WO2024192258A2 - Procédés d'évaluation de l'état de santé immunitaire - Google Patents

Procédés d'évaluation de l'état de santé immunitaire Download PDF

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Publication number
WO2024192258A2
WO2024192258A2 PCT/US2024/019966 US2024019966W WO2024192258A2 WO 2024192258 A2 WO2024192258 A2 WO 2024192258A2 US 2024019966 W US2024019966 W US 2024019966W WO 2024192258 A2 WO2024192258 A2 WO 2024192258A2
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subject
lymphocytes
frequency
immune
sample
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WO2024192258A3 (fr
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Allan Kirk
Dimitrios MORIS
Richard BARFIELD
Shelley HWANG
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Duke University
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Duke University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/569—Immunoassay; Biospecific binding assay; Materials therefor for microorganisms, e.g. protozoa, bacteria, viruses
    • G01N33/56966—Animal cells
    • G01N33/56972—White blood cells
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/24—Immunology or allergic disorders
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/52—Predicting or monitoring the response to treatment, e.g. for selection of therapy based on assay results in personalised medicine; Prognosis

Definitions

  • the present disclosure provides compositions and methods related to assessing the immune fitness of a subject.
  • the present disclosure provides novel methods for assessing immune fitness of a subject through the measurement of immune cell differentiation, maturation, exhaustion, and senescence. Based on this assessment, the present disclosure provides prognostic information and risk/resilience stratification for surgical procedures and critical illness.
  • BACKGROUND Humans endure numerous stresses, environmental insults, and immunological challenges throughout their lifespan. Their physiologic ability to adapt to these insults defines their resiliency, and evidence of prior injury and adaptation manifests in several ways. For example, clinicians intuitively estimate their patients’ prior experience with injury and assess their suitability for subsequent injury by noting evidence of prior insults, such as physical scars, and ordered return to original structure and function. Practically, this is relevant when considering a patient for elective surgery; for example, surgeons noting that an unviolated abdomen is more permissive for surgical intervention than a scarred one, and preservation of form and function in previously insulted areas (minimal residual scar from prior surgeries) suggests less extensive prior injury and a general capability for ordered recovery following a subsequent operation.
  • Embodiments of the present disclosure include a method of assessing a subject’s immune fitness for a physiological stressor.
  • the method includes determining a frequency of CD4+ lymphocytes and a frequency CD8+ lymphocytes in population of cells isolated from a sample from a subject, and determining a frequency of at least one lymphocyte maturation marker in the population of cells isolated from the sample from the subject.
  • determining the frequency of CD4+ lymphocytes, the frequency CD8+ lymphocytes, and/or the frequency of the at least one lymphocyte maturation marker comprises using flow cytometry to determine a relative frequency based on absolute count data.
  • the at least one lymphocyte maturation marker comprises CD45RA, CCR7, CD27, CD28 CD57, CD244, and/or PD1. In some embodiments, the method further comprises determining a ratio of CD4+ lymphocytes to CD8+ lymphocytes. In some embodiments, the at least one lymphocyte comprises a T lymphocyte. In some embodiments, the at least one lymphocyte comprises a B lymphocyte. In some embodiments, the method further comprises determining a frequency of CD3+ lymphocytes in the population of cells isolated from the sample from the subject, and DUKE-42907.601 wherein the at least one lymphocyte maturation marker is determined from the population of CD3+ lymphocytes.
  • the frequency of the at least one lymphocyte maturation marker is determined from the population of the CD4+ and/or CD8+ lymphocytes in the cells isolated from the sample from the subject.
  • the sample is a blood sample, a serum sample, a tissue sample, a bone marrow sample, a lavage fluid sample, a cerebral spinal fluid sample, a urine sample, or a fecal sample.
  • the subject is a non-human mammal.
  • the subject is a human.
  • the physiological stressor comprises a surgical procedure performed on the subject.
  • the surgical procedure comprises head and neck surgery, hernia repair surgery, a mastectomy, or a thyroidectomy.
  • the surgical procedure comprises cardiovascular surgery, bariatric surgery, a colectomy, a cystectomy, a nephrectomy, a pancreatectomy, a prostatectomy, or a thoracotomy.
  • the physiological stressor comprises a non-surgical medical procedure.
  • the physiological stressor comprises prolonged physical exertion.
  • the method comprises assessing the subject’s immune fitness within 60 days prior to exposure to the physiological stressor.
  • the method comprises assessing the subject’s immune fitness within 30 days prior to exposure to the physiological stressor.
  • the method further comprises reassessing the subject’s immune fitness after exposure to the physiological stressor.
  • the method further comprises assessing the subject’s immune fitness before and after exposure to the physiological stressor.
  • an increased frequency of CD57+ lymphocytes is indicative of immune system senescence and decreased immune fitness in the subject.
  • an increased frequency of PD1+ lymphocytes is indicative of immune system exhaustion and decreased immune fitness in the subject.
