between the costs and benefits of genomic instability. ITH and genomic instability thus have the potential to be useful measures that can universally be applied to all cancers.

Computational pathology: Exploring the spatial dimension of tumor ecology

Tumors are evolving ecosystems where cancer subclones and the microenvironment interact. This is analogous to interaction dynamics between species in their natural habitats, which is a prime area of study in ecology. Spatial statistics are frequently used in ecological studies to infer complex relations including predator-prey, resource dependency and co-evolution. Recently, the emerging field of computational pathology has enabled high-throughput spatial analysis by using image processing to identify different cell types and their locations within histological tumor samples. We discuss how these data may be analyzed with spatial statistics used in ecology to reveal patterns and advance our understanding of ecological interactions occurring among cancer cells and their microenvironment.

Combined bacterial and viral treatment: a novel anticancer strategy

An idea for a new combination therapy will be described herein. It is a proposition to combine viral and bacterial anticancer therapies and make them fight cancer in concert. We analyzed biological anticancer therapies and found overlapping advantages and disadvantages which led us to the conclusion that the combination therapy has the potential to create a new therapeutic quality. It is surprising how many weaknesses of viral anticancer therapy are the strengths of bacterial anticancer therapies and the other way round. We review the facts behind this concept and try to assess its value. We propose a few strategies how to combine these two therapies but as far as the review can go, final answers will have to come from the experiments. This review is the first attempt to describe a new strategy and understand the means for this idea but also to raise new questions and discuss new ways to look at anti-cancer treatment.

Intra-tumor heterogeneity of cancer cells and its implications for cancer treatment

Recent studies have revealed extensive genetic and non-genetic variation across different geographical regions of a tumor or throughout different stages of tumor progression, which is referred to as intra-tumor heterogeneity. Several causes contribute to this phenomenon, including genomic instability, epigenetic alteration, plastic gene expression, signal transduction, and microenvironmental differences. These variables may affect key signaling pathways that regulate cancer cell growth, drive phenotypic diversity, and pose challenges to cancer treatment. Understanding the mechanisms underlying this heterogeneity will support the development of effective therapeutic strategies.

In situ single-cell analysis identifies heterogeneity for PIK3CA mutation and HER2 amplification in HER2-positive breast cancer

Detection of minor, genetically distinct subpopulations within tumors is a key challenge in cancer genomics. Here we report STAR-FISH (specific-to-allele PCR-FISH), a novel method for the combined detection of single-nucleotide and copy number alterations in single cells in intact archived tissues. Using this method, we assessed the clinical impact of changes in the frequency and topology of PIK3CA mutation and HER2 (ERBB2) amplification within HER2-positive breast cancer during neoadjuvant therapy. We found that these two genetic events are not always present in the same cells. Chemotherapy selects for PIK3CA-mutant cells, a minor subpopulation in nearly all treatment-naive samples, and modulates genetic diversity within tumors. Treatment-associated changes in the spatial distribution of cellular genetic diversity correlated with poor long-term outcome following adjuvant therapy with trastuzumab. Our findings support the use of in situ single cell-based methods in cancer genomics and imply that chemotherapy before HER2-targeted therapy may promote treatment resistance.

An Emerging Allee Effect Is Critical for Tumor Initiation and Persistence

Tumor cells develop different strategies to cope with changing microenvironmental conditions. A prominent example is the adaptive phenotypic switching between cell migration and proliferation. While it has been shown that the migration-proliferation plasticity influences tumor spread, it remains unclear how this particular phenotypic plasticity affects overall tumor growth, in particular initiation and persistence. To address this problem, we formulate and study a mathematical model of spatio-temporal tumor dynamics which incorporates the microenvironmental influence through a local cell density dependence. Our analysis reveals that two dynamic regimes can be distinguished. If cell motility is allowed to increase with local cell density, any tumor cell population will persist in time, irrespective of its initial size. On the contrary, if cell motility is assumed to decrease with respect to local cell density, any tumor population below a certain size threshold will eventually extinguish, a fact usually termed as Allee effect in ecology. These results suggest that strategies aimed at modulating migration are worth to be explored as alternatives to those mainly focused at keeping tumor proliferation under control.

