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Method of using non-rare cells to detect rare cells

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Method of using non-rare cells to detect rare cells


The invention provides seminal computational approaches utilizing data from non-rare cells to detect rare cells, such as circulating tumor cells (CTCs). The invention is applicable at two distinct stages of CTC detection; the first being to make decisions about data collection parameters and the second being to make decisions during data reduction and analysis. Additionally, the invention utilizes both one and multi-dimensional parameterized data in a decision making process.
Related Terms: Circulating Tumor Cells

Inventors: Peter Kuhn, Anand Kolatkar, Joshua Kunken, Dena Marrinucci, Xing Yang, John R. Stuelpnagel
USPTO Applicaton #: #20120276555 - Class: 435 723 (USPTO) - 11/01/12 - Class 435 
Chemistry: Molecular Biology And Microbiology > Measuring Or Testing Process Involving Enzymes Or Micro-organisms; Composition Or Test Strip Therefore; Processes Of Forming Such Composition Or Test Strip >Involving Antigen-antibody Binding, Specific Binding Protein Assay Or Specific Ligand-receptor Binding Assay >Involving A Micro-organism Or Cell Membrane Bound Antigen Or Cell Membrane Bound Receptor Or Cell Membrane Bound Antibody Or Microbial Lysate >Animal Cell >Tumor Cell Or Cancer Cell



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The Patent Description & Claims data below is from USPTO Patent Application 20120276555, Method of using non-rare cells to detect rare cells.

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BACKGROUND OF THE INVENTION

1. Field of the Invention

The invention relates generally to medical diagnostics and more specifically to detection and categorization of rare cells, such as circulating tumor cells (CTCs).

2. Background Information

Significant unmet medical need exists for the longitudinal disease monitoring in patients with epithelial cancers at the cellular level. Predicting and monitoring therapy response and disease progression are particularly important in epithelial cancer patients due to the natural history of the disease and the selective selection process in response to the therapeutic pressure. While progress has been made in understanding the primary and metastatic tumors in their respective microenvironments, a substantial barrier exists in understanding carcinoma behavior during the fluid phase, as it spreads within and occupies the bloodstream. The circulating component of cancer contains within it the cells giving rise to future metastases, and as such, represents a compelling target for investigation.

Research to fully characterize the clinical significance of this fluid phase of solid tumors has been hindered by the lack of easily accessible and reliable experimental tools for the identification of CTCs. The unknown character and low and unknown frequency of CTCs in the blood, combined with the difficulty of distinguishing between cancerous versus normal epithelial cells, has significantly impeded research into how the fluid phase might be clinically important. The ideal fluid phase biopsy should find significant numbers of a specific CTC population in most epithelial cancer patients and preserve and present CTCs to a pathologist and/or researcher in a format that enables not only enumeration but further molecular, morphologic and/or phenotypic analysis. In addition, it should preserve the remaining rare populations for further analysis.

CTCs are generally, although not exclusively, epithelial cells that originate from a solid tumor in very low concentration and enter into the blood stream of patients with various types of cancer. CTCs are also thought to be capable of originating in the blood, forming small colonies throughout the body. The shedding of CTCs by an existing tumor or metastasis often results in formation of secondary tumors. Secondary tumors typically go undetected and lead to 90% of all cancer deaths. Circulating tumor cells provide the link between the primary and metastatic tumors. This leads to the promise of using the identification and characterization of circulating tumor cells for the early detection and treatment management of metastatic epithelial malignancies. Detection of CTCs in cancer patients offers an effective tool in early diagnosis of primary or secondary cancer growth and determining the prognosis of cancer patients undergoing cancer treatment because number and characterization of CTCs present in the blood of such patients has been correlated with overall prognosis and response to therapy. Accordingly, CTCs serve as an early indicator of tumor expansion or metastasis before the appearance of clinical symptoms.

While the detection of CTCs has important prognostic and potential therapeutic implications in the management and treatment of cancer, because of their occult nature in the bloodstream, these rare cells are not easily detected. CTCs were first described in the 1800s, however only recent technological advances have allowed their reliable detection. The challenge in the detection of circulating tumor cells is that they are present in relatively low frequency compared to other nucleated cells, commonly less than 1:100,000. To compensate for this challenge, most conventional approaches for detecting circulating tumor cells rely on experimental enrichment methods, whereby the CTCs are preferentially separated from the other cellular components (e.g., non-CTCs), most importantly other nucleated cells that are the most similar to CTCs.

Currently, the most utilized methods of positive enrichment for enumeration/characterization of CTCs are immunomagnetic enrichment methods targeting the surface protein EpCAM and the “CTC chip”. The most widely used methodology to detect CTCs, J&J's Veridex technology, utilizes immunomagnetic enrichment. The technology relies upon immunomagnetic enrichment of tumor cell populations using magnetic ferrofluids linked to an antibody which binds epithelial cell adhesion molecule (EpCAM), expressed only on epithelial derived cells. This methodology requires 7.5 mL of blood for analysis and finds greater than 2 CTCs in only some metastatic cancer patients.

Microfluidic or “CTC-Chip” technology, is another positive enrichment method for enumeration/characterization of CTCs. The methods utilizes 1-3 mL of blood in which whole blood flows past 78,000 EpCAM-coated microposts. EpCAM+ cells stick to the posts and are subsequently stained with cytokeratin, CD45, and DAPI. With this methodology, CTCs are found in virtually all metastatic cancer patients at a relatively high purity and not in healthy controls. Additionally, CTC-chip technology identifies CTCs in all patients and in higher numbers than other technologies by a factor of approximately 10 to 100 fold as reported in two recent publications.

