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Zheng Q, Yan H, He Y, Wang J, Zhang N, Huo L, Liu Y, Wang L, Xu L, Fan Z. An ultrasound-based nomogram for predicting axillary node pathologic complete response after neoadjuvant chemotherapy in breast cancer: Modeling and external validation. Cancer 2024; 130:1513-1523. [PMID: 38427584 DOI: 10.1002/cncr.35248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 12/18/2023] [Accepted: 12/20/2023] [Indexed: 03/03/2024]
Abstract
INTRODUCTION The staging and treatment of axillary nodes in breast cancer have become a focus of research. For breast cancer patients with fine-needle aspiration-or core needle biopsy-confirmed positive nodes, axillary lymph node dissection (ALND) after neoadjuvant chemotherapy (NAC) is still a standard treatment. However, some patients achieve an axillary pathologic complete response (pCR) after NAC. In this study, the authors sought to construct a model to predict axillary pCR in patients with positive axillary lymph nodes (cN+) breast cancer. METHODS Data from patients with pathologically proven cN+ breast cancer treated with NAC followed by ALND between January 2010 and April 2019 at the Peking University Cancer Hospital were reviewed. Axillary lymph node status was assessed using ultrasonography before and after NAC. The patient cohort was assigned to the construction and internal validation cohorts according to admission time. A nomogram was constructed based on the significant factors associated with axillary pCR. The predictive performance of the model was externally validated using data from Peking University First Hospital. RESULTS This study included 953 and 267 patients from Peking University Cancer Hospital and Peking University First Hospital, respectively. In the construction cohort, 39.7% (238 of 600) of patients achieved axillary pCR after NAC. The result of multivariate logistic regression analysis showed that tumor grade, clinical nodal response, NAC regimen, tumor pCR, lymphovascular invasion, and tumor biologic subtype were significant independent predictors of ypN0 (p < 0.05). The areas under the receiver operating characteristic curves for the construction, validation, and independent testing cohorts were 0.87 (95% confidence interval [CI], 0.84-0.90), 0.83 (95% CI, 0.79-0.87), and 0.84 (0.79-0.89), respectively. CONCLUSIONS A nomogram was constructed to predict the pCR of axillary lymph nodes after NAC for breast cancer. Validation of both the internal and external cohorts achieved good predictive performance, indicating that the model has preliminary clinical application prospects.
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Affiliation(s)
- Qijun Zheng
- Breast Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | - Huicui Yan
- Department of Thyroid and Breast Surgery, Peking University First Hospital, Beijing, China
| | - Yingjian He
- Breast Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | - Jiwei Wang
- Breast Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | - Nan Zhang
- Breast Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | - Ling Huo
- Breast Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | - Yiqiang Liu
- Department of Pathology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | - Lize Wang
- Breast Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | - Ling Xu
- Department of Thyroid and Breast Surgery, Peking University First Hospital, Beijing, China
| | - Zhaoqing Fan
- Breast Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
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Lee EJ, Chang YW. Prediction of complete response after neoadjuvant chemotherapy for invasive breast cancers: The utility of shear wave elastography and superb microvascular imaging in pretreatment breast ultrasound. Eur J Radiol 2024; 175:111432. [PMID: 38554672 DOI: 10.1016/j.ejrad.2024.111432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 03/03/2024] [Accepted: 03/15/2024] [Indexed: 04/02/2024]
Abstract
PURPOSE To investigate whether multiparametric parameters of pretreatment breast ultrasound (US) and clinicopathologic factors are associated with pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) for breast cancer. METHODS Between November 2018 and September 2022, 88 patients who underwent NAC and subsequent surgery were included in this study (median age, 55 years; interquartile range [IQR], 45, 59.3). Multiparametric breast US including grayscale, shear wave elastography (SWE) and superb microvascular imaging (SMI) of pathologically proven invasive breast cancers were retrospectively reviewed. Clinicopathological and multiparametric parameters of breast US, including size, SWEmax, SWEratio and vascular index on SMI (SMIVI) were compared between the groups. Univariate and multivariate logistic regression analyses were performed to determine factors predicting pCR after NAC. AUROC curve analysis was performed to determine the predictors' optimal cut-off values and diagnostic performance. RESULTS The pCR group (n = 24) showed a significantly smaller tumor size, lower SWEmax, higher Ki-67 index, higher hormone receptor negativity and negative axillary lymph node metastasis compared to the non-pCR group (n = 64). Multivariate regression analysis showed that SWEmax (adjusted odds ratio[aOR] = 0.956, 95 % confidence interval [CI] = 0.919-0.994, P = 0.025) and Ki-67 index (aOR = 1.083, 95 % CI = 1.012-1.159, P = 0.021) were independently associated with pathologically complete response. The optimal cut-off values for predicting pCR were 27.5 % for Ki-67 with an AUC of 0.743 and 134.8 kPa for SWEmax with an AUC of 0.779. A combination model including clinical factors and SWEmax showed the best diagnostic performance with an AUC of 0.876. CONCLUSION A higher Ki-67 index and lower SWEmax measured on pretreatment breast US were independently associated with pCR in invasive breast cancer after NAC.
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Affiliation(s)
- Eun Ji Lee
- Department of Radiology, Soonchunhyang University Seoul Hospital, Seoul, Korea
| | - Yun-Woo Chang
- Department of Radiology, Soonchunhyang University Seoul Hospital, Seoul, Korea.
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3
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Butner JD, Farhat M, Cristini V, Chung C, Wang Z. Protocol for mathematical prediction of patient response and survival to immune checkpoint inhibitor immunotherapy. STAR Protoc 2022; 3:101886. [PMID: 36595890 PMCID: PMC9719106 DOI: 10.1016/j.xpro.2022.101886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 10/03/2022] [Accepted: 11/03/2022] [Indexed: 12/03/2022] Open
Abstract
This protocol describes the application of a mechanistic mathematical model of immune checkpoint inhibitor (ICI) immunotherapy to patient tumor imaging data for predicting solid tumor response and patient survival under ICI intervention. We describe steps for data collection and processing, data pipelines, and approaches to increase precision. The protocol is highly predictive as early as the first restaging after treatment start and can be used with standard-of-care imaging measures. For complete details on the use and execution of this protocol, please refer to Butner et al. (2020)1 and Butner et al. (2021).2.
