FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4362543587> ?p ?o ?g. }

• W4362543587 endingPage "1120" @default.
• W4362543587 startingPage "1120" @default.
• W4362543587 abstract "Abstract Translational oncology studies increasingly emphasize deriving hypotheses from single-cell datasets by evaluating groups of patients (e.g., treated vs. untreated, responders vs. non-responders, early vs. late-stage tumors) for differences in gene signatures/pathways to guide subsequent investigations. However, when evaluating these questions using tumor single-cell data, current statistical methods for this task are limited in two major aspects: (i) They are not representative of the hierarchical nature of tumor single-cell datasets (where cells are more similar within the patient than between patients); and (ii) They often do not contextualize statistical significance (p-values) in the context of variability that is expected biologically between patients. This can lead to molecular hypotheses that are skewed by patient-specific biology with high false positive rates. To address these challenges, we developed a nonparametric statistical method, BEANIE (group Biology EstimAtioN in sIngle cEll), for estimating group biology in single-cell transcriptomic datasets. BEANIE uses monte carlo simulations to estimate the p-value distribution for the gene signature of interest and normalizes with respect to a background distribution of the expected patient-to-patient variability. The method then performs leave-one-out cross-validation to infer robustness of the gene signatures to patient exclusion. We applied our method to public cancer single-cell datasets to evaluate candidate differences in tumor cell populations between known groups. When comparing tumor cell programs derived from treatment-naive vs. chemotherapy-treated Triple-Negative Breast Cancer (TNBC) patients, BEANIE identified upregulation of hallmark gene signatures for fatty acid metabolism and hypoxia to be statistically significant and robust in the treatment-naive group, indicative of response to chemotherapy treatment. We also compared BEANIE to conventional methods like Mann-Whitney U (MWU) test followed by Benjamini-Hochberg (BH) correction and Generalized Linear Models (GLM). MWU test and GLM also identified these signatures to be upregulated, but they additionally identified other immune cell gene signatures like T-cell markers, B-cell markers and NK cell markers, to also be upregulated, which was not expected since we were comparing between tumor cells. Similarly, BEANIE identified candidate tumor cell intrinsic programs relevant to hypotheses in lung cancer patients (early vs. late-stage) and melanoma patients (Immune Checkpoint Blockade (ICB)-naive vs. ICB-treated). Therefore, BEANIE greatly increased the specificity of analysis, and reduced false positive rates. This method is publicly available and may overcome existing methodological limitations to guide tumor-intrinsic hypothesis generation in rapidly growing clinical cancer single-cell cohorts. Citation Format: Shreya Johri, Kevin Bi, Breanna M. Titchen, Jingxin Fu, Jake Conway, Jett Crowdis, Natalie I. Volkes, Zenghua Fan, Lawrence Fong, Jihye Park, David Liu, Meng Xiao He, Eliezer M. Van Allen. Dissecting tumor cell programs through group biology estimation in clinical single-cell transcriptomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1120." @default.
• W4362543587 created "2023-04-06" @default.
• W4362543587 creator A5023009480 @default.
• W4362543587 creator A5023865932 @default.
• W4362543587 creator A5033689713 @default.
• W4362543587 creator A5039044023 @default.
• W4362543587 creator A5041837817 @default.
• W4362543587 creator A5046713411 @default.
• W4362543587 creator A5051755724 @default.
• W4362543587 creator A5059533375 @default.
• W4362543587 creator A5065102493 @default.
• W4362543587 creator A5067450389 @default.
• W4362543587 creator A5068525042 @default.
• W4362543587 creator A5073970384 @default.
• W4362543587 creator A5074600939 @default.
• W4362543587 date "2023-04-04" @default.
• W4362543587 modified "2023-10-18" @default.
• W4362543587 title "Abstract 1120: Dissecting tumor cell programs through group biology estimation in clinical single-cell transcriptomics" @default.
• W4362543587 doi "https://doi.org/10.1158/1538-7445.am2023-1120" @default.
