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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

MO-F-CAMPUS-T-04: Development and Evaluation of a Knowledge-Based Model for Treatment Planning of Lung Cancer Patients Using Stereotactic Body Radiotherapy (SBRT)

Purpose: To describe the development of a knowledge-based treatment planning model for lung cancer patients treated with SBRT, and to evaluate the model performance and applicability to different planning techniques and tumor locations. Methods: 105 lung SBRT plans previously treated at our institution were included in the development of the model using Varian’s RapidPlan DVH estimation algorithm. The model was trained with a combination of IMRT, VMAT, and 3D–CRT techniques. Tumor locations encompassed lesions located centrally vs peripherally (43:62), upper vs lower (62:43), and anterior vs posterior lobes (60:45). The model performance was validated with 25 cases independent of the training set, for both IMRT and VMAT. Model generated plans were created with only one optimization and no planner intervention. The original, general model was also divided into four separate models according to tumor location. The model was also applied using different beam templates to further improve workflow. Dose differences to targets and organs-at-risk were evaluated. Results: IMRT and VMAT RapidPlan generated plans were comparable to clinical plans with respect to target coverage and several OARs. Spinal cord dose was lowered in the model-based plans by 1Gy compared to the clinical plans, p=0.008. Splitting the model according to tumor location resulted in insignificant differences in DVH estimation. The peripheral model decreased esophagus dose to the central lesions by 0.5Gy compared to the original model, p=0.025, and the posterior model increased dose to the spinal cord by 1Gy compared to the anterior model, p=0.001. All template beam plans met OAR criteria, with 1Gy increases noted in maximum heart dose for the 9-field plans, p=0.04. Conclusion: A RapidPlan knowledge-based model for lung SBRT produces comparable results to clinical plans, with increased consistency and greater efficiency. The model encompasses both IMRT and VMAT techniques, differing tumor locations, and beam arrangements. Research supported in part by a grant from Varian Medical Systems, Palo Alto CA.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

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↗
DOE OSTI2016

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↗
DOE OSTI2012

The Benefits of Including Clinical Factors in Rectal Normal Tissue Complication Probability Modeling After Radiotherapy for Prostate Cancer

Purpose: To study the impact of clinical predisposing factors on rectal normal tissue complication probability modeling using the updated results of the Dutch prostate dose-escalation trial. Methods and Materials: Toxicity data of 512 patients (conformally treated to 68 Gy [n = 284] and 78 Gy [n = 228]) with complete follow-up at 3 years after radiotherapy were studied. Scored end points were rectal bleeding, high stool frequency, and fecal incontinence. Two traditional dose-based models (Lyman-Kutcher-Burman (LKB) and Relative Seriality (RS) and a logistic model were fitted using a maximum likelihood approach. Furthermore, these model fits were improved by including the most significant clinical factors. The area under the receiver operating characteristic curve (AUC) was used to compare the discriminating ability of all fits. Results: Including clinical factors significantly increased the predictive power of the models for all end points. In the optimal LKB, RS, and logistic models for rectal bleeding and fecal incontinence, the first significant (p = 0.011-0.013) clinical factor was 'previous abdominal surgery.' As second significant (p = 0.012-0.016) factor, 'cardiac history' was included in all three rectal bleeding fits, whereas including 'diabetes' was significant (p = 0.039-0.048) in fecal incontinence modeling but only in the LKB and logistic models. High stool frequency fits only benefitted significantly (p = 0.003-0.006) from the inclusion of the baseline toxicity score. For all models rectal bleeding fits had the highest AUC (0.77) where it was 0.63 and 0.68 for high stool frequency and fecal incontinence, respectively. LKB and logistic model fits resulted in similar values for the volume parameter. The steepness parameter was somewhat higher in the logistic model, also resulting in a slightly lower D{sub 50}. Anal wall DVHs were used for fecal incontinence, whereas anorectal wall dose best described the other two endpoints. Conclusions: Comparable prediction models were obtained with LKB, RS, and logistic NTCP models. Including clinical factors improved the predictive power of all models significantly.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

Modeling Pathologic Response of Esophageal Cancer to Chemoradiation Therapy Using Spatial-Temporal {sup 18}F-FDG PET Features, Clinical Parameters, and Demographics

