Cancer will touch 92% of people, either directly or through a close family member, the World Health Organization’s latest annual report on cancer warns. One in five people will develop the disease themselves. The report also found widespread global inequities in access to prevention, diagnosis, treatment and care.
This global view of cancer occurrence, diagnosis, and treatment, comes at a time when some in industry feel oncology is undergoing some dramatic technological transformations. Companies positioned to help spark change in how we meet the growing challenge of cancer around the world include those that provide direct to consumer genomic (e.g. Grail) or MRI (e.g. Prenuvo) testing for the disease. But whole new disciplines are likely to emerge, such as PreOncology, a new service that offers very early, comprehensive, cancer screening through primary care physicians (PCPs). PreOncology launched in Florida July 1.
“The number of oncology cases is not declining, unlike cardiology and other conditions,” Jose Barreau, MD, one of PreOncology’s co-founders, told Inside Precision Medicine. “Primary care doctors in particular, can use help with these patients.”
It is estimated there will be 20.6M cases of cancer and 10M deaths from the disease every year going forward. The number of cancer patients worldwide is expected to rise to nearly 35M cases by 2050. The report emphasizes inequities and points out that in developed countries, 85% of patients with breast or childhood cancers will survive at least five years. But in poorer countries that figure drops to less than 30%.
Approximately 38% of cancers can currently be prevented by avoiding risk factors, such as tobacco and alcohol, eating well and exercising, and implementing existing other “evidence-based prevention strategies,” WHO said.
But the rate of cancer and cancer deaths can also be reduced through early detection and appropriate treatment and care of patients: Many cancers have a very high cure rate if diagnosed early and treated appropriately.
The most common new cancer cases in 2024 were in: lung, breast, colon and rectum, prostate, skin (non-melanoma), and stomach. While the most common causes of cancer death last year were: Lung, colon and rectum, liver, breast, and stomach. All of which can be detected early using advanced diagnostics, but they are almost almost only screened for in older adults.
The incidence of cancer rises dramatically with age, but it is also a leading cause of death among children and adolescents. WHO reports that it is estimated that each year approximately 400,000 children and adolescents develop cancer. The rise of certain cancers in young adults, such as colorectal cancer and lung cancer in non-smoking, younger women, has been particularly alarming.
Cancer screening is still largely based on age and averages, and many cancers have no routine screening at all. But early screening proponents, such as PreOncology have integrated advanced technologies and proprietary AI to establish ways to accurately identify cancers as early as stage I, or the Alpha stage. This is when they are usually curable by the simplest means, and chemotherapy or other harsh treatments may be avoided.
PreOncology “looks at each person’s unique risk profile, from genetics and family history to lifestyle, to create a personalized monitoring plan,” the company said in a recent press release. An oncologist-led Signal Board reviews any concerning findings and helps guide physicians on the next steps.The goal is to detect cancer at the earliest point where something impactful can be done and when there are still the most options.
Cancer is the first or second leading cause of death, depending on which statistics you use.
The start-up hasa risk engine drawing on ~3.1M de-identified patient records from the studies behind national screening guidelines. Its lung model alone was modeled on 611,000+ people. The program’s whole genome sequencing component covers 61 cancer-related genes. Ambry genomics does the sequencing. The program also comprises whole-body MRIs and a variety of liquid biopsy type tests, CT-DNA, circulating tumor cells.
“There is a lot of emerging technology and early detection coming to market. Some of it is not FDA-approved, but it’s being advertised to physicians, and we can take advantage of that,” Barreau said.
To tie it all together, he added, “We have the deep medical oncology expertise to interpret and understand all this data in the context of any patient’s specific circumstances.”
Will such new approaches, along with new treatments such as cancer vaccines, be the basis for a new age of oncology? Let’s hope, as the rise in global prevalence of cancer continues.
For decades, psychiatry has used trial-and-error symptom-based diagnoses and treatments. Standardized diagnostic frameworks brought much-needed consistency to the field, but they also grouped diverse patients with different biology. Consequently, many people receive unsuitable treatments.
On this episode of Behind the Breakthroughs, Alto Neuroscience founder and CEO Amit Etkin, MD, PhD, discusses how precision medicine will change mental health care. Etkin explains how objective biological measures like cognitive testing, EEG brain activity, sleep and circadian rhythm monitoring, and advanced computational analysis can help identify patients who will benefit from specific therapies rather than just symptoms. Comparing psychiatry to precision oncology, he explains why it is at a turning point. Instead of finding a perfect biomarker, the field is developing practical, scalable tools to link brain function to targeted drug development.
Etkin shows from Alto’s clinical pipeline how matching therapies to biologically defined patient populations can improve outcomes and reduce psychiatric treatment uncertainty. We also examine the potential and limitations of genetics, multi-omics, wearables, and AI in precision psychiatry. Etkin explains why brain measurements may be more clinically useful than peripheral biomarkers and how AI can help find patterns in complex biological data. Finally, we discuss how precision psychiatry will become routine clinical practice, from regulatory acceptance and standardized data collection to the first biomarker-guided therapies. If successful, these advances could transform psychiatric disorder diagnosis, treatment, and understanding.
