A Mathematical Methodology for Determining the Temporal Order of Pathway Alterations Arising during Gliomagenesis

Abstract

Human cancer is caused by the accumulation of genetic alterations in cells. Of special importance are changes that occur early during malignant transformation because they may result in oncogene addiction and thus represent promising targets for therapeutic intervention. We have previously described a computational approach, called Retracing the Evolutionary Steps in Cancer (RESIC), to determine the temporal sequence of genetic alterations during tumorigenesis from cross-sectional genomic data of tumors at their fully transformed stage. Since alterations within a set of genes belonging to a particular signaling pathway may have similar or equivalent effects, we applied a pathway-based systems biology approach to the RESIC methodology. This method was used to determine whether alterations of specific pathways develop early or late during malignant transformation. When applied to primary glioblastoma (GBM) copy number data from The Cancer Genome Atlas (TCGA) project, RESIC identified a temporal order of pathway alterations consistent with the order of events in secondary GBMs. We then further subdivided the samples into the four main GBM subtypes and determined the relative contributions of each subtype to the overall results: we found that the overall ordering applied for the proneural subtype but differed for mesenchymal samples. The temporal sequence of events could not be identified for neural and classical subtypes, possibly due to a limited number of samples. Moreover, for samples of the proneural subtype, we detected two distinct temporal sequences of events: (i) RAS pathway activation was followed by TP53 inactivation and finally PI3K2 activation, and (ii) RAS activation preceded only AKT activation. This extension of the RESIC methodology provides an evolutionary mathematical approach to identify the temporal sequence of pathway changes driving tumorigenesis and may be useful in guiding the understanding of signaling rearrangements in cancer development.

Cancer stem cells in glioblastoma

“…As the accepted functional definition of a stem cell is the ability to self-renew and generate differentiated progeny, any claims for a CSC population must also demonstrate this capacity (Fig. 1 Cheng et al 2012), the microenvironment, including medium composition and culture conditions, does necessarily affect the characteristics of CSCs (Pastrana et al 2011).…”

“…Clinically relevant parameters, such as identifying optimal radiation schedules, have also been modeled using genetically engineered mice (Leder et al 2014). Additionally, quantitative approaches have been developed to model the events leading to intertumoral and intratumoral heterogeneity in both human patient specimens (Sottoriva et al 2013) and mouse models (Cheng et al 2012). Integrating mathematical approaches into future CSC studies will provide an opportunity to identify key pathways essential for self-renewal and will predict responses to therapeutic perturbations.…”

Inferring mutational timing and reconstructing tumour evolutionary histories

“…Branched tumour 70 evolution gives rise to multiple genetically distinct subclones which 71 may co-exist in the tumour mass and produce varying degrees of ITH 72 [3]. A recent study in colorectal cancer shows that most detectable ITH 73 occurs early after the transition to an advanced tumour as a result of 74 fairly neutral subclonal evolution [4]. Critically, when heterogeneous 75 tumours are sampled via a single biopsy the true extent of ITH is 76 underestimated [5].…”

Simultaneous Inference of Cancer Pathways and Tumor Progression from Cross-Sectional Mutation Data

“…Two recent works (Gerstung et al, 2011;Cheng et al, 2012) have considered the inference of the progression model at the pathway level, where the assignment of genes into pathways was defined a priori and provided in input to the method. As pointed out in the introduction, the a priori assignment of genes into pathways is complicated by the fact that such pathways are large and moreover often a gene is assigned to multiple pathways, which limit the ability to detect smaller sets of interacting genes related to tumor progression.…”

“…There has been some initial work in inferring pathway order (Gerstung et al, 2011;Cheng et al, 2012). These approaches demonstrated some advantages over gene-based approaches, but restricted attention to known, annotated pathways.…”