From Prevention to Precision Therapy
Lung Cancer
Lung cancer UCs 4.1–4.4 combine risk assessment, early detection, and biological model data to advance understanding and improve patient care.
Addressing High Aggression
The Challenge: Combating Late-Stage Diagnosis
Lung cancer remains one of the most serious and challenging cancer types, affecting large and diverse populations and often being diagnosed at a late stage. Within this broad disease group, Small Cell Lung Cancer (SCLC) is particularly aggressive. It grows and spreads quickly, responds only briefly to treatment, and has seen little improvement in survival over many decades. Because SCLC progresses rapidly and is usually detected only after it has advanced, patients often have very limited treatment options, which contributes to poor outcomes.
Rapid Progression
Tumours change quickly, making it hard to predict therapy response. SCLC is usually detected only after it has advanced, leaving limited treatment options.
Data Bottlenecks
Fragmented care pathways and a lack of surgical samples for research limit our biological understanding of the disease.
Managing SCLC is especially difficult due to its biological complexity. Tumours change rapidly, making it hard to predict how patients will respond to therapy, and surgery is rarely possible, meaning researchers have fewer samples to study. This limits understanding of the disease and reduces opportunities to discover more effective treatments. More broadly, lung‑cancer care pathways can be fragmented, with important clinical information spread across institutions. For a fast‑moving disease like SCLC or for early‑stage lung cancer nodules, delays in diagnosis or incomplete data can greatly affect patient survival. Better data availability, integration, and reuse are therefore essential.
UC4.1 tackles a different part of the challenge: how to identify people at high risk before cancer develops. Many individuals—especially long‑term smokers—are not reached by current screening programmes. UC4.1 reviews and compares validated risk‑prediction models and evaluates which key data elements are available in European health systems. This helps ensure that high‑risk individuals are found earlier, making screening more effective.
UC4.2 focuses on another critical bottleneck: the early detection of small lung nodules. In many cases, tiny nodules are difficult to characterise with standard imaging alone. UC4.2 improves this by combining radiological data, radiomics, and liquid‑biopsy markers with AI‑based analysis to better distinguish harmless nodules from early cancers. This can reduce uncertainty for patients and support more timely diagnostic decisions.
UC4.3 complements this by exploring the biology of non‑small cell lung cancer (NSCLC) through advanced experimental models such as 3D cultures and bioprinted tumour systems. These models help researchers understand how NSCLC starts, progresses, and responds to treatment, generating valuable molecular and functional data that can inform personalised therapies.
UC4.4 addresses this by generating rich experimental datasets using cell lines and patient‑derived models to shed light on treatment responses, biological subtypes, and the drivers of resistance. When these data are combined with clinical information, they help build a more complete understanding of how SCLC develops and behaves.
Together, UC4.1, UC4.2, UC4.3, and UC4.4 address different but interconnected challenges across the lung‑cancer pathway: UC4.1 improves early identification, UC4.2 enhances early diagnosis, UC4.3 deepens understanding of NSCLC biology, and UC4.4 advances insight into SCLC mechanisms and treatment options. Their combined contributions strengthen UNCAN‑Connect’s mission to support data‑driven, personalised lung‑cancer care—from early risk estimation to diagnosis, biological understanding, and treatment innovation.
The End-to-End Lung Cancer Pathway
The Use Case Objectives
UC4.1 – Risk Prediction
UC4.1 complements this by addressing the front end of the lung‑cancer pathway: early risk identification and screening. Many programmes still rely on age and smoking history, but multivariable risk‑prediction models can more accurately identify people who would benefit from low‑dose CT screening. UC4.1 systematically reviews, compares, and validates these risk models and maps which risk‑factor data are available across European health systems. Its goal is practical: define an optimal data set, recommend which models are suitable for national programmes, and identify how often individuals’ risk should be re‑evaluated. In short, UC4.1 strengthens who gets screened and when, while UC4.4 strengthens how we treat and why treatments work or fail.
Data being collected:
- Harmonised risk factor variables (demographics, smoking history, comorbidities, family/occupational exposures).
- Metadata on data availability, quality, and re evaluation intervals for screening programmes.
- Metadata on data availability, quality, and re-evaluation intervals for screening programmes.
