Big Bang Tumor Growth and Clonal Evolution

A glycolytic metabolite bypasses "two-hit" tumor suppression by BRCA2

Knudson's "two-hit" paradigm posits that carcinogenesis requires inactivation of both copies of an autosomal tumor suppressor gene. Here, we report that the glycolytic metabolite methylglyoxal (MGO) transiently bypasses Knudson's paradigm by inactivating the breast cancer suppressor protein BRCA2 to elicit a cancer-associated, mutational single-base substitution (SBS) signature in nonmalignant mammary cells or patient-derived organoids. Germline monoallelic BRCA2 mutations predispose to these changes. An analogous SBS signature, again without biallelic BRCA2 inactivation, accompanies MGO accumulation and DNA damage in Kras-driven, Brca2-mutant murine pancreatic cancers and human breast cancers. MGO triggers BRCA2 proteolysis, temporarily disabling BRCA2's tumor suppressive functions in DNA repair and replication, causing functional haploinsufficiency. Intermittent MGO exposure incites episodic SBS mutations without permanent BRCA2 inactivation. Thus, a metabolic mechanism wherein MGO-induced BRCA2 haploinsufficiency transiently bypasses Knudson's two-hit requirement could link glycolysis activation by oncogenes, metabolic disorders, or dietary challenges to mutational signatures implicated in cancer evolution.

Colorectal cancer murine models: Initiation to metastasis

Despite significant advancements in prevention and treatment, colorectal cancer (CRC) remains the third leading cause of cancer-related deaths. Animal models, including xenografts, syngeneic, and genetically engineered, have emerged as indispensable tools in cancer research. These models offer a valuable platform to address critical questions regarding molecular pathogenesis and test therapeutic interventions before moving on to clinical trials. Advancements in CRC animal models have also facilitated the advent of personalized and precision medicine. Patient-derived xenografts and genetically engineered mice that mirror features of human tumors allow for tailoring treatments to specific CRC subtypes, improving treatment outcomes and quality of life. To overcome the limitations of individual model systems, recent studies have employed a multi-modal approach, combining different animal models, 3D organoids, and in vitro studies. This integrative approach provides a comprehensive understanding of CRC biology, including the tumor microenvironment and therapeutic responses, driving the development of more effective and personalized therapeutic interventions. This review discusses the animal models used for CRC research, including recent advancements and limitations of these animal models.

Global lineage evolution pattern of sars-cov-2 in Africa, America, Europe, and Asia: A comparative analysis of variant clusters and their relevance across continents

Objective

The objective of this study is to provide a comparative analysis of variant clusters and their relevance across Africa, America, Europe, and Asia, in order to understand the evolutionary patterns of the virus across different regions and to inform the development of targeted interventions and genomic surveillance eforts.

Methods

The study analyzed the global lineage evolution pattern of 74, 075 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes from 32 countries across four continents, focusing on variant clusters and their relevance across regions. Variants were weighted according to their hierarchical level. The correlation between variants was visualized through Dimensionality reduction analysis and Pairwise Pearson's correlation. We presented a reconstructed phylogenetic tree based on correlation analysis and variant weights.

Results

The analysis revealed that each continent had distinct variant clusters and different evolutionary patterns. The Americas had two clustered variants before lineage divergence and a downstream confluence lineage, Europe had bifurcation into two global lineages with an early occurrence of certain cluster while Asia had a downstream confluence of two large lineages diverging by two distinct clusters. Based on the cluster patterns of shared variants of the SARS-CoV-2 virus, Africa demonstrated a relatively clear distinction among three distinct regions.

Conclusions

The study provides insights into the evolutionary patterns of SARS-CoV-2 and highlights the importance of international collaboration in tracking and responding to emerging variants. The study found that the global pandemic was driven by Omicron variants that evolved with significant differences between countries and regions, and with different patterns across continents.

Unravelling the Complexity of Colorectal Cancer: Heterogeneity, Clonal Evolution, and Clinical Implications

Colorectal cancer (CRC) is a global health concern and a leading cause of death worldwide. The disease's course and response to treatment are significantly influenced by its heterogeneity, both within a single lesion and between primary and metastatic sites. Biomarkers, such as mutations in KRAS, NRAS, and BRAF, provide valuable guidance for treatment decisions in patients with metastatic CRC. While high concordance exists between mutational status in primary and metastatic lesions, some heterogeneity may be present. Circulating tumor DNA (ctDNA) analysis has proven invaluable in identifying genetic heterogeneity and predicting prognosis in RAS-mutated metastatic CRC patients. Tumor heterogeneity can arise from genetic and non-genetic factors, affecting tumor development and response to therapy. To comprehend and address clonal evolution and intratumoral heterogeneity, comprehensive genomic studies employing techniques such as next-generation sequencing and computational analysis are essential. Liquid biopsy, notably through analysis of ctDNA, enables real-time clonal evolution and treatment response monitoring. However, challenges remain in standardizing procedures and accurately characterizing tumor subpopulations. Various models elucidate the origin of CRC heterogeneity, highlighting the intricate molecular pathways involved. This review focuses on intrapatient cancer heterogeneity and genetic clonal evolution in metastatic CRC, with an emphasis on clinical applications.

