Publications

Key Points

Question  Considering their risk compared with current smokers, when should former smokers begin lung cancer screening?

Findings  In this cohort study of 86 035 former or current heavy smokers, former heavy smokers could be screened between 3 and 17 years later than current heavy smokers, depending on the length of smoking cessation, in order to more closely reflect their risk levels. Whereas the US Preventive Services Task Force currently recommends a unified starting age of 50 years, the derived risk-adapted screening start ages for former heavy smokers ranged between 53 and 67 years of age.

Meaning  This study’s findings suggest that using differentiated, risk-adapted starting ages of former smokers could allow for enhanced lung cancer screening strategies.

This policy brief summarises recommendations from the third “Understanding (Risk Factors & Determinants)” cluster meeting to strengthen cancer prevention, early detection and equity access in the context of the EU Cancer Mission. It calls for support on interoperable, FAIR-by-design cancer data ecosystems that connect project-level resources with national cancer data nodes and emerging European hubs such as EUCAIM, UNCAN and CANDLE, ensuring sustainable reuse of legacy and new datasets. The report urges EU policymakers to prioritise integrated risk-stratification tools and minimally invasive biomarkers, backed by trustworthy AI, federated analytics and shared quality standards to accelerate translation into screening, surveillance, and personalised care. It further recommends that EU-level action on cancer inequalities adopt a broader perspective, including prevention, diagnostics, treatment and survivorship, while systematically embedding patient and citizen voices and clear governance frameworks for digital health and AI. Finally, the brief proposes that the European Commission and Member States should use these suggestions to guide future legislation, funding programmes, and mission-oriented initiatives, ensuring that scientific advances are matched by equitable access and long-term infrastructure support.

This document summarises the discussions and conclusions as presented by the participating projects. It does not introduce new content. This document reports on the third annual meeting of the Horizon Europe “Understanding (Risk Factors & Determinants)” cluster, hosted by the MELCAYA project in Barcelona the 15th of October 2025. It brought together the five cluster projects (GENIAL, LUCIA, ELMUMY, DISCERN and MELCAYA) focused on how risk factors and health determinants drive cancer development and progression. The first part of the meeting reviewed collaborative work on FAIR data management convergence on healthcare data standards and plans for long-term interoperability with emerging EU infrastructures such as UNCAN and EUCAIM. Scientific sessions showcased cross-project results on genetic susceptibility, exposome, omics and functional models, as risk stratification and early diagnosis tools, including AI-based models, smart sensors and proteomic signatures.

Further sessions addressed health policy implementation, inequalities and citizen engagement, highlighting gaps in rare cancers, the value of patient-generated evidence and methods such as social labs and Delphi surveys to co-create recommendations. The meeting concluded with presentations from UNCAN-Connect, CANDLE and EUCAIM projects, outlining how new federated European cancer data hubs will provide sustainable, secure environments for reusing cluster data and models. Overall, the cluster is progressing towards integrated, policy-relevant evidence and interoperable infrastructures that support the EU Missions on Cancer.

Screening heavy smokers by low-dose computed tomography (LDCT) can reduce lung cancer (LC) mortality, but defining the population that benefits most, a prerequisite for cost-effective screening, is challenging. In order to contribute to a more nuanced risk stratification of high-risk target populations, we developed and validated a blood-based protein marker model for LC. A two-stage design was implemented in this study, and the derivation set comprised 18 868 participants from the UK Biobank, which included 200 incident LC cases identified at 6 years of follow-up. The independent validation set included 101 LC cases identified at 6 years of follow-up. A total of 2025 protein markers measured by proximity extension assays available for both datasets were used for analysis. A risk prediction algorithm by least absolute shrinkage and selection operator regression with bootstrap method was developed in the derivation set and then externally evaluated in the independent validation set. The risk discriminatory performance of the protein marker model was compared with the established PLCO_m2012 model, USPSTF 2020 guidelines and trial criteria used in different LDCT trials. The protein marker model comprising of four protein biomarkers—CEACAM5, CXCL17, MMP12, and WFDC2—outperformed the PLCO_m2012 model, and the areas under the receiver operating curve (AUCs) for the protein marker model in the derivation and validation sets were 0.814 [95% confidence interval (95% CI), 0.785–0.843] and 0.814 (95% CI, 0.756–0.873), respectively. The addition of the protein marker model to the PLCO_m2012 model increased the AUCs up to 0.056 and 0.057 and yielded up to 16 and 12 percentage points higher sensitivities to identify future LC cases compared to the LDCT trial criteria, in the derivation and validation sets, respectively. The protein marker model improves the selection of high LC risk individuals for LDCT screening and thereby enhances screening efficacy.

Background

Nonsense-mediated mRNA decay (NMD) is a quality-control pathway that degrades mRNA bearing premature termination codons (PTCs) resulting from mutation or mis-splicing, and that additionally participates in gene regulation of unmutated transcripts. While NMD activity is known to differ between examples of PTCs, it is less well studied if human tissues differ in NMD activity, or if individuals differ.

