scholarly journals Predicting Cellular Drug Sensitivity using Conditional Modulation of Gene Expression

2021 ◽  
Author(s):  
Will Connell ◽  
Michael Keiser
Keyword(s):  
Gene Expression ◽  
Small Molecule ◽  
Drug Sensitivity ◽  
Expression Patterns ◽  
Structural Features ◽  
Precision Oncology ◽  
Transcriptional Signature ◽  
Ongoing Work ◽  
Important Challenge ◽  
Modulation Of Gene Expression

AbstractSelecting drugs most effective against a tumor’s specific transcriptional signature is an important challenge in precision medicine. To assess oncogenic therapy options, cancer cell lines are dosed with drugs that can differentially impact cellular viability. Here we show that basal gene expression patterns can be conditioned by learned small molecule structure to better predict cellular drug sensitivity, achieving an R2 of 0.7190±0.0098 (a 5.61% gain). We find that 1) transforming gene expression values by learned small molecule representations outperforms raw feature concatenation, 2) small molecule structural features meaningfully contribute to learned representations, and 3) an affine transformation best integrates these representations. We analyze conditioning parameters to determine how small molecule representations modulate gene expression embeddings. This ongoing work formalizes in silico cellular screening as a conditional task in precision oncology applications that can improve drug selection for cancer treatment.

scholarly journals Protein structure-based gene expression signatures

2020 ◽  
Author(s):  
R. Rahman ◽  
Y. Xiong ◽  
J. G. C. van Hasselt ◽  
J. Hansen ◽  
E. A. Sobie ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Structure ◽  
Mrna Expression ◽  
Expression Patterns ◽  
Drug Effects ◽  
Tissue Expression ◽  
Structural Features ◽  
Biological Phenomena ◽  
Gene Expression Signatures ◽  
Cellular Phenotypes

AbstractGene expression signatures (GES) connect phenotypes to mRNA expression patterns, providing a powerful approach to define cellular identity, function, and the effects of perturbations. However, the use of GES has suffered from vague assessment criteria and limited reproducibility. The structure of proteins defines the functional capability of genes, and hence, we hypothesized that enrichment of structural features could be a generalizable representation of gene sets. We derive structural gene expression signatures (sGES) using features from various levels of protein structure (e.g. domain, fold) encoded by the transcribed genes in GES, to describe cellular phenotypes. Comprehensive analyses of data from the Genotype-Tissue Expression Project (GTEx), ARCHS4, and mRNA expression of drug effects on cardiomyocytes show that structural GES (sGES) are useful for identifying robust signatures of biological phenomena. sGES also enables the characterization of signatures across experimental platforms, facilitates the interoperability of expression datasets, and can describe drug action on cells.

scholarly journals Classification of Colorectal Carcinoma Based on Ferroptosis-related Gene Signature

2021 ◽  
Author(s):  
QingFang Yue ◽  
Yuan Zhang ◽  
Fei Wang ◽  
Fei Cao ◽  
Xianglong Duan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Carcinoma ◽  
Drug Sensitivity ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Mutations ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Related Gene ◽  
Molecular Features

Abstract Background: Ferroptosis is an iron-dependent form of programmed cell death, involves in the development of many cancers. However, systematic analysis of ferroptosis related-genes in colorectal carcinoma (CRC) remains to be clarified. We herein analyzed the public databases to identify the molecular features of CRC by the development of a classification based on the gene expression profile of ferroptosis-related genes.Methods: We collected the gene expression data and clinical information from The Cancer Atlas and Gene Expression Omnibus to explore the correlation of ferroptosis-related gene expression of CRC. Consensus clustering was performed to determine the clusters of colorectal cancer patients, then we analyzed the prognostic value, transcriptome features, immune microenvironment, drug sensitivity, gene mutations differences of the subclasses. Results: Four subclasses of CRC (C1, C2, C3 and C4) were identified. There were significant differences in the prognosis of patients between the four subtypes. Functional enrichment suggested that these ferroptosis related-genes were associated with biological processes such as metabolic processes and oxidative stress, and the KEGG pathway suggested that it was closely related to ferroptosis and glutathione metabolic pathway. There were significant differences in immune cell infiltrations, immune score, stromal score and tumor purity among four subclasses. The expression of PD-L1 was differentially expressed in four subclasses and PD-L1 was significantly correlated with the expression of several ferroptosis-related genes in the differentially expressed subtypes. There were significant differences in stemness indices and drug sensitivity in four subclasses, and gene mutations analysis showed that ferroptosis-related genes such as TP53 had high mutations in different subtypes, and there were significant differences in mutation frequencies in the subclasses.Conclusion: This study established a new CRC classification based on ferroptosis-related genes expression profiles, and different subgroups may have their unique gene expression patterns, indicating that the heterogeneity within CRC and the classification might provide valuable stratification for the design of future treatment.

