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At least 289 records · Page 16

All sources NASA NTRS DOE OSTI

DOE OSTI2022

Super-resolution and segmentation deep learning for breast cancer histopathology image analysis

Traditionally, a high-performance microscope with a large numerical aperture is required to acquire high-resolution images. However, the images’ size is typically tremendous. Therefore, they are not conveniently managed and transferred across a computer network or stored in a limited computer storage system. As a result, image compression is commonly used to reduce image size resulting in poor image resolution. Here, we demonstrate custom convolution neural networks (CNNs) for both super-resolution image enhancement from low-resolution images and characterization of both cells and nuclei from hematoxylin and eosin (H&E) stained breast cancer histopathological images by using a combination of generator and discriminator networks so-called super-resolution generative adversarial network-based on aggregated residual transformation (SRGAN-ResNeXt) to facilitate cancer diagnosis in low resource settings. The results provide high enhancement in image quality where the peak signal-to-noise ratio and structural similarity of our network results are over 30 dB and 0.93, respectively. The derived performance is superior to the results obtained from both the bicubic interpolation and the well-known SRGAN deep-learning methods. In addition, another custom CNN is used to perform image segmentation from the generated high-resolution breast cancer images derived with our model with an average Intersection over Union of 0.869 and an average dice similarity coefficient of 0.893 for the H&E image segmentation results. Finally, we propose the jointly trained SRGAN-ResNeXt and Inception U-net Models, which applied the weights from the individually trained SRGAN-ResNeXt and inception U-net models as the pre-trained weights for transfer learning. The jointly trained model’s results are progressively improved and promising. We anticipate these custom CNNs can help resolve the inaccessibility of advanced microscopes or whole slide imaging (WSI) systems to acquire high-resolution images from low-performance microscopes located in remote-constraint settings.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Using case-level context to classify cancer pathology reports

Individual electronic health records (EHRs) and clinical reports are often part of a larger sequence—for example, a single patient may generate multiple reports over the trajectory of a disease. In applications such as cancer pathology reports, it is necessary not only to extract information from individual reports, but also to capture aggregate information regarding the entire cancer case based off case-level context from all reports in the sequence. In this paper, we introduce a simple modular add-on for capturing case-level context that is designed to be compatible with most existing deep learning architectures for text classification on individual reports. We test our approach on a corpus of 431,433 cancer pathology reports, and we show that incorporating case-level context significantly boosts classification accuracy across six classification tasks—site, subsite, laterality, histology, behavior, and grade. We expect that with minimal modifications, our add-on can be applied towards a wide range of other clinical text-based tasks.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Metabolic Signaling Cascades Prompted by Glutaminolysis in Cancer

Aberrant glutamatergic signaling has been implicated in altered metabolic activity and the demand to synthesize biomass in several types of cancer including melanoma. In the last decade, there has been a significant contribution to our understanding of metabolic pathways. An increasing number of studies are now emphasizing the importance of glutamate functioning as a signaling molecule and a building block for cancer progression. To that end, our group has previously illustrated the role of glutamatergic signaling mediated by metabotropic glutamate receptor 1 (GRM1) in neoplastic transformation of melanocytes in vitro and spontaneous development of metastatic melanoma in vivo. Glutamate, the natural ligand of GRM1, is one of the most abundant amino acids in humans and the predominant excitatory neurotransmitter in the central nervous system. Elevated levels of glutaminolytic mitochondrial tricarboxylic acid (TCA) cycle intermediates, especially glutamate, have been reported in numerous cancer cells. Herein, we highlight and critically review metabolic bottlenecks that are prevalent during tumor evolution along with therapeutic implications of limiting glutamate bioavailability in tumors.