The Concept of Cancer Stem Cells: Elaborating on ALDH1B1 as an Emerging Marker of Cancer Progression

Cancer is a multifactorial, complex disease exhibiting extraordinary phenotypic plasticity and diversity. One of the greatest challenges in cancer treatment is intratumoral heterogeneity, which obstructs the efficient eradication of the tumor. Tumor heterogeneity is often associated with the presence of cancer stem cells (CSCs), a cancer cell sub-population possessing a panel of stem-like properties, such as a self-renewal ability and multipotency potential. CSCs are associated with enhanced chemoresistance due to the enhanced efflux of chemotherapeutic agents and the existence of powerful antioxidant and DNA damage repair mechanisms. The distinctive characteristics of CSCs make them ideal targets for clinical therapeutic approaches, and the identification of efficient and specific CSCs biomarkers is of utmost importance. Aldehyde dehydrogenases (ALDHs) comprise a wide superfamily of metabolic enzymes that, over the last years, have gained increasing attention due to their association with stem-related features in a wide panel of hematopoietic malignancies and solid cancers. Aldehyde dehydrogenase 1B1 (ALDH1B1) is an isoform that has been characterized as a marker of colon cancer progression, while various studies suggest its importance in additional malignancies. Here, we review the basic concepts related to CSCs and discuss the potential role of ALDH1B1 in cancer development and its contribution to the CSC phenotype.

Cancer genome and tumor microenvironment: Reciprocal crosstalk shapes lung cancer plasticity

Lung cancer classification and treatment has been revolutionized by improving our understanding of driver mutations and the introduction of tumor microenvironment (TME)-associated immune checkpoint inhibitors. Despite the significant improvement of lung cancer patient survival in response to either oncogene-targeted therapy or anticancer immunotherapy, many patients show initial or acquired resistance to these new therapies. Recent advances in genome sequencing reveal that specific driver mutations favor the development of an immunosuppressive TME phenotype, which may result in unfavorable outcomes in lung cancer patients receiving immunotherapies. Clinical studies with follow-up after immunotherapy, assessing oncogenic driver mutations and the TME immune profile, not only reveal the underlying potential molecular mechanisms in the resistant lung cancer patients but also hold the key to better treatment choices and the future of personalized medicine. In this review, we discuss the crosstalk between cancer cell genomic features and the TME to reveal the impact of genetic alterations on the TME phenotype. We also provide insights into the regulatory role of cellular TME components in defining the genetic landscape of cancer cells during tumor development.

Unravelling the tumour genome: The evolutionary and clinical impacts of structural variants in tumourigenesis

Structural variants (SVs) represent a major source of aberration in tumour genomes. Given the diversity in the size and type of SVs present in tumours, the accurate detection and interpretation of SVs in tumours is challenging. New classes of complex structural events in tumours are discovered frequently, and the definitions of the genomic consequences of complex events are constantly being refined. Detailed analyses of short-read whole-genome sequencing (WGS) data from large tumour cohorts facilitate the interrogation of SVs at orders of magnitude greater scale and depth. However, the inherent technical limitations of short-read WGS prevent us from accurately detecting and investigating the impact of all the SVs present in tumours. The expanded use of long-read WGS will be critical for improving the accuracy of SV detection, and in fully resolving complex SV events, both of which are crucial for determining the impact of SVs on tumour progression and clinical outcome. Despite the present limitations, we demonstrate that SVs play an important role in tumourigenesis. In particular, SVs contribute significantly to late-stage tumour development and to intratumoural heterogeneity. The evolutionary trajectories of SVs represent a window into the clonal dynamics in tumours, a comprehensive understanding of which will be vital for influencing patient outcomes in the future. Recent findings have highlighted many clinical applications of SVs in cancer, from early detection to biomarkers for treatment response and prognosis. As the methods to detect and interpret SVs improve, elucidating the full breadth of the complex SV landscape and determining how these events modulate tumour evolution will improve our understanding of cancer biology and our ability to capitalise on the utility of SVs in the clinical management of cancer patients. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Spatial structure governs the mode of tumour evolution

Characterizing the mode-the way, manner or pattern-of evolution in tumours is important for clinical forecasting and optimizing cancer treatment. Sequencing studies have inferred various modes, including branching, punctuated and neutral evolution, but it is unclear why a particular pattern predominates in any given tumour. Here we propose that tumour architecture is key to explaining the variety of observed genetic patterns. We examine this hypothesis using spatially explicit population genetics models and demonstrate that, within biologically relevant parameter ranges, different spatial structures can generate four tumour evolutionary modes: rapid clonal expansion, progressive diversification, branching evolution and effectively almost neutral evolution. Quantitative indices for describing and classifying these evolutionary modes are presented. Using these indices, we show that our model predictions are consistent with empirical observations for cancer types with corresponding spatial structures. The manner of cell dispersal and the range of cell-cell interactions are found to be essential factors in accurately characterizing, forecasting and controlling tumour evolution.

