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. Author manuscript; available in PMC: 2020 Jun 15.
Published in final edited form as: Clin Cancer Res. 2019 Sep 23;25(24):7463–7474. doi: 10.1158/1078-0432.CCR-19-1106

ImmunoPET, [64Cu]Cu-DOTA-anti-CD33 PET-CT, imaging of an AML Xenograft model

Srideshikan Sargur Madabushi 1, Jamison Brooks 1, Darren Zuro 1, Bijender Kumar 1, James Sanchez 3, Liliana Echavarria Parra 1, Marvin Orellana 1, Paresh Vishwasrao 1, Indu Nair 1, Junie Chea 2, Kofi Poku 2, Nicole Bowles 2, Aaron Miller 2, Todd Ebner 2, Justin Molnar 2, Joseph Rosenthal 6, Daniel A Vallera 4, Jeffrey Y C Wong 1, Anthony S Stein 5, David Colcher 2, John E Shively 2, Paul J Yazaki 2, Susanta K Hui 1
PMCID: PMC6911626  NIHMSID: NIHMS1540349  PMID: 31548348

Abstract

Purpose

Acute myeloid leukemia (AML) is a highly aggressive form of leukemia that results in poor survival outcomes. Currently, diagnosis and prognosis are based on invasive single-point bone marrow biopsies (iliac crest). There is currently no AML-specific non-invasive imaging method to detect disease, including in extramedullary organs, representing an unmet clinical need. About 85–90% of human myeloid leukemia cells express CD33 cell surface receptors, highlighting CD33 as an ideal candidate for AML immunoPET.

Experimental Design

We evaluated whether [64Cu]Cu-DOTA-anti-CD33 murine monoclonal antibody (mAb) can be used for immunoPET imaging of AML in a preclinical model. MicroCT was adjusted to detect spatial/anatomical details of PET activity. For translational purposes, a humanized anti-CD33 antibody was produced; we confirmed its ability to detect disease and its distribution. We reconfirmed/validated CD33 antibody-specific targeting with an antibody-drug conjugate (ADC) and radioimmunotherapy (RIT).

Results

[64Cu]Cu-DOTA-anti-CD33-based PET-CT imaging detected CD33+ AML in mice with high sensitivity (95.65%) and specificity (100%). The CD33+ PET activity was significantly higher in specific skeletal niches [femur (p<0.00001), tibia (p=0.0001), humerus (p=0.0014), and lumber spine (p<0.00001)] in AML-bearing mice (over non-leukemic control mice). Interestingly, the hybrid PET-CT imaging showed high disease activity in the epiphysis/metaphysis of the femur, indicating regional spatial heterogeneity. Anti-CD33 therapy using newly developed humanized anti-CD33 mAb as an ADC (p=0.02) and [225Ac]Ac-anti-CD33-RIT (p<0.00001) significantly reduced disease burden over that of respective controls.

Conclusion

We have successfully developed a novel anti-CD33 immunoPET-CT–based non-invasive modality for AML and its spatial distribution, indicating a preferential skeletal niche.

Keywords: AML-ImmunoPET-CT, AML heterogeneity, Anti-CD33-PET, [225Ac]Ac-RIT, CD33-RIT

Introduction

Acute myeloid leukemia (AML) is a highly aggressive hematopoietic malignancy with an extremely poor prognosis, as reflected by an overall 5-year survival rate of 40%−45% in young adults and <10% in the elderly (>65 years of age) (1). Research over the past decades has helped us understand the pathobiology, classification and genomic landscape of the disease, which has resulted in improving current treatment options. Despite advances, the prognosis for elderly patients, who account for the majority of new AML cases, remains discouraging (2). More than 70% of elderly AML patients (> 65 years old) will die of their disease within 1 year of diagnosis and treatment (3). Therefore, new diagnostic and therapeutic approaches are necessary to improve outcomes.

Currently, the diagnostic criteria for AML is the presence of ≥20% blasts in the bone marrow or peripheral blood (4). AML diagnosis and prognosis are currently achieved by single-point bone marrow biopsies (iliac crest) followed by cytogenetics and mutation analysis. However, the iliac crest may not always be accurately representative of disease distribution within the entire body, particularly in the context of extramedullary disease, which is common in AML. Hence, there is need for new diagnostic tools that are non-invasive, specific and sensitive to AML in the whole body, including extramedullary organs, and useful to longitudinally monitor disease and treatment response.

CD33, or SIGLEC3, is a cell surface marker found on myeloid stem cells, monoblasts, myeloblasts, monocytes/macrophages, and granulocytic precursors. However, CD33 is not expressed on erythrocytes, platelets, B cells, T cells, or NK cells, making it a suitable myeloid marker and therefore commonly used in the diagnosis of AML. CD33 has been shown to be expressed on more than 85% of AML cells (blasts) (5), and an increased level of CD33 has been correlated with poor survival (6). An anti-CD33 antibody drug conjugate (ADC) (Mylotarg) is an FDA-approved immunotherapy for AML.

Although an anti-CD33 monoclonal antibody (mAb) is used for therapeutic purposes, no effort has been reported for developing it as a quantitative diagnostic and prognostic imaging biomarker. An anti-CD33 imaging modality may significantly improve these therapies, from patient stratification to post-therapy monitoring. Therefore, in this study, we aimed to develop an AML-specific diagnostic imaging modality; we hypothesized that an anti-CD33 mAb would be an ideal candidate for immuno-PET imaging of AML.

