The Somatic Evolution of Cancer

Chapter 18 - Obstacles to the Darwinian Framework of Somatic Cancer Evolution

Despite recent notable progress in medical sciences, cancer remains a leading cause of human death worldwide, accounting for about one-quarter of human deaths in wealthy countries and about one-eighth worldwide (Ferlay et al. 2010). In fact, the progress in cancer research has been slower compared with that achieved related to other pathologies, such as cardiovascular disorders. This is mainly because of the enormous complexity of this disease, which exhibits sophisticated cellular mechanisms that are the targets of evolutionary processes driven by random genetic and epigenetic mutations.

The insulin-like growth factor type 1 receptor and colorectal neoplasia: insights into invasion

In this review, we will discuss how the cell of origin may modulate breast cancer intratumoral heterogeneity (ITH) as well as the role of ITH in the evolution of cancer. The clonal evolution and the cancer stem cell (CSC) models, as well as a model that integrates clonal evolution with a CSC hierarchy, have all been proposed to explain the development of ITH. The extent of ITH correlates with clinical outcome and reflects the cellular complexity and dynamics within a tumor. A unique subtype of breast cancer, the claudin-low subtype that is highly resistant to chemotherapy and most closely resembles mammary epithelial stem cells, will be discussed. Furthermore, we will review how the interactions among various tumor cells, some with distinct mutations, may impact breast cancer treatment. Finally, novel technologies that may help advance our understanding of ITH and lead to improvements in the design of new treatments also will be discussed.

The need for a highly specific system of recognition in immunity has resulted in the evolution of several somatic mechanisms such as V(D)J recombination, to diversify the repertoire of B cells. Therefore, repertoire diversification is the driving force for the cells that constitute the bulk of the response to unpredictable pathogens, the B2 naive B cells. Predictability of antigen, on the other hand, has played a major role in shaping the neonatal repertoire, in which evolution to recognize commonly encountered pathogens has driven the germline sequence of several VH segments that are used frequently in the neonatal repertoire. A third population, the memory B cell population, is generated to respond to a known pathogen, but predictability of the pathogen is not acquired until after a first exposure. Therefore, it is somatic evolution in germinal centers that drives the generation of high-affinity memory B cells.

Recent studies have shown that some silent mutations can be harmful to various processes. In this study, we performed a comprehensive in silico analysis to elucidate the effects of silent mutations on cancer pathogenesis using exome sequencing data derived from the Cancer Genome Atlas. We focused on the codon optimality scores of silent mutations, which were defined as the difference between the optimality of synonymous codons, calculated using the codon usage table. The relationship between cancer evolution and silent mutations showed that the codon optimality score of the mutations that occurred later in carcinogenesis was significantly higher than of those that occurred earlier. In addition, mutations with higher scores were enriched in genes involved in the cell cycle and cell division, while those with lower scores were enriched in genes involved in apoptosis and cellular senescence. Our results demonstrate that some silent mutations can be involved in cancer pathogenesis.

Two-phased evolution: Genome chaos-mediated information creation and maintenance

Somatic evolution of cancer involves a series of mutations, and attendant changes, in one or more clones of cells. A “bad luck” type model assumes chance accumulation of mutations. The clonal expansion model assumes, on the other hand, that any mutation leading to partial loss of regulation of cell proliferation will give a selective advantage to the mutant. However, a number of experiments show that an intermediate pre-cancer mutant has only a conditional selective advantage. Given that tissue microenvironmental conditions differ across individuals, this selective advantage to a mutant could be widely distributed over the population. We evaluate three models, namely “bad luck”, context-independent, and context-dependent selection, in a comparative framework, on their ability to predict patterns in total incidence, age-specific incidence, stem cell number-incidence relationship and other known phenomena associated with cancers. Results show that among the factors considered in the model, context dependence is necessary and sufficient to explain observed epidemiological patterns, and that cancer evolution is largely selection-limited, rather than mutation-limited. A wide range of physiological, genetic and behavioural factors influence the tissue micro-environment, and could therefore be the source of this context dependence in somatic evolution of cancer. The identification and targeting of these micro-environmental factors that influence the dynamics of selection offer new possibilities for cancer prevention.

The distribution of fitness effects (DFE) defines how new mutations spread through an evolving population. The ratio of non-synonymous to synonymous mutations (dN/dS) has become a popular method to detect selection in somatic cells. However the link, in somatic evolution, between dN/dS values and fitness coefficients is missing. Here we present a quantitative model of somatic evolutionary dynamics that determines the selective coefficients of individual driver mutations from dN/dS estimates. We then measure the DFE for somatic mutant clones in ostensibly normal oesophagus and skin. We reveal a broad distribution of fitness effects, with the largest fitness increases found for TP53 and NOTCH1 mutants (proliferative bias 1-5%). This study provides the theoretical link between dN/dS values and selective coefficients in somatic evolution, and measures the DFE of mutations in human tissues.