The breast pre-cancer atlas illustrates the molecular and micro-environmental diversity of ductal carcinoma in situ

Microenvironmental and molecular factors mediating the progression of Breast Ductal Carcinoma In Situ (DCIS) are not well understood, impeding the development of prevention strategies and the safe testing of treatment de-escalation. We addressed methodological barriers and characterized the mutational, transcriptional, histological, and microenvironmental landscape across 85 multiple microdissected regions from 39 cases. Most somatic alterations, including whole-genome duplications, were clonal, but genetic divergence increased with physical distance. Phenotypic and subtype heterogeneity was frequently associated with underlying genetic heterogeneity and regions with low-risk features preceded those with high-risk features according to the inferred phylogeny. B- and T-lymphocytes spatial analysis identified three immune states, including an epithelial excluded state located preferentially at DCIS regions, and characterized by histological and molecular features of immune escape, independently from molecular subtypes. Such breast pre-cancer atlas with uniquely integrated observations will help scope future expansion studies and build finer models of outcomes and progression risk.

Alterations of Ion Homeostasis in Cancer Metastasis: Implications for Treatment

We have previously reported that metastases from all malignancies are characterized by a core program of gene expression that suppresses extracellular matrix interactions, induces vascularization/tissue remodeling, activates the oxidative metabolism, and alters ion homeostasis. Among these features, the least elucidated component is ion homeostasis. Here we review the literature with the goal to infer a better mechanistic understanding of the progression-associated ionic alterations and identify the most promising drugs for treatment. Cancer metastasis is accompanied by skewing in calcium, zinc, copper, potassium, sodium and chloride homeostasis. Membrane potential changes and water uptake through Aquaporins may also play roles. Drug candidates to reverse these alterations are at various stages of testing, with some having entered clinical trials. Challenges to their utilization comprise differences among tumor types and the involvement of multiple ions in each case. Further, adverse effects may become a concern, as channel blockers, chelators, or supplemented ions will affect healthy and transformed cells alike.

Biomarkers of Response to Neoadjuvant Androgen Deprivation in Localised Prostate Cancer

Simple Summary

Prostate cancer is the second leading cause of cancer deaths in men. Attempts to improve patient outcomes include trials of neoadjuvant androgen deprivation therapy for patients with high-risk disease. Neoadjuvant treatment refers to androgen deprivation therapy that is administered prior to surgery (or radiation therapy). Patients typically respond well to this treatment regimen, showing a decrease in tumour size, but a significant proportion of patients eventually relapse and progress to metastatic disease. The mechanisms driving this resistance to neoadjuvant treatment are currently unknown. This review explores theories of resistance broadly, and their possible applications in the prostate cancer setting. Additionally, this review draws comparisons between breakthrough resistance and neoadjuvant resistance, and lastly investigates the current biomarkers for treatment sensitivity.

Abstract

Prostate cancer (PCa) is a hormone driven cancer, characterised by defects in androgen receptor signalling which drive the disease process. As such, androgen targeted therapies have been the mainstay for PCa treatment for over 70 years. High-risk PCa presents unique therapeutic challenges, namely in minimising the primary tumour, and eliminating any undetected micro metastases. Trials of neoadjuvant androgen deprivation therapy aim to address these challenges. Patients typically respond well to neoadjuvant treatment, showing regression of the primary tumour and negative surgical margins at the time of resection, however the majority of patients relapse and progress to metastatic disease. The mechanisms affording this resistance are largely unknown. This commentary attempts to explore theories of resistance more broadly, namely, clonal evolution, cancer stem cells, cell persistence, and drug tolerance. Moreover, it aims to explore the application of these theories in the PCa setting. This commentary also highlights the distinction between castration resistant PCa, and neoadjuvant resistant disease, and identifies the markers and characteristics of neoadjuvant resistant disease presented by current literature.

Clone decomposition based on mutation signatures provides novel insights into mutational processes

Intra-tumor heterogeneity is a phenomenon in which mutation profiles differ from cell to cell within the same tumor and is observed in almost all tumors. Understanding intra-tumor heterogeneity is essential from the clinical perspective. Numerous methods have been developed to predict this phenomenon based on variant allele frequency. Among the methods, CloneSig models the variant allele frequency and mutation signatures simultaneously and provides an accurate clone decomposition. However, this method has limitations in terms of clone number selection and modeling. We propose SigTracer, a novel hierarchical Bayesian approach for analyzing intra-tumor heterogeneity based on mutation signatures to tackle these issues. We show that SigTracer predicts more reasonable clone decompositions than the existing methods against artificial data that mimic cancer genomes. We applied SigTracer to whole-genome sequences of blood cancer samples. The results were consistent with past findings that single base substitutions caused by a specific signature (previously reported as SBS9) related to the activation-induced cytidine deaminase intensively lie within immunoglobulin-coding regions for chronic lymphocytic leukemia samples. Furthermore, we showed that this signature mutates regions responsible for cell-cell adhesion. Accurate assignments of mutations to signatures by SigTracer can provide novel insights into signature origins and mutational processes.

