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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Landscape analysis of environmental data sources for linkage with SEER cancer patients database

Abstract One of the challenges associated with understanding environmental impacts on cancer risk and outcomes is estimating potential exposures of individuals diagnosed with cancer to adverse environmental conditions over the life course. Historically, this has been partly due to the lack of reliable measures of cancer patients’ potential environmental exposures before a cancer diagnosis. The emerging sources of cancer-related spatiotemporal environmental data and residential history information, coupled with novel technologies for data extraction and linkage, present an opportunity to integrate these data into the existing cancer surveillance data infrastructure, thereby facilitating more comprehensive assessment of cancer risk and outcomes. In this paper, we performed a landscape analysis of the available environmental data sources that could be linked to historical residential address information of cancer patients’ records collected by the National Cancer Institute’s Surveillance, Epidemiology, and End Results Program. The objective is to enable researchers to use these data to assess potential exposures at the time of cancer initiation through the time of diagnosis and even after diagnosis. The paper addresses the challenges associated with data collection and completeness at various spatial and temporal scales, as well as opportunities and directions for future research.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

COVID ‐19 outcomes in patients with cancer: Findings from the University of California health system database

Abstract Background The interaction between cancer diagnoses and COVID‐19 infection and outcomes is unclear. We leveraged a state‐wide, multi‐institutional database to assess cancer‐related risk factors for poor COVID‐19 outcomes. Methods We conducted a retrospective cohort study using the University of California Health COVID Research Dataset, which includes electronic health data of patients tested for severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) at 17 California medical centers. We identified adults tested for SARS‐CoV‐2 from 2/1/2020–12/31/2020 and selected a cohort of patients with cancer. We obtained demographic, clinical, cancer type, and antineoplastic therapy data. The primary outcome was hospitalization within 30d after the first positive SARS‐CoV‐2 test. Secondary outcomes were SARS‐CoV‐2 positivity and severe COVID‐19 (intensive care, mechanical ventilation, or death within 30d after the first positive test). We used multivariable logistic regression to identify cancer‐related factors associated with outcomes. Results We identified 409,462 patients undergoing SARS‐CoV‐2 testing. Of 49,918 patients with cancer, 1781 (3.6%) tested positive. Patients with cancer were less likely to test positive (RR 0.70, 95% CI: 0.67–0.74, p < 0.001). Among the 1781 SARS‐CoV‐2‐positive patients with cancer, BCR/ABL‐negative myeloproliferative neoplasms (RR 2.15, 95% CI: 1.25–3.41, p = 0.007), venetoclax (RR 2.96, 95% CI: 1.14–5.66, p = 0.028), and methotrexate (RR 2.72, 95% CI: 1.10–5.19, p = 0.032) were associated with greater hospitalization risk. Cancer and therapy types were not associated with severe COVID‐19. Conclusions In this large, diverse cohort, cancer was associated with a decreased risk of SARS‐CoV‐2 positivity. Patients with BCR/ABL‐negative myeloproliferative neoplasm or receiving methotrexate or venetoclax may be at increased risk of hospitalization following SARS‐CoV‐2 infection. Mechanistic and comparative studies are needed to validate findings.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

MEX3A promotes development and progression of breast cancer through regulation of PIK3CA

Breast cancer has been identified as the most common malignant tumors among women and the morbidity of breast cancer is still increasing rapidly. MEX3A possesses important functions in the regulation of mRNAs and may be involved in a variety of human diseases including cancer, whose relationship with breast cancer is still not clear. In this study, MEX3A was identified as a potential promotor in breast cancer, whose expression was strongly higher in breast cancer tissues than normal tissues. The in vitro experiments showed that MEX3A is capable of promoting the development of breast cancer through stimulating cell proliferation, inhibiting cell apoptosis, arresting cell cycle and promoting cell migration. The functions of MEX3A were also verified in vivo. Furthermore, a combination of genechip analysis and Ingenuity pathway analysis (IPA) identified PIK3CA as a potential downstream target of MEX3A, knockdown of which executes similar inhibitory effects on breast cancer and could alleviate MEX3A-induced progression of breast cancer. In conclusion, our study unveiled, as the first time, MEX3A as a tumor promotor for breast cancer, whose function was carried out probably through the regulation of PIK3CA.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Dichloroacetate enhances the anti-tumor effect of sorafenib via modulating the ROS-JNK-Mcl-1 pathway in liver cancer cells

