Prediction Of Intratumor Transcriptional Heterogeneity From BulkTumors
Agnieszka Kraft1,2, Auguste Lefevre1, Michaela B Kirschner2, Mayura Meerang2,Isabelle Opitz2, Mitchell P Levesque3,4, TUPRO Consortium, Valentina Boeva1,5,6
1

ETH Zürich, Department of Computer Science, Institute for Machine Learning,Zurich, Switzerland
2
Department of Thoracic Surgery, University Hospital Zurich, Zurich, Switzerland
3
Department of Dermatology, University Hospital Zurich, Zurich, Switzerland
4
Faculty of Medicine, University of Zurich, Zurich, Switzerland
5
Swiss Institute of Bioinformatics (SIB), Zurich, Switzerland
6Institut Cochin, Inserm U1016, CNRS UMR 8104, Université Paris Descartes
UMR-S1016, Paris, FranceAbstract
In recent years tumor cell composition was associated with patients’ survival andresponse to treatment. Although there were many studies on the composition oftumor microenvironment (TME), for many tumors the knowledge about
differences between transcriptional profiles of cancer cells (here referred to as
Intratumor Heterogeneity, ITH) is still limited. Previous studies on melanoma 1,2,gastric adenocarcinoma3 and neuroblastoma4 identified subpopulations ofmesenchymal-like malignant cells expressing markers of epithelial-tomesenchymal transformation (EMT), which were often associated with worse
prognosis, metastases and resistance to treatment 3,4. On the other hand, a vast
majority of cancers have not been studied in this aspect, mostly due to thelimited availability of scRNA-seq data. The majority of available methods for bulk
data deconvolution assume prior knowledge of mixed components (DeMix 5,UNDO6, ESTIMATE7) or require pure expression of components provided as areference (DeconRNAseq8, CIBERSORT9). More recent method10 proposes anapproach based on cancer subtyping into distinct molecular clusters, but does
not address the possibility that tumors may consist of a mixture of cancer-cell
subtypes. To overcome these limitations, we propose an unsupervised method topredict and analyze cancer-cell subpopulations from bulk data. By using matchedbulk RNA-seq and scRNA-seq data from skin melanoma patients we established
an approach which successfully separates bulk signal into respective
components, separates cancer-cell subpopulations from TME-associated ones
and measures ITH as a gradient between proportions of identified cancer-cell
subpopulations within each tumor. Our method characterizes identified cancercell components by association with patients’ survival, enriched pathways anddriver mutations. Finally, we apply our method to 33 TCGA datasets and provide
an overview of cancer-cells transcriptional profiles on a pan-cancer scale.
Keywords cancer, deconvolution, bulk RNA-seq, intratumor heterogeneity
Relevant topics(1) Graphical representation of biological knowledge(2) Multi-scale networks (genome, epigenome, transcriptome, proteome,metabolome, lipidome..)(3) Single-cell data and network inferenceReferences
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