The evolution and ecology of benign tumors

Tumors are usually classified into two main categories - benign or malignant, with much more attention being devoted to the second category given that they are usually associated with more severe health issues (i.e., metastatic cancers). Here, we argue that the mechanistic distinction between benign and malignant tumors has narrowed our understanding of neoplastic processes. This review provides the first comprehensive discussion of benign tumors in the context of their evolution and ecology as well as interactions with their hosts. We compare the genetic and epigenetic profiles, cellular activities, and the involvement of viruses in benign and malignant tumors. We also address the impact of intra-tumoral cell composition and its relationship with the tumoral microenvironment. Lastly, we explore the differences in the distribution of benign and malignant neoplasia across the tree of life and provide examples on how benign tumors can also affect individual fitness and consequently the evolutionary trajectories of populations and species. Overall, our goal is to bring attention to the non-cancerous manifestations of tumors, at different scales, and to stimulate research on the evolutionary ecology of host-tumor interactions on a broader scale. Ultimately, we suggest that a better appreciation of the differences and similarities between benign and malignant tumors is fundamental to our understanding of malignancy both at mechanistic and evolutionary levels.

Reconstructing single-cell karyotype alterations in colorectal cancer identifies punctuated and gradual diversification patterns

Central to tumor evolution is the generation of genetic diversity. However, the extent and patterns by which de novo karyotype alterations emerge and propagate within human tumors are not well understood, especially at single-cell resolution. Here, we present 3D Live-Seq-a protocol that integrates live-cell imaging of tumor organoid outgrowth and whole-genome sequencing of each imaged cell to reconstruct evolving tumor cell karyotypes across consecutive cell generations. Using patient-derived colorectal cancer organoids and fresh tumor biopsies, we demonstrate that karyotype alterations of varying complexity are prevalent and can arise within a few cell generations. Sub-chromosomal acentric fragments were prone to replication and collective missegregation across consecutive cell divisions. In contrast, gross genome-wide karyotype alterations were generated in a single erroneous cell division, providing support that aneuploid tumor genomes can evolve via punctuated evolution. Mapping the temporal dynamics and patterns of karyotype diversification in cancer enables reconstructions of evolutionary paths to malignant fitness.

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

Cancer is traditionally labeled a "cellular growth problem." However, it is fundamentally an issue of macroevolution where new systems emerge from tissue by breaking various constraints. To study this process, we used experimental platforms to "watch evolution in action" by comparing the profiles of karyotypes, transcriptomes, and cellular phenotypes longitudinally before, during, and after key phase transitions. This effort, alongside critical rethinking of current gene-based genomic and evolutionary theory, led to the development of the Genome Architecture Theory. Following a brief historical review, we present four case studies and their takeaways to describe the pattern of genome-based cancer evolution. Our discoveries include 1. The importance of non-clonal chromosome aberrations or NCCAs; 2. Two-phased cancer evolution, comprising a punctuated phase and a gradual phase, dominated by karyotype changes and gene mutation/epigenetic alterations, respectively; 3. How the karyotype codes system inheritance, which organizes gene interactions and provides the genomic basis for physiological regulatory networks; and 4. Stress-induced genome chaos, which creates genomic information by reorganizing chromosomes for macroevolution. Together, these case studies redefine the relationship between cellular macro- and microevolution: macroevolution does not equal microevolution + time. Furthermore, we incorporate genome chaos and gene mutation in a general model: genome reorganization creates new karyotype coding, then diverse cancer gene mutations can promote the dominance of tumor cell populations. Finally, we call for validation of the Genome Architecture Theory of cancer and organismal evolution, as well as the systematic study of genomic information flow in evolutionary processes.

To portray clonal evolution in blood cancer, count your stem cells

Clonal evolution, the process of expansion and diversification of mutated cells, plays an important role in cancer development, resistance, and relapse. Although clonal evolution is most often conceived of as driven by natural selection, recent studies uncovered that neutral evolution shapes clonal evolution in a significant proportion of solid cancers. In hematological malignancies, the interplay between neutral evolution and natural selection is also disputed. Because natural selection selects cells with a greater fitness, providing a growth advantage to some cells relative to others, the architecture of clonal evolution serves as indirect evidence to distinguish natural selection from neutral evolution and has been associated with different prognoses for the patient. Linear architecture, when the new mutant clone grows within the previous one, is characteristic of hematological malignancies and is typically interpreted as being driven by natural selection. Here, we discuss the role of natural selection and neutral evolution in the production of linear clonal architectures in hematological malignancies. Although it is tempting to attribute linear evolution to natural selection, we argue that a lower number of contributing stem cells accompanied by genetic drift can also result in a linear pattern of evolution, as illustrated by simulations of clonal evolution in hematopoietic stem cells. The number of stem cells contributing to long-term clonal evolution is not known in the pathological context, and we advocate that estimating these numbers in the context of cancer and aging is crucial to parsing out neutral evolution from natural selection, 2 processes that require different therapeutic strategies.