This interview has been edited for length and clarity.
IPM: What has been the key limiting factor to advancing precision psychiatry?
Etkin: There was a period of time in the 1940s, 1950s, and 1960s when psychiatry was beginning to develop. Our definition of diseases was bespoke to how you practiced them. They really made very little sense.
Amit Etkin, MD, PhD, co-founder and CEO of Alto Neuroscience [Alto Neuroscience]
The DSM did a great job of bringing everybody under the same diagnostic umbrella. We can talk about a common set of symptoms and everybody’s talking about them. Yes, we have wonky definitions of diseases, but at least we’re starting to use the same definition.
We are finding large, heterogeneous groups of people that fit into a category. If you examine the words used to describe some psychiatric labels from the past, they sound antiquated because they are linked to outdated concepts and are inconsistently defined. That was good. The problem is that we never transitioned beyond that.
I try to focus on a much simpler approach to what we’re doing. Instead of thinking about biomarkers, machine learning, and so forth, it’s about knowing what we are doing. If what you’re doing is developing a drug for a population with depression, it’s a massive category where some people just symptomatically might sleep more and others sleep less, and some eat more and others eat less. There seems like it’s a bit of a mess. What would you want to know? At a very basic level, that would simply allow you to know what you’re doing better. What would you like to measure?
There are any number of answers. We know that some patients with depression have cognitive problems and others don’t and that people with cognitive problems have worse outcomes regarding standards of care and treatment. We’ve known that for a long time. But why aren’t we doing that measurement systematically in every single drug trial or in clinical care? Because if we did that one little thing systematically, we would see that some treatments or people saw it one way and others saw it another way just by collecting that data more systematically. That’s what we had been doing in the lab: figuring out what kind of data to collect systematically.
That’s what led us to the form Alto, understanding that there are certain measures, like cognition and brain activity. We do this non-invasively with electroencephalogram (EEG) brainwave recordings and wearables to look at circadian rhythms that are physiologically and biologically meaningful, easily scalable, low cost to meet our health system’s needs, and, if done consistently, insightful for separating populations and identifying mechanisms to develop for them and for translating back to an animal model, which is not possible if you only look at depressive symptoms.
Do we know what we’re doing now? No. We’re really just beginning on this journey as a field. But I do think people now recognize that this is the direction of travel. You can see that more and more companies and academic research centers are emerging under this theme.
IPM: What are your thoughts on the use of genomics, multi-omics, or blood-based biomarkers of the central nervous system?
Etkin: The way I interpret that literature is that there’s smoke but not yet fire. The classical molecular marker is genetics. We get that on every patient, but we do not use stratification here, as genetics cannot achieve meaningful stratification in our populations. It’s predominantly common variance with each of them, or in combination, having a very rare large effect size variance, which is really not going to be clinically meaningful from a drug perspective because you’re treating one out of every 10,000 people or whatever the prevalence is.
I don’t know the best polygenic risk score for schizophrenia, determined from 100,000 people, which is probably the high watermark for psychiatric genetics and may explain 1–2% of the variance. You needed to explain at least 10% to stratify the population even a little bit. I don’t think genetics will ever get us there for that purpose.
Where there’s smoke but not yet fire are immune measures. There have been many implications for different immune measures in psychiatric disorders, but every time it gets tested to see whether people with high inflammation respond to something that targets our process directly or indirectly, those studies never really work out.
Then there’s the even larger world of multi-omics, where you have a multiple testing issue and, fundamentally, the problem that what you’re sampling is very peripheral to the organ that matters. Where is the serotonin in the blood coming from? It’s mainly coming from platelets—it’s not coming from the brain. The bulk of serotonin in the body is in the gut. So you can measure… a ton of different proteins, different configurations, and modifications to those proteins probably have relatively little purchase on what is going on in the brain.
The simplest example is a protein called brain-derived neurotrophic factor (BDNF), which is a really important neuroplasticity protein in the brain. It’s also found in the blood, and people tried over and over and over again. You see some positive studies, but mainly studies that are negative and some that are just not published that come to the conclusion that there’s very little bearing of what you’re measuring peripherally to what’s going on centrally.
Measuring the brain directly with EEG or the output of specific brain circuits through behavioral tests is much more amenable and has better performance statistics and interpretability for gaining insights.
IPM: Has a specific layer or test modality enabled precision psychiatry programs for Alto Neuroscience, or are they ultimately based on aggregate measures?
Etkin: Less so in aggregate as measured together but each alone. We try not to combine everything into one model because it becomes very complicated, and we have already been told by the FDA in no uncertain terms that a multimodal biomarker is not something we will readily consider. To get a multimodal biomarker approved for some sort of use, you have to validate each and every component alone and their combination, which sounds like a headache. But I’m not sure you necessarily need to either.
