With the increase in next generation sequencing generating large amounts of genomic data, gene expression signatures are becoming critically important tools, poised to make a large impact on the diagnosis, management and prognosis for a number of diseases. Increasingly, it is becoming necessary to determine whether a gene expression signature may apply to a dataset, but no standard quality control methodology exists. In this work, we introduce the first protocol, implemented in an R package sigQC, enabling a streamlined methodological and standardised approach for the quality control validation of gene signatures on independent data sets. The emphasis in this work is in showing the critical quality control steps involved in the generation of a clinically and biologically useful, transportable gene signature, including ensuring sufficient expression, variability, and autocorrelation of a signature. We demonstrate the application of the protocol in this work, showing how the outputs created from sigQC may be used for the evaluation of gene signatures on large-scale gene expression data in cancer.
Despite major strides in the treatment of cancer, the development of drug resistance remains a major hurdle. To address this issue, researchers have proposed sequential drug therapies with which the resistance developed by a previous drug can be relieved by the next one, a concept called collateral sensitivity. The optimal times of these switches, however, remains unknown. We therefore developed a dynamical model and study the effect of sequential therapy on heterogeneous tumors comprised of resistant and sensitivity cells. A pair of drugs (DrugA, DrugB) are utilized and switched in turn within the therapy schedule. Assuming that they are collaterally sensitive to each other, we classified cancer cells into two groups, and explored their population dynamics: A_R and B_R, each of which is subpopulation of cells resistant to the indicated drug and concurrently sensitive to the other. Based on a system of ordinary differential equations for A_R and B_R, we determined that the optimal treatment strategy consists of two stages: initial stage in which a chosen better drug is utilised until a specific time point, T, and afterward; a combination of the two drugs with relative durations (i.e. f Δt-long for DrugA and (1-f)Δt-long for DrugB with 0≤f≤1 and Δt≥0). Of note, we prove that the initial period, in which the first drug is administered, T, is shorter than the period in which it remains effective in lowing total population, contrary to current clinical intuition. We further analyzed the relationship between population makeup, ApB=A_R/B_R, and effect of each drug. We determine a specific makeup, ApB*, at which the two drugs are equally effective. While the optimal strategy is applied, ApB is changing monotonically to ApB* and then remains at ApB* thereafter. Beyond our analytic results, we explored an individual based stochastic model and presented the distribution of extinction times for the classes of solutions found. Taken together, our results suggest opportunities to improve therapy scheduling in clinical oncology.
Experiments show that fitness landscapes can have a rich combinatorial structure due to epistasis and yet theory assumes that local peaks can be reached quickly. I introduce a distinction between easy landscapes where local fitness peaks can be found in a moderate number of steps and hard landscapes where finding evolutionary equilibria requires an infeasible amount of time. Hard examples exist even among landscapes with no reciprocal sign epistasis; on these, strong selection weak mutation dynamics cannot find the unique peak in polynomial time. On hard rugged fitness landscapes, no evolutionary dynamics -- even ones that do not follow adaptive paths -- can find a local fitness peak quickly; and the fitness advantage of nearby mutants cannot drop off exponentially fast but has to follow a power-law that long term evolution experiments have associated with unbounded growth in fitness. I present candidates for hard landscapes at scales from singles genes, to microbes, to complex organisms with costly learning (Baldwin effect). Even though hard landscapes are static and finite, local evolutionary equilibrium cannot be assumed.
Antibiotic resistance represents a growing health crisis that necessitates the immediate discovery of novel treatment strategies. One such strategy is the identification of sequences of drugs exhibiting collateral sensitivity, wherein the evolution of resistance to a first drug renders a population more susceptible to a second. Here, we demonstrate that sequential multi-drug therapies derived from in vitro evolution experiments can have overstated therapeutic benefit - potentially suggesting a collaterally sensitive response where cross resistance ultimately occurs. The evolution of drug resistance need not be genetically or phenotypically convergent, and where resistance arises through divergent mechanisms, the efficacy of a second drug can vary substantially. We first quantify the likelihood of this occurring by use of a mathematical model parametrised by a set of small combinatorially complete fitness landscapes for Escherichia coli. We then verify, through in vitro experimental evolution, that a second-line drug can indeed stochastically exhibit either increased susceptibility or increased resistance when following a first. Genetic divergence is confirmed as the driver of this differential response through targeted sequencing. These results indicate that the present methodology of designing drug regimens through experimental collateral sensitivity analysis may be flawed under certain ecological conditions. Further, these results suggest the need for a more rigorous probabilistic understanding of the contingencies that can arise during the evolution of drug resistance.