The only routinely used technology for CTC detection is based on immunomagnetic enrichment. This current “gold standard” and FDA approved test is called CellSearch® and employs an immunomagnetic enrichment step to isolate cells that express the epithelial cells adhesion molecule (EpCAM). Additionally, to be identified as a CTC, the cell must contain a nucleus, express cytoplasmic cytokeratin, and have a diameter larger than five microns. This system has uncovered the prognostic utility of enumerating and monitoring CTC counts in patients with metastatic breast, prostate, and colorectal cancers; however, the sensitivity of this system is low, finding no or few CTCs in most patients. Most follow-on CTC technologies have reported higher sensitivity and are pursuing variations of the enrichment strategy, however this directly biases the detectable events towards those that have sufficient expression of the protein selected for the initial enrichment step.

A standardized microscope based approach has also been previously utilized to identify and morphologically characterize and credential CTCs in case studies of breast, colorectal, and lung cancer patients.

Although many CTC detection approaches are currently in use, significant limitations have been identified with the current approaches. For example, one significant limitation of positive selection methods to enumerate/characterize CTCs is that positive physical selection invariably leads to loss of CTCs and is less than 100% efficient. Thus the number of CTCs detected per sample using current methods is often too low to provide robust interpretation or clinically meaningful content of a particular sample. Additional limitations of current methods include low CTC detection due to CTC heterogeneity. For example, differences in individual CTC features within the CTC population of interest further hinder the number of CTCs detected using current methodologies. Such differences may include size variations between individual CTCs, and variable or down regulated expression between individual CTCs of the cell surface markers used to detect CTCs. A further limitation of existing methodologies includes limitations in purity levels and variable purity. Any enrichment will have a certain number of false positives, for instance other nucleated blood cells that stick to the enrichment. For example, the Veridex magnet has typically 5,000 to 10,000 false positives on top of the 5 to 10 positives.

SUMMARY

OF THE INVENTION

The present invention is based in part on the discovery of innovative methods for analyzing samples to detect, enumerate and characterize rare cells, such as CTCs. Accordingly, the present invention provides methods for improved detection and characterization allowing for clinically meaningful analysis of samples for use in clinical, research and development settings.

Accordingly, the present invention provides methods for the improved detection and characterization of rare cells in a sample by utilizing data from non-rare cells (cells present at a concentration of 10, 50, 100, 200, 300, 400, 500, 1,000, 5,000, 10,000 times or greater as compared to the rare cell) in the sample. Thus the method of the invention utilizes similarity measures to assess non-similarity of cells, requiring both the biggest distance exclusion, e.g., events that are clearly non-rare cell related and the fine distinction of a cutoff based on similarities of surrounding non-rare cells.

The method includes providing a sample suspected of having at least one rare cell and at least one cell that is present at a concentration that is at least 10 times that of the rare cell; contacting the sample with at least one detectable agent, such as an agent that binds a cell marker; performing cell imaging on the sample to generate an image; and detecting the at least one rare cell as compared with other cells in the sample by analyzing the cell from the image, thereby detecting the rare cell in the sample. In various aspects of the invention, the method further includes plating of the suspected rare cell and at least one cell on a solid support, such as a slide, to facilitate contacting the cells with the detectable agent and cell imaging. In various aspects of the invention, the detectable agent is any agent used to stain the cells, such as an agent that binds a cell marker, including, but not limited to, a positive marker, negative marker, nuclear marker, content marker, or any combination thereof.

In various aspects of the invention, the methods described herein are performed on an apparatus for efficiently imaging a slide containing a detectable signal, such as a fluorescent signal. The apparatus may typically include a computer having at least one system processor with image processing capability, a computer monitor, an input device, a power supply and a microscope subsystem. Thus the apparatus includes a computer having executable code for performing the various analysis required to practice the invention. The microscope subsystem includes an optical sensing array for acquiring images. A two-dimensional motion stage for sample movement and for focus adjustment, and input and output mechanisms for multiple sample analysis and storage. The apparatus may also include a transmitted light source as well as an illuminating/fluorescent excitation light source for fluorescing samples.

In one embodiment of the invention, the method includes establishing optimal exposure limits for performing the cell imaging that facilitate detection of rare cells present. In one aspect, the exposure limit for the detectable agent is determined using a signal from at least one cell. In various aspects, the detectable marker may be a positive marker, negative marker, nuclear marker or content marker. In a related aspect, the exposure limits may be set using data relating to the cells and/or suspected rare cells gathered from a first image, to re-image the slide.

In another embodiment, the method includes minimizing exposure settings to minimize data collection time and maximize throughput to facilitate detection of rare cells.

In another embodiment, the method includes utilizing data associated with non-rare cells to generate a quality control parameter that facilitates detection of rare cells. In various aspects, the quality control parameter is distribution of at least one non-rare cell on the slide, alignment of multiple cell images via alignment of non-rare cell markers, quality of cell staining, distribution of a positive marker throughout the non-rare cells, or cell loss from repeated processing.

In another embodiment, the method includes determining intensity cut-off limits to minimize false negatives, as well as false positives and to facilitate rare cell detection. In one aspect, the detectable agent is a positive marker and the intensity limits are determined using mean, standard deviation, coefficient of variation, other statistical parameters or any combination thereof, for a background signal of the positive marker. In another aspect, the detectable agent is a positive marker and the intensity limits are determined within a single image, or portions of that image, by identifying the highest signal event from a positive marker and comparing the highest signal to the mean and standard deviation calculated from signals of all, or a subset of events. In yet another aspect, the detectable agent is a negative marker and the intensity limit for the negative marker is determined using mean and standard deviation of signals from the negative markers from non-rare cells (either all non-rare cells or a specific subset).

In another embodiment, cytological features of non-rare cells, such as cellular and nuclear size (absolute and relative; overall and apparent) and distribution, are utilized to facilitate detection of non-rare cells.