Previous clinical studies have reported whole-body-gamma imaging of patients with myeloid leukemia using anti-CD33 mAb (HuM195) conjugated with Iodine-131 and Bismuth-213, but these studies were not intended for diagnosing AML but rather studying pharmacokinetics and the targeting of the mAb to the bone marrow, the primary site of the disease (7–10). Additionally, the imaging modality used was planar or two-dimensional and hence only qualitative.

On the other hand, whole body positron emission tomography-computed tomography (PET-CT) is an important dual-imaging modality used in nuclear medicine. In PET-CT diagnostic three-dimensional imaging, PET imaging provides physiological and biochemical quantitative information to identify normal versus malignant lesions, and inclusion/optimization of the whole body microCT imaging may provide spatial distribution of the disease in the whole body. Furthermore, PET-CT is currently used in several investigative non-invasive whole body diagnostic imaging studies of hematological and non-hematological malignancy (11). Particularly in AML, there are several clinical trials ongoing using PET-CT diagnostic imaging (e.g., and our ) suggesting an unmet clinical need for an AML-specific imaging modality.

The primary goal of this study was to evaluate CD33 as a diagnostic imaging marker of AML using an anti-CD33 monoclonal antibody (mAb) in a preclinical mouse model. For proof of concept, we first used the murine [64Cu]Cu-DOTA-anti-CD33 (clone p67.6) mAb for immuno-PET-CT imaging of CD33+ AML cells (diagnostic) in a mouse xenograft model. However, besides detecting AML, this imaging modality interestingly also provided information about the spatial distribution of disease in the whole body. Then, with a translational objective, we produced a humanized anti-CD33 monoclonal antibody (clone Hu-M195) and showed detection of CD33+ AML in preclinical models using the newly developed CD33 PET-CT imaging.

Materials and Methods

Antibody

Murine anti-human CD33 clone p67.6 is an IgG1 kappa mAb that targets human CD33-positive cells of the myeloid lineage (12,13). The hybridoma was produced in a hollow fiber bioreactor and purified by Protein G and cation exchange chromatography. Anti-human CD33 (Clone WM 53, # 555450) was obtained from BD Biosciences. Anti-human CD45 (clone: 2D1, # 368516) was obtained from BioLegend, San Diego, CA.

Humanized anti-CD33

The murine anti-CD33 M195 mAb was humanized by CDR grafting to reduce human anti-mouse antibody responses (14,15). The scFv was reformatted to a human IgG1 antibody by cDNA synthesis, transiently expressed in HEK293 cells and purified by Protein A chromatography.

Anti-human CD33 antibody DOTA and Copper-64 conjugation

The mouse and humanized anti-human CD33 mAb were conjugated with the metal chelator 1,4,7,10-tetraazacyclododecane-N,N′,N″,N′″-tetraacetic acid (NHS-DOTA; Macrocyclics, Dallas, TX) as previously described (16). The detailed methods are provided in the Supplemental Methods section.

Cell lines

Human AML cells (HL60, MV4–11, Kg1a), multiple myeloma (MM) cells (MM.1S), and Daudi cells (lymphoma) were cultured using standard tissue culture condition techniques. The use of human samples was approved by the City of Hope Institutional Review Board, and informed consent was obtained from patients. Human subjects research was in accordance with the Declaration of Helsinki. Detailed information regarding the cells and culture conditions are described in the Supplementary Methods section.

Flow cytometry

Flow cytometry was used to analyze CD33 and CD45 expression in human AML (MV4–11, HL 60 and Kg1a) and multiple myeloma cells (MM1S), using mouse anti-human CD33 and CD45 antibodies. Staining and flow cytometry analysis were performed as per standard protocols. Data were acquired from a BD Fortessa cytometer and analyzed with FlowJo V 10.0 software.

microPET-CT imaging and biodistribution studies

Mice bearing CD33+ AML cells (MV4–11, HL-60) or CD33− MM cells (MM.1S) or non-leukemic control mice were injected IV with murine [64Cu]Cu-DOTA-anti-CD33 (100 μCi/ 10 μg), or [64Cu]Cu-DOTA-anti-CD33 (100 μCi/ 10 μg) + 500 μg of unlabeled anti-CD33-DOTA (1:50). For the humanized anti-CD33 mAb, human IVIg was injected (~1 mg/mouse, ip) 2–3h prior to injecting humanized [64Cu]Cu-DOTA-anti-CD33 mAb (100 μCi/ 1 μg). However, murine mAb did not necessitate human IVIg for imaging and biodistribution studies. Each group consisted of at least 5 mice; representative data for each group are presented. Static PET scans were acquired at 1 day (40 min scan) and 2 days (60 min scan, with whole body CT at 100 μm resolution) post-injection using InVeon PET-CT (Siemens). MicroCT parameters were adjusted (voltage 80 kV, current 500 μA, 0.5 mm Al filter, 220 projection, scan time 264s) to obtain anatomical details of skeleton. Image reconstruction was performed using Inveon Acquisition Workplace (Siemens). Post-processed image display and analysis was performed by Vivoquant (InviCro, Boston, USA). For biodistribution studies, mice were euthanized at 24h and/or 48h, as both time points showed activity. Various organs were obtained from control and AML leukemia-bearing mice. Wet weighs of each organ were determined, and radioactive counts from each tissue/organs were measured using a WIZARD2 automatic gamma counter (PerkinElmer). Biodistribution and imaged PET activity of [64Cu]Cu-DOTA-anti-CD33 was presented as the percent injected dose per gram or organ/tissue (% ID/g). MV4–11 with cold-CD33 block and CD33− MM.1S served as negative controls. [64Cu]Cu-DOTA-anti-CD33- PET-CT imaging and biodistribution studies were conducted at least twice independently and the data is representative of one exepeirment (n≥5).