Heterogeneity in Signaling Pathway Activity within Primary and between Primary and Metastatic Breast Cancer

Targeted therapy aims to block tumor-driving signaling pathways and is generally based on analysis of one primary tumor (PT) biopsy. Tumor heterogeneity within PT and between PT and metastatic breast lesions may, however, impact the effect of a chosen therapy. Whereas studies are available that investigate genetic heterogeneity, we present results on phenotypic heterogeneity by analyzing the variation in the functional activity of signal transduction pathways, using an earlier developed platform to measure such activity from mRNA measurements of pathways' direct target genes. Statistical analysis comparing macro-scale variation in pathway activity on up to five spatially distributed PT tissue blocks (n = 35), to micro-scale variation in activity on four adjacent samples of a single PT tissue block (n = 17), showed that macro-scale variation was not larger than micro-scale variation, except possibly for the PI3K pathway. Simulations using a "checkerboard clone-size" model showed that multiple small clones could explain the higher micro-scale variation in activity found for the TGFβ and Hedgehog pathways, and that intermediate/large clones could explain the possibly higher macro-scale variation of the PI3K pathway. While within PT, pathway activities presented a highly positive correlation, correlations weakened between PT and lymph node metastases (n = 9), becoming even worse for PT and distant metastases (n = 9), including a negative correlation for the ER pathway. While analysis of multiple sub-samples of a single biopsy may be sufficient to predict PT response to targeted therapies, metastatic breast cancer treatment prediction requires analysis of metastatic biopsies. Our findings on phenotypic intra-tumor heterogeneity are compatible with emerging ideas on a Big Bang type of cancer evolution in which macro-scale heterogeneity appears not dominant.

Elements and evolutionary determinants of genomic divergence between paired primary and metastatic tumors

Can metastatic-primary (M-P) genomic divergence measured from next generation sequencing reveal the natural history of metastatic dissemination? This remains an open question of utmost importance in facilitating a deeper understanding of metastatic progression, and thereby, improving its prevention. Here, we utilize mathematical and computational modeling to tackle this question as well as to provide a framework that illuminates the fundamental elements and evolutionary determinants of M-P divergence. Our framework facilitates the integration of sequencing detectability of somatic variants, and hence, paves the way towards bridging the measurable between-tumor heterogeneity with analytical modeling and interpretability. We show that the number of somatic variants of the metastatic seeding cell that are experimentally undetectable in the primary tumor, can be characterized as the path of the phylogenetic tree from the last appearing variant of the seeding cell back to the most recent detectable variant. We find that the expected length of this path is principally determined by the decay in detectability of the variants along the seeding cell's lineage; and thus, exhibits a significant dependence on the underlying tumor growth dynamics. A striking implication of this fact, is that dissemination from an advanced detectable subclone of the primary tumor can lead to an abrupt drop in the expected measurable M-P divergence, thereby breaking the previously assumed monotonic relation between seeding time and M-P divergence. This is emphatically verified by our single cell-based spatial tumor growth simulation, where we find that M-P divergence exhibits a non-monotonic relationship with seeding time when the primary tumor grows under branched and linear evolution. On the other hand, a monotonic relationship holds when we condition on the dynamics of progressive diversification, or by restricting the seeding cells to always originate from undetectable subclones. Our results highlight the fact that a precise understanding of tumor growth dynamics is the sine qua non for exploiting M-P divergence to reconstruct the chronology of metastatic dissemination. The quantitative models presented here enable further careful evaluation of M-P divergence in association with crucial evolutionary and sequencing parameters.

RNA Based Approaches to Profile Oncogenic Pathways From Low Quantity Samples to Drive Precision Oncology Strategies

