The findings, published in the Journal of the American College of Radiology, suggest that AI could serve as a second set of eyes for radiologists, while highlighting limitations that could affect its value in different clinical settings.

Researchers at Northwell Health evaluated an FDA-cleared algorithm from Aidoc designed to identify suspected intracranial aneurysms on CT angiography (CTA). The deep learning software analyzes scans for findings suggestive of an aneurysm and flags suspicious cases for radiologist attention.

The study included 3,856 CTA examinations across a large health system. The AI operated in “shadow mode,” meaning it processed scans without showing its results to radiologists or influencing patient care. Independent neuroradiologists reviewed cases in which the AI and clinical interpretations disagreed.

Radiologists and AI agreed on more than 96% of examinations, but each caught aneurysms the other missed. AI detected 55 confirmed aneurysms that radiologists had not reported, increasing the number of cases found by 39% compared with radiologists alone. Radiologists, however, detected 30 confirmed aneurysms that AI missed.

The AI’s greater sensitivity came with an important tradeoff. It detected 84.6% of true aneurysms compared with 71.8% for radiologists, but its positive predictive value was considerably lower—78.2% versus 92.7%. In other words, more than one in five aneurysms flagged by AI were not confirmed as true aneurysms.

The limitation was even more apparent among findings identified only by AI. Of 101 AI-only findings, 55 were confirmed aneurysms and 46 were false positives.

AI performance also varied by clinical setting. Among inpatients, the algorithm added 18 true aneurysm detections while producing seven false-positive alerts. Results were also favorable in the emergency department. In outpatient care, however, AI added only four true detections and generated more false-positive than true-positive findings.

Many of the additional aneurysms detected by AI were small, which could provide opportunities for risk assessment and monitoring before rupture.

Overall, the findings suggest that AI may help radiologists catch additional aneurysms, but its added sensitivity comes at the cost of false-positive findings requiring physician review. The differences across care settings also underscore the importance of evaluating medical AI in the clinical environments where it will actually be used.

The post AI Boosts Brain Aneurysm Detection but Produces More False Positives appeared first on Inside Precision Medicine.

Alessandro Di Federico, MD, from the Memorial Sloan Kettering Cancer Center in New York City, told delegates that “BRAF III alterations may be associated with worse prognosis,” and that “STK11, KEAP1, and SMARCA4 co-mutations are linked to worse outcomes” in patients with either BRAF class II or III alterations, a finding he said was “similar to observations previously reported in KRAS-mutated NSCLC.”

Di Federico explained that approximately four percent of patients with NSCLC harbor BRAF alterations, which are classified into three groups according to their impact on BRAF kinase activity.

At present, approved targeted therapies for BRAF inhibitors are limited to class I alterations (BRAFV600), which make up around a third of BRAF-altered NSCLC. Class II and III alterations together account for a further 60% of BRAF-altered NSCLC but these groups lack approved targeted therapy options, and their clinical characteristics and responses to immune checkpoint inhibitors are not well defined.

Di Federico and team therefore carried out a retrospective, multicenter study among 256 patients with NSCLC with a BRAF class II (n=124) or class III (n=132) alterations who received first-line immune checkpoint inhibitor therapy with or without chemotherapy.

They found that patients with class III alterations had significantly shorter median overall survival (OS) than those with class II alterations (12.7 vs. 20.5 months), despite a statistically similar objective response rate (47 vs. 52%) and median progression-free survival (5.8 vs. 10.0 months).

The researchers then performed complementary clinicopathologic and genomic analyses in a broader cohort of 15,212 patients with metastatic NSCLC treated at Memorial Sloan Kettering Cancer Center or Dana-Farber Cancer Institute. Of these, 1.6% had a BRAF class II alteration and 1.5% had a BRAF class III alteration.

Di Frederico reported that, compared with BRAF class II mutations, class III mutations were more strongly associated with a history of tobacco use (89.6 vs 81.6%) and a high tumor mutational burden (≥10 mutations/mb; 52.3 vs 39.5%).

Concurrent oncogene alterations in non-BRAF genes were found in around 25% of cases with either class II or III alterations, the most common of these was KRAS, which had a higher prevalence among class III altered cases (17.8 vs 12.1%).

