Online tool enhances diagnostic precision in lung cancer with multiple tumors

For lung cancer patients with multiple tumors, a crucial question is whether these represent separate primary tumors or metastases of an existing tumor. Modern sequencing methods, which are often carried out routinely in the context of finding a suitable therapy, provide a genetic fingerprint of the tumor that can be used to compare tumors. The answer has a direct impact on disease staging and the choice of therapy. Classical histological methods remain valuable, but often reach their limits, especially with small biopsy samples. To address this, the IASLC recently recommended an algorithm that combines pathological assessment with targeted molecular testing. The pathologists, physicians, and scientists led by Professor Stenzinger and Dr. Kirchner wanted to determine whether this approach also works reliably under routine conditions—i.e., with only small tissue samples and limited sequencing panels available.

In one of the largest real-world studies to date, the Heidelberg team analyzed 240 tumor samples from 120 patients with non-small cell lung cancer (NSCLC). The cohort included patients with and without lymph node or distant metastases, different histological subtypes, and preserved (FFPE) tissue— i.e. typical conditions in clinical practice. Most samples were examined using panels of only 31–54 genes—the standard in many European clinics. Larger panels with more than 500 genes were usually only used when the initial diagnosis did not provide a clear answer.

The team developed a new bioinformatic tool that takes into account how common or rare specific genetic mutations are. This allows for a more reliable classification of clonality. The basis for this was the mutation frequency in an independent NSCLC reference cohort of 3,477 samples. With the new tool, the proportion of unclear results dropped to just 2%, even when using small panels. “Precisely distinguishing whether multiple lung tumors are related or have developed independently is critical for treatment planning. This question is often raised in clinical diagnostics,” says Dr. Michael Allgäuer, first author of the study. “Our results show that even with the limited panels available in many clinics today, reliable answers are possible —especially with the support of our freely accessible bioinformatic tool,” adds Dr. Martina Kirchner, who reanalyzed the extensive genetic data. Importantly:  patients who were classified as carriers of separate primary tumors survived significantly longer than those with metastatic disease — clear evidence of the clinical relevance of accurate classification.

The freely available Clonality Checker from Heidelberg combines the IASLC approach with the new bioinformatic method. This provides clinics and pathology departments worldwide with an easily accessible, evidence-based tool to improve diagnostic accuracy—even in settings with limited sequencing capacity. This work was made possible through close collaboration within an interdisciplinary team of pathologists and scientists from Heidelberg University Hospital and physicians from the Thoraxklinik Heidelberg, in cooperation with experts from Portugal, France, Switzerland, and the Netherlands. The study was published in the renowned Journal of Thoracic Oncology.

Original Publication:  Allgäuer M, Kluck K, Christopoulos P, Ball M, Volckmar A-L, Radonic T, Bubendorf L, Hofmann P, Heußel CP, Winter H, Herth F, Thomas M, Ylstra B, Peters S, Schirmacher P, Kazdal, D, Budczies J, Stenzinger A, Kirchner M. Ad­van­cing Lung Can­cer Sta­ging: In­teg­rat­ing IASLC Re­com­mend­a­tions and Bioin­form­at­ics to De­lin­eate Tumor Ori­gins. Journal of Thoracic On­co­logy, Open Access, Article in Press, October 23. DOI: 10.1016/j.jtho.2025.10.010.

Source: Bioinformatics online tool improves accuracy in staging of multiple lung tumors - TLRC Heidelberg