FIELD OF THE INVENTION

This invention relates generally to image processing techniques. In particular embodiments, the invention relates to processing of an MM image for mapping of cell types based on their endogenous iron concentrations.

BACKGROUND

Stromal infiltration of macrophages is implicated in the progression of many types of cancer, including prostate cancer (PCa). Depletion of tumor-associated macrophages (TAMs) inhibits tumor growth and enhances radiation therapy response. An important factor determining macrophage infiltration is tissue iron load because their high innate iron metabolism primes them towards an iron scavenging immune response. Currently, exogenous iron contrast agents are used with MRI (magnetic resonance imaging) to monitor macrophage levels in vivo. However, iron contrast agents can hyperpolarize the iron metabolism of macrophages promoting a pro-tumor phenotype, therefore biasing the measurement of TAM infiltration.

Thus, current ability to non-invasively assess TAM number, and monitor TAM response over the full course of therapeutic regimens using MRI is limited, leaving a critical gap in the understanding of immune response to cancer therapy and how to enhance it further. This is because label-based MRI approaches are hampered by the contrast agent itself due to various factors, such as variable cell uptake and limited lifetime of the cellular label (<1 week).

There is a need for systems and methods for mapping of cell-types based on their endogenous iron concentrations which do not require administration of iron contrast agents.

SUMMARY OF INVENTION

Described herein are systems and methods for image-based spatial and temporal mapping of macrophages and other cell types, without the need for image contrast agents. These systems and methods are particularly useful for imaging macrophages because they naturally store metabolites such as iron. Alternatively, the systems and methods described herein can be used where contrast agents are administered, rather than looking only at endogenous metabolite (e.g., iron) deposits.

In various embodiments, the approach described herein is referred to as FeMRI. The FeMRI technique exploits the high affinity of macrophages for iron(III) to quantitatively monitor TAM infiltration in vivo without contrast agents. In certain embodiments, the FeMRI technique creates an iron(III) map that covers a discrete quantifiable concentration range at a given magnetic field. The sizes of the concentration ranges are magnetic field dependent, and the detected iron concentration ranges can be divided into an arbitrary number of sub-ranges. Corresponding pixels in these ranges are stratified from the FeMRT maps, and the location and size of pixel clusters having corresponding iron concentrations is measured. The technique has been validated with quantitative histology, and used to measure infiltration of TAMs in orthotopic models of prostate cancer during iron(III) chelation therapy, as described herein. The TAM number was found to be higher in larger tumors, but chelation was able to reduce TAMs and achieve impressive reductions in tumor growth. These results demonstrate the ability to monitor macrophages and their immune response to cancer therapy with the non-perturbing MM approach described herein, and modulate tumor growth and TAM infiltration by targeting their innate metabolism of iron.

Macrophage infiltration appears to be correlated with their innate iron(III) metabolism. The TAM number as estimated non-invasively by FeMRT may serve as a predictor of therapeutic efficacy. By using embodiments of the FeMRT quantitative imaging approach described herein, macrophage infiltration can be monitored over a range of physiological and therapeutic conditions, with the goal of exploiting the immune system to improve therapeutic outcomes and monitor this process. Furthermore, TAMs identified and/or monitored/tracked via the methods and systems described herein may serve as predictive biomarkers of therapeutic response, e.g., during cancer therapy. In certain embodiments, the techniques described herein image, map, and/or monitor iron(III) concentration ranges at a given magnetic field that are helpful for macrophage tracking. For example, the techniques described herein cover a discrete quantifiable concentration range at a given magnetic field (e.g., wherein the concentration range is a range equivalent to or within endpoints of about 0 to about 0.3 mg/g iron, e.g., at 7 T).

In certain embodiments, the systems and methods described herein are applicable to other image types. In other embodiments, the systems and methods described herein are applicable to other image types in addition to the quantitative FeMRI approach described above. For example, macrophage infiltration can be identified in other types of functional (e.g., nuclear) imaging modalities. In particular, FeMRI images can be recorded in combination with CT images and/or PET images in a similar fashion to FeMRI images. Accordingly, in certain embodiments, the systems and methods described herein can be applied to analysis of PET/CT images in order to identify regions of FeMRI images that correspond to particular cell types, as is relevant for assessing states of cancer in a subject.

