
Researchers at The University of Texas MD Anderson Cancer Center and The University of Texas at Austin have identified specific blood-based genomic biomarkers that distinguish inflammatory breast cancer from other subtypes, providing a new and less invasive method for early diagnosis, disease progression monitoring and treatment development for patients with this aggressive disease.
The study, published in Science Advances, used an improved method of RNA sequencing, called TGIRT sequencing, that allows for a more comprehensive overview of all RNA types and amounts present in a given sample. The research was led by Savitri Krishnamurthy, M.D., professor of Anatomic Pathology at UT MD Anderson, in collaboration with Alan Lambowitz, Ph.D., at UT Austin, and Naoto Ueno M.D, Ph.D., at the University of Hawai’i Cancer Center.
“These findings provide new insights into inflammatory breast cancer that should enable clinicians to monitor disease progression simply through liquid biopsy,” Krishnamurthy said. “Because it is so difficult to obtain tumor samples, these blood-based biomarkers could be truly transformative in developing treatments for this patient population.”
Why is it so hard to identify biomarkers for IBC and what did the researchers do differently?
IBC is considered the most lethal and aggressive breast cancer type, but most genome- sequencing techniques have been unable to distinguish IBC from non-inflammatory breast cancer because their cancer-related gene mutations are so similar.
Additionally, standard RNA-sequencing methods use enzymes that often struggle with more complex RNA, meaning a lot of information gets skipped or missed in a given sample.
In this study, the researchers used a specialized sequencing method – TGIRT – which employs a more robust enzyme that can handle extreme environments, making it more reliable and able to capture the difficult, complex and fragmented RNAs.
What distinguishes IBC from other breast cancers?
TGIRT sequencing helped the researchers develop methods to analyze different protein-coding genes specific to IBC tumors. They also found that blood samples from IBC patients had high levels of noncoding RNAs and higher levels of white blood cells compared to those of healthy or non-IBC patients. This suggests that the immune system is activated and that there are imbalances in RNA splicing, which decreases mRNA production.
Similarly, in plasma samples, the overrepresented genes in IBC were intron RNA fragments, the noncoding parts inside genes that normally are spliced out. However, healthy blood mostly had mRNA fragments, which are shorter, broken-down segments of messenger RNA that normally are degraded to control gene activity.
What does this mean for patients who may have IBC?
Overall, the researchers were able to identify several potential blood-based biomarkers of IBC in tumors, peripheral blood cells and plasma, leading to potentially more effective ways to diagnose and monitor disease progression. These biomarkers can also help with the development of new therapeutic strategies to address the unique features of this aggressive breast cancer subtype.
Source – Newswise
Researchers at The University of Texas MD Anderson Cancer Center and The University of Texas at Austin have identified specific blood-based genomic biomarkers that distinguish inflammatory breast cancer from other subtypes, providing a new and less invasive method for early diagnosis, disease progression monitoring and treatment development for patients with this aggressive disease.
The study, published in Science Advances, used an improved method of RNA sequencing, called TGIRT sequencing, that allows for a more comprehensive overview of all RNA types and amounts present in a given sample. The research was led by Savitri Krishnamurthy, M.D., professor of Anatomic Pathology at UT MD Anderson, in collaboration with Alan Lambowitz, Ph.D., at UT Austin, and Naoto Ueno M.D, Ph.D., at the University of Hawai’i Cancer Center.
Why is it so hard to identify biomarkers for IBC and what did the researchers do differently?
IBC is considered the most lethal and aggressive breast cancer type, but most genome- sequencing techniques have been unable to distinguish IBC from non-inflammatory breast cancer because their cancer-related gene mutations are so similar.
Additionally, standard RNA-sequencing methods use enzymes that often struggle with more complex RNA, meaning a lot of information gets skipped or missed in a given sample.
In this study, the researchers used a specialized sequencing method – TGIRT – which employs a more robust enzyme that can handle extreme environments, making it more reliable and able to capture the difficult, complex and fragmented RNAs.
What distinguishes IBC from other breast cancers?
TGIRT sequencing helped the researchers develop methods to analyze different protein-coding genes specific to IBC tumors. They also found that blood samples from IBC patients had high levels of noncoding RNAs and higher levels of white blood cells compared to those of healthy or non-IBC patients. This suggests that the immune system is activated and that there are imbalances in RNA splicing, which decreases mRNA production.