Cancer evolution: Special focus on the immune aspect of cancer

Cancer cells evolve throughout disease progression and tumor relapse. Such evolution is a dynamic process resulting in genotypic and phenotypic cellular changes, conferring a high level of cell plasticity. Data derived from next-generation sequencing strategies have implicated that cancer cell plasticity could be driven by genetic changes induced by genomic instability during cancer evolution. Understanding the mechanisms of how genomic instability promotes mutagenesis and cancer cell plasticity could thus be critical avenue for cancer prevention and intervention. In this review, we discuss the relationships between cancer cell plasticity, genomic instability and mutagenesis during cancer evolution. We offer our insight and opinion on therapeutic strategies in this rapidly progressing research field.

Background: There is a lack of effective predictive biomarkers to efficiently assess the optimal treatment for each patient and overcome resistance. When effective death signaling is initiated by a targeted therapy, an increase in mitochondrial apoptotic sensitivity (or ‘priming for death’) can be observed within hours with Dynamic BH3 Profiling (DBP). Most chemotherapeutic agents kill via the mitochondrial pathway of apoptosis, but unfortunately tumors frequently are able to survive adapting their antiapoptotic strategy. By using different BH3 peptides we can determine the tumor's defense adaptation over time and find novel ways to rationally develop combination therapies and restore apoptotic cell death. Hypothesis: Analyzing the tumor's antipoptotic defense over time with DBP we can predict how the tumor will adapt to the first-line therapy and restore cell death using BH3 mimetics. Results: Our first set of experiments consisted in characterizing the tumor's adaptation to therapy over time. We performed DBP over time using different BH3 peptides: BIM, BAD, MS1, PUMA and NOXA. In GIST and melanoma cell lines, we observed that when they were effectively treated with imatinib (KIT inhibitor) and dabrafenib (BRAFV600E inhibitor) respectively, or to selumetinib (MEK inhibitor) in both cases, there was an increased dependence in MCL-1 between 24 and 48 hours. This observation explains a possible mechanism by which tumors adapt their antiapoptotic dependencies. Based on these observations, we exploit these altered dependencies by pretreating the cancer cells with the above mentioned targeted therapies and then add a specific MCL-1 inhibitor to restore cell death. Other cell lines, including breast cancer and melanoma, were subsequently studied, and the combination of specific targeted therapies with BH3 mimetics (ABT-199, ABT-263 and MCL-1 inhibitors) was also assessed. Exploiting DBP's capacity to measure changes in priming without the requirement for prolonged ex vivo culture, we assessed if this antipoptotic evolution was also present in primary melanoma samples. We tested combinations of targeted therapies with BH3 mimetics to incorporate antiapoptotic targets to cancer treatment. Conclusions: Our cell line and clinical experiments demonstrate the potential for Dynamic BH3 profiling to be used as a powerful real-time tool to predict chemotherapy response and evaluate induced dependencies in relapsed patients. By studying combinations of targeted agents with BH3 mimetics we aim to improve cancer treatment. Citation Format: Joan Montero, Dorota E. Sadowicz, Rizwan Haq*, Anthony Letai*. Antiapoptotic defense evolution in cancer: novel therapeutic strategies to restore cell death using dynamic BH3 profiling. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr C6.

The evolution of cancer is a Darwinian process, the composition of cancer cell population varying with time, especially after chemotherapy. Such changes occurring in the genetic landscape of tumours are possibly responsible for the chemotherapeutic resistance. Due to the recent advances in single cellular sequencing, the evolution of cancers can be monitored at the single cellular resolution. We reanalysed a previously published dataset composed of single cell RNAseq data, collected before, during and after the establishment of Paclitaxel tolerance, to identify single cell heterogeneity. We used gene expression analysis, gene expression rank correlation analysis, pathway analysis and mutation analysis to identify within group variations of cancer cells after chemotherapy. We identified a decrease in cancer cell within group diversity during the transition to drug-tolerance. Additionally, we observed high mutation rate in stressed single cells, which suggests genetic instability of cancer cells that could ultimately result in the development of drug resistance. Our analysis carries significant implications for developing personalised and efficient therapeutics against cancer.

Exploring the changes in the polarity of intracellular lipid droplets (LDs) during the evolution of cancer is important for cancer detection and treatment.

Multicellularity rests on a social contract: cells give up autonomy in exchange for shared resources, division of labour, and protection. Cancer is what happens when that contract collapses, and somatic evolution runs loose inside the body. Here, the multicellular social contract is used as a framework to recast tumour suppressors, tissue architecture, and immune surveillance as enforcement devices, and cancer as a ‘rogue society’ where cheater lineages exploit public goods, remodel niches, and build hierarchies. This perspective links clonal evolution, phenotypic plasticity, and therapy resistance to the ‘politics’ of rules and enforcement, and points towards therapies that do not just kill malignant cells but actively reshape the conditions under which somatic evolution proceeds.