The Valley-of-Death: Reciprocal sign epistasis constrains adaptive trajectories in a constant, nutrient limiting environment

Chiotti, Kami; Kvitek, Daniel J.; Schmidt, Karen; Koniges, Gregory; Schwartz, Katja; Donckels, Elizabeth A.; Rosenzweig, Frank; Sherlock, Gavin

doi:10.1016/j.ygeno.2014.10.011

Cited by 23 publications

(23 citation statements)

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“…In other words, acquiring driver mutations (when their dependencies on other genes are satisfied) cannot decrease fitness, which implies that the fitness landscapes assumed by CPMs only have a single global fitness maximum (the genotype with all drivers mutated -see Figure 1). But it is likely that many cancer fitness landscapes have several local fitness maxima (i.e., they are rugged, multi-peaked landscapes): this can happen if there are many combinations of a small number of drivers, out of a larger pool of drivers (Tomasetti et al, 2015), that result in the escape genotypes; moreover, synthetic lethality is common in both cancer cells (Beijersbergen et al, 2017;O'Neil et al, 2017) and the human genome (Blomen et al, 2015), and it can lead to local fitness maxima when it affects mutations that individually increase fitness -see also Chiotti et al, 2014. Thus, to examine if CPMs can be used to predict paths of tumor progression we will need to assess how the quality of the predictions is affected by multi-peaked fitness landscapes.…”

Section: Introductionmentioning

confidence: 99%

Every which way? On predicting tumor evolution using cancer progression models

Dı́az-Uriarte

Vega

2018

Preprint

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Cancer progression models (CPMs) use cross-sectional samples to identify restrictions in the order of accumulation of driver mutations. CPMs implicitly encode all the possible tumor progression paths or evolutionary trajectories during cancer progression, which can be of help for diagnostic, prognostic, and treatment purposes. Here we examine whether CPMs can be used to predict the true distribution of tumor progression paths and to estimate evolutionary unpredictability. Using simulations we show that the agreement between the true and the predicted distributions of paths is generally poor, unless sample sizes are very large and fitness landscapes are single peaked (have a single global fitness maximum). Under other fitness landscapes, performance is poor and only improves slightly with increasing sample size. Detection regime can be a key determinant of performance, and evolutionary unpredictability hurts performance except under regimes with very low sample variability. Estimates of evolutionary unpredictability from CPMs tend to overestimate the true unpredictability and the bias is affected by detection regime; CPMs could be useful for estimating upper bounds to the true evolutionary unpredictability. Analysis of eleven cancer data sets supports the relevance of detection regime and shows estimates of evolutionary unpredictability in regions where useful prediction might possible for at least some data sets. But the evolutionary trajectory predictions themselves are unreliable. Our results indicate that, currently, obtaining useful predictions of tumor progression paths from CPMs is dubious and emphasize the need for methodological work that can account for the probably multi-peaked fitness landscapes in cancer.

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Section: Introductionmentioning

confidence: 99%

Every which way? On predicting tumor evolution using cancer progression models

Dı́az-Uriarte

Vega

2018

Preprint

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“…The loss of MTH1 also likely upregulates the expression of HXT6 and HXT7 under the low glucose concentrations, and thus also likely leads to increased glucose influx. Intriguingly, the combined effect of the loss of MTH1 and the 10-fold expansion of the HXT6/7 cluster is deleterious, most likely because the combined effect of both mutations is to increase the amount of hexose transporters beyond the optimal level under these conditions (Kvitek and Sherlock 2011;Chiotti et al 2014).…”

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confidence: 99%

Heterozygote Advantage Is a Common Outcome of Adaptation inSaccharomyces cerevisiae

