NITUMID: Nonnegative matrix factorization-based Immune-TUmor MIcroenvironment Deconvolution

Tang, Daiwei; Park, Seyoung; Zhao, Hongyu

doi:10.1093/bioinformatics/btz748

Cited by 22 publications

(19 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, although limited in sample size, our analysis detected an association between anti-PD-1 immunotherapy response and elevated CD8+ T cell and NK cell levels and revealed the potential of TICPE for providing important insights into dynamic immune cell infiltration during immunotherapy. The TIICs found in our study make a significant contribution to patient survival and treatment and our findings are corroborated by previous studies (17,39). Overall, the proportions of immune cells measured by the TICPE could serve as a prognostic factor or a potential predictive model for the response to immune checkpoint blockade therapy in solid tumors.…”

Section: Discussionsupporting

confidence: 90%

A Cancer-Specific Qualitative Method for Estimating the Proportion of Tumor-Infiltrating Immune Cells

et al. 2021

View full text Add to dashboard Cite

Tumor-infiltrating immune cells are important components in the tumor microenvironment (TME) and different types of these cells exert different effects on tumor development and progression; these effects depend upon the type of cancer involved. Several methods have been developed for estimating the proportion of immune cells using bulk transcriptome data. However, there is a distinct lack of methods that are capable of predicting the immune contexture in specific types of cancer. Furthermore, the existing methods are based on absolute gene expression and are susceptible to experimental batch effects, thus resulting in incomparability across different datasets. In this study, we considered two common neoplasms as examples (colorectal cancer [CRC] and melanoma) and introduced the Tumor-infiltrating Immune Cell Proportion Estimator (TICPE), a cancer-specific qualitative method for estimating the proportion of tumor-infiltrating immune cells. The TICPE was based on the relative expression orderings (REOs) of gene pairs within a sample and is notably insensitive to batch effects. Performance evaluation using public expression data with mRNA mixtures, single-cell RNA-Seq (scRNA-Seq) data, immunohistochemistry data, and simulated bulk RNA-seq samples, indicated that the TICPE can estimate the proportion of immune cells with levels of accuracy that are clearly superior to other methods. Furthermore, we showed that the TICPE could effectively detect prognostic signals in patients with tumors and changes in the fractions of immune cells during immunotherapy in melanoma. In conclusion, our work presented a unique novel method, TICPE, to estimate the proportion of immune cells in specific cancer types and explore the effect of the infiltration of immune cells on the efficacy of immunotherapy and the prognosis of cancer. The source code for TICPE is available at https://github.com/huitingxiao/TICPE.

show abstract

Section: Discussionsupporting

confidence: 90%

A Cancer-Specific Qualitative Method for Estimating the Proportion of Tumor-Infiltrating Immune Cells

et al. 2021

View full text Add to dashboard Cite

show abstract

“…These algorithms represent a more flexible alternative to reference-based algorithms; however, once estimated, the interpretation and the labeling of different components might be problematic. Recently, Tang et al [17] proposed an algorithm based on non-negative matrix factorization. This method recovers the identifiability and the labeling of different components using a penalized regression, in which markers expected to be less expressed in a particular cell type shrink towards zero.…”

Section: Introductionmentioning

confidence: 99%

BayesDeBulk: A Flexible Bayesian Algorithm for the Deconvolution of Bulk Tumor Data

Petralia

Krek

Calinawan

et al. 2021

Preprint

View full text Add to dashboard Cite

Characterizing the tumor microenvironment is crucial in order to improve responsiveness to immunotherapy and develop new therapeutic strategies. The fraction of different cell-types in the tumor microenvironment can be estimated based on transcriptomic profiling of bulk tumor data via deconvolution algorithms. One class of such algorithms, known as reference-based, rely on a reference signature containing gene expression data for various cell-types. The limitation of these methods is that such a signature is derived from the gene expression of pure cell-types, which might not be consistent with the transcriptomic profiling in solid tumors. On the other hand, reference-free methods usually require only a set of cell-specific markers to perform deconvolution; however, once the different components have been estimated from the data, their labeling can be problematic. To overcome these limitations, we propose BayesDeBulk - a new reference-free Bayesian method for bulk deconvolution based on gene expression data. Given a list of markers expressed in each cell-type (cell-specific markers), a repulsive prior is placed on the mean of gene expression in different cell-types to ensure that cell-specific markers are upregulated in a particular component. Contrary to existing reference-free methods, the labeling of different components is decided a priori through a repulsive prior. Furthermore, the advantage over reference-based algorithms is that the cell fractions as well as the gene expression of different cells are estimated from the data, simultaneously. Given its flexibility, BayesDeBulk can be utilized to perform bulk deconvolution beyond transcriptomic data, based on other data types such as proteomic profiles or the integration of both transcriptomic and proteomic profiles.

show abstract

“…Limitations inevitably exist in this study and the most significant one is the fidelity of CIBERSORT results. Transcriptome-based celltype quantification methods for immuno-oncology are indeed all facing similar questionings (43)(44)(45). The computer-based algorithms can introduce a technical bias in a basis matrix towards the microarray platform used for transcriptome profiling, resulting in lower deconvolution accuracy for samples that are profiled using different platforms.…”

Section: Discussionmentioning

confidence: 99%

Establishment of an Immune Cell Infiltration Score to Help Predict the Prognosis and Chemotherapy Responsiveness of Gastric Cancer Patients

et al. 2021

View full text Add to dashboard Cite

The immune microenvironment plays a critical role in tumor biology. The molecular profiles of immune components and related genes are of tremendous value for the study of primary resistance to immune checkpoint blockers (ICBs) for gastric cancer (GC) and serve as prognostic biomarkers to predict GC survival. Recent studies have revealed that tumor immune cell infiltration (ICI) is an indicator of the survival and responsiveness to chemotherapy in GC patients. Here, we describe the immune cell landscape based on the ESTIMATE and CIBERSORT algorithms to help separate GC into 3 ICI clusters using the unsupervised clustering method. Further in-depth analyses, such as differential expression gene (DEG) analysis and principal component analysis (PCA), help to establish an ICI scoring system. A low ICI score is characterized by an increased tumor mutation burden (TMB). The combination of the ICI score and TMB score better predicts the survival of GC patients. Analyses based on public and our own database revealed that the ICI scoring system could also help predict the survival and chemotherapy responsiveness of GC patients. The present study demonstrated that the ICI score may be an effective prognostic biomarker and predictive indicator for chemotherapy and immunotherapy.

show abstract

NITUMID: Nonnegative matrix factorization-based Immune-TUmor MIcroenvironment Deconvolution

Cited by 22 publications

References 28 publications

A Cancer-Specific Qualitative Method for Estimating the Proportion of Tumor-Infiltrating Immune Cells

A Cancer-Specific Qualitative Method for Estimating the Proportion of Tumor-Infiltrating Immune Cells

BayesDeBulk: A Flexible Bayesian Algorithm for the Deconvolution of Bulk Tumor Data

Establishment of an Immune Cell Infiltration Score to Help Predict the Prognosis and Chemotherapy Responsiveness of Gastric Cancer Patients

Contact Info

Product

Resources

About