Liver cancer is one of the most common and high recurrence malignancies. Besides radiotherapy and surgery, chemotherapy also plays an essential role in the treatment of liver cancer. Sorafenib and sorafenib-based combination therapies have been proven efficacy against tumors. However, previous clinical studies have indicated that some patients with liver cancer are resistant to sorafenib treatment and the existing strategies are not satisfactory in the clinic. Therefore, it is urgent to investigate strategies to improve the effectiveness of sorafenib for liver cancer and to explore effective drug combinations. In the present study, we found that dichloroacetate (DCA) could significantly enhance the anti-tumor effect of sorafenib on liver cancer cells, including reduced viability and dramatically promoted apoptosis in liver cancer cells. Moreover, compared to sorafenib alone, the combination of DCA and sorafenib markedly increased the degradation of anti-apoptotic protein Mcl-1 by enhancing its phosphorylation. Overexpression of Mcl-1 could significantly attenuate the synergetic effect of DCA and sorafenib on apoptosis induction in liver cancer cells. Furthermore, we found that the ROS-JNK pathway was obviously activated in the DCA combined sorafenib group. The levels of ROS and p-JNK were dramatically up-regulated in the two drug combination groups. Antioxidant NAC could alleviate the synergetic effects of DCA and sorafenib on ROS generation, JNK activation, Mcl-1 degradation, and cell apoptosis. Moreover, DCA and sorafenib's effects on Mcl-1 degradation and apoptosis could also be inhibited by JNK inhibitor ‘SP’600125. Finally, the synergetic effects of DCA and sorafenib on tumor growth suppression, Mcl-1 degradation and induction of apoptosis were also validated in liver cancer xenograft in vivo. These findings indicate that DCA enhances the anti-tumor effect of sorafenib via the ROS-JNK-Mcl-1 pathway in liver cancer cells. This study may provide new insights to improve the chemotherapeutic effect of sorafenib, which may be benecial for further clinical application of sorafenib in liver cancer treatment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

MiR-10b-5p inhibits tumorigenesis in gastric cancer xenograft mice model through down-regulating Tiam1

Highlights: • MiR-10b-5p acted as a tumor suppressor in gastric cancer by targeting and down-regulating Tiam1. • Overexpression of miR-10b-5p induced apoptosis of gastric cancer cells. • As a specifically chemically modified miRNA mimics, agomir-10b-5p can accumulate in target cells after overcoming membrane and tissue barriers in vivo. • Agomir-10b-5p could be a potential therapeutic agent for the treatment of gastric cancer. The miR-10b-5p plays an important role in gastric cancer development but its exact effect on gastric cancer development in vivo has not been fully studied. We showed that miR-10b-5p inhibited the proliferation and migration of gastric cancer cells by down-regulating Tiam1 which was up-regulated in both gastric cancer cells and tissues. Gastric cancer xenograft experiment showed that lenti-miR-10b-5p treatment and agomir-10b-5p injection could significantly retard tumor growth and reduce tumor size and induced apoptosis. Therefore, our results elucidate the tumor suppressor role of miR-10b-5p in gastric cancer in which it acts as a negative regulator of Tiam1 and also provide a molecular mechanism for agomir-10b-5p to treat gastric cancer.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

A data-driven approach to modeling cancer cell mechanics during microcirculatory transport

