ISOpureR: an R implementation of a computational purification algorithm of mixed tumour profiles

Computational identification and experimental verification of a novel signature based on SARS-CoV-2-related genes for predicting prognosis, immune microenvironment and therapeutic strategies in lung adenocarcinoma patients

Background

Early research indicates that cancer patients are more vulnerable to adverse outcomes and mortality when infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Nonetheless, the specific attributes of SARS-CoV-2 in lung Adenocarcinoma (LUAD) have not been extensively and methodically examined.

Methods

We acquired 322 SARS-CoV-2 infection-related genes (CRGs) from the Human Protein Atlas database. Using an integrative machine learning approach with 10 algorithms, we developed a SARS-CoV-2 score (Cov-2S) signature across The Cancer Genome Atlas and datasets GSE72094, GSE68465, and GSE31210. Comprehensive multi-omics analysis, including assessments of genetic mutations and copy number variations, was conducted to deepen our understanding of the prognosis signature. We also analyzed the response of different Cov-2S subgroups to immunotherapy and identified targeted drugs for these subgroups, advancing personalized medicine strategies. The expression of Cov-2S genes was confirmed through qRT-PCR, with GGH emerging as a critical gene for further functional studies to elucidate its role in LUAD.

Results

Out of 34 differentially expressed CRGs identified, 16 correlated with overall survival. We utilized 10 machine learning algorithms, creating 101 combinations, and selected the RFS as the optimal algorithm for constructing a Cov-2S based on the average C-index across four cohorts. This was achieved after integrating several essential clinicopathological features and 58 established signatures. We observed significant differences in biological functions and immune cell statuses within the tumor microenvironments of high and low Cov-2S groups. Notably, patients with a lower Cov-2S showed enhanced sensitivity to immunotherapy. We also identified five potential drugs targeting Cov-2S. In vitro experiments revealed a significant upregulation of GGH in LUAD, and its knockdown markedly inhibited tumor cell proliferation, migration, and invasion.

Conclusion

Our research has pioneered the development of a consensus Cov-2S signature by employing an innovative approach with 10 machine learning algorithms for LUAD. Cov-2S reliably forecasts the prognosis, mirrors the tumor's local immune condition, and supports clinical decision-making in tumor therapies.

Performance of computational algorithms to deconvolve heterogeneous bulk ovarian tumor tissue depends on experimental factors

BACKGROUND

Single-cell gene expression profiling provides unique opportunities to understand tumor heterogeneity and the tumor microenvironment. Because of cost and feasibility, profiling bulk tumors remains the primary population-scale analytical strategy. Many algorithms can deconvolve these tumors using single-cell profiles to infer their composition. While experimental choices do not change the true underlying composition of the tumor, they can affect the measurements produced by the assay.

RESULTS

We generated a dataset of high-grade serous ovarian tumors with paired expression profiles from using multiple strategies to examine the extent to which experimental factors impact the results of downstream tumor deconvolution methods. We find that pooling samples for single-cell sequencing and subsequent demultiplexing has a minimal effect. We identify dissociation-induced differences that affect cell composition, leading to changes that may compromise the assumptions underlying some deconvolution algorithms. We also observe differences across mRNA enrichment methods that introduce additional discrepancies between the two data types. We also find that experimental factors change cell composition estimates and that the impact differs by method.

CONCLUSIONS

Previous benchmarks of deconvolution methods have largely ignored experimental factors. We find that methods vary in their robustness to experimental factors. We provide recommendations for methods developers seeking to produce the next generation of deconvolution approaches and for scientists designing experiments using deconvolution to study tumor heterogeneity.

Histone deacetylase-mediated tumor microenvironment characteristics and synergistic immunotherapy in gastric cancer

