In sigven/oncoEnrichR: Cancer-dedicated gene set interpretation

Cancer associations {.tabset}

Each target in the query set is annotated with known associations to cancer phenotypes (ontology terms) established from multiple data types through the Open Targets Platform. Annotations are provided both through descriptive associations and rank scores
Indication of putative cancer driver genes require support from at least two of the following sources:
- Network of Cancer Genes (NCG)
- IntOGen
- Cancer Gene Census (CGC) - TIER1/2
- TCGA's predicted cancer driver genes
- CancerMine literature mining resource (considering only entries with > 10 citations)
Evidence for genes harboring tumor suppressive or oncogenic functions are provided with three levels of confidence:
- Very Strong
  - Manually curated entry in NCG or CGC, and with significant support (> 10 citations) in the biomedical literature (CancerMine), OR
  - Strong_support (> 20 citations) in the biomedical literature, no entry in NCG/CGC
- Strong
  - Curated entry in NCG or CGC, with moderate support (<= 10 citations, > 5 citations) in the biomedical literature (CancerMine), OR
  - Significant support (> 10 citations, <= 20 citations) in the biomedical literature (CancerMine), no entry in CGC/NCG
- Moderate
  - Curated entry in NCG/CGC, with no or weak support (<= 5 citations) in the biomedical literature (CancerMine), OR
  - Moderate support (<= 10 citations, > 5 citations) in the biomedical literature (CancerMine), no entry in CGC/NCG
Gene function summary is retrieved from NCBI and UniProt Knowledgebase

Query set - cancer association rank

Query set genes are ranked according to their overall strength of association to cancer, visualized in varying shades of blue (from weak associations to strong associations ). Specifically, ranking is based on the sum of association scores pr. tumor type/tissue, and scaled as percent rank within the query set (column targetset_cancer_rank)

target_assocs <- onc_enrich_report[['data']][['disease']][['target']] |>
  dplyr::mutate(targetset_cancer_rank = round(
    .data$targetset_cancer_rank, digits = 2)) |>
  dplyr::mutate(global_cancer_rank = round(
    .data$global_cancer_rank, digits = 2)) |>
  dplyr::select(-c("tumor_suppressor","oncogene")) |>
  dplyr::rename(tumor_suppressive = tsg_confidence_level,
                oncogenic = oncogene_confidence_level) |>
  dplyr::mutate(tumor_suppressive = dplyr::case_when(
    tumor_suppressive == "VERY STRONG" ~ "Very Strong",
    tumor_suppressive == "STRONG" ~ "Strong",
    tumor_suppressive == "MODERATE" ~ "Moderate",
    TRUE ~ as.character(tumor_suppressive)
  )) |>
   dplyr::mutate(oncogenic = dplyr::case_when(
    oncogenic == "VERY STRONG" ~ "Very Strong",
    oncogenic == "STRONG" ~ "Strong",
    oncogenic == "MODERATE" ~ "Moderate",
    TRUE ~ as.character(oncogenic)
  )) |>
  dplyr::mutate(tumor_suppressive = dplyr::if_else(
    tumor_suppressive == "NONE/LIMITED",
    "",
    as.character(tumor_suppressive)
  )) |>
  dplyr::mutate(oncogenic = dplyr::if_else(
    oncogenic == "NONE/LIMITED",
    "",
    as.character(oncogenic)
  ))

tsg_confidence_levels <- levels(as.factor(
  target_assocs$tumor_suppressive))

oncogene_confidence_levels <- levels(as.factor(
  target_assocs$oncogenic))

targets_shared <- crosstalk::SharedData$new(target_assocs)

crosstalk::bscols(
  list(
    crosstalk::filter_select("symbol", "Target", targets_shared, ~symbol),
    crosstalk::filter_select("tumor_suppressive", "Tumor suppressor evidence", 
                             targets_shared, ~tumor_suppressive),
    crosstalk::filter_select("oncogenic", "Oncogene evidence", 
                             targets_shared, ~oncogenic)
  ),
  list(
    crosstalk::filter_select("cancer_associations", "Associated cancer types", 
                             targets_shared, ~cancer_associations),
    crosstalk::filter_select("disease_associations", "Associated diseases (non-cancer)", 
                             targets_shared, ~disease_associations),
    crosstalk::filter_checkbox("cancer_driver", "Potential cancer driver", 
                               targets_shared, ~cancer_driver)
  )
)