In order to understand the effect of cellular level features on the transport of circulating cancer cells in the microcirculation, there has been an increasing reliance on high-resolution in silico models. Accurate simulation of cancer cells flowing with blood cells requires resolving cellular-scale interactions in 3D, which is a significant computational undertaking warranting a cancer cell model that is both computationally efficient yet sufficiently complex to capture relevant behavior. Given that the characteristics of metastatic spread are known to depend on cancer type, it is crucial to account for mechanistic behavior representative of a specific cancer’s cells. To address this gap, in the present work we develop and validate a means by which an efficient and popular membrane model-based approach can be used to simulate deformable cancer cells and reproduce experimental data from specific cell lines. Here, cells are modeled using the immersed boundary method (IBM) within a lattice Boltzmann method (LBM) fluid solver, and the finite element method (FEM) is used to model cell membrane resistance to deformation. Through detailed comparisons with experiments, we (i) validate this model to represent cancer cells undergoing large deformation, (ii) outline a systematic approach to parameterize different cell lines to optimally fit experimental data over a range of deformations, and (iii) provide new insight into nucleated vs. non-nucleated cell models and their ability to match experiments. While many works have used the membrane-model based method employed here to model generic cancer cells, no quantitative comparisons with experiments exist in the literature for specific cell lines undergoing large deformation. Here, we describe a phenomenological, data-driven approach that can not only yield good agreement for large deformations, but explicitly detail how it can be used to represent different cancer cell lines. This model is readily incorporated into cell-resolved hemodynamic transport simulations, and thus offers significant potential to complement experiments towards providing new insights into various aspects of cancer progression.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2007

Second Cancers After Fractionated Radiotherapy: Stochastic Population Dynamics Effects

When ionizing radiation is used in cancer therapy it can induce second cancers in nearby organs. Mainly due to longer patient survival times, these second cancers have become of increasing concern. Estimating the risk of solid second cancers involves modeling: because of long latency times, available data is usually for older, obsolescent treatment regimens. Moreover, modeling second cancers gives unique insights into human carcinogenesis, since the therapy involves administering well characterized doses of a well studied carcinogen, followed by long-term monitoring. In addition to putative radiation initiation that produces pre-malignant cells, inactivation (i.e. cell killing), and subsequent cell repopulation by proliferation can be important at the doses relevant to second cancer situations. A recent initiation/inactivation/proliferation (IIP) model characterized quantitatively the observed occurrence of second breast and lung cancers, using a deterministic cell population dynamics approach. To analyze ifradiation-initiated pre-malignant clones become extinct before full repopulation can occur, we here give a stochastic version of this I I model. Combining Monte Carlo simulations with standard solutions for time-inhomogeneous birth-death equations, we show that repeated cycles of inactivation and repopulation, as occur during fractionated radiation therapy, can lead to distributions of pre-malignant cells per patient with variance >> mean, even when pre-malignant clones are Poisson-distributed. Thus fewer patients would be affected, but with a higher probability, than a deterministic model, tracking average pre-malignant cell numbers, would predict. Our results are applied to data on breast cancers after radiotherapy for Hodgkin disease. The stochastic IIP analysis, unlike the deterministic one, indicates: a) initiated, pre-malignant cells can have a growth advantage during repopulation, not just during the longer tumor latency period that follows; b) weekend treatment gaps during radiotherapy, apart from decreasing the probability of eradicating the primary cancer, substantially increase the risk of later second cancers.

