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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A Roadmap for the Future of Systems Biology in Cancer Research

Cancer systems biology seeks to understand how cancer arises as a system of interconnected molecules, cells, and tissues, with the goal of understanding, predicting, and controlling the disease. In the last decade, the field has rapidly grown as advances in experimental, computational, and analytic technologies have improved our ability to capture and recapitulate the complexities of cancer at multiple scales. However, the field’s promise to understand how specific molecular changes give rise to altered cancer outcomes remains incompletely fulfilled. Fortunately, an opportunity exists to accelerate progress by better coordinating modeling and data-gathering efforts across the cancer systems biology community. This will create the foundation for building accurate, multiscale cancer models that can better predict and identify improved therapeutic interventions. Here, in this study, we outline some of the current challenges in cancer systems biology research, how they can be addressed, and actions that the community can take to accelerate progress in the field.

Modeling & Simulation↗
DOE OSTI2023

Reuniting philosophy and science to advance cancer research

Cancers rely on multiple, heterogeneous processes at different scales, pertaining to many biomedical fields. Therefore, understanding cancer is necessarily an interdisciplinary task that requires placing specialised experimental and clinical research into a broader conceptual, theoretical, and methodological framework. Without such a framework, oncology will collect piecemeal results, with scant dialogue between the different scientific communities studying cancer. We argue that one important way forward in service of a more successful dialogue is through greater integration of applied sciences (experimental and clinical) with conceptual and theoretical approaches, informed by philosophical methods. By way of illustration, we explore six central themes: (i) the role of mutations in cancer; (ii) the clonal evolution of cancer cells; (iii) the relationship between cancer and multicellularity; (iv) the tumour microenvironment; (v) the immune system; and (vi) stem cells. In each case, we examine open questions in the scientific literature through a philosophical methodology and show the benefit of such a synergy for the scientific and medical understanding of cancer.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Current status and future outlook for patient‐derived cancer models from a rare cancer research perspective

Abstract Rare cancers are a group of approximately 200 malignancies with extremely low incidences and with a wide variety of genotypes and phenotypes. Collectively, they are more common than any single malignancy. However, given the small numbers of individuals diagnosed with rare cancers, it is difficult to design clinical trials with sufficient patient numbers. Therefore, few effective anticancer drugs have been developed, and evidence‐based medicine is not always feasible for rare cancers. Consequently, their clinical outcomes are generally poorer. Cancer research requires adequate models that faithfully recapitulate molecular features and reproduce treatment responses of the original tumors. Such models allow us to focus on more efficacious drugs in the clinical studies. For rare cancers, patient‐derived cancer models are particularly important because the enrollment of sufficient patients is rarely attainable within a reasonable period of time. However, extremely few models are available for rare cancers. For example, cell lines and xenografts are available for only a limited number of histological subtypes of sarcomas; therefore, most sarcoma research is performed without such models, and a lack of adequate cancer models causes a lag in therapeutic development. The establishment of novel rare cancer models will dramatically facilitate rare cancer research and treatment development in the near future. This review focuses on the status of patient‐derived rare cancer models and discusses their pivotal problems and possibilities, using sarcomas as a representative rare cancer type. Multi‐institutional collaboration will help address the scarcity of patient‐derived rare cancer models.

Kondo, Tadashi↗
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 OSTI2021

Artificial intelligence in cancer research, diagnosis and therapy

Artificial intelligence and machine learning techniques are breaking into biomedical research and health care, which importantly includes cancer research and oncology, where the potential applications are vast. These include detection and diagnosis of cancer, subtype classification, optimization of cancer treatment and identification of new therapeutic targets in drug discovery. While big data used to train machine learning models may already exist, leveraging this opportunity to realize the full promise of artificial intelligence in both the cancer research space and the clinical space will first require significant obstacles to be surmounted. In this Viewpoint article, we asked four experts for their opinions on how we can begin to implement artificial intelligence while ensuring standards are maintained so as transform cancer diagnosis and the prognosis and treatment of patients with cancer and to drive biological discovery.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Engineered bone marrow as a clinically relevant ex vivo model for primary bone cancer research and drug screening

Osteosarcoma (OS) is the most common primary malignant bone cancer in children and adolescents. While numerous other cancers now have promising therapeutic advances, treatment options for OS have remained unchanged since the advent of standard chemotherapeutics and offer less than a 25% 5-y survival rate for those with metastatic disease. This dearth of clinical progress underscores a lack of understanding of OS progression and necessitates the study of this disease in an innovative system. Here, we adapt a previously described engineered bone marrow (eBM) construct for use as a three-dimensional platform to study how microenvironmental and immune factors affect OS tumor progression. We form eBM by implanting acellular bone-forming materials in mice and explanting the cellularized constructs after 8 wk for study. We interrogate the influence of the anatomical implantation site on eBM tissue quality, test ex vivo stability under normoxic (5% O 2 ) and standard (21% O 2 ) culture conditions, culture OS cells within these constructs, and compare them to human OS samples. We show that eBM stably recapitulates the composition of native bone marrow. OS cells exhibit differential behavior dependent on metastatic potential when cultured in eBM, thus mimicking in vivo conditions. Furthermore, we highlight the clinical applicability of eBM as a drug-screening platform through doxorubicin treatment and show that eBM confers a protective effect on OS cells that parallel clinical responses. Combined, this work presents eBM as a cellular construct that mimics the complex bone marrow environment that is useful for mechanistic bone cancer research and drug screening.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1991

X ray imaging microscope for cancer research

The NASA technology employed during the Stanford MSFC LLNL Rocket X Ray Spectroheliograph flight established that doubly reflecting, normal incidence multilayer optics can be designed, fabricated, and used for high resolution x ray imaging of the Sun. Technology developed as part of the MSFC X Ray Microscope program, showed that high quality, high resolution multilayer x ray imaging microscopes are feasible. Using technology developed at Stanford University and at the DOE Lawrence Livermore National Laboratory (LLNL), Troy W. Barbee, Jr. has fabricated multilayer coatings with near theoretical reflectivities and perfect bandpass matching for a new rocket borne solar observatory, the Multi-Spectral Solar Telescope Array (MSSTA). Advanced Flow Polishing has provided multilayer mirror substrates with sub-angstrom (rms) smoothnesss for the astronomical x ray telescopes and x ray microscopes. The combination of these important technological advancements has paved the way for the development of a Water Window Imaging X Ray Microscope for cancer research.

Hoover, Richard B.↗
DOE OSTI2020

RCSB Protein Data Bank tools for 3D structure-guided cancer research: human papillomavirus (HPV) case study

Abstract Atomic-level three-dimensional (3D) structure data for biological macromolecules often prove critical to dissecting and understanding the precise mechanisms of action of cancer-related proteins and their diverse roles in oncogenic transformation, proliferation, and metastasis. They are also used extensively to identify potentially druggable targets and facilitate discovery and development of both small-molecule and biologic drugs that are today benefiting individuals diagnosed with cancer around the world. 3D structures of biomolecules (including proteins, DNA, RNA, and their complexes with one another, drugs, and other small molecules) are freely distributed by the open-access Protein Data Bank (PDB). This global data repository is used by millions of scientists and educators working in the areas of drug discovery, vaccine design, and biomedical and biotechnology research. The US Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) provides an integrated portal to the PDB archive that streamlines access for millions of worldwide PDB data consumers worldwide. Herein, we review online resources made available free of charge by the RCSB PDB to basic and applied researchers, healthcare providers, educators and their students, patients and their families, and the curious public. We exemplify the value of understanding cancer-related proteins in 3D with a case study focused on human papillomavirus.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Exascale Deep Learning to Accelerate Cancer Research

Deep learning, through the use of neural networks, has demonstrated remarkable ability to automate many routine tasks when presented with sufficient data for training. The neural network architecture (e.g. number of layers, types of layers, connections between layers, etc.) plays a critical role in determining what, if anything, the neural network is able to learn from the training data. The trend for neural network architectures, especially those trained on ImageNet, has been to grow ever deeper and more complex. The result has been ever increasing accuracy on benchmark datasets with the cost of increased computational demands. In this paper we demonstrate that neural network architectures can be automatically generated, tailored for a specific application, with dual objectives: accuracy of prediction and speed of prediction. Using MENNDL- an HPC-enabled software stack for neural architecture search-we generate a neural network with comparable accuracy to state-of-the-art networks on a cancer pathology dataset that is also 16× faster at inference. The speedup in inference is necessary because of the volume and velocity of cancer pathology data; specifically, the previous state-of-the-art networks are too slow for individual researchers without access to HPC systems to keep pace with the rate of data generation. Our new model enables researchers with modest computational resources to analyze newly generated data faster than it is collected.