htmltools::br()
dt <- DT::datatable(
  targets_shared, escape = F, 
  extensions=c("Buttons","Responsive"), 
  width = "100%",
  options=list(buttons = c('csv','excel'),
               #columnDefs = list(list(className = 'dt-center', targets = 3:5)),
               pageLength = 20,
               dom = 'Blfrtip')) |>
  DT::formatStyle('oncogenic', textAlign = 'center') |>
  DT::formatStyle('tumor_suppressive', textAlign = 'center') |>
  DT::formatStyle("symbol",
                  "targetset_cancer_rank", 
                  color = "white", 
                  backgroundColor = DT::styleInterval(
                    onc_enrich_report[['config']][['disease']][['breaks']],
                    onc_enrich_report[['config']][['disease']][['colors']])
  )

if(length(tsg_confidence_levels) > 0){
  dt <- dt |>
    DT::formatStyle(
      "tumor_suppressive","tumor_suppressive",
      color = "white", 
      backgroundColor = DT::styleEqual(
        c("Very Strong", "Strong", "Moderate"),
       c("#5A5A5A", "#898989", "#BBBBBB"))
    )
}

if(length(oncogene_confidence_levels) > 0){
  dt <- dt |>
    DT::formatStyle(
      "oncogenic", "oncogenic",
      color = "white",
      backgroundColor = DT::styleEqual(
        c("Very Strong", "Strong", "Moderate"),
        c("#5A5A5A", "#898989", "#BBBBBB"))
    )
}
dt

cat('<i>No genes with disease associations from Open Targets Platform were found.</i>',sep='\n')
cat('\n')

Query set - association strength pr. tumor type

Top cancer-associated genes (maximum 100) in the query set are shown with their specific tumor-type association strengths (percent rank)

oe_ttype_matrix <- 
  onc_enrich_report[['data']][['disease']][['ttype_matrix']]

if(NROW(oe_ttype_matrix) > 100){
  oe_ttype_matrix <- oe_ttype_matrix[1:100,]
}

if(NROW(oe_ttype_matrix) > 1){
  oe_ttype_matrix <- 
    oe_ttype_matrix[nrow(oe_ttype_matrix):1, ]
}

n_percent_ticks <- 10
if(NROW(oe_ttype_matrix) < 20){
  n_percent_ticks <- 5
}

target_ttype_rank_fig <- plotly::plot_ly(
  colors = "Blues",
  width = 800,
  height = 400 + (14.67 * NROW(oe_ttype_matrix))) |>
  #width = 1040,
  #height = 520 + (19.071 * NROW(oe_ttype_matrix))) |>
  plotly::add_heatmap(
    y = rownames(oe_ttype_matrix),
    x = colnames(oe_ttype_matrix),
    z = oe_ttype_matrix,
    hovertext = "Tumor type percent rank",
    yaxis = "y") |>
  plotly::layout(
    title = 'Tumor type association rank',
    xaxis = list(tickfont = list(size = 13, family = "Helvetica"), 
                 tickangle = -50),
    yaxis = list(tickfont = list(size = 12, family = "Helvetica")),
    margin = list(l = 75, r = 20, b = 150, t = 30, pad = 4)
  ) |>
  plotly::colorbar(
    nticks = n_percent_ticks, 
    title = list(text = "Percent rank",side = "bottom"),
    limits = c(0, plyr::round_any(max(oe_ttype_matrix), 100, ceiling)))

rm(oe_ttype_matrix)
target_ttype_rank_fig

cat('<br>')
cat('\n <ul><li>  <i> <span style="font-size: 100%; padding: 3px; background-color:#989898; color:white">&nbsp;&nbsp;<b>NOT SHOWN</b> - limited/few tumor-type specific associations found for genes in the query set.&nbsp;&nbsp;</span></i></li></ul>',sep='\n')
cat('\n')
cat('<br><br>')

{.unlisted .unnumbered .toc-ignore}

Citation Note : If you use the output of the Cancer Association module of oncoEnrichR in your research, please cite the following resources and tools as approriate: