Order Matters: The Order of Somatic Mutations Influences Cancer Evolution

RAS-mutant leukaemia stem cells drive clinical resistance to venetoclax

Cancer driver mutations often show distinct temporal acquisition patterns, but the biological basis for this, if any, remains unknown. RAS mutations occur invariably late in the course of acute myeloid leukaemia, upon progression or relapsed/refractory disease1-6. Here, by using human leukaemogenesis models, we first show that RAS mutations are obligatory late events that need to succeed earlier cooperating mutations. We provide the mechanistic explanation for this in a requirement for mutant RAS to specifically transform committed progenitors of the myelomonocytic lineage (granulocyte-monocyte progenitors) harbouring previously acquired driver mutations, showing that advanced leukaemic clones can originate from a different cell type in the haematopoietic hierarchy than ancestral clones. Furthermore, we demonstrate that RAS-mutant leukaemia stem cells (LSCs) give rise to monocytic disease, as observed frequently in patients with poor responses to treatment with the BCL2 inhibitor venetoclax. We show that this is because RAS-mutant LSCs, in contrast to RAS-wild-type LSCs, have altered BCL2 family gene expression and are resistant to venetoclax, driving clinical resistance and relapse with monocytic features. Our findings demonstrate that a specific genetic driver shapes the non-genetic cellular hierarchy of acute myeloid leukaemia by imposing a specific LSC target cell restriction and critically affects therapeutic outcomes in patients.

RB1 Mutations Induce Smoking-Related Bladder Cancer by Modulating the Cytochrome P450 Pathway

Cigarette smoking causes multiple cancers by directly influencing mutation burden of driver mutations. However, the mechanism between somatic mutation caused by cigarette smoking and bladder tumorigenesis remains elusive. Smoking-related mutation profile of bladder cancer was characterized by The Cancer Genome Atlas cohort. Integraticve OncoGenomics database was utilized to detect the smoking-related driver genes, and its biological mechanism predictions were interpreted based on bulk transcriptome and single-cell transcriptome, as well as cell experiments. Cigarette smoking was associated with an increased tumor mutational burden under 65 years old (p = 0.031), and generated specific mutational signatures in smokers. RB1 was identified as a differentially mutated driver gene between smokers and nonsmokers, and the mutation rate of RB1 increased twofold after smoking (p = 0.008). RB1 mutations and the 4-aminobiphenyl interference could significantly decrease the RB1 expression level and thus promote the proliferation, invasion, and migration ability of bladder cancer cells. Enrichment analysis and real-time quantitative PCR (RT-qPCR) data showed that RB1 mutations inhibited cytochrome P450 pathway by reducing expression levels of UGT1A6 and AKR1C2. In addition, we also observed that the component of immunological cells was regulated by RB1 mutations through the stronger cell-to-cell interactions between epithelial scissor+ cells and immune cells in smokers. This study highlighted that RB1 mutations could drive smoking-related bladder tumorigenesis through inhibiting cytochrome P450 pathway and regulating tumor immune microenvironment.

Orchard: Building large cancer phylogenies using stochastic combinatorial search

Phylogenies depicting the evolutionary history of genetically heterogeneous subpopulations of cells from the same cancer, i.e., cancer phylogenies, offer valuable insights about cancer development and guide treatment strategies. Many methods exist that reconstruct cancer phylogenies using point mutations detected with bulk DNA sequencing. However, these methods become inaccurate when reconstructing phylogenies with more than 30 mutations, or, in some cases, fail to recover a phylogeny altogether. Here, we introduce Orchard, a cancer phylogeny reconstruction algorithm that is fast and accurate using up to 1000 mutations. Orchard samples without replacement from a factorized approximation of the posterior distribution over phylogenies, a novel result derived in this paper. Each factor in this approximate posterior corresponds to a conditional distribution for adding a new mutation to a partially built phylogeny. Orchard optimizes each factor sequentially, generating a sequence of incrementally larger phylogenies that ultimately culminate in a complete tree containing all mutations. Our evaluations demonstrate that Orchard outperforms state-of-the-art cancer phylogeny reconstruction methods in reconstructing more plausible phylogenies across 90 simulated cancers and 14 B-progenitor acute lymphoblastic leukemias (B-ALLs). Remarkably, Orchard accurately reconstructs cancer phylogenies using up to 1,000 mutations. Additionally, we demonstrate that the large and accurate phylogenies reconstructed by Orchard are useful for identifying patterns of somatic mutations and genetic variations among distinct cancer cell subpopulations.

Increased CXCL10 (IP-10) is associated with advanced myeloproliferative neoplasms and its loss dampens erythrocytosis in mouse models

Key studies in pre-leukemic disorders have linked increases in pro-inflammatory cytokines with accelerated phases of the disease, but the precise role of the cellular microenvironment in disease initiation and evolution remains poorly understood. In myeloproliferative neoplasms (MPNs), higher levels of specific cytokines have been previously correlated with increased disease severity (tumor necrosis factor-alpha [TNF-α], interferon gamma-induced protein-10 [IP-10 or CXCL10]) and decreased survival (interleukin 8 [IL-8]). Whereas TNF-α and IL-8 have been studied by numerous groups, there is a relative paucity of studies on IP-10 (CXCL10). Here we explore the relationship of IP-10 levels with detailed genomic and clinical data and undertake a complementary cytokine screen alongside functional assays in a wide range of MPN mouse models. Similar to patients, levels of IP-10 were increased in mice with more severe disease phenotypes (e.g., JAK2V617F/V617F TET2-/- double-mutant mice) compared with those with less severe phenotypes (e.g., CALRdel52 or JAK2+/V617F mice) and wild-type (WT) littermate controls. Although exposure to IP-10 did not directly alter proliferation or survival in single hematopoietic stem cells (HSCs) in vitro, IP-10-/- mice transplanted with disease-initiating HSCs developed an MPN phenotype more slowly, suggesting that the effect of IP-10 loss was noncell-autonomous. To explore the broader effects of IP-10 loss, we crossed IP-10-/- mice into a series of MPN mouse models and showed that its loss reduces the erythrocytosis observed in mice with the most severe phenotype. Together, these data point to a potential role for blocking IP-10 activity in the management of MPNs.

Predictability: A new distinguishing feature of cancer?

Cancer is a consequence of stochastic (mutations, genetic, and epigenetic instabilities) and deterministic (evolutionary bottlenecks) events. Stochastic events are less amenable to prediction, whereas deterministic events yield more predictable results. The relative contribution of these opposing forces determines cancer predictability, which affects the accuracy of our prognostic predictions and is critical for treatment planning. In this study, we attempted to quantify predictability. The predictability index (PI) was defined as the median overall-survival at any time point divided by the standard error at that time. Using data obtained from the SEER program, we found striking differences in the PI of different tumors. Highly predictable tumors were malignancies of the breast, thyroid, prostate, and testis (5-year PI of 3516, 1920, 1919, and 1805, respectively). Less predictable tumors were colorectal, melanoma, and bladder (5-year PI of 1264, 1197, and 760, respectively). Least predictable were pancreatic cancer and chronic myelogenous leukemia (5-year PI of 129, and 42). PI decreased during follow-up in all examined tumors and showed sex differences in some cases. Thyroid cancer was significantly more predictable in women (5-year PI of 2579 vs. 748, p = 0.00017) and bladder cancer more predictable in men (5-year PI of 723 vs. 385, p = 0.012), Predictability is a potentially new distinguishing feature of malignancy. This study sheds light on prognostic accuracy and provides insight into the relative roles of stochastic and deterministic forces during carcinogenesis.

Biological Basis of Breast Cancer-Related Disparities in Precision Oncology Era

Precision oncology is based on deep knowledge of the molecular profile of tumors, allowing for more accurate and personalized therapy for specific groups of patients who are different in disease susceptibility as well as treatment response. Thus, onco-breastomics is able to discover novel biomarkers that have been found to have racial and ethnic differences, among other types of disparities such as chronological or biological age-, sex/gender- or environmental-related ones. Usually, evidence suggests that breast cancer (BC) disparities are due to ethnicity, aging rate, socioeconomic position, environmental or chemical exposures, psycho-social stressors, comorbidities, Western lifestyle, poverty and rurality, or organizational and health care system factors or access. The aim of this review was to deepen the understanding of BC-related disparities, mainly from a biomedical perspective, which includes genomic-based differences, disparities in breast tumor biology and developmental biology, differences in breast tumors' immune and metabolic landscapes, ecological factors involved in these disparities as well as microbiomics- and metagenomics-based disparities in BC. We can conclude that onco-breastomics, in principle, based on genomics, proteomics, epigenomics, hormonomics, metabolomics and exposomics data, is able to characterize the multiple biological processes and molecular pathways involved in BC disparities, clarifying the differences in incidence, mortality and treatment response for different groups of BC patients.

Trade-offs, trade-ups, and high mutational parallelism underlie microbial adaptation during extreme cycles of feast and famine

Microbes are evolutionarily robust organisms capable of rapid adaptation to complex stress, which enables them to colonize harsh environments. In nature, microbes are regularly challenged by starvation, which is a particularly complex stress because resource limitation often co-occurs with changes in pH, osmolarity, and toxin accumulation created by metabolic waste. Often overlooked are the additional complications introduced by eventual resource replenishment, as successful microbes must withstand rapid environmental shifts before swiftly capitalizing on replenished resources to avoid invasion by competing species. To understand how microbes navigate trade-offs between growth and survival, ultimately adapting to thrive in environments with extreme fluctuations, we experimentally evolved 16 Escherichia coli populations for 900 days in repeated feast/famine conditions with cycles of 100-day starvation before resource replenishment. Using longitudinal population-genomic analysis, we found that evolution in response to extreme feast/famine is characterized by narrow adaptive trajectories with high mutational parallelism and notable mutational order. Genetic reconstructions reveal that early mutations result in trade-offs for biofilm and motility but trade-ups for growth and survival, as these mutations conferred positively correlated advantages during both short-term and long-term culture. Our results demonstrate how microbes can navigate the adaptive landscapes of regularly fluctuating conditions and ultimately follow mutational trajectories that confer benefits across diverse environments.

Pulmonary Adenocarcinoma In Situ and Minimally Invasive Adenocarcinomas in European Patients Have Less KRAS and More EGFR Mutations Compared to Advanced Adenocarcinomas

Pulmonary adenocarcinoma (ADC) is a very diverse disease, both genetically and histologically, which displays extensive intratumor heterogeneity with numerous acquired mutations. ADC is the most common type of lung cancer and is believed to arise from adenocarcinoma in situ (AIS) which then progresses to minimally invasive adenocarcinoma (MIA). In patients of European ethnicity, we analyzed genetic mutations in AIS (n = 10) and MIA (n = 18) and compared the number of genetic mutations with advanced ADC (n = 2419). Using next-generation sequencing, the number of different mutations detected in both AIS (87.5%) and MIA (94.5%) were higher (p < 0.001) than in advanced ADC (53.7%). In contrast to the high number of mutations in Kirsten rat sarcoma virus gene (KRAS) in advanced ADC (34.6%), there was only one case of AIS with KRAS G12C mutation (3.5%; p < 0.001) and no cases of MIA with KRAS mutation (p < 0.001). In contrast to the modest prevalence of epidermal growth factor receptor (EGFR) mutations in advanced ADC (15.0%), the fraction of EGFR mutant cases was higher in both in AIS (22.2%) and MIA (59.5%; p < 0.001). The EGFR exon 19 deletion mutation was more common in both MIA (50%; n = 6/12) and ADC (41%; n = 149/363), whereas p.L858R was more prevalent in AIS (75%; n = 3/4). In contrast to pulmonary advanced ADC, KRAS driver mutations are less common, whereas mutations in EGFR are more common, in detectable AIS and MIA.

The dark side of stemness - the role of hematopoietic stem cells in development of blood malignancies

Hematopoietic stem cells (HSCs) produce all blood cells throughout the life of the organism. However, the high self-renewal and longevity of HSCs predispose them to accumulate mutations. The acquired mutations drive preleukemic clonal hematopoiesis, which is frequent among elderly people. The preleukemic state, although often asymptomatic, increases the risk of blood cancers. Nevertheless, the direct role of preleukemic HSCs is well-evidenced in adult myeloid leukemia (AML), while their contribution to other hematopoietic malignancies remains less understood. Here, we review the evidence supporting the role of preleukemic HSCs in different types of blood cancers, as well as present the alternative models of malignant evolution. Finally, we discuss the clinical importance of preleukemic HSCs in choosing the therapeutic strategies and provide the perspective on further studies on biology of preleukemic HSCs.

Order-of-Mutation Effects on Cancer Progression: Models for Myeloproliferative Neoplasm

In some patients with myeloproliferative neoplasms (MPN), two genetic mutations are often found: JAK2 V617F and one in the TET2 gene. Whether one mutation is present influences how the other subsequent mutation will affect the regulation of gene expression. In other words, when a patient carries both mutations, the order of when they first arose has been shown to influence disease progression and prognosis. We propose a nonlinear ordinary differential equation, the Moran process, and Markov chain models to explain the non-additive and non-commutative mutation effects on recent clinical observations of gene expression patterns, proportions of cells with different mutations, and ages at diagnosis of MPN. Combined, these observations are used to shape our modeling framework. Our key proposal is that bistability in gene expression provides a natural explanation for many observed order-of-mutation effects. We also propose potential experimental measurements that can be used to confirm or refute predictions of our models.

The lifelong natural history of clonal hematopoiesis and its links to myeloid neoplasia

ABSTRACT

The study of somatic mutations and the associated clonal mosaicism across the human body has transformed our understanding of aging and its links to cancer. In proliferative human tissues, stem cells compete for dominance, and those with an advantage expand clonally to outgrow their peers. In the hematopoietic system, such expansion is termed clonal hematopoiesis (CH). The forces driving competition, namely heterogeneity of the hematopoietic stem cell (HSC) pool and attrition of their environment, become increasingly prominent with age. As a result, CH becomes progressively more common through life to the point of becoming essentially ubiquitous. We are beginning to unravel the specific intracellular and extracellular factors underpinning clonal behavior, with somatic mutations in specific driver genes, inflammation, telomere maintenance, extraneous exposures, and inherited genetic variation among the important players. The inevitability of CH with age combined with its unequivocal links to myeloid cancers poses a scientific and clinical challenge. Specifically, we need to decipher the factors determining clonal behavior and develop prognostic tools to identify those at high risk of malignant progression, for whom preventive interventions may be warranted. Here, we discuss how recent advances in our understanding of the natural history of CH have provided important insights into these processes and helped define future avenues of investigation.

Time-resolved, integrated analysis of clonally evolving genomes

Clonal genome evolution is a key feature of asexually reproducing species and human cancer development. While many studies have described the landscapes of clonal genome evolution in cancer, few determine the underlying evolutionary parameters from molecular data, and even fewer integrate theory with data. We derived theoretical results linking mutation rate, time, expansion dynamics, and biological/clinical parameters. Subsequently, we inferred time-resolved estimates of evolutionary parameters from mutation accumulation, mutational signatures and selection. We then applied this framework to predict the time of speciation of the marbled crayfish, an enigmatic, globally invasive parthenogenetic freshwater crayfish. The results predict that speciation occurred between 1986 and 1990, which is consistent with biological records. We also used our framework to analyze whole-genome sequencing datasets from primary and relapsed glioblastoma, an aggressive brain tumor. The results identified evolutionary subgroups and showed that tumor cell survival could be inferred from genomic data that was generated during the resection of the primary tumor. In conclusion, our framework allowed a time-resolved, integrated analysis of key parameters in clonally evolving genomes, and provided novel insights into the evolutionary age of marbled crayfish and the progression of glioblastoma.

Order-of-mutation effects on cancer progression: models for myeloproliferative neoplasm

In some patients with myeloproliferative neoplasms (MPN), two genetic mutations are often found, JAK2 V617F and one in the TET2 gene. Whether or not one mutation is present will influence how the other subsequent mutation affects the regulation of gene expression. When both mutations are present, the order of their occurrence has been shown to influence disease progression and prognosis. We propose a nonlinear ordinary differential equation (ODE), Moran process, and Markov chain models to explain the non-additive and non-commutative mutation effects on recent clinical observations of gene expression patterns, proportions of cells with different mutations, and ages at diagnosis of MPN. These observations consistently shape our modeling framework. Our key proposal is that bistability in gene expression provides a natural explanation for many observed order-of-mutation effects. We also propose potential experimental measurements that can be used to confirm or refute predictions of our models.

Order-of-mutation effects on cancer progression: models for myeloproliferative neoplasm

In some patients with myeloproliferative neoplasms (MPN), two genetic mutations are often found, JAK2 V617F and one in the TET2 gene. Whether or not one mutation is present will influence how the other subsequent mutation affects the regulation of gene expression. When both mutations are present, the order of their occurrence has been shown to influence disease progression and prognosis. We propose a nonlinear ordinary differential equation (ODE), Moran process, and Markov chain models to explain the non-additive and non-commutative mutation effects on recent clinical observations of gene expression patterns, proportions of cells with different mutations, and ages at diagnosis of MPN. These observations consistently shape our modeling framework. Our key proposal is that bistability in gene expression provides a natural explanation for many observed order-of-mutation effects. We also propose potential experimental measurements that can be used to confirm or refute predictions of our models.

A Systematic Review of Mesenchymal Epithelial Transition Factor (MET) and Its Impact in the Development and Treatment of Non-Small-Cell Lung Cancer

Lung cancer represents the leading cause of annual cancer-related deaths worldwide, accounting for 12.9%. The available treatment options for patients who experience disease progression remain limited. Targeted therapeutic approaches are promising but further understanding of the role of genetic alterations in tumorigenesis is imperative. The MET gene has garnered great interest in this regard. The aim of this systematic review was to analyze the findings from multiple studies to provide a comprehensive and unbiased summary of the evidence. A systematic search was conducted in the reputable scientific databases Embase and PubMed, leading to the inclusion of twenty-two articles, following the PRISMA guidelines, elucidating the biological role of MET in lung cancer and targeted therapies. The systematic review was registered in PROSPERO with registration ID: CRD42023437714. MET mutations were detected in 7.6-11.0% of cases while MET gene amplification was observed in 3.9-22.0%. Six studies showed favorable treatment outcomes utilizing MET inhibitors compared to standard treatment or placebo, with increases in PFS and OS ranging from 0.9 to 12.4 and 7.2 to 24.2 months, respectively, and one study reporting an increase in ORR by 17.3%. Furthermore, patients with a higher mutational burden may derive greater benefit from treatment with MET tyrosine kinase inhibitors (TKIs) than those with a lower mutational burden. Conversely, two studies reported no beneficial effect from adjunctive treatment with a MET targeted therapy. Given these findings, there is an urgent need to identify effective therapeutic strategies specifically targeting the MET gene in lung cancer patients.

A phylogenetic approach to inferring the order in which mutations arise during cancer progression

Although the role of evolutionary process in cancer progression is widely accepted, increasing attention is being given to the evolutionary mechanisms that can lead to differences in clinical outcome. Recent studies suggest that the temporal order in which somatic mutations accumulate during cancer progression is important. Single-cell sequencing (SCS) provides a unique opportunity to examine the effect that the mutation order has on cancer progression and treatment effect. However, the error rates associated with single-cell sequencing are known to be high, which greatly complicates the task. We propose a novel method for inferring the order in which somatic mutations arise within an individual tumor using noisy data from single-cell sequencing. Our method incorporates models at two levels in that the evolutionary process of somatic mutation within the tumor is modeled along with the technical errors that arise from the single-cell sequencing data collection process. Through analyses of simulations across a wide range of realistic scenarios, we show that our method substantially outperforms existing approaches for identifying mutation order. Most importantly, our method provides a unique means to capture and quantify the uncertainty in the inferred mutation order along a given phylogeny. We illustrate our method by analyzing data from colorectal and prostate cancer patients, in which our method strengthens previously reported mutation orders. Our work is an important step towards producing meaningful prediction of mutation order with high accuracy and measuring the uncertainty of predicted mutation order in cancer patients, with the potential to lead to new insights about the evolutionary trajectories of cancer.

Impact of a haplotype (composed of the APC, KRAS, and TP53 genes) on colorectal adenocarcinoma differentiation and patient prognosis

BACKGROUND

Many types of gene mutation are associated with the drug resistance of cancer cells. XELOX is a new and efficient surgical adjuvant chemotherapy for colorectal adenocarcinoma. However, drug-resistant related genetic mutations associated with this treatment remain unknown.

METHODS

Next-generation sequencing (NGS) was performed on 36 colorectal cancer patients to identify mutations among patients with residual tumors following preoperative chemotherapy. Enrichment and prognosis of these mutations were evaluated in a TCGA cohort. The pathology of cases with poor prognosis-related mutations was also determined.

RESULTS

A sequence of SNPs associated with the APC, KRAS, and TP53 genes in 13 of 19 subjects with residual tumors after preoperative chemotherapy was identified. Using survival analysis data from 317 cases in the TCGA database, a prognosis-related haplotype composed of SNPs from APC, KRAS, and TP53 was assembled. Colorectal cancer patients with these mutations had a lower 5-year tumor-specific survival rate than those without (p < 0.05). Most patients with these mutations were at a higher clinical stage (III-IV) of disease. Enrolled subjects with the identified haplotype tended to have poor cancer cell differentiation.

CONCLUSIONS

The prognosis-related haplotype can be used as a marker of drug resistance and prognosis in colorectal cancer patients after preoperative chemotherapy.

HOXA9 has the hallmarks of a biological switch with implications in blood cancers

Blood malignancies arise from the dysregulation of haematopoiesis. The type of blood cell and the specific order of oncogenic events initiating abnormal growth ultimately determine the cancer subtype and subsequent clinical outcome. HOXA9 plays an important role in acute myeloid leukaemia (AML) prognosis by promoting blood cell expansion and altering differentiation; however, the function of HOXA9 in other blood malignancies is still unclear. Here, we highlight the biological switch and prognosis marker properties of HOXA9 in AML and chronic myeloproliferative neoplasms (MPN). First, we establish the ability of HOXA9 to stratify AML patients with distinct cellular and clinical outcomes. Then, through the use of a computational network model of MPN, we show that the self-activation of HOXA9 and its relationship to JAK2 and TET2 can explain the branching progression of JAK2/TET2 mutant MPN patients towards divergent clinical characteristics. Finally, we predict a connection between the RUNX1 and MYB genes and a suppressive role for the NOTCH pathway in MPN diseases.

Discovering significant evolutionary trajectories in cancer phylogenies

MOTIVATION

Tumors are the result of a somatic evolutionary process leading to substantial intra-tumor heterogeneity. Single-cell and multi-region sequencing enable the detailed characterization of the clonal architecture of tumors and have highlighted its extensive diversity across tumors. While several computational methods have been developed to characterize the clonal composition and the evolutionary history of tumors, the identification of significantly conserved evolutionary trajectories across tumors is still a major challenge.

RESULTS

We present a new algorithm, MAximal tumor treeS TRajectOries (MASTRO), to discover significantly conserved evolutionary trajectories in cancer. MASTRO discovers all conserved trajectories in a collection of phylogenetic trees describing the evolution of a cohort of tumors, allowing the discovery of conserved complex relations between alterations. MASTRO assesses the significance of the trajectories using a conditional statistical test that captures the coherence in the order in which alterations are observed in different tumors. We apply MASTRO to data from nonsmall-cell lung cancer bulk sequencing and to acute myeloid leukemia data from single-cell panel sequencing, and find significant evolutionary trajectories recapitulating and extending the results reported in the original studies.

