TISSUE-LOCATION SPECIFIC TRANSCRIPTIONAL PROGRAMS IN COLON DETERMINE MOLECULAR DEPENDENCIES FOR TUMOR INITIATION
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA903910 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR22361300_1.fastq.gz | Fastqsanger.gz | |||
| SRR22361300_2.fastq.gz | Fastqsanger.gz | |||
| SRR22362200_1.fastq.gz | Fastqsanger.gz | |||
| SRR22362200_2.fastq.gz | Fastqsanger.gz | |||
| SRR22361301_1.fastq.gz | Fastqsanger.gz |
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2023-12-12 | GSE218480 | GEO
Project description:It is not known why cancers arising in anatomically distinct locations but within the same tissue type can exhibit stark differences in molecular, pathological and clinical features. Cancers arising in proximal and distal sites of the colon are emblematic of these above differences as the proximal and distal colon cancers harbor differences in key molecular features, with BRAFV600E oncogene predominantly occurring in proximal colon cancers in the context of the increased promoter DNA methylation phenotype (CIMP-high), while distal colon cancers lack these molecular features. We determined the molecular basis of tissue-location specificity of driver mutations using normal mouse derived organoid models for colon cancer initiation. We show that the homeobox transcription factor, Cdx2, which is downregulated by DNA methylation in proximal colon cancers, plays distinct roles in regulating stem cell and differentiation in proximal compared to distal colon stem cells. Loss of Cdx2 altered the differentiation and stem cell programs and result in transformation by BRAFV600E specifically in the proximal but not distal colon stem cells. Analyses of RNA expression and Cdx2-binding to target genomic regions revealed that Cdx2 was a key effector of the transcriptional response to differentiation cues in proximal colon cells. This proximal colon-specific transcriptional program concurrently is tumor suppressive, and its loss sufficiently creates permissive state for oncogenic-BRAF mutations. The transcriptional program associated with Cdx2 loss in mouse proximal organoids matches that of human proximal colon cancers with downregulated CDX2 expression due to epigenetic silencing. Our analyses reveal that developmental and lineage-restricted transcription factors maintain tissue-location specific transcriptional programs which create critical dependencies for tumor initiation.
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Project description:Cancers of the same tissue-type but in anatomically distinct locations exhibit different molecular dependencies for tumorigenesis. Proximal and distal colon cancers exemplify such characteristics, with BRAFV600E predominantly occurring in proximal colon cancers along with increased DNA methylation phenotype. Using mouse colon organoids, here we show that proximal and distal colon stem cells have distinct transcriptional programs that regulate stemness and differentiation. We identify that the homeobox transcription factor, CDX2, which is silenced by DNA methylation in proximal colon cancers, is a key mediator of the differential transcriptional programs. Cdx2-mediated proximal colon-specific transcriptional program concurrently is tumor suppressive, and Cdx2 loss sufficiently creates permissive state for BRAFV600E-driven transformation. Human proximal colon cancers with CDX2 downregulation showed similar transcriptional program as in mouse proximal organoids with Cdx2 loss. Developmental transcription factors, such as CDX2, are thus critical in maintaining tissue-location specific transcriptional programs that create tissue-type origin specific dependencies for tumor development.
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Project description:MotivationKnowing the location of a protein within the cell is important for understanding its function, role in biological processes, and potential use as a drug target. Much progress has been made in developing computational methods that predict single locations for proteins. Most such methods are based on the over-simplifying assumption that proteins localize to a single location. However, it has been shown that proteins localize to multiple locations. While a few recent systems attempt to predict multiple locations of proteins, their performance leaves much room for improvement. Moreover, they typically treat locations as independent and do not attempt to utilize possible inter-dependencies among locations. Our hypothesis is that directly incorporating inter-dependencies among locations into both the classifier-learning and the prediction process can improve location prediction performance.ResultsWe present a new method and a preliminary system we have developed that directly incorporates inter-dependencies among locations into the location-prediction process of multiply-localized proteins. Our method is based on a collection of Bayesian network classifiers, where each classifier is used to predict a single location. Learning the structure of each Bayesian network classifier takes into account inter-dependencies among locations, and the prediction process uses estimates involving multiple locations. We evaluate our system on a dataset of single- and multi-localized proteins (the most comprehensive protein multi-localization dataset currently available, derived from the DBMLoc dataset). Our results, obtained by incorporating inter-dependencies, are significantly higher than those obtained by classifiers that do not use inter-dependencies. The performance of our system on multi-localized proteins is comparable to a top performing system (YLoc+), without being restricted only to location-combinations present in the training set.
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