Connectivity mapping using a combined gene signature from multiple colorectal cancer datasets identified candidate drugs including existing chemotherapies

BACKGROUND: While the discovery of new drugs is a complex, lengthy and costly process, identifying new uses for existing drugs is a cost-effective approach to therapeutic discovery. Connectivity mapping integrates gene expression profiling with advanced algorithms to connect genes, diseases and small molecule compounds and has been applied in a large number of studies to identify potential drugs, particularly to facilitate drug repurposing. Colorectal cancer (CRC) is a commonly diagnosed cancer with high mortality rates, presenting a worldwide health problem. With the advancement of high throughput omics technologies, a number of large scale gene expression profiling studies have been conducted on CRCs, providing multiple datasets in gene expression data repositories. In this work, we systematically apply gene expression connectivity mapping to multiple CRC datasets to identify candidate therapeutics to this disease. RESULTS: We developed a robust method to compile a combined gene signature for colorectal cancer across multiple datasets. Connectivity mapping analysis with this signature of 148 genes identified 10 candidate compounds, including irinotecan and etoposide, which are chemotherapy drugs currently used to treat CRCs. These results indicate that we have discovered high quality connections between the CRC disease state and the candidate compounds, and that the gene signature we created may be used as a potential therapeutic target in treating the disease. The method we proposed is highly effective in generating quality gene signature through multiple datasets; the publication of the combined CRC gene signature and the list of candidate compounds from this work will benefit both cancer and systems biology research communities for further development and investigations

Transcriptional upregulation of c-MET is associated with invasion and tumor budding in colorectal cancer

c-MET and its ligand HGF are frequently overexpressed in colorectal cancer (CRC) and increased c-MET levels are found in CRC liver metastases. This study investigated the role of the HGF/c-MET axis in regulating migration/invasion in CRC, using pre-clinical models and clinical samples. Pre-clinically, we found marked upregulation of c-MET at both protein and mRNA levels in several invasive CRC cells. Down-regulation of c-MET using RNAi suppressed migration/invasion of parental and invasive CRC cells. Stimulation of CRC cells with rh-HGF or co-culture with HGF-expressing colonic myofibroblasts, resulted in significant increases in their migratory/invasive capacity. Importantly, HGF-induced c-MET activation promoted rapid downregulation of c-MET protein levels, while the MET transcript remained unaltered. Using RNA in situ hybridization (RNA ISH), we further showed that MET mRNA, but not protein levels, were significantly upregulated in tumor budding foci at the invasive front of a cohort of stage III CRC tumors (p < 0.001). Taken together, we show for the first time that transcriptional upregulation of MET is a key molecular event associated with CRC invasion and tumor budding. This data also indicates that RNA ISH, but not immunohistochemistry, provides a robust methodology to assess MET levels as a potential driving force of CRC tumor invasion and metastasis

Immune-derived PD-L1 gene expression defines a subgroup of stage II/III colorectal cancer patients with favorable prognosis that may be harmed by adjuvant chemotherapy

Abstract A recent phase II study of patients with metastatic colorectal carcinoma showed that mismatch repair gene status was predictive of clinical response to PD-1–targeting immune checkpoint blockade. Further examination revealed strong correlation between PD-L1 protein expression and microsatellite instability (MSI) in stage IV colorectal carcinoma, suggesting that the amount of PD-L1 protein expression could identify late-stage patients who might benefit from immunotherapy. To assess whether the clinical associations between PD-L1 gene expression and MSI identified in metastatic colorectal carcinoma are also present in stage II/III colorectal carcinoma, we used in silico analysis to elucidate the cell types expressing the PD-L1 gene. We found a statistically significant association of PD-L1 gene expression with MSI in early-stage colorectal carcinoma (P &amp;lt; 0.001) and show that, unlike in non–colorectal carcinoma tumors, PD-L1 is derived predominantly from the immune infiltrate. We demonstrate that PD-L1 gene expression has positive prognostic value in the adjuvant disease setting (PD-L1low vs. PD-L1high HR = 9.09; CI, 2.11–39.10). PD-L1 gene expression had predictive value, as patients with high PD-L1 expression appear to be harmed by standard-of-care treatment (HR = 4.95; CI, 1.10–22.35). Building on the promising results from the metastatic colorectal carcinoma PD-1–targeting trial, we provide compelling evidence that patients with PD-L1high/MSI/immunehigh stage II/III colorectal carcinoma should not receive standard chemotherapy. This conclusion supports the rationale to clinically evaluate this patient subgroup for PD-1 blockade treatment. Cancer Immunol Res; 4(7); 582–91. ©2016 AACR.</jats:p