  • an increased frequency of CD244+ lymphocytes is indicative of immune system activation and decreased immune fitness in the subject.
  • a decreased frequency of CD28+ lymphocytes is indicative of reduced na ⁇ ve T cell activation and decreased immune fitness in the subject.
  • a decreased frequency of CD45RA+ lymphocytes and/or CCR7+ lymphocytes is indicative of increased T cell maturation and decreased immune fitness in the subject.
  • decreased immune fitness comprises an increased likelihood of at least one of hospital readmission, infection, system dysfunction, and/or post-surgical complications.
  • the method further comprises administering a treatment to the subject to increase immune fitness.
  • Boxplots showing relative frequency of CD4+ T-cell subsets expressing activation and exhaustion markers (B). Association of relative frequency of cell subsets pre-surgery with various post- surgery outcomes (C). Each row corresponds to a flow cytometry-gated cell subset. Cells in table are shaded by magnitude and direction of the test statistic (e.g., an increase in CD3+ CD4+ na ⁇ ve cells was associated with a decreased odds of infection). The number in each cell corresponds to un-adjusted p-values with ** indicating that FDR was significant when adjusting for other tests within the same column. Clustering was performed using hierarchical clustering on a matrix of the Euclidean distance between rows (based on their test-statistics).
  • FIGS.2A-2C tSNE plots showing qualitative differences between groups (A).
  • the first column displays information about cell density; all other columns display information about expression of a specific marker (from left to right: CD3, CD4, CD8, CD127, CD45RA, CCR7, CD57, CD244, PD-1, and CD28).
  • Row 1 (Pooled) is pseudo-colored by marker expression levels using samples pooled across all outcomes and is provided to guide interpretation of the distribution of different cell types. All other rows show the relative difference, after density estimation and smoothing, in marker expression between two different pooled sample groups (from top to bottom: Pooled, Pre/Post Surgery, Age, Infection, Any Complication, System Dysfunction, and Readmission).
  • Rows 4-7 groups (Infection, Any Complication, System Dysfunction, and Readmission) samples by various outcomes after surgery.
  • Red relative over-expression
  • Blue relative underrepresent in the group name displayed in the left-hand row.
  • Cells in table are shaded by size and direction of the test statistic (e.g., an increase in CD3+ CD4+ na ⁇ ve cells was associated with a decreased odds of infection).
  • the number in each cell corresponds to un- adjusted p-values with ** indicating that FDR was significant when adjusting for other tests within the same column.
  • Clustering is done using hierarchical clustering on a matrix of the Euclidean distance between rows (based on their test-statistics). Visualization of changes in three cell subsets from Pre-surgery to Recovery (C). Each color corresponds to a different cell subset.
  • Plot is facet wrapped by procedure. Circles indicate pre-surgery observations, triangles indicate recovery.
  • FIG. 3A-3B Boxplot showing logit change of relative frequency of CD19+ lymphocytes at immediate post-surgery by visceral surgery (A). Boxplot showing logit change of relative frequency of cell subsets at immediate post-surgery by laparoscopic surgery (B).
  • FIG. 4 Boxplot showing logit change of relative frequency of cell subsets at immediate post-surgery by major surgery.
  • FIG.5 TruCount and Phenoflow Gating strategies.
  • FIG.7 Flow tree for CD4+ and CD8+ subsets.
  • Another phenotypic characteristic of immune senescence is the expression of late-differentiated markers on memory cells reflecting the magnitude and diversity of antigens challenging the immune system throughout life. These memory cells display significant differences compared to na ⁇ ve cells such as CD28 and CD27 expression, and increased expression of exhaustion and senescence markers such as CD57, CTLA-4, and PD-1. Results of the present disclosure indicate that higher levels of T cells with exhaustive/senescent phenotypes (e.g., CD57 + or EM or EMRA subsets) at baseline were related to a higher likelihood of postoperative complications after elective surgery. The clinical importance of cumulative immune experience, especially in the human response to injury and healing, has not been fully elucidated.
  • the numbers 7 and 8 are contemplated in addition to 6 and 9, and for the range 6.0-7.0, the number 6.0, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 6.9, and 7.0 are explicitly contemplated.
  • “Correlated to” as used herein refers to compared to.
  • the terms “administration of” and “administering” a composition as used herein refers to providing a composition of the present disclosure to a subject in need of treatment (e.g., antiviral treatment).
  • compositions of the present disclosure may be administered by oral, parenteral (e.g., intramuscular, intraperitoneal, intravenous, ICV, intracisternal injection or infusion, subcutaneous injection, nebulization, or implant), by inhalation spray, nasal, vaginal, rectal, sublingual, or topical routes of administration and may be formulated, alone or together, in suitable dosage unit formulations containing conventional non-toxic DUKE-42907.601 pharmaceutically acceptable carriers, adjuvants and vehicles appropriate for each route of administration.