Purpose: To construct predictive models using comprehensive tumor features for the evaluation of tumor response to neoadjuvant chemoradiation therapy (CRT) in patients with esophageal cancer. Methods and Materials: This study included 20 patients who underwent trimodality therapy (CRT + surgery) and underwent {sup 18}F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) both before and after CRT. Four groups of tumor features were examined: (1) conventional PET/CT response measures (eg, standardized uptake value [SUV]{sub max}, tumor diameter); (2) clinical parameters (eg, TNM stage, histology) and demographics; (3) spatial-temporal PET features, which characterize tumor SUV intensity distribution, spatial patterns, geometry, and associated changes resulting from CRT; and (4) all features combined. An optimal feature set was identified with recursive feature selection and cross-validations. Support vector machine (SVM) and logistic regression (LR) models were constructed for prediction of pathologic tumor response to CRT, cross-validations being used to avoid model overfitting. Prediction accuracy was assessed by area under the receiver operating characteristic curve (AUC), and precision was evaluated by confidence intervals (CIs) of AUC. Results: When applied to the 4 groups of tumor features, the LR model achieved AUCs (95% CI) of 0.57 (0.10), 0.73 (0.07), 0.90 (0.06), and 0.90 (0.06). The SVM model achieved AUCs (95% CI) of 0.56 (0.07), 0.60 (0.06), 0.94 (0.02), and 1.00 (no misclassifications). With the use of spatial-temporal PET features combined with conventional PET/CT measures and clinical parameters, the SVM model achieved very high accuracy (AUC 1.00) and precision (no misclassifications)—results that were significantly better than when conventional PET/CT measures or clinical parameters and demographics alone were used. For groups with many tumor features (groups 3 and 4), the SVM model achieved significantly higher accuracy than did the LR model. Conclusions: The SVM model that used all features including spatial-temporal PET features accurately and precisely predicted pathologic tumor response to CRT in esophageal cancer.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2012

Low Dose Radiation Cancer Risks: Epidemiological and Toxicological Models

The basic purpose of this one year research grant was to extend the two stage clonal expansion model (TSCE) of carcinogenesis to exposures other than the usual single acute exposure. The two-stage clonal expansion model of carcinogenesis incorporates the biological process of carcinogenesis, which involves two mutations and the clonal proliferation of the intermediate cells, in a stochastic, mathematical way. The current TSCE model serves a general purpose of acute exposure models but requires numerical computation of both the survival and hazard functions. The primary objective of this research project was to develop the analytical expressions for the survival function and the hazard function of the occurrence of the first cancer cell for acute, continuous and multiple exposure cases within the framework of the piece-wise constant parameter two-stage clonal expansion model of carcinogenesis. For acute exposure and multiple exposures of acute series, it is either only allowed to have the first mutation rate vary with the dose, or to have all the parameters be dose dependent; for multiple exposures of continuous exposures, all the parameters are allowed to vary with the dose. With these analytical functions, it becomes easy to evaluate the risks of cancer and allows one to deal with the various exposure patterns in cancer risk assessment. A second objective was to apply the TSCE model with varing continuous exposures from the cancer studies of inhaled plutonium in beagle dogs. Using step functions to estimate the retention functions of the pulmonary exposure of plutonium the multiple exposure versions of the TSCE model was to be used to estimate the beagle dog lung cancer risks. The mathematical equations of the multiple exposure versions of the TSCE model were developed. A draft manuscript which is attached provides the results of this mathematical work. The application work using the beagle dog data from plutonium exposure has not been completed due to the fact that the research project did not continue beyond its first year.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

New insights into the autotaxin/LPA axis in cancer development and metastasis

Lysophosphatidic acid (LPA) is a simple lipid with a single fatty acyl chain linked to a glycerophosphate backbone. Despite the simplicity of its structure but owing to its interactions with a series of at least six G protein-coupled receptors (LPA{sub 1–6}), LPA exerts pleiotropic bioactivities including stimulation of proliferation, migration and survival of many cell types. Autotaxin (ATX) is a unique enzyme with a lysophospholipase D (lysoPLD) activity that is responsible for the levels of LPA in the blood circulation. Both LPA receptor family members and ATX/LysoPLD are aberrantly expressed in many human cancers. This review will present the more striking as well as novel experimental evidences using cell lines, cancer mouse models and transgenic animals identifying the roles for ATX and LPA receptors in cancer progression, tumor cell invasion and metastasis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