Background: Studies have shown that the expression of histone deacetylases (HDACs) is significantly related to the tumor microenvironment (TME) in gastric cancer. However, the expression of a single molecule or several molecules does not accurately reflect the TME characteristics or guide immunotherapy in gastric cancer. Methods: We constructed an HDAC score (HDS) based on the expression level of HDACs. The single-cell transcriptome was used to analyze the underlying factors contributing to differences in immune infiltration between patients with a high and low HDS. In vitro and in vivo experiments validated the strategy of transforming cold tumors into hot tumors to guide immunotherapy. Results: According to the expression characteristics of HDACs, we constructed an HDS model to characterize the TME. We found that patients with a high HDS had stronger immunogenicity and could benefit more from immunotherapy than those with a low score. The AUC value of the HDS combined with the combined positive score (CPS)for predicting the efficacy of immunotherapy was as high as 0.96. By single-cell and paired bulk transcriptome sequencing analysis, we found that the infiltration levels of CD4+ T cells, CD8+ T cells and NK cells were significantly decreased in the low HDS group, which may be induced by MYH11+ fibroblasts, CD234+ endothelial cells and CCL17+ pDCs via the MIF signaling pathway. Inhibition of the MIF signaling pathway was confirmed to potentially enhance immune infiltration. In addition, our analysis revealed that GPX4 inhibitors might be effective for patients with a low HDS. GPX4 knockout significantly inhibited PD-L1 expression and promoted the infiltration and activation of CD8+ T cells. Conclusion: We constructed an HDS model based on the HDAC expression characteristics of gastric cancer. This model was used to evaluate TME characteristics and predict immunotherapy efficacy. Inhibition of the MIF signaling pathway in the TME and GPX4 expression in tumor cells may be an important strategy for cold tumor synergistic immunotherapy for gastric cancer.

Tumor microenvironment: barrier or opportunity towards effective cancer therapy

Tumor microenvironment (TME) is a specialized ecosystem of host components, designed by tumor cells for successful development and metastasis of tumor. With the advent of 3D culture and advanced bioinformatic methodologies, it is now possible to study TME’s individual components and their interplay at higher resolution. Deeper understanding of the immune cell’s diversity, stromal constituents, repertoire profiling, neoantigen prediction of TMEs has provided the opportunity to explore the spatial and temporal regulation of immune therapeutic interventions. The variation of TME composition among patients plays an important role in determining responders and non-responders towards cancer immunotherapy. Therefore, there could be a possibility of reprogramming of TME components to overcome the widely prevailing issue of immunotherapeutic resistance. The focus of the present review is to understand the complexity of TME and comprehending future perspective of its components as potential therapeutic targets. The later part of the review describes the sophisticated 3D models emerging as valuable means to study TME components and an extensive account of advanced bioinformatic tools to profile TME components and predict neoantigens. Overall, this review provides a comprehensive account of the current knowledge available to target TME.

Identification of Novel Molecular Subgroups in Esophageal Adenocarcinoma to Predict Response to Neo-Adjuvant Therapies

Simple Summary

Gene expression of esophageal adenocarcinoma is highly heterogeneous. In general, these cancers have poor prognosis and unpredictable responses to chemo- and radiotherapy. Investigating expression profiles from RNA from pre-treatment biopsies are highly attractive to investigate the existence of diverse biological groups and signatures associated with the clinical response to current treatment strategies. We identified and validated three distinct biological esophageal adenocarcinoma subgroups and identified immune signatures with association to therapy response using RNA sequencing. These findings aid in understanding biological mechanisms’ underlying response to neo-adjuvant treatment.

Abstract

Esophageal adenocarcinoma (EAC) is a highly aggressive cancer and its response to chemo- and radiotherapy is unpredictable. EACs are highly heterogeneous at the molecular level. The aim of this study was to perform gene expression analysis of EACs to identify distinct molecular subgroups and to investigate expression signatures in relation to treatment response. In this prospective observational study, RNA sequencing was performed on pre-treatment endoscopic EAC biopsies from a discovery cohort included between 2012 and 2017 in one Dutch Academic Center. Four additional cohorts were analyzed for validation purposes. Unsupervised clustering was performed on 107 patients to identify biological EAC subgroups. Specific cell signaling profiles were identified and evaluated with respect to predicting response to neo-adjuvant chemo(radio)therapy. We identified and validated three distinct biological EAC subgroups, characterized by (1) p38 MAPK/Toll-like receptor signaling; (2) activated immune system; and (3) impaired cell adhesion. Subgroup 1 was associated with poor response to chemo-radiotherapy. Moreover, an immune signature with activated T-cell signaling, and increased number of activated CD4 T memory cells, neutrophils and dendritic cells, and decreased M1 and M2 macrophages and plasma cells, was associated with complete histopathological response. This study provides a novel molecular classification for EACs. EAC subgroup 1 proved to be more therapy-resistant, while immune signaling was increased in patients with complete response to chemo-radiotherapy. Our findings give insight into the biology of EACs and in cellular signaling mechanisms underlying response to neo-adjuvant treatment. Future implementation of this classification will improve patient stratification and enhance the development of targeted therapies.