In addition, class III tumors more frequently harbored co-mutations in STK11 (27.6 vs 19.4%), KEAP1 (24.0 vs 15.8%) and SMARCA4 (28.0 vs 16.2%) than class II tumors. Of note, these mutations are “thought to drive worse outcomes to immunotherapy mainly in KRAS-driven, but not in KRAS wild-type tumors,” Di Frederico remarked.

There were no significant differences between the two groups, however, in age, sex, PD-L1 expression, NSCLC histology, or tumor aneuploidy levels.

Lastly, the team evaluated the impact of concurrent mutations on outcomes using pooled data from the patients with class II and III alterations. This analysis revealed that patients with STK11, KEAP1, and SMARCA4 had significantly shorter OS than those without such co-mutations.

The post <i>BRAF</i> Mutation Class Influences Outcomes in NSCLC appeared first on Inside Precision Medicine.

The findings, published in Nature Medicine, come from I³LUNG, an international effort to develop an AI-based physician decision-support system for immunotherapy in NSCLC. The study included 2,396 patients with stage IIIC to IVB disease treated across six centers in six countries. Researchers incorporated clinical and blood measurements alongside CT imaging, digital pathology, and genomic information into machine-learning and deep-learning models.

Routine clinical data outperform established biomarkers

One of the study’s most notable findings was that relatively simple information already routinely collected in clinical practice carried substantial predictive power.

Models using clinical and blood data, including PD-L1 expression, ECOG performance status, smoking status, metastatic sites, neutrophil-to-lymphocyte ratio, and lactate dehydrogenase, achieved area under the curve values of up to 0.77 in the independent test set.

The AI models significantly outperformed individual predictors including PD-L1, ECOG performance status, neutrophil-to-lymphocyte ratio, lactate dehydrogenase, and the Lung Immune Prognostic Index. PD-L1 remains the principal clinically approved biomarker used to help guide immunotherapy decisions in NSCLC, despite its limited ability to accurately distinguish patients who will experience durable benefit.

Explainable AI improved physician predictions

The researchers also tested whether providing physicians with AI predictions could improve clinical decision-making rather than simply comparing algorithmic performance with conventional biomarkers.

Twenty physicians, including 10 lung cancer experts and 10 nonexperts, assessed real-world patient cases before and after receiving predictions from the model together with explanations showing which features contributed to its conclusions.

For predicting disease control, sensitivity increased from 0.72 without AI assistance to 0.87 with the explainable AI tool, while overall accuracy increased from 0.57 to 0.65. Both lung cancer specialists and nonexperts showed improvements. Agreement between the two groups also increased after AI support was introduced.

The findings suggest that explainability may be important for translating predictive algorithms into clinical tools, allowing physicians to examine the factors underlying an individual prediction instead of receiving an unexplained risk score.

More data did not always mean better predictions

I³LUNG also tested whether combining clinical information with CT imaging, digital pathology, and genomic data could further improve prediction.

Some multimodal machine-learning models showed substantial improvements during cross-validation, particularly in selected patient subgroups. However, those gains were not consistently reproduced in independent testing or external validation. Deep-learning approaches similarly showed no consistent benefit from adding additional modalities.

Performance of the clinical-data models also declined in the geographically distinct external validation cohort, with AUCs ranging from 0.55 to 0.72, highlighting the challenges of transferring AI models between patient populations and healthcare systems.

The study is retrospective, and the researchers caution that the incremental value of multimodal AI remains uncertain. The I³LUNG decision-support system is now undergoing prospective validation in more than 2,000 patients.

If confirmed prospectively, the results suggest that precision immunotherapy may not always require increasingly complex molecular datasets. Combining routinely available patient information with explainable AI could provide a more scalable route toward individualized treatment decisions.

The post AI Tool Outperforms PD-L1 for Predicting Lung Cancer Immunotherapy Outcomes appeared first on Inside Precision Medicine.

“In India and similar countries, the primary model for reaching rural communities has long been the ‘eye camp’—temporary outreach clinics where ophthalmologists travel from urban hospitals to rural sites on fixed dates,” said Prabhu Krishna Ravilla, MD, medical officer at Aravind Eye Hospital in India. “This model has serious constraints: it’s expensive, logistically complex, entirely dependent on specialist availability and reaches only a fraction of those who need it.”

Cataracts remain the major cause of blindness worldwide, disproportionately impacting low-resource settings with limited access to eye specialists. A previous study conducted at Aravind Eye Hospital found that eye camps only screened seven percent of local rural residents, and that a third of those who didn’t attend were in need of cataract surgery. Women, the elderly, and poorer people are the most underserved. 

