News
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.
Diagnosis
Lung cancer drug shows breast cancer potential
Ovarian cancer cells quickly activate survival responses after PARP inhibitor treatment, and a lung cancer drug could help block this, research suggests.
PARP inhibitors are a common treatment for ovarian cancer, particularly in tumours with faulty DNA repair. They stop cancer cells fixing DNA damage, which leads to cell death, but many tumours later stop responding.
Researchers identified a way cancer cells may survive PARP inhibitor treatment from the outset, pointing to a potential way to block that response. A Mayo Clinic team found ovarian cancer cells rapidly switch on a pro-survival programme after exposure to PARP inhibitors. A key driver is FRA1, a transcription factor (a protein that turns genes on and off) that helps cancer cells adapt and avoid death.
The team then tested whether brigatinib, a drug approved for certain lung cancers, could block this response and boost the effect of PARP inhibitors. Brigatinib was chosen because it inhibits multiple signalling pathways involved in cancer cell survival.
In laboratory studies, combining brigatinib with a PARP inhibitor was more effective than either treatment alone. Notably, the effect was seen in cancer cells but not normal cells, suggesting a more targeted approach.
Brigatinib also appeared to act in an unexpected way. Rather than working through the usual DNA repair routes, it shut down two signalling molecules, FAK and EPHA2, that aggressive ovarian cancer cells rely on. FAK and EPHA2 are proteins that relay survival signals inside cells. Blocking both at once weakened the cells’ ability to adapt and resist treatment, making them more vulnerable to PARP inhibitors.
Tumours with higher levels of FAK and EPHA2 responded better to the drug combination. Other data link high levels of these molecules to more aggressive disease, pointing to potential benefit in harder-to-treat cases.
Arun Kanakkanthara, an oncology investigator at Mayo Clinic and a senior author of the study, said: “This work shows that drug resistance does not always emerge slowly over time; cancer cells can activate survival programmes very early after treatment begins.”
John Weroha, a medical oncologist at Mayo Clinic and a senior author of the study, said: “From a clinical perspective, resistance remains one of the biggest challenges in treating ovarian cancer. By combining mechanistic insights from Dr Kanakkanthara’s laboratory with my clinical experience, this preclinical work supports the strategy of targeting resistance early, before it has a chance to take hold. This strategy could improve patient outcomes.”
Insight
Higher nighttime temps linked to increased risk of autism diagnosis in children – study
Entrepreneur
Kindbody unveils next-gen fertility platform
-
Wellness4 weeks agoDesigner perfumes recalled over banned chemical posing fertility risk
-
Insight2 weeks agoParents sue IVF clinic after delivering someone else’s baby
-
Insight3 weeks agoWomen’s health could unlock US$100bn by 2030
-
Insight4 weeks agoChina’s birth rate hits record low despite government fertility efforts
-
Menopause3 weeks agoHRT linked to greater weight loss on tirzepatide
-
Entrepreneur6 days agoUS startup builds wearable hormone tracker
-
Menopause2 weeks agoFlo Health and Mayo Clinic publish global perimenopause awareness study
-
News4 weeks agoVerdane invest in Clue to accelerate the future of women’s health







