Loss of LPAR6 and CAB39L dysregulates the basal-to-luminal urothelial differentiation program, contributing to bladder carcinogenesis.

We describe a strategy that combines histologic and molecular mapping that permits interrogation of the chronology of changes associated with cancer development on a whole-organ scale. Using this approach, we present the sequence of alterations around RB1 in the development of bladder cancer. We show that RB1 is not involved in initial expansion of the preneoplastic clone. Instead, we found a set of contiguous genes that we term "forerunner" genes whose silencing is associated with the development of plaque-like field effects initiating carcinogenesis. Specifically, we identified five candidate forerunner genes (ITM2B, LPAR6, MLNR, CAB39L, and ARL11) mapping near RB1. Two of these genes, LPAR6 and CAB39L, are preferentially downregulated in the luminal and basal subtypes of bladder cancer, respectively. Their loss of function dysregulates urothelial differentiation, sensitizing the urothelium to N-butyl-N-(4-hydroxybutyl)nitrosamine-induced cancers, which recapitulate the luminal and basal subtypes of human bladder cancer.

Inferring Bladder Cancer Evolution from Mucosal field Effects by Whole-Organ Spatial Mutational, Proteomic, and Metabolomic Mapping.

Multi-platform mutational, proteomic, and metabolomic spatial mapping was used on the whole-organ scale to identify the molecular evolution of bladder cancer from mucosal field effects. We identified complex proteomic and metabolomic dysregulations in microscopically normal areas of bladder mucosa adjacent to dysplasia and carcinoma in situ. The mutational landscape developed in a background of complex defects of protein homeostasis which included dysregulated nucleocytoplasmic transport, splicesome, ribosome biogenesis, and peroxisome. These changes were combined with altered urothelial differentiation which involved lipid metabolism and protein degradations controlled by PPAR. The complex alterations of proteome were accompanied by dysregulation of gluco-lipid energy-related metabolism. The analysis of mutational landscape identified three types of mutations based on their geographic distribution and variant allele frequencies. The most common were low frequency α mutations restricted to individual mucosal samples. The two other groups of mutations were associated with clonal expansion. The first of this group referred to as ß mutations occurred at low frequencies across the mucosa. The second of this group called γ mutations increased in frequency with disease progression. Modeling of the mutations revealed that carcinogenesis may span nearly 30 years and can be divided into dormant and progressive phases. The α mutations developed gradually in the dormant phase. The progressive phase lasted approximately five years and was signified by the advent of ß mutations, but it was driven by γ mutations which developed during the last 2-3 years of disease progression to invasive cancer. Our study indicates that the understanding of complex alterations involving mucosal microenvironment initiating bladder carcinogenesis can be inferred from the multi-platform whole-organ mapping.

A Countable-Type Branching Process Model for the Tug-of-War Cancer Cell Dynamics.

We consider a time-continuous Markov branching process of proliferating cells with a countable collection of types. Among-type transitions are inspired by the Tug-of-War process introduced by McFarland et al. (Proc Natl Acad Sci 111(42):15138-15143, 2014) as a mathematical model for competition of advantageous driver mutations and deleterious passenger mutations in cancer cells. We introduce a version of the model in which a driver mutation pushes the type of the cell L-units up, while a passenger mutation pulls it 1-unit down. The distribution of time to divisions depends on the type (fitness) of cell, which is an integer. The extinction probability given any initial cell type is strictly less than 1, which allows us to investigate the transition between types (type transition) in an infinitely long cell lineage of cells. The analysis leads to the result that under driver dominance, the type transition process escapes to infinity, while under passenger dominance, it leads to a limit distribution. Implications in cancer cell dynamics and population genetics are discussed.

Correction: Kurpas et al. Genomic Analysis of SARS-CoV-2 Alpha, Beta and Delta Variants of Concern Uncovers Signatures of Neutral and Non-Neutral Evolution. Viruses 2022, 14, 2375.

Missing Funding [...].

Detection and characterization of constitutive replication origins defined by DNA polymerase epsilon.

