Deconvolution of breast tumors for optimal chemotherapy selection
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PROVIDER: PRJNA707323 | ENA |
REPOSITORIES: ENA
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Project description:Extensive work in pre-clinical models has shown that microenvironmental cells influence many aspects of cancer cell behavior including metastatic potential and their sensitivity to therapeutics. In the human setting, this behavior is mainly correlated with the presence of immune cells, whilst the relevance of other cell types have been largely ignored. Here, in addition to T cells, B cells, macrophages and mast cells, we identified the relevance of non-immune cell types for breast cancer survival and therapy benefit, including fibroblast, myoepithelial cells, muscle cells, endothelial cells, and 7 distinct epithelial cell types. Using single-cell sequencing data, we generated reference profiles for all these cell types. We used these reference profiles in deconvolution algorithms to optimally detangle the cellular composition of over 3500 primary breast tumors of patients that were enrolled in the SCAN-B and MATADOR clinical trials, and for which bulk mRNA sequencing data was available. This large data set enables us to identify and subsequently validate the cellular composition of microenvironments that distinguish differential survival and treatment benefit for different treatment regiments in primary breast cancer patients.
2021-12-10 | GSE168410 | GEO
Project description:<notes xmlns="http://www.sbml.org/sbml/level2/version4">
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<pre>Optimal control of mixed immunotherapy and chemotherapy of tumors
Lisette Depillis, K. R. Fister , W. Gu, Tiffany Head, Kenny Maples, Todd Neal, Anand Murugan and Kenji Kozai
Abstract
We investigate a mathematical population model of tumor-immune interactions. Thepopulations involved are tumor cells, specific and non-specific immune cells, and con-centrations of therapeutic treatments. We establish the existence of an optimal con-trol for this model and provide necessary conditions for the optimal control triple forsimultaneous application of chemotherapy, tumor infiltrating lymphocyte (TIL) ther-apy, and interleukin-2 (IL-2) treatment. We discuss numerical results for the combina-tion of the chemo-immunotherapy regimens. We find that the qualitative nature of ourresults indicates that chemotherapy is the dominant intervention with TIL interactingin a complementary fashion with the chemotherapy. However, within the optimal con-trol context, the interleukin-2 treatment does not become activated for the estimatedparameter ranges.
2020-01-21 | BIOMD0000000913 | BioModels
Project description:Optimal cytoreduction to no residual disease (R0) correlates with improved disease outcome in the management of high-grade serous ovarian cancer (HGSOC) patients. Neoadjuvant chemotherapy (NACT) followed by interval debulking surgery (IDS) offers an alternate approach to management of HGSOC patients to achieve complete resection. This study assessed proteomic alterations in matched, chemotherapy naïve and NACT-treated patients tumors obtained from HGSOC patients with suboptimal (R1) versus optimal (R0) debulking at IDS. We describe distinct proteome profiles in pre- and post-NACT HGSOC tumors correlating with residual disease status providing prognostic biomarkers for residual disease at IDS as well as candidate proteins associated with NACT resistance warranting further pre-clinical investigation.
2022-10-05 | PXD031929 | Pride
Project description:Deconvolution is the problem of estimating proportions of mixed cell types from tissue samples. DNA methylation is commonly used for deconvolution because individual CpG sequences can reflect cell type identity and can be accurately measured at either the population or single-molecule level. Genomic sequencing techniques can profile multiple CpGs on a single DNA molecule, but few deconvolution models have been developed to exploit these single-molecule methylation haplotypes for cell type deconvolution. We use simulated whole-genome methylation data and in silico mixtures of real data to compare existing computational tools with two new models developed here. We find that adapting an existing model CelFiE to incorporate methylation haplotype information improves deconvolution accuracy by ~30% over the original CelFiE and the next best tool. Our new tool, CelFiE Integrated Single-molecule Haplotypes (or CelFiE-ISH), also outperforms other tools in detecting rare cell types present at 0.1% and below, which can be used to improve detection of rare cell types in circulating DNA. CelFiE-ISH is publicly available under a permissive open-source license. Finally, we investigate the selection of cell-type specific marker regions from the genome, a prerequisite for all of these tools. We find that the selection method used has a strong effect on accuracy in our benchmarks, and identify specific marker features that contribute to deconvolution accuracy. We show that tailoring the marker selection method to specific attributes of the deconvolution model, such as the use of DNA methylation haplotypes, can improve deconvolution accuracy.