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Affiliation(s)
- Joseph D. Butner
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA,Corresponding author
| | - Maguy Farhat
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Vittorio Cristini
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA,Neal Cancer Center, Houston Methodist Research Institute, Houston, TX 77030, USA,Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77230, USA,Physiology, Biophysics, and Systems Biology Program, Graduate School of Medical Sciences, Weill Cornell Medicine, New York, NY 10065, USA
| | - Caroline Chung
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA,Corresponding author
| | - Zhihui Wang
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA,Neal Cancer Center, Houston Methodist Research Institute, Houston, TX 77030, USA,Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA,Department of Medical Education, Texas A&M University School of Medicine, Bryan, TX 77807, USA,Corresponding author
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4
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Dedifferentiation-mediated stem cell niche maintenance in early-stage ductal carcinoma in situ progression: insights from a multiscale modeling study. Cell Death Dis 2022; 13:485. [PMID: 35597788 PMCID: PMC9124196 DOI: 10.1038/s41419-022-04939-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 05/11/2022] [Accepted: 05/11/2022] [Indexed: 12/14/2022]
Abstract
We present a multiscale agent-based model of ductal carcinoma in situ (DCIS) to study how key phenotypic and signaling pathways are involved in the early stages of disease progression. The model includes a phenotypic hierarchy, and key endocrine and paracrine signaling pathways, and simulates cancer ductal growth in a 3D lattice-free domain. In particular, by considering stochastic cell dedifferentiation plasticity, the model allows for study of how dedifferentiation to a more stem-like phenotype plays key roles in the maintenance of cancer stem cell populations and disease progression. Through extensive parameter perturbation studies, we have quantified and ranked how DCIS is sensitive to perturbations in several key mechanisms that are instrumental to early disease development. Our studies reveal that long-term maintenance of multipotent stem-like cell niches within the tumor are dependent on cell dedifferentiation plasticity, and that disease progression will become arrested due to dilution of the multipotent stem-like population in the absence of dedifferentiation. We have identified dedifferentiation rates necessary to maintain biologically relevant multipotent cell populations, and also explored quantitative relationships between dedifferentiation rates and disease progression rates, which may potentially help to optimize the efficacy of emerging anti-cancer stem cell therapeutics.
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5
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Butner JD, Martin GV, Wang Z, Corradetti B, Ferrari M, Esnaola N, Chung C, Hong DS, Welsh JW, Hasegawa N, Mittendorf EA, Curley SA, Chen SH, Pan PY, Libutti SK, Ganesan S, Sidman RL, Pasqualini R, Arap W, Koay EJ, Cristini V. Early prediction of clinical response to checkpoint inhibitor therapy in human solid tumors through mathematical modeling. eLife 2021; 10:70130. [PMID: 34749885 PMCID: PMC8629426 DOI: 10.7554/elife.70130] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 10/25/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Checkpoint inhibitor therapy of cancer has led to markedly improved survival of a subset of patients in multiple solid malignant tumor types, yet the factors driving these clinical responses or lack thereof are not known. We have developed a mechanistic mathematical model for better understanding these factors and their relations in order to predict treatment outcome and optimize personal treatment strategies. Methods: Here, we present a translational mathematical model dependent on three key parameters for describing efficacy of checkpoint inhibitors in human cancer: tumor growth rate (α), tumor-immune infiltration (Λ), and immunotherapy-mediated amplification of anti-tumor response (µ). The model was calibrated by fitting it to a compiled clinical tumor response dataset (n = 189 patients) obtained from published anti-PD-1 and anti-PD-L1 clinical trials, and then validated on an additional validation cohort (n = 64 patients) obtained from our in-house clinical trials. Results: The derived parameters Λ and µ were both significantly different between responding versus nonresponding patients. Of note, our model appropriately classified response in 81.4% of patients by using only tumor volume measurements and within 2 months of treatment initiation in a retrospective analysis. The model reliably predicted clinical response to the PD-1/PD-L1 class of checkpoint inhibitors across multiple solid malignant tumor types. Comparison of model parameters to immunohistochemical measurement of PD-L1 and CD8+ T cells confirmed robust relationships between model parameters and their underlying biology. Conclusions: These results have demonstrated reliable methods to inform model parameters directly from biopsy samples, which are conveniently obtainable as early as the start of treatment. Together, these suggest that the model parameters may serve as early and robust biomarkers of the efficacy of checkpoint inhibitor therapy on an individualized per-patient basis. Funding: We gratefully acknowledge support from the Andrew Sabin Family Fellowship, Center for Radiation Oncology Research, Sheikh Ahmed Center for Pancreatic Cancer Research, GE Healthcare, Philips Healthcare, and institutional funds from the University of Texas M.D. Anderson Cancer Center. We have also received Cancer Center Support Grants from the National Cancer Institute (P30CA016672 to the University of Texas M.D. Anderson Cancer Center and P30CA072720 the Rutgers Cancer Institute of New Jersey). This research has also been supported in part by grants from the National Science Foundation Grant DMS-1930583 (ZW, VC), the National Institutes of Health (NIH) 1R01CA253865 (ZW, VC), 1U01CA196403 (ZW, VC), 1U01CA213759 (ZW, VC), 1R01CA226537 (ZW, RP, WA, VC), 1R01CA222007 (ZW, VC), U54CA210181 (ZW, VC), and the University of Texas System STARS Award (VC). BC acknowledges support through the SER Cymru II Programme, funded by the European Commission through the Horizon 2020 Marie Skłodowska-Curie Actions (MSCA) COFUND scheme and the Welsh European Funding Office (WEFO) under the European Regional Development Fund (ERDF). EK has also received support from the Project Purple, NIH (U54CA210181, U01CA200468, and U01CA196403), and the Pancreatic Cancer Action Network (16-65-SING). MF was supported through NIH/NCI center grant U54CA210181, R01CA222959, DoD Breast Cancer Research Breakthrough Level IV Award W81XWH-17-1-0389, and the Ernest Cockrell Jr. Presidential Distinguished Chair at Houston Methodist Research Institute. RP and WA received serial research awards from AngelWorks, the Gillson-Longenbaugh Foundation, and the Marcus Foundation. This work was also supported in part by grants from the National Cancer Institute to SHC (R01CA109322, R01CA127483, R01CA208703, and U54CA210181 CITO pilot grant) and to PYP (R01CA140243, R01CA188610, and U54CA210181 CITO pilot grant). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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Affiliation(s)