BACKGROUND: Despite the process of DNA replication being mechanistically highly conserved, the location of origins of replication (ORI) may vary from one tissue to the next, or between rounds of replication in eukaryotes, suggesting flexibility in the choice of locations to initiate replication. Lists of human ORI therefore vary widely in number and location, and there are currently no methods available to compare them. Here, we propose a method of detection of ORI based on somatic mutation patterns generated by the mutator phenotype of damaged DNA polymerase epsilon (POLE). RESULTS: We report the genome-wide localization of constitutive ORI in POLE-mutated human tumors using whole genome sequencing data. Mutations accumulated after many rounds of replication of unsynchronized dividing cell populations in tumors allow to identify constitutive origins, which we show are shared with high fidelity between individuals and tumor types. Using a Smith-Waterman-like dynamic programming approach, we compared replication origin positions obtained from multiple different methods. The comparison allowed us to define a consensus set of replication origins, identified consistently by multiple ORI detection methods. Many DNA features co-localized with the consensus set of ORI, including chromatin loop anchors, G-quadruplexes, S/MARs, and CpGs. Among all features, the H2A.Z histone exhibited the most significant association. CONCLUSIONS: Our results show that mutation-based detection of replication origins is a viable approach to determining their location and associated sequence features.

Genomic Analysis of SARS-CoV-2 Alpha, Beta and Delta Variants of Concern Uncovers Signatures of Neutral and Non-Neutral Evolution.

Due to the emergence of new variants of the SARS-CoV-2 coronavirus, the question of how the viral genomes evolved, leading to the formation of highly infectious strains, becomes particularly important. Three major emergent strains, Alpha, Beta and Delta, characterized by a significant number of missense mutations, provide a natural test field. We accumulated and aligned 4.7 million SARS-CoV-2 genomes from the GISAID database and carried out a comprehensive set of analyses. This collection covers the period until the end of October 2021, i.e., the beginnings of the Omicron variant. First, we explored combinatorial complexity of the genomic variants emerging and their timing, indicating very strong, albeit hidden, selection forces. Our analyses show that the mutations that define variants of concern did not arise gradually but rather co-evolved rapidly, leading to the emergence of the full variant strain. To explore in more detail the evolutionary forces at work, we developed time trajectories of mutations at all 29,903 sites of the SARS-CoV-2 genome, week by week, and stratified them into trends related to (i) point substitutions, (ii) deletions and (iii) non-sequenceable regions. We focused on classifying the genetic forces active at different ranges of the mutational spectrum. We observed the agreement of the lowest-frequency mutation spectrum with the Griffiths-Tavaré theory, under the Infinite Sites Model and neutrality. If we widen the frequency range, we observe the site frequency spectra much more consistently with the Tung-Durrett model assuming clone competition and selection. The coefficients of the fitting model indicate the possibility of selection acting to promote gradual growth slowdown, as observed in the history of the variants of concern. These results add up to a model of genomic evolution, which partly fits into the classical drift barrier ideas. Certain observations, such as mutation "bands" persistent over the epidemic history, suggest contribution of genetic forces different from mutation, drift and selection, including recombination or other genome transformations. In addition, we show that a "toy" mathematical model can qualitatively reproduce how new variants (clones) stem from rare advantageous driver mutations, and then acquire neutral or disadvantageous passenger mutations which gradually reduce their fitness so they can be then outcompeted by new variants due to other driver mutations.

Alternatively spliced CSF3R isoforms in SRSF2 P95H mutated myeloid neoplasms.

Alternatively spliced colony stimulating factor 3 receptor (CSF3R) isoforms Class III and Class IV are observed in myelodysplastic syndromes (MDS), but their roles in disease remain unclear. We report that the MDS-associated splicing factor SRSF2 affects the expression of Class III and Class IV isoforms and perturbs granulopoiesis. Add-back of the Class IV isoform in Csf3r-null mouse progenitor cells increased granulocyte progenitors with impaired neutrophil differentiation, while add-back of the Class III produced dysmorphic neutrophils in fewer numbers. These CSF3R isoforms were elevated in patients with myeloid neoplasms harboring SRSF2 mutations. Using in vitro splicing assays, we confirmed increased Class III and Class IV transcripts when SRSF2 P95 mutations were co-expressed with the CSF3R minigene in K562 cells. Since SRSF2 regulates splicing partly by recognizing exonic splicing enhancer (ESE) sequences on pre-mRNA, deletion of either ESE motifs within CSF3R exon 17 decreased Class IV transcript levels without affecting Class III. CD34+ cells expressing SRSF2 P95H showed impaired neutrophil differentiation in response to G-CSF and was accompanied by increased levels of Class IV. Our findings suggest that SRSF2 P95H promotes Class IV splicing by binding to key ESE sequences in CSF3R exon 17, and that SRSF2, when mutated, contributes to dysgranulopoiesis.

