Insuk Sohn

Phase III Trial Comparing Capecitabine Plus Cisplatin Versus Capecitabine Plus Cisplatin With Concurrent Capecitabine Radiotherapy in Completely Resected Gastric Cancer With D2 Lymph Node Dissection: The ARTIST Trial

PURPOSE The ARTIST (Adjuvant Chemoradiation Therapy in Stomach Cancer) trial was the first study to our knowledge to investigate the role of postoperative chemoradiotherapy therapy in patients with curatively resected gastric cancer with D2 lymph node dissection. This trial was designed to compare postoperative treatment with capecitabine plus cisplatin (XP) versus XP plus radiotherapy with capecitabine (XP/XRT/XP). PATIENTS AND METHODS The XP arm received six cycles of XP (capecitabine 2,000 mg/m2 per day on days 1 to 14 and cisplatin 60 mg/m2 on day 1, repeated every 3 weeks) chemotherapy. The XP/XRT/XP arm received two cycles of XP followed by 45-Gy XRT (capecitabine 1,650 mg/m2 per day for 5 weeks) and two cycles of XP. RESULTS Of 458 patients, 228 were randomly assigned to the XP arm and 230 to the XP/XRT/XP arm. Treatment was completed as planned by 75.4% of patients (172 of 228) in the XP arm and 81.7% (188 of 230) in the XP/XRT/XP arm. Overall, the addition of XRT to XP chemotherapy did not significantly prolong disease-free survival (DFS; P = .0862). However, in the subgroup of patients with pathologic lymph node metastasis at the time of surgery (n = 396), patients randomly assigned to the XP/XRT/XP arm experienced superior DFS when compared with those who received XP alone (P = .0365), and the statistical significance was retained at multivariate analysis (estimated hazard ratio, 0.6865; 95% CI, 0.4735 to 0.9952; P = .0471). CONCLUSION The addition of XRT to XP chemotherapy did not significantly reduce recurrence after curative resection and D2 lymph node dissection in gastric cancer. A subsequent trial (ARTIST-II) in patients with lymph node-positive gastric cancer is planned.

Molecular analysis of gastric cancer identifies subtypes associated with distinct clinical outcomes

Gastric cancer, a leading cause of cancer-related deaths, is a heterogeneous disease. We aim to establish clinically relevant molecular subtypes that would encompass this heterogeneity and provide useful clinical information. We use gene expression data to describe four molecular subtypes linked to distinct patterns of molecular alterations, disease progression and prognosis. The mesenchymal-like type includes diffuse-subtype tumors with the worst prognosis, the tendency to occur at an earlier age and the highest recurrence frequency (63%) of the four subtypes. Microsatellite-unstable tumors are hyper-mutated intestinal-subtype tumors occurring in the antrum; these have the best overall prognosis and the lowest frequency of recurrence (22%) of the four subtypes. The tumor protein 53 (TP53)-active and TP53-inactive types include patients with intermediate prognosis and recurrence rates (with respect to the other two subtypes), with the TP53-active group showing better prognosis. We describe key molecular alterations in each of the four subtypes using targeted sequencing and genome-wide copy number microarrays. We validate these subtypes in independent cohorts in order to provide a consistent and unified framework for further clinical and preclinical translational research.

Phase III Trial to Compare Adjuvant Chemotherapy With Capecitabine and Cisplatin Versus Concurrent Chemoradiotherapy in Gastric Cancer: Final Report of the Adjuvant Chemoradiotherapy in Stomach Tumors Trial, Including Survival and Subset Analyses

