Resistance is Futile: Physical Science, Systems Biology and Single-Cell Analysis to Understanding the Plastic and Heterogeneous Nature of Melanoma and Their Role in Non-Genetic Drug Resistance

Melanoma is the most deadly form of skin cancer due to its great metastatic potential. Targeted therapy that inhibits the BRAF-V600E driver mutation has shown impressive initial responses in melanoma patients. However, drug resistance, as the universal phenomenon for any cancer therapy, always limits treatment efficacy and compromises outcomes. As the early-step of resistance development, non-genetic mechanisms enable cancer cells to transition into a drug-resistant state in as early as a few days after drug treatment without alteration of the genome. This early mechanism is, to a large extent, due to the heterogeneous and highly plastic nature of tumor cells. Therefore, it imperative to understand the plastic and heterogeneous nature of the melanoma cells in order to identify combination therapies that can overcome resistance. In this thesis, we investigate these two fundamental natures of non-genetic drug resistance using BRAF inhibition of BRAF-mutant melanomas as the model system. These melanoma cells undergo multi-step, reversible drug-induced cell-state transitions from the original sensitive phenotype to a drug-resistant one. We first conducted bulk analysis to characterize the detailed kinetics of the entire transition from drug-sensitive state towards drug-resistant state, revealing expression changes of thousands of genes and extensive chromatin remodeling. A 3-step computational biology approach greatly simplified the complexity and revealed that the whole cell-state transition was controlled by a gene module activated within just the first three days of drug treatment, with the RelA transcription factor driving chromatin remodeling to establish an epigenetic program encoding long-term phenotype changes towards resistance. From there, a detailed mechanism connecting tumor epigenetic plasticity with non-genetic drug resistance was resolved through in-depth molecular biology experiments. The mechanism was validated in clinical patient samples. We further investigated heterogeneity by moving from bulk cellular studies to single-cell analysis. The single-cell view further revealed that two driving forces from both cell-state interconversions and phenotype-specific drug selection control the cell-state transition dynamics. The single-cell studies also pinpointed the signaling network hub, RelA, as the driver molecule of the initiation of the adaptive transition. These two competing driving forces were further quantitatively modeled via a thermodynamic-inspired surprisal analysis and a modified Fokker-Planck-type kinetic model. Finally, using integrated single-cell proteomic and metabolic technology I developed to characterize the early-stage signaling and metabolic changes upon initial drug responses, we further identified two distinct paths connecting drug-sensitive and drug-tolerant states. Melanoma cells exclusively traverse one of the two paths depending on the level of MITF in the drug-naïve cells. The two trajectories are associated with distinct signaling and metabolic susceptibilities and are independently druggable. In total, this thesis combines and synergizes various physical science and systems biology approaches together with several unique single-cell technologies and analysis to obtain a deep and comprehensive understanding of non-genetic drug resistance in cancer. The findings from this thesis provide several novel insights into the rational design of effective combination therapy for overcoming the development of resistance in response to cancer treatments.</p

Computational models of melanoma.

Genes, proteins, or cells influence each other and consequently create patterns, which can be increasingly better observed by experimental biology and medicine. Thereby, descriptive methods of statistics and bioinformatics sharpen and structure our perception. However, additionally considering the interconnectivity between biological elements promises a deeper and more coherent understanding of melanoma. For instance, integrative network-based tools and well-grounded inductive in silico research reveal disease mechanisms, stratify patients, and support treatment individualization. This review gives an overview of different modeling techniques beyond statistics, shows how different strategies align with the respective medical biology, and identifies possible areas of new computational melanoma research

Melanoma Plasticity Induced by Pro-Inflammatory Cytokines in Response to Immunotherapy