What I would consider to be a win is getting a drug for the whole population with a marker that enriches finding ways to show additional value in a drug program through a biomarker perspective and, over time, an iteration. The field then transforms into one where oncology already exists, which means they expect you to know what you’re doing. You have to understand the population. You have to understand how your drug impacts the biology that defines the population. We’re not there yet.
But they weren’t there yet either, in the same kind of single stroke that we now envision. It was like the first precision therapeutics, like Herceptin, were approved well over a decade before the immuno-oncology (IO) revolution. That really brought precision oncology into maturity, as we understand it now. That history suggests we probably need our IO moment as an inflection point, but we are not yet ready for it.
We need that Herceptin moment first: start transitioning how people think and collect data and create a bit more of a common language across programs so that different drug makers and different academic labs aren’t collecting their own unique data sets that aren’t then harmonized across them. You can’t speak about a thing as an invariant measure of a process that doesn’t matter who is measuring; they get the same outcome.
The field has been somewhat resistant, probably for cultural reasons related to how people have historically operated, to a lot of data sharing and harmonizing of what we’re collecting, how we’re collecting it, and how we’re analyzing it. We’re just starting to really move in that direction. Those are all limits that gate the early-stage biomarker collection efforts.
IPM: Is there a future where someone walks in with a psychiatric condition and undergoes a battery of measurements that spits out a drug that has a high probability of being effective?
Etkin: I think that bar is a lot lower than that. It doesn’t have to be very effective—it just has to be more effective than chance. because that’s where we are. If I told you that instead of a 30% chance of remission with a drug, I could increase it to 40% or 45%, would that be helpful? That’s meaningful. You convert that to a number needed to treat it. For a clinician, this represents a significant effect, although it does not achieve perfect precision. All you need is something better than nothing, which is what we have. Of course, once you have something that’s better than nothing, now you have a new benchmark, and things will continue to improve, which is great. But the field’s got to start somewhere.
I don’t think it is that far away. I think in our efforts and the efforts of others in the field who have followed suit and taken a precision approach, something will work. When that changes, all of a sudden you can’t envision going back; it’s only going forward. That’ll be super exciting. It’s not like a “by the time I retire” kind of thing. It’s within the next five or a maximum of ten years that we will be at that inflection point.
IPM: Does precision psychiatry apply to the rest of neurology?
Etkin: If you anchored on the way I framed brain circuit function earlier. And what’s measurable is that there is no line between psychiatry and neurology. You have neurologists who are called “functional neurologists” or something in that vein, where they think about what I would call the “psychiatric aspects of neurology.”
A big part of Parkinson’s is cognitive impairment in a substantial portion of people, leading to dementia. Nothing to do with the movement disorder, but everything to do with the biology affecting different circuits. The right mood, in fact, is one of the earliest areas of perturbation in Parkinson’s that will then predict the development of the motor symptoms. Some people have perfectly well-controlled motor problems but have cognitive problems and mood problems that are even more prominent and lead to more, especially on the cognitive side, of their ultimate disabilities. Cognitive impairments are even a contraindication for deep brain stimulation.
Because of these interactions, all of these boundaries are artificial. It’s just a core engineering question of, can I know what I am measuring and what I am manipulating? There’s no reason we need to draw that line in an artificial way. It’s just about whether I can leverage the tools and the drugs in a useful way together.
Because Mung beans (Vigna radiata) are nutritious, inexpensive, have nitrogen-fixing capacity, and are relatively easy to grow with a short growing cycle, they are an important crop for food security in many parts of Asia, Africa, and other regions. Now, researchers have made a significant contribution to a landmark international study that has uncovered tens of thousands of previously hidden structural variations influencing yield, nutritional quality, insect resistance, and other relevant mung bean traits.
The study entitled, “Graph-based pan-genome reveals structural variations associated with agronomic traits in mung bean,” published in Nature Genetics, presents the world’s first graph-based pan-genome for the pulse crop (a legume grown specifically for its dry edible seeds) offering a comprehensive resource for understanding the genetic basis of key agronomic traits and accelerating crop improvement.
The international research team, co-led by researchers at the Chinese Academy of Agricultural Sciences and Murdoch University’s Centre for Crop and Food Innovation (CCFI), assembled chromosome-scale genomes from genetically diverse mung bean accessions and analyzed genomic variation across 580 global accessions. The resulting graph-based pan-genome captures more than 75,000 gene families and identifies over 66,000 structural variants, offering important insights that will help breeders target key agronomic traits and accelerate crop improvement.
More specifically, the authors note that, “integrating these structural variants and single nucleotide polymorphisms, genome-wide association studies across five environments identified candidate genes for 20 agronomic traits, underscoring the pivotal roles of these variants in driving mung bean domestication and improvement.”
