Though not biomarkers per se, these scores for certain genetic traits may someday be used by clinical laboratories to identify individuals’ risk for specific diseases
Can polygenic risk scores (a number that denotes a person’s genetic predisposition for certain traits) do a better job at predicting the likelihood of developing specific diseases, perhaps even before the onset of symptoms? Researchers at the Broad Institute of MIT and Harvard (Broad Institute) believe so, and their study could have implications for clinical laboratories nationwide.
In cooperation with medical centers across the US, the scientists “optimized 10 polygenic scores for use in clinical research as part of a study on how to implement genetic risk prediction for patients,” according to a Broad Institute news release.
The research team “selected, optimized, and validated the tests for 10 common diseases [selected from a total of 23 conditions], including heart disease, breast cancer, and type 2 diabetes. They also calibrated the tests for use in people with non-European ancestries,” the news release notes.
As these markers for genetic risk become better understood they may work their way into clinical practice. This could mean clinical laboratories will have a role in sequencing patients’ DNA to provide physicians with information about the probability of a patient’s elevated genetic risk for certain conditions.
However, the effectiveness of polygenic risk scores has faced challenges among diverse populations, according to the news release, which also noted a need to appropriately guide clinicians in use of the scores.
“With this work, we’ve taken the first steps toward showing the potential strength and power of these scores across a diverse population,” said Niall Lennon, PhD (above), Chief Scientific Officer of Broad Clinical Labs. “We hope in the future this kind of information can be used in preventive medicine to help people take actions that lower their risk of disease.” Clinical laboratories may eventually be tasked with performing DNA sequencing to determine potential genetic risk for certain diseases. (Photo copyright: Broad Institute.)
Polygenic Scores Need to Reflect Diversity
“There have been a lot of ongoing conversations and debates about polygenic risk scores and their utility and applicability in the clinical setting,” said Niall Lennon, PhD, Chair and Chief Scientific Officer of Broad Clinical Labs and first author of the study, in the news release. However, he added, “It was important that we weren’t giving people results that they couldn’t do anything about.”
In the paper, Lennon and colleagues explained polygenic risk scores “aggregate the effects of many genetic risk variants” to identify a person’s genetic predisposition for a certain disease or phenotype.
“But their development and application to clinical care, particularly among ancestrally diverse individuals, present substantial challenges,” they noted. “Clinical use of polygenic risk scores may ultimately prevent disease or enable its detection at earlier, more treatable stages.”
The scientists set a research goal to “optimize polygenic risk scores for a diversity of people.”
While performing the polygenic risk score testing on participants, Broad Clinical Labs focused on 10 conditions—including cardiometabolic diseases and cancer—selected by the research team based on “polygenic risk score performance, medical actionability, and clinical utility,” the Nature Medicine paper explained.
For each condition, the researchers:
Identified “exact spots in the genome that they would analyze to calculate the risk score.”
Used information from the NIH’s All of Us Research Program to “create a model to calibrate a person’s polygenic risk score according to that individual’s genetic ancestry.”
The All of Us program, which aims to collect health information from one million US residents, has three times more people of non-European ancestry than other data sources developing genetic risk scores, HealthDay News reported.
20% of Study Participants Showed High Risk for Disease
To complete their studies, Broad Institute researchers processed a diverse group of eMERGE participants to determine their clinical polygenic risk scores for each of the 10 diseases between July 2022 and August 2023.
Listed below are all conditions studied, as well as the number of participants involved in each study and the number of people with scores indicating high risk of the disease, according to their published paper:
Over 500 people (about 20%) of the 2,500 participants, had high risk for at least one of the 10 targeted diseases, the study found.
Participants in the study self-reported their race/ancestry as follows, according to the paper:
White: 32.8%
Black: 32.8%
Hispanic: 25.4%
Asian: 5%
American Indian: 1.5%
Middle Eastern: 0.9%
No selection: 0.8%
“We can’t fix all biases in the risk scores, but we can make sure that if a person is in a high-risk group for a disease, they’ll get identified as high risk regardless of what their genetic ancestry is,” Lennon said.
