The prospects of microphysiological systems in modeling platelet pathophysiology in cancer
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Lopamudra D. Ghosh↗
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Utilizing ensemble learning for performance and power modeling and improvement of parallel cancer deep learning CANDLE benchmarks
Abstract Machine learning (ML) continues to grow in importance across nearly all domains in modeling to learn from data. Often a tradeoff exists between a model's ability to minimize bias and variance. In this article, we utilize ensemble learning to combine linear, nonlinear, and tree‐/rule‐based ML methods to cope with the bias‐variance tradeoff and result in more accurate models. We use the datasets collected for two parallel cancer deep learning CANDLE benchmarks, NT3 and P1B2, to build performance and power models based on hardware performance counters using single‐object and multiple‐objects ensemble learning to identify the most important counters for improvement on the Cray XC40 Theta at Argonne National Laboratory. Based on the insights from these models, we improve the performance and energy of P1B2 and NT3 by optimizing the deep learning environments TensorFlow, Keras, Horovod, and Python under the huge page size of 8 MB. Experimental results show that ensemble learning not only produces more accurate models but also provides more robust performance counter ranking. We achieve up to 61.15% performance improvement and up to 62.58% energy saving for P1B2 and up to 55.81% performance improvement and up to 52.60% energy saving for NT3 on up to 24,576 cores.
Wu, Xingfu↗
Comparison of Health and Performance Risk for Accelerated Mars Mission Scenarios
This document details the results for a quantitative estimate of the difference in human health and performance risk that crews would face for two hypothetical variations on Mars mission scenarios: an Accelerated Mars Mission (AMM) and a Standard Mars Mission(SMM).NASA goals for Mars mission concepts, duration, and tasks have varied over the years. While neither of the cases considered here are likely to accurately represent the initial mission to Mars, the exercise of evaluating significant differences in mission duration from an astronaut health perspective can provide bounding insight to the level of risk that is likely to be encountered in an eventual Mars mission. Medical Probabilistic Risk Assessment using the Integrated Medical Model (IMM)(1–3)and the NASA Space Radiation Cancer Risk Model (NSCR)(4,5) are used here to help mission planners gain the best insight currently available into the expected magnitude of impacts to astronaut health when undertaking a Mars mission. These impacts occur both in-mission as well as in the long-term health of the astronauts post-mission. These evaluations are currently the best available modeling estimates to characterize and bound the health risks in a proposed mission domain where humans have no experience.
Erik Antonsen↗
Automatic information extraction from childhood cancer pathology reports
The International Classification of Childhood Cancer (ICCC) facilitates the effective classification of a heterogeneous group of cancers in the important pediatric population. However, there has been no development of machine learning models for the ICCC classification. We developed deep learning-based information extraction models from cancer pathology reports based on the ICD-O-3 coding standard. In this article, we describe extending the models to perform ICCC classification. We developed 2 models, ICD-O-3 classification and ICCC recoding (Model 1) and direct ICCC classification (Model 2), and 4 scenarios subject to the training sample size. We evaluated these models with a corpus consisting of 29206 reports with age at diagnosis between 0 and 19 from 6 state cancer registries. Our findings suggest that the direct ICCC classification (Model 2) is substantially better than reusing the ICD-O-3 classification model (Model 1). Applying the uncertainty quantification mechanism to assess the confidence of the algorithm in assigning a code demonstrated that the model achieved a micro-F1 score of 0.987 while abstaining (not sufficiently confident to assign a code) on only 14.8% of ambiguous pathology reports. Our experimental results suggest that the machine learning-based automatic information extraction from childhood cancer pathology reports in the ICCC is a reliable means of supplementing human annotators at state cancer registries by reading and abstracting the majority of the childhood cancer pathology reports accurately and reliably.
