Privacy-Preserving Deep Learning NLP Models for Cancer Registries
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60 APPLIED LIFE SCIENCES↗
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Deep learning uncertainty quantification for clinical text classification
Machine learning algorithms are expected to work side-by-side with humans in decision-making pipelines. Thus, the ability of classifiers to make reliable decisions is of paramount importance. Deep neural networks (DNNs) represent the state-of-the-art models to address real-world classification. Although the strength of activation in DNNs is often correlated with the network’s confidence, in-depth analyses are needed to establish whether they are well calibrated. In this paper, we demonstrate the use of DNN-based classification tools to benefit cancer registries by automating information extraction of disease at diagnosis and at surgery from electronic text pathology reports from the US National Cancer Institute (NCI) Surveillance, Epidemiology, and End Results (SEER) population-based cancer registries. In particular, we introduce multiple methods for selective classification to achieve a target level of accuracy on multiple classification tasks while minimizing the rejection amount—that is, the number of electronic pathology reports for which the model’s predictions are unreliable. We evaluate the proposed methods by comparing our approach with the current in-house deep learning-based abstaining classifier. Overall, all the proposed selective classification methods effectively allow for achieving the targeted level of accuracy or higher in a trade-off analysis aimed to minimize the rejection rate. On in-distribution validation and holdout test data, with all the proposed methods, we achieve on all tasks the required target level of accuracy with a lower rejection rate than the deep abstaining classifier (DAC). Interpreting the results for the out-of-distribution test data is more complex; nevertheless, in this case as well, the rejection rate from the best among the proposed methods achieving 97% accuracy or higher is lower than the rejection rate based on the DAC. We show that although both approaches can flag those samples that should be manually reviewed and labeled by human annotators, the newly proposed methods retain a larger fraction and do so without retraining—thus offering a reduced computational cost compared with the in-house deep learning-based abstaining classifier.
59 BASIC BIOLOGICAL SCIENCES↗
Machine learning and deep learning tools for the automated capture of cancer surveillance data
The National Cancer Institute and the Department of Energy strategic partnership applies advanced computing and predictive machine learning and deep learning models to automate the capture of information from unstructured clinical text for inclusion in cancer registries. Applications include extraction of key data elements from pathology reports, determination of whether a pathology or radiology report is related to cancer, extraction of relevant biomarker information, and identification of recurrence. With the growing complexity of cancer diagnosis and treatment, capturing essential information with purely manual methods is increasingly difficult. These new methods for applying advanced computational capabilities to automate data extraction represent an opportunity to close critical information gaps and create a nimble, flexible platform on which new information sources, such as genomics, can be added. This will ultimately provide a deeper understanding of the drivers of cancer and outcomes in the population and increase the timeliness of reporting. These advances will enable better understanding of how real-world patients are treated and the outcomes associated with those treatments in the context of our complex medical and social environment.
60 APPLIED LIFE SCIENCES↗
Deformable phrase level attention: A flexible approach for improving AI based medical coding
Objective: Improving the AI-driven automated medical encoding of clinical text plays a vital role in gathering information on the occurrence of diseases to improve population-level health. This work presents a novel attention mechanism designed to enhance text classification models and ensure appropriate classification of medical concepts in unstructured electronic health records. Materials and Methods: We developed a deformable, phrase-level attention mechanism to identify important lexical word-level and contextual phrase-level information from clinical text documents. We evaluated conventional and transformer-based deep learning models that we extended with our attention mechanism on the extraction of critical cancer information (e.g., site, subsite, laterality, histology, behavior) from 629,908 electronic pathology reports and on the automated medical encoding of 52,722 hospital discharge summaries. Results: Transformer-based models with the deformable, phrase-level attention mechanism achieved the best performance on the extraction of critical cancer information from pathology reports. Conventional- and transformer-based models show similar or better performance than their baseline counterparts on the automated medical encoding of clinical documents. Discussion: The addition of phrase-level information allowed models extended with our proposed method to outperform standard word-level attention. Our method showed favorable properties for the real-world application in terms of model robustness and phenotyping. These results indicate that our method is promising for automated data harmonization for common data models. Conclusion: This work proposes a novel deformable, phrase-level attention mechanism that enhances text classification models in the extraction of medical concepts from clinical text documents. We demonstrate strong performances on two clinical text datasets and showcase real-world deployability of our method.
