Elisa Baldelli

Phosphoinositide-3-Kinase Catalytic Alpha and KRAS Mutations are Important Predictors of Resistance to Therapy with Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitors in Patients with Advanced Non-small Cell Lung Cancer

Background: Specific mutations of the epidermal growth factor receptor (EGFR) gene are predictive for favorable response to tyrosine kinase inhibitors (TKIs) and are associated with a good prognosis. In contrast, Kirsten rat sarcoma viral oncogene homolog (KRAS) mutation has been shown to predict poor response to such therapy. Nevertheless, tumor that initially responds to EGFR-TKIs almost inevitably becomes resistant later. Other mechanisms of resistance to EGFR inhibitors could involve activating mutations of the other main EGFR effector pathway, i.e., the phosphoinositide-3-kinase/phosphate and tensin homologue deleted from chromosome 10 (PTEN)/alpha serine/threonine protein kinase (AKT) pathway. The aim of this study was to investigate the role of phosphoinositide-3-kinase catalytic alpha (PIK3CA), EGFR, and KRAS gene mutations in predicting response and survival in patients with non-small cell lung cancer (NSCLC) treated with EGFR-TKIs. Patients and Methods: A total of 166 patients with advanced NSCLC treated with EGFR-TKI with available archival tissue specimens were included. PIK3CA, EGFR, and KRAS mutations were analyzed using polymerase chain reaction-based sequencing. Results: EGFR mutation was detected in 25.3% of patients, PIK3CA mutation in 4.1%, and KRAS mutation in 6.7%. PIK3CA mutation correlated with shorter median time to progression (TTP) (p = 0.01) and worse overall survival (OS) (p < 0.001). EGFR mutation (p < 0.0001) correlated with favorable response to TKIs treatment and longer TTP (p < 0.0001). KRAS mutation correlated with progressive disease (p = 0.05) and shorter median TTP (p = 0.003) but not with OS. Cox multivariate analysis including histology and performance status showed that PIK3CA mutation was an independent factor to predict worse OS (p = 0.0001) and shorter TTP (p = 0.03), while KRAS mutation to predict shorter TTP (p = 0.01). Conclusion: PIK3CA and KRAS mutations seem to be indicators of resistance and poor survival in patients with NSCLC treated with EGFR-TKIs.

Association of Cytidine Deaminase and Xeroderma Pigmentosum Group D Polymorphisms with Response, Toxicity, and Survival in Cisplatin/Gemcitabine-Treated Advanced Non-small Cell Lung Cancer Patients

Background: Selecting patients according to key genetic characteristics may help to tailor chemotherapy and optimize the treatment in non-small cell lung cancer (NSCLC). Genetic variations in drug metabolism may affect the clinical response, toxicity, and prognosis of NSCLC patients treated with cisplatin/gemcitabine-based therapy. Patients and Methods: We evaluated seven single-nucleotide polymorphisms of six genes CDA Lys27Gln (A/C); CDA C435T; ERCC1 C118T; XRCC3 Thr241Met (C/T); XPD Lys751Gln (A/C); P53 Arg72Pro (G/C), and RRM1 C524T in 192 chemotherapy-naive patients with advanced NSCLC treated with cisplatin/gemcitabine-based regimen by TaqMan probe-based assays with 7300 Real-Time PCR System, using genomic DNA extracted from blood samples. Results: The CDA 435 T/T genotype was significantly associated with better response (p = 0.03). The CDA 435 C/T genotype was associated with a significantly increased risk of nonhematological toxicity of grade ≥3 (p = 0.02) after adjusting for performance status, age, and type of treatment regimen. In the multivariate Cox model, the XPD 751 C/C genotype was a significant prognostic factor of longer progression-free survival (p = 0.006). Conclusion: Our data suggest polymorphic variations of drug metabolic gene were associated with response and toxicity of cisplatin/gemcitabine-based therapy and progression-free survival of patients with advanced NSCLC.

