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Integrated multi-omics landscape of non-small cell lung cancer with distant metastasis

Front Immunol. 2025 Mar 17;16:1560724. doi: 10.3389/fimmu.2025.1560724. eCollection 2025.

ABSTRACT

BACKGROUND: Distant metastasis is one of the important factors affecting the prognosis of lung cancer patients. Extracellular vesicles (EVs) play an important role in the occurrence, development, and metastasis of cancer. However, it is currently unclear whether EVs in BALF are involved in distant tumor metastasis.

METHODS: we collected bronchoalveolar lavage fluid (BALF) from patients with metastatic and non-metastatic non-small cell lung cancer (NSCLC) to isolate exosomes, which were then characterized by nanoparticle tracking analysis (NTA) and transmission electron microscopy (TEM), followed by comprehensive metabolomic and proteomic analysis to ultimately construct a distant metastasis prediction model for non-small cell lung cancer.

RESULTS: Our research has found that the BALF of NSCLC patients is rich in EVs, which have typical morphology and size. There are significant differences in protein expression and metabolite types between patients with distant metastasis and those without distant metastasis. Sphingolipid metabolism pathways may be a key factor influencing distant metastasis in NSCLC. Subsequently, we constructed a predictive model for distant metastasis in NSCLC based on differentially expressed proteins identified by proteomics. This model has been proven to have high predictive value.

CONCLUSION: The multi-omic analysis generated in this study provided a global overview of the molecular changes, which may provide useful insight into the therapy and prognosis of NSCLC metastasis.

PMID:40165954 | PMC:PMC11956740 | DOI:10.3389/fimmu.2025.1560724

  • ✇MIT Technology Review
  • How a bankruptcy judge can stop a genetic privacy disaster Keith Porcaro
    Stop me if you’ve heard this one before: A tech company accumulates a ton of user data, hoping to figure out a business model later. That business model never arrives, the company goes under, and the data is in the wind.  The latest version of that story emerged on March 24, when the onetime genetic testing darling 23andMe filed for bankruptcy. Now the fate of 15 million people’s genetic data rests in the hands of a bankruptcy judge. At a hearing on March 26, the judge gave 23andMe permission
     

How a bankruptcy judge can stop a genetic privacy disaster

28 March 2025 at 21:00

Stop me if you’ve heard this one before: A tech company accumulates a ton of user data, hoping to figure out a business model later. That business model never arrives, the company goes under, and the data is in the wind. 

The latest version of that story emerged on March 24, when the onetime genetic testing darling 23andMe filed for bankruptcy. Now the fate of 15 million people’s genetic data rests in the hands of a bankruptcy judge. At a hearing on March 26, the judge gave 23andMe permission to seek offers for its users’ data. But, there’s still a small chance of writing a better ending for users.

After the bankruptcy filing, the immediate take from policymakers and privacy advocates was that 23andMe users should delete their accounts to prevent genetic data from falling into the wrong hands. That’s good advice for the individual user (and you can read how to do so here). But the reality is most people won’t do it. Maybe they won’t see the recommendations to do so. Maybe they don’t know why they should be worried. Maybe they have long since abandoned an account that they don’t even remember exists. Or maybe they’re just occupied with the chaos of everyday life. 

This means the real value of this data comes from the fact that people have forgotten about it. Given 23andMe’s meager revenue—fewer than 4% of people who took tests pay for subscriptions—it seems inevitable that the new owner, whoever it is, will have to find some new way to monetize that data. 

This is a terrible deal for users who just wanted to learn a little more about themselves or their ancestry. Because genetic data is forever. Contact information can go stale over time: you can always change your password, your email, your phone number, or even your address. But a bad actor who has your genetic data—whether a cybercriminal selling it to the highest bidder, a company building a profile of your future health risk, or a government trying to identify you—will have it tomorrow and the next day and all the days after that. 

Users with exposed genetic data are not only vulnerable to harm today; they’re vulnerable to exploits that might be developed in the future. 