Further Studies, Scoring Implications
With 10 tests in hand, Broad Clinical Labs plans to calculate risk scores for all 25,000 people in the eMERGE network. The researchers also aim to conduct follow-up studies to discover what role polygenic risk scores may play in patients’ overall healthcare.
“Ultimately, the network wants to know what it means for a person to receive information that says they’re at high risk for one of these diseases,” Lennon said.
The researchers’ findings about disease risk are likely also relevant to healthcare systems, which want care teams to make earlier, pre-symptomatic diagnosis to keep patients healthy.
Clinical laboratory leaders may want to follow Broad Clinical Labs’ studies as they perform the 10 genetic tests and capture information about what participants may be willing to do—based on risk scores—to lower their risk for deadly diseases.
Speedy DNA sequencing and on-the-spot digital imaging may change the future of anatomic pathology procedures during surgery
Researchers at the Center for Molecular Medicine (CMM) at UMC Utrecht, a leading international university medical center in the Netherlands, have paired artificial intelligence (AI) and machine learning with DNA sequencing to develop a diagnostic tool cancer surgeons can use during surgeries to determine in minutes—while the patient is still on the operating table—whether they have fully removed all the cancerous tissue.
The method, “involves a computer scanning segments of a tumor’s DNA and alighting on certain chemical modifications that can yield a detailed diagnosis of the type and even subtype of the brain tumor,” according to The New York Times, which added, “That diagnosis, generated during the early stages of an hours-long surgery, can help surgeons decide how aggressively to operate, … In the future, the method may also help steer doctors toward treatments tailored for a specific subtype of tumor.”
This technology has the potential to reduce the need for frozen sections, should additional development and studies confirm that it accurately and reliably shows surgeons that all cancerous cells were fully removed. Many anatomic pathologists would welcome such a development because of the time pressure and stress associated with this procedure. Pathologists know that the patient is still in surgery and the surgeons are waiting for the results of the frozen section. Most pathologists would consider fewer frozen sections—with better patient outcomes—to be an improvement in patient care.
“It’s imperative that the tumor subtype is known at the time of surgery,” Jeroen de Ridder, PhD (above), associate professor in the Center for Molecular Medicine at UMC Utrecht and one of the study leaders, told The New York Times. “What we have now uniquely enabled is to allow this very fine-grained, robust, detailed diagnosis to be performed already during the surgery. It can figure out itself what it’s looking at and make a robust classification,” he added. How this discovery affects the role of anatomic pathologists and pathology laboratories during cancer surgeries remains to be seen. (Photo copyright: UMC Utrecht.)
Rapid DNA Sequencing Impacts Brain Tumor Surgeries
The UMC Utrecht scientists employed Oxford Nanopore’s “real-time DNA sequencing technology to address the challenges posed by central nervous system (CNS) tumors, one of the most lethal type of tumor, especially among children,” according to an Oxford Nanopore news release.
The researchers called their new machine learning AI application the “Sturgeon.”
According to The New York Times, “The new method uses a faster genetic sequencing technique and applies it only to a small slice of the cellular genome, allowing it to return results before a surgeon has started operating on the edges of a tumor.”
Jeroen de Ridder, PhD, an associate professor in the Center for Molecular Medicine at UMC Utrecht, told The New York Times that Sturgeon is “powerful enough to deliver a diagnosis with sparse genetic data, akin to someone recognizing an image based on only 1% of its pixels, and from an unknown portion of the image.” Ridder is also a principal investigator at the Oncode Institute, an independent research center in the Netherlands.
The researchers tested Sturgeon during 25 live brain surgeries and compared the results to an anatomic pathologist’s standard method of microscope tissue examination. “The new approach delivered 18 correct diagnoses and failed to reach the needed confidence threshold in the other seven cases. It turned around its diagnoses in less than 90 minutes, the study reported—short enough for it to inform decisions during an operation,” The New York Times reported.