In collaboration with researchers from Johns Hopkins University, Ravilla’s team developed a small attachment that clips onto a smartphone camera. The device contains a magnifying lens, two small white LEDs powered by the phone, and a silicone scope that rests against the patient’s eye socket to block ambient light and ensure clear pictures can be taken. The complete system costs less than £150 including the smartphone.

“A low-cost, easy-to-use smartphone imaging device could allow community health workers with minimal ophthalmic training to capture diagnostic-quality eye images in the field, which could then be reviewed remotely by ophthalmologists,” Ravilla said. “This would decouple the need for a specialist to be physically present at every screening.”

After receiving three hours of training, community health workers used the device to screen 1,093 patients across 19 rural camps near Puducherry in South India. The smartphone images were reviewed remotely by ophthalmologists, and their diagnoses and referral decisions were then compared with those made by ophthalmologists who examined the same patients in person at the eye camps. 

Both approaches showed 89% agreement when identifying cataracts, rising to 96% for patients with mature cataracts. “The most clinically important finding was that when the remote ophthalmologist reviewed the smartphone images and the in-person eye camp doctor independently decided whether a patient needed to be referred to hospital for further care, they agreed in 96 out of every 100 cases,” said Ravilla.

Community health workers were able to screen each eye within two and a half minutes, with more than 90% of images considered suitable for diagnosis. The researchers also found that diagnostic agreement improved with image quality.

“These findings challenge the assumption that specialist presence is necessary for accurate cataract screening,” said Ravilla. “They suggest a model where a trained ophthalmologist’s time is used for diagnosis and decision-making, where it is most valuable, rather than travel and in-person examination. For patients, particularly in rural and underserved communities, door-to-door screening becomes imaginable.”

“For policymakers and public health planners, it offers a potential path to scaling up cataract screening without a proportional increase in a specialist workforce. The platform integrates with existing community health worker infrastructure, works in low-bandwidth environments, and has demonstrated patient acceptability. It is also compatible with future AI-based grading, which could eventually reduce dependence on remote ophthalmologist review for initial triage.”

The researchers are planning further work to continue improving the system, including adding slit-beam and blue-light imaging, as well as taking images after pupil dilation. These techniques are part of comprehensive eye examinations and can make diagnosis easier for ophthalmologists remotely assessing patients.

“This is a promising approach for underserved regions, where access to ophthalmologists can be limited,” said Burkhard Dick, ESCRS president and chair of the ophthalmology department at the University Eye Hospital Bochum in Germany. “It should not be regarded as a replacement for a comprehensive ophthalmological examination. However, as a carefully governed screening and triage tool, it could enable ophthalmologists to use their expertise remotely and help more people access timely cataract assessment and treatment. Further research in other settings will now be important to establish how widely the approach could be applied.”

The post Smartphone Attachment Screens for Cataracts in Remote Communities appeared first on Inside Precision Medicine.

“Our results support the hypothesis that the molecular profile of CKMD appears early in the life course with proteomic signals relevant to long-term clinical outcomes appearing even in childhood,” write Joshua Landman, PhD, form Vanderbilt University Medical Center, and co-authors in Nature Medicine. “In total, these observations underscore the potential for precision proteomic risk assessment in children to identify at-risk groups who may benefit from earlier preventive strategies aimed at improving CKMD-free longevity.”

The researchers explain that current CKMD prevention efforts focus on adults but “defining precise biomarkers in childhood that reflect relevant early CKMD traits and have prognostic and genetic links to CKMD outcomes in adults are critical to deploying surveillance and treatment strategies to prevent CKMD at its origin.”

Landman and team measured the levels of more than 5000 proteins in blood samples from 273 children and adolescents (13.1 years, 53% girls) and linked them to 25 clinical and imaging-based physical traits, or phenotypes, of CKMD including body composition, liver fat and fibrosis, renal function, hypertension, and insulin resistance. More than a third of the children presented with obesity, lipid levels that were associated with an increased risk for cardiovascular disease, hypertension, and insulin resistance.

The investigators found that 1064 proteins were associated with at least one CKMD phenotype across six domains that captured shared molecular risk and mechanistic pathways.

They then showed that these proteomic signatures were strongly associated with CKMD clinical outcomes in 685 adults from the same community as the children and in 28,256 adults from UK Biobank.

