Cancers adapt to their mutational load by buffering protein misfolding stress.

In asexual populations that don't undergo recombination, such as cancer, deleterious mutations are expected to accrue readily due to genome-wide linkage between mutations. Despite this mutational load of often thousands of deleterious mutations, many tumors thrive. How tumors survive the damaging consequences of this mutational load is not well understood.

Pervasive fitness trade-offs revealed by rapid adaptation in large experimental populations of .

Life-history trade-offs are an inherent feature of organismal biology that evolutionary theory posits play a key role in patterns of divergence within and between species. Efforts to quantify trade-offs are largely confined to phenotypic measurements and the identification of negative genetic-correlations among fitness-relevant traits. Here, we use time-series genomic data collected during experimental evolution in large, genetically diverse populations of to directly measure the manifestation of trade-offs in response to temporally fluctuating selection pressures on ecological timescales.

Most cancers carry a substantial deleterious load due to Hill-Robertson interference.

Cancer genomes exhibit surprisingly weak signatures of negative selection (Martincorena et al., 2017; Weghorn, 2017). This may be because selective pressures are relaxed or because genome-wide linkage prevents deleterious mutations from being removed (Hill-Robertson interference; Hill and Robertson, 1966).

Direct observation of adaptive tracking on ecological time scales in .

Direct observation of evolution in response to natural environmental change can resolve fundamental questions about adaptation, including its pace, temporal dynamics, and underlying phenotypic and genomic architecture. We tracked the evolution of fitness-associated phenotypes and allele frequencies genome-wide in 10 replicate field populations of over 10 generations from summer to late fall. Adaptation was evident over each sampling interval (one to four generations), with exceptionally rapid phenotypic adaptation and large allele frequency shifts at many independent loci.

Broad geographic sampling reveals the shared basis and environmental correlates of seasonal adaptation in .

To advance our understanding of adaptation to temporally varying selection pressures, we identified signatures of seasonal adaptation occurring in parallel among populations. Specifically, we estimated allele frequencies genome-wide from flies sampled early and late in the growing season from 20 widely dispersed populations. We identified parallel seasonal allele frequency shifts across North America and Europe, demonstrating that seasonal adaptation is a general phenomenon of temperate fly populations.

Accurate, ultra-low coverage genome reconstruction and association studies in Hybrid Swarm mapping populations.

Genetic association studies seek to uncover the link between genotype and phenotype, and often utilize inbred reference panels as a replicable source of genetic variation. However, inbred reference panels can differ substantially from wild populations in their genotypic distribution, patterns of linkage-disequilibrium, and nucleotide diversity. As a result, associations discovered using inbred reference panels may not reflect the genetic basis of phenotypic variation in natural populations.

Accurate Allele Frequencies from Ultra-low Coverage Pool-Seq Samples in Evolve-and-Resequence Experiments.

Evolve-and-resequence (E+R) experiments leverage next-generation sequencing technology to track the allele frequency dynamics of populations as they evolve. While previous work has shown that adaptive alleles can be detected by comparing frequency trajectories from many replicate populations, this power comes at the expense of high-coverage (>100x) sequencing of many pooled samples, which can be cost-prohibitive. Here, we show that accurate estimates of allele frequencies can be achieved with very shallow sequencing depths (<5x) via inference of known founder haplotypes in small genomic windows.

Publisher Correction: Clonal replacement and heterogeneity in breast tumors treated with neoadjuvant HER2-targeted therapy.

The original version of this Article omitted from the Author Contributions statement that 'R.S. and J.

Clonal replacement and heterogeneity in breast tumors treated with neoadjuvant HER2-targeted therapy.

Genomic changes observed across treatment may result from either clonal evolution or geographically disparate sampling of heterogeneous tumors. Here we use computational modeling based on analysis of fifteen primary breast tumors and find that apparent clonal change between two tumor samples can frequently be explained by pre-treatment heterogeneity, such that at least two regions are necessary to detect treatment-induced clonal shifts. To assess for clonal replacement, we devise a summary statistic based on whole-exome sequencing of a pre-treatment biopsy and multi-region sampling of the post-treatment surgical specimen and apply this measure to five breast tumors treated with neoadjuvant HER2-targeted therapy.

Deep sequencing of natural and experimental populations of reveals biases in the spectrum of new mutations.

Mutations provide the raw material of evolution, and thus our ability to study evolution depends fundamentally on having precise measurements of mutational rates and patterns. We generate a data set for this purpose using (1) de novo mutations from mutation accumulation experiments and (2) extremely rare polymorphisms from natural populations. The first, mutation accumulation (MA) lines are the product of maintaining flies in tiny populations for many generations, therefore rendering natural selection ineffective and allowing new mutations to accrue in the genome.