Nat Commun. 2025 Oct 17;16(1):9232. doi: 10.1038/s41467-025-64292-3.
ABSTRACT
Recent advancements in spatial transcriptomics technologies have significantly enhanced resolution and throughput, underscoring an urgent need for systematic benchmarking. Here, we generate serial tissue sections from colon adenocarcinoma, hepatocellular carcinoma, and ovarian cancer samples for systematic evaluation. Using these uniformly processed samples, we generate spatial transcriptomics data across four high-throughput platforms with subcellular resolution: Stereo-seq v1.3, Visium HD FFPE, CosMx 6K, and Xenium 5K. To establish ground truth datasets, we profile proteins on tissue sections adjacent to all platforms using CODEX and perform single-cell RNA sequencing on the same samples. Leveraging manual nuclear segmentation and detailed annotations, we systematically assess each platform's performance across capture sensitivity, specificity, diffusion control, cell segmentation, cell annotation, spatial clustering, and concordance with adjacent CODEX. The uniformly generated and processed multi-omics dataset could advance computational method development and biological discoveries. The dataset is accessible via SPATCH, a user-friendly web server for visualization and download.
Mol Med Rep. 2026 Jan;33(1):6. doi: 10.3892/mmr.2025.13716. Epub 2025 Oct 17.
ABSTRACT
R‑loops, three‑stranded nucleic acid structures composed of an RNA:DNA hybrid and displaced single‑stranded DNA, have emerged as important regulators of gene expression and genome maintenance. Although physiological R‑loops participate in normal cellular processes, their dysregulation can threaten genomic integrity by inducing DNA damage and replication stress. The present review explores the role of R‑loops in hepatocellular carcinoma (HCC), a malignancy characterized by marked genomic instability. In the present review, the formation mechanisms of R‑loops, their dual functions in transcriptional regulation and DNA damage, and their specific implications for HCC pathophysiology were discussed. HCC cells exhibit altered R‑loop homeostasis with aberrant accumulation linked to hepatitis B virus infection, inflammatory signaling and oncogene activation. The present review highlighted how HCC cells exploit or manage R‑loops to promote tumor progression, particularly through the epigenetic silencing of differentiation genes and modulation of replication stress responses. Furthermore, emerging therapeutic strategies targeting R‑loop biology were examined, including small molecules that induce synthetic lethality, gene‑based interventions and combination approaches that exploit R‑loop vulnerabilities. Challenges in targeting R‑loops and future directions, including multi‑omics profiling and biomarker development, were also addressed. Understanding the complex interplay between R‑loops and HCC offers promising avenues for novel diagnostic and therapeutic approaches for this malignancy.
Google has announced DeepSomatic, an AI tool that can identify cancer-related mutations in tumour genetic sequences more accurately.
Cancer starts when the controls governing cell division malfunction. Finding the specific genetic mutations driving a tumour’s growth is essential for creating effective treatment plans. Doctors now regularly sequence tumour cell genomes from biopsies to inform treatments that can target how a particular cancer grows and spreads.
Published in Nature Biotechnology, this work presents a tool that uses convolutional neural networks to identify genetic variants in tumour cells with greater accuracy than current methods. Google has made both DeepSomatic and the high-quality training dataset created for it openly available.
The challenge of somatic variants
Cancer genetics is complex. While genome sequencing finds genetic cancer variations, distinguishing real variants from sequencing errors is difficult and where an AI tool would provide welcome assistance. Most cancers are driven by ‘somatic’ variants acquired after birth rather than inherited ‘germline’ variants from parents.
Somatic mutations happen when environmental factors like UV light damage DNA, or when random errors occur during DNA replication. When these variants alter normal cell behaviour, they can cause uncontrolled replication, driving cancer development and progression.
Identifying somatic variants is harder than finding inherited ones because they can exist at low frequencies within tumour cells, sometimes at rates lower than the sequencing error rate itself.