- Joseph D Butner
- The Houston Methodist Research Institute, Houston, United States
| | - Geoffrey V Martin
- The University of Texas MD Anderson Cancer Center, Houston, United States
| | - Zhihui Wang
- The Houston Methodist Research Institute, Houston, United States
| | - Bruna Corradetti
- The Houston Methodist Research Institute, Houston, United States
| | - Mauro Ferrari
- The Houston Methodist Research Institute, Houston, United States
| | - Nestor Esnaola
- The Houston Methodist Research Institute, Houston, United States
| | - Caroline Chung
- The University of Texas MD Anderson Cancer Center, Houston, United States
| | - David S Hong
- The University of Texas MD Anderson Cancer Center, Houston, United States
| | - James W Welsh
- The Houston Methodist Research Institute, Houston, United States
| | - Naomi Hasegawa
- University of Texas Health Science Center, Houston, United States
| | | | | | - Shu-Hsia Chen
- The Houston Methodist Research Institute, Houston, United States
| | - Ping-Ying Pan
- The Houston Methodist Research Institute, Houston, United States
| | | | | | - Richard L Sidman
- Department of Neurology, Harvard Medical School, Boston, United States
| | | | - Wadih Arap
- Hematology and Oncology, Rutgers Cancer Institute of New Jersey, Newark, United States
| | - Eugene J Koay
- University of Texas MD Anderson Cancer Center, Houston, United States
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Staquicini FI, Hajitou A, Driessen WHP, Proneth B, Cardó-Vila M, Staquicini DI, Markosian C, Hoh M, Cortez M, Hooda-Nehra A, Jaloudi M, Silva IT, Buttura J, Nunes DN, Dias-Neto E, Eckhardt B, Ruiz-Ramírez J, Dogra P, Wang Z, Cristini V, Trepel M, Anderson R, Sidman RL, Gelovani JG, Cristofanilli M, Hortobagyi GN, Bhujwalla ZM, Burley SK, Arap W, Pasqualini R. Targeting a cell surface vitamin D receptor on tumor-associated macrophages in triple-negative breast cancer. eLife 2021; 10:e65145. [PMID: 34060472 PMCID: PMC8169110 DOI: 10.7554/elife.65145] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 04/23/2021] [Indexed: 02/06/2023] Open
Abstract
Triple-negative breast cancer (TNBC) is an aggressive tumor with limited treatment options and poor prognosis. We applied the in vivo phage display technology to isolate peptides homing to the immunosuppressive cellular microenvironment of TNBC as a strategy for non-malignant target discovery. We identified a cyclic peptide (CSSTRESAC) that specifically binds to a vitamin D receptor, protein disulfide-isomerase A3 (PDIA3) expressed on the cell surface of tumor-associated macrophages (TAM), and targets breast cancer in syngeneic TNBC, non-TNBC xenograft, and transgenic mouse models. Systemic administration of CSSTRESAC to TNBC-bearing mice shifted the cytokine profile toward an antitumor immune response and delayed tumor growth. Moreover, CSSTRESAC enabled ligand-directed theranostic delivery to tumors and a mathematical model confirmed our experimental findings. Finally, in silico analysis showed PDIA3-expressing TAM in TNBC patients. This work uncovers a functional interplay between a cell surface vitamin D receptor in TAM and antitumor immune response that could be therapeutically exploited.
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Affiliation(s)
- Fernanda I Staquicini
- Rutgers Cancer Institute of New JerseyNewarkUnited States
- Division of Cancer Biology, Department of Radiation Oncology, Rutgers New Jersey Medical SchoolNewarkUnited States
| | - Amin Hajitou
- Phage Therapy Group, Department of Brain Sciences, Imperial College LondonLondonUnited Kingdom
| | | | - Bettina Proneth
- Institute of Metabolism and Cell Death, Helmholtz Zentrum MuenchenNeuherbergGermany
| | - Marina Cardó-Vila
- Department of Cellular and Molecular Medicine, The University of Arizona Cancer Center, University of ArizonaTucsonUnited States
- Department of Otolaryngology-Head and Neck Surgery, The University of Arizona Cancer Center, University of ArizonaTucsonUnited States
| | - Daniela I Staquicini
- Rutgers Cancer Institute of New JerseyNewarkUnited States
- Division of Cancer Biology, Department of Radiation Oncology, Rutgers New Jersey Medical SchoolNewarkUnited States
| | - Christopher Markosian
- Rutgers Cancer Institute of New JerseyNewarkUnited States
- Division of Cancer Biology, Department of Radiation Oncology, Rutgers New Jersey Medical SchoolNewarkUnited States
| | - Maria Hoh
- Division of Cancer Imaging Research, The Russell H Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of MedicineBaltimoreUnited States
| | - Mauro Cortez
- Department of Parasitology, Institute of Biomedical Sciences, University of São PauloSão PauloBrazil
| | - Anupama Hooda-Nehra
- Rutgers Cancer Institute of New JerseyNewarkUnited States
- Division of Hematology/Oncology, Department of Medicine, Rutgers New Jersey Medical SchoolNewarkUnited States
| | - Mohammed Jaloudi
- Rutgers Cancer Institute of New JerseyNewarkUnited States
- Division of Hematology/Oncology, Department of Medicine, Rutgers New Jersey Medical SchoolNewarkUnited States
| | - Israel T Silva
- Laboratory of Computational Biology, A.C. Camargo Cancer CenterSão PauloBrazil
| | - Jaqueline Buttura
- Laboratory of Computational Biology, A.C. Camargo Cancer CenterSão PauloBrazil
| | - Diana N Nunes
- Laboratory of Medical Genomics, A.C. Camargo Cancer CenterSão PauloBrazil
| | - Emmanuel Dias-Neto
- Laboratory of Computational Biology, A.C. Camargo Cancer CenterSão PauloBrazil
- Laboratory of Medical Genomics, A.C. Camargo Cancer CenterSão PauloBrazil
| | - Bedrich Eckhardt
- Translational Breast Cancer Program, Olivia Newton-John Cancer Research InstituteMelbourneAustralia
| | - Javier Ruiz-Ramírez
- Mathematics in Medicine Program, The Houston Methodist Research InstituteHoustonUnited States
| | - Prashant Dogra
- Mathematics in Medicine Program, The Houston Methodist Research InstituteHoustonUnited States
| | - Zhihui Wang
- Mathematics in Medicine Program, The Houston Methodist Research InstituteHoustonUnited States
| | - Vittorio Cristini
- Mathematics in Medicine Program, The Houston Methodist Research InstituteHoustonUnited States
| | - Martin Trepel
- Department of Oncology and Hematology, University Medical Center Hamburg-EppendorfHamburgGermany
- Department of Oncology and Hematology, University Medical Center AugsburgAugsburgGermany
| | - Robin Anderson
