Advancing Cancer Patient Care by Integrating ... - Wiley Online Library

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DRUG DEVELOPMENT RESEARCH 75 : 384–392 (2014)

Research Overview

Advancing Cancer Patient Care by Integrating Circulating Tumor Cell Technology to Understand the Spatial and Temporal Dynamics of Cancer Mariam Rodriguez-Lee,1 Peter Kuhn,2 and David R. Webb3* 1 Department of Cell and Molecular Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA 2 Dornsife College of Letters, Arts and Sciences, University of Southern California, 3551 Trousdale Parkway, ADM 304, Los Angeles, CA 90089, USA 3 Department of Integrative Structural and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA

Strategy, Management and Health Policy Enabling Technology, Genomics, Proteomics

Preclinical Research

Preclinical Development Toxicology, Formulation Drug Delivery, Pharmacokinetics

Clinical Development Phases I-III Regulatory, Quality, Manufacturing

Postmarketing Phase IV

ABSTRACT Spatial and temporal dynamics of cancer, studied with physical science approaches at critical transition points of the disease can provide insight into the biology of cancer and the evolutionary changes that occur both naturally and in response to therapy. A very promising development in translational cancer medicine has been the emergence of circulating tumor cells (CTC) as minimally invasive “liquid biopsies.” We envision that the future utility of CTC will not simply be confined to enumeration, but also include their routine characterization using a high-content approach that investigates morphometrics, protein expression and genomic profiling. This novel approach guided by mathematical models to predict the spread of disease from the primary site to secondary site can bring the bench to the bedside for cancer patients. It is agnostic with reference to drug choice and treatment regimen, which also means that each patient is unique. The approach is Bayesian from a data collection perspective and is patient-centric rather than drug or new chemical entity–centric. The analysis of data comes from an understanding of commonalities and differences that are detected among patients with a given cancer type. Thus, patients are treated over the course of their disease with various drug regimens that reflects our real-time understanding of their evolving tumor genomics and response to treatment. This likely means that smaller cohorts of patients receive any given regimen but we hypothesize that it would lead to better patient outcomes than with the current classic approach to drug testing and development. Drug Dev Res 75 : 384–392, 2014. © 2014 Wiley Periodicals, Inc. Key words: circulating tumor cells (CTC); high-content analysis; copy number variation (CNV); mathematical modeling; cancer care

Disclosures of commercial interests and roles: The HD-CTC technology described here has been licensed to Epic Sciences. P.K. has an ownership interest in Epic Sciences. *Correspondence to: David R. Webb, Department of Integrative Structural and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA. E-mail: [email protected] Published online in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/ddr.21225 © 2014 Wiley Periodicals, Inc.

CIRCULATING TUMOR CELLS

INTRODUCTION

Cancer is an evolutionary process taking place within a heterogeneous population of cells [Fidler, 2003; Michor et al., 2004; Axelrod et al., 2006; Barabasi et al., 2011] that traffic from one anatomical site to another via hematogenous and lymphatic routes [Weinberg, 2007]. The population of cells associated with the primary and metastatic tumors evolve, adapt, respond, and disseminate in an environment in which a fitness landscape controls both survival and replication [Gatenby and Vincent, 2003; Nowak and Sigmund, 2004; Nowak, 2006]. Generally, cancer develops in individuals over decades as a result of inherited and acquired genetic, epigenetic and other abnormalities that emerge in otherwise normal cells [Fidler, 2003; Frank, 2007]. The cells in tumors compete for space and resources in a Darwinian struggle for existence in tissues that progressively age, evolve, and sometimes develop tumors. There can be little doubt that the ability to detect and diagnose cancer remains a major goal in oncology. Major strides have been made toward this goal in the last few years both in blood borne and solid tumor diseases. The lessons learned in the study of the biochemistry and genomics of blood borne cancers such as leukemias and lymphomas have contributed greatly to the development of targeted therapies and in some cases, such as chronic lymphocytic leukemia and multiple myeloma, led to considerable increases in both progression-free survival and overall survival [Schnaiter and Stilgenbauer, 2011]. The focus of this overview is the concept of bringing the bench to the bedside for cancer patients with solid tumors. This involves carrying out a detailed and ongoing high-content analysis of their tumor based on frequent sampling of tumor cells in patient blood samples that is guided by mathematical models of cancer progression and used to direct the clinical approach to treatment. This requires that preclinical basic genomics/biomarkers research be applied in a translational way to develop just-in-time treatments that may be applied directly to a patient. Solid tumor samples taken during biopsy or tumor resection allow for an initial assessment of the biochemical and genetic changes that have taken place in the tumor, but does not allow either the clinician or the researchers to follow the evolution of solid tumor disease during remission and possible, subsequent reemergence of the tumor. It is in this context that over the last several years, a number of groups have begun to study a unique and accessible form of carcinomas (epithelial cell based), namely those that may be found in the circulation and are thus potentially routinely available for study and analysis.

