Size-based gating for analysis of flow cytometry data

Abstract

Disclosed herein is a method for analyzing flow cytometry data for cells derived from a homogenized whole tumor sample.

Description

Background Art

[0001] Cancer is a disease characterized by the uncontrolled proliferation of abnormal cells. In normal tissues, cells divide and organize within the tissue in response to signals from surrounding cells, resulting in normal cellular behavior that is carefully coordinated by the tissue environment. Cancer cells are unresponsive to growth-restricting environmental cues from surrounding tissues, and they often have genetic variations that drive them to proliferate and form tumors in many organs. As tumors grow, genetic and phenotypic variation accumulates, allowing the cancer cell population to overcome other "checkpoints," such as anti-tumor immune responses, and to exhibit a more aggressive cancer cell growth phenotype. If left untreated, metastasis may occur, where cancer cells spread to distant parts of the body via the lymphatic system or blood flow. Metastasis leads to the formation of secondary tumors at multiple sites, damaging healthy tissue. Most cancer deaths are caused by such secondary tumors.

[0002] Despite decades of progress in cancer diagnosis and treatment, many cancers are not discovered until the late stages. As a result, many solid tumors contain genetically and / or phenotypically heterogeneous tumor cell populations that are usually spatially separated at the initial growth site. One or more of these cancer cell populations in the primary tumor may give rise to secondary metastatic tumors. In addition, the tumor mass is often composed of normal cells that are either recruited by the tumor to form a supportive environment (e.g., blood vessels) or are initially attracted to the tumor by the host as a defense mechanism (e.g., immune cells), but are later suppressed as the cancer develops.

[0003] A technique for counting, examining and sorting multiple analytes, including cell populations, present in a biological sample, such as a sample derived from a tumor sample or tumor mass, involves flow cytometry. Flow cytometry allows for simultaneous multi-parameter analysis of the physical and / or chemical characteristics of particles flowing through an optical and / or electronic detection device. Current optical detection systems monitor changes in light scattering and fluorescence. Electronic detection is achieved by suspending the particles in a conducting fluid and then passing them through a small hole or orifice. An electric field is applied across the hole or orifice, thereby generating an electric current. As the particle passes through the hole, the resistance across the orifice increases. The increase in resistance under constant current results in an increase in voltage across the orifice, which is directly related to the volume of the particle. A measurable voltage pulse is generated, which can be analyzed and used for further operations, such as conductivity, resistivity, capacitance, and shape modeling of the particle. Summary of the invention

[0004] Applicants have developed a new method for sampling tumors so that meaningful information can be obtained from sampled tumors. Applicants have shown that cells isolated from homogenized whole tumor samples are relatively more representative of different types of cells with whole tumors, for example, homogenized whole tumor samples include heterogeneous collections of cells or cell populations (including tumor cells, immune cells, stromal cells, etc.) (see, for example, PCT Publication Nos. PCT / US2016 / 060861 and PCT / US2016 / 060835, the disclosures of which are incorporated herein by reference in their entirety). Similarly, applicants have shown that residual surgical materials from fixed tumors can be mixed and broken down into single cells for further downstream analysis by flow cytometry.

[0005] Although providing more representative information, heterogeneous populations of cell types (from homogenates) make data analysis relatively more complicated when presented for flow cytometry analysis. For example, a specific cell subset (e.g., those that are CD8 positive) may be plotted on the same density plot with other cell types, and if the subset represents a minority of cells within the homogenate, it may be difficult to detect them within the heterogeneous mixture (see, e.g., FIG6 ). Again, this stems from the fact that flow cytometry has traditionally been used to analyze more homogeneous cell populations, such as peripheral blood mononuclear cells (PBMCs) ( Figure 5A Compare with Figure 6.) Therefore, it is believed that for samples derived from homogenates, data obtained from flow cytometry may be unfamiliar to those skilled in the art and more difficult to interpret.

[0006] Applicants have developed a method that reduces the complexity of flow cytometry data from heterogeneous samples such as whole tumor samples. Applicants have developed a method of digital sorting or gating by which a heterogeneous mixture of tumor cell particles and normal cell particles (including immune cells) can be sorted into well-defined populations (e.g., tumor populations or normal cell populations) to improve the efficiency of research, tumor analysis, and other downstream processing tasks. The gating strategy described herein provides substantially homogeneous populations and subpopulations that are easy to analyze, thereby providing improved data quality and the ability to obtain correlations between different data subsets.

[0007] One aspect of the present disclosure is a method for quantifying the percentage of cells expressing one or more biomarkers, the method comprising: homogenizing a whole tumor sample to provide a homogenized sample; isolating single cells from the homogenate; staining the cells in the homogenized sample for the presence of one or more biomarkers; performing at least a first primary gating on the cells in the homogenized sample based on forward scatter (associated with cell size) and backscatter (associated with cell granularity) to provide at least a first population of cells having a first predicted cell type; and determining the percentage of cells expressing a first biomarker of the one or more biomarkers in a first subpopulation within the first population of cells. In some embodiments, the method does not require a physical sorting step prior to determining the percentage of cells in at least the first subpopulation of cells.

[0008] In some embodiments, the first predicted cell type is an immune cell. In some embodiments, the size of the immune cell is less than about 12 μm. In some embodiments, the homogenized tissue sample is further processed prior to staining, wherein the further processing comprises at least one of: digesting proteins within the homogenized sample, heating the sample, or filtering the homogenized sample.

[0009] In some embodiments, a first percentage of cells in a first subpopulation expressing a first biomarker of the one or more biomarkers is determined by performing secondary gating on cells in the first population based on the presence of the first biomarker of the one or more biomarkers. In some embodiments, the first biomarker of the one or more biomarkers is selected from the group consisting of: CD3, CD4, CD8, CD25, CD163, CD45LCA, CD45RA, and CD45RO clusters of differentiation biomarkers.

[0010] In some embodiments, the method further includes determining the percentage of cells in the second subgroup of the first group of cells expressing the second biomarker in the one or more biomarkers, wherein the first biomarker and the second biomarker in the one or more biomarkers are different. In some embodiments, the second biomarker in the one or more biomarkers is a differentiation marker cluster. In some embodiments, the second biomarker in the one or more biomarkers is a biomarker other than a differentiation biomarker cluster. In some embodiments, the second biomarker in the one or more biomarkers is selected from the group consisting of the following items: PD-1, TIM-3, LAG-3, CD28, CD57 and FOXP3, EPCAM, CK8 / 18. In some embodiments, the first biomarker in the one or more biomarkers is CD3, and the second biomarker in the one or more biomarkers is a biomarker that identifies the immune cell as a regulatory T cell, a helper T cell, or a cytotoxic T cell.

[0011] In some embodiments, the method further includes performing a second primary gating on the cells in the homogenized sample to provide at least a second population of cells having a second predicted cell type. In some embodiments, the second predicted cell type is a tumor cell. In some embodiments, the method further includes determining the percentage of cells in the second population of cells having chromosomal abnormalities. In some embodiments, the method further includes associating the percentage of cells having chromosomal abnormalities (e.g., aneuploidy) with the percentage of cells expressing the first biomarker in one or more biomarkers (e.g., the correlation between cell CD8 positivity and aneuploidy).

[0012] In some embodiments, the method further comprises making a treatment decision based in part on the determined percentage of cells in the first subpopulation expressing a first biomarker of the one or more biomarkers.