In another embodiment, the method includes utilizing data associated with non-rare cells to enumerate rare cells, thus facilitating their detection. In various embodiments, data may include, but is not limited to, total intensity, mean intensity, segmented intensity, fixed circle, variable circle, or any combination thereof.

In another embodiment, the method includes determination of the expression level of a content marker in rare cells and non-rare cells to facilitate detection of rare cells.

In various aspects of the invention, a rare cell is a CTC or subpopulation thereof.

As such, in another embodiment, the invention provides a method for diagnosing or prognosing cancer in a subject. The method includes performing the method of improved detection and characterization of CTCs as described herein and analyzing detected CTCs and provide a diagnosis or prognosis based on analysis of the CTCs, thereby diagnosing or prognosing cancer in a subject.

In another embodiment, the invention provides a method for determining responsiveness of a subject to a therapeutic regime. The method includes performing the method of improved detection and characterization of CTCs as described herein and analyzing the CTCs, thereby determining the responsiveness of the subject to a therapeutic regime.

In another embodiment, the invention provides a method for determining a candidate subject for a clinical trial. The method includes performing the method of improved detection and characterization of CTCs as described herein and analyzing the CTCs, thereby determining a candidate subject for a clinical trial.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a graphical representation of mean observed SKBR3s plotted against expected SKBR3s. Four aliquots of normal control blood were spiked with varying numbers of SKBR2 cells to produce 4 slides with approximately 10, 30, 100, and 300 cancer cells per slide. The mean of each quadruplicate is displayed as well as error bars noting standard deviation.

FIG. 2 is a pictorial representation of a gallery of a representative subpopulation of CTCs found in cancer patients. Each CTC of the subpopulation is cytokeratin positive, CD45 negative, contains a DAPI nucleus, and is morphologically distinct from surrounding white blood cells which are circular in shape.

FIG. 3 is a graphical representation comparing CTC counts between two separate processors on 9 different cancer patient samples. CTC/mL counts ranged from 0 to 203.

FIG. 4 is a graphical representation including four graphs plotting CTC and PSA levels of serial blood draws from 4 different prostate cancer patients over a three month time period. Two patients had increasing CTC and PSA levels and two patients had decreasing/stable CTC and PSA levels. PSA levels increased in patients that had increasing CTC counts and decreased in patients that had decreasing/stable CTC counts.

FIG. 5 is a graphical representation showing the incidence rate of a putative rare cell population across patients relative to a CTC subpopulation (HD-CTC).

DETAILED DESCRIPTION

OF THE INVENTION

The present invention provides a method which omits physical methods for positively enriching for rare cells, such as CTCs, from a mixed population, thereby minimizing the loss of rare cells. This methodology further allows for the capture/identification of subsets of cell populations, such as subpopulations of CTCs or other rare populations by detection of the same or different markers using different parameters, such as cutoff values, that allow for distinguishing between events and non-events. For example, as discussed in detail herein, different cutoffs may be utilized to characterize different cell subpopulations.

While the disclosure highlights CTCs and subpopulations thereof, the same methodologies may be used to find any other rare cell type in a background of non-rare cells. As used herein, a “rare cell” is intended to include a cell that is either 1) of a cell type that is less than about 5%, 4%, 3%, 2%, 1%, 0.1%, 0.01% or 0.001% of the total nucleated cell population in a fluid sample, or 2) of a cell type that is present at less than one million cells per milliliter of fluid sample. Exemplary rare cells include, but are not limited to CTCs, circulating endothelial cells (CECs), white blood cells in emboli, cancer stem cells, activated or infected cells, such as activated or infected blood cells, and fetal cells.

Accordingly, it will be understood by one in the art that references to CTCs throughout the specification include reference to rare cells and vice versa.

The present method allows for identification of rare cells, such as CTCs or subpopulations of CTCs from the background of other blood cells using microscopy, cytometry, automation, and computation. The present invention utilizes these components, individually and collectively, to identify rare cells. The benefits include the ability to find more rare cells, to present them in a way that enables subsequent analyses for content markers, and to do so in a time and resource efficient manner.

Further, the present disclosure is based in part on a next generation assay capable of identifying subpopulations of CTCs in cancer patients. One particular subpopulation identified was from a small cohort of cancer patients. In addition to using specific parameters defining subpopulations of CTCs, such as one referred to herein as the High-Definition-CTC (HD-CTC) subpopulation, the assay affords greater sensitivity with a smaller volume of blood than previous efforts. The key innovative aspects of this assay are driven by the need for simplicity and minimal processing of the blood specimen as well as conforming to the need to enable professional interpretation with diagnostic quality imagery.

The approach used to identify a rare cell population, such as CTCs, or subpopulation thereof, is distinct in that it does not rely on any single protein enrichment strategies. All nucleated blood cells are imaged in multiple colors to locate and morphologically evaluate rare events. This enrichment-free strategy results in an assay capable of ‘tunable specificity/sensitivity’ allowing high sensitivity and high specificity while still enabling the study of a rare cell population known to be heterogeneous. A key advantage and difference to physical enrichment is that one may ‘tune’ the outcome, while physical enrichment is ‘yes’ or ‘no’. Another key advantage of this approach is that one or multiple analysis parameters can be pursued to identify and characterize specific populations of interest.

Before the present compositions and methods are described, it is to be understood that this invention is not limited to particular compositions, methods, and experimental conditions described, as such compositions, methods, and conditions may vary. It is also to be understood that the terminology used herein is for purposes of describing particular embodiments only, and is not intended to be limiting, since the scope of the present invention will be limited only in the appended claims.

