WO2023004055A1

WO2023004055A1 - Metabolic and inflammatory markers for car-t cell therapy

Info

Publication number: WO2023004055A1
Application number: PCT/US2022/037894
Authority: WO
Inventors: Neetu Gupta; Brian Hill; Daniel ROTROFF
Original assignee: The Cleveland Clinic Foundation
Priority date: 2021-07-21
Filing date: 2022-07-21
Publication date: 2023-01-26

Abstract

The present invention relates to CAR T cell-independent and dependent metabolic and inflammatory biomarkers of CRS, ICANS, CAR T cell expansion and patient survival, that can be used to predict patient response, enable mitigating strategies for toxicities in the clinic and improve patient outcomes and survival.

Description

METABOLIC AND INFLAMMATORY MARKERS FOR CAR-T CELL THERAPY

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of priority to U.S. Provisional Application No. 63/224,315 filed July 21, 2021, the contents of which are hereby incorporated by reference in their entirety and for all purposes.

FIELD OF INVENTION

[0002] The present invention relates to CAR T cell-independent and dependent metabolic and inflammatory biomarkers of CRS, ICANS, CAR T cell expansion and patient survival, that can be used to predict patient response, enable mitigating strategies for toxicities in the clinic and improve patient outcomes and survival.

BACKGROUND OF THE INVENTION

[0003] A significant proportion of DLBCL (Diffuse Large B-Cell Lymphoma) patients are either refractory or suffer relapse despite stem cell transplantation (r/r DLBCL), and face poor prognosis (Crump, Neelapu et al., 2017, Hu, Winter et al., 2019). Chimeric antigen receptors (CARs) are hybrid proteins consisting of (i) single chain variable fragment (scFv) of heavy (VH) and light (VL) chains of an antibody molecule directed against a tumor antigen, (ii) hinge region of CD8 or CD28, (iii) transmembrane region of CD8 or CD28, (iv) co-stimulatory domain of CD28 or 4-1BB, and (v) intracellular signaling domain of Oϋ3z (Geldres, Savoldo et al., 2016, Ghobadi, 2018, Porter, Kalos et al., 2011). Adoptive immunotherapy for r/r DLBCL involves administration of T-cells engineered to express a CAR (i.e. CAR-T cells) that targets the tumor antigen, CD19 (Kohn, Doth et al., 2011) (Chow, Shadman et al., 2018). CAR-T cells are manufactured by collection of peripheral blood mononuclear cells (PBMCs) from patients through apheresis with subsequent purification of T cells which are then virally transduced, stimulated, and expanded. Patients undergo a lymphodepleting chemotherapy to allow for expansion of CAR T cells. This is followed by infusion of the patient’s own bio-engineered CAR T cells to target the specific tumor antigen and thereby kill the tumor cells (Zhao & Cao, 2019).

[0004] Two different CARs targeting the antigen CD 19 on B cells were approved by FDA for treatment of relapsed/ refractory DLBCL - axicabtagene ciloleucel (Axi-cel [Yescarta®]) by Gilead Sciences, Inc. and tisagenlecleucel (Tisa-cel) [Kymriah®]) by Novartis (2018, Kallam & Vose, 2019, Maziarz, Waller et al., 2020, Neelapu, Locke et al., 2017, Schwartz, 2019, Zavras, Wang et al., 2019). While these CD19-directed CAR T cell therapies have demonstrated high anti-tumor efficacy the benefit is still limited to 30-40% of the patients. The molecular basis of CAR T cell expansion also remains poorly characterized.

[0005] Furthermore, CD 19 CAR T cell therapy is associated with unique toxi cities in 70% of patients, including cytokine release syndrome (CRS) and CAR T cell-related immune effector cell-associated neurotoxicity syndrome (ICANS) (Hay, Hanafi et al., 2017, Wang & Han, 2018). CRS is indeed a life-threatening complication of several new immunotherapies and thought to derive from a surge of proinflammatory cytokines in patients undergoing treatment (Lee, Gardner et al., 2014). There have been reports which have discussed the factors associated with CRS and ICANS but a multidimensional analysis of neurotoxicity biomarkers remains to be described (Murthy, Iqbal et al., 2019, Wang & Han, 2018).

[0006] In response to CAR T cell therapy, immune cells undergo profound metabolic changes to endure proliferation and production of cytokines which in turn lead to pyrexia, hypotension, pulmonary edema, reduced renal perfusion, and various cardiovascular toxicities (Wang & Han, 2018, Xu, Gnanaprakasam et al., 2019). It has been well recognized that neurotoxicity (ICANS) occurs in the presence of CRS but it appears later and is more persistent than CRS (Karschnia, Jordan et al., 2019). A recent report showed association between neurotoxicity and elevated cerebrospinal fluid (CSF) cytokines as well as elevated serum cytokines levels (Santomasso, Park et al., 2018, Wang & Han, 2018). Despite these observations, the pathobiology of the neurotoxicity remains poorly understood.