Mouse AML models: immunodeficient (NSG)

All animal experiments were carried out in accordance to the guidelines of Institutional Animal Care and Use Committee (IACUC). The NSG (NOD-scidIL2Rgnull) mice were purchased from the Jackson laboratory and were bred in a City of Hope animal breeding facility. The NSG mice were treated with 1–2 Gy radiation 24h before transplant as a preconditioning regime to ensure faster engraftment. Human AML and MM cells (1–2 × 106 cells) were injected via tail vein, and engraftment was determined using bioluminescent imaging (BLI) 7–10 days post-transplant. Biodistribution and [64Cu]Cu-DOTA-anti-CD33 mAb imaging studies and immunotherapy interventions were performed around 2–3 weeks post-transplant.

Murine and humanized anti-CD33 mAb blood pharmacokinetics

Humanized anti-CD33-DOTA mAb was radiolabeled as mentioned before with Indium-111 (Triad, Isotope, Los Angeles, CA). The blood activity clearance of murine and humanized [111In]In-DOTA-anti-CD33 mAb was tested by collecting blood from tail veins at different points post injections (0, 2, 4, 24, 48, 72h) and using a gamma counter. The detailed method is described in the Supplemental Methods section.

Humanized anti-CD33 and secondary (2°) Ab-ADC in vitro and in vivo

The therapeutic potential of humanized anti-CD33 mAb as an ADC was evaluated indirectly using anti-human IgG (Fc) 2° Ab conjugated to monomethyl auristatin E, (MMAE) (Moradec, LLC, San Diego, CA). The in vitro cytotoxicity assay was performed using a 1:1 molar ratio of anti-CD33 and 2°-ADC antibody. The primary and secondary antibody were first allowed to bind (~15 min) in complete IMDM medium and were then added to cells and incubated at 37°C, 5% CO2. The cytotoxicity was assayed after 5 days by measuring Annexin V and PI staining in these cells. The antibody amount used ranged from 1 ng-2.5 μg. As control, CD33− Daudi cells (lymphoma) and secondary Ab-ADC alone (1 μg) were used.

For in vivo study, 2.5 μg of humanized anti-CD33 antibody and 2.5 μg of secondary Ab-ADC were mixed, incubated for 15 min at 4°C, and then resuspended in 100 μl of PBS 0.1% BSA and retro-orbitally injected into mice bearing MV4–11 leukemia on day 0 and day 3. As controls, leukemic mice were untreated or given secondary Ab-ADC alone. Disease progression was monitored using BLI at indicated time points. The whole body of the mouse was contoured, and average BLI intensity (photons/s) for each group was plotted at different time points.

Humanized anti-CD33 radioimmunotherapy (CD33-RIT)

Humanized anti-CD33-DOTA mAb was radiolabeled as mentioned before with Actinium-225 (Oak Ridge National Laboratory, Tennessee) at a specific activity of 50 nCi/μg. For therapy, three different activities, 100 nCi, 200 nCi and 300 nCi of [225Ac]Ac-DOTA-anti-CD33, were used, with one single dose given to AML-bearing NSG mice (n=5). Untreated AML-bearing mice served as controls.

Statistical analysis

Statistical analysis was performed using ANOVA and the Student’s t-test. The Pearson correlation coefficients were calculated assuming a Gaussian distribution. The difference was considered significant when the p value was <0.05. All graphs and statistical analysis were generated using GraphPad Prism software V 7.2.

Other materials and methods

Detailed methods for anti-human CD33 (clone P67.6 Hu-M195) mAb DOTA and Copper-64 conjugation, CD33 surface expression quantification, BLI, lentiviral production and transduction in MV4–11 cells, CD33 antibody immunoreactivity, in vitro and in vivo stability, and blood clearance and diagnostic accuracy calculations are provided in the Supplemental Methods section.

Results

Generation of [64Cu]Cu-DOTA-anti-CD33 (p67.6) monoclonal antibody

For use as an immunoPET tracer, the anti-human CD33 mAb, mouse hybridoma P67.7, was produced, purified, conjugated to the metal chelate DOTA and radiolabeled with Copper-64. Coomassie stained SDS-PAGE gel electrophoresed under reducing conditions showed the purity, with 2 bands corresponding to the light and heavy chains (Supplemental Figure S 1A). Post-DOTA conjugation, iso-electrofocusing gel analysis revealed a shift to a more acidic pH, confirming the conjugation process (Supplemental Figure S 1B). The radiolabeled [64Cu]Cu-DOTA-anti-CD33 mAb was analyzed by size exclusion chromatography (SEC), and the radiochromatogram showed a single peak, with a retention time corresponding to a mAb (Supplemental Figure S 1C). The immunoreactivity of DOTA-anti-CD33 mAb was evaluated by incubating the [64Cu]Cu-DOTA-anti-CD33 with soluble CD33-Fc antigen and analyzed by SEC. The radiochromatogram showed a faster retention time (~30 min), indicating an increase in molecular size consistent with binding to CD33 Fc soluble antigen (67–85 kDa) (Supplemental Figure S1D, E). The [64Cu]Cu-DOTA-anti-CD33 mAb was shown to be stable in serum both in vitro and in vivo for 48h (Supplemental Figure S 1F,G).