But there were issues. Where the minute samples contain healthy brain tissue, identifying an adequate number of tumor markers could become problematic. Under those conditions, surgeons can ask an anatomic pathologist to “flag the [tissue samples] with the most tumor for sequencing, said PhD candidate Marc Pagès-Gallego, a bioinformatician at UMC Utrecht and a co-author of the study,” The New York Times noted.
“Implementation itself is less straightforward than often suggested,” Sebastian Brandner, MD, a professor of neuropathology at University College London, told The Times. “Sequencing and classifying tumor cells often still required significant expertise in bioinformatics as well as workers who are able to run, troubleshoot, and repair the technology,” he added.
“Brain tumors are also the most well-suited to being classified by the chemical modifications that the new method analyzes; not all cancers can be diagnosed that way,” The Times pointed out.
Thus, the research continues. The new method is being applied to other surgical samples as well. The study authors said other facilities are utilizing the method on their own surgical tissue samples, “suggesting that it can work in other people’s hands.” But more work is needed, The Times reported.
UMC Utrecht Researchers Receive Hanarth Grant
To expand their research into the Sturgeon’s capabilities, the UMC Utrecht research team recently received funds from the Hanarth Fonds, which was founded in 2018 to “promote and enhance the use of artificial intelligence and machine learning to improve the diagnosis, treatment, and outcome of patients with cancer,” according to the organization’s website.
The researchers will investigate ways the Sturgeon AI algorithm can be used to identify tumors of the central nervous system during surgery, a UMC Utrecht news release states. These type of tumors, according to the researchers, are difficult to examine without surgery.
“This poses a challenge for neurosurgeons. They have to operate on a tumor without knowing what type of tumor it is. As a result, there is a chance that the patient will need another operation,” said de Ridder in the news release.
The Sturgeon application solves this problem. It identifies the “exact type of tumor during surgery. This allows the appropriate surgical strategy to be applied immediately,” the news release notes.
The Hanarth funds will enable Jeroen and his team to develop a variant of the Sturgeon that uses “cerebrospinal fluid instead of (part of) the tumor. This will allow the type of tumor to be determined already before surgery. The main challenge is that cerebrospinal fluid contains a mixture of tumor and normal DNA. AI models will be trained to take this into account.”
The UMC Utrecht scientists’ breakthrough is another example of how organizations and research groups are working to shorten time to answer, compared to standard anatomic pathology methods. They are combining developing technologies in ways that achieve these goals.
Research findings could lead to new biomarkers for genetic tests and give clinical laboratories new capabilities to diagnose different health conditions
New insights continue to emerge about “junk DNA” (aka, non-coding DNA). For pathologists and clinical laboratories, these discoveries may have value and eventually lead to new biomarkers for genetic testing.
One recent example comes from researchers at Stanford Medicine in California who recently learned how non-coding DNA—which makes up 98% of the human genome—affects gene expression, the function that leads to observable characteristics in an organism (phenotypes).
The research also could lead to a better understanding of how short tandem repeats (STRs)—the number of times a gene is copied into RNA for protein use—affect gene expression as well, according to Stanford.
“We’ve known for a while that short tandem repeats or STRs, aren’t junk because their presence or absence correlates with changes in gene expression. But we haven’t known how they exert these effects,” said study lead Polly Fordyce, PhD (above), Associate Professor of Bioengineering and Genetics at Stanford University, in a news release. The research could lead to new clinical laboratory biomarkers for genetic testing. (Photo copyright: Stanford University.)
To Bind or Not to Bind
In their Science paper, the Stanford researchers described an opportunity to explore a new angle to transcription factors binding to some sequences, also known as sequence motifs.
“Researchers have spent a lot of time characterizing these transcription factors and figuring out which sequences—called motifs—they like to bind to the most,” said the study lead Polly Fordyce, PhD, Associate Professor of Bioengineering and Genetics at Stanford University, in a Stanford Medicine news release.
“But current models don’t adequately explain where and when transcription factors bind to non-coding DNA to regulate gene expression. Sometimes, no transcription factor is attached to something that looks like a perfect motif. Other times, transcription factors bind to stretches of DNA that aren’t motifs,” the news release explains.