In certain embodiments, the systems and methods described herein are applicable in drug development and personalized medicine, and can be helpful for monitoring response to various treatments.

In one aspect, the invention is directed to a method of mapping one or more cell types in an image, the method comprising: (a) accessing (e.g., obtaining) an image of a subject; (b) generating a parametric map (e.g., a metabolite (e.g., iron(III)) map) (2D or 3D map) from the image; (c) generating a spatial (2D or 3D) map of pixels (or voxels) within one or more discrete metabolite (e.g., iron) concentration ranges using the parametric map in step (b), thereby identifying each of one or more regions of the image with a corresponding metabolite concentration range; and (d) for each of the one or more regions of the image, identifying a particular cell type according to its corresponding metabolite concentration range.

In certain embodiments, the image comprises an MRI image, a computational tomography (CT) image, a positron emission tomography (PET) image, a single-photon emission computed tomography (SPECT) image, an optical image, fluorescence image, an ultrasound image, and/or any combination thereof. In certain embodiments, the image comprises an MM image.

In certain embodiments, the metabolite depicted in the spatial map comprises a member selected from group consisting of iron, glucose, amino acids, nucleotides, vitamins, antioxidants, proteins, and lipids. In certain embodiments, the metabolite depicted in the spatial map comprises iron (e.g., iron(III)).

In certain embodiments, the image comprises an MRI image, and wherein the metabolite depicted in the spatial map comprises iron (e.g., iron(III)).

In certain embodiments, the parametric map is a visual representation of an indirectly measured parameter that is mapped as a function of a directly recorded imaging parameter. In certain embodiments, the indirectly measured parameter is a member selected from the group consisting of a concentration, a perfusion rate, and a diffusion coefficient, and wherein the directly measured imaging parameter is a member selected from the group consisting of a signal contrast level and a signal relaxation time.

In certain embodiments, the one or more cell types comprise macrophages. In certain embodiments, the one or more cell types comprise tumor-associated macrophages (TAMs). In certain embodiments, the one or more cell types comprise cancer cells (e.g., prostate cancer cells, e.g., breast cancer cells, e.g., melanoma cancer cells) (e.g., wherein the cancer cells comprise a member selected from the group consisting of adrenocortical carcinoma cells, astrocytoma cells, basal cell carcinoma cells, carcinoid cells, cardiac cells, cholangiocarcinoma cells, chordoma cells, chronic myeloproliferative neoplasms cells, craniopharyngioma cells, ductal carcinoma in situ, ependymoma cells, intraocular melanoma cells, gastrointestinal carcinoid tumor cells, gastrointestinal stromal tumor (GIST) cells, gestational trophoblastic disease cells, glioma, histiocytosis cells, leukemia cells, merkel cell carcinoma cells, mesothelioma cells, myeloma cells, myelodysplastic syndrome cells, papillomatosis cells, paraganglioma cells, pheochromacytoma cells, pleuropulmonary blastoma cells, retinoblastoma cells, sarcoma cells, and Wilms' tumor cells).

In certain embodiments, step (b) comprises generating the map (2D or 3D map) from the image using a calibration curve. In certain embodiments, step (b) comprises generating the map (2D or 3D map) from the image using a regression analysis (e.g., wherein the metabolite is iron, e.g., of R2*=1/T2* vs. mg iron(III) g−1).

In certain embodiments, the map covers a discrete quantifiable concentration range at a given magnetic field.

In certain embodiments, the parametric map comprises an iron(III) map, and wherein the concentration range is a range equivalent to or within endpoints of about 0 to about 0.3 mg/g iron, e.g., at 7 T. In certain embodiments, the parametric map comprises an iron(III) map, and wherein the concentration range is a range having at least 0.2 or at least 0.3 mg/g iron as an upper bound. In certain embodiments, the parametric map is an iron(III) map, and wherein the concentration range spans a range at least about 0.1 mg/g iron, e.g., at least about 0.15 mg/g iron, e.g., at least about 0.2 mg/g iron).