As used in this specification and the appended claims, the singular forms “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise. Thus, for example, references to “the method” includes one or more methods, and/or steps of the type described herein which will become apparent to those persons skilled in the art upon reading this disclosure and so forth.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the invention, the preferred methods and materials are now described.

In general, reference to “a circulating tumor cell” is intended to refer to a single cell, while reference to “circulating tumor cells” or “cluster of circulating tumor cells” is intended to refer to more than one cell. However, one of skill in the art would understand that reference to “circulating tumor cells” is intended to include a population of circulating tumor cells including one or more circulating tumor cells.

The term “circulating tumor cell” (CTC) or CTC “cluster” is intended to mean any cancer cell or cluster of cancer cells that is found in a subject's sample. Typically CTCs have been exfoliated from a solid tumor. As such, CTCs are often epithelial cells shed from solid tumors found in very low concentrations in the circulation of patients with advanced cancers. CTCs may also be mesothelial from sarcomas or melanocytes from melanomas. CTCs may also be cells originating from a primary, secondary, or tertiary tumor. CTCs may also be circulating cancer stem cells. While the term “circulating tumor cell” (CTC) or CTC “cluster” includes cancer cells, it also is intended to include non-tumor cells that are not commonly found in circulation, for example, circulating epithelial or endothelial cells. Accordingly tumor cells and non-tumor epithelial cells are encompassed within the definition of CTCs.

The term “cancer” as used herein, includes a variety of cancer types which are well known in the art, including but not limited to, dysplasias, hyperplasias, solid tumors and hematopoietic cancers. Many types of cancers are known to metastasize and shed circulating tumor cells or be metastatic, for example, a secondary cancer resulting from a primary cancer that has metastasized. Additional cancers may include, but are not limited to, the following organs or systems: brain, cardiac, lung, gastrointestinal, genitourinary tract, liver, bone, nervous system, gynecological, hematologic, skin, breast, and adrenal glands. Additional types of cancer cells include gliomas (Schwannoma, glioblastoma, astrocytoma), neuroblastoma, pheochromocytoma, paraganlioma, meningioma, adrenalcortical carcinoma, medulloblastoma, rhabdomyoscarcoma, kidney cancer, vascular cancer of various types, osteoblastic osteocarcinoma, prostate cancer, ovarian cancer, uterine leiomyomas, salivary gland cancer, choroid plexus carcinoma, mammary cancer, pancreatic cancer, colon cancer, and megakaryoblastic leukemia; and skin cancers including malignant melanoma, basal cell carcinoma, squamous cell carcinoma, Karposi's sarcoma, moles dysplastic nevi, lipoma, angioma, dermatofibroma, keloids, sarcomas such as fibrosarcoma or hemangiosarcoma, and melanoma.

Using the methods described herein, rare cells, such as CTCs may be detected and characterized from any suitable sample type. As used herein, the term “sample” refers to any sample suitable for the methods provided by the present invention. The sample may be any sample that includes rare cells suitable for detection. Sources of samples include whole blood, bone marrow, pleural fluid, peritoneal fluid, central spinal fluid, urine, saliva and bronchial washes. In one aspect, the sample is a blood sample, including, for example, whole blood or any fraction or component thereof. A blood sample, suitable for use with the present invention may be extracted from any source known that includes blood cells or components thereof, such as veinous, arterial, peripheral, tissue, cord, and the like. For example, a sample may be obtained and processed using well known and routine clinical methods (e.g., procedures for drawing and processing whole blood). In one aspect, an exemplary sample may be peripheral blood drawn from a subject with cancer.

The term “blood component” is intended to include any component of whole blood, including red blood cells, white blood cells, platelets, endothelial cells, mesotheial cells or epithelial cells. Blood components also include the components of plasma, such as proteins, lipids, nucleic acids, and carbohydrates, and any other cells that may be present in blood, due to pregnancy, organ transplant, infection, injury, or disease.

As used herein, a “white blood cell” is a leukocyte, or a cell of the hematopoietic lineage that is not a reticulocyte or platelet. Leukocytes can include nature killer cells (“AK cells”) and lymphocytes, such as B lymphocytes (“B cells”) or T lymphocytes (“T cells”). Leukocytes can also include phagocytic cells, such as monocytes, macrophages, and granulocytes, including basophils, eosinophils and neutrophils. Leukocytes can also comprise mast cells.

As used herein, a “red blood cell” or “RBC” is an erythrocyte. Unless designated a “nucleated red blood cell” (“nRBC”) or “fetal nucleated red blood cell”, as used herein, “red blood cell” is used to mean a non-nucleated red blood cell.

The present invention provides a method whereby a biological sample may be assayed or examined in many different ways to detect and characterize rare cells. A sample may be stained or labeled with one or more detectable markers and examined by fluorescent microscopy and/or light microscopy. Unlike conventional enrichment schemes whose goal it is to eliminate the non-rare or non-CTCs from evaluation, the present invention relies on the non-rare cells or non-CTCs present in the sample to aid in the identification and characterization of the rare cells or CTCs. In the presently described non-enrichment method, the sample (e.g., blood or other body fluid, including urine, peritoneal, pleural, saliva, cerebral spinal, and the like) is minimally processed, and the rare cells, such as CTCs are not separated from other nucleated cells (e.g., non-rare or non-CTCs).

As used herein, the terms “non-rare cell” and “non-rare cells”, generally refer to any cell that is not a rare cell as defined herein. Similarly, as used herein, the terms “non-CTC” and “non-CTCs”, generally refer to any cell that is not a CTC as defined herein. Non-rare and non-CTCs may include nucleated or enucleated cells, such as, in the case of blood, white blood cells (also called leukocytes) including neutrophils, eosinophils, basophils, lymphocytes, and monocytes; red blood cells (also known as erythrocytes); and platelets.

