Biology subjects

DeGregori, J.

Publications and source records attributed to DeGregori, J..

5 recordsLinked to original sources

bioRxiv2018

A somatic evolutionary model of the dynamics of aneuploid cells during hematopoietic reconstitution

Aneuploidy is associated with many cancers. Recent studies demonstrate that in thehematopoietic stem and progenitor cell (HSPC) compartment aneuploid cells havereduced fitness and are efficiently purged from the bone marrow. However, early phasesof hematopoietic reconstitution following bone marrow transplantation provide awindow of opportunity whereby aneuploid cells rise in frequency, only to decline to basallevels thereafter. Here we demonstrate by Monte Carlo modeling that two mechanismscould underlie this aneuploidy peak: rapid expansion of the engrafted HSPC populationand bone marrow microenvironment degradation caused by pre-transplantationradiation treatment. Both mechanisms reduce the strength of purifying selection actingin early post-transplantation bone marrow. We explore the contribution of other factorssuch as alterations in cell division rates that affect the strength of purifying selection, thebalance of drift and selection imposed by the HSPC population size, and the mutationselectionbalance dependent on the rate of aneuploidy generation per cell division. Wepropose a somatic evolutionary model for the dynamics of cells with aneuploidy or otherfitness-reducing mutations during hematopoietic reconstitution following bone marrowtransplantation.\n\nSignificanceBone marrow transplantations (BMT) following ablative irradiation pose a great health risk. Its been shown that additionally the bone microenvironment is conducive to elevated frequencies of aneuploid cells in mice during bone marrow reconstitution post-BMT. As aneuploidy is linked with many cancers, we explore the reasons of such aberrant cell frequency peaks by Monte Carlo modeling. We demonstrate that elevated rates of aneuploidy early post-BMT are likely to be caused by reduced purifying somatic selection resulting from the expansion of the reconstituting population and the damage to stem cell niches caused by ablative radiation

cell biology↗

bioRxiv2018

Targeting glutamine metabolism and redox state for leukemia therapy

Acute myeloid leukemia (AML) is a hematological malignancy characterized by the accumulation of immature myeloid precursor cells. AML is poorly responsive to conventional genotoxic chemotherapy and a diagnosis of AML is usually fatal. More effective and less toxic forms of therapy are desperately needed. AML cells are known to be highly dependent on the amino acid glutamine for their survival. Here, we show that blocking glutamine metabolism through the use of a glutaminase inhibitor (CB-839) significantly impairs antioxidant glutathione production in multiple types of AML, resulting in accretion of mitochondrial reactive oxygen species (mitoROS) and apoptotic cell death. Moreover, glutaminase inhibition makes AML cells susceptible to adjuvant drugs that further perturb mitochondrial redox state, such as arsenic trioxide (ATO) and homoharringtonine (HHT). Indeed, the combination of ATO or HHT with CB-839 exacerbates mitoROS and apoptosis, and leads to more complete cell death in AML cell lines, primary AML patient samples and in vivo using mouse models of AML. In addition, these redox-targeted combination therapies are effective in eradicating acute lymphoblastic leukemia cells in vitro and in vivo. Thus, targeting glutamine metabolism in combination with drugs that perturb mitochondrial redox state represents an effective and potentially widely applicable therapeutic strategy for treating multiple types of leukemia.\n\nKey PointsO_LIGlutaminase inhibition commonly impairs glutathione metabolism and induces mitochondrial oxidative stress in acute myeloid leukemia cells\nC_LIO_LIA glutaminase inhibitor synergizes with pro-oxidant drugs in inducing apoptosis and eliminating leukemia cells in vitro and in vivo\nC_LI

cancer biology↗

bioRxiv2018

A generalized theory of somatic evolution

The modern Multi-Stage Model of Carcinogenesis (MMC) was developed in the 1950s through the 70s and postulated carcinogenesis as a process of rounds of Darwinian selection favoring progressively more malignant cell phenotypes. Through this period, almost nothing was known about driver mutations in cancers. Also, stem cells and cellular tissue organization were poorly characterized. The general multi-stage process was later confirmed by experimental studies, and cancer risk and incidence has been explained as primarily a function of mutation occurrence. However, the MMC has never been formally tested for its ability to account for current knowledge about cancer evolution. In particular, different numbers of cancer drivers required for different cancers and vast discrepancies in the organization of stem cell compartments for different tissues appear inconsistent with the very similar age distribution of the vast majority of cancers. In this regard, the initial theoretical idea underlying MMC is often over-interpreted with little connection to modern evidence, and a general theory of somatic evolution still does not exist. In this study, we applied Monte Carlo modeling and demonstrated the effect of various parameters, such as mutation rate, mutation effects and cell division, on the MMC performance. Our modeling demonstrates that the MMC requires considerable modification in order to describe cancer incidence. We elucidate the required conditions for how somatic cell selection should operate within the MMC in order to explain modern data on stem cell clonality and cancer, and propose a generalized theory of somatic evolution based on these results.

cancer biology↗

bioRxiv2017

Conserved patterns of somatic mutations in human peripheral blood cells

With growing interest in monitoring mutational processes in normal tissues, tumor heterogeneity, and cancer evolution under therapy, the ability to accurately and economically detect ultra-rare mutations is becoming increasingly important. However, this capability has often been compromised by significant sequencing, PCR and DNA preparation error rates. Here, we describe FERMI (Fast Extremely Rare Mutation Identification) - a novel method designed to eliminate majority of these sequencing and library preparation errors in order to significantly improve rare somatic mutation detection. This method leverages barcoded targeting probes to capture and sequence DNA of interest with single copy resolution. The variant calls from the barcoded sequencing data then further filtered in a position-dependent fashion against an adaptive, context-aware null model in order to distinguish true variants. As a proof of principle, we employ FERMI to probe bone marrow biopsies from leukemia patients, and show that rare mutations and clonal evolution can be tracked throughout cancer treatment, including during historically intractable periods like minimum residual disease. Importantly, FERMI is able to accurately detect nascent clonal expansions within leukemias in a manner that may facilitate the early detection and characterization of cancer relapse.

genetics↗

bioRxiv2017

On somatic constraints in the evolution of multicellularity

The evolution of multi-cellular animals has produced a conspicuous trend toward increased body size. This trend has introduced at least two novel problems: an elevated risk of somatic disorders, such as cancer, and declining evolvability due to reduced population size, lower reproduction rate and extended generation time. Low population size is widely recognized to explain the high mutation rates in animals by limiting the presumed universally negative selection acting on mutation rates. Here, we present evidence from stochastic modeling that the direction and strength of selection acting on mutation rates is highly dependent on the evolution of somatic maintenance, and thus longevity, which modulates the cost of somatic mutations. We argue that this mechanism may have been critical in facilitating animal evolution.

evolutionary biology↗