[0013] Another aspect of the present disclosure is a method for quantifying the percentage of cells expressing at least a first biomarker in a plurality of biomarkers in a tissue sample, the method comprising: homogenizing the tissue sample to provide a homogenized sample; staining the cells in the homogenized sample for the presence of a plurality of biomarkers; performing primary gating on the cells in the homogenized sample based on scattering to provide at least a first population of cells having a first predicted size range; performing at least one secondary gating on the cells in the first population, wherein the at least one secondary gating is based at least on the presence of a first biomarker from the plurality of biomarkers, and wherein the at least one secondary gating provides a first subpopulation of cells expressing the first biomarker; and determining the percentage of cells expressing the first biomarker in the first subpopulation of cells. In some embodiments, the method does not require a physical sorting step before determining the percentage of those cells in the first subpopulation. In some embodiments, the first population of cells is gated (i.e., selected) based on forward scatter and backscatter (i.e., size and granularity) of the cells.

[0014] In some embodiments, the homogenized sample is stained for the presence of at least a CD3 biomarker. In some embodiments, the homogenized sample is stained for the presence of at least one other differentiation biomarker cluster. In some embodiments, at least one secondary gating is performed based on the presence of at least a CD3 biomarker (e.g., a density plot of forward scatter (FSC) versus CD3 staining intensity). In some embodiments, separate secondary gating is performed independently based on the presence of a CD3 biomarker and at least one of a CD4 biomarker and / or a CD8 biomarker to provide a CD3 subpopulation, and at least one of a CD4 subpopulation and / or a CD8 subpopulation. In some embodiments, the percentage of cells in a CD3 subpopulation and at least one of a CD4 subpopulation and / or a CD8 subpopulation is independently determined / quantified.

[0015] In some embodiments, the tissue sample is derived from at least one of the following: a whole tumor, a portion of a tumor, a metastatic tumor, a portion of a metastatic tumor, or a lymph node. In some embodiments, the tissue sample is derived from at least one of residual surgical material or a biopsy sample. In some embodiments, the homogenized tissue sample is further processed prior to staining, wherein the further processing comprises at least one of the following: digesting proteins within the homogenized sample, heating the sample, or filtering the homogenized sample.

[0016] In some embodiments, the plurality of biomarkers are selected from the group consisting of: differentiation biomarker cluster, PD-1, TIM-3, LAG-3 and FOXP3. In some embodiments, the differentiation biomarker cluster is selected from the group consisting of: CD3, CD4, CD8, CD45RA, CD45RO, CD28 and CD57.

[0017] Another aspect of the present disclosure is a method for quantifying the percentage of cells expressing at least a first biomarker in a homogenized tissue sample of one or more biomarkers, the method comprising: separating the homogenized sample into single cells (e.g., including further mixing and filtering) to provide a sample consisting of single cells (e.g., a sample consisting mainly of single cells); staining the cells for the presence of the one or more biomarkers; probing the cells with a flow cytometer; and analyzing the resulting data. In some embodiments, the data analysis comprises performing at least two sequential gatings to provide at least a first subpopulation of cells expressing a first biomarker in the one or more biomarkers; and determining the percentage of cells expressing the first biomarker in the first subpopulation.

[0018] In some embodiments, the at least two sequential gatings include: primary gating to provide a first population of cells; and secondary gating to provide the first subpopulation of cells. In some embodiments, primary gating is based on forward scattered light and side scattered light. In some embodiments, the cutoff values ​​of forward scattered light and side scattered light are selected so that the first population is rich in immune cells. In some embodiments, multiple secondary gatings are performed.

[0019] In some embodiments, secondary gating is performed on at least two differentiation biomarker clusters. In some embodiments, the differentiation biomarker cluster is selected from the group consisting of the following items: CD3, CD4, CD8, CD45RA, CD45RO, CD28 and CD57. In some embodiments, the method further includes determining the percentage of cells in a second subpopulation of cells expressing a second biomarker in the one or more biomarkers, wherein the first biomarker and the second biomarker in the one or more biomarkers are different. In some embodiments, the second biomarker in the one or more biomarkers is a biomarker other than the differentiation marker cluster. In certain embodiments, the second biomarker in the one or more biomarkers is selected from the group consisting of the following items: PD-1, TIM-3, LAG-3 and FOXP3.

[0020] The methods disclosed herein have several clinical applications. In one embodiment, the methods can be used to assess specific and nonspecific immune responses. For example, the methods can be used to determine changes in immune function after tumor development, determine the course of cancer treatment, or determine whether an applied treatment is effective. Alternatively, the methods can be used to determine changes in immune function after treating a patient with a vaccine, immunosuppressant, antiviral agent, antitumor agent, or other therapeutic agent. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] For a general understanding of the features of the present disclosure, reference is made to the drawings, in which like reference numerals are used throughout to identify like elements.

[0022] Figure 1 Listed is a flow chart for quantifying different cell types in a homogenate according to some embodiments of the present disclosure.

[0023] Figure 2 A flowchart for performing sequential gating according to some embodiments of the present disclosure is listed.

[0024] Figure 3 Listed is a flow chart for quantifying different cell types in a homogenate according to some embodiments of the present disclosure.

[0025] Figure 4 A flow chart showing steps for selecting a gate size according to some embodiments of the present disclosure is listed.

[0026] Figure 5A The density map of CD8 vs. CD4 is shown, which clearly shows the population of CD4 and CD8 cells, and the sample is derived from a fresh lymphocyte sample. The figure shows how the flow cytometry data display different populations (CD4 positive, CD8 positive and double negative) in the dot plot. The data shown are only from the analysis of immune cells (homogeneous populations) in fresh blood (not a cell mixture).

[0027] Figure 5B Density plots of FSC versus CD8 are shown, showing gating of the CD8 cell population. The figure shows flow cytometric analysis of isolated cells from whole tumors (e.g., a highly heterogeneous population consisting of immune cells, tumor cells, epithelial cells). Figure 5B Different cell populations are not shown ( Figure 5A circles in the dot plot), which makes the data more difficult for flow cytometry users to accept.

[0028] Fig. 6A Density plots (FSC vs. SSC (side scatter)) of filtered homogenates are shown, with two gates selected based on scatter. All filtered cells from the homogenate are shown.

[0029] Figure 6B Shown are density plots (FSC vs. SSC) of filtered homogenates after primary gating, where only digitally sorted small cells are retained.

[0030] Figure 7 Shown are density plots (FSC vs. SSC) of filtered homogenates with five gates selected based on scatter.

[0031] Fig. 8A The analysis of gated large cells for CD3 markers is shown (two pictures at the top). The picture on the upper right shows a control, in which no primary antibody is added, and the picture on the upper left shows an actual sample. The rectangle in each picture shows the position where the positive cells (CD3 positive cells) should be. Large cells do not show any CD3 positive, which is expected because large cells are mostly tumor cells that do not express CD3 markers. The bottom picture shows the analysis of small cells for the same CD3 marker. Most small cells do express CD3, which is expected because small cells are immune cells.

