Heterogeneity, turn-over rate and karyotype space shape susceptibility to missegregation-induced extinction

The incidence of somatic copy number alterations (SCNAs) per base pair of the genome is orders of magnitudes larger than that of point mutations. This makes SCNAs phenotypically effective. One mitotic event stands out in its potential to significantly change a cell's SCNA burden -- a chromosome missegregation. We have presented a general deterministic framework for modeling whole chromosome missegregations and use it to evaluate the possibility of missegregation-induced population extinction (MIE). The model predicts critical curves that separate viable from non-viable populations as a function of their turnover- and mis-segregation rates. Missegregation- and turnover rates estimated for nine cancer types are then compared to these predictions for various biological assumptions. The assumption of heterogeneous missegregation rates within a tumor was sufficient to explain the observed data. By contrast, when assuming constant mis-segregation rates, several cancers were located in regions predicted as unviable. Intra-tumor heterogeneity, including heterogeneity in mis-segregation rates, increases as tumors progress. Our predictions suggest that this intra-tumor heterogeneity hinders the chance of success of therapies aimed at MIE.

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Patient Derived Xenografts for Genome-Driven Therapy of Osteosarcoma

Cells ◽

10.3390/cells10020416 ◽

2021 ◽

Vol 10 (2) ◽

pp. 416

Author(s):

Lorena Landuzzi ◽

Maria Cristina Manara ◽

Pier-Luigi Lollini ◽

Katia Scotlandi

Keyword(s):

Clinical Trials ◽

Tumor Heterogeneity ◽

Functional Studies ◽

Ngs Data Analysis ◽

The Many ◽

Orthotopic Xenografts ◽

Next Generation Sequencing Ngs ◽

Ngs Data ◽

Generation Sequencing ◽

Somatic Copy Number Alterations

Osteosarcoma (OS) is a rare malignant primary tumor of mesenchymal origin affecting bone. It is characterized by a complex genotype, mainly due to the high frequency of chromothripsis, which leads to multiple somatic copy number alterations and structural rearrangements. Any effort to design genome-driven therapies must therefore consider such high inter- and intra-tumor heterogeneity. Therefore, many laboratories and international networks are developing and sharing OS patient-derived xenografts (OS PDX) to broaden the availability of models that reproduce OS complex clinical heterogeneity. OS PDXs, and new cell lines derived from PDXs, faithfully preserve tumor heterogeneity, genetic, and epigenetic features and are thus valuable tools for predicting drug responses. Here, we review recent achievements concerning OS PDXs, summarizing the methods used to obtain ectopic and orthotopic xenografts and to fully characterize these models. The availability of OS PDXs across the many international PDX platforms and their possible use in PDX clinical trials are also described. We recommend the coupling of next-generation sequencing (NGS) data analysis with functional studies in OS PDXs, as well as the setup of OS PDX clinical trials and co-clinical trials, to enhance the predictive power of experimental evidence and to accelerate the clinical translation of effective genome-guided therapies for this aggressive disease.

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Multi-omic tumor data reveal diversity of molecular mechanisms that correlate with survival

10.1101/267245 ◽

2018 ◽

Cited By ~ 2

Author(s):

Daniele Ramazzotti ◽

Avantika Lal ◽

Bo Wang ◽

Serafim Batzoglou ◽

Arend Sidow

Keyword(s):

Molecular Mechanisms ◽

Molecular Subtypes ◽

Point Mutations ◽

Integrated Analysis ◽

Tumor Type ◽

Cancer Subtypes ◽

Cancer Types ◽

Copy Number Changes ◽

Omic Data

Outcomes for cancer patients vary greatly even within the same tumor type, and characterization of molecular subtypes of cancer holds important promise for improving prognosis and personalized treatment. This promise has motivated recent efforts to produce large amounts of multidimensional genomic (‘multi-omic’) data, but current algorithms still face challenges in the integrated analysis of such data. Here we present Cancer Integration via Multikernel Learning (CIMLR), a new cancer subtyping method that integrates multi-omic data to reveal molecular subtypes of cancer. We apply CIMLR to multi-omic data from 36 cancer types and show significant improvements in both computational efficiency and ability to extract biologically meaningful cancer subtypes. The discovered subtypes exhibit significant differences in patient survival for 27 of 36 cancer types. Our analysis reveals integrated patterns of gene expression, methylation, point mutations and copy number changes in multiple cancers and highlights patterns specifically associated with poor patient outcomes.

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The transcriptional hallmarks of intra-tumor heterogeneity across a thousand tumors

10.1101/2021.12.19.473368 ◽

2021 ◽

Author(s):

Avishai Gavish ◽

Michael Tyler ◽

Dor Simkin ◽

Daniel Kovarsky ◽

L. Nicolas Gonzalez Castro ◽

...