CPDR: An R Package of Recommending Personalized Drugs for Cancer Patients by Reversing the Individual’s Disease-Related Signature

Due to cancer heterogeneity, only some patients can benefit from drug therapy. The personalized drug usage is important for improving the treatment response rate of cancer patients. The value of the transcriptome of patients has been recently demonstrated in guiding personalized drug use, and the Connectivity Map (CMAP) is a reliable computational approach for drug recommendation. However, there is still no personalized drug recommendation tool based on transcriptomic profiles of patients and CMAP. To fill this gap, here, we proposed such a feasible workflow and a user-friendly R package—Cancer-Personalized Drug Recommendation (CPDR). CPDR has three features. 1) It identifies the individual disease signature by using the patient subgroup with transcriptomic profiles similar to those of the input patient. 2) Transcriptomic profile purification is supported for the subgroup with high infiltration of non-cancerous cells. 3) It supports in silico drug efficacy assessment using drug sensitivity data on cancer cell lines. We demonstrated the workflow of CPDR with the aid of a colorectal cancer dataset from GEO and performed the in silico validation of drug efficacy. We further assessed the performance of CPDR by a pancreatic cancer dataset with clinical response to gemcitabine. The results showed that CPDR can recommend promising therapeutic agents for the individual patient. The CPDR R package is available at https://github.com/AllenSpike/CPDR.

Identification of Tumor Microenvironment and DNA Methylation-Related Prognostic Signature for Predicting Clinical Outcomes and Therapeutic Responses in Cervical Cancer

Background: Tumor microenvironment (TME) has been reported to have a strong association with tumor progression and therapeutic outcome, and epigenetic modifications such as DNA methylation can affect TMB and play an indispensable role in tumorigenesis. However, the potential mechanisms of TME and DNA methylation remain unclear in cervical cancer (CC).

Methods: The immune and stromal scores of TME were generated by the ESTIMATE algorithm for CC patients in The Cancer Genome Atlas (TCGA) database. The TME and DNA methylation-related genes were identified by the integrative analysis of DNA promoter methylation and gene expression. The least absolute shrinkage and selection operator (LASSO) Cox regression was performed 1,000 times to further identify a nine-gene TME and DNA methylation-related prognostic signature. The signature was further validated in Gene Expression Omnibus (GEO) dataset. Then, the identified signature was integrated with the Federation International of Gynecology and Obstetrics (FIGO) stage to establish a composite prognostic nomogram.

Results: CC patients with high immunity levels have better survival than those with low immunity levels. Both in the training and validation datasets, the risk score of the signature was an independent prognosis factor. The composite nomogram showed higher accuracy of prognosis and greater net benefits than the FIGO stage and the signature. The high-risk group had a significantly higher fraction of genome altered than the low-risk group. Eleven genes were significantly different in mutation frequencies between the high- and low-risk groups. Interestingly, patients with mutant TTN had better overall survival (OS) than those with wild type. Patients in the low-risk group had significantly higher tumor mutational burden (TMB) than those in the high-risk group. Taken together, the results of TMB, immunophenoscore (IPS), and tumor immune dysfunction and exclusion (TIDE) score suggested that patients in the low-risk group may have greater immunotherapy benefits. Finally, four drugs (panobinostat, lenvatinib, everolimus, and temsirolimus) were found to have potential therapeutic implications for patients with a high-risk score.

Conclusions: Our findings highlight that the TME and DNA methylation-related prognostic signature can accurately predict the prognosis of CC and may be important for stratified management of patients and precision targeted therapy.

Comparative molecular genomic analyses of a spontaneous rhesus macaque model of mismatch repair-deficient colorectal cancer

Colorectal cancer (CRC) remains the third most common cancer in the US with 15% of cases displaying Microsatellite Instability (MSI) secondary to Lynch Syndrome (LS) or somatic hypermethylation of the MLH1 promoter. A cohort of rhesus macaques from our institution developed spontaneous mismatch repair deficient (MMRd) CRC with a notable fraction harboring a pathogenic germline mutation in MLH1 (c.1029C Collapse

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MESH Headings

• Animals
• Colonic Neoplasms/genetics
• Colorectal Neoplasms/genetics
• Colorectal Neoplasms/pathology
• Colorectal Neoplasms, Hereditary Nonpolyposis/genetics
• Colorectal Neoplasms, Hereditary Nonpolyposis/pathology
• DNA Methylation/genetics
• DNA Mismatch Repair/genetics
• Genomics
• Humans
• Macaca mulatta/genetics
• Mice
• Microsatellite Instability
• MutL Protein Homolog 1/genetics