- Translational Breast Cancer Program, Olivia Newton-John Cancer Research InstituteMelbourneAustralia
| | - Richard L Sidman
- Department of Neurology, Harvard Medical SchoolBostonUnited States
| | - Juri G Gelovani
- Department of Biomedical Engineering, College of Engineering, Wayne State UniversityDetroitUnited States
- Department of Oncology, School of Medicine, Wayne State UniversityDetroitUnited States
- Department of Neurosurgery, School of Medicine, Wayne State UniversityDetroitUnited States
| | - Massimo Cristofanilli
- Robert H Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University ChicagoChicagoUnited States
| | - Gabriel N Hortobagyi
- Department of Breast Medical Oncology, The University of Texas M.D. Anderson Cancer CenterHoustonUnited States
| | - Zaver M Bhujwalla
- Division of Cancer Imaging Research, The Russell H Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of MedicineBaltimoreUnited States
| | - Stephen K Burley
- Rutgers Cancer Institute of New JerseyNew BrunswickUnited States
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California-San DiegoLa JollaUnited States
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayUnited States
| | - Wadih Arap
- Rutgers Cancer Institute of New JerseyNewarkUnited States
- Division of Hematology/Oncology, Department of Medicine, Rutgers New Jersey Medical SchoolNewarkUnited States
| | - Renata Pasqualini
- Rutgers Cancer Institute of New JerseyNewarkUnited States
- Division of Cancer Biology, Department of Radiation Oncology, Rutgers New Jersey Medical SchoolNewarkUnited States
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7
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Anaya DA, Dogra P, Wang Z, Haider M, Ehab J, Jeong DK, Ghayouri M, Lauwers GY, Thomas K, Kim R, Butner JD, Nizzero S, Ramírez JR, Plodinec M, Sidman RL, Cavenee WK, Pasqualini R, Arap W, Fleming JB, Cristini V. A Mathematical Model to Estimate Chemotherapy Concentration at the Tumor-Site and Predict Therapy Response in Colorectal Cancer Patients with Liver Metastases. Cancers (Basel) 2021; 13:cancers13030444. [PMID: 33503971 PMCID: PMC7866038 DOI: 10.3390/cancers13030444] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 01/21/2021] [Indexed: 12/22/2022] Open
Abstract
Simple Summary It is known that drug transport barriers in the tumor determine drug concentration at the tumor site, causing disparity from the systemic (plasma) drug concentration. However, current clinical standard of care still bases dosage and treatment optimization on the systemic concentration of drugs. Here, we present a proof of concept observational cohort study to accurately estimate drug concentration at the tumor site from mathematical modeling using biologic, clinical, and imaging/perfusion data, and correlate it with outcome in colorectal cancer liver metastases. We demonstrate that drug concentration at the tumor site, not in systemic circulation, can be used as a credible biomarker for predicting chemotherapy outcome, and thus our mathematical modeling approach can be applied prospectively in the clinic to personalize treatment design to optimize outcome. Abstract Chemotherapy remains a primary treatment for metastatic cancer, with tumor response being the benchmark outcome marker. However, therapeutic response in cancer is unpredictable due to heterogeneity in drug delivery from systemic circulation to solid tumors. In this proof-of-concept study, we evaluated chemotherapy concentration at the tumor-site and its association with therapy response by applying a mathematical model. By using pre-treatment imaging, clinical and biologic variables, and chemotherapy regimen to inform the model, we estimated tumor-site chemotherapy concentration in patients with colorectal cancer liver metastases, who received treatment prior to surgical hepatic resection with curative-intent. The differential response to therapy in resected specimens, measured with the gold-standard Tumor Regression Grade (TRG; from 1, complete response to 5, no response) was examined, relative to the model predicted systemic and tumor-site chemotherapy concentrations. We found that the average calculated plasma concentration of the cytotoxic drug was essentially equivalent across patients exhibiting different TRGs, while the estimated tumor-site chemotherapeutic concentration (eTSCC) showed a quadratic decline from TRG = 1 to TRG = 5 (p < 0.001). The eTSCC was significantly lower than the observed plasma concentration and dropped by a factor of ~5 between patients with complete response (TRG = 1) and those with no response (TRG = 5), while the plasma concentration remained stable across TRG groups. TRG variations were driven and predicted by differences in tumor perfusion and eTSCC. If confirmed in carefully planned prospective studies, these findings will form the basis of a paradigm shift in the care of patients with potentially curable colorectal cancer and liver metastases.
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Affiliation(s)
- Daniel A. Anaya
- Department of Gastrointestinal Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (M.H.); (J.E.); (R.K.); (J.B.F.)
- Correspondence: (D.A.A.); (V.C.); Tel.: +1-813-745-1432 (D.A.A.); +1-505-934-1813 (V.C.); Fax: +1-813-745-7229 (D.A.A.)
| | - Prashant Dogra
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA; (P.D.); (Z.W.); (J.D.B.); (S.N.); (J.R.R.)
| | - Zhihui Wang
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA; (P.D.); (Z.W.); (J.D.B.); (S.N.); (J.R.R.)
| | - Mintallah Haider
- Department of Gastrointestinal Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (M.H.); (J.E.); (R.K.); (J.B.F.)
| | - Jasmina Ehab
- Department of Gastrointestinal Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (M.H.); (J.E.); (R.K.); (J.B.F.)
| | - Daniel K. Jeong
- Department of Diagnostic Imaging and Interventional Radiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (D.K.J.); (M.G.); (G.Y.L.); (K.T.)
| | - Masoumeh Ghayouri
- Department of Diagnostic Imaging and Interventional Radiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (D.K.J.); (M.G.); (G.Y.L.); (K.T.)
| | - Gregory Y. Lauwers
- Department of Diagnostic Imaging and Interventional Radiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (D.K.J.); (M.G.); (G.Y.L.); (K.T.)
| | - Kerry Thomas
- Department of Diagnostic Imaging and Interventional Radiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (D.K.J.); (M.G.); (G.Y.L.); (K.T.)
| | - Richard Kim
- Department of Gastrointestinal Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (M.H.); (J.E.); (R.K.); (J.B.F.)
| | - Joseph D. Butner
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA; (P.D.); (Z.W.); (J.D.B.); (S.N.); (J.R.R.)
| | - Sara Nizzero
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA; (P.D.); (Z.W.); (J.D.B.); (S.N.); (J.R.R.)
| | - Javier Ruiz Ramírez
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA; (P.D.); (Z.W.); (J.D.B.); (S.N.); (J.R.R.)