Erratum: The origin of bladder cancer from mucosal field effects.

[This corrects the article DOI: 10.1016/j.isci.2022.104551.].

The origin of bladder cancer from mucosal field effects.

Whole-organ mapping was used to study molecular changes in the evolution of bladder cancer from field effects. We identified more than 100 dysregulated pathways, involving immunity, differentiation, and transformation, as initiators of carcinogenesis. Dysregulation of interleukins signified the involvement of inflammation in the incipient phases of the process. An aberrant methylation/expression of multiple HOX genes signified dysregulation of the differentiation program. We identified three types of mutations based on their geographic distribution. The most common were mutations restricted to individual mucosal samples that targeted uroprogenitor cells. Two types of mutations were associated with clonal expansion and involved large areas of mucosa. The α mutations occurred at low frequencies while the ß mutations increased in frequency with disease progression. Modeling revealed that bladder carcinogenesis spans 10-15 years and can be divided into dormant and progressive phases. The progressive phase lasted 1-2 years and was driven by ß mutations.

Identification of lung cancer drivers by comparison of the observed and the expected numbers of missense and nonsense mutations in individual human genes.

Largely, cancer development is driven by acquisition and positive selection of somatic mutations that increase proliferation and survival of tumor cells. As a result, genes related to cancer development tend to have an excess of somatic mutations in them. An excess of missense and/or nonsense mutations in a gene is an indicator of its cancer relevance. To identify genes with an excess of potentially functional missense or nonsense mutations one needs to compare the observed and expected numbers of mutations in the gene. We estimated the expected numbers of missense and nonsense mutations in individual human genes using (i) the number of potential sites for missense and nonsense mutations in individual transcripts and (ii) histology-specific nucleotide context-dependent mutation rates. To estimate mutation rates defined as the number of mutations per site per tumor we used silent mutations reported in the Catalog Of Somatic Mutations In Cancer (COSMIC). The estimates were nucleotide context dependent. We have identified 26 genes with an excess of missense and/or nonsense mutations for lung adenocarcinoma, 18 genes for small cell lung cancer, and 26 genes for squamous cell carcinoma of the lung. These genes include known genes and novel lung cancer gene candidates.

A general calculus of fitness landscapes finds genes under selection in cancers.

Genetic variants drive the evolution of traits and diseases. We previously modeled these variants as small displacements in fitness landscapes and estimated their functional impact by differentiating the evolutionary relationship between genotype and phenotype. Conversely, here we integrate these derivatives to identify genes steering specific traits. Over cancer cohorts, integration identified 460 likely tumor-driving genes. Many have literature and experimental support but had eluded prior genomic searches for positive selection in tumors. Beyond providing cancer insights, these results introduce a general calculus of evolution to quantify the genotype-phenotype relationship and discover genes associated with complex traits and diseases.

Modes of Selection in Tumors as Reflected by Two Mathematical Models and Site Frequency Spectra.

The tug-of-war model was developed in a series of papers of McFarland and co-authors to account for existence of mutually counteracting rare advantageous driver mutations and more frequent slightly deleterious passenger mutations in cancer. In its original version, it was a state-dependent branching process. Because of its formulation, the tug-of-war model is of importance for tackling the problem as to whether evolution of cancerous tumors is "Darwinian" or "non-Darwinian." We define two Time-Continuous Markov Chain versions of the model, including identical mutation processes but adopting different drift and selection components. In Model A, drift and selection process preserves expected fitness whereas in Model B it leads to non-decreasing expected fitness. We investigate these properties using mathematical analysis and extensive simulations, which detect the effect of the so-called drift barrier in Model B but not in Model A. These effects are reflected in different structure of clone genealogies in the two models. Our work is related to the past theoretical work in the field of evolutionary genetics, concerning the interplay among mutation, drift and selection, in absence of recombination (asexual reproduction), where epistasis plays a major role. Finally, we use the statistics of mutation frequencies known as the Site Frequency Spectra (SFS), to compare the variant frequencies in DNA of sequenced HER2+ breast cancers, to those based on Model A and B simulations. The tumor-based SFS are better reproduced by Model A, pointing out a possible selection pattern of HER2+ tumor evolution. To put our models in context, we carried out an exploratory study of how publicly accessible data from breast, prostate, skin and ovarian cancers fit a range of models found in the literature.