A machine learning platform to optimize the translation of personalized network models to the clinic

PURPOSE Dynamic network models predict clinical prognosis and inform therapeutic intervention by elucidating disease-driven aberrations at the systems level. However, the personalization of model predictions requires the profiling of multiple model inputs, which hampers clinical translation. PATIENTS AND METHODS We applied APOPTO-CELL, a prognostic model of apoptosis signaling, to showcase the establishment of computational platforms that require a reduced set of inputs. We designed two distinct and complementary pipelines: a probabilistic approach to exploit a consistent subpanel of inputs across the whole cohort (Ensemble) and a machine learning approach to identify a reduced protein set tailored for individual patients (Tree). Development was performed on a virtual cohort of 3,200,000 patients, with inputs estimated from clinically relevant protein profiles. Validation was carried out in an in-house stage III colorectal cancer cohort, with inputs profiled in surgical resections by reverse phase protein array (n = 120) and/or immunohistochemistry (n = 117). RESULTS Ensemble and Tree reproduced APOPTO-CELL predictions in the virtual patient cohort with 92% and 99% accuracy while decreasing the number of inputs to a consistent subset of three proteins (40% reduction) or a personalized subset of 2.7 proteins on average (46% reduction), respectively. Ensemble and Tree retained prognostic utility in the in-house colorectal cancer cohort. The association between the Ensemble accuracy and prognostic value (Spearman ρ = 0.43; P = .02) provided a rationale to optimize the input composition for specific clinical settings. Comparison between profiling by reverse phase protein array (gold standard) and immunohistochemistry (clinical routine) revealed that the latter is a suitable technology to quantify model inputs. CONCLUSION This study provides a generalizable framework to optimize the development of network-based prognostic assays and, ultimately, to facilitate their integration in the routine clinical workflow

Transcriptional Subtyping and CD8 Immunohistochemistry Identifies Patients With Stage II and III Colorectal Cancer With Poor Prognosis Who Benefit From Adjuvant Chemotherapy

This work was funded by Cancer Research UK: C212/A13721 and C11884/A24387Peer reviewedPublisher PD