Automated medical encoding↗
Multimodal Data Representation with Deep Learning for Extracting Cancer Characteristics from Clinical Text
This paper presents a multimodal data representation to improve the performance of deep learning models for extracting cancer key characteristics from unstructured text in pathology reports. Specifically, in addition to using the text as the input to deep learning models, we use concept unique identifiers (CUIs) as another source of information to the models. We analyze the performance of different text and CUI data representations, including word embeddings and bag of embeddings (BOE), with a convolutional neural network (CNN) and a fully connected multilayer perceptron neural network (MLP-NN). The high level document embeddings from text and CUI inputs are combined by concatenating them and then applying a classifier. The model is used for extracting cancer subsite and histology from pathology reports. These two classification tasks have a large number of labels, i.e. 317 for subsite and 556 for histology, with extreme class imbalance. We compare the performance of the developed DL models across the two tasks based on micro- and macro-F1 scores. The evaluation shows that a multi-channel DL model that utilizes text represented by word embeddings and CUIs represented by BOE outperforms other DL models. Also, this approach significantly improves the model performance on low prevalence classes.
Alawad, Mohammed↗
A Comprehensive Urine Proteome Database Generated From Patients With Various Renal Conditions and Prostate Cancer
Urine proteins can serve as viable biomarkers for diagnosing and monitoring various diseases. A comprehensive urine proteome database, generated from a variety of urine samples with different disease conditions, can serve as a reference resource for facilitating discovery of potential urine protein biomarkers. Herein, we present a urine proteome database generated from multiple datasets using 2D LC-MS/MS proteome profiling of urine samples from healthy individuals (HI), renal transplant patients with acute rejection (AR) and stable graft (STA), patients with non-specific proteinuria (NS), and patients with prostate cancer (PC). A total of ~28,000 unique peptides spanning ~2,200 unique proteins were identified with a false discovery rate of <0.5% at the protein level. Over one third of the annotated proteins were plasma membrane proteins and another one third were extracellular proteins according to gene ontology analysis. Ingenuity Pathway Analysis of these proteins revealed 349 potential biomarkers in the literature-curated database. Forty-three percentage of all known cluster of differentiation (CD) proteins were identified in the various human urine samples. Interestingly, following comparisons with five recently published urine proteome profiling studies, which applied similar approaches, there are still ~400 proteins which are unique to this current study. These may represent potential disease-associated proteins. Among them, several proteins such as serpin B3, renin receptor, and periostin have been reported as pathological markers for renal failure and prostate cancer, respectively. Taken together, our data should provide valuable information for future discovery and validation studies of urine protein biomarkers for various diseases.
60 APPLIED LIFE SCIENCES↗
Direct evidence that tumor cells soften when navigating confined spaces.
The mechanical properties of cells strongly regulate many physiological and pathological processes. For example, in cancer, invasive and metastatic tumor cells have often been reported to be softer than nontumor cells, raising speculation that cancer cells might adaptively soften to facilitate migration through narrow tissue spaces. Despite growing interest in targeting cell softening to impede invasion and metastasis, it remains to be directly demonstrated that tumor cells soften as they migrate through confined spaces. Here, we address this open question by combining topographically patterned substrates with atomic force microscopy (AFM). Using a polydimethylsiloxane open-roof microdevice featuring tapered, fibronectin-coated channels, we followed the migration of U2OS cells through various stages of confinement while simultaneously performing AFM indentation. As cells progress from unconfined migration to fully confined migration, cells soften and exclude Yes-associated protein from the nucleus. Superresolution imaging reveals that confinement induces remodeling of actomyosin stress fiber architecture. Companion studies with flat one-dimensional microlines indicate that the changes in cytoarchitecture and mechanics are intrinsically driven by topographical confinement rather than changes in cellular aspect ratio. Our studies represent among the most direct evidence to date that tumor cells soften during confined migration and support cell softening as a mechanoadaptive mechanism during invasion.
59 BASIC BIOLOGICAL SCIENCES↗
Prognostic Importance of MRI-Detected Extramural Venous Invasion in Rectal Cancer: A Literature Review and Systematic Meta-Analysis
Extramural venous invasion (EMVI) is recognized as a poor prognostic factor in rectal cancer. There are well-documented limitations associated with pathology detection of EMVI, including variable reporting and the inability to use it preoperatively to guide neoadjuvant treatment. Magnetic resonance imaging (MRI)-detected EMVI (mrEMVI) has been proposed as an imaging biomarker. This review assesses the prognostic significance of mrEMVI on survival outcomes and whether regression of mrEMVI after neoadjuvant therapy is associated with improvements in survival.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
Biology of extracellular vesicles secreted from senescent cells as senescence‐associated secretory phenotype factors
The increase of the morbidity rate in age‐related diseases, such as cancer, Alzheimer’s disease, arteriosclerosis and pulmonary fibrosis, has become a profound social problem. Recent reports have pointed out that senescent cells accumulated in the body with aging might cause these aged‐related pathologies. Cellular senescence is known as an irreversible cell cycle arrest induced by various stresses, and can function as an important tumor suppression mechanism to exclude the premalignant cells. In contrast, senescent cells provoke the phenomenon, termed the senescence‐associated secretory phenotype, which causes the secretion of various inflammatory proteins, and it is at risk of facilitating chronic inflammation and oncogenic transformation to surrounding cells. We have previously reported that senescent cells secrete not only inflammatory proteins, but also extracellular vesicles (EV). EV include various cellular components, such as proteins, lipids and nucleic acids, which are proven to be important factors for cell‐to‐cell communication. Recent evidence suggests that EV secreted from senescent cells might contribute to tumorigenesis and age‐associated pathologies as new senescence‐associated secretory phenotype factors. In addition, we also showed that the EV secretion pathway is one of the essential defense mechanisms to maintain cellular homeostasis by excretion of intercellular toxic substances into extracellular space. Herein, this review shows the biological functions of EV secreted from senescent cells. Geriatr Gerontol Int 2020; ••: ••–•• .
Misawa, Tomoka↗
Colonoscopy Screening in the US Astronaut Corps
BACKGROUND: Historically, colonoscopy screenings for astronauts have been conducted to ensure that astronauts are in good health for space missions. Recently this historical data has been identified as being useful for developing an occupational surveillance requirement. It can be used to assess overall colon health and to have a point of reference for future tests in current and former astronauts, as well as to follow-up and track rates of colorectal cancer and polyps. These rates can be compared to military and other terrestrial populations. In 2003, the active astronaut colonoscopy requirements changed to require less frequent colonoscopies. Since polyp removal during a colonoscopy is an intervention that prevents the polyp from potentially developing into cancer, the procedure decreases the individual's risk for colon cancer. The objective of this study is to evaluate the possible effect of increased follow-up times between colonoscopies on the number and severity of polyps identified during the procedures among both current and former NASA astronauts. Initial results and forward work regarding astronaut colonoscopy screenings will be presented. METHODS: A retrospective study of all colonoscopy procedures performed on NASA astronauts between 1962 and 2015 (both during active career and retirement) was conducted by review of the JSC Clinic Electronic Medical Record and Lifetime Surveillance of Astronaut Health (LSAH) database for colonoscopy screening procedures and pathology reports. The timeframe of interest was from the time of selection into the Astronaut Corps through May 2015 or death. For each colonoscopy report, the following data were captured: date of procedure, age at time of procedure, reason for procedure, quality of bowel prep, completion of procedure and/or reason for termination of procedure, findings of procedure, subsequent treatment (if any), recommended follow-up interval, actual follow up interval, family history of polyps or colon cancer, and other significant items or discrepancies. The population consisted of 338 astronauts: 52 females, 286 males. Of these, 56 were deceased, and 11 astronauts had no record of any colonoscopies. Because of a screening requirement change in 2003, analyses were conducted to determine if there were differences between the two time periods. One-sided Wilcoxon rank sum tests were used to identify statistically significant differences between the two time periods. RESULTS: There was a combined total of 1,964 colonoscopy screenings identified. The average follow-up intervals between colonoscopies were indeed longer after the screening requirement change than before the change. The mean follow-up interval pre -2003 was 3.59 years, while post-2003 it increased to 4.35 years. The statistical significance of this difference was confirmed using a one sided Wilcoxon rank sum test which yielded p<.001. Colonoscopies performed after the requirement change tended to have a higher incidence and greater severity of polyps. From pre-2003 to post-2003 the percentage of colonoscopy procedures yielding no polyps decreased from 83.77% to 74.70%. Not only did post-2003 procedures yield more polyp findings, but the polyps recorded were more often of severe pathology. Before 2003 3.62% of colonoscopy findings were polyps of the hyperplastic type (the least severe polyp type) and only 3.35% were of greater severity. Post-2003, 4.21% of findings were hyperplastic polyps while 11.44% were of greater severity. Upon the investigation of other possible contributing factors to these results, we also found that mean age post-2003 was 54.55 years which was significantly higher than during the pre-2003 timeframe (47.32 years). This was observed with a one-sided Wilcoxon rank sum test, resulting in a p<0.001. The increased average age of astronauts could also be a contributing factor to the greater number of polyps found since the risk of developing polyps increases with age. Further work is needed to better understand the increased incidence and greater severity of polyps found in astronaut colonoscopy outcomes.