Keyword(s):

Tumor Heterogeneity ◽

Cancer Therapeutics ◽

Cell Types ◽

Cancer Type ◽

Malignant Cells ◽

Cellular Processes ◽

Narrow View ◽

Cancer Types ◽

Context Specific ◽

Tumor Types

Each tumor contains malignant cells that differ in genotype, phenotype, and in their interactions with the tumor micro-environment (TME). This results in distinct integrated cellular states that govern intra-tumor heterogeneity (ITH), a central challenge of cancer therapeutics. Dozens of recent studies have begun to describe ITH by single cell RNA-seq, but each study typically profiledonly a small number of tumors and provided a narrow view of transcriptional ITH. Here, we curate, annotate and integrate the data from 77 different studies to reveal the patterns of ITH across 1,163 tumor samples covering 24 tumor types. Focusing on the malignant cells, we find thousands of transcriptional ITH programs that can be described by 41 consensus meta-programs (MPs), each consisting of dozens of genes that are coordinately upregulated in subpopulations of cells within many different tumors. The MPs cover diverse cellular processes and differ in their cancer-type distribution. General MPs associated with processes such as cell cycle and stress vary within most tumors, while context-specific MPs reflect the unique biology of particular cancer types, often resembling developmental cell types and suggesting the co-existence of variable differentiation states within tumors. Some of the MPs are further associated with overall tumor proliferation or immune state, highlighting their potential clinical significance. Based on functional similarities among MPs, we propose a set of 11 hallmarks that together account for the majority of observed ITH programs. Given the breadth and scope of the investigated cohort, the MPs and hallmarks described here reflect the first comprehensive pan-cancer description of transcriptional ITH.

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The landscape of long noncoding RNA-involved and tumor-specific fusions across various cancers

Nucleic Acids Research ◽

10.1093/nar/gkaa1119 ◽

2020 ◽

Vol 48 (22) ◽

pp. 12618-12631

Author(s):

Mengbiao Guo ◽

Zhen-Dong Xiao ◽

Zhiming Dai ◽

Ling Zhu ◽

Hang Lei ◽

...

Keyword(s):

Cancer Progression ◽

Long Noncoding Rna ◽

Noncoding Rna ◽

Chimeric Proteins ◽

Protein Coding ◽

Cellular Processes ◽

Cancer Types ◽

Approved Drugs ◽

Fda Approved Drugs ◽

Somatic Copy Number Alterations

Abstract The majority of the human genome encodes long noncoding RNA (lncRNA) genes, critical regulators of various cellular processes, which largely outnumber protein-coding genes. However, lncRNA-involved fusions have not been surveyed and characterized yet. Here, we present a systematic study of the lncRNA fusion landscape across cancer types and identify >30 000 high-confidence tumor-specific lncRNA fusions (using 8284 tumor and 6946 normal samples). Fusions positively correlated with DNA damage and cancer stemness and were specifically low in microsatellite instable (MSI)-High or virus-infected tumors. Moreover, fusions distribute differently among cancer molecular subtypes, but with shared enrichment in tumors that are microsatellite stable (MSS), with high somatic copy number alterations (SCNA), and with poor survival. Importantly, we find a potentially new mechanism, mediated by enhancer RNAs (eRNA), which generates secondary fusions that form densely connected fusion networks with many fusion hubs targeted by FDA-approved drugs. Finally, we experimentally validate functions of two tumor-promoting chimeric proteins derived from mRNA-lncRNA fusions, KDM4B–G039927 and EPS15L1–lncOR7C2–1. The EPS15L1 fusion protein may regulate (Gasdermin E) GSDME, critical in pyroptosis and anti-tumor immunity. Our study completes the fusion landscape in cancers, sheds light on fusion mechanisms, and enriches lncRNA functions in tumorigenesis and cancer progression.

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Senescent cell turnover slows with age providing an explanation for the Gompertz law

Nature Communications ◽

10.1038/s41467-019-13192-4 ◽

2019 ◽

Vol 10 (1) ◽

Cited By ~ 20

Author(s):

Omer Karin ◽

Amit Agrawal ◽

Ziv Porat ◽

Valery Krizhanovsky ◽

Uri Alon

Keyword(s):