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Historically, strides have been made in cancer treatments that integrate a combination of direct characterization of the disease in its context (biopsy histopathology) and molecular characterization of a therapeutic target therein (companion diagnostic) matched to a treatment (surgical resection, radiation therapy or targeted therapy). The majority of conventional cancer treatments have had limited success in curing metastatic disease. As tumors evolve, even an effective response to therapy is typically short lived, and patients often relapse within 12–24 months of therapeutic intervention [Lacy et al., 1998; Cristofanilli et al., 2005; Ushijima, 2010]. Solving the problems associated with the currently imperfect performance of our therapeutic interventions and making real improvements in patient outcomes require understanding of the disease as a system undergoing constant transitions both spatially and temporally. Related to this is a parallel development to develop mathematical-computational models that describe and ultimately help to predict the spread of disease from the primary site to secondary, metastatic sites. These models recapitulate clinically observed cancer progression and are used to inform the design of high-content, time resolved investigations in clinically relevant and also feasible settings. The correlations derived from these clinical observations drive model system experimental design to derive causation, and test potential interventions. The clinical correlates and model system-derived causation then integrates into the computational model systems to drive subsequent cycles. Growth and complexity of the mathematical model, informed by further patient and model data, is expected to improve accuracy of the model for increasingly specific patient populations. This is founded in the previously demonstrated ability of quantifying metastatic pathways using a Markovian model [Newton et al., 2012] that is consistent with clinical observations [Newton et al., 2013] and enabled to substantiate case studies around potential benefits in a treatment setting [Bazhenova et al., 2014]. This mathematical framework is grounded in previous descriptions of the correlation between the solid phase and fluid phase of cancer [Disibio and French, 2008], the description of cellular structures that contribute to disease [Carlsson et al., 2014], which are enabled through a high-content single-cell approach that is applicable across specimen origins [Dago et al., 2014]. The second innovation is a unique technique for the acquisition and banking of solid and fluid biopsy specimens that give researchers the rare ability to not only repeatedly sample tumor specimens from blood but also preserve them for secondary analysis using as yet undeveloped assay techniques. The technology thus Drug Dev. Res.

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permits hypothesis testing in the human and the biologic model system on an iterative basis that can adapt to changes in research questions and technological advancement. The third arena of focus involves the leveraging of ongoing scientific developments in rapidly advancing fields of rare cell biology and especially the integration of co-clinical models using a clinically annotated biorepository. As one example, it is reasonable to expect significant impact on the iterative cycle from work by groups that have recently reported advances in seeding cells from the circulatory system into animal models based on in-human observations. Similarly, advances in in vivo imaging technologies are starting to allow direct visualization of homing and dislocation of cells, for example, from the bone marrow. Both of these types of approaches are just examples of what we believe will add real value to the organizing framework of this approach in the future and shed light on the physical dynamics of cancer as it evolves in the human body. Detection and High-Content Analysis of Circulating Tumor Cells (CTCs) CTCs are rare cancer cells released from tumors into the bloodstream that play a critical role in establishing metastases. The presence of CTCs has been associated with worse prognosis in several major cancer types, including breast, prostate and colorectal cancer. The ability to detect CTCs to guide patient treatment and inform prognosis is highly attractive, especially since it has the potential to provide multiple samples by way of serial, minimally invasive “liquid biopsies.” The incentive for attempting to find alternatives to repeated biopsies of solid tumors (carcinomas) is strong, as outlined above. Although CTCs were first identified over a century ago, CTC research has been hampered by the inability to reliably detect these rare cells. The fact that CTCs occur at extremely low levels in the circulation has also hindered their isolation and molecular characterization. There have been numerous efforts, and many technologies developed to enrich and analyze CTCs, many of which have been explored and evaluated with samples from cancer patients. The CTC enumeration platform, CellSearch® (Veridex LLC, NJ) was approved for clinical use by the Food and Drug Administration (FDA) in the United States, initially for patients with breast cancer in 2004, then colorectal cancer in 2007 and prostate cancer in 2008. This platform has been widely used for the enumeration of CTCs and utilizes immunomagnetic enrichment to isolate tumor cells that express epithelial cell adhesion molecule (EpCAM). While this approach Drug Dev. Res.