[0007] As such, there remains a need for new CAR T cell-independent and dependent metabolic and inflammatory biomarkers of CRS, ICANS, CAR T cell expansion and patient survival, that can be used to predict patient response, enable mitigating strategies for toxicities in the clinic, and improve treatment efficacy and/or patient survival. Specifically, low molecular weight metabolites not belonging to the well-studied cytokine family have not been considered as potential biomarkers of CRS, ICANS, CAR T cell expansion and patient survival. The current invention addresses these and other unmet needs. SUMMARY OF INVENTION

[0008] Provided are methods, kits and compositions related to toxicity associated with administration of cell therapy for the treatment of diseases or conditions, e.g., cancer, including methods for use in determining whether to administer such cell therapy in the first instance, and for predicting and treating a toxicity after administration of the therapy. In some embodiments, the toxicity is a neurotoxicity, CAR T cell-related immune effector cell-associated neurotoxicity syndrome (ICANS), or cytokine release syndrome (CRS), such as a severe neurotoxicity, a severe ICANS, or a severe CRS. The methods generally involve detecting one or more of the metabolic biomarkers identified and characterized herein, either individually or in a panel of biomarkers, and comparing the detected concentration or amount to a reference value to determine if the subject is at risk for developing the toxicity, such as neurotoxicity, ICANS or CRS.

[0009] In some embodiments, the biological sample is obtained from the subject prior to induction of the adoptive cell therapy, and the risk assessment is used determine whether to administer the cell therapy in the first instance. In some embodiments, additional biological samples are derived from a subject after administration of the cell therapy, but at a time at which the subject does or did not exhibit a physical sign or symptom of severe neurotoxicity or severe CRS, and the methods involve administering an agent or therapy for treating, ameliorating, preventing, delaying and/or attenuating the development of the toxicity, such as neurotoxicity, ICANS or CRS, such as severe neurotoxicity, severe ICANS or severe CRS.

[0010] In one aspect, the present invention relates to a biomarker assay intended to identify recipients of CD 19 chimeric antigen receptor CAR-T cell therapy for diffuse large B-cell lymphoma who are at increased risk for neurologic toxicity. CAR-T cell therapy, which while it has increased survival rates of relapsed/ refractory (r/r) DLBCL patients compared to standard of care, the benefit is only seen in 30-40% of the patients due to serious side effects like ICANS or CRS toxicity. In exemplary embodiments, the metabolite markers are extracted from plasma samples, analyzed by LC-MS and metabolomics analyses performed.

[0011] In an exemplary embodiment, eight metabolites, identified as N-acetyl glycine, 2- hydroxyglutarate, l-stearoyl-2-oleoyl-GPI (18:0/18:1)*, palmitoleoylcamitine (C16:l)*, gamma- glutamyl-alpha-lysine, octadecadienedioate (C18:2-DC)*, hydroxyproline, and glutamine are associated with ICANS on day of apheresis (DA), and predict the onset of ICANS with 89% balanced accuracy with sensitivity and specificity of 74% and 86%, respectively.

[0012] In an exemplary embodiment, eight metabolites, identified as o-cresol sulfate, isoursodeoxycholate, N-delta-acetylomithine, 4-allylcatechol sulfate, 2,4-di-tert-butylphenol, 1- methyladenosine, succinate, and phytanate predicted the onset of CRS with 90% balanced accuracy, and sensitivity and specificity of 90% and 90%, respectively.

[0013] In an alternative exemplary embodiment, nine metabolites, identified as o-cresol sulfate, isoursodeoxycholate, N-delta-acetylomithine, 4-allylcatechol sulfate, 2,4-di-tert- butylphenol, 1-methyladenosine, succinate, phytanate, and glucose predicted the onset of CRS with 87% balanced accuracy, and sensitivity and specificity of 84% and 90%, respectively.

[0014] In one aspect, methods of treating a patient in need thereof with an adoptive cell therapy, e.g. CAR-T cell therapy, including a CD 19 CAR-T cell therapy, are provided. In some aspects, the methods involve detecting one or more of the foregoing biomarkers individually or in a panel of biomarkers in a biological sample, comparing each biomarker to a reference value, wherein the comparison indicates whether the subject is or is not at risk for developing a toxicity, e.g. neurotoxicity, ICANS or CRS, and if the comparison indicates that the subject is not at risk for developing the toxicity, and/or indicates that the risk is below a threshold level, administering the adoptive CAR-T therapy to the subject. In some cases, the biological sample is derived from a subject at a time at which the subject does or did not exhibit a physical sign or symptom of severe neurotoxicity or severe CRS. Preferably, the biological sample is derived from a subject prior to the induction of the adoptive cell therapy.

[0015] In another aspect, methods of ameliorating the development of toxicity, such as severe neurotoxicity, severe ICANS or severe CRS, are provided for a subject following administration of an adoptive cell therapy, e.g. CAR-T cell therapy. In some aspects, the methods involve detecting a biomarker, individually, or each biomarker in a panel of biomarkers in a biological sample, comparing each biomarker to a reference value, wherein the comparison indicates whether the subject is or is not at risk for developing a toxicity, e.g. neurotoxicity, ICANS or CRS, and if the comparison indicates that the subject is at risk for developing the toxicity, and/or indicates that the risk is above a threshold level, administering a preventive therapy for the toxicity. In some cases, the biological sample is derived from a subject at a time at which the subject does or did not exhibit a physical sign or symptom of severe neurotoxicity or severe CRS. In some cases, the biological sample is derived from a subject before and/or after the induction of the adoptive cell therapy.

[0016] In some embodiments, the method involves comparing the detected parameter for the biomarker or each of the biomarkers in the panel, individually, to a reference value for the parameter. In some cases, the comparison indicates whether the subject is or is not at risk for developing a toxicity and/or indicates a degree of risk for developing a toxicity. In some instances, the toxicity is neurotoxicity or severe neurotoxicity. In some embodiments the toxicity is or is related to CAR T cell-related immune effector cell-associated neurotoxicity syndrome (ICANS) or severe ICANS. In some embodiments the toxicity is related to cytokine release syndrome (CRS) or severe CRS. In some embodiments, if the comparison indicates that the subject is at risk for developing the toxicity and/or indicates that the risk is above a threshold level, the method involves administering to the subject an agent or therapy that is capable of treating, preventing, delaying, or attenuating the development of toxicity.

