Genome instability model of metastatic neuroblastoma tumorigenesis by a dictionary learning algorithm

Abstract

Background: Metastatic neuroblastoma (NB) occurs in pediatric patients as stage 4S or stage 4 and it is characterized by heterogeneous clinical behavior associated with diverse genotypes. Tumors of stage 4 contain several structural copy number aberrations (CNAs) rarely found in stage 4S. To date, the NB tumorigenesis is not still elucidated, although it is evident that genomic instability plays a critical role in the genesis of the tumor. Here we propose a mathematical approach to decipher genomic data and we provide a new model of NB metastatic tumorigenesis.

Method: We elucidate NB tumorigenesis using Enhanced Fused Lasso Latent Feature Model (E-FLLat) modeling the array comparative chromosome hybridization (aCGH) data of 190 metastatic NBs (63 stage 4S and 127 stage 4). This model for aCGH segmentation, based on the minimization of functional dictionary learning (DL), combines several penalties tailored to the specificities of aCGH data. In DL, the original signal is approximated by a linear weighted combination of atoms: the elements of the learned dictionary.

Results: The hierarchical structures for stage 4S shows at the first level of the oncogenetic tree several whole chromosome gains except to the unbalanced gains of 17q, 2p and 2q. Conversely, the high CNA complexity found in stage 4 tumors, requires two different trees. Both stage 4 oncogenetic trees are marked diverged, up to five sublevels and the 17q gain is the most common event at the first level (2/3 nodes). Moreover the 11q deletion, one of the major unfavorable marker of disease progression, occurs before 3p loss indicating that critical chromosome aberrations appear at early stages of tumorigenesis. Finally, we also observed a significant (p = 0.025) association between patient age and chromosome loss in stage 4 cases.

Conclusion: These results led us to propose a genome instability progressive model in which NB cells initiate with a DNA synthesis uncoupled from cell division, that leads to stage 4S tumors, primarily characterized by numerical aberrations, or stage 4 tumors with high levels of genome instability resulting in complex chromosome rearrangements associated with high tumor aggressiveness and rapid disease progression.

Fig. 1

An aCGH signal before and after segmentation. An aCGH profile can be thought of as the concatenation of the log-ratio values ordered by chromosomes and by chromosomal location. In the top plot, each black dot corresponds to a probe placed at a given chromosomal location (x-axis) and with a corresponding estimated a log-ratio of the CNAs for the hybridized control and patient (y-axis). Probes are sorted according to their chromosomal location, as example, from chromosome 1 to chromosome 4 and each dotted red line represents boundaries among chromosomes. The bottom plot shows the same aCGH profile after segmentation. The thin black line is a piecewise constant signal obtained as a result of the segmentation. The red dots indicate gain whereas the green dots correspond to probes where a loss occurred

Fig. 2

A piecewise constant signal as weighted linear combination of atoms. The piecewise constant signal at the bottom of the figure can be obtained by linearly combining a set of three elementary alterations (atoms). Each atom is multiplied by a weighting factor (coefficient) β and then added up to obtain the final signal

Fig. 3

Representation of Θ coefficients for stage 4S and stage 4 tumors. The stage 4S (a) and stage 4 (b) tumors are reported in the columns, whereas the atoms are in the rows. Each sample is approximated by a linear combination of atoms weighted by the Θ coefficients. The atoms in the Θ matrix are sorted according to their use in the sample representation, i.e., the most used atoms are in the top rows. The coefficients range from 0 to 1, as indicated by the underlying color bar, and darker hues correspond to higher coefficient values

Fig. 4

Oncogenetic Trees. The reconstructed atom tree for stage 4S (top) shows several initial events of which only one with sublevels. Conversely, both stage 4 reconstructed atom trees (bottom) are marked branched (up to five sublevels) The root node R (yellow) is associated with a weight corresponding to the portion of mutation patterns represented by the tree. The missing portion is associated with the random mutations tree (data not shown). The edges are weighted according to the frequency of the corresponding mutation occurrence. Node color codes: the red node is associated with chromosome gain, the green node is associated with chromosome loss, and the green and red node indicates co-occurring loss and gain

Fig. 5

Schematic representation of the Genome Instability Progressive model of metastatic Neuroblastoma. The GIP model suggests a common ancestor for metastatic “stage 4S and 4 NBs”, showing that a deregulated endomitosis, chromosome mis-segregation and abnormal mitosis in the neural crest progenitors lead to aneuploid cells. This cell may generate “Stage 4S cell clone”, characterized mainly by numerical aberrations. However the clone maintains the capacity to active cell death or differentiation programs as mechanism to escape the catastrophic mitosis. Conversely the deregulated neural crest cells (NNC) may also generate “Stage 4 cell clone” with high genomic instability resulting in complex chromosome rearrangements. Finally, in the GIP model chromosomal deletions are late events, resulting in increasing of genomic chaos and progressive increase of genomic instability, with consequent tumor progression