Testing Multi-Task Cancer Evolution: How Do We Test Ecological Hypotheses in Cancer?

Plutynski, Anya

doi:10.3389/fevo.2021.666262

Cited by 4 publications

(3 citation statements)

References 33 publications

(37 reference statements)

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“…There is no agreement on how to infer tasks from functional data. For example, Plutynski points out that an analysis of life history and trade‐offs should draw on phenotypes and genotypes [ 28 ]. We chose the 10 cancer hallmarks to signify tasks important to the survival and growth of cancer cells.…”

Section: Discussionmentioning

confidence: 99%

Cancer cells forgo translating mRNA transcribed from genes of nonspecialized tasks

Ahmed,

Pham,

Kim

et al. 2024

FEBS Open Bio

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The coupling of transcription and translation enables prokaryotes to regulate mRNA stability and reduce nonfunctional transcripts. Eukaryotes evolved other means to perform these functions. Here, we quantify the disparity between gene expression and protein levels and attempt to explain its origins. We collected publicly available simultaneous measurements of gene expression, protein level, division rate, and growth inhibition of breast cancer cells under drug perturbation. We used the cell lines as entities with shared origin, different evolutionary trajectories, and cancer hallmarks to define tasks subject to specializing and trading‐off. We observed varying average mRNA and protein correlation across cell lines, and it was consistently higher for the gene products in the cancer hallmarks. The enrichment of hallmark gene products signifies the resources invested in it as a task. Enrichment based on mRNA or protein abundance corresponds to the relative resources dedicated to transcription and translation. The differences in gene‐ and protein‐based enrichment correlated with nominal division rates but not growth inhibition under drug perturbations. Comparing the range of enrichment scores of the hallmarks within each cell signifies the resources dedicated to each. Cells appear to have a wider range of enrichment in protein synthesis relative to gene transcription. The difference and range of enrichment of the hallmark genes and proteins correlated with cell division and inhibition in response to drug treatments. We posit that cancer cells may express the genes coding for seemingly nonspecialized tasks but do not translate them to the corresponding proteins. This trade‐off may cost the cells under normal conditions but confer benefits during stress.

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Section: Discussionmentioning

confidence: 99%

Cancer cells forgo translating mRNA transcribed from genes of nonspecialized tasks

Ahmed,

Pham,

Kim

et al. 2024

FEBS Open Bio

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“…For this reason, evo-eco thinking has not achieved tangible clinic success. As indicated by Korolev et al (2014), it is crucial to pursue the identified parallels further by making them quantitative, testable, and eventually useful insights for devising therapy strategies and methods (Plutynski, 2021). DNA sequencing technologies, and experiments with microbes and animal models have created unprecedented opportunities to revolutionize the eco-evolutionary studies on cancers.…”

Section: Key Concepts/hypotheses In Cancer Ecologymentioning

confidence: 99%

Ecology of Human Medical Enterprises: From Disease Ecology of Zoonoses, Cancer Ecology Through to Medical Ecology of Human Microbiomes

Sam

Zhang²

2022

Front. Ecol. Evol.

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In nature, the interaction between pathogens and their hosts is only one of a handful of interaction relationships between species, including parasitism, predation, competition, symbiosis, commensalism, and among others. From a non-anthropocentric view, parasitism has relatively fewer essential differences from the other relationships; but from an anthropocentric view, parasitism and predation against humans and their well-beings and belongings are frequently related to heinous diseases. Specifically, treating (managing) diseases of humans, crops and forests, pets, livestock, and wildlife constitute the so-termed medical enterprises (sciences and technologies) humans endeavor in biomedicine and clinical medicine, veterinary, plant protection, and wildlife conservation. In recent years, the significance of ecological science to medicines has received rising attentions, and the emergence and pandemic of COVID-19 appear accelerating the trend. The facts that diseases are simply one of the fundamental ecological relationships in nature, and the study of the relationships between species and their environment is a core mission of ecology highlight the critical importance of ecological science. Nevertheless, current studies on the ecology of medical enterprises are highly fragmented. Here, we (i) conceptually overview the fields of disease ecology of wildlife, cancer ecology and evolution, medical ecology of human microbiome-associated diseases and infectious diseases, and integrated pest management of crops and forests, across major medical enterprises. (ii) Explore the necessity and feasibility for a unified medical ecology that spans biomedicine, clinical medicine, veterinary, crop (forest and wildlife) protection, and biodiversity conservation. (iii) Suggest that a unified medical ecology of human diseases is both necessary and feasible, but laissez-faire terminologies in other human medical enterprises may be preferred. (iv) Suggest that the evo-eco paradigm for cancer research can play a similar role of evo-devo in evolutionary developmental biology. (v) Summarized 40 key ecological principles/theories in current disease-, cancer-, and medical-ecology literatures. (vi) Identified key cross-disciplinary discovery fields for medical/disease ecology in coming decade including bioinformatics and computational ecology, single cell ecology, theoretical ecology, complexity science, and the integrated studies of ecology and evolution. Finally, deep understanding of medical ecology is of obvious importance for the safety of human beings and perhaps for all living things on the planet.

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“…Considering many tasks may hinder defining trade-offs for several of the tumors that were included in the study (Hausser et al, 2019). Plutynski pointed out The Cancer Genome Atlas (TCGA) data, on which the study was based, might be biased toward the task that the study explains (Plutynski, 2021). In addition, an analysis of life history and trade-offs should draw on phenotypes as well as Correlation between hallmark enrichment in archetypes and in response to drug perturbations.…”

Section: Cell Linementioning

confidence: 99%

Disruption of cancer cell functions by task-specific drug perturbations

Ahmed

Kim

2022

Front. Pharmacol.

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Cancer expands clonally, capitalizing on the variations between growing cells. Cancer cells specialize in one or more functions to gain an advantage. This study examined the prediction that cells would be vulnerable to drugs that perturb their specific tasks. We analyzed the correlation between gene expression and the response to drug perturbations in different cancer cells. Next, we assigned every cancer cell to an archetype based on gene expression. Finally, we calculated the enrichment of the cancer hallmark gene sets in each cell, archetypes, and response to drug treatment. We found that the extremes of gene expression were susceptible to change in response to perturbations. This correlation predicted the growth rate inhibition of breast cancer cells. Cancer hallmarks were enriched differently in the archetypes, and this enrichment predicted the cell’s response to perturbations. We present evidence that specialized cancer cells are sensitive to compounds that perturb their tasks.

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Testing Multi-Task Cancer Evolution: How Do We Test Ecological Hypotheses in Cancer?

Cited by 4 publications

References 33 publications

Cancer cells forgo translating mRNA transcribed from genes of nonspecialized tasks

Cancer cells forgo translating mRNA transcribed from genes of nonspecialized tasks

Ecology of Human Medical Enterprises: From Disease Ecology of Zoonoses, Cancer Ecology Through to Medical Ecology of Human Microbiomes

Disruption of cancer cell functions by task-specific drug perturbations

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