Integrating Systems Biology and an Ex Vivo Human Tumor Model Elucidates PD-1 Blockade Response Dynamics

Agent-based computational modeling of glioblastoma predicts that stromal density is central to oncolytic virus efficacy

Oncolytic viruses (OVs) are emerging cancer immunotherapy. Despite notable successes in the treatment of some tumors, OV therapy for central nervous system cancers has failed to show efficacy. We used an ex vivo tumor model developed from human glioblastoma tissue to evaluate the infiltration of herpes simplex OV rQNestin (oHSV-1) into glioblastoma tumors. We next leveraged our data to develop a computational, model of glioblastoma dynamics that accounts for cellular interactions within the tumor. Using our computational model, we found that low stromal density was highly predictive of oHSV-1 therapeutic success, suggesting that the efficacy of oHSV-1 in glioblastoma may be determined by stromal-to-tumor cell regional density. We validated these findings in heterogenous patient samples from brain metastatic adenocarcinoma. Our integrated modeling strategy can be applied to suggest mechanisms of therapeutic responses for central nervous system cancers and to facilitate the successful translation of OVs into the clinic.

Circadian and Immunity Cycle Talk in Cancer Destination: From Biological Aspects to In Silico Analysis

Simple Summary

The circadian cycle is a natural cycle of the body repeated every 24 h, based on a day and night rhythm, and it affects many body processes. The present article reviews the importance and role of the circadian cycle in cancer and its association with the immune system and immunotherapy drugs at the cellular and molecular levels. It also examines the genes and cellular pathways involved in both circadian and immune systems. It offers possible computational solutions to increase the effectiveness of cancer treatment concerning the circadian cycle.

Abstract

Cancer is the leading cause of death and a major problem to increasing life expectancy worldwide. In recent years, various approaches such as surgery, chemotherapy, radiation, targeted therapies, and the newest pillar, immunotherapy, have been developed to treat cancer. Among key factors impacting the effectiveness of treatment, the administration of drugs based on the circadian rhythm in a person and within individuals can significantly elevate drug efficacy, reduce adverse effects, and prevent drug resistance. Circadian clocks also affect various physiological processes such as the sleep cycle, body temperature cycle, digestive and cardiovascular processes, and endocrine and immune systems. In recent years, to achieve precision patterns for drug administration using computational methods, the interaction of the effects of drugs and their cellular pathways has been considered more seriously. Integrated data-derived pathological images and genomics, transcriptomics, and proteomics analyses have provided an understanding of the molecular basis of cancer and dramatically revealed interactions between circadian and immunity cycles. Here, we describe crosstalk between the circadian cycle signaling pathway and immunity cycle in cancer and discuss how tumor microenvironment affects the influence on treatment process based on individuals’ genetic differences. Moreover, we highlight recent advances in computational modeling that pave the way for personalized immune chronotherapy.

Systems biology informed neural networks (SBINN) predict response and novel combinations for PD-1 checkpoint blockade

Anti-PD-1 immunotherapy has recently shown tremendous success for the treatment of several aggressive cancers. However, variability and unpredictability in treatment outcome have been observed, and are thought to be driven by patient-specific biology and interactions of the patient's immune system with the tumor. Here we develop an integrative systems biology and machine learning approach, built around clinical data, to predict patient response to anti-PD-1 immunotherapy and to improve the response rate. Using this approach, we determine biomarkers of patient response and identify potential mechanisms of drug resistance. We develop systems biology informed neural networks (SBINN) to calculate patient-specific kinetic parameter values and to predict clinical outcome. We show how transfer learning can be leveraged with simulated clinical data to significantly improve the response prediction accuracy of the SBINN. Further, we identify novel drug combinations and optimize the treatment protocol for triple combination therapy consisting of IL-6 inhibition, recombinant IL-12, and anti-PD-1 immunotherapy in order to maximize patient response. We also find unexpected differences in protein expression levels between response phenotypes which complement recent clinical findings. Our approach has the potential to aid in the development of targeted experiments for patient drug screening as well as identify novel therapeutic targets.

Nanoengineered Disruption of Heat Shock Protein 90 Targets Drug-Induced Resistance and Relieves Natural Killer Cell Suppression in Breast Cancer

Drug-induced resistance, or tolerance, is an emerging yet poorly understood failure of anticancer therapy. The interplay between drug-tolerant cancer cells and innate immunity within the tumor, the consequence on tumor growth, and therapeutic strategies to address these challenges remain undescribed. Here, we elucidate the role of taxane-induced resistance on natural killer (NK) cell tumor immunity in triple-negative breast cancer (TNBC) and the design of spatiotemporally controlled nanomedicines, which boost therapeutic efficacy and invigorate "disabled" NK cells. Drug tolerance limited NK cell immune surveillance via drug-induced depletion of the NK-activating ligand receptor axis, NK group 2 member D, and MHC class I polypeptide-related sequence A, B. Systems biology supported by empirical evidence revealed the heat shock protein 90 (Hsp90) simultaneously controls immune surveillance and persistence of drug-treated tumor cells. On the basis of this evidence, we engineered a "chimeric" nanotherapeutic tool comprising taxanes and a cholesterol-tethered Hsp90 inhibitor, radicicol, which targets the tumor, reduces tolerance, and optimally reprimes NK cells via prolonged induction of NK-activating ligand receptors via temporal control of drug release in vitro and in vivo. A human ex vivo TNBC model confirmed the importance of NK cells in drug-induced death under pressure of clinically approved agents. These findings highlight a convergence between drug-induced resistance, the tumor immune contexture, and engineered approaches that consider the tumor and microenvironment to improve the success of combinatorial therapy. SIGNIFICANCE: This study uncovers a molecular mechanism linking drug-induced resistance and tumor immunity and provides novel engineered solutions that target these mechanisms in the tumor and improve immunity, thus mitigating off-target effects.