The first part of the ERC Starting Grant CombaTCancer focused on addressing a longstanding question in oncology: Do resistant clones pre-exist in therapy-naïve tumors, or does therapy itself induce resistance? To address this question, we created the CaTCH lineage tracing technology (Umkehrer et al., Nature Biotechnology, 2020). CaTCH enables precise mapping of the lineage history of millions of cells and facilitates the isolation any clone as rare as 0.002% through a CRIPRa activatable BC-linked GFP reporter with exceptional purity (98%, ~20,000-fold enrichment), allowing the direct functional comparison of resistant clones with their therapy-naive founder clones.
In its initial applications, CaTCH provided critical insights into resistance mechanisms to targeted RAF/MEK inhibitor therapy in vivo. Our findings revealed that resistance is often acquired during treatment rather than pre-existing. While in most clones resistance emerged due to altered signaling output, we also identified de novo mutations in the MAPK pathway in resistant clones, which were absent in their treatment-naïve counterparts, offering experimental evidence that resistance conferring mutations can be acquired during drug treatment. These data challenge the prevailing notion that drug-resistance mutations generally pre-exist before therapy and suggest potential preventive strategies. CaTCH is now extensively utilized in our lab and globally for advancing research in therapy resistance, metastasis, and immune evasion.
The second part of CombaTCancer was dedicated to understanding how targeted therapies reshape the entire tumor-immune ecosystem. We developed novel, clinically relevant models and discovered that when BRAF-mutated melanomas acquire resistance to MAPK pathway-targeted therapies, they also develop cross-resistance to immunotherapy, despite the distinct modes of action of these treatments (Haas et al., Nature Cancer, 2021). We demonstrated that this cross-resistance is mediated by an immune-evasive tumor microenvironment (TME), provided mechanistic insights into the drivers of cross-resistance and potential therapeutic strategies to overcome it. Supported by recent prospective clinical trials, the work by us and others has influenced clinical practice by emphasizing the prioritization of immunotherapy as a first-line treatment.
These studies have opened up several exciting avenues for further research. Most recently, we used our models to explore how cancer cells shape a TME that is either permissive or suppressive of a T cell response. Our findings have provided crucial new insights into the fundamental principles of anti-tumor immunity, reshaping our understanding of immunity in both health and disease (in revision).
Beyond these discoveries, the technologies established within the CombaTCancer framework have enabled us to extend our research to cancer types where little is known about the drivers and mechanism-based treatments. For example, we identified new therapeutic entry points for rare skin cancers (Leiendecker et al., EMBO Molecular Medicine 2020) and uncovered a new oncogenic virus (Human papillomavirus 42) as the driver of a rare skin cancer (digital papillary adenocarcinoma). Through machine learning approaches, we found that oncogenic papillomaviruses induce a germ cell-like program in cancer cells, which we are working towards exploiting both diagnostically and therapeutically (Leiendecker et al., Cancer Discovery, 2023).