Sachs, Rainer K.↗
NASA NTRS2017

Cancer Risk in Astronauts: A Constellation of Uncommon Consequences

Excess cancers resulting from external radiation exposures have been noted since the early 1950s, when a rise in leukemia rates was first reported in young atomic bomb survivors [1]. Further studies in atomic bomb survivors, cancer patients treated with radiotherapy, and nuclear power plant workers have confirmed that radiation exposure increases the risk of not only leukemia, but also a wide array of solid cancers [2,3]. NASA has long been aware of this risk and limits astronauts' risk of exposure-induced death (REID) from cancer by specifying permissible mission durations (PMD) for astronauts on an individual basis. While cancer is present among astronauts, current data does not suggest any excess of known radiation-induced cancers relative to a comparable population of U.S. adults; however, very uncommon cancers have been diagnosed in astronauts including nasopharyngeal cancer, lymphoma of the brain, and acral myxoinflammatory fibroblastic sarcoma. In order to study cancer risk in astronauts, a number of obstacles must be overcome. Firstly, several factors make the astronaut cohort considerably different from the cohorts that have previously been studied for effects resulting from radiation exposure. The high rate of accidents and the much healthier lifestyle of astronauts compared to the U.S. population make finding a suitable comparison population a problematic task. Space radiation differs substantially from terrestrial radiation exposures studied in the past; therefore, analyses of galactic cosmic radiation (GCR) in animal models must be conducted and correctly applied to the human experience. Secondly, a large enough population of exposed astronauts must exist in order to obtain the data necessary to see any potential statistically significant differences between the astronauts and the control population. Thirdly, confounders and effect modifiers, such as smoking, diet, and other space stressors, must be correctly identified and controlled for in those analyses. In order to begin work assessing the astronaut population, the earliest groups of astronauts (selection groups 1-7) provide a unique model. These astronauts were relatively homogenous, white males whose lifestyle characteristics were similar to an average U.S. citizen of the same birth cohort. This work reviews radiation exposure levels, age, and causes of mortality among these early NASA astronauts and discusses the benefits and limitations of assessing such a cohort for radiation-induced cancer risk.

Milder, Caitlin M.↗
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

Concepts of association between cancer and ionising radiation: accounting for specific biological mechanisms

The probability that an observed cancer was caused by radiation exposure is usually estimated using cancer rates and risk models from radioepidemiological cohorts and is called assigned share (AS). This definition implicitly assumes that an ongoing carcinogenic process is unaffected by the studied radiation exposure. However, there is strong evidence that radiation can also accelerate an existing clonal development towards cancer. In this work, we define different association measures that an observed cancer was newly induced, accelerated, or retarded. The measures were quantified exemplarily by Monte Carlo simulations that track the development of individual cells. Three biologically based two-stage clonal expansion (TSCE) models were applied. In the first model, radiation initiates cancer development, while in the other two, radiation has a promoting effect, i.e. radiation accelerates the clonal expansion of pre-cancerous cells. The parameters of the TSCE models were derived from breast cancer data from the atomic bomb survivors of Hiroshima and Nagasaki. For exposure at age 30, all three models resulted in similar estimates of AS at age 60. For the initiation model, estimates of association were nearly identical to AS. However, for the promotion models, the cancerous clonal development was frequently accelerated towards younger ages, resulting in associations substantially higher than AS. This work shows that the association between a given cancer and exposure in an affected person depends on the underlying biological mechanism and can be substantially larger than the AS derived from classic radioepidemiology.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2024

Urban relatives ameliorate survival disparities for genitourinary cancer in rural patients

Patients living in rural areas have worse cancer-specific outcomes. This study examines the effect of family-based social capital on genitourinary cancer survival. We hypothesized that rural patients with urban relatives have improved survival relative to rural patients without urban family. We examined rural and urban based Utah individuals diagnosed with genitourinary cancers between 1968 and 2018. Familial networks were determined using the Utah Population Database. Patients and relatives were classified as rural or urban based on 2010 rural–urban commuting area codes. Overall survival was analyzed using Cox proportional hazards models. We identified 24,746 patients with genitourinary cancer with a median follow-up of 8.72 years. Rural cancer patients without an urban relative had the worst outcomes with cancer-specific survival hazard ratios (HRs) at 5 and 10 years of 1.33 (95% CI 1.10–1.62) and 1.46 (95% CI 1.24–1.73), respectively relative to urban patients. Rural patients with urban first-degree relatives had improved survival with 5- and 10-year survival HRs of 1.21 (95% CI 1.06–1.40) and 1.16 (95% CI 1.03–1.31), respectively. Our findings suggest rural patients who have been diagnosed with a genitourinary cancer have improved survival when having relatives in urban centers relative to rural patients without urban relatives. Further research is needed to better understand the mechanisms through which having an urban family member contributes to improved cancer outcomes for rural patients. Better characterization of this affect may help inform policies to reduce urban–rural cancer disparities.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

MIER3 induces epithelial-mesenchymal transition and promotes breast cancer cell aggressiveness via forming a co-repressor complex with HDAC1/HDAC2/Snail