[0017] In some instances, the comparison to the reference value, or each of the comparisons to each of the reference values, thereby determines a relative value for the biomarker, as compared to the reference value. In some aspects, the relative value or combination thereof indicates whether the subject is at risk. In some cases, the relative value is a percentage or fold increase or percentage or fold decrease, compared to the reference value, or is an indication that the biomarker is at, within, above, or below the reference value. In some embodiments, the reference value contains a range of values. In some cases, the relative value is an indication that the detected parameter is within the range or is not within the range.

[0018] In accordance with one aspect of the invention, there is provided a method for predicting the likelihood of a toxicity to a CAR-T cell therapy, e.g. a CD 19 CAR-T cell therapy, the subject having, or suspected of having cancer, the method comprising determining the presence, absence, amount or relative levels of one or more metabolites selected from the group comprising or consisting of o-cresol sulfate, isoursodeoxycholate, N-delta-acetylornithine, 4- allylcatechol sulfate, 2,4-di-tert-butylphenol, 1-methyladenosine, succinate, phytanate, N- acetylglycine, 2-hydroxyglutarate, l-stearoyl-2-oleoyl-GPI (18:0/18:1)*, palmitoleoylcamitine (C16:l)*, gamma-glutamyl-alpha-lysine, octadecadienedioate (C18:2-DC)*, hydroxyproline, glutamine or glucose in a sample collected from said patient on the day of apheresis, wherein the presence, absence, amount or relative level of the metabolite is indicative of the potential onset of ICANS and/or CRS in the subject as a result of the CAR-T cell therapy.

[0019] In accordance with another aspect of the invention, there is provided a method for selecting a subject for receiving a CAR-T cell therapy, e.g. a CD 19 CAR-T cell therapy, the subject having, or suspected of having cancer, the method comprising determining the presence, absence, amount or relative levels of one or more metabolites selected from the group comprising or consisting of comprising or consisting of o-cresol sulfate, isoursodeoxycholate, N-delta-acetyl ornithine, 4-allylcatechol sulfate, 2,4-di-tert-butylphenol, 1-methyladenosine, succinate, phytanate, N-acetylglycine, 2-hydroxyglutarate, l-stearoyl-2-oleoyl-GPI (18:0/18:1)*, palmitoleoylcamitine (06:1)*, gamma-glutamyl-alpha-lysine, octadecadienedioate (08:2- DC)*, hydroxyproline, glutamine or glucose in a sample collected from said patient on the day of apheresis, wherein the presence, absence, amount or relative level of the metabolite is indicative of the risk of a toxicity, e.g., onset of ICANS and/or CRS, in the subject as a result of the CAR-T cell therapy.

[0020] In accordance with another aspect of the invention, there is provided a method of treating cancer in a mammalian subject in need thereof, the method comprising administering to the subject a a CAR-T cell therapy, e.g. a CD 19 CAR-T cell therapy, the subject having, or suspected of having cancer, the method comprising determining the presence, absence, amount or relative levels of one or more metabolites selected from the group comprising or consisting of o-cresol sulfate, isoursodeoxycholate, N-delta-acetylornithine, 4-allylcatechol sulfate, 2,4-di-tert- butylphenol, 1-methyladenosine, succinate, phytanate, N-acetylglycine, 2-hydroxyglutarate, 1- stearoyl-2-oleoyl-GPI (18:0/18:1)*, palmitoleoylcamitine (06:1)*, gamma-glutamyl-alpha- lysine, octadecadienedioate (C18:2-DC)*, hydroxyproline, glutamine or glucose in a sample collected from said patient on the day of apheresis, wherein the presence, absence, amount or relative level of the metabolite is indicative of the efficacy and/or risk of toxicity of the CAR-T cell therapy.

BRIEF DESCRIPTION OF THE DRAWINGS [0021] FIG. 1A-C illustrates the scientific approach of the subject invention.

[0022] FIG. 2 illustrates the technical approach of the subject invention for CRS. [0023] FIG. 3 illustrates the performance of the invention when applied to CRS prediction.

[0024] FIG. 4 illustrates the performance of an alternative approach to the invention when applied to CRS prediction.

[0025] FIG. 5 illustrates the technical approach of the subject invention for ICANS.

[0026] FIG. 6 illustrates the performance of the invention when applied to ICANS prediction.

[0027] FIG. 7 illustrates metabolites that are significantly associated with the outcomes of CRS. Relative abundance of the most statistically significant metabolites that were positively or negatively associated with the occurrence of CRS.

[0028] FIG. 8 illustrates metabolites that are significantly associated with the outcomes of CRS. FIG. 8 A is a heatmap of median relative abundance of 166 metabolites (rows) significantly associated with CRS severity. FIG. 8 B illustrates the relative abundance of the most statistically significant metabolites that were positively or negatively associated with maximum observed CRS grade.

[0029] FIG. 9 illustrates metabolites that are significantly associated with the outcomes of CRS. FIG. 9 A is a forest plot of hazard ratios and 95% confidence intervals for the 155 metabolites, associated with time to CRS onset from day of treatment initiation. FIG. 9 B includes Kaplan Meier curves for the six metabolites with the highest or lowest hazard ratios.

