Fortunato Bianconi

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.

High coexpression of both insulin-like growth factor receptor-1 (IGFR-1) and epidermal growth factor receptor (EGFR) is associated with shorter disease-free survival in resected non-small-cell lung cancer patients

BACKGROUND Insulin-like growth factor receptor-1 (IGFR-1) represents a novel molecular target in non-small-cell lung cancer (NSCLC). IGFR-1 and epidermal growth factor receptor (EGFR) activation is essential to mediate tumor cell survival, proliferation and invasion. We explored the correlation between IGFR-1 and EGFR, their relationship with clinicopathological parameters and their impact on outcome in resected stage I-III NSCLC patients. PATIENTS AND METHODS Tumors from 125 surgical NSCLC patients were evaluated for IGFR-1 and EGFR expression by immunohistochemistry. Kaplan-Meier estimates of survival and time to recurrence were calculated for clinical variables and biologic markers using the Cox model for multivariate analysis. RESULTS IGFR-1 protein overexpression was detected in 36.0% of NSCLC patients and was associated with larger tumor size (P = 0.04) but not with other clinical or biological characteristics. EGFR protein overexpression was observed in 55.2% of NSCLC, more frequently in squamous cell carcinoma (SCC) than non-SCC (63.7% versus 36.3%, chi(2) = 9.8, P = 0.001). IGFR-1 protein expression was associated with EGFR protein expression (P = 0.03). At the multivariate analysis, high coexpression of both IGFR-1 and EGFR was a significant prognostic factor of worse disease-free survival (DFS) (hazard ratio 2.51, P = 0.01). CONCLUSION A statistically significant association was observed between high coexpression of both IGFR-1 and EGFR and worse DFS in early NSCLC patients.

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.

Information Technology as Tools for Cancer Registry and Regional Cancer Network Integration

Background. With the publication of large studies from different health systems comparing survival probabilities, cancer registries are increasingly involved in clinical evaluation research. The changing role of registries strictly depends on the integration between the oncology system and proper information technology (IT) tools. IT is fundamental to improving validity and timeliness of data diffusion when both the number of sources linked and the number of variables registered are on the rise. Aims. In this paper, we present a modern web-based management system that allows to integrate different sources, validate and elaborate data thus providing a new evaluation system for the oncology network based on cancer registries. Materials and methods. We developed a Web 2.0 management system for the Umbria Cancer Registry (S.G.RTUP) based on AMPAX technology (Apache, Mysql, PHP, Ajax and XML) and object-oriented programming. ISO/IEC 27001:2005 standard is followed to ensure security access to the information. The S.G.RTUP architecture is modular and extensible and information consistency is guaranteed by entity-relationship principles. Cancer sites, topology, morphology, and behavior are coded according to the International Classification of Diseases. Classical epidemiological indices for a cancer registry are implemented: incidence, mortality, years of potential life lost, and cumulative risk. S.G.RTUP has tools to prepare data for trend analysis and relative survival analysis. Geographical analysis is also implemented. Results. S.G.RTUP is integrated with the Oncology Network and gives timely epidemiological indices for evaluation of oncological activities. The registration system that we developed can effectively manage different data sources. Automatic importing of routinely available data from pathology archives, screening services, and hospital discharge records will reduce the time needed to produce data and will also allow the expansion of registered information. Several services for data visualization and statistical analysis are implemented. A geographic information system based on Google maps API is used for geolocalization of cases and map plotting of incidence and mortality rates. We implemented Besag York and Mollies algorithm for real-time smoothed maps. All services can be dynamically performed over a subset of data that the user can select through an innovative filtering system. Discussion and conclusion. IT contributed to shortening all phases of cancer registration, including linkage with external sources, coding, quality control, data management and analysis and publication of results. Integration in the oncology network and secure Web access allowed us to design with clinicians innovative population-based collaborative studies. Our geographic analysis system enables us to develop sophisticated dynamic geostatistic tools.

Seasonal variation in the month of birth in patients with skin cancer

Background:Month of birth influences the risk of developing several diseases. We investigated the influence of date of birth on melanoma skin cancer (MSC) and non-melanoma skin cancer (NMSC) incidence.Methods:Enhanced cancer registry data were analysed including 1751 MSC and 15 200 NMSC.Results:People born in February to April showed significantly elevated risks of NMSC compared with those born in summertime.Conclusions:We demonstrated seasonality by date of birth for skin cancer incidence. Neonatal UV exposure may explain this finding.

Changes in cervical cancer incidence following the introduction of organized screening in Italy.

OBJECTIVE To quantify the impact of organized cervical screening programs (OCSPs) on the incidence of invasive cervical cancer (ICC), comparing rates before and after activation of OCSPs. METHODS This population-based investigation, using individual data from cancer registries and OCSPs, included 3557 women diagnosed with ICC at age 25-74years in 1995-2008. The year of full-activation of each OCSP was defined as the year when at least 40% of target women had been invited. Incidence rate ratios (IRRs) with 95% confidence intervals (95% CIs) were calculated as the ratios between age-standardized incidence rates observed in periods after full-activation of OCSPs vs those observed in the preceding quinquennium. RESULTS ICC incidence rates diminished with time since OCSPs full-activation: after 6-8years, the IRR was 0.75 (95% CI: 0.67-0.85). The reduction was higher for stages IB-IV (IRR=0.68, 95% CI: 0.58-0.80), squamous cell ICCs (IRR=0.74, 95% CI: 0.64-0.84), and particularly evident among women aged 45-74years. Conversely, incidence rates of micro-invasive (stage IA) ICCs increased, though not significantly, among women aged 25-44years (IRR=1.34, 95% CI: 0.91-1.96). Following full-activation of OCSPs, micro-invasive ICCs were mainly and increasingly diagnosed within OCSPs (up to 72%). CONCLUSION(S) Within few years from activation, organized screening positively impacted the already low ICC incidence in Italy and favored down-staging.

