ASSESSMENT OF SYSTEMIC IMMUNITY USING [F 18]F-Ara-G IN POSITRON EMISSION TOMOGRAPHY

Abstract

Embodiments of the invention disclosed herein provide methods for using certain18F-based PET tracers in selected imaging techniques including studies of T cell localization and function that is associated with systemic immunity. Embodiments of the invention include methods that use multiple PET tracers selected for their ability to observe certain regions in the subject in which the tracers accumulate and then determining a ratio of the signal intensity observed in the region with the first tracer with the signal intensity observed in the region in second tracer as a way to characterize systemic immunity. The methods disclosed herein that allow the quantification of T cell function in relation to tumor burden are useful in patient selection and therapy guidance, both of which are expected to dramatically increase success of immunotherapies and improve patients' clinical outcome.

Description

[0001] ASSESSMENT OF SYSTEMIC IMMUNITY USING [F18]F-Ara-G IN POSITRON EMISSION TOMOGRAPHY

[0002] CROSS-REFERENCE TO REEATED APPLICATIONS

[0003] This application claims the benefit under 35 U.S.C. Section 119(e) of copending and commonly-assigned U.S. Provisional Patent Application Serial No. 63 / 592,728, filed on October 24, 2023, and entitled "ASSESSMENT OF SYSTEMIC IMMUNITY USING [F18]F-Ara-G IN POSITRON EMISSION TOMOGRAPHY” which application is incorporated by reference herein.

[0004] TECHNICAL FIELD

[0005] The technology generally relates to non-invasive imaging methods for diagnosis, prognosis, and treatment of disease.

[0006] BACKGROUND OF THE INVENTION

[0007] By the time they are diagnosed, most cancers have already developed mechanisms by which they evade control by the immune system. Immunotherapy, a rapidly advancing field, aims to overcome the immunosuppressive environment in the tumors by utilizing patients’ innate immune defenses. Unfortunately, immunotherapy works only in a relatively small fraction of patients with solid tumors. Although the reasons for immunotherapy failure are not entirely clear, it is believed that the immune activity within tumors plays a crucial role. Numerous studies have shown an association between tumor infiltrating T cells and clinical prognosis in many solid cancers. Pathologic examination of tumor biopsies revealed three basic cancer- immune phenotypes: immune inflamed, immune excluded and immune desert tumors. Not surprisingly, inflamed tumors, characterized by high numbers of immune cell infiltrates in the tumor and its margin show the best response to immunotherapy. However, even within the inflamed phenotype there is a wide variation in response to therapy, indicating the existence of other factors, that can affect immunotherapy outcome. Recent findings, such as the immune modulation by the microbiome highlight the critical significance of processes on the periphery and clearly call for a better understanding of the global immune context.

[0008] Positron emission tomography (PET) is a nuclear medicine functional imaging technique that is used to observe physiological processes in a body. In PET studies, a molecule carrying a radioactive tracer is first introduced into a patient's body. The imaging system then detects gamma rays (also referred to as PET data) emitted by the tracer and constructs images of the tracer concentration within the body by analyzing the detected signals. In this way, PET can aid the evaluation of the physiology (functionality) of the target organ, tissue or cell, as well as its biochemical properties. Moreover, changes in these properties of the target organ, tissue or cell can provide information on a disease process before other phenomena relating to the disease become detectable by other diagnostic tests, such as computed tomography (CT) or magnetic resonance imaging (MR1).

[0009] There is a need in the art for PET methodologies useful to assess the systemic immunity of individuals, for example in patients being evaluated for a course of therapy and / or treated with a selected therapeutic regimen.

[0010] SUMMARY

[0011] In studies on the utility' of [18F]F-AraG as a PET tracer in tracking activated T cells, we have discovered unique properties of [18F]F-AraG that make it an ideally- suited agent for tracking immune processes on a whole-body level. Embodiments of the invention build upon these discoveries using methods designed to observe aspects of systemic immunity in vivo, including aspects that have been discovered to be useful to assess a likelihood of a patient’s response to a therapeutic regimen; as well as aspects that correlate with a patient’s response to a therapeutic regimen.

[0012] [18F]-F-arabinofuranosyl guanine, was initially developed as a PET imaging agent for activated T cells. This compound is an18F-labeled analog of arabinofuranosyl guanine (AraG), one that can be phosphorylated, and trapped intracellularly, by two kinases: cytoplasmic deoxycytidine kinase (dCK) as well as deoxy guanosine kinase (dGK), a key enzyme that makes [18F]F-AraG useful in clinical applications. As discussed below, we have discovered that, at tracer levels, [18F]F-AraG, both alone, and in combination with other PET tracer compounds such as18F-fluorodeoxyglucose (FDG) can be used in a number of new PET methodologies that are designed to observe and quantify PET tracer signal accumulation in a number of selected organs or regions in vivo. Such observations of tracer accumulation in particular in situ locations can then be used in methods for observing aspects of systemic immunity in vivo.

[0013] The invention disclosed herein has a number of embodiments. Embodiments of the invention include, for example methods of observing T cell localization associated with systemic immunity in a subject via positron emission tomography (PET). Such methods typically comprise administering [F18]F-Ara-G to the subject and a performing a first [F18]F-Ara-G PET scan so as to observe [F18]F-Ara-G accumulation in at least 3 regions of [F18]F-Ara-G accumulation; and then quantifying levels of [F18]F-Ara-G observed in the at least 3 regions; such that T cell localization associated with systemic immunity in the subject is observed. In illustrative embodiments of the invention, the at least 3 regions in which tracer accumulation that are observed includes at least 2 organs selected from lymph nodes, bone marrow, heart, spleen, liver, thyroid, brain, choroid plexus, salivary glands, nose and mouth.

[0014] In certain methods for observing T cell localization associated with systemic immunity, the subject is selected to be a patient diagnosed with a malignancy, an infectious disease or an immune disorder. In some embodiments of the invention, the methods are selected to observe [F18]F-Ara-G accumulation in antigen experienced T cells. In one illustrative working embodiment of the invention that is disclosed herein, the subject is selected to be a patient infected with C0VID19. or having a post-acute COVID 19 condition; and the method comprises observing virus-affected regions in the patient. In other embodiments of the invention, the subject is selected to be a patient diagnosed with a malignancy and the method observes inflammation in relation to the tumor burden in the patient; and / or the subject is selected to be a patient receiving chimeric antigen receptor (CAR) T-cell therapy. In some embodiments of the invention, the method further comprises selecting a therapeutic regimen for the patient using information obtained from the first [F18]F-Ara-G PET scan.

[0015] Certain embodiments of the invention include methods designed to observe systemic immunity in a patient at different points of time (e.g., where a period of time between the first scan and the second scan is at least 1, 4, 7, 10 or 14 days). Such embodiments include administering [F18]F-Ara-G to the subject and performing a first [F18]F-Ara-G PET scan as discussed above, and subsequently performing a second [F18]F-Ara-G PET scan to observe [F18]F-Ara-G accumulation in the at least 3 regions of [F18]F-Ara-G accumulation; quantifying levels of [F18]F-Ara-G observed in the at least 3 regions of [F18]F-Ara-G accumulation in the second [F18]F-Ara-G PET scan; and then comparing levels of [F18]F-Ara-G observed in the first [F18]F-Ara-G PET scan with levels of [F18]F-Ara-G observed in the second [F18]F-Ara-G PET scan. Typically, in these methods, levels of accumulated [F18]F-Ara-G observed in the first [F18]F-Ara-G PET scan are compared with levels of accumulated [F18]F-Ara-G observed in the second [F18]F-Ara-G PET scan so as to obtain information on changes in systemic immunity that occur in response to the therapeutic regimen. Typically in these methods, the subject is treated with a therapeutic regimen between the first [F18]F-Ara-G PET scan and the second [F18]F-Ara-G PET scan; and levels of [F18]F- Ara-G observed in the first [F18]F-Ara-G PET scan are compared with levels of [F18]F-Ara-G observed in the second [F18]F-Ara-G PET scan so as to obtain information on efficacy of treatment and / or off target adverse T cell localization that occurs following the therapeutic regimen. Related embodiments of the invention include methods of using a [F18]F-Ara-G PET scan to assess systemic immunity7following a therapeutic regimen in the absence of a [F18]F-Ara-G PET scan prior to the therapeutic regimen.

[0016] In embodiments of the invention that include methods designed to observ e systemic immunity7in a patient at different points of time, artisans can quantify levels of [F18]F-Ara-G observed in the at least 3 regions of [F18]F-Ara-G accumulation by identifying at least one region in the subject to which [F18]F-Ara-G accumulates in the first PET scan. Typically this is accomplished by determining at least one standardized [F18]F-Ara-G uptake value (SUV) observed in the first PET scan for the region selected from SUVmax. SUVmean and SUVtotai; determining at least one standardized [F18]F-Ara-G uptake value observed in the second PET scan for the region selected from SUVmax, SUVmean and SUVtotai; and then comparing the standardized [F18]F-Ara-G uptake value observed in the first PET scan with the standardized [F18]F-Ara-G uptake value observed in the second PET scan. Typically, these methods further include identifying a region having greater than a 10%, 20% or 30% increase in [F18]F-Ara-G uptake as exhibiting immune activation / increased mitochondrial metabolism; and / or identifying a region having greater than a 10%, 20% or 30% decrease in [F18]F-Ara-G uptake as exhibiting immune deactivation / mitochondrial dysfunction. In some of these methods, the subject is selected to be a patient diagnosed with a pathological condition; and the method further comprises selecting a therapeutic regimen for the patient using the information obtained from the signal intensities.

[0017] Embodiments of the invention include methods that use multiple PET tracer compounds. For example, as discussed below, embodiments of the invention include administering [F18]F-Ara-G to the subject and a performing a first [F18]F-Ara-G PET scan; and also administering18F-fluorodeoxyglucose (FDG) to the subject and a performing a FDG PET scan to observe FDG accumulation in the subject (e.g. in at least 3 selected regions); and then comparing levels of [F18]F-Ara-G observed in the first [F18]F-Ara-G PET scan with levels FDG observed in the FDG PET scan. For example, embodiments of the invention include methods of observing a region in the subject to which [F18]F-Ara-G accumulates in the [F18]F-Ara-G PET scan; observing the region in the subject in which FDG accumulates in the FDG PET scan; comparing the signal observed in the region in the [F18]F-Ara-G PET scan with the signal observed in the region in the FDG PET scan; and then determining a ratio of the signal intensity observed in the region in the [F18]F-Ara-G PET scan with the signal intensity observed in the region in the FDG PET scan. In some embodiments of the invention, the subject is a patient diagnosed with a malignancy and the ratio of the signal intensity observed provides information on T cell function in relation to tumor burden. In certain embodiments, regions with a ratio of signal intensity of at least 0.3 (0.25-0.4) show that the observed T cells are antigen experienced, druggable T cells.

