Skip to main content

Table 3 Actionable genes identified by the MTB report workflow

From: Explaining decisions of graph convolutional neural networks: patient-specific molecular subnetworks responsible for metastasis prediction in breast cancer

Patient Gene Expression Known Var Predicts
615695 HSPB1 Normal expression Response to gemcitabine
  ABL1 High GoF Response to ABL TK inhibitors (imatinib, desatininb, ponatinib, regorafenib …)
  AKT1 High GoF Response to PI3K, AKT, MTOR inhibitors; resistance to BRAF inhibitors
  ERBB2 High GoF Response to ERBB2, EGFR, MTOR, AKT inhibitors
  MAPK3 High GoF Resistance to EGFR inhibition
519217 HSPB1 Normal expression Response to gemcitabine
  CTNNB1 High GoF Response to everolimus + letrozole; resistance to Tankyrase inhibitors
  EGFR High GoF Response to EGFR, ERBB2, HSP90 and MEK inhibitors
  ERBB2 High GoF Response to ERBB2, EGFR, MTOR, AKT inhibitors
  JUN High overexpr Response to irbesartan (angiotensin II antagonist)
  MCL1 High GoF Resistance to anti-tubulin agents
  PTPN11 High GoF Response to MEK inhibitors
615233 FOS High overexpr Response to irbesartan (angiotensin II antagonist)
  PTPN11 High GoF Response to MEK inhibitors
150990 HSPB1 Normal expression Response to gemcitabine
  ESR1 High GoF Response to novel ER degraders, fulvestrant, tamoxifen
  1. Genes from the PPI subnetworks were matched to known genomic alterations (Known Var) that predict either response or resistance to drugs (Predicts). High and low gene expression were matched to gain of function (GoF) and loss of function (LoF) genomic variants, respectively