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Grants

• P30 CA016672 NCI NIH HHS
• R24 OD011173 NIH HHS
• Feinberg Family Foundation
• MDACC Institutional Research Grant Program
• Cattleman for Cancer Research
• National Institutes of Health
• National Cancer Institute

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Affiliation(s)

Nejla Ozirmak Lermi Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America School of Health Professions, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
Stanton B. Gray Michale E. Keeling Center for Comparative Medicine and Research, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
Charles M. Bowen Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
Laura Reyes-Uribe Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
Beth K. Dray Charles River Laboratories, Ashland, Ohio, United States of America
Nan Deng Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
R. Alan Harris Human Genome Sequencing Center and Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America
Muthuswamy Raveendran Human Genome Sequencing Center and Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America
Fernando Benavides Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
Carolyn L. Hodo Michale E. Keeling Center for Comparative Medicine and Research, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
Melissa W. Taggart Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
Karen Colbert Maresso Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
Krishna M. Sinha Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
Jeffrey Rogers Human Genome Sequencing Center and Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America
Eduardo Vilar Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America Clinical Cancer Genetics Program, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America

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Comprehensive Characterization of Alternative mRNA Splicing Events in Glioblastoma: Implications for Prognosis, Molecular Subtypes, and Immune Microenvironment Remodeling

Alternative splicing (AS) of pre-mRNA has been widely reported to be associated with the progression of malignant tumors. However, a systematic investigation into the prognostic value of AS events in glioblastoma (GBM) is urgently required. The gene expression profile and matched AS events data of GBM patients were obtained from The Cancer Genome Atlas Project (TCGA) and TCGA SpliceSeq database, respectively. 775 AS events were identified as prognostic factors using univariate Cox regression analysis. The least absolute shrinkage and selection operator (LASSO) cox model was performed to narrow down candidate AS events, and a risk score model based on several AS events were developed subsequently. The risk score-based signature was proved as an efficient predictor of overall survival and was closely related to the tumor purity and immunosuppression in GBM. Combined similarity network fusion and consensus clustering (SNF-CC) analysis revealed two distinct GBM subtypes based on the prognostic AS events, and the associations between this novel molecular classification and clinicopathological factors, immune cell infiltration, as well as immunogenic features were further explored. We also constructed a regulatory network to depict the potential mechanisms that how prognostic splicing factors (SFs) regulate splicing patterns in GBM. Finally, a nomogram incorporating AS events signature and other clinical-relevant covariates was built for clinical application. This comprehensive analysis highlights the potential implications for predicting prognosis and clinical management in GBM.

The effect of tissue composition on gene co-expression

Variable cellular composition of tissue samples represents a significant challenge for the interpretation of genomic profiling studies. Substantial effort has been devoted to modeling and adjusting for compositional differences when estimating differential expression between sample types. However, relatively little attention has been given to the effect of tissue composition on co-expression estimates. In this study, we illustrate the effect of variable cell-type composition on correlation-based network estimation and provide a mathematical decomposition of the tissue-level correlation. We show that a class of deconvolution methods developed to separate tumor and stromal signatures can be applied to two component cell-type mixtures. In simulated and real data, we identify conditions in which a deconvolution approach would be beneficial. Our results suggest that uncorrelated cell-type-specific markers are ideally suited to deconvolute both the expression and co-expression patterns of an individual cell type. We provide a Shiny application for users to interactively explore the effect of cell-type composition on correlation-based co-expression estimation for any cell types of interest.

Systematic Assessment of Tumor Purity and Its Clinical Implications

PURPOSE

The tumor microenvironment is complex, comprising heterogeneous cellular populations. As molecular profiles are frequently generated using bulk tissue sections, they represent an admixture of multiple cell types (including immune, stromal, and cancer cells) interacting with each other. Therefore, these molecular profiles are confounded by signals emanating from many cell types. Accurate assessment of residual cancer cell fraction is crucial for parameterization and interpretation of genomic analyses, as well as for accurately interpreting the clinical properties of the tumor.