CD33 cell surface expression in AML cells

The murine DOTA-anti-CD33 mAb and a validated commercial anti-CD33 antibody (clone WM53) were tested for binding and specificity by flow cytometry using CD33 positive MV4–11, HL-60 cells, Kg1a, THP-1 and the CD33-negative MM.1S-GFP cells. The DOTA-anti-CD33 antibody showed immunoreactivity towards CD33-positive AML cell lines but not in CD33−negative MM.1S cells (Supplemental Figure S 2A, B). The immunoreactivity of the antibody was further confirmed by immunofluorescence: HL60 cells showed brighter staining than MV4–11, whereas negative control MM.1S showed no staining (Supplemental Figure S 2C). The total number of CD33 cell surface receptors per cell was determined using the BD Quantibrite PE kit (Supplemental Figure S 3A–C). The HL-60 AML cell line expressed ~55,000 cell surface CD33/cell, versus ~26,000/cell in MV4–11 (Supplemental Figure S 3C).

PET-CT imaging and biodistribution of CD33+ AML cells in mouse model

[64Cu]Cu-DOTA-anti-CD33 mAb immuno PET-CT in vivo imaging was performed in NSG mice bearing CD33+ AML cells and CD33− MM cells, and in non-leukemic control mice. Mice were injected with [64Cu]Cu-DOTA-anti-CD33 mAb (100 μCi/10 μg) 24–48h prior to PET imaging. As an additional control for specificity, [64Cu]Cu-DOTA-anti-CD33 mAb (100 μCi/ 10 μg) + 500 μg of unlabeled DOTA-anti-CD33-mAb (1:50) were injected into mice bearing CD33+ AML cells. Bioluminescent imaging of AML and MM.1S cells in NSG mice was carried out 24h prior to [64Cu]Cu-DOTA-anti-CD33 mAb injections. The engraftment of AML and MM cells was also measured in the femur, L-spine, and spleen using flow cytometry upon harvesting tissues/organs after imaging and biodistribution studies. Three-dimensional intensity projection based PET-CT images clearly show CD33+ activity only in MV4–11 AML mice, but not in mice given cold-blocked anti-CD33, mice with MM.1S, and non-leukemic control mice (Fig. 1A, B), while the BLI images of the respective mice shows engraftment (Fig. 1C). CD33+ PET activity was detected in the epiphysis/metaphysis of the femur, tibia, and humerus; L-spine; and pelvic bone in CD33+ MV4–11 bearing mice (Fig. 1D). Although BLI and flow cytometry indicated high levels of myeloma cells in MM.1S bearing mice, there was no detectable CD33+ activity; similarly, the mice injected with cold-blocked unlabeled CD33 antibody showed no CD33+ PET signal, suggesting high specificity (Fig. 1 A–C).

Figure 1: PET-CT images of [64Cu]-DOTA-anti-CD33 antibody in AML and MM bearing mice.

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Representative PET-CT and bioluminescence images (BLI) are shown from AML, MM bearing, and no leukemia control mice. [64Cu]Cu-anti-CD33-DOTA (100 μCi/10 μg) was injected into these mice via tail vein 24–48h before PET-CT imaging or biodistribution was carried out. (A) Three-dimensional intensity projection based PET-CT images showing CD33 activity in AML bearing mice. (B) Matching PET-CT images rotated 90° showing CD33 activity in AML mice. (C) Bioluminescence (BLI) images of AML, cold-blocked AML, MM.1S and non-leukemic control mice. (D) PET-CT image of AML bearing mice highlighting CD33+ regions in the skeletal system. Imaged PET activity of [64Cu]Cu-DOTA-anti-CD33 is presented as the percentage of the injected activity per gram of organ/tissue. [64Cu]Cu-DOTA-anti-CD33 PET-CT imaging of AML bearing mice and non-leukemic control mice is representative of at least 2 independent experiments.

Biodistribution studies were carried out in mice from respective groups by harvesting different organs/tissues 24–48h post injection of the [64Cu]Cu-DOTA-anti-CD33 mAb, and the activity was measured using a gamma counter. The % ID/g of tissue/organs was plotted to determine the activity. The biodistribution of [64Cu]Cu-DOTA-anti-CD33 mAb was similar in all groups for blood, liver, lung, heart, muscle, intestine and kidney (Fig. 2 A, B). However, the % ID/g was particularly high in bones (femur, humerus, tibia and lumber spine) in CD33+ bearing MV4–11 AML mice, but not in CD33− MM mice, cold-blocked AML mice, or control non-leukemic mice (Fig. 2C). The mean % ID/g in all skeletal sites showed no significant difference between control, MM.1S bearing mice and cold blocked MV4–11 bearing mice. However, there was a significant difference in mean % ID/g between control and MV4–11 AML bearing mice in skeletal sites: femur (2.4 vs 4.77 %ID/g, p<0.00001); humerus (2.8 vs 5.83 % ID/g, p<0.00001); tibia (2.35 vs 4.07 %ID/g, p=<0.00001) and L spine (2.1 vs 4.41 %ID/g, p<0.00001). There was an average 2-fold increase in PET signal in CD33+ AML bearing mice over non-leukemic control mice. [64Cu]Cu-DOTA-anti-CD33 targeting was validated independently using a second AML cell line, HL-60, and similar biodistribution results were obtained in the skeleton: femur (2.3 vs 9.9 %ID/g p<0.00001); humerus (2.7 vs 10.3 %ID/g, p<0.00001); L spine (2.1 vs 8 %ID/g, p=0.0013) and tibia (2.2 vs 8 %ID/g, p=0.0015) (Fig. 2D, E). However, there was an average 4-fold increase in CD33+ activity in skeletal sites of HL-60 bearing mice.

Figure 2: Biodistribution of [64Cu]-DOTA-anti-CD33 antibody in AML and MM bearing mice.

