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

Successful prediction of the likely paths of tumor progression is valuable for diagnostic, prognostic, and treatment purposes. Cancer progression models (CPMs) use cross-sectional samples to identify restrictions in the order of accumulation of driver mutations and thus CPMs encode the paths of tumor progression. Here we analyze the performance of four CPMs to examine whether they can be used to predict the true distribution of paths of tumor progression and to estimate evolutionary unpredictability. Employing simulations we show that if fitness landscapes are single peaked (have a single fitness maximum) there is good agreement between true and predicted distributions of paths of tumor progression when sample sizes are large, but performance is poor with the currently common much smaller sample sizes. Under multi-peaked fitness landscapes (i.e., those with multiple fitness maxima), performance is poor and improves only slightly with sample size. In all cases, detection regime (when tumors are sampled) is a key determinant of performance. Estimates of evolutionary unpredictability from the best performing CPM, among the four examined, 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 twenty-two cancer data sets shows low evolutionary unpredictability for several of the data sets. But most of the predictions of paths of tumor progression are very unreliable, and unreliability increases with the number of features analyzed. Our results indicate that CPMs could be valuable tools for predicting cancer progression but that, currently, obtaining useful predictions of paths of tumor progression from CPMs is dubious, and emphasize the need for methodological work that can account for the probably multi-peaked fitness landscapes in cancerWork partially supported by BFU2015- 67302-R (MINECO/FEDER, EU) to RDU. CV supported by PEJD-2016-BMD-2116 from Comunidad de Madrid to RD

The Szeg\"o curve and Laguerre polynomials with large negative parameters

We study the asymptotic zero distribution of the rescaled Laguerre polynomials, $\displaystyle L_n^{(\alpha_n)}(nz)$, with the parameter $\alpha_n$ varying in such a way that $\displaystyle \lim_{n\rightarrow \infty}\alpha_n/n=-1$. The connection with the so-called Szeg\"{o} curve will be showed

Cellular covers of local groups

We prove that, in the category of groups, the composition of a cellularization and a localization functor need not be idempotent. This provides a negative answer to a question of Emmanuel Dror Farjoun.Ministerio de Educación y CienciaJunta de Andalucí

On soft/hard handoff for packet data services in cellular CDMA mobiles systems

Benefits of macrodiversity operation for packet data services in third generation mobile systems are not obvious. Retransmission procedures to enhance link performance and higher downlink bandwidth requirements could question macrodiversity usage. This paper describes a simple methodology to compare soft and hard handoff performance in terms of transmission delay for packet data services. The handover procedures are based exclusively on power criteria and hysteresis margins.Peer ReviewedPostprint (published version

Multiple slot allocation for voice/data transmission over PRMA++ applied to FRAMES multiple access mode 1

This paper presents some simulation results of PRMA++ for voice and data transmission over a physical air interface platform defined in the ACTS European project FRAMES. Variations on the statistics of speech sources (activity factor, petition rate) are studied and conclusions are obtained for optimal frame dimensioning. For data transmission, a multiple slot allocation scheme is presented and results are shown for different source rates and packet lengths.Peer ReviewedPostprint (published version

OncoSimulR: Genetic simulation with arbitrary epistasis and mutator genes in asexual populations

OncoSimulR implements forward-time genetic simulations of biallelic loci in asexual populations with special focus on cancer progression. Fitness can be defined as an arbitrary function of genetic interactions between multiple genes or modules of genes, including epistasis, restrictions in the order of accumulation of mutations, and order effects. Mutation rates can differ among genes, and can be affected by (anti)mutator genes. Also available are sampling from simulations (including single-cell sampling), plotting the genealogical relationships of clones and generating and plotting fitness landscapesSupported by BFU2015-67302-R (MINECO/FEDER, EU