MATERIALS AND METHODS

To benchmark cancer cell fraction estimation methods, 10 estimators were applied to a clinical cohort of 333 patients with prostate cancer. These methods include gold-standard multiobserver pathology estimates, as well as estimates inferred from genome, epigenome, and transcriptome data. In addition, two methods based on genomic and transcriptomic profiles were used to quantify tumor purity in 4,497 tumors across 12 cancer types. Bulk mRNA and microRNA profiles were subject to in silico deconvolution to estimate cancer cell-specific mRNA and microRNA profiles.

RESULTS

We present a systematic comparison of 10 tumor purity estimation methods on a cohort of 333 prostate tumors. We quantify variation among purity estimation methods and demonstrate how this influences interpretation of clinico-genomic analyses. Our data show poor concordance between pathologic and molecular purity estimates, necessitating caution when interpreting molecular results. Limited concordance between DNA- and mRNA-derived purity estimates remained a general pan-cancer phenomenon when tested in an additional 4,497 tumors spanning 12 cancer types.

CONCLUSION

The choice of tumor purity estimation method may have a profound impact on the interpretation of genomic assays. Taken together, these data highlight the need for improved assessment of tumor purity and quantitation of its influences on the molecular hallmarks of cancers.

Towards multi-omics characterization of tumor heterogeneity: a comprehensive review of statistical and machine learning approaches

The multi-omics molecular characterization of cancer opened a new horizon for our understanding of cancer biology and therapeutic strategies. However, a tumor biopsy comprises diverse types of cells limited not only to cancerous cells but also to tumor microenvironmental cells and adjacent normal cells. This heterogeneity is a major confounding factor that hampers a robust and reproducible bioinformatic analysis for biomarker identification using multi-omics profiles. Besides, the heterogeneity itself has been recognized over the years for its significant prognostic values in some cancer types, thus offering another promising avenue for therapeutic intervention. A number of computational approaches to unravel such heterogeneity from high-throughput molecular profiles of a tumor sample have been proposed, but most of them rely on the data from an individual omics layer. Since the heterogeneity of cells is widely distributed across multi-omics layers, methods based on an individual layer can only partially characterize the heterogeneous admixture of cells. To help facilitate further development of the methodologies that synchronously account for several multi-omics profiles, we wrote a comprehensive review of diverse approaches to characterize tumor heterogeneity based on three different omics layers: genome, epigenome and transcriptome. As a result, this review can be useful for the analysis of multi-omics profiles produced by many large-scale consortia. Contact:sunkim.bioinfo@snu.ac.kr.

contamDE-lm: linear model-based differential gene expression analysis using next-generation RNA-seq data from contaminated tumor samples

MOTIVATION

Tumor and adjacent normal RNA samples are commonly used to screen differentially expressed genes between normal and tumor samples or among tumor subtypes. Such paired-sample design could avoid numerous confounders in differential expression (DE) analysis, but the cellular contamination of tumor samples can be an important noise and confounding factor, which can both inflate false-positive rate and deflate true-positive rate. The existing DE tools that use next-generation RNA-seq data either do not account for cellular contamination or are computationally extensive with increasingly large sample size.

RESULTS

A novel linear model was proposed to avoid the problem that could arise from tumor-normal correlation for paired samples. A statistically robust and computationally very fast DE analysis procedure, contamDE-lm, was developed based on the novel model to account for cellular contamination, boosting DE analysis power through the reduction in individual residual variances using gene-wise information. The desired advantages of contamDE-lm over some state-of-the-art methods (limma and DESeq2) were evaluated through the applications to simulated data, TCGA database and Gene Expression Omnibus (GEO) database.

AVAILABILITY AND IMPLEMENTATION

The proposed method contamDE-lm was implemented in an updated R package contamDE (version 2.0), which is freely available at https://github.com/zhanghfd/contamDE.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A reference profile-free deconvolution method to infer cancer cell-intrinsic subtypes and tumor-type-specific stromal profiles

BACKGROUND

Patient stratification based on molecular subtypes is an important strategy for cancer precision medicine. Deriving clinically informative cancer molecular subtypes from transcriptomic data generated on whole tumor tissue samples is a non-trivial task, especially given the various non-cancer cellular elements intertwined with cancer cells in the tumor microenvironment.

METHODS

We developed a computational deconvolution method, DeClust, that stratifies patients into subtypes based on cancer cell-intrinsic signals identified by distinguishing cancer-type-specific signals from non-cancer signals in bulk tumor transcriptomic data. DeClust differs from most existing methods by directly incorporating molecular subtyping of solid tumors into the deconvolution process and outputting molecular subtype-specific tumor reference profiles for the cohort rather than individual tumor profiles. In addition, DeClust does not require reference expression profiles or signature matrices as inputs and estimates cancer-type-specific microenvironment signals from bulk tumor transcriptomic data.