Half Life ◽

Removal Rate ◽

Cell Turnover ◽

Turnover Rates ◽

C Elegans ◽

Slowing Down ◽

Gompertz Law ◽

Dietary Shifts ◽

Turn Over ◽

Old Mice

AbstractA causal factor in mammalian aging is the accumulation of senescent cells (SnCs). SnCs cause chronic inflammation, and removing SnCs decelerates aging in mice. Despite their importance, turnover rates of SnCs are unknown, and their connection to aging dynamics is unclear. Here we use longitudinal SnC measurements and induction experiments to show that SnCs turn over rapidly in young mice, with a half-life of days, but slow their own removal rate to a half-life of weeks in old mice. This leads to a critical-slowing-down that generates persistent SnC fluctuations. We further demonstrate that a mathematical model, in which death occurs when fluctuating SnCs cross a threshold, quantitatively recapitulates the Gompertz law of mortality in mice and humans. The model can go beyond SnCs to explain the effects of lifespan-modulating interventions in Drosophila and C. elegans, including scaling of survival-curves and rapid effects of dietary shifts on mortality.

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Towards multi-omics characterization of tumor heterogeneity: a comprehensive review of statistical and machine learning approaches

Briefings in Bioinformatics ◽

10.1093/bib/bbaa188 ◽

2020 ◽

Author(s):

Dohoon Lee ◽

Youngjune Park ◽

Sun Kim

Keyword(s):

Cancer Biology ◽

Tumor Heterogeneity ◽

Large Scale ◽

Bioinformatic Analysis ◽

Learning Approaches ◽

Individual Layer ◽

Tumor Biopsy ◽

Comprehensive Review ◽

Cancer Types

Abstract The multi-omics molecular characterization of cancer opened a new horizon for our understanding of cancer biology and therapeutic strategies. However, a tumor biopsy comprises diverse types of cells limited not only to cancerous cells but also to tumor microenvironmental cells and adjacent normal cells. This heterogeneity is a major confounding factor that hampers a robust and reproducible bioinformatic analysis for biomarker identification using multi-omics profiles. Besides, the heterogeneity itself has been recognized over the years for its significant prognostic values in some cancer types, thus offering another promising avenue for therapeutic intervention. A number of computational approaches to unravel such heterogeneity from high-throughput molecular profiles of a tumor sample have been proposed, but most of them rely on the data from an individual omics layer. Since the heterogeneity of cells is widely distributed across multi-omics layers, methods based on an individual layer can only partially characterize the heterogeneous admixture of cells. To help facilitate further development of the methodologies that synchronously account for several multi-omics profiles, we wrote a comprehensive review of diverse approaches to characterize tumor heterogeneity based on three different omics layers: genome, epigenome and transcriptome. As a result, this review can be useful for the analysis of multi-omics profiles produced by many large-scale consortia. Contact:[email protected]

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Principal Effectiveness and Principal Turnover

Education Finance and Policy ◽

10.1162/edfp_a_00256 ◽

2019 ◽

Vol 14 (3) ◽

pp. 355-382 ◽

Cited By ~ 9

Author(s):

Jason A. Grissom ◽

Brendan Bartanen

Keyword(s):

Low Income ◽

Evaluation System ◽

Relative Effectiveness ◽

School Level ◽

Principal Effectiveness ◽

Turnover Rates ◽

Effective Principals ◽

Principal Turnover ◽

Principal Performance ◽

Turn Over

Research demonstrates the importance of principal effectiveness for school performance and the potentially negative effects of principal turnover. However, we have limited understanding of the factors that lead principals to leave their schools or about the relative effectiveness of those who stay and those who turn over. We investigate the association between principal effectiveness and principal turnover using longitudinal data from Tennessee, a state that has invested in multiple measures of principal performance through its educator evaluation system. Using three measures of principal performance, we show that less-effective principals are more likely to turn over, on average, though we find some evidence that the most effective principals have elevated turnover rates as well. Moreover, we demonstrate the importance of differentiating pathways out of the principalship, which vary substantially by effectiveness. Low performers are more likely to exit the education system and to be demoted to other school-level positions, whereas high performers are more likely to exit and to be promoted to central office positions. The link between performance and turnover suggests that prioritizing hiring or placing effective principals in schools with large numbers of low-income or low-achieving students can serve to lower principal turnover rates in high-needs environments.

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Structural dynamics is a determinant of the functional significance of missense variants

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1715896115 ◽

2018 ◽

Vol 115 (16) ◽

pp. 4164-4169 ◽

Cited By ~ 29

Author(s):

Luca Ponzoni ◽

Ivet Bahar

Keyword(s):

Structural Dynamics ◽

Protein Function ◽

Biological Function ◽

Point Mutations ◽

Computational Tools ◽

Missense Variants ◽

Pathogenicity Prediction ◽

Cancer Types ◽

The Impact ◽

Insight Into

Accurate evaluation of the effect of point mutations on protein function is essential to assessing the genesis and prognosis of many inherited diseases and cancer types. Currently, a wealth of computational tools has been developed for pathogenicity prediction. Two major types of data are used to this aim: sequence conservation/evolution and structural properties. Here, we demonstrate in a systematic way that another determinant of the functional impact of missense variants is the protein’s structural dynamics. Measurable improvement is shown in pathogenicity prediction by taking into consideration the dynamical context and implications of the mutation. Our study suggests that the class of dynamics descriptors introduced here may be used in conjunction with existing features to not only increase the prediction accuracy of the impact of variants on biological function, but also gain insight into the physical basis of the effect of missense variants.