Shah, Raj (ORCID:0000000204400189)↗

DOE OSTI2022

Pathophysiological Integration of Metabolic Reprogramming in Breast Cancer

Metabolic changes that facilitate tumor growth are one of the hallmarks of cancer. The triggers of these metabolic changes are located in the tumor parenchymal cells, where oncogenic mutations induce an imperative need to proliferate and cause tumor initiation and progression. Cancer cells undergo significant metabolic reorganization during disease progression that is tailored to their energy demands and fluctuating environmental conditions. Oxidative stress plays an essential role as a trigger under such conditions. These metabolic changes are the consequence of the interaction between tumor cells and stromal myofibroblasts. The metabolic changes in tumor cells include protein anabolism and the synthesis of cell membranes and nucleic acids, which all facilitate cell proliferation. They are linked to catabolism and autophagy in stromal myofibroblasts, causing the release of nutrients for the cells of the tumor parenchyma. Metabolic changes lead to an interstitium deficient in nutrients, such as glucose and amino acids, and acidification by lactic acid. Together with hypoxia, they produce functional changes in other cells of the tumor stroma, such as many immune subpopulations and endothelial cells, which lead to tumor growth. Thus, immune cells favor tissue growth through changes in immunosuppression. This review considers some of the metabolic changes described in breast cancer.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Mechanistic Insights into the Successful Development of Combination Therapy of Enfortumab Vedotin and Pembrolizumab for the Treatment of Locally Advanced or Metastatic Urothelial Cancer

Antibody–drug conjugates (ADCs) consist of an antibody backbone that recognizes and binds to a target antigen expressed on tumor cells and a small molecule chemotherapy payload that is conjugated to the antibody via a linker. ADCs are one of the most promising therapeutic modalities for the treatment of various cancers. However, many patients have developed resistance to this form of therapy. Extensive efforts have been dedicated to identifying an effective combination of ADCs with other types of anticancer therapies to potentially overcome this resistance. A recent clinical study demonstrated that a combination of the ADC enfortumab vedotin (EV) with the immune checkpoint inhibitor (ICI) pembrolizumab can achieve remarkable clinical efficacy as the first-line therapy for the treatment of locally advanced or metastatic urothelial carcinoma (la/mUC)—leading to the first approval of a combination therapy of an ADC with an ICI for the treatment of cancer patients. In this review, we highlight knowledge and understanding gained from the successful development of EV and the combination therapy of EV with ICI for the treatment of la/mUC. Using urothelial carcinoma as an example, we will focus on dissecting the underlying mechanisms necessary for the development of this type of combination therapy for a variety of cancers.

Oncology↗

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↗

NASA NTRS1993

Automated System for Early Breast Cancer Detection in Mammograms

The increasing demand on mammographic screening for early breast cancer detection, and the subtlety of early breast cancer signs on mammograms, suggest an automated image processing system that can serve as a diagnostic aid in radiology clinics. We present a fully automated algorithm for detecting clusters of microcalcifications that are the most common signs of early, potentially curable breast cancer. By using the contour map of the mammogram, the algorithm circumvents some of the difficulties encountered with standard image processing methods. The clinical implementation of an automated instrument based on this algorithm is also discussed.

Bankman, Isaac N.↗

NASA NTRS2001

Cancer Risk Assessment for Space Radiation

Predicting the occurrence of human cancer following exposure to any agent causing genetic damage is a difficult task. This is because the uncertainty of uniform exposure to the damaging agent, and the uncertainty of uniform processing of that damage within a complex set of biological variables, degrade the confidence of predicting the delayed expression of cancer as a relatively rare event within any given clinically normal individual. The radiation health research priorities for enabling long-duration human exploration of space were established in the 1996 NRC Report entitled 'Radiation Hazards to Crews of Interplanetary Missions: Biological Issues and Research Strategies'. This report emphasized that a 15-fold uncertainty in predicting radiation-induced cancer incidence must be reduced before NASA can commit humans to extended interplanetary missions. That report concluded that the great majority of this uncertainty is biologically based, while a minority is physically based due to uncertainties in radiation dosimetry and radiation transport codes. Since that report, the biologically based uncertainty has remained large, and the relatively small uncertainty associated with radiation dosimetry has increased due to the considerations raised by concepts of microdosimetry. In a practical sense, however, the additional uncertainties introduced by microdosimetry are encouraging since they are in a direction of lowered effective dose absorbed through infrequent interactions of any given cell with the high energy particle component of space radiation. Additional information is contained in the original extended abstract.

Richmond, Robert C.↗

NASA NTRS1998

Cancer Detection Device

Ratcom, Inc., has joined NASA Johnson Space Center in an active program to develop cytometry capabilities for space station freedom. This agreement results from a cooperative program that NASA entered into with the American Cancer Society to aid in cancer prevention, diagnosis, and treatment. The flow cytometer is used by cancer researchers to make cellular measurements.

