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Clustering Digestive Tract Tumors Using Transcriptomic and Mutation Data
Dwayne G Tally1, Polina Bombina2, Jake Reed3
1Department of Informatics, Indiana University, Bloomington, IN 47408, USA.
Cancers
|May 13, 2026
Summary
A new method called Newmanization improves cancer classification by analyzing gene expression data. This technique enhances the separation of digestive tract cancer subtypes, paving the way for personalized medicine.
Area of Science:
- Genomics
- Bioinformatics
- Cancer Research
Background:
- Digestive tract cancers are typically classified by their tissue of origin.
- Transcriptome-based molecular clustering often yields similar classifications.
- Existing methods may not fully capture distinct molecular subtypes.
Purpose of the Study:
- To introduce Newmanization, a novel method for reducing tissue-specific signals in transcriptomic analysis.
- To evaluate the effectiveness of Newmanized data in classifying digestive tract cancers.
- To compare Newmanization with traditional mutation data for cancer subtyping.
Main Methods:
- Developed the Newmanization technique to refine transcriptomic data.
- Analyzed 1635 digestive tract cancer samples from The Cancer Genome Atlas.
- Utilized RNA-Seq and whole exome sequencing data.
- Compared Newmanized transcriptome and mutation data using silhouette widths and dimension reduction plots.
Main Results:
- Newmanized transcriptome data demonstrated clearer separation and higher average silhouette widths compared to unadjusted data.
- Newmanized clusters revealed a higher frequency of specific messenger RNAs (mRNAs).
- Clusters derived from Newmanized data showed distinct molecular features.
Conclusions:
- The Newmanization method significantly improves the molecular classification of digestive tract cancers.
- Newmanized data provides a more refined basis for identifying cancer subtypes.
- This approach shows promise for advancing personalized transcriptomic medicine.