Masterova, K.↗
Macromolecular Expression and Function: A New Paradigm for NASA Risk Assessment
Predicting risks in humans of either acute effects such as bone loss or muscle wasting, or late effects such as cancer, is challenging. To an approximation, this is because uncertainties of exposure to stress factors or toxic agents and the uniformity of processing subsequent damage at the cellular level within a complex set of biological variables degrade the confidence of predicting pathologic outcome. A cellular biodosimeter that simultaneously reports 1) the type of damage due to that exposure, 2) the quantity of damage incurred by that exposure, and 3) the dataset used to assess risk of developing pathologic outcome caused by that exposure would therefore be useful for predicting ultimate risks faced by an individual, such as an astronaut. It is suggested that such a biodosimeter can be based upon analyses of gene-expression and protein expression whereby large datasets of cellular response to damage are obtained and analyzed for expression-profiles correlated with established end points and molecular markers predictive for risks being assessed. The usefulness of multiparametric cellular biodosimeters could be realized by quantitatively profiling these datasets using techniques of bioinformatics. Such an approach contributes to the foundation of molecular epidemiology as a new scientific discipline, and represents a new paradigm of risk assessment.
Richmond, Robert↗
Evidence Report: Risk of Cardiovascular Disease and Other Degenerative Tissue Effects from Radiation Exposure
Occupational radiation exposure from the space environment may result in non-cancer or non-CNS degenerative tissue diseases, such as cardiovascular disease, cataracts, and respiratory or digestive diseases. However, the magnitude of influence and mechanisms of action of radiation leading to these diseases are not well characterized. Radiation and synergistic effects of radiation cause DNA damage, persistent oxidative stress, chronic inflammation, and accelerated tissue aging and degeneration, which may lead to acute or chronic disease of susceptible organ tissues. In particular, cardiovascular pathologies such as atherosclerosis are of major concern following gamma-ray exposure. This provides evidence for possible degenerative tissue effects following exposures to ionizing radiation in the form of the GCR or SPEs expected during long-duration spaceflight. However, the existence of low dose thresholds and dose-rate and radiation quality effects, as well as mechanisms and major risk pathways, are not well-characterized. Degenerative disease risks are difficult to assess because multiple factors, including radiation, are believed to play a role in the etiology of the diseases. As additional evidence is pointing to lower, space-relevant thresholds for these degenerative effects, particularly for cardiovascular disease, additional research with cell and animal studies is required to quantify the magnitude of this risk, understand mechanisms, and determine if additional protection strategies are required.The NASA PEL (Permissive Exposure Limit)s for cataract and cardiovascular risks are based on existing human epidemiology data. Although animal and clinical astronaut data show a significant increase in cataracts following exposure and a reassessment of atomic bomb (A-bomb) data suggests an increase in cardiovascular disease from radiation exposure, additional research is required to fully understand and quantify these adverse outcomes at lower doses (less than 0.5 gray (SI unit for ionizing radiation dosage, i.e. one joule of radiation energy per one kilogram of matter)) to facilitate risk prediction. This risk has considerable uncertainty associated with it, and no acceptable model for projecting degenerative tissue risk is currently available. In particular, risk factors such as obesity, alcohol, and tobacco use can act as confounding factors that contribute to the large uncertainties. The PELs could be violated under certain scenarios, including following a large SPE (solar proton event) or long-term GCR (galactic cosmic ray) exposure. Specifically, for a Mars mission, the accumulated dose is sufficiently high that epidemiology data and preliminary risk estimates suggest a significant risk for cardiovascular disease. Ongoing research in this area is intended to provide the evidence base for accurate risk quantification to determine criticality for extended duration missions. Data specific to the space radiation environment must be compiled to quantify the magnitude of this risk to decrease the uncertainty in current PELs and to determine if additional protection strategies are required. New research results could lead to estimates of cumulative radiation risk from CNS and degenerative tissue diseases that, when combined with the cancer risk, may have major negative impacts on mission design, costs, schedule, and crew selection. The current report amends an earlier report (Human Research Program Requirements Document, HRP-47052, Rev. C, dated Jan 2009) in order to provide an update of evidence since 2009.
