Reproducibility of Gene Expression Signatures in Diffuse Large B-Cell Lymphoma

SIMPLE SUMMARY: Multiple gene expression signatures with biological or prognostic subgroups have been published in diffuse large B-cell lymphoma (DLBCL). With exception of the cell of origin (COO) classifier, these were not validated in independent cohorts. The aim of the study was to reproduce four gene expression signatures capturing multiple biological subgroups using the NanoString platform. In addition, we aimed to identify potential associations between the signatures and portray the heterogeneity of DLBCL. We show that, in an independent cohort of clinically well-defined patients, these signatures can co-occur in the same patient and that each classifier captures a different aspect of the biological heterogenous panorama of DLBCL. Beside COO, there is clear evidence of different immune and MYC signatures. A direct comparison in our cohort showed that these signatures reflect independent biological features. More comparative studies with gene expression profiles need to be conducted to enable a further integration and to help develop new taxonomy systems for clinical utility. ABSTRACT: Multiple gene expression profiles have been identified in diffuse large B-cell lymphoma (DLBCL). Besides the cell of origin (COO) classifier, no signatures have been reproduced in independent studies or evaluated for capturing distinct aspects of DLBCL biology. We reproduced 4 signatures in 175 samples of the HOVON-84 trial on a panel of 117 genes using the NanoString platform. The four gene signatures capture the COO, MYC activity, B-cell receptor signaling, oxidative phosphorylation, and immune response. Performance of our classification algorithms were confirmed in the original datasets. We were able to validate three of the four GEP signatures. The COO algorithm resulted in 94 (54%) germinal center B-cell (GCB) type, 58 (33%) activated B-cell (ABC) type, and 23 (13%) unclassified cases. The MYC-classifier revealed 77 cases with a high MYC-activity score (44%) and this MYC-high signature was observed more frequently in ABC as compared to GCB DLBCL (68% vs. 32%, p < 0.00001). The host response (HR) signature of the consensus clustering was present in 55 (31%) patients, while the B-cell receptor signaling, and oxidative phosphorylation clusters could not be reproduced. The overlap of COO, consensus cluster and MYC activity score differentiated six gene expression clusters: GCB/MYC-high (12%), GCB/HR (16%), GCB/non-HR (27%), COO-Unclassified (13%), ABC/MYC-high (25%), and ABC/MYC-low (7%). In conclusion, the three validated signatures identify distinct subgroups based on different aspects of DLBCL biology, emphasizing that each classifier captures distinct molecular profiles

Pulsar Search Using Supervised Machine Learning

Pulsars are rapidly rotating neutron stars which emit a strong beam of energy through mechanisms that are not entirely clear to physicists. These very dense stars are used by astrophysicists to study many basic physical phenomena, such as the behavior of plasmas in extremely dense environments, behavior of pulsar-black hole pairs, and tests of general relativity. Many of these tasks require information to answer the scientific questions posed by physicists. In order to provide more pulsars to study, there are several large-scale pulsar surveys underway, which are generating a huge backlog of unprocessed data. Searching for pulsars is a very labor-intensive process, currently requiring skilled people to examine and interpret plots of data output by analysis programs. An automated system for screening the plots will speed up the search for pulsars by a very large factor. Research to date on using machine learning and pattern recognition has not yielded a completely satisfactory system, as systems with the desired near 100% recall have false positive rates that are higher than desired, causing more manual labor in the classification of pulsars. This work proposed to research, identify, propose and develop methods to overcome the barriers to building an improved classification system with a false positive rate of less than 1% and a recall of near 100% that will be useful for the current and next generation of large pulsar surveys. The results show that it is possible to generate classifiers that perform as needed from the available training data. While a false positive rate of 1% was not reached, recall of over 99% was achieved with a false positive rate of less than 2%. Methods of mitigating the imbalanced training and test data were explored and found to be highly effective in enhancing classification accuracy