Precision treatment of cancer requires knowledge on active tumor driving signal transduction pathways to select the optimal effective targeted treatment. Currently only a subset of patients derive clinical benefit from mutation based targeted treatment, due to intrinsic and acquired drug resistance mechanisms. Phenotypic assays to identify the tumor driving pathway based on protein analysis are difficult to multiplex on routine pathology samples. In contrast, the transcriptome contains information on signaling pathway activity and can complement genomic analyses. Here we present the validation and clinical application of a new knowledge-based mRNA-based diagnostic assay platform (OncoSignal) for measuring activity of relevant signaling pathways simultaneously and quantitatively with high resolution in tissue samples and circulating tumor cells, specifically with very small specimen quantities. The approach uses mRNA levels of a pathway's direct target genes, selected based on literature for multiple proof points, and used as evidence that a pathway is functionally activated. Using these validated target genes, a Bayesian network model has been built and calibrated on mRNA measurements of samples with known pathway status, which is used next to calculate a pathway activity score on individual test samples. Translation to RT-qPCR assays enables broad clinical diagnostic applications, including small analytes. A large number of cancer samples have been analyzed across a variety of cancer histologies and benchmarked across normal controls. Assays have been used to characterize cell types in the cancer cell microenvironment, including immune cells in which activated and immunotolerant states can be distinguished. Results support the expectation that the assays provide information on cancer driving signaling pathways which is difficult to derive from next generation DNA sequencing analysis. Current clinical oncology applications have been complementary to genomic mutation analysis to improve precision medicine: (1) prediction of response and resistance to various therapies, especially targeted therapy and immunotherapy; (2) assessment and monitoring of therapy efficacy; (3) prediction of invasive cancer cell behavior and prognosis; (4) measurement of circulating tumor cells. Preclinical oncology applications lie in a better understanding of cancer behavior across cancer types, and in development of a pathophysiology-based cancer classification for development of novel therapies and precision medicine.

A High-Dimensional Window into the Micro-Environment of Triple Negative Breast Cancer

Providing effective personalized immunotherapy for triple negative breast cancer (TNBC) patients requires a detailed understanding of the composition of the tumor microenvironment. Both the tumor cell and non-tumor components of TNBC can exhibit tremendous heterogeneity in individual patients and change over time. Delineating cellular phenotypes and spatial topographies associated with distinct immunological states and the impact of chemotherapy will be necessary to optimally time immunotherapy. The clinical successes in immunotherapy have intensified research on the tumor microenvironment, aided by a plethora of high-dimensional technologies to define cellular phenotypes. These high-dimensional technologies include, but are not limited to, single cell RNA sequencing, spatial transcriptomics, T cell repertoire analyses, advanced flow cytometry, imaging mass cytometry, and their integration. In this review, we discuss the cellular phenotypes and spatial patterns of the lymphoid-, myeloid-, and stromal cells in the TNBC microenvironment and the potential value of mapping these features onto tumor cell genotypes.

Genotype-phenotype associations in colorectal adenocarcinomas and their matched metastases

Although primary colorectal carcinomas (CRCs) frequently share genetic alterations with their metastases, morphologic surrogates reflecting the genotype contexture of metastases remain largely unknown. We investigated phenotype/genotype associations in paired primary and metastatic colorectal adenocarcinomas from 75 patients. Thirty-three (44%) metastatic lesions were synchronous and 42 (56%) were metachronous. Tumor budding, micronecrosis, and tumor-infiltrating lymphocyte (TIL) density were compared with matched next-generation sequencing genotypes. Micronecrosis in the primary were significantly associated with nodal status (P = 0.0054) and with micronecrosis in metastatic sites (P = 0.0216), particularly in metachronous metastases (P = 0.0033). With a 57-gene panel, one or more mutations were identified in 64 (85.3%) cases. In metastases, high (brisk) TILs were associated with overall mutational burden (P = 0.0058) and with mutations in EGF (P = 0.0325), RAS genes (P = 0.0043), and MMR genes (P = 0.0069), whereas high-level micronecrosis correlated with mutations in APC (P = 0.0004) and MSH6 (P = 0.0385) genes. Genomic alterations were shared in 90.1% of primary/metastatic pairs, but clonality of the same mutation was shared in only 57.1% of paired lesions. Compared with synchronous, metachronous metastases had more private clonal alterations (P = 0.0291); in this group, clonal alterations coincided with brisk TILs (P = 0.0334) and high micronecrosis (P = 0.0133). High TILs in metastatic lesions were predictive of favorable overall survival (log-rank P = 0.044). The observed phenotype/genotype associations favor the clonal evolution model in CRC metastases that seems accompanied by intense host immune response. If the role of micronecrosis and brisk TILs in metachronous metastases is validated in larger studies, these histologic parameters will be worth adding in the armamentarium for the evaluation of metastatic CRC.

Origins and Consequences of Chromosomal Instability: From Cellular Adaptation to Genome Chaos-Mediated System Survival

When discussing chromosomal instability, most of the literature focuses on the characterization of individual molecular mechanisms. These studies search for genomic and environmental causes and consequences of chromosomal instability in cancer, aiming to identify key triggering factors useful to control chromosomal instability and apply this knowledge in the clinic. Since cancer is a phenomenon of new system emergence from normal tissue driven by somatic evolution, such studies should be done in the context of new genome system emergence during evolution. In this perspective, both the origin and key outcome of chromosomal instability are examined using the genome theory of cancer evolution. Specifically, chromosomal instability was linked to a spectrum of genomic and non-genomic variants, from epigenetic alterations to drastic genome chaos. These highly diverse factors were then unified by the evolutionary mechanism of cancer. Following identification of the hidden link between cellular adaptation (positive and essential) and its trade-off (unavoidable and negative) of chromosomal instability, why chromosomal instability is the main player in the macro-cellular evolution of cancer is briefly discussed. Finally, new research directions are suggested, including searching for a common mechanism of evolutionary phase transition, establishing chromosomal instability as an evolutionary biomarker, validating the new two-phase evolutionary model of cancer, and applying such a model to improve clinical outcomes and to understand the genome-defined mechanism of organismal evolution.