Transcription factors are “like light switches that can turn genes on or off depending on what cells need,” notes a King’s College LondonEDIT Labblog post.
But why do transcription factors target some places in the genome and not others?
“To solve the puzzle of why transcription factors go to some places in the genome and not to others, we needed to look beyond the highly preferred motifs,” Fordyce added. “In this study, we’re showing that the STR sequence around the motif can have a really big effect on transcription factor binding, providing clues as to what these repeated sequences might be doing.”
Such information could aid in understanding certain hereditary conditions and diseases.
“Variations in STR length have been associated with changes in gene expression and implicated in several complex phenotypes such as schizophrenia, cancer, autism, and Crohn’s disease. However, the mechanism by which STRs affect transcription remains unknown,” the researchers wrote in Science.
Special Assays Explore Binding
According to their paper, the research team turned to the Fordyce Lab’s previously developed microfluidic binding assays (MITOMI, k–MITOMI, and STAMMP) to analyze the impact of different DNA sequences on transcription factor binding.
“In the experiment we asked, ‘How do these changes impact the strength of transcription factor binding?’ We saw a surprisingly large effect. Varying the STR sequence around a motif can have a 70-fold impact on the binding,” Fordyce wrote.
In an accompanying Science article titled, “Repetitive DNA Regulates Gene Expression,” Thomas Kuhlman, PhD, Assistant Professor, Physics and Astronomy, University of California, Riverside, wrote that the study “demonstrates that STRs exert their effects by directly binding transcription factor proteins, thus explaining how STRs might influence gene expression in both normal and diseased states.”
“This research unveils, for the first time, the intricate connection between how variants in the non-coding genome affect genes that are associated with blood pressure and with hypertension. What we’ve created is a kind of functional map of the regulators of blood pressure genes, “said Philipp Maass, PhD, Lead Researcher and Assistant Professor Molecular Genetics, University of Toronto, in a news release.
The research team used massively parallel reporter assay (MPRA) technology to analyze 4,608 genetic variants associated with blood pressure.
The findings could aid precision medicine for cardiovascular health and may possibly be adopted to other conditions, according to The Hospital for Sick Children.
“The variants we have characterized in the non-coding genome could be used as genomic markers for hypertension, laying the groundwork for future genetic research and potential therapeutic targets for cardiovascular disease,” Maass noted.
Why All the ‘Junk’ DNA?
Clinical laboratory scientists may wonder why genetic research has primarily focused on 20,000 genes within the genome, leaving the “junk” DNA for later investigation. So did researchers at Harvard University.
“After the Human Genome Project, scientists found that there were around 20,000 genes within the genome, a number that some researchers had already predicted. Remarkably, these genes comprise only about 1-2% of the three billion base pairs of DNA. This means that anywhere from 98-99% of our entire genome must be doing something other than coding for proteins,” they wrote in a blog post.
“Imagine being given multiple volumes of encyclopedias that contained a coherent sentence in English every 100 pages, where the rest of the space contained a smattering of uninterpretable random letters and characters. You would probably start to wonder why all those random letters and characters were there in the first place, which is the exact problem that has plagued scientists for decades,” they added.
Not only is junk DNA an interesting study subject, but ongoing research may also produce useful new biomarkers for genetic diagnostics and other clinical laboratory testing. Thus, medical lab professionals may want to keep an eye on new developments involving non-coding DNA.
Clinical laboratories and pathology groups may soon have new assays for diagnosis, treatment identification, patient monitoring
It’s here at last! The human Y chromosome now has a full and complete sequence. This achievement by an international team of genetic researchers is expected to open the door to significant insights in how variants and mutations in the Y chromosome are involved in various diseases and health conditions. In turn, these insights could lead to new diagnostic assays for use by clinical laboratories and pathology groups.
Pathologists and clinical laboratories involved in genetic research will understand the significance of this accomplishment. The full Y chromosome sequence “fills in gaps across more than 50% of the Y chromosome’s length, [and] uncovers important genomic features with implications for fertility, such as factors in sperm production,” SciTechDaily noted.