While 23andMe promises that it will not voluntarily share data with insurance providers, employers, or public databases, its new owner could unwind those promises at any time with a simple change in terms. 

In other words: If a bankruptcy court makes a mistake authorizing the sale of 23andMe’s user data, that mistake is likely permanent and irreparable. 

All this is possible because American lawmakers have neglected to meaningfully engage with digital privacy for nearly a quarter-century. As a result, services are incentivized to make flimsy, deceptive promises that can be abandoned at a moment’s notice. And the burden falls on users to keep track of it all, or just give up.

Here, a simple fix would be to reverse that burden. A bankruptcy court could require that users individually opt in before their genetic data can be transferred to 23andMe’s new owners, regardless of who those new owners are. Anyone who didn’t respond or who opted out would have the data deleted. 

Bankruptcy proceedings involving personal data don’t have to end badly. In 2000, the Federal Trade Commission settled with the bankrupt retailer ToySmart to ensure that its customer data could not be sold as a stand-alone asset, and that customers would have to affirmatively consent to unexpected new uses of their data. And in 2015, the FTC intervened in the bankruptcy of RadioShack to ensure that it would keep its promises never to sell the personal data of its customers. (RadioShack eventually agreed to destroy it.) 

The ToySmart case also gave rise to the role of the consumer privacy ombudsman. Bankruptcy judges can appoint an ombuds to help the court consider how the sale of personal data might affect the bankruptcy estate, examining the potential harms or benefits to consumers and any alternatives that might mitigate those harms. The U.S. Trustee has requested the appointment of an ombuds in this case. While scholars have called for the role to have more teeth and for the FTC and states to intervene more often, a framework for protecting personal data in bankruptcy is available. And ultimately, the bankruptcy judge has broad power to make decisions about how (or whether) property in bankruptcy is sold.

Here, 23andMe has a more permissive privacy policy than ToySmart or RadioShack. But the risks incurred if genetic data falls into the wrong hands or is misused are severe and irreversible. And given 23andMe’s failure to build a viable business model from testing kits, it seems likely that a new business would use genetic data in ways that users wouldn’t expect or want. 

An opt-in requirement for genetic data solves this problem. Genetic data (and other sensitive data) could be held by the bankruptcy trustee and released as individual users gave their consent. If users failed to opt in after a period of time, the remaining data would be deleted. This would incentivize 23andMe’s new owners to earn user trust and build a business that delivers value to users, instead of finding unexpected ways to exploit their data. And it would impose virtually no burden on the people whose genetic data is at risk: after all, they have plenty more DNA to spare.

Consider the alternative. Before 23andMe went into bankruptcy, its then-CEO made two failed attempts to buy it, at reported valuations of $74.7 million and $12.1 million. Using the higher offer, and with 15 million users, that works out to a little under $5 per user. Is it really worth it to permanently risk a person’s genetic privacy just to add a few dollars in value to the bankruptcy estate?    

Of course, this raises a bigger question: Why should anyone be able to buy the genetic data of millions of Americans in a bankruptcy proceeding? The answer is simple: Lawmakers allow them to. Federal and state inaction allows companies to dissolve promises about protecting Americans’ most sensitive data at a moment’s notice. When 23andMe was founded, in 2006, the promise was that personalized health care was around the corner. Today, 18 years later, that era may really be almost here. But with privacy laws like ours, who would trust it?

Keith Porcaro is the Rueben Everett Senior Lecturing Fellow at Duke Law School.

Worldwide Innovative Network (WIN) Consortium in Personalized Cancer Medicine: Bringing next-generation precision oncology to patients

Oncotarget. 2025 Mar 12;16:140-162. doi: 10.18632/oncotarget.28703.