  • a mammal e.g., cow, pig, camel, llama, horse, goat, rabbit, sheep, hamsters, guinea pig, cat, dog, rat, and mouse
  • a non-human primate e.g., a monkey, such as a cynomolgus or rhesus monkey, chimpanzee, macaque, etc.
  • the subject may be a human
  • sample generally refers to a biological sample obtained from a subject.
  • the sample comprises a tissue and/or biological fluid obtained from a human patient.
  • the sample comprises whole blood, blood plasma, or serum.
  • the sample comprises cellular fluid, ascites, urine, feces, pancreatic fluid, fluid obtained during endoscopy, blood, mucus, or saliva.
  • the sample is a stool sample.
  • Such samples can be obtained by any number of means known in the art, such as will be apparent to the skilled person.
  • urine and fecal samples are easily attainable, while blood, ascites, serum, or pancreatic fluid samples can be obtained parenterally by using a needle and syringe, for instance.
  • Cell free or substantially cell free samples can be obtained by subjecting the sample to various techniques known to those of skill in the art which include, but are not limited to, centrifugation and filtration.
  • the term “treat,” “treating” or “treatment” are each used interchangeably herein to describe reversing, alleviating, or inhibiting the progress of a disease and/or injury, or one or more symptoms of such disease, to which such term applies.
  • the term also refers to preventing a disease, and includes preventing the onset of a disease, or preventing the symptoms associated with a disease (e.g., viral infection).
  • a treatment may be either performed in an acute or chronic way.
  • the term also refers to reducing the severity of a disease or symptoms associated with such disease prior to affliction with the disease.
  • Such prevention or reduction of the severity of a disease prior to affliction refers to administration of a treatment to a subject that is not at the time of administration afflicted with the disease.
  • Preventing also refers to preventing the recurrence of a disease or of one or more symptoms associated with such disease.
  • expression level refers to the amount of a molecule expressed in a cell that corresponds to the physiological state of the cell.
  • the expression level of a molecule can be represented by the amount of messenger RNA (mRNA) encoded by a gene, the amount DUKE-42907.601 of polypeptide corresponding to a given amino acid sequence encoded by a gene, or the amount of biochemical forms of molecules expressed in a cell, including the amount of particular post- synthetic modifications of a molecule such as a polypeptide, nucleic acid or small molecule.
  • mRNA messenger RNA
  • DUKE-42907.601 the amount of polypeptide corresponding to a given amino acid sequence encoded by a gene
  • biochemical forms of molecules expressed in a cell including the amount of particular post- synthetic modifications of a molecule such as a polypeptide, nucleic acid or small molecule.
  • an expression level is intended to include a “gene expression level,” a “cellular expression level,” or both.
  • the expression level can refer to an absolute amount of the molecule in a specimen or to a relative amount of the molecule.
  • the expression level of a molecule can be determined relative to a control molecule in the specimen.
  • an “expression profile” refers to a characteristic representation of the expression level of at least two molecules in a specimen such as a cell or tissue. The determination of an expression profile in a specimen from an individual is representative of the expression state of the individual. An expression profile reflects the gene expression level and/or cellular expression level of at least two molecules in a specimen such as a cell or tissue.
  • a “reference expression level” refers to the expression level of a molecule that is correlated with an associated reference expression level.
  • a reference expression level can be any level suitable for measuring and comparing expression levels of molecules between different samples.
  • the term “associated with” or “association with” refers to a change in one or more biomarkers (e.g., differences in frequency, level, or amount, e.g., greater or less than a reference) that occurs under a given set of conditions or parameters (e.g., as a correlation between the biomarker and the condition). The association can be made at any time point (e.g., prior to, during, or after development or onset of the condition(s)).
  • biomarker need not be causative of the condition, simply present at any point during the time course of the condition.
  • scientific and technical terms used in connection with the present disclosure shall have the meanings that are commonly understood by those of ordinary skill in the art.
  • any nomenclatures used in connection with, and techniques of, cell and tissue culture, molecular biology, immunology, microbiology, genetics and protein and nucleic acid chemistry and hybridization described herein are those that are well known and commonly used in the art.
  • the meaning and scope of the terms should be clear; in the event, however of any latent ambiguity, definitions provided herein take precedent over any dictionary or extrinsic definition.
  • singular terms shall include pluralities and plural terms shall include the singular.
  • Embodiments of the present disclosure include compositions and methods related to assessing the immune fitness of a subject.
  • the present disclosure provides novel methods for assessing immune fitness of a subject through the measurement of immune cell differentiation, maturation, exhaustion, and senescence. Based on this assessment, the present disclosure provides prognostic information and risk/resilience stratification for surgical procedures and critical illness.
  • the ability to characterize and quantify accumulating immunological alterations has been greatly improved by advances in multiparameter flow cytometry, and numerous changes in cell surface phenotype are now accepted as indicative of cellular lineage, antigen experience, effector function and cell fate.
  • Embodiments of the present disclosure include the assessment of these changes (immune “scarring”) as a systemic measure of accumulated insult to a patient’s ability to sustain and recover from impending injury.
