Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors

Intratumoral heterogeneity is a major obstacle to cancer treatment and a significant confounding factor in bulk-tumor profiling. We performed an unbiased analysis of transcriptional heterogeneity in colorectal tumors and their microenvironments using single-cell RNA–seq from 11 primary colorectal tumors and matched normal mucosa. To robustly cluster single-cell transcriptomes, we developed reference component analysis (RCA), an algorithm that substantially improves clustering accuracy. Using RCA, we identified two distinct subtypes of cancer-associated fibroblasts (CAFs). Additionally, epithelial–mesenchymal transition (EMT)-related genes were found to be upregulated only in the CAF subpopulation of tumor samples. Notably, colorectal tumors previously assigned to a single subtype on the basis of bulk transcriptomics could be divided into subgroups with divergent survival probability by using single-cell signatures, thus underscoring the prognostic value of our approach. Overall, our results demonstrate that unbiased single-cell RNA–seq profiling of tumor and matched normal samples provides a unique opportunity to characterize aberrant cell states within a tumor.

[1]  J. Concordet,et al.  Illegitimate transcription: transcription of any gene in any cell type. , 1989, Proceedings of the National Academy of Sciences of the United States of America.

[2]  A. Jemal,et al.  Global cancer statistics , 2011, CA: a cancer journal for clinicians.

[3]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[4]  A. Evens,et al.  Motexafin gadolinium: a redox-active tumor selective agent for the treatment of cancer , 2004, Current opinion in oncology.

[5]  S. Ramaswamy,et al.  Twist, a Master Regulator of Morphogenesis, Plays an Essential Role in Tumor Metastasis , 2004, Cell.

[6]  Mehmet Toner,et al.  Application of genome-wide expression analysis to human health and disease. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[7]  M. Monden,et al.  Stromal Myofibroblasts Predict Disease Recurrence for Colorectal Cancer , 2007, Clinical Cancer Research.

[8]  E. Ross,et al.  Clinical Implications of Fibroblast Activation Protein in Patients with Colon Cancer , 2007, Clinical Cancer Research.

[9]  Jeffrey W. Pollard,et al.  Macrophage Diversity Enhances Tumor Progression and Metastasis , 2010, Cell.

[10]  A. Jemal,et al.  Global Cancer Statistics , 2011 .

[11]  Pradeep S Rajendran,et al.  Single-cell dissection of transcriptional heterogeneity in human colon tumors , 2011, Nature Biotechnology.

[12]  Hans Clevers,et al.  The intestinal stem cell signature identifies colorectal cancer stem cells and predicts disease relapse. , 2011, Cell stem cell.

[13]  Douglas Hanahan,et al.  Accessories to the Crime: Functions of Cells Recruited to the Tumor Microenvironment Prospects and Obstacles for Therapeutic Targeting of Function-enabling Stromal Cell Types , 2022 .

[14]  P. Jung,et al.  Dependency of colorectal cancer on a TGF-β-driven program in stromal cells for metastasis initiation. , 2012, Cancer cell.

[15]  Katie M. Reindl,et al.  Overexpression of peptide deformylase in breast, colon, and lung cancers , 2013, BMC Cancer.

[16]  S. Gottschalk,et al.  Cancer-associated fibroblasts as targets for immunotherapy. , 2012, Immunotherapy.

[17]  J. Isaacs,et al.  Rationale Behind Targeting Fibroblast Activation Protein–Expressing Carcinoma-Associated Fibroblasts as a Novel Chemotherapeutic Strategy , 2012, Molecular Cancer Therapeutics.

[18]  Peptide deformylase inhibitor actinonin reduces celastrol’s HSP70 induction while synergizing proliferation inhibition in tumor cells , 2014, BMC Cancer.

[19]  Mira Ayadi,et al.  Gene Expression Classification of Colon Cancer into Molecular Subtypes: Characterization, Validation, and Prognostic Value , 2013, PLoS medicine.

[20]  M. Junttila,et al.  Influence of tumour micro-environment heterogeneity on therapeutic response , 2013, Nature.

[21]  Florian Markowetz,et al.  Poor-prognosis colon cancer is defined by a molecularly distinct subtype and develops from serrated precursor lesions , 2013, Nature Medicine.

[22]  A. G. de Herreros,et al.  Functional Heterogeneity of Cancer-Associated Fibroblasts from Human Colon Tumors Shows Specific Prognostic Gene Expression Signature , 2013, Clinical Cancer Research.

[23]  Corbin E. Meacham,et al.  Tumour heterogeneity and cancer cell plasticity , 2013, Nature.

