Publications

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.

Lung cancer remains one of the deadliest cancers and a major public health concern. Although numerous studies have identified various risk factors, further research is essential, particularly in the biological domain. Existing data sources compile biological information on lung cancer and its subtypes but differ in structure and format, complicating data extraction and integration for artificial intelligence (AI) models. Ontologies and semantic technologies address this challenge by enabling the construction of unified knowledge graphs that promote interoperability. Lung-CABO is an ontology specifically designed for lung cancer, supporting the creation of a knowledge graph for risk factor identification and AI applications. Its modular design allows expansion to integrate additional data, such as environmental factors, further enhancing its utility and reusability.

This deliverable has been conceived in the frame of T4.3 “General population screening”, T4.4 “Diagnosis and precision follow-up and stratification” and T4.5 “Contextual-empirical investigations to evaluate the realization of identified values”. These tasks are devoted to the recruitment of a prospective cohort of around 3,000 volunteers that will be followed up for 2 years and on the diagnosis, including Indeterminate Pulmonary Nodules (IPN) characterization, with an accentuation on the Never Smokers and Reduced Smokers (NSRS) patients, incorporating Breath Analyser (BAN), Wide-biomarker-spectrum Multi-Use Sensing Patch (WBSP) and spectrometry-on-card (SPOC) into clinical studies.

Regarding Task 4.5, it examines whether identified socio-technical values (in WP1-3) (e.g., transparency, bias, accountability, explanability) are realized when the technology is used. To achieve this goal, the different contexts of the use of technology are to be analysed as different contextual variables come into play to impact the way values are understood.

Volunteers have been recruited from different clinical centers (“Servicio Andaluz de Salud” (SAS) and “Osakidetza Servicio Vasco de Salud” (OSA), in Spain; “Centre Hospitalier Universitaire de Liège” (CHUL) in Belgium, and “Centre for Tuberculosis and Lung Diseases (CTLD) of Riga East University Hospital (REUH)” in Latvia). Non-invasive devices such as Breath Analyzer (BAN), Multiomics (MO) and spectrometry-on-card (SPOC) are monitoring these participants.

The entire study cohort is currently being followed up. For 2 years, participants of the study will attend to 4 visits: baseline, month 6, month 12 and month 24. During these visits, the following tests and procedures will be carried out:

– Baseline visit: blood test, spirometry, lifestyle questionnaires, sociodemographic data, medical record data, exposure to harmful agents data, physical exploration, LDCT scan and new lung cancer screening devices testing (breath analyzer and spectrometry-on-card)
– 6 months visit: remote visit where sociodemographic data, medical record data and exposure to harmful agents data will be recorded.
– 12 months visit: spirometry, lifestyle questionnaires, sociodemographic data, medical record data, exposure to harmful agents’ data and physical exploration.
– 24 months visit: spirometry, ifestyle questionnaires, sociodemographic data, medical record data, exposure to harmful agents’ data, physical exploration, low-dose computed tomography (LDCT) scan and new lung cancer screening devices testing (breath analyzer and spectrometry-on-card).

(See the following of the Executive Summary in file).

BACKGROUND
The US Preventive Services Task Force (USPSTF) recommends lung cancer screening for individuals aged 50–80 with ≥20 pack-years of smoking and ≤15 quit-years. This implies that former heavy smokers with ≥20 pack-years would either be offered screening from age 50 onwards or not at all, depending on a dichotomous classification by time since cessation. An alternative strategy that would better match individual risks could be to define risk-adapted starting ages of screening, according to time since cessation.

METHODS
Based on data from the UK Biobank cohort, we assessed the relationship between smoking cessation time, pack-years, age, and lung cancer risk among ever heavy smokers using multivariable Cox proportional hazards models. We estimated “risk postponement periods” (RPPs) from the regression coefficients for the time since cessation and age and used these RPP estimates to derive risk-adapted starting ages for lung cancer screening among former heavy smokers, using 50 years as the reference starting age for current heavy smokers.

RESULTS
The RPPs for smoking cessation ranged from 3.1 (95% CI 1.7, 4.5) years for former heavy smokers who quit 6-10 years ago to 14.6 (95% CI 13.0, 16.3) years for former heavy smokers who quit more than 15 years ago, which translate to risk-adapted starting ages of screening between 53 and 65 years.

CONCLUSIONS
Our analysis provides an empirical basis for risk-adapted starting ages of lung cancer screening among former heavy smokers.

Somatic cells can transform into tumors due to mutations, and the tumors further evolve towards increased aggressiveness and therapy resistance. We develop DiffInvex, a framework for identifying changes in selection acting on individual genes in somatic genomes, drawing on an empirical mutation rate baseline derived from non-coding DNA that accounts for shifts in neutral mutagenesis during cancer evolution. We apply DiffInvex to >11,000 somatic whole-genome sequences from ~30 cancer types or healthy tissues, identifying genes where point mutations are under conditional positive or negative selection during exposure to specific chemotherapeutics, suggesting drug resistance mechanisms occurring via point mutation. DiffInvex identifies 11 genes exhibiting treatment-associated selection for different classes of chemotherapies, linking selected mutations in PIK3CA, APC, MAP2K4, SMAD4, STK11 and MAP3K1 with drug exposure. Various gene-chemotherapy associations are further supported by differential functional impact of mutations pre- versus post-therapy, and are also replicated in independent studies. In addition to nominating drug resistance genes, we contrast the genomes of healthy versus cancerous cells of matched human tissues. We identify noncancerous expansion-specific drivers, including NOTCH1 and ARID1A. DiffInvex can also be applied to diverse analyses in cancer evolution to identify changes in driver gene repertoires across time or space.

Biological Field Effect Transistors (Bio-FETs) are redefining the standard of biosensing by enabling label-free, real-time, and extremely sensitive detection of biomolecules. At the center of this innovation is the fundamental empowering role of advanced materials, such as graphene, molybdenum disulfide, carbon nanotubes, and silicon. These materials, when harnessed with the downstream biomolecular probes like aptamers, antibodies, and enzymes, allow Bio-FETs to offer unrivaled sensitivity and precision. This review is an exposition of how advancements in materials science have permitted Bio-FETs to detect biomarkers in extremely low concentrations, from femtomolar to attomolar levels, ensuring device stability and reliability. Specifically, the review examines how the incorporation of cutting-edge materials architectures, like flexible / stretchable and multiplexed designs, is expanding the frontiers of biosensing and contributing to the development of more adaptable and user-friendly Bio-FET platforms. A key focus is placed on the synergy of Bio-FETs with artificial intelligence (AI), the Internet of Things (IoT), and sustainable materials approaches as fast-tracking toward transition from research into practical healthcare applications. The review also explores current challenges such as material reproducibility, operational durability, and cost-effectiveness. It outlines targeted strategies to address these hurdles and facilitate scalable manufacturing. By emphasizing the transformative role played by advanced materials and their cementing position in Bio-FETs, this review positions Bio-FETs as a cornerstone technology for the future healthcare solution for precision applications. These advancements would lead to an era where material innovation would herald massive strides in biomedical diagnostics and subsume.

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.

How is the LUCIA Project Study Conducted in Latvia

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