Melanoma dedifferentiation has been reported as a state of cellular resistance to targeted therapy and immunotherapy as cancer cells revert to a more primitive cellular phenotype. In a patient with metastatic melanoma who received adoptive T-cell transfer therapy using T cells with receptors against the melanoma antigen recognized by T cells 1 (MART-1/ Melan-A), we observed dedifferentiation as a resistance mechanism after initial response. However, biopsies obtained from responding patients during anti-programmed cell death receptor 1 (PD-1) therapy had decreased expression of melanocytic markers and increased neural crest markers. When modeling the effects in vitro, we documented that melanoma cell lines that were originally melanocytic differentiated underwent a process of neural crest dedifferentiation when continuously exposed to interferon gamma (IFNγ), through a global chromatin landscape change leading to enrichment in specific hyperaccessible chromatin regions. The IFNγ-induced dedifferentiation signature corresponded with improved outcomes in patients with melanoma, challenging the notion that neural crest dedifferentiation is an adverse phenotype

Melanoma Single-Cell Biology in Experimental and Clinical Settings

Cellular heterogeneity is regarded as a major factor for treatment response and resistance in a variety of malignant tumors, including malignant melanoma. More recent developments of single-cell sequencing technology provided deeper insights into this phenomenon. Single-cell data were used to identify prognostic subtypes of melanoma tumors, with a special emphasis on immune cells and fibroblasts in the tumor microenvironment. Moreover, treatment resistance to checkpoint inhibitor therapy has been shown to be associated with a set of differentially expressed immune cell signatures unraveling new targetable intracellular signaling pathways. Characterization of T cell states under checkpoint inhibitor treatment showed that exhausted CD8+ T cell types in melanoma lesions still have a high proliferative index. Other studies identified treatment resistance mechanisms to targeted treatment against the mutated BRAF serine/threonine protein kinase including repression of the melanoma differentiation gene microphthalmia-associated transcription factor (MITF) and induction of AXL receptor tyrosine kinase. Interestingly, treatment resistance mechanisms not only included selection processes of pre-existing subclones but also transition between different states of gene expression. Taken together, single-cell technology has provided deeper insights into melanoma biology and has put forward our understanding of the role of tumor heterogeneity and transcriptional plasticity, which may impact on innovative clinical trial designs and experimental approaches

Emerging properties of signaling networks in cancer: a data-derived modeling approach

Mammalian signal transduction pathways are highly integrated within extended networks, with crosstalk emerging in space and time. This dynamic circuitry is dependent on changing activity states for proteins and organelles. Network structures govern specificity of cellular responses to external stimuli, including proliferation and cell death. Loss of regulation virtually underlies all disease. However, while the contributions of individual components to phenotype are mostly well understood, systematic elucidation for the emergence or loss of crosstalk and impact on phenotype remains a fundamental challenge in classical biology that can be investigated by systems biology. To that end, we established a mathematical modeling platform, at the interface between experimental and theoretical approaches, to integrate prior literature knowledge with high-content, heterogeneous datasets for the non-intuitive prediction of adaptive signaling events. In the first part of this work, we investigated high-content microscopy datasets of morphological, bio-energetic and functional features of mitochondria in response to pro- apoptotic treatment in MCF-7 breast cancer cells. Data pretreatment techniques were used to unify the heterogeneous datasets. Using fuzzy logic, we established a generalized data-driven modeling formalism to model signaling events solely based on measurements, capable of high simulation accuracy via non-discrete rule sets. Employing neural networks, a generalized fuzzy logic system, i.e. its rules and membership functions, could be parameterized for each potential signaling interaction. An exhaustive search approach identified models with least error, i.e. the most related signaling events, and predicted a hierarchy of apoptotic events, in which upon activation of pro-apoptotic Bax, mitochondrial fragmentation propagates apoptosis, which is consistent with reported literature. Hence, we established a predictive approach for investigation of protein and organelle interactions utilizing cell-to-cell heterogeneity, a critical source of biologically relevant information. In the second part of this work, we sought to identify network evolution in the topology of MAPK signaling in the A-375 melanoma cell line. To that end, the modeling method was extended to incorporate temporal and topological structure from phosphorylation profiles of key MAPK intermediates treated with different pharmacological inhibitors and acquired over 96 hours. To increase prediction power, a parameter reduction strategy was developed to identify and fix parameters with lowest contribution to model performance. Therefore, training datasets were bootstrapped and signatures of deviation in flexibility and accuracy were calculated. This novel strategy achieved an optimal set of free parameters. Finally, a reduced multi-treatment model encoding the behavior of the full MAPK dataset was systematically trained to a sequentially increasing subset of time points, enabling time-defined identification of discrepancies in reported vs. acquired network topology. To that end, an objective function for fuzzy logic model optimization was implemented, which accounted for time-defined model training. Analysis led to the identification of emerging discrepancies between model and data at specific time points, thus characterizing a potential network rearrangement upstream of MAPK kinase MEK1, consistent with studies reporting increased resistance to apoptosis exhibited by A-375 melanoma cell line. The approach presented here was successfully benchmarked against a recently published fuzzy-logic-based analysis of signal transduction