In the case of blood, while the CTCs may not be separated from other nucleated cells, red blood cells, which are typically only found nucleated in the blood of newborns, are removed from the sample before plating. This is commonly performed by lysing the red blood cells, although several alternative approaches are well known in the literature and may be utilized with the present methods, for example, removing the cells by filtration or density gradient centrifugation. After removing the red blood cells, the remaining cells may be processed by spinning, re-suspending, and plating the cells onto a solid support that may be used in cell imaging.

A variety of solid supports are well known in the art and include slides that may be treated to promote cellular attachment to the slide surface. The slide may be constructed from a variety of materials sufficient to provide a support for performing a biological assay. In an exemplary aspect, the support is composed of a material that may be coated with a compound that promotes electrostatic interaction of biological material to the support. A variety of substrate materials are well known in the art and suitable for use with the present invention. Such materials may include one or more of glass; organoplastics such as polycarbonate and polymethylmethacrylate, polyolefins; polyamides; polyesters; silicones; polyurethanes; epoxies; acrylics; polyacrylates; polyesters; polysulfones; polymethacrylates; polycarbonate; PEEK; polyimide; polystyrene; and fluoropolymers. In an exemplary aspect, the slide is manufactured from glass or plastic and includes one or more biologically interactive coatings.

Slides may include one or more active areas defined on the surface thereof. An active field, as used herein, is intended to include areas in which the slide has been chemically or electrically treated, such as with a biologically interactive coating, for example to promote the adhesion of cells to the slide. For example, the slide may be treated such that the surface is positively charged which allows for cells to be anchored to the surface though the electrostatic adhesion of a negatively charged cell. The slide may include from 1 to any number of active areas depending on the size of the slide and the intended application. In various aspects, the slide includes a single active area.

The total number of rare cells or CTCs that are adhered to a given slide is dependent, in part, on the initial sample volume. In various aspects, a wide range of initial sample volumes may be used to practice the present method and provide clinically significant results. As such, the initial sample volume may be less than about 1 μl, 2 μl, 2.5 μl, 3 μl, 4 μl, 5 μl, 6 μl, 7 μl, 7.5 μl, 8 μl, 9 μl, 10 μl, 12.5 μl, 15 μl, 17.5 μl, 20 μl, 25 μl, 50 μl, 75 μl, 100 μl, 125 μl, 150 μl, 175 μl, 200 μl, 225 μl, 250 μl, 300 μl, 400 μl, 500 μl, 750 μl, 1 ml, 2 ml, 3 ml, 4 ml, 5 ml, 6 ml, 7 ml, 8 ml, 9 ml or greater than about 10 ml. In an exemplary aspect, the initial sample volume is between about 200 and 500 μl, 200 and 1000 μl, 1000 to 2000 μl, 1000 to 3000 μl or 1000 to 5000 μl. In another exemplary aspect, a sample processed as described herein includes greater than about 1, 2, 5, 7, 10, 15, 20, 30, 40, 50, 100, 200, 300, 400, 500, 600, 700, 800, 900, or even 1000 rare cells or CTCs.

After adhering the minimally processed cells to a solid support, for example by plating the cells on a slide, the cells are contacted with one or more detectable markers to facilitate cell imaging via examination of the cells by fluorescent microscopy and/or light microscopy. In general, detectable markers include a variety of agents useful in detecting and characterizing cellular phenomenon. For example, detectable markers may include agents such as polynucleotides, polypeptides, small molecules, and/or antibodies that specifically bind to a marker present in a sample and which are labeled such that the agent is detectable when bound or hybridized to its target marker or ligand. For example, detectable markers may include enzymatic, fluorescent, or radionuclide labels. Additional reporter means and labels are well known in the art.

A marker can be any cell component present in a sample that is identifiable by known microscopic, histologic, or molecular biology techniques. Markers can be used, for example, to detect and characterize rare cells, including CTCs, and distinguish rare cells from non-rare cells and non-CTCs. In general a marker can be, for example, a molecule present on a cell surface, an overexpressed target protein, a nucleic acid mutation or a morphological characteristic of a cell present in a sample. Thus markers may include any cellular component that may be detected within or on the surface of a cell, or a macromolecule bound or aggregated to the surface of the cell. As such, markers are not limited to markers physically on the surface of a cell. For example, markers may include, but are not limited to surface antigens, transmembrane receptors or coreceptors, macromolecules bound to the surface, such as bound or aggregated proteins or carbohydrates, internal cellular components, such as cytoplasmic or nuclear components, and the like. A marker may also include a blood component that binds preferentially to specific cell types, such as platelets or fibrin.

In one aspect, a detectable marker may be a detectably labeled antibody. Antibodies useful in the methods of the invention include intact polyclonal or monoclonal antibodies, as well as any fragments thereof, such as Fab and F(ab′)2, as well as combinations of such antibodies or fragments. Methods for generating fluorescently labeled antibodies are well known in the art, for example, fluorescent molecules may be bound to an immunoglobulin either directly or indirectly by using an intermediate functional group. In related aspects, a detectable marker may be a nucleic acid molecule (e.g., an oligonucleotide or polynucleotide). For example, in situ nucleic acid hybridization techniques are well known in the art and can be used to identify an RNA or DNA marker present in a sample or subsample (e.g., individual cell).

In various aspects of the invention, the detectable markers used to stain the cells include one or more detectable markers that are tissue specific and thus used as a positive marker for a specific type of cell and/or tissue. As used herein, a “positive marker” is a detectable marker that specifically binds to a rare cell such as a CTC, but not a non-rare cell or non-CTC. For instance the positive marker may be epithelial and/or tissue specific, for example, cytokeratin and/or EpCAM marker may be used which bind preferentially to epithelial cells. Similarly, markers that are tissue specific may be employed. There are numerous examples of tissue-specific markers known in the art and suitable for use in practicing the invention, such as PSA and PSMA for prostate tissue, CDX2 for colon tissue and TTF1 for lung tissue (of the subpopulation of lung cancer patients that are TTF1 positive). As used herein a “positive marker” may also be a detectable marker that specifically binds to subpopulations of rare cells or CTCs, but not all rare cells or CTCs of a population. For example, a “positive marker” may specifically bind to HD-CTCs, but not all CTCs.