The special issue on cancer and evolution: Lessons learned

This special issue of evolutionary applications focused on the evolution of cancer has provided a wealth of different viewpoints and results from leaders in the field. Together, these papers emphasize the importance of a broad perspective in order to understand why we and other animals get cancer, how it evolves within an individual, and what we can do about it. We can no longer take reductionist approaches that consider only the cancer cells and their genes. Instead, we need to understand how millions of years of evolution have guided strategies that shape cancer risk, why cancer risk varies across different animals, how cancer risk can vary in a population and be influenced by ecology (and influence this ecology), and of course how cancers evolve within us and the evolutionarily informed strategies to counter their impact. My goal here will be to "bring it all home," providing a refresher of lessons learned with added kibitzing.

The three dimensions of somatic evolution: Integrating the role of genetic damage, life-history traits, and aging in carcinogenesis

Tumors result from genetic and epigenetic alterations that change cellular survival and differentiation probabilities, promoting clonal dominance. Subsequent genetic and selection processes in tumors allow cells to lose their tissue fidelity and migrate to other parts of the body, turning tumors into cancer. However, the relationship between genetic damage and cancer is not linear, showing remarkable and sometimes seemingly counterintuitive patterns for different tissues and across animal taxa. In the present paper, we attempt to integrate our understanding of somatic evolution and cancer as a product of three major orthogonal processes: occurrence of somatic mutations, evolution of species-specific life-history traits, and physiological aging. Patterns of cancer risk have been shaped by selective pressures experienced by animal populations over millions of years, influencing and influenced by selection acting on traits ranging from mutation rate to reproductive strategies to longevity. We discuss how evolution of species shapes their cancer profiles alongside and in connection with other evolving life-history traits and how this process explains the patterns of cancer incidence we observe in humans and other animals.

Convergent Evolution, Evolving Evolvability, and the Origins of Lethal Cancer

Advances in curative treatment to remove the primary tumor have increased survival of localized cancers for most solid tumor types, yet cancers that have spread are typically incurable and account for >90% of cancer-related deaths. Metastatic disease remains incurable because, somehow, tumors evolve resistance to all known compounds, including therapies. In all of these incurable patients, de novo lethal cancer evolves capacities for both metastasis and resistance. Therefore, cancers in different patients appear to follow the same eco-evolutionary path that independently manifests in affected patients. This convergent outcome, that always includes the ability to metastasize and exhibit resistance, demands an explanation beyond the slow and steady accrual of stochastic mutations. The common denominator may be that cancer starts as a speciation event when a unicellular protist breaks away from its multicellular host and initiates a cancer clade within the patient. As the cancer cells speciate and diversify further, some evolve the capacity to evolve: evolvability. Evolvability becomes a heritable trait that influences the available variation of other phenotypes that can then be acted upon by natural selection. Evolving evolvability may be an adaptation for cancer cells. By generating and maintaining considerable heritable variation, the cancer clade can, with high certainty, serendipitously produce cells resistant to therapy and cells capable of metastasizing. Understanding that cancer cells can swiftly evolve responses to novel and varied stressors create opportunities for adaptive therapy, double-bind therapies, and extinction therapies; all involving strategic decision making that steers and anticipates the convergent coevolutionary responses of the cancers.

The microcosmos of intratumor heterogeneity: the space-time of cancer evolution

The Cancer Genome Atlas consortium brought us terabytes of information about genetic alterations in different types of human tumors. While many cancer-driver genes have been identified through these efforts, interrogating cancer genomes has also shed new light on tumor complexity. Mutations were found to vary tremendously in their allelic frequencies within the same tumor. Based on those variant allelic frequencies grouping, an estimate of genetically distinct "clones" of cancer cells can be determined for each tumor. It was estimated that 4-8 clones are present in every human tumor. Presence of distinct clones, cells that differ in their genotype and/or phenotype, is one of the roots for the major challenge of effectively curing cancer patients. Any given treatment applied to a heterogeneous mixture of cancer cells will yield distinct responses in different cells and may be ineffective in killing particular clones. Moreover, in highly heterogeneous tumors, stochastically, there is a higher chance of presence of traits, such as point mutations in key receptor tyrosine kinases, that drive drug resistance. Thus, intratumor heterogeneity is like an arsenal, providing a variety of weapons for self-defense against cancer-targeted therapy. However, in this arsenal the supplies are constantly changing, as cancer cells are accumulating new mutations. What is also changing is the battlefield-the tumor microenvironment including all noncancerous cells within the tumor and surrounding tissue, which also contribute to the diversification of cancer's forces. In order to design more effective therapies that would target this ever-changing landscape, we need to learn more about the two elusive variables that shape the tumor ecosystem: the space-how could we exploit the organization of tumor microenvironment? and the time-how could we predict the changes in heterogeneous tumors?