[0018] Other objects features and advantages of the present invention will become apparent to those skilled in the art from the following detailed description. It is to be understood, however, that the detailed description and specific examples, while indicating some embodiments of the present invention are given by way of illustration and not limitation. Many changes and modifications within the scope of the present invention may be made without departing from the spirit thereof, and the invention includes all such modifications.

[0019] BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Further aspects of the present disclosure will be more readily appreciated upon review of the detailed description of its various embodiments, described below , when taken in conjunction with the accompanying drawings.

[0021] Figures 1(A)-1(D) provide schematics and data on the proposed mechanism of [18F]F-AraG uptake. [18F]F-AraG is transported into cells via nucleoside transporters, followed by the rate-limiting phosphorylation by mitochondrial deoxyguanosine kinase (dGK). As shown in Figure 1(A), once triphosphorylated, )18F]F-AraG can be incorporated into mtDNA or be exported from mitochondria where SAMHD1, can dephosphorylate triphosphate providing an opportunity for the unphosphorylated [18F]F-AraG to be exported from the cell. Overall, optimal trapping of [18F]F-AraG may be achieved in cells with high mitochondrial biogenesis and low expression of SAMHD1 Figure 1(B). Accumulation of [18F]F-AraG in CEM, unstimulated and antigen stimulated T cells. Antigen- stimulated T cells show' 6-fold higher uptake than the unstimulated T cells. Figure 1(C). Antigen-stimulated CD8 cells show higher levels of PD-1 and CD69 and lower levels of SAMHD-1 than unstimulated cells [18F]F-AraG shows dynamic range of accumulation in T cells. Figure 1(D). Within tumor microenvironment [18F]F-AraG accumulates in fully functional antigen experienced effector cells (round dark green cells, highest accumulation), early dysfunctional antigen experienced cells that can be reprogrammed (round light green cells, medium accumulation) but is not taken up in terminally dysfunctional T cells resistant to immunomodulating therapies (distorted green cells). Glowing cell indicate the ones that accumulate [18F]F-AraG.

[0022] Figures 2(A)-2(B) provide the results of patient PET scans, with Figure 2(A) showing Al-assisted assessment of the change in [18F]F-AraG signal post immunotherapy showing heterogeneous response to therapy (green indicates a responding lesion, red a non-responding and grey correspond to stable lesions). Figure 2(B). A PET scan showing that the signal is also evaluated in the bone marrow, spleen, liver, thyroid and heart. In this patient no increase in [18F]F-AraG these organs was observed. This information can be used to assess systemic response to immunotherapy.

[0023] Figure 3 provides two PET scan images of a long COVID 19 infected patient illustrating how [18F]F-AraG uptake in the nose, mouth, brain and bone marrow (arrows) indicates systemic immune effects in long COVID patients.

[0024] Figure 4 provides PET scan image (left panel) of a representative example of a FDG / [18F]F- AraG analysis that can allow assessment of inflammation to obtain a “T score"’ (right image). The colors in the composite image show the following: red are hotspots with only [18F]F-AraG signal, orange is for the ratio of [18F]F-AraG / FDG > 0.6, green for 0.3<[18F]F-AraG / FDG<0.6, turquoise for [18F]F-AraG / FDG <0.3 and blue are hotspots with only FDG signal.

[0025] Figure 5 provides side by side PET scan images (left patient image shows progressive disease while the right patient image shows a partial response) designed to observe the inflammation status of two subjects examined in embodiments of the invention where multiple hotspots (areas of interest) showing accumulation of two tracers ([18F]F-AraG and18F-fluorodeoxyglucose) are compared such that ratios of these tracer signal that are associated with indicia of systemic immunity are determined. In the right most image and the patient images, cold lesions are characterized as having a low [18F]F-AraG to FDG (inflammation to tumor burden) ratio (i.e. , lower than 0.1, 0.2 or 0.3) Hot lesions are characterized as having a high [18F]F-AraG to FDG (inflammation to tumor burden) ratio (i.e., greater than 0.3). The patient on the left exhibits primarily cold (not inflamed) lesions (colored light and dark blue) and in a follow-up consequently shows progressive disease. The patient on the right exhibits primarily hot (inflamed) lesions and in a follow-up consequently shows partial response to therapy. This indicates a potential of [18F]F-AraG / FDG ratio to assess the likelihood of response to therapy based on the inflammatory status of lesions on a whole-body level. The FDG / [18F]F-AraG ratiometric analysis that allows quantification of T cell function in relation to tumor burden can find great use in patient selection and therapy guidance, both of which are expected to dramatically increase success of immunotherapies and improve patients’ clinical outcome.

[0026] Figure 6 provides data from tumor profiling studies on CD8 T cell functional status. Figure 6A: images showing that colon tumor, MC38 and breast tumor, 4T1 show different tracer signal characteristics: in MC38 tumors a high [18F]F-AraG signal is observed in the tumor core and in the tumor draining lymph node, while 4T1 tumors show no signal in the tumor and a weak signal in the tumor draining lymph node. Figure 6B: Graphed data showing these tumors had very different growth, expected from the differences in signal: 4T1 tumors showing low [18F]F-AraG signal were much bigger and grew faster than the inflamed MC38 tumors with a high signal in the tumor and tumor draining lymph node. Figure 6C: Graphed data showing that an analysis of the excised tumors revealed that the bigger 4T1 tumors that w ere void of [18F]F-AraG signal were actually abundant in CD8 cells. Figure 6D: Graphed data showing that an analysis of these tumor infiltrating lymphocytes cells indicated that, unlike the CD8s in MC38, T cells in 4T1 tumor were largely antigen unexperienced, as seen from their PD-1 status. Figure 6E. Graphed data showing that overall, across multiple tumor models we found that [18F]F-AraG does not correlate with the total number of T lymphocytes but with the CD8 cells expressing PD-1 and thus a target of checkpoint inhibitor, anti-PD-1 therapy. This information is highly relevant for the clinic, as a study in a small cohort of lung cancer patients treated with anti PD-1 found that patients with T cells expressing high levels of PD-1 with high number of mitochondria as shown in Figure 6(F) have survival advantage over the ones that do not have this particular T cell subset as show n in Figure 6(G).

[0027] Figure 7 provides side by side PET scan images (left patient image shows pretreatment scan while the right patient image shows a post treatment scan). This data provides an example on how [18F]F-AraG PET can be used to simultaneously observe a treatment response and treatment toxicity in a patient / subject (e.g., aberrant T cell activity such as accumulation in off target tissues). In this embodiment, accumulation of a single tracer ([18F]F-AraG) is observed at multiple selected sites. The image on the left shows the subject (a melanoma patient) pretreatment and exhibits a physiological uptake in the thyroid and heart (green arrows). The image on the right shows the subject on treatment (after only one infusion of immunotherapy) and exhibits a significantly higher signal in the thyroid and heart compared to the pretreatment scan. This indicates aberrant T cell accumulation at off-target sites. The ability to say early on how toxic the therapy is would be very valuable information for clinicians, potentially allowing educated decision on therapy continuation especially in patients with low or no clinical benefit.

[0028] Figure 8 provides side by side views a frontal (right patient scan) and side (left patient scan) PET scan of a patient / subject designed to observe treatment effects in a patient / subject (e.g., aberrant T cell activity such as accumulation in off target tissues as well as response to therapy in lymphoid organs such as spleen, bone marrow, etc.). In this embodiment, accumulation of a single tracer ([18F]F-AraG) is observed at multiple selected sites. The patient image on the left shows the changes in [18F]F-AraG signal posttreatment in a subject treated with immunotherapy and exhibits increase in signal in the kidneys and heart (green color) and decrease in signal in the thyroid (red color). This indicates changes in T cell / mitochondrial biogenesis activity in different organs. The patient image on the right shows the same subject and scan only from a side view for better visualization of kidneys. The bars at the right identify various organs in the patient images. Assessing changes in off target organs can provide useful information about the response to therapy and / or aberrant T cell / mitochondrial biogenesis activity.

[0029] Figure 9 provides cartoon schematics and graphical data showing [18F]F- AraG compound tracking cell function through mitochondrial activity. The bar graphs of [18F]F-AraG uptake on the right of the figure show how this uptake is believed to correlate with T cell function in resting, effector and exhausted T cells (e.g., in functional effector T cells versus resting and exhausted T cells).

[0030] Figure 10 provides cartoon schematics showing how [18F]F-AraG can be used in PET imaging of T cells, and activated T cells in particular, as well as conditions in which such scans can provide information (left panel with various cell types). The upper right panel shows different T cell functional states (e.g., functional antigen experienced effector T cells versus dysfunctional T cells), while the lower right panel provides a cartoon of cells in such scans.

[0031] Figure 11 shows data from tumor burden and overall survival. A. Diagnostic [18F]FDG scan was used to assess extent of the disease. Tumor burden differed greatly between patients. B. Kaplan Meier analysis did not show statistically significant difference in the probability of survival between patients with different TLG (cut-off 1654.3). C. Kaplan Meier analysis did not show statistically significant difference in the probability of survival between patients with different tMTV (cut-off 362.6 cm3).

[0032] Figure 12. shows data on inter- and intra-lesional differences between [18F]FDG and [18F]F-AraG. A. Representative [18F]FDG and [18F]F-AraG patient images. [18F]F-AraG uptake differed between [18F]FDG avid lesions. [18F]F-AraG accumulated in some (red arrows) but not all (white arrows) lesions. B. Representative [18F]FDG and [18F]F-AraG transaxial slices showing a lung lesion (red arrow) in three patients. Intralesional signal distribution differed between [18F]FDG and [18F]F-AraG. [18F]F-AraG signal distribution resembles distribution of immune infiltrates in common cancer-immune phenotypes: immune excluded (immune cells on the tumor margins, top row), immune desert (very' few immune cells, middle row) and inflamed (immune cells in the tumor core, bottom row).

[0033] Figure 13 shows data on [18F]F-AraG accumulation in lymphoid organs. A. Accumulation in lymphoid and non-lymphoid organs differed between patients. L= liver, S=spleen, white arrows pointing to the bone marrow' in the lumbar vertebrae and iliac bone. B. [18F]F-AraG SUV total in the lumbar bone marrow' differed significantly between patients with progressive disease and those without (p = 0.046). C. [18F]FDG SUVtotai in the lumbar bone marrow did not differ significantly between patients who had progressive disease and those that did not (p = 0.44).

[0034] Figure 14 show s data on high [18F]F-AraG uptake in the lumbar bone marrow correlates with decreased overall survival. A. Kaplan Meier analysis revealed significantly lower probability of survival in patients with high accumulation of [18F]F-AraG in the lumbar bone marrow. B. Tumor burden in two patients with different clinical outcomes. Patient 1 (TLG = 1800) had progressive disease and died less than 5 months after starting therapy. Patient 2 (TLG = 2162) had a complete response and was alive at the time of analysis, more than 12 months after the start of therapy. C. Lung lesion in the two patients showed comparable [18F]F-AraG accumulation (top row). [18F]F-AraG accumulation in the bone marrow' L4 vertebrae of the patient with shorter survival w as considerably higher than the uptake in the patient with longer survival.