Similarly, in plasma samples, the overrepresented genes in IBC were intron RNA fragments, the noncoding parts inside genes that normally are spliced out. However, healthy blood mostly had mRNA fragments, which are shorter, broken-down segments of messenger RNA that normally are degraded to control gene activity.
What does this mean for patients who may have IBC?
Overall, the researchers were able to identify several potential blood-based biomarkers of IBC in tumors, peripheral blood cells and plasma, leading to potentially more effective ways to diagnose and monitor disease progression. These biomarkers can also help with the development of new therapeutic strategies to address the unique features of this aggressive breast cancer subtype.
Source – Newswise
Wylie D, Wang X, Yao J, Xu H, Ferrick-Kiddie E, Iwase T, Krishnamurthy S, Ueno N, Lambowitz A. (2026) Pervasive enhanced transcription in inflammatory breast cancer tumors and PBMCs impacts RNA splicing and intronic RNAs in plasma. Science Advances 12(18): eadu0031. [article]
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Researchers at The University of Texas MD Anderson Cancer Center and The University of Texas at Austin have identified specific blood-based genomic biomarkers that distinguish inflammatory breast cancer from other subtypes, providing a new and less invasive method for early diagnosis, disease progression monitoring and treatment development for patients with this aggressive disease.
The study, published in Science Advances, used an improved method of RNA sequencing, called TGIRT sequencing, that allows for a more comprehensive overview of all RNA types and amounts present in a given sample. The research was led by Savitri Krishnamurthy, M.D., professor of Anatomic Pathology at UT MD Anderson, in collaboration with Alan Lambowitz, Ph.D., at UT Austin, and Naoto Ueno M.D, Ph.D., at the University of Hawai’i Cancer Center.
Why is it so hard to identify biomarkers for IBC and what did the researchers do differently?
IBC is considered the most lethal and aggressive breast cancer type, but most genome- sequencing techniques have been unable to distinguish IBC from non-inflammatory breast cancer because their cancer-related gene mutations are so similar.
Additionally, standard RNA-sequencing methods use enzymes that often struggle with more complex RNA, meaning a lot of information gets skipped or missed in a given sample.
In this study, the researchers used a specialized sequencing method – TGIRT – which employs a more robust enzyme that can handle extreme environments, making it more reliable and able to capture the difficult, complex and fragmented RNAs.
What distinguishes IBC from other breast cancers?
TGIRT sequencing helped the researchers develop methods to analyze different protein-coding genes specific to IBC tumors. They also found that blood samples from IBC patients had high levels of noncoding RNAs and higher levels of white blood cells compared to those of healthy or non-IBC patients. This suggests that the immune system is activated and that there are imbalances in RNA splicing, which decreases mRNA production.
Similarly, in plasma samples, the overrepresented genes in IBC were intron RNA fragments, the noncoding parts inside genes that normally are spliced out. However, healthy blood mostly had mRNA fragments, which are shorter, broken-down segments of messenger RNA that normally are degraded to control gene activity.
What does this mean for patients who may have IBC?
Overall, the researchers were able to identify several potential blood-based biomarkers of IBC in tumors, peripheral blood cells and plasma, leading to potentially more effective ways to diagnose and monitor disease progression. These biomarkers can also help with the development of new therapeutic strategies to address the unique features of this aggressive breast cancer subtype.
Source – Newswise
Wylie D, Wang X, Yao J, Xu H, Ferrick-Kiddie E, Iwase T, Krishnamurthy S, Ueno N, Lambowitz A. (2026) Pervasive enhanced transcription in inflammatory breast cancer tumors and PBMCs impacts RNA splicing and intronic RNAs in plasma. Science Advances 12(18): eadu0031. [article]
Related Posts
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
From bench to bytes: a practical guide to RNA sequencing data analysis
RNA sequencing resolves cryptic pathogenic variants in mitochondrial disease
Unlocking the past – new method helps gain insights into old tissue
New RNA sequencing model improves sequencing depth planning for UMI transcriptomics
Combining RNA sequencing and pathology images identifies glioblastoma subgroups linked to survival
Novel AI model trained on RNA-Seq data accurately detects key gene mutations and predicts biomarkers across 32 cancer types
MitoClipSplice – machine learning improves RNA sequencing analysis of mitochondrial RNA processing
Transcriptomic aging clock reveals age-related molecular patterns in opioid dependence
Machine learning improves RNA sequencing annotation of active genes
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