RESULTS

DeClust was evaluated on both simulated data and 13 solid tumor datasets from The Cancer Genome Atlas (TCGA). DeClust performed among the best, relative to existing methods, for estimation of cellular composition. Compared to molecular subtypes reported by TCGA or other similar approaches, the subtypes generated by DeClust had higher correlations with cancer-intrinsic genomic alterations (e.g., somatic mutations and copy number variations) and lower correlations with tumor purity. While DeClust-identified subtypes were not more significantly associated with survival in general, DeClust identified a poor prognosis subtype of clear cell renal cancer, papillary renal cancer, and lung adenocarcinoma, all of which were characterized by CDKN2A deletions. As a reference profile-free deconvolution method, the tumor-type-specific stromal profiles and cancer cell-intrinsic subtypes generated by DeClust were supported by single-cell RNA sequencing data.

CONCLUSIONS

DeClust is a useful tool for cancer cell-intrinsic molecular subtyping of solid tumors. DeClust subtypes, together with the tumor-type-specific stromal profiles generated by this pan-cancer study, may lead to mechanistic and clinical insights across multiple tumor types.

Optimization and expansion of non-negative matrix factorization

Background

Non-negative matrix factorization (NMF) is a technique widely used in various fields, including artificial intelligence (AI), signal processing and bioinformatics. However existing algorithms and R packages cannot be applied to large matrices due to their slow convergence or to matrices with missing entries. Besides, most NMF research focuses only on blind decompositions: decomposition without utilizing prior knowledge. Finally, the lack of well-validated methodology for choosing the rank hyperparameters also raises concern on derived results.

Results

We adopt the idea of sequential coordinate-wise descent to NMF to increase the convergence rate. We demonstrate that NMF can handle missing values naturally and this property leads to a novel method to determine the rank hyperparameter. Further, we demonstrate some novel applications of NMF and show how to use masking to inject prior knowledge and desirable properties to achieve a more meaningful decomposition.

Conclusions

We show through complexity analysis and experiments that our implementation converges faster than well-known methods. We also show that using NMF for tumour content deconvolution can achieve results similar to existing methods like ISOpure. Our proposed missing value imputation is more accurate than conventional methods like multiple imputation and comparable to missForest while achieving significantly better computational efficiency. Finally, we argue that the suggested rank tuning method based on missing value imputation is theoretically superior to existing methods. All algorithms are implemented in the R package NNLM, which is freely available on CRAN and Github.

Computational deconvolution of transcriptomics data from mixed cell populations

Summary

Gene expression analyses of bulk tissues often ignore cell type composition as an important confounding factor, resulting in a loss of signal from lowly abundant cell types. In this review, we highlight the importance and value of computational deconvolution methods to infer the abundance of different cell types and/or cell type-specific expression profiles in heterogeneous samples without performing physical cell sorting. We also explain the various deconvolution scenarios, the mathematical approaches used to solve them and the effect of data processing and different confounding factors on the accuracy of the deconvolution results.

Contact

katleen.depreter@ugent.be.

Supplementary information

Supplementary data are available at Bioinformatics online.

Landscape of transcriptomic interactions between breast cancer and its microenvironment

Solid tumours comprise mixtures of tumour cells (TCs) and tumour-adjacent cells (TACs), and the intricate interconnections between these diverse populations shape the tumour’s microenvironment. Despite this complexity, clinical genomic profiling is typically performed from bulk samples, without distinguishing TCs from TACs. To better understand TC–TAC interactions, we computationally distinguish their transcriptomes in 1780 primary breast tumours. We show that TC and TAC mRNA abundances are divergently associated with clinical phenotypes, including tumour subtypes and patient survival. These differences reflect distinct responses of TCs and TACs to specific somatic driver mutations, particularly TP53. These data further elucidate how the molecular interplay between breast tumours and their microenvironment drives aggressive tumour phenotypes.

The transcriptomic profile of tumour-adjacent cells provides important information about tumour context but its clinical utility is unclear. Here, in breast cancer, Fox et al. show that the mRNA abundances of tumour and tumour-adjacent cells hold prognostic information.

