The Role of Tumor Heterogeneity in Therapy Resistance
{The Role of Tumor Heterogeneity in Therapy Resistance
Cancers are highly adaptable, leveraging diverse signaling pathways, cellular transitions, and interactions with the tumor microenvironment to survive under stress and therapy. Given the complexity of the genome, transcriptome, and methylome, tumors have a vast arsenal of resistance strategies. Their heterogeneity enables rapid adaptation or dormancy when advantageous, making a single mutational snapshot insufficient for treatment decisions. Instead, therapies must evolve dynamically, mirroring cancer’s own adaptability. By mapping tumor heterogeneity within an evolutionary framework, we can better predict its trajectory and develop more effective, personalized treatment strategies.
Bringing evolutionary medicine to the clinic
{Cancer evolution and multi-omic profile of relapsing metastases after treatment
Colorectal cancer poses significant challenges in patient survival due to disease relapse and development of resistance. Current comparative studies on relapse mechanisms often lack depth, relying on unpaired samples and single-genomic analyses.
To address this limitation, we present a patient-specific analysis of paired relapse mechanisms in eight individuals with colorectal liver metastases, leveraging a multi-genomic approach. By examining multiple samples from liver metastases and their corresponding relapses, we integrate DNA, RNA, and methylome data. Our analysis, encompassing DNA mutations, somatic copy number alterations, DNA signatures, RNA differential expression, immune cell infiltration, neoantigens, and epigenomic methylation, reveals low spatial and temporal heterogeneity in somatic mutations and copy number alterations.
Clonal evolution analysis uncovers two relapsing mechanisms: one originating from the ancestral liver metastasis and another from an unknown separate origin of metastatic cells. Individualized multi-omic analysis of each patient deepens our understanding of relapse mechanisms in late-stage colorectal cancer. Our findings offer nuanced insights into patient-specific alterations, providing valuable perspectives on the intricacies of disease progression.
Building Patient-Derived Tumoroid Platforms: Mimicking, Monitoring, and Steering Cancer Evolution
{Building Patient-Derived Tumoroid Platforms: Mimicking, Monitoring, and Steering Cancer Evolution
Therapy Resistance in 3D Neuroblastoma Models
{Therapy Resistance in 3D Neuroblastoma Models
Neuroblastoma is the most common solid tumor outside the brain in children and remains a major clinical challenge. Despite aggressive multimodal treatment more than half of patients with high-risk disease relapse, often within two years of diagnosis. This therapy failure is largely driven by the tumor’s ability to adapt. Resistance can emerge during or after treatment, fueled by neuroblastoma’s remarkable cellular plasticity and genetic evolution.
We want to understanding how neuroblastoma tumor subclones respond to therapy. Using patient-derived 3D tumoroids, we aim to map how subpopulations evolve spatially and temporally under therapeutic pressure. We are particularly interested in identifying patterns of phenotypic plasticity and tracking lineage trajectories during drug exposure.
Monitoring genetic resistance and plasticity dynamics under treatment.
{Monitoring genetic resistance and plasticity dynamics under treatment.
cancer cells fighting
Our team aims to explore the evolving subclonality of sensitive and resistant cells, assess their fitness costs and examine various selective pressures of malignancy.
CanEvo has begun to establish a patient-derived organoid and tumoroid platform at the Charité that enables genetic and fluorescent tracing of resistant and sensitive populations in experiments exploring tumor evolution and its molecular mechanisms The platform creates, combines and tracks resistant and sensitive cell populations to investigate evolutionary concepts that can inform future treatment strategies using evolutionary dynamics. We delve deeper into the fitness acquired by resistant cells from treatments or selective pressures (e.g. hypoxia, serum-deprivation).