ABSTRACT

The human genome project ushered in a genomic medicine era that was largely unimaginable three decades ago. Discoveries of druggable cancer drivers enabled biomarker-driven gene- and immune-targeted therapy and transformed cancer treatment. Minimizing treatment not expected to benefit, and toxicity-including financial and time-are important goals of modern oncology. The Worldwide Innovative Network (WIN) Consortium in Personalized Cancer Medicine founded by Drs. John Mendelsohn and Thomas Tursz provided a vision for innovation, collaboration and global impact in precision oncology. Through pursuit of transcriptomic signatures, artificial intelligence (AI) algorithms, global precision cancer medicine clinical trials and input from an international Molecular Tumor Board (MTB), WIN has led the way in demonstrating patient benefit from precision-therapeutics through N-of-1 molecularly-driven studies. WIN Next-Generation Precision Oncology (WINGPO) trials are being developed in the neoadjuvant, adjuvant or metastatic settings, incorporate real-world data, digital pathology, and advanced algorithms to guide MTB prioritization of therapy combinations for a diverse global population. WIN has pursued combinations that target multiple drivers/hallmarks of cancer in individual patients. WIN continues to be impactful through collaboration with industry, government, sponsors, funders, academic and community centers, patient advocates, and other stakeholders to tackle challenges including drug access, costs, regulatory barriers, and patient support. WIN's collaborative next generation of precision oncology trials will guide treatment selection for patients with advanced cancers through MTB and AI algorithms based on serial liquid and tissue biopsies and exploratory omics including transcriptomics, proteomics, metabolomics and functional precision medicine. Our vision is to accelerate the future of precision oncology care.

PMID:40073368 | PMC:PMC11907938 | DOI:10.18632/oncotarget.28703

Systems-level immunomonitoring in children with solid tumors to enable precision medicine

In a population-based cohort of 191 children with diverse solid tumors, systems-level analyses unravel immune variation with age and tumor type and provide a reference for future precision immunotherapies tailored for the evolving immune systems of children.

Liquid biopsy into the clinics: Current evidence and future perspectives

J Liq Biopsy. 2024 Feb 11;4:100146. doi: 10.1016/j.jlb.2024.100146. eCollection 2024 Jun.

ABSTRACT

As precision oncology has become a major part of the treatment landscape in oncology, liquid biopsies have developed as a particularly powerful tool as it surmounts several limitations of traditional tissue biopsies. These biopsies involve most commonly the isolation of circulating extracellular nucleic acids, including cell-free DNA (cfDNA) and circulating tumor DNA (ctDNA), as well as circulating tumor cells (CTCs), typically from blood. The clinical applications of liquid biopsies are diverse, encompassing the initial diagnosis and cancer detection, the application as a tool for prognostication in early and advanced tumor settings, the identification of potentially actionable alterations, the monitoring of response and resistance under systemic therapy and the detection of resistance mechanisms, the differentiation of distinct immune checkpoint blockade response patterns through serial samples, the prediction of immune checkpoint blockade responses based on initial liquid biopsy characteristics and the assessment of tumor heterogeneity. Moreover, molecular relapse monitoring in early-stage cancers and the personalization of adjuvant or additive therapy via MRD have become a major field of research in recent years. Compared to tissue biopsies, liquid biopsies are less invasive and can be collected serially, offering real-time molecular insights. Furthermore, liquid biopsies may allow for a more holistic evaluation of a patient's disease, as they assess material from all tumor sites and can theoretically reflect tumor heterogeneity. Furthermore, quicker turnaround-time also constitutes an advantage of liquid biopsies. Disadvantages or hurdles include the challenge of detecting low amounts of tumor deposits in peripheral blood or other fluids and the potential of different amounts tumor-shedding from different metastatic sites, as well as potentially false-positive from clonal hematopoietic mutations of indeterminate potential (CHIP) mutations. The clinical utility of liquid biopsies still must be validated in most settings and further research has to be done. Clinal trials including alternate bodily fluids and leveraging AI-technology are expected to revolutionize the field of liquid biopsies.

PMID:40027149 | PMC:PMC11863819 | DOI:10.1016/j.jlb.2024.100146

Liquid biopsy for monitoring minimal residual disease in localized and locally-advanced non-small cell lung cancer after radical-intent treatment

J Liq Biopsy. 2024 Feb 10;4:100145. doi: 10.1016/j.jlb.2024.100145. eCollection 2024 Jun.