In certain embodiments, the given magnetic field affects the discrete quantifiable concentration range, e.g., the specific concentration range (e.g., specific iron concentration range) that is utilized is field strength dependent and varies with magnetic field).

In certain embodiments, the metabolite (e.g., iron) being mapped is endogenous metabolite (e.g., iron) only (i.e., where no metabolite-containing contrast agent has been administered to the subject).

In certain embodiments, the step of generating the spatial (2D or 3D) map of pixels (or voxels) within one or more discrete metabolite concentration ranges using the map in step (b) is conducted by performing histogram-based thresholding of the map.

In certain embodiments, the method comprises quantifying extent of infiltration of tumor-associated macrophages (TAMs) by mapping regions of the image (e.g., MRI image) with high metabolite (e.g., iron) concentration as a function of percent infiltration of a tumor.

In certain embodiments, the metabolite comprises iron, and the high iron concentration is in a range from about 0.1 to about 0.2 mg/g iron (e.g., from 0.15 to 0.16 mg/g iron).

In certain embodiments, the map covers multiple discrete quantifiable ranges, e.g., two or more ranges, e.g., a high range and a low range (e.g., wherein the range is defined by the median of the range at a given magnetic field strength).

In certain embodiments, the metabolite (e.g., iron) concentration is quantified by performing concentric rake sampling.

In certain embodiments, the method further comprises: (e) performing steps (a) to (d) for each of a plurality of images obtained from the subject at different times to monitor progression of macrophages in the subject.

In certain embodiments, the method comprises monitoring immune response to cancer therapy.

In another aspect, the invention is directed to a system for mapping one or more cell types in an image, the system comprising: a processor; and a memory having instructions stored thereon, wherein the instructions, when executed by the processor, cause the processor to: (a) access an image of a subject; (b) generate a parametric map (e.g., a metabolite (e.g., iron(III)) map) (2D or 3D map) from the image; (c) generate a spatial (2D or 3D) map of pixels (or voxels) within one or more discrete metabolite concentration ranges using the map in step (b), thereby identifying each of one or more regions of the image with a corresponding metabolic concentration range; and(d) for each of the one or more regions of the MRI image, identify a particular cell type according to its corresponding metabolite concentration range.

In certain embodiments, the image comprises an MRI image, a computational tomography (CT) image, a positron emission tomography (PET) image, a single-photon emission computed tomography (SPECT) image, an optical image, fluorescence image, an ultrasound image, and/or any combination thereof. In certain embodiments, the image comprises an MM image.

In certain embodiments, the metabolite depicted in the spatial map comprises a member selected from group consisting of iron, glucose, amino acids, nucleotides, vitamins, antioxidants, proteins, and lipids. In certain embodiments, the metabolite depicted in the spatial map comprises iron (e.g., iron(III)).

In certain embodiments, the image comprises an MM image, and wherein the metabolite depicted in the spatial map comprises iron (e.g., iron(III)).

In certain embodiments, the parametric map is a visual representation of an indirectly measured parameter that is mapped as a function of a directly recorded imaging parameter. In certain embodiments, the indirectly measured parameter is a member selected from the group consisting of a concentration, a perfusion rate, and a diffusion coefficient, and wherein the directly measured imaging parameter is a member selected from the group consisting of a signal contrast level and a signal relaxation time.

In certain embodiments, the one or more cell types comprise macrophages. In certain embodiments, the one or more cell types comprise tumor-associated macrophages (TAMs). In certain embodiments, the one or more cell types comprise cancer cells (e.g., prostate cancer cells, e.g., breast cancer cells, e.g., melanoma cancer cells) (e.g., wherein the cancer cells comprise a member selected from the group consisting of adrenocortical carcinoma cells, astrocytoma cells, basal cell carcinoma cells, carcinoid cells, cardiac cells, cholangiocarcinoma cells, chordoma cells, chronic myeloproliferative neoplasms cells, craniopharyngioma cells, ductal carcinoma in situ, ependymoma cells, intraocular melanoma cells, gastrointestinal carcinoid tumor cells, gastrointestinal stromal tumor (GIST) cells, gestational trophoblastic disease cells, glioma, histiocytosis cells, leukemia cells, merkel cell carcinoma cells, mesothelioma cells, myeloma cells, myelodysplastic syndrome cells, papillomatosis cells, paraganglioma cells, pheochromacytoma cells, pleuropulmonary blastoma cells, retinoblastoma cells, sarcoma cells, and Wilms' tumor cells).