  • Results of the present disclosure indicate that an assessment of a patient’s T-cell maturation (e.g., quantifying relative accumulation of T cells with more advanced maturation states of memory, exhaustion and senescence) provides information relevant to the likelihood of the development of complications following a future physiological stressor (e.g., surgical intervention).
  • embodiments of the present disclosure provide a prospective study of the relationship between peripheral immune phenotype and outcomes after elective surgery. Results indicate a significant association between pre-operative advanced immune experience and post-operative risk of complications.
  • the present disclosure includes a method of assessing a subject’s immune fitness for a physiological stressor.
  • the method includes determining a frequency of CD4+ lymphocytes and a frequency CD8+ lymphocytes in population of cells isolated from a sample from a subject.
  • the method includes determining a frequency of at least one lymphocyte maturation marker in the population of cells isolated from the sample from the subject.
  • determining the frequency of CD4+ lymphocytes, the frequency CD8+ lymphocytes, and/or the frequency of the at least one lymphocyte maturation marker comprises using flow cytometry to determine a relative frequency based on absolute count data.
  • the at least one lymphocyte maturation marker comprises CD45RA, CCR7, CD27, CD28 CD57, CD244, and/or PD1.
  • DUKE-42907.601 In general, flow cytometry involves the passage of individual cells through the path of one or more laser beams. A scattering of a beam and excitation of any fluorescent molecule attached to, or found within, a cell is detected by photomultiplier tubes to create a readable output. Often optical filters and beam splitters direct various scattered light to detectors, which generate electronic signals proportional to intensity of light signals received. Data can be collected, stored in computer memory, and cell characteristics analyzed based on fluorescent and light scattering properties.
  • flow cytometry involves analysis of a single sample or involves high-throughput screening (e.g.96-well or greater microtiter plates).
  • cells may be labeled with one or more fluorophores and then excited by one or more lasers to emit light at the fluorophore emission frequency or frequencies.
  • fluorescence is measured as cells pass through multiple laser beams simultaneously.
  • detection elements e.g. fluorophore-conjugated antibodies or fluorescence markers, can be used simultaneously, so measurements made as one cell passes through a laser beam may consist of scattered light intensities as well as light intensities from each fluorophore.
  • At least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, or more fluorescence markers are used.
  • a combination of fluorescence markers is used.
  • Characterization of a single cell can comprise a set of measured light intensities that may be represented as a coordinate position in a multidimensional space (e.g., a feature coordinate space).
  • a number of coordinate axes (the dimensions of the space) is often the number of fluorophores used plus one or more forward scatter or side scatter measurements.
  • fluorophores can be used as consistent with this application.
  • Alexa-Fluor dyes e.g., Alexa Fluor® 350, Alexa Fluor® 405, Alexa Fluor® 430, Alexa Fluor® 488, Alexa Fluor® 500, Alexa Fluor® 514, Alexa Fluor® 532, Alexa Fluor® 546, Alexa Fluor® 555, Alexa Fluor® 568, Alexa Fluor® 594, Alexa Fluor® 610, Alexa Fluor® 633, Alexa Fluor® 647, Alexa Fluor® 660, Alexa Fluor® 680, Alexa Fluor® 700, and Alexa Fluor® 750), APC, Cascade Blue, Cascade Yellow and R- phycoerythrin (PE), DyLight 405, DyLight 488, DyLight 550, DyLight 650, DyLight 680, DyLight 755, DyLight 800, FITC, Pacific Blue, PerCP, Rhodamine, Texas Red, Cy5, Cy5.5, and Cy7.
  • flow cytometry may measure at least one of cell size, cell volume, cell morphology, cell granularity, the amounts of cell components such as total DNA, newly synthesized DNA, gene expression as the amount messenger RNA for a particular gene, amounts of specific surface receptors, amounts of DUKE-42907.601 intracellular proteins, or signaling or binding events in cells.
  • cell analysis by flow cytometry on the basis of fluorescent level is combined with a determination of other flow cytometry readable outputs, such as granularity or cell size to provide a correlation between the activation level of a multiplicity of elements and other cell qualities measurable by flow cytometry for single cells.
  • flow cytometry data is presented as a single parameter histogram.
  • flow cytometry data is presented as 2-dimensional (2D) plots of parameters called cytograms.
  • two measurement parameters are depicted such as one on an x-axis and one on a y-axis.
  • parameters depicted comprise at least one of side scatter signals (SSCs), forward scatter signals (FSCs), and fluorescence.
  • data in a cytogram is displayed as at least one of a dot plot, a pseudo-color dot plot, a contour plot, or a density plot. For example, data regarding cells of interest is determined by a position of the cells of interest in a contour or density plot.
  • the contour or density plot can represent a number of cells that share a characteristic such as expression of particular biomarkers.