et al. 2016

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Adaptation in diploids is predicted to proceed via mutations that are at least partially dominant in fitness. Recently, we argued that many adaptive mutations might also be commonly overdominant in fitness. Natural (directional) selection acting on overdominant mutations should drive them into the population but then, instead of bringing them to fixation, should maintain them as balanced polymorphisms via heterozygote advantage. If true, this would make adaptive evolution in sexual diploids differ drastically from that of haploids. The validity of this prediction has not yet been tested experimentally. Here, we performed four replicate evolutionary experiments with diploid yeast populations (Saccharomyces cerevisiae) growing in glucose-limited continuous cultures. We sequenced 24 evolved clones and identified initial adaptive mutations in all four chemostats. The first adaptive mutations in all four chemostats were three copy number variations, all of which proved to be overdominant in fitness. The fact that fitness overdominant mutations were always the first step in independent adaptive walks supports the prediction that heterozygote advantage can arise as a common outcome of directional selection in diploids and demonstrates that overdominance of de novo adaptive mutations in diploids is not rare.KEYWORDS heterozygote advantage; experimental evolution; adaptation; diploid T HE most immediate difference between diploids and haploids is that diploids have twice as many gene copies and thus roughly twice as many expected mutations per individual per generation, assuming the same mutation rate per nucleotide. If adaptation is limited by the waiting time for new adaptive mutations, this suggests that diploids might enjoy an adaptive advantage over haploids; indeed, it has been argued that the rate of adaptive evolution in diploids might be double that in haploids (Paquin and Adams 1983;Anderson et al. 2004) [although see Zeyl et al. (2003) and Gerstein et al. (2011)].Diploids, however, might also suffer an adaptive disadvantage. New mutations in diploids are heterozygous, and their effect is thus "diluted" or even completely masked by the presence of the ancestral allele. Unless mutations are fully dominant in fitness, this reduces the probability of fixation of new beneficial mutations and increases the expected frequency that can be reached by deleterious mutations. This fitness effect "dilution" also slows down fixation of adaptive alleles in diploids by roughly twofold when adaptive mutations are codominant in fitness, and by more than that when adaptive mutations are either recessive (they spread in the population slowly) or fully dominant (they suffer a slowdown at high frequencies). This suggests that haploids should gain an advantage in adapting to new environments when the rate of spread of adaptive mutations is the limiting step in adaptation.These considerations have underpinned a large body of theory (Otto and Gerstein 2008) and generated some specific predictions. One of these is known as Hal...

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“…Cancer progression models (CPMs) assume restrictive fitness landscapes that, for instance, are devoid of reciprocal sign epistasis. Yet reciprocal sign epistasis may be common in cancer fitness landscapes [11]. What would be the consequences of using CPMs if tumors evolved on fitness landscapes that cannot be represented by DAGs from CPMs?…”

Section: Discussionmentioning

confidence: 99%

“…for apparent exceptions). This is a potentially serious limitation of CPMs because reciprocal sign epistasis is probably common in cancer [11], given the extent of synthetic lethality both in the human genome [6] and in cancer cells [4,28,44] (synthetic lethality is an epistatic interaction where the combination of two mutations is lethal when each individual mutation is not -synthetic lethality between mutations that individually increase fitness constitutes reciprocal sign epistasis). Moreover, reciprocal sign epistasis is a key structural feature of fitness landscapes: it can lead to multiple peaks and affects ruggedness and predictability of the evolutionary process [13,14,19,38], which itself affects our opportunities to block tumor progression [23,29].…”

Section: Introductionmentioning

confidence: 99%

Cancer progression models and fitness landscapes: a many-to-many relationship

Dı́az-Uriarte

2017

Preprint

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The identification of constraints, due to gene interactions, in the order of accumulation of mutations during cancer progression can allow us to single out therapeutic targets. Cancer progression models (CPMs) use genotype frequency data from cross-sectional samples to try to identify these constraints, and return Directed Acyclic Graphs (DAGs) of genes. On the other hand, fitness landscapes, which map genotypes to fitness, contain all possible paths of tumor progression. Thus, we expect a correspondence between DAGs from CPMs and the fitness landscapes where evolution happened. But many fitness landscapes -e.g., those with reciprocal sign epistasiscannot be represented by CPMs. Using simulated data under 500 fitness landscapes, I show that CPMs' performance (prediction of genotypes that can exist) degrades with reciprocal sign epistasis. There is large variability in the DAGs inferred from each landscape, which is also affected by mutation rate, detection regime, and fitness landscape features, in ways that depend on CPM method. And the same DAG is often observed in very different landscapes, which differ in more than 50% of their accessible genotypes. Using a pancreatic data set, I show that this many-to-many relationship affects the analysis of empirical data. Fitness landscapes that are widely different from each other can, when evolutionary processes run repeatedly on them, both produce data similar to the empirically observed one, and lead to DAGs that are very different among themselves. Because reciprocal sign epistasis can be common in cancer, these results question the use and interpretation of CPMs.

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The Valley-of-Death: Reciprocal sign epistasis constrains adaptive trajectories in a constant, nutrient limiting environment

Cited by 23 publications

References 61 publications

Every which way? On predicting tumor evolution using cancer progression models

Every which way? On predicting tumor evolution using cancer progression models

Heterozygote Advantage Is a Common Outcome of Adaptation inSaccharomyces cerevisiae

Cancer progression models and fitness landscapes: a many-to-many relationship

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