Controlling tumor growth remains a major medical challenge. Current clinical therapies focus on strategies to reduce tumor cell proliferation. However, during tumor progression, tumor cells may switch between proliferative and migratory behaviors, thereby allowing adaptation to microenvironmental changes that result in variations in local tumor cell density. We herein explore by means of a mathematical model the impact of migration-proliferation plasticity on tumor initiation and persistence. Our work suggests that small tumors can become extinct solely by their intrinsic cell population dynamics if cell motility decreases along with local cell density. In contrast, if cell motility increases with cell density, the tumor inevitably grows. Our model suggests that the regulation of cell migration plays a key role in tumor growth as a whole, making this feature a potential target for clinical studies.

The dynamic control of signal transduction networks in cancer cells

Cancer is often considered a genetic disease. However, much of the enormous plasticity of cancer cells to evolve different phenotypes, to adapt to challenging microenvironments and to withstand therapeutic assaults is encoded by the structure and spatiotemporal dynamics of signal transduction networks. In this Review, we discuss recent concepts concerning how the rich signalling dynamics afforded by these networks are regulated and how they impinge on cancer cell proliferation, survival, invasiveness and drug resistance. Understanding this dynamic circuitry by mathematical modelling could pave the way to new therapeutic approaches and personalized treatments.

Genomic profiling of breast cancers

PURPOSE OF REVIEW

To describe recent advances in the application of advanced genomic technologies towards the identification of biomarkers of prognosis and treatment response in breast cancer.

RECENT FINDINGS

Advances in high-throughput genomic profiling such as massively parallel sequencing have enabled researchers to catalogue the spectrum of somatic alterations in breast cancers. These tools also hold promise for precision medicine through accurate patient prognostication, stratification, and the dynamic monitoring of treatment response. For example, recent efforts have defined robust molecular subgroups of breast cancer and novel subtype-specific oncogenes. In addition, previously unappreciated activating mutations in human epidermal growth factor receptor 2 have been reported, suggesting new therapeutic opportunities. Genomic profiling of cell-free tumor DNA and circulating tumor cells has been used to monitor disease burden and the emergence of resistance, and such 'liquid biopsy' approaches may facilitate the early, noninvasive detection of aggressive disease. Finally, single-cell genomics is coming of age and will contribute to an understanding of breast cancer evolutionary dynamics.

SUMMARY

Here, we highlight recent studies that employ high-throughput genomic technologies in an effort to elucidate breast cancer biology, discover new therapeutic targets, improve prognostication and stratification, and discuss the implications for precision cancer medicine.

Tumorigenesis: it takes a village

Although it is widely accepted that most cancers exhibit some degree of intratumour heterogeneity, we are far from understanding the dynamics that operate among subpopulations within tumours. There is growing evidence that cancer cells behave as communities, and increasing attention is now being directed towards the cooperative behaviour of subclones that can influence disease progression. As expected, these interactions can add a greater layer of complexity to therapeutic interventions in heterogeneous tumours, often leading to a poor prognosis. In this Review, we highlight studies that demonstrate such interactions in cancer and postulate ways to overcome them with better-designed therapeutic strategies.

Control of cancer formation by intrinsic genetic noise and microenvironmental cues

Differentiation therapies that induce malignant cells to stop growing and revert to normal tissue-specific differentiated cell types are successful in the treatment of a few specific haematological tumours. However, this approach has not been widely applied to solid tumours because their developmental origins are less well understood. Recent advances suggest that understanding tumour cell plasticity and how intrinsic factors (such as genetic noise and microenvironmental signals, including physical cues from the extracellular matrix) govern cell state switches will help in the development of clinically relevant differentiation therapies for solid cancers.