| | - Marija Plodinec
- Biozentrum and the Swiss Nanoscience Institute & ARTIDIS AG, University of Basel, 4056 Basel, Switzerland;
| | - Richard L. Sidman
- Department of Neurology, Harvard Medical School, Boston, MA 02115, USA;
| | - Webster K. Cavenee
- Ludwig Institute for Cancer Research, University of California-San Diego, La Jolla, CA 92093, USA;
| | - Renata Pasqualini
- Rutgers Cancer Institute of New Jersey & Division of Cancer Biology, Department of Radiation Oncology, Rutgers New Jersey Medical School, Newark, NJ 07103, USA;
| | - Wadih Arap
- Rutgers Cancer Institute of New Jersey & Division of Hematology/Oncology, Department of Medicine Rutgers New Jersey Medical School, Newark, NJ 07103, USA;
| | - Jason B. Fleming
- Department of Gastrointestinal Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (M.H.); (J.E.); (R.K.); (J.B.F.)
| | - Vittorio Cristini
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA; (P.D.); (Z.W.); (J.D.B.); (S.N.); (J.R.R.)
- Correspondence: (D.A.A.); (V.C.); Tel.: +1-813-745-1432 (D.A.A.); +1-505-934-1813 (V.C.); Fax: +1-813-745-7229 (D.A.A.)
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8
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Butner JD, Wang Z, Elganainy D, Al Feghali KA, Plodinec M, Calin GA, Dogra P, Nizzero S, Ruiz-Ramírez J, Martin GV, Tawbi HA, Chung C, Koay EJ, Welsh JW, Hong DS, Cristini V. A mathematical model for the quantification of a patient's sensitivity to checkpoint inhibitors and long-term tumour burden. Nat Biomed Eng 2021; 5:297-308. [PMID: 33398132 PMCID: PMC8669771 DOI: 10.1038/s41551-020-00662-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Accepted: 11/14/2020] [Indexed: 02/06/2023]
Abstract
A large proportion of patients with cancer are unresponsive to treatment with immune checkpoint blockade and other immunotherapies. Here, we report a mathematical model of the time-course of tumour responses to immune-checkpoint inhibitors. The model takes into account intrinsic tumour-growth rates, the rates of immune activation and of tumour–immune-cell interactions, and the efficacy of immune-mediated tumour killing. For 124 patients, four cancer types and two immunotherapy agents, the model reliably described the immune responses and final tumour burden across all different cancers and drug combinations examined. In validation cohorts from four clinical trials of checkpoint inhibitors (with a total of 177 patients), the model accurately stratified the patients according to reduced or increased long-term tumour burden. We also provide model-derived quantitative measures of treatment sensitivity for specific drug–cancer combinations. The model can be used to predict responses to therapy and to quantify specific drug–cancer sensitivities in individual patients.
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Affiliation(s)
- Joseph D Butner
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
| | - Zhihui Wang
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA. .,Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
| | - Dalia Elganainy
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Karine A Al Feghali
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Marija Plodinec
- Biozentrum and the Swiss Nanoscience Institute, University of Basel, Basel, Switzerland
| | - George A Calin
- Department of Translational Molecular Pathology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Prashant Dogra
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
| | - Sara Nizzero
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
| | - Javier Ruiz-Ramírez
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
| | - Geoffrey V Martin
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Hussein A Tawbi
- Department of Melanoma Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Caroline Chung
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Eugene J Koay
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - James W Welsh
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - David S Hong
- Department of Investigational Cancer Therapeutics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Vittorio Cristini
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA. .,Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA. .,Physiology, Biophysics, and Systems Biology Program, Graduate School of Medical Sciences, Weill Cornell Medicine, New York, NY, USA.
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9
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Dogra P, Butner JD, Ramirez JR, Cristini V, Wang Z. Investigating the Effect of Aging on the Pharmacokinetics and Tumor Delivery of Nanomaterials using Mathematical Modeling. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:2447-2450. [PMID: 33018501 DOI: 10.1109/embc44109.2020.9175322] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The application of nanomedicine for diagnosis and treatment of cancer has immense potential, but has witnessed only limited clinical success, in part due to insufficient understanding of the role of nanomaterial properties and physiological variables in governing nanoparticle (NP) pharmacology. Here, we present a multiscale mathematical model to examine the effects of physiological changes associated with patient age on the pharmacokinetics and tumor delivery efficiency of NPs. We show that physiological changes due to aging prolong the residence of NPs in the systemic circulation, thereby improving passive accumulation of NPs in tumors.Clinical Relevance - Understanding the effect of inter-individual variability on the pharmacological behavior of nanomaterials will improve their clinical translatability.
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10
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Pichler M, Rodriguez-Aguayo C, Nam SY, Dragomir MP, Bayraktar R, Anfossi S, Knutsen E, Ivan C, Fuentes-Mattei E, Lee SK, Ling H, Ivkovic TC, Huang G, Huang L, Okugawa Y, Katayama H, Taguchi A, Bayraktar E, Bhattacharya R, Amero P, He WR, Tran AM, Vychytilova-Faltejskova P, Klec C, Bonilla DL, Zhang X, Kapitanovic S, Loncar B, Gafà R, Wang Z, Cristini V, Hanash S, Bar-Eli M, Lanza G, Slaby O, Goel A, Rigoutsos I, Lopez-Berestein G, Calin GA. Therapeutic potential of FLANC, a novel primate-specific long non-coding RNA in colorectal cancer. Gut 2020; 69:1818-1831. [PMID: 31988194 PMCID: PMC7382985 DOI: 10.1136/gutjnl-2019-318903] [Citation(s) in RCA: 75] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Revised: 11/21/2019] [Accepted: 12/24/2019] [Indexed: 12/12/2022]
Abstract
OBJECTIVE To investigate the function of a novel primate-specific long non-coding RNA (lncRNA), named FLANC, based on its genomic location (co-localised with a pyknon motif), and to characterise its potential as a biomarker and therapeutic target. DESIGN FLANC expression was analysed in 349 tumours from four cohorts and correlated to clinical data. In a series of multiple in vitro and in vivo models and molecular analyses, we characterised the fundamental biological roles of this lncRNA. We further explored the therapeutic potential of targeting FLANC in a mouse model of colorectal cancer (CRC) metastases. RESULTS FLANC, a primate-specific lncRNA feebly expressed in normal colon cells, was significantly upregulated in cancer cells compared with normal colon samples in two independent cohorts. High levels of FLANC were associated with poor survival in two additional independent CRC patient cohorts. Both in vitro and in vivo experiments demonstrated that the modulation of FLANC expression influenced cellular growth, apoptosis, migration, angiogenesis and metastases formation ability of CRC cells. In vivo pharmacological targeting of FLANC by administration of 1,2-dioleoyl-sn-glycero-3-phosphatidylcholine nanoparticles loaded with a specific small interfering RNA, induced significant decrease in metastases, without evident tissue toxicity or pro-inflammatory effects. Mechanistically, FLANC upregulated and prolonged the half-life of phosphorylated STAT3, inducing the overexpression of VEGFA, a key regulator of angiogenesis. CONCLUSIONS Based on our findings, we discovered, FLANC as a novel primate-specific lncRNA that is highly upregulated in CRC cells and regulates metastases formation. Targeting primate-specific transcripts such as FLANC may represent a novel and low toxic therapeutic strategy for the treatment of patients.