Computational model of EGFR and IGF1R pathways in lung cancer: A Systems Biology approach for Translational Oncology

In this paper we propose a Systems Biology approach to understand the molecular biology of the Epidermal Growth Factor Receptor (EGFR, also known as ErbB1/HER1) and type 1 Insulin-like Growth Factor (IGF1R) pathways in non-small cell lung cancer (NSCLC). This approach, combined with Translational Oncology methodologies, is used to address the experimental evidence of a close relationship among EGFR and IGF1R protein expression, by immunohistochemistry (IHC) and gene amplification, by in situ hybridization (FISH) and the corresponding ability to develop a more aggressive behavior. We develop a detailed in silico model, based on ordinary differential equations, of the pathways and study the dynamic implications of receptor alterations on the time behavior of the MAPK cascade down to ERK, which in turn governs proliferation and cell migration. In addition, an extensive sensitivity analysis of the proposed model is carried out and a simplified model is proposed which allows us to infer a similar relationship among EGFR and IGF1R activities and disease outcome.

Functional characterization of epithelial ovarian cancer histotypes by drug target based protein signaling activation mapping: implications for personalized cancer therapy.

Epithelial ovarian carcinoma (EOC) is a deadly disease, with a 5‐year survival of 30%. The aim of the study was to perform broad‐scale protein signaling activation mapping to evaluate if EOC can be redefined based on activated protein signaling network architecture rather than histology. Tumor cells were isolated using laser capture microdissection (LCM) from 72 EOCs. Tumors were classified as serous (n = 38), endometrioid (n = 13), mixed (n = 8), clear cell (CCC; n = 7), and others (n = 6). LCM tumor cells were lysed and subjected to reverse‐phase protein microarray to measure the expression/activation level of 117 protein drug targets. Unsupervised hierarchical clustering analysis was utilized to explore the overall signaling network. ANOVA was used to detect significant differences among the groups (p < 0.05). Regardless of histology, unsupervised analysis revealed five pathway‐driven clusters. When the EOC histotypes were compared by ANOVA, only CCC showed a distinct signaling network, with activation of EGFR, Syk, HER2/ErbB2, and SHP2 (p = 0.0007, p = 0.0021, p < 0.0001, and p = 0.0410, respectively). The histological classification of EOC fails to adequately describe the underpinning protein signaling network. Nevertheless, CCC presents unique signaling characteristics compared to the other histotypes. EOC may need to be characterized by functional signaling activation mapping rather than pure histology.

Impact of upfront cellular enrichment by laser capture microdissection on protein and phosphoprotein drug target signaling activation measurements in human lung cancer: Implications for personalized medicine.

The aim of this study was to evaluate whether upfront cellular enrichment via laser capture microdissection (LCM) is necessary for accurately quantifying predictive biomarkers in nonsmall cell lung cancer tumors.

A pilot study exploring the molecular architecture of the tumor microenvironment in human prostate cancer using laser capture microdissection and reverse phase protein microarray

The cross‐talk between tumor epithelium and surrounding stromal/immune microenvironment is essential to sustain tumor growth and progression and provides new opportunities for the development of targeted treatments focused on disrupting the tumor ecology. Identification of novel approaches to study these interactions is of primary importance. Using laser capture microdissection (LCM) coupled with reverse phase protein microarray (RPPA) based protein signaling activation mapping we explored the molecular interconnection between tumor epithelium and surrounding stromal microenvironment in 18 prostate cancer (PCa) specimens. Four specimen‐matched cellular compartments (normal‐appearing epithelium and its adjacent stroma, and malignant epithelium and its adjacent stroma) were isolated for each case. The signaling network analysis of the four compartments unraveled a number of molecular mechanisms underlying the communication between tumor cells and stroma in the context of the tumor microenvironment. In particular, differential expression of inflammatory mediators like IL‐8 and IL‐10 by the stroma cells appeared to modulate specific cross‐talks between the tumor cells and surrounding microenvironment.The cross-talk between tumor epithelium and surrounding stromal/immune microenvironment is essential to sustain tumor growth and progression and provides new opportunities for the development of targeted treatments focused on disrupting the tumor ecology. Identification of novel approaches to study these interactions is of primary importance. Using laser capture microdissection (LCM) coupled with reverse phase protein microarray (RPPA) based protein signaling activation mapping we explored the molecular interconnection between tumor epithelium and surrounding stromal microenvironment in 18 prostate cancer (PCa) specimens. Four specimen-matched cellular compartments (normal-appearing epithelium and its adjacent stroma, and malignant epithelium and its adjacent stroma) were isolated for each case. The signaling network analysis of the four compartments unraveled a number of molecular mechanisms underlying the communication between tumor cells and stroma in the context of the tumor microenvironment. In particular, differential expression of inflammatory mediators like IL-8 and IL-10 by the stroma cells appeared to modulate specific cross-talks between the tumor cells and surrounding microenvironment.

