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Article ; Online: Nicotinamide drives T cell activation in the mammary tumor microenvironment

Yang Hu / Norma Bloy / Olivier Elemento / Aitziber Buqué

Journal of Translational Medicine, Vol 20, Iss 1, Pp 1-

2022 Volume 3

Abstract: Abstract Nicotinamide (NAM, a variant of vitamin B3) has recently been shown to accelerate the activation of human CD4+ and CD8+ T cells exposed to repeated CD3/CD28 agonism in vitro. Here, we demonstrate that T cells infiltrating mouse mammary ... ...

Abstract	Abstract Nicotinamide (NAM, a variant of vitamin B3) has recently been shown to accelerate the activation of human CD4+ and CD8+ T cells exposed to repeated CD3/CD28 agonism in vitro. Here, we demonstrate that T cells infiltrating mouse mammary carcinomas that are therapeutically controlled by NAM also express multiple markers of late-stage activation. Taken together, these findings lend additional support to the notion that the antineoplastic effects of NAM involve at least some degree of restored cancer immunosurveillance.
Keywords	CTLA4 ; Immune checkpoint inhibitors ; Immunotherapy ; PD-1 ; LAG3 ; TIM-3 ; Medicine ; R
Language	English
Publishing date	2022-06-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Prediction of primary venous thromboembolism based on clinical and genetic factors within the U.K. Biobank

David A. Kolin / Scott Kulm / Olivier Elemento

Scientific Reports, Vol 11, Iss 1, Pp 1-

2021 Volume 9

Abstract: Abstract Both clinical and genetic factors drive the risk of venous thromboembolism. However, whether clinically recorded risk factors and genetic variants can be combined into a clinically applicable predictive score remains unknown. Using Cox ... ...

Abstract	Abstract Both clinical and genetic factors drive the risk of venous thromboembolism. However, whether clinically recorded risk factors and genetic variants can be combined into a clinically applicable predictive score remains unknown. Using Cox proportional-hazard models, we analyzed the association of risk factors with the likelihood of venous thromboembolism in U.K. Biobank, a large prospective cohort. We then created a polygenic risk score of 36 single nucleotide polymorphisms and a clinical score determined by age, sex, body mass index, previous cancer diagnosis, smoking status, and fracture in the last 5 years. Participants were at significantly increased risk of venous thromboembolism if they were at high clinical risk (subhazard ratio, 4.37 [95% CI, 3.85–4.97]) or high genetic risk (subhazard ratio, 3.02 [95% CI, 2.63–3.47]) relative to participants at low clinical or genetic risk, respectively. The combined model, consisting of clinical and genetic components, was significantly better than either the clinical or the genetic model alone (P < 0.001). Participants at high risk in the combined score had nearly an eightfold increased risk of venous thromboembolism relative to participants at low risk (subhazard ratio, 7.51 [95% CI, 6.28–8.98]). This risk score can be used to guide decisions regarding venous thromboembolism prophylaxis, although external validation is needed.
Keywords	Medicine ; R ; Science ; Q
Language	English
Publishing date	2021-11-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Publisher Correction

David A. Kolin / Scott Kulm / Olivier Elemento

Scientific Reports, Vol 11, Iss 1, Pp 1-

Prediction of primary venous thromboembolism based on clinical and genetic factors within the U.K. Biobank

2021 Volume 4

Keywords	Medicine ; R ; Science ; Q
Language	English
Publishing date	2021-11-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Clinical and Genetic Characteristics of Covid-19 Patients from UK Biobank

David A Kolin / Scott Kulm / Olivier Elemento

Abstract: We conducted an analysis of 669 Covid-19 positive patients within the UK Biobank cohort, a prospective cohort including over 500,000 participants. Our analyses led to several findings. We found that black participants in the cohort were over four times ... ...