How DeepSomatic works
In clinical settings, scientists sequence both tumour cells from a biopsy and normal cells from the patient. DeepSomatic spots the differences, identifying variations in tumour cells that aren’t inherited. These variations reveal what’s fuelling the tumour’s growth.
The model converts raw genetic sequencing data from both tumour and normal samples into images representing various data points, including the sequencing data and its alignment along the chromosome. A convolutional neural network analyses these images to differentiate between the standard reference genome, the individual’s normal inherited variants, and cancer-causing somatic variants while filtering out sequencing errors. The output is a list of cancer-related mutations.
DeepSomatic can also work in ‘tumour-only’ mode when normal cell samples are unavailable, which happens frequently with blood cancers like leukaemia. This makes the tool applicable across many research and clinical scenarios.
Training a more precise AI cancer research tool
Training an accurate AI model requires high-quality data. For its AI tool, Google and its partners at the UC Santa Cruz Genomics Institute and the National Cancer Institute created a benchmark dataset called CASTLE. They sequenced tumour and normal cells from four breast cancer samples and two lung cancer samples.
These samples were analysed using three leading sequencing platforms to create a single, accurate reference dataset by combining the outputs and removing platform-specific errors. The data shows how even the same cancer type can have vastly different mutational signatures, information that can help predict patient response to specific treatments.
DeepSomatic models performed better than other established methods across all three major sequencing platforms. The tool excelled at identifying complex mutations called insertions and deletions, or ‘Indels’. For these variants, DeepSomatic achieved a 90% F1-score on Illumina sequencing data, compared to 80% for the next-best method. The improvement was more dramatic on Pacific Biosciences data, where DeepSomatic scored over 80% while the next-best tool scored less than 50%.
The AI performed well when analysing challenging samples. Testing included a breast cancer sample preserved with formalin-fixed-paraffin-embedded (FFPE), a common method that can introduce DNA damage and complicate analysis. It was also tested on data from whole exome sequencing (WES), a more affordable method that sequences only the 1% of the genome coding for proteins. In both scenarios, DeepSomatic outperformed other tools, suggesting its utility for analysing lower-quality or historical samples.
An AI tool for all cancers
The AI tool has shown it can apply its learning to new cancer types it wasn’t trained on. When used to analyse a glioblastoma sample, an aggressive brain cancer, it successfully pinpointed the few variants known to drive the disease. In a partnership with Children’s Mercy in Kansas City, it analysed eight samples of paediatric leukaemia and found the previously known variants while identifying 10 new ones, despite working with tumour-only samples.
Google hopes research labs and clinicians will adopt this tool to better understand individual tumours. By detecting known cancer variants, it could help guide choices for existing treatments. By identifying new ones, it could lead to new therapies. The goal is to advance precision medicine and deliver more effective treatments to patients.
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The surge of multi-billion-dollar investments in AI has sparked growing debate over whether the industry is heading for a bubble similar to the dot-com boom.
Investors are watching closely for signs that enthusiasm might be fading or that the heavy spending on infrastructure and chips is failing to deliver expected returns. A recent survey by BofA Global Research found that 54% of fund managers believe AI stocks are already in bubble territory, while 38% disagree.
Echoes of the dot-com era
Despite the optimism surrounding AI, sceptics remain unconvinced of its real-world impact. Some even call it a bluff or a bubble waiting to burst.
Speaking during Cisco’s recent Virtual Media Roundtable — AI Readiness Index 2025: Readiness Leads to Value, Ben Dawson, Senior Vice President and President for Asia Pacific, Japan, and Greater China (APJC), compared the current wave of AI hype to the early days of the internet. He said technological shifts of this scale often follow a familiar pattern — early excitement, heavy investment, and eventual market correction before long-term value takes hold.
Dawson noted that while some AI projects or business models may not last, the overall transformation is real and lasting. He added that, much like the internet revolution, AI will permanently reshape business and society, and organisations that ignore it do so at their own risk.