In certain embodiments, step (b) comprises generating the map (2D or 3D map) from the image using a calibration curve. In certain embodiments, step (b) comprises generating the map (2D or 3D map) from the image (e.g., Mill) using a regression analysis (e.g., wherein the metabolite is iron, e.g., of R2*=1/T2* vs. mg iron(III) g−1).

In certain embodiments, the map covers a discrete quantifiable concentration range at a given magnetic field.

In certain embodiments, the map comprises an iron(III) map, and wherein the concentration range is a range equivalent to or within endpoints of about 0 to about 0.3 mg/g iron, e.g., at 7 T. In certain embodiments, the parametric map comprises an iron(III) map, and wherein the concentration range is a range having at least 0.2 or at least 0.3 mg/g iron as an upper bound. In certain embodiments, the parametric map comprises an iron(III) map, and wherein the concentration range spans a range at least about 0.1 mg/g iron, e.g., at least about 0.15 mg/g iron, e.g., at least about 0.2 mg/g iron.

In certain embodiments, the given magnetic field affects the discrete quantifiable concentration range, e.g., the specific concentration range (e.g., specific iron concentration range) that is utilized is field strength dependent and varies with magnetic field).

In certain embodiments, the metabolite (e.g., iron) being mapped is endogenous metabolite only (i.e., where no metabolite-containing contrast agent has been administered to the subject).

In certain embodiments, the step of generating the spatial (2D or 3D) map of pixels (or voxels) within one or more discrete metabolite concentration ranges using the map in step (b) is conducted by performing histogram-based thresholding of the map.

In certain embodiments, the instructions cause the processor to quantify extent of infiltration of tumor-associated macrophages (TAMs) by mapping regions of the image (e.g., MRI image) with high metabolite (e.g., iron) concentration as a function of percent infiltration of a tumor.

In certain embodiments, the metabolite comprises iron, and wherein the high metabolite concentration is in a range from about 0.1 to about 0.2 mg/g iron (e.g., from 0.15 to 0.16 mg/g iron).

In certain embodiments, the map covers multiple discrete quantifiable ranges, e.g., two or more ranges, e.g., a high range and a low range (e.g., wherein the range is defined by the median of the range at a given magnetic field strength).

In certain embodiments, the metabolite (e.g., iron) concentration is quantified by performing concentric rake sampling.

In certain embodiments, the instructions cause the processor to: (e) perform steps (a) to (d) for each of a plurality of images (e.g., MM) obtained from the subject at different times to monitor progression of macrophages in the subject.

In certain embodiments, the system monitors immune response to cancer therapy.

Elements of embodiments involving one aspect of the invention (e.g., methods) can be applied in embodiments involving other aspects of the invention (e.g., systems), and vice versa.

DEFINITIONS

In order for the present disclosure to be more readily understood, certain terms are first defined below. Additional definitions for the following terms and other terms are set forth throughout the specification.

In this application, the use of “or” means “and/or” unless stated otherwise. As used in this application, the term “comprise” and variations of the term, such as “comprising” and “comprises,” are not intended to exclude other additives, components, integers or steps. As used in this application, the terms “about” and “approximately” are used as equivalents. Any numerals used in this application with or without about/approximately are meant to cover any normal fluctuations appreciated by one of ordinary skill in the relevant art. In certain embodiments, the term “approximately” or “about” refers to a range of values that fall within 25%, 20%, 19%, 18%, 17%, 16%, 15%, 14%, 13%, 12%, 11%, 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1%, or less in either direction (greater than or less than) of the stated reference value unless otherwise stated or otherwise evident from the context (except where such number would exceed 100% of a possible value).