Patel, Zarana↗
Optical Microsystems for Molecular Sensing (CRADA Final Report)
As part of the Cyclotron Road program, NuSight Photonics sought to implement and investigate an optical Raman spectroscopy instrument for material and molecular analysis. Raman spectroscopy is widely used for quick and non-invasive material measurement when accurate results are needed – e.g., for monitoring manufacturing process in pharmaceutical plants, or identifying illicit substances and explosives by Law Enforcement. This technique is also growing rapidly in popularity as it is applied successfully to a broad range of applications, from pathology and non-invasive early cancer detection, to food and drug safety. The biggest challenge for Raman spectroscopy to achieve its full potential is the hardware cost, ranging from $\$ $25k to $\$ $250k, with the lower cost solutions significantly limited in sensitivity—or detection limit. NuSight Photonics aimed to tackle this obstacle through a novel hardware approach: Swept-Source Raman Spectroscopy.
36 MATERIALS SCIENCE↗
Rutin is a potent senomorphic agent to target senescent cells and can improve chemotherapeutic efficacy
Aging is a major risk factor for most chronic disorders, for which cellular senescence is one of the central hallmarks. Senescent cells develop the pro-inflammatory senescence-associated secretory phenotype (SASP), which significantly contributes to organismal aging and age-related disorders. Development of senotherapeutics, an emerging class of therapeutic agents to target senescent cells, allows to effectively delay aging and alleviate chronic pathologies. Here we report preliminary outputs from screening of a natural medicinal agent (NMA) library for senotherapeutic candidates and validated several agents with prominent potential as senomorphics. Rutin, a phytochemical constituent found in a number of plants, showed remarkable capacity in targeting senescent cells by dampening expression of the full spectrum SASP. Further analysis indicated that rutin restrains the acute stress-associated phenotype (ASAP) by specifically interfering with the interactions of ATM with HIF1α, a master regulator of cellular and systemic homeostasis activated during senescence, and of ATM with TRAF6, part of a key signaling axis supporting the ASAP development toward the SASP. Conditioned media produced by senescent stromal cells enhanced the malignant phenotypes of prostate cancer cells, including in vitro proliferation, migration, invasion, and more importantly, chemoresistance, while rutin remarkably downregulated these gain-of-functions. Although classic chemotherapy reduced tumor progression, the treatment outcome was substantially improved upon combination of a chemotherapeutic agent with rutin. Our study provides a proof of concept for rutin as an emerging natural senomorphic agent, and presents an effective therapeutic avenue for alleviating age-related pathologies including cancer.
