Data_Sheet_1_Network-Based Predictors of Progression in Head and Neck Squamous Cell Carcinoma.zip

<p>The heterogeneity in head and neck squamous cell carcinoma (HNSCC) has made reliable stratification extremely challenging. Behavioral risk factors such as smoking and alcohol consumption contribute to this heterogeneity. To help elucidate potential mechanisms of progression in HNSCC, we focused on elucidating patterns of gene interactions associated with tumor progression. We performed de-novo gene co-expression network inference utilizing 229 patient samples from The Cancer Genome Atlas (TCGA) previously annotated by Bornstein et al. (2016). Differential network analysis allowed us to contrast progressor and non-progressor cohorts. Beyond standard differential expression (DE) analysis, this approach evaluates changes in gene expression variance (differential variability DV) and changes in covariance, which we denote as differential wiring (DW). The set of affected genes was overlaid onto the co-expression network, identifying 12 modules significantly enriched in DE, DV, and/or DW genes. Additionally, we identified modules correlated with behavioral measures such as alcohol consumption and smoking status. In the module enriched for differentially wired genes, we identified network hubs including IL10RA, DOK2, APBB1IP, UBASH3A, SASH3, CELF2, TRAF3IP3, GIMAP6, MYO1F, NCKAP1L, WAS, FERMT3, SLA, SELPLG, TNFRSF1B, WIPF1, AMICA1, PTPN22; the network centrality and progression specificity of these genes suggest a potential role in tumor evolution mechanisms related to inflammation and microenvironment. The identification of this network-based gene signature could be further developed to guide progression stratification, highlighting how network approaches may help improve clinical research end points and ultimately aid in clinical utility.</p

Presentation_1_Network-Based Predictors of Progression in Head and Neck Squamous Cell Carcinoma.pdf

<p>The heterogeneity in head and neck squamous cell carcinoma (HNSCC) has made reliable stratification extremely challenging. Behavioral risk factors such as smoking and alcohol consumption contribute to this heterogeneity. To help elucidate potential mechanisms of progression in HNSCC, we focused on elucidating patterns of gene interactions associated with tumor progression. We performed de-novo gene co-expression network inference utilizing 229 patient samples from The Cancer Genome Atlas (TCGA) previously annotated by Bornstein et al. (2016). Differential network analysis allowed us to contrast progressor and non-progressor cohorts. Beyond standard differential expression (DE) analysis, this approach evaluates changes in gene expression variance (differential variability DV) and changes in covariance, which we denote as differential wiring (DW). The set of affected genes was overlaid onto the co-expression network, identifying 12 modules significantly enriched in DE, DV, and/or DW genes. Additionally, we identified modules correlated with behavioral measures such as alcohol consumption and smoking status. In the module enriched for differentially wired genes, we identified network hubs including IL10RA, DOK2, APBB1IP, UBASH3A, SASH3, CELF2, TRAF3IP3, GIMAP6, MYO1F, NCKAP1L, WAS, FERMT3, SLA, SELPLG, TNFRSF1B, WIPF1, AMICA1, PTPN22; the network centrality and progression specificity of these genes suggest a potential role in tumor evolution mechanisms related to inflammation and microenvironment. The identification of this network-based gene signature could be further developed to guide progression stratification, highlighting how network approaches may help improve clinical research end points and ultimately aid in clinical utility.</p

Differential Network Analysis Reveals Genetic Effects on Catalepsy Modules

<div><p>We performed short-term bi-directional selective breeding for haloperidol-induced catalepsy, starting from three mouse populations of increasingly complex genetic structure: an F₂ intercross, a heterogeneous stock (HS) formed by crossing four inbred strains (HS4) and a heterogeneous stock (HS-CC) formed from the inbred strain founders of the Collaborative Cross (CC). All three selections were successful, with large differences in haloperidol response emerging within three generations. Using a custom differential network analysis procedure, we found that gene coexpression patterns changed significantly; importantly, a number of these changes were concordant across genetic backgrounds. In contrast, absolute gene-expression changes were modest and not concordant across genetic backgrounds, in spite of the large and similar phenotypic differences. By inferring strain contributions from the parental lines, we are able to identify significant differences in allelic content between the selected lines concurrent with large changes in transcript connectivity. Importantly, this observation implies that genetic polymorphisms can affect transcript and module connectivity without large changes in absolute expression levels. We conclude that, in this case, selective breeding acts at the subnetwork level, with the same modules but not the same transcripts affected across the three selections.</p> </div

<i>Bcl11b</i> connectivity and allelic differences between High and Low selected lines – HS-CC founders.

<p>A: <i>Bcl11b</i> connectivity patterns in the High network. For visual clarity, only edges involving <i>Bcl11b</i> are represented. Edge thickness and opacity are proportional with the edge weight (adjacency). Node size (except <i>Bcl11b</i>) is proportional with modular connectivity. B: Low network <i>Bcl11b</i> connectivity pattern. C: Allele distribution for <i>Bcl11b</i> in the naïve HS-CC animals (“Green”, top) and in the High and Low selected lines (red and blue, bottom). NOD and A/J alleles are more prevalent in the High group (blue) while NZO, B6 and A129 are more prevalent in the Low group (red). Strains: C57BL/6J (B6); A/J (A); 129S1/SvImJ (129); NOD/LtJ (NOD); NZO/HILtJ (NZO). CAST/EiJ (CAST). PWK/PhJ (PWK), WSB/EiJ (WSB).</p

Genes with connectivity and allele origin differences.

2<p>For the three modules affected by selection, a number of genes change connectivity significantly, as indicated by change in connectivity rank and z Score. The same genes fall within genomic regions that segregate between High and Low populations.</p

Hierarchical clustering of gene modules and module color assignments.

<p>Top: clustering tree. Bottom: initial unmerged colors and subsequent merged (final) module color assignments.</p

Phenotypic and genetic differences between naïve animals and selected lines.

<p>“Green”, naïve animals; blue, High line; red, Low line. A–C: Top, distribution of catalepsy responses in the naïve populations—high scores denote responders. Bottom, distribution of catalepsy responses in the selected lines. The three selected populations display differences in scores, showing successful selection. D–F: Multidimensional scaling of the genetic distances between individuals. The selected populations appear distinct from each other and closer together due to allele loss/fixation.</p

List of disruption Z scores.

1<p>Three modules (Green, Grey60 and Pink) displayed significant disruption (cor.kIM absolute value z scores above 2) in all three datasets.</p