Source record↗

NASA NTRS2002

Cancer Risk-Assessment of Radiation Damage in Ataxia Telangiectasia Heterozygous Human Breast Epithelial Cell Cultures

This paper describes the study of the markers of cellular changes that are found during the onset of carcinogenesis. Several of the biological factors are markers of stress response, oncoprotein expression, and differentiation factors. Oxidative stress response agents such as heat shock proteins (HSPs) protect cells from oxidative stresses such as ionizing radiation. The onocoprotein HER-2/neu, a specific breast cancer marker, indicates early onset of cancer. Additional structural and morphogenetic markers of differentiation were considered in order to determine initial cellular changes at the initial onset of cancer. As an additional consideration, all-trans retinoic acid (RA), a differentiation agent, was considered because of its known role in regulating normal differentiation and inhibiting tumor proliferation via specific nuclear receptors. This paper discusses study and results of the preliminary analyses of gamma irradiation of AT heterozygous human breast epithelial cells (WH). Comparisons are also made of the effects various RA concentrations post-irradiation.

Lisa C. Applewhite↗

NASA NTRS2001

Space Radiation Cancer Risks and Uncertainties for Mars Missions

Projecting cancer risks from exposure to space radiation is highly uncertain because of the absence of data for humans and because of the limited radiobiology data available for estimating late effects from the high-energy and charge (HZE) ions present in the galactic cosmic rays (GCR). Cancer risk projections involve many biological and physical factors, each of which has a differential range of uncertainty due to the lack of data and knowledge. We discuss an uncertainty assessment within the linear-additivity model using the approach of Monte Carlo sampling from subjective error distributions that represent the lack of knowledge in each factor to quantify the overall uncertainty in risk projections. Calculations are performed using the space radiation environment and transport codes for several Mars mission scenarios. This approach leads to estimates of the uncertainties in cancer risk projections of 400-600% for a Mars mission. The uncertainties in the quality factors are dominant. Using safety standards developed for low-Earth orbit, long-term space missions (>90 days) outside the Earth's magnetic field are currently unacceptable if the confidence levels in risk projections are considered. Because GCR exposures involve multiple particle or delta-ray tracks per cellular array, our results suggest that the shape of the dose response at low dose rates may be an additional uncertainty for estimating space radiation risks.

NASA Discipline Radiation Health↗

NASA NTRS2001

Fatty acid regulates gene expression and growth of human prostate cancer PC-3 cells

It has been proposed that the omega-6 fatty acids increase the rate of tumor growth. Here we test that hypothesis in the PC-3 human prostate tumor. We found that the essential fatty acids, linoleic acid (LA) and arachidonic acid (AA), and the AA metabolite PGE(2) stimulate tumor growth while oleic acid (OA) and the omega-3 fatty acid, eicosapentaenoic acid (EPA) inhibited growth. In examining the role of AA in growth response, we extended our studies to analyze changes in early gene expression induced by AA. We demonstrate that c-fos expression is increased within minutes of addition in a dose-dependent manner. Moreover, the immediate early gene cox-2 is also increased in the presence of AA in a dose-dependent manner, while the constitutive cox-1 message was not increased. Three hours after exposure to AA, the synthesis of PGE(2) via COX-2 was also increased. Previous studies have demonstrated that AA was primarily delivered by low density lipoprotein (LDL) via its receptor (LDLr). Since it is known that hepatomas, acute myelogenous leukemia and colorectal tumors lack normal cholesterol feedback, we examined the role of the LDLr in growth regulation of the PC-3 prostate cancer cells. Analysis of ldlr mRNA expression and LDLr function demonstrated that human PC-3 prostate cancer cells lack normal feedback regulation. While exogenous LDL caused a significant stimulation of cell growth and PGE(2) synthesis, no change was seen in regulation of the LDLr by LDL. Taken together, these data show that normal cholesterol feedback of ldlr message and protein is lost in prostate cancer. These data suggest that unregulated over-expression of LDLr in tumor cells would permit increased availability of AA, which induces immediate early genes c-fos and cox-2 within minutes of uptake.