[0035] DETAILED DESCRIPTION

[0036] Before the present disclosure is described in greater detail, it is to be understood that this disclosure is not limited to particular embodiments described, and the embodiment of the invention as such may. of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. All publications and patents cited in this specification are herein incorporated by reference as if each individual publication or patent were specifically and individually indicated to be incorporated by reference and are incorporated herein by reference to disclose and describe the methods and / or materials in connection with which the publications are cited. As will be apparent to those of skill in the art upon reading this disclosure, each of the individual embodiments described and illustrated herein has discrete components and features which may be readily separated from or combined with the features of any of the other several embodiments without departing from the scope or spirit of the present disclosure. Any recited method can be carried out in the order of events recited or in any other order that is logically possible. Unless otherwise indicated, the present disclosure is not limited to particular materials, reagents, reaction materials, manufacturing processes, or the like, as such can vary. It is also to be understood that the terminology used herein is for purposes of describing particular embodiments only and is not intended to be limiting. It is also possible in the present disclosure that steps can be executed in different sequences where this is logically possible.

[0037] It must be noted that, as used in the specification and the appended claims, the singular forms “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to “a compound” includes a plurality of compounds. In this specification and in the claims that follow, reference will be made to a number of terms that shall be defined to have the following meanings unless a contrary intention is apparent.

[0038] Each of the applications and patents cited in this text, as well as each document or reference cited in each of the applications and patents (including during the prosecution of each issued patent; “application cited documents”), and each of the PCT and foreign applications or patents corresponding to and / or claiming priority from any of these applications and patents, and each of the documents cited or referenced in each of the application cited documents, are hereby expressly incorporated herein by reference. Further, documents or references cited in this text (e.g., U.S. patent Publication Nos. 20150230762, 20150297760 and 20190054198, and Philip et al.. Nat Rev Immunol. 2022 Apr;22(4):209-223), in a Reference List before the claims, or in the text itself; and each of these documents or references (“herein cited references"), as well as each document or reference cited in each of the herein-cited references (including any manufacturer’s specifications, instructions, etc.) are hereby expressly incorporated herein by reference.

[0039] Herein, the term "tracer" is used to identify each individual tracer or administration of a tracer and will be used generically to refer to both tracers of different chemical form and multiple administrations of the same tracer at different times and / or under different physiological conditions (e.g., in a patient undergoing therapy). Likewise, the term "multi-tracer" refers to data containing contributions from more than one tracer as defined above (such that systemic immunity imaging constitutes multi-tracer imaging in that there are two tracer administrations— wherein a part of the PET data contains signals arising from both tracer administrations). The term PET "signal" is broadly used to describe the essence of the PET measurement under discussion. To varying degrees multi-tracer PET signal separation can be performed on the raw scanner data, partially processed data, reconstructed dynamic images, and / or time-activity curves; similarly, for each tracer, the imaging endpoint(s) may be a static image, standardized uptake value (SUV), pseudo-quantitative measure, kinetic parameter(s) and / or macro parameter(s). For a given dataset and imaging endpoint, "signal" is used to identify the element or elements of the dataset necessary for computing the desired endpoint. Likewise, "signal separation" (and "signal recovery") refer to the process of separating a multi-tracer dataset into individual tracer components, thereby recovering the necessary signal for each tracer for computing the desired endpoint.

[0040] The term “detectable” refers to the ability to detect a signal or presence of an embodiment of the present disclosure over a background signal. The term “detectable signal” or the phrases “detection of a labeled compound” or “detectable labeled compound" refers to the detection (directly or indirectly) of a labeled compound in a host or sample. The detection of a labeled compound refers to the ability to detect and distinguish the presence of a labeled compound in a host or sample from other background signals derived from the host or sample. In other words, there is a measurable and statistically significant difference (e.g., a statistically significant difference is enough of a difference to distinguish among the detectable signal and the background, such as about 0.1%, 1%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, or 40% or more difference betw een the detectable signal and the background) between detectable signal and the background. Standards and / or calibration curves can be used to determine the relative intensity of the detectable signal and / or the background (e.g. in methods designed to assess systemic immunity). The detectable signal can be generated from a small to large concentration of a labeled compound.

[0041] As used herein, ‘‘agent7’, “active agent”, or the like, can include a compound (e.g., labeled compound) of the present disclosure. The agent can be disposed in a composition or a pharmaceutical composition. As used herein, “pharmaceutical composition” refers to the combination of an active agent with a pharmaceutically acceptable carrier. As used herein, a "pharmaceutical composition" refers to a composition suitable for administration to a subject, such as a mammal, especially a human. In general a “pharmaceutical composition” is sterile, and preferably free of contaminants that are capable of eliciting an undesirable response within the subject (e.g., the compound(s) in the pharmaceutical composition is pharmaceutical grade). Pharmaceutical compositions can be designed for administration to subjects or patients in need thereof via a number of different routes of administration including oral, intravenous, buccal, rectal, parenteral, intraperitoneal, intradermal, intracheal, intramuscular, subcutaneous, inhalational and the like. For compositions suitable for administration to humans, the term "excipient" is meant to include, but is not limited to, those ingredients described in Remington: The Science and Practice of Pharmacy, Lippincott Williams & Wilkins, 21st ed. (2006) the contents of which are incorporated by reference herein. As used herein, the term “host” or “subject” includes humans, mammals (e.g., cats, dogs, horses, etc.), and other living animals. In particular, the host is a human subject. Typical hosts to which embodiments of the present disclosure may be administered will be mammals, particularly primates, especially humans. For veterinary applications, a wide variety of subjects will be suitable, e.g., livestock such as cattle, sheep, goats, cows, swine, and the like; poultry' such as chickens, ducks, geese, turkeys, and the like; and domesticated animals particularly pets such as dogs and cats. For diagnostic or research applications, a wide variety of mammals will be suitable subjects, including rodents (e.g.. mice, rats, hamsters), rabbits, primates, and swine such as inbred pigs and the like. Additionally, for in vitro applications, such as in vitro diagnostic and research applications, body fluids and cell samples of the above subjects will be suitable for use as a “sample”, such as mammalian (particularly primate such as human) blood, urine, or tissue samples, or blood, urine, or tissue samples of the animals mentioned for veterinary applications.

[0042] The present invention relates to positron emission tomography (PET) methods in which the systemic immunity of a subject is examined, and an image of the subject is reconstructed from information obtained during the examination. Conventional PET methods can be adapted for observing component signals or estimates of component signals from single tracers or from combined signals of multiple tracers (used either in separate PET scans which can then be compared in the context of imaging multiple PET tracers, a single tracer injected repeatedly, or a combination of tracers using multiple-timepoint or dynamic scanning, where the tracer administrations staggered in time such that some or all of the PET timeframes, images, data, and / or datasets contain overlapping signals from more than one of the tracer administrations.

[0043] Embodiments of the invention disclosed herein are based upon the discovery that different populations of T cells in vivo exhibit strikingly different [18F]F-AraG tracer uptake patterns. For example, it has been discovered that [18F]F-AraG uptake patterns are observably distinct in “functional” T cell populations as compared to “exhausted” T cell populations (as illustrated, for example in the data presented in Figure 5). Unlike “exhausted” T cell populations functional T cell populations are amenable to therapeutic intervention (e.g.. are “druggable”). Briefly, T cells follow a pathway of activation, proliferation and differentiation before becoming functionally and phenotypically "exhausted" (for example in settings of chronic infection, autoimmunity and in cancer). Exhausted T cells progressively lose canonical effector functions, exhibit altered transcriptional networks and epigenetic signatures and gain expression of a coinhibitory receptor molecules. We have discovered that [18F]F- AraG can be used as a marker for these different functional states. Without being bound by a specific theory or mechanism of action, it is believed that the use of [18F]F-AraG as a PET tracer provides insight into the functional biology of T cell populations in this manner because the accumulation of [18F]F-AraG in T cells rests on its phosphorylation by kinases, enzymes whose activity depends on the functional state of T cells (e.g., as [18F]F-AraG is associated with mitochondria, and mitochondria are at the center of T cell function).

[0044] Building upon this discovery, we have designed methods that harness this feature of [18F]F-AraG and utilize its properties as a tool in order to, for example, observe aspects of systemic immunity in the subject and, for example, evaluate the immunomodulatory effects of chemotherapy. Aspects of T cell biology including functional / druggable T cell populations and exhausted T cell populations are well known in the art and discussed, for example in: Levi et al, J Nucl Med 2021; 62:802- 807; Blank et al, Nat Rev Immunol. 2019 Nov;19(l l):665-674; Dolina et al, Front Immunol. 2021 Jul 20:12:715234; Rha et al, Cell Mol Immunol. 2021 Oct;18(10):2325-2333; Soerens et al, Nature. 2023 Feb;614(7949):762-766; and Verdon et al, Int J Mol Sci. 2020 Oct 5;21 (19):7357, the contents of each which are incorporated by reference.

[0045] Embodiments of the invention include methods of non-invasively PET imaging a subject to observe aspects of systemic immunity' in the subject. Where the subject has a disease, such as cancer, neurodegenerative disease, autoimmune disease, metabolic disease or an infectious disease, the aspects of systemic immunity observed by these methods may provide diagnostic or prognostic information about the disease, and / or or prognostic information about the subject's response to therapy. The present disclosure provides methods for determining the aspects of systemic immunity in a subject using imaging agents (e g., PET tracer) for non-invasive imaging, such as positron emission tomography (PET). The obtained distribution and / or abundance of the binding targets, e.g., antigen experienced druggable T cells, provides information on various aspects of systemic immunity.

[0046] PET imaging studies in multiple patients using certain of the methods disclosed herein confirm that embodiments of the invention can be used for assessing patient likelihood of response to a therapeutic regimen. These studies found that patients showing a progressive disease in a standard of care follow up had a statistically significant lower pretherapy [18F]F-AraG / FDG ratio as compared to patients who did not show a progressive disease. These studies also found that a threshold [18F]F-AraG / FDG ratio of 0.36 can be used to indicate which patients who are undergoing immunotherapy have a higher probability of survival. These studies demonstrate the utility of whole-body [18F]F-AraG / FDG ratio in patient selection and therapy guidance.

