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Deconvolution of drug screening data delineates drug sensitivity of stem-like cancer cells in Acute Myeloid Leukemia [RNA-Seq]

ABSTRACT: Ex-vivo drug sensitivity screening (DSS) only provides a readout on mixtures of cells, potentially occulting important information on clinically relevant cell subtypes. Here we developed a machine-learning framework to deconvolute bulk RNA expression matched with bulk drug sensitivity into cell subtype composition and cell subtype drug sensitivity. We first determined that our method could decipher the cellular composition of bulk samples more accurately than current state-of-the-art methods. We then optimized an algorithm capable of estimating cell subtype- and single-cell-specific drug sensitivity, which we evaluated by performing in-vitro drug studies

ORGANISM(S): Homo sapiens

PROVIDER: GSE217919 | GEO | 2022/11/16

REPOSITORIES: GEO

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Deconvolution of drug screening data delineates drug sensitivity of stem-like cancer cells in Acute Myeloid Leukemia [scRNA-Seq]

Project description:Ex-vivo drug sensitivity screening (DSS) only provides a readout on mixtures of cells, potentially occulting important information on clinically relevant cell subtypes. Here we developed a machine-learning framework to deconvolute bulk RNA expression matched with bulk drug sensitivity into cell subtype composition and cell subtype drug sensitivity. We first determined that our method could decipher the cellular composition of bulk samples more accurately than current state-of-the-art methods. We then optimized an algorithm capable of estimating cell subtype- and single-cell-specific drug sensitivity, which we evaluated by performing in-vitro drug studies

2022-11-16 | GSE217922 | GEO

Deconvolution of breast tumors for optimal chemotherapy selection

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

Dissecting Cell Composition and Drug Sensitivity in Human Adenoid Cystic Carcinomas (ACCs)

Project description:Dissecting Cell Composition and Drug Sensitivity in Human Adenoid Cystic Carcinomas (ACCs)

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Dissecting Cell Composition and Drug Sensitivity in Human Adenoid Cystic Carcinomas (ACCs)

Project description:Dissecting Cell Composition and Drug Sensitivity in Human Adenoid Cystic Carcinomas (ACCs)

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Proteomics informed deconvolution of 3 cell lines

Project description:The project aimed to investigate the possibility to use proteomics data to deconvolute cell line proportions in mixed samples. Samples containing either HEK 293, Caco-2, or A549 cells and mixtures of the three cell lines was analysed using the total protein approach. This was then used for proteomics informed deconvolution. The results show that proteome deconvolution provides an effective tool for investigating cellular composition in mixed samples. This was later applied also to in silico mixtures of primary human liver cells and liver tissue. However, those data are presented elsewhere.

2023-10-24 | PXD027282 | Pride

Single-cell RNA sequencing of human cervical cancer and adjacent normal tissue

Project description:Cervical cancer (CC) is the fourth leading cause of deaths in gynecological malignancies. Although the etiology of CC has been extensively investigated, the exact pathogenesis of CC remains incomplete. Recently, single-cell technologies demonstrated advantages in exploring intra-tumoral diversification among various tumor cells. However, single-cell transcriptome (scRNA-seq) analysis of CC cells and microenvironment has not been conducted. In this study, a total of 6 samples (3 CC and 3 adjacent normal tissues) were examined by scRNA-seq. Here, we performed single-cell RNA sequencing (scRNA-seq) to survey the transcriptomes of 57,669 cells derived from three CC tumors with paired normal adjacent non-tumor (NAT) samples. Single-cell transcriptomics analysis revealed extensive heterogeneity in malignant cells of human CCs, wherein epithelial subpopulation exhibited different genomic and transcriptomic signatures. We also identified cancer-associated fibroblasts (CAF) that may promote tumor progression of CC, and further distinguished inflammatory CAF (iCAF) and myofibroblastic CAF (myCAF). CD8+ T cell diversity revealed both proliferative (MKI67+) and non-cycling exhausted (PDCD1+) subpopulations at the end of the trajectory path. We used the epithelial signature genes derived from scRNA-seq to deconvolute bulk RNA-seq data of CC, identifying four different CC subtypes, namely hypoxia (S-H subtype), proliferation (S-P subtype), differentiation (S-D subtype), and immunoactive (S-I subtype) subtype. Our results lay the foundation for precision prognostic and therapeutic stratification of CC.