has established CTC enumeration as a prognostic marker and predictor of patient outcome in metastatic breast [Hayes et al., 2006], prostate [Danila et al., 2007] and colon cancers [Cohen et al., 2008], it has methodological constraints that cannot be neglected such as inadequate cell recovery, analytical sensitivity and sampling accuracy [Nagrath et al., 2007; Tanaka et al., 2009; Flores et al., 2010]. This system is also not applicable to all types of cancer and is limited in its ability to permit single-cell analysis. An alternative approach is to detect CTCs in situ, without requiring a specific enrichment step by carrying out high-throughput assaying of the entire population of cells in the blood. Using this direct analysis approach to detect CTCs, the Kuhn laboratory has developed the high-definition single-cell assay (HD-SCA) over the past several years [Hsieh et al., 2006; Marrinucci et al., 2007, 2008, 2012; Cho et al., 2012; Lazar et al., 2012; Nieva et al., 2012; Wendel et al., 2012], capable of analyzing 300,000 cells per second. Whole blood is first treated for erythrocyte lysis, and then all remaining cells are plated onto several glass slides. All nucleated cells are identified and stained with monoclonal antibodies (via immunofluorescence) that target cytokeratin (CK) found exclusively on epithelial cells, a pan leukocyte specific anti-CD45 monoclonal antibody, and a nuclear stain, DAPI. Thus, all nucleated blood cells are image captured using multiple fluorescence wavelengths leading to high-quality and high-resolution digital images. This is an enrichment-free approach that has high sensitivity, high specificity and adds a highdefinition cytomorphology that enables a detailed morphological characterization of CTCs, which are quite heterogeneous. This approach offers a considerable advantage in that one may carry out the analysis of multiple parameters that allows the identification and characterization of specific populations. Figure 1 summarizes the most recent data collected by our group, interrogating a wide array of solid tumor types in ongoing studies that have been specifically designed as prospective studies to investigate the spatiotemporal dynamics of cancer. The corresponding de-identified patient clinical information is annotated in a separate biorepository. Simple enumeration of CTCs will not contribute significantly to the development of improved or more personalized cancer treatments. Instead, the contribution of CTCs will stem from obtaining a better understanding of this cell population through complete characterization and functional analysis in in vivo, in situ or ex vivo models [Helzer et al., 2009; Ameri et al., 2010; Kaiser, 2010; Kirby et al., 2012]. Our high-content assay detection platform has been extensively validated and goes beyond cell


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Fig. 1. Overview of sample collection over time interrogating a wide array of solid tumor types in past and ongoing prospective studies specifically designed to investigate the spatiotemporal dynamics of cancer using the rare cell high-content assay platform. Number of samples collected per 30-day period for each study are shown stacked in the y-axis.