This breakthrough will make it possible for other research teams to gain further understanding of the functions of the Y chromosome and how specific gene variants and mutations contribute to specific health conditions and diseases. In turn, knowledge of those genetic sequences and mutations would give clinical laboratories the assays that help diagnosis, identify relevant therapies, and monitor a patient’s progress.
“When you find variation that you haven’t seen before, the hope is always that those genomic variants will be important for understanding human health,” said Adam Phillippy, PhD, a senior investigator and head of the Genome Informatics Section at the National Human Genome Research Institute, in a press release. Clinical laboratories and anatomic pathology groups may soon have new assays based on the T2T study findings. (Photo copyright: National Human Genome Research Institute.)
Study Background and Recognition
Revolutionary thinking by the Telomere-to-Telomere (T2T) scientists led to the team’s breakthrough. The researchers “applied new DNA sequencing technologies and sequence assembly methods, as well as knowledge gained from generating the first gapless sequences for the other 23 human chromosomes,” SciTechDaily reported.
In 1977, the first complete genome of an organism was sequenced. Thus began the commencement of sequencing technology research. Twenty years ago the first human genome sequence was completed. The result was thanks to years of work through the preferred “chain termination” (aka, Sanger Sequencing) method developed by Fred Sanger and a $2.7 billion contribution from the Human Genome Project, according to a study published in the African Journal of Laboratory Medicine (AJLM).
By 2005, a new era in genomic sequencing emerged. Scientists now employed a technique called pyrosequencing and the change had great benefits. “Massively parallel or next-generation sequencing (NGS) technologies eliminated the need for multiple personnel working on a genome by automating DNA cleavage, amplification, and parallel short-read sequencing on a single instrument, thereby lowering costs and increasing throughput,” the AJLM paper noted.
The new technique brought great results. “Next-generation sequencing technologies have made sequencing much easier, faster and cheaper than Sanger sequencing,” the AJLM study authors noted.
The changes allowed more sequencing to be completed. Nevertheless, more than half of the Y chromosome sequence was still unknown until the new findings from the T2T study, SciTechDaily reported.
Why the TDT Breakthrough Is So Important
“The biggest surprise was how organized the repeats are,” said Adam Phillippy, PhD, a senior investigator and head of the NHGRI. “We didn’t know what exactly made up the missing sequence. It could have been very chaotic, but instead, nearly half of the chromosome is made of alternating blocks of two specific repeating sequences known as satellite DNA. It makes a beautiful, quilt-like pattern.”
Much can be gained in knowing more about the Y chromosome. Along with the X chromosome, it is significant in sexual development. Additionally, current research is showing that genes on the Y chromosome are linked to the risk and severity of cancer.
Might What Comes Next Give Clinical Labs New Diagnostic Tools?
The variety of new regions of the Y chromosome that the T2T team discovered bring into focus several areas of new genetic research. For instance, the “azoospermia factor region, a stretch of DNA containing several genes known to be involved in sperm production” was uncovered, and “with the newly completed sequence, the researchers studied the structure of a set of inverted repeats or palindromes in the azoospermia factor region,” SciTechDaily reported.
“This structure is very important because occasionally these palindromes can create loops of DNA. Sometimes, these loops accidentally get cut off and create deletions in the genome,” said Arang Rhie, PhD, a staff scientist at NHGRI and first author of the Nature study.
Missing regions would challenge the production of sperm, impacting fertility, so being able to finally see a complete sequence will help research in this area.
Scientists are only just beginning to recognize the value of this breakthrough to future genetic research and development. As genetic sequencing costs continue to drop, the T2T research findings could mean new treatment options for pathologists and diagnostic assays for clinical laboratories are just around the corner.
And in less than eight hours, they had diagnosed a child with a rare genetic disorder, results that would take clinical laboratory testing weeks to return, demonstrating the clinical value of the genomic process
In another major genetic sequencing advancement, scientists at Stanford University School of Medicine have developed a method for rapid sequencing of patients’ whole human genome in as little as five hours. And the researchers used their breakthrough to diagnose rare genetic diseases in under eight hours, according to a Stanford Medicine news release. Their new “ultra-rapid genome sequencing approach” could lead to significantly faster diagnostics and improved clinical laboratory treatments for cancer and other diseases.