  • Flow cytometry data is conventionally analyzed by gating. Often sub-populations of cells are gated or demarcated within a plot. Gating can be performed manually or automatically. Manual gates, by way of non-limiting example, can take the form of polygons, squares, or dividing a cytogram into quadrants or other sectional measurements. In some instances, an operator can create or manually adjust the demarcations to generate new sub-populations of cells. Alternately or in combination, gating is performed automatically. Gating can be performed, in some part, manually or in some part automatically.
  • the methods described herein comprise using a flow cytometry instrument (also referred to as a flow cytometer) to collect flow cytometry data.
  • Flow cytometry is a technology for analyzing the physical and chemical characteristics of particles in a fluid that are passed in a stream through the beam of at least one laser.
  • One way to analyze cell characteristics using flow cytometry is to label cells with a fluorophore and then excite the fluorophore with at least one laser to emit light at the fluorophore emission frequency. The fluorescence is measured as cells pass through one or more laser beams simultaneously. Up to thousands of cells per second can be analyzed as they pass through the laser beams in the liquid stream.
  • Flow cytometer instruments generally comprise three main systems: fluidics, optics, and electronics.
  • the fluidic system may transport the cells in a stream of fluid through the laser DUKE-42907.601 beams where they are illuminated.
  • the optics system may be made up of lasers which illuminate the cells in the stream as they pass through the laser light and scatter the light from the laser. When a fluorophore is present on the cell, it will fluoresce at its characteristic frequency, which fluorescence is then detected via a lensing system.
  • the intensity of the light in the forward scatter direction and side scatter direction may be used to determine size and granularity (i.e., internal complexity) of the cell.
  • Optical filters and beam splitters may direct the various scattered light signals to the appropriate detectors, which generate electronic signals proportional to the intensity of the light signals they receive. Data may be thereby collected on each cell, may be stored in computer memory, and then the characteristics of those cells can be analyzed based on their fluorescent and light scattering properties.
  • the electronic system may convert the light signals detected into electronic pulses that can be processed by a computer. Information on the quantity and signal intensity of different subsets within the overall cell sample can be identified and measured.
  • Flow cytometry data may be presented in the form of single parameter histograms or as 2-dimensional plots of parameters, generally referred to as cytograms, which display two measurement parameters, one on the x-axis and one on the y-axis, and the cell count as a density (dot) plot or contour map.
  • FIGS.4A and 4B show examples of 2-dimensional plots and some gates.
  • parameters are side scattering (SSC) intensity, forward scattering (FSC) intensity, or fluorescence.
  • SSC and FSC intensity signals can be categorized as Area, Height, or Width signals (SSC-A, SSC-H, SSC-W and FSC-A, FSC-H, FSC-W) and represent the area, height, and width of the photo intensity pulse measured by the flow cytometer electronics.
  • the area, height, and width of the forward and side scatter signals can provide information about the size and granularity, or internal structure, of a cell as it passes through the measurement lasers.
  • parameters which consist of various characteristics of forward and side scattering intensity, and fluorescence intensity in particular channels, are used as axes for the histograms or cytograms.
  • biomarkers represent dimensions as well.
  • Cytograms display the data in various forms, such as a dot plot, a pseudo-color dot plot, a contour plot, or a density plot.
  • the data can be used to count cells in particular populations by detection of biomarkers and light intensity scattering parameters.
  • a biomarker is detected when the intensity of the fluorescent emitted light for that biomarker reaches a particular threshold level.
  • embodiments of the methods of the present disclosure further comprises determining a ratio of CD4+ lymphocytes to CD8+ lymphocytes. In some DUKE-42907.601 embodiments, determining a ratio of CD4+ lymphocytes to CD8+ lymphocytes is used to assess the immune fitness of a subject for a given physiological stressor.
  • the at least one lymphocyte comprises a T lymphocyte. In some embodiments, the at least one lymphocyte comprises a B lymphocyte. In some embodiments, a population of T cells are isolated from peripheral blood lymphocytes by lysing the red blood cells and depleting the monocytes, for example, by centrifugation through a gradient or by counterflow centrifugal elutriation. The methods described herein can include selection of a specific subpopulation of lymphocytes, e.g., T cells, B cells, etc., and assessing these isolated lymphocytes for expression of at least one lymphocyte maturation marker comprises CD45RA, CCR7, CD27, CD28 CD57, CD244, and/or PD1.
  • the methods of the present disclosure further comprise determining a frequency of CD3+ lymphocytes in a population of cells isolated from a sample from a subject.
  • at least one lymphocyte maturation marker is determined from the population of CD3+ lymphocytes.
  • the frequency of the at least one lymphocyte maturation marker is determined from the population of the CD4+ and/or CD8+ lymphocytes in the cells isolated from the sample from the subject.
  • determining a frequence comprises determining a relative frequency that is calculated using the respective absolute count data, with relative frequency equal to count+1 divided by parent count+1.
  • embodiments of the present disclosure include methods of assessing a subject’s immune fitness for a physiological stressor by determining a frequency of at least one lymphocyte maturation marker in a population of cells isolated from a sample from a subject using flow cytometry.