[0032] Figure 8B The analysis of gated large cells for CD3 markers is shown (two pictures at the top). The picture on the upper right shows a control, in which no primary antibody is added, and the picture on the upper left shows an actual sample. The rectangle in each picture shows the position where the positive cells (CD3 positive cells) should be. Large cells do not show any CD3 positive, which is expected because large cells are mostly tumor cells that do not express CD3 markers. The bottom picture shows the analysis of small cells for the same CD3 marker. Most small cells do express CD3, which is expected because small cells are immune cells.

[0033] Fig. 9A Density plots (FSC vs. CD3 staining intensity) of the first subpopulation expressing the CD3 biomarker are shown.

[0034] Fig. 9B Density plots (FSC vs. CD8 staining intensity) of the first subpopulation expressing the CD8 biomarker are shown.

[0035] Fig. 9C Density plots (FSC vs. CD4 staining intensity) of the first subpopulation expressing the CD4 biomarker are shown.

[0036] Fig.10 Density plots (FSC vs. CD8 staining intensity) of subpopulations of cells derived from different homogenized lung tumor samples are shown.

[0037] Figure 11 compares unsorted cells from a homogenized tumor sample (Panel C), as compared to CD3 expression after physical sorting (Panel A) and CD3 expression after digital sorting (Panel B), both of which improved data quality and enabled accurate percentage values ​​to be derived.

[0038] Fig.12 A method for staining cells derived from a homogenized sample is shown.

[0039] Fig.13 Density plots of CK8 / 18 versus forward scattered light are shown. DETAILED DESCRIPTION

[0040] It should also be understood that, unless indicated to the contrary, in any method claimed herein that includes more than one step or action, the order of the steps or actions of the method is not necessarily limited to the order in which the steps or actions of the method are described.

[0041] As used herein, the singular forms "a", "an", and "the" include plural referents unless the context clearly indicates otherwise. Similarly, the word "or" is intended to include "and" unless the context clearly indicates otherwise. The term "comprising" is defined as inclusive, such as "comprising A or B" means comprising A, B, or A and B.

[0042] As used herein in the specification and claims, "or" shall be understood to have the same meaning as "and / or" defined above. For example, when separating items in a list, "or" or "and / or" shall be interpreted as being inclusive, i.e., including at least one element from a number of elements or a list of elements, but also including more than one element, and optionally including additional unlisted items. Only terms indicating the contrary, such as "only one" or "exactly one", or "consisting of..." as used in the claims, will refer to the inclusion of exactly one element from a number of elements or a list of elements. In general, the term "or" as used herein shall be interpreted as indicating an exclusive alternative (i.e., "one or the other, but not both") only when preceded by an exclusive term such as "or", "one of", "only one", or "exactly one". "Substantially consisting of..." as used in the claims shall have the ordinary meaning used in the field of patent law.

[0043] The terms "include", "comprises", "have", etc. are used interchangeably and have the same meaning. Similarly, "includes", "includes", "have", etc. are used interchangeably and have the same meaning. Specifically, the definition of each term is consistent with the definition of "include" in general U.S. patent law, so each term can be understood as an open term, which means "at least the following" and can also be interpreted as not excluding additional features, limitations, aspects, etc. Therefore, for example, "a device having components a, b and c" means that the device includes at least components a, b and c. Similarly, the phrase: "a method involving steps a, b and c" means that the method includes at least steps a, b and c. In addition, although the steps and processes may be outlined in a particular order herein, those skilled in the art will recognize that the order steps and processes may vary.

[0044] As used herein in the specification and claims, with respect to a list of one or more elements, the phrase "at least one" should be understood as at least one element selected from any one or more elements in the list of elements, but does not necessarily include at least one of each element specifically listed in the list of elements, nor does it exclude any combination of elements in the list of elements. In addition to the elements specifically identified in the list of elements to which the phrase "at least one" refers, the definition also allows other elements to be optionally present, whether or not they are related to the specifically identified elements. Thus, as a non-limiting example, "at least one of A and B" (or equivalently, "at least one of A or B", or equivalently, "at least one of A and / or B") may refer to at least one optionally including more than one A, but without B (and optionally including elements other than B) in one embodiment; in another embodiment, to at least one selectively including more than one B, but without A (and optionally including elements other than A); in yet another embodiment, to at least one selectively including more than one A, and at least one selectively including more than one B (and optionally including other elements), etc.

[0045] As used herein, the terms "biological sample," "tissue sample," "specimen," or similar terms refer to any sample including biomolecules (e.g., proteins, peptides, nucleic acids, lipids, carbohydrates, or combinations thereof) obtained from any organism, including viruses. Examples of other organisms include mammals (e.g., humans; therians, such as cats, dogs, horses, cows, and pigs; and laboratory animals, such as mice, rats, and primates), insects, annelids, arachnids, marsupials, reptiles, amphibians, bacteria, and fungi. Biological samples include tissue samples (e.g., tissue sections and puncture biopsies of tissues), cell samples (e.g., cytological smears, such as cervical smears or blood smears, or obtained by microdissection), or cell fractions, fragments, or organelles (e.g., obtained by lysing cells and separating their components by centrifugation or other means). Other examples of biological samples include blood, serum, urine, semen, feces, cerebrospinal fluid, interstitial fluid, mucus, tears, sweat, pus, biopsy tissue (e.g., obtained by surgical biopsy or needle biopsy), nipple aspirate, cerumen, breast milk, vaginal secretions, saliva, swabs (e.g., oral swabs), or any material containing biomolecules and derived from a first biological sample. In certain embodiments, the term "biological sample" as used herein refers to a sample (e.g., a homogenized or liquefied sample) prepared from a tumor or a portion thereof obtained from a subject.

[0046] As used herein, the term "biomarker" refers to a biological molecule found in blood, other body fluids or tissues that serves as a marker for a normal or abnormal process or disorder or disease (e.g., cancer). Biomarkers can be used to determine the degree of response of the body to the treatment of a disease or disorder or whether a subject is susceptible to a disease or disorder. In the case of cancer, a biomarker refers to a biological substance that indicates the presence of cancer in the body. A biomarker can be a molecule secreted by a tumor or a specific response of the body to the presence of cancer. Genetic, epigenetic, proteomic, carbohydrate and imaging biomarkers can be used for the diagnosis, prognosis and epidemiology of cancer. Such biomarkers can be measured in non-invasively collected biological fluids (e.g., blood or serum). Several gene- and protein-based biomarkers have been used in patient care, including but not limited to AFP (liver cancer), BCR-ABL (chronic myeloid leukemia), BRCA1 / BRCA2 (breast cancer / ovarian cancer), BRAF V600E (melanoma / colorectal cancer), CA-125 (ovarian cancer), CA19.9 (pancreatic cancer), CEA (colorectal cancer), EGFR (non-small cell lung cancer), HER-2 (breast cancer), KIT (gastrointestinal stromal tumor), PSA (prostate-specific antigen), S100 (melanoma), etc. Biomarkers can be used as diagnostics (identifying early-stage cancer) and / or prognostics (predicting the aggressiveness of the cancer and / or predicting how well a subject will respond to a particular treatment and / or the likelihood of cancer recurrence).