60 APPLIED LIFE SCIENCES↗
Antitumor activity of a novel and orally available inhibitor of serine palmitoyltransferase
Metabolic reprogramming is an essential hallmark of neoplasia. Therefore, targeting cancer metabolism, including lipid synthesis, has attracted much interest in recent years. Serine palmitoyltransferase (SPT) plays a key role in the initial and rate-limiting step of de novo sphingolipid biosynthesis, and inhibiting SPT activity prevents the proliferation of certain cancer cells. Here, we identified a novel and orally available SPT inhibitor, compound-2. Compound-2 showed an anti-proliferative effect in several cancer cell models, reducing the levels of the sphingolipids ceramide and sphingomyelin. In the presence of compound-2, exogenously added S1P partially compensated the intracellular sphingolipid levels through the salvage pathway by partially rescuing compound-2-induced cytotoxicity. This suggested that the mechanism underlying the anti-proliferative effect of compound-2 involved the reduction of sphingolipid levels. Indeed, compound-2 promoted multinuclear formation with reduced endogenous sphingomyelin levels specifically in a compound-2-sensitive cell line, indicating that the effect was induced by sphingolipid reduction. Furthermore, compound-2 showed potent antitumor activity without causing significant body weight loss in the PL-21 acute myeloid leukemia mouse xenograft model. Therefore, SPT may be an attractive therapeutic anti-cancer drug target for which compound-2 may be a promising new drug. - Highlights: • We discovered compound-2, a novel and orally available SPT inhibitor. • Compound-2 was cytotoxic against PL-21 acute myeloid leukemia cells. • Compound-2 showed antitumor activity in the PL-21 mouse xenograft model.
60 APPLIED LIFE SCIENCES↗
Comparative Molecular Analysis of Cancer Behavior Cultured In Vitro, In Vivo, and Ex Vivo
Current pre-clinical models of cancer fail to recapitulate the cancer cell behavior in primary tumors primarily because of the lack of a deeper understanding of the effects that the microenvironment has on cancer cell phenotype. Transcriptomic profiling of 4T1 murine mammary carcinoma cells from 2D and 3D cultures, subcutaneous or orthotopic allografts (from immunocompetent or immunodeficient mice), as well as ex vivo tumoroids, revealed differences in molecular signatures including altered expression of genes involved in cell cycle progression, cell signaling and extracellular matrix remodeling. The 3D culture platforms had more in vivo-like transcriptional profiles than 2D cultures. In vivo tumors had more cells undergoing epithelial-to-mesenchymal transition (EMT) while in vitro cultures had cells residing primarily in an epithelial or mesenchymal state. Ex vivo tumoroids incorporated aspects of in vivo and in vitro culturing, retaining higher abundance of cells undergoing EMT while shifting cancer cell fate towards a more mesenchymal state. Cellular heterogeneity surveyed by scRNA-seq revealed that ex vivo tumoroids, while rapidly expanding cancer and fibroblast populations, lose a significant proportion of immune components. This study emphasizes the need to improve in vitro culture systems and preserve syngeneic-like tumor composition by maintaining similar EMT heterogeneity as well as inclusion of stromal subpopulations.
59 BASIC BIOLOGICAL SCIENCES↗
SU-F-R-34: Quantitative Perfusion Measurement in Rectal Cancer Using Three Different Pharmacokinetic Models: Implications for Prospective Study Design
Purpose: To compare three different pharmacokinetic models for analysis of dynamic-contrast-enhanced (DCE)-CT data with respect to different acquisition times and location of region of interest. Methods: Eight rectal cancer patients with pre-treatment DCE-CTs were included. The dynamic sequence started 4–10seconds(s) after the injection of contrast agent. The scan included a 110s acquisition with intervals of 40×1s+15×3s+4×6s. An experienced oncologist outlined the tumor region. Hotspots with top-5%-enhancement were also identified. Pharmacokinetic analysis was performed using three different models: deconvolution method, Patlak model, and modified Toft’s model. Perfusion parameters as blood flow (BF), blood volume (BV), mean transit time (MTT), permeability-surface-area-product (PS), volume transfer constant (Ktrans), and flux rate constant (Kep), were compared with respect to different acquisition times of 45s, 65s, 85s and 105s. Both hotspot and whole-volume variances were also assessed. The differences were compared using the Wilcoxon matched-pairs test and Bland-Altman plots. Results: Moderate correlation was observed for various perfusion parameters (r=0.56–0.72, p<0.0001) but the Wilcoxon test revealed a significant difference among the three models (P < .001). Significant differences in PS were noted between acquisitions of 45s versus longer time of 85s or 105s (p<0.05) using Patlak but not with the deconvolution method. In addition, measurements varied substantially between whole-volume vs. hotspot analysis. Conclusion: The radiation dose of DCE-CT was on average 1.5 times of an abdomen/pelvic CT, which is not insubstantial. To take the DCE-CT forward as a biomarker in oncology, prospective studies should be carefully designed with the optimal image acquisition and analysis technique. Our study suggested that: (1) different kinetic models are not interchangeable; (2) a 45s acquisition might not be sufficient for reliable permeability measurement in rectal cancer using Patlak model, but might be achievable using deconvolution method; and (3) local variations existed inside the tumor, and both whole-volume-averaged and local-heterogeneity analysis is recommended for future quantitative studies. This work is supported by the National High-tech R&D program for Young Scientists by the Ministry of Science and Technology of China (Grant No. 2015AA020917), Natural Science Foundation of China (NSFC Grant No. 81201091).