2023-08-02 | GSE239605 | GEO
Project description:Mixed immunotherapy and chemotherapy of tumors: feedback design and model updating schemes.
Chareyron S1, Alamir M.
Author information
Abstract
In this paper, a recently developed model governing the cancer growth on a cell population level with combination of immune and chemotherapy is used to develop a reactive (feedback) mixed treatment strategy. The feedback design proposed here is based on nonlinear constrained model predictive control together with an adaptation scheme that enables the effects of unavoidable modeling uncertainties to be compensated. The effectiveness of the proposed strategy is shown under realistic human data showing the advantage of treatment in feedback form as well as the relevance of the adaptation strategy in handling uncertainties and modeling errors. A new treatment strategy defined by an original optimal control problem formulation is also proposed. This new formulation shows particularly interesting possibilities since it may lead to tumor regression under better health indicator profile.
2020-01-16 | MODEL2001160002 | BioModels
Project description:Using whole genome mRNA expression profiling of primary human tumors and unsupervised hierarchical cluster analyses, 3 novel molecular subsets (basal, luminal and p53-like subsets) of muscle-invasive bladder cancer (MIBC) were identified. 23 MVAC treated samples were used for the validation of three MIBC subsets MVAC cohort consisted of 23 FFPE pretreatment tumors (TURs) from a Phase III clinical trial were used to validate 3 molecular subsets including basal, luminal and p53-like subsets We used Illumina cDNA-mediated Annealing, Selection, Extension, and Ligation (DASL) assay that is specifically designed for the gene expression of RNA that is extracted from FFPE.
2014-02-10 | E-GEOD-52219 | biostudies-arrayexpress
Project description:Gene expression profiling of six chemotherapy and two untreated WT1 mutant tumors; see also GSE63616
2018-06-17 | GSE102723 | GEO
Project description:Whole blood is a highly convenient and informative tissue from which to sample DNA and RNA in epigenomic and functional genomic studies, but it is comprised of multiple distinct cell types and this complexity significantly impairs our ability to interpret downstream differential methylation and/or differential expression results. In this multiple sclerosis (MS)-focused study we utilised an application of current statistical deconvolution methods to interrogate whole blood DNA methylation data thereby enabling the methylome of several immune cell types to be analysed independently. Methylome profiling on cell type-purified blood samples revealed optimal CpG sets for use as robust immune cell markers in the statistical deconvolution process. We show that it is possible to identify differentially methylated (DM) loci in a cell type specific manner using statistical deconvolution. Finally, we demonstrate that deconvolution improved the biological relevance and interpretability of our DM results, significantly enhancing concordance of the identified DM loci with loci previously shown to be genetically or epigenetically associated with MS.
2017-06-30 | GSE88824 | GEO
Project description:Cancer progression is dependent on both cell-intrinsic processes and interactions between different cell types that constitute tumor tissue. To access this information, we develop Epigenomic Deconvolution (EDec), an in silico method that provides estimates of cell type composition of complex tissues, such as solid tumors, as well as CpG methylation and gene transcription within constituent cell types. By applying EDec to breast tumors from TCGA we detect changes in immune cell infiltration, and a striking change in stromal fibroblast to adipocyte ratio across breast cancer subtypes. We further show that a decrease in stromal adipocyte content and increase in fibroblast content is associated with a reduction of mitochondrial activity in stromal cells and a concomitant increase in oxidative metabolism in cancerous epithelial cells. These findings highlight the role of stromal cell type composition in the establishment of patterns of metabolic coupling such as the previously proposed reverse Warburg effect. Raw data files were not provided for this Series. Submitters did not have permission to share the raw data.
2016-11-17 | GSE87297 | GEO