For example, the signatures related to pro-inflammatory adiposity, liver fat/fibrosis, and insulin resistance were associated with an increased risk for all-cause mortality, type 2 diabetes, cardiovascular disease, sleep apnea, and fatty liver disease over 10 years of follow-up.

“Collectively, these findings underscore the potential consistency of early CKMD risk, as reflected in the childhood proteome, with the diagnosis of adult cardiovascular and metabolic disease,” Landman and co-authors remark.

The researchers also evaluated whether the proteins implicated in pediatric CKMD were modified during treatment with the glucagon-like peptide (GLP)-1 inhibitor semaglutide in adults. This analysis revealed that protein levels associated with more adverse CKMD in children and adolescents were altered in a favorable direction following semaglutide therapy.

“This association is really one of the most exciting messages, because those proteins that predict the risk of cardiometabolic diseases all moved in a healthy direction after these adults took GLP-1. This makes this a modifiable risk state rather than a fixed marker of fate,” said corresponding author Kari North, PhD, director of the Border Health Research Center in Houston. “Childhood is the time that we really need to start intervening because it’s reversible.”

While additional research is needed to validate the findings, the study suggests that proteomic signatures, by “opening a window into the earliest roots of cardiovascular disease at its most interruptible phase,” may help identify children most at risk, and therefore most likely to benefit from targeted therapy, the paper concluded.

The post Childhood Proteome Reveals Precision Prevention Approach for Cardiometabolic Disease appeared first on Inside Precision Medicine.

Researchers at Sun Yat-sen University in China profiled 55 different RNA modifications in peripheral blood from patients with HCC, individuals with liver cirrhosis, and healthy controls. Their analysis identified a combination of RNA modifications and gene-expression markers capable of distinguishing HCC with area under the receiver operating characteristic curve (AUC) values approaching 0.97.

RNA modifications offer a new liquid biopsy approach

RNA molecules can undergo numerous chemical modifications after they are produced, altering their stability, structure, translation, and other biological functions. More than 170 RNA modifications have been identified, but relatively little research has explored whether their patterns in peripheral blood could serve as diagnostic biomarkers for liver cancer.

Using liquid chromatography-tandem mass spectrometry, the investigators quantified 55 modifications. Eleven were significantly altered in both the overall HCC group and patients with stage I disease compared with the combined non-HCC group of healthy participants and patients with cirrhosis.

One modification in particular, N²,N²-dimethylguanosine, or m²,²G—emerged as a prominent component of the HCC-associated signature.

Multi-omics model reaches AUC of 0.97

A model based on RNA modifications was able to distinguish HCC from non-HCC samples, including patients with stage I disease. The researchers then added two blood-based gene-expression markers, IFI27 and CCR2, to see whether combining different types of molecular information could improve detection.

The combined multi-omics model performed even better, reaching an AUC of about 0.97 for both HCC overall and stage I disease. The final signature brought together three RNA modifications with IFI27 and CCR2 expression.

Larger studies needed before clinical use

The work represents an emerging precision diagnostics approach sometimes described as epitranscriptomics, the study of chemical modifications made to RNA. Most liquid biopsy development has focused on circulating tumor DNA mutations, DNA methylation, circulating tumor cells, or RNA expression. Profiling RNA modifications could potentially provide another layer of biological information.

The results are particularly notable for stage I HCC, where earlier detection can substantially expand treatment options. However, the authors characterize the findings as preliminary.

The performance of the signature will require validation in independent and larger populations before it can be considered for clinical screening or surveillance. If validated, combining RNA modification patterns with gene-expression markers could provide a new multi-omics strategy for distinguishing liver cancer from the cirrhosis and other chronic liver conditions that frequently complicate HCC detection.

The post Blood RNA Modification Signature Shows Promise for Early Liver Cancer Detection appeared first on Inside Precision Medicine.

The approach, described in Science Advances, addresses a persistent problem in cancer immunotherapy: Even when the immune system can recognize cancer, the environment surrounding a solid tumor can prevent immune cells from mounting an effective attack.

Researchers at Adelaide University in Australia focused on tumor-associated macrophages (TAMs), immune cells that are abundant in many tumors. These macrophages can suppress anticancer immune responses and make it difficult for cytotoxic T cells to infiltrate tumors. Rather than eliminating the macrophages, the researchers aimed to change their behavior.