60 APPLIED LIFE SCIENCES↗
Comparison of All Solid Cancer Mortality and Incidence Dose-Response in the Life Span Study of Atomic Bomb Survivors, 1958–2009
Recent analysis of all solid cancer incidence (1958–2009) in the Life Span Study (LSS) revealed evidence of upward curvature in the radiation dose response among males but not females. Upward curvature in sex-averaged excess relative risk (ERR) for all solid cancer mortality (1950–2003) was also observed in the 0–2 Gy dose range. As reasons for non-linearity in the LSS are not completely understood, we conducted dose response analyses for all solid cancer mortality and incidence applying similar methods (1958–2009 follow-up, DS02R1 doses, including subjects notin-city (NIC) at the time of the bombing) and statistical models. Incident cancers were ascertained from Hiroshima and Nagasaki cancer registries, while cause of death was ascertained from death certificates over entire Japan. The study included 105,444 LSS subjects who were alive and not known to have cancer before Jan 1, 1958 (80,205 with dose estimates and 25,239 NIC subjects). Between 1958 and 2009, there were 3.1 million person-years (PY) and 22,538 solid cancers for incidence analysis and 3.8 million PY and 15,419 solid cancer deaths for mortality analysis. We fitted sex-specific ERR models adjusted for smoking to both types of data. Over the entire range of doses, solid cancer mortality dose response exhibited a borderline significant upward curvature among males (P=0.062) and significant upward curvature among females (P=0.010); for solid cancer incidence, as before, we found a significant upward curvature among males (P=0.001) but not among females (P=0.624). The sex difference in magnitude of dose response curvature was statistically significant for cancer incidence (P=0.017) but not for cancer mortality (P=0.781). The results of analyses in the 0–2 Gy range and restricted lower dose ranges generally supported inferences made about the sex-specific dose response shape over the entire range of doses for each outcome. Patterns of sex-specific curvature by calendar period (1958–1987 vs 1988–2009) and age at exposure (0–19 vs 20–83) varied between mortality and incidence data, particularly among females, although for each outcome there was an indication of curvature among 0–19 year old male survivors in both calendar periods and among 0–19 year old female survivors in the recent period. Collectively, our findings indicate that the upward curvature in all solid cancer dose response in the LSS is neither specific to males nor to incidence data; it appears to depend on composition of case series and age at exposure or time. Further follow-up and site-specific analyses of cancer mortality and incidence will be important to confirm the emerging trend in dose response curvature among young survivors and unveil the contributing factors and sites.
63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
Adversarial Training for Privacy-Preserving Deep Learning Model Distribution
Collaboration among cancer registries is essential to develop accurate, robust, and generalizable deep learning models for automated information extraction from cancer pathology reports. Sharing data presents a serious privacy issue, especially in biomedical research and healthcare delivery domains. Distributing pretrained deep learning (DL) models has been proposed to avoid critical data sharing. However, there is growing recognition that collaboration among clinical institutes through DL model distribution exposes new security and privacy vulnerabilities. These vulnerabilities increase in natural language processing (NLP) applications, in which the dataset vocabulary with word vector representations needs to be associated with the other model parameters. In this paper, we propose a novel privacy-preserving DL model distribution across cancer registries for information extraction from cancer pathology reports with privacy and confidentiality considerations. The proposed approach exploits the adversarial training framework to distinguish private features from shared features among different datasets. It only shares registry-invariant model parameters, without sharing raw data nor registry-specific model parameters among cancer registries. Thus, it protects both the data and the trained model simultaneously. We compare our proposed approach to single-registry models, and a model trained on centrally hosted data from different cancer registries. The results show that the proposed approach significantly outperforms the single-registry models and achieves statistically indistinguishable micro and macro F1-score as compared to the centralized model.