PURPOSE The Adjuvant Chemoradiotherapy in Stomach Tumors (ARTIST) trial tested whether the addition of radiotherapy to adjuvant chemotherapy improved disease-free survival (DFS) in patients with D2-resected gastric cancer (GC). PATIENTS AND METHODS Between November 2004 and April 2008, 458 patients with GC who received gastrectomy with D2 lymph node dissection were randomly assigned to either six cycles of adjuvant chemotherapy with capecitabine and cisplatin (XP) or to two cycles of XP followed by chemoradiotherapy and then two additional cycles of XP (XPRT). This final update contains the first publication of overall survival (OS), together with updated DFS and subset analyses. RESULTS With 7 years of follow-up, DFS remained similar between treatment arms (hazard ratio [HR], 0.740; 95% CI, 0.520 to 1.050; P=.0922). OS also was similar (HR, 1.130; 95% CI, 0.775 to 1.647; P=.5272). The effect of the addition of radiotherapy on DFS and OS differed by Lauren classification (interaction P=.04 for DFS; interaction P=.03 for OS) and lymph node ratio (interaction P<.01 for DFS; interaction P<.01 for OS). Subgroup analyses also showed that chemoradiotherapy significantly improved DFS in patients with node-positive disease and with intestinal-type GC. There was a similar trend for DFS and OS by stage of disease. CONCLUSION In D2-resected GC, both adjuvant chemotherapy and chemoradiotherapy are tolerated and equally beneficial in preventing relapse. Because results suggest a significant DFS effect of chemoradiotherapy in subsets of patients, the ARTIST 2 trial evaluating adjuvant chemotherapy and chemoradiotherapy in patients with node-positive, D2-resected GC is under way.

Randomized phase II study of gefitinib versus erlotinib in patients with advanced non-small cell lung cancer who failed previous chemotherapy ☆

PURPOSE Gefitinib and erlotinib are potent EGFR TKIs, with antitumor activity. In this randomized, single-center, non-comparative phase II trial, the efficacy and safety of gefitinib and erlotinib was evaluated as the second-line therapy for advanced non-small cell lung cancer (NSCLC). PATIENTS AND METHODS Patients with locally advanced, metastatic stage IIIB/IV NSCLC who failed first-line chemotherapy and had either EGFR mutation or at least two out of three clinical factors associated with higher incidence of EGFR mutations (female, adenocarcinoma histology, and never-smoker) were eligible. RESULTS A total of 96 (48 per arm) patients were randomly assigned to gefitinib- or erlotinib-arm, respectively. Baseline characteristics were well-balanced between the two arms. The response rates (RR) were 47.9% in the gefitinib arm and 39.6% in the erlotinib arm. Median PFS was 4.9 months (95% CI, 1.3-8.5) in the gefitinib arm and 3.1 months (95% CI, 0.0-6.4) in the erlotinib arm. The most common grade 3/4 toxicity was skin rash. Exploratory analyses showed that there was no significant difference in RR and PFS in the gefitinib arm compared to the erlotinib arm (RR (%) 47.9 vs. 39.6, p=0.269; median survival (months) 4.9 vs. 3.1, p=0.336). There was no significant difference in QOL between the two arms. CONCLUSION Both gefitinib and erlotinib showed effective activity and tolerable toxicity profiles as second-line treatment for the selected population of NSCLC. We may consider conducting a phase III trial to directly compare the efficacy and toxicity between gefitinib and erlotinib in an enriched patient population.

Gradient lasso for Cox proportional hazards model

MOTIVATION There has been an increasing interest in expressing a survival phenotype (e.g. time to cancer recurrence or death) or its distribution in terms of a subset of the expression data of a subset of genes. Due to high dimensionality of gene expression data, however, there is a serious problem of collinearity in fitting a prediction model, e.g. Coxs proportional hazards model. To avoid the collinearity problem, several methods based on penalized Cox proportional hazards models have been proposed. However, those methods suffer from severe computational problems, such as slow or even failed convergence, because of high-dimensional matrix inversions required for model fitting. We propose to implement the penalized Cox regression with a lasso penalty via the gradient lasso algorithm that yields faster convergence to the global optimum than do other algorithms. Moreover the gradient lasso algorithm is guaranteed to converge to the optimum under mild regularity conditions. Hence, our gradient lasso algorithm can be a useful tool in developing a prediction model based on high-dimensional covariates including gene expression data. RESULTS Results from simulation studies showed that the prediction model by gradient lasso recovers the prognostic genes. Also results from diffuse large B-cell lymphoma datasets and Norway/Stanford breast cancer dataset indicate that our method is very competitive compared with popular existing methods by Park and Hastie and Goeman in its computational time, prediction and selectivity. AVAILABILITY R package glcoxph is available at http://datamining.dongguk.ac.kr/R/glcoxph.