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Diatoms diversify and turn over faster in freshwater than marine environments

10.1101/406165 ◽

2018 ◽

Cited By ~ 1

Author(s):

Teofil Nakov ◽

Jeremy Michael Beaulieu ◽

Andrew James Alverson

Keyword(s):

Turnover Rate ◽

Turnover Rates ◽

High Turnover ◽

Diversification Rates ◽

Extinction Rates ◽

State Dependent ◽

Freshwater Diatoms ◽

Marine Realm ◽

Turn Over ◽

Freshwater Environments

AbstractMany clades that span the marine-freshwater boundary are disproportionately more diverse in the younger, shorter-lived, and scarcer freshwater environments than they are in the marine realm. This disparity is thought to be related to differences in diversification rates between marine and freshwater lineages. However, marine and freshwaters are not ecologically homogeneous, so the study of diversification across the salinity divide should also account for other potentially interacting variables. In diatoms, freshwater and substrate-associated (benthic) lineages are several-fold more diverse than their marine and suspended (planktonic) counterparts. These imbalances provide an excellent system to understand whether these variables interact with diversification. Using multistate hidden-state speciation and extinction models we found that freshwater lineages diversify faster than marine lineages regardless of whether they inhabit the plankton or the benthos. Freshwater lineages also had higher turnover rates (speciation + extinction), suggesting that habitat transitions impact speciation and extinction rates jointly. The plankton-benthos contrast was also consistent with state-dependent diversification, but with modest differences in diversification and turnover rates. Asymmetric, and bidirectional transitions rejected hypotheses about the plankton and freshwaters as absorbing, inescapable habitats. Our results further suggest that the high turnover rate of freshwater diatoms is related to high turnover of freshwater systems themselves.

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Distinct DNA metabolism and anti-proliferative effects of goat urine metabolites: An explanation for xeno-tumor heterogeneity

10.1101/815548 ◽

2019 ◽

Author(s):

Ajay Kumar ◽

Swati Swami ◽

Nilesh Kumar Sharma

Keyword(s):

Colorectal Cancer ◽

Genomic Dna ◽

Tumor Heterogeneity ◽

Significant Loss ◽

Urine Metabolites ◽

Hct 116 ◽

Cancer Types ◽

In Vitro Treatment ◽

Mcf 7

ABSTRACTBackgroundThe tumor microenvironment, including microbiome populations in the local niche of several types of solid tumors like mammary and colorectal cancer are distinct. The occurrence of one type of cancer over another varies from animals to human individuals. Further, clinical data suggest that specific cancer types such as mammary and colorectal cancer are rare in ruminant like goat.MethodsFresh urine samples were collected from healthy ruminates including cow, goat, buffalo, ox, horse, jenny and human and subjected to fractionation using drying, vortexing, centrifugation and sterile filtration in DMSO solvent. Collected urine DMSO fraction (UDF) samples from all sources were subjected DNA metabolizing assay with plasmid DNA pBR322 and genomic DNA of MCF-7 cells. Further, based on the discernible DNA metabolizing effects, goat UDF was tested for anti-proliferative effects upon HCT-116 and MCF-7 cells using Trypan blue due exclusion assay.ResultsThis paper reports that goat UDF possesses very clear DNA metabolizing effects (up to 95%) upon plasmid and genomic DNA compared to other ruminants and human UDF samples. Interestingly autoclaving of goat UDF and other sample results in the significant loss of DNA metabolizing effects. In this way, data potentially indicate that the goat UDF sample contains metabolite or similar organic compounds. Further, in vitro treatment of the goat UDF sample shows discernible anti-proliferative effects upon HCT-116 (up to 75%) and MCF-7 (up to 40%).ConclusionThis study signifies the clear differences in DNA metabolizing effects of goat UDF and well correlated with anti-proliferative effects upon HCT-116 and MCF-7 cells. This study is of first report to show the comparison of urine metabolites and an indirect link to support the possible reasons behind xeno-tumor heterogeneity as rare occurrences of colorectal and mammary cancer in goat over other ruminants and human.

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