Alawad, Mohammed↗
AI Meets Exascale Computing: Advancing Cancer Research With Large-Scale High Performance Computing
The application of data science in cancer research has been boosted by major advances in three primary areas: (1) Data: diversity, amount, and availability of biomedical data; (2) Advances in Artificial Intelligence (AI) and Machine Learning (ML) algorithms that enable learning from complex, large-scale data; and (3) Advances in computer architectures allowing unprecedented acceleration of simulation and machine learning algorithms. These advances help build in silico ML models that can provide transformative insights from data including: molecular dynamics simulations, next-generation sequencing, omics, imaging, and unstructured clinical text documents. Unique challenges persist, however, in building ML models related to cancer, including: (1) access, sharing, labeling, and integration of multimodal and multi-institutional data across different cancer types; (2) developing AI models for cancer research capable of scaling on next generation high performance computers; and (3) assessing robustness and reliability in the AI models. In this paper, we review the National Cancer Institute (NCI) -Department of Energy (DOE) collaboration, Joint Design of Advanced Computing Solutions for Cancer (JDACS4C), a multi-institution collaborative effort focused on advancing computing and data technologies to accelerate cancer research on three levels: molecular, cellular, and population. This collaboration integrates various types of generated data, pre-exascale compute resources, and advances in ML models to increase understanding of basic cancer biology, identify promising new treatment options, predict outcomes, and eventually prescribe specialized treatments for patients with cancer.
60 APPLIED LIFE SCIENCES↗
Tracing and Forecasting Metabolic Indices of Cancer Patients Using Patient-Specific Deep Learning Models
We develop a patient-specific dynamical system model from the time series data of the cancer patient’s metabolic panel taken during the period of cancer treatment and recovery. The model consists of a pair of stacked long short-term memory (LSTM) recurrent neural networks and a fully connected neural network in each unit. It is intended to be used by physicians to trace back and look forward at the patient’s metabolic indices, to identify potential adverse events, and to make short-term predictions. When the model is used in making short-term predictions, the relative error in every index is less than 10% in the L ∞ norm and less than 6.3% in the L 1 norm in the validation process. Once a master model is built, the patient-specific model can be calibrated through transfer learning. As an example, we obtain patient-specific models for four more cancer patients through transfer learning, which all exhibit reduced training time and a comparable level of accuracy. This study demonstrates that this modeling approach is reliable and can deliver clinically acceptable physiological models for tracking and forecasting patients’ metabolic indices.
60 APPLIED LIFE SCIENCES↗
4-Hydroxybenzoic acid (4-HBA) enhances the sensitivity of human breast cancer cells to adriamycin as a specific HDAC6 inhibitor by promoting HIPK2/p53 pathway
Highlights: • 4-Hydroxybenzoic acid enhances the sensitivity of MCF-7/ADM to ADM-induced apoptosis. • 4-Hydroxybenzoic acid induces apoptosis in breast cancer cell lines. • 4-Hydroxybenzoic acid induces cell cycle arrest in G2/M phase. Breast cancer is reported a very complex disease along with heterogeneous morphological characteristics and unrelated clinical behavior, and is a leading cancer among female. Nevertheless, chemo-resistance is frequently observed. Adriamycin (ADM) is a always employed drug to treat clinical breast cancer. However, strong resistance to ADM limited its clinical efficacy. Deregulation of HDAC6 activity is linked to various diseases including cancer resulting in accumulating interest for developing HDAC6 inhibitors. In the present study, for the first time, we found that 4-Hydroxybenzoic acid (4-HBA), as histone deacetylase 6 (HDAC6) inhibitor, could successfully reverse ADM resistance in human breast cancer cells. 4-HBA significantly promoted the anticancer effect of ADM on apoptosis induction, as evidenced by the increased expressions of Caspase-3 and PARP cleavage, which was associated with the promotion p53 and homeodomain interacting protein kinase-2 (HIPK2) expressions in ADM-resistant breast cancer cells. Furthermore, the suppressive effect of ADM on drug-resistant breast cancer cells was accelerated by 4-HBA through increasing the number of cells distributed in G2/M phase of cell cycle arrest. Inhibiting HIPK2/p53 pathway could abolish 4-HBA/ADM co-treatment-induced apoptosis and G2/M cell cycle arrest. Importantly, HDAC6 expressions were also significantly down-regulated in ADM-resistance breast cancer cells co-treated with ADM and 4-HBA. Additionally, 4-HBA clearly potentiated the anticancer role of ADM in the MCF-7 breast cancer animal model with low toxicity. Therefore, 4-HBA could be applied as an effective HDAC6 inhibitor to reverse human breast cancer resistance. Herein, the 4-HBA and ADM combination might represent as a useful therapeutic strategy to prevent human breast cancer.
