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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

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↗
DOE OSTI2023

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↗
DOE OSTI2016

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↗
NASA NTRS2007

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.↗
DOE OSTI2014

SU-F-BRD-01: A Logistic Regression Model to Predict Objective Function Weights in Prostate Cancer IMRT

Purpose: To develop a statistical model that predicts optimization objective function weights from patient geometry for intensity-modulation radiotherapy (IMRT) of prostate cancer. Methods: A previously developed inverse optimization method (IOM) is applied retrospectively to determine optimal weights for 51 treated patients. We use an overlap volume ratio (OVR) of bladder and rectum for different PTV expansions in order to quantify patient geometry in explanatory variables. Using the optimal weights as ground truth, we develop and train a logistic regression (LR) model to predict the rectum weight and thus the bladder weight. Post hoc, we fix the weights of the left femoral head, right femoral head, and an artificial structure that encourages conformity to the population average while normalizing the bladder and rectum weights accordingly. The population average of objective function weights is used for comparison. Results: The OVR at 0.7cm was found to be the most predictive of the rectum weights. The LR model performance is statistically significant when compared to the population average over a range of clinical metrics including bladder/rectum V53Gy, bladder/rectum V70Gy, and mean voxel dose to the bladder, rectum, CTV, and PTV. On average, the LR model predicted bladder and rectum weights that are both 63% closer to the optimal weights compared to the population average. The treatment plans resulting from the LR weights have, on average, a rectum V70Gy that is 35% closer to the clinical plan and a bladder V70Gy that is 43% closer. Similar results are seen for bladder V54Gy and rectum V54Gy. Conclusion: Statistical modelling from patient anatomy can be used to determine objective function weights in IMRT for prostate cancer. Our method allows the treatment planners to begin the personalization process from an informed starting point, which may lead to more consistent clinical plans and reduce overall planning time.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Combinatorial DNA Damage Pairing Model Based on X-Ray-Induced Foci Predicts the Dose and LET Dependence of Cell Death in Human Breast Cells

In contrast to the classic view of static DNA double-strand breaks (DSBs) being repaired at the site of damage, we hypothesize that DSBs move and merge with each other over large distances (m). As X-ray dose increases, the probability of having DSB clusters increases as does the probability of misrepair and cell death. Experimental work characterizing the X-ray dose dependence of radiation-induced foci (RIF) in nonmalignant human mammary epithelial cells (MCF10A) is used here to validate a DSB clustering model. We then use the principles of the local effect model (LEM) to predict the yield of DSBs at the submicron level. Two mechanisms for DSB clustering, namely random coalescence of DSBs versus active movement of DSBs into repair domains are compared and tested. Simulations that best predicted both RIF dose dependence and cell survival after X-ray irradiation favored the repair domain hypothesis, suggesting the nucleus is divided into an array of regularly spaced repair domains of ~;;1.55 m sides. Applying the same approach to high-linear energy transfer (LET) ion tracks, we are able to predict experimental RIF/m along tracks with an overall relative error of 12percent, for LET ranging between 30 350 keV/m and for three different ions. Finally, cell death was predicted by assuming an exponential dependence on the total number of DSBs and of all possible combinations of paired DSBs within each simulated RIF. Relative biological effectiveness (RBE) predictions for cell survival of MCF10A exposed to high-LET showed an LET dependence that matches previous experimental results for similar cell types. Overall, this work suggests that microdosimetric properties of ion tracks at the submicron level are sufficient to explain both RIF data and survival curves for any LET, similarly to the LEM assumption. Conversely, high-LET death mechanism does not have to infer linear-quadratic dose formalism as done in the LEM. In addition, the size of repair domains derived in our model are based on experimental RIF and are three times larger than the hypothetical LEM voxel used to fit survival curves. Our model is therefore an alternative to previous approaches that provides a testable biological mechanism (i.e., RIF). In addition, we propose that DSB pairing will help develop more accurate alternatives to the linear cancer risk model (LNT) currently used for regulating exposure to very low levels of ionizing radiation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

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↗
DOE OSTI2021

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↗
DOE OSTI2012

Chronic occupational exposure to arsenic induces carcinogenic gene signaling networks and neoplastic transformation in human lung epithelial cells