In various aspects of the invention, the detectable markers used to stain the cells include one or more detectable markers that specifically bind to non-rare cells or non-CTCs and may be used as a negative selector. As used herein a “negative marker” is a detectable marker that specifically binds to non-rare cells or non-CTCs and is a negative selector. The most commonly used negative marker for non-CTCs is CD45, which binds preferentially to WBCs. There are other detectable markers or combinations of detectable markers that bind to the various subpopulations of WBCs. These may be used in various combinations, including in combination with or as an alternative to CD45. As used herein a “negative marker” may also be a detectable marker that specifically binds to subpopulations of non-rare cells or non-CTCs and is a negative selector.

In addition to positive and negative detectable markers to identify CTCs, additional detectable markers may be used to stain cells that specifically bind to the nucleus of the cell allowing differentiation of cells from non-cellular material. As used herein, a “nuclear marker” is a detectable marker that binds to a nuclear component of a cell and allows differentiation of cells from non-cellular material. The most common nuclear marker for use in the present invention is DAPI.

In various aspects of the invention, the detectable markers used to stain the cells include one or more detectable markers referred to herein as “content markers”. Content markers typically may include, detectably labeled oligonucleotide probes, such as FISH probes or immunohistochemistry probes. In one embodiment, content markers are applied to the slide at the same time as the positive and negative markers, or are applied to the slide after the positive and negative markers and after the identification of the rare cells by imaging. Content markers include detectable markers directed to EGFR, HER2, ERCC1, CXCR4, EpCAM, E-Cadherin, Mucin-1, Cytokeratin, PSA, PSMA, RRM1, Androgen Receptor, Estrogen Receptor, Progesterone Receptor, IGF1, cMET, EML4, or leukocyte associated receptor (LAR). In some cases, a content marker may also be a positive marker.

The intensity of signal from a positive marker, or any marker, is detectable on a scale of intensities, which based on the methodology of the disclosure, is highly quantifiable. The scale of intensity allows for vastly improved quantification and ranking of detectable events enabling further categorization. For example, a CTC that emits a low intensity signal for cytokeratin may be a cancer stem cell; or the change in the number of high/low cytokeratin cells might be either predictive of response or a readout of response (or resistance course). The same is true for positive, negative and content markers.

The present invention utilizes detectable markers to facilitate cell imaging via examination of the cells by fluorescent microscopy and/or light microscopy. In an exemplary aspect, the minimally processed cells are stained with several fluorescent markers, and then imaged using a fast, automated microscope. Typically, a prepared slide may be loaded onto the automated system or may be placed in a slide carrier that holds any number of additional slides. The slide carriers are loaded into an input hopper of the automated system. An operator may then enter data identifying the size, shape, and location of a scan area on each slide or the system can automatically locate a scan area for each slide during slide processing. The processing parameters of the slide may be identified by a bar code present on the slide or slide carrier. At system activation, a slide carrier is positioned on an X-Y stage, the entire slide, or portion thereof, is rapidly scanned. This may be done at low or high magnification and may be repeated at various levels of magnification and/or for various regions of the slide. Images may be stored on an appropriate storage medium and analyzed using executable code as is well known in the art for performing the various analysis discussed herein. As discussed herein, various parameters may be adjusted throughout the imaging process to facilitate detection of rare cells, for example, CTCs using data regarding non-rare or non-CTCs, such as exposure limits and intensity settings.

As used herein, the terms “image” and “sample image” generally refer to an image, digital or otherwise, of a minimally processed sample including various cells, such as rare cells and CTCs. Typically, a sample image is an image of all or a portion of a sample slide having cells adhered to its surface and optionally stained with one or more detectable markers.

One advantage of the present invention, which allows for tunable specificity/sensitivity and focuses on data reduction and analysis rather than enrichment, is that minimal processing is expected to minimize bias. In alternative techniques that require enrichment, rare cells are invariably lost in the process. Specifically, in the use of immunocapture or size filtration to distinguish between WBCs and CTCs, variation in the expression of the targeted antigen in the case of immunocapture or variation in the size differential between the WBC and CTC causes some CTCs to be lost during the enrichment phase. This can lead to (i) inaccurate counts of CTCs; (ii) too few CTCs for downstream characterization or content analysis; and (iii) the creation of a selection bias as some types of CTCs are preferentially lost based upon their type of variation.

The challenge with the minimal processing approach is that it is difficult to find the low frequency rare cells or CTCs in the background of the non-rare cells or non-CTCs. The low frequency may be 1 rare cell or CTC: 1,000 non-rare cells or non-CTCs, 1:10,000, 1:100,000, 1:1,000,000, and even 1:10,000,000, or anywhere between those ratios. Complicating the ability to find and characterize the rare cells is that the positive and negative markers, while very selective, are not perfect resulting in either false positives or false negatives. In other words, it is common to have some background staining of the negative markers on the rare cells and/or some background staining of the positive markers on the non-rare cells. While assay optimization is used to minimize this background staining, it is challenging to completely eliminate the phenomenon with assay optimization.