[0030] FIG. 10 illustrates metabolites that are significantly associated with the outcomes of ICANS. Relative abundance of the most statistically significant metabolites that were negatively associated with the occurrence of ICANS.

[0031] FIG. 11 illustrates metabolites that are significantly associated with the outcomes of ICANS. Relative abundance of the most statistically significant metabolites that were negatively associated with the severity of ICANS.

[0032] FIG. 12 illustrates metabolites that are significantly associated with the outcomes of ICANS. FIG. 12 A is a forest plot of hazard ratios and 95% confidence intervals for the 11 metabolites, associated with time to ICANS onset from day of treatment initiation. FIG. 12 B includes Kaplan Meier curves for the four metabolites with the highest or lowest hazard ratios. [0033] FIG. 13 A illustrates validation of the metabolites associated with any of the outcomes of CRS and ICANS using targeted assays. Differential association of metabolite concentration with occurrence, severity or time-to-onset of CRS. FIG. 13 B illustrates association of metabolite with occurrence, severity, or time-to-onset of ICANS.

DETAILED DESCRIPTION

[0034] CD 19 chimeric antigen receptor (CAR) T cell therapy has increased survival rates of relapsed/refractory (r/r) DLBCL patients compared to standard of care, yet the benefit is only seen in 30-40% of the patients. Additionally, patients experience a variety of toxicities associated with therapy, such as cytokine release syndrome (CRS) and CAR T cell associated immune effector cell-associated neurotoxicity syndrome (ICANS). This invention addresses the above challenges by identifying CAR T cell independent and dependent metabolic and inflammatory biomarkers of CRS, ICANS, CAR T cell expansion and patient survival. These biomarkers may be used to predict patient response, enable mitigating strategies for toxicities in the clinic and the development of strategies through further research for improving patient survival.

[0035] Metabolomics is a new approach in system biology which is used to characterize small biochemical entities which change in response to various stimuli and serve as effector molecules in biological processes. Therefore, identification of metabolic biomarkers related to the severity of adverse outcomes and/or favorable response to therapy is important for prediction and management of patient outcomes. Further, it is important to identify metabolic pathways for better understanding of immune cell modulation in response to therapy. As demonstrated herein for the first time, alterations in the metabolome of patients treated with CAR T cell therapy may regulate both favorable and adverse clinical outcomes.

Subject recruitment and collection of plasma

[0036] The collection of clinical samples was conducted according to the principles of the Declaration of Helsinki and was approved by the Institutional Review Board of Cleveland Clinic (IRB# ?) in accordance with guidelines for the protection of human subjects. All study participants provided written informed consent for the collection of samples and subsequent analyses. Blood samples were collected from 41 r/rLBCL patients at the time of apheresis, prior to treatment with Axicabtagene Ciloleucel (Axi-cel; n=31) or Tisagenlecleucel (Tisa-cel; n=10), in sodium citrate tubes. Plasma was separated from cellular material by centrifuging the tubes at 1500 xg for 30 minutes, stored at -80 °C until further use.]

Patient data

[0037] Clinical and demographic details of 41 patients treated with Axi-cel and Tisa-cel were collected by review of the electronic medical record and maintained in the password-protected and HIPPA-compliant Research Electronic Data Capture (REDCap) system. Gender, age, diagnosis, laboratory values prior to treatment including blood glucose levels were recorded. Toxicity grading on each inpatient day after treatment with CAR-T Cell therapy was recorded using the ASTCT consensus criteria. Clinical outcomes including response to treatment and overall survival were collected.

[0038] Random forest was used to identify a collection of metabolites that could predict CRS. Leave-one-out cross-validation was used. A random forest model with 808 metabolites was generated and metabolites were ranked by their Gini Score, and the metabolite with the lowest Gini score was removed. This process was repeated until the only one metabolite remained. Of 808 models, the model including eight metabolites has the lowest misclassification rates on the training set: o-cresol sulfate, isoursodeoxycholate, N-delta-acetylornithine, 4-allylcatechol sulfate, 2,4-di-tert-butylphenol, 1-methyladenosine, succinate, and phytanate. An additional model utilizing glucose, which was significantly associated with CRS (P< 05), was included with the eight metabolites above to create a model with nine metabolites. All reported accuracies reported here were determined based on the left out samples from the leave-one-out cross- validation.

[0039] In addition, we have taken a similar approach to predict onset of ICANS in r/r DLBCL patients from plasma samples obtained at DA. We identified eight metabolites, N- acetylglycine, 2-hydroxyglutarate, l-stearoyl-2-oleoyl-GPI (18:0/18:1)*, palmitoleoylcamitine (C16:l)*, gamma-glutamyl -alpha-lysine, octadecadienedioate (C18:2-DC)*, hydroxyproline, and glutamine are associated with ICANS on day of apheresis (DA), and predicted the onset of ICANS with 89% balanced accuracy with sensitivity and specificity of 74% and 86%, respectively.