Mechanistically, they add, the work demonstrates that “a 68-bp promoter insertion in VrTIFY6B and a 136-bp promoter deletion in VrPGIP1 regulate flavonoid content and confer bruchid resistance, respectively.”
In Australia, mung bean generates over $100 million annually in export revenue. At roughly three times the price of wheat, it represents a highly profitable break crop opportunity for Australian growers; however, seasonal rainfall variability continues to drive significant year-to-year swings in the size and value of the crop.
By cataloging tens of thousands of previously invisible structural variations and linking them to agronomic traits through genome-wide association analysis, this new genomic resource gives breeders a far more complete map of the genetic variation they can work with.
Globally, where mung bean underpins the diets and incomes of millions of smallholder farmers across Asia and Africa, the study’s findings on genes governing seed nutritional compounds and resistance to bruchids, a major storage pest, have direct implications for global food security.
Rajeev Varshney FRS FAA, CCFI director, said the research represents a major advance in crop genomics and demonstrates how next-generation genomic technologies are transforming plant breeding. “Traditional reference genomes capture only part of the genetic diversity within a crop species. By constructing a graph-based pan-genome, we can now identify structural variations that were previously invisible but often have profound effects on important agricultural traits.
“These discoveries provide breeders with powerful new genomic tools to accelerate the development of higher-yielding, more nutritious and climate-resilient mung bean varieties,” Varshney continues. “The genomic resources generated through this work will support marker-assisted breeding, genomic selection and genome editing, enabling breeders to deliver improved varieties to farmers much faster.”
The objective is to report a patient with Gerstmann-Sträussler-Scheinker syndrome caused by a pathogenic PRNP P102L variant harboring an unexpected concomitant pathogenic GRN variant p.R110X and to discuss the potential contribution of combined genetic pathology to the clinical and neuroimaging phenotype confirmed by autopsy. Moreover, we discuss the potential role of TMEM106B as an important modifier of the protein TDP-43 neuropathology associated with the GRN mutation in this case. The patient underwent detailed clinical assessment, serial neuropsychological evaluation, brain MRI, cerebrospinal fluid analysis, whole-exome sequencing, and next generation sequencing. A postmortem neuropathologic examination was performed to confirm the diagnosis. The patient presented slowly progressive paresthesia, cerebellar ataxia, dysarthria, and later cognitive and behavioral changes. Genetic testing revealed a heterozygous PRNP P102L variant and an unpenetrated GRN p.R110X variant; a protective TMEM106B polymorphism associated with TDP-43 pathology was also identified. Neuroimaging demonstrated progressive cerebellar and parietal atrophy with asymmetric left frontal opercular and insular involvement. The clinical course was dominated by a cerebellar GSS phenotype. The patient died 4 years after symptom onset. Neuropathology confirmed GSS, nevertheless without detectable TDP-43-associated neuropathology. This case highlights the diagnostic complexity of rare neurodegenerative disorders and illustrates that pathogenic variants may not influence phenotypic expression. Comprehensive genetic testing should be considered in atypical cases, as certain genetic variants may contribute to phenotypic variability and represent potential modifiers of phenotypic expression.
Precision oncology has transformed lung cancer care, but its reach remains uneven. Targeted therapies and immunotherapies have improved outcomes for selected patients, yet many people with lung cancer still receive systemic treatment without a reliable way to know whether their tumor will respond.
For most patients, treatment decisions are still guided by tumor type, stage, standard-of-care guidelines, and a limited set of biomarkers. That works for some, but not enough. Lung cancer is biologically heterogeneous, and tumors with similar clinical features can respond very differently to the same drug.
A new study in npj Precision Oncology describes a rapid ex vivo tumor platform designed to address that gap. Instead of relying only on genomic markers, the method tests drug response directly on patient-derived 3D lung tumor replicas generated from routine clinical samples.
Moving from genomic prediction to functional testing
Genomic profiling has become central to lung cancer treatment, especially for patients with actionable alterations such as EGFR mutations. But many tumors do not carry targetable drivers, and even when they do, genomic information does not always predict resistance, chemotherapy response, or sensitivity to combination regimens.
That is where functional precision oncology is gaining attention. The idea is straightforward: take a patient’s tumor cells, grow them in a clinically relevant model, expose them to candidate drugs, and measure whether the tumor responds.
The challenge is making this fast, reliable, and feasible from the small biopsies that are actually available in routine lung cancer care. Many patient-derived organoid approaches require surgical specimens, have low establishment rates, or take longer than the treatment decision window.
The authors frame the problem clearly: “Effective prediction models are needed” to enable more personalized lung cancer care.
Tumor replicas from routine biopsies
The new platform was developed in a prospective multicenter cohort of 129 treatment-naïve lung cancer patients. Researchers generated 3D lung tumor replicas from resection material as well as small diagnostic samples obtained through EUS-FNA and EBUS-TBNA biopsy procedures.