[0047] Without wishing to be bound by theory, the aspects of systemic immunity observed in a tissue associated with a disease, (e.g., a tumor, an organ or an anatomical region) may be a prognostic marker for predicting the response of the disease to treatment and / or survival. The aspects of systemic immunity of a tissue may include abundance and / or distribution of immune cells in the tissue. The aspects of systemic immunity may include one or more immune cell types, such as, but not limited to, cytotoxic T cells, helper T cells, memory T cells, and regulatory T cells (Tregs). In some embodiments, the aspects of systemic immunity of a tissue can include abundance and / or distribution of immune cells in the tissue. In some embodiments, the aspects of systemic immunity of a tissue can include abundance and / or distribution of mitochondrial biogenesis in the tissue. Embodiments of the methods disclosed herein can allow aspects of systemic immunity of a tissue associated with disease to be determined by non-invasive imaging of T cells such as CD8+, CD4+and CD3+cells in a subject.

[0048] In some cases, the aspects of systemic immunity of a tissue may be represented by the pattern and / or level of expression of one or more PET tracers in the tissue. In some cases, the aspects of systemic immunity of a tissue may include the activity of immune cells in the tissue and / or a functional environment of the tissue. The functional environment of the tissue may include tumor metabolism, the presence of immune checkpoints or tumor immune suppression status. The functional activity of immune cells may include, without limitation, antitumor T-cell activity. In some embodiments, the aspects of systemic immunity may include a tumor infiltrating lymphocyte (TIL) status. In some embodiments of the instant invention, the aspects of systemic immunity may be represented by an ”[18F]F-AraG immunoscore7’. Immunoscores of this type are described for example, in, e.g., Jerome Galon, et al; “Towards the introduction of the Tmmunoscore’ in the classification of malignant tumors’’; J Pathol. 2014 January'; 232(2): 199-209; or Frank Pages et al., “International validation of the consensus Immunoscore for the classification of colon cancer: a prognostic and accuracy study.”; Lancet. 2018 391 :2128-39. The functional activity of immune cells may include, without limitation, aberrant (e.g., autoreactive) T-cell activity.

[0049] The present disclosure provides methods that can allow the non-invasive imaging for measurement of immune cells in tissues which are (or are not) amenable to biopsy. Examples of monitoring such tissues include assessment of immunotherapy, recovery from stroke, brain injury or cardiac event, or transplant rejection, none of which are recommended for biopsy. Methods of the present disclosure may allow obtaining an aspect of systemic immunity for such conditions based on non-invasive visualization of a patient's immune system. In some embodiments, the aspects of systemic immunity may be represented by an [18F]F- AraG immunoscore, as described herein. As used herein, “[18F]F-AraG immunoscore” may apply to any disease or condition (e.g., cancer, autoimmune disorders, infectious disease, etc.) where the aspects of systemic immunity7of a tissue is relevant to diagnosis and / or treatment of the disease or condition.

[0050] Embodiments of the invention use one or more tracer molecules. [18F]F- AraG, also known as VisAcT, was developed as a PET tracer intended for imaging activated T cells in-vivo. [18F]F-AraG is an18F-labeled analog of arabinofuranosylguanine (AraG), a compound that has shown selective accumulation in T cells. Nelarabine, AraG's prodrug, is US Food and Drug Administration (FDA) approved for treatment of T cell acute lymphoblastic leukemia and T cell lymphoblastic lymphoma. [18F]F-AraG enters T cells via nucleoside transporters and is trapped intracellularly through rate-limiting phosphory lation primarily by deoxyguanosine kinase (dGK), an enzyme, present solely in mitochondria, and critical in supplying nucleotides for mitochondrial DNA synthesis. Phosphorylation by dGK leads to entrapment and potential downstream accumulation into mtDNA allowing visualization via PET imaging (Fig. 1). [18F]F-AraG has already shown great promise in evaluating T cell involvement in graft versus host disease (GVHD), rheumatoid arthritis, cancer and multiple sclerosis (see. e.g. Ronald, J.A., et al.. A PET Imaging Strategy to Visualize Activated T Cells in Acute Graft-versus-Host Disease Elicited by Allogenic Hematopoietic Cell Transplant. Cancer Res, 2017. 77(11): p. 2893- 2902; Franc, B.L., et al., In Vivo PET Imaging of the Activated Immune Environment in a Small Animal Model of Inflammatory Arthritis. Mol Imaging, 2017. 16: p. 1536012117712638; Levi, J., et al.. Imaging of Activated T Cells as an Early Predictor of Immune Response to Anti-PD-1 Therapy. Cancer Research, 2019. 79(13): p. 3455-3465; Levi, J., et al., (18)F-AraG PET for CD8 Profiling of Tumors and Assessment of Immunomodulation by Chemotherapy. J Nucl Med, 2021. 62(6): p. 802-807; and Guglielmetti. C., et al., Longitudinal imaging of T-cells and inflammatory demyelination in a preclinical model of multiple sclerosis using ( 18)F- FAraG PET and MRI. J Nucl Med, 2021).

[0051] In studies on the utility of [18F]F-AraG in tracking activated T cells, we have discovered unique properties of [18F]F-AraG that make it an ideally suited agent for tracking immune processes on a whole-body level. While it was previously recognized that [18F]F-AraG visualizes activated T cells, we have found that [18F]F- AraG specifically tracks antigen experienced, reprogrammable T cells and thus can be used in many imaging applications, in immunooncology, neurodegenerative diseases (Alzheimer’s disease, Parkinson’s’ disease and the like) autoimmune diseases (multiple sclerosis, diabetes and the like), as well as viral responses (COVID19. HIV and the like) etc. (Figure 1). As discussed below, we have discovered that [18F]F- AraG shows a dynamic range of accumulation in T cells, varying from very high and high in fully functional antigen experienced T cells and antigen experienced druggable T cells, to insignificant accumulation in terminally dysfunctional and antigen inexperienced T cells. Such characteristics give [18F]F-AraG a unique ability to be used in drug discovery, patient selection, therapy response and assessment of off target adverse effects.

[0052] To fully take advantage of this characteristic we developed a number of methods for assessing systemic immunity, methods that rely on evaluating [18F]F- AraG signals on a whole-body level: for example tracer accumulation in at least 2, 3, 4 or 5 organs such as lymph nodes, bone marrow, liver spleen, heart, etc. As shown in the illustrative examples, such methods can be adapted for use in immunooncology in therapy response and patient selection (Figure 2 and 9) as well as in assessing aberrant immune activity in viral infections such as long COVID (Figure 3). In embodiments of the invention, in order to get an assessment of the changes in signal post therapy, the regions of uptake in the pretreatment and on-treatment scans can be registered and matched according to conventional PET methods. Standardized uptake values, such as SUVmax, SUVmean and SUV total can be extracted from all areas of tracer uptake in both scans and % changes in signal calculated to assess therapy effects. In methods of the invention, signal ’’hotspots” with an increase in signal (e.g., at least 10%, 20% or 30% increase) post therapy can be characterized as showing immune activation. Correspondingly, hotspots with a reduction in signal (e.g., at least 10%, 20% or 30% decrease) can be characterized as showing lower immune activity post treatment. Non-hotspots with a minimal increase or decrease in activity' can be characterized as stable.

[0053] In addition, it has been discovered that [18F]F-AraG’s accumulation in antigen experienced druggable (reprogrammable) T cells allows for the baseline, pretreatment [18F]F-AraG scan to be informative of patient’s overall immune status and be used in patient selection / therapy guidance. Importantly, we discovered that [18F]F- AraG signal in the lumbar bone marrow and sacrum can be used to predict progressive disease and overall survival (Figure 9) Besides using baseline [18F]F- AraG scan on its own, as discussed below, we have also developed a ratiometric FDG / [18F]F-AraG method that takes advantage of both baseline [18F]F-AraG scans and standard of care18F-fluorodeoxyglucose (FDG) scans, which are widely used for diagnosis and staging of cancer patients. Embodiments of the invention in which FDG scans provide tumor-centric context for observing T cell specific [18F]F-AraG uptake can sen e as powerful tools for patient selection and therapy guidance.

[0054] Tumor burden is known to affect immunity', and recent studies indicate that the assessment of tumor burden in relation to the activity of the immune system can predict response to immunotherapy. In the methods disclosed herein, we have discovered that the ratio of FDG to [18F]F-AraG uptake in different lesions and hotspots which allows clear visualization of cold and hot lesions as well as tumor unrelated inflammation (Figure 4). In addition to easy visualization, this method provides quantification of the number of lesions with a certain inflammation degree in relation to tumor burden that is used to analyze patient-level [18F]F-AraG immunity or inflammation score. The proof-of-the-concept ratiometric analysis performed in 5 lung cancer patients indicates high potential of this method to assess T cell involvement in different lesions and thus provide [18F]F-AraG “inflammation score” or, more specifically, [18F]F-AraG “T score” on a patient level. This method revealed a high degree of intra-patient heterogeneity' that may prove a critical determinant in immunotherapy response, similar to what is observed in standard cancer therapies. The FDG / [18F]F-AraG ratiometric analysis disclosed herein that allows quantification of T cell function in relation to tumor burden is useful in patient selection and therapy guidance, both of which are expected to dramatically increase success of immunotherapies and improve patients' clinical outcome (see also the bone marrow studies discussed in the Example below). For example, patients with low T score (high FDG / [18F]F-AraG ratio) could benefit from undergoing immune priming strategies that increase tumor inflammation and make lesions more likely to respond to immunotherapy. On the other hand, patients with high T score (low FDG / [18F]F- AraG ratio) could undergo immunotherapy without any immune priming therapies, reducing the cost of the therapy and avoiding side effects of additional, but unnecessary' treatment. In addition, this method can be used to optimize therapy as well. For instance, direct radiotherapy to liver metastasis has been shown to greatly impact response to immunotherapy and overall survival but it is currently not known what are the lesions that are best to target: the inflamed ones where radiotherapy might destroy effector cells or cold lesions that may not produce adequate immune response. The quantification of FDG / [18F]F-AraG ratio per metastatic lesion allows the exploration of the role of inflammation in getting the most out of radiotherapy / immunotherapy synergy and can lead to identification of those lesions whose direct targeting w ould lead to the optimal clinical outcome.

[0055] Furthermore, preclinical studies with [18F]F-AraG signal indicate that the signal in draining lymph nodes holds key information to immunomodulating therapies. In current clinical practice, lymph nodes are often resected but the critical role they play in immunotherapy has motivated calls for reevaluation of the necessity and investigation of the damaging effects of TDLN resection. Embodiments of the methods disclosed herein can serve as a tool to assess tumor draining lymph nodes and allow informed decisions regarding the need for their resection.

[0056] Embodiments of the invention include, for example methods of observing T cell localization associated with systemic immunity in a subject via positron emission tomography (PET). Such methods typically comprise administering [F18]F-Ara-G to the subject and a performing a first [F18]F-Ara-G PET scan so as to observe [F18]F- Ara-G accumulation in at least 2, 3, 4, 5 or 6 regions of [F18]F-Ara-G accumulation; and then quantifying levels of [F18]F-Ara-G observed in the at least 2. 3, 4, 5 or 6 regions (optionally separated in vivo by at least 10 cm); such that T cell localization associated with systemic immunity in the subject is observed. Tn illustrative embodiments of the invention, the at least 2, 3, 4, 5 or 6 regions in which tracer accumulation is observed includes at least 2, 3, 4, 5 or 6 organs selected from lymph nodes, bone marrow, heart, spleen, liver, thyroid, brain, choroid plexus, salivary glands, nose and mouth. In certain embodiments of these methods, such observation are then used for predicting progressive disease and / or survival. In certain embodiments of these methods, such observation are then used for monitoring mitochondrial biogenesis in organs such as liver and kidney and / or for also assigning weighting factors for each organ based on their importance for the response to a therapy.

