Acquired resistance in cancer: towards targeted therapeutic strategies

Abstract

Development of acquired therapeutic resistance limits the efficacy of cancer treatments and accounts for therapeutic failure in most patients. How resistance arises, varies across cancer types and differs depending on therapeutic modalities is incompletely understood. Novel strategies that address and overcome the various and complex resistance mechanisms necessitate a deep understanding of the underlying dynamics. We are at a crucial time when innovative technologies applied to patient-relevant tumour models have the potential to bridge the gap between fundamental research into mechanisms and timing of acquired resistance and clinical applications that translate these findings into actionable strategies to extend therapy efficacy. Unprecedented spatial and time-resolved high-throughput platforms generate vast amounts of data, from which increasingly complex information can be extracted and analysed through artificial intelligence and machine learning-based approaches. This Roadmap outlines key mechanisms that underlie the acquisition of therapeutic resistance in cancer and explores diverse modelling strategies. Clinically relevant, tractable models of disease and biomarker-driven precision approaches are poised to transform the landscape of acquired therapy resistance in cancer and its clinical management. Here, we propose an integrated strategy that leverages next-generation technologies to dissect the complexities of therapy resistance, shifting the paradigm from reactive management to predictive and proactive prevention.

Figure 1.. Genetic and phenotypic heterogeneity in tumours drives the development of acquired resistance.

Tumours exhibit both genetic heterogeneity (top) and phenotypic, non-genetic heterogeneity (bottom), which collectively influence cellular fitness and determine the response to therapy and emergence of resistance. Active therapies impose a selective bottleneck, creating pressure for cells to survive or outcompete others. The top part is a simplified fish plot depicting the progression of genetic heterogeneity over time, highlighting intrinsic resistance (pre-existing genetic variants) and acquired resistance (genetic selection and evolution under selective therapeutic pressures). In the context of intrinsic resistance, pre-existing tumour cells harbour resistance-conferring mutations, providing an immediate fitness advantage. These resistant clones rapidly dominate the tumour population, minimizing reliance on non-genetic adaptations. Acquired resistance emerges when therapy induces evolutionary processes that lead to the selection and expansion of genetically resistant clones, driving tumour survival and progression. The bottom panel illustrates phenotypic heterogeneity and its role in the development of resistance. Concurrently, tumour cells exist in various phenotypic states, including pre-drug-tolerant persister (pre-DTP) cells. Upon therapeutic pressure, pre-DTP cells can convert into non-cycling DTP cells, and finally into cycling DTP cells. To survive therapy, tumours that lack pre-existing resistance mutations rely heavily on dynamic phenotypic adaptations such as transcriptional priming and epigenetic plasticity, which begin immediately upon drug exposure. Over time, phenotypic adaptations enable survival and drive the emergence of resistant cells with increased phenotypic fitness. Genetic and non-genetic processes are often coupled, whereby changes in tumour genotype are associated with phenotypic alterations. Although the extent to which phenotypic adaptations sculpt tumour genomes is less well established, they enable survival and provide time for subsequent genetic evolution to occur under therapeutic pressure. DTP cells can probably arise from multiple genetic backgrounds, driven by non-genetic adaptive processes. Together, tumours leverage both genetic diversity and phenotypic plasticity to adapt, survive and evolve under therapeutic pressure, ultimately facilitating the development of acquired resistance.

Figure 2.. Acquired resistance mechanisms arising from dynamic evolutionary processes under different therapeutic strategies.

When patients are treated with chemotherapy, which is typically administered in cycles to allow for patient recovery, discontinuous treatment can lead to the selection and survival of rare pre-existing drug-tolerant cells, which persist as minimal residual disease. Over time, these surviving populations undergo recovery and subsequent expansion. Here, acquired resistance is driven by poorly understood mechanisms that may include genetic alterations, changes in protein efflux or other unknown factors. When patients are treated with targeted therapy, continuous treatment induces acquired resistance through both genetic and non-genetic mechanisms. Genetic evolution can involve mutations of the targets of the therapy, such as epidermal growth factor receptor (EGFR), or survival mechanisms driven by mutations in master cancer regulator genes such as TP53 and RB1. Non-genetic adaptations include phenotypic changes such as epithelial–mesenchymal transition (EMT) or transdifferentiation, the loss of target dependence (as seen with androgen receptor (AR) signalling) and transcriptionally primed drug-tolerant persister cells. When patients are treated with immunotherapy, treatment schedules may vary depending on the agent, treatment protocol or clinical trial design. Acquired resistance emerges through tumour-intrinsic and tumour-extrinsic mechanisms. Tumour-intrinsic factors include genetic alterations, such as mutations in β2-microglobulin (β2m), and epigenetic changes, such as defects in interferon (IFN) signalling pathways. Tumour-extrinsic resistance involves changes in components of the tumour immune microenvironment (TIME), including the expansion of myeloid-derived suppressor cells (MDSCs) and immune exhaustion, which collectively impair effective antitumour immune responses. Together, these mechanisms illustrate how different therapeutic pressures shape the emergence and evolution of acquired resistance, enabling cancer cell populations to adapt, survive and progress during ongoing treatment. CAF, cancer-associated fibroblast.

Figure 3.. A dynamic framework for intercepting and rationally targeting acquired therapy resistance in cancer.

Tumours exhibit genetic, transcriptional and epigenetic heterogeneity, which evolves under therapeutic pressure, driving tumour progression and acquired resistance. The fish plot shows changes in tumour clonal composition over time. Each colour represents a genetically or epigenetically distinct subpopulation, with width reflecting relative abundance. Therapy induces a selective bottleneck (centre), followed by expansion of resistant cells (right). Although cancer treatment modalities and mechanisms of acquired resistance can vary widely, the overall strategy to intercept and target resistance remains consistent. Future approaches should optimize clinical care by integrating real-time monitoring and modelling, as well as adaptive treatment strategies to inform and adjust therapies to target (and eventually prevent) acquired resistance, ultimately improving patient outcomes. Functional and molecular characteristics of emerging resistance can be identified through data generated with advanced omics approaches such as single-cell transcriptomics, spatial proteomics and genomic sequencing, which can be applied to tumour samples obtained at diagnosis, at surgery (pre- or post-therapy) and during treatment (on-treatment biopsies). These analyses, when paired with patient-derived models, provide crucial insight into tumour trajectories and enable the development of therapies tailored to the evolving tumour landscape. The horizontal brackets mark potential intervention points for deploying rational precision approaches informed by biomarkers and functional models, timed to intercept or redirect tumour evolution. To track the emergence of resistance, advanced biomarkers, including longitudinal genetic and epigenetic analyses from liquid biopsies, can provide real-time insights into clonal dynamics. On-treatment biopsies further offer key windows of opportunity to anticipate resistance by capturing transitional cell states and adaptation pathways. Functional models that reflect the molecular features of a patient’s tumour, such as organoids or patient-derived xenografts, can be used to reveal therapeutic vulnerabilities and test rational combinations. To effectively counter resistance, emphasis should be placed on combinatorial strategies that target multiple pathways, tumour and stromal components, or induce synthetic lethality. CAF, cancer-associated fibroblast; MDSC, myeloid-derived suppressor cell; TME, tumour microenvironment.