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(A-C) Biodistribution of [64Cu]Cu-DOTA-anti-CD33 in bones and different tissues was conducted 24h/48h post injection. Plot of %ID/g of different tissues has been shown, indicating that CD33 activity is high in bones of MV4–11 mice, whereas no activity was seen in CD33− MM.1S, cold-blocked MV4–11, or non-leukemic control mice. The %ID/g between groups was insignificant for blood, heart, liver, lung and kidney. (D and E) Biodistribution of [64Cu]Cu-DOTA-anti-CD33 in bones and soft tissues of HL-60 bearing mice in comparison to that of control mice. Statistical significance was determined using ANOVA/ ‘t’ test and considered significant when <0.05. Biodistribution of [64Cu]Cu-DOTA-anti-CD33 is presented as the percentage of the injected activity per gram of organ/tissue.

The sensitivity and specificity were calculated from biodistribution of the [64Cu]Cu-DOTA-anti-CD33 mAb injected AML mice. The ROC curves showing sensitivity vs 100-specificity for biodistribution data from the femur indicate high sensitivity (~95%) and specificity (100%) (Fig. 3A).

Figure 3: Sensitivity and specificity of anti-CD33 imaging method and correlation with leukemia engraftment.

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Sensitivity and specificity were calculated as mentioned in the methods. (A) The ROC curve showing sensitivity vs 100-specificity for the [64Cu]Cu-DOTA-anti-CD33 imaging was generated using biodistribution data (n≥15 mice). The imaging method has a sensitivity of ~95.5% and specificity of 100%. Therefore, this is a very reliable imaging method to detect AML. (B and C) A correlation curve for % engraftment vs % ID/g for femur and L-spine (n≥5 mice). A very high correlation was observed between leukemia engraftment and [64Cu]Cu-DOTA-anti-CD33 activity in the left femur (R2 = 0.9854) and L-spine (R2 = 0.8027). Engraftment was determined using flow cytometry. (D) A correlation curve for CD33 PET contour signal vs BLI signal (n≥5 mice). A strong correlation (R2 = 0.9262) was seen between BLI signal vs PET signal; however, the spatial resolution was high in PET whereas in BLI it was poor. The data were assumed for Gaussian distribution, and Pearson’s correlation coefficients were calculated using Prism software. The p value was calculated using a two tailed t test and considered significant if <0.05.

Further, the femur and L-spine of AML-bearing mice were contoured in PET-CT images. The extent of engraftment from these bones was then determined by flow cytometry of human CD45+ cells/ MV4–11-RFP. A high correlation was observed between PET-CT signals and percent engraftment (R2 value for femur and L-spine is 0.9854 and 0.8027, respectively) (Fig. 3 B, C). Additionally, the femur of AML bearing mice was contoured both in BLI and PET-CT images, and a high correlation were observed between BLI and PET-CT signals (R2= 0.9262) (Fig. 3D). However, spatial localization was evident in PET-CT in comparison to BLI images, which were diffuse.

Spatial heterogeneity in AML in vivo

Besides detecting a CD33+ specific signal, anti-CD33 PET-CT imaging also indicated the spatial heterogeneity of AML. The adjustment in whole body MicroCT allowed a better anatomical visualization of the skeletal system (Supplemental Figure S 4A). CD33+ PET activity was significantly heterogeneous within the femur; for example, the distal and proximal femur showed higher CD33 activity compared to that in the long bone area in mice with low leukemia burden; however, heightened activity in the long bone was observed when leukemic burden increased (Fig. 4A, Supplemental Figure S 4B,C). A similar localization pattern was seen in other bones including the tibia, humerus and L-spine (Fig. 1D). CD33 activity was mostly concentrated in the proximal/distal end of the bone. Further supporting the concept of heterogeneous spatial distribution of AML, in the L-spine (Fig. 4B) at areas of low leukemic burden (5–10%), the disease is strongly localized to one of the segments (L1-L5) of the L-spine. However, with increasing leukemic burden (>15%) the leukemia is distributed across the L-spine and can be seen even in the T-spine, which was initially undetectable in the low leukemia group (Fig. 4B), suggesting a preferential niche for AML during the early stages of disease progression.

Figure 4: Spatial distribution of AML.

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(A) Spatial distribution of AML in femur and tibia. Three dimensional intensity projections PET-CT images of representative AML bearing mice show a preferential niche to the joints in the early stages of the disease. (B) A representative three dimensional intensity projection-based PET-CT image of the spine from AML-bearing mice showing leukemia burden-dependent CD33+ activity. The leukemia is localized in the early disease stage (<10% engraftment); however, with increased leukemic burden (>10%), it spreads and appears systemic.

Humanized anti-CD33 antibody detects CD33 AML

After validating the use of CD33 mAb as an imaging agent, with the intent of translating this promising technology to humans, we produced a humanized anti-CD33 mAb, as described in the Methods section. The radiolabeling, immunoreactivity and stability (48h, in vivo) were analyzed as mentioned for the murine version (Supplemental Figure S 5A–D). We tested the humanized [64Cu]Cu-DOTA-anti-CD33 mAb immunoreactivity and found that it bound specifically to CD33+ AML cell lines (MV4–11, HL-60, Kg1a, THP-1) and human patient samples (Supplemental Figure S 6A, B). We then compared the murine and humanized CD33 mAb blood retention and the ability of the mAb to detect AML in mice bearing CD33+ AML cells. Significant differences between humanized and murine antibody clearance were observed at 4, 24, 48, and 72h post injection (P=0.0021, 0.00044, 0.00073 and 0.00015, respectively), and faster blood pharmacokinetics was observed with the humanized antibody. The concentration of humanized anti-CD33 mAb in blood dropped to <5% of initial concentration within 24 h post injection, while the murine antibody maintained >25% activity of initial concentration even at 72h post injection (Supplemental Figure S 6C).