Chronic arsenic exposure remains a human health risk; however a clear mode of action to understand gene signaling-driven arsenic carcinogenesis is currently lacking. This study chronically exposed human lung epithelial BEAS-2B cells to low-dose arsenic trioxide to elucidate cancer promoting gene signaling networks associated with arsenic-transformed (B-As) cells. Following a 6 month exposure, exposed cells were assessed for enhanced cell proliferation, colony formation, invasion ability and in vivo tumor formation compared to control cell lines. Collected mRNA was subjected to whole genome expression microarray profiling followed by in silico Ingenuity Pathway Analysis (IPA) to identify lung carcinogenesis modes of action. B-As cells displayed significant increases in proliferation, colony formation and invasion ability compared to BEAS-2B cells. B-As injections into nude mice resulted in development of primary and secondary metastatic tumors. Arsenic exposure resulted in widespread up-regulation of genes associated with mitochondrial metabolism and increased reactive oxygen species protection suggesting mitochondrial dysfunction. Carcinogenic initiation via reactive oxygen species and epigenetic mechanisms was further supported by altered DNA repair, histone, and ROS-sensitive signaling. NF-κB, MAPK and NCOR1 signaling disrupted PPARα/δ-mediated lipid homeostasis. A ‘pro-cancer’ gene signaling network identified increased survival, proliferation, inflammation, metabolism, anti-apoptosis and mobility signaling. IPA-ranked signaling networks identified altered p21, EF1α, Akt, MAPK, and NF-κB signaling networks promoting genetic disorder, altered cell cycle, cancer and changes in nucleic acid and energy metabolism. In conclusion, transformed B-As cells with their whole genome expression profile provide an in vitro arsenic model for future lung cancer signaling research and data for chronic arsenic exposure risk assessment. Highlights: ► Chronic As{sub 2}O{sub 3} exposure to lung epithelial cells resulted in a cancer-like phenotype. ► Mice injected with arsenic transformed (B-As) cells displayed metastatic tumors. ► Microarray profiling revealed changes in mitochondrial metabolism and ROS response. ► p21, EF1α, Akt, MAPK, PPARα and NF-κB networks promoted pro-cancer signaling. ► B-As cells represent a lung cancer model to explore As-associated carcinogenesis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Low responsiveness of machine learning models to critical or deteriorating health conditions

Machine learning (ML) based mortality prediction models can be immensely useful in intensive care units. Such a model should generate warnings to alert physicians when a patient’s condition rapidly deteriorates, or their vitals are in highly abnormal ranges. Before clinical deployment, it is important to comprehensively assess a model’s ability to recognize critical patient conditions. We develop multiple medical ML testing approaches, including a gradient ascent method and neural activation map. We systematically assess these machine learning models’ ability to respond to serious medical conditions using additional test cases, some of which are time series. Guided by medical doctors, our evaluation involves multiple machine learning models, resampling techniques, and four datasets for two clinical prediction tasks. We identify serious deficiencies in the models’ responsiveness, with the models being unable to recognize severely impaired medical conditions or rapidly deteriorating health. For in-hospital mortality prediction, the models tested using our synthesized cases fail to recognize 66% of the injuries. In some instances, the models fail to generate adequate mortality risk scores for all test cases. Our study identifies similar kinds of deficiencies in the responsiveness of 5-year breast and lung cancer prediction models. Using generated test cases, we find that statistical machine-learning models trained solely from patient data are grossly insufficient and have many dangerous blind spots. Most of the ML models tested fail to respond adequately to critically ill patients. How to incorporate medical knowledge into clinical machine learning models is an important future research direction.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Fully automatic multi-organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks

Experiments on clinical datasets of H&N patients demonstrated the effectiveness of the proposed deep neural network segmentation method for multi-organ segmentation on volumetric CT scans. Additionally, the accuracy and robustness of the segmentation were further increased by incorporating shape priors using SMR. The proposed method showed competitive performance and took shorter time to segment multiple organs in comparison to state of the art methods.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

Treatment planning for prostate focal laser ablation in the face of needle placement uncertainty

