Acquired drug resistance is a major limitation for the successful treatment of cancer. Resistance can emerge due to a variety of reasons including host environmental factors as well as genetic or ...epigenetic alterations in the cancer cells. Evolutionary theory has contributed to the understanding of the dynamics of resistance mutations in a cancer cell population, the risk of resistance pre-existing before the initiation of therapy, the composition of drug cocktails necessary to prevent the emergence of resistance, and optimum drug administration schedules for patient populations at risk of evolving acquired resistance. Here we review recent advances towards elucidating the evolutionary dynamics of acquired drug resistance and outline how evolutionary thinking can contribute to outstanding questions in the field.
•Acquired drug resistance is a major limitation for the successful treatment of cancer.•Evolutionary theory has contributed to understanding the dynamics of drug resistance in cancer.•We review recent advances in evolutionary models of resistance in cancer.•We outline how evolutionary thinking can contribute to outstanding questions in the field.
Drug delivery schedules are key factors in the efficacy of cancer therapies, and mathematical modeling of population dynamics and treatment responses can be applied to identify better drug ...administration regimes as well as provide mechanistic insights. To capitalize on the promise of this approach, the cancer field must meet the challenges of moving this type of work into clinics.
Moving findings for optimized cancer therapy regimens derived from mathematical modeling studies into the clinic faces steep challenges, but the benefits may well be worthwhile.
Human cancers are thought to be sustained in their growth by a pathologic counterpart of normal adult stem cells: cancer stem cells. This concept was first developed in human myeloid leukemias and is ...today being extended to solid tumors such as breast and brain cancers. A quantitative understanding of cancer stem cells requires a mathematical framework to describe the dynamics of cancer initiation and progression, the response to treatment, and the evolution of resistance. In this review, I use chronic myeloid leukemia as an example to discuss how mathematical and computational techniques have been used to gain insights into the biology of cancer stem cells.
An unstable genome is a hallmark of many cancers. It is unclear, however, whether some mutagenic features driving somatic alterations in cancer are encoded in the genome sequence and whether they can ...operate in a tissue-specific manner. We performed a genome-wide analysis of 663,446 DNA breakpoints associated with somatic copy-number alterations (SCNAs) from 2,792 cancer samples classified into 26 cancer types. Many SCNA breakpoints are spatially clustered in cancer genomes. We observed a significant enrichment for G-quadruplex sequences (G4s) in the vicinity of SCNA breakpoints and established that SCNAs show a strand bias consistent with G4-mediated structural alterations. Notably, abnormal hypomethylation near G4s-rich regions is a common signature for many SCNA breakpoint hotspots. We propose a mechanistic hypothesis that abnormal hypomethylation in genomic regions enriched for G4s acts as a mutagenic factor driving tissue-specific mutational landscapes in cancer.
The identification of optimal drug administration schedules to battle the emergence of resistance is a major challenge in cancer research. The existence of a multitude of resistance mechanisms ...necessitates administering drugs in combination, significantly complicating the endeavor of predicting the evolutionary dynamics of cancers and optimal intervention strategies. A thorough understanding of the important determinants of cancer evolution under combination therapies is therefore crucial for correctly predicting treatment outcomes. Here we developed the first computational strategy to explore pharmacokinetic and drug interaction effects in evolutionary models of cancer progression, a crucial step towards making clinically relevant predictions. We found that incorporating these phenomena into our multiscale stochastic modeling framework significantly changes the optimum drug administration schedules identified, often predicting nonintuitive strategies for combination therapies. We applied our approach to an ongoing phase Ib clinical trial (TATTON) administering AZD9291 and selumetinib to EGFR-mutant lung cancer patients. Our results suggest that the schedules used in the three trial arms have almost identical efficacies, but slight modifications in the dosing frequencies of the two drugs can significantly increase tumor cell eradication. Interestingly, we also predict that drug concentrations lower than the MTD are as efficacious, suggesting that lowering the total amount of drug administered could lower toxicities while not compromising on the effectiveness of the drugs. Our approach highlights the fact that quantitative knowledge of pharmacokinetic, drug interaction, and evolutionary processes is essential for identifying best intervention strategies. Our method is applicable to diverse cancer and treatment types and allows for a rational design of clinical trials.