AVAILABILITY AND IMPLEMENTATION

MASTRO is available at https://github.com/VandinLab/MASTRO.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

In Silico Pleiotropy Analysis in KEGG Signaling Networks Using a Boolean Network Model

Pleiotropy, which refers to the ability of different mutations on the same gene to cause different pathological effects in human genetic diseases, is important in understanding system-level biological diseases. Although some biological experiments have been proposed, still little is known about pleiotropy on gene–gene dynamics, since most previous studies have been based on correlation analysis. Therefore, a new perspective is needed to investigate pleiotropy in terms of gene–gene dynamical characteristics. To quantify pleiotropy in terms of network dynamics, we propose a measure called in silico Pleiotropic Scores (sPS), which represents how much a gene is affected against a pair of different types of mutations on a Boolean network model. We found that our model can identify more candidate pleiotropic genes that are not known to be pleiotropic than the experimental database. In addition, we found that many types of functionally important genes tend to have higher sPS values than other genes; in other words, they are more pleiotropic. We investigated the relations of sPS with the structural properties in the signaling network and found that there are highly positive relations to degree, feedback loops, and centrality measures. This implies that the structural characteristics are principles to identify new pleiotropic genes. Finally, we found some biological evidence showing that sPS analysis is relevant to the real pleiotropic data and can be considered a novel candidate for pleiotropic gene research. Taken together, our results can be used to understand the dynamics pleiotropic characteristics in complex biological systems in terms of gene–phenotype relations.

Dissecting the Genetic and Non-Genetic Heterogeneity of Acute Myeloid Leukemia Using Next-Generation Sequencing and In Vivo Models

Simple Summary

Acute myeloid leukemia (AML) is an extremely aggressive form of blood cancer with high rates of treatment failure. AML arises from the stepwise acquisition of genetic aberrations and is a highly heterogeneous disorder. Recent research has shown that individual AML samples often contain several clones that are defined by a distinct combination of genetic lesions, epigenetic patterns and cell surface marker expression profiles. A better understanding of the clonal dynamics of AML is required to develop novel treatment strategies against this disease. In this review, we discuss the recent developments that have further deepened our understanding of clonal evolution and heterogeneity in AML.

Abstract

Acute myeloid leukemia (AML) is an extremely aggressive and heterogeneous disorder that results from the transformation of hematopoietic stem cells. Although our understanding of the molecular pathology of AML has greatly improved in the last few decades, the overall and relapse free survival rates among AML patients remain quite poor. This is largely due to evolution of the disease and selection of the fittest, treatment-resistant leukemic clones. There is increasing evidence that most AMLs possess a highly complex clonal architecture and individual leukemias are comprised of genetically, phenotypically and epigenetically distinct clones, which are continually evolving. Advances in sequencing technologies as well as studies using murine AML models have provided further insights into the heterogeneity of leukemias. We will review recent advances in the field of genetic and non-genetic heterogeneity in AML.

Genetic Determinants of Somatic Selection of Mutational Processes in 3,566 Human Cancers

The somatic landscape of the cancer genome results from different mutational processes represented by distinct "mutational signatures." Although several mutagenic mechanisms are known to cause specific mutational signatures in cell lines, the variation of somatic mutational activities in patients, which is mostly attributed to somatic selection, is still poorly explained. Here, we introduce a quantitative trait, mutational propensity (MP), and describe an integrated method to infer genetic determinants of variations in the mutational processes in 3,566 cancers with specific underlying mechanisms. As a result, we report 2,314 candidate determinants with both significant germline and somatic effects on somatic selection of mutational processes, of which, 485 act via cancer gene expression and 1,427 act through the tumor-immune microenvironment. These data demonstrate that the genetic determinants of MPs provide complementary information to known cancer driver genes, clonal evolution, and clinical biomarkers. SIGNIFICANCE: The genetic determinants of the somatic mutational processes in cancer elucidate the biology underlying somatic selection and evolution of cancers and demonstrate complementary predictive power across cancer types.

Putative homeostatic role of cancer driver mutations

Somatic mutations have traditionally been associated with cancer, yet more recently, it was realized that they also appear in nontransformed cells beginning in early life. Remarkably, some of these mutations, commonly viewed as cancer driver mutations, are widely spread among cells of noncancerous tissues, sometimes affecting the majority of the tissue cells. This spreading process intensifies upon aging or exposure to extrinsic insults, such as UV irradiation, inhaling smoke, and inflammatory cues. Whereas classic driver mutations in normal cells are mostly viewed as a first step in the carcinogenesis process, here, we speculate that in certain states, they can play beneficial homeostatic roles while confronting stress and aging tissue repair.

Exploiting Single-Cell Tools in Gene and Cell Therapy

Single-cell molecular tools have been developed at an incredible pace over the last five years as sequencing costs continue to drop and numerous molecular assays have been coupled to sequencing readouts. This rapid period of technological development has facilitated the delineation of individual molecular characteristics including the genome, transcriptome, epigenome, and proteome of individual cells, leading to an unprecedented resolution of the molecular networks governing complex biological systems. The immense power of single-cell molecular screens has been particularly highlighted through work in systems where cellular heterogeneity is a key feature, such as stem cell biology, immunology, and tumor cell biology. Single-cell-omics technologies have already contributed to the identification of novel disease biomarkers, cellular subsets, therapeutic targets and diagnostics, many of which would have been undetectable by bulk sequencing approaches. More recently, efforts to integrate single-cell multi-omics with single cell functional output and/or physical location have been challenging but have led to substantial advances. Perhaps most excitingly, there are emerging opportunities to reach beyond the description of static cellular states with recent advances in modulation of cells through CRISPR technology, in particular with the development of base editors which greatly raises the prospect of cell and gene therapies. In this review, we provide a brief overview of emerging single-cell technologies and discuss current developments in integrating single-cell molecular screens and performing single-cell multi-omics for clinical applications. We also discuss how single-cell molecular assays can be usefully combined with functional data to unpick the mechanism of cellular decision-making. Finally, we reflect upon the introduction of spatial transcriptomics and proteomics, its complementary role with single-cell RNA sequencing (scRNA-seq) and potential application in cellular and gene therapy.