Classical pathology and mutational load of breast cancer - integration of two worlds

Breast cancer is a complex molecular disease comprising several biological subtypes. However, daily routine diagnosis is still based on a small set of well‐characterized clinico‐pathological variables. Here, we try to link the two worlds of surgical pathology and multilayered molecular profiling by analyzing the relationships between clinico‐pathological phenotypes and mutational loads of breast cancer. We evaluated the number of mutated genes with somatic non‐silent mutations in different subgroups of breast cancer based on clinico‐pathological, including immunohistochemical and tumour characteristics. The analysis was performed for a cohort of 687 primary breast cancer patients with mutational profiling, gene expression and clinico‐pathological data available from The Cancer Genome Atlas (TCGA) project. The number of mutated genes was strongly positively associated with higher tumour grade (p = 1.4e−14) and with the different immunohistochemical and PAM50 molecular subtypes of breast cancer (p = 1.4e−10 and p = 4.3e−10, respectively). We observed significant associations (|R| > 0.4) between the abundance of mutated genes and expression levels of genes related to proliferation in the overall cohort and hormone receptor positive cohort, including the Recurrence Score gene signature (e.g., MYBL2 and BIRC5). Specific mutated genes (TP53, NCOR1, NF1, PTPRD and RB1) were highly significantly associated with high loads of mutated genes. Multivariate analysis for overall survival (OS) revealed a worse survival for patients with high numbers of mutated genes (hazard ratio = 4.6, 95% CI: 1.0 – 20.0, p = 0.044). Here, we report a strong association of the number of mutated genes with immunohistochemical and PAM50 subtypes and tumour grade in breast cancer. We provide evidence that specific levels of the mutational load underlie different morphological and biological phenotypes, which collectively constitute the current basis of pathological diagnosis. Our study is a step towards genomics‐informed breast pathology and will provide a basis for future studies in this field bridging the gap between morphology, tumour biology and medical oncology.

Cancer modelling in the NGS era - Part I: Emerging technology and initial modelling

It is today indisputable that great progresses have been made in our molecular understanding of cancer cells, but an effective implementation of such knowledge into dramatic cancer-cures is still belated and yet desperately needed. This review gives a snapshot at where we stand today in this search for cancer understanding and definitive treatments, how far we have progressed and what are the major obstacles we will have to overcome both technologically and for disease modelling. In the first part, promising 3rd/4th Generation Sequencing Technologies will be summarized (particularly IonTorrent and OxfordNanopore technologies). Cancer modelling will be then reviewed from its origin in XIX Century Germany to today's NGS applications for cancer understanding and therapeutic interventions. Developments after Molecular Biology revolution (1953) are discussed as successions of three phases. The first, PH1, labelled "Clonal Outgrowth" (from 1960s to mid 1980s) was characterized by discoveries in cytogenetics (Nowell, Rowley) and viral oncology (Dulbecco, Bishop, Varmus), which demonstrated clonality. Treatments were consequently dominated by a "cytotoxic eradication" strategy with chemotherapeutic agents. In PH2, (from the mid 1980s to our days) the description of cancer as "Gene Networks" led to targeted-gene-therapies (TGTs). TGTs are the focus of Section 3: in view of their apparent failing (Ephemeral Therapies), alternative strategies will be discussed in review part II (particularly cancer immunotherapy, CIT). Additional Pitfalls impinge on the concepts of tumour heterogeneity (inter/intra; ITH). The described pitfalls set the basis for a new phase, PH3, which is called "NGS Era" and will be also discussed with ten emerging cancer models in the Review 2nd part.

Subclonal diversification of primary breast cancer revealed by multiregion sequencing

The sequencing of cancer genomes may enable tailoring of therapeutics to the underlying biological abnormalities driving a particular patient's tumor. However, sequencing-based strategies rely heavily on representative sampling of tumors. To understand the subclonal structure of primary breast cancer, we applied whole-genome and targeted sequencing to multiple samples from each of 50 patients' tumors (303 samples in total). The extent of subclonal diversification varied among cases and followed spatial patterns. No strict temporal order was evident, with point mutations and rearrangements affecting the most common breast cancer genes, including PIK3CA, TP53, PTEN, BRCA2 and MYC, occurring early in some tumors and late in others. In 13 out of 50 cancers, potentially targetable mutations were subclonal. Landmarks of disease progression, such as resistance to chemotherapy and the acquisition of invasive or metastatic potential, arose within detectable subclones of antecedent lesions. These findings highlight the importance of including analyses of subclonal structure and tumor evolution in clinical trials of primary breast cancer.

