Biology subjects

Persi, E.

Publications and source records attributed to Persi, E..

4 recordsLinked to original sources

bioRxiv2026

Evolutionary implications of phenotype switching for growth and survival of tumors in stochastic environments.

Tumors evolve under constantly changing microenvironmental conditions, and a key major mechanism by which cancer cells adapt to fluctuating environments is stochastic phenotypic switch via epithelial-mesenchymal plasticity (EMP). Here we demonstrate the existence of EMP in human breast epithelial cell lines and assess the fitness of E and M cells in environments that fluctuate between favorable and harsh conditions. We then develop a theoretical framework for analysis of tumor evolution with E-M stochastic phenotypic switch that integrates the behaviors of E and M cells. We characterize the outcomes of tumor evolution via three characteristic times describing i) tumor growth, ii) phenotypic adaptation and iii) fluctuations of the environment. We identify a tradeoff between tumor growth and survival, with different phenotype switching rates maximizing each of these objectives. An anticancer therapeutic strategy is proposed based on tumor survival analysis, revealing that blocking mesenchymal to epithelial transition, rather than epithelial to mesenchymal transition, is critical under fluctuating conditions. Thus, this study elucidates fundamental evolutionary mechanisms of tumor phenotypic adaptation in fluctuating environments with potentially important clinical implications.

evolutionary biology↗

bioRxiv2022

Compensatory Relationship between Low Complexity Regions and Gene Paralogy in the Evolution of Prokaryotes

Evolution of genomes in all life forms involves two distinct, dynamic types of genomic changes: gene duplication (and loss) that shape families of paralogous genes and extension (and contraction) of low complexity regions (LCR), which occurs through dynamics of short repeats in protein-coding genes. Although the roles of each of these types of events in genome evolution have been studied, their co-evolutionary dynamics is not thoroughly understood. Here, by analyzing a wide range of genomes from diverse bacteria and archaea, we show that LCR and paralogy represent two distinct routes of evolution that are inversely correlated. Emergence of LCR is a prominent evolutionary mechanism in fast evolving, young protein families, whereas paralogy dominates the comparatively slow evolution of old protein families. Analysis of multiple prokaryotic genomes shows that the formation of LCR is likely a widespread, transient evolutionary mechanism that temporally and locally affects also ancestral functions, but apparently, fades away with time, under mutational and selective pressures, yielding to gene paralogy. We propose that compensatory relationships between short-term and longer-term evolutionary mechanisms are universal in the evolution of life. SignificanceEvolution of genomes in all organisms involves a variety of changes occurring on different spatial and temporal scales, from point mutations to whole genome duplication. Here we demonstrate that during the evolution of bacterial and archaeal genomes, there is a universal inverse relationship between the formation of low complexity regions in protein sequences through proliferation of short repeats and gene duplication. The former process apparently is a route of short-term adaptation whereas the latter one dominates evolution on longer temporal scales. We propose that compensatory relationships between evolutionary mechanisms acting at different spatial and temporal scales are a general feature of the process of evolution.

evolutionary biology↗

bioRxiv2018

Proteomic and Genomic Signatures of Repeat-instability in Cancer and Adjacent Normal Tissues

Repetitive sequences are hotspots of evolution at multiple levels. However, due to technical difficulties involved in their assembly and analysis, the role of repeats in tumor evolution is poorly understood. We developed a rigorous motif-based methodology to quantify variations in the repeat content of proteomes and genomes, directly from proteomic and genomic raw sequence data, and applied it to analyze a wide range of tumors and normal tissues. We identify high similarity between the repeat-instability in tumors and their patient-matched normal tissues, but also tumor-specific signatures, both in protein expression and in the genome, that strongly correlate with cancer progression and robustly predict the tumorigenic state. In a patient, the hierarchy of genomic repeat instability signatures accurately reconstructs tumor evolution, with primary tumors differentiated from metastases. We find an inverse relationship between repeat-instability and point mutation load, within and across patients, and independently of other somatic aberrations. Thus, repeat-instability is a distinct, transient and compensatory adaptive mechanism in tumor evolution.

cancer biology↗

bioRxiv2018

Criticality in Tumor Evolution and Clinical Outcome

How mutation and selection determine the fitness landscape of tumors and hence clinical outcome is an open fundamental question in cancer biology, crucial for the assessment of therapeutic strategies and resistance to treatment. Here we explore the mutation-selection phase-diagram of 6721 primary tumors representing 23 cancer types, by quantifying the overall somatic point mutation load (ML) and selection (dN/dS) in the entire proteome of each tumor. We show that ML strongly correlates with patient survival, revealing two opposing regimes around a critical point. In low ML cancers, high number of mutations indicates poor prognosis, whereas high ML cancers show the opposite trend, due to mutational meltdown. Although the majority of cancers evolve near neutrality, deviations are observed at extreme MLs. Cancers with the highest ML evolve under purifying selection, whereas those with the lowest ML show signatures of positive selection, demonstrating how selection affects cancer fitness. Moreover, different cancers occupy specific positions on the ML-dN/dS plane, revealing a diversity of evolutionary trajectories. These results support and expand the theory of tumor evolution and its non-linear effects on survival.\n\nSignificance StatementIt remains an open fundamental question how mutation and selection co-determine the course of cancer evolution. We construct a selection-mutation phase diagram, using tumor mutation load and selection strength as key variables, and assess their association with clinical outcome. We demonstrate the existence of a biphasic evolutionary regime, whereby beyond a critical ML, the fitness of tumors decreases with the number of mutations, while the proteome evolves near neutrality. Deviations from neutrality in extreme ML elucidate how positive and purifying selections maintain tumor fitness. These results empirically corroborate the existence of a critical state in cancer evolution predicted by theory, and have fundamental and likely clinical implications.

cancer biology↗