Small-cell lung cancer (SCLC) is an aggressive subtype of lung cancer, accounting for 13-15% of cases. While treatment outcomes of many cancers have improved significantly the last decades, there have been few improvements for small-cell lung cancer. A part of the problem is that all SCLC patients receive the same treatment, although we know that treatment outcomes vary significantly between patients.

Development of diagnostic classification

In PETS, we will build on translational SCLC research to

• Develop an affordable, cost-effective, and easily implemented diagnostic classification system
• Assess the extent of disease
• Predict treatment response and disease development, including risk of brain metastases
• Estimate time to relapse and survival time

Data from clinical trials

Data and biological material collected through international randomized clinical trials will be analyzed in the project. These analyses will include molecular signals in patient blood (microRNAs, tumor DNA, single cell RNA) and tissue (RNA and DNA). Further, we will develop in vitro human lung microtissue models and use these with patient tumor samples to evaluate disease development and responses to treatment alternatives. We will then integrate these diverse data into a common model for classifying patients into different treatment modalities.

One size does not fit all

Better knowledge of the biology of non-small-cell lung cancer (NSCLC) has enabled us to move from “one size fits all” chemotherapy to specific therapy for subgroups characterized by biomarkers. This has been key for improving NSCLC survival in recent years. Based on this we believe that improved classification will promote effective, tailored, individualised therapy and increase feasibility and validity of future research also in SCLC.

Hypotheses

1. The blood immune profile assessed by single-cell RNA sequencing is different in patients who achieve long term disease control from ICI therapy than in those who do not.
2. A multi-cell culture system with controlled structure and perfusion can emulate in vivo alveolar tissue homeostasis and dynamic monitoring of patient-derived cancer cells. This model system will enable us to track disease development, understand the mechanisms behind growth and spread of tumor cells, and to test the efficacy of individual SCLC therapies. Consequently, the system enables improved prediction of patient-specific therapeutic interventions for SCLC cells classified by comprehensive molecular classification. By performing similar molecular profiling of treatment resistant tumor cells in the system, we can understand the biological mechanisms behind treatment failure.
3. Combining these data and clinical data on treatment outcomes in tumors with similar molecular profile, we will be able to predict treatment sensitivity in individual SCLC tumors.