[0047] As used herein, the term "gating" refers to selecting a population of particles from a sample based on the properties of the particles. For example, the properties of the particles can be defined based on forward scatter (FSC), side scatter (SSC) and / or fluorescence intensity. Therefore, particles with the desired properties will pass through the gating and be selected for further analysis, while particles without the desired properties will not be selected for further analysis. Digital gating means that after analyzing a mixture of all cells, a specific population is selected to be displayed on the graph. After all cells are analyzed, the selected cell population will be "digitally sorted". Physical sorting is the process of moving a selected population from all cells into a separate test tube and then analyzing the selected cell population. Both digital sorting and physical sorting are considered to be able to achieve analysis of certain cell populations described herein.

[0048] As used herein, the term "homogenizing" refers to a process by which a biological sample is brought to a state whereby all fractions of the sample are compositionally equal. In the present disclosure, "homogenization" will generally maintain the integrity of most cells within the sample, e.g., as a result of the homogenization process, at least 50%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.9% or more of the cells in the sample are not broken or dissolved. A homogenate can be substantially resolved into individual cells (or cell clusters), and the resulting homogenate or homogenates are substantially homogeneous (composed of or composed of similar elements, or uniform as a whole).

[0049] As used herein, the term "label" or "stain" means an agent that can bind to an analyte, be internalized or otherwise absorbed and be detected, for example, by shape, morphology, color, fluorescence, luminescence, phosphorescence, absorbance, magnetism or radioactive emission. Similarly, as used herein, the terms "labeling", "staining" or similar terms generally refer to any treatment of a biological specimen that detects and / or distinguishes the presence, location and / or amount (e.g., concentration) of a specific molecule (e.g., lipid, protein or nucleic acid) or a specific structure (e.g., normal or malignant cells, cytoplasm, nucleus, Golgi apparatus or cytoskeleton) in a biological specimen. For example, staining can compare a specific molecule or a specific cell structure of a biological specimen with the surrounding part, and the intensity of the staining can determine the amount of the specific molecule in the specimen. Staining can be used not only with bright field microscopy, but also with other observation tools such as phase contrast microscopy, electron microscopy and fluorescence microscopy to assist in the observation of molecules, cell structures and organisms. Some staining performed by the system can make the outline of the cell clearly visible. Other staining performed by the system may rely on specific cellular components (e.g., molecules or structures) that are stained and do not stain other cellular components or stain other cellular components relatively little. Examples of various types of staining methods performed by the system include, but are not limited to, histochemical methods, immunohistochemical methods, and other methods based on intermolecular reactions such as hybridization reactions between nucleic acid molecules (including non-covalent binding interactions). Specific staining methods include, but are not limited to, primary staining methods (such as H&E staining, cervical staining, etc.), enzyme-linked immunohistochemical methods, and in situ RNA and DNA hybridization methods, such as fluorescence in situ hybridization (FISH).

[0050] As used herein, the terms "representative sample" and "representative sampling" refer to a sample (or a subset of a sample) that accurately reflects the overall composition and is therefore an unbiased indication of the entire population. Typically, this means that different types of cells and their relative proportions or percentages in a representative sample or a portion thereof substantially accurately reflect or simulate the relative proportions or percentages of these cell types in a whole tissue specimen (usually a solid tumor or a portion thereof). As used herein, "sequencing" or "DNA sequencing" refers to a biochemical method for determining the order of nucleotide bases, adenine, guanine, cytosine, and thymine in a DNA oligonucleotide. As the term is used herein, sequencing may include, but is not limited to, parallel sequencing or any other sequencing method known to those skilled in the art, such as chain termination, rapid DNA sequencing, wandering-spot analysis, Maxam-Gilbert sequencing, dye terminator sequencing, or use any other modern automated DNA sequencing instrument.

[0051] As used herein, the term "tumor" refers to a mass or growth that is itself defined as an abnormal new growth of cells that generally grow faster than normal cells and will continue to grow if untreated, sometimes causing damage to adjacent structures. Tumors can vary widely in size. Tumors can be solid or fluid-filled. Tumors can refer to benign (non-malignant, generally harmless) or malignant (able to metastasize) growths. Some tumors may contain benign neoplastic cells (e.g., carcinoma in situ) while also containing malignant cancer cells (e.g., adenocarcinoma). It is to be understood to include growths located at multiple positions throughout the body. Thus, for the purposes of this disclosure, tumors include primary tumors, lymph nodes, lymphoid tissue, and metastatic tumors.

[0052] As used herein, the term "tumor sample" includes a sample prepared from a tumor or from a sample that may contain or is suspected of containing cancer cells, or a sample to be tested for the potential presence of cancer cells, such as a lymph node.

[0053] Isolation of cells and / or nuclei from tissues

[0054] Referring Figure 1 and Figure 3 , in some embodiments, cells, cell nuclei, and / or small tissue aggregates are separated from a sample (e.g., a tissue sample). In some embodiments, cells, cell nuclei, and / or small tissue aggregates are separated from the tissue sample by homogenizing the tissue sample (step 100). In some embodiments, the cells, cell nuclei, and / or small tissue aggregates present in the homogenized sample are then presented for flow cytometry analysis (step 110). The separated cells, cell nuclei, and / or small tissue aggregates can then be gated (step 120), followed by further analysis.

[0055] In some embodiments, the tissue sample used for homogenization (step 100) is derived from a tumor (cancerous or non-cancerous), a metastatic lesion, normal tissue, whole blood, or a lymph node. In some embodiments, the tissue sample is a residual surgical sample, a biopsy sample, or a histological sample. In some embodiments, the tissue sample is a fresh sample, i.e., a sample that has not been preserved. In some embodiments, the tissue sample is a fixed tissue sample. In some embodiments, the tissue sample is derived from a formalin-fixed paraffin-embedded tissue block. In some embodiments, multiple tissue sources can be combined, and then the cells and / or cell nuclei are separated from the pooled tissue sample.

[0056] Homogenization of tissue samples

[0057] In some embodiments, the tumor sample, lymph node sample, and / or other tissue sample is homogenized (step 300) by placing the sample in a mechanical shearing device (e.g., a blender or sonicator). Homogenization produces a series of tissue fragments. Methods for preparing homogenized tumor samples or lymph node samples are disclosed in PCT Application No. PCT / US2016 / 060861, the disclosure of which is incorporated herein by reference in its entirety.

[0058] After sufficient mechanical shearing is performed to separate the tumor, lymph node, and / or other tissue sample, all subpopulations of tumor cells that were initially spatially isolated within the original sample are distributed throughout the new homogenized sample. That is, due to the homogenization of the tumor, one or more lymph nodes, or any combination thereof, any heterogeneity of cells within the tumor is substantially homogenously (evenly) distributed within the resulting homogenate or a portion or fraction thereof, such that the homogenate (or any fraction thereof) substantially homogenously expresses the heterogeneity of the tumor and / or lymph node sample as input. By homogenizing the tumor and / or lymph node to produce a sample (or homogenate) that is representative of the tumor as a whole, in some embodiments, it is possible to characterize the landscape (e.g., heterogeneity) of the tumor, for example, each different cell or cell nucleus population contained in the entire homogenate may be analyzed (e.g., to determine the number of a particular type of immune cell in order to determine whether a patient is suitable for a particular type of treatment). In some embodiments, the method can be used to detect tumor infiltrating lymphocytes, such as the relative abundance of different tumor infiltrating lymphocytes.

[0059] Isolation of cells, nuclei and / or small tissue aggregates from tumors

[0060] The homogenized sample (from step 200) may be further separated and / or processed to provide separated cells, cell nuclei and / or small tissue aggregates (step 210). Generally, there are three main methods for tissue separation, including enzymatic separation, chemical separation and mechanical separation or any combination thereof. The separation method is usually selected based on the tissue type and tissue source.