[0040] In one embodiment, therefore, the invention provides a method of evaluating, e.g., predicting, a subject's risk of developing CAR T cell associated immune effector cell-associated neurotoxicity syndrome (ICANS), e.g., severe ICANS, comprising: acquiring a ICANS risk status for the subject, e.g., in response to a CAR-expressing cell therapy (e.g., a CAR19- expressing cell therapy), wherein said ICANS risk status comprises a measure of one, two, three, four, five, six, seven, eight (all) of the following metabolites in a patient sample: N- acetylglycine, 2-hydroxyglutarate, l-stearoyl-2-oleoyl-GPI (18:0/18:1)*, palmitoleoylcamitine (C16:l)*, gamma-glutamyl -alpha-lysine, octadecadienedioate (C18:2-DC)*, hydroxyproline, and glutamine wherein the ICANS risk status is indicative of the subject's risk for developing ICANS, e.g., severe ICANS. In particular, if the detected level of one or more of these biomarkers is greater than a reference value, the subject is more likely to develop ICANS (e.g., severe ICANS) than a subject having a detected level or activity at the reference value.

[0041] In one embodiment, therefore, the invention provides a method of evaluating, e.g., predicting, a subject's risk of developing cytokine release syndrome (CRS), e.g., severe CRS, comprising: acquiring a CRS risk status for the subject, e.g., in response to a CAR-expressing cell therapy (e.g., a CAR19-expressing cell therapy), wherein said CRS risk status comprises a measure of one, two, three, four, five, six, seven, eight, nine (all) of the following metabolites in a patient sample: o-cresol sulfate, isoursodeoxycholate, N-delta-acetylornithine, 4-allylcatechol sulfate, 2,4-di-tert-butylphenol, 1-methyladenosine, succinate, phytanate, and glucose, wherein the CRS risk status is indicative of the subject's risk for developing CRS, e.g., severe CRS. In particular, if the detected level of one or more of these biomarkers is greater than a reference value, the subject is more likely to develop CRS (e.g., severe CRS) than a subject having a detected level or activity at the reference value.

[0042] In yet another aspect, the invention features a system or method for evaluating, e.g., predicting, a subject's risk of developing ICANS and/or CRS, preferably prior to induction of CAR expressing cell therapy. The system includes at least one processor operatively connected to a memory, wherein the at least one processor when executing is configured to acquire a ICANS and/or CRS risk status, e.g., in response to an immune cell based therapy (e.g., a CAR- expressing cell therapy (e.g., a CARl 9-expressing cell therapy)) for the subject, wherein said ICANS and/or CRS risk status comprises the above. The method includes: acquiring a CRS risk status for the subject, e.g., in response to an immune cell-based therapy (e.g., a CAR-expressing cell therapy (e.g., a CAR 19-expressing cell therapy)), wherein said CRS risk status is determined by: (i) acquiring a first biomarker level or activity;

(ii) determining whether the first biomarker level or activity is above or below a first reference level or activity,

(iii) optionally acquiring a second biomarker level or activity;

(iv) optionally determining whether the second biomarker level or activity is above or below a second reference level or activity,

(v) optionally acquiring a third biomarker level or activity; and

(vi) optionally determining whether the third biomarker level or activity is above or below a third reference level or activity, thereby evaluating, e.g., predicting, the subject's risk of developing ICANS and/or CRS; wherein the biomarker is selected from the group consisting of o-cresol sulfate, isoursodeoxycholate, N-delta-acetylomithine, 4-allylcatechol sulfate, 2,4-di-tert- butylphenol, 1-methyladenosine, succinate, phytanate, N-acetyl glycine, 2-hydroxyglutarate, 1- stearoyl-2-oleoyl-GPI (18:0/18:1)*, palmitoleoyl carnitine (C16:l)*, gamma-glutamyl -alpha- lysine, octadecadienedioate (C18:2-DC)*, hydroxyproline, glutamine or glucose.

[0043] In some aspects, the present disclosure provides a method of treating one or more of a neurological toxicity, CRS, or CAR T cell-related immune effector cell-associated neurotoxicity syndrome (ICANS), comprising administering to a subject in need thereof a therapeutically effective amount of cyclophosphamide. In related aspects, the present disclosure provides cyclophosphamide for use in treating neurological toxicity, CRS, or ICANS. In embodiments, the administration of cyclophosphamide is subsequent to a cell-based therapy, e.g., a cell-based therapy for cancer, a CD19-inhibiting therapy, or a CD19-depleting therapy, or the subject has been previously treated with a cell-based therapy, e.g., a cell-based therapy for cancer, a CD19-inhibiting therapy, or a CD19-depleting therapy. In embodiments, the administration of cyclophosphamide is prior to, at the same time as, or after the cell-based therapy.

[0044] The amount of the respective biomarker(s) in the subject can be determined according to any technique known to those of skill in the art without limitation. For instance, in particular embodiments, methods of the invention can comprise the single step of comparing total biomarker amount in a subject to a reference total biomarker amount in order to assess risk for the systemic inflammatory condition without regard to how either amount is measured. In further embodiments, total biomarker of the subject is evaluated by a technique described herein followed by comparing to a reference total biomarker in order to assess risk for the systemic inflammatory condition. In certain embodiments, total biomarker is evaluated by spectrometry, chromatography, immunoassay, electrophoresis or enzymatic assay as described in detail below.