They developed lipid nanoparticles coated with an antibody against TREM2, a protein found on immunosuppressive TAMs. The antibody helped steer the nanoparticles toward these macrophages while limiting uptake by cancer cells.

Once inside the macrophages, the nanoparticles delivered two components designed to work together. Resiquimod, which activates Toll-like receptors 7 and 8, helped shift the macrophages away from their immune-suppressing state. At the same time, mRNA instructed the cells to produce CXCL9, a chemical signal that attracts cancer-fighting CD8+ T cells.

In mice with 4T1 breast tumors, treatment reduced immunosuppressive macrophages by more than 60% and increased CXCL9 levels in tumors fourfold. More CD8+ T cells subsequently entered the tumors, and their anticancer activity increased. The treatment moderately reduced tumor growth.

The researchers also tested the nanoparticles alongside checkpoint inhibitors targeting PD-L1 and CTLA-4. Adding the drugs produced additional changes in the immune response, including more cancer-fighting T cells in tumors and an increase in central memory T cells, which can help the immune system mount a response if it encounters the same threat again.

But those immune changes did not translate into additional tumor control. Tumor growth was not reduced further by adding checkpoint blockade to the nanoparticle treatment. The researchers noted that the 4T1 model responds poorly to PD-L1 and CTLA-4 inhibitors and contains other immune-suppressing cells that were not targeted by their approach.

The nanoparticles were given intravenously and were well tolerated in the mice, with minimal effects detected in healthy organs. The researchers cautioned that systemic inflammation and long-term toxicity will need further investigation.

“Overall, our study offers a previously unreported system that specifically delivers resiquimod and mRNA- CXCL9 to TAMs, relieving immunosuppression and expressing a CTL chemoattractant to overcome the immune barriers for CTL immunotherapy,” the authors write.

The study provides proof of concept for using targeted mRNA delivery to alter the immune environment surrounding solid tumors. Rather than directly attacking cancer cells, the strategy reprograms immune cells that tumors can exploit—potentially making tumors more vulnerable to the body’s own cancer-fighting T cells.

The post Targeted mRNA Nanoparticles Turn Tumor Macrophages Against Cancer appeared first on Inside Precision Medicine.

Prior research has identified platelet factor 4 (PF4) as a protein of interest in HIT. PF4 is a protein produced in the body with a role in blood clotting. However, in some patients treated with heparin, PF4 changes shape, resulting in irregular proteins that are targeted by antibodies, triggering an immune response resulting in HIT. Though rate, the blood clots developed through HIT can be life-threatening, potentially leading to stroke, heart attack, limb loss, or death.

Using an interdisciplinary approach, the McMasters team focused on identifying the “molecular switch” that spurs the shape change seen in PF4. Their work is published in Nature Communications.

“This study provides a molecular explanation for how PF4 becomes a pathogenic antigen,” said lead author Giuseppe Melacini, PhD, professor from the chemistry and chemical biology and biochemistry and biomedical sciences departments. “By identifying the structural switch that controls this process, we’ve revealed a new way of thinking about how to detect or prevent these dangerous immune reactions.”

Advanced nuclear magnetic resonance (NMR) spectroscopy, allowed the researchers to identify the sought after molecular switch for PF4. They began their work by exploring the native structure of PF4.

“To understand how PF4 transitions from a benign protein into an immunogenic antigen, it is essential to understand the PF4’s structural dynamics underlying neoepitope exposure,” the authors wrote.

The team used “an integrative approach combining high-resolution NMR spectroscopy, SEC-MALS, mutagenesis, network modeling, and molecular dynamics simulations. The integration of these methods has revealed structural and mechanistic insights into the conformational asymmetry of PF4 tetramers in solution.”

The combination of approaches enabled the team to clarify how the dimers interacted in dynamic ways through differing activations and the use of mutant molecules.

“Mutations that target symmetry-switching sites stabilize a symmetric PF4 tetramer with markedly reduced affinity for HIT antibodies. These findings overturn the long-standing hypothesis that electrostatics alone drive PF4 asymmetry,” they wrote.

Typically, PF4 is assembled from two dimers. Shifts in conformation of these dimers form an asymmetrical tetramer with variable open and closed ends, and can lead to variable interactions between PF4 and heparin or antibodies which can trigger a reaction leading to HIT. While previous data supported the idea that electrostatic charges were a primary driver of the tetramer conformation changes, the team demonstrated that conformation changes can be modulated through structural changes—mutations—altering the switch ability, keeping PF4 in its closed form, dramatically reducing its ability to provoke an immune response.