EGFR activity as a determinant of response to EGFR-targeted therapy

Afgezien van de aanwezigheid van activerende EGFR mutaties, die in niet- kleincellige long tumoren (NKLT) gecorreleerd werden met respons aan EGF R tyrosine kinase remmers (TKR), werden er tot op heden geen voorspellen de factoren voor respons aan EGFR-TKR in andere tumoren of aan EGFR-TKR in combinatie met chemotherapie geindentificeerd. We toonden de waarde aan van de constitutieve EGFR activiteit als een pr edictieve factor voor respons aan EGFR-gerichte therapieën in colorectal e kanker (CRC) cellijnen. We toonden eveneens de predictieve waarde aan van chemotherapie-geinduceerde EGFR activatie voor de gecombineerde beha ndeling van chemotherapie met EGFR-gerichte TKR: CRC en NKLT cellijnen w aarin we een verhoging in EGFR activatie waarnamen na chemotherapie toed iening, waren gevoelig aan EGFR-gerichte TKR. Onze bevindingen suggerere n dat door het bepalen van de pEGFR expressie na chemotherapie toedienin g, CRC en NKLT patiënten die een voordeel ondervinden van de toevoeging van een EGFR-TKR aan hun chemotherapie schema, kunnen geïdentificeerd wo rden. We identificeerden verder het belang van Src-family kinases (SFK), ADAMs (a desintegrin and metalloprotease) en TGF-&#945; als kritische mediato ren van EGFR fosforylatie na chemotherapie toediening. We suggereren dat een verandering in serum waarden van TGF-&#945; na chemotherapie toedie ning zou kunnen gebruikt worden om een subgroep patiënten die zullen bea ntwoorden aan gecombineerde behandeling van chemotherapie met gefitinib, te identificeren. Gerichte therapieën tegen SFKs en ADAMs (ADAM-17) in combinatie met chemotherapie kunnen belangrijke therapeutische benaderin gen zijn voor de behandeling van CRC en NKLT. Tot slot toonden we de waarde aan van de combinatie van een dual EGFR/He r2 remmer met chemotherapie in CRC cellijnen en we stelden voor dat deze combinatie geïntroduceerd kan worden in plaats van of in vergelijking m et de combinatie van een EGFR specifieke remmer met chemotherapie.Chapter 1: Introduction. 1 1.1. Biology of EGFR family receptors 3 1.1.1.The Her receptors in development and normal physiology 3 1.1.2. The Her receptors and their ligands 4 1.1.2.1. EGFR 4 1.1.2.2. Her2 4 1.1.2.3. Her3 6 1.1.2.4. Her4 6 1.1.2.5. Biochemical properties of Her receptor ligands 6 1.1.2.6. Expression of Her receptors and ligands in cancer 7 1.1.3. Ligand-induced dimerization and activation 8 1.1.4. Signalling pathways activated by EGFR 10 1.1.4.1. Shc, Grb2 and Ras/MAPK pathway 11 1.1.4.2. PI3-K/Akt pathway 12 1.1.4.3. STAT pathway 13 1.1.4.4. PLC pathway 13 1.1.4.5. Nuclear EGFR as a transcriptional regulator 13 1.1.5. Internalization and recycling of the Her receptors 14 1.1.6. EGFR activation by heterologous mechanisms 15 1.1.6.1. EGFR overexpression 15 1.1.6.2. Increased expression receptor ligands 15 1.1.6.3. EGFR mutations 15 1.1.6.4. Transactivation of EGFR 15 1.2. The zinc protease superfamily and matrixins 16 1.2.1. ADAMs, multidomain proteins with multiple functions 16 1.2.2. The matrix metalloproteases 17 1.3. Non-receptor tyrosine kinase proteins 17 1.3.1. The Src-family kinases 17 1.3.2. The Abl kinase 19 1.3.3. The Jak-STAT pathway 19 1.4. The EGFR family as targets for cancer therapy 20 1.4.1. EGFR family targeted monoclonal antibodies 20 1.4.1.1. Cetuximab (C225, Erbitux) 20 1.4.1.2. Traztuzumab (Herceptin) 21 1.4.1.3. Pertuzumab (Omnitarg, 2C4) 21 1.4.2. EGFR family targeted tyrosine kinase inhibitors 22 1.4.2.1. EGFR-targeted TKIs 22 1.4.2.2. Her2-targeted TKIs 23 1.4.2.3. Pan-Her TKIs. 23 1.4.2.4. Dual Her1/Her2 TKIs 23 1.4.3. Downstream intracellular signalling targets 24 1.4.3.1. Inhibitors of the Ras/B-Raf/MAPK pathway 24 1.4.3.2. PI3K/Akt Inhibitors 24 1.5. Predictors for response to EGFR targeted therapies 24 1.5.1. EGFR and Her2 expression 24 1.5.2. Constitutive levels of pAkt and pErk1/2 24 1.5.3. EGFR gene mutations 25 1.5.4. Link between EGFR mutations, amplification and prognosis 26 1.5.5. K-Ras and B-Raf gene mutations 27 1.5.6. PTEN and PI3K gene mutations 27 1.5.7. Clinical surrogate markers 27 1.6. Aims of the study 28 Chapter 2: Materials and methods. 