[0061] Enzyme separation is the process of using enzyme digestion tissue fragments to release cells from tissue. In this process, many different types of enzymes can be used, and, as those skilled in the art will appreciate, some enzymes are more effective for some tissue types. Those skilled in the art will also appreciate that any enzyme separation process can be combined with one or more enzymes, or with other chemical and / or mechanical separation methods. The example of suitable enzyme includes but is not limited to collagenase, trypsin, elastase, hyaluronidase, papain, DNase I, neutral protease and trypsin inhibitor.

[0062] Collagenase is a proteolytic enzyme that digests proteins found in the extracellular matrix. Unique to proteases, collagenases attack and degrade triple-helical native collagen fibers commonly found in connective tissues. There are four basic types of collagenases, namely: Type 1, which is suitable for epithelial, liver, lung, adipose, and adrenal tissue cell specimens; Type 2, which is suitable for cardiac, bone, muscle, thyroid, and cartilage tumor origin tissues due to its high proteolytic activity; Type 3, which is suitable for breast cells due to its low proteolytic activity; and Type 4: which is suitable for pancreatic islets and other research protocols where receptor integrity is critical due to its trypsin-like activity.

[0063] Trypsin is described as a pancreatic serine (an amino acid) protease that is specific for peptide bonds involving the carboxyl groups of arginine and lysine amino acids. It is considered one of the most highly specific proteases. Trypsin alone is generally ineffective for tissue dissociation because of its extremely low selectivity for extracellular proteins. It is often used in combination with other enzymes such as collagenase or elastase.

[0064] Elastase is another pancreatic serine protease that has specificity for peptide bonds next to neutral amino acids. It has a unique ability among proteases to hydrolyze native elastin. Elastase can also be found in blood components and bacteria. In certain embodiments, it is suitable for separating type II cells from lung tissue.

[0065] Hyaluronidase is a polysaccharidase, an enzyme often used for tissue breakdown when used in conjunction with more natural proteases such as collagenase. It has an affinity for the bonds present in nearly all connective tissues.

[0066] Papain is a thiol protease that has a broad specificity and can therefore degrade most protein substrates more thoroughly than pancreatic proteases (i.e., trypsin or elastase). Papain is often used to isolate neuronal material from tissues.

[0067] Deoxyribonuclease I (DNase I) is often included in enzymatic cell separation procedures to digest nucleic acids that leak into the separation medium and can address increased viscosity and recovery issues. Without wishing to be bound by any particular theory, it is believed that DNase I does not damage intact cells.

[0068] Neutral proteases, e.g. (available from Worthington Biochemical) is a bacterial enzyme with mild proteolytic activity. Can be used to separate primary and secondary cell cultures due to its ability to maintain cell membrane integrity. It has been found to be more effective in separating fibroblast-like cells than epithelial-like cells. It is inhibited by EDTA.

[0069] Trypsin inhibitors, primarily derived from soy, inactivate trypsin and are therefore sometimes used in certain cell isolation protocols.

[0070] Chemical dissociation exploits the fact that cations are involved in the maintenance of intracellular bonds and the intracellular matrix. By introducing EDTA or EGTA, which bind to these cations, the intercellular bonds are disrupted, allowing the separation of tissue structures.

[0071] Finally, mechanical separation requires cutting, scraping or scraping the tissue into small pieces, then washing the minced tissue in culture medium to separate the cells from the tissue, sometimes using gentle agitation and / or sonication to help loosen the cells. In other embodiments, mechanical separation may involve homogenizing the sample, as further described herein.

[0072] In some embodiments, cells in a homogenized sample or a filtered homogenized sample are lysed to release cellular components. For example, cells can be lysed using a French press or similar type of lysing device, a microfluidizer, grinding, grinding, chemical or enzymatic lysis (including those described above) and / or other techniques known in the art. In some embodiments, membrane lipids and proteins (including histones) are removed from a sample containing cellular components (e.g., by adding a surfactant or enzyme (protease)).

[0073] Homogenized, representative or separated samples are further processed into single cell nuclei and need to remove cell membranes. The current nuclear separation method for fresh cells does not require enzymes to release the nuclei, and it is not a common method to carry out nuclear separation from formalin fixed samples. In order to effectively separate single cell nuclei, while maintaining the cytoskeleton markers that can identify normal and tumor cell nuclei, enzymes (e.g., pronase, proteinase K, pepsin, trypsin, Accumax, collagenase H) can be used to display the nuclei without causing excessive damage that DNA will be released from the processed nuclei. The specific method for separating nuclei from homogenate or representative samples is disclosed in co-pending PCT application number PCT / US2016 / 060861, the disclosure of which is incorporated herein by reference in its entirety.

[0074] In some embodiments, the homogenate is further mixed with a typical mixer (IKA mixer) and then filtered with a set of cell screens of different sizes (e.g., about 20 um, about 10 um, etc.). In some embodiments, a metal mesh is used to remove large tissue fragments before filtering with a cell screen. In some embodiments, the sample obtained consists primarily of single cells (some small cell aggregates, such as double cells) that can be stained with the desired marker.

[0075] Cell staining in homogenized samples

[0076] In some embodiments, prior to evaluating the sample by flow cytometry, the isolated cells and / or cell nuclei are labeled or stained so that different cell types can be identified (see, e.g., Fig.12 The marker or stain can be any detectable marker or reporter moiety that can identify different cell types by flow cytometry, such as a fluorescent marker ( Figure 3 Step 310).

[0077] In some embodiments, the homogenized sample is contacted with one or more detection probes, which can be visualized by applying one or more detection reagents (see, e.g., PCT Publication No. WO / 2017 / 085307, the disclosure of which is incorporated herein by reference in its entirety). For example, in some embodiments, the detection probe utilized is specific for immune cell markers. In some embodiments, the detection probe is selected from a primary antibody specific for a marker of lymphocytes, including T lymphocytes and B lymphocytes. In other embodiments, the detection probe is selected from a primary antibody specific for a marker of leukocytes, T helper cells, T regulatory cells, and / or cytotoxic T cells. In other embodiments, the detection probe is selected from a primary antibody specific for a broad spectrum of lymphocyte second biomarkers.

[0078] In some embodiments, antibodies can be conjugated to different fluorescent dyes by any conventional procedure, for example, Wofsy et al., "Modification and Use of Antibodies to Label Cell Surface Antigens", Selected methods in Cellular Immunology, BB Mishell and SM Siigi, ed., WH Freeman and Co. (1980). For example, fluorescently labeled antibodies specific for antigens on different cell types can be used to determine different types of immune cells. Examples of fluorescently labeled antibodies specific for different cell types include, but are not limited to, CD3 FITC / CD16+CD56PE for NK cells; CD3 FITC / CD19PE for B cells; CD45 FITC / CD14PE for monocytes; CD3 FITC / CD4PerCP for T helper cells; CD3 FITC / CD8PE for CD8 cells; CD4PerCP / CD45 RO PE for T helper memory cells; CD8 FITC / CD45RO Pe for CD8 memory cells; CD62 FITC / CD45RA PE / CD4PerCP for naive T helper cells; CD62 FITC / CD45RA PE / CD8PerCP for naive CD8 cells. In other embodiments, cells or cell nuclei are stained with 4',6-diamidino-2-phenylindole (DAPI).