“This work shows the power of collaboration across disciplines,” said Ishac Nazy, PhD, associate professor in the departments of medicine and biochemistry and biomedical sciences and the Michael G. DeGroote Centre for Transfusion Research. “By combining advanced molecular imaging approaches with clinical expertise, we were able to answer a longstanding question about what causes PF4 to become a target of harmful antibodies.”

“We have identified a single point mutation (L8A) that is sufficient to reprogram PF4’s entire tetrameric architecture and allosteric networks into symmetric closed-closed tetramers with reduced affinity for HIT-like IgG antibodies,” they wrote.

While the authors conceded that this work doesn’t identify a single therapeutic avenue, the point out that is provides a “conceptual foundational blueprint to develop tool compounds for a better understanding of PF4 pathophysiology.” They also write that this work could be used to better understand other auto-immune conditions, stating that this could be “a generalizable strategy for attenuating autoimmune responses by selectively stabilizing non‑immunogenic conformational states of self‑proteins rather than globally suppressing immunity.”

The post Mechanism Driving Blood-Clotting Disorder Identified appeared first on Inside Precision Medicine.

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Panelists:

Surbhi Jain, PhD, MBBS

Staff Scientist
Bio-Rad Laboratories

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Panelist

Surbhi Jain, PhD, MBBS

Surbhi Jain, PhD, MBBS, is a staff scientist at Bio-Rad Laboratories and the R&D lead for the ddPLEX EGFR/KRAS/BRAF multiplexed droplet digital PCR panel. She is focused on developing ddPCR assay solutions for precision oncology and has more than 16 years of experience in diagnostic assay development, with expertise spanning liquid biopsy biomarkers and high-throughput molecular workflows. Before joining Bio-Rad, Surbhi developed reagents for CRISPR-enabled massively parallel genome editing and served as principal investigator on SBIR programs developing cell-free DNA technologies for cancer detection and monitoring. She earned her PhD in microbiology and immunology from Temple University in Philadelphia and her medical degree (MBBS) from B.Y.L. Nair Hospital in Mumbai.

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Prithwish Pal, PhD

Director, Global Product Marketing – Oncology
Bio-Rad Laboratories

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Panelist

Prithwish Pal, PhD

Prithwish Pal, PhD, is director of global product marketing for oncology at Bio-Rad Laboratories, leading the company’s ddPCR oncology portfolio for liquid biopsy, MRD, and treatment monitoring applications. With over 15 years of experience in precision oncology, he has held leadership roles in product management, market development, and medical affairs at Illumina and other diagnostics companies. He holds a PhD in biophysics from the University of Rochester and an MBA from the University of California, San Diego.

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National and international guidelines recommend biomarker testing for oncogenic driver mutations in metastatic non-small cell lung cancer (NSCLC). Emerging technologies such as Droplet Digital PCR (ddPCR) enable sensitive detection and quantification of nucleic acid targets and have been widely used in scientific research involving solid and hematologic cancer samples. Such technologies support oncology research applications, including biomarker characterization and molecular profiling studies.

In this IPM webinar, two experts will discuss the growing role of ddPCR in clinical research and translational oncology. In the first session, Prithwish Pal, PhD, will explore the use of ddPCR in liquid biopsies that require highly sensitive variant detection and precise quantification in research settings. In the second session, Surbhi Jain, PhD, will present analytical data for ddPLEX EGFR/KRAS/BRAF Mutation Detection Assay Kit*, a research-use-only kit developed for the QX600 Droplet Digital PCR System. She will share results from an external analytical validation study of the assay against alternative reference methods that showed 100% concordance in archived plasma cell-free DNA and 96.5% in FFPE specimens.

Key takeaways from the webinar include:

• Workflow and precision of ddPCR systems ideal for cancer research, including biomarker discovery, development of biomarker driven therapy, and monitoring applications in research settings
• How multiplexing extracts maximum information from limited testing research material with greater sensitivity and precision than qPCR and with less time and cost than next-generation sequencing
• A streamlined RUO ddPCR assay for metastatic NSCLC research that quantifies 37 EGFR, KRAS, and BRAF variants in a single well, paired with a mutation-agnostic total quantification well for variant allele frequency calculations.

A live Q&A session will follow the presentation offering you a chance to pose questions to our expert panelists.