ABSTRACT

Blood-based biomarkers investigation does not require invasive tissue biopsies and may explore diverse tumoral components such as proteins, microRNAs, circulating tumor cells, ctDNA, and exosomes and may better reflect tumor molecular heterogeneity, either temporal or spatial. ctDNA is related to tumor burden and represents a more objective measure of the total body disease burden than imaging findings. ctDNA profiling can be therefore useful to determine minimal residual disease (MRD), which is defined as the remaining tumor cells or tumor-derived material after definitive treatment in patients with no clinical evidence of disease. The detection of MRD is highly predictive of future disease recurrence. Although detectable MRD is associated with a poor prognosis, it is not clear whether MRD detection can guide therapy escalation to improve patient outcomes. In this review, we present four cases of epidermal growth factor receptor (EGFR) mutant NSCLC patients who received standard of care curative treatment and periodic radiological assessment and liquid biopsy analyses were carried out as follow-up. A tumor-informed 52 genes Oncomine Pan-Cancer Cell-Free assay (Thermo Fisher Scientific, Waltham, MA, USA), was used to identify single-nucleotide variants (SNVs), indels, copy number variations (CNVs), and RNA fusions in blood based liquid biopsy ctDNA in three of four patients. In one patient the approach used was through Commercial Kit Cobas EGFR Mutation Test v2 CE-IVD (Roche Diagnostics SL) that identifies 42 mutations in the EGFR gene. In one patient, an actionable oncogene driver alteration was identified in the ctDNA analysis, four months after radical intent concurrent chemoradiotherapy and six weeks before radiological distant relapse was clearly confirmed. There is no evidence of ctDNA or radiological disease relapse in the other three patients. Finally, a review of the literature addressing the potential value of MRD detection in this clinical setting is presented and discussed as well.

PMID:40027142 | PMC:PMC11863883 | DOI:10.1016/j.jlb.2024.100145

Single-cell multiome and spatial profiling reveals pancreas cell type-specific gene regulatory programs driving type 1 diabetes progression

bioRxiv [Preprint]. 2025 Feb 17:2025.02.13.637721. doi: 10.1101/2025.02.13.637721.

ABSTRACT

Cell type-specific regulatory programs that drive type 1 diabetes (T1D) in the pancreas are poorly understood. Here we performed single nucleus multiomics and spatial transcriptomics in up to 32 non-diabetic (ND), autoantibody-positive (AAB+), and T1D pancreas donors. Genomic profiles from 853,005 cells mapped to 12 pancreatic cell types, including multiple exocrine sub-types. Beta, acinar, and other cell types, and related cellular niches, had altered abundance and gene activity in T1D progression, including distinct pathways altered in AAB+ compared to T1D. We identified epigenomic drivers of gene activity in T1D and AAB+ which, combined with genetic association, revealed causal pathways of T1D risk including antigen presentation in beta cells. Finally, single cell and spatial profiles together revealed widespread changes in cell-cell signaling in T1D including signals affecting beta cell regulation. Overall, these results revealed drivers of T1D progression in the pancreas, which form the basis for therapeutic targets for disease prevention.

PMID:40027657 | PMC:PMC11870426 | DOI:10.1101/2025.02.13.637721

Biomarkers, omics and artificial intelligence for early detection of pancreatic cancer

Semin Cancer Biol. 2025 Jun;111:76-88. doi: 10.1016/j.semcancer.2025.02.009. Epub 2025 Feb 20.

ABSTRACT

Pancreatic ductal adenocarcinoma (PDAC) is frequently diagnosed in its late stages when treatment options are limited. Unlike other common cancers, there are no population-wide screening programmes for PDAC. Thus, early disease detection, although urgently needed, remains elusive. Individuals in certain high-risk groups are, however, offered screening or surveillance. Here we explore advances in understanding high-risk groups for PDAC and efforts to implement biomarker-driven detection of PDAC in these groups. We review current approaches to early detection biomarker development and the use of artificial intelligence as applied to electronic health records (EHRs) and social media. Finally, we address the cost-effectiveness of applying biomarker strategies for early detection of PDAC.