Temporal order of mutations influences cancer initiation dynamics

Cancer is a set of genetic diseases that are driven by mutations. It was recently discovered that the temporal order of genetic mutations affects the cancer evolution and even the nature of the decease itself. The mechanistic origin of these observations, however, remain not well understood. Here we present a theoretical model for cancer initiation dynamics that allows us to quantify the impact of the temporal order of mutations. In our approach, the cancer initiation process is viewed as a set of stochastic transitions between discrete states defined by the different numbers of mutated cells. Using a first-passage analysis, probabilities and times before the cancer initiation are explicitly evaluated for two alternative sequences of two mutations. It is found that the probability of cancer initiation is determined only by the first mutation, while the dynamics depends on both mutations. In addition, it is shown that the acquisition of a mutation with higher fitness before mutation with lower fitness increases the probability of the tumor formation but delays the cancer initiation. Theoretical results are explained using effective free-energy landscapes.

Sequential and co-occurring DNA damage response genetic mutations impact survival in stage III colorectal cancer patients receiving adjuvant oxaliplatin-based chemotherapy

BACKGROUND

Certain sequences of genomic mutations can lead to cancer formation and affect treatment outcomes and drug resistance. We constructed a cancer evolutionary tree using bulk-targeted deep sequencing to explore the impact of sequential and co-occurring somatic mutations on patients with stage III colorectal cancer (CRC).

METHODS

A total of 108 stage III CRC patients from National Cheng Kung University Hospital (NCKUH) were recruited for this study between Jan. 2014 and Jan. 2019. Clinical information and tumor-targeted deep sequencing data were collected. Phylogenetic trees were reconstructed for evolutionary trajectories. We used a machine learning model for survival analysis.

RESULTS

Six sequential somatic mutations stratified patients into seven subgroups based on survival. Patients carrying sequential germline followed by DNA damage response-related ATM or BRCA2 somatic mutations or non-TP53, APC somatic mutations had a better outcome than those without such mutations. The 4-year recurrence-free survival (RFS) probability was 88% in the low-risk group (G1) and 46% in the high-risk group (G2) (log-rank p-value 2e-05). The predictive efficacy by the area under the curve (AUC) was 0.73, 0.7, 0.797, and 0.88 at 2, 4, 6, and 8 years, respectively. The mutation status of mismatch repair (MMR) genes was not associated with RFS. Different genomic features were found between the groups. The orders of APC, KRAS and APC, BRCA2 sequential somatic mutations were associated with clinical outcomes. The occurrence of somatic mutations in BRCA2, such as TP53 somatic mutations, affected recurrence-free survival.

CONCLUSIONS

According to the evolution model, DNA damage response (DDR)-related ATM or BRCA2 somatic mutations are promising biomarkers for assessing the response of stage III CRC patients to oxaliplatin-based chemotherapy. The sequential order and co-occurring DDR somatic mutations are associated with recurrence-free survival.

Structure and contingency determine mutational hotspots for flower color evolution

Evolutionary genetic studies have uncovered abundant evidence for genomic hotspots of phenotypic evolution, as well as biased patterns of mutations at those loci. However, the theoretical basis for this concentration of particular types of mutations at particular loci remains largely unexplored. In addition, historical contingency is known to play a major role in evolutionary trajectories, but has not been reconciled with the existence of such hotspots. For example, do the appearance of hotspots and the fixation of different types of mutations at those loci depend on the starting state and/or on the nature and direction of selection? Here, we use a computational approach to examine these questions, focusing the anthocyanin pigmentation pathway, which has been extensively studied in the context of flower color transitions. We investigate two transitions that are common in nature, the transition from blue to purple pigmentation and from purple to red pigmentation. Both sets of simulated transitions occur with a small number of mutations at just four loci and show strikingly similar peaked shapes of evolutionary trajectories, with the mutations of the largest effect occurring early but not first. Nevertheless, the types of mutations (biochemical vs. regulatory) as well as their direction and magnitude are contingent on the particular transition. These simulated color transitions largely mirror findings from natural flower color transitions, which are known to occur via repeated changes at a few hotspot loci. Still, some types of mutations observed in our simulated color evolution are rarely observed in nature, suggesting that pleiotropic effects further limit the trajectories between color phenotypes. Overall, our results indicate that the branching structure of the pathway leads to a predictable concentration of evolutionary change at the hotspot loci, but the types of mutations at these loci and their order is contingent on the evolutionary context.

Is cancer latency an outdated concept? Lessons from chronic myeloid leukemia

Our concept of cancer latency, the interval from when a cancer starts until it is diagnosed, has changed dramatically. A prior widely-used definition was the interval between an exposure to a cancer-causing substance and cancer diagnosis. However, this definition does not accurately reflect current knowledge of how most cancers develop assuming, mostly incorrectly, one exposure is the sole cause of a cancer, ignoring the possibility the cancer being considered would have developed anyway but that the exposure accelerated cancer development and eliding the randomness in when a cancer is diagnosed. We show, using chronic myeloid leukaemia as a model, that defining cancer latency is not as simple as it once seemed. It is difficult or impossible to know at which event or mutation to start to clock to measure cancer latency. It is equally difficult to know when to stop the clock given the stochastic nature of when cancers are diagnosed. Importantly, even in genetically-identical twins with the same driver mutation intervals to develop cancer vary substantially. And we discuss other confonders. Clearly we need a new definition of cancer latency or we need to abandon the concept of cancer latency in the modern era of cancer biology.

Genetic and Immune Changes Associated with Disease Progression under the Pressure of Oncolytic Therapy in A Neuroblastoma Outlier Patient

Little is known about the effect of oncolytic adenovirotherapy on pediatric tumors. Here we present the clinical case of a refractory neuroblastoma that responded positively to Celyvir (ICOVIR-5 oncolytic adenovirus delivered by autologous mesenchymal stem cells) for several months. We analyzed samples during tumor evolution in order to identify molecular and mutational features that could explain the interactions between treatment and tumor and how the balance between both of them evolved. We identified a higher adaptive immune infiltration during stabilized disease compared to progression, and also a higher mutational rate and T-cell receptor (TCR) diversity during disease progression. Our results indicate an initial active role of the immune system controlling tumor growth during Celyvir therapy. The tumor eventually escaped from the control exerted by virotherapy through acquisition of resistance by the tumor microenvironment that exhausted the initial T cell response.