[0045] Further exemplary biomarkers for the prognosis or diagnosis of a systemic inflammatory condition, and methods of their evaluation, are described in U.S. Patent Application Publication Nos. 20030194752, 20040096917, 20040097460, 20040106142, 20040157242, and U.S. Provisional Application Nos. 60/671,620, filed Apr. 15, 2005, 60/671,941, filed Apr. 15, 2005, and 60/674,046, filed Apr. 22, 2005, the contents of which are hereby incorporated by reference in their entireties. Further exemplary biomarkers for sepsis include endotoxin; bacterial DNA; acute phase proteins such as protein C, procalcitonin, LBP- LPS-binding protein; coagulation factors such as fibrin degrading products, antithrombin III, dimer D; membrane cell markers such as HLA-DR, CD-64, E-selectin; hormones such as cortisol, ACTH; soluble receptors such as CD-14, sTNFRI, sTNF-RII; and cytokines such as TNF, IL-6, IL-8 and IL-10; and others such as D-dimer, prothrombin time, activated partial thromboplastin time, plasminogen activator inhibitor-1, soluble thrombomodulin, IL-6, IL-10, IL-8, protein C, thrombin activatable fibrinolysis inhibitor, protein S, anti thrombin, TNF-a. See, e.g., Kinasewitz et ak, 2004, Critical Care 8:R82-R90, Bozza et ah, 2005, Mem. Inst. Oswaldo Cruz 100(s) 1:217-221, the contents of which are hereby incorporated by reference in their entireties. Preferred biomarkers include C reactive protein, procalcitonin and IL-6.

EXAMPLES

Methods

Patients

[0046] The collection of specimens was in accordance with guidelines of the Internal Review Board of the Cleveland Clinic, and all patients provided written informed consent before participating in the studies. Specimens were collected from 41 r/r DLBCL patients treated with axi-cel or tisa-cel at six different time points - Day of Apheresis (DA), day of but 8 h prior to infusion of axi-cel or tisa-cel (DO). Blood was drawn in CPT tubes containing EDTA as an anti coagulation agent. Plasma was isolated from blood by centrifugation at 1500xg for 30 minutes. Aliquots of plasma were frozen on dry ice and stored at -80°C until further analysis. The baseline clinical characteristics of patients are shown in Table 1. The on-treatment and post treatment clinical characteristics are shown in Table 2.

Extraction of metabolites from the plasma samples

[0047] Recovery standards in methanol were added to the plasma samples under vigorous shaking conditions for 2 min (Glen Mills GenoGrinder 2000) followed by centrifugation. The resulting extract was divided into five fractions: two for analysis by two separate reverse phase (RP)/UPLC -MS/MS methods with positive ion mode electrospray ionization (ESI), one for analysis by RP/UPLC-MS/MS with negative ion mode ESI, one for analysis by HILIC/UPLC- MS/MS with negative ion mode ESI, and one sample was reserved for backup. Samples were placed briefly on a TurboVap® (Zymark) to remove the organic solvent. The sample extracts were stored overnight under nitrogen before preparation for analysis.

Metabolomics data acquisition

[0048] A pooled matrix sample generated by taking a small volume of each experimental sample; extracted water samples served as process blanks, and a cocktail of QC standards were spiked into every analyzed sample. The sample extract was dried, followed by reconstitution in solvents compatible to each of the four methods. Each reconstitution solvent contained a series of standards at fixed concentrations to ensure injection and chromatographic consistency. One aliquot was analyzed using acidic positive ion conditions, chromatographically optimized for more hydrophilic compounds. In this method, the extract was gradient eluted from a C18 column (Waters UPLC BEH C18-2.1xl00 mm, 1.7 pm) using water and methanol, containing 0.05% perfluoropentanoic acid (PFPA) and 0.1% formic acid (FA). Another aliquot was also analyzed using acidic positive ion conditions; however, it was chromatographically optimized for more hydrophobic compounds. In this method, the extract was gradient eluted from the same aforementioned C18 column using methanol, acetonitrile, water, 0.05% PFPA and 0.01% FA and was operated at an overall higher organic content. Another aliquot was analyzed using basic negative ion optimized conditions using a separate dedicated C18 column. The basic extracts were gradient eluted from the column using methanol and water, however with 6.5 mM Ammonium Bicarbonate at pH 8. The fourth aliquot was analyzed via negative ionization following elution from a HILIC column (Waters UPLC BEH Amide 2.1x150 mm, 1.7 pm) using a gradient consisting of water and acetonitrile with 10 mM Ammonium Formate, pH 10.8. The MS analysis alternated between MS and data-dependent MSn scans using dynamic exclusion. The scan range varied slightly between methods but covered 70-1000 m/z.

Data Analysis

[0049] The relative abundance for each metabolite across the cohort was normalized for batch effects by dividing each value by the median of the samples in each instrument batch. The normalized relative abundances were then log transformed to meet parametric assumptions. Metabolites that were missing in >20% of patient samples were excluded from analysis (n=203 metabolites) and metabolites with < 20% missingness were imputed with the minimum observed value across all samples. Principal component analysis identified no outlier samples among the cohort. Fourteen individual metabolite measurements with median absolute deviation (MAD) > seven were removed as outliers. A total of 808 metabolites were retained for analysis after processing and quality control.

[0050] A Fisher’s exact test was used to determine if the occurrence of CRS was significantly associated with the occurrence of ICANS. Kendall rank correlation was performed on the maximum grade of CRS and ICANS to determine if the severity of CRS was correlated with the severity of ICANS. Pearson’s correlation coefficient was used to examine correlation between the number of days to onset of CRS and ICANS. Results with P< 05 were considered to be statistically significant.