Development of Machine Learning Tutorials for R

Machine learning (ML) techniques developed in computer science have revolutionized nearly every sector of industry. Despite the prevalence and usefulness of ML, students outside of computer science rarely receive training in ML. Students frequently receive training in statistical analysis, often using the software package R, which is free, open source, and has additional downloadable modules. A popular module is the ML package caret, which contains 238 different ML algorithms, each with 0-9 hyperparameters. caret is powerful, flexible, and provides consistent syntax across algorithms. In the hands of an experienced practitioner, this tunability is welcomed and can increase accuracy. However, when used by a beginning student, the large number of options can become overwhelming and hinder their learning. babyCaret is an ML package for R developed in this work to reduce this complexity and support student learning while matching caret\u27s syntax. The goal is to teach users about the application of ML directly inside their familiar R environment. baby- Caret contains integrated tutorials activated by a function call. These tutorials teach users about the application, interpretation, and technical aspects of four algorithms: k-nearest neighbors, apriori, k-prototypes, and decision tree. The k-nearest neighbors implementation was designed by the author. Decision trees are computed via the rpart R package for its visualization capabilities, k-prototypes uses a modified implementation by Gero Szepannek, and apriori uses the arules R package. A limited number of hyperparameters are available for tuning. The rest have either been automated or fixed to their simplest configuration to reduce complexity, which may affect accuracy. babyCaret is an open-source teaching tool, a simple and functional beginner ML package, and a stepping-stone to the more complex caret. Evaluation includes runtime comparison between k-nearest neighbors computed using caret and babyCaret, and runtime comparison between Szepannek\u27s implementation and our modified version of k-prototypes. babyCaret\u27s KNN implementation had lower runtime than caret\u27s and the modified version of Szepannek\u27s k-prototypes implementation had lower runtime than the original. Evaluation of the tutorials involved distributing them to an intelligent systems class as supplemental course content. Tutorials were successfully used in the course setting

Characterization and Diagnostic Potential

Funding Information: R.M. is supported by Fundação para a Ciência e a Tecnologia (CEEC position, 2019–2025 investigator). This article is a result of the projects (iNOVA4Health—UIDB/04462/2020), supported by Lisboa Portugal Regional Operational Programme (Lisboa2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). This work is also funded by FEDER funds through the COMPETE 2020 Programme and National Funds through FCT—Portuguese Foundation for Science and Technology under the projects number PTDC/BTM-TEC/30087/2017 and PTDC/BTM-TEC/30088/2017. Publisher Copyright: © 2022 by the authors.Diffuse large B cell lymphoma (DLBCL) is an aggressive B cell lymphoma characterized by a heterogeneous behavior and in need of more accurate biological characterization monitoring and prognostic tools. Extracellular vesicles are secreted by all cell types and are currently established to some extent as representatives of the cell of origin. The present study characterized and evaluated the diagnostic and prognostic potential of plasma extracellular vesicles (EVs) proteome in DLBCL by using state-of-the-art mass spectrometry. The EV proteome is strongly affected by DLBCL status, with multiple proteins uniquely identified in the plasma of DLBCL. A proof-of-concept classifier resulted in highly accurate classification with a sensitivity and specificity of 1 when tested on the holdout test data set. On the other hand, no proteins were identified to correlate with non-germinal center B-cell like (non-GCB) or GCB subtypes to a significant degree after correction for multiple testing. However, functional analysis suggested that antigen binding is regulated when comparing non-GCB and GCB. Survival analysis based on protein quantitative values and clinical parameters identified multiple EV proteins as significantly correlated to survival. In conclusion, the plasma extracellular vesicle proteome identifies DLBCL cancer patients from healthy donors and contains potential EV protein markers for prediction of survival.publishersversionpublishe

Cigarette smoke induces endoplasmic reticulum stress and the unfolded protein response in normal and malignant human lung cells

<p>Abstract</p> <p>Background</p> <p>Although lung cancer is among the few malignancies for which we know the primary etiological agent (i.e., cigarette smoke), a precise understanding of the temporal sequence of events that drive tumor progression remains elusive. In addition to finding that cigarette smoke (CS) impacts the functioning of key pathways with significant roles in redox homeostasis, xenobiotic detoxification, cell cycle control, and endoplasmic reticulum (ER) functioning, our data highlighted a defensive role for the unfolded protein response (UPR) program. The UPR promotes cell survival by reducing the accumulation of aberrantly folded proteins through translation arrest, production of chaperone proteins, and increased degradation. Importance of the UPR in maintaining tissue health is evidenced by the fact that a chronic increase in defective protein structures plays a pathogenic role in diabetes, cardiovascular disease, Alzheimer's and Parkinson's syndromes, and cancer.</p> <p>Methods</p> <p>Gene and protein expression changes in CS exposed human cell cultures were monitored by high-density microarrays and Western blot analysis. Tissue arrays containing samples from 110 lung cancers were probed with antibodies to proteins of interest using immunohistochemistry.</p> <p>Results</p> <p>We show that: 1) CS induces ER stress and activates components of the UPR; 2) reactive species in CS that promote oxidative stress are primarily responsible for UPR activation; 3) CS exposure results in increased expression of several genes with significant roles in attenuating oxidative stress; and 4) several major UPR regulators are increased either in expression (i.e., BiP and eIF2α) or phosphorylation (i.e., phospho-eIF2α) in a majority of human lung cancers.</p> <p>Conclusion</p> <p>These data indicate that chronic ER stress and recruitment of one or more UPR effector arms upon exposure to CS may play a pivotal role in the etiology or progression of lung cancers, and that phospho-eIF2α and BiP may have diagnostic and/or therapeutic potential. Furthermore, we speculate that upregulation of UPR regulators (in particular BiP) may provide a pro-survival advantage by increasing resistance to cytotoxic stresses such as hypoxia and chemotherapeutic drugs, and that UPR induction is a potential mechanism that could be attenuated or reversed resulting in a more efficacious treatment strategy for lung cancer.</p

