Characterizing disease states from topological properties of transcriptional regulatory networks

BACKGROUND: High throughput gene expression experiments yield large amounts of data that can augment our understanding of disease processes, in addition to classifying samples. Here we present new paradigms of data Separation based on construction of transcriptional regulatory networks for normal and abnormal cells using sequence predictions, literature based data and gene expression studies. We analyzed expression datasets from a number of diseased and normal cells, including different types of acute leukemia, and breast cancer with variable clinical outcome. RESULTS: We constructed sample-specific regulatory networks to identify links between transcription factors (TFs) and regulated genes that differentiate between healthy and diseased states. This approach carries the advantage of identifying key transcription factor-gene pairs with differential activity between healthy and diseased states rather than merely using gene expression profiles, thus alluding to processes that may be involved in gene deregulation. We then generalized this approach by studying simultaneous changes in functionality of multiple regulatory links pointing to a regulated gene or emanating from one TF (or changes in gene centrality defined by its in-degree or out-degree measures, respectively). We found that samples can often be separated based on these measures of gene centrality more robustly than using individual links. We examined distributions of distances (the number of links needed to traverse the path between each pair of genes) in the transcriptional networks for gene subsets whose collective expression profiles could best separate each dataset into predefined groups. We found that genes that optimally classify samples are concentrated in neighborhoods in the gene regulatory networks. This suggests that genes that are deregulated in diseased states exhibit a remarkable degree of connectivity. CONCLUSION: Transcription factor-regulated gene links and centrality of genes on transcriptional networks can be used to differentiate between cell types. Transcriptional network blueprints can be used as a basis for further research into gene deregulation in diseased states

Incidence of the V600K mutation among melanoma patients with BRAF mutations, and potential therapeutic response to the specific BRAF inhibitor PLX4032

Activating mutations in BRAF kinase are common in melanomas. Clinical trials with PLX4032, the mutant-BRAF inhibitor, show promising preliminary results in patients selected for the presence of V600E mutation. However, activating V600K mutation is the other most common mutation, yet patients with this variant are currently excluded from the PLX4032 trials. Here we present evidence that a patient bearing the BRAF V600K mutation responded remarkably to PLX4032, suggesting that clinical trials should include all patients with activating BRAF V600E/K mutations

The utility of single nucleotide DNA variations as predictors of postoperative pain

Objectives: Genetic variation is an important contributor to postsurgical pain and thereby analgesia requirements. A description of the potential predictive power of genetic variants in pain should instruct improvements in pain management postoperatively. We set out to examine whether a set of genetic variants in pain related genes would show any association with actual pain outcomes in a typical surgical population. Methods: A candidate gene study was carried out in 135 surgical patients with 12 DNA variants (single nucleotide polymorphisms or ‘SNPs’) in known or putative pain pathway genes to detect associations with postoperative pain - measured by a verbal rating score (VRS) and patient-controlled analgesia (PCA) usage rate. Standard PCR based molecular biology approaches were used. Results: At 20-24h after surgery, patients with the 1032G/1032G variant pair for the A1032G variant of the potassium channel KCNJ6 gene had a slightly higher median VRS than those with 1032A/1032A or 1032A/1032G pairs (p=0.04; dominant genetic model). This small difference was most apparent in the orthopaedic surgery patients where the 1032G/1032G pair associated with VRS (median(interquartile range)) of 5(4-6) vs. 3(0.5-4) in 1032A/1032A or 1032A/1032G groups. For PCA, patients with 3435C/3435C or 3435C/3435T pairs for ATPdependent efflux pump gene ABCB1 variant C3435T used PCA at a considerably higher rate of 0.89(0.07-1.66) mg.h-1 compared with just 0.11 (0-0.52) mg.h-1 for the 3435T/3435T pair (p=0.03; dominant model). A significantly higher usage rate was also detected for opioid receptor OPRM1 variant IVS2-691 with usage of 0.77(0.01-1.56) mg.h-1 for the IVS2C/IVS2C or IVS2C/IVS2G group vs. 0.24(0-1.26) mg.h-1 in the IVS2G/IVS2G group (p=0.04; recessive model). Conclusion: While this study has identified some significant statistical associations the potential utility of the studied DNA variants in prediction of postoperative pain and patient-controlled opioid analgesia requirements appears to be quite limited at present

Benefits of biomarker selection and clinico-pathological covariate inclusion in breast cancer prognostic models