Heat shock factor 1 (HSF1) cooperates with estrogen receptor α (ERα) in the regulation of estrogen action in breast cancer cells.

Heat shock factor 1 (HSF1), a key regulator of transcriptional responses to proteotoxic stress, was linked to estrogen (E2) signaling through estrogen receptor α (ERα). We found that an HSF1 deficiency may decrease ERα level, attenuate the mitogenic action of E2, counteract E2-stimulated cell scattering, and reduce adhesion to collagens and cell motility in ER-positive breast cancer cells. The stimulatory effect of E2 on the transcriptome is largely weaker in HSF1-deficient cells, in part due to the higher basal expression of E2-dependent genes, which correlates with the enhanced binding of unliganded ERα to chromatin in such cells. HSF1 and ERα can cooperate directly in E2-stimulated regulation of transcription, and HSF1 potentiates the action of ERα through a mechanism involving chromatin reorganization. Furthermore, HSF1 deficiency may increase the sensitivity to hormonal therapy (4-hydroxytamoxifen) or CDK4/6 inhibitors (palbociclib). Analyses of data from The Cancer Genome Atlas database indicate that HSF1 increases the transcriptome disparity in ER-positive breast cancer and can enhance the genomic action of ERα. Moreover, only in ER-positive cancers an elevated HSF1 level is associated with metastatic disease.

About 70% of breast cancers rely on supplies of a hormone called estrogen ­ which is the main hormone responsible for female physical characteristics ­ to grow. Breast cancer cells that are sensitive to estrogen possess proteins known as estrogen receptors and are classified as estrogen-receptor positive. When estrogen interacts with its receptor in a cancer cell, it stimulates the cell to grow and migrate to other parts of the body. Therefore, therapies that decrease the amount of estrogen the body produces, or inhibit the receptor itself, are widely used to treat patients with estrogen receptor-positive breast cancers. When estrogen interacts with an estrogen receptor known as ERα it can also activate a protein called HSF1, which helps cells to survive under stress. In turn, HSF1 regulates several other proteins that are necessary for ERα and other estrogen receptors to work properly. Previous studies have suggested that high levels of HSF1 may worsen the outcomes for patients with estrogen receptor-positive breast cancers, but it remains unclear how HSF1 acts in breast cancer cells. Vydra, Janus, Kus et al. used genetics and bioinformatics approaches to study HSF1 in human breast cancer cells. The experiments revealed that breast cancer cells with lower levels of HSF1 also had lower levels of ERα and responded less well to estrogen than cells with higher levels of HSF1. Further experiments suggested that in the absence of estrogen, HSF1 helps to keep ERα inactive. However, when estrogen is present, HSF1 cooperates with ERα and enhances its activity to help cells grow and migrate. Vydra, Janus, Kus et al. also found that cells with higher levels of HSF1 were less sensitive to two drug therapies that are commonly used to treat estrogen receptor-positive breast cancers. These findings reveal that the effect HSF1 has on ERα activity depends on the presence of estrogen. Therefore, cancer therapies that decrease the amount of estrogen a patient produces may have a different effect on estrogen receptor-positive tumors with high HSF1 levels than tumors with low HSF1 levels.

Predicting Time to Relapse in Acute Myeloid Leukemia through Stochastic Modeling of Minimal Residual Disease Based on Clonality Data.

Event-free and overall survival remain poor for patients with acute myeloid leukemia. Chemoresistant clones contributing to relapse arise from minimal residual disease (MRD) or newly-acquired mutations. However, the dynamics of clones comprising MRD is poorly understood. We developed a predictive stochastic model, based on a multitype age-dependent Markov branching process, to describe how random events in MRD contribute to the heterogeneity in treatment response. We employed training and validation sets of patients who underwent whole genome sequencing and for whom mutant clone frequencies at diagnosis and relapse were available. The disease evolution and treatment outcome are subject to stochastic fluctuations. Estimates of malignant clone growth rates, obtained by model fitting, are consistent with published data. Using the estimates from the training set, we developed a function linking MRD and time of relapse, with MRD inferred from the model fits to clone frequencies and other data. An independent validation set confirmed our model. In a third data set, we fitted the model to data at diagnosis and remission and predicted the time to relapse. As a conclusion, given bone marrow genome at diagnosis and MRD at or past remission, the model can predict time to relapse, and help guide treatment decisions to mitigate relapse.