  • the method includes assessing the isolated lymphocytes for expression of at least one lymphocyte maturation marker comprises CD45RA, CCR7, CD27, CD28 CD57, CD244, and/or PD1.
  • an increased frequency of CD57+ lymphocytes is indicative of immune system senescence and decreased immune fitness in the subject.
  • an increased frequency of PD1+ lymphocytes is indicative of immune system exhaustion and decreased immune fitness in the subject.
  • an increased frequency of CD244+ lymphocytes is indicative of immune system activation and decreased immune fitness in the subject.
  • a decreased frequency of CD28+ lymphocytes is indicative of reduced na ⁇ ve T cell activation and decreased immune fitness in the subject.
  • a decreased frequency of CD45RA+ lymphocytes and/or CCR7+ lymphocytes is indicative of increased T cell maturation and decreased immune fitness in the DUKE-42907.601 subject.
  • decreased immune fitness comprises an increased likelihood of at least one of hospital readmission, infection, system dysfunction, and/or post-surgical complications.
  • the method further comprises administering a treatment to the subject to increase immune fitness.
  • a sample is obtained from a subject and used to isolate a population of cells for assessment, as described further herein.
  • the sample is a blood sample, a serum sample, a tissue sample, a bone marrow sample, a lavage fluid sample, a cerebral spinal fluid sample, a urine sample, or a fecal sample.
  • the subject is a non-human mammal.
  • the subject is a human.
  • embodiments of the present disclosure include methods of assessing a subject’s immune fitness for a physiological stressor.
  • the methods of the present disclosure can be used to assess the impact (e.g., prognostication) of any physiological stressor on a subject’s immune fitness.
  • the physiological stressor comprises a surgical procedure performed on the subject.
  • the surgical procedure comprises head and neck surgery, hernia repair surgery, a mastectomy, or a thyroidectomy.
  • the surgical procedure comprises cardiovascular surgery, bariatric surgery, a colectomy, a cystectomy, a nephrectomy, a pancreatectomy, a prostatectomy, or a thoracotomy.
  • the physiological stressor comprises a non-surgical medical procedure.
  • the physiological stressor comprises prolonged physical exertion.
  • the method comprises assessing the subject’s immune fitness within 60 days prior to exposure to the physiological stressor. In some embodiments, the method comprises assessing the subject’s immune fitness within 50 days prior to exposure to the physiological stressor. In some embodiments, the method comprises assessing the subject’s immune fitness within 40 days prior to exposure to the physiological stressor. In some embodiments, the method comprises assessing the subject’s immune fitness within 30 days prior to exposure to the physiological stressor. In some embodiments, the method comprises assessing the subject’s immune fitness within 20 days prior to exposure to the physiological stressor. In some embodiments, the method comprises assessing the subject’s immune fitness within 10 days prior to exposure to the physiological stressor.
  • the method further comprises reassessing the subject’s immune fitness after exposure to the DUKE-42907.601 physiological stressor. In some embodiments, the method further comprises assessing the subject’s immune fitness before and after exposure to the physiological stressor.
  • biomarker e.g., protein or polypeptide
  • methods to measure biomarkers described herein include, but are not limited to: Western blot, immunoblot, enzyme-linked immunosorbant assay (ELISA), radioimmunoassay (RIA), immunoprecipitation, surface plasmon resonance, chemiluminescence, fluorescent polarization, phosphorescence, immunohistochemical analysis, liquid chromatography mass spectrometry (LC-MS), matrix-assisted laser desorption/ionization time-of-flight (MALDI- TOF) mass spectrometry, microcytometry, microarray, microscopy, fluorescence activated cell sorting (FACS), magnetic activated cell sorting (MACS), flow cytometry, laser scanning cytometry, hematology analyzer and assays based on a property of the protein including but not limited to DNA binding, ligand binding, or interaction with other protein partners.
  • LC-MS liquid chromatography mass spectrometry
  • MALDI- TOF matrix-assisted laser desorption/ionization time-of-f
  • the activity or level of a marker protein can also be detected and/or quantified by detecting or quantifying the expressed polypeptide.
  • the polypeptide can be detected and quantified by any of a number of means well known to those of skill in the art. These can include analytic biochemical methods such as electrophoresis, capillary electrophoresis, high performance liquid chromatography (HPLC), thin layer chromatography (TLC), hyperdiffusion chromatography, and the like, or various immunological methods such as fluid or gel precipitin reactions, immunodiffusion (single or double), immunoelectrophoresis, radioimmunoassay (RIA), enzyme-linked immunosorbent assays (ELISAs), immunofluorescent assays, Western blotting, immunohistochemistry and the like.
  • analytic biochemical methods such as electrophoresis, capillary electrophoresis, high performance liquid chromatography (HPLC), thin layer chromatography (TLC), hyperdiffusion chromatography, and the like
  • Another agent for detecting a polypeptide is an antibody capable of binding to a polypeptide corresponding to a marker described herein, e.g., an antibody with a detectable label.