Development of a predictive model for stromal content in prostate cancer samples to improve signature performance

Prostate cancer is heterogeneous in both cellular composition and patient outcome, and development of biomarker signatures to distinguish indolent from aggressive tumours is a high priority. Stroma plays an important role during prostate cancer progression and undergoes histological and transcriptional changes associated with disease. However, identification and validation of stromal markers is limited by a lack of datasets with defined stromal/tumour ratio. We have developed a prostate‐selective signature to estimate the stromal content in cancer samples of mixed cellular composition. We identified stromal‐specific markers from transcriptomic datasets of developmental prostate mesenchyme and prostate cancer stroma. These were experimentally validated in cell lines, datasets of known stromal content, and by immunohistochemistry in tissue samples to verify stromal‐specific expression. Linear models based upon six transcripts were able to infer the stromal content and estimate stromal composition in mixed tissues. The best model had a coefficient of determination R² of 0.67. Application of our stromal content estimation model in various prostate cancer datasets led to improved performance of stromal predictive signatures for disease progression and metastasis. The stromal content of prostate tumours varies considerably; consequently, deconvolution of stromal proportion may yield better results than tumour cell deconvolution. We suggest that adjusting expression data for cell composition will improve stromal signature performance and lead to better prognosis and stratification of men with prostate cancer. © 2019 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

Pan-cancer transcriptomic analysis dissects immune and proliferative functions of APOBEC3 cytidine deaminases

APOBEC3 cytidine deaminases are largely known for their innate immune protection from viral infections. Recently, members of the family have been associated with a distinct mutational activity in some cancer types. We report a pan-tissue, pan-cancer analysis of RNA-seq data specific to the APOBEC3 genes in 8,951 tumours, 786 cancer cell lines and 6,119 normal tissues. By deconvolution of levels of different cell types in tumour admixtures, we demonstrate that APOBEC3B (A3B), the primary candidate as a cancer mutagen, shows little association with immune cell types compared to its paralogues. We present a pipeline called RESPECTEx (REconstituting SPecific Cell-Type Expression) and use it to deconvolute cell-type specific expression levels in a given cohort of tumour samples. We functionally annotate APOBEC3 co-expressing genes, and create an interactive visualization tool which 'barcodes' the functional enrichment (http://fraternalilab.kcl.ac.uk/apobec-barcodes/). These analyses reveal that A3B expression correlates with cell cycle and DNA repair genes, whereas the other APOBEC3 members display specificity for immune processes and immune cell populations. We offer molecular insights into the functions of individual APOBEC3 proteins in antiviral and proliferative contexts, and demonstrate the diversification this family of enzymes displays at the transcriptomic level, despite their high similarity in protein sequences and structures.

Quantifying tumor-infiltrating immune cells from transcriptomics data

By exerting pro- and anti-tumorigenic actions, tumor-infiltrating immune cells can profoundly influence tumor progression, as well as the success of anti-cancer therapies. Therefore, the quantification of tumor-infiltrating immune cells holds the promise to unveil the multi-faceted role of the immune system in human cancers and its involvement in tumor escape mechanisms and response to therapy. Tumor-infiltrating immune cells can be quantified from RNA sequencing data of human tumors using bioinformatics approaches. In this review, we describe state-of-the-art computational methods for the quantification of immune cells from transcriptomics data and discuss the open challenges that must be addressed to accurately quantify immune infiltrates from RNA sequencing data of human bulk tumors.

Cancer transcriptome profiling at the juncture of clinical translation

Methodological breakthroughs over the past four decades have repeatedly revolutionized transcriptome profiling. Using RNA sequencing (RNA-seq), it has now become possible to sequence and quantify the transcriptional outputs of individual cells or thousands of samples. These transcriptomes provide a link between cellular phenotypes and their molecular underpinnings, such as mutations. In the context of cancer, this link represents an opportunity to dissect the complexity and heterogeneity of tumours and to discover new biomarkers or therapeutic strategies. Here, we review the rationale, methodology and translational impact of transcriptome profiling in cancer.

Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data

Immune cells infiltrating tumors can have important impact on tumor progression and response to therapy. We present an efficient algorithm to simultaneously estimate the fraction of cancer and immune cell types from bulk tumor gene expression data. Our method integrates novel gene expression profiles from each major non-malignant cell type found in tumors, renormalization based on cell-type-specific mRNA content, and the ability to consider uncharacterized and possibly highly variable cell types. Feasibility is demonstrated by validation with flow cytometry, immunohistochemistry and single-cell RNA-Seq analyses of human melanoma and colorectal tumor specimens. Altogether, our work not only improves accuracy but also broadens the scope of absolute cell fraction predictions from tumor gene expression data, and provides a unique novel experimental benchmark for immunogenomics analyses in cancer research (http://epic.gfellerlab.org).

