Spatiotemporal T790M Heterogeneity in Individual Patients with EGFR-Mutant Non–Small-Cell Lung Cancer after Acquired Resistance to EGFR-TKI

IntroductionEpidermal growth factor receptor (EGFR) mutation T790M accounts for approximately half of acquired resistances to EGFR-tyrosine kinase inhibitor (TKI). Because T790M is mediated by TKI exposure, its penetration and “on–off” may affect T790M status.MethodsWe retrospectively reviewed T790M status and clinical course of patients who had undergone multiple rebiopsies after acquired resistance to EGFR-TKI.ResultsOf 145 patients with EGFR-mutant NSCLC receiving rebiopsy after acquired resistance, 30 underwent multiple site rebiopsies, and 24 received repeated rebiopsies at the same lesion. In 22 patients who underwent rebiopsies from both central nervous system (CNS; 20 cerebrospinal fluids [CSF] and 2 brain tumoral tissues) and thoracic lesions (7 lung tissues, 14 pleural effusions, and 1 lymph node), 12 were thoracic-T790M-positive. Of these 12 patients, 10 were CNS-T790M-negative, despite exhibiting thoracic-T790M-positive. All 10 thoracic-T790M-negatives were CNS-T790M-negative. Three patients revealed a spatial heterogeneous T790M status among their thoracic lesions. In 24 patients receiving repeated rebiopsies at the same lesion (12 lung tissues, 6 CSFs, and 6 pleural effusions), T790M status of lung lesions varied in five patients after TKI-free interval. In all five patients whose T790M status changed from positive to negative, EGFR-TKI rechallenge was effective. In three of these five patients, after further TKI exposure, T790M status changed from negative to positive again. There was also a patient whose CSF T790M status changed from negative to positive after high-dose erlotinib therapy.ConclusionsT790M status in an individual patient can be spatiotemporally heterogeneous because of selective pressure from EGFR-TKI

How Sensitive Are Epidermal Growth Factor Receptor–Tyrosine Kinase Inhibitors for Squamous Cell Carcinoma of the Lung Harboring EGFR Gene–Sensitive Mutations?

Introduction:Epidermal growth factor receptor (EGFR) mutations are found mostly in adenocarcinoma, and rarely in squamous cell carcinoma (SQC). Little is known about SQC harboring EGFR mutations.Methods:Between April 2006 and October 2010, we investigated the incidence of EGFR activating mutations in SQC of the lung using the peptide nucleic acid-locked nucleic acid polymerase chain reaction clamp method. The efficacy of EGFR-tyrosine kinase inhibitors (TKIs) was retrospectively evaluated in patients with EGFR-mutated SQC. Further pathologic analyses were performed using immunohistochemistry.Results:Thirty-three of 249 patients with SQC (13.3%) had EGFR mutations, including exon 19 deletion (19 of 33 patients, 58%), L858R point mutation in exon 21 (12 of 33, 36%), and G719S point mutation in exon 18 (2 of 33, 6%). Twenty of these 33 patients received EGFR-TKI therapy, and five of these 20 responded to EGFR-TKIs with a response rate of 25.0% (95% confidence interval [CI], 8.7%–49.1%). The patients’ median progression-free survival and median overall survival were 1.4 months (95% CI, 0.7–5.8 months) and 14.6 months (95% CI, 2.9–undeterminable months), respectively. Approximately one third of the EGFR-mutated SQC patients achieved progression-free survival for longer than 6 months. Some of these patients had high carcinoembryonic antigen levels or a history of never smoking, or were positive for thyroid transcription factor-1.Conclusions:Although EGFR-TKIs seem to be generally less effective in EGFR-mutated SQC than in EGFR-mutated adenocarcinoma, some EGFR-mutated SQC patients can obtain clinical benefit from EGFR-TKIs. To better identify these patients, not only EGFR mutation status, but also clinical factors and pathologic findings should be taken into consideration

Investigating and Projecting Population Structures in Open Source Software Projects: A Case Study of Projects in GitHub

GitHub is a developers' social networking service that hosts a great number of open source software (OSS) projects. Although some of the hosted projects are growing and have many developers, most projects are organized by a few developers and face difficulties in terms of sustainability. OSS projects depend mainly on volunteer developers, and attracting and retaining these volunteers are major concerns of the project stakeholders. To investigate the population structures of OSS development communities in detail and conduct software analytics to obtain actionable information, we apply a demographic approach. Demography is the scientific study of population and seeks to identify the levels and trends in the size and components of a population. This paper presents a case study, investigating the characteristics of the population structures of OSS projects on GitHub, and shows population projections generated with the well-known cohort component method. We found that there are four types of population structures in OSS development communities in terms of experiences and contributions. In addition, we projected the future population accurately using a cohort component population projection method. This method predicts a population of the next period using a survival rate calculated from past population. To the best of our knowledge, this is the first study that applied demography to the field of OSS research. Our approach addressing OSS-related problems based on demography will bring new insights, since studying population is novel in OSS research. Understanding current and future structures of OSS projects can help practitioners to monitor a project, gain awareness of what is happening, manage risks, and evaluate past decisions

Automatic Unsupervised Bug Report Categorization

2014 6th International Workshop on Empirical Software Engineering in Practice, 12-13 Nov. 2014, Osaka, JapanBackground: Information in bug reports is implicit and therefore difficult to comprehend. To extract its meaning, some processes are required. Categorizing bug reports is a technique that can help in this regard. It can be used to help in the bug reports management or to understand the underlying structure of the desired project. However, most researches in this area are focusing on a supervised learning approach that still requires a lot of human afford to prepare a training data. Aims: Our aim is to develop an automated framework than can categorize bug reports, according to their hidden characteristics and structures, without the needed for training data. Method: We solve this problem using clustering, unsupervised learning approach. It can automatically group bug reports together based on their textual similarity. We also propose a novel method to label each group with meaningful and representative names. Results: Experiment results show that our framework can achieve performance comparable to the supervised learning approaches. We also show that our labeling process can label each cluster with representative names according to its characteristic. Conclusion: Our framework could be used as an automated categorization system that can be applied without prior knowledge or as an automated labeling suggestion system

Bug report recommendation for code inspection

SWAN 2015 : 2015 IEEE 1st International Workshop on Software Analytics, 2 March 2015, Montreal, QC, CanadaLarge software projects such as Mozilla Firefox and Eclipse own more than ten thousand bug reports that have been reported but left unresolved. To utilize such a great amount of unresolved bug reports and accelerate bug detection and removal, we propose to a way recommend programmers a bug report that is likely to contain failure descriptions related to a source file being inspected. We employ the vector space model (VSM) to make a relevancy ranking of bug reports to a given source file. The result of an experiment using data of three open source software projects showed that the accuracies of recommendations ranged from 21.74% to 60.05% in terms of the percentage of recommendations that contained relevant bug reports in a top 10 recommended list