Mapping tumor heterogeneity: development of barcoding approaches for clonal tracking during tumor progression, metastasis and therapy response

Tumor heterogeneity is a fundamental characteristic of cancer and plays a pivotal role in disease progression, metastasis and the emergence of therapy resistance. As cancer develops, tumor cells gradually accumulate genetic and epigenetic changes, resulting in various clonal lineages and subgroups. These linages undergo selective pressure based on growth and fitness advantages, influencing their disease progression or treatment resistance. Tumor heterogeneity can be investigated with cellular barcoding. This technique uses a short (semi-)random DNA sequence, or barcode, which is stably introduced into the genome of target cells and allows for the identification and tracking of individual cancer cells and subsequent progeny. Due to its broad range of applications, DNA barcoding has become a powerful tool for identifying novel therapeutic compounds and drug targets. Additionally, DNA barcoding can provide valuable insights into tumor heterogeneity, tumor evolution, and clonal dynamics, with potential implications for personalized cancer therapies in the future. With this in mind, two barcoding approaches were developed in this thesis to investigate tumor heterogeneity in solid tumors and serve as tools to study clonal dynamics and ultimately uncover new treatment options. In the first approach, the high-complexity CORE barcode library was developed to label and track tens of thousands of uniquely marked pancreatic cancer cells. The CORE barcode library was further used to monitor the clonal dynamics of PDAC cells under FOLFIRINOX treatment. It was observed that the ASPC1 cells have a high intrinsic resistance to FOLFIRINOX. In contrast, cell expansion after treatment followed a stochastic pattern. Using the CORE system to track clonal dynamics during metastasis revealed that ASPC1 cells are characterized by a consistently high inherent metastatic ability regarding their metastatic seeding. This observation contrasts a patient-derived cell line that displayed a significantly higher degree of heterogeneity and consisted of subpopulations with varying metastatic abilities. Lastly, the expertise gained from developing the CORE library was further applied to conceptualize the sCORE approach based on mathematical modeling to allow the integration of clonal tracking into single-cell transcriptome analysis to enable the longitudinal tracking of transcriptome changes. Following the comprehensive exploration of cellular barcoding's potential in understanding tumor heterogeneity and clonal dynamics in PDAC, the second approach delved into understanding drug resistance complexities in gastrointestinal stromal tumors. For this, a preclinical GIST barcoding platform was developed that provided a lens to study genotype-specific drug responses and identify promising drug candidates. Although the introduction of selective tyrosine kinase inhibitors such as imatinib has revolutionized GIST therapy, resistance eventually emerges in nearly all patients. Overcoming resistance and developing new drugs are complicated by highly diverse resistance mechanisms mediated by a broad range of genetic mutations. The preclinical GIST barcoding platform was deployed to further study the secondary drug resistance landscape regarding their overall fitness and genotype-specific drug response. It allowed to assess the individual fitness of various sublines for the first time in a competitive setting and revealed a fitness hierarchy; ultimately demonstrating that drug resistance comes at a fitness cost. A sophisticated Bayesian model was employed to allow multiplex screening of drugs and to monitor genotype-specific susceptibility. This joint approach of the preclinical GIST screening platform, combined with the Bayesian model, was subsequently validated by in vitro and in vivo drug screens of the standard approved treatment schedules. The expected genotypespecific effects, as seen in preclinical and clinical studies, could be faithfully recapitulated, thus confirming the effectiveness and accuracy of the platform. Additionally, the platform was used to screen the novel compound M4205 in vitro and revealed a strong inhibition in all sublines. These data support M4205 as a promising drug candidate for further preclinical studies and demonstrate that the preclinical GIST barcoding platform can also be used to identify novel compounds. Overall, DNA barcoding technologies are emerging as a powerful tool for studying tumor heterogeneity as it allows for the tracking of clonal dynamics and the evolution of cellular states during metastasis and/or under treatment. The barcoding systems established in this thesis further progress the evolving technology and provide excellent methodological approaches to better understand the heterogeneity of solid tumors and to help decipher vulnerabilities for improving patient care.