NASA Discipline Cell Biology↗

NASA NTRS2000

Androgen and taxol cause cell type-specific alterations of centrosome and DNA organization in androgen-responsive LNCaP and androgen-independent DU145 prostate cancer cells

We investigated the effects of androgen and taxol on the androgen-responsive LNCaP and androgen-independent DU145 prostate cancer cell lines. Cells were treated for 48 and 72 h with 0.05-1 nM of the synthetic androgen R1881 and with 100 nM taxol. Treatment of LNCaP cells with 0.05 nM R1881 led to increased cell proliferation, whereas treatment with 1 nM R1881 resulted in inhibited cell division, DNA cycle arrest, and altered centrosome organization. After treatment with 1 nM R1881, chromatin became clustered, nuclear envelopes convoluted, and mitochondria accumulated around the nucleus. Immunofluorescence microscopy with antibodies to centrosomes showed altered centrosome structure. Although centrosomes were closely associated with the nucleus in untreated cells, they dispersed into the cytoplasm after treatment with 1 nM R1881. Microtubules were only faintly detected in 1 nM R1881-treated LNCaP cells. The effects of taxol included microtubule bundling and altered mitochondria morphology, but not DNA organization. As expected, the androgen-independent prostate cancer cell line DU145 was not affected by R1881. Treatment with taxol resulted in bundling of microtubules in both cell lines. Additional taxol effects were seen in DU145 cells with micronucleation of DNA, an indication of apoptosis. Simultaneous treatment with R1881 and taxol had no additional effects on LNCaP or DU145 cells. These results suggest that LNCaP and DU145 prostate cancer cells show differences not only in androgen responsiveness but in sensitivity to taxol as well. Copyright 2000 Wiley-Liss, Inc.

NASA Program Fundamental Space Biology↗

NASA NTRS1999

Predicting cancer rates in astronauts from animal carcinogenesis studies and cellular markers

The radiation space environment includes particles such as protons and multiple species of heavy ions, with much of the exposure to these radiations occurring at extremely low average dose-rates. Limitations in databases needed to predict cancer hazards in human beings from such radiations are significant and currently do not provide confidence that such predictions are acceptably precise or accurate. In this article, we outline the need for animal carcinogenesis data based on a more sophisticated understanding of the dose-response relationship for induction of cancer and correlative cellular endpoints by representative space radiations. We stress the need for a model that can interrelate human and animal carcinogenesis data with cellular mechanisms. Using a broad model for dose-response patterns which we term the "subalpha-alpha-omega (SAO) model", we explore examples in the literature for radiation-induced cancer and for radiation-induced cellular events to illustrate the need for data that define the dose-response patterns more precisely over specific dose ranges, with special attention to low dose, low dose-rate exposure. We present data for multiple endpoints in cells, which vary in their radiosensitivity, that also support the proposed model. We have measured induction of complex chromosome aberrations in multiple cell types by two space radiations, Fe-ions and protons, and compared these to photons delivered at high dose-rate or low dose-rate. Our data demonstrate that at least three factors modulate the relative efficacy of Fe-ions compared to photons: (i) intrinsic radiosensitivity of irradiated cells; (ii) dose-rate; and (iii) another unspecified effect perhaps related to reparability of DNA lesions. These factors can produce respectively up to at least 7-, 6- and 3-fold variability. These data demonstrate the need to understand better the role of intrinsic radiosensitivity and dose-rate effects in mammalian cell response to ionizing radiation. Such understanding is critical in extrapolating databases between cellular response, animal carcinogenesis and human carcinogenesis, and we suggest that the SAO model is a useful tool for such extrapolation.

Non-NASA Center↗

NASA NTRS2005

Managing Lunar and Mars Mission Radiation Risks: Cancer Risks, Uncertainties, and Shielding Effectiveness - Part 1

This document addresses calculations of probability distribution functions (PDFs) representing uncertainties in projecting fatal cancer risk from galactic cosmic rays (GCR) and solar particle events (SPEs). PDFs are used to test the effectiveness of potential radiation shielding approaches. Monte-Carlo techniques are used to propagate uncertainties in risk coefficients determined from epidemiology data, dose and dose-rate reduction factors, quality factors, and physics models of radiation environments. Competing mortality risks and functional correlations in radiation quality factor uncertainties are treated in the calculations. The cancer risk uncertainty is about four-fold for lunar and Mars mission risk projections. For short-stay lunar missins (<180 d), SPEs present the most significant risk, but one effectively mitigated by shielding. For long-duration (>180 d) lunar or Mars missions, GCR risks may exceed radiation risk limits. While shielding materials are marginally effective in reducing GCR cancer risks because of the penetrating nature of GCR and secondary radiation produced in tissue by relativisitc particles, polyethylene or carbon composite shielding cannot be shown to significantly reduce risk compared to aluminum shielding. Therefore, improving our knowledge of space radiobiology to narrow uncertainties that lead to wide PDFs is the best approach to ensure radiation protection goals are met for space exploration.