* The content presented in these sessions is intended for research use only and is not intended for use in diagnostic procedures. The views and opinions expressed in the recordings are those of the individual presenters and do not necessarily reflect the official policy, position, or claims of Bio-Rad Laboratories. 

Any clinical applications, test validations, or performance claims discussed are the sole responsibility of the presenting institution or speaker and do not reflect the views or endorsements of Bio-Rad Laboratories.

Any reference to clinical use, patient testing, or performance claims reflects the practices and experience of the individual institutions or researchers and should not be interpreted as endorsement by Bio-Rad.

Produced with support from:

The post Highly Multiplexed Droplet Digital PCR for Ultra-Sensitive NSCLC Biomarker Detection in Research Workflows appeared first on Inside Precision Medicine.

Results published today in Science uncover the extent of DNA damage caused by platinum-based chemotherapy in children treated for cancer, including a previously unseen pattern of damage in the liver that could help explain some of the long-term health problems experienced by survivors. 

“Our study represents a milestone in revealing the DNA damage that chemotherapy causes in normal tissues,” said Anna Wenger, PhD, postdoctoral fellow at the Wellcome Sanger Institute and the University of Gothenburg. “We explored what this might mean for children who undergo this life-saving treatment at a young age, as delayed side effects from treatment often unfold over a lifetime.”

The researchers stress that these findings should not discourage the use of chemotherapy, which remains essential for treating childhood cancer. Instead, understanding how treatment affects healthy tissues could eventually help scientists develop ways to reduce health risks later in life. “Our finding enables us to begin to think about ways in which we could protect healthy tissues from DNA damage,” said Wenger.

Chemotherapy kills cancer cells by damaging their DNA as they rapidly divide. While healthy cells can survive this damage, the treatment can leave mutational signatures with unknown effects on the long-term health of survivors. 

To understand these changes, Wenger’s team used a genomic sequencing method developed at the Wellcome Sanger institute known as nanorate sequencing (NanoSeq), which sequences both strands of DNA independently and compares results to achieve high resolution. The technique is especially useful when sequencing childhood tumors, as these have fewer shared mutations with healthy tissues that can be missed by conventional bulk sequencing methods. 

The researchers analyzed 186 samples covering a wide range of tissues from nine children with liver cancer, collected after treatment with platinum-based chemotherapy. Another 77 samples were collected from children with either other forms of cancer, who had received non-platinum treatment, or who had not undergone treatment at all. 

The analysis revealed substantially more DNA changes in healthy cells following platinum-based chemotherapy, with some children showing levels of genetic damage comparable to those found in adult tissues. Some of these mutations are considered cancer drivers, meaning they can potentially contribute to the development of cancer, although secondary cancers following childhood cancer treatment remain rare.

Importantly, results unveiled a distinctive pattern of DNA damage in liver tissue that had not previously been observed. The signature was not found in other tissues and appeared to be specifically associated with platinum-based chemotherapy. Because platinum-based drugs are broken down in the liver, the organ may be particularly exposed to the processes responsible for this type of DNA damage.

“Our findings have unearthed something quite unprecedented about chemotherapy and DNA damage: The same chemotherapy drug can cause different types of DNA damage across tissues,” said Foad J. Rouhani, MD, PhD, group leader at the Francis Crick Institute and honorary consultant transplant surgeon at King’s College Hospital. “This is a fundamental observation that questions our assumption that chemotherapy causes the same DNA damage in all tissues. In the case of the liver, we have seen that chemotherapy can cause distinctive DNA damage which may plausibly be the contributor to liver disease in adult life in these patients.”

The findings may partly explain why childhood cancer survivors commonly face health issues related to premature aging. However, further research will be needed to understand exactly how these mutations affect the long-term health of survivors and whether the damage can be prevented or reduced. 

“Chemotherapy is the key to curing cancer in children, and there is no alternative,” said Sam Behjati, MD, PhD, head of pediatrics at the University of Cambridge and director of the Cambridge Children’s Research Institute. “Our work now reveals a plausible mechanism, DNA damage in normal tissues, through which chemotherapy in childhood may cause late adverse effects. The next step will be to gain a deeper understanding of this damage which may enable us to develop protective treatments to reduce long-term health risks for childhood cancer survivors.”

The post Childhood Chemotherapy Leaves Lasting DNA Damage in Healthy Tissues appeared first on Inside Precision Medicine.