Insight
Researchers share tool to improve newborn genetic screening

More than a decade ago, researchers launched the BabySeq Project, a pilot programme to return newborn genomic sequencing results to parents and measure the effects on newborn care.
Today, over 30 international initiatives are exploring the expansion of newborn screening using genomic sequencing (NBSeq), but a new study by researchers from Mass General Brigham highlights the substantial variability in gene selection among those programmes.
In a paper published in Genetics in Medicine, an official journal of the American College of Medical Genetics and Genomics, they offer a data-driven approach to prioritising genes for public health consideration.
“It’s critical that we be thoughtful about which genes and conditions are included in genomic newborn screening programmes,” said co-senior author Nina Gold, director of Prenatal Medical Genetics and Metabolism at Massachusetts General Hospital (MGH), a founding member of the Mass General Brigham healthcare system.
“By leveraging machine learning, we can provide a tool that helps policymakers and clinicians make more informed choices, ultimately improving the impact of genomic screening programmes.”
The authors introduce a machine learning model that brings structure and consistency to the selection of genes for NBSeq programmes. This is the first publication from the International Consortium of Newborn Sequencing (ICoNS), founded in 2021 by senior author Robert Green, director of the Genomes2People Research Program at Mass General Brigham, and David Bick of Genomics England in the United Kingdom.
Researchers analysed 4,390 genes included across 27 NBSeq programmes, identifying key factors influencing gene inclusion. While the number of genes analysed by each program ranged from 134 to 4,299, only 74 genes (1.7 per cent) were consistently included in over 80 per cent of programmes.
The strongest predictors of gene inclusion were whether the condition is on the U.S. Recommended Uniform Screening Panel, has robust natural history data, and if there is strong evidence of treatment efficacy.
Using these insights, the team developed a machine learning model incorporating 13 predictors, achieving high accuracy in predicting gene selection across programs. The model provides a ranked list of genes that can adapt to new evidence and regional needs, enabling more consistent and informed decision-making in NBSeq initiatives worldwide.
“This research represents a significant step toward harmonising NBSeq programs and ensuring that gene selection reflects the latest scientific evidence and public health priorities,” said Green.
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Common cancer marker may play active role in preventing the disease, study finds

Ki-67, a protein used to measure tumour growth, may also help prevent chromosome errors that drive cancer, a study suggests.
The findings could change how scientists view Ki-67, a marker commonly used in breast cancer and other tumours to assess how quickly cancer cells are growing.
Researchers found the protein may help preserve genome stability by maintaining the structural integrity of centromeres, key parts of chromosomes that help ensure DNA is shared correctly during cell division.
The research was led by professor Paola Vagnarelli at Brunel University of London in collaboration with scientists at the University of Edinburgh and the Technical University of Berlin.
Professor Vagnarelli said: “Doctors already measure Ki-67 to see how aggressive a cancer might be. But our results suggest it is actually helping maintain genome stability.
“That means it may be more than a marker. It could potentially also be a therapeutic target.”
The study examined three proteins that attach to chromosomes during cell division and help rebuild the molecular system that tells each new cell what kind of cell it is.
Every human cell carries identical DNA. What makes a liver cell different from a brain cell is which genes are switched on and which are kept inactive.
When a cell divides, that entire system of switches must be rebuilt. The three proteins involved in this process were Ki-67, Repo-Man and PNUTS.
Vagnarelli’s team developed a method that individually removes each protein from a living cell at the precise point of division. Older techniques could not isolate that moment cleanly.
They found that cells rely on all three proteins to reset themselves after division, but each failed in a different way when removed.
Without PNUTS, gene activity spiralled out of control and thousands of genes switched on at once.
Without Repo-Man, cells escaped safety checkpoints that usually stop damaged or abnormal cells from continuing to divide.
“What we didn’t expect was how clean the separation was,” said Vagnarelli.
Each protein fails in its own specific way. There is no redundancy, no safety net. Which means there are three separate points at which this process can go wrong.
“When the system breaks down, cells can emerge with the wrong number of chromosomes. That condition, called aneuploidy, is seen in disorders such as Down syndrome and in many cancers.
“We also found that these chromosome errors can trigger inflammatory signals inside the cell.”
Aneuploidy means a cell has too many or too few chromosomes, which can disrupt normal growth and function.
Inflammatory signals are chemical messages that can make a cell behave as if it is responding to injury or infection.
“These cells behave almost as if they are under attack,” said Vagnarelli.
“The immune response switches on because the genome is unstable.
“That link between chromosome imbalance and inflammation could help explain patterns we see in several diseases.”
The researchers said the findings may help cancer scientists better understand how chromosome instability, loss of gene regulation and cells dividing before they are ready contribute to tumour growth.
They said understanding the normal machinery that prevents these errors may help researchers find ways to push cancer cells into making mistakes they cannot survive.
“We now have a clearer map of the machinery that resets the cell after division,” said Vagnarelli.
“That knowledge gives us a starting point for thinking about new therapeutic approaches.”
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