enumeration (Fig. 2). Multiple characteristics of single rare cells from the solid and the fluid phases of carcinomas can be evaluated using this technique that identifies rare cells at the single-cell level. It provides morphological characterization, biomarker expression and localization, and single-cell genomic copy number variation (CNV) analysis as well as single gene re-sequencing. It is also a flexible platform that allows evaluation of additional biomarkers including estrogen receptor (ER), human epidermal growth factor receptor 2 (Her-2), VE-Cadherin, platelet-derived growth factor receptor alpha (PDGFRα) and androgen receptor (AR) for breast, lung and prostate cancer, respectively. Multiple types of carcinomas are being studied with our rare cell high-content assay platform, including lung, prostate, pancreatic, liver, ovarian, breast and colon cancer. All research is conducted using patient blood samples, bone marrow and tissue. We also have the ability to extract plasma from patient whole blood and bone marrow samples for use in cell-free experiments. Frozen storage of samples is a unique feature of this detection platform and permits for secondary analysis and iterative scientific exploration on archived samples. Advances in the genomic analysis of CTCs provide an ongoing look at the evolution of disease in individual patients. The ability to sequence whole human genomes has continued to progress since the 1990s/early 2000s when the first fully sequenced human genome was presented. Not only has the cost come down, but the capacity to sequence large segments of the genome very rapidly means that it is possible within the space of a few days, to assess the status of known cancer genes in individual patient biopsies. Moreover, the amount of cells/tissue required to com-

plete these analyses has also declined bringing us closer to the time when it will be possible to analyze the genome of a cancer patient using only the CTCs. However, that is not currently possible. At present the most powerful tools that may be used to analyze small tumor samples are single nucleotide polymorphism (SNP) analysis and CNV. Current thinking suggests that tumors may either be driven by mutations or by CNV—i.e., changes in the number of copies of a gene > 2N. There are arguments suggesting that detecting either mutations or copy number aberrations may be the best measure of changes in the tumor genomics reflecting the need for rethinking the therapeutic approach. Certainly detecting mutations has been key in guiding drug discovery efforts toward specific genes and proteins. However, CNV analysis also appears to be relevant both on the diagnostic and predictive biomarker fronts. The high-content assay platform has the ability to isolate single and grouped cells for further analysis including single-cell genomics. We can then integrate phenotypic and genotypic data to understand the alterations that occur in the tumor and its microenvironment as it evolves in the spatiotemporal dimensions. Studying cancer at these resolutions presents unique opportunities and challenges. Drawing useful conclusions from comparisons between single cells from a biopsy will require robust biostatistical analysis in order to extract the most useful data. Sets of genomically profiled single cells may present in one extreme as a completely homogeneous population, while in the other as highly diverse with complex sub-clonal structures [Navin et al., 2011; Ni et al., 2013]. We believe the added resolution will provide an unprecedented view of the time-resolved state of cancer. We have recently shown [Carlsson Drug Dev. Res.

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Fig. 2. High-content assay platform. This platform when applied to liquid biopsies obtained in prospectively followed cohorts of subjects results in the generation of diverse types of experimental and modeling data such as cell enumeration, morphometric characterization, protein expression, subcellular localization and next-generation sequencing. A unique output of this approach is the integration of high-definition single-cell assay (HD-SCA) results from samples of specific cohorts of cancer patients and the corresponding HD-SCA-independent data such as patient clinical information and cell-free DNA assays, to ultimately describe transitions of the disease.

et al., 2014], how morphometric traits (e.g., clustering) can be leveraged using statistical analysis to draw clinically relevant conclusions. When adding genomic analysis on the CNV and SNV levels, the number of parameters that can be used to describe a population of cells increases and each new parameter can be biologically investigated. As a consequence of this and of the fact that we will never be able to measure every cell and every genomic change, we must look for representative events in both spatial and temporal dimensions when assessing a cell population’s composition in terms of clonal, non-clonal but altered, or “normal-like” cells. Corralling the unwieldy amounts of data extracted from this type of single cell analyses, and focusing it in concert with emerging potentially relevant clinically Drug Dev. Res.

applicable information, represents an innovative data handling method. Another area of innovation lies in the strategy we employ to perform genomic analysis within a “tiered” structure that permits a first-level look at genome-wide copy number profiles for as little as $25/ cell. This is the result of a unique strategy for read depth counting and bar coding that allows us to run highly multiplexed, short read sequencing lanes. We group cells into lineage trees to determine whether they are closely related to cells from tissue, or whether a given sample has a single genetic type, multiple distinct genetic lineages, or is a mixture of tumor cells and other disease-associated cells. Based on this first level evidence, we can decide whether selecting additional cells would likely provide additional information.