Effects of ordered mutations on dynamics in signaling networks

BACKGROUND

Many previous clinical studies have found that accumulated sequential mutations are statistically related to tumorigenesis. However, they are limited in fully elucidating the significance of the ordered-mutation because they did not focus on the network dynamics. Therefore, there is a pressing need to investigate the dynamics characteristics induced by ordered-mutations.

METHODS

To quantify the ordered-mutation-inducing dynamics, we defined the mutation-sensitivity and the order-specificity that represent if the network is sensitive against a double knockout mutation and if mutation-sensitivity is specific to the mutation order, respectively, using a Boolean network model.

RESULTS

Through intensive investigations, we found that a signaling network is more sensitive when a double-mutation occurs in the direction order inducing a longer path and a smaller number of paths than in the reverse order. In addition, feedback loops involving a gene pair decreased both the mutation-sensitivity and the order-specificity. Next, we investigated relationships of functionally important genes with ordered-mutation-inducing dynamics. The network is more sensitive to mutations subject to drug-targets, whereas it is less specific to the mutation order. Both the sensitivity and specificity are increased when different-drug-targeted genes are mutated. Further, we found that tumor suppressors can efficiently suppress the amplification of oncogenes when the former are mutated earlier than the latter.

CONCLUSION

Taken together, our results help to understand the importance of the order of mutations with respect to the dynamical effects in complex biological systems.

Molecular Biology and Evolution of Cancer: From Discovery to Action

Cancer progression is an evolutionary process. During this process, evolving cancer cell populations encounter restrictive ecological niches within the body, such as the primary tumor, circulatory system, and diverse metastatic sites. Efforts to prevent or delay cancer evolution-and progression-require a deep understanding of the underlying molecular evolutionary processes. Herein we discuss a suite of concepts and tools from evolutionary and ecological theory that can inform cancer biology in new and meaningful ways. We also highlight current challenges to applying these concepts, and propose ways in which incorporating these concepts could identify new therapeutic modes and vulnerabilities in cancer.

Exploiting Cancer Cells Metabolic Adaptability to Enhance Therapy Response in Cancer

Despite all the progresses developed in prevention and new treatment approaches, cancer is the second leading cause of death worldwide, being chemoresistance a pivotal barrier in cancer management. Cancer cells present several mechanisms of drug resistance/tolerance and recently, growing evidence have been supporting a role of metabolism reprograming per se as a driver of chemoresistance. In fact, cancer cells display several adaptive mechanisms that allow the emergency of chemoresistance, revealing cancer as a disease that adapts and evolve along with the treatment. Therefore, clinical protocols that take into account the adaptive potential of cancer cells should be more effective than the current traditional standard protocols on the fighting against cancer.In here, some of the recent findings on the role of metabolism reprograming in cancer chemoresistance emergence will be discussed, as the potential evolutionary strategies that could unable these adaptations, hence allowing to prevent the emergency of treatment resistance, changing cancer outcome.

Modeling Leukemia with Human Induced Pluripotent Stem Cells

The reprogramming of human somatic cells into induced pluripotent stem cells (iPSCs) a little over a decade ago raised exciting prospects to transform the study and potentially also the therapy of human diseases. iPSC models have now been created for a multitude of hematologic diseases, including malignancies. Here we discuss practical aspects of iPSC modeling of malignant diseases, review recent studies, and discuss the new opportunities that iPSC models offer, as well as their current limitations and prospects for future development.

Relating evolutionary selection and mutant clonal dynamics in normal epithelia

Cancer develops from mutated cells in normal tissues. Whether somatic mutations alter normal cell dynamics is key to understanding cancer risk and guiding interventions to reduce it. An analysis of the first incomplete moment of size distributions of clones carrying cancer-associated mutations in normal human eyelid skin gives a good fit with neutral drift, arguing mutations do not affect cell fate. However, this suggestion conflicts with genetic evidence in the same dataset that argues for strong positive selection of a subset of mutations. This implies cells carrying these mutations have a competitive advantage over normal cells, leading to large clonal expansions within the tissue. In the normal epithelium, clone growth is constrained by the limited size of the proliferating compartment and competition with surrounding cells. We show that if these factors are taken into account, the first incomplete moment of the clone size distribution is unable to exclude non-neutral behaviour. Furthermore, experimental factors can make a non-neutral clone size distribution appear neutral. We validate these principles with a new experimental dataset showing that when experiments are appropriately designed, the first incomplete moment can be a useful indicator of non-neutral competition. Finally, we discuss the complex relationship between mutant clone sizes and genetic selection.

Estimating the predictability of cancer evolution

MOTIVATION

How predictable is the evolution of cancer? This fundamental question is of immense relevance for the diagnosis, prognosis and treatment of cancer. Evolutionary biologists have approached the question of predictability based on the underlying fitness landscape. However, empirical fitness landscapes of tumor cells are impossible to determine in vivo. Thus, in order to quantify the predictability of cancer evolution, alternative approaches are required that circumvent the need for fitness landscapes.

RESULTS

We developed a computational method based on conjunctive Bayesian networks (CBNs) to quantify the predictability of cancer evolution directly from mutational data, without the need for measuring or estimating fitness. Using simulated data derived from >200 different fitness landscapes, we show that our CBN-based notion of evolutionary predictability strongly correlates with the classical notion of predictability based on fitness landscapes under the strong selection weak mutation assumption. The statistical framework enables robust and scalable quantification of evolutionary predictability. We applied our approach to driver mutation data from the TCGA and the MSK-IMPACT clinical cohorts to systematically compare the predictability of 15 different cancer types. We found that cancer evolution is remarkably predictable as only a small fraction of evolutionary trajectories are feasible during cancer progression.