2022-10-04 | E-MTAB-11948 | biostudies-arrayexpress

DECIPHERING THE SIGNALING NETWORK LANDSCAPE OF BREAST CANCER IMPROVES DRUG SENSITIVITY PREDICTION

Project description:Although genetic and epigenetic abnormalities in breast cancer have been extensively studied, it remains difficult to identify those patients who will respond to particular therapies. This is due in part to our lack of understanding of how the variability of cellular signaling affects drug sensitivity. Here, we used mass cytometry to characterize the single-cell signaling landscapes of 62 breast cancer cell lines and five lines from healthy tissue. We quantified 34 markers in each cell line upon stimulation by the growth factor EGF in the presence or absence of five kinase inhibitors. These data – on more than 80 million single cells from 4,000 conditions – were used to fit mechanistic signaling network models that provide unprecedented insights into the biological principles of how cancer cells process information. Our dynamic single-cell-based models more accurately predicted drug sensitivity than static bulk measurements for drugs targeting the PI3K-MTOR signaling pathway. Finally, we identified genomic features associated with drug sensitivity by using signaling phenotypes as proxies, including a missense mutation in DDIT3 predictive of PI3K-inhibition sensitivity. This provides proof of principle that single-cell measurements and modeling could inform matching of patients with appropriate treatments in the future.

2021-04-20 | PXD017199 | Pride

Deconvolution of drug screening data delineates drug sensitivity of stem-like cancer cells in Acute Myeloid Leukemia

Project description:This SuperSeries is composed of the SubSeries listed below.

2022-11-16 | GSE217924 | GEO

Single-cell based elucidation of molecularly distinct GBM states and drug sensitivity

Project description:Glioblastoma heterogeneity and plasticity remain controversial, with proposed subtypes representing the average of highly heterogeneous admixtures of independent transcriptional states. Single-cell, protein-activity-based analysis identified four novel, molecularly distinct subtypes that successfully harmonize across multiple GBM datasets, including previously published bulk and single-cell profiles and single cell profiles from seven orthotopic PDX models, representative of prior subtype diversity. From these studies, GBM emerges as the plastic equilibrium of single tumor cells coexisting in two mutually-exclusive developmental states, with additional stratification provided by their proliferative potential. Consistent with these findings, all previously proposed subtypes could be recapitulated by mixtures of single cells representing these newly identified states. Most critically, drug sensitivity was predicted and experimentally confirmed as highly state-dependent, both in single-cell assays from patient-derived explants and in PDX models treated with clinically relevant drugs, suggesting that successful therapeutic strategies will likely require combinations of multiple drugs targeting these distinct tumor states.

2022-04-11 | GSE129671 | GEO

Molecular phenotyping of breast cancer cell lines at the single-cell level for automated tumour diagnosis and prediction of drug sensitivity

Project description:Breast Cancer (BC) patient stratification is mainly driven by tumour receptor status and histological grading and subtyping, with about twenty percent of patients for which absence of any actionable biomarkers results in no clear therapeutic intervention to apply. Here, we evaluated the potential of single-cell transcriptomics for automated diagnosis and drug treatment of breast cancer. We transcriptionally profiled 35,276 individual cells from 32 BC cell-lines covering all main BC subtypes to yield a Breast Cancer Single Cell Atlas. We show that single cell transcriptomics can successfully detect clinically relevant BC biomarkers and that atlas can be used to automatically predict cancer subtype and composition from a patient’s tumour biopsy. We found that BC cell lines harbour a high degree of heterogeneity in the expression of clinically relevant BC biomarkers and that such heterogeneity enables cells with differential drug sensitivity to co-exist even within a genomically stable isogenic cell line. Finally, we developed a novel bioinformatics approach named DREEP (Drug Response Estimation from Expression Profiles) to automatically predict responses to more than 450 anticancer agents starting from single-cell transcriptional profiles. We validated DREEP both in-silico and in-vitro by selectively inhibiting the growth of the HER2-deficient subpopulation in the MDAMB361 cell line. Our work shows that transcriptional heterogeneity is common, dynamic and that its plasticity plays a relevant role in determining drug sensitivity. Moreover, our Breast Cancer Single Cell Atlas and DREEP approach are a unique resource for the BC research community and to advance the use of single-cell sequencing in the clinic.

2022-01-28 | GSE173634 | GEO

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