Highlights: • The expression of MIER3 was correlated with poor outcome of breast cancer patients. • MIER3 induced EMT and promoted cell migration. • MIER3 interacted with HDAC1/HDAC2 and Snail to form a repressive complex. • The repressive complex bound to E-cadherin promoter and reduced its acetylation. Breast cancer is one of the most frequently diagnosed cancers and the leading cause of cancer death in women. MIER3 (Mesoderm induction early response 1, family member3) is considered as a potential oncogene for breast cancer. However, the role of MIER3 in breast cancer remain largely unknown. The expression of MIER3 was detected and the relationship between its expression and clinicopathological characteristics was also analyzed. The effect of MIER3 on proliferation and migration of breast cancer cells was detected in vitro and in vivo. Western blot, IF, and Co-IP were employed to detect the relationship between MIER3, HDAC1, HDAC2, and Snail. ChIP assay was performed to determine the binding of MIER3/HDAC1/HDAC2/Snail complex to the promoter of E-cadherin. In this study, we found that MIER3 was upregulated in breast cancer tissue and closely associated with poor prognosis of patients. MIER3 could promote the proliferation, migration, and epithelial-mesenchymal transition (EMT) of breast cancer cells. Further studies showed that MIER3 interacted with HDAC1/HDAC2 and Snail to form a repressive complex which could bind to E-cadherin promoter and was related to its deacetylation. Our study concluded that MIER3 was involved in forming a co-repressor complex with HDAC1/HDAC2/Snail to promote EMT by silencing E-cadherin.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Anticancer effect of AZD2461 PARP inhibitor against colon cancer cells carrying wt or dysfunctional p53

Colon cancer is one of the most common cancers, currently treated with traditional chemotherapies or alternative therapies. However, these treatments are still not enough effective and induce several side effects, so that the search of new therapeutic strategies is needed. The use of Poly-(ADP-ribose)-polymerase (PARP) inhibitors, although originally approved against BRCA-1 or BRCA-2 mutated cancers, has been extended, particularly in combination with other treatments, to cure cancers that do not display defects in DNA repair signaling pathways. The role of p53 oncosuppressor in the regulating the outcome of PARP inhibitor treatment remains an open issue. In this study, we addressed this topic by using a well-tolerated PARP 1/2/3 inhibitor, namely AZD2461, against colon cancer cell lines with different p53 status. We found that AZD2461 reduced cell proliferation in wtp53 and p53−/− cancer cells by increasing ROS and DNA damage, while R273H mutant (mut) p53 counteracted these effects. Moreover, AZD2461 improved the reduction of cell proliferation by low dose radiation (IR) in wtp53 cancer cells, in which a down-regulation of BRCA-1 occurred. AZD2461 did not affect cell proliferation of mutp53 colon cancer cells also in combination with low dose radiation, suggesting that only wt p53 or p53 null colon cancer cells could benefit AZD2461 treatment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

CLEC12B suppresses lung cancer progression by inducing SHP-1 expression and inactivating the PI3K/AKT signaling pathway

Lung cancer is the leading cause of cancer mortality worldwide. CLEC12B, a C-type lectin-like receptor, is low-expressed in lung cancer tissues. However, the function of CLEC12B in lung cancer and its underlying mechanism remain unclear. Here, an obvious down-regulation of CLEC12B was observed in lung cancer cells compared with the normal lung epithelial cells. CLEC12B over-expression suppressed cell viability and cell cycle entry in lung cancer, along with the reduction of PCNA and cyclin D1 expressions, while silencing CLEC12B possessed the opposite effects. Over-expression of CLEC12B promoted lung cancer cell apoptosis, accompanied by decreased Bcl-2 and increased Bax, cleaved caspase-3 and cleaved caspase-9. Moreover, CLEC12B decreased phosphorylation of PI3K-p85 and AKT proteins. By contrast, CLEC12B knockdown activated the PI3K/AKT pathway. In vivo, CLEC12B inhibited tumor growth in lung cancer, which can be reversed by CLEC12B inhibition. Co-IP and immunofluorescence assays confirmed the interaction between CLEC12B and SHP-1, and CLEC12B over-expression increased SHP-1 level. Furthermore, knocking down SHP-1 abrogated the above biological phenotypes caused by CLEC12B elevation. Taken together, our findings demonstrate that CLEC12B serves as a tumor-suppressing gene in lung cancer through positively regulating SHP-1 expression, which may be mediated by the PI3K/AKT signaling pathway.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Risk Factors and Trends for HPV-Associated Subsequent Malignant Neoplasms among Adolescent and Young Adult Cancer Survivors