[0057] As will be appreciated by one skilled in the art, embodiments of the invention may be implemented as a method, a data processing system, or a computer program product. Accordingly, one embodiment may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, implementations of the preferred embodiment may take the form of a computer program product on a computer- readable storage medium having computer-readable program instructions (e.g., computer software) embodied in the storage medium. More particularly, implementations of the preferred embodiments may take the form of web- implemented computer software. Any suitable computer-readable storage medium may be utilized including hard disks, CD-ROMs, optical storage devices, or magnetic storage devices.

[0058] In certain methods for observing [18F]F-AraG signal associated with systemic immunity the subject is selected to be a patient diagnosed with a malignancy, an infectious disease or an immune disorder. In some embodiments of the invention, the methods are selected to observe [F18]F-Ara-G accumulation in antigen experienced T cells. Optionally, the method comprises observing aberrant T cell activity using information obtained from the first [F18]F-Ara-G PET scan. Optionally, the method comprises observing inflammation-related immunosupression using information obtained from the first, second or both [18F]F-AraG PET scans. In illustrative working embodiments of the invention that are disclosed herein, the subject is selected to be a patient infected with C0VID19 or other virus; and the method comprises observing virus-affected regions in the patient. In other embodiments of the invention, the subject is selected to be a patient diagnosed with a malignancy and the method observes tumor-related systemic inflammation in the patient; and / or the subject is selected to be a patient receiving chimeric antigen receptor (CAR) T-cell therapy. In some embodiments of the invention, the method further comprises selecting a therapeutic regimen for the patient using information obtained from the first [F18]F-Ara-G PET scan.

[0059] Certain embodiments of the invention include methods designed to observe systemic immunity in a patient at different points of time (e.g., where a period of time between the first scan and the second scan is at least 1, 4. 7, 10, 14 or 21 days). Such embodiments include administering [18F]F-AraG to the subject and performing a first [18F]F-AraG PET scan as discussed above, and subsequently performing a second [F18]F-Ara-G PET scan to observe [18F]F-AraG accumulation in the at least 3 regions of [F18]F-Ara-G accumulation; quantifying levels of [18F]F-AraG observed in the at least 3 regions of [18F]F-AraG accumulation in the second [F18]F-Ara-G PET scan; and then comparing levels of [F18]F-Ara-G observed in the first [F18]F-Ara-G PET scan with levels of [F18]F-Ara-G observed in the second [F18]F-Ara-G PET scan. Typically, in these methods, levels of accumulated [F18]F-Ara-G observed in the first [F18]F-Ara-G PET scan are compared with levels of accumulated [F18]F-Ara-G observed in the second [F18]F-Ara-G PET scan so as to obtain information on changes in systemic immunity that occur in response to the therapeutic regimen. Typically in these methods, the subject is treated with a therapeutic regimen between the first [18F]F-AraG PET scan and the second [F18]F-Ara-G PET scan; and levels of [F18]F- Ara-G observed in the first [F18]F-Ara-G PET scan are compared with levels of [F18]F-Ara-G observed in the second [F18]F-Ara-G PET scan so as to obtain information on efficacy of treatment and / or off target adverse T cell localization that occurs following the therapeutic regimen.

[0060] In embodiments of the invention, artisans can quantify levels of [F18]F-Ara-G observed in the at least 2, 3, 4, 5 or 6 regions of [F18]F-Ara-G accumulation by identifying at least one region in the subject to which [F18]F-Ara-G accumulates in the first PET scan; determining at least one quantification metric used in the art. Typically, a standardized [F18]F-Ara-G uptake value (SUV) observed in the first PET scan. Embodiment of the invention can use, for example, a SUV selected from SUVmax, SUVmean and SUVtotai; by determining at least one standardized [F18]F-Ara-G uptake value observed in the second PET scan for the region (e.g. one selected from SUVmax, SUVmean and SUVtotai); and then comparing the standardized [F18]F-Ara-G uptake value observed in the first PET scan with the standardized [F18]F-Ara-G uptake value observed in the second PET scan. Illustrative methods useful for such SUV analysis are disclosed in Vanderhoek et al, J Nucl Med 2013; 54: 1188-1194 and US Patent Publication 20080230703, the contents of which are incorporated by reference. In addition, image analysis software is known in the art that can be used to extract SUV parameters, for example MIMsoftware. Typically, these SUV methods further include identifying a region having greater than a 10%, 20% or 30% increase in | F'S|F-Ara-G signal / uptake as exhibiting immune activation; and / or identifying a region having greater than a 10%, 20% or 30% decrease in [F18]F-Ara-G uptake / signal as exhibiting immune deactivation. In some of these SUV methods, the subject is selected to be a patient diagnosed with a pathological condition; and the method further comprises selecting a therapeutic regimen for the patient using the information obtained from the ratio of the signal intensity.

[0061] Embodiments of the invention include methods that use multiple PET tracer compounds, typically in single tracer discreet PET scans which are then compared. For example, as discussed below, embodiments of the invention include administering [18F]F-AraG to the subject and a performing a [F18]F-Ara-G PET scan; and also administering18F-fluorodeoxyglucose (FDG) to the subject and a performing a FDG PET scan to observe FDG accumulation in the subject (e.g. in at least 2, 3, 4, 5 or 6 selected regions); and then comparing levels of [F18]F-Ara-G observed in the [F18]F- Ara-G PET scan with levels FDG observed in the FDG PET scan. For example, embodiments of the invention include methods of observing a region in the subject to which [F18]F-Ara-G accumulates in the [F18]F-Ara-G PET scan; observing the region in the subject in which FDG accumulates in the FDG PET scan; comparing the signal observed in the region in the [F18]F-Ara-G PET scan with the signal observed in the region in the FDG PET scan; and then determining a ratio of the signal intensity observed in the region in the [F18]F-Ara-G PET scan with the signal intensity observed in the region in the FDG PET scan. In some embodiments of the invention, the subject is a patient diagnosed with a malignancy and the ratio of the signal intensity observed provides information on T cell function in relation to tumor burden. In certain embodiments, regions with a ratio of signal intensity of at least 0.3 (0.25-0.4) are identified to indicate that the observed T cells are antigen experienced druggable T cells.

[0062] EXAMPLE 1: [18F]F-ARAG UPTAKE IN VERTEBRAL BONE MARROW TO PREDICT SURVIVAL IN PATIENTS WITH NON-SMALL CELL LUNG

[0063] CANCER TREATED WITH ANTI-PD-(L)! IMMUNOTHERAPY

[0064] Lung cancer is the leading cause of cancer-related mortality’ in both women and men worldwide. Approximately 85% of lung cancers are classified as non-small lung cancer (NSCLC), a heterogenous group of diseases with diverse molecular features and pathology (7). Immunotherapy has emerged as a breakthrough treatment for patients without driver mutations, becoming a fundamental element in the treatment of advanced disease. Immune checkpoint inhibitors, monoclonal antibodies directed at immune checkpoints, such as programmed death- 1 (PD-1), or its ligand PD-L1, disrupt immune tolerance to cancer and enable effective and durable antitumor responses that lead to improved overall survival (2.3) Unfortunately, many patients do not benefit from the treatment and identifying patients who will most likely respond to checkpoint inhibitors remains a difficult clinical challenge. The most extensively studied biomarkers of response rely on invasive biopsies and are confined to assessment only within the tumor microenvironment; they include PD-L1 expression, tumor mutational burden, and tumor-infiltrating lymphocytes. However, owing to the inherent limitations of biopsy, those biomarkers cannot adequately capture heterogeneity of the immune contexture, both within a tumor lesion and between different lesions in patients with extensive disease (4). Furthermore, historical knowledge (5) and recent discoveries (6, 7), underscore the significance of the global immune context, and urge a shift away from exclusive focus on the tumor microenvironment and local immune aspects.

[0065] The capacity of positron emission tomography (PET) to non-invasively monitor processes on a whole-body level make it a particularly well-suited method for assessing system-wide immune status. A number of PET tracers, differing in size and what they assess, are currently being investigated as biomarkers for immune profiling and assessment of immunotherapy response (S). Radiotracers that target immune activation, such as those assessing activation pathways ( ) or markers like CD69 (10) and 0X40 (77), offer the ability to evaluate not only the presence but also the functional state of key players in adaptive antitumoral immune response. Smallmolecule metabolic radiotracers are particularly appealing in this context, as their size facilitates effective penetration through biological barriers and assessment of systemic immune status (12). [18F]F-AraG is a small molecule mitochondrial metabolic tracer that tracks activated T cells by assessing metabolic reprogramming associated with their function (13). Preclinically. [18F]F-AraG has shown utility in evaluation of immunotherapy response (74), but also in immune profiling prior to initiating therapy (75). Characterization of pretreatment immune status and its correlation with response to therapy and clinical outcome are of critical importance in understanding the value of [18F]F-AraG PET in patient selection. In this study we examine the systemic immune status of patients with NSCLC using pretreatment [18F]F-AraG scans and demonstrate that [18F]F-AraG uptake in vertebral bone marrow can predict clinical outcome.

[0066] Methods

[0067] Study Participants and Clinical Trials

[0068] This study included patients enrolled in a clinical trial aimed at imaging T cell activation in patients with advanced NSCLC (NCT04726215). Participants were enrolled at two institutions: Palo Alto Veterans Affairs (five patients) and Sutter Medical Center in Sacramento (six patients). The studies were approved by the local Institutional Review Board and all participants signed a written informed consent. Standard of care, diagnostic, [18F]FDG scans were also collected. The response to therapy was defined according to RECIST criteria (for patients at Sutter Medical Center) and based on the treating oncologist’s discretion (for patients at Palo Alto Veterans Affairs).

[0069] [18F]F-AraG PET / CT Imaging

[0070] [18F]F-AraG PET / CT imaging was performed at the institution where participants were enrolled following the same imaging protocol. Patients received a bolus venous injection of 185 ± 10% MBq of [18F]F-AraG, followed by whole-body imaging at approximately 60 minutes after injection. The GE Discover}' MI and GE Discovery STE scanners were used at Palo Alto Veterans Affairs while Siemens Biograph 40 mCT was employed at Sutter Medical Center. Images were reconstructed using iterative algorithms and using CT for attenuation correction. The following image reconstruction parameters were used: 3D IR (3D ordered subset expectation maximization (OSEM)) with time of flight (TOF) ON, 8 subsets, 3 iterations and a 6.0mm filter cutoff; No Z-axis filter (GE Discovery' MI); 3D IR (3D OSEM) with no TOF, 20 subsets, 2 iterations and a 6.0mm filter cutoff; No Z-axis filter (GE Discovery STE) and TrueX with TOF (ultra HD-PET) recon, 2 iterations, 21 subsets, Gaussian filter with FWHM 2.0mm (Siemens Biograph).