60 APPLIED LIFE SCIENCES↗
Utility of Physiologically Based Pharmacokinetic Modeling to Investigate the Impact of Physiological Changes of Pregnancy and Cancer on Oncology Drug Pharmacokinetics
The treatment of cancer during pregnancy remains challenging with knowledge gaps in drug dosage, safety, and efficacy due to the under-representation of this population in clinical trials. Our aim was to investigate physiological changes reported in both pregnancy and cancer populations into a PBPK modeling framework that allows for a more accurate estimation of PK changes in pregnant patients with cancer. Methods: Paclitaxel and docetaxel were selected to validate a population model using clinical data from pregnant patients with cancer. The validated population model was subsequently used to predict the PK of acalabrutinib in pregnant patients with cancer. Results: The Simcyp pregnancy population model reasonably predicted the PK of docetaxel in pregnant patients with cancer, while a modified model that included a 2.5-fold increase in CYP2C8 abundance, consistent with the increased expression during pregnancy, was needed to reasonably predict the PK of paclitaxel in pregnant patients with cancer. Changes in protein binding levels of patients with cancer had a minimal impact on the predicted clearance of paclitaxel and docetaxel. PBPK modeling predicted approximately 60% lower AUC and Cmax for acalabrutinib in pregnant versus non-pregnant patients with cancer. Conclusions: Our results suggest that PBPK modeling is a promising approach to investigate the effects of pregnancy and cancer on the PK of oncology drugs and potentially inform dosing for pregnant patients with cancer. Further evaluation and refinement of the population model are needed for pregnant patients with cancer with additional compounds and clinical PK data.
60 APPLIED LIFE SCIENCES↗
Prospective study of ultraviolet radiation exposure and risk of breast cancer in the United States
Although there are few environmental risk factors for breast cancer, some epidemiologic studies found that exposure to solar UV radiation (UVR) may lower risk. Prior epidemiologic studies are limited by narrow ambient UVR ranges and lack lifetime exposure assessment. To address these issues, we studied a cohort with residences representing a wide range of ambient UVR. Using the nationwide U.S. Radiologic Technologists study (USRT), we examined the association between breast cancer risk and UVR based on ambient UVR, time outdoors, a combined variable of ambient UVR and time outdoors (combined UVR), and sun susceptibility factors. Participants reported location of residence and hours spent outdoors during five age periods. Ambient UVR was derived by linking satellite-based annual UVR estimates to self-reported residences. Lifetime values were calculated by averaging these measures accounting for years spent in that location. We examined the risk of breast cancer among 36,725 participants (n=716 cases) from baseline questionnaire completion (2003–2005) through 2012–2013 using Cox proportional hazards models. Breast cancer risk was unrelated to ambient UVR (HR for lifetime 5th vs 1st quintile=1.22, 95% CI: 0.95–1.56, p-trend=0.36), time outdoors (HR for lifetime 5th vs 1st quintile=0.87, 95% confidence interval (CI): 0.68–1.10, p-trend=0.46), or combined UVR (HR lifetime 5th vs 1st quintile =0.85, 95% CI: 0.67–1.08, p-trend=0.46). Breast cancer risk was not associated with skin complexion, eye or hair color, or sunburn history. This study does not support the hypothesis that UVR exposure lowers breast cancer risk. - Highlights: • Prior studies of UVR and breast cancer are inconsistent and limited. • We assessed UVR exposure across the lifetime and in three ways. • Conducted a prospective analysis of UVR exposure and breast cancer risk. • No association of any UVR exposure metric at any age with breast cancer risk. • Our findings suggest UVR exposure does not reduce risk of breast cancer.