[0079] In some embodiments, the detection probe is selected from a primary antibody specific for certain receptors and / or ligands, including but not limited to CD45, CD45LCA (wherein "LCA" refers to a common leukocyte antigen), CD3, CD4, CD8, CD20, CD25, CD19, and CD163 (e.g., an anti-CD antibody). In other embodiments, the detection probe is a primary antibody specific for CD45LCA. In some embodiments, the detection probe is an anti-CD45LCA primary antibody, such as that available from Ventana Medical Systems, Tucson, AZ, and available under the trade name CONFIRM anti-CD45, LCA (RP2 / 18) (a mouse monoclonal antibody (IgGI) that specifically binds to an antigen located on the membrane of leukocytes).

[0080] In some embodiments, the sample is stained for the presence of at least lymphocyte markers. Lymphocyte markers include CD3, CD4 and CD8. Generally, CD3 is a "universal marker" for T cells. In some embodiments, further analysis (staining) is performed to identify specific types of T cells, such as regulatory T cells, helper T cells or cytotoxic T cells. For example, CD3+T cells can be further distinguished as cytotoxic T lymphocytes that are positive for CD8 biomarkers (CD8 is a specific marker for cytotoxic T lymphocytes). CD3+T cells can also be distinguished as cytotoxic T lymphocytes that are positive for perforin (perforin is a membrane-dissolving protein expressed in the cytoplasmic granules of cytotoxic T cells and natural killer cells). Cytotoxic T cells are effector cells that actually "kill" tumor cells. It is believed that they work by direct contact, introducing the digestive enzyme granzyme B into the cytoplasm of tumor cells, thereby killing them. Similarly, CD3+T cells can be further distinguished as regulatory T cells that are positive for FOXP3 biomarkers. FOXP3 is a nuclear transcription factor and is the most specific marker for regulatory T cells. Likewise, CD3+ T cells can be further differentiated into helper T cells that are positive for the CD4 biomarker.

[0081] In view of the foregoing, in some embodiments, the homogenized sample can be stained for one or more immune cell markers, including at least CD3 or total lymphocytes detected by hematoxylin and eosin staining. In some embodiments, at least one additional T cell-specific marker may also be included, such as CD8 (a marker for cytotoxic T lymphocytes), CD4 (a marker for helper T lymphocytes), FOXP3 (a marker for regulatory T lymphocytes), CD45RA (a marker for naive T lymphocytes), and CD45RO (a marker for memory T lymphocytes). In a specific embodiment, at least two markers are used, including human CD3 (or total lymphocytes detected by H&E staining) and human CD8.

[0082] In some embodiments, T cells, such as CD8 positive cytotoxic T cells, can be further distinguished by multiple biomarkers including PD-1, TIM-3, LAG-3, CD28 and CD57. Thus, in some embodiments, T cells are stained with at least one of multiple lymphocyte biomarkers (e.g., CD3, CD4, CD8, FOXP3) to identify them, and stained with additional biomarkers (LAG-3, TIM-3, PD-L1, etc.) for further identification.

[0083] Cell population gating

[0084] After isolating cells, cell nuclei, and / or small tissue aggregates from a tissue sample, or more specifically, after homogenization (step 300) and staining of the cells, cell nuclei, and / or small tissue aggregates (step 310), the cells, cell nuclei, and / or small tissue aggregates are provided for flow cytometry analysis. In some embodiments, the sample is sorted electronically or virtually ( Figure 1 step 120; Figure 3 steps 320 and 330).

[0085] In some embodiments, due to the nature of the heterogeneous cell population being analyzed, flow cytometry data from a homogenized sample (e.g., a population of all cells isolated from a whole tumor including immune cells, tumor cells, epithelial cells) is considered to have "low quality". Such a heterogeneous population of cells being analyzed is composed of cells with different sizes and granularities, and in flow cytometry data, these cells are not "typically" "organized" into distinct populations. Since flow cytometry analysis is mainly based on viewing different cell populations in dot plots, the data from a homogenized sample is considered to look "odd" due to the inability to define distinct cell populations. When gating on small cells, a more homogeneous cell population (e.g., immune cells with similar size and granularity, thus having distinct populations on the dot plot) can be analyzed, and thus, the data will look more like traditional flow cytometry data (e.g., distinct populations), and it may be easier to depict an increase in fluorescence due to staining of certain subpopulations of the immune cell population. This gating process does not change the data, i.e., it only depicts it in a way that flow cytometry users consider themselves familiar with. Even when analyzing a mixture of all cells in a sample, the gating process allows viewing of certain cell populations (e.g., immune cells). Thus, the resulting selected cell population is more homogeneous in size and granularity and appears as distinct cell populations on flow cytometry data plots.

[0086] In some embodiments, the present disclosure provides a gating protocol whereby a homogenized mixture of cells or cell nuclei is first gated based on size (primary gating) (step 200) to provide a first population of cells or cell nuclei having a first size range or predicted cell type; and then a second gating (secondary gating) is performed based at least on biomarker expression (step 210). The primary and / or secondary gating can be performed one or more times separately.

[0087] In some embodiments, primary gating is performed to distinguish one or more populations of cells based on forward scatter (FSC) and side scatter (SSC) properties. It is believed that forward scatter and side scatter provide estimates of cell size and granularity, respectively, although this may depend on several factors, such as the sample, the wavelength of the laser, the collection angle and refractive index of the sample and sheath fluid. In some embodiments, for cell lines where only one type of cell is present (see Figure 5A ), which can more directly distinguish cell populations, but may be more complicated for samples with multiple cell types (such as in homogenized tissue samples). That is, the cellular heterogeneity in homogenized samples greatly reduces the quality of flow cytometry data ( Figure 5A and Figure 5B for comparison). Figure 5A and Figure 5B The stained samples are shown, while Fig. 6A and Figure 6B Forward scattered light and side scattered light are shown (markers not shown).

[0088] In embodiments where the homogenized sample is derived from a tumor sample, it will be understood by those skilled in the art that the homogenized sample will contain a tumor cell population and other cell populations, such as immune cells. Applicants have previously shown that such populations of cells can be physically sorted (see PCT Application No. PCT / EP2018 / 058809, the disclosure of which is incorporated herein by reference in its entirety). It will be appreciated by those skilled in the art that scatter-based gating strategies can be used to digitally sort those cells in a tumor cell population from those cells in other populations present in a homogenized sample.

[0089] In some embodiments, such a primary gating strategy enables selection of cells having a specific predicted size range (e.g., a size between about 3 μm and about 6 μm) or a predicted cell type (e.g., immune cells). In some embodiments, the relative proportions of cells having a first size (e.g., tumor cells having a size greater than 12 μm) and cells having a second size (e.g., immune cells having a size greater than 12 μm) can be determined and / or quantified by placing a gate in an area of ​​the density map where the different populations to be estimated or predicted are located. For example, if it is desired to determine the percentages of different types of immune cells within a homogenized tumor sample, the operator can select a primary gating trace that takes into account the different characteristics of such immune cells from tumor cells, such as the size difference between immune cells and tumor cells.