Purpose: To study the effect of needle placement uncertainty on the expected probability of achieving complete focal target destruction in focal laser ablation (FLA) of prostate cancer. Methods: Using a simplified model of prostate cancer focal target, and focal laser ablation region shapes, Monte Carlo simulations of needle placement error were performed to estimate the probability of completely ablating a region of target tissue. Results: Graphs of the probability of complete focal target ablation are presented over clinically relevant ranges of focal target sizes and shapes, ablation region sizes, and levels of needle placement uncertainty. In addition, a table is provided for estimating the maximum target size that is treatable. The results predict that targets whose length is at least 5 mm smaller than the diameter of each ablation region can be confidently ablated using, at most, four laser fibers if the standard deviation in each component of needle placement error is less than 3 mm. However, targets larger than this (i.e., near to or exceeding the diameter of each ablation region) require more careful planning. This process is facilitated by using the table provided. Conclusions: The probability of completely ablating a focal target using FLA is sensitive to the level of needle placement uncertainty, especially as the target length approaches and becomes greater than the diameter of ablated tissue that each individual laser fiber can achieve. The results of this work can be used to help determine individual patient eligibility for prostate FLA, to guide the planning of prostate FLA, and to quantify the clinical benefit of using advanced systems for accurate needle delivery for this treatment modality.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

A Comprehensive Investigation of Active Learning Strategies for Conducting Anti-Cancer Drug Screening

It is well-known that cancers of the same histology type can respond differently to a treatment. Thus, computational drug response prediction is of paramount importance for both preclinical drug screening studies and clinical treatment design. To build drug response prediction models, treatment response data need to be generated through screening experiments and used as input to train the prediction models. In this study, we investigate various active learning strategies of selecting experiments to generate response data for the purposes of (1) improving the performance of drug response prediction models built on the data and (2) identifying effective treatments. Here, we focus on constructing drug-specific response prediction models for cancer cell lines. Various approaches have been designed and applied to select cell lines for screening, including a random, greedy, uncertainty, diversity, combination of greedy and uncertainty, sampling-based hybrid, and iteration-based hybrid approach. All of these approaches are evaluated and compared using two criteria: (1) the number of identified hits that are selected experiments validated to be responsive, and (2) the performance of the response prediction model trained on the data of selected experiments. The analysis was conducted for 57 drugs and the results show a significant improvement on identifying hits using active learning approaches compared with the random and greedy sampling method. Active learning approaches also show an improvement on response prediction performance for some of the drugs and analysis runs compared with the greedy sampling method.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

A physical sciences network characterization of non-tumorigenic and metastatic cells

To investigate the transition from non-cancerous to metastatic from a physical sciences perspective, the Physical Sciences–Oncology Centers (PS-OC) Network performed molecular and biophysical comparative studies of the non-tumorigenic MCF-10A and metastatic MDA-MB-231 breast epithelial cell lines, commonly used as models of cancer metastasis. Experiments were performed in 20 laboratories from 12 PS-OCs. Each laboratory was supplied with identical aliquots and common reagents and culture protocols. Analyses of these measurements revealed dramatic differences in their mechanics, migration, adhesion, oxygen response, and proteomic profiles. Model-based multi-omics approaches identified key differences between these cells’ regulatory networks involved in morphology and survival. These results provide a multifaceted description of cellular parameters of two widely used cell lines and demonstrate the value of the PS-OC Network approach for integration of diverse experimental observations to elucidate the phenotypes associated with cancer metastasis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Effects of activated fibroblasts on phenotype modulation, EGFR signalling and cell cycle regulation in OSCC cells

Crosstalk between carcinoma associated fibroblasts (CAFs) and oral squamous cell carcinoma (OSCC) cells is suggested to mediate phenotype transition of cancer cells as a prerequisite for tumour progression, to predict patients’ outcome, and to influence the efficacy of EGFR inhibitor therapies. Here we investigate the influence of activated fibroblasts as a model for CAFs on phenotype and EGFR signalling in OSCC cells in vitro. For this, immortalised hTERT-BJ1 fibroblasts were activated with TGFβ1 and PDGFAB to generate a myofibroblast or proliferative phenotype, respectively. Conditioned media (FCM{sub TGF}, FCM{sub PDGF}) were used to stimulate PE/CA-PJ15 OSCC cells. Results were compared to the effect of conditioned media of non-stimulated fibroblasts (FCM{sub B}). FCM{sub TGF} stimulation leads to an up-regulation of vimentin in the OSCC cells and an enhancement of invasive behaviour, indicating EMT-like effects. Similarly, FCM{sub TGF}≫FCM{sub PDGF} induced up-regulation of EGFR, but not of ErbB2/ErbB3. In addition, we detected an increase in basal activities of ERK, PI3K/Akt and Stat3 (FCM{sub TGF}>FCM{sub PDGF}) accompanied by protein interaction of vimentin with pERK. These effects are correlated with an increased proliferation. In summary, our results suggest that the activated myofibroblast phenotype provides soluble factors which are able to induce EMT-like phenomena and to increase EGFR signalling as well as cell proliferation in OSCC cells. Our results indicate a possible influence of activated myofibroblasts on EGFR-inhibitor therapy. Therefore, CAFs may serve as promising novel targets for combined therapy strategies. - Highlights: • A cell culture model for cancer associated fibroblasts is described. • The mutual interaction with OSCC cells leads to up-regulation of EGFR in tumour cells. • mCAF induces EGFR downstream signalling with increased proliferation in OSCC. • Erk activation is associated with protein interaction with vimentin as sign of EMT. • Results qualify CAF as promising new therapeutic targets in OSCC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Treatment of Prostate Cancer with CD46-targeted 225 Ac Alpha Particle Radioimmunotherapy