Modulation of Gene Expression via Disruption of NF- B Signaling by a Bacterial Small Molecule

Science ◽  
2008 ◽  
Vol 321 (5886) ◽  
pp. 259-263 ◽  
Author(s):  
V. V. Kravchenko ◽  
G. F. Kaufmann ◽  
J. C. Mathison ◽  
D. A. Scott ◽  
A. Z. Katz ◽  
...  
Keyword(s):  
Gene Expression ◽  
Small Molecule ◽  
Modulation Of Gene Expression

scholarly journals Precision Oncology With Radiogenomics: Non-invasive Interrogation of Liver Cancer Genomics to Select Drug Therapy and Monitor Patient Response

2018 ◽  
Vol 4 (Supplement 2) ◽  
pp. 45s-45s
Author(s):  
P. Chow ◽  
J. Sarkar ◽  
N. Roy
Keyword(s):  
Gene Expression ◽  
Drug Therapy ◽  
Liver Cancer ◽  
Cancer Genomics ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Intratumoral Heterogeneity ◽  
Asia Pacific ◽  
Ct Scans ◽  
Precision Oncology

Background: Significant intratumoral heterogeneity is present in hepatocellular carcinoma (HCC) and single biopsy samples severely underread the genomic profile of the tumor. This confounds drug development in HCC and makes it extremely challenging to stratify and prognosticate patients to select therapy on the basis of genomic biomarkers. Multiregion sampling of surgically resected HCC allows for these intratumoral heterogeneity to be unraveled and this is currently carried out in our prospective PLANET (Precision Medicine in Liver Cancer across an Asia-Pacific Network) program. However, multiregion sampling is not practical in unresectable HCC cases, which comprise 80% of the disease burden. Radiogenomics studies on liver, lung and head-and-neck cancers have established the capability of radiomics analysis in capturing intratumor heterogeneity with the identification of radiomics signatures that are representative of underlying gene expression patterns. Aim: On this basis, our PLANET program has adopted a radiogenomics approach to characterize the intratumoral heterogeneity in HCC. Methods: This approach will leverage on the correlation of distinctive imaging traits in CT scans against the gene expression profiles of surgically resected HCC patients. Our PLANET program and the data-science enterprise Holmusk have embarked on close collaborative efforts to develop an algorithm that characterizes intratumoral heterogeneity based on distinctive traits in the CT scans. Results and conclusion: The development of such an algorithm is potentially ground-breaking as it allows for the noninvasive interrogation of liver cancer genomics and prognostication of HCC patients. This allows the easy screening of patients for therapeutic targets in vivo and will significantly improve the selection of drug therapy and monitoring of therapeutic response in HCC.

scholarly journals Deep learning transcriptomic model for prediction of pan-drug chemotherapeutic sensitivity

2022 ◽  
pp. 1-16
Author(s):  
Eddie Guo ◽  
Pouria Torabi ◽  
Daiva E. Nielsen ◽  
Matthew Pietrosanu
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Cell Lines ◽  
Drug Sensitivity ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Precision Oncology ◽  
Chemotherapeutic Response ◽  
Chemotherapeutic Sensitivity ◽  
Response Groups

The emergence of precision oncology approaches has begun to inform clinical decision-making in diagnostic, prognostic, and treatment contexts. High-throughput technology has enabled machine learning algorithms to use the molecular characteristics of tumors to generate personalized therapies. However, precision oncology studies have yet to develop a predictive biomarker incorporating pan-cancer gene expression profiles to stratify tumors into similar drug sensitivity profiles. Here we show that a neural network with ten hidden layers accurately classifies pancancer cell lines into two distinct chemotherapeutic response groups based on a pan-drug dataset with 89.0% accuracy (AUC = 0.904). Using unsupervised clustering algorithms, we found a cohort of cell line gene expression data from the Genomics of Drug Sensitivity in Cancer could be clustered into two response groups with significant differences in pan-drug chemotherapeutic sensitivity. After applying the Boruta feature selection algorithm to this dataset, a deep learning model was developed to predict chemotherapeutic response groups. The model’s high classification efficacy validates our hypothesis that cell lines with similar gene expression profiles present similar pan-drug chemotherapeutic sensitivity. This finding provides evidence for the potential use of similar combinatorial biomarkers to select potent candidate drugs that maximize therapeutic response and minimize the cytotoxic burden. Future investigations should aim to recursively subcluster cell lines within the response clusters defined in this study to provide a higher resolution of potential patient response to chemotherapeutics.

scholarly journals Evidence in support of gene regulatory hypothesis: Gene expression profiling manifests homeopathy effect as more than placebo

2021 ◽  
Vol 12 (45) ◽  
pp. 162-167
Author(s):  
Anisur Rahman Khuda-Bukhsh ◽  
Santu Kumar Saha ◽  
Sourav Roy
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Expression Patterns ◽  
Gene Expressions ◽  
Microarray Gene Expression ◽  
Hydrastis Canadensis ◽  
Modulation Of Gene Expression ◽  
Homeopathic Remedies