PMID:39986585 | DOI:10.1016/j.semcancer.2025.02.009

Rare disease gene association discovery in the 100,000 Genomes Project

Nature, Published online: 26 February 2025; doi:10.1038/s41586-025-08623-w

A rare variant burden analytical framework for Mendelian diseases was developed and applied to data from the 100,000 Genomes Project, identifying 69 probable new disease–gene associations.

Imaging and outcome correlates of ctDNA methylation markers in prostate cancer: a comparative, cross-sectional [⁶⁸Ga]Ga-PSMA-11 PET/CT study

To validate the clinical utility of a previously identified circulating tumor DNA methylation marker (meth-ctDNA) panel for disease detection and survival outcomes, meth-ctDNA markers were compared to PSA leve...

Identification of circulating tumor DNA as a biomarker for diagnosis and response to therapies in cancer patients

12 February 2025 at 19:00

Int Rev Cell Mol Biol. 2025;391:43-93. doi: 10.1016/bs.ircmb.2024.08.006. Epub 2024 Sep 7.

ABSTRACT

The sampling of circulating biomarkers provides an opportunity for non-invasive evaluation and monitoring of cancer activity. In modern day practice, this has typically been in the form of circulating tumor DNA (ctDNA) detected in plasma. The field of ctDNA has been a burgeoning technology, with prominent applications for blood-based cancer screening and in disease status assessment, especially after curative-intent surgery to evaluate for minimal residual disease (MRD). Clinical applications for the latter show an incredibly high sensitivity in certain cancer types with a need for additional studies to determine how much clinical decision-making should be adapted based on ctDNA results and which cancer types, stages, and treatments are best informed by ctDNA results. This chapter provides an overview of ctDNA detection as tool for cancer screening, detecting MRD, and/or molecularly characterizing a cancer, highlighting the rapidly amassing research as a prognostic biomarker and emerging data on ctDNA as a predictive biomarker.

PMID:39939078 | DOI:10.1016/bs.ircmb.2024.08.006

Synthetic lethality of mRNA quality control complexes in cancer

Nature, Published online: 05 February 2025; doi:10.1038/s41586-024-08398-6

PELO–HBS1L and SKI complexes in the human mRNA quality control pathway exhibit a synthetic lethal interaction and may represent novel targets for the development of cancer therapies.

A statistical framework for multi-trait rare variant analysis in large-scale whole-genome sequencing studies

Nat Comput Sci. 2025 Feb 7. doi: 10.1038/s43588-024-00764-8. Online ahead of print.

ABSTRACT

Large-scale whole-genome sequencing (WGS) studies have improved our understanding of the contributions of coding and noncoding rare variants to complex human traits. Leveraging association effect sizes across multiple traits in WGS rare variant association analysis can improve statistical power over single-trait analysis, and also detect pleiotropic genes and regions. Existing multi-trait methods have limited ability to perform rare variant analysis of large-scale WGS data. We propose MultiSTAAR, a statistical framework and computationally scalable analytical pipeline for functionally informed multi-trait rare variant analysis in large-scale WGS studies. MultiSTAAR accounts for relatedness, population structure and correlation among phenotypes by jointly analyzing multiple traits, and further empowers rare variant association analysis by incorporating multiple functional annotations. We applied MultiSTAAR to jointly analyze three lipid traits in 61,838 multi-ethnic samples from the Trans-Omics for Precision Medicine (TOPMed) Program. We discovered and replicated new associations with lipid traits missed by single-trait analysis.

PMID:39920506 | DOI:10.1038/s43588-024-00764-8

Deep learning in microbiome analysis: a comprehensive review of neural network models

Front Microbiol. 2025 Jan 22;15:1516667. doi: 10.3389/fmicb.2024.1516667. eCollection 2024.