The role of governments and global policy
Public policy is also shaping how the AI cycle unfolds — and how governments might cushion the risks of a potential AI bubble. As Harvard Business Review pointed out, in the US, government involvement has helped define past technology eras — often through incentives and early investments that encourage private innovation. The same pattern is now visible in AI. Both the Trump and Biden administrations have positioned AI as a matter of economic strength and national security, sending a clear message that speed matters.
China has taken a state-led approach, directing capital toward local AI firms to reduce reliance on US technology. In Europe, efforts have focused more on regulation, though fears of overregulation have led to new programs — such as the AI Continent Action Plan and a €1 billion Apply AI fund — to boost adoption and competitiveness.
Meanwhile, venture capital and sovereign wealth funds are investing heavily, even before widespread AI demand exists. These early bets assume that adoption will eventually justify the buildout. But if that demand slows, some investors could be left with stranded assets, much like the unused fibre networks that followed the dot-com bubble.
For businesses, the challenge is different. Instead of financing the next infrastructure wave, they face the question of how to use AI to strengthen their operations. The companies that survived the dot-com downturn — such as Amazon — succeeded by aligning technology with real business value rather than market hype.
Market warnings over a possible AI bubble
The Bank of England recently warned that markets could suffer a sharp correction if confidence in AI falters, calling the potential impact on the UK’s financial system “material.” The warning reflects growing caution among policymakers about how quickly AI-related valuations have climbed.
This concern is shared by some investors and economists who believe the rapid pace of AI spending may outstrip short-term returns. Others, however, argue that building AI infrastructure now is essential groundwork for future innovation.
Building long-term AI infrastructure amid bubble fears
When asked whether companies are worried about AI infrastructure costs and energy demand, Simon Miceli, Managing Director of Cloud and AI Infrastructure for APJC at Cisco, said he views the issue from the opposite angle.
Rather than fearing overcapacity, he said what’s happening now is a large-scale buildout to support the industrialisation of AI. The question, he said, isn’t whether AI demand exists today, but whether the world is preparing fast enough for what’s coming.
Miceli acknowledged that some correction in the AI market is likely, but he believes the long-term need for AI computing power justifies current investment levels. “There’s a race to develop AI and build the capability behind it,” he said, adding that demand will eventually meet supply as applications mature.
Different shades of caution
Across the industry, opinions vary on whether AI’s momentum represents hype or healthy growth.
According to Reuters, at the Milken Institute Asia Summit 2025, Singapore’s GIC Chief Investment Officer Bryan Yeo said valuations in early-stage AI ventures appear inflated, with many startups commanding “huge multiples” despite modest revenues. He suggested that while some firms may justify their valuations, others are unlikely to deliver returns that match investor expectations.
Jeff Bezos, Amazon’s founder, said that during periods of excitement like this, investors often struggle to separate good ideas from bad ones — though he also noted that innovation-driven bubbles often leave behind real progress once the market settles.
At Goldman Sachs, economist Joseph Briggs argued that the current surge in AI infrastructure spending remains economically sustainable. He said the long-term case for AI investment is strong, but the ultimate winners are still uncertain given how quickly technology changes and how easily companies can switch providers.
Meanwhile, ABB CEO Morten Wierod told Reuters that while he doesn’t see an AI bubble, supply chain and construction limits could slow the rollout of new data centres. IMF Chief Economist Pierre-Olivier Gourinchas added that even if there’s a downturn, it’s unlikely to cause a systemic financial crisis since AI investments aren’t debt-driven.
OpenAI CEO Sam Altman also acknowledged market overexcitement, predicting that some investors will lose large sums while others will profit heavily — an outcome that mirrors past technology bubbles.
Despite growing talk of an AI bubble, many investors remain committed to the sector. UBS equity strategists said that about 90% of investors who think the market is overheated are still holding AI-related assets, suggesting most believe the industry has not yet peaked.