As mentioned previously, most other approaches for finding rare cells attempt to remove the non-rare cells. The present invention uses the non-rare cells or non-CTCs to aid in finding and characterizing the rare cells or CTCs. The numerous ways in which non-rare cells and non-CTCs may be analyzed are discussed throughout the disclosure. Throughout this disclosure, non-rare cells or non-CTCs are typically referred to as a single group and may be analyzed using the methods described herein as such. However, the invention also recognizes that non-rare cells may contain various discrete subgroups. For example, in the case of CTCs, the various discrete subgroups may include neutrophils, macrophages, lymphocytes, eosinophils and basophils, and cells in varying states such as various states of apoptosis or cell division, that may be distinguished using the methods described herein by size, shape, nuclear characteristics, and staining pattern. In some embodiments of the invention, it may be useful to use one of these subgroups to aid in finding rare cells or CTCs, rather than to use the entire group. The use of non-rare or non-CTCs in the present invention is not meant to limit the invention to using only the entire group when it may be appropriate in some of the embodiments to use just one or more of the subgroups.

An enabling aspect of this invention is that the low frequency of rare cells or CTCs to non rare cells or non-CTCs allows one to treat the majority of cells as non-rare cells or non-CTCs even if they have not been definitively identified as such. The low frequency of rare cells and CTCs allows one to ignore such cells and assume the cells are non-rare cells or non-CTCs to derive quality control, cut-off, normalization, and calibration metrics. Since the rare cells are in low abundance, if these metrics are to be refined taking into consideration the population of rare cells, outlier removal techniques may be utilized. The outlier removal techniques mathematically ensure that the population of rare cells does not factor into the metrics.

As discussed herein, the disclosed methodology allows detection, enumeration and characterization of populations of rare cells or subpopulations of rare cells. The methodology utilizes data from non-rare cells in the sample to identify and characterize rare cells by applying defined parameters pertaining to exposure limits, exposure settings, quality control, intensity cut-off limits, cell size and shape calibration, cell enumeration and content evaluation, each of which is further discussed in turn. In various aspects, the assay allows for simultaneous cytomorphologic review of fluorescent images with individual channel images, augmented with cell-by-cell annotation with ancillary semi-quantitative data regarding size and fluorescent intensity of objects both absolute and relative to the non-rare cells or non-rare cell candidates, e.g., non-CTCs or non-CTC candidates, from either the full experiment or the local environment.

Establishing Exposure Limits.

While variation should be minimized through assay optimization and instrument standardization, variation in the staining of the markers is common, slide-to-slide, batch-to-batch, operator-to-operator, and day-to-day. Thus selecting the right exposure for a particular slide is non-trivial, as setting it too low or too high will cause one to miss information. While standard approaches work for those markers that are common on the majority of events on the slide, it is challenging for those that are specific to rare cells or CTCs. Within the dynamic range of the imaging system, the signal in rare cells or CTCs and background in non-rare cells or non-CTCs are proportional to the exposure time. But noise which is random variation in both signal and background caused by electronics in the imaging system decreases when exposure increases. Ideally, exposure should be set to maximize the signal without saturating the imaging system. But this is impractical due to the impact on data collection time. Because a rare cell or CTC is present in very low frequency, it is unlikely that a rare cell or CTC would be found in a small number of Sample Images, preventing one from using the Sample Images to set the exposure for the positive marker. Complicating this further, there is a natural variation in the expression of and staining of both positive and negative markers to their target cells. A small number of Sample Images to set exposure may not capture this natural variation on the target rare cells or CTCs.

In one embodiment of this invention, the signal from the non-rare cells or non-CTCs is utilized to set the exposure limit for the positive marker. This is somewhat counter-intuitive as the non-rare cell or non-CTC is not the target of choice for the positive marker. However in this embodiment, the exposure is adjusted so that a visible but low signal is observed from the non-rare cells or non-CTCs in the Sample Images originating from fluorescent sources such as nonspecific staining, autofluorescence and optical system properties. The brightfield imagery, nuclear marker and the negative marker may be used to identify the non-rare cells or non-CTCs in the Sample Images. The low signal is a distinguishable cellular signal in the non-rare cells or non-CTCs when compared to the non-cellular areas in the Sample Image. This process provides a method to set the exposure for the positive marker when the target of those markers are in low frequency and also helps to maximize the Signal/Background of the positive marker, both of which are aids to finding rare cells or CTCs while still minimizing the total time required to collect data. This phenomenon is especially true when the signal is low or dim. Once cellular background is statistically significant above non-cellular background, the exposure time for this particular marker is optimized for speed of data collection. All subsequent optimization can be performed in silico. Once the exposure is set, the entire slide is ready to be imaged at that setting.

While the above embodiment facilitates the setting of exposure for the positive marker, non-rare cells or non-CTCs may also be used to set the exposure for the negative markers, nuclear markers and content markers in a way that is relevant for the clinical interpretation of rare cells or CTCs. In one embodiment, the nuclear marker on non-rare cells or non-CTCs in the Sample Images is set to a level that allows the evaluation of the nuclear content of a cell, and in particular whether the cell is classified as live or dead, facilitating the calculations of live:dead ratios for cells by cell type. These exposure levels for the nuclear marker for the non-rare cells or non-CTCs will be satisfactory for the same evaluation of the nuclear marker for the rare cells or CTCs, in particular to distinguish nuclear shape on normal vs. malignant cells.

In another embodiment, the exposure for the negative marker is set from the Sample Images by looking at the distribution of the signal from that marker on the non-rare or non-CTCs where the exposure is chosen to maximize the signal/background ratio, especially at the critical low end of the dynamic range where a faint signal to a negative marker in a rare cell or CTC may occur.