[24]  N. McGranahan,et al.  The causes and consequences of genetic heterogeneity in cancer evolution , 2013, Nature.

[25]  J. Utikal,et al.  Dose-dependent roles for canonical Wnt signalling in de novo crypt formation and cell cycle properties of the colonic epithelium , 2013, Development.

[26]  Lewis C Cantley,et al.  A colorectal cancer classification system that associates cellular phenotype and responses to therapy , 2013, Nature Medicine.

[27]  D. Haussler,et al.  Exploring TCGA Pan-Cancer Data at the UCSC Cancer Genomics Browser , 2013, Scientific Reports.

[28]  R. Lothe,et al.  BCL-XL Mediates the Strong Selective Advantage of a 20q11.21 Amplification Commonly Found in Human Embryonic Stem Cell Cultures , 2013, Stem cell reports.

[29]  Andreas Krämer,et al.  Causal analysis approaches in Ingenuity Pathway Analysis , 2013, Bioinform..

[30]  W. Mesker,et al.  Interaction with colon cancer cells hyperactivates TGF-β signaling in cancer-associated fibroblasts , 2014, Oncogene.

[31]  N. Neff,et al.  Reconstructing lineage hierarchies of the distal lung epithelium using single cell RNA-seq , 2014, Nature.

[32]  Xin Wang,et al.  Reconciliation of classification systems defining molecular subtypes of colorectal cancer , 2014, Cell cycle.

[33]  M. Augsten.  Cancer-Associated Fibroblasts as Another Polarized Cell Type of the Tumor Microenvironment , 2014, Front. Oncol..

[34]  Shawn M. Gillespie,et al.  Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma , 2014, Science.

[35]  Ash A. Alizadeh,et al.  Abstract PR09: The prognostic landscape of genes and infiltrating immune cells across human cancers , 2015 .

[36]  Jeffrey S. Morris,et al.  The Consensus Molecular Subtypes of Colorectal Cancer , 2015, Nature Medicine.

[37]  P. Linsley,et al.  MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data , 2015, Genome Biology.

[38]  Stephen T. C. Wong,et al.  EMT is not required for lung metastasis but contributes to chemoresistance , 2015, Nature.

[39]  Hans Clevers,et al.  Single-cell messenger RNA sequencing reveals rare intestinal cell types , 2015, Nature.

[40]  P. Aloy,et al.  Isolation of Human Colon Stem Cells Using Surface Expression of PTK7 , 2015, Stem cell reports.

[41]  S. Linnarsson,et al.  Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq , 2015, Science.

[42]  L. Liang,et al.  Improved ancestry estimation for both genotyping and sequencing data using projection procrustes analysis and genotype imputation. , 2015, American journal of human genetics.

[43]  N. Navin,et al.  The first five years of single-cell cancer genomics and beyond , 2015, Genome research.

[44]  A. Jemal,et al.  Global cancer statistics, 2012 , 2015, CA: a cancer journal for clinicians.

[45]  Mingxiang Teng,et al.  On the widespread and critical impact of systematic bias and batch effects in single-cell RNA-Seq data , 2015 .

[46]  Camille Stephan-Otto Attolini,et al.  Stromal gene expression defines poor-prognosis subtypes in colorectal cancer , 2015, Nature Genetics.

[47]  M. Tsuboi,et al.  Cancer cell invasion driven by extracellular matrix remodeling is dependent on the properties of cancer-associated fibroblasts , 2016, Journal of Cancer Research and Clinical Oncology.

[48]  V. LeBleu,et al.  EMT Program is Dispensable for Metastasis but Induces Chemoresistance in Pancreatic Cancer , 2015, Nature.

[49]  Wolfgang Huber,et al.  Single-cell transcriptome analysis reveals coordinated ectopic gene expression patterns in medullary thymic epithelial cells , 2015, Nature Immunology.

[50]  G. Inghirami,et al.  Stromal contribution to the colorectal cancer transcriptome , 2015, Nature Genetics.

[51]  A. Regev,et al.  Spatial reconstruction of single-cell gene expression data , 2015 .

[52]  M. Cugmas,et al.  On comparing partitions , 2015 .

[53]  Mauro J. Muraro,et al.  De Novo Prediction of Stem Cell Identity using Single-Cell Transcriptome Data , 2016, Cell stem cell.

[54]  Debarka Sengupta,et al.  Fast, scalable and accurate differential expression analysis for single cells , 2016, bioRxiv.

[55]  D. Zwijnenburg,et al.  Collagen-rich stroma in aggressive colon tumors induces mesenchymal gene expression and tumor cell invasion , 2016, Oncogene.

[56]  Charles H. Yoon,et al.  Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq , 2016, Science.