Parameter, noise, and tree topology effects in tumor phylogeny inference

BACKGROUND

Accurate inference of the evolutionary history of a tumor has important implications for understanding and potentially treating the disease. While a number of methods have been proposed to reconstruct the evolutionary history of a tumor from DNA sequencing data, it is not clear how aspects of the sequencing data and tumor itself affect these reconstructions.

METHODS

We investigate when and how well these histories can be reconstructed from multi-sample bulk sequencing data when considering only single nucleotide variants (SNVs). Specifically, we examine the space of all possible tumor phylogenies under the infinite sites assumption (ISA) using several approaches for enumerating phylogenies consistent with the sequencing data.

RESULTS

On noisy simulated data, we find that the ISA is often violated and that low coverage and high noise make it more difficult to identify phylogenies. Additionally, we find that evolutionary trees with branching topologies are easier to reconstruct accurately. We also apply our reconstruction methods to both chronic lymphocytic leukemia and clear cell renal cell carcinoma datasets and confirm that ISA violations are common in practice, especially in lower-coverage sequencing data. Nonetheless, we show that an ISA-based approach can be relaxed to produce high-quality phylogenies.

CONCLUSIONS

Consideration of practical aspects of sequencing data such as coverage or the model of tumor evolution (branching, linear, etc.) is essential to effectively using the output of tumor phylogeny inference methods. Additionally, these factors should be considered in the development of new inference methods.

The Many Facets of Tumor Heterogeneity: Is Metabolism Lagging Behind?

Tumor functional heterogeneity has been recognized for decades, and technological advancements are fueling renewed interest in uncovering the cell-intrinsic and extrinsic factors that influence tumor development and therapeutic response. Intratumoral heterogeneity is now arguably one of the most-studied topics in tumor biology, leading to the discovery of new paradigms and reinterpretation of old ones, as we aim to understand the profound implications that genomic, epigenomic, and functional heterogeneity hold with regard to clinical outcomes. In spite of our improved understanding of the biological complexity of cancer, characterization of tumor metabolic heterogeneity has lagged behind, lost in a century-old controversy debating whether glycolysis or mitochondrial respiration is more influential. But is tumor metabolism really so simple? Here, we review historical and current views of intratumoral heterogeneity, with an emphasis on summarizing the emerging data that begin to illuminate just how vast the spectrum of metabolic strategies a tumor can employ may be, and what this means for how we might interpret other tumor characteristics, such as mutational landscape, contribution of microenvironmental influences, and treatment resistance.

Understanding aneuploidy in cancer through the lens of system inheritance, fuzzy inheritance and emergence of new genome systems

BACKGROUND

In the past 15 years, impressive progress has been made to understand the molecular mechanism behind aneuploidy, largely due to the effort of using various -omics approaches to study model systems (e.g. yeast and mouse models) and patient samples, as well as the new realization that chromosome alteration-mediated genome instability plays the key role in cancer. As the molecular characterization of the causes and effects of aneuploidy progresses, the search for the general mechanism of how aneuploidy contributes to cancer becomes increasingly challenging: since aneuploidy can be linked to diverse molecular pathways (in regards to both cause and effect), the chances of it being cancerous is highly context-dependent, making it more difficult to study than individual molecular mechanisms. When so many genomic and environmental factors can be linked to aneuploidy, and most of them not commonly shared among patients, the practical value of characterizing additional genetic/epigenetic factors contributing to aneuploidy decreases.

RESULTS

Based on the fact that cancer typically represents a complex adaptive system, where there is no linear relationship between lower-level agents (such as each individual gene mutation) and emergent properties (such as cancer phenotypes), we call for a new strategy based on the evolutionary mechanism of aneuploidy in cancer, rather than continuous analysis of various individual molecular mechanisms. To illustrate our viewpoint, we have briefly reviewed both the progress and challenges in this field, suggesting the incorporation of an evolutionary-based mechanism to unify diverse molecular mechanisms. To further clarify this rationale, we will discuss some key concepts of the genome theory of cancer evolution, including system inheritance, fuzzy inheritance, and cancer as a newly emergent cellular system.

CONCLUSION

Illustrating how aneuploidy impacts system inheritance, fuzzy inheritance and the emergence of new systems is of great importance. Such synthesis encourages efforts to apply the principles/approaches of complex adaptive systems to ultimately understand aneuploidy in cancer.