Immunological landscape of consensus clusters in colorectal cancer

Recent, large-scale expression–based subtyping has advanced our understanding of the genomic landscape of colorectal cancer (CRC) and resulted in a consensus molecular classification that enables the categorization of most CRC tumors into one of four consensus molecular subtypes (CMS). Currently, major progress in characterization of immune landscape of tumor-associated microenvironment has been made especially with respect to microsatellite status of CRCs. While these studies profoundly improved the understanding of molecular and immunological profile of CRCs heterogeneity less is known about repertoire of the tumor infiltrating immune cells of each CMS.

In order to comprehensively characterize the immune landscape of CRC we re-analyzed a total of 15 CRC genome-wide expression data sets encompassing 1597 tumors and 125 normal adjacent colon tissues. After quality filtering, CRC clusters were discovered using a combination of multiple clustering algorithms and multiple validity metrics. CIBERSORT algorithm was used to compute relative proportions of 22 human leukocyte subpopulations across CRC clusters and normal colon tissue. Subsequently, differential expression specific to tumor epithelial cells was calculated to characterize mechanisms of tumor escape from immune surveillance occurring in particular CRC clusters.

Our results not only characterize the common and cluster-specific influx of immune cells into CRCs but also identify several deregulated gene targets that may contribute to improvement of immunotherapeutic strategies in CRC.

Prediction of Primary Tumors in Cancers of Unknown Primary

A cancer of unknown primary (CUP) is a metastatic cancer for which standard diagnostic tests fail to identify the location of the primary tumor. CUPs account for 3–5% of cancer cases. Using molecular data to determine the location of the primary tumor in such cases can help doctors make the right treatment choice and thus improve the clinical outcome. In this paper, we present a new method for predicting the location of the primary tumor using gene expression data: locating cancers of unknown primary (LoCUP). The method models the data as a mixture of normal and tumor cells and thus allows correct classification even in impure samples, where the tumor biopsy is contaminated by a large fraction of normal cells. We find that our method provides a significant increase in classification accuracy (95.8% over 90.8%) on simulated low-purity metastatic samples and shows potential on a small dataset of real metastasis samples with known origin.

MixChIP: a probabilistic method for cell type specific protein-DNA binding analysis

Background

Transcription factors (TFs) are proteins that bind to DNA and regulate gene expression. To understand details of gene regulation, characterizing TF binding sites in different cell types, diseases and among individuals is essential. However, sometimes TF binding can only be measured from biological samples that contain multiple cell or tissue types. Sample heterogeneity can have a considerable effect on TF binding site detection. While manual separation techniques can be used to isolate a cell type of interest from heterogeneous samples, such techniques are challenging and can change intra-cellular interactions, including protein-DNA binding. Computational deconvolution methods have emerged as an alternative strategy to study heterogeneous samples and numerous methods have been proposed to analyze gene expression. However, no computational method exists to deconvolve cell type specific TF binding from heterogeneous samples.

Results

We present a probabilistic method, MixChIP, to identify cell type specific TF binding sites from heterogeneous chromatin immunoprecipitation sequencing (ChIP-seq) data. Our method simultaneously estimates the binding strength in different cell types as well as the proportions of different cell types in each sample when only partial prior information about cell type composition is available. We demonstrate the utility of MixChIP by analyzing ChIP-seq data from two cell lines which we artificially mix to generate (simulated) heterogeneous samples and by analyzing ChIP-seq data from breast cancer patients measuring oestrogen receptor (ER) binding in primary breast cancer tissues. We show that MixChIP is more accurate in detecting TF binding sites from multiple heterogeneous ChIP-seq samples than the standard methods which do not account for sample heterogeneity.

Conclusions

Our results show that MixChIP can estimate cell-type proportions and identify cell type specific TF binding sites from heterogeneous ChIP-seq samples. Thus, MixChIP can be an invaluable tool in analyzing heterogeneous ChIP-seq samples, such as those originating from cancer studies. R implementation is available at http://research.ics.aalto.fi/csb/software/mixchip/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0834-3) contains supplementary material, which is available to authorized users.