“Administration”: The term “administration” refers to introducing a substance (e.g., an iron-containing contrast agent) into a subject. In general, any route of administration may be utilized including, for example, parenteral (e.g., intravenous), oral, topical, subcutaneous, peritoneal, intraarterial, inhalation, vaginal, rectal, nasal, introduction into the cerebrospinal fluid, or instillation into body compartments. In some embodiments, administration is oral. Additionally or alternatively, in some embodiments, administration is parenteral. In some embodiments, administration is intravenous.

“Cancer”: As used herein, the term “cancer” refers to a disease, disorder, or condition in which cells exhibit relatively abnormal, uncontrolled, and/or autonomous growth, so that they display an abnormally elevated proliferation rate and/or aberrant growth phenotype characterized by a significant loss of control of cell proliferation. In certain embodiments, a cancer may be characterized by one or more tumors. Those skilled in the art are aware of a variety of types of cancer including, for example, adrenocortical carcinoma, astrocytoma, basal cell carcinoma, carcinoid, cardiac, cholangiocarcinoma, chordoma, chronic myeloproliferative neoplasms, craniopharyngioma, ductal carcinoma in situ, ependymoma, intraocular melanoma, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), gestational trophoblastic disease, glioma, histiocytosis, leukemia (e.g., acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL), chronic myelogenous leukemia (CML), hairy cell leukemia, myelogenous leukemia, myeloid leukemia), lymphoma (e.g., Burkitt lymphoma [non-Hodgkin lymphoma], cutaneous T-cell lymphoma, Hodgkin lymphoma, mycosis fungoides, Sezary syndrome, AIDS-related lymphoma, follicular lymphoma, diffuse large B-cell lymphoma), melanoma, merkel cell carcinoma, mesothelioma, myeloma (e.g., multiple myeloma), myelodysplastic syndrome, papillomatosis, paraganglioma, pheochromacytoma, pleuropulmonary blastoma, retinoblastoma, sarcoma (e.g., Ewing sarcoma, Kaposi sarcoma, osteosarcoma, rhabdomyosarcoma, uterine sarcoma, vascular sarcoma), Wilms' tumor, and/or cancer of the adrenal cortex, anus, appendix, bile duct, bladder, bone, brain, breast, bronchus, central nervous system, cervix, colon, endometrium, esophagus, eye, fallopian tube, gall bladder, gastrointestinal tract, germ cell, head and neck, heart, intestine, kidney (e.g., Wilms' tumor), larynx, liver, lung (e.g., non-small cell lung cancer, small cell lung cancer), mouth, nasal cavity, oral cavity, ovary, pancreas, rectum, skin, stomach, testes, throat, thyroid, penis, pharynx, peritoneum, pituitary, prostate, rectum, salivary gland, ureter, urethra, uterus, vagina, or vulva.

“Cells”: As used herein, the term “cells” includes any normal or diseased cells including, but not limited to, cells such as trichocytes, keratinocytes, gondaotrophes, corticotropes, thyrotropes, somatorropes, lacroptrophs, neurons, glia cells, chromaffin cells, parafollicular cells, glomus cells, melanocytes, Merkel cells, odontoblasts, cementoblasts, keratocytes, ependymocytes, pinealocytes, enteroendocrines, S cells, delta cells, goblet cells, Paneth cells, tuft cells, enterocytes, hepatocytes, hepatic stellates, Kuppfer cells, cholestocytes, centroacinar cells, pancreatic stellate cells, alpha cells, beta cells, F cells, thyroid cells, parathyroid cells, urothelial cells, osteoblasts, osteolytes, chrodblasts, chondrocytes, lipoblasts, adipocytes, myoblasts, mycocytes, myosatellaites, tendon cells, cardiac cells, fibroblast cells, fibrocytes, interstitial cells of Cajal, angioblasts, endothelial cells, mesangial cells, juxtaglomerular cells, macula densa cells, stroma cells, telocytes, simple epithelial cells, podocytes, kidney proximal tubule brush border cells, sertoli cells, leydig cells, granulosa cells, peg cells, germ cells, lymphoid cells, myeloid cells, endothelial progenitor cells, endothelial colony forming cells, endothelial stem cells, angioblasts, pericytes, and mural cells.