Impact of epidermal growth factor receptor and KRAS mutations on clinical outcome in resected non-small cell lung cancer patients.

Objectives:Surgery yields best results for non–small cell lung cancer (NSCLC) patients. Epidermal growth factor receptor (EGFR) and its downstream factor Kirsten rat sarcoma viral oncogene homolog (KRAS) are variably mutated in NSCLC. Such mutations predict clinical response to tyrosine kinase inhibitors. This study evaluated incidence and correlation of EGFR and KRAS mutations with clinicopathologic parameters and outcome in resected stage I to III NSCLC. Methods:We analyzed the clinical characteristics and outcome data for 230 patients who underwent resection at our institution for stage I to III NSCLC. The tumors were assessed for both EGFR (exons 18 to 21) and KRAS (exons 2 and 3) mutations by nested polymerase chain reaction and sequenced in both sense and antisense direction. Kaplan-Meier estimates of overall survival and disease-free survival were calculated for clinical and biological variables using Cox model. Results:EGFR and KRAS mutations were detected in 22 (9.6%) and 39 (16.9%) patients, respectively. In the whole population, both EGFR and KRAS mutations were significantly correlated with adenocarcinoma (ADC). Overall, EGFR mutations were more frequent in women (P<0.0001) and in nonsmokers (P<0.0001). In the ADC/BAC group, KRAS mutations were more frequent in man (P<0.02) and EGFR mutations (exon 19 deletion and L858R) demonstrated a tendency towards worse disease-free survival (P=0.056). No difference in outcome was seen between patients harboring KRAS mutations compared with KRAS wild type. Conclusions:EGFR and KRAS mutations are frequent in ADCs and are not prognostic factors for survival. EGFR mutations could be used to identify patients suitable for adjuvant treatment with targeted therapy resulting in potentially improved outcomes.

Gene identification for risk of relapse in stage I lung adenocarcinoma patients: a combined methodology of gene expression profiling and computational gene network analysis.

Risk assessment and treatment choice remains a challenge in early non-smallcell lung cancer (NSCLC). The aim of this study was to identify novel genes involved in the risk of early relapse (ER) compared to no relapse (NR) in resected lung adenocarcinoma (AD) patients using a combination of high throughput technology and computational analysis. We identified 18 patients (n.13 NR and n.5 ER) with stage I AD. Frozen samples of patients in ER, NR and corresponding normal lung (NL) were subjected to Microarray technology and quantitative-PCR (Q-PCR). A gene network computational analysis was performed to select predictive genes. An independent set of 79 ADs stage I samples was used to validate selected genes by Q-PCR. From microarray analysis we selected 50 genes, using the fold change ratio of ER versus NR. They were validated both in pool and individually in patient samples (ER and NR) by Q-PCR. Fourteen increased and 25 decreased genes showed a concordance between two methods. They were used to perform a computational gene network analysis that identified 4 increased (HOXA10, CLCA2, AKR1B10, FABP3) and 6 decreased (SCGB1A1, PGC, TFF1, PSCA, SPRR1B and PRSS1) genes. Moreover, in an independent dataset of ADs samples, we showed that both high FABP3 expression and low SCGB1A1 expression was associated with a worse disease-free survival (DFS). Our results indicate that it is possible to define, through gene expression and computational analysis, a characteristic gene profiling of patients with an increased risk of relapse that may become a tool for patient selection for adjuvant therapy.

MYC and human telomerase gene (TERC) copy number gain in early-stage non-small cell lung cancer.

Objectives:We investigated the frequency of MYC and TERC increased gene copy number (GCN) in early-stage non–small cell lung cancer (NSCLC) and evaluated the correlation of these genomic imbalances with clinicopathologic parameters and outcome. Materials and Methods:Tumor tissues were obtained from 113 resected NSCLCs. MYC and TERC GCNs were tested by fluorescence in situ hybridization (FISH) according to the University of Colorado Cancer Center (UCCC) criteria and based on the receiver operating characteristic (ROC) classification. Results:When UCCC criteria were applied, 41 (36%) cases for MYC and 41 (36%) cases for TERC were considered FISH-positive. MYC and TERC concurrent FISH-positive was observed in 12 cases (11%): 2 (17%) cases with gene amplification and 10 (83%) with high polysomy. By using the ROC analysis, high MYC (mean ≥2.83 copies/cell) and TERC (mean ≥2.65 copies/cell) GCNs were observed in 60 (53.1%) cases and 58 (51.3%) cases, respectively. High TERC GCN was associated with squamous cell carcinoma (SCC) histology (P=0.001). In univariate analysis, increased MYC GCN was associated with shorter overall survival (P=0.032 [UCCC criteria] or P=0.02 [ROC classification]), whereas high TERC GCN showed no association. In multivariate analysis including stage and age, high MYC GCN remained significantly associated with worse overall survival using both the UCCC criteria (P=0.02) and the ROC classification (P=0.008). Conclusions:Our results confirm MYC as frequently amplified in early-stage NSCLC and increased MYC GCN as a strong predictor of worse survival. Increased TERC GCN does not have prognostic impact but has strong association with squamous histology.

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.