The overall establishment success rate was 65%. Success was highest from surgical resection material, at 100%, and remained strong for EUS-FNA biopsy material, at 82.7%. EBUS-TBNA samples had a lower but still clinically relevant success rate of 47.1%.
This is important because most patients who need systemic treatment do not undergo upfront surgery. A platform intended for real-world treatment selection must work from limited biopsy material, not only from large resection specimens.
The tumor replicas formed compact 3D clusters and retained key features of the original tumor. Histology and immunohistochemistry showed that replicas preserved subtype-specific characteristics of lung adenocarcinoma, lung squamous cell carcinoma, and small cell lung cancer. In selected samples, genetic features such as KRAS mutations were also retained.
Drug results within the treatment decision window
A central strength of the platform is speed. The researchers report that ex vivo drug response results were available within a median of 12 days from biopsy acquisition. In 80.4% of patients, results were generated within two weeks.
That timing matters. A functional drug test has limited clinical value if results arrive after treatment has already started. In this cohort, patients receiving standard systemic therapy began treatment after a median of 21 days, suggesting that the platform could produce results early enough to inform decision-making.
The researchers tested standard lung cancer therapies, including platinum-based chemotherapy backbones and other agents such as etoposide, pemetrexed, paclitaxel, afatinib, and sotorasib. The tumor replicas showed heterogeneous drug responses, mirroring the variability seen clinically.
In patient-derived xenograft models, ex vivo responses were consistent with in vivo treatment responses or expected mutation-drug relationships. For example, tumor replicas harboring EGFR L858R responded to the EGFR inhibitor afatinib, while KRAS G12C-mutant replicas showed sensitivity to sotorasib.
Early clinical validation shows promise
The study also compared ex vivo drug responses with patient outcomes. In stage 3 lung cancer patients treated with chemoradiation, ex vivo chemotherapy sensitivity was significantly associated with overall survival.
In another group of 20 patients treated with platinum-doublet chemotherapy, with or without immunotherapy, the platform showed a sensitivity of 73% and a positive predictive value of 92% for predicting treatment response in the biopsy lesion.
That high positive predictive value is clinically meaningful. It suggests that when the platform classified a tumor as sensitive, the patient was likely to experience clinical benefit. However, the negative predictive value was lower, meaning the test was less reliable at identifying tumors that would not respond.
This distinction matters for clinical use. At this stage, the platform may be better suited to helping identify promising treatment options than to ruling therapies out definitively.
Why this matters for precision lung cancer care
The study signals a broader shift in precision oncology. Molecular testing asks what alterations a tumor carries. Functional testing asks what the tumor actually does when exposed to therapy. Both approaches are valuable, but they answer different questions.
For lung cancer, this could be especially important because chemotherapy remains a backbone of treatment across multiple stages and subtypes. Yet chemotherapy selection is rarely personalized in the same way targeted therapy is. A rapid ex vivo assay could help distinguish patients likely to benefit from a specific chemotherapy combination from those who may need an alternative approach.
The authors describe the platform as following the same concept as an antibiogram: testing patient-specific tumor material to identify effective treatment options before therapy begins.
If validated in larger studies, this type of approach could reduce ineffective treatment, avoid unnecessary toxicity, and support more rational selection of systemic therapies.
Not yet ready for routine care
The results are promising, but still early. The clinical validation cohort was small, and the authors describe the patient-response data as proof-of-concept. Larger prospective trials will be needed to determine whether using the platform to guide treatment improves outcomes compared with standard care.
The system also has biological limitations. The current tumor replicas primarily capture intrinsic tumor cell drug sensitivity. They do not fully reproduce the tumor microenvironment, including immune cells, stromal cells, endothelial cells, or extracellular matrix components. That means the platform may be less suited, in its current form, to predicting responses to therapies where the immune microenvironment is central, such as immune checkpoint inhibitors.
The short-term 72-hour drug readout also cannot capture delayed effects, acquired resistance, or long-term tumor evolution. Future versions may need to incorporate immune co-cultures, repeated sampling at progression, or more complex microenvironmental features.
A step toward faster functional precision oncology
The promise of this platform lies in its practicality. It uses routine biopsy material, produces results quickly, and tests actual drug response rather than inferring sensitivity from biomarkers alone.
As the authors conclude, the platform enables upfront screening of anti-cancer drug responses “within a clinically relevant timeframe of two weeks.”
That is the key translational point. For functional precision oncology to become clinically useful, it must fit the pace and constraints of real cancer care. This study suggests that, at least for lung cancer, rapid patient-derived tumor replicas may bring that goal closer.
A novel sequencing strategy that combines low-pass whole-genome sequencing with deep whole-exome sequencing in a single assay could significantly lower the cost of large-scale genomic studies without sacrificing analytical performance, according to a study published in Nature Genetics. The approach, known as blended genome–exome (BGE) sequencing, may help accelerate precision medicine initiatives by making comprehensive genomic profiling more accessible across diverse populations.