Statistical Challenges in Preprocessing in Microarray Experiments in Cancer

Many clinical studies incorporate genomic experiments to investigate the potential associations between high-dimensional molecular data and clinical outcome. A critical first step in the statistical analyses of these experiments is that the molecular data are preprocessed. This article provides an overview of preprocessing methods, including summary algorithms and quality control metrics for microarrays. Some of the ramifications and effects that preprocessing methods have on the statistical results are illustrated. The discussions are centered around a microarray experiment based on lung cancer tumor samples with survival as the clinical outcome of interest. The procedures that are presented focus on the array platform used in this study. However, many of these issues are more general and are applicable to other instruments for genome-wide investigation. The discussions here will provide insight into the statistical challenges in preprocessing microarrays used in clinical studies of cancer. These challenges should not be viewed as inconsequential nuisances but rather as important issues that need to be addressed so that informed conclusions can be drawn.

A copula method for modeling directional dependence of genes

BackgroundGenes interact with each other as basic building blocks of life, forming a complicated network. The relationship between groups of genes with different functions can be represented as gene networks. With the deposition of huge microarray data sets in public domains, study on gene networking is now possible. In recent years, there has been an increasing interest in the reconstruction of gene networks from gene expression data. Recent work includes linear models, Boolean network models, and Bayesian networks. Among them, Bayesian networks seem to be the most effective in constructing gene networks. A major problem with the Bayesian network approach is the excessive computational time. This problem is due to the interactive feature of the method that requires large search space. Since fitting a model by using the copulas does not require iterations, elicitation of the priors, and complicated calculations of posterior distributions, the need for reference to extensive search spaces can be eliminated leading to manageable computational affords. Bayesian network approach produces a discretely expression of conditional probabilities. Discreteness of the characteristics is not required in the copula approach which involves use of uniform representation of the continuous random variables. Our method is able to overcome the limitation of Bayesian network method for gene-gene interaction, i.e. information loss due to binary transformation.ResultsWe analyzed the gene interactions for two gene data sets (one group is eight histone genes and the other group is 19 genes which include DNA polymerases, DNA helicase, type B cyclin genes, DNA primases, radiation sensitive genes, repaire related genes, replication protein A encoding gene, DNA replication initiation factor, securin gene, nucleosome assembly factor, and a subunit of the cohesin complex) by adopting a measure of directional dependence based on a copula function. We have compared our results with those from other methods in the literature. Although microarray results show a transcriptional co-regulation pattern and do not imply that the gene products are physically interactive, this tight genetic connection may suggest that each gene product has either direct or indirect connections between the other gene products. Indeed, recent comprehensive analysis of a protein interaction map revealed that those histone genes are physically connected with each other, supporting the results obtained by our method.ConclusionThe results illustrate that our method can be an alternative to Bayesian networks in modeling gene interactions. One advantage of our approach is that dependence between genes is not assumed to be linear. Another advantage is that our approach can detect directional dependence. We expect that our study may help to design artificial drug candidates, which can block or activate biologically meaningful pathways. Moreover, our copula approach can be extended to investigate the effects of local environments on protein-protein interactions. The copula mutual information approach will help to propose the new variant of ARACNE (Algorithm for the Reconstruction of Accurate Cellular Networks): an algorithm for the reconstruction of gene regulatory networks.