Abstract	We conducted an analysis of 669 Covid-19 positive patients within the UK Biobank cohort, a prospective cohort including over 500,000 participants. Our analyses led to several findings. We found that black participants in the cohort were over four times more likely to be diagnosed with Covid-19 than white participants. In order to assess for confounding, we produced - to our knowledge - the first multivariable adjusted estimate of the association of racial characteristics with Covid-19. Our adjusted estimates indicated that black participants remained at over threefold increased risk of Covid-19 relative to white participants. Exploratory analyses identified that 22.9% of Covid-19 positive black patients were using either angiotensin converting enzyme inhibitors or angiotensin II receptor blockers, relative to just 6.7% of all black participants. Our genetic analyses confirmed the finding of a previous report noting an association of blood type A with Covid-19, and we discovered a novel genetic association with HLA DQA1_509 that remained significant even after Bonferroni correction.
Keywords	covid19
Publisher	medrxiv
Document type	Article ; Online
DOI	10.1101/2020.05.05.20075507
Database	COVID19

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Article ; Online: Application of Deep Learning on Single-cell RNA Sequencing Data Analysis

Matthew Brendel / Chang Su / Zilong Bai / Hao Zhang / Olivier Elemento / Fei Wang

Genomics, Proteomics & Bioinformatics, Vol 20, Iss 5, Pp 814-

A Review

2022 Volume 835

Abstract: Single-cell RNA sequencing (scRNA-seq) has become a routinely used technique to quantify the gene expression profile of thousands of single cells simultaneously. Analysis of scRNA-seq data plays an important role in the study of cell states and ... ...

Abstract	Single-cell RNA sequencing (scRNA-seq) has become a routinely used technique to quantify the gene expression profile of thousands of single cells simultaneously. Analysis of scRNA-seq data plays an important role in the study of cell states and phenotypes, and has helped elucidate biological processes, such as those occurring during the development of complex organisms, and improved our understanding of disease states, such as cancer, diabetes, and coronavirus disease 2019 (COVID-19). Deep learning, a recent advance of artificial intelligence that has been used to address many problems involving large datasets, has also emerged as a promising tool for scRNA-seq data analysis, as it has a capacity to extract informative and compact features from noisy, heterogeneous, and high-dimensional scRNA-seq data to improve downstream analysis. The present review aims at surveying recently developed deep learning techniques in scRNA-seq data analysis, identifying key steps within the scRNA-seq data analysis pipeline that have been advanced by deep learning, and explaining the benefits of deep learning over more conventional analytic tools. Finally, we summarize the challenges in current deep learning approaches faced within scRNA-seq data and discuss potential directions for improvements in deep learning algorithms for scRNA-seq data analysis.
Keywords	Single-cell RNA sequencing ; Single-cell sequencing ; Deep learning ; Deep neural network ; Artificial intelligence ; Biology (General) ; QH301-705.5
Subject code	006
Language	English
Publishing date	2022-10-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Clinical, regional, and genetic characteristics of Covid-19 patients from UK Biobank.

David A Kolin / Scott Kulm / Paul J Christos / Olivier Elemento

PLoS ONE, Vol 15, Iss 11, p e

2020 Volume 0241264

Abstract: Background Coronavirus disease 2019 (Covid-19) has rapidly infected millions of people worldwide. Recent studies suggest that racial minorities and patients with comorbidities are at higher risk of Covid-19. In this study, we analyzed the effects of ... ...