• W4362543587 hasPublicationYear "2023" @default.
• W4362543587 type Work @default.
• W4362543587 citedByCount "0" @default.
• W4362543587 crossrefType "journal-article" @default.
• W4362543587 hasAuthorship W4362543587A5023009480 @default.
• W4362543587 hasAuthorship W4362543587A5023865932 @default.
• W4362543587 hasAuthorship W4362543587A5033689713 @default.
• W4362543587 hasAuthorship W4362543587A5039044023 @default.
• W4362543587 hasAuthorship W4362543587A5041837817 @default.
• W4362543587 hasAuthorship W4362543587A5046713411 @default.
• W4362543587 hasAuthorship W4362543587A5051755724 @default.
• W4362543587 hasAuthorship W4362543587A5059533375 @default.
• W4362543587 hasAuthorship W4362543587A5065102493 @default.
• W4362543587 hasAuthorship W4362543587A5067450389 @default.
• W4362543587 hasAuthorship W4362543587A5068525042 @default.
• W4362543587 hasAuthorship W4362543587A5073970384 @default.
• W4362543587 hasAuthorship W4362543587A5074600939 @default.
• W4362543587 hasConcept C104317684 @default.
• W4362543587 hasConcept C121608353 @default.
• W4362543587 hasConcept C143998085 @default.
• W4362543587 hasConcept C150194340 @default.
• W4362543587 hasConcept C151730666 @default.
• W4362543587 hasConcept C162317418 @default.
• W4362543587 hasConcept C2779343474 @default.
• W4362543587 hasConcept C2780110267 @default.
• W4362543587 hasConcept C530470458 @default.
• W4362543587 hasConcept C54355233 @default.
• W4362543587 hasConcept C60644358 @default.
• W4362543587 hasConcept C70721500 @default.
• W4362543587 hasConcept C71924100 @default.
• W4362543587 hasConcept C86803240 @default.
• W4362543587 hasConceptScore W4362543587C104317684 @default.
• W4362543587 hasConceptScore W4362543587C121608353 @default.
• W4362543587 hasConceptScore W4362543587C143998085 @default.
• W4362543587 hasConceptScore W4362543587C150194340 @default.
• W4362543587 hasConceptScore W4362543587C151730666 @default.
• W4362543587 hasConceptScore W4362543587C162317418 @default.
• W4362543587 hasConceptScore W4362543587C2779343474 @default.
• W4362543587 hasConceptScore W4362543587C2780110267 @default.
• W4362543587 hasConceptScore W4362543587C530470458 @default.
• W4362543587 hasConceptScore W4362543587C54355233 @default.
• W4362543587 hasConceptScore W4362543587C60644358 @default.
• W4362543587 hasConceptScore W4362543587C70721500 @default.
• W4362543587 hasConceptScore W4362543587C71924100 @default.
• W4362543587 hasConceptScore W4362543587C86803240 @default.
• W4362543587 hasIssue "7_Supplement" @default.
• W4362543587 hasLocation W43625435871 @default.
• W4362543587 hasOpenAccess W4362543587 @default.
• W4362543587 hasPrimaryLocation W43625435871 @default.
• W4362543587 hasRelatedWork W1966181922 @default.
• W4362543587 hasRelatedWork W1999292713 @default.
• W4362543587 hasRelatedWork W2009966535 @default.
• W4362543587 hasRelatedWork W2101673433 @default.
• W4362543587 hasRelatedWork W2102579905 @default.
• W4362543587 hasRelatedWork W2366029130 @default.
• W4362543587 hasRelatedWork W2554176272 @default.
• W4362543587 hasRelatedWork W3012091774 @default.
• W4362543587 hasRelatedWork W3044626609 @default.
• W4362543587 hasRelatedWork W80971072 @default.
• W4362543587 hasVolume "83" @default.
• W4362543587 isParatext "false" @default.
• W4362543587 isRetracted "false" @default.
• W4362543587 workType "article" @default.