“Detector”: As used herein, the term “detector” includes any detector of electromagnetic radiation used during the acquisition of an image using techniques such as, but not limited to, X-ray radiography, magnetic resonance imaging, ultrasonography, endoscopy, elastography, tactile imaging, thermography, photography, nuclear, positron emission tomography, and single-photon emission computed tomography.

“Disease”: As used herein, the term “disease” refers to any abnormal medical condition caused by factors including, but not limited to, cancer, cutaneous conditions, endocrine disorder, infection, acquired disorders congenital, genetic, hereditary, latrogenic disorder, idiopathic disorders, liver disorders, heart disorders, and metabolic disorders.

“Image”: The term “image”, as used herein, includes any visual representation, such as a photo, a video frame, streaming video, as well as any electronic, digital or mathematical analogue of a photo, video frame, or streaming video. An image can be two-dimensional or three-dimensional. A three-dimensional image may be stored and/or rendered as a dataset in three dimensions and/or may be displayed as a set of two-dimensional representations, or as a three-dimensional representation. Systems described herein, in certain embodiments, includes a display for displaying an image or any other result produced by the processor. Methods described herein, in certain embodiments, include a step of displaying an image or any other result produced via the method. For example, an image comprises a magnetic resonance image, a computational tomography (CT) image, a positron emission tomography (PET) image, an optical image, a fluorescence image, an ultrasound image, or any combination thereof.

“Metabolite”: The term “metabolite”, as used herein, includes any exogenous or endogenous chemical substance, or chemical reaction products from these substances. Metabolites can be, but are not limited to, iron, glucose, amino acids, nucleotides, vitamins, antioxidants, proteins, and lipids.

“Parametric map”: The term “parametric map”, as used herein, is a visual representation of an indirectly measured parameter that is mapped as a function of a directly recorded imaging parameter using a theoretical or empirical mathematical relationship between the variables. A directly recorded imaging parameter may include, but is not limited to, a signal contrast level or a signal relaxation time, while an indirectly recorded imaging parameter may include, but is not limited to, a concentration, perfusion rate, or diffusion coefficient, for example. The mathematical relationship allowing the directly measured parameter to be expressed in terms of the indirectly measured parameter may include, but is not limited to, linear regression, non-linear regression, or other statistical analysis.

“Subject”: As used herein, the term “subject” includes humans and mammals (e.g., mice, rats, pigs, cats, dogs, and horses). In many embodiments, subjects are mammals, particularly primates, especially humans. In some embodiments, subjects are 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. In some embodiments (e.g., particularly in research contexts) subject mammals will be, for example, rodents (e.g., mice, rats, hamsters), rabbits, primates, or swine such as inbred pigs and the like.

“Therapeutic agent”: As used herein, the phrase “therapeutic agent” refers to any agent that has a therapeutic effect and/or elicits a desired biological and/or pharmacological effect, when administered to a subject.

“Treatment”: As used herein, the term “treatment” (also “treat” or “treating”) refers to any administration of a substance that partially or completely alleviates, ameliorates, relieves, inhibits, delays onset of, reduces severity of, and/or reduces incidence of one or more symptoms, features, and/or causes of a particular disease, disorder, and/or condition. Such treatment may be of a subject who does not exhibit signs of the relevant disease, disorder and/or condition and/or of a subject who exhibits only early signs of the disease, disorder, and/or condition. Alternatively or additionally, such treatment may be of a subject who exhibits one or more established signs of the relevant disease, disorder and/or condition. In certain embodiments, treatment may be of a subject who has been diagnosed as suffering from the relevant disease, disorder, and/or condition. In certain embodiments, treatment may be of a subject known to have one or more susceptibility factors that are statistically correlated with increased risk of development of the relevant disease, disorder, and/or condition. In certain embodiments, treatment comprises delivery of therapeutics, including but not limited to, small molecule delivery, radiotherapy, immunotherapy, intrinsic therapeutic properties (e.g., ferroptosis), and particle-driven regulation of the tumor microenvironment. In certain embodiments, therapeutics are attached to particles, such as those described herein.