Background: Use of ultra-high diluted remedies in homeopathy and their claimed efficacy in curing diseases has been challenged time and again by non-believers despite many evidence-based positive results published in favor of their efficacy in curing/ameliorating disease symptoms. Aims: To test the ability of ultra-high diluted homeopathic remedies beyond Avogadro’s limit, if any, in manifesting gene modulating effects in controlled in vitro experimental model. Methods: Since cancer cells manifest aberrant epigenetic gene expressions, we conducted global microarray gene expression profiling of HeLa cells (an established epigenetic model of HPV18 positive cell line) treated with two different potentized homeopathic remedies, namely, Condurango 30c and Hydrastis canadensis 30C (used in the treatment of cancer), as compared to that of placebo (succussed alcohol 30c). Results: Data revealed distinctly different expression patterns of over 100 genes as a consequence of treatment with both homeopathc remedies compared to placebo. Conclusion: Results indicate that action of the potentized drugs was “more than placebo” and these ultra-highly diluted drugs acted primarily through modulation of gene expression.

scholarly journals 1721. A Transcriptional Signature of Acute Aspergillus Infection Offers High Diagnostic Accuracy Despite the Presence of Immunosuppression

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S630-S631
Author(s):  
Julie M Steinbrink ◽  
Aimee K Zaas ◽  
Marisol Βncourt-Quiroz ◽  
Jennifer L Modliszewski ◽  
David Corcoran ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Immunosuppressive Drugs ◽  
Host Cells ◽  
Specific Gene ◽  
Transcriptomic Response ◽  
Aspergillus Infection ◽  
Bayesian Techniques ◽  
Transcriptional Signature ◽  
Transcriptomic Responses

Abstract Background Invasive aspergillosis (IA) is a major cause of critical illness in immunocompromised (IC) patients. However, current fungal testing methods have significant limitations and there is a clear need for new diagnostic options. Disease-specific gene expression patterns in circulating host cells show promise as novel diagnostics; however, it is unknown whether such a “signature” exists for IA. Additionally, there is a need for better understanding of the effect of iatrogenic immunosuppression (present in most cases of IA) on such host response-driven biomarkers. Methods Male BALB/c mice were separated into an Aspergillus fumigatus inhalational exposure group and a placebo group. These two groups were each subdivided into three additional sets based on immunocompromised status (no immunosuppression, cyclophosphamide, and corticosteroids) for a total of six experimental groups. Mice were sacrificed 4 days post-infection. Whole blood was assayed for transcriptomic responses via microarray. Bayesian techniques were utilized to develop classifiers of IA and leave one out cross-validation was used to estimate predictive probabilities. Results Aspergillus infection triggers a powerful response in non-IC hosts, with 2996 genes differentially expressed between IA and controls. We generated a 146-gene expression classifier able to discriminate between non-IC mice with IA and uninfected non-IC mice with 100% accuracy. However, the presence of immunosuppressive drugs exhibited a strong confounding effect on the transcriptomic classifier that was derived in the absence of immunosuppression. After controlling for the genomic effects of immunosuppressive drugs, we were able to generate a 187-gene classifier with a sensitivity of 100% and specificity of 97% across all IC states. Conclusion The host transcriptomic response to IA is robust and highly conserved. Pharmacologic perturbation of the host immune response unsurprisingly has powerful effects on gene expression-based classifier performance and must be taken into account when developing novel diagnostics. When appropriately designed, host-derived peripheral blood transcriptomic responses to IA demonstrate the ability to accurately diagnose Aspergillus infection, even in the presence of immunosuppression. Disclosures All authors: No reported disclosures.

scholarly journals DeltaNp63-dependent super enhancers define molecular identity in pancreatic cancer by an interconnected transcription factor network

2018 ◽  
Vol 115 (52) ◽  
pp. E12343-E12352 ◽  
Author(s):  
Feda H. Hamdan ◽  
Steven A. Johnsen
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Cell Lines ◽  
Cancer Cell ◽  
Pancreatic Cancer Cell ◽  
Transcriptional Regulatory Network ◽  
Expression Patterns ◽  
Gene Expression Signature ◽  
Cancer Cell Lines ◽  
Precision Oncology

Molecular subtyping of cancer offers tremendous promise for the optimization of a precision oncology approach to anticancer therapy. Recent advances in pancreatic cancer research uncovered various molecular subtypes with tumors expressing a squamous/basal-like gene expression signature displaying a worse prognosis. Through unbiased epigenome mapping, we identified deltaNp63 as a major driver of a gene signature in pancreatic cancer cell lines, which we report to faithfully represent the highly aggressive pancreatic squamous subtype observed in vivo, and display the specific epigenetic marking of genes associated with decreased survival. Importantly, depletion of deltaNp63 in these systems significantly decreased cell proliferation and gene expression patterns associated with a squamous subtype and transcriptionally mimicked a subtype switch. Using genomic localization data of deltaNp63 in pancreatic cancer cell lines coupled with epigenome mapping data from patient-derived xenografts, we uncovered that deltaNp63 mainly exerts its effects by activating subtype-specific super enhancers. Furthermore, we identified a group of 45 subtype-specific super enhancers that are associated with poorer prognosis and are highly dependent on deltaNp63. Genes associated with these enhancers included a network of transcription factors, including HIF1A, BHLHE40, and RXRA, which form a highly intertwined transcriptional regulatory network with deltaNp63 to further activate downstream genes associated with poor survival.

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