29 2.1. Materials 31 2.2. Cell culture 31 2.3. MTT cell Viability Assay 32 2.4. Crystal Violet assay 32 2.5. Flow cytometric analysis and cell death measurement 32 2.6. Detection of cell surface EGFR and Her2 expression 33 2.7. Western Blotting 33 2.8. Quantitative real-time PCR 34 2.9. Epidermal Growth Factor Receptor sequencing 34 2.10. siRNA transfections 35 2.11. Statistical analysis 35 Chapter 3: Sensitivity of CRC and NSCLC cells to EGFR- targeted therapy alone. 37 3.1. Introduction 39 3.2 Correlation between sensitivity to gefitinib or cetuximab and constitutive levels of pEGFR 39 3.3. Correlation between sensitivity to gefitinib and inhibition of pEGFR, pHer2, pAkt and pErk1/2 by gefitinib 44 3.4. Discussion 47 Chapter 4: Interaction between gefitinib and chemotherapy in CRC cells. 49 4.1. Introduction 51 4.2. Evaluation of the gefitinib/chemotherapy combination 51 4.2.1. Gefitinib in combination with oxaliplatin 51 4.2.2. Gefitinib in combination with 5-FU 57 4.2.3. Gefitinib in combination with SN-38 60 4.3. Effect of chemotherapy on EGFR phosphorylation 62 4.4. Discussion 66 Chapter 5: Interaction between gefitinib and chemotherapy in NSCLC cells. 69 5.1 Introduction 71 5.2. Evaluation of the gefitinib/chemotherapy interaction 71 5.2.1. Gefitinib in combination with cisplatin 71 5.2.2. Gefitinib in combination with taxol 75 5.3. Effect of chemotherapy on EGFR phosphorylation 77 5.4. Discussion 79 Chapter 6: Mechanism of increased EGFR activation following chemotherapy. 83 6.1 Introduction 85 6.2 Effect of chemotherapy on EGFR and SFK phosphorylation and expression 86 6.3. Effect of SFK inhibition and gefitinib on constitutive and activated EGFR and SFK phosphorylation following chemotherapy 86 6.4. Evaluation of the interaction between the SFK inhibitor PP2 and chemotherapy 88 6.5. Effect of metalloproteinase inhibition on EGFR and SFK phosphorylation following chemotherapy 93 6.6. Evaluation of the interaction between GM6001 and chemotherapy 94 6.7. Evaluation of the role of ADAM-17 in basal and activated EGFR and SFK phosphorylation following chemotherapy 94 6.8. Effect of the EGFR monoclonal antibody cetuximab on constitutive and chemotherapy-activated pEGFR and pSFK levels 96 6.9. Effect of TGF-alpha inhibition on constitutive and chemotherapy-activated pEGFR and pSFK levels 97 6.10. Evaluation of the role of ROS in chemotherapy-activated pEGFR and pSFK levels 99 6.11. Discussion 100 Chapter 7: Sensitivity of CRC to Her2 and dual Her1/Her2 inhibition. 105 7.1. Introduction 107 7.2. Correlation between the sensitivity of CRC cells to the DKI or Her2I and constitutive levels of pEGFR 107 7.3.Characterization of the dual EGFR/Her2 TKI (DKI, M880588) 109 7.3.1. Effect of the DKI on pEGFR/pHer2/pAkt/pErk1/2 109 7.3.2. Interaction between DKI and chemotherapy 110 7.3.2.1. Interaction between DKI and oxaliplatin 110 7.3.2.2. Interaction between DKI and 5-FU 113 7.3.2.3. Interaction between DKI and SN-38 115 7.4. Characterization of the specific Her2 TKI (Her2I, M578440) 117 7.4.1. Evaluation of the antiproliferative activity of the Her2I in combination with chemotherapy 117 7.4.2. Comparison of the effect of the Her2I, DKI or gefitinib on chemotherapy-induced apoptosis in CRC cells 119 7.4.3. Effect of Her2I on Akt activation 121 7.5. Interaction between the PI3-K inhibitor LY294002 and chemotherapy 122 7.6. Discussion 123 Chapter 8: Conclusions and future directions. 127 8.1. Summary 129 8.2. General conclusions 134 8.3. Future directions 135 Samenvatting. 138 References. 143status: publishe