AVAILABILITY AND IMPLEMENTATION

https://github.com/cbg-ethz/predictability\_of\_cancer\_evolution.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The Roles of Initiating Truncal Mutations in Human Cancers: The Order of Mutations and Tumor Cell Type Matters

We propose that initiating truncal mutations plays a special role in tumor formation by both enhancing the survival of the initiating cancer cell and by selecting for secondary mutations that contribute to tumor progression, and that these mutations often act in a tissue-preferred fashion. Here, we explain why inherited mutations often have different tissue specificities compared with spontaneous mutations in the same gene. Initiating truncal mutations make excellent neo-antigens for immunotherapy, and understanding why one mutation selects for a second mutation in a particular tissue type could one day aid in the design of gene-targeted combination therapies.

Functional characterization of phospholipase C-γ2 mutant protein causing both somatic ibrutinib resistance and a germline monogenic autoinflammatory disorder

Depending on its occurrence in the germline or somatic context, a single point mutation, S707Y, of phospholipase C-γ₂ (PLCγ₂) gives rise to two distinct human disease states: acquired resistance of chronic lymphocytic leukemia cells (CLL) to inhibitors of Brutons´s tyrosine kinase (Btk) and dominantly inherited autoinflammation and PLCγ₂-associated antibody deficiency and immune dysregulation, APLAID, respectively. The functional relationships of the PLCγ₂S707Y mutation to other PLCG2 mutations causing (i) Btk inhibitor resistance of CLL cells and (ii) the APLAID-related human disease PLCγ₂-associated antibody deficiency and immune dysregulation, PLAID, revealing different clinical characteristics including cold-induced urticaria, respectively, are currently incompletely understood. Here, we show that PLCγ₂S707 point mutants displayed much higher activities at 37° C than the CLL Btk inhibitor resistance mutants R665W and L845F and the two PLAID mutants, PLCγ₂Δ19 and PLCγ₂Δ20-22. Combinations of CLL Btk inhibitor resistance mutations synergized to enhance PLCγ₂ activity, with distinct functional consequences for different temporal orders of the individual mutations. Enhanced activity of PLCγ₂S707Y was not observed in a cell-free system, suggesting that PLCγ₂ activation in intact cells is dependent on regulatory rather than mutant-enzyme-inherent influences. Unlike the two PLAID mutants, PLCγ₂S707Y was insensitive to activation by cooling and retained marked hyperresponsiveness to activated Rac upon cooling. In contrast to the PLAID mutants, which are insensitive to activation by endogenously expressed EGF receptors, the S707Y mutation markedly enhanced the stimulatory effect of EGF, explaining some of the pathophysiological discrepancies between immune cells of PLAID and APLAID patients in response to receptor-tyrosine-kinase activation.

Big Bang Tumor Growth and Clonal Evolution

The advent and application of next-generation sequencing (NGS) technologies to tumor genomes has reinvigorated efforts to understand clonal evolution. Although tumor progression has traditionally been viewed as a gradual stepwise process, recent studies suggest that evolutionary rates in tumors can be variable with periods of punctuated mutational bursts and relative stasis. For example, Big Bang dynamics have been reported, wherein after transformation, growth occurs in the absence of stringent selection, consistent with effectively neutral evolution. Although first noted in colorectal tumors, effective neutrality may be relatively common. Additionally, punctuated evolution resulting from mutational bursts and cataclysmic genomic alterations have been described. In this review, we contrast these findings with the conventional gradualist view of clonal evolution and describe potential clinical and therapeutic implications of different evolutionary modes and tempos.

Generation of autochthonous mouse models of clear cell renal cell carcinoma: mouse models of renal cell carcinoma

Renal cell carcinoma (RCC) is one of the 10 most common cancers worldwide, and to date, a strong systemic therapy has not been developed to treat RCC, even with the remarkable modern advances in molecular medicine mostly due to our incomplete understanding of its tumorigenesis. There is a dire unmet need to understand the etiology and progression of RCC, especially the most common subtype, clear cell RCC (ccRCC), and to develop new treatments for RCC. Genetically engineered mouse (GEM) models are able to mimic the initiation, progression, and metastasis of cancer, thus providing valuable insights into tumorigenesis and serving as perfect preclinical platforms for drug testing and biomarker discovery. Despite substantial advances in the molecular investigation of ccRCC and monumental efforts that have been performed to try to establish autochthonous animal models of ccRCC, this goal has not been achieved until recently. Here we present a review of the most exciting progress relevant to GEM models of ccRCC.

A vicious interplay between genetic and environmental insults in the etiology of blood cancers

Over the years, the etiology of cancer has been attributed alternatively to genetic and environmental insults. According to the genetic hypothesis, cancer cells arise from the acquisition of mutations that dysregulate the intrinsic mechanisms controlling normal cell growth and survival. In contrast, the environmental hypothesis holds that cancer can be caused by multiple extrinsic challenges that alter normal tissue homeostasis, but may not necessarily involve mutations. It is, however, quite possible that these two mechanisms are not mutually exclusive. According to this third hypothesis, environmental challenges, by mechanisms still poorly understood, may act by imposing a selection pressure that confers a proliferative advantage on cells carrying specific driver mutations, leading to disease initiation. The authors of a brief report published in this journal (Exp Hematol. 2017;56:1-6) tested this third hypothesis experimentally and provide new evidence that chronic inflammation, by increasing tumor necrosis factor (TNF)-α, may positively select malignant hematopoietic stem cells (HSCs) carrying loss-of-function TET2 mutations that switch the TNF-α signaling responses to activate proliferation rather than inducing quiescence. Furthermore, these mutations skew hematopoietic differentiation toward the myelomonocytic lineage and the output of macrophages producing TNF-α constitutively, promoting further environment-independent expansion of the malignant HSCs. These findings support a model in which the formation of a malignant population is triggered by a vicious interplay between genetic (TET2 mutations) and environmental (inflammation) insults, suggesting the need for strategies that target both the malignant cells and their environment.