60 APPLIED LIFE SCIENCES↗
High-Content Clonogenic Survival Screen to Identify Chemoradiation Sensitizers
The combination of cytotoxic chemotherapy with radiation therapy (CRT) has resulted in significant improvements in clinical outcomes for patients with many locally advanced unresectable cancers. Only a small proportion of patients achieve pathologic complete responses to CRT; combination of CRT with targeted agents offers the promise of further improving treatment responses. However, numerous clinical trials have failed to show an improvement in clinical outcomes with the addition of targeted agents. To increase the accessibility of our screening method and accelerate the pace at which novel combinations with CRT are identified and incorporated into standard practices for treatments, we report details on screening method optimization, data generation, and downstream data analysis.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
Next-generation poly-L-histidine formulations for miRNA mimic delivery
Many diseases, especially cancer, are caused by the abnormal expression of non-coding microRNAs (miRNAs), which regulate gene expression, leading to the development of miRNA-based therapeutics. Synthetic miRNA inhibitors have shown promising efficacy in blocking the activity of aberrant miRNAs that are upregulated in disease-specific pathologies. On the other hand, miRNAs that aid in preventing certain diseases and are reduced in expression in the disease state need different strategies. To tackle this, miRNA mimics, which mimic the activity of endogenous miRNAs, can be delivered for those miRNAs downregulated in different disease states. However, the delivery of miRNA mimics remains a challenge. Here, we report a cationic polylactic-co-glycolic acid (PLGA)-poly-L-histidine delivery system to deliver miRNA mimics. We chose miR-34a mimics as a proof of concept for miRNA delivery. miR-34a-loaded PLGA-poly-L-histidine nanoparticles (NPs) were formulated and biophysically characterized to analyze the structural properties of miRNA mimic-loaded NPs. In vitro efficacy was determined by investigating miR-34a and downstream target levels and performing cell viability and apoptosis assays. We confirmed in vivo efficacy through prolonged survival of miR-34a NP-treated A549-derived xenograft mice treated intratumorally. The results of these studies establish PLGA-poly-L-histidine NPs as an effective delivery system for miRNA mimics for treating diseases characterized by downregulated miRNAs.
60 APPLIED LIFE SCIENCES↗
Structural Insights into the SPRED1-Neurofibromin-KRAS Complex and Disruption of SPRED1-Neurofibromin Interaction by Oncogenic EGFR
Sprouty-related, EVH1 domain-containing (SPRED) proteins negatively regulate RAS/mitogen-activated protein kinase (MAPK) signaling following growth factor stimulation. This inhibition of RAS is thought to occur primarily through SPRED1 binding and recruitment of neurofibromin, a RasGAP, to the plasma membrane. Here, we report the structure of neurofibromin (GTPase-activating protein [GAP]-related domain) complexed with SPRED1 (EVH1 domain) and KRAS. The structure provides insight into how the membrane targeting of neurofibromin by SPRED1 allows simultaneous interaction with activated KRAS. SPRED1 and NF1 loss-of-function mutations occur across multiple cancer types and developmental diseases. Analysis of the neurofibromin-SPRED1 interface provides a rationale for mutations observed in Legius syndrome and suggests why SPRED1 can bind to neurofibromin but no other RasGAPs. We show that oncogenic EGFR(L858R) signaling leads to the phosphorylation of SPRED1 on serine 105, disrupting the SPRED1-neurofibromin complex. The structural, biochemical, and biological results provide new mechanistic insights about how SPRED1 interacts with neurofibromin and regulates active KRAS levels in normal and pathologic conditions.
59 BASIC BIOLOGICAL SCIENCES↗
A comparison of histopathology imaging comprehension algorithms based on multiple instance learning
Whole slide imaging (WSI), also called digital virtual microscopy, is a new imaging modality. It allows for the application of AI and machine learning methods to cancer pathology to help establish a means for the automatic diagnosis of cancer cases. However, designing machine-learning models for WSI is computationally challenging due to its required ultra-high resolution. The current state-of-the-art models use multiple instance learning (MIL). MIL is a weakly-supervised learning method in which the model uses an array of inferences from many smaller instances to make a final classification about the entire set. In the context of WSI, researchers divide the ultra-high-resolution image into many patches. The model then classifies the slide based on an array of inferences from the patches. Among several ways of making the final classification, attention-based mechanisms have resulted in superb accuracy scores. The Transformer, one attention-based algorithm, has reported substantial improvements for WSI comprehension tasks. In this project, we studied and compared several WSI comprehension algorithms. We used the following three datasets: CAMELYON16+17, TCGALung, and TCGA-Kidney. We found that attention-based MIL algorithms performed better than standard MIL algorithms for classifying WSI images, achieving a higher mean accuracy and AUC. However, none of the attention-based algorithms performed significantly better than the others, reporting accuracy scores that varied widely. Presumably, it is due to the limited availability of training samples in the data corpus. Since it is not easy to increase the samples from human subjects, some machine learning techniques like transfer learning could help mitigate this issue.
Saunders, Adam↗