Patton, Robert↗
NASA NTRS2000

Simian virus 40, poliovirus vaccines, and human cancer: research progress versus media and public interests

From 1955 through early 1963, millions of people were inadvertently exposed to simian virus 40 (SV40) as a contaminant of poliovirus vaccines; the virus had been present in the monkey kidney cultures used to prepare the vaccines and had escaped detection. SV40 was discovered in 1960 and subsequently eliminated from poliovirus vaccines. This article reviews current knowledge about SV40 and considers public responses to reports in the media. SV40 is a potent tumour virus with broad tissue tropism that induces tumours in rodents and transforms cultured cells from many species. It is also an important laboratory model for basic studies of molecular processes in eukaryotic cells and mechanisms of neoplastic transformation. SV40 neutralizing antibodies have been detected in individuals not exposed to contaminated poliovirus vaccines. There have been many reports of detection of SV40 DNA in human tumours, especially mesotheliomas, brain tumours and osteosarcomas; and DNA sequence analyses have ruled out the possibility that the viral DNA in tumours was due to laboratory contamination or that the virus had been misidentified. However, additional studies are necessary to prove that SV40 is the cause of certain human cancers. A recently published review article evaluated the status of the field and received much media attention. The public response emphasized that there is great interest in the possibility of health risks today from vaccinations received in the past.

Review, Tutorial↗
DOE OSTI2024

Advances in statistical methods for cancer surveillance research: an age-period-cohort perspective

Background: Analysis of Lexis diagrams (population-based cancer incidence and mortality rates indexed by age group and calendar period) requires specialized statistical methods. However, existing methods have limitations that can now be overcome using new approaches. Methods: We assembled a “toolbox” of novel methods to identify trends and patterns by age group, calendar period, and birth cohort. We evaluated operating characteristics across 152 cancer incidence Lexis diagrams compiled from United States (US) Surveillance, Epidemiology and End Results Program data for 21 leading cancers in men and women in four race and ethnicity groups (the “cancer incidence panel”). Results: Nonparametric singular values adaptive kernel filtration (SIFT) decreased the estimated root mean squared error by 90% across the cancer incidence panel. A novel method for semi-parametric age-period-cohort analysis (SAGE) provided optimally smoothed estimates of age-period-cohort (APC) estimable functions and stabilized estimates of lack-of-fit (LOF). SAGE identified statistically significant birth cohort effects across the entire cancer panel; LOF had little impact. As illustrated for colon cancer, newly developed methods for comparative age-period-cohort analysis can elucidate cancer heterogeneity that would otherwise be difficult or impossible to discern using standard methods. Conclusions: Cancer surveillance researchers can now identify fine-scale temporal signals with unprecedented accuracy and elucidate cancer heterogeneity with unprecedented specificity. Birth cohort effects are ubiquitous modulators of cancer incidence in the US. The novel methods described here can advance cancer surveillance research.

60 APPLIED LIFE SCIENCES↗
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↗
DOE OSTI2021

Science and Technology Review (May 2021)

Lawrence Livermore has been on the forefront of cancer research for over 60 years. Early interest in cancer statistics stemmed from the nature of work, particularly how radiation affects humans. The Department of Energy funded research to investigate the effects of radiation on workers with long term exposure. This research quickly morphed into a wider breadth of cancer research topics, including the use of advanced computational models to investigate mutations in genes. Livermore is regarded as a leader in cancer research, from the Human Genome Center to its participation in the National Cancer Institute’s “Moonshot” project. The highly interdisciplinary Laboratory unites research in one more example: bringing together cancer biology, 3D printing, high-performance computing, big data, and materials science to address this pressing medical challenge.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Extraction of Tumor Site from Cancer Pathology Reports using Deep Filters