[0071] Image Analysis

[0072] All individual [18F]FDG and [18F]F-AraG PET / CT scans were analyzed using TRAQinform IQ, a software only medical device intended for use by trained medical professionals (AIQ Solutions, Madison, WI). A nuclear medicine physician manually identified regions of interest (ROI) which included lesions on [18F]FDG PET / CT images and hotspots, with PET uptake elevated from background, on [18F]F- AraG PET / CT images. [18F]F-AraG hotspot contours were generated independently of [18F]FDG contours by referencing both CT and PET images. If the local SUV value was significantly higher than the surrounding tissue or similar parts of the normal image, the ROI was included. Additionally, the ROls were included if there was an abnormal presentation on the CT images. Automated organ contours were manually reviewed and corrected for motion and spillover. The TRAQinform IQ software performs automated matching of ROI across the [18F]FDG and [18F]F-AraG images (16-19) and performs quantitative analysis of ROI on PET / CT images. Moreover, the software also performs automatic organ segmentation, trained using the method outlined in Weisman et aL, to provide quantification and locations of lesion-ROI (20).

[0073] From every PET / CT image, TRAQinform IQ software extracted single timepoint image features from the scans in each individual ROE SUVmax (the highest SUV within ROI), SUV mean (the average SUV in ROI), Volume (the total volume of ROI, which for [18F]FDG is equivalent to metabolic tumor volume or MTV), SUVtotai (the total SUV in ROI, which for [18F]FDG is equivalent to total lesion glycolysis or TLG). Features SUVmax. SUVmean, SUVtotai and SUVvoiume were extracted for organs liver, spleen, thyroid, kidneys, bowel, heart, and spine.

[0074] Statistical Analysis Means, medians, standard deviations of each quantitative variables and percentages for qualitative variables were computed. Kolmogorov-Smirnov tests were computed to assess normal distribution assumptions. The ability of the [18F]FDG and [18F]F-AraG measures for various patient organ and skeletal elements to distinguish between progressive disease and non-progressive disease was investigated by Step- wise multivariate discriminant analysis. The aim of this analysis was to obtain measures that would yield the largest total correct classification percentage. The cutoff point for this discriminant analysis was then used by the univariate survival analysis using Kaplan-Meier plots and Mantel-Cox tests. All these analyses were performed using IBM SPSS Statistics for Windows, Version 28.0. Armonk, NY. As a further validation of the cut-point, a classification and regression tree analysis was performed using Minitab version 21.4.1.

[0075] Patient demographics

[0076] Eleven patients with advanced NSCLC were enrolled and imaged at two institutions: Palo Alto Veterans Affairs (five patients) and Sutter Medical Center in Sacramento (six patients) (Table 1). The cohort included 8 males and 3 females; all patients had stage IV disease. Two patients had liver metastasis and no patients had brain metastasis. One patient received atezolizumab (a PD-L1 inhibitor), four patients received nivolumab (a PD-1 inhibitor), and eight patients were treated with pembrolizumab (a PD-1 inhibitor). PD-L1 status was known for five patients (46%). The patients were followed for 12 months, dunng which time three patients (27%) died. Four patients (36%) developed progressive disease (PD), five patients (46%) had a partial response (PR), one (9%) had stable disease (SD) and one (9%) had a complete response (CR).

[0077] Table 1. Patient characteristics [18F]FDG biomarkers were not predictive of survival in patients with metastatic NSCLC. As tumor burden affects patients’ responses to therapy (27), we analyzed baseline diagnostic [18F]FDG PET / CT scans to assess differences in the extent of disease among patients. Diagnostic [18F]FDG PET / CT scans were available for all patients and were acquired 16-56 days (median 36 days) before the [18F]F-AraG scan. We analyzed standardized uptake values (SUV) most commonly associated with tumor burden: SUVmax, total metabolic tumor volume (tMTV) and total lesion glycolysis (TLG). The extent of the disease differed between patients (Figure 11 A). In the patient cohort, SUVmax values ranged from 5.2 to 44.5, tMTV spanned from 27.8 to 790.5 cm3, and TLG ranged from 70.78 to 3531.4. The Kaplan Meier analysis did not show significant differences in the probability7of survival between patients with different TLG (cut-off 1654.3, P = 0.40) or tMTV (cut-off 362.6 cm3, P = 0.11) (Figure 1 1B.C). Moreover, no significant differences in SUVmax were noted between patients who progressed on therapy and those who did not.

[0078] [18F]FDG and [18F]F-AraG have different inter- and intra-lesional uptake distribution. Our preclinical studies have shown that [18F]F-AraG, unlike [18F]FDG, selectively accumulates in activated T lymphocytes over tumor cells, providing information on immune contexture within the tumor microenvironment (74). In this patient cohort, differences between [18F]FDG and [18F]F-AraG uptake were noted across various lesions within the same patient (Figure 12A), as well as within the same lesion (Figure 12B), capturing distinct aspects of the tumor microenvironment. Similar to what was observed in the preclinical studies (75), [18F]F-AraG’s intralesional distribution patterns resembled discrete cancer-immune phenotypes: inflamed, immune excluded, and immune desert (Figure 12B). The observed differences between [18F]FDG and [18F]F-AraG, as well as the [18F]F-AraG’s pattern of lesional uptake indicate that within tumors, [18F]F-AraG does not accumulate in cancer cells (14,22). [18F]F-AraG SUVtotai offers a dynamic range needed for stratification of immune activity. Standardized uptake values are commonly used for quantitative assessment of [18F]FDG uptake. To understand the use of SUV measurements in the context of [18F]F-AraG and assessment of immune status, we extracted different parameters, SUV max, S TVmean, and SUVtotai and determined the extent of their variation within the patient cohort. The median maximum value for all three SUV parameters was considerably lower for [18F]F-AraG compared to [18F]FDG (Table 2). The range of values for these parameters was also notably narrower for [18F]F-AraG than for [18F]FDG. Expectedly, the narrowest range was observed for SUVmean, whereas values for SUVtotai displayed the widest range. Although [18F]F-AraG SUVtotai values provide a dynamic range essential for stratifying tumors or patients according to their immune activity, it is important to acknowledge that the variability in SUVtotai stems primarily from differences in lesion volume. Given its reliance on lesion size. [18F]F- AraG SUVtotai is most reliable for comparisons within lesions of similar sizes or among patients with comparable tumor burdens.

[0079] [18F]FDG [18F]F-AraG

[0080] Table 2. Standardized Uptake Values detected for [18F]FDG and [18F]F-AraG in the patient cohort.

[0081] [18F]F-AraG uptake in the vertebral bone marrow identifies patients with progressive disease. As cancer, metastatic disease in particular, represents a complex systemic condition that affects immunity as well as distant organs (23). we assessed [18F]F-AraG accumulation in the spleen, vertebral bone marrow, liver, thyroid, heart and bowel. [18F]F-AraG uptake in those organs differed greatly between patients (Figure 13A). To comprehensively evaluate systemic immunity, we performed discriminant analysis using SUV values extracted from these organs along with those from all tumor lesions and active lymph nodes. [18F]F-AraG SUVtotai measured in the lumbar (Figure 13B) and sacral vertebrae differentiated between patients who progressed on therapy from those who did not with 90.9 % and 81.8 % accuracy, respectively. That stands in contrast to [18F]FDG SUVtotai measured in the vertebral bone marrow, which showed no significant distinctions (p = 0.44) between these two groups (Figure 13C). [18F]F-AraG SUVtotai measured in the cervical and thoracic bone marrow was similar between patients who had progressive disease and those who did not. The volume of vertebral ROIs was not different between patients with progressive and non-progressive disease, indicating that the findings for lumbar and sacral [18F]F-AraG SUVtotai were independent of volume effects.

[0082] [18F]F-AraG SUVtotai in the lumbar bone marrow predicts overall survival. The discriminant analysis identified an optimal cut-off value of 485.93 for lumbar [18F]F- AraG SUVtotai, which stratified patients into high and low signal groups. The Kaplan- Meier analysis revealed lower probability of survival for patients with high [18F]F- AraG SUVtotai in the lumbar bone marrow compared to those with low signal (Figure 14A). Notably, two patients with comparable tumor burden as assessed by [18F]FDG and similar immune tumor microenvironment as determined by intralesional [18F]F- AraG, but with contrasting lumbar bone marrow [18F]F-AraG SUVtotai, exhibited distinct clinical outcomes (Figure 14). These results suggest that [18F]F-AraG signal in the vertebral bone marrow could serve as a predictive biomarker of patient outcome. Further investigation is warranted to elucidate biological mechanisms underlying the observed survival disparities.

[0083] Discussion

[0084] Cancer is a systemic disease that affects the entire organism, including the immune terrain. Despite the systemic impact of both cancer and the body ’s associated immune response, tumor immunology is predominantly centered on the immune milieu within the tumor microenvironment. There is an urgent need for a more comprehensive understanding of the organismic immune context. In this study, we investigated [18F]F-AraG PET imaging as a non-invasive method for assessment of system-wide immune status of patients with advanced NSCLC prior to starting immunotherapy. We extracted potential [18F]F-AraG biomarkers from patients’ baseline scans and tested their ability to predict patient response and overall survival.

[0085] The patient cohort studied was heterogenous, displaying large variations in the extent of the disease. As disease load affects immunity and response to immunotherapy (24), we first examined the value of [18F]FDG biomarkers associated with tumor burden in predicting patient overall survival. None of the analyzed [18F]FDG biomarkers, SUVmax, tMTV and TLG, correlated with overall survival. Studies that investigated predictive value of baseline [18F]FDG biomarkers in immunotherapy-treated NSCLC patients have reported conflicting findings. While Evangelista et al. did not observe significant differences in the whole body TLG and MTV between responders and non-responders (25), other studies reported an association between higher MTV and poor overall survival (26,27). The inconsistent findings could stem from the differences in how lesions are segmented, as well as the variations in patient characteristics across the studies. In our study, the expression of PD-L1, a biomarker of response to anti-PD-1 therapy, was unknown for more than half of the patients, and at least three patients had an expression level below the threshold linked with a favorable response to therapy. This may explain discrepancies with studies reporting the predictive value of tMTV and TLG, which included only patients with high PD-L1 expression (>50%). However, our findings remain clinically relevant as immunotherapy commonly extends to patients with lower PD-L1 expression (<50%).