ABSTRACT

Microbiome research, the study of microbial communities in diverse environments, has seen significant advances due to the integration of deep learning (DL) methods. These computational techniques have become essential for addressing the inherent complexity and high-dimensionality of microbiome data, which consist of different types of omics datasets. Deep learning algorithms have shown remarkable capabilities in pattern recognition, feature extraction, and predictive modeling, enabling researchers to uncover hidden relationships within microbial ecosystems. By automating the detection of functional genes, microbial interactions, and host-microbiome dynamics, DL methods offer unprecedented precision in understanding microbiome composition and its impact on health, disease, and the environment. However, despite their potential, deep learning approaches face significant challenges in microbiome research. Additionally, the biological variability in microbiome datasets requires tailored approaches to ensure robust and generalizable outcomes. As microbiome research continues to generate vast and complex datasets, addressing these challenges will be crucial for advancing microbiological insights and translating them into practical applications with DL. This review provides an overview of different deep learning models in microbiome research, discussing their strengths, practical uses, and implications for future studies. We examine how these models are being applied to solve key problems and highlight potential pathways to overcome current limitations, emphasizing the transformative impact DL could have on the field moving forward.

PMID:39911715 | PMC:PMC11794229 | DOI:10.3389/fmicb.2024.1516667

Interplay between gut microbial communities and metabolites modulates pan-cancer immunotherapy responses

Cell Metab. 2025 Jan 28:S1550-4131(24)00495-9. doi: 10.1016/j.cmet.2024.12.013. Online ahead of print.

ABSTRACT

Immune checkpoint blockade (ICB) therapy has revolutionized cancer treatment but remains effective in only a subset of patients. Emerging evidence suggests that the gut microbiome and its metabolites critically influence ICB efficacy. In this study, we performed a multi-omics analysis of fecal microbiomes and metabolomes from 165 patients undergoing anti-programmed cell death protein 1 (PD-1)/programmed death ligand 1 (PD-L1) therapy, identifying microbial and metabolic entities associated with treatment response. Integration of data from four public metagenomic datasets (n = 568) uncovered cross-cohort microbial and metabolic signatures, validated in an independent cohort (n = 138). An integrated predictive model incorporating these features demonstrated robust performance. Notably, we characterized five response-associated enterotypes, each linked to specific bacterial taxa and metabolites. Among these, the metabolite phenylacetylglutamine (PAGln) was negatively correlated with response and shown to attenuate anti-PD-1 efficacy in vivo. This study sheds light on the interplay among the gut microbiome, the gut metabolome, and immunotherapy response, identifying potential biomarkers to improve treatment outcomes.

PMID:39909032 | DOI:10.1016/j.cmet.2024.12.013

The hype around ctDNA guiding an informed perioperative therapeutic strategy in early-stage non-small cell lung cancer

Discov Oncol. 2025 Jan 29;16(1):100. doi: 10.1007/s12672-025-01826-7.

ABSTRACT

Non-small cell lung cancer (NSCLC) remains a dire disease being the first cause of cancer death among both genders. Early-stage NSCLC often has better treatment outcomes despite it being a highly heterogeneous disease. So far, the neo-adjuvant chemotherapy strategies have led to a small benefit with an improvement of 5% in overall survival as an absolute benefit. Recently, the introduction of immune checkpoint inhibitors combined with chemotherapy has shown robust efficacy in terms of event-free survival and overall survival. Thus, these combinations are today considered a new standard of care in early-stage NSCLC. The application of these strategies to all-comer population lead to confounding definitive results regarding the efficacy and predictive biomarkers are urgently needed balancing the promise of healing than toxicities. At present, the clinical staging TNM system guides the clinical choice, however it is not entirely sufficient. Circulant tumoral DNA (ctDNA) emerged as a promising prognostic and predictive biomarker that may guide the future perioperative strategy and pave the way to personalized medicine also in this exciting field. This narrative review aims to put in the context the employment of ctDNA, give some perspective and suggestions weighing the pros and cons of this technique for our tomorrow clinical practice.

PMID:39881042 | PMC:PMC11780067 | DOI:10.1007/s12672-025-01826-7

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