In another embodiment, the exposure is set from the non-rare cells or non-CTCs for the content marker. The setting of the signal for the content marker using the non-rare cells or non-CTCs in Sample Images will depend on the specific content marker. For instance, some content markers may have relatively high expression in the non-rare cells or non-CTCs when compared to rare cells or CTCs, in which case one would use the information from the non-rare cells or non-CTCs in the Sample Images to set the upper boundary for the content marker. Conversely, the content marker may have relatively low expression in the non-rare cells or non-CTCs when compared to the rare cells or CTCs, in which case one would use the information from the non-rare cells or non-CTCs in the Sample Images to set the lower boundary for the content marker.

While the above embodiments use non-rare cells or non-CTCs to set the exposure limits from Sample Images for various markers prior to imaging the slide to find rare cells or CTCs, in another embodiment information from the non-rare cells or non-CTCs and/or rare cells or CTCs from the images taken during the first imaging event of the entire slide is used to set the exposure limits when selected areas of the slide are re-imaged. Selected areas are re-imaged for a variety of reasons, including collecting images that are in optimal focus or that are in a higher magnification. In this embodiment, the distribution of signals for the various markers in the non-rare cells or non-CTCs and the rare cells or CTCs across the entire slide may be used to calculate a better exposure that maximizes the desired signal or the desired dynamic range.

Minimizing Exposure Setting to Minimize Data Collection Time and Maximize Throughput.

As described above, exposure settings can be adjusted to optimize the signal or signal:background parameters. However, in another embodiment, exposure settings are adjusted with a goal of minimizing data collection time and maximizing throughput. For example, one might determine that it takes 5 seconds of exposure time to fully utilize the dynamic range of the CCD camera but only 500 milliseconds to get the cellular background above the non-cellular background, hence saving 10× data collection time. Thus exposure times can be optimized either for maximum signal (or signal:background) or for minimum time.

Quality Control.

In another embodiment, the use of non-rare cells or non-CTCs to aid in identifying rare cells or CTCs also includes their use as quality control parameters. Since the non-rare cells or non-CTCs are represented in much higher frequency and distributed throughout the slide, they provide an available resource to evaluate the quality of the processing and imaging of the slide, both relative to a particular slide as well as across slides and across data sets.

In one embodiment, the invention provides observing the distribution of the non-rare cells or non-CTCs using the nuclear markers to identify the non-rare cells or non-CTCs. In this embodiment, the goal is to find cells, not necessarily to distinguish between non-rare cells or non-CTCs and rare cells or CTCs, and thus the positive or negative markers may not be utilized to distinguish between these categories; however, since the vast majority of the cells are non-rare cells or non-CTCs, most of the cells that utilized to determine distribution of cells on the slide are non-rare cells or non-CTCs. The distribution of the cells is important from a quality control standpoint as the desired distribution is an even distribution of cells with minimal overlap between the cells. If there is a substantial deviation from that ideal distribution, one may elect to reject the slide from further processing. While a nuclear marker is used in this example, any method for identifying the cells would suffice, including brightfield imaging and conventional stains such as Wright Giemsa.

In another embodiment, the co-location of the nuclear marker and the negative marker is used as a quality control method to evaluate whether the alignment of different images is satisfactory. In this embodiment, the nuclear marker and the negative marker should have significant overlap.

In another embodiment, the ratio between negative marker events and the nuclear marker events is a measure for the effectiveness of the negative marker staining, where the higher the ratio without exceeding 12 is desirable. A desirable negative marker may have a 0.8, 0.9, 1.0 or 1.1 ratio. In another embodiment, the distribution, including mean, standard deviation and coefficient of variation (CV) of the negative marker over the population of the non-rare cells or non-CTCs is used as a quality control parameter, where the distribution of the negative marker is consistent with expected distribution patterns of past experiments and/or consistent with the distribution of WBC\'s normal expression patterns.

In another embodiment, the distribution, including mean, standard deviation and CV of the positive marker over the population of the non-rare cells or non-CTCs is used as a quality control parameter, where the distribution of the positive marker is consistent with expected distribution patterns from past experiments.

While the quality control methods described above describe methods to evaluate overall slide quality, the same methods may be used to evaluate an image or a group of images. In some instances, the parameters derived from an image or a group of images in a region may be compared to the same parameters calculated over the entire slide. In another instance, the quality control parameters described above may be compared across different slides.

In another embodiment, cell loss may be calculated from the slide during processing by comparing the ratio of the nuclear marker events or negative marker events to the known number of non-rare cells or non-CTCs placed on the slide, where the known number of non-rare cells or non-CTCs is derived from the WBC count and the volume used in the experiment.

Setting Intensity Cut-Off Limits for CTC Detection (Minimizing False Negatives).

As mentioned above, the challenges in this approach to rare cell and CTC detection are 1) that the relative frequency of rare cells, such as CTCs, to non-rare cells or non-CTCs is low; and 2) the imperfect staining of the positive and negative markers to CTCs and non-CTCs respectively. However, the present method takes those challenges and turns them into strengths. In one embodiment, the background signal from the positive markers on the highly abundant non-rare cells or non-CTCs is used to calculate mean, standard deviation and CV. Those metrics are subsequently used to determine detection cut-offs to separate rare cells, such as CTCs from non-rare cells or non-CTCs. In one aspect, the factor of 10 multiplied by the standard deviation and added to the mean for the non-rare cell or non-CTC positive marker signal, is used as a cut-off to distinguish rare cells or CTCs, where putative rare cells or CTCs are determined to have a positive marker signal greater than that cut-off. In other aspects, the metric uses a factor of 5, 7.5, 12.5, 15, 17.5, 20 or more, or any number between those numbers. The calculation of the metric may be set on a global slide basis. Alternatively, it may be set on an image basis or a regional basis.



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stats Patent Info
Application #
US 20120276555 A1
Publish Date
11/01/2012
Document #
13503014
File Date
10/20/2010
USPTO Class
435/723
Other USPTO Classes
International Class
01N21/64
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