UC4.2 – Early Detection
UC4.2 focuses on a different but equally critical bottleneck: early detection and diagnosis of small pulmonary nodules. Many early‑stage cancers present as subcentimetric nodules that are difficult to evaluate with standard imaging alone. UC4.2 brings together radiology, radiomics, AI‑based image analysis, and liquid‑biopsy markers to better distinguish benign from malignant nodules. By improving characterisation and risk stratification at this very early stage, UC4.2 aims to reduce uncertainty, avoid unnecessary procedures, and support more timely diagnostic decision‑making.
Data being collected:
- Clinical and imaging data for subcentimetric nodules.
- Radiomics features and harmonised imaging datasets.
- Liquid biopsy markers and multimodal datasets suitable for AI based malignancy prediction.
UC4.3 – NSCLC Biology
UC4.3 deepens biological understanding on the NSCLC side of the disease spectrum. It employs advanced experimental cancer models, including patient‑derived 3D cultures and bioprinted tumour constructs, to study NSCLC onset, progression, metastasis, and treatment response. This produces multimodal biological datasets that help explain how NSCLC evolves and why tumours behave differently across patients. UC4.3 therefore strengthens the translational pipeline between bench science, clinical data, and predictive modelling.
Data being collected:
- Molecular (genomic, proteomic), imaging, and functional data from patient derived 3D models and bioprinted NSCLC systems.
- Data describing tumour progression, microenvironment interactions, and therapy responses.
UC4.4 – SCLC Biology
UC4.4 has a distinctive role in UNCAN‑Connect: it brings a mechanistic, model‑driven lens to lung cancer, focusing on Small Cell Lung Cancer (SCLC)—a fast‑growing, treatment‑resistant disease with poor outcomes. UC4.4 investigates how epigenetic alterations and glypicans (especially GPC3) shape SCLC development, progression, and treatment response. What makes UC4.4 unique is its translational experimentation at scale: it combines standard SCLC cell lines with patient‑derived cancer models grown in 2D and advanced 3D cultures and systematically tests responses to radiotherapy, standard drugs, epigenetic modifiers, and GPC3‑targeted therapies. By doing so, UC4.4 aims to uncover more effective combination treatments and to explain why resistance occurs, thereby bridging laboratory insight with real‑world clinical needs.
Data being collected:
- Epigenetic and proteomic profiles, single cell transcriptomics, drug response and DNA damage datasets, imaging data from 2D/3D and patient derived SCLC models.
- Data supporting subtype identification, resistance mechanisms, and combination therapy evaluation.
UNCAN‑Connect aims to federate high‑value cancer data and make it usable for research and innovation across Europe. The four lung‑cancer use cases deliver complementary datasets that together cover the entire pathway from risk → detection → biology → treatment:
- UC4.1 → from population to clinic: contributes harmonised risk factor datasets, model performance evidence, and metadata on data availability and quality to support risk-based screening and integration of risk models into routine care.
- UC4.2 → from detection to diagnosis: contributes harmonised imaging datasets, radiomic features, liquid biopsy markers, and AI ready diagnostic pipelines to improve early characterization of pulmonary nodules.
- UC4.3 → from biology to targeted therapy: contributes molecular, imaging, and functional data from advanced NSCLC models, improving understanding of tumour progression and therapeutic vulnerabilities.
- UC4.4 → from bench to bedside: contributes mechanistic, high quality experimental datasets from 2D/3D and patient derived SCLC models, enabling subtype mapping, resistance analyses, and testing of therapeutic combinations.
By feeding all four streams into the UNCAN.eu meta‑catalogue with standardisation and quality control, the project enables federated, cross‑domain analyses—linking population‑level risk, early‑detection markers, tumour biology, and treatment response. Together, UC4.1–UC4.4 demonstrate end‑to‑end interoperability: identifying who is at risk, understanding what early changes to monitor, uncovering why tumours behave as they do, and informing how to select effective therapies.