Genomic trajectory in leukemogenesis of myeloproliferative neoplasms: a case report.

BACKGROUND: We report a patient with Essential Thrombocythemia (ET), subsequently diagnosed with concurrent myeloid and lymphoid leukemia. Generally, the molecular mechanisms underlying leukemic transformation of Philadelphia-negative myeloproliferative neoplasms (Ph-MPN) are poorly understood. Risk of transformation to acute myelogenous leukemia (AML) is low; transformation to both AML and acute lymphoblastic leukemia (ALL) is extremely low. Genetic defects, including allele burden, order of mutation acquisition, clonal heterogeneity and epigenetic mechanisms are important contributors to disease acceleration. CASE PRESENTATION: A 78-year-old Caucasian female originally treated for stable ET, underwent disease acceleration and transition to myeloid sarcoma and B-cell ALL. Genomic reconstruction based on targeted sequencing revealed the presence of a large del(5q) in all three malignancies and somatic driver mutations: TET2, TP53, SF3B1, and ASXL1 at high allele frequency. We propose that the combination of genetic and molecular abnormalities led to hematopoietic stem cell (HSC) injury and disease progression through sub-clone branching. We hypothesize that ancestral reconstruction of genomic data is a useful tool to uncover subclonal events leading to transformation. CONCLUSIONS: The use of ancestral reconstruction of genomic data sheds light on the unique clinical scenario described in this case report. By determining the mutational profile of tumors at several timepoints and deducing the most parsimonious relationship between them, we propose a reconstruction of their origin. We propose that blast progression originated from subclonal events with malignant potential, which coexisted with but did not originate from JAK2 p.V617F-positive ET. We conclude that the application of genomic reconstruction enhances our understanding of leukemogenesis by identifying the timing of molecular events, potentially leading to better chemotherapy choices as well as the development of new targeted therapies.

Chronic infection drives Dnmt3a-loss-of-function clonal hematopoiesis via IFNγ signaling.

Age-related clonal hematopoiesis (CH) is a risk factor for malignancy, cardiovascular disease, and all-cause mortality. Somatic mutations in DNMT3A are drivers of CH, but decades may elapse between the acquisition of a mutation and CH, suggesting that environmental factors contribute to clonal expansion. We tested whether infection provides selective pressure favoring the expansion of Dnmt3a mutant hematopoietic stem cells (HSCs) in mouse chimeras. We created Dnmt3a-mosaic mice by transplanting Dnmt3a-/- and WT HSCs into WT mice and observed the substantial expansion of Dnmt3a-/- HSCs during chronic mycobacterial infection. Injection of recombinant IFNγ alone was sufficient to phenocopy CH by Dnmt3a-/- HSCs upon infection. Transcriptional and epigenetic profiling and functional studies indicate reduced differentiation associated with widespread methylation alterations, and reduced secondary stress-induced apoptosis accounts for Dnmt3a-/- clonal expansion during infection. DNMT3A mutant human HSCs similarly exhibit defective IFNγ-induced differentiation. We thus demonstrate that IFNγ signaling induced during chronic infection can drive DNMT3A-loss-of-function CH.

Interferon Gamma Mediates Hematopoietic Stem Cell Activation and Niche Relocalization through BST2.

During chronic infection, the inflammatory cytokine interferon gamma (IFNγ) damages hematopoietic stem cells (HSCs) by disrupting quiescence and promoting excessive terminal differentiation. However, the mechanism by which IFNγ hinders HSC quiescence remains undefined. Using intravital 3-dimensional microscopy, we find that IFNγ disrupts the normally close interaction between HSCs and CXCL12-abundant reticular (CAR) cells in the HSC niche. IFNγ stimulation increases expression of the cell surface protein BST2, which we find is required for IFNγ-dependent HSC relocalization and activation. IFNγ stimulation of HSCs increases their E-selectin binding by BST2 and homing to the bone marrow, which depends on E-selectin binding. Upon chronic infection, HSCs from mice lacking BST2 are more quiescent and more resistant to depletion than HSCs from wild-type mice. Overall, this study defines a critical mechanism by which IFNγ promotes niche relocalization and activation in response to inflammatory stimulation and identifies BST2 as a key regulator of HSC quiescence. VIDEO ABSTRACT.