Researchers from Massachusetts General Hospital and the Broad Institute of MIT and Harvard developed BGE to overcome a persistent tradeoff in human genomics. High-coverage whole-genome sequencing offers the most comprehensive view of genetic variation but remains prohibitively expensive for many population studies. Genotyping arrays and exome sequencing are more affordable but either miss large portions of the genome or introduce bias by relying on variants selected primarily from European ancestry populations.
The BGE workflow integrates low-pass whole-genome sequencing at 1–4x coverage with deep exome sequencing at 30–40x coverage within a single library preparation and sequencing run on Illumina’s NovaSeqS4. The result is a unified dataset capable of supporting genome-wide association studies, rare variant discovery, copy number variant (CNV) detection, and polygenic analyses at approximately 28% of the cost of conventional 30x whole-genome sequencing.
The investigators validated the approach in more than 53,000 participants enrolled in the Populations Underrepresented in Mental Illness Associations Studies (PUMAS) Project, which includes African, African American, Hispanic/Latino, and Colombian cohorts. The scale and diversity of the study allowed the researchers to assess performance in populations that have historically been underrepresented in genomic research.
Imputed genotypes generated from BGE showed excellent agreement with Illumina Global Screening Array data, achieving concordance exceeding 95% for variants with minor allele frequencies above 1%. Importantly, performance remained consistent across multiple ancestry groups and local ancestry backgrounds, addressing one of the major limitations of conventional array-based genotyping.
The platform also demonstrated strong performance for clinically relevant structural variation. Using established computational pipelines, investigators achieved approximately 90% positive predictive value for protein-coding CNVs spanning three or more exons compared with deep whole-genome sequencing. In benchmarking studies, the method successfully detected all validated de novo coding CNVs in a reference autism cohort while maintaining low false-positive rates.
Beyond analytical performance, the study highlights potential operational advantages. By combining genome and exome sequencing into a single workflow, BGE simplifies laboratory processing, reduces the need for multiple assays, and minimizes sample attrition between sequencing platforms. These efficiencies could prove valuable for national biobanks, health system sequencing programs, and pharmaceutical research efforts that increasingly require genomic datasets from hundreds of thousands of participants.
The technology may also advance equity in precision medicine. Because low-pass genome sequencing does not depend on predefined variant content, it avoids many of the ascertainment biases associated with traditional genotyping arrays. The authors found that BGE captured substantially more coding and noncoding variants than array-based approaches while maintaining high-quality rare variant detection through deep exome coverage.
The researchers acknowledge that imputation performance remains influenced by the diversity of available reference panels, particularly for Indigenous American ancestry. However, as more globally representative reference datasets become available, they expect the accuracy of low-pass genome imputation to improve further.
As precision medicine increasingly depends on large, ancestrally diverse genomic datasets, technologies that balance cost, scalability, and comprehensive variant detection will be essential. BGE sequencing offers a practical alternative to deep whole-genome sequencing, enabling broader participation in genomic discovery while preserving much of the analytical power needed to identify clinically meaningful genetic variation.
A genome-wide association study (GWAS) among almost 2.8 million individuals has revealed a causal link between irritable bowel syndrome (IBS) and elevated triglyceride levels.
IBS affects between 5% and 15% of the general population, is more common in women, and considerably reduces quality of life. It is known as a disorder of gut–brain interaction, but understanding of underlying disease mechanisms is limited, which has hindered the development of effective biomarkers and mechanism-based therapies.
“Our findings support a more integrated view of IBS that extends beyond the traditional gut–brain axis,” said lead author Mauro D’Amato, PhD, professor of medical genetics at LUM University in Bari, Italy, and Ikerbasque research professor at CIC bioGUNE in Bilbao, Spain. “The specific pathways we highlighted may contribute to mechanism-based patient stratifications and the identification of new or existing drugs to be tested in patients who do not respond to current therapies.”
D’Amato and colleagues used genetic, questionnaire, and electronic health records (EHR) data for 2,775,539 individuals from 22 international biobanks, to investigate the genetic architecture of IBS across three case definitions (self-reported, Rome III criteria, and International Classification of Diseases criteria), three subtypes (IBS-C, characterized by constipation; IBS-D, characterized by diarrhea; and IBS-Mixed, which combines both), and by ancestry.
They found that IBS is an heritable condition among people of European ancestry but not among those of other ancestries. However, the investigators that caution that the finding should not be interpreted as “evidence against a genetic contribution to IBS in these [non-European] populations,” rather that the data they had was not powerful enough to prove a link.
Further analyses revealed 35 regions of the human genome associated with IBS risk. While several of these regions involved the brain and enteric nervous system, the researchers also identified a strong, unexpected overlap with cardiometabolic traits.
Specifically, they demonstrated a likely causal link between genetic liability for IBS and elevated levels of blood triglycerides, which was not explained by BMI.
