Biology subjects

Hochberg, M. E.

Publications and source records attributed to Hochberg, M. E..

5 recordsLinked to original sources

bioRxiv2025

Disease as a mediator of somatic mutation-life history coevolution

Multicellular organisms are confronted not only with germline mutations, but also with mutations emerging in somatic cells. Somatic mutations can lead to diverse pathologies, including cancer. Somatic mutation rate (SMR) can be constrained by evolved protection mechanisms, notably those repairing damaged DNA, or eliminating or slowing the growth of mutated cells. However, in a broader context, life history traits such as body mass, age of first reproduction and reproductive lifespan can also be subject to selection due to the negative fitness impacts of disease. Here, I analyze a simple coevolutionary model of somatic mutation rate and lifespan. The trait dynamics are formulated as an evolutionary-response system through an explicit, frequency-dependent invasion-fitness function. Evolution in the model is driven in part by the negative fitness impacts of disease, represented phenomenologically as increasing with both SMR and lifespan. I investigate relations between selective forces and (co)evolutionary responses, notably showing the possibility of either monotone or oscillatory non-equilibrium dynamics and fast or slow returns to equilibrium. Model predictions are then compared to recently published data on body mass, lifespan and somatic mutation rate. I show that the model (1) can explain the non-linear empirical relationship between somatic mutation and lifespan, (2) predicts the evolution of longer lifespans through a heretofore ignored feedback loop, (3) predicts opposite body-mass scaling of cumulative mutational burden and lifespan-normalized mutation rate, and (4) is compatible with observed linear mutation accumulation with age reflecting a hypothesized washing-out of highly mutated cells. I discuss the findings and their generality to somatic diseases, notably cancers and to aging itself.

evolutionary biology↗

bioRxiv2020

Paracrine behaviors arbitrate parasite-like interactions between tumor subclones

Explaining the emergence and maintenance of intratumor heterogeneity is an important question in cancer biology. Tumor cells can generate considerable subclonal diversity, which influences tumor growth rate, treatment resistance, and metastasis, yet we know remarkably little about how cells from different subclones interact. Here, we confronted two murine mammary cancer cell lines to determine both the nature and mechanisms of subclonal cellular interactions in vitro. Surprisingly, we found that, compared to monoculture, growth of the winner was enhanced by the presence of the loser cell line, whereas growth of the latter was reduced. Mathematical modeling and laboratory assays indicated that these interactions are mediated by the production of paracrine metabolites resulting in the winner subclone effectively farming the loser. Our findings add a new level of complexity to the mechanisms underlying subclonal growth dynamics.

cancer biology↗

bioRxiv2019

When, why and how clonal diversity predicts future tumour growth

Intratumour heterogeneity holds promise as a prognostic biomarker in multiple cancer types. However, the relationship between this marker and its clinical impact is mediated by an evolutionary process that is not well understood. Here we employ a spatial computational model of tumour evolution to assess when, why and how intratumour heterogeneity can be used to forecast tumour growth rate, an important predictor of clinical progression. We identify three conditions that can lead to a positive correlation between clonal diversity and subsequent growth rate: diversity is measured early in tumour development; selective sweeps are rare; and/or tumours vary in the rate at which they acquire driver mutations. Opposite conditions typically lead to negative correlation. Our results further suggest that prognosis can be better predicted on the basis of both clonal diversity and genomic instability than either factor alone. Nevertheless, we find that, for predicting tumour growth, clonal diversity is likely to perform worse than conventional measures of tumour stage and grade. We thus offer explanations - grounded in evolutionary theory - for empirical findings in various cancers. Our work informs the search for new prognostic biomarkers and contributes to the development of predictive oncology.

cancer biology↗

bioRxiv2019

Phage steering of antibiotic-resistance evolution in the bacterial pathogen Pseudomonas aeruginosa

Antimicrobial resistance is a growing global concern and has spurred increasing efforts to find alternative therapeutics. Bacteriophage therapy has seen near constant use in eastern Europe since its discovery over a century ago. One promising approach is to use phages that not only reduce bacterial pathogen loads, but also select for phage resistance mechanisms that trade-off with antibiotic resistance - so called phage steering. Recent work has shown that phage OMKO1 can interact with efflux pumps and in so doing select for both phage resistance and antibiotic sensitivity. We tested the robustness of this approach to three different antibiotics in vitro and one in vivo. We show that in vitro OMKO1 can reduce antibiotic resistance either in the absence or the presence of antibiotics. Our in vivo experiment showed that phage increased the survival times of wax moth larvae and increased bacterial sensitivity to erythromycin, both in the absence and presence of the antibiotic. We discuss the implications of our findings for future research on this promising therapeutic approach using OMKO1.

evolutionary biology↗

bioRxiv2017

The network structure of cancer ecosystems

Ever since Pagets seed-and-soil and Ewings connectivity hypotheses to explain tumor metastasis (1,2), it has become clear that cancer progression can be envisaged as an ecological phenomenon. This connection has flourished during the past two decades (3-7), giving rise to important insights into the ecology and evolution of cancer progression, with therapeutic implications (8-10). Here, we take a metapopulation view of metastasis (i.e. the migration to and colonization of, habitat patches) and represent it as a bipartite network, distinguishing source patches, or organs that host a primary tumor, and acceptor patches, or organs colonized ultimately from the source through metastasis. Using 20,326, biomedical records obtained from literature, we show that: (i) the network structure of cancer ecosystems is non-random, exhibiting a nested subset pattern as has been found both in the distribution of species across islands and island-like habitats (11-13), and in the distribution of among species interactions across different ecological networks (14-16); (ii) similar to ecological networks, there is a heterogeneous distribution of degree (i.e., number of connections associated with a source or acceptor organ); (iii) there is a significant correlation between metastatic incidence (or the frequency with which tumor cells from a source organ colonize an acceptor one) and arterial blood supply, suggesting that more irrigated organs have a higher probability of developing metastasis or being invaded; (iv) there is a positive correlation between metastatic incidence and acceptor organ degree (or number of different tumor-bearing source organs that generate metastasis in a given acceptor organ), and a negative one between acceptor organ degree and number of stem cell divisions, implying that there are preferred sink organs for metastasis and that this could be related to average acceptor organ cell longevity; (v) there is a negative association between organ cell turnover and source organ degree, implying that organs with rapid cell turnovers tend to generate more metastasis, a process akin to the phenomenon of propagule pressure in ecology (17); and (vi) the cancer ecosystem network exhibits a modular structure in both source and acceptor patches, suggesting that some of them share more connections among themselves than with the rest of the network. We show that both niche-related processes occurring at the organ level as well as spatial connectivity and propagule pressure contribute to metastaticspread and result in a non-random cancer network, which exhibits a truncated power law degree distribution, clustering and a nested subset structure. The similarity between the cancer network and ecological networks highlights the importance of ecological approaches in increasing our understanding of patterns in cancer incidence and dynamics, which may lead to new strategies to control tumor spread within the human ecosystem.

ecology↗