Researchers have discovered an alternative way to classify distinct types of colon cancer, making the information more valuable to patients and their doctors as they consider treatment.
A team at Wilmot Cancer Institute collaborated with a German company, Indivumed Therapeutics, on the project. Currently, colon cancers can be classified into four subtypes based on gene expression patterns, yet this method can be unreliable and is very costly, scientists said. In the new proof-of-concept study, researchers found that using RNA splicing events rather than gene-expression analysis offers more precise and lower cost tumor-type identification. When a patient is diagnosed, this step — identifying the unique characteristics and molecular properties of tumors — is crucial to determining prognosis and what medications may work best to attack the disease.
The journal Gastroenterology reported the research today. Hucky Land, PhD, deputy director at Wilmot and chair of the University of Rochester Medical Center Department of Biomedical Genetics, is corresponding author for the publication. He credits Aslihan Ambeskovic, PhD, lead bioinformatics analyst in the Land lab, for conducting most of the work using RNA sequencing data from hundreds of human colon cancer tissue samples. Matthew N. McCall, PhD, associate professor of Biostatistics, is also a co-author.

Calling the discovery “a significant advance based on biological principles that is highly translational,” Land noted that the next step is to develop a diagnostic test suitable for the clinic.
Colorectal cancers have a complex landscape of genetic and epigenetic alterations. Some subtypes, for example, may respond better to immunotherapy while certain chemotherapy regimens may be the correct approach for other subtypes.
Researchers believe their newly discovered subtype identifier is accurate and reliable because variation in RNA splicing holds more relevant information in each cancer specimen.
Source – University of Rochester Medical Center
Researchers have discovered an alternative way to classify distinct types of colon cancer, making the information more valuable to patients and their doctors as they consider treatment.
A team at Wilmot Cancer Institute collaborated with a German company, Indivumed Therapeutics, on the project. Currently, colon cancers can be classified into four subtypes based on gene expression patterns, yet this method can be unreliable and is very costly, scientists said. In the new proof-of-concept study, researchers found that using RNA splicing events rather than gene-expression analysis offers more precise and lower cost tumor-type identification. When a patient is diagnosed, this step — identifying the unique characteristics and molecular properties of tumors — is crucial to determining prognosis and what medications may work best to attack the disease.
The journal Gastroenterology reported the research today. Hucky Land, PhD, deputy director at Wilmot and chair of the University of Rochester Medical Center Department of Biomedical Genetics, is corresponding author for the publication. He credits Aslihan Ambeskovic, PhD, lead bioinformatics analyst in the Land lab, for conducting most of the work using RNA sequencing data from hundreds of human colon cancer tissue samples. Matthew N. McCall, PhD, associate professor of Biostatistics, is also a co-author.
Colorectal cancers have a complex landscape of genetic and epigenetic alterations. Some subtypes, for example, may respond better to immunotherapy while certain chemotherapy regimens may be the correct approach for other subtypes.
Researchers believe their newly discovered subtype identifier is accurate and reliable because variation in RNA splicing holds more relevant information in each cancer specimen.
Source – University of Rochester Medical Center
Ambeskovic A, McCall MN, Woodsmith J, Juhl H, Land H. (2024) Exon-Skipping-Based Subtyping of Colorectal Cancers. Gastroenterology [Epub ahead of print]. [article]
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Researchers have discovered an alternative way to classify distinct types of colon cancer, making the information more valuable to patients and their doctors as they consider treatment.
A team at Wilmot Cancer Institute collaborated with a German company, Indivumed Therapeutics, on the project. Currently, colon cancers can be classified into four subtypes based on gene expression patterns, yet this method can be unreliable and is very costly, scientists said. In the new proof-of-concept study, researchers found that using RNA splicing events rather than gene-expression analysis offers more precise and lower cost tumor-type identification. When a patient is diagnosed, this step — identifying the unique characteristics and molecular properties of tumors — is crucial to determining prognosis and what medications may work best to attack the disease.
The journal Gastroenterology reported the research today. Hucky Land, PhD, deputy director at Wilmot and chair of the University of Rochester Medical Center Department of Biomedical Genetics, is corresponding author for the publication. He credits Aslihan Ambeskovic, PhD, lead bioinformatics analyst in the Land lab, for conducting most of the work using RNA sequencing data from hundreds of human colon cancer tissue samples. Matthew N. McCall, PhD, associate professor of Biostatistics, is also a co-author.
Colorectal cancers have a complex landscape of genetic and epigenetic alterations. Some subtypes, for example, may respond better to immunotherapy while certain chemotherapy regimens may be the correct approach for other subtypes.
Researchers believe their newly discovered subtype identifier is accurate and reliable because variation in RNA splicing holds more relevant information in each cancer specimen.
Source – University of Rochester Medical Center
Ambeskovic A, McCall MN, Woodsmith J, Juhl H, Land H. (2024) Exon-Skipping-Based Subtyping of Colorectal Cancers. Gastroenterology [Epub ahead of print]. [article]
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POND-seq enables non-destructive RNA sequencing in living cells
Worm’s radical transformation shows metamorphosis can change the functions of cells
New method allows scientists to follow gene activity over time in the same cells
Single-cell and single-embryo RNA sequencing
RNA sequencing reveals functional chimeric mRNAs in mammalian immunity
Deep learning improves microRNA target prediction from sequence
Atlas of the brain’s striatum could guide researchers to new drug treatments
scLS – a computationally efficient differentially expressed gene detection algorithm
Spatial mapping of RNA turnover kinetics in the mouse brain
Immune cells offer insights on billion-dollar virus
SPIDER improves spatial transcriptomics data using single-cell RNA sequencing
Ultrafast and reference-free sequence discovery in single-cell data
ARCADIA combines RNA sequencing and spatial proteomics to reveal how tissue location shapes cell behavior
An end-to-end computational framework for “Record-seq” transcriptional recording data
A functionally integrated cross-tissue alternative splicing program during short-term calorie restriction
ExoShorkie – predicting RNA-seq coverage of exogenous genomes in yeast by transfer learning
Dietary oxidized plant sterol shifts macrophage state to fuel aortic inflammation
Bonsai reconstructs tree representations for distortion-free visualization and exploration of high-dimensional data
MiRQuery – a user-friendly web app for the interactive analysis and visualization of microRNA sequencing data
RNA sequencing resolves cryptic pathogenic variants in mitochondrial disease
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