Background Radiation therapy (RT) may produce immunomodulatory effects that can interact with immunotherapy. Our pilot study (NCT05371132) uses 89Zr-Df-crefmirlimab, a radiolabeled CD8-specific minibody, to image intratumoral changes in CD8+ T cell activity during and after RT. Here, we present the results of our first four patients. Methods Eligible patients have no change in systemic treatment within two months prior to RT and no splenic disorders. Patients receive a 1 mCi dose of 89Zr-Df-crefmirlimab before and 1–2 weeks after a 5-fraction RT course. Each dose is followed by a whole-body positron emission tomography (PET) scan, and each radiation fraction is followed by a region-of-interest PET. Maximum standardized uptake values (SUVmax) of lesions are extracted from each CD8 ImmunoPET scan. Results 3 metastatic renal cell carcinoma (mRCC) patients and 1 diffuse large B-cell lymphoma (DLBCL) patient have completed treatment. Median age was 64 years (range 61–65). Concurrent therapies included cabozantinib, nivolumab, and an experimental chemotherapy XL092 (table 1). The mRCC patients (P1-P3) received stereotactic body RT (30–40 Gy in 5 fractions) to one metastatic site. The DLBCL patient (P4) received RT (20 Gy in 5 fractions) to two lesions in the legs, in the bridging setting prior to chimeric antigen receptor (CAR) T-cell therapy. No toxicities attributable to the CD8 PET tracer were observed. Mean peak increase in SUVmax of the treated lesion in P1-P3 was 14.5 ± 8.1 (standard deviation). Mean size reduction by longest diameter of P1-P3 was 36.3% ± 20.2% (standard deviation). Each patient's results are included in table 1. In P1, a normal lymph node saw an unusual 4.4 increase in SUVmax post-RT. At 8-month follow-up, this was found to have developed into a lesion (figure 1). In P4, peak increase in SUVmax was 1.2 and 2.7 in the left and right leg treated lesions, respectively. Images are shown in figure 2. Two non-target lesions proximal to the left leg target lesion that received ~1% of the dose achieved an increase in CD8 SUVmax of 1.2 and 1.7 during radiation and resolved post-RT pre-CAR T. CD8 ImmunoPET taken 7 days post-CAR T infusion did not demonstrate any significant CD8 PET signal. All lesions resolved by day 30 post-CAR T on FDG PET imaging. Conclusions Increase in intratumoral CD8+ T cell activity was observed during RT in mRCC and lymphoma patients. Follow-up may reveal the prognostic implications of visualizing the immunogenic effects of radiation using CD8 ImmunoPET. Trial Registration NCT05371132 Ethics Approval This clinical trial was approved by the City of Hope Institutional Review Board (IRB) and has IRB number 21221. All trial participants provided informed consent prior to taking part in this study.

Background Zr-Df-Crefmirlimab is a humanized, engineered, 80-kDa minibody (an antibody fragment), with high affinity to human CD8 (kd of 0.4 nM). It has been evaluated as an imaging agent in a Phase-II open-label multi-dose study (NCT03802123) in patients with metastatic solid malignancies (figure 1) scheduled to receive standard of care immunotherapy (Nivolumab, Pembrolizumab and Ipilimumab-Nivolumab). Supervised machine learning (ML) combined with tumor growth-inhibition (TGI) modeling was applied to predict clinical response using various baseline patient characteristics including CD8 density (via biopsy) and CD8 PET imaging SUV (Standardized Uptake Value). Methods Modeling framework for prediction of response to immunotherapy was developed leveraging multimodal data (figure 2) including CD8 PET imaging readouts. Random forest was used for classification of response using only baseline characteristics from 32 patients. Data was randomly assigned into training and test data (65:35). Performance of the model on test data was evaluated using area under the receiver operating characteristic curve (ROC AUC). Rates of tumor growth and kill were estimated from a TGI model developed using early tumor kinetic data (first three time points). Baseline CD8 density and SUV were used along with estimated TGI model parameters to classify patient response using supervised ML. Explainable ML techniques like partial dependence and individual conditional expectation were leveraged to further explore contribution of features of interest towards model outcome. Results Using only baseline characteristics, AUC of 0.75 was achieved on the test data with an overall prediction accuracy of 82%. Using partial dependence of model features, increase in the likelihood of patient response was observed with increasing CD8 density and SUV at baseline. Early tumor kinetic data was described reasonably well by the developed TGI model. AUC of 0.88 was achieved on the same test data (as above) with an overall prediction accuracy of 91% using individual estimates of tumor growth and kill from the TGI model and baseline CD8 density and SUV. Decrease in likelihood of response was associated with increasing rate of tumor growth and smaller baseline CD8 density. Conclusions Baseline CD8 density and PET SUV data were used along with other patient characteristics to predict clinical outcome to immunotherapy with a reasonable degree of accuracy. Using a combined approach of tumor growth inhibition modelling and supervised machine learning, high precision in prediction of clinical outcome was achieved leveraging baseline CD8 PET information and early tumor kinetic data.