The humanized [64Cu]Cu-DOTA-anti-CD33 mAb PET-CT images (Fig. 5A,B) clearly show targeting of AML cells to the skeletal system similarly to that of the murine Ab, and the respective BLI image showed AML engraftment (Fig. 5C). As in the murine Ab PET imaging, humanized mAb also signaled CD33+ PET activity localized to the epiphysis/diaphysis of the bones, which was dependent on disease burden, indicating spatial heterogeneity (Fig. 5D). The humanized Ab also revealed CD33+ activity in the spleen of mouse, with ~20% AML engraftment (Fig. 5A), suggesting its ability to detect extramedullary disease. Biodistribution studies were carried out in mice from respective groups as mentioned before. There was no significant difference in the biodistribution of [64Cu]Cu-DOTA-anti-CD33 mAb in both groups in the heart, lung, muscle and kidney (Fig. 5E); however, the signal was significantly reduced in the blood while increased in the liver. Also, noticeably, there was reduction of the PET signal in the heart of the AML-bearing mice with the humanized antibody in comparison to that from the murine Ab, which may be due to lower blood activity. The % ID/g was particularly high in bones in CD33+ bearing AML mice, but not in control non-leukemic mice: femur (2.4 vs 22.4 %ID/g, p<0.0001); humerus (2.2 vs 14.5 %ID/g, p=0.004); L spine (1.7 vs 15.2 %ID/g, p<0.0001 and tibia (1.9 vs 13.4 %ID/g, p<0.0001) (Fig. 5F). An average of 7–10–fold higher %ID/g PET activity was observed in bones of AML-bearing mice over non-AML–bearing control mice. Furthermore, the humanized mAb (21.2 %ID/g) showed ~3–4 times higher CD33+ activity in bones than with murine mAb (4.7 %ID/g) (Supplemental Figure S 6D).

Figure 5: PET-CT images and biodistribution of humanized [64Cu]Cu-DOTA-anti-CD33 antibody in AML bearing mice.

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Representative PET-CT and bioluminescence images (BLI) are shown from AML-bearing mice. Humanized [64Cu]Cu-DOTA-anti-CD33 (100 μCi/1 μg) was injected into these mice via tail vein 24–48h before PET-CT imaging or biodistribution was carried out. (A) Three-dimensional intensity projection-based PET-CT images showing CD33 activity in AML-bearing mice (B) PET-CT images showing CD33 activity in AML mice. (C) Bioluminescence (BLI) images of AML bearing mice. (D) PET-CT images (left femur) showing spatial heterogeneity in distribution of AML disease (E, F) Biodistribution of [64Cu]Cu-DOTA-anti-CD33 in bones and different tissues was conducted 24h/48h post injection. Plot of %ID/g of different tissues has been shown, indicating that CD33 activity is high in bones of MV4–11 mice, whereas no activity was seen in non-leukemic control mice (n=6). Statistical significance was determined using ANOVA and considered significant when p<0.05. Biodistribution of [64Cu]Cu-DOTA-anti-CD33 is presented as the percentage of the injected activity per gram of organ/tissue.

Humanized anti-CD33 mAb targets CD33 in vivo

Additionally, to further asses in vivo CD33 specific targeting of the humanized anti-CD33 mAb, as a proof of concept we have used two methods, first an ADC, an anti-human IgG (Fc) 2° Ab conjugated to MMAE, and second, [225Ac]Ac-DOTA-anti-CD33 radioimmunotherapy (CD33-RIT). The in vitro cytotoxicity assay on MV4–11 cells using anti-CD33 mAb + 2°-ADC Ab (CD33–2°-ADC) was carried out as mentioned in the Methods section. As control, 2°-ADC Ab alone (1 μg) was used, which showed no significant cell killing over untreated cells, while the same concentration of CD33–2°-ADC yielded ~55% cell killing (Fig. 6A), and maximum (>95%) cell killing was observed at 55 nM (2.5 μg). Lymphoma cells (Daudi) were used to show that the CD33–2°-ADC had no significant cell killing on the CD33− cell line, indicating specificity. For in vivo study, we used 2.5 μg CD33–2°-ADC, which showed maximum cell killing in the in vitro study. The MV4–11 AML-bearing mice treated with CD33–2°-ADC showed lower BLI intensity 24 days post treatment than in mice treated with 2°-ADC alone (p=0.02) or in the untreated AML-bearing mice (p=0.0008) (Fig. 6B, Supplemental Figure S 7A).

Figure 6: Humanized anti-CD33 mAb antibody therapeutic potential.

Figure 6:

Figure 6:

Figure 6:

Open in a new tab

The humanized anti-CD33 mAb in vitro and in vivo CD33 targeting as an ADC and RIT was explored. A) In vitro cell killing of MV4–11 cells by anti-CD33 + ADC. Significant cell killing over untreated cells was seen from 500 ng −2.5 μg of Ab. (B) AML bearing mice were treated with anti-CD33 mAb + 2° Ab (anti-Human Fc) conjugated to MMAE. Untreated mice or 2° Ab-ADC only treated mice was used as controls. The BLI intensity (photons/s) of whole mouse was calculated and plotted at day 24 days post intervention. The mice treated with the anti-CD33 + 2° Ab-ADC combination reduced disease burden significantly in comparison to ADC only treated mice (p=0.02) or untreated mice (p=0.0008) (n≥4). (C) Three different activities of humanized [225Ac]Ac-DOTA-anti-CD33 (100 nCi, 200 nCi, 300 nCi) were injected into AML bearing mice. All activities efficiently reduced leukemia burden as measured by BLI intensity (photons/s) at 14 days post intervention in comparison to levels in untreated AML-bearing mice (n=5). Statistical significance was determined using ANOVA and considered significant when p<0.05.