Radiopharmaceutical therapy is changing the standard of care in prostate cancer and other malignancies. We previously reported high CD46 expression in prostate cancer and developed an antibody–drug conjugate and immunoPET agent based on the YS5 antibody, which targets a tumor-selective CD46 epitope. Here, we present the preparation, preclinical efficacy, and toxicity evaluation of [ 225 Ac]DOTA-YS5, a radioimmunotherapy agent based on the YS5 antibody. [ 225 Ac]DOTA-YS5 was developed, and its therapeutic efficiency was tested on cell-derived (22Rv1, DU145), and patient-derived (LTL-545, LTL484) prostate cancer xenograft models. Biodistribution studies were carried out on 22Rv1 tumor xenograft models to confirm the targeting efficacy. Toxicity analysis of the [ 225 Ac]DOTA-YS5 was carried out on nu/nu mice to study short-term (acute) and long-term (chronic) toxicity. Biodistribution study shows that [ 225 Ac]DOTA-YS5 agent delivers high levels of radiation to the tumor tissue (11.64% ± 1.37%ID/g, 28.58% ± 10.88%ID/g, 29.35% ± 7.76%ID/g, and 31.78% ± 5.89%ID/g at 24, 96, 168, and 408 hours, respectively), compared with the healthy organs. [ 225 Ac]DOTA-YS5 suppressed tumor size and prolonged survival in cell line–derived and patient-derived xenograft models. Toxicity analysis revealed that the 0.5 μCi activity levels showed toxicity to the kidneys, likely due to redistribution of daughter isotope 213 Bi. [ 225 Ac]DOTA-YS5 suppressed the growth of cell-derived and patient-derived xenografts, including prostate-specific membrane antigen–positive and prostate-specific membrane antigen–deficient models. Overall, this preclinical study confirms that [ 225 Ac]DOTA-YS5 is a highly effective treatment and suggests feasibility for clinical translation of CD46-targeted radioligand therapy in prostate cancer.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

184AA3: a xenograft model of ER+ breast adenocarcinoma

Despite the prevalence and significant morbidity resulting from estrogen receptor positive (ER + ) breast adenocarcinomas, there are only a few models of this cancer subtype available for drug development and arguably none for studying etiology. Those models that do exist have questionable clinical relevance. Given our goal of developing luminal models, we focused on six cell lines derived by minimal mutagenesis from normal human breast cells, and asked if any could generate clinically relevant xenografts, which we then extensively characterized. Xenografts of one cell line, 184AA3, consistently formed ER + adenocarcinomas that had a high proliferative rate and other features consistent with “luminal B” intrinsic subtype. Squamous and spindle cell/mesenchymal differentiation was absent, in stark contrast to other cell lines that we examined or others have reported. We explored intratumoral heterogeneity produced by 184AA3 by immunophenotyping xenograft tumors and cultured cells, and characterized marker expression by immunofluorescence and flow cytometry. A CD44 High subpopulation was discovered, yet their tumor forming ability was far less than CD44 Low cells. Single cell cloning revealed the phenotypic plasticity of 184AA3, consistent with the intratumoral heterogeneity observed in xenografts. Characterization of ER expression in cultures revealed ER protein and signaling is intact, yet when estrogen was depleted in culture, and in vivo, it did not impact cell or tumor growth, analogous to therapeutically resistant ER + cancers. In conclusion, this model is appropriate for studies of the etiology of ovarian hormone independent adenocarcinomas, for identification of therapeutic targets, predictive testing, and drug development.

59 BASIC BIOLOGICAL SCIENCES↗