This concept was recently investigated in a patient with castrate-resistant prostate cancer progressing through both chemotherapy and targeted therapy [Dago et al., 2014]. We integrated across four time points a total of 41 complete genome-wide CNV profiles with AR protein levels and phenotypic changes. Remarkably, little change was observed in response to standard chemotherapy, but the clinical response and subsequent progression after targeted therapy was associated with the sequential emergence of two distinct subpopulations that differed in both AR genotype and expression phenotype. Understanding the process of adaptation to targeted therapy may provide for new strategies based on combined or sequential application of treatments chosen to maximize the extent and duration of response and delay therapeutic resistance. These data demonstrated a framework for the multi-scale analysis of fluid biopsies to quantify disease evolution in individual patients and provides an example of the time frame of tumor evolution in response to therapy. In addition to single cells, CTC aggregates, also known as circulating tumor microemboli (CTM), can be identified and analyzed using the high-content assay platform. When combined with clinical data, it can provide a way to diagnose lung cancer [Carlsson et al., 2014]. We have routinely detected and characterized CTM in blood samples from lung cancer patients using our rare cell high-content assay platform. The CTM are commonly found in a wide range of sizes, some containing hundreds of cells. We performed a prospective clinical study of patients with undiagnosed lung nodules that were undergoing diagnostic PET scans [Nair, 2013]. Data from the CTM assay were combined with patient clinical information and analyzed to create a predictive model, called LungDx, for the diagnosis of lung cancer. This model will be available to the public as a web-based application where clinicians can enter patient characteristics, imaging and CTM assay results in order to obtain a probability that a given lung nodule is malignant. LungDx has demonstrated its initial prognostic potential in nonsmall cell lung cancer, a traditionally difficult disease to diagnose and monitor. CTM proved to be highly specific in detecting cancerous versus benign nodules. These aforementioned results demonstrate that it is indeed possible to couple high-content analysis of CTC to give an ongoing, real-time monitoring of therapy and to identify precisely when such therapy ceases to be of use. This approach has the potential to take personalized treatment into a new era; one in which continuous monitoring of solid tumors during therapy becomes routine, giving clinicians an accurate, ongoing view of disease remission and relapse. This should allow the clinicians to rapidly change treatment directions based

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on direct evidence of changes in the tumor possibly even anticipating what the tumor may do next allowing a more rapid interdiction with other therapeutics. Mathematical Models Generated to Understand Tumor Cell Spread Support and Quantify a Multidirectional Pathway to Metastatic Progression. Predictions Based on These Models Can Modify Cancer Management Guidelines Mathematical tools can be used to develop mathematical and computational models that bridge the vast range of spatial and temporal scales of human systems biology [Barillot et al., 2012]. These tools, used in conjunction with the time-resolved CTC data available from prospective studies, can prove useful to quantitatively model the spatiotemporal pathways and mechanisms of cancer metastasis. Over the past 5 years, our group has developed mathematical models to understand metastatic cancer progression for many of the major cancer types, including primary lung, breast and colorectal cancers. This modeling framework is a Markov model construct in which each anatomical site in the human body is represented as a node in a directed graph, linked by an edge that has an associated weight and direction. The edge weights (stochastic model parameters) are probabilities of metastatic spread from one anatomical site to another, obtained from the Markov transition matrix. Following the initial mathematical development [Newton et al., 2012], we applied the model to a research hypothesis of lung cancer progression [Newton et al., 2013] and translated the outcomes to clinically actionable recommendations [Bazhenova et al., 2014]. These mathematical models categorize each metastatic site as either a “spreader” site (has a higher chance of spreading CTCs throughout the body) or a “sponge” site (has a higher chance of attracting CTCs from within the body) based on the ratio of “probability out” over “probability in” of all of the edges at each node. One of the lessons we have learned from our lung cancer models (using autopsy data [Disibio and French, 2008] only) is that it is the combined characteristics of the primary and first metastatic site to which it spreads that largely determines the future pathways and timescales of disease progression. This allows us to group subpopulations of patients according to their combination of primary tumor plus first metastatic site. One example is the controversy concerning primary lung cancer and adrenal metastases. In lung cancer, although adrenal metastases are common, there is no clear consensus as to whether they occur via a lymphatic route or through the blood. Although the lymphatic theory of adrenal gland spread is not novel, to date this finding is Drug Dev. Res.