Next, CD33-RIT was carried out using three different Actinium-225 activities (100 nCi, 200 nCi and 300 nCi). Similar to the ADC study, CD33-RIT treated mice showed a significant decrease (p<0.00001) in leukemia burden 14 days post intervention (Fig. 6C, Supplemental Figure S 7B). These results suggest that the humanized anti-CD33 antibody targets CD33+ AML cells and can be used in anti-CD33 therapy.

Currently, a cGMP-grade humanized antibody is under preparation for a planned phase I/II clinical trial of anti-CD33 PET-CT imaging of AML. Efforts are also in progress for generating a primary anti-CD33 mAb-ADC, to explore treatment response monitoring post anti-CD33 immunotherapy using our newly developed anti-CD33 PET-CT imaging.

Discussion

We have developed a non-invasive anti-CD33 immunoPET-CT imaging method for the in vivo detection of AML disease with high sensitivity and specificity. Diagnostic and prognostic markers facilitate stratifying patients for treatment management. However, the current clinically approved diagnostic method is invasive, relying on single point (iliac crest) biopsies, which may 1) not always be representative of the actual disease state, and 2) limit the number of times it is performed. Therefore, in leukemia, non-invasive PET-imaging using PET tracers including 18F-flourodeoxyglucose (FDG) (metabolic activity) and 18F-flourothymidine (FLT) (cell proliferation) have been tested for diagnosis and monitoring treatment response (11,17–19). Although 18F-FDG-PET has shown some success in diagnosing extramedullary disease in AML (20), it has also yielded highly inconsistent results because of changes in the metabolic activity of normal/tumor cells post treatment (21,22). However, these PET tracers are non-specific in that they detect metabolically active or highly proliferating cells, which could be affected by treatment, a contrast to CD33-PET imaging, which specifically detects cells expressing CD33, an accepted biomarker for AML. Studies have shown that even relapsed AML blasts post anti-CD33 therapy have CD33 expression (23,24). Therefore, CD33-based imaging is expected to improve detection of AML with high specificity in the whole body. This approach may also provide a blueprint for selecting biopsy sites (image-guided) that would be greatly useful for early detection and also in longitudinal monitoring of treatment response.

In this study, [64Cu]Cu-DOTA-anti-CD33 mAb PET-CT was used to detect AML in vivo, showing very high specificity and sensitivity to CD33+ AML. The spatial information was achieved with whole body CT-based 3D anatomical imaging. Notably, a spatial heterogeneity in the distribution of AML within the skeletal regions was observed, which would not be established with point biopsies. For example, CD33 activity was especially prominent in the L-spine and epiphysis/diaphysis of femurs, indicating a preferential skeletal niche for the disease at early stages. However, when the disease progresses, this spatial heterogeneity was reduced, and the disease appeared to be more systemic. This finding suggests, contrary to previous beliefs that leukemia was a systemic disease, that leukemia may be initiated in multiple preferential niches, characterized by multifocal disease before becoming more systemic. This imaging observation warrants future investigation to understand the role of the micro-environment in fostering preferential disease localization. Also, a previous study has shown that chemoresistance was due to localization of leukemic cells to the endosteal niche in the bone (25), further indicating the relevance and importance of spatial localization of the disease in the whole body. One small clinical study suggested that a heterogeneous distribution of leukemia may indicate resistance to chemotherapy, as shown through 18F-FLT-PET-based imaging of cell proliferation (26). Also, a recent study indicated that 18F-FLT-PET could identify the risk of early relapse in the spine prior to evidence of relapse by detection of minimal residual disease (MRD) (27). Therefore, future clinical studies may be designed to assess the prognostic value of this anti-CD33 oncoPET imaging. As immuno-PET combines the sensitivity and resolution of a PET imaging system with the high specificity of a monoclonal antibody, it may represent what is described as a “comprehensive immunohistochemical staining in vivo” (28), therefore guiding precise locations for biopsies. Also, precisely specifying sites of disease could yield valuable information about the tumor in terms of location, phenotype, treatment susceptibility and response (29). The spatial heterogeneity of leukemia also suggests caution should be taken when interpreting data from single-point biopsies.

As the initial study was conducted using a murine anti-CD33 mAb, clone p67.6, for translational purposes, a humanized anti-CD33 monoclonal antibody (Hu-M195) was generated. We carried out preclinical PET-CT imaging using humanized anti-CD33 mAb and showed that targeting was significantly improved over that of the murine version. Currently, a cGMP grade mAb has been made for future phase I/II clinical trials. The Hu-M195 mAb labelled with Iodine-131 or Bismuth-213 has been shown to be useful for whole-body-gamma imaging of patients with myeloid leukemia (7–10,30). However, this modality is planar or two-dimensional and is hence qualitative and lacks three-dimensional quantitative information with spatial heterogeneity assessment of the disease. Also, none of the previous whole-body gamma imaging studies were conducted with an intent for AML diagnosis. They were used for studying targeting and pharmacokinetics of the radiolabeled anti-CD33 mAb. In contrast, our study was mainly conducted to evaluate CD33 as a non-invasive diagnostic imaging marker for AML. Additionally, the imaging modality used is PET-CT, which is more advanced than the gamma imaging conducted previously. Our study used PET-CT 3D imaging, which is quantitative and provides high resolution images that reveal information about spatial distribution of the CD33+ AML in the whole body, hence resulting in a significantly improved diagnostic imaging of AML.