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Affiliation(s)
- Martin Pichler
- Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Research Unit of Non-Coding RNA and Genome Editing, Division of Oncology, Department of Internal Medicine, Medical University of Graz (MUG), Graz, Austria
| | - Cristian Rodriguez-Aguayo
- Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Center for RNA interference and Non-coding RNA, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Su Youn Nam
- Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Present address: Gastroenterology Department, Kyungpook National University Chilgok Hospital, Daegu, Korea
| | - Mihnea P. Dragomir
- Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Recep Bayraktar
- Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Simone Anfossi
- Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Erik Knutsen
- Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Present address: Department of Medical Biology, Faculty of Health Sciences, UiT - The Arctic University of Norway, Tromsø, Norway
| | - Cristina Ivan
- Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Center for RNA interference and Non-coding RNA, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Enrique Fuentes-Mattei
- Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Sang Kil Lee
- Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Present address: Institute of Gastroenterology, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea
| | - Hui Ling
- Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Tina Catela Ivkovic
- Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Present address: Division of Molecular Medicine, Ruder Boskovic Institute, Zagreb, Croatia
| | - Guoliang Huang
- Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Present address: China-America Cancer Research Institute, Dongguan Scientific Research Center, Guangdong Medical University, Dongguan 523808, Guangdong, P.R. China
| | - Li Huang
- Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yoshinaga Okugawa
- Center for Gastrointestinal Research and Center for Translational Genomics and Oncology, Baylor Scott & White Research Institute and Charles A. Sammons Cancer Center, Baylor University Medical Center, Dallas, TX
| | - Hiroyuki Katayama
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA
| | - Ayumu Taguchi
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA
| | - Emine Bayraktar
- Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Rajat Bhattacharya
- Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Paola Amero
- Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - William Ruixian He
- Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Anh M. Tran
- Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Petra Vychytilova-Faltejskova
- Molecular Oncology II - Solid Cancers, Molecular Medicine, Central European Institute of Technology, Masaryk University, Brno, Czech Republic.,Department of Comprehensive Cancer Care, Masaryk Memorial Cancer Institute, Czech Republic
| | - Christiane Klec
- Research Unit of Non-Coding RNA and Genome Editing, Division of Oncology, Department of Internal Medicine, Medical University of Graz (MUG), Graz, Austria
| | - Diana L. Bonilla
- Department of Stem Cell Transplantation, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Xinna Zhang
- Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Present address: Medical and Molecular Genetics Department, Indiana University, Indianapolis, IN, USA
| | - Sanja Kapitanovic
- Laboratory for Personalized Medicine, Division of Molecular Medicine, Ruder Boskovic Institute, Zagreb, Croatia
| | - Bozo Loncar
- Department of Surgery, Clinical Hospital Dubrava, Zagreb, Croatia
| | - Roberta Gafà
- Department of Morphology, Surgery and Experimental Medicine, University of Ferrara, Ferrara, Italy
| | - Zhihui Wang
- Mathematics in Medicine Program, The Houston Methodist Research Institute HMRI R8-122, 6670 Bertner Ave, Houston, TX 77030
| | - Vittorio Cristini
- Mathematics in Medicine Program, The Houston Methodist Research Institute HMRI R8-122, 6670 Bertner Ave, Houston, TX 77030
| | - Samir Hanash
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA
| | - Menashe Bar-Eli
- Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Giovanni Lanza
- Department of Medical Sciences, University of Ferrara, Ferrara, Italy
| | - Ondrej Slaby
- Molecular Oncology II - Solid Cancers, Molecular Medicine, Central European Institute of Technology, Masaryk University, Brno, Czech Republic.,Department of Comprehensive Cancer Care, Masaryk Memorial Cancer Institute, Czech Republic
| | - Ajay Goel
- Center for Gastrointestinal Research and Center for Translational Genomics and Oncology, Baylor Scott & White Research Institute and Charles A. Sammons Cancer Center, Baylor University Medical Center, Dallas, TX,Present address: Department of Molecular Diagnostics, Therapeutics and Translational Oncology, City of Hope Comprehensive Cancer Center, Duarte, CA, USA
| | - Isidore Rigoutsos
- Computational Medicine Center and Department of Pathology, Anatomy and Cell Biology, Thomas Jefferson University, 1020 Locust Street, Philadelphia, PA, USA
| | - Gabriel Lopez-Berestein
- Department of Experimental Therapeutics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA .,Center for RNA interference and Non-coding RNA, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - George A. Calin
- Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Center for RNA interference and Non-coding RNA, The University of Texas MD Anderson Cancer Center, Houston, TX, USA,Corresponding authors George A. Calin, M.D., Ph.D. Professor, Department of Experimental Therapeutics, Center for RNA Interference and Non-Coding RNAs, Department of Experimental Therapeutics - Unit 1950, The University of Texas MD Anderson Cancer Center, P.O. Box 301429, Houston, Texas 77030-1429, and Gabriel Lopez-Berestein, M.D., Professor, Department of Experimental Therapeutics, Center for RNA Interference and Non-Coding RNAs, Department of Experimental Therapeutics - Unit 1950, The University of Texas MD Anderson Cancer Center, P.O. Box 301429, Houston, Texas 77030-1429,
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11
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Butner JD, Elganainy D, Wang CX, Wang Z, Chen SH, Esnaola NF, Pasqualini R, Arap W, Hong DS, Welsh J, Koay EJ, Cristini V. Mathematical prediction of clinical outcomes in advanced cancer patients treated with checkpoint inhibitor immunotherapy. SCIENCE ADVANCES 2020; 6:eaay6298. [PMID: 32426472 PMCID: PMC7190324 DOI: 10.1126/sciadv.aay6298] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Accepted: 01/21/2020] [Indexed: 05/20/2023]
Abstract
We present a mechanistic mathematical model of immune checkpoint inhibitor therapy to address the oncological need for early, broadly applicable readouts (biomarkers) of patient response to immunotherapy. The model is built upon the complex biological and physical interactions between the immune system and cancer, and is informed using only standard-of-care CT. We have retrospectively applied the model to 245 patients from multiple clinical trials treated with anti-CTLA-4 or anti-PD-1/PD-L1 antibodies. We found that model parameters distinctly identified patients with common (n = 18) and rare (n = 10) malignancy types who benefited and did not benefit from these monotherapies with accuracy as high as 88% at first restaging (median 53 days). Further, the parameters successfully differentiated pseudo-progression from true progression, providing previously unidentified insights into the unique biophysical characteristics of pseudo-progression. Our mathematical model offers a clinically relevant tool for personalized oncology and for engineering immunotherapy regimens.
