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

Abstract

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 cancer.

Identification of Conserved Evolutionary Trajectories in Tumors

“…To ensure that T S is a tree, we require that each node can have only one parent; this is ensured by constraint (6). We enforce that the nodes of T S form a connected component of size |S| by considering a fictitious network flow originating at seed node s of |S| − 1 units -by constraint (7). The flow loses 1 unit at each node -by constraint (8).…”

“…As multi-region, time-series and single cell sequencing data become more widely available, it is becoming clear that certain tumors share evolutionary characteristics with others. With new computational methods to identify recurrent cancer progression patterns from multi-dimensional tumor sequencing data, it may become possible to predict the likely course of evolution and perhaps an effective treatment strategy for certain cancer types [14,21,7]. E.g.…”

Mechanical influences onin silicotumor evolution

“…To determine the fitness of cells experimentally, the lineage of cells has to be tracked over time, making it necessary to track each individual cell over a long time, which is currently not possible in in vivo tumors. Models of the fitness landscape based on genetic changes and driver mutations have been introduced [5], yet phenotypic changes in cells can also drive mutation [6,7]. Naturally, an ensemble of cells with different cell types evolves towards the cell type of highest fitness.…”

Efficient Agony Based Transfer Learning Algorithms for Survival Forecasting

“…Cancer progression modeling is a mature subfield of cancer informatics [7]. The desirable models seek to recapitulate or forecast the accumulation of genomic events in the course of a patient's disease.…”