sigQC: A procedural approach for standardising the evaluation of gene signatures

Andrew Dhawan, Alessandro Barberis, Wei-Chen Cheng, Enric Domingo, Catharine West, Tim Maughan, Jacob Scott, Adrian L Harris, Francesca M Buffa

doi: https://doi.org/10.1101/203729

This article is a preprint and has not been peer-reviewed [what does this mean?].

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Abstract

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.

https://www.biorxiv.org/content/early/2017/10/16/203729

Optimal Therapy Scheduling Based on a Pair of Collaterally Sensitive Drugs

 View ORCID ProfileNara Yoon, Robert Vander Velde, Andriy Marusyk,  View ORCID ProfileJacob Scott

doi: https://doi.org/10.1101/196824

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Abstract

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.

https://www.biorxiv.org/content/early/2017/10/11/196824