“This association of IBS with pro-atherogenic lipid profiles is consistent with large-scale observational studies that have repeatedly documented a strong link between IBS and metabolic syndrome, including cardiovascular traits and elevated triglycerides, but not BMI,” write D’Amato and co-authors in Gut.
The molecular basis for the connection was a single nucleotide polymorphism in the gene for glucokinase regulatory protein (GKRP), which is a regulator of triglyceride metabolism. The variant is also associated with metabolic dysfunction-associated fatty liver disease, supporting a shared metabolic background.
“We have long known that IBS involves a complex dialogue between the gut and the brain, but these results show that the conversation includes the body’s metabolic system too,” said D’Amato. “The genetic link to triglyceride regulation and liver function gives us a completely new framework for understanding the condition.”
The researchers also looked for potential drug targets. They identified 276 chemical agents with possible therapeutic relevance, of which 156 had a defined mechanism of action. These included many with known applications in the treatment of IBS and related symptoms, including off-label prescriptions (nortriptyline, sertraline, lorazepam, curcumin, and capsaicin) as well others that are typically used to treat cardiovascular conditions (urapidil, fenoldopam, indapamide, nicardipine, picotamide, torsemide), and triglyceride-modifying compounds (rosuvastatin, simvastatin).
The remaining 120 small molecules, without a known target or mechanism of act, could represent “potential novel opportunities for translational research in IBS,” D’Amato and co-authors suggest.
They conclude that their findings provide “the most comprehensive assessment to date of IBS genetic architecture, its subtypes, and the biological and translational inferences that can be drawn from common-variant association signals.”
Agrigenomics is, to use an analogy, spring looking toward summer. Wielding modern genomics tools fine-tuned to agriculture, along with AI-driven insights from field technologies and satellites in low Earth orbit, traditional agriculture is transitioning to precision farming and the bounty it brings. Insights are compressing 30 years of field trials into a single growing season and enabling growers to express specific traits without introducing foreign genes and without hit-and-miss Mendelian hybridization.
This intensive application of science starts with comprehensive soil analyses and extends, not only to the major row crops, but also to such lesser-studied areas as fruits and forestry.
It’s all about the soil
Nate Kelly CEO, Miraterra
Healthy, productive crops all need a good growing environment. “Soil is the most complex, beautiful system on the planet,” Nate Kelly, CEO, Miraterra, points out. Yet, “The soil measurement system in place today is quite archaic,” with manual systems and complex chemistries.
Miraterra’s lead technology is the Digitizer. With shifted-excitement Raman difference spectroscopy as its core, it also employs lasers and advanced signal processing to remove the noise that previously made Raman spectroscopy untenable for soil samples. Robust modeling transforms those data into a molecular fingerprint of soil to 30 ppm, with significantly enhanced resolution anticipated. The Digitizer shortens analysis time from hours to minutes, and makes toxic chemical analysis unnecessary. “Today, we measure texture, organic matter, and carbon and—soon—nutrients,” Kelly says.
After acquiring Trace Genomics and its large soil microbiology data set last year, the company can identify approximately 400,000 different biological components of soil, “things like nitrogen fixers, phosphorus solubilizers, pathogens, and nematodes,” Kelly says.
Miraterra Fill Tray. Healthy crops begin with deep knowledge of soil’s chemistry, biology, and structure. [Miraterra]
As its capabilities increase, Miraterra plans to measure soil chemistry, texture, and biological content. It already accesses public data, satellite imaging, and LiDAR to develop reports, such as nutrient flow, on plots as small as 10 square meters. By adding AI to identify patterns predictive of pest infestations and disease prevalence, he says the company expects to correlate soil health with plant conditions and pathogens.
“Soil is too complex [chemically and biologically] to stick a simple probe in the ground and try to get a reading,” Kelly says. “My aspiration is to cut the cost [of soil analysis] by about 90%, to democratize measurement,” Kelly says. “That means turning a $40 analysis into one that costs less than $5.”
CRISPR: Beyond corn and soy
CRISPR technology is being applied to agrigenomics with the same revolutionary results it created for medicine. As the basis of Pairwise’s Fulcrum Platform, it is expanding agrigenomics well beyond the major crops of corn and soybeans.
Ryan Bartlett, PhD CTO, Pairwise
Fulcrum technology produced the world’s first seedless blackberry—now in field trials—and a more compact blackberry plant that is commercialized in the Americas. With this compact Fontana blackberry, “Growers can plant more blackberry plants per acre, which helps them be more efficient and produce more yield,” Ryan Bartlett, PhD, CTO, says. A thornless blackberry plant is on the horizon.
The goal is to “make growers more sustainable and efficient, and to give consumers the opportunity to consume more healthy things, like blackberries,” Bartlett says. Fast, precision editing will also help plant breeders quickly adjust to climate change.