The effect of intrinsic and acquired resistances on chemotherapy effectiveness

Although chemotherapy is one of the most common treatments for cancer, it can be only partially successful. Drug resistance is the main cause of the failure of chemotherapy. In this work, we present a mathematical model to study the impact of both intrinsic (preexisting) and acquired (induced by the drugs) resistances on chemotherapy effectiveness. Our simulations show that intrinsic resistance could be as dangerous as acquired resistance. In particular, our simulations suggest that tumors composed by even a small fraction of intrinsically resistant cells may lead to an unsuccessful therapy very quickly. Our results emphasize the importance of monitoring both intrinsic and acquired resistances during treatment in order to succeed and the importance of doing more experimental and genetic research in order to develop a pretreatment clinical test to avoid intrinsic resistance.

Regional Bias of Intratumoral Genetic Heterogeneity of Apoptosis-Related Genes BAX, APAF1, and FLASH in Colon Cancers with High Microsatellite Instability

BACKGROUND

Apoptosis inactivation and intratumoral heterogeneity (ITH) are common features of cancers, including colorectal cancer (CRC). Inactivation of apoptosis prolongs cancer cell survival, and ITH may contribute to CRC progression.

AIM

To examine the presence and extent of mutational ITH in the pro-apoptotic genes APAF1, BAX, and FLASH and the association of mutational ITH with pathologic parameters of CRC.

METHODS

The ITH of mutations in the mononucleotide repeats of APAF1, BAX and FLASH in different tumors were analyzed in 16 cases of CRC with high microsatellite instability (MSI-H) and 41 cases of CRC with stable MSI/low MSI (MSS/MSI-L) by single-strand conformation polymorphism and DNA sequencing analyses.

RESULTS

Frameshift mutations of APAF1, BAX, and FLASH were identified in 19, 31, and 6 % of CRC with MSI-H, respectively, but also in cases of CRC with MSS/MSI-L. All but one CRC with a mutation (8/9) harbored regional ITH of the APAF1, BAX and FLASH frameshift mutations. ITH, however, was not associated with histopathologic features of CRC with MSI-H, suggesting that ITH might not be related to development of the MSI-H phenotype itself, but rather to disease progression.

CONCLUSIONS

Our results indicate that the APAF1, BAX, and FLASH genes not only harbor frameshift mutations but also demonstrate mutational ITH, which together might play a role in the tumorigenesis of CRC with MSI-H by affecting the apoptosis of cancer cells. Our data also suggest that multiregional mutation analysis is needed for a better evaluation of the mutation status in CRC.

Turning the headlights on novel cancer biomarkers: Inspection of mechanics underlying intratumor heterogeneity

Although the existence of intratumoral heterogeneity (ITH) in the expression of common biomarkers has been described by pathologists since the late 1890s, we have only recently begun to fathom the staggering extent and near ubiquity of this phenomenon. From the tumor's perspective, ITH provides a stabilizing diversity that allows for the evolution of aggressive cancer phenotypes. As the weight of the evidence correlating ITH to poor prognosis burgeons, it has become increasingly important to determine the mechanisms by which a tumor acquires ITH, find clinically-adaptable means to quantify ITH and design strategies to deal with the numerous profound clinical ramifications that ITH forces upon us. Elucidation of the drivers of ITH could enable development of novel biomarkers whose interrogation might permit quantitative evaluation of the ITH inherent in a tumor in order to predict the poor prognosis risk associated with that tumor. This review proposes centrosome amplification (CA), aided and abetted by centrosome clustering mechanisms, as a critical driver of chromosomal instability (CIN) that makes a key contribution to ITH generation. Herein we also evaluate how a tumor's inherent mitotic propensity, which reflects the cell cycling kinetics within the tumor's proliferative cells, functions as the indispensable engine underpinning CIN, and determines the rate of CIN. We thus expound how the forces of centrosome amplification and mitotic propensity collaborate to sculpt the genetic landscape of a tumor and spawn extensive subclonal diversity. As such, centrosome amplification and mitotic propensity profiles could serve as clinically facile and powerful prognostic biomarkers that would enable more accurate risk segmentation of patients and design of individualized therapies.