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↗
DOE OSTI2015

Combination Effect of Regulatory T-Cell Depletion and Ionizing Radiation in Mouse Models of Lung and Colon Cancer

Purpose: To investigate the potential of low-dose cyclophosphamide (LD-CTX) and anti-CD25 antibody to prevent activation of regulatory T cells (Tregs) during radiation therapy. Methods and Materials: We used LD-CTX and anti-CD25 monoclonal antibody as a means to inhibit Tregs and improve the therapeutic effect of radiation in a mouse model of lung and colon cancer. Mice were irradiated on the tumor mass of the right leg and treated with LD-CTX and anti-CD25 antibody once per week for 3 weeks. Results: Combined treatment of LD-CTX or anti-CD25 antibody with radiation significantly decreased Tregs in the spleen and tumor compared with control and irradiation only in both lung and colon cancer. Combinatorial treatments resulted in a significant increase in the effector T cells, longer survival rate, and suppressed irradiated and distal nonirradiated tumor growth. Specifically, the combinatorial treatment of LD-CTX with radiation resulted in outstanding regression of local and distant tumors in colon cancer, and almost all mice in this group survived until the end of the study. Conclusions: Our results suggest that Treg depletion strategies may enhance radiation-mediated antitumor immunity and further improve outcomes after radiation therapy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2012

Novel, dually radiolabeled peptides for simultaneous monitoring of enzymatic activity and protein targets

This application investigated a novel imaging approach to develop methods to incorporate multiple radionuclides into a single peptide at chemoselective sites for simultaneous monitoring of cell-bound protein targets as well as specific enzymatic activity, both of which are associated with enhanced tumor growth and metastasis. This imaging construct was synthesized in such a manner so that the PET radionuclide will remain associated with the tumor cells and the SPECT radionuclide was cleaved from the imaging agent. Measurement of the PET agent only will yield information about the tumor marker density while measurement of the amount of co-localization and mismatch of the two radionuclides will yield information about the enzymatic activity. This coincident measuring technique using both PET and SPECT agents allows us to draw correlations involving the interactions of enzymes (cathepsin, serine-protease urokinase (uPA) and matrix metalloproteases) and other cellular proteins which play a role in cancer growth and metastasis. This technique will allow for studies in xenograft or genetic models of cancer in the same animal at the same time, thus eliminating problems that may occur when trying to invoke comparisons across animals or timepoints. By using radionuclide imaging as opposed to other imaging modalities, this technique has the potential to be translatable and can exploit the high specific activity probes which can be generated with radiotracers. The proof of principle test of this system investigated simultaneous monitoring of matrix metalloprotease (MMP) activity in the extracellular matrix (ECM) as well as density of integrins on the cell surface, both of which can serve as tumor markers. The outcomes/deliverables of this project were as follows: 1. Peptides were synthesized dually labeled at chemospecific sites with PET and SPECT agents. 2. Stability (intrinsic and to radiolysis) and specific activity of these labeled compounds were determined. 3. The feasibility of using these agents for simultaneous monitoring of MMP-2 enzymatic activity and $\alpha v \beta 3$ integrin density was demonstrated in several in vitro assays Radiotracers can be detected at concentrations up to 1000 fold lower than those labeled with non-radioactive markers (e.g. MRI contrast agents), thus using this technique has the advantage of very high sensitivity to measure these processes in vivo. Hence, the development of an efficient approach to the dual labeling of these molecular probes is embodied within this project, with the end result yielding a molecular imaging probe with the highest specific activity possible. An advantage to this dual labeling approach is the ability to measure two different biochemical processes at the same time, a benefit which is not possible in scans involving protocols utilizing two different radiolabeled agents injected sequentially. Another advantage to this technique is the ability to measure enzymatic activity in the form of substrate cleavage. This can only be achieved with a dually labeled compound as has been demonstrated in the case of FRET1. To our knowledge this is the first instance of a measurement of enzymatic substrate cleavage by a dually labeled PET/SPECT radionuclide imaging agent.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2019

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↗
DOE OSTI2013

Boron neutron capture therapy for oral precancer: proof of principle in an experimental animal model

Field-cancerized tissue can give rise to second primary tumours, causing therapeutic failure. Boron neutron capture therapy (BNCT) is based on biological targeting and would serve to treat undetectable foci of malignant transformation. The aim of this study was to optimize BNCT for the integral treatment for oral cancer, with particular emphasis on the inhibitory effect on tumour development originating in precancerous conditions, and radiotoxicity of different BNCT protocols in a hamster cheek pouch oral precancer model.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2012