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Affiliation(s)
- Joseph D. Butner
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
| | - Dalia Elganainy
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Charles X. Wang
- MD/PhD Program, Drexel University College of Medicine, Philadelphia, PA 19102, USA
| | - Zhihui Wang
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Shu-Hsia Chen
- Immunotherapy Research Center, Houston Methodist Research Institute, Houston, TX, USA
- Cancer Center, Houston Methodist Research Institute, Houston, TX, USA
| | - Nestor F. Esnaola
- Cancer Center, Houston Methodist Research Institute, Houston, TX, USA
- Department of Surgical Oncology, Fox Chase Cancer Center—Temple Health, Philadelphia, PA, USA
| | - Renata Pasqualini
- Rutgers Cancer Institute of New Jersey at University Hospital, Newark, NJ, USA
- Division of Cancer Biology, Department of Radiation Oncology, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Wadih Arap
- Rutgers Cancer Institute of New Jersey at University Hospital, Newark, NJ, USA
- Division of Hematology/Oncology, Department of Medicine, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - David S. Hong
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - James Welsh
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Eugene J. Koay
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Corresponding author. (V.C.); (E.J.K.)
| | - Vittorio Cristini
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
- MD/PhD Program, Drexel University College of Medicine, Philadelphia, PA 19102, USA
- Corresponding author. (V.C.); (E.J.K.)
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12
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Dogra P, Butner JD, Nizzero S, Ruiz Ramírez J, Noureddine A, Peláez MJ, Elganainy D, Yang Z, Le AD, Goel S, Leong HS, Koay EJ, Brinker CJ, Cristini V, Wang Z. Image-guided mathematical modeling for pharmacological evaluation of nanomaterials and monoclonal antibodies. WILEY INTERDISCIPLINARY REVIEWS-NANOMEDICINE AND NANOBIOTECHNOLOGY 2020; 12:e1628. [PMID: 32314552 PMCID: PMC7507140 DOI: 10.1002/wnan.1628] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 02/06/2020] [Accepted: 02/15/2020] [Indexed: 12/13/2022]
Abstract
While plasma concentration kinetics has traditionally been the predictor of drug pharmacological effects, it can occasionally fail to represent kinetics at the site of action, particularly for solid tumors. This is especially true in the case of delivery of therapeutic macromolecules (drug-loaded nanomaterials or monoclonal antibodies), which can experience challenges to effective delivery due to particle size-dependent diffusion barriers at the target site. As a result, disparity between therapeutic plasma kinetics and kinetics at the site of action may exist, highlighting the importance of target site concentration kinetics in determining the pharmacodynamic effects of macromolecular therapeutic agents. Assessment of concentration kinetics at the target site has been facilitated by non-invasive in vivo imaging modalities. This allows for visualization and quantification of the whole-body disposition behavior of therapeutics that is essential for a comprehensive understanding of their pharmacokinetics and pharmacodynamics. Quantitative non-invasive imaging can also help guide the development and parameterization of mathematical models for descriptive and predictive purposes. Here, we present a review of the application of state-of-the-art imaging modalities for quantitative pharmacological evaluation of therapeutic nanoparticles and monoclonal antibodies, with a focus on their integration with mathematical models, and identify challenges and opportunities. This article is categorized under: Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease Diagnostic Tools > in vivo Nanodiagnostics and Imaging Nanotechnology Approaches to Biology > Nanoscale Systems in Biology.
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Affiliation(s)
- Prashant Dogra
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, USA
| | - Joseph D Butner
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, USA
| | - Sara Nizzero
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, USA
| | - Javier Ruiz Ramírez
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, USA
| | - Achraf Noureddine
- Department of Chemical and Biological Engineering, University of New Mexico, Albuquerque, New Mexico, USA
| | - María J Peláez
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, USA.,Applied Physics Graduate Program, Rice University, Houston, Texas, USA
| | - Dalia Elganainy
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Zhen Yang
- Center for Bioenergetics, Houston Methodist Research Institute, Houston, Texas, USA
| | - Anh-Dung Le
- Nanoscience and Microsystems Engineering, University of New Mexico, Albuquerque, New Mexico, USA
| | - Shreya Goel
- Cancer Systems Imaging, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Hon S Leong
- Biological Sciences Platform, Sunnybrook Research Institute, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Eugene J Koay
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - C Jeffrey Brinker
- Department of Chemical and Biological Engineering and UNM Comprehensive Cancer Center, University of New Mexico, Albuquerque, New Mexico, USA
| | - Vittorio Cristini
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, USA
| | - Zhihui Wang
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, USA
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13
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Padmanabhan R, Kheraldine HS, Meskin N, Vranic S, Al Moustafa AE. Crosstalk between HER2 and PD-1/PD-L1 in Breast Cancer: From Clinical Applications to Mathematical Models. Cancers (Basel) 2020; 12:E636. [PMID: 32164163 PMCID: PMC7139939 DOI: 10.3390/cancers12030636] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Revised: 02/12/2020] [Accepted: 02/18/2020] [Indexed: 12/12/2022] Open
Abstract
Breast cancer is one of the major causes of mortality in women worldwide. The most aggressive breast cancer subtypes are human epidermal growth factor receptor-positive (HER2+) and triple-negative breast cancers. Therapies targeting HER2 receptors have significantly improved HER2+ breast cancer patient outcomes. However, several recent studies have pointed out the deficiency of existing treatment protocols in combatting disease relapse and improving response rates to treatment. Overriding the inherent actions of the immune system to detect and annihilate cancer via the immune checkpoint pathways is one of the important hallmarks of cancer. Thus, restoration of these pathways by various means of immunomodulation has shown beneficial effects in the management of various types of cancers, including breast. We herein review the recent progress in the management of HER2+ breast cancer via HER2-targeted therapies, and its association with the programmed death receptor-1 (PD-1)/programmed death ligand-1 (PD-L1) axis. In order to link research in the areas of medicine and mathematics and point out specific opportunities for providing efficient theoretical analysis related to HER2+ breast cancer management, we also review mathematical models pertaining to the dynamics of HER2+ breast cancer and immune checkpoint inhibitors.