Functional signaling pathway analysis of lung adenocarcinomas identifies novel therapeutic targets for KRAS mutant tumors

Little is known about the complex signaling architecture of KRAS and the interconnected RAS-driven protein-protein interactions, especially as it occurs in human clinical specimens. This study explored the activated and interconnected signaling network of KRAS mutant lung adenocarcinomas (AD) to identify novel therapeutic targets. Thirty-four KRAS mutant (MT) and twenty-four KRAS wild-type (WT) frozen biospecimens were obtained from surgically treated lung ADs. Samples were subjected to laser capture microdissection and reverse phase protein microarray analysis to explore the expression/activation levels of 150 signaling proteins along with co-activation concordance mapping. An independent set of 90 non-small cell lung cancers (NSCLC) was used to validate selected findings by immunohistochemistry (IHC). Compared to KRAS WT tumors, the signaling architecture of KRAS MT ADs revealed significant interactions between KRAS downstream substrates, the AKT/mTOR pathway, and a number of Receptor Tyrosine Kinases (RTK). Approximately one-third of the KRAS MT tumors had ERK activation greater than the WT counterpart (p<0.01). Notably 18% of the KRAS MT tumors had elevated activation of the Estrogen Receptor alpha (ER-α) (p=0.02). This finding was verified in an independent population by IHC (p=0.03). KRAS MT lung ADs appear to have a more intricate RAS linked signaling network than WT tumors with linkage to many RTKs and to the AKT-mTOR pathway. Combination therapy targeting different nodes of this network may be necessary to treat this group of patients. In addition, for patients with KRAS MT tumors and activation of the ER-α, anti-estrogen therapy may have important clinical implications.

Robustness of complex feedback systems: application to oncological biochemical networks

Biochemical transduction networks can be modelled through a proper set of differential equations, and at the same time they can be experimentally analysed only by measuring a few signals at the end of the cascades. The study of those networks is of special importance in oncology. The approach proposed in this paper is aimed at characterising the network robustness with respect to both parameters and initial condition perturbations. The key idea is to introduce a robustness index, the proliferation index, and to study its behaviour over the parameter space. The index relates the measurable signals, and the shape of their time behaviour, to the model parameters and initial conditions. In addition, the paper will also provide specific results for a dynamic model of the EGFR–IGFR receptor pathway, which turns out to be relevant to the study of some specific pathologies, such as lung cancer. The connection between the proposed robustness index and the pathology will also be addressed.

An approach to the conditional robustness problem for biochemical networks

Robustness analysis of mathematical models is of high importance for studying cancer and its proliferation. In this paper, we introduce the concept of “conditional robustness” for nonlinear ODE models of cancer, and we propose a method to identify regions in the parameter space which exhibits desired behaviors. The proposed approach allows the selection of key parameters influencing system robustness, that is, the selection of key nodes in the biochemical network whose inhibition should improve drug response. We illustrate our approach using a model of the EGFR-IGF1R signal transduction system, which is an important network for translational oncology and cancer therapy.

An approach for optimally extending mathematical models of signaling networks using omics data.

Mathematical modeling is a key process in Systems Biology and the use of computational tools such as Cytoscape for omics data processing, need to be integrated in the modeling activity. In this paper we propose a new methodology for modeling signaling networks by combining ordinary differential equation models and a gene recommender system, GeneMANIA. We started from existing models, that are stored in the BioModels database, and we generated a query to use as input for the GeneMANIA algorithm. The output of the recommender system was then led back to the kinetic reactions that were finally added to the starting model. We applied the proposed methodology to EGFR-IGF1R signal transduction network, which plays an important role in translational oncology and cancer therapy of non small cell lung cancer.