Microscopic Disease Extension in Three Dimensions for Non-Small-Cell Lung Cancer: Development of a Prediction Model Using Pathology-Validated Positron Emission Tomography and Computed Tomography Features

Purpose: One major uncertainty in radiotherapy planning of non-small-cell lung cancer concerns the definition of the clinical target volume (CTV), meant to cover potential microscopic disease extension (MDE) around the macroscopically visible tumor. The primary aim of this study was to establish pretreatment risk factors for the presence of MDE. The secondary aim was to establish the impact of these factors on the accuracy of positron emission tomography (PET) and computed tomography (CT) to assess the total tumor-bearing region at pathologic examination (CTV{sub path}). Methods and Materials: 34 patients with non-small-cell lung cancer who underwent CT and PET before lobectomy were included. Specimens were examined microscopically for MDE. The gross tumor volume (GTV) on CT and PET (GTV{sub CT} and GTV{sub PET}, respectively) was compared with the GTV and the CTV at pathologic examination, tissue deformations being taken into account. Using multivariate logistic regression, image-based risk factors for the presence of MDE were identified, and a prediction model was developed based on these factors. Results: MDE was found in 17 of 34 patients (50%). The MDE did not exceed 26 mm in 90% of patients. In multivariate analysis, two parameters (mean CT tumor density and GTV{sub CT}) were significantly associated with MDE. The area under the curve of the two-parameter prediction model was 0.86. Thirteen tumors (38%, 95% CI: 24-55%) were identified as low risk for MDE, being potential candidates for reduced-intensity therapy around the GTV. In the low-risk group, the effective diameter of the GTV{sub CT/PET} accurately represented the CTV{sub path}. In the high-risk group, GTV{sub CT/PET} underestimated the CTV{sub path} with, on average, 19.2 and 26.7 mm, respectively. Conclusions: CT features have potential to predict the presence of MDE. Tumors identified as low risk of MDE show lower rates of disease around the GTV than do high-risk tumors. Both CT and PET accurately visualize the CTV{sub path} in low-risk tumors but underestimate it in high-risk tumors.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2012

Optimized Planning Target Volume for Intact Cervical Cancer

Purpose: To model interfraction clinical target volume (CTV) variation in patients with intact cervical cancer and design a planning target volume (PTV) that minimizes normal tissue dose while maximizing CTV coverage. Methods and Materials: We analyzed 50 patients undergoing external-beam radiotherapy for intact cervical cancer using daily online cone-beam computed tomography (CBCT). The CBCTs (n = 972) for each patient were rigidly registered to the planning CT. The CTV was delineated on the planning CT (CTV{sub 0}) and the set of CBCTs ({l_brace}CTV{sub 1}-CTV{sub 25}{r_brace}). Manual (n = 98) and automated (n = 668) landmarks were placed over the surface of CTV{sub 0} with reference to defined anatomic structures. Normal vectors were extended from each landmark, and the minimum length required for a given probability of encompassing CTV{sub 1}-CTV{sub 25} was computed. The resulting expansions were used to generate an optimized PTV. Results: The mean (SD; range) normal vector length to ensure 95% coverage was 4.3 mm (2.7 mm; 1-16 mm). The uniform expansion required to ensure 95% probability of CTV coverage was 13 mm. An anisotropic margin of 20 mm anteriorly and posteriorly and 10 mm superiorly, inferiorly, and laterally also would have ensured a 95% probability of CTV coverage. The volume of the 95% optimized PTV (1470 cm{sup 3}) was significantly lower than both the anisotropic PTV (2220 cm{sup 3}) and the uniformly expanded PTV (2110 cm{sup 3}) (p < 0.001). For a 95% probability of CTV coverage, normal lengths of 1-3 mm were found along the superior and lateral regions of CTV{sub 0}, 5-10 mm along the interfaces of CTV{sub 0} with the bladder and rectum, and 10-14 mm along the anterior surface of CTV{sub 0} at the level of the uterus. Conclusion: Optimizing PTV definition according to surface landmarking resulted in a high probability of CTV coverage with reduced PTV volumes. Our results provide data justifying planning margins to use in practice and clinical trials.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

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↗
DOE OSTI2022

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↗
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↗