  • Antibodies can be polyclonal or monoclonal. An intact antibody, or a fragment thereof ")%*%$ &'( +, &"'(-# 2 ) can be used.
  • labeled with regard to the probe or antibody, is intended to encompass direct labeling of the probe or antibody by coupling (i.e., physically linking) a detectable substance to the probe or antibody, as well as indirect labeling of the probe or antibody by reactivity with another reagent that is directly labeled.
  • indirect labeling include detection of a primary antibody using a fluorescently labeled secondary DUKE-42907.601 antibody and end-labeling of a DNA probe with biotin such that it can be detected with fluorescently labeled streptavidin.
  • the antibody is labeled, e.g., a radio-labeled, chromophore- labeled, fluorophore-labeled, or enzyme-labeled antibody.
  • an antibody derivative e.g., an antibody conjugated with a substrate or with the protein or ligand of a protein-ligand pair (e.g., biotin-streptavidin)
  • an antibody fragment e.g., a single-chain antibody, an isolated antibody hypervariable domain, etc.
  • a protein corresponding to the marker such as the protein encoded by the open reading frame corresponding to the marker or such a protein which has undergone all or a portion of its normal post-translational modification, is used.
  • Proteins from cells can be isolated using techniques that are well known to those of skill in the art.
  • the protein isolation methods employed can, for example, be such as those described in Harlow and Lane (Harlow and Lane, 1988, Antibodies: A Laboratory Manual, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y.).
  • methods of the present disclosure can be applied as part of a clinical decision support algorithm.
  • the present disclosure provides algorithms and methods that can be used for diagnosis and monitoring of a subject’s immune fitness before, during, and/or after exposure to one or more physiological stressors.
  • data obtained using the methods of the present disclosure can be input into a clinical decision support algorithm designed to normalize and or improve the reliability of the data, as well as integrate the data with other data pertaining to various aspects of the patient.
  • data analysis requires a computer or other device, machine or apparatus for application of the various algorithms described herein due to the large number of individual data points that are processed.
  • a “machine learning algorithm” refers to a computational-based prediction methodology, also known to persons skilled in the art as a “classifier,” employed for characterizing, for example, a biomarker profile or an immune fitness profile. Signals can correspond to certain biomarker levels and be input into the algorithm for processing.
  • Supervised learning generally involves “training” a classifier to recognize the distinctions among classes and then “testing” the accuracy of the classifier on an independent test set.
  • meta-analysis can be used to generate the clinical support decision algorithm, such as that described by Fishel and Kaufman et al. 2007 Bioinformatics 23(13): 1599-606.
  • the algorithm may be supplemented with a meta-analysis approach such as a repeatability analysis.
  • the repeatability analysis selects biomarkers that appear in at least one predictive expression product marker set.
  • the clinical support decision algorithm can include Bayesian analysis.
  • the data can be subjected to a classification step, including any of the classification methods known in the art, and subsequently ranked according to a probability function.
  • a probability function may be derived from examining various patient parameters, and error rates may be calculated. These probabilities may then be combined with other patient profiling datasets, including the assessments of a patient’s immune fitness according to the present disclosure.
  • a statistical evaluation of the results of the patient profiling obtained using the clinical decision support algorithm may provide a quantitative value or values that can be presented directly to a physician in its most useful form to guide patient care.
  • the results of the molecular profiling can be statistically evaluated using a number of methods known to the art including, but not limited to: the students T test, the two sided T test, pearson rank sum analysis, hidden markov model analysis, analysis of q-q plots, principal component analysis, one way ANOVA, two way ANOVA, LIMMA and the like.
  • accuracy may be determined by tracking the patient over time to determine the accuracy of an original diagnosis. In other cases, accuracy may be established in a deterministic manner or using statistical methods. For example, receiver operator characteristic (ROC) analysis may be used to determine the optimal assay parameters to achieve a specific level of accuracy, specificity, positive predictive value, negative predictive value, and/or false discovery rate.
  • ROC receiver operator characteristic
  • Baseline demographics included gender, self- reported race, decade of life at the time of surgery, cancer-related procedure and body mass DUKE-42907.601 index (BMI).
  • Self-reported race included White, Black or African American, Native American/Alaskan Native, Asian, Native Hawaiian/ Pacific islander, more than one and unknown/not reported.
  • Body Mass Index was classified according to WHO guidelines as follows: underweight (below 18.5), normal weight (18.5-24.9), pre-obesity (25-29.9) and obese (30 or above).
  • Inclusion criteria included patients who were to undergo one of 12 well-described, relatively standardized elective surgical procedures chosen to include a broad spectrum of anatomic locations and magnitude of systemic perturbations.