[0051] Using leave-one-out cross-validation, Random ForestTM was initially run using all 808 metabolites. Metabolites were ranked by their Gini score, and the metabolite with the lowest Gini score was removed. This process was repeated until the only a single metabolite remained. Of 808 models, the model including the following eight metabolites had the lowest misclassification rate on the training set: o-cresol sulfate, isoursodeoxycholate, N-delta- acetyl ornithine, 4-allylcatechol sulfate, 2,4-di-tert-butylphenol, 1-methyladenosine, succinate, and phytanate predicted the onset of CRS with 90% balanced accuracy with sensitivity and specificity of 90%. An additional model utilizing glucose, which was significantly associated with CRS [HR: 5.04; 95% Cl: 1.02-24.9; q=.08], was included with the eight metabolites above to create a model with nine metabolites, which predicted the onset of CRS with 87% balanced accuracy, and sensitivity and specificity of 84% and 90%, respectively. Results

Metabolites which appeared after CAR T cell therapy

[0052] There were approximately 13,771 ions detected in positive mode and 4,911 ions detected in negative mode. Among these, there were only 14 ions which were having a putative ID and statistically significant appeared after CAR T cell therapy in all 30 patients. These ions were not detected on the Day of Apheresis (DA) and Day 0, suggesting that they may have a role in CAR T expansion, activation or adverse effects of therapy.

Baseline Clinical Characteristics Associated with Neurotoxicity of CAR T cell patients

[0053] In order to identify clinical and biological factors that are associated with severe neurotoxicity, we examined age, gender, weight, body mass index, number of prior therapies, CAR T-cell doses, prior hematopoietic stem cell transplant (HSCT) status, conditioning chemotherapy regimen, and infused CAR T-cell product characteristics (Table 1). After CAR T therapy, the grade of CRS and ICANS, as well as response rate of the patient were recorded. Other clinical parameters such as serum lactate dehydrogenase (LDH) levels, ferritin and CRP levels were also captured at DA, for use in association studies with metabolite abundance and CRS or ICANS.

[0054] Association of metabolite abundance at Day of Apheresis with the appearance of ICANS/CRS event.

[0055] We performed machine learning analyses to determine which metabolite abundances in the 41 patients on DA were associated with ICANS occurrence using Random Forest. The model was iteratively trained on 40 patients and then validated on the remaining one patient. We found that there were eight metabolites whose abundance was predictive of ICANS . Based on the optimal model, as determined by the minimal misclassification rate, nine metabolites, identified as N-acetylglycine, 2-hydroxyglutarate, l-stearoyl-2-oleoyl-GPI (18:0/18:1)*, palmitoleoylcamitine (06:1)*, gamma-glutamyl-alpha-lysine, octadecadienedioate (08:2- DC)*, hydroxyproline, and glutamine predicted the onset of ICANS with 88% balanced accuracy with sensitivity and specificity of 95% and 86% based on the ROC curve, which identified 0.44 as the optimal threshold. [0056] Next, we performed machine learning to determine which metabolite abundances in the 41 patients on DA were associated with CRS occurrence using Random Forest, using the same approach described above. Predictive modeling, as described above, optimized with eight metabolites, identified as o-cresol sulfate, isoursodeoxycholate, N-delta-acetylomithine, 4- allylcatechol sulfate, 2,4-di-tert-butylphenol, 1-methyladenosine, succinate, and phytanate predicted the onset of CRS with 90% balanced accuracy, and sensitivity and specificity of 90% and 90%, respectively. Analysis of the ROC curve identified 0.67 as the optimal threshold for CRS. In an additional analysis, we developed the model described above with the addition of glucose for nine metabolites, identified as o-cresol sulfate, isoursodeoxycholate, N-delta- acetylomithine, 4-allylcatechol sulfate, 2,4-di-tert-butylphenol, 1-methyladenosine, succinate, phytanate, and glucose, which predicted the onset of CRS with 87% balanced accuracy, and sensitivity and specificity of 84% and 90%, respectively. Analysis of the ROC curve identified 0.63 as the optimal threshold.

Table 1: Baseline Clinical Characteristics of Patients Treated with Axi-Cel or Tisa-Cel

Table 2: Post-treatment Clinical Characteristics of Patients Treated with Axi-Cel or Tisa- Cel

aCRS: Cytokine release syndrome; ^bICANS: Immune effector cell-associated neurotoxicity syndrome; ^CCR: Complete response, PR: Partial response

Table 3. Metabolite performance for predicting CRS based on leave-one-out cross- validation.

^a All results are based on a model threshold of 0.5; ^b positive predictive value; ^c negative predictive value; ^darea under the receiver operating characteristic curve.

Table 4. Metabolite performance for predicting ICANS based on leave-one-out cross- validation.

^a All results are based on a model threshold of 0.5, positive predictive value; negative predictive value; ^darea under the receiver operating characteristic curve. Example 2

[050] Metabolites were identified in plasma samples collected from a cohort of patients (as shown in Table 1, above) prior to initiation of treatment with FDA-approved CD19 CAR T-cells Axicabtagene Ciloleucel or Tisagenlecleucel, and association of metabolite abundance with risk, severity, and onset of toxicities analysed using post-treatment clinical data. Both untargeted metabolomics analysis and validation of identified metabolites using clinical assays revealed that high abundance of glucose was associated with increased risk of development and time-to-onset of CRS, whereas cholesterol showed a negative correlation with these outcomes. On the other hand, low levels of amino acids hydroxyproline and glutamine were associated with increased risk of developing ICANS. Our results demonstrate that pre-existing host metabolites are associated with the development of toxicities in response to anti-CD 19 CAR T-cell treatment, justifying measurement of plasma metabolite concentrations as a novel means to monitoring the risk of toxicities prior to treatment initiation. Further, these results lay the foundation for future development of metabolite-based targeted clinical interventions to mitigate the risk of toxicities associated with anti-CD 19 CAR T-cell therapy.