Nanostring-Based Multigene Assay to Predict Recurrence for Gastric Cancer Patients after Surgery

Despite the benefits from adjuvant chemotherapy or chemoradiotherapy, approximately one-third of stage II gastric cancer (GC) patients developed recurrences. The aim of this study was to develop and validate a prognostic algorithm for gastric cancer (GCPS) that can robustly identify high-risk group for recurrence among stage II patients. A multi-step gene expression profiling study was conducted. First, a microarray gene expression profiling of archived paraffin-embedded tumor blocks was used to identify candidate prognostic genes (N = 432). Second, a focused gene expression assay including prognostic genes was used to develop a robust clinical assay (GCPS) in stage II patients from the same cohort (N = 186). Third, a predefined cut off for the GCPS was validated using an independent stage II cohort (N = 216). The GCPS was validated in another set with stage II GC who underwent surgery without adjuvant treatment (N = 300). GCPS was developed by summing the product of Cox regression coefficients and normalized expression levels of 8 genes (LAMP5, CDC25B, CDK1, CLIP4, LTB4R2, MATN3, NOX4, TFDP1). A prospectively defined cut-point for GCPS classified 22.7% of validation cohort treated with chemoradiotherapy (N = 216) as high-risk group with 5-year recurrence rate of 58.6% compared to 85.4% in the low risk group (hazard ratio for recurrence = 3.16, p = 0.00004). GCPS also identified high-risk group among stage II patients treated with surgery only (hazard ratio = 1.77, p = 0.0053).

Clinical relevance of gain-of-function mutations of p53 in high-grade serous ovarian carcinoma.

Purpose Inactivation of TP53, which occurs predominantly by missense mutations in exons 4–9, is a major genetic alteration in a subset of human cancer. In spite of growing evidence that gain-of-function (GOF) mutations of p53 also have oncogenic activity, little is known about the clinical relevance of these mutations. Methods The clinicopathological features of high-grade serous ovarian carcinoma (HGS-OvCa) patients with GOF p53 mutations were evaluated according to a comprehensive somatic mutation profile comprised of whole exome sequencing, mRNA expression, and protein expression profiles obtained from the Cancer Genome Atlas (TCGA). Results Patients with a mutant p53 protein (mutp53) with a GOF mutation showed higher p53 mRNA and protein expression levels than patients with p53 mutation with no evidence of GOF (NE-GOF). GOF mutations were more likely to occur within mutational hotspots, and at CpG sites, and resulted in mutp53 with higher functional severity (FS) scores. Clinically, patients with GOF mutations showed a higher frequency of platinum resistance (22/58, 37.9%) than patients with NE-GOF mutations (12/56, 21.4%) (p=0.054). Furthermore, patients with GOF mutations were more likely to develop distant metastasis (36/55, 65.5%) than local recurrence (19/55, 34.5%), whereas patients with NE-GOF mutations showed a higher frequency of locoregional recurrence (26/47, 55.3%) than distant metastasis (21/47, 44.7%) (p=0.035). There were no differences in overall or progression-free survival between patients with GOF or NE-GOF mutp53. Conclusion This study demonstrates that patient with GOF mutp53 is characterized by a greater likelihood of platinum treatment resistance and distant metastatic properties in HGS-OvCa.

Classification of gene functions using support vector machine for time-course gene expression data

Since most biological systems are developmental and dynamic, time-course gene expression profiles provide an important characterization of gene functions. Assigning functions for genes with unknown functions based on time-course gene expressions is an important task in functional genomics. Recently, various methods have been proposed for the classification of gene functions based on time-course gene expression data. In this paper, we consider the classification of gene functions from functional data analysis viewpoint, where a functional support vector machine is adopted. The functional support vector machine can model temporal effects of time-course gene expression data by incorporating the coefficients as well as the basis matrix obtained from a finite expansion of gene expressions on a set of basis functions. We apply the functional support vector machine to both real microarray and simulated data. Our results indicate that the functional support vector machine is effective in discriminating gene functions of time-course gene expressions with predefined functions. The method also provides valuable functional information about interactions between genes and allows the assignment of new functions to genes with unknown functions.