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not commonly acknowledged in clinical practice. Using our mathematical models of primary lung cancer [Newton et al., 2013], we found that metastasis to the adrenal gland occurs as a very early event. Furthermore, the adrenal gland also serves as a spreader site. By integrating this finding with existing literature, we can identify a subpopulation of patients should be treated very differently than those lung cancer patients with non-adrenal metastasis who might indeed benefit from a primary intervention, i.e., aggressive treatment of their adrenal metastases with surgery or radiation [Bazhenova et al., 2014]. More recently, we have created a specific Markov model for breast cancer based on clinical data points from 453 newly diagnosed breast cancer patients diagnosed between 1975 and 2009 that validates and extends (with calibrated temporal data) the autopsyderived model [Newton et al., 2014b]. That validation uses anatomical spread data only, with specific anatomic sites of metastases recorded as they developed. The subtle differences in the likelihood of spread to each of the sites (e.g., the “shape” of the probability distribution function associated with spread to each of the sites) in different types of cancers are accurately captured in the Shannon entropy metric we have developed for this purpose [Newton et al., 2014a, 2014b]. The next step will be to integrate the mathematical models with CTC high-content analysis including single-cell genomic CNV using noninvasive sequential sampling strategies to determine the relationship between genome variation at the population level and the metastatic entropy. These approaches will allow us to develop statistical forecasting tools for cancer progression, including mathematical models that incorporate the important evolutionary biology aspects of cancer cell driver mutations, adaptation and response. These models will help us pinpoint ways in which CTCs adapt to and survive in a complex heterogeneous environment [Axelrod et al., 2006]. We can use these models to study response and adaptation to therapies that are locally targeted at the spreader and sponge metastatic sites. CONCLUDING REMARKS

There is great interest in obtaining reliable molecular information from CTCs, as they may provide real-time and noninvasive surrogates for diagnosis and prognosis, predictive biomarkers for making treatment decisions, and samples for monitoring drug resistance. CTCs may provide a source for longitudinal molecular analysis of tumors during the clinical management of patients that could facilitate both clinical investigations and cancer patient care. Drug Dev. Res.

The eventual integration of CTC technology into clinical patient care requires a robust approach that is cost-effective with a fast turn-around time. Assays with improved sensitivity could not only be applicable to different types of cancers, but also may detect cancers at early stages when patients are more likely to benefit from therapies. Prescribing the right treatment at the right time to the right patient can prolong a high quality of life, reduce side effects and reduce hospital time. The rare cell high-content assay platform we have developed gives the ability to not only repeatedly sample tumor specimens from blood but also preserves and isolates single and grouped cells for secondary analysis using as yet undeveloped assay techniques. This approach recently demonstrated the emergence of different cancer cell populations over time in response to therapy in a prostate cancer patient, and additional studies in other cancer types are underway. Our research efforts are aimed at integrating patient, model system and high-content single-cell data to translate clinically observed correlations into a mechanistic understanding of the physical and biological underpinnings of cancer dynamics. The expected outcomes of this approach are both individual advances of our understanding of cancer physical biology in specific settings as well as global understanding of disease progression in the patient. A better understanding of how cell populations differ within tumors is necessary to make full use of new therapies that target cancer at the molecular level. This technology thus permits hypothesis testing on an iterative basis that can adapt to changes in research questions and technological advancement. We propose that integrating single-cell high-content characterization of CTCs with patient clinical information will ultimately describe key clinical and actionable transitions of the disease.

ACKNOWLEDGMENTS

This is manuscript # 27096 from The Scripps Research Institute. We thank Dr. Anders Carlsson for the design of Figure 1. This work was supported by Award Number U54CA143906 from the National Cancer Institute. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health.

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