Purpose: Pathology reports are the primary source of information concerning the millions of cancer cases across the United States. Cancer registries manually process the pathology reports to extract the pertinent information including primary tumor site, behavior, histology, laterality, and grade. Processing a large volume of the pathology reports in a timely manner is a continuing challenge for cancer registries. The purpose of this study is to develop an information extraction pipeline to reliably and efficiently extract reportable information. Method: We have developed a novel inverse-regression (IR) based information extraction pipeline. The inverse-regression based supervised filter has been successfully applied to many application domains. However, its application to the information extraction from unstructured text is hindered primarily by the extreme high-dimensionality of n-gram representations of text. In this study, we attempt to overcome the obstacles by a novel bootstrapping strategy. First, we use an information-theoretic mutual information based filter to discard the excessive and redundant n-gram features. This step reduces the size and improves the condition number of the sample covariance matrix, thus reducing the computational cost and improving the numerical stability of the subsequent inverse-regression step. Then we use localized sliced inverse-regression (LSIR) to learn a low-dimensional discriminatory subspace for information inference. In particular, we use the k-nearest neighbors of an unlabeled pathology report in the learned representation to infer the desired information from the labeled data in a supervised manner. Results: The experiments were conducted on a set of de-identified pathology reports with human expert labels as the ground truth. Our pipeline consistently performed better than or comparable to the best performing state-of-the-art methods while reducing the training and inference times substantially. Conclusion: Our results demonstrate the potential of inverse-regression based information extraction pipeline for reliable and efficient information extraction from unstructured text. The information extracted from the pathology reports can be used along with clinical information, medical imaging, and genomic information to instigate discoveries in cancer research.

Dubey, Abhishek↗
DOE OSTI2024

UNNT: A novel Utility for comparing Neural Net and Tree-based models

The use of deep learning (DL) is steadily gaining traction in scientific challenges such as cancer research. Advances in enhanced data generation, machine learning algorithms, and compute infrastructure have led to an acceleration in the use of deep learning in various domains of cancer research such as drug response problems. In our study, we explored tree-based models to improve the accuracy of a single drug response model and demonstrate that tree-based models such as XGBoost (eXtreme Gradient Boosting) have advantages over deep learning models, such as a convolutional neural network (CNN), for single drug response problems. However, comparing models is not a trivial task. To make training and comparing CNNs and XGBoost more accessible to users, we developed an open-source library called UNNT (A novel Utility for comparing Neural Net and Tree-based models). The case studies, in this manuscript, focus on cancer drug response datasets however the application can be used on datasets from other domains, such as chemistry.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2004

Software architecture for large scale, distributed, data-intensive systems

This paper presents our experience with OODT, a novel software architectual style, and middlware-based implementation for data-intensive systems. To date, OODT has been successfully evaluated in several different science domains including Cancer Research with the National Cancer Institute (NCI), and Planetary Science with NASA's Planetary Data System (PDS).

software architecture↗
NASA NTRS2002

Commercial Instrumentation Technology Associates Inc. Biomedical Experiments Payload (CIBX-2)

Experiments to find solutions for a range of biomedical issues are being hosted by the Commercial Instrumentation Technology Associates Inc. (ITA) Biomedical Experiments (CIBX-2) payload. This research encompasses more than 20 separate experiments including cancer research, commercial experiments and hands-on student experiments from 10 schools as part of ITA's ongoing University Among the Stars program. Protein crystal growth experiments will address the structure of urokinase - a protein that has been identified as a key enzyme in the spread of brain, lung, colon, prostate and breast cancers. Crystals of Bence Jones, a protein associated with bone cancer, will also be grown. Understanding their structures may help scientists develop treatments. In a related area, the Microencapsulation of Drugs (MEPS) is an anti-cancer drug delivery system, based on a 10-year partnership with NASA's Johnson Space Center. On this mission, the co-encapsulation of antibodies and immune stimulants will be made in submicron microcapsules to target pulmonary and bacterial infections.

Morrison, Dennis↗
DOE OSTI2020

Small animal PET: a review of what we have done and where we are going

Abstract Small animal research is an essential tool in studying both pharmaceutical biodistribution and disease progression over time. Furthermore, through the rapid development of in vivo imaging technology over the last few decades, small animal imaging (also referred to as preclinical imaging) has become a mainstay for all fields of biologic research and a center point for most preclinical cancer research. Preclinical imaging modalities include optical, MRI and MRS, microCT, small animal PET, ultrasound, and photoacoustic, each with their individual strengths. The strong points of small animal PET are its translatability to the clinic; its quantitative imaging capabilities; its whole-body imaging ability to dynamically trace functional/biochemical processes; its ability to provide useful images with only nano- to pico- molar concentrations of administered compounds; and its ability to study animals serially over time. This review paper gives an overview of the development and evolution of small animal PET imaging. It provides an overview of detector designs; system configurations; multimodality PET imaging systems; image reconstruction and analysis tools; and an overview of research and commercially available small animal PET systems. It concludes with a look toward developing technologies/methodologies that will further enhance the impact of small animal PET imaging on medical research in the future.

Engineering↗