Effector T cells need to form immunological synapses (IS) with recognized target cells to elicit cytolytic effects. Facilitating IS formation is the principal pharmacological action of most T cell-based cancer immunotherapies. However, the dynamics of IS formation at the cell population level, the primary driver of the pharmacodynamics of many cancer immunotherapies, remains poorly defined. Using classic immunotherapy CD3/CD19 bispecific T cell engager (BiTE) as our model system, we integrate experimental and theoretical approaches to investigate the population dynamics of IS formation and their relevance to clinical pharmacodynamics and treatment resistance. Our models produce experimentally consistent predictions when defining IS formation as a series of spatiotemporally coordinated events driven by molecular and cellular interactions. The models predict tumor-killing pharmacodynamics in patients and reveal trajectories of tumor evolution across anatomical sites under BiTE immunotherapy. Our models highlight the bone marrow as a potential sanctuary site permitting tumor evolution and antigen escape. The models also suggest that optimal dosing regimens are a function of tumor growth, CD19 expression, and patient T cell abundance, which confer adequate tumor control with reduced disease evolution. This work has implications for developing more effective T cell-based cancer immunotherapies.

Bispecific T cell engagers (BiTEs) engage T cells for anti-tumor activity. Despite early successes such as blinatumomab, the therapeutic potential of BiTEs for cancer immunotherapy remains limited by low response rates and high potential for relapse. Formation of the immunological synapse (IS) is principal to BiTE pharmacology and BiTE-directed lysis of tumor cells by T cells. Unfortunately, our knowledge of BiTE-induced IS formation dynamics and its relation to treatment resistance remains rudimentary. This work develops an integrated experimental and theoretical approach to investigate IS formation dynamics induced by BiTEs under diverse conditions. Our theoretical models recapitulate experimental observations by defining IS formation as spatiotemporally coordinated events driven by molecular and cellular interactions. These models adequately predicted tumor-killing profiles in patients and revealed a trajectory of antigen escape and tumor evolution across anatomical sites, particularly highlighting the role of the bone marrow in disease relapse. Our work yields implications for developing more effective cancer immunotherapies.

Type 2 diabetes mellitus (T2DM) agent sodium-glucose co-transporter 2 (SGLT2) inhibitors show special benefits in reducing body weight and heart failure risks.To accelerate clinical development for novel SGLT2 inhibitors, a quantitative relationship among pharmacokinetics, pharmacodynamics, and disease end points (PK/PD/end points) in healthy subjects and patients with T2DM was developed. PK/PD/end point data in published clinical studies for three globally marketed SGLT2 inhibitors (dapagliflozin, canagliflozin, and empagliflozin) were collected according to pre-set criteria. Overall, 80 papers with 880 PK, 27 PD, 848 fasting plasma glucose (FPG), and 1219 hemoglobin A1c (HbA1c) data were collected.A two-compartmental model with Hill's equation was utilized to capture PK/ PD profiles. A novel translational biomarker, the change of urine glucose excretion (UGE) from baseline normalized by FPG (ΔUGE c ) was identified to bridge healthy subjects and patients with T2DM with different disease statuses. ΔUGE c was found to have a similar maximum increase with different half-maximal effective concentration values of 56.6, 2310, and 841 mg/mL•h for dapagliflozin, canagliflozin, and empagliflozin respectively. ΔUGE c will change FPG based on linear function. HbA1c profiles were captured by indirect response model. Additional placebo effect was also considered for both end points. The PK/ΔUGE c /FPG/ HbA1c relationship was validated internally using diagnostic plots and visual assessment and further validated externally using the fourth globally approved same-in-class drug (ertugliflozin). This validated quantitative PK/PD/end point relationship offers novel insight into long-term efficacy prediction for SGLT2 inhibitors. The novelty identified ΔUGE c could make the comparison of different SGLT2 inhibitors' efficacy characteristics easier, and achieve early prediction from healthy subjects to patients.

Considerable lesion-specific response heterogeneity exists in metastatic colorectal cancer patients, largely due to organ-specific ecological environments and evolutionary pressures. Metastatic lesions with poor response to therapy often become tumor sanctuary sites, leading to systemic resistance and tumor relapse. To map the lesion-specific response and relapse patterns, we investigated the longitudinal dynamics of individual lesions in metastatic colorectal cancer patients. Tumor longitudinal data in 4,308 colorectal cancer patients with 40,612 individual lesions were collected from eight Phase III trials in Project Data Sphere. First, tumor response dynamics (regression after treatment and progression upon resistance) were characterized using an empirical mathematical model. Next, tumor response time (when the lesion size decreases ≥20% from baseline) and relapse time (when the lesion size increases ≥30% from tumor nadir) were estimated for each individual lesion in patients being treated with bevacizumab, panitumumab, and/or chemotherapy. Random effect cox proportional models were applied to predict lesion-specific response and relapse probabilities and temporal sequence. We then took machine learning algorithm k-means to cluster patients based on their lesion relapse sequence. We found the response probabilities across organs are: Liver > Distal Lymph Nodes (LN) > Abdomen > Spleen > Lung > Regional LN > Adrenal > Muscle/Soft Tissue > Bone > Brain/CNS. Lesion relapse temporal sequence are: Brain/CNS > Liver > Adrenal > Muscle/Soft tissue > Abdomen > Bone > Spleen > Lung > Distal LN > Regional LN. Of note, lesions in the bone, brain, adrenal, and muscle/soft tissues often had low responses and high relapse probabilities, implying the greatest potential as tumor sanctuary sites. Liver, the most common metastatic organ in colorectal cancer, showed highest response rate but high relapse probabilities. Interestingly, the organ-specific response rate and relapse probabilities are respectively in line with drug distribution profiles and organ-specific immune landscape. Organ-specific relapse sequence in each patient is significantly correlated with patient long-term survival (p<0.0001). Patients with relapse lesions occurring in multiple organs had worst survival. Patients whose liver lesions relapsed first had worse survival than those who first relapsed in other organs. In conclusion, our study provides insights into the lesion-specific response and relapse heterogeneity in metastatic colorectal cancer, and yields substantial implications for designing lesion-specific therapeutics. Citation Format: Jiawei Zhou, Quefeng Li, Amber Cipriani, Yanguang Cao. Mapping lesion specific response and relapse patterns in metastatic colorectal cancer patients [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 3789.

Achieving systemic tumor control across metastases is vital for long-term patient survival but remains intractable in many patients. High intrapatient heterogeneity persists, conferring many dissociated responses across metastatic lesions. Most studies of metastatic disease focus on tumor molecular and cellular features, which are crucial to elucidating the mechanisms underlying intrapatient heterogeneity. However, our understanding of intrapatient heterogeneity on the macroscopic level, such as lesion dynamics in growth, response, and relapse during treatment, remains rudimentary. This study investigated intrapatient heterogeneity through analyzing 116,542 observations of 40,612 lesions in 4,308 metastatic colorectal cancer (mCRC) patients. Despite significant differences in their response and relapse dynamics, metastatic lesions converged on four phenotypes that varied with anatomical site. Importantly, we found that organ-level relapse sequence was closely associated with patient survival, and that patients with the first relapses in the liver often had worse survival. In conclusion, our study provides insights into intrapatient response heterogeneity in mCRC and creates impetus for metastasis-specific therapeutics.