Stationary Distribution of Telomere Lengths in Cells with Telomere Length Maintenance and its Parametric Inference.

Telomeres are nucleotide caps located at the ends of each eukaryotic chromosome. Under normal physiological conditions as well as in culture, they shorten during each DNA replication round. Short telomeres initiate a proliferative arrest of cells termed 'replicative senescence'. However, cancer cells possessing limitless replication potential can avoid senescence by the telomere maintenance mechanism, which offsets telomeric loss. Therefore, cancer cells have sufficiently long telomeres even though their lengths are significantly shorter than their normal counterparts. This implies that the attrition and elongation rates play crucial roles in deciding whether and when cells ultimately become carcinogenic. In this research, we propose a concise mathematical model that shows the shortest telomere length at each cell division and prove mathematical conditions related to the attrition and elongation rates, which are necessary and sufficient for the existence of stationary distribution of telomere lengths. Moreover, we estimate the parameters of the telomere length maintenance process based on frequentist and Bayesian approaches. This study expands our knowledge of the mathematical relationship between the telomere attrition and elongation rates in cancer cells, which is important because the telomere length dynamics is a useful biomarker of cancer diagnosis and prognosis.

Mathematical model predicts response to chemotherapy in advanced non-resectable non-small cell lung cancer patients treated with platinum-based doublet.

We developed a computational platform including machine learning and a mechanistic mathematical model to find the optimal protocol for administration of platinum-doublet chemotherapy in a palliative setting. The platform has been applied to advanced metastatic non-small cell lung cancer (NSCLC). The 42 NSCLC patients treated with palliative intent at Maria Sklodowska-Curie National Research Institute of Oncology, Gliwice Branch, were collected from a retrospective cohort of patients diagnosed in 2004-2014. Patients were followed-up, for three years. Clinical data collected include complete information about the clinical course of the patients including treatment schedule, response according to RECIST classification, and survival. The core of the platform is the mathematical model, in the form of a system of ordinary differential equations, describing dynamics of platinum-sensitive and platinum-resistant cancer cells and interactions reflecting competition for space and resources. The model is simulated stochastically by sampling the parameter values from a joint probability distribution function. The machine learning model is applied to calibrate the mathematical model and to fit it to the overall survival curve. The model simulations faithfully reproduce the clinical cohort at three levels long-term response (OS), the initial response (according to RECIST criteria), and the relationship between the number of chemotherapy cycles and time between two consecutive chemotherapy cycles. In addition, we investigated the relationship between initial and long-term response. We showed that those two variables do not correlate which means that we cannot predict patient survival solely based on the initial response. We also tested several chemotherapy schedules to find the best one for patients treated with palliative intent. We found that the optimal treatment schedule depends, among others, on the strength of competition among various subclones in a tumor. The computational platform developed allows optimizing chemotherapy protocols, within admissible limits of toxicity, for palliative treatment of metastatic NSCLC. The simplicity of the method allows its application to chemotherapy optimization in different cancers.

Application of the Moran Model in Estimating Selection Coefficient of Mutated CSF3R Clones in the Evolution of Severe Congenital Neutropenia to Myeloid Neoplasia.

Bone marrow failure (BMF) syndromes, such as severe congenital neutropenia (SCN) are leukemia predisposition syndromes. We focus here on the transition from SCN to pre-leukemic myelodysplastic syndrome (MDS). Stochastic mathematical models have been conceived that attempt to explain the transition of SCN to MDS, in the most parsimonious way, using extensions of standard processes of population genetics and population dynamics, such as the branching and the Moran processes. We previously presented a hypothesis of the SCN to MDS transition, which involves directional selection and recurrent mutation, to explain the distribution of ages at onset of MDS or AML. Based on experimental and clinical data and a model of human hematopoiesis, a range of probable values of the selection coefficient s and mutation rate µ have been determined. These estimates lead to predictions of the age at onset of MDS or AML, which are consistent with the clinical data. In the current paper, based on data extracted from published literature, we seek to provide an independent validation of these estimates. We proceed with two purposes in mind: (i) to determine the ballpark estimates of the selection coefficients and verify their consistency with those previously obtained and (ii) to provide possible insight into the role of recurrent mutations of the G-CSF receptor in the SCN to MDS transition.