Kinetic Inference Resolves Epigenetic Mechanism of Drug Resistance in Melanoma

We resolved a mechanism connecting tumor epigenetic plasticity with non-genetic adaptive resistance to therapy, with MAPK inhibition of BRAF-mutant melanomas providing the model. These cancer cells undergo multiple, reversible drug-induced cell-state transitions, ultimately yielding a drug-resistant mesenchymal-like phenotype. A kinetic series of transcriptome and epigenome data, collected over two months of drug treatment and release, revealed changing levels of thousands of genes and extensive chromatin remodeling. However, a 3-step computational algorithm greatly simplified the interpretation of these changes, and revealed that the whole adaptive process was controlled by a gene module activated within just three days of treatment, with RelA driving chromatin remodeling to establish an epigenetic program encoding long-term phenotype changes. These findings were confirmed across several patient-derived cell lines and in melanoma patients under MAPK inhibitor treatment. Co-targeting BRAF and histone-modifying enzymes arrests adaptive transitions towards drug tolerance in epigenetically plastic melanoma cells and may be exploited therapeutically

The Role of Melanocyte Lineage Genes in Melanoma

Malignant melanoma accounts for the highest number of deaths among all skin cancer types, and its incidence has increased dramatically over the past decades. Despite the tremendous therapeutic advances, treatment resistant cells emerge in the vast heterogeneity of melanoma, driving tumor relapse and poor patient outcome. The aims of the studies conducted in this thesis were to contribute to the knowledge with regards to therapy-resistant melanomas, and to explore tumor heterogeneity in relation to cancer progression among the chronic sun-damaged (CSD) melanomas.Therapy resistant cells have lost the melanocyte lineage-specific transcriptional program, which is mainly driven by the master-melanocyte regulator MITF. Paper I validated the MITF-negative (MITFNeg) melanomas to be highly aggressive and associated with inferior patient survival compared with the MITF-high (MITFHigh) cases. We herein discovered an even more undifferentiated melanoma subtype that lacks the MITF upstream marker SOX10 (MITFNegSOX10Neg), characterized by superior metastatic potential and resistance to targeted therapy. Importantly, we found gene methylation explaining the silencing of both MITF and SOX10 in these melanomas. To discriminate the role of SOX10 in MITFNeg cells, in Paper II we engineered SOX10KO by CRISPR-Cas9 technology. Depletion of SOX10 in MITFNeg cells lead to a hyper-undifferentiated phenotype: a new distinct lineage identity state in melanoma. Paper III uncovered a novel layer of regulation of MITF at the translational level. We showed that MITF is regulated by the RNA-helicase DDX3X. DDX3X loss in melanoma leads to decreased MITF, and results in enhanced metastasis and therapy resistance. Interestingly, DDX3X is located on the X-chromosome. Thus, mutations affecting DDX3X associate with poor prognosis in male melanoma patients, implying an exclusive window of opportunity in this gender. Paper IV investigated the molecular features of a unique cohort of high and low CSD (CSDhigh, CSDlow) melanomas. Focusing on the less investigated CSDhigh subtype in view of cancer progression, we found no mutational difference between in situ or invasive phases. We further observed dissimilarity in the heterogeneity levels between CSDhigh and CSDlow melanomas, which suggests distinguishable molecular entities that progress via different routes.Overall, we unraveled the role of melanocyte-specific genes in defining diverse melanoma lineage states, while investigating novel biological mechanisms behind their regulation. Our findings further highlighted the variable heterogeneity in CSD melanoma subtypes, which should be taken into consideration for an improved diagnosis, and when choosing the best treatment options for melanoma patients