Abstract	Background Coronavirus disease 2019 (Covid-19) has rapidly infected millions of people worldwide. Recent studies suggest that racial minorities and patients with comorbidities are at higher risk of Covid-19. In this study, we analyzed the effects of clinical, regional, and genetic factors on Covid-19 positive status. Methods The UK Biobank is a longitudinal cohort study that recruited participants from 2006 to 2010 from throughout the United Kingdom. Covid-19 test results were provided to UK Biobank starting on March 16, 2020. The main outcome measure in this study was Covid-19 positive status, determined by the presence of any positive test for a single individual. Clinical risk factors were derived from UK Biobank at baseline, and regional risk factors were imputed using census features local to each participant's home zone. We used robust adjusted Poisson regression with clustering by testing laboratory to estimate relative risk. Blood types were derived using genetic variants rs8176719 and rs8176746, and genomewide tests of association were conducted using logistic-Firth hybrid regression. Results This prospective cohort study included 397,064 UK Biobank participants, of whom 968 tested positive for Covid-19. The unadjusted relative risk of Covid-19 for Black participants was 3.66 (95% CI 2.83-4.74), compared to White participants. Adjusting for Townsend deprivation index alone reduced the relative risk to 2.44 (95% CI 1.86-3.20). Comorbidities that significantly increased Covid-19 risk included chronic obstructive pulmonary disease (adjusted relative risk [ARR] 1.64, 95% CI 1.18-2.27), ischemic heart disease (ARR 1.48, 95% CI 1.16-1.89), and depression (ARR 1.32, 95% CI 1.03-1.70). There was some evidence that angiotensin converting enzyme inhibitors (ARR 1.48, 95% CI 1.13-1.93) were associated with increased risk of Covid-19. Each standard deviation increase in the number of total individuals living in a participant's locality was associated with increased risk of Covid-19 (ARR 1.14, 95% CI 1.08-1.20). ...
Keywords	Medicine ; R ; Science ; Q
Subject code	610
Language	English
Publishing date	2020-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Deep learning predicts chromosomal instability from histopathology images

Zhuoran Xu / Akanksha Verma / Uska Naveed / Samuel F. Bakhoum / Pegah Khosravi / Olivier Elemento

iScience, Vol 24, Iss 5, Pp 102394- (2021)

2021

Abstract: Summary: Chromosomal instability (CIN) is a hallmark of human cancer yet not readily testable for patients with cancer in routine clinical setting. In this study, we sought to explore whether CIN status can be predicted using ubiquitously available ... ...

Abstract	Summary: Chromosomal instability (CIN) is a hallmark of human cancer yet not readily testable for patients with cancer in routine clinical setting. In this study, we sought to explore whether CIN status can be predicted using ubiquitously available hematoxylin and eosin histology through a deep learning-based model. When applied to a cohort of 1,010 patients with breast cancer (Training set: n = 858, Test set: n = 152) from The Cancer Genome Atlas where 485 patients have high CIN status, our model accurately classified CIN status, achieving an area under the curve of 0.822 with 81.2% sensitivity and 68.7% specificity in the test set. Patch-level predictions of CIN status suggested intra-tumor heterogeneity within slides. Moreover, presence of patches with high predicted CIN score within an entire slide was more predictive of clinical outcome than the average CIN score of the slide, thus underscoring the clinical importance of intra-tumor heterogeneity.
Keywords	Cell Biology ; Automation in Bioinformatics ; Neural Networks ; Cancer Systems Biology ; Science ; Q
Subject code	616
Language	English
Publishing date	2021-05-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A machine learning and network framework to discover new indications for small molecules.

Coryandar Gilvary / Jamal Elkhader / Neel Madhukar / Claire Henchcliffe / Marcus D Goncalves / Olivier Elemento

PLoS Computational Biology, Vol 16, Iss 8, p e

2020 Volume 1008098

Abstract: Drug repurposing, identifying novel indications for drugs, bypasses common drug development pitfalls to ultimately deliver therapies to patients faster. However, most repurposing discoveries have been led by anecdotal observations (e.g. Viagra) or ... ...

Abstract	Drug repurposing, identifying novel indications for drugs, bypasses common drug development pitfalls to ultimately deliver therapies to patients faster. However, most repurposing discoveries have been led by anecdotal observations (e.g. Viagra) or experimental-based repurposing screens, which are costly, time-consuming, and imprecise. Recently, more systematic computational approaches have been proposed, however these rely on utilizing the information from the diseases a drug is already approved to treat. This inherently limits the algorithms, making them unusable for investigational molecules. Here, we present a computational approach to drug repurposing, CATNIP, that requires only biological and chemical information of a molecule. CATNIP is trained with 2,576 diverse small molecules and uses 16 different drug similarity features, such as structural, target, or pathway based similarity. This model obtains significant predictive power (AUC = 0.841). Using our model, we created a repurposing network to identify broad scale repurposing opportunities between drug types. By exploiting this network, we identified literature-supported repurposing candidates, such as the use of systemic hormonal preparations for the treatment of respiratory illnesses. Furthermore, we demonstrated that we can use our approach to identify novel uses for defined drug classes. We found that adrenergic uptake inhibitors, specifically amitriptyline and trimipramine, could be potential therapies for Parkinson's disease. Additionally, using CATNIP, we predicted the kinase inhibitor, vandetanib, as a possible treatment for Type 2 Diabetes. Overall, this systematic approach to drug repurposing lays the groundwork to streamline future drug development efforts.
Keywords	Biology (General) ; QH301-705.5
Subject code	006
Language	English
Publishing date	2020-08-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images