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Affiliation(s)
- Regina Padmanabhan
- Department of Electrical Engineering, Qatar University, 2713 Doha, Qatar
- Biomedical Research Centre, Qatar University, 2713 Doha, Qatar
| | - Hadeel Shafeeq Kheraldine
- Biomedical Research Centre, Qatar University, 2713 Doha, Qatar
- College of Pharmacy, QU Health, Qatar University, 2713 Doha, Qatar
| | - Nader Meskin
- Department of Electrical Engineering, Qatar University, 2713 Doha, Qatar
| | - Semir Vranic
- College of Medicine, QU Health, Qatar University, 2713 Doha, Qatar
| | - Ala-Eddin Al Moustafa
- Biomedical Research Centre, Qatar University, 2713 Doha, Qatar
- College of Medicine, QU Health, Qatar University, 2713 Doha, Qatar
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14
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Dogra P, Butner JD, Ruiz Ramírez J, Chuang YL, Noureddine A, Jeffrey Brinker C, Cristini V, Wang Z. A mathematical model to predict nanomedicine pharmacokinetics and tumor delivery. Comput Struct Biotechnol J 2020; 18:518-531. [PMID: 32206211 PMCID: PMC7078505 DOI: 10.1016/j.csbj.2020.02.014] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 02/14/2020] [Accepted: 02/22/2020] [Indexed: 02/07/2023] Open
Abstract
Towards clinical translation of cancer nanomedicine, it is important to systematically investigate the various parameters related to nanoparticle (NP) physicochemical properties, tumor characteristics, and inter-individual variability that affect the tumor delivery efficiency of therapeutic nanomaterials. Comprehensive investigation of these parameters using traditional experimental approaches is impractical due to the vast parameter space; mathematical models provide a more tractable approach to navigate through such a multidimensional space. To this end, we have developed a predictive mathematical model of whole-body NP pharmacokinetics and their tumor delivery in vivo, and have conducted local and global sensitivity analyses to identify the factors that result in low tumor delivery efficiency and high off-target accumulation of NPs. Our analyses reveal that NP degradation rate, tumor blood viscosity, NP size, tumor vascular fraction, and tumor vascular porosity are the key parameters in governing NP kinetics in the tumor interstitium. The impact of these parameters on tumor delivery efficiency of NPs is discussed, and optimal values for maximizing NP delivery are presented.
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Affiliation(s)
- Prashant Dogra
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Joseph D. Butner
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Javier Ruiz Ramírez
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Yao-li Chuang
- Department of Mathematics, California State University, Northridge, CA 91330, USA
| | - Achraf Noureddine
- Department of Chemical and Biological Engineering, University of New Mexico, Albuquerque, NM 87106, USA
| | - C. Jeffrey Brinker
- Department of Chemical and Biological Engineering, University of New Mexico, Albuquerque, NM 87106, USA
- UNM Comprehensive Cancer Center, University of New Mexico, Albuquerque, NM 87102, USA
| | - Vittorio Cristini
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Zhihui Wang
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA
- Corresponding author at: Mathematics in Medicine Program, The Houston Methodist Research Institute, HMRI R8-122, 6670 Bertner Ave, Houston, TX 77030, USA.
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15
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Dogra P, Chuang YL, Butner JD, Cristini V, Wang Z. Development of a Physiologically-Based Mathematical Model for Quantifying Nanoparticle Distribution in Tumors. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:2852-2855. [PMID: 31946487 DOI: 10.1109/embc.2019.8856503] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Nanomedicine holds promise for the treatment of cancer, as it enables tumor-targeted drug delivery. However, reports on translation of most nanomedicine strategies to the clinic so far have been less than satisfactory, in part due to insufficient understanding of the effects of nanoparticle (NP) physiochemical properties and physiological variables on their pharmacological behavior. In this paper, we present a multiscale mathematical model to examine the efficacy of NP delivery to solid tumors; as a case example, we apply the model to a clinically detectable primary pancreatic ductal adenocarcinoma (PDAC) to assess tissue-scale spatiotemporal distribution profiles of NPs. We integrate NP systemic disposition kinetics with NP-cell interactions in PDAC abstractly described as a two-dimensional structure, which is then parameterized with human physiological data obtained from published literature. Through model analysis of delivery efficiency, we verify the multiscale approach by showing that NP concentration kinetics of interest in various compartments predicted by the whole-body scale model were in agreement with those obtained from the tissue-scale model. We also found that more NPs were trapped in the outer well-perfused tumor region than the inner semi-necrotic domain. Further development of the model may provide a useful tool for optimal NP design and physiological interventions.
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Dogra P, Ramírez JR, Peláez MJ, Wang Z, Cristini V, Parasher G, Rawat M. Mathematical Modeling to Address Challenges in Pancreatic Cancer. Curr Top Med Chem 2020; 20:367-376. [PMID: 31893993 PMCID: PMC7279939 DOI: 10.2174/1568026620666200101095641] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Revised: 09/10/2019] [Accepted: 10/20/2019] [Indexed: 12/30/2022]
Abstract
Pancreatic Ductal Adenocarcinoma (PDAC) is regarded as one of the most lethal cancer types for its challenges associated with early diagnosis and resistance to standard chemotherapeutic agents, thereby leading to a poor five-year survival rate. The complexity of the disease calls for a multidisciplinary approach to better manage the disease and improve the status quo in PDAC diagnosis, prognosis, and treatment. To this end, the application of quantitative tools can help improve the understanding of disease mechanisms, develop biomarkers for early diagnosis, and design patient-specific treatment strategies to improve therapeutic outcomes. However, such approaches have only been minimally applied towards the investigation of PDAC, and we review the current status of mathematical modeling works in this field.
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Affiliation(s)
- Prashant Dogra
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Javier Ruiz Ramírez
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA
| | - María J. Peláez
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA
- Applied Physics Graduate Program, Rice University, Houston, TX 77005, USA
| | - Zhihui Wang
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Vittorio Cristini
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Gulshan Parasher
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA
| | - Manmeet Rawat
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA
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