[0086] The overlap in metabolic needs between cancer cells and activated immune cells leads to [18F]FDG accumulation in both, rendering [18F]FDG a non-specific tracer for immune cells (28). In contrast to [18F]FDG and due to the intricate regulation of nucleotide pools and mitochondrial biogenesis in T cells (13), [18F]F- AraG has shown preferential accumulation in activated T lymphocytes. We observed differences in the distribution of these two tracers, both within and between lesions, which aligned with our expectations considering their respective targets. In contrast to [18F]FDG, which primarily indicates metabolically active cancer cells, the uptake patterns of [18F]F-AraG within tumor lesions resembled distinct immune landscapes observed clinically. The level of intralesional [18F]FDG uptake exceeded that of [18F]F-AraG, an expected finding considering differences in number of cancer and immune cells present within a lesion, as well as the level of glycolysis in relation to mitochondrial biogenesis (29). In addition, the range of the values for the three metrices tested was considerably lower for [18F]F-AraG than for [18F]FDG. The limited variability observed in [18F]F-AraG SUVmax across patients suggests that the highest image pixel intensity within the volume of interest may not be a suitable metric for accurate classification of immunological activity within the tumor microenvironment. [18F]F-AraG SUVmean, indicative of average immune activity, also exhibited minimal variation between patients, while [18F]F-AraG SUVtotai, representing total immune activity, demonstrated a wider range of values across the patient cohort.

[0087] Interestingly, our study revealed substantial variability in the [18F]F-AraG SUVtotai within the lymphoid organs across patients. While studies suggest the importance of the [18F]FDG signal in the spleen in immunotherapy response (30), in this patient cohort, neither [18F]FDG or [18F]F-AraG SUVs in the spleen showed significant differences between responders and non-responders. Notably, [18F]F-AraG SUVtotai in the lumbar bone marrow significantly differed between patients who experienced disease progression during immunotherapy and those who did not. In addition, the patients with high lumbar SUVtotai exhibited significantly lower overall survival compared to those with low vertebral signal. This finding is particularly intriguing in light of studies that suggest continuous communication between lung cancer and the bone marrow niche (31). In patients with metastatic melanoma, high baseline [18F]FDG signal in the bone marrow' has been linked to varying clinical outcomes. One study found that elevated [18F]FDG signal in the bone marrow indicated a favorable response to immunotherapy (32), whereas other studies reported a correlation with poor clinical outcomes (33-35). While in NSCLC, increased [18F]FDG metabolism in the bone marrow' has been reported to inversely correlate with survival (36), in our study w'e did not observe significant differences in bone marrow' [18F]FDG uptake between responders and non-responders.

[0088] Previous studies have reported elevated [18F]F-AraG bone marrow in both cancer patients (37) and post-acute severe acute respiratory syndrome coronavirus 2 patients (38). The precise etiology of increased metabolism in the bone marrow remains elusive, despite various mechanisms having been proposed, including increased hematopoiesis (32), micrometastases (36), and systemic inflammation (34). In our preclinical studies, we observed elevated [18F]F-AraG uptake in the vertebral bone marrow in adrenergically stimulated mice as well as in mice with neuroinflammation (39). Imaging of mice deficient in T cells (Ragl knockout) and bone marrow adipocytes (Letmdl knockout) (40) following adrenergic stimulation demonstrated the crucial role of T cells in the increased [18F]F-AraG accumulation in the bone marrow'. However, a comprehensive analysis of the bone marrow indicates that, in the context of adrenergic stimulation, the [18F]F-AraG signal in the lumbar bone marrow primarily originates from the tracer’s uptake in metabolically active bone marrow adipocytes, rather than from its accumulation in T cells (39). The ongoing studies are focused on identifying the particular cause of heightened [18F]F- AraG signal in the bone marrow7.

[0089] In this study, we used diagnostic [18F]FDG scans that, in some patients, were acquired close to two months prior to the [18F]F-AraG scan. This time gap may have contributed to the observed differences between [18F]FDG and [18F]F-AraG findings. To improve the accuracy of future comparisons between these tw'o tracers, we will aim to acquire [18F]FDG and [18F]F-AraG scans within a 7-day window. This study was conducted with a small number of patients, and we anticipate that the cut-off values determined in this patient group will need to be reassessed and refined in a larger patient cohort. Despite the limitations of the study size, the results underscore the significance of evaluating systemic immunity and offer initial evidence suggesting the potential of [18F]F-AraG bone marrow signal as a predictive imaging biomarker for patient stratification and treatment guidance.

[0090] EXAMPLE 1 REFERENCES

[0091] 1. Chen Z, Fillmore CM, Hammerman PS, Kim CF, Wong KK. Non-small-cell lung cancers: a heterogeneous set of diseases. Nat Rev Cancer. 2014;14:535-546.

[0092] 2. Reck M, Remon J, Hellmann MD. First-Line Immunotherapy for Non-Small- Cell Lung Cancer. J Clin Oncol. 2022;40:586-597.

[0093] 3. Spigel DR, Faivre-Finn C, Gray JE, et al. Five-Year Survival Outcomes From the PACIFIC Trial: Durvalumab After Chemoradiotherapy in Stage III Non-Small- Cell Lung Cancer. J Clin Oncol. 2022;40: 1301-1311.

[0094] 4. Jimenez-Sanchez A, Memon D, Pourpe S, et al. Heterogeneous Tumor- Immune Microenvironments among Differentially Growing Metastases in an Ovarian Cancer Patient. Cell. 2017;170:927-938 e920.

[0095] 5. Coley WB. II. Contribution to the Knowledge of Sarcoma. Ann Surg. 1891;14: 199-220.

[0096] 6. Spitzer MH, Carmi Y, Reticker-Flynn NE. et al. Systemic Immunity Is Required for Effective Cancer Immunotherapy. Cell. 2017;168:487-502 e415.

[0097] 7. Gopalakrishnan V, Spencer CN, Nezi L, et al. Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science. 2018;359:97- 103.

[0098] 8. Slebe M, Pouw JEE. Hashemi SMS, Menke-van der Houven van Oordt CW, Yaqub MM, Bahce I. Current state and upcoming opportunities for immunoP ET biomarkers in lung cancer. Lung Cancer. 2022;169:84-93. 9. Kim W, Le TM, Wei L, et al. [18F]CFA as a clinically translatable probe for PET imaging of deoxy cytidine kinase activity. Proc Natl Acad Sci U S A. 2016;113:4027-4032.

[0099] 10. Edwards KJ. Chang B, Babazada H, et al. Using CD69 PET Imaging to Monitor Immunotherapy-Induced Immune Activation. Cancer Immunol Res. 2022;10: 1084-1094.

[0100] 11. Alam IS, Mayer AT, Sagiv-Barfi I, et al. Imaging activated T cells predicts response to cancer vaccines. J Clin Invest. 2018;128:2569-2580.

[0101] 12. Levi J, Song H. The other immuno-PET: Metabolic tracers in evaluation of immune responses to immune checkpoint inhibitor therapy for solid tumors. Frontiers in Immunology. 2023; 13.

[0102] 13. Levi J, Duan H, Yaghoubi S, et al. Biodistribution of a Mitochondrial Metabolic Tracer, [(18)F]F-AraG. in Healthy Volunteers. Mol Imaging. 2022;2022:3667417.

[0103] 14. Levi J, Lam T, Goth SR, et al. Imaging of Activated T Cells as an Early Predictor of Immune Response to Anti-PD-1 Therapy. Cancer Res. 2019:79:3455- 3465.

[0104] 15. Levi J, Goth S, Huynh L, et al. (18)F-AraG PET for CD8 Profiling of Tumors and Assessment of Immunomodulation by Chemotherapy. J Nucl Med. 2021 ;62: 802- 807.

[0105] 16. Santoro-Fernandes V, Huff D, Scarpelli ML. et al. Development and validation of a longitudinal soft-tissue metastatic lesion matching algorithm. Phys Med Biol. 2021 ;66.

[0106] 17. Rueckert D, Sonoda LI, Hayes C, Hill DL, Leach MO, Hawkes DJ. Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans Med Imaging. 1999;18:712-721.

[0107] 18. Rigaud B, Simon A, Castelli J, et al. Deformable image registration for radiation therapy: principle, methods, applications and evaluation. Acta Oncol. 2019;58: 1225-1237. 19. Huff DT, Santoro-Fernandes V, Chen S, et al. Performance of an automated registration-based method for longitudinal lesion matching and comparison to interreader variability. Phys Med Biol. 2023;68.

[0108] 20. Weisman AJ, Huff DT, Govindan RM, Chen S, Perk TG. Multi-organ segmentation of CT via convolutional neural network: impact of training setting and scanner manufacturer. Biomed Phys Eng Express. 2023;9.

[0109] 21. Kim SI, Cassella CR, Byrne KT. Tumor Burden and Immunotherapy: Impact on Immune Infiltration and Therapeutic Outcomes. Front Immunol. 2020; 11 :629722.

[0110] 22. Ronald JA, Kim BS, Gowrishankar G, et al. A PET Imaging Strategy to Visualize Activated T Cells in Acute Graft-versus-Host Disease Elicited by Allogenic Hematopoietic Cell Transplant. Cancer Res. 20\TOC2 9i' -29Q2.

[0111] 23. Aleckovic M, McAllister SS, Polyak K. Metastasis as a systemic disease: molecular insights and clinical implications. Biochim Biophys Acta Rev Cancer. 2019;1872:89-102.

[0112] 24. Dall'Olio FG, Marabelle A, Caramelia C, et al. Tumour burden and efficacy of immune-checkpoint inhibitors. Nat Rev Clin Oncol. 2022;19:75-90.

[0113] 25. Evangelista L, Cuppari L, Menis J, et al. 18F-FDG PET / CT in non-small-cell lung cancer patients: a potential predictive biomarker of response to immunotherapy. NuclMed Commun. 2019;40:802-807.

[0114] 26. Dall'Olio FG, Calabro D, Conci N, et al. Baseline total metabolic tumour volume on 2-deoxy-2-[18F]fluoro-d-glucose positron emission tomography -computed tomography as a promising biomarker in patients with advanced non-small cell lung cancer treated with first-line pembrolizumab. Eur J Cancer. 2021;150:99-107.

[0115] 27. Yamaguchi O, Kaira K, Hashimoto K, et al. Tumor metabolic volume by (18)F-FDG-PET as a prognostic predictor of first-line pembrolizumab for NSCLC patients with PD-L1 > / = 50. Sci Rep. 2020; 10: 14990.

[0116] 28. Reinfeld BI, Madden MZ, Wolf MM, et al. Cell -programmed nutrient partitioning in the tumour microenvironment. Nature. 2021;593:282-288.

[0117] 29. Wallace DC. Mitochondria and cancer. Nat Rev Cancer. 2012;12:685-698. 30. Seith F, Forschner A, Weide B, et al. Is there a link between very early changes of primary and secondary' lymphoid organs in (18)F-FDG-PET / MRI and treatment response to checkpoint inhibitor therapy? J Immunother Cancer. 2020;8.

