Asset Details
Do you wish to reserve the book?
Comprehensive Enumeration of Cancer Stem-like Cell Heterogeneity Using Deep Neural Network
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Comprehensive Enumeration of Cancer Stem-like Cell Heterogeneity Using Deep Neural Network
Do you wish to request the book?
Comprehensive Enumeration of Cancer Stem-like Cell Heterogeneity Using Deep Neural Network
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Paper
Comprehensive Enumeration of Cancer Stem-like Cell Heterogeneity Using Deep Neural Network
2024
Overview
Cancer stem cells (CSCs), a distinct subpopulation within tumors, are pivotal in driving treatment resistance and tumor recurrence, posing substantial challenges to conventional therapeutic strategies. Precise quantification and profiling of these cells are essential for improving cancer treatment outcomes. We present ACSCeND, an advanced deep neural network model accompanied by a robust workflow, specifically developed to quantify cellular compositions from bulk RNA-seq data, enabling accurate CSC profiling. By integrating bulk RNA-seq data with insights derived from single-cell RNA-seq datasets, ACSCeND effectively captures the diversity and hierarchical organization of tumor-resident cell states, alongside cell-specific gene expression profiles (GEPs). Compared to current tissue deconvolution models, ACSCeND exhibits superior performance, achieving significantly higher Concordance Correlation Coefficient (CCC) values and lower Root Mean Square Error (RMSE) across various pseudobulk and real-world bulk tissue samples. Application of ACSCeND to TCGA and PRECOG datasets reveals a strong association between CSC abundance and poorer disease-free survival outcomes, underscoring the clinical relevance of CSCs in cancer progression. Furthermore, cell-specific GEPs for distinct CSC states unveil novel molecular signatures and illuminate the origins of CSC-driven tumor heterogeneity. In summary, ACSCeND provides a powerful, scalable platform for high-throughput quantification of cellular compositions and distinct potency states within normal tissues as well as highly heterogeneous tissues, such as tumors.Competing Interest StatementThe authors have declared no competing interest.
Publisher
Cold Spring Harbor Laboratory Press,Cold Spring Harbor Laboratory
Subject