Learning to tie the knot: The acquisition of functional object representations by physical and observational experience

Here we examined neural substrates for physically and observationally learning to construct novel objects, and characterized brain regions associated with each kind of learning using fMRI. Each participant was assigned a training partner, and for five consecutive days practiced tying one group of knots (“tied” condition) or watched their partner tie different knots (“watched” condition) while a third set of knots remained untrained. Functional MRI was obtained prior to and immediately following the week of training while participants performed a visual knot-matching task. After training, a portion of left superior parietal lobule demonstrated a training by scan session interaction. This means this parietal region responded selectively to knots that participants had physically learned to tie in the post-training scan session but not the pre-training scan session. A conjunction analysis on the post-training scan data showed right intraparietal sulcus and right dorsal premotor cortex to respond when viewing images of knots from the tied and watched conditions compared to knots that were untrained during the post-training scan session. This suggests that these brain areas track both physical and observational learning. Together, the data provide preliminary evidence of engagement of brain regions associated with hand-object interactions when viewing objects associated with physical experience, and with observational experience without concurrent physical practice

Aero-Assisted Spacecraft Missions Using Hypersonic Waverider Aeroshells

This work examines the use of high-lift, low drag vehicles which perform orbital transfers within a planet’s atmosphere to reduce propulsive requirements. For the foreseeable future, spacecraft mission design will include the objective of limiting the mass of fuel required. One means of accomplishing this is using aerodynamics as a supplemental force, with what is termed an aero-assist maneuver. Further, the use of a lifting body enables a mission designer to explore candidate trajectory types wholly unavailable to non-lifting analogs. Examples include missions to outer planets by way of an aero-gravity assist, aero-assisted plane change, aero-capture, and steady atmospheric periapsis probing missions. Engineering level models are created in order to simulate both atmospheric and extra-atmospheric space flight. Each mission is parameterized using discrete variables which control multiple areas of design. This work combines the areas of hypersonic aerodynamics, re-entry aerothermodynamics, spacecraft orbital mechanics, and vehicle shape optimization. In particular, emphasis is given to the parametric design of vehicles known as “waveriders” which are inversely designed from known shock flowfields. An entirely novel means of generating a class of waveriders known as “starbodies” is presented. A complete analysis is performed of asymmetric starbody forms and compared to a better understood parameterization, “osculating cone” waveriders. This analysis includes characterization of stability behavior, a critical discipline within hypersonic flight. It is shown that asymmetric starbodies have significant stability improvement with only a 10% reduction in the lift-to-drag ratio. By combining the optimization of both the shape of the vehicle and the trajectory it flies, much is learned about the benefit that can be expected from lifting aero-assist missions. While previous studies have conceptually proven the viability, this work provides thorough quantification of the optimized outcome. In examining an aero-capture of Mars, it was found that with a lifting body, the increased maneuverability can allow completion of multiple mission objectives along with the aero-capture, such as atmospheric profiling or up to 80 degrees of orbital plane change. Completing a combined orbital plane change and aero-capture might save as much as 4.5 km/s of velocity increment while increasing the feasible entry corridor by an order of magnitude. Analyzing a higher energy mission type, a database of maximum aero-gravity assist performance is developed at Mars, Earth and Venus. Finally, a methodology is presented for designing end-toend interplanetary missions using aero-gravity assists. As a means of demonstrating the method, promising trajectories are propagated which reduce the time of flight of an interstellar probe mission by up to 50%