Subsequent malignant neoplasms (SMN; new cancers that arise after an original diagnosis) contribute to premature mortality among adolescent and young adult (AYA) cancer survivors. Because of the high population prevalence of human papillomavirus (HPV) infection, we identify demographic and clinical risk factors for HPV-associated SMNs (HPV-SMN) among AYA cancer survivors in the SEER-9 registries diagnosed from 1976 to 2015. Outcomes included any HPV-SMN, oropharyngeal-SMN, and cervical-SMN. Follow-up started 2 months after their original diagnosis. Standardized incidence ratios (SIR) compared risk between AYA survivors and general population. Age-period-cohort (APC) models examined trends over time. Fine and Gray's models identified therapy effects controlling for cancer and demographic confounders. Of 374,408 survivors, 1,369 had an HPV-SMN, occurring on average 5 years after first cancer. Compared with the general population, AYA survivors had 70% increased risk for any HPV-SMN [95% confidence interval (CI), 1.61–1.79] and 117% for oropharyngeal-SMN (95% CI, 2.00–2.35); cervical-SMN risk was generally lower in survivors (SIR, 0.85; 95% CI, 0.76–0.95), but Hispanic AYA survivors had a 8.4 significant increase in cervical-SMN (SIR, 1.46; 95% CI, 1.01–2.06). AYAs first diagnosed with Kaposi sarcoma, leukemia, Hodgkin, and non-Hodgkin lymphoma had increased HPV-SMN risks compared with the general population. Oropharyngeal-SMN incidence declined over time in APC models. Chemotherapy and radiation were associated with any HPV-SMN among survivors with first HPV-related cancers, but not associated among survivors whose first cancers were not HPV-related. HPV-SMN in AYA survivors are driven by oropharyngeal cancers despite temporal declines in oropharyngeal-SMN. Hispanic survivors are at risk for cervical-SMN relative to the general population. Encouraging HPV vaccination and cervical and oral cancer screenings may reduce HPV-SMN burden among AYA survivors.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2012

Technical Evaluation of the NASA Model for Cancer Risk to Astronauts Due to Space Radiation

At the request of NASA, the National Research Council's (NRC's) Committee for Evaluation of Space Radiation Cancer Risk Model1 reviewed a number of changes that NASA proposes to make to its model for estimating the risk of radiation-induced cancer in astronauts. The NASA model in current use was last updated in 2005, and the proposed model would incorporate recent research directed at improving the quantification and understanding of the health risks posed by the space radiation environment. NASA's proposed model is defined by the 2011 NASA report Space Radiation Cancer Risk Projections and Uncertainties--2010 . The committee's evaluation is based primarily on this source, which is referred to hereafter as the 2011 NASA report, with mention of specific sections or tables. The overall process for estimating cancer risks due to low linear energy transfer (LET) radiation exposure has been fully described in reports by a number of organizations. The approaches described in the reports from all of these expert groups are quite similar. NASA's proposed space radiation cancer risk assessment model calculates, as its main output, age- and gender-specific risk of exposure-induced death (REID) for use in the estimation of mission and astronaut-specific cancer risk. The model also calculates the associated uncertainties in REID. The general approach for estimating risk and uncertainty in the proposed model is broadly similar to that used for the current (2005) NASA model and is based on recommendations by the National Council on Radiation Protection and Measurements. However, NASA's proposed model has significant changes with respect to the following: the integration of new findings and methods into its components by taking into account newer epidemiological data and analyses, new radiobiological data indicating that quality factors differ for leukemia and solid cancers, an improved method for specifying quality factors in terms of radiation track structure concepts as opposed to the previous approach based on linear energy transfer, the development of a new solar particle event (SPE) model, and the updates to galactic cosmic ray (GCR) and shielding transport models. The newer epidemiological information includes updates to the cancer incidence rates from the life span study (LSS) of the Japanese atomic bomb survivors, transferred to the U.S. population and converted to cancer mortality rates from U.S. population statistics. In addition, the proposed model provides an alternative analysis applicable to lifetime never-smokers (NSs). Details of the uncertainty analysis in the model have also been updated and revised. NASA's proposed model and associated uncertainties are complex in their formulation and as such require a very clear and precise set of descriptions. The committee found the 2011 NASA report challenging to review largely because of the lack of clarity in the model descriptions and derivation of the various parameters used. The committee requested some clarifications from NASA throughout its review and was able to resolve many, but not all, of the ambiguities in the written description.