[0090] As shown in the two-parameter density plot of FIG. 6 , a primary gating strategy (step 200 or step 330) can be selected so that small cells can be separated from large cells, including tumor cells, based on forward scatter and side scatter. As shown in FIG. 6 , by gating traces onto a two-parameter density plot of FSC versus SSC (gating that estimates the physical properties of immune cells relative to other cells in the sample), relatively small immune cells can be digitally separated from larger tumor cells that are also present in a homogenized tumor sample. In some embodiments, given that immune cells are relatively smaller than tumor cells, an upper limit on the gating trace on the forward scatter axis is selected so that the cell population produced after primary gating is enriched in smaller immune cells (see Figure 4 Likewise, given that immune cells have lower complexity and therefore lower granularity than tumor cells, an upper limit on side scatter is selected to provide a population enriched for those cells that have reduced granularity compared to tumor cells (see Figure 4 Step 410).

[0091] To confirm that the primary gating in the above example correctly predicted the population of immune cells (i.e., immune cells digitally separated from tumor cells in the homogenized sample), FSC versus biomarker staining intensity plots were made for the same marker (CD3) for two different cell populations (large cell and small cell populations) derived from the same sample. Fig. 8A Density plots of FSC versus CD3 biomarker staining are provided, illustrating the presence of cells in the expected immune cell populations returned after primary gating. Control samples are illustrated in a comparative manner for each population that was not stained with antibodies targeting CD3. Likewise, Figure 8B Density plots of FSC versus CK8 / 18 tumor biomarker staining are provided, illustrating the presence of cells in the predicted tumor cell populations returned after primary gating. Again, control samples are illustrated in a comparative manner for each population that was not stained with antibodies targeting the CK8 / 18 tumor biomarker. Fig. 8A and Figure 8B , it is clear that the gating strategy correctly predicts the immune cell population based on the size of the cells.

[0092] exist Figure 7The use of primary gating in predicting cell populations derived from heterogeneous homogenized tumor samples based on size and / or granularity is further illustrated in Figure 5, which shows five different gated traces (labeled 1, 2, 3, 4, and 5) performed for a single homogenized sample. Each gated trace, trace 1, 2, 3, 4, and 5, is selected to provide different populations of cells with different characteristics based on scattering. In this particular example, gating is selected based on those cells that may have aneuploidy, i.e., the presence of an abnormal number of chromosomes. It is reported that such abnormal cells are likely to be tumor cells, and therefore large cells, and gates 4 and 5 can be traced to obtain a population enriched in large cells. Compared with gated trace 1 (expected to have small cells, such as immune cells), in which only 0.64% of the cells have aneuploidy, 13.22% and 8.89% of the cells in the subpopulations generated by gated traces 4 and 5 have aneuploidy. Based on the selection of tracers predicted to cover those cells with higher granularity compared to the tracers selected for gate 1, 2.14% and 2.32% of the cells were aneuploid in traces 2 and 3, respectively. Here, the optimal gate for capturing the subpopulation of cells with the most aneuploidy was gate 4, which takes into account high granularity and large cell size, both of which are considered to be characteristics of cells with aneuploidy. Applicants have shown that primary gating based on forward scatter and side scatter provides better estimates of aneuploidy than primary gating based on the presence of tumor markers (e.g., CK8 / 18) as described in Example 3.

[0093] In some embodiments, secondary gating (step 210 or step 240) is performed to determine the expression pattern of a specific cell type within a population of cells identified during primary gating. It is believed that this is particularly useful as the number of biomarkers increases. For example, sequential secondary gating (step 350, as shown by the dotted line) can be used in multiplex determinations to identify specific cell subpopulations expressing different biomarkers in a homogenized sample. In some embodiments, each secondary gating is performed by applying a tracer to a two-parameter density map of forward scatter (FSC) versus biomarker staining intensity on the first population of cells identified after the primary gating step.

[0094] For example, lymphocytes can be identified by primary gating based on forward scatter and side scatter (see Figure 6). A first secondary gating can be performed to identify and quantify CD3 positive T cells ( Fig. 9A Based on the expression of CD4 and CD8, a second secondary gating and a third secondary gating can also be performed to identify CD3 positive T cells (see FIG. 9B to FIG. 9C). In this particular example, three secondary gates are performed and traces with specific intensity cutoffs are applied to identify those subpopulations that express each of the CD3, CD8, and CD4 biomarkers. After secondary gating, the percentage of biomarker expression for each subpopulation can also be determined. Fig.10 The generation of the percentage of CD8 positive immune cells derived from homogenized samples from different patient samples is shown. In some embodiments, this type of data can be used to quantitatively measure the expression of different markers from whole tumor samples separated into single cells. In some embodiments, these expression levels can be associated with the patient's diagnosis, prognosis and treatment status. In some embodiments, flow cytometry is more conducive to quantification and suitable for multiplexing compared to IHC. In some embodiments, the fact of analyzing the whole tumor can increase tumor heterogeneity and can provide better correlation. The applicant has shown that this data obtained by the method of the present invention is very comparable to the data obtained from the actual physical sorting method, such as described in Example 1.

[0095] In some embodiments, the same or different gates for each marker are selected based on the variability between flow cytometry rounds. Gates that help identify positive cells (cells that are not present in the control and only appear in the sample) are then selected. In some processes, computer-implemented algorithms are used to automate the process.

[0096] The above principles can be extended to other biomarkers to identify and / or quantify other types of immune cells, and or to identify other subpopulations within any population or subpopulation. For example, the relative expression of CD28 and CD45RA biomarkers can be used to identify CD45RA+CD28+ naive cells, CD45RA-CD28+ memory cells, and CD45RA+CD28- effector cells, for example, based on CD4 and CD8 populations. Other markers can be used to continue this principle.

[0097] The method confirmed above is not limited to the amount or relative amount of cells that quantitatively express a cluster of differentiation markers. In some embodiments, the above method can be used to determine the percentage of a certain type of immune cell in different patients, and determine, for example, whether (i) those patients will benefit from a specific type of treatment; or (ii) monitor the progression and / or treatment of tumors. In other embodiments, the method of the present disclosure can be used to identify new correlations between the expression of two or more biomarkers or the expression of a certain biomarker and chromosomal abnormalities, as described in Example 2.

[0098] Examples

[0099] Example 1 - Comparison of physical and digital sorting

[0100] According to the method described herein, the results from the physical sorting process have been compared with the results from the digital gating method. Physically sorted minicells (minicells from overall homogenate) were analyzed for CD3 markers. Minicells (immune cells) express CD3 markers as expected (see Figure 11, picture B, which shows separate and different CD3 populations (73.71% positive cells)). In general, Figure 11 shows that physical and digital sorting methods provide similar results. Figure 11, picture A presents all cell staining from isolated tumors for CD3 and DAPI, and small cells are gated by flow cytometry analysis. Gating is a way to view a population (here, immune cells) in a mixed cell population. All cells from isolated tumors are stained only for DAPI and then analyzed by flow cytometry. Minicells (immune cells) are sorted (physically separated from a cell mixture), stained for CD3, and then analyzed again by flow cytometry. Data are presented in Figure 11, picture A. The percentage in both cases represents the percentage of positive cells in the analyzed population. In the case of Figure 11, Picture A, the analyzed population is sorted cells (pure sample), so the percentages are quite high. In the case of Figure 11, Picture B, the analyzed sample was gated into a small cell population of lower purity (some larger cells may find their way into this analyzed population), so the percentages are lower. Figure 11, Picture C shows the analysis of the same cell mixture, without gating or sorting. The data quality is much worse (tailing effect, lower percentages, and the positive cell population is not obvious).