Challenges in the management of advanced, ER-positive, HER2-negative breast cancer

Hormone-receptor-positive breast cancer accounts for the majority-up to 80%-of all breast cancers. The evolution of breast cancer from early stage to the metastatic setting leads to increased heterogeneity, the occurrence of new mutations, and the development of treatment resistance representing a great challenge for management decisions. Unfortunately, little data exist to offer guidance in this context, and a reliance on traditional clinical parameters remains when deciding on optimal treatment. In advanced-stage oestrogen receptor-positive (ER+) disease, ongoing issues include the choice between endocrine therapy and chemotherapy, the appropriate sequence of treatment agents, and the incorporation of biological agents, such as everolimus, into the treatment armamentarium. In metastatic disease, repeated biopsies can help to reassess the receptor or genetic mutational status; however, the evidence to support this approach is limited. In this Review, we examine the current evidence that can guide treatment decisions in patients with advanced-stage ER+ breast cancer, discuss how to tackle these therapeutic challenges and provide suggestions for the optimal management of this patient population.

Molecular characterization and targeted therapeutic approaches in breast cancer

Despite the wide improvements in breast cancer (BC) detection and adjuvant treatment, BC is still responsible for approximately 40,000 deaths annually in the United States. Novel biomarkers are fundamental to assist clinicians in BC detection, risk stratification, disease subtyping, prediction of treatment response, and surveillance, allowing a more tailored approach to therapy in both primary and metastatic settings. In primary BC, the development of molecular profiling techniques has added prognostic and predictive information to conventional biomarkers--estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2. Moreover, the application of next-generation sequencing and reverse-phase protein microarray methods in the metastatic setting holds the promise to further advance toward a personalized management of cancer. The improvement in our understanding on BC biology associated with the study of the genomic aberrations characterizing the most common molecular subtypes allows us to explore new targets for drug development. Finally, the integration of cancer stem cell-targeted therapies and immune therapies in future combination regimens increases our chances to successfully treat a larger proportion of women with more aggressive and resistant metastatic disease. This article reviews the current state of novel biological markers for BC, the evidence to demonstrate their clinical validity and utility, and the implication for therapeutic targeting.

An implantable microdevice to perform high-throughput in vivo drug sensitivity testing in tumors

Current anticancer chemotherapy relies on a limited set of in vitro or indirect prognostic markers of tumor response to available drugs. A more accurate analysis of drug sensitivity would involve studying tumor response in vivo. To this end, we have developed an implantable device that can perform drug sensitivity testing of several anticancer agents simultaneously inside the living tumor. The device contained reservoirs that released microdoses of single agents or drug combinations into spatially distinct regions of the tumor. The local drug concentrations were chosen to be representative of concentrations achieved during systemic treatment. Local efficacy and drug concentration profiles were evaluated for each drug or drug combination on the device, and the local efficacy was confirmed to be a predictor of systemic efficacy in vivo for multiple drugs and tumor models. Currently, up to 16 individual drugs or combinations can be assessed independently, without systemic drug exposure, through minimally invasive biopsy of a small region of a single tumor. This assay takes into consideration physiologic effects that contribute to drug response by allowing drugs to interact with the living tumor in its native microenvironment. Because these effects are crucial to predicting drug response, we envision that these devices will help identify optimal drug therapy before systemic treatment is initiated and could improve drug response prediction beyond the biomarkers and in vitro and ex vivo studies used today. These devices may also be used in clinical drug development to safely gather efficacy data on new compounds before pharmacological optimization.

Molecular portraits: the evolution of the concept of transcriptome-based cancer signatures

Cancer results from dysregulation of multiple steps of gene expression programs. We review how transcriptome profiling has been widely explored for cancer classification and biomarker discovery but resulted in limited clinical impact. Therefore, we discuss alternative and complementary omics approaches.

Intratumoral heterogeneity in a Trp53-null mouse model of human breast cancer

Intratumoral heterogeneity correlates with clinical outcome and reflects the cellular complexity and dynamics within a tumor. Such heterogeneity is thought to contribute to radio- and chemoresistance because many treatments may target only certain tumor cell subpopulations. A better understanding of the functional interactions between various subpopulations of cells, therefore, may help in the development of effective cancer treatments. We identified a unique subpopulation of tumor cells expressing mesenchymal-like markers in a Trp53-null mouse model of basal-like breast cancer using fluorescence-activated cell sorting and microarray analysis. Both in vitro and in vivo experiments revealed the existence of cross-talk between these "mesenchymal-like" cells and tumor-initiating cells. Knockdown of genes encoding ligands upregulated in the mesenchymal cells and their corresponding receptors in the tumor-initiating cells resulted in reduced tumorigenicity and increased tumor latency. These studies illustrate the non-cell-autonomous properties and importance of cooperativity between tumor subpopulations.