  • SSSI Superficial Surgical Site Infection
  • DSSI Deep Surgical Site Infection
  • IDL organ space soft tissue infection
  • CLABSI central line-associated bloodstream infection
  • UTI urinary tract infection
  • System dysfunction included complications arising from the cardiovascular (myocardial infarction, pulmonary embolism, deep venous thrombosis, and stroke), renal (acute kidney injury), respiratory (unplanned intubation), and/or skin/musculoskeletal (decubitus ulcer) systems. Unplanned readmission included readmission to the hospital or intensive care unit (ICU).
  • Flow cytometry Flow cytometry was performed as previously described. Briefly, blood was collected in a Streck Cyto-Chex® BCT tube and stored at room temperature for 2 to 5 days before being processed for phenotyping into 27 distinct phenotypic subsets.
  • NK Bright CD56 + Bright
  • NK Dim CD56 + Dim
  • Adaptive response immune cells included markers for pan T cells (CD3 + ) and their broad subsets (CD4, CD8), pan B-cells (CD19), and validated markers of progressive immune maturation including T cell memory [CD45RA, CCR7 defining na ⁇ ve, central memory (CM), Effector Memory (EM), and terminal effectors (EMRA) based on the classification of Salusto, et al 7 ), exhaustion (PD1), senescence (CD57), and activation (CD244).50 ul of mixed blood was carefully added by reverse pipetting to the TruCount tube containing a lyophilized bead pellet and antibodies. Samples were incubated, and then red cells lysed by adding 1X BD PharmLyse Buffer.
  • FDR Benjamini-Hochberg false discovery rate
  • Non-visceral surgery includes any of the following types of procedures: Head and neck, Vascular, Mastectomy, Thyroidectomy or Hernia repair. Otherwise, the procedures were classified as a visceral surgery. 4. Examples It will be readily apparent to those skilled in the art that other suitable modifications and adaptations of the methods of the present disclosure described herein are readily applicable and appreciable, and may be made using suitable equivalents without departing from the scope of the present disclosure or the aspects and embodiments disclosed herein.
  • Example 1 Demographics and Baseline immune phenotypes.
  • Table 1 Patient Demographics and Procedures.
  • Table 2 Complications and Types of Surgical Procedures.
  • Each patient’s immune repertoire was defined by its relative frequency of distinct flow cytometry defined cell surface phenotypes (FIG.7), as well as by the CD4/CD8 ratio.
  • the pre-operative immune phenotype was diverse, characterized by wide expression ranges for parameters associated with T-cell maturation (Table 3).
  • Table 3 Characteristics Pre-Op of CD19+, CD3, CD4, and CD8. Data shows Median [min, max] for relative frequencies.
  • DUKE-42907.601 BMI body mass index An analysis was performed and no significant differences were observed in lymphocyte phenotypes by sex, BMI, cancer diagnosis, race, or procedure, and no age-related changes were observed in absolute T (CD3, CD4, CD8) or relative B cell (CD19) frequencies through age 80 (Tables 4-7).
  • Table 4 Characteristics Pre-Op of CD4 memory cells. Data shows Median [min, max] for relative frequencies.
  • EM effector memory
  • EMRA terminally differentiated effector memory
  • BMI body mass index
  • Table 5 Characteristics Pre-Op of CD4 cytokine cells. Data shows Median [min, max] for relative frequencies.
  • DUKE-42907.601 BMI body mass index Table 6: Characteristics Pre-Op of CD8 Memory cells. Data shows Median [min, max] for relative frequencies.
  • DUKE-42907.601 CM central memory
  • EM effector memory
  • EMRA terminally differentiated effector memory
  • BMI body mass index
  • Table 7 Characteristics Pre-Op of CD8 Cytokine cells. Data shows Median [min, max] for relative frequencies.
  • substantial age-related patterns were observed in the relative maturation phenotypes within individuals’ T-cell populations, consistent with an anticipated accumulation of immune experience over the lifespan.
  • T cell lymphopenia apparent in all T cells (CD3+) including CD4 and CD8 populations, and the concomitant emergence of an innate NK response (CD56+), particularly DUKE-42907.601 evident in the CD56 dim population, which significantly increased in prevalence postoperatively (FIG. 2C).
  • CD56+ innate NK response
  • Most T-cell phenotypes were generally depressed immediately postoperatively, with the marked exception of cells that expressed CD57 (CD4 and CD8), or CD244, or lacked CD28.
  • CD57 CD4 and CD8

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Abstract

La présente invention concerne des compositions et des procédés associés à l'évaluation de l'état de santé immunitaire d'un sujet. En particulier, la présente invention concerne de nouveaux procédés d'évaluation de l'état de santé immunitaire d'un sujet par la mesure de la différenciation, de la maturation, de l'épuisement et de la sénescence de cellules immunitaires. Sur la base de cette évaluation, la présente invention fournit des informations de pronostic et une stratification de risque/résilience pour des procédures chirurgicales et une maladie critique.
PCT/US2024/019966 2023-03-14 2024-03-14 Procédés d'évaluation de l'état de santé immunitaire Ceased WO2024192258A2 (fr)

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