[0101] Example 2 - Correlation between cells identified as CD8+ and having aneuploidy

[0102] It is believed that in different cancer types, tumor aneuploidy and tumor infiltrating lymphocytes are inversely proportional. We use tumor homogenate and flow cytometry analysis samples of aneuploidy and tumor infiltrating lymphocytes, and observe the same correlation. For example, the whole tumor is homogenized and filtered according to the technology described herein. Then the aliquot of the separated cells is stained for the presence of CD3 or CD3, and also stained with DAPI (2- (4- amidinophenyl) -1H- indole -6- carboxamidine). The sample of dyeing is provided for flow cytometry analysis, and the data generated are analyzed for aneuploidy (from DAPI) and markers (CD3 or CD8). For different cancer types, such as lung cancer and colon cancer, the sample process is repeated. It is observed again that in different cancer types, tumor aneuploidy and tumor infiltrating lymphocytes are inversely proportional.

[0103] Example 3 - Comparison between primary gating based on scatter and primary gating based on biomarker expression

[0104] Figure 7A dot plot is provided, which shows the forward and lateral size (size and granularity). When different positions on the analysis graph are used, it is observed that aneuploidy increases with size and granularity, which is expected because tumor cells are larger and more granular than aneuploid cells. The same analysis was also performed, but using a plot showing CK8 / 18 staining versus forward scattered light ( Fig.13 ). As expected, larger cells were determined to be more aneuploid, and CK8 / 18 was also expected to be more aneuploid. Size was also determined to be a better predictor of tumor cells than CK8 / 18, because small cells ( Fig.13 The lower right quadrant of the ) has almost no aneuploidy, which means that they are most likely non-tumor cells. All large cells (CK8 / 18 positive or negative) have much higher aneuploidy, which means that they are tumor cells. Figure 7 and Fig.13 Both demonstrated that appropriate gating for size was sufficient to define cells as tumor cells or immune cells without the need for staining for tumor biomarkers.

[0105] All U.S. patents, U.S. patent application publications, U.S. patent applications, foreign patents, foreign patents, and non-patent publications mentioned in this specification and / or listed in the application data sheet are incorporated herein by reference in their entirety. If it is necessary to adopt the concepts of the various patents, applications, and publications to provide other embodiments, various aspects of the embodiments may be modified.

[0106] Although the present disclosure has been described with reference to some illustrative embodiments, it should be understood that those skilled in the art can design many other modifications and embodiments within the spirit and scope of the principles of the present disclosure. More specifically, without violating the spirit of the present disclosure, within the scope of the above disclosure, the drawings and the appended claims, the components and / or arrangements of the subject combination arrangement may be reasonably varied and modified. In addition to the variations and modifications in the components and / or arrangements, alternative uses will also be apparent to those skilled in the art.

Claims

1. A method for quantifying the percentage of cells expressing one or more biomarkers, the method comprising: include: The fixed whole tumor sample was homogenized to provide a homogenized sample; staining cells in the homogenized sample for the presence of the one or more biomarkers; performing at least a first primary gating of the cells in the homogenized sample based on size and granularity scatter to provide at least a first population of cells having a first predicted cell type; as well as determining a first percentage of cells in a first subpopulation within the first population of cells that express a first biomarker of the one or more biomarkers; The method further comprises: performing a second primary gating on the cells in the homogenized sample to provide at least a second population of cells having a second predicted cell type, wherein the second predicted cell type is a tumor cell; determining a third percentage of cells within the second population of cells having a chromosomal abnormality; and The determined third percentage of cells having the chromosomal abnormality is correlated to the determined first percentage of cells expressing the first biomarker of the one or more biomarkers.

2. The method of claim 1, wherein the method does not require a physical sorting step prior to determining the first percentage of cells in the at least first subpopulation of cells. The method of claim 1 , wherein the first predicted cell type is an immune cell.

4. The method of claim 3, wherein the size of the immune cells is less than 12 μm.

5. The method of claim 3, wherein the first percentage of cells in the first subpopulation expressing the first biomarker among the one or more biomarkers is determined by performing secondary gating on cells in the first population based on the presence of the first biomarker among the one or more biomarkers.

6. The method of claim 5, wherein the first of the one or more biomarkers is selected from the group consisting of: CD3, CD4, CD8, CD45RA, and CD45RO cluster of differentiation biomarkers.

7. The method of claim 6, further comprising determining a second percentage of cells in a second subpopulation within the first population of cells that express a second biomarker of the one or more biomarkers, wherein the first biomarker and the second biomarker of the one or more biomarkers are different.

8. The method of claim 7, wherein the second biomarker among the one or more biomarkers is a differentiation marker cluster.

9. The method of claim 7, wherein the second biomarker of the one or more biomarkers is selected from the group consisting of: PD-1, TIM-3, LAG-3, CD28, CD57, and FOXP3.

10. The method of claim 7, wherein the first biomarker of the one or more biomarkers is CD3, and wherein the second biomarker of the one or more biomarkers is a biomarker that identifies the immune cell as a regulatory T cell, a helper T cell, or a cytotoxic T cell.

11. The method according to any one of claims 1 to 10, wherein the homogenized sample is further processed before staining, wherein the further processing comprises at least one of the following: digesting proteins within the homogenized sample, heating the sample, or filtering the homogenized sample.

12. A method for quantifying the percentage of cells expressing at least a first biomarker among one or more biomarkers in a fixed tissue sample, the method include: preparing a homogenized sample from the fixed tissue sample, wherein any heterogeneity of cells within the fixed tissue sample is substantially homogenously distributed within the homogenized sample such that any aliquot taken from the homogenized sample expresses the heterogeneity of the fixed tissue sample; filtering the homogenized sample to provide a filtered homogenized sample; staining cells in the filtered homogenized sample for the presence of the one or more biomarkers; performing at least two sequential gatings to provide at least a first subpopulation of cells expressing the first biomarker of the one or more biomarkers; as well as determining a first percentage of cells in the first subpopulation that express the first biomarker of the one or more biomarkers; wherein the at least two sequential gatings include: a primary gating to provide a first population of cells; and secondary gating to provide said first subpopulation of cells, wherein said primary gating is based on forward scatter and side scatter, and wherein a cutoff value for forward scatter is selected such that said first population is enriched for immune cells; The method further includes determining a second percentage of cells in a second subpopulation of cells that express a second biomarker of the one or more biomarkers, wherein the first and second biomarkers of the one or more biomarkers are different. The method according to claim 12 , wherein multiple secondary gating is performed. The method of claim 13 , wherein secondary gating is performed on at least two differentiation biomarker clusters.

15. The method of claim 14, wherein the differentiation biomarker cluster is selected from the group consisting of: CD3, CD4, CD8, CD45RA, CD45RO, CD28, and CD57.

16. The method of claim 12, wherein the second biomarker of the one or more biomarkers is a biomarker other than a differentiation marker cluster.

17. The method of claim 16, wherein the second biomarker of the one or more biomarkers is selected from the group consisting of: PD-1, TIM-3, LAG-3, and FOXP3.

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