Fulcrum features plant-specific CRISPR editing that fine-tunes gene expression rather than simply turning genes on or off. The platform includes additional gene editing tools, enzymes, and trait libraries to help breeders design and test changes and quickly advance specific phenotypes into field trials.
Bartlett calls this “traditional breeding on an accelerated basis.” With Fulcrum, these new blackberries were produced in about three years. Using traditional Mendelian breeding would have taken 10 to 40 years.
Genomics tools coupled with advanced analytics enable growers to plant more per acre. [Pairwise]
The speed comes from genome editing that generates the desired phenotype in one cycle, eliminating the need for generations of crosses to develop the features growers want.
Pairwise is also working with “additional permanent crops like cherries,” Bartlett says. “There haven’t been a lot of advancements in the past 50 years.” That may soon change, as Pairwise is working with Sun World to develop a pitless cherry.
New opportunities
“Accelerating timelines and lowering the cost of crop improvement [will] unlock expansive opportunities in agriculture, new species, new geographics, and new traits,” Brad Zamft, PhD, CEO and co-founder of Heritable Agriculture, says.
Heritable focuses on breeding indoor vegetables and forestry, although it also develops fruits and row crops with strategic partners. “The majority of the unmet need comes from placing existing plants in the right places and determining what crosses would make them better,” Zamft says.
Brad Zamft, PhD CEO, Heritable Agriculture
Consider strawberries. “We think we’re going to bring a new strawberry variety to the market—start to finish—within four years. The status quo is around a decade.” Heritable can identify desired traits, determine the genes involved, and validate them in growing plants within 18 months, versus 3 to 12 years using traditional methods. Currently, Heritable is adapting genetics from Consorzio Italiano Vivaisti’s (CIV’s) strawberries to grow indoors in Canada.
To speed the process, Heritable uses digital field trials and an AI engine developed in-house specifically for agrigenomics. Potential applications are global. “We have scalable integration of weather and soil to 10-meter resolution anywhere in the world,” Zamft says.
Increasingly accurate correlations of genes to phenotypes shorten the development timeline, Zamft adds. For example, three of the top eight genes Heritable’s team discovered for flowering time in corn proved causative, and three of three in a flavor project for leafy greens. In contrast, he cited a literature analysis that reported that out of 1,671 unique genes that were field-tested across multiple traits, only 22 were identified as validated leads.
To produce and commercialize these innovations, Heritable relies on an alliance of partners in which each partner brings vital strengths to the endeavor, from genomics through production, distribution, and sales. The company also engages in trait development with seed developers and growers, and recently launched a software-as-a-service platform to enable users to predict plant performance under multiple variables.
The company is also active in commercial forestry, using AI to help breeders make placement and breeding decisions that could shorten the breeding cycle by more than half.
Gene editing = no GMOs
Verinomics built its foundation on plant genomics, computational biology, and trait mapping to enable breakthroughs in multiple crops. Most recently, it developed Nonpareil+, a self-pollinating Nonpareil almond variety that may increase per-acre productivity by 30%.
That anticipated productivity gain is directly tied to self-pollination. Because the Nonpareil variety—which is the dominant commercial almond variety in California—is self-incompatible, it requires cross-pollination. That, in turn, requires planting alternating rows of other almond varieties as pollinators and then renting beehives (at about $400 per acre) to pollinate the orchard.
Stephen Dellaporta, PhD Founder, Verinomics
“There’s no way to create a self-compatible Nonpareil using conventional breeding,” Stephen Dellaporta, PhD, founder of Verinomics and professor at Yale University, points out. Therefore, using Verinomics’ Genesis gene editing platform, “we identified the self-incompatibility gene and edited it to a self-compatible gene.” The resultant Nonpareil+ is genetically identical to the Nonpareil except that it is self-pollinating.
Growers are beginning to plant it this year, with the expectation of reducing or eliminating the need for multi-variety almond orchards and bee hives. If it works as expected, growers can harvest all the trees at once, rather than needing separate harvests for multiple varieties.
Almonds aren’t the only crop on Dellaporta’s mind. Back in the lab, “we just completed 60 whole genome sequences, assemblies, and annotations,” he says. The company works with partners to address concerns in multiple crops, making adaptations without introducing foreign DNA.
The bacterial disease known as citrus greening is an example. It’s killed millions of acres of citrus trees, according to the U.S. Department of Agriculture. “There’s no known resistance within the domesticated citrus germplasm,” Dellaporta says. Crossing a wild-type, resistant citrus with a domesticated citrus would take decades to produce a resistant, domesticated citrus, he points out. “Gene editing may create a modification to allow the plant to be resistant without losing varietal identity.”
Precision farming can bring incredible benefits to agrigenomics, just as precision genomics is doing for medicine. As agrigenomics begins to bear fruit, developers can look forward to a genomics-fueled bounty very, very soon.
Platform, it is expanding agrigenomics well beyond the major crops of corn and soybeans.