Source record↗
DOE OSTI2024

Evaluating county-level lung cancer incidence from environmental radiation exposure, PM 2.5 , and other exposures with regression and machine learning models

Characterizing the interplay between exposures shaping the human exposome is vital for uncovering the etiology of complex diseases. For example, cancer risk is modified by a range of multifactorial external environmental exposures. Environmental, socioeconomic, and lifestyle factors all shape lung cancer risk. However, epidemiological studies of radon aimed at identifying populations at high risk for lung cancer often fail to consider multiple exposures simultaneously. For example, moderating factors, such as PM 2.5 , may affect the transport of radon progeny to lung tissue. This ecological analysis leveraged a population-level dataset from the National Cancer Institute’s Surveillance, Epidemiology, and End-Results data (2013–17) to simultaneously investigate the effect of multiple sources of low-dose radiation (gross γ activity and indoor radon) and PM 2.5 on lung cancer incidence rates in the USA. County-level factors (environmental, sociodemographic, lifestyle) were controlled for, and Poisson regression and random forest models were used to assess the association between radon exposure and lung and bronchus cancer incidence rates. Tree-based machine learning (ML) method perform better than traditional regression: Poisson regression: 6.29/7.13 (mean absolute percentage error, MAPE), 12.70/12.77 (root mean square error, RMSE); Poisson random forest regression: 1.22/1.16 (MAPE), 8.01/8.15 (RMSE). The effect of PM 2.5 increased with the concentration of environmental radon, thereby confirming findings from previous studies that investigated the possible synergistic effect of radon and PM 2.5 on health outcomes. In summary, the results demonstrated (1) a need to consider multiple environmental exposures when assessing radon exposure’s association with lung cancer risk, thereby highlighting (1) the importance of an exposomics framework and (2) that employing ML models may capture the complex interplay between environmental exposures and health, as in the case of indoor radon exposure and lung cancer incidence.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2023

Deep Learning on Multimodal Chemical and Whole Slide Imaging Data for Predicting Prostate Cancer Directly from Tissue Images

Prostate cancer is one of the most common cancers globally and is the second most common cancer in the male population in the US. Here we develop a study based on correlating the hematoxylin and eosin (H&E)-stained biopsy data with MALDI mass-spectrometric imaging data of the corresponding tissue to determine the cancerous regions and their unique chemical signatures and variations of the predicted regions with original pathological annotations. We obtain features from high-resolution optical micrographs of whole slide H&E stained data through deep learning and spatially register them with mass spectrometry imaging (MSI) data to correlate the chemical signature with the tissue anatomy of the data. We then use the learned correlation to predict prostate cancer from observed H&E images using trained coregistered MSI data. This multimodal approach can predict cancerous regions with ~80% accuracy, which indicates a correlation between optical H&E features and chemical information found in MSI. Further, we show that such paired multimodal data can be used for training feature extraction networks on H&E data which bypasses the need to acquire expensive MSI data and eliminates the need for manual annotation saving valuable time. Two chemical biomarkers were also found to be predicting the ground truth cancerous regions. This study shows promise in generating improved patient treatment trajectories by predicting prostate cancer directly from readily available H&E-stained biopsy images aided by coregistered MSI data.

60 APPLIED LIFE SCIENCES↗