At present, cancers are described in both biological and clinical settings with static models that characterize tumors by the phenotypic and genotypic features observed at a given time point. Although these tumor snapshots proved valuable to predict the short-term response to cancer treatments, mounting evidence supports the idea that the evolutionary properties of cancers should be taken into account to improve the prediction of clinical outcomes. Indeed, cancers are evolutionary processes of populations of cells spiraling through cycles of uncontrolled duplication and acquisition and selection of (epi)genomic alterations. Tumors have different propensities to acquire variable degrees of heterogeneous alterations (intra-tumor heterogeneity), and specific patterns of alteration selection have been observed (conditional selection). In particular, anticancer treatments have been shown to impose selective pressures, calling for a better understanding of cancer evolution. Indeed, although being a stochastic process, the properties of cancer evolution are not unpredictable. Characterizing the evolutionary properties of cancer has significant clinical relevance, such as anticipating mechanisms of resistance to therapy and estimating the likely trajectories of cancer progression.
Historically, the first barrier to the characterization of the evolutionary properties of cancer was the limited amount of collectable data, which was usually based on a single tissue biopsy. There are several factors that changed this paradigm, including the advancement of molecular profiling technologies, the substantial decrease in sequencing costs, and the introduction of multimodal serial sampling in clinical and research settings, such as circulating tumor cells (CTC) and cell-free DNA (cfDNA) in peripheral blood samples. Although these tools enable the study of the evolutionary properties of cancers, to date such technological advancements have not been matched by sufficient advancements on modeling the properties of cancer evolution and on understanding the clinical utility of these processes. Only joint efforts from computational, clinical, and experimental perspectives can lead to an improved understanding of evolutionary properties of cancer.
Hence, in this Research Topic we propose a collection of articles of computational, experimental or translational nature, examining clinically relevant properties of cancer evolution, with particular focus on the following aspects:
? The characterization of evolutionary properties of cancers, such as intratumor heterogeneity and the subclonal structure of tumors. Both systematic computational analyses on big data cohorts and detailed experimental studies of small-scale evolutionary cancer dynamics are of interest.
? Experimental or large-scale systematic characterization of cellular-level mechanisms of alteration emergence and selection, including conditional selection between alterations.
? Development and validation of machine learning methods to predict axes of cancer evolution. This may include the preferential trajectories or acquisition of specific alterations (e.g. the canonical APC -> KRAS progression in colorectal adenocarcinoma).
? Development of machine learning methods to predict evolutionary mechanisms leading to resistance to a given therapy. These predictors should have clinical relevance to help forecast mechanisms of resistance to optimally stratify patients and to better inform clinical decision making.
Topic Editor Marco Mina is employed by Sophia Genetics. All other Topic Editors declare no competing interests with regards to the Research Topic subject.
At present, cancers are described in both biological and clinical settings with static models that characterize tumors by the phenotypic and genotypic features observed at a given time point. Although these tumor snapshots proved valuable to predict the short-term response to cancer treatments, mounting evidence supports the idea that the evolutionary properties of cancers should be taken into account to improve the prediction of clinical outcomes. Indeed, cancers are evolutionary processes of populations of cells spiraling through cycles of uncontrolled duplication and acquisition and selection of (epi)genomic alterations. Tumors have different propensities to acquire variable degrees of heterogeneous alterations (intra-tumor heterogeneity), and specific patterns of alteration selection have been observed (conditional selection). In particular, anticancer treatments have been shown to impose selective pressures, calling for a better understanding of cancer evolution. Indeed, although being a stochastic process, the properties of cancer evolution are not unpredictable. Characterizing the evolutionary properties of cancer has significant clinical relevance, such as anticipating mechanisms of resistance to therapy and estimating the likely trajectories of cancer progression.
Historically, the first barrier to the characterization of the evolutionary properties of cancer was the limited amount of collectable data, which was usually based on a single tissue biopsy. There are several factors that changed this paradigm, including the advancement of molecular profiling technologies, the substantial decrease in sequencing costs, and the introduction of multimodal serial sampling in clinical and research settings, such as circulating tumor cells (CTC) and cell-free DNA (cfDNA) in peripheral blood samples. Although these tools enable the study of the evolutionary properties of cancers, to date such technological advancements have not been matched by sufficient advancements on modeling the properties of cancer evolution and on understanding the clinical utility of these processes. Only joint efforts from computational, clinical, and experimental perspectives can lead to an improved understanding of evolutionary properties of cancer.
Hence, in this Research Topic we propose a collection of articles of computational, experimental or translational nature, examining clinically relevant properties of cancer evolution, with particular focus on the following aspects:
? The characterization of evolutionary properties of cancers, such as intratumor heterogeneity and the subclonal structure of tumors. Both systematic computational analyses on big data cohorts and detailed experimental studies of small-scale evolutionary cancer dynamics are of interest.
? Experimental or large-scale systematic characterization of cellular-level mechanisms of alteration emergence and selection, including conditional selection between alterations.
? Development and validation of machine learning methods to predict axes of cancer evolution. This may include the preferential trajectories or acquisition of specific alterations (e.g. the canonical APC -> KRAS progression in colorectal adenocarcinoma).
? Development of machine learning methods to predict evolutionary mechanisms leading to resistance to a given therapy. These predictors should have clinical relevance to help forecast mechanisms of resistance to optimally stratify patients and to better inform clinical decision making.
Topic Editor Marco Mina is employed by Sophia Genetics. All other Topic Editors declare no competing interests with regards to the Research Topic subject.