[051] Metabolite relative abundances were compared to commercially available standards, evaluated using a known volume of standard and a known volume of media. The comparison of metabolite abundance in the patient sample to the commercially available standard provides the concentration of metabolite in the patient’s biospecimen. Upon determination of the metabolite concentrations in a patient’s biospecimen, these values are incorporated into the random forest model in order to determine the patient’s risk of developing CRS or ICANS. The quantification of metabolite concentrations uses methods known in the art (see e.g., Lei Z, Huhman DV, Sumner LW. Mass spectrometry strategies in metabolomics. Journal of Biological Chemistry. 2011 Jul 22;286(29):25435-42) and is analogous to current clinical practice for basic and comprehensive metabolic panels (see e.g., Complete metabolic panel (MedlinePlus) available on the world wide web at medlineplus.gov/lab-tests/comprehensive-metabolic-panel-cmp/ and Complete metabolic panel (Lab Tests Online) available on the world wide web at labtestsonline.org/tests/comprehensive-metabolic-panel-cmp).

[052] Results are shown in Figures 28-34. We found that pre-existing biochemical signatures present in the plasma at the time of apheresis are strongly associated with toxicities observed in response to commercial CD19 CAR T-cell therapies. Accordingly, these endogenous metabolites may serve as biomarkers for monitoring risk of toxicity associated with CD 19 CAR T-cell treatment and provide insight into rational clinical interventions to mitigate such risks.

[053] All patents and patent publications referred to herein are hereby incorporated by reference in their entirety.

[054] Accordingly, the preceding merely illustrates the principles of the invention. It will be appreciated that those skilled in the art will be able to devise various arrangements which, although not explicitly described or shown herein, embody the principles of the invention and are included within its spirit and scope. Furthermore, all examples and conditional language recited herein are principally intended to aid the reader in understanding the principles of the invention and the concepts contributed by the inventors to furthering the art, and are to be construed as being without limitation to such specifically recited examples and conditions. Moreover, all statements herein reciting principles and aspects of the invention as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents and equivalents developed in the future, i.e., any elements developed that perform the same function, regardless of structure. The scope of the present invention, therefore, is not intended to be limited to the exemplary aspects shown and described herein. Rather, the scope and spirit of present invention is embodied by the appended claims.

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Claims

Claims:

1. A method of ameliorating the development of toxicity resulting from CAR-T therapy, preferably CD 19 CAR-T therapy, in a subject, the method comprising:

(a) detecting one or more biomarkers selected from the group comprising or consisting of o- cresol sulfate, isoursodeoxycholate, N-delta-acetyl ornithine, 4-allyl catechol sulfate, 2,4-di-tert- butylphenol, 1-methyladenosine, succinate, phytanate, N-acetyl glycine, 2-hydroxyglutarate, 1- stearoyl-2-oleoyl-GPI (18:0/18:1)*, palmitoleoyl carnitine (C16:l)*, gamma-glutamyl-alpha- lysine, octadecadienedioate (C18:2-DC)*, hydroxyproline, glutamine and glucose in a biological sample from the subject, the detecting comprising detecting directly in the biological sample or indirectly in a test sample obtained from said biological sample;

(b) comparing the level, concentration or amount of the one or more biomarkers to a reference value for said biomarker(s), wherein the comparison indicates whether the subject is or is not at risk for developing toxicity, which optionally is neurotoxicity, ICANS or CRS, and/or indicates a degree of risk for developing said toxicity; and

(c) if the comparison indicates that the subject is at risk for developing the toxicity, and/or indicates that the risk is above a threshold level, administering to the subject an agent or therapy that is capable of treating, preventing, delaying, or attenuating the development of the toxicity.

2. The method of claim 1, wherein said toxicity is ICANS and said one or more biomarkers are selected from the group consisting of N-acetyl glycine, 2-hydroxyglutarate, 1- stearoyl-2-oleoyl-GPI (18:0/18:1)*, palmitoleoylcamitine (C16:l)*, gamma-glutamyl -alpha- lysine, octadecadienedioate (C18:2-DC)*, hydroxyproline, and glutamine.

3. The method of claim 1, wherein said toxicity is CRS and said one or more biomarkers are selected from the group consisting of o-cresol sulfate, isoursodeoxycholate, N-delta- acetyl ornithine, 4-allylcatechol sulfate, 2,4-di-tert-butylphenol, 1-methyladenosine, succinate, phytanate, and glucose.

4. A method of treating a patient in need thereof with a CAR-T cell therapy, preferably a CD 19 CAR-T cell therapy, the method comprising: (a) detecting one or more biomarkers selected from the group comprising or consisting of o- cresol sulfate, isoursodeoxycholate, N-delta-acetyl ornithine, 4-allyl catechol sulfate, 2,4-di-tert- butylphenol, 1-methyladenosine, succinate, phytanate, N-acetyl glycine, 2-hydroxyglutarate, 1- stearoyl-2-oleoyl-GPI (18:0/18:1)*, palmitoleoyl carnitine (C16:l)*, gamma-glutamyl-alpha- lysine, octadecadienedioate (C18:2-DC)*, hydroxyproline, glutamine and glucose in a biological sample from the subject, the detecting comprising detecting directly in the biological sample or indirectly in a test sample obtained from said biological sample,

(c) if the comparison indicates that the subject is not at risk for developing the toxicity, and/or indicates that the risk is below a threshold level, administering the CAR-T cell therapy to the subject.