4551 Background: Novel imaging modalities using frequently expressed RCC antigens, such as CAIX, have shown promise in early-stage disease (Shuch B et al ASCO GU 2023). In advanced RCC, no tissue-based biomarkers have been well established to predict outcome with contemporary regimens, e.g., checkpoint inhibitors (CPIs) or targeted therapy (TT). We hypothesize that functional imaging of CD8 T-cells (CD8s) with crefmirlimab (a ~80 kDA 89 Zr-labelled minibody with high affinity for CD8) may predict response given the essential role of CD8 T-cells (CD8s) in mediating CPI response. Methods: Eligible pts had pathologically verified RCC, metastatic disease and an intent to initiate standard of care CPI therapy. Patients received crefmirlimab PET/CT within 1 wk of CPI infusion and 4-6 weeks after initiating therapy. Baseline biopsy was mandated, along with repeat biopsy 0-2 weeks following the second PET/CT scan. PET signal was characterized as SUV max , SUV peak and SUV mean of the biopsied lesions, up to 5 index lesions and representative CD8 avid lymph nodes. Mean SUV max in responders and non-responders were compared using students t-test (1-sided). CD8 expression in tissue was characterized as the number of positive cells per mm 2 ; PET avidity and CD8 expression were compared using the Spearman correlation coefficient. Results: 17 pts (9 M: 8 F) were enrolled; most pts had clear cell histology (12; 71%) followed by unclassified (3; 17%) and papillary (2; 12%). The most commonly rendered CPI-based regimens were nivolumab alone (6 pts; 35%) and cabozantinib/nivolumab (3 pts; 17%). Follow-up data was available in 15 of the patients. By RECIST v1.1, 3 of 15 patients were classified as responders (best overall response [BOR] of complete response or partial response) and 12 patients were classified as non-responders (BOR of stable disease or progressive disease). Average SUV max , SUV peak and SUV mean per patient among all quantified index lesions and representative lymph nodes were 10.02, 6.95 and 6.11 for baseline and 8.82, 6.23 and 5.39 during treatment, respectively. Average SUV max at baseline was 14.68 in responders to CPI and 8.28 in non-responders (P=0.006). On treatment SUV max was 10.93 in responders to CPI and 8.22 in non-responders (P=0.19). A strong correlation between CD8 expression in baseline tissue and normalized SUV mean was observed (r=0.77; 95%CI 0.53-0.91). Conclusions: To our knowledge, this is the first series in RCC to demonstrate that functional imaging of immune cells (here, CD8s) may segregate response to CPIs, with responders having a higher baseline SUV and a larger decrement in SUV with therapy. Our results are bolstered by a significant correlation between tissue and imaging CD8 expression. Larger studies are underway to validate this noninvasive strategy. Clinical trial information: NCT03802123 .

Background: Immunotherapy (IOT) of cancer depends on intratumoral CD8+ cells overcoming multiple obstacles to their localization and function. CD8+ cell content and its changes with treatment are important to understand tumor immunobiology, prognosis, and to guide therapy. Trial design: ImaginAb has developed an imaging agent, 89Zr-crefmirlimab berdoxam (formerly 89Zr-Df-IAB22M2C/89Zr-Df-crefmirlimab), with an 80 kDa minibody lacking a functional Fc domain, conferring high affinity to CD8, conjugated via deferoxamine to 89Zr for PET imaging: specific binding is to both CD8αα and CD8αβ, facilitating recognition of mature CD8+ cells and a small number of other cell types expressing CD8. The minibody structure was optimized for organ perfusion and pharmacokinetics, thereby maximizing the signal-to-background ratio. The long T1/2 (~3 d) of 89Zr permits repeat scanning during the week following an infusion. A previous Phase I study of single-dose 89Zr-crefmirlimab berdoxam was designed to select the optimum Phase II dose [Pandit-Taskar et al J Nuc Med 2020; Farwell et al J Nuc Med 2021]. After establishing the lack of toxicity across several dose levels, 1 mCi of 89Zr and 1.5 mg of minibody are now being used in Phase II studies. Our recently-completed Ph IIA study was designed to test the correlation of CD8+ cells in tumor biopsies with CD8 PET/CT before and during IOT in several tumor types routinely treated with IOT (including melanoma, renal, driver oncogene-negative lung). The present Ph IIB study, (first patient/first visit accomplished in 12/2021) tests the correlation of CD8 PET/CT with subject response by RECIST 1.1 (primary objective), and with lesion response (secondary objective). Adults with solid tumors receive 89Zr-crefmirlimab berdoxam i.v. and undergo PET-CT scan 24 hours later (non-contrast CT for localization). IOT (usually one or two immune checkpoint antibodies, using most commonly-used standard-of-care regimen for the disease) is then initiated. A second 89Zr-crefmirlimab berdoxam infusion and PET/CT are performed 4-6 weeks after the start of IOT followed 6-8 weeks later by standard tumor assessments (preferably CT with i.v. contrast to obtain RECIST-qualifying measurements). Thereafter, IOT and standard tumor assessments are continued according to standard practice and data for the present study collected up to 12 months after the start of treatment. For participants who progress on treatment there is an optional third 89Zr-crefmirlimab berdoxam infusion and PET/CT. For all participants optional biopsies are collected to coincide with PET/CT timepoints. Accrual is ongoing at three sites in USA, and up to 20 sites globally (USA, Europe and Australia) will participate. The overall accrual goal is 80 patients, distributed across the four tumor types. Citation Format: Kim Margolin, David Hays, Delphine L. Chen, Gary Ulaner, Ron Korn, Katherine Young, Michael Ferris, William Le, Ian Wilson. iPREDICT trial: A phase IIb, open label study of 89Zr-Crefmirlimab Berdoxam PET/CT in subjects with selected advanced malignancies (melanoma, merkel cell, renal cell and non-small cell lung cancers), scheduled to receive standard-of-care immunotherapy, to predict response to therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr CT132.