Cucinotta, Francis A.↗

NASA NTRS2007

Space Radiation Cancer Risks

Space radiation presents major challenges to astronauts on the International Space Station and for future missions to the Earth s moon or Mars. Methods used to project risks on Earth need to be modified because of the large uncertainties in projecting cancer risks from space radiation, and thus impact safety factors. We describe NASA s unique approach to radiation safety that applies uncertainty based criteria within the occupational health program for astronauts: The two terrestrial criteria of a point estimate of maximum acceptable level of risk and application of the principle of As Low As Reasonably Achievable (ALARA) are supplemented by a third requirement that protects against risk projection uncertainties using the upper 95% confidence level (CL) in the radiation cancer projection model. NASA s acceptable level of risk for ISS and their new lunar program have been set at the point-estimate of a 3-percent risk of exposure induced death (REID). Tissue-averaged organ dose-equivalents are combined with age at exposure and gender-dependent risk coefficients to project the cumulative occupational radiation risks incurred by astronauts. The 95% CL criteria in practice is a stronger criterion than ALARA, but not an absolute cut-off as is applied to a point projection of a 3% REID. We describe the most recent astronaut dose limits, and present a historical review of astronaut organ doses estimates from the Mercury through the current ISS program, and future projections for lunar and Mars missions. NASA s 95% CL criteria is linked to a vibrant ground based radiobiology program investigating the radiobiology of high-energy protons and heavy ions. The near-term goal of research is new knowledge leading to the reduction of uncertainties in projection models. Risk projections involve a product of many biological and physical factors, each of which has a differential range of uncertainty due to lack of data and knowledge. The current model for projecting space radiation cancer risk relies on the three assumptions of linearity, additivity, and scaling along with the use of population averages. We describe uncertainty estimates for this model, and new experimental data that sheds light on the accuracy of the underlying assumptions. These methods make it possible to express risk management objectives in terms of quantitative metrics, i.e., the number of days in space without exceeding a given risk level within well defined confidence limits. The resulting methodology is applied to several human space exploration mission scenarios including lunar station, deep space outpost, and a Mars mission. Factors that dominate risk projection uncertainties and application of this approach to assess candidate mitigation approaches are described.

Cucinotta, Francis A.↗

NASA NTRS2003

A Virtual Bioinformatics Knowledge Environment for Early Cancer Detection

Discovery of disease biomarkers for cancer is a leading focus of early detection. The National Cancer Institute created a network of collaborating institutions focused on the discovery and validation of cancer biomarkers called the Early Detection Research Network (EDRN). Informatics plays a key role in enabling a virtual knowledge environment that provides scientists real time access to distributed data sets located at research institutions across the nation. The distributed and heterogeneous nature of the collaboration makes data sharing across institutions very difficult. EDRN has developed a comprehensive informatics effort focused on developing a national infrastructure enabling seamless access, sharing and discovery of science data resources across all EDRN sites. This paper will discuss the EDRN knowledge system architecture, its objectives and its accomplishments.

knowledge systems↗

NASA NTRS2011

CPRIT/Johnson Space Center, September, 2011 (Cancer Prevention and Research Institute of Texas)

JSC researchers study carcinogenesis, cancer prevention and treatment along with epidemiological (primarily retrospective and longitudinal) studies, modeling, and interactions with the environment such as radiation, nutritional, and endocrine changes related to space flight along with behaviors such as smoking. Cancer research is a major focus for human space flight due to the exposure to space radiation which consists of particles of varying charges and energies, and secondary neutrons. The JSC laboratories collaborate with investigators from the U.S. as well as our European and Japanese partners. We use accelerator facilities at the Brookhaven National Laboratory, Loma Linda University and Los Alamos National Laboratory that generate high energy charged particles and neutrons to simulate cosmic radiation and solar particle events. The research using cultured cells and animals concentrates on damage and repair from the level of DNA to organ tissues, due to exposure to simulated space radiation exposure, that contribute to the induction of leukemia and solid tumors in most major tissues such as lung, colon, liver and breast. The goal of the research is to develop a mathematical model that can predict cancer morbidity and mortality risks with sufficient accuracy for a given space mission.

Davis, Jeffrey↗