[0118] 31. Calderon-Espinosa E, De Ridder K, Benoot T. et al. The crosstalk between lung cancer and the bone marrow niche fuels emergency myelopoiesis. Front Immunol. 2024; 15: 1397469.

[0119] 32. Schwenck J, Schorg B, Fiz F, et al. Cancer immunotherapy is accompanied by distinct metabolic patterns in primary’ and secondary lymphoid organs observed by non-invasive in vivo (18)F-FDG-PET. Theranostics. 2020;10:925-937.

[0120] 33. Jeong SY, Kim SJ, Pak K, Lee SW, Ahn BC, Lee J. Prognostic value of 18F- fluorodeoxy glucose bone marrow uptake in patients with solid tumors: A metaanalysis. Medicine (Baltimore). 2018;97:el2859.

[0121] 34. Nakamoto R. Zaba LC. Liang T. et al. Prognostic Value of Bone Marrow Metabolism on Pretreatment (18)F-FDG PET / CT in Patients with Metastatic Melanoma Treated with Anti-PD-1 Therapy. J NuclMed. 2021;62: 1380-1383.

[0122] 35. Seban RD, Nemer JS, Marabelle A, et al. Prognostic and theranostic 18F-FDG PET biomarkers for anti-PDl immunotherapy in metastatic melanoma: association with outcome and transcriptomics. Eur J Nucl Med Mol Imaging. 2019;46:2298-2310.

[0123] 36. Prevost S, Boucher L, Larivee P, Boileau R, Benard F. Bone marrow hypermetabolism on 18F-FDG PET as a survival prognostic factor in non-small cell lung cancer. J Nucl Med. 2006;47:559-565.

[0124] 37. Jessica E. Wijngaarden MS, Johanna E. E. Pouw et al. . Pharmacokinetic analysis and simplified uptake measures for tumour lesion [18F]F-AraG PET imaging in patients with non-small cell lung cancer. PREPRINT (Version 1) available at Research Square

[0125] 38. Peluso MJ, Ryder D. Flavell R, et al. Multimodal Molecular Imaging Reveals Tissue-Based T Cell Activation and Viral RNA Persistence for Up to 2 Years Following COVID-19. medRxiv. 2023. 39. Levi J, Guglielmetti C, Henrich TJ, et al. [(18)F]F-AraG imaging reveals association between neuroinflammation and brown- and bone marrow adipose tissue. Commun Biol. 2024;7:793.

[0126] 40. Choi KM. Kim JH, Kong X, et al. Defective brown adipose tissue thermogenesis and impaired glucose metabolism in mice lacking Letmdl . Cell Rep. 2021;37: 110104.

[0127] It should be noted that ratios, concentrations, amounts, and other numerical data may be expressed herein in a range format. It is to be understood that such a range format is used for convenience and brevity, and thus, should be interpreted in a flexible manner to include not only the numerical values explicitly recited as the limits of the range, but also to include all the individual numerical values or subranges encompassed within that range as if each numerical value and sub-range is explicitly recited. To illustrate, a concentration range of “about 0.1% to about 5%” should be interpreted to include not only the explicitly recited concentration of about 0.1 wt% to about 5 wt%, but also include individual concentrations (e.g., 1%, 2%, 3%, and 4%) and the sub-ranges (e.g., 0.5%, 1.1%, 2.2%, 3.3%, and 4.4%) within the indicated range. In an embodiment, the term “about” can include traditional rounding according to significant figures of the numerical value. In addition, the phrase “about ‘x’ to V” includes “about ‘x’ to aboutcy”’.

[0128] It should be emphasized that the above-described embodiments of the present disclosure are merely possible examples of implementations and are set forth only for a clear understanding of the principles of the disclosure. Many variations and modifications may be made to the above-described embodiments of the disclosure without departing substantially from the spirit and principles of the disclosure. All such modifications and variations are intended to be included herein within the scope of this disclosure.

Claims

CLAIMS1. A method of observing T cell localization and function associated with systemic immunity' in a subject via positron emission tomography (PET) comprising: administering [F18]F-Ara-G to the subject and a performing a first [F18]F-Ara- G PET scan so as to observe [F18]F-Ara-G accumulation in at least 3 regions of [F18]F-Ara-G accumulation; and quantifying levels of [F18]F-Ara-G observed in the at least 3 regions; such that T cell localization and function / mitochondrial biogenesis associated with systemic immunity in the subject is observed.

2. The method of claim 1, wherein the subject is selected to be a patient diagnosed with a malignancy, neurodegenerative disease, metabolic disease, an infectious disease or an immune disorder.

3. The method of claim 2, wherein the method further comprises selecting a therapeutic regimen for the patient using information obtained from the first [F18]F- Ara-G PET scan.

4. The method of claim 2, wherein the method comprises observing aberrant T cell activity7using information obtained from the first [F18]F-Ara-G PET scan or a subsequent [F18]F-Ara-G PET scan.

5. The method of claim 2. wherein the subject is selected to be a patient infected with COVID19 or HIV; and the method comprises observing virus-affected regions in the patient.

6. The method of claim 2. wherein: the subject is selected to be a patient diagnosed with a malignancy and the method observes inflammation in relation to tumor burden in the patient; and / orthe subject is selected to be a patient receiving chimeric antigen receptor (CAR) T-cell therapy.

7. The method of claim 1. further comprising: administering [F18]F-Ara-G to the subject and a performing a second [F18]F- Ara-G PET scan to observe [F18]F-Ara-G accumulation in the at least 3 regions of [F18]F-Ara-G accumulation; quantifying levels of [F18]F-Ara-G observed in the at least 3 regions of [F18]F- Ara-G accumulation; in the second [F18]F-Ara-G PET scan; and comparing levels of [F18]F-Ara-G observed in the first [F18]F-Ara-G PET scan with levels of [F18]F-Ara-G observed in the second [F18]F-Ara-G PET scan.

8. The method of claim 7. wherein: the subject is treated with a therapeutic regimen between the first [F18]F-Ara- G PET scan and the second [F18]F-Ara-G PET scan; and levels of accumulated [F18]F-Ara-G observed in the first [F18]F-Ara-G PET scan are compared with levels of accumulated [F18]F-Ara-G observed in the second [F18]F-Ara-G PET scan so as to obtain information on changes in systemic immunity that occur in response to the therapeutic regimen.

9. The method of claim 7. wherein: the subject is treated with a therapeutic regimen between the first |F18]F-Ara- G PET scan and the second [F18]F-Ara-G PET scan; and levels of [F18]F-Ara-G observed in the first [F18]F-Ara-G PET scan are compared with levels of [F18]F-Ara-G observed in the second [F18]F-Ara-G PET scan so as to obtain information on off target adverse T cell localization and function that occurs following the therapeutic regimen.

10. The method of claim 1, wherein the method comprises:administering18F-fluorodeoxyglucose (FDG) to the subject and a performing a FDG PET scan to observe FDG accumulation in the subject; and comparing levels of [F18]F-Ara-G observed in the first or a subsequent [F18]F- Ara-G PET scan with levels FDG observed in the FDG PET scan.

11. The method of claim 10, wherein the method comprises: observing a region in the subject to which [F18]F-Ara-G accumulates in the first [F18]F-Ara-G PET scan; observing the region in the subject in which FDG accumulates in the FDG PET scan; comparing the signal observed in the region in the first [F18]F-Ara-G PET scan with the signal observed in the region in the FDG PET scan; and determining a ratio of the signal intensity observed in the region in the first [F18]F-Ara-G PET scan with the signal intensity observed in the region in the FDG PET scan.

12. The method of claim 10, wherein the subject is selected to be a patient diagnosed with a pathological condition; and the method further comprises selecting a therapeutic regimen for the patient using the information obtained from the ratio of the signal intensity.

13. The method of claim 1, wherein the at least 3 regions includes at least 2 organs selected from lymph nodes, bone marrow, heart, spleen, liver, thyroid, brain, choroid plexus, salivary glands, nose and mouth.

14. The method of claim 1. wherein the method is selected to observe [F18]F-Ara- G accumulation in antigen experienced T cells.

15. The method of claim 7, wherein quantifying levels of [F18]F-Ara-G observed in the at least 3 regions of [F18]F-Ara-G accumulation comprises: identifying at least one region in the subject to which [F18]F-Ara-G accumulates in the first PET scan; determining at least one standardized [F18]F-Ara-G uptake value (SUV) observed in the first PET scan for the region selected from SUVmax, SUVmean and SUVtotai; determining at least one standardized [F18]F-Ara-G uptake value observed in the second PET scan for the region selected from SUVmax, SUVmean and SUVtotai; comparing the standardized [F18]F-Ara-G uptake value observed in the first PET scan with the standardized [F18]F-Ara-G uptake value observed in the second PET scan; and identifying a region having greater than a 10% increase in [F18]F-Ara-G uptake as exhibiting immune activation; and / or identify ing a region having greater than a 10% decrease in [F18]F-Ara-G uptake as exhibiting immune deactivation.

16. The method of claim 11, wherein the subject is a patient diagnosed with a malignancy and the ratio of the signal intensity observed provides information on T cell localization and function in relation to tumor burden.

17. The method of claim 7, wherein a period of time between the first scan and the second scan is at least 1, 4, 7, 10 or 14 days.

18. The method of claim 11 , wherein a ratio of signal intensity of at least 0.3 (0.25-0.4) identifies the observed T cells as antigen experienced druggable T cells.

19. A method of observing the immunomodulatory effects of a chemotherapy regimen comprising:selecting a patient diagnosed a pathological condition treatable with a chemotherapy or immunomodulatory regimen;(a) observing T cell localization and function / mitochondrial biogenesis associated with systemic immunity in a subject prior to initiation of the chemotherapy regimen via positron emission tomography (PET) comprising administering [F18]F- Ara-G to the subject and a performing a first [F18]F-Ara-G PET scan so as to observe [F18]F-Ara-G accumulation in at least 3 regions of [F18]F-Ara-G accumulation;(b) observing T cell localization and function / mitochondrial biogenesis associated with systemic immunity in a subject following initiation of the chemotherapy regimen via PET comprising administering [F18]F-Ara-G to the subject and a performing a first [F18]F-Ara-G PET scan so as to observe [F18]F-Ara-G accumulation in the at least 3 regions of [F18]F-Ara-G accumulation; observing the presence of functional T cells in the patient in (a) and (b) by observing accumulation of [F18]F-Ara-G in the at least 3 regions of [F18]F-Ara-G accumulation such that the immunomodulatory effects of a chemotherapy regimen are observed.

20. The method of claim 19, wherein the method comprises: quantifying levels of [F18]F-Ara-G observed in (a) and (b); and comparing levels of [F18]F-Ara-G observed in (a) and (b).

Images