Laura van Zelst
A5054030058- Estrogen and related hormone effects
- HER2/EGFR in Cancer Research
- Cancer Immunotherapy and Biomarkers
- Colorectal Cancer Treatments and Studies
- Computational Drug Discovery Methods
- Cytokine Signaling Pathways and Interactions
- Monoclonal and Polyclonal Antibodies Research
- Chemical Reactions and Isotopes
- Pharmacogenetics and Drug Metabolism
- Cancer therapeutics and mechanisms
- Radiopharmaceutical Chemistry and Applications
- Immune cells in cancer
- CAR-T cell therapy research
- Steroid Chemistry and Biochemistry
- Histone Deacetylase Inhibitors Research
- Epigenetics and DNA Methylation
Abstract Drugs that kill tumors through multiple mechanisms have the potential for broad clinical benefits. Here, we first developed an in silico multiomics approach (BipotentR) to find cancer cell–specific regulators simultaneously modulate tumor immunity and another oncogenic pathway then used it identify 38 candidate immune–metabolic regulators. We show activities of these stratify patients with melanoma by their response anti–PD-1 using machine learning deep neural approaches, which...
<div>Abstract<p>Drugs that kill tumors through multiple mechanisms have the potential for broad clinical benefits. Here, we first developed an <i>in silico</i> multiomics approach (BipotentR) to find cancer cell–specific regulators simultaneously modulate tumor immunity and another oncogenic pathway then used it identify 38 candidate immune–metabolic regulators. We show activities of these stratify patients with melanoma by their response anti–PD-1 using machine...
<div>Abstract<p>Drugs that kill tumors through multiple mechanisms have the potential for broad clinical benefits. Here, we first developed an <i>in silico</i> multiomics approach (BipotentR) to find cancer cell–specific regulators simultaneously modulate tumor immunity and another oncogenic pathway then used it identify 38 candidate immune–metabolic regulators. We show activities of these stratify patients with melanoma by their response anti–PD-1 using machine...
<p>Machine learning evaluation of immune-metabolic targets in predicting patient response to anti-PD1 monotherapy.</p>
<p>The description and validation of the BipotentR immune module</p>
<p>Evaluation of scRNA-BipotentR (BipotentR without bulk-tumor sub-module).</p>
<p>deepBTAS improve the performance of current ICB biomarkers.</p>
<p>ESRRAi does not affect CD8+T T-cells, and ESRRA activity increases post-immunotherapy in resistant tumors.</p>
<p>Validation of bipotent regulators angiogenesis or growth-suppressor.</p>
<p>ESRRA inhibition induces antitumor immunity in vivo.</p>
<p>Evaluation of bulk-BipotentR (BipotentR without single-cell sub-module).</p>
<p>ESRRA inhibition is associated with immune infiltrations and proinflammatory signaling in patient tumors.</p>
<p>Expression of markers M1 and M2 in scRNA data ESRRAi Vehicle treated mice.</p>
<p>Robust in vivo antitumor elimination by ESRRA inhibition.</p>
<p>The effect of ESRRA inhibition on tumor microenvironments at single-cell resolutions.</p>
<p>Evaluation of BipotentR.</p>
<p>ESRRA inhibition correlated with immune infiltrations and proinflammatory signaling in patient tumors.</p>
<p>Kaplan–Meier plots showing progression-free survival and overall differences for patients receiving anti-PD1 between the low-risk high-risk groups defined by median value of deepBTAS.</p>
<p>Machine learning evaluation of immune-metabolic targets in predicting patient response to anti-PD1 and anti-CTLA4 combination therapy.</p>
<p>We detailed a comparison of BipotentR with competing and alternative approaches in Supplementary notes 1, 2, 6, 7. Note 3-5 describes the vitro findings ESRRA, potential clinical relevance ESRRA across cancer-types, association tumor activity immune infiltration.</p>
<p>Energy metabolism and immune signaling by targeting ESRRA in vitro.</p>
<p>Overall design.</p>
<p>Energy metabolism and immune signaling by targeting ESRRA in vitro.</p>
<p>Energy metabolism and immune signaling by targeting ESRRA in vitro.</p>