Pegah Khosravi / Ehsan Kazemi / Marcin Imielinski / Olivier Elemento / Iman Hajirasouliha

EBioMedicine, Vol 27, Iss C, Pp 317-

2018 Volume 328

Abstract: Pathological evaluation of tumor tissue is pivotal for diagnosis in cancer patients and automated image analysis approaches have great potential to increase precision of diagnosis and help reduce human error. In this study, we utilize several ... ...

Abstract	Pathological evaluation of tumor tissue is pivotal for diagnosis in cancer patients and automated image analysis approaches have great potential to increase precision of diagnosis and help reduce human error. In this study, we utilize several computational methods based on convolutional neural networks (CNN) and build a stand-alone pipeline to effectively classify different histopathology images across different types of cancer. In particular, we demonstrate the utility of our pipeline to discriminate between two subtypes of lung cancer, four biomarkers of bladder cancer, and five biomarkers of breast cancer. In addition, we apply our pipeline to discriminate among four immunohistochemistry (IHC) staining scores of bladder and breast cancers. Our classification pipeline includes a basic CNN architecture, Google's Inceptions with three training strategies, and an ensemble of two state-of-the-art algorithms, Inception and ResNet. Training strategies include training the last layer of Google's Inceptions, training the network from scratch, and fine-tunning the parameters for our data using two pre-trained version of Google's Inception architectures, Inception-V1 and Inception-V3. We demonstrate the power of deep learning approaches for identifying cancer subtypes, and the robustness of Google's Inceptions even in presence of extensive tumor heterogeneity. On average, our pipeline achieved accuracies of 100%, 92%, 95%, and 69% for discrimination of various cancer tissues, subtypes, biomarkers, and scores, respectively. Our pipeline and related documentation is freely available at https://github.com/ih-_lab/CNN_Smoothie.
Keywords	Biomarkers ; Classification ; Convolutional Neural Network ; Deep learning ; Digital pathology imaging ; Tumor heterogeneity ; Medicine ; R ; Medicine (General) ; R5-920
Subject code	006
Language	English
Publishing date	2018-01-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: The Role of Next-Generation Sequencing in Precision Medicine

Margaret Morash / Hannah Mitchell / Himisha Beltran / Olivier Elemento / Jyotishman Pathak

Journal of Personalized Medicine, Vol 8, Iss 3, p

A Review of Outcomes in Oncology

2018 Volume 30

Abstract: Precision medicine seeks to use genomic data to help provide the right treatment to the right patient at the right time. Next-generation sequencing technology allows for the rapid and accurate sequencing of many genes at once. This technology is becoming ...

Abstract	Precision medicine seeks to use genomic data to help provide the right treatment to the right patient at the right time. Next-generation sequencing technology allows for the rapid and accurate sequencing of many genes at once. This technology is becoming more common in oncology, though the clinical benefit of incorporating it into precision medicine strategies remains under significant debate. In this manuscript, we discuss the early findings of the impact of next-generation sequencing on cancer patient outcomes. We investigate why not all patients with genomic variants linked to a specific therapy receive that therapy and describe current barriers. Finally, we explore the current state of health insurance coverage for individual genome sequencing and targeted therapies for cancer. Based on our analysis, we recommend increased transparency around the determination of “actionable mutations” and a heightened focus on investigating the variations in health insurance coverage across patients receiving sequencing-matched therapies.
Keywords	precision medicine ; next generation sequencing ; oncology ; patient outcomes ; health insurance coverage ; Medicine ; R
Language	English
Publishing date	2018-09-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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