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Jenner AL, Smalley M, Goldman D, Goins WF, Cobbs CS, Puchalski RB, Chiocca EA, Lawler S, Macklin P, Goldman A, Craig M. Agent-based computational modeling of glioblastoma predicts that stromal density is central to oncolytic virus efficacy. iScience 2022; 25:104395. [PMID: 35637733 PMCID: PMC9142563 DOI: 10.1016/j.isci.2022.104395] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 03/18/2022] [Accepted: 04/08/2022] [Indexed: 11/26/2022] Open
Abstract
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.
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Affiliation(s)
- Adrianne L. Jenner
- Department of Mathematics and Statistics, Université de Montréal, Montréal, QC, Canada
- Sainte-Justine University Hospital Research Centre, Montréal, QC, Canada
| | - Munisha Smalley
- Division of Engineering in Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | | | - William F. Goins
- Department of Microbiology and Molecular Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Charles S. Cobbs
- Ben and Catherine Ivy Center for Advanced Brain Tumor Treatment, Swedish Neuroscience Institute, Seattle, WA, USA
| | - Ralph B. Puchalski
- Ben and Catherine Ivy Center for Advanced Brain Tumor Treatment, Swedish Neuroscience Institute, Seattle, WA, USA
| | - E. Antonio Chiocca
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Sean Lawler
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Paul Macklin
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - Aaron Goldman
- Division of Engineering in Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Morgan Craig
- Department of Mathematics and Statistics, Université de Montréal, Montréal, QC, Canada
- Sainte-Justine University Hospital Research Centre, Montréal, QC, Canada
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Circadian and Immunity Cycle Talk in Cancer Destination: From Biological Aspects to In Silico Analysis. Cancers (Basel) 2022; 14:cancers14061578. [PMID: 35326729 PMCID: PMC8945968 DOI: 10.3390/cancers14061578] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 03/16/2022] [Accepted: 03/17/2022] [Indexed: 02/01/2023] Open
Abstract
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.
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Przedborski M, Smalley M, Thiyagarajan S, Goldman A, Kohandel M. Systems biology informed neural networks (SBINN) predict response and novel combinations for PD-1 checkpoint blockade. Commun Biol 2021; 4:877. [PMID: 34267327 PMCID: PMC8282606 DOI: 10.1038/s42003-021-02393-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 06/25/2021] [Indexed: 02/06/2023] Open
Abstract
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.
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Affiliation(s)
- Michelle Przedborski
- Department of Applied Mathematics, University of Waterloo, Waterloo, ON, Canada.
| | - Munisha Smalley
- Integrative Immuno Oncology Center, Mitra Biotech, Woburn, MA, USA
| | | | - Aaron Goldman
- Division of Engineering in Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Mohammad Kohandel
- Department of Applied Mathematics, University of Waterloo, Waterloo, ON, Canada
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Smalley M, Natarajan SK, Mondal J, Best D, Goldman D, Shanthappa B, Pellowe M, Dash C, Saha T, Khiste S, Ramadurai N, Eton EO, Smalley JL, Brown A, Thayakumar A, Rahman M, Arai K, Kohandel M, Sengupta S, Goldman A. Nanoengineered Disruption of Heat Shock Protein 90 Targets Drug-Induced Resistance and Relieves Natural Killer Cell Suppression in Breast Cancer. Cancer Res 2020; 80:5355-5366. [PMID: 33077554 DOI: 10.1158/0008-5472.can-19-4036] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2019] [Revised: 08/16/2020] [Accepted: 10/09/2020] [Indexed: 12/24/2022]
Abstract
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.
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Affiliation(s)
- Munisha Smalley
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Siva Kumar Natarajan
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Jayanta Mondal
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Douglas Best
- Integrative Immuno-Oncology Center, Farcast Biosciences, Woburn, Massachusetts
| | | | | | - Moriah Pellowe
- Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada
| | - Chinmayee Dash
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Tanmoy Saha
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Sachin Khiste
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Nithya Ramadurai
- Integrative Immuno-Oncology Center, Farcast Biosciences, Woburn, Massachusetts
| | - Elliot O Eton
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | | | - Andrew Brown
- Division of Computational Genomics, Arrayo, Boston, Massachusetts
| | - Allen Thayakumar
- Integrative Immuno-Oncology Center, Farcast Biosciences, Woburn, Massachusetts
| | - Mamunur Rahman
- Medical and Biological Laboratories International, Woburn, Massachusetts
| | | | - Mohammad Kohandel
- Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada
| | - Shiladitya Sengupta
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts.
| | - Aaron Goldman
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts.
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