Determinación de relaciones entre actividades y costos:

To determine if the molecular drivers of the ccA/ccB expression subgroups could be associated with the genetic factors assessed in the prognostic analyses described in Chapter 4, the following analysis was performed.

All the genetic prognostic factors that were found in the literature search (n=17*, duplicate entries of Chrom9p and Chrom20 were removed based on lower (HRs)), were categorised as those that validated in the log-rank tests (n=9) and those that failed to validate (n=8). The ccB expression signature was then investigated to see if it might reflect the transcriptomic impact of the poor risk genetic alterations, which were significant in logrank analysis but failed in the multivariate analysis. For this analysis, the cohort of all 350 cases, as devised in Chapter 4, was used. Seven out of the nine poor prognosis genetic alterations (BAP1 and TP53 mutations; Chrom8q, Chrom12 and Chrom20q focal amplifications; Chrom9p and Chrom22q deletions) were significantly enriched (p<0.05) in the ccB subgroup (Figure 5.2). In contrast, on repeating the analyses for the eight candidate genetic markers that had failed univariate validation, only two were found to be enriched in ccB samples (Figure 5.3).

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Figure 5.2: Enrichment analysis for the poor prognosis genetic events in the ccA/ccB subgroups

The top part of the figure depicts a heatmap, showing the gene expression of the 103/110 gene panel (Brannon et al.). The ccA subgroup is represented on the left and the ccB subgroup on the right. The bars below the heatmap depict the occurrence of the genetic events in each patient. The odds ratio of the occurrence of these events in the ccB subgroup with respect to the ccA subgroup is given on the right side, along with a p-value of significance for the odds (Fisher’s exact test). The barchart at the bottom of the figure depicts the total number of these events per patient. The highest number of these events occurring in a single patient is seven, with both of these cases belonging to the ccB subgroup. This figure is as presented in (Gulati et al.).

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Figure 5.3: Enrichment analysis for the genetic events, which failed validation in the ccA/ccB subgroups

The top part of the figure depicts a heatmap, showing the gene expression of the 103/110 gene panel (Brannon et al.). The ccA subgroup is represented on the left and the ccB subgroup on the right. The bars below the heatmap depict the occurrence of the genetic events in each patient. The odds ratio of the occurrence of these events in the ccB subgroup with respect to the ccA subgroup is given on the right side, along with a p-value of significance for the odds (Fisher’s exact test). The barchart at the bottom of the figure depicts the total number of these events per patient. The highest number of these events occurring in a single patient is seven, with both of these cases belonging to the ccB subgroup. This figure is as presented in (Gulati et al.).

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Further assessment of these aberrations showed that about 72% of the ccB samples had at least one of the seven enriched aberrations in contrast to only 30% of ccA samples (Figure 5.4A). Both, the maximum and the median number of the poor prognosis aberrations per sample were higher in the ccB group than in the ccA group (Figure 5.4A and 5.4B). However, when the distribution of aberrations which failed validation in the prognostic analysis was compared, the median number of these aberrations between ccA and ccB samples was not statistically different (Figure 5.4C and 5.4D).

Figure 5.4: Comparison of genetic markers in the ccA/ccB subgroups

A. Comparison of the number of poor prognosis genetic aberrations per sample between ccA and ccB subgroups. Only aberrations, which are enriched in the ccB subgroup, were considered. B. Box and whisker plot comparing median number of poor prognosis genetic aberrations between samples assigned to the ccA and the ccB group. C. Comparison of the number of number of genetic aberrations, which did not pass univariate validation per sample between ccA and ccB subgroups. D. Boxplot and whisker plot showing the median number of genetic aberrations, which did not pass univariate validation between ccA and ccB subgroups.

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Chromosomal instability is known to foster the acquisition of SCNAs and has been associated with poor prognosis in several cancers (McGranahan et al., 2012). To reveal whether enrichment of chromosomal aberrations in ccB was a result of increased chromosomal instability, the weighted Genomic Instability Index (wGII), a measure of overall copy number aberrations, was calculated for each sample (wGII ≥ 0.2 is considered unstable (Lee et al.)). The ccB samples had significantly higher wGII scores when compared to ccA samples (p<0.001, Figure 5.5A). However, the mutation load was not significantly different between the two cohorts (p>0.05, Figure 5.5B and 5.5C). Based on these results, it appears possible that the aggressive ccB phenotype is partially driven by several poor prognosis genetic alterations, co-occurring within these samples, which may be permitted by a cancer genomic background of elevated chromosomal instability.

Figure 5.5: Comparison of genomic measures in the ccA/ccB subgroups Box and whisker plots comparing genomic factors between the ccA/ccB subgroups. A. Comparison of wGII between the two cohorts where wGII ≥ 0.2 is deemed to be genomically unstable; ccB patients were observed to be more genomically unstable. B. and C. compare the total mutation load and the number of non-syn mutations between the two subgroups respectively. No statistical differences were seen between the two cohorts.

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5.3.3 Random forests elucidate the most important determinants of the ccB