SIGNIFICANCE

Intratumoral heterogeneity has been considered one important factor in assessing a patient's initial response to treatment and selecting drug regimens to effectively increase tumor response rate. Elucidating the functional interactions between various subpopulations of tumor cells will help provide important new insights in understanding treatment response and tumor progression.

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.

A Big Bang model of human colorectal tumor growth

What happens in the early, still undetectable human malignancy is unknown because direct observations are impractical. Here we present and validate a “Big Bang” model, whereby tumors grow predominantly as a single expansion producing numerous intermixed sub-clones that are not subject to stringent selection, and where both public (clonal) and most detectable private (subclonal) alterations arise early during growth. Genomic profiling of 349 individual glands from 15 colorectal tumors revealed the absence of selective sweeps, uniformly high intra-tumor heterogeneity (ITH), and sub-clone mixing in distant regions, as postulated by our model. We also verified the prediction that most detectable ITH originates from early private alterations, and not from later clonal expansions, thus exposing the profile of the primordial tumor. Moreover, some tumors appear born-to-be-bad, with sub-clone mixing indicative of early malignant potential. This new model provides a quantitative framework to interpret tumor growth dynamics and the origins of ITH with significant clinical implications.

A journey to uncharted territory: new technical frontiers in studying tumor-stromal cell interactions

The crosstalk between tumor cells and cells of the tumor stroma dictate malignant progression and represent an intriguing and viable anticancer therapeutic target. The successful development of therapeutics targeting tumor-stroma interactions is tied to the insight provided by basic research on such crosstalk. Tumor-stroma interactions can be transient and dynamic, and they occur within defined spatiotemporal contexts among genetically and compositionally heterogeneous populations of cells, yet methods currently applied to study the said crosstalk do not sufficiently address these features. Emerging imaging and genetic methods, however, can overcome limitations of traditional approaches and provide unprecedented insight into tumor-stroma crosstalk with unparalleled accuracy. The comprehensive data obtained by applying emerging methods will require processing and analysis by multidisciplinary teams, but the efforts will ultimately rejuvenate hope in developing novel therapies against pro-tumorigenic tumor-stroma crosstalk.

Optical metabolic imaging quantifies heterogeneous cell populations

The genetic and phenotypic heterogeneity of cancers can contribute to tumor aggressiveness, invasion, and resistance to therapy. Fluorescence imaging occupies a unique niche to investigate tumor heterogeneity due to its high resolution and molecular specificity. Here, heterogeneous populations are identified and quantified by combined optical metabolic imaging and subpopulation analysis (OMI-SPA). OMI probes the fluorescence intensities and lifetimes of metabolic enzymes in cells to provide images of cellular metabolism, and SPA models cell populations as mixed Gaussian distributions to identify cell subpopulations. In this study, OMI-SPA is characterized by simulation experiments and validated with cell experiments. To generate heterogeneous populations, two breast cancer cell lines, SKBr3 and MDA-MB-231, were co-cultured at varying proportions. OMI-SPA correctly identifies two populations with minimal mean and proportion error using the optical redox ratio (fluorescence intensity of NAD(P)H divided by the intensity of FAD), mean NAD(P)H fluorescence lifetime, and OMI index. Simulation experiments characterized the relationships between sample size, data standard deviation, and subpopulation mean separation distance required for OMI-SPA to identify subpopulations.

Higher gene expression variability in the more aggressive subtype of chronic lymphocytic leukemia

Background

Chronic lymphocytic leukemia (CLL) presents two subtypes which have drastically different clinical outcomes, IgVH mutated (M-CLL) and IgVH unmutated (U-CLL). So far, these two subtypes are not associated to clear differences in gene expression profiles. Interestingly, recent results have highlighted important roles for heterogeneity, both at the genetic and at the epigenetic level in CLL progression.

Methods

We analyzed gene expression data of two large cohorts of CLL patients and quantified expression variability across individuals to investigate differences between the two subtypes using different measures and statistical tests. Functional significance was explored by pathway enrichment and network analyses. Furthermore, we implemented a random forest approach based on expression variability to classify patients into disease subtypes.