Additionally, we showed CD33 targeting of HuM195 anti-CD33 mAb using CD33–2°-ADC or CD33-RIT. Both treatment studies resulted in reduced disease burden in treated groups over untreated AML bearing mice, suggesting CD33-specific targeting. Such approaches are very effective against receptors like CD33, which is endocytosed after Ab binding, enabling the cytotoxic payload to internalize and result in cell killing (31,32). Furthermore, after CD33 antibody therapy, blasts in relapsed/refractory disease continue to express CD33, suggesting that AML cells may not use downregulation of CD33 as an epigenetic “escape mechanism” (24), in contrast to the use of anti-CD38 mAb daratumumab which may result in relapse due to a CD38− myeloma clone (33). Anti-CD33 mAb has been clinically validated as an ADC (34–36) and for RIT (37–39,40 ). Therefore, CD33 PET imaging may also be useful in monitoring treatment response post intervention (such as ADC, RIT, or any other treatment modalities).

Several investigational CD33-targeted therapeutics have been developed for AML (41), including the recently FDA-approved anti-CD33 monoclonal antibody-drug conjugate gemtuzumab ozogamicin (GO, Mylotarg) (42), bispecific antibodies such as CD33/CD3 antibodies (43), and CD33/CD123-directed CAR T cells (44). Although positive results have been reported from clinical trials of CD33-targeting drugs, dose-limiting toxicities such as hepatotoxicities (45) during treatment are of concern. Therefore, an imaging tool as presented here would help determine optimal doses as well as monitoring treatment response non-invasively, providing an opportunity to improve treatment efficacy while decreasing toxicities.

There are some limitations of this study. Although CD33 is expressed in all patients on at least a subset of AML blasts, high inter-patient variability in terms of level of expression (> 2 fold) is known (6,46,47). This differential expression is not random but correlated with the presence of particular mutations; for example, very high CD33 expression has been shown in AML with NPM1 and FLT3/ITD mutations, whereas lower expression has been associated with core binding factor translocations (6,47,48). A properly controlled imaging study with subset (mutation) analysis will be essential to understand whether specific mutations impact imaging intensity and/or heterogeneity.

In conclusion, to the best of our knowledge, this is the first preclinical report of an anti-CD33-PET-CT imaging modality to successfully detect AML in vivo. The imaging tool may be used for diagnosis as well as monitoring treatment response in the whole body, including extramedullary organs. The molecular imaging method also detected heterogeneity in the spatial distribution of the AML, warranting caution in interpreting results from single-point biopsies. Therefore, the CD33 PET imaging modality also could provide guidance for preferential sites for biopsy when there is large disease heterogeneity, and it may also indicate location of chemo/radio-resistant leukemic cells post treatment that have been suggested to be responsible for relapse (25). Future studies to understand the relationship between genomic mutation (like NPM1, FLT3-ITD, etc.) and spatial heterogeneity (as observed in the CD33 PET-CT imaging) would be beneficial in the diagnosis and prognosis of AML. In summary, anti-CD33 PET-CT is a viable AML diagnostic imaging method with significant translational potential.

Supplementary Material

1

Translational Relevance.

Acute myeloid leukemia (AML) exhibits extensive heterogeneity in therapeutic response, even in patients sharing similar characteristics. There is a critical need for a non-invasive quantitative imaging modality specific to AML, to assess disease in the whole body, including extramedullary organs, and to longitudinally monitor treatment response. CD33, an accepted AML biomarker, is expressed on >85–90% of blast cells. Mylotarg (anti-CD33-ADC) is an FDA-approved immunotherapy for AML. However, the non-invasive diagnostic imaging ability of CD33 mAb has not been validated. In this preclinical study, anti-CD33 PET imaging using [64Cu]Cu-DOTA-anti-CD33 was developed, and AML was detected with high sensitivity and specificity. Furthermore, combining PET with whole body microCT revealed a topologically heterogeneous disease development pattern with a preferential initial skeletal niche in the L spine and femoral growth plate. This finding supports developing non-invasive imaging in patients with AML and warranting caution in using single-point biopsies, in particular during early disease development.

Acknowledgments

We appreciate administrative support at our institutions. Research reported in this publication included work performed by the Small Animal Imaging Core for PET-CT imaging and imaging precision radiation delivery system supported by the National Cancer Institute of the National Institutes of Health under award number P30CA033572 and partly supported by National Institutes of Health grant 1R01CA154491–01. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Abbreviations

3D

three dimensional

64Cu

copper 64

AML

acute myeloid leukemia

BM

bone marrow

CBCT

cone beam CT

CT

computed tomography

FDG

fluorodeoxyglucose

FLT

fluorothymidine

FLT3/ITD

FLT3 internal tandem duplication

fTMI

functional total marrow irradiation

Gy

Gray

HSCT

hematopoietic stem cell transplant

MM

multiple myeloma

NHS-DOTA

1,4,7,10-tetraazacyclododecane-N,N′,N″,N′″-tetraacetic acid

NPM1

nucleophosmin 1

NSG

NOD-scidIL2Rgnull

PET

positron emission tomography

SIGLEC3

Sialic Acid-Binding Ig-Like Lectin 3

TBI

total body irradiation

ADC

Antibody drug Conjugate

RIT

Radioimmunotherapy

MMAE

monomethyl auristatin E

Footnotes

Conflict of interest

Anthony S Stein provides consulting for Amgen and Stemline and is on the speakers’ bureau for the former company. Joseph Rosenthal receives honoraria from and consults for Shire and Bayer, and receives research funding from Daiichi Sankyo and Kite Pharma. Susanta K Hui receives honoraria from and consults for Janssen Research & Development, LLC. All other authors declare no competing financial interests.

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NIHMS1540349-supplement-1.docx (14.8MB, docx)

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