54 ENVIRONMENTAL SCIENCES↗
Finding Uncertainties that Cause the Age Dependence of Dose Limits to Be Immature
Space radiation permissible exposure limits (PEL) are intended to set acceptable levels of cancer risks, and avoid any clinical significant non-cancer effects. The 1989 recommendation of the National Council of Radiation Protection and Measurements (NCRP) recommended a strong age dependence of dose limits that departed drastically from the then mature 1970 dose limits recommendations from the National Academy of Science, which were independent of age. In 2000, the NCRP recommended revised limits that showed a similar trend of risk with age to the 1989 report. In this model, the cancer risk per Sv varies by more than 2-fold for ages between 30- and 50-yr. Therefore for galactic cosmic rays exposure, astronaut age has a larger influence on risk then radiation shielding mass or material composition, vehicle propulsion method, or position in the solar cycle. For considering the control of mission costs and resources, the possibility of using astronaut age as a trade variable in mission design could be considered. However, the uncertainties in describing the age dependence on risk have not been fully explored. We discuss biological factors that influence the age dependence of radiation risks, including susceptibility, expression and latency, and radiation quality. These factors depend not only on the individual s age, but also their genetic sensitivity and interaction with other environmental factors. Epidemiological data is limited in describing the age dependence on risk. The 2005, BEIR VII report recommends an age dependence for cancer risk attributable solely to the life-table disagreeing strongly with the NCRP model. However, BEIR VII also noted the limited power of human data for concomitantly describing both age and age after exposure dependences of cancer risks. Many experimental studies have shown that high LET radiation (e.g., high charge and energy (HZE) nuclei and neutrons) display reduced latency compared to low LET radiation, suggesting distinct biological factors are important. We discuss potential molecular mechanisms that would influence the age dependence of radiation risks. A probability distribution function for the uncertainties in age-dependence of risk models is described and predictions for Mars missions discussed. Our report suggests that theoretical considerations based on new experimental studies are needed to ensure the correct age dependence in space radiation risk models and the resulting Astronaut PEL.
Cucinotta, Francis A.↗
CD46 targeted 212 Pb alpha particle radioimmunotherapy for prostate cancer treatment
We recently identified CD46 as a novel prostate cancer cell surface antigen that shows lineage independent expression in both adenocarcinoma and small cell neuroendocrine subtypes of metastatic castration resistant prostate cancer (mCRPC), discovered an internalizing human monoclonal antibody YS5 that binds to a tumor selective CD46 epitope, and developed a microtubule inhibitor-based antibody drug conjugate that is in a multi-center phase I trial for mCRPC (NCT03575819). Here we report the development of a novel CD46-targeted alpha therapy based on YS5. We conjugated 212 Pb, an in vivo generator of alpha-emitting 212 Bi and 212 Po, to YS5 through the chelator TCMC to create the radioimmunoconjugate, 212 Pb-TCMC-YS5. We characterized 212 Pb-TCMC-YS5 in vitro and established a safe dose in vivo. We next studied therapeutic efficacy of a single dose of 212 Pb-TCMC-YS5 using three prostate cancer small animal models: a subcutaneous mCRPC cell line-derived xenograft (CDX) model (subcu-CDX), an orthotopically grafted mCRPC CDX model (ortho-CDX), and a prostate cancer patient-derived xenograft model (PDX). In all three models, a single dose of 0.74 MBq (20 µCi) 212 Pb-TCMC-YS5 was well tolerated and caused potent and sustained inhibition of established tumors, with significant increases of survival in treated animals. A lower dose (0.37 MBq or 10 µCi 212 Pb-TCMC-YS5) was also studied on the PDX model, which also showed a significant effect on tumor growth inhibition and prolongation of animal survival. These results demonstrate that 212 Pb-TCMC-YS5 has an excellent therapeutic window in preclinical models including PDXs, opening a direct path for clinical translation of this novel CD46-targeted alpha radioimmunotherapy for mCRPC treatment.
212Pb↗
Increased Cancer Mortality Risk for NASA's ISS Astronauts: The Contribution of Diagnostic Radiological Examinations
This viewgraph presentation reviews the radiation exposures and risks associated with long-term spaceflight on the International Space Station. NASA's risk model of cancer mortality is also presented.
Dodge, C.W.↗
Magneto mitochondrial dysfunction mediated cancer cell death using intracellular magnetic nano-transducers
Mitochondria are crucial regulators of the intrinsic pathway of cancer cell death. The high sensitivity of cancer cells to mitochondrial dysfunction offers opportunities for emerging targets in cancer therapy. Herein, magnetic nano-transducers, which convert external magnetic fields into physical stress, are designed to induce mitochondrial dysfunction to remotely kill cancer cells. Spindle-shaped iron oxide nanoparticles were synthesized to maximize cellular internalization and magnetic transduction. The magneto-mechanical transduction of nano-transducers in mitochondria enhances cancer cell apoptosis by promoting a mitochondrial quality control mechanism, referred to as mitophagy. Furthermore, in the liver cancer animal model, nano-transducers are infused into the local liver tumor via the hepatic artery. After treatment with a magnetic field, in vivo mitophagy-mediated cancer cell death was also confirmed by mitophagy markers, mitochondrial DNA damage assay, and TUNEL staining of tissues. This study is expected to contribute to the development of nanoparticle-mediated mitochondria-targeting cancer therapy and biological tools, such as magneto-genetics.
60 APPLIED LIFE SCIENCES↗