Results

We found that U-CLL, the more aggressive type of the disease, shows significantly increased variability of gene expression across patients and that, overall, genes that show higher variability in the aggressive subtype are related to cell cycle, development and inter-cellular communication. These functions indicate a potential relation between gene expression variability and the faster progression of this CLL subtype. Finally, a classifier based on gene expression variability was able to correctly predict the disease subtype of CLL patients.

Conclusions

There are strong relations between gene expression variability and disease subtype linking significantly increased expression variability to phenotypes such as aggressiveness and resistance to therapy in CLL.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-014-0125-z) contains supplementary material, which is available to authorized users.

Cancer evolution: mathematical models and computational inference

Cancer is a somatic evolutionary process characterized by the accumulation of mutations, which contribute to tumor growth, clinical progression, immune escape, and drug resistance development. Evolutionary theory can be used to analyze the dynamics of tumor cell populations and to make inference about the evolutionary history of a tumor from molecular data. We review recent approaches to modeling the evolution of cancer, including population dynamics models of tumor initiation and progression, phylogenetic methods to model the evolutionary relationship between tumor subclones, and probabilistic graphical models to describe dependencies among mutations. Evolutionary modeling helps to understand how tumors arise and will also play an increasingly important prognostic role in predicting disease progression and the outcome of medical interventions, such as targeted therapy.

Self-digitization microfluidic chip for absolute quantification of mRNA in single cells

Quantification of mRNA in single cells provides direct insight into how intercellular heterogeneity plays a role in disease progression and outcomes. Quantitative polymerase chain reaction (qPCR), the current gold standard for evaluating gene expression, is insufficient for providing absolute measurement of single-cell mRNA transcript abundance. Challenges include difficulties in handling small sample volumes and the high variability in measurements. Microfluidic digital PCR provides far better sensitivity for minute quantities of genetic material, but the typical format of this assay does not allow for counting of the absolute number of mRNA transcripts samples taken from single cells. Furthermore, a large fraction of the sample is often lost during sample handling in microfluidic digital PCR. Here, we report the absolute quantification of single-cell mRNA transcripts by digital, one-step reverse transcription PCR in a simple microfluidic array device called the self-digitization (SD) chip. By performing the reverse transcription step in digitized volumes, we find that the assay exhibits a linear signal across a wide range of total RNA concentrations and agrees well with standard curve qPCR. The SD chip is found to digitize a high percentage (86.7%) of the sample for single-cell experiments. Moreover, quantification of transferrin receptor mRNA in single cells agrees well with single-molecule fluorescence in situ hybridization experiments. The SD platform for absolute quantification of single-cell mRNA can be optimized for other genes and may be useful as an independent control method for the validation of mRNA quantification techniques.

Applying evolutionary biology to address global challenges

Two categories of evolutionary challenges result from escalating human impacts on the planet. The first arises from cancers, pathogens, and pests that evolve too quickly and the second, from the inability of many valued species to adapt quickly enough. Applied evolutionary biology provides a suite of strategies to address these global challenges that threaten human health, food security, and biodiversity. This Review highlights both progress and gaps in genetic, developmental, and environmental manipulations across the life sciences that either target the rate and direction of evolution or reduce the mismatch between organisms and human-altered environments. Increased development and application of these underused tools will be vital in meeting current and future targets for sustainable development.

Tumor evolution in response to chemotherapy: phenotype versus genotype

In this issue of Cell Reports, Almendro et al. report one of the first comprehensive studies on the intratumor heterogeneity of cell phenotypes and genotypes before and after chemotherapy in breast cancer. These data challenge the concept of genetic population bottlenecks and suggest that cellular phenotypes play an important role in developing resistance to therapy.

Statistical measures of transcriptional diversity capture genomic heterogeneity of cancer

BACKGROUND

Molecular heterogeneity of tumors suggests the presence of multiple different subclones that may limit response to targeted therapies and contribute to acquisition of drug resistance, but its quantification has remained challenging.