Introduction: Multi-marker molecular assays have impacted management of early stage breast cancer, facilitating adjuvant chemotherapy decisions. We generated prognostic models that incorporate protein-based molecular markers and clinico-pathological variables to improve survival prediction. Methods: We used a quantitative immunofluorescence method to study protein expression of 14 markers included in the Oncotype DX™ assay on a 638 breast cancer patient cohort with 15-year follow-up. We performed cross-validation analyses to assess performance of multivariate Cox models consisting of these markers and standard clinico-pathological covariates, using an average time-dependent Area Under the Receiver Operating Characteristic curves and compared it to nested Cox models obtained by robust backward selection procedures. Results: A prognostic index derived from of a multivariate Cox regression model incorporating molecular and clinico-pathological covariates (nodal status, tumor size, nuclear grade, and age) is superior to models based on molecular studies alone or clinico-pathological covariates alone. Performance of this composite model can be further improved using feature selection techniques to prune variables. When stratifying patients by Nottingham Prognostic Index (NPI), the most prognostic markers in high and low NPI groups differed. Similarly, for the node-negative, hormone receptor-positive sub-population, we derived a compact model with three clinico-pathological variables and two protein markers that was superior to the full model. Conclusions: Prognostic models that include both molecular and clinico-pathological covariates can be more accurate than models based on either set of features alone. Furthermore, feature selection can decrease the number of molecular variables needed to predict outcome, potentially resulting in less expensive assays.This work was supported by a grant from the Susan G Komen Foundation (to YK)

Tumor Microvessel Density as a Prognostic Marker in High-Risk Renal Cell Carcinoma Patients Treated on ECOG-ACRIN E2805

Purpose—Increased vascularity is a hallmark of renal cell carcinoma (RCC). Microvessel density (MVD) is one measurement of tumor angiogenesis; however, its utility as a biomarker of outcome is unknown. ECOG-ACRIN 2805 (E2805) enrolled 1,943 resected high-risk RCC patients randomized to adjuvant sunitinib, sorafenib, or placebo. We aimed to determine the prognostic and predictive role of MVD in RCC. Experimental Design—We obtained pretreatment primary RCC nephrectomy tissues from 822 patients on E2805 and constructed tissue microarrays. Using quantitative immunofluorescence, we measured tumor MVD as the area of CD34-expressing cells. We determined the association with disease-free survival (DFS), overall survival (OS), treatment arm, and clinicopathologic variables. Results—High MVD (above the median) was associated with prolonged OS for the entire cohort (p = 0.021) and for patients treated with placebo (p = 0.028). The association between high MVD and OS was weaker in patients treated with sunitinib or sorafenib (p = 0.060). MVD was not associated with DFS (p = 1.00). On multivariable analysis, MVD remained independently associated with improved OS (p = 0.013). High MVD correlated with Fuhrman grade 1–2 (p \u3c 0.001), clear cell histology (p \u3c 0.001), and absence of necrosis (p \u3c 0.001) but not with gender, age, sarcomatoid features, lymphovascular invasion, or tumor size. Conclusions—High MVD in resected high-risk RCC patients is an independent prognostic, rather than predictive, biomarker of improved OS. Further studies should assess whether incorporating MVD into clinical models will enhance our ability to predict outcome and if low MVD can be used for selection of high-risk patients for adjuvant therapy trials

Safety & efficacy of lifileucel (LN-144) tumor infiltrating lymphocyte therapy in metastatic melanoma patients after progression on multiple therapies – independent review committee data update

Treatment options are limited for patients with advanced melanoma who have progressed on checkpoint inhibitors and targeted therapies such as BRAF/MEK inhibitors (if BRAF-V600E mutated). Adoptive cell therapy utilizing tumor-infiltrating lymphocytes (TIL) has shown antitumor efficacy with durable responses in heavily pretreated melanoma patients. Safety and efficacy of lifileucel, a centrally manufactured cryopreserved autologous TIL therapy assessed by both investigator and an independent review committee (IRC), are presented

Multi-Level Targeting of the Phosphatidylinositol-3-Kinase Pathway in Non-Small Cell Lung Cancer Cells

Introduction: We assessed expression of p85 and p110a PI3K subunits in non-small cell lung cancer (NSCLC) specimens and the association with mTOR expression, and studied effects of targeting the PI3K/AKT/mTOR pathway in NSCLC cell lines. Methods: Using Automated Quantitative Analysis we quantified expression of PI3K subunits in two cohorts of 190 and 168 NSCLC specimens and correlated it with mTOR expression. We studied effects of two PI3K inhibitors, LY294002 and NVP-BKM120, alone and in combination with rapamycin in 6 NSCLC cell lines. We assessed activity of a dual PI3K/mTOR inhibitor