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Mathematical modelling approaches have become increasingly abundant in cancer research. The complexity of cancer is well suited to quantitative approaches as it provides challenges and opportunities ...for new developments. In turn, mathematical modelling contributes to cancer research by helping to elucidate mechanisms and by providing quantitative predictions that can be validated. The recent expansion of quantitative models addresses many questions regarding tumour initiation, progression and metastases as well as intra-tumour heterogeneity, treatment responses and resistance. Mathematical models can complement experimental and clinical studies, but also challenge current paradigms, redefine our understanding of mechanisms driving tumorigenesis and shape future research in cancer biology.
Oscillations of the cellular circadian clock have emerged as an important regulator of many physiological processes, both in health and in disease. One such process, cellular proliferation, is being ...increasingly recognized to be affected by the circadian clock. Here, we review how a combination of experimental and theoretical work has furthered our understanding of the way circadian clocks couple to the cell cycle and play a role in tissue homeostasis and cancer. Finally, we discuss recently introduced methods for modeling coupling of clocks based on techniques from survival analysis and machine learning and highlight their potential importance for future studies.
Somatic copy-number alterations (SCNA) are a hallmark of many cancer types, but the mechanistic basis underlying their genome-wide patterns remains incompletely understood. Here we integrate data on ...DNA replication timing, long-range interactions between genomic material, and 331,724 SCNAs from 2,792 cancer samples classified into 26 cancer types. We report that genomic regions of similar replication timing are clustered spatially in the nucleus, that the two boundaries of SCNAs tend to be found in such regions, and that regions replicated early and late display distinct patterns of frequencies of SCNA boundaries, SCNA size and a preference for deletions over insertions. We show that long-range interaction and replication timing data alone can identify a significant proportion of SCNAs in an independent test data set. We propose a model for the generation of SCNAs in cancer, suggesting that data on spatial proximity of regions replicating at the same time can be used to predict the mutational landscapes of cancer genomes.
EGFR-mutant lung cancer was first described as a new clinical entity in 2004. Here, we present an update on new controversies and conclusions regarding the disease.
This article reviews the clinical ...implications of EGFR mutations in lung cancer with a focus on epidermal growth factor receptor tyrosine kinase inhibitor resistance.
The discovery of EGFR mutations has altered the ways in which we consider and treat non-small-cell lung cancer (NSCLC). Patients whose metastatic tumors harbor EGFR mutations are expected to live longer than 2 years, more than double the previous survival rates for lung cancer.
The information presented in this review can guide practitioners and help them inform their patients about EGFR mutations and their impact on the treatment of NSCLC. Efforts should now concentrate on making EGFR-mutant lung cancer a chronic rather than fatal disease.
Triple-negative breast cancer (TNBC) is an aggressive subtype characterized by extensive intratumoral heterogeneity. To investigate the underlying biology, we conducted single-cell RNA-sequencing ...(scRNA-seq) of >1500 cells from six primary TNBC. Here, we show that intercellular heterogeneity of gene expression programs within each tumor is variable and largely correlates with clonality of inferred genomic copy number changes, suggesting that genotype drives the gene expression phenotype of individual subpopulations. Clustering of gene expression profiles identified distinct subgroups of malignant cells shared by multiple tumors, including a single subpopulation associated with multiple signatures of treatment resistance and metastasis, and characterized functionally by activation of glycosphingolipid metabolism and associated innate immunity pathways. A novel signature defining this subpopulation predicts long-term outcomes for TNBC patients in a large cohort. Collectively, this analysis reveals the functional heterogeneity and its association with genomic evolution in TNBC, and uncovers unanticipated biological principles dictating poor outcomes in this disease.
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