RESULTS

We performed simulations to evaluate statistical measures that best capture the molecular diversity within a group of tumors for either continuous (gene expression) or discrete (mutations, copy number alterations) molecular data. Dispersion based metrics in the principal component space best captured the underlying heterogeneity. To demonstrate utility of these measures, we characterized the diversity in transcriptional and genomic profiles of different breast tumor subtypes, and showed that basal-like or triple-negative breast cancers (TNBC) are significantly more heterogeneous molecularly than other subtypes. Our analysis also suggests that transcriptional diversity is a global characteristic of the tumors observed across the majority of molecular pathways. Among basal-like tumors, those that were resistant to multi-agent chemotherapy showed greater transcriptional diversity compared to chemotherapy-sensitive tumors, suggesting that potentially multiple mechanisms may be contributing to chemotherapy resistance.

CONCLUSIONS

We proposed and validated measures of transcriptional and genomic diversity that can quantify the molecular diversity of tumors. We applied the new measures to genomic data from breast tumors and demonstrated that basal-like breast cancers are significantly more diverse than other breast cancers. The observation that chemo-resistant tumors are significantly more diverse molecularly than chemosensitive tumors implies that multiple resistance mechanisms may be active, thus limiting the sensitivity and accuracy of predictive markers of chemotherapy response.

Cancer systems biology: embracing complexity to develop better anticancer therapeutic strategies

The transformation of normal cells into cancer cells and maintenance of the malignant state and phenotypes are associated with genetic and epigenetic deregulations, altered cellular signaling responses and aberrant interactions with the microenvironment. These alterations are constantly evolving as tumor cells face changing selective pressures induced by the cells themselves, the microenvironment and drug treatments. Tumors are also complex ecosystems where different, sometime heterogeneous, subclonal tumor populations and a variety of nontumor cells coexist in a constantly evolving manner. The interactions between molecules and between cells that arise as a result of these alterations and ecosystems are even more complex. The cancer research community is increasingly embracing this complexity and adopting a combination of systems biology methods and integrated analyses to understand and predictively model the activity of cancer cells. Systems biology approaches are helping to understand the mechanisms of tumor progression and design more effective cancer therapies. These approaches work in tandem with rapid technological advancements that enable data acquisition on a broader scale, with finer accuracy, higher dimensionality and higher throughput than ever. Using such data, computational and mathematical models help identify key deregulated functions and processes, establish predictive biomarkers and optimize therapeutic strategies. Moving forward, implementing patient-specific computational and mathematical models of cancer will significantly improve the specificity and efficacy of targeted therapy, and will accelerate the adoption of personalized and precision cancer medicine.

Tumour heterogeneity and the evolution of polyclonal drug resistance

Cancer drug resistance is a major problem, with the majority of patients with metastatic disease ultimately developing multidrug resistance and succumbing to their disease. Our understanding of molecular events underpinning treatment failure has been enhanced by new genomic technologies and pre-clinical studies. Intratumour genetic heterogeneity (ITH) is a prominent contributor to therapeutic failure, and it is becoming increasingly apparent that individual tumours may achieve resistance via multiple routes simultaneously - termed polyclonal resistance. Efforts to target single resistance mechanisms to overcome therapeutic failure may therefore yield only limited success. Clinical studies with sequential analysis of tumour material are needed to enhance our understanding of inter-clonal functional relationships and tumour evolution during therapy, and to improve drug development strategies in cancer medicine.

Cancer genomics: one cell at a time

The study of single cancer cells has transformed from qualitative microscopic images to quantitative genomic datasets. This paradigm shift has been fueled by the development of single-cell sequencing technologies, which provide a powerful new approach to study complex biological processes in human cancers.

GoIFISH: a system for the quantification of single cell heterogeneity from IFISH images

Molecular analysis has revealed extensive intra-tumor heterogeneity in human cancer samples, but cannot identify cell-to-cell variations within the tissue microenvironment. In contrast, in situ analysis can identify genetic aberrations in phenotypically defined cell subpopulations while preserving tissue-context specificity. GoIFISHGoIFISH is a widely applicable, user-friendly system tailored for the objective and semi-automated visualization, detection and quantification of genomic alterations and protein expression obtained from fluorescence in situ analysis. In a sample set of HER2-positive breast cancers GoIFISHGoIFISH is highly robust in visual analysis and its accuracy compares favorably to other leading image analysis methods. GoIFISHGoIFISH is freely available at www.sourceforge.net/projects/goifish/.