Results

We analyzed exomes and matched transcriptomes from Human tumors and healthy tissues to quantify individual-level NMD efficiency, and assess its variability between tissues, tumors, and individuals. This was done by monitoring mRNA levels of endogenous NMD target transcripts, and additionally supported by allele-specific expression of germline PTCs. Nervous system and reproductive system tissues have lower NMD efficiency than other tissues, such as the digestive tract. Next, there is systematic inter-individual variability in NMD efficiency, and we identify two underlying mechanisms. First, somatic copy number alterations can robustly associate with NMD efficiency, prominently the commonly-occurring gain at chromosome 1q that encompasses two core NMD genes: SMG5 and SMG7 and additional functionally interacting genes such as PMF1 and GON4L. Second, deleterious germline variants in genes such as the KDM6B chromatin modifier can associate with higher or lower NMD efficiency in individuals. Variable NMD efficiency modulates positive selection upon somatic nonsense mutations in tumor suppressor genes, and is associated with cancer patient survival and immunotherapy responses.

Conclusions

NMD efficiency is variable across human tissues, and it is additionally variable across individuals and tumors thereof due to germline and somatic genetic alterations.

Background
Mutational signatures are increasingly used to understand the mechanisms causing cancer. However, their important applications in predicting prognosis and stratifying patients for therapy are hampered by inaccurate inference of the various featureless, dense trinucleotide mutational spectra, which are often confounded with one another. One of them is the homologous recombination deficiency (HRd)-associated signature SBS3, relevant because of its association with prognosis in ovarian and breast cancer and because of its potential as a biomarker for synthetic lethality therapies.

Methods
Here, we highlight strong benefits of a multimodal approach for mutational signature extraction, applied on top of standard bioinformatic pipelines. By jointly operating on single-base substitution (SBS) and indel (ID) spectra, this method enables accurate identification of various DNA repair deficiency signatures and patient survival prediction.

Results
Across four different cohorts of whole-genome sequenced high-grade serous ovarian cancers (HGSOC), the multimodal SBS + ID approach correctly distinguished the commonly confused signatures SBS3, SBS5, SBS8, SBS39, and SBS40. Importantly, we robustly identified two different multimodal SBS3 signatures, m-SBS3a and m-SBS3b, with distinct patterns in the indel spectrum. Multimodal SBS3b signature was strongly predictive of longer survival in ovarian cancer patients, replicating across four cohorts, with effect sizes greatly exceeding other genetic markers. Our m-SBS3 also predicted survival in platinum-treated patients with various cancer types, and moreover, the SBS + ID joint inference was successfully applied to mismatch repair-deficient colorectal cancer and immunotherapy response, supporting a general utility of the multimodal mutational signatures approach.

Conclusions
Overall, combining SBS and ID mutations improves detection of HR deficiency-associated signatures and reveals distinct SBS3 subtypes with prognostic value. This multimodal approach outperforms existing markers and is readily applicable to therapy stratification.

Precision and personalized medicine for disease management necessitates real-time, continuous monitoring of biomarkers and therapeutic drugs to adjust treatment regimens based on individual patient responses. This study introduces a wearable Microneedle-based Continuous Biomarker/Drug Monitoring (MCBM) system, designed for the simultaneous, in vivo pharmacokinetic and pharmacodynamic evaluation for diabetes. Utilizing a dual-sensor microneedle and a layer-by-layer nanoenzyme immobilization strategy, the MCBM system achieves high sensitivity and specificity in measuring glucose and metformin concentrations in skin interstitial fluid (ISF). Seamless integration with a smartphone application enables real-time data analysis and feedback, fostering a pharmacologically informed approach to diabetes management. The MCBM system’s validation and in vivo trials demonstrate its precise monitoring of glucose and metformin, offering a tool for personalized treatment adjustments. Its proven biocompatibility and safety suit long-term usage. This system advances personalized diabetes care, highlighting the move towards wearables that adjust drug dosages in real-time, enhancing precision and personalized medicine.

As part of the LUCIA project, funded under Horizon Europe, Yaghma facilitated Social Lab workshops to identify and address challenges in implementing novel lung cancer screening technologies including an AI-driven risk prediction algorithm. Engaging healthcare professionals, policymakers, and patient representatives, these participatory sessions helped uncover key barriers of implementing LUCIA technologies to lung cancer screening.

Through structured discussions, 80 actionable barriers have been identified across 10 key categories, including trust, feasibility, ethical considerations, and regulation. These insights were mapped in an Impact-Influence map through a prioritization survey, ensuring that the project focuses on the most critical and actionable challenges.

Yaghma’s role in this process underscores the importance of stakeholder engagement in responsible AI adoption, ensuring that AI technologies in cancer screening are practical, inclusive, and aligned with real-world healthcare and patient needs.

Cancer is among the most common causes of mortality worldwide. Screening for cancer involves surveillance of populations towards identification of cancer that was unknown to the patient. Therapeutic treatments for cancer generally are more effective when applied earlier on in the condition’s development, as such, screening for cancer has the potential to improve the standard for patient care and improving mortality and morbidity associated with the disease. Cancer screening has increasingly become dependent on advanced technologies to assist in the identification and characterization of tumours. This article Collection showcases current research towards the development of new methods for cancer screening, including the development of new advanced technologies in this domain such as novel methods incorporating liquid biopsies, tailed primer isothermal amplification assays, and infrared spectroscopy. As is common in modern research, approaches make use of computational techniques as a critical component in cancer screening, and research is highlighted on the use of artificial intelligence, which is now a common technological innovation contributing to the overall cancer screening process in research studies. Future directions for cancer screening are discussed.