Integrated Research Innovation
Few initiatives link risk identification (UC4.1), early diagnostic precision (UC4.2), deep biological modelling of NSCLC (UC4.3), and mechanistic treatment insight in SCLC (UC4.4) within a single federated cancer‑data infrastructure. Together, they make the lung‑cancer pillar of UNCAN‑Connect uniquely capable of accelerating early detection, personalised therapy development, and continuous learning across Europe—all from standardised, interoperable datasets designed for long‑term impact.
A Collaborative Framework for Europe
Expected Impact
Use Case 4.4
Use Case 4.4 (UC4.4) is designed to generate lasting scientific, technical, and clinical value both within UNCAN‑Connect and across the wider cancer research ecosystem. Its long‑term impact stems from the depth, quality, and interoperability of the data it produces, as well as its unique focus on the biological mechanisms driving Small Cell Lung Cancer (SCLC), one of the most aggressive and underserved cancer types. By providing high‑quality molecular, functional, and imaging‑based datasets from 2D systems and advanced 3D patient‑derived cancer models, UC4.4 enriches the UNCAN.eu meta‑catalogue with data rarely available at scale in SCLC research. These datasets help characterise tumour heterogeneity, treatment‑response patterns, and resistance mechanisms, enabling more robust modelling, hypothesis generation, and cross‑Use Case integration. Over time, this elevates the scientific value of the UNCAN.eu platform and strengthens its ability to support multimodal, data‑driven oncology research.
Use Case 4.1
In parallel, Use Case 4.1 (UC4.1) extends long‑term impact upstream—on prevention and early detection. By systematically reviewing, comparing, and validating multivariable lung‑cancer risk‑prediction models, and mapping the availability and quality of required risk‑factor data across European systems, UC4.1 generates durable assets for risk‑based screening and policy harmonisation. Its outputs—harmonised risk variables, model‑performance evidence, and recommendations on minimal data sets and risk‑re‑evaluation intervals—will remain reusable for national programmes and EHR integrations, improving identification of high‑risk individuals and accelerating the pathway to timely diagnosis.
Use Case 4.2
Use Case 4.2 (UC4.2) adds long‑term impact at the earliest point of clinical presentation: the evaluation of subcentimetric pulmonary nodules. By combining harmonised imaging datasets, radiomics features, liquid‑biopsy markers, and AI‑enabled analytical pipelines, UC4.2 delivers tools that can improve diagnostic certainty, reduce unnecessary interventions, and enable earlier detection of malignant transformation. These datasets will support future validation of nodule‑risk models, development of more efficient follow‑up protocols, and integration of radiomics and liquid‑biopsy markers into federated European workflows—benefiting clinical decision‑making long after the project concludes.
Use Case 4.3
Use Case 4.3 (UC4.3) contributes long‑term value through its mechanistic exploration of non‑small cell lung cancer (NSCLC). By generating multimodal biological datasets from patient‑derived 3D cultures, bioprinted constructs, and advanced tumour‑microenvironment models, UC4.3 enhances understanding of NSCLC onset, progression, and treatment response. These datasets will remain valuable for future drug‑development efforts, modelling of metastatic behaviour, and discovery of new biomarkers for patient stratification. The translational platforms developed in UC4.3 will also provide a replicable framework for connecting in‑vitro disease modelling with federated clinical datasets.
Beyond the project, the combined outputs of UC4.1, UC4.2, UC4.3, and UC4.4 address major gaps across the entire lung‑cancer continuum. UC4.1’s assets can guide sustained improvements in screening efficiency and equity; UC4.2’s diagnostic datasets can support future AI models and early‑detection guidelines; UC4.3’s mechanistic NSCLC resources can drive ongoing therapeutic innovation; and UC4.4’s experimental SCLC datasets can underpin future research into epigenetic regulators, glypican biology, and treatment‑resistance mechanisms. Together, they demonstrate how population‑level risk modelling, early‑diagnostic innovation, NSCLC biology, and SCLC mechanistic data can be seamlessly connected through a federated data infrastructure—creating a reusable, interoperable framework for lung‑cancer research in Europe.
A SUSTAINABLE RESEARCH LEGACY
Ultimately, the long‑term impact lies in enabling earlier and more accurate detection, advancing personalised treatment approaches across lung‑cancer subtypes, and strengthening Europe’s capacity for high‑quality, federated cancer research and clinical decision‑support well beyond the lifetime of the project.