A cycle, not a collapse
While concerns about an AI bubble are valid, most experts agree that the technology’s long-term impact is undeniable. As Cisco’s Ben Dawson put it, every major technological transition goes through a cycle of hype, correction, and consolidation — but what remains afterward reshapes industries for decades.
For now, the question isn’t whether AI will endure, but how well businesses and investors can navigate the growing pains that come with every market bubble.
Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information.
AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.
As part of its bid to transform the future of cancer screening and seize control of a potentially vast and increasingly competitive market, Grail announced fresh data on Friday from a large U.S. study of its flagship blood-based test for detecting dozens of tumor types. The results reinforce some of the company’s arguments for a new approach to screening; experts said the firm seems to have improved the test’s accuracy but noted that major questions about the real-world impacts of such tests remain.
The Pathfinder 2 study enrolled nearly 36,000 adults over the age of 50 to evaluate the biotech’s screening test, Galleri. When researchers measured the test’s performance in participants who’d been followed for over a year, they found that it caught 40.4% of cancer cases, a test feature known as sensitivity. A little more than half of these cancers were found early, in stage 1 or 2, and approximately three-quarters of them aren’t part of current screening regimens, such as pancreatic, liver, and head and neck cancers.
Among patients who had a positive test, nearly 62% were found to in fact have cancer, while 38% of positive results were false alarms. In earlier studies, Galleri’s positive predictive value was a bit lower, ranging from 43% to 50%. Conversely, nearly all participants who got a negative result did not have cancer, with a negative predictive value of 99.1%.
Background: Artificial intelligence-assisted conversational agents have been applied and developed in outpatient departments to improve health services in China. However, there has been little research that evaluates the effect of artificial intelligence-assisted conversational agents on the patient experience related to physicians during outpatient visits. Objective: This aim of this study was to examine whether the use of artificial intelligence-assisted conversational agents improves the patient experience related to physicians during outpatient visits and to further find out the difference in the patient experience between conversational agent users and nonusers. Methods: We used the Chinese Outpatient Experience Questionnaire to survey the patient experience related to physicians during outpatient visits. A sample of 392 adult residents who sought outpatient services from tertiary public hospitals in China was selected by random sampling. The t tests were used to test the mean difference in the patient experience scores between conversational agent users and nonusers. Multiple linear regression analysis was further performed to determine whether the use of artificial intelligence-assisted conversational agents during outpatient visits was associated with a better patient experience related to physicians. Results: Conversational agent user reported significantly higher scores than nonusers in the total patient experience scores (t392=5.589, P<.001 the items and dimensions of physician-patient communication p=".006)," health information short-term outcome general satisfaction multiple linear regression results further showed that after controlling for other factors on participant characteristics use artificial intelligence-assisted conversational agents during outpatient visits significantly influenced total patient experience scores related to physicians averagely increased by conclusions: could improve especially in terms making better accessing more targeted ameliorating outcomes increasing satisfaction. therefore we suggest public hospitals should consider benefits actively deploy departments so as continuously visits.>
For much of the 20th century, the academic medicine ideal was clear: a physician-researcher supported by NIH grants, publishing in high-impact journals, climbing a predictable ladder of assistant to associate to full professor. Research was formal, structured, and often slow. Productivity was measured in citations, and prestige came from peer-reviewed work and institutional affiliation. But that model no longer holds uncontested sway.
Academic medicine is undergoing a radical democratization driven by falling public research funding, the proliferation of alternative modes of scholarly communication, and the transformative influence of artificial intelligence (which, yes, did help me proofread this article). In this evolving landscape, junior faculty, clinicians, and nontraditional scholars can exert as much influence as tenured researchers with decades of conventional output. In some cases, they might even have more influence than their established counterparts. While this may be frightening to some, this shift holds tremendous promise for medicine, opening the door to potentially more inclusive and important contributions.
A second blood test has been been approved by the US Food and Drug Administration to assist in diagnosing Alzheimer’s disease. Will these tests change how cognitive decline is measured?
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