“A few weeks is what most clinicians call ‘rapid’ when it comes to sequencing a patient’s genome and returning results,” said cardiovascular disease specialist Euan Ashley, MD, PhD (above), professor of medicine, genetics, and biomedical data science, at Stanford University in the news release. “The right people suddenly came together to achieve something amazing. We really felt like we were approaching a new frontier.” Their results could lead to faster diagnostics and clinical laboratory treatments. (Photo copyright: Stanford Medicine.)
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Need for Fast Genetic Diagnosis
In their NEJM paper, the Stanford scientists argue that rapid genetic diagnosis is key to clinical management, improved prognosis, and critical care cost savings.
“Although most critical care decisions must be made in hours, traditional testing requires weeks and rapid testing requires days. We have found that nanopore genome sequencing can accurately and rapidly provide genetic diagnoses,” the authors wrote.
To complete their study, the researchers sequenced the genomes of 12 patients from two hospitals in Stanford, Calif. They used nanopore genome sequencing, cloud computing-based bioinformatics, and a “custom variant prioritization.”
Their findings included:
Five people received a genetic diagnosis from the sequencing information in about eight hours.
Diagnostic rate of 42%, about 12% higher than the average rate for diagnosis of genetic disorders (the researchers noted that not all conditions are genetically based and appropriate for sequencing).
Five hours and two minutes to sequence a patient’s genome in one case.
Seven hours and 18 minutes to sequence and diagnose that case.
How the Nanopore Process Works
To advance sequencing speed, the researchers used equipment by Oxford Nanopore Technologies with 48 sequencing units called “flow cells”—enough to sequence a person’s whole genome at one time.
The Oxford Nanopore PromethION Flow Cell generates more than 100 gigabases of data per hour, AI Time Journal reported. The team used a cloud-based storage system to enable computational power for real-time analysis of the data. AI algorithms scanned the genetic code for errors and compared the patients’ gene variants to variants associated with diseases found in research data, Stanford explained.
According to an NVIDIA blog post, “The researchers accelerated both base calling and variant calling using NVIDIA GPUs on Google Cloud. Variant calling, the process of identifying the millions of variants in a genome, was also sped up with NVIDIA Clara Parabricks, a computational genomics application framework.”
Rapid Genetic Test Produces Clinical Benefits
“Together with our collaborators and some of the world’s leaders in genomics, we were able to develop a rapid sequencing analysis workflow that has already shown tangible clinical benefits,” said Mehrzad Samadi, PhD, NVIDIA Senior Engineering Manager and co-author of the NEJM paper, in the blog post. “These are the kinds of high-impact problems we live to solve.”
In their paper, the Stanford researchers described their use of the rapid genetic test to diagnose and treat an infant who was experiencing epileptic seizures on arrival to Stanford’s pediatric emergency department. In just eight hours, their diagnostic test found that the infant’s convulsions were attributed to a mutation in the gene CSNK2B, “a variant and gene known to cause a neurodevelopmental disorder with early-onset epilepsy,” the researchers wrote.
“By accelerating every step of this process—from collecting a blood sample to sequencing the whole genome to identifying variants linked to diseases—[the Stanford] research team took just hours to find a pathogenic variant and make a definitive diagnosis in a three-month-old infant with a rare seizure-causing genetic disorder. A traditional gene panel analysis ordered at the same time took two weeks to return results,” AI Time Journal reported.
New Benchmarks
The Stanford research team wants to cut the sequencing time in half. But for now, the five-hour rapid whole genome sequence can be considered by clinical laboratory leaders, pathologists, and research scientists a new benchmark in genetic sequencing for diagnostic purposes.
Stories like Stanford’s rapid diagnosis of the three-month old patient with epileptic seizures, point to the ultimate value of advances in genomic sequencing technologies.
