Epitope-positive truncating MLH1 mutation and loss of PMS2: implications for IHC-directed genetic testing for lynch syndrome

We assessed mismatch repair by immunohistochemistry (IHC) and microsatellite instability (MSI) analysis in an early onset endometrial cancer and a sister’s colon cancer. We demonstrated high-level MSI and normal expression for MLH1, MSH2 and MSH6. PMS2 failed to stain in both tumors, strongly implicating a PMS2 defect. This family did not meet clinical criteria for Lynch syndrome. However, early onset endometrial cancers in the proband and her sister, a metachronous colorectal cancer in the sister as well as MSI in endometrial and colonic tumors suggested a heritable mismatch repair defect. PCR-based direct exonic sequencing and multiplex ligation-dependent probe amplification (MLPA) were undertaken to search for PMS2 mutations in the germline DNA from the proband and her sister. No mutation was identified in the PMS2 gene. However, PMS2 exons 3, 4, 13, 14, 15 were not evaluated by MLPA and as such, rearrangements involving those exons cannot be excluded. Clinical testing for MLH1 and MSH2 mutation revealed a germline deletion of MLH1 exons 14 and 15. This MLH1 germline deletion leads to an immunodetectable stable C-terminal truncated MLH1 protein which based on the IHC staining must abrogate PMS2 stabilization. To the best of our knowledge, loss of PMS2 in MLH1 truncating mutation carriers that express MLH1 in their tumors has not been previously reported. This family points to a potential limitation of IHC-directed gene testing for suspected Lynch syndrome and the need to consider comprehensive MLH1 testing for individuals whose tumors lack PMS2 but for whom PMS2 mutations are not identified

EFEKTIVITAS DESENTRALISASI PENDIDIKAN DALAM MENINGKATKAN PROFESIONALISME GURU DI SMA NEGERI 1 LAKEA

Skripsi ini membahas tentang efektivitas desentralisasi pendidikan dalammeningkatkan profesionalisme guru di SMA Negeri 1 Lakea dengan pokok pembahasana bagaimana deskripsi desentralisasi pendidikan dalam meningkatkan profesionalisme guru di SMA Negeri 1 Lakea? dan bagaimana implikasi desentralisasi pendidikan dalam meningkatkan profesionalisme guru di SMA Negeri 1 Lakea. Tujuan penelitian ini untuk mengetahuideskripsi dan implikasi desentralisasi pendidikan dalam meningkatkan prodfesionalisme guru di sekolah tersebut.Untuk menjawab permsalahan tersebut, penelitian ini menggunakan metode kualitatif dengan teknik pengumpulan data melalui observasi, wawancara dan dokumentasi, serta menggunakan teknik analisis data melalui reduksi data, penyajian data, verifikasi data dan penarikan kesimpulan.Hasil penelitian skripsi ini yaitu: deskripsi Desentralisasi Pendidikan dalam Meningkatkan Profesionalisme Guru di SMA Negeri 1 Lakea: (1) Desentralisasi pendidikan merupakan pemberian kewenangan kepada daerah untuk mengelola pendidikannya, lalu daerah melimpahkan kepada masing-masing sekolah (2) Pemerintah pusat tetap mengontrol pendidikan yang diselenggarakan oleh pemerintah daerah dan masing-masing sekolah melalui akreditasi nasional yang dilaksanakan oleh BAN-SM (3) Aspek-aspek yang menjadi kewenangan sekolah dalam melaksanakan desentralisai pendidikan yakni: (a) Perencanaan dan evaluasi program sekolah dalam hal ini sekolah berupaya meningkatkan mutu pendidikan dengan cara merencanakan kegiatan-kegiatan yang dapat meningkatkan profesionalisme guru, misalnya mengutus guru untuk mengikuti Musyawarah Guru Mata Pelajaran (MGMP) dan merencanakan bimbingan teknis untuk meningkatkan keterampilan guru dan mengevaluasi berbagai perencaan kegiatan program sekolah (b) Aspek pengelolaan proses belajar, memberikan kewenangan kepada masing-masing guru untuk mengelola proses pembelajaran pada mata pelajaran yang dipegangnya dan kepala sekolah melukan supervisi kepada guru untuk mengevaluasi dan membimbing pelaksanaan proses pembelajaran guru tersebut (c) Aspek pengelolaan ketenagaan, SMA Negeri 1 Lakea mengelola 25 orang guru dan 3 orang tenaga administrasi, pengelolaan yang dilakukan misalnya memberikan guru mata pelajaran yang sesuai dengan keahliannya, dan mengutus guru sebagai perwakilannya dalam kegiatan seminar mapun workshop (d) Aspek pengelolaan keuangan, sekolah mengelola dana BOS dengan prosedur sesuai dengan aturan penggunaan dana tersebut yakni sebagai operasional sekolah, misalnya mengupayakan pembayaran honor guru honorer yang tidak pernah terlambat dibayarkan. Implikasi Desentralisasi Pendidikan dalam Meningkatkan Profesionalisme Guru di SMA Negeri 1 Lakea: (1) Program sekolah dalam meningkatkan profesionalisme dapat terlaksana dengan baik (2) Masing-masing guru berupaya meningkatkan pengelolaan proses belajarnya sebagai implikasi dari pelaksanaan supervisi (3) Sekolah memiliki tenaga pendidik sesuai dengan kebutuhan dan (4) Pengelolaan keuangan yang dilaksanakan oleh sekolah dapat memotivasi guru untuk melaksanakan tugasnya dengan baik.Kata Kunci        :    Desentralisasi pendidikan, profesionalisme gur

Identifying hypermethylated CpG islands using a quantile regression model

<p>Abstract</p> <p>Background</p> <p>DNA methylation has been shown to play an important role in the silencing of tumor suppressor genes in various tumor types. In order to have a system-wide understanding of the methylation changes that occur in tumors, we have developed a differential methylation hybridization (DMH) protocol that can simultaneously assay the methylation status of all known CpG islands (CGIs) using microarray technologies. A large percentage of signals obtained from microarrays can be attributed to various measurable and unmeasurable confounding factors unrelated to the biological question at hand. In order to correct the bias due to noise, we first implemented a quantile regression model, with a quantile level equal to 75%, to identify hypermethylated CGIs in an earlier work. As a proof of concept, we applied this model to methylation microarray data generated from breast cancer cell lines. However, we were unsure whether 75% was the best quantile level for identifying hypermethylated CGIs. In this paper, we attempt to determine which quantile level should be used to identify hypermethylated CGIs and their associated genes.</p> <p>Results</p> <p>We introduce three statistical measurements to compare the performance of the proposed quantile regression model at different quantile levels (95%, 90%, 85%, 80%, 75%, 70%, 65%, 60%), using known methylated genes and unmethylated housekeeping genes reported in breast cancer cell lines and ovarian cancer patients. Our results show that the quantile levels ranging from 80% to 90% are better at identifying known methylated and unmethylated genes.</p> <p>Conclusions</p> <p>In this paper, we propose to use a quantile regression model to identify hypermethylated CGIs by incorporating probe effects to account for noise due to unmeasurable factors. Our model can efficiently identify hypermethylated CGIs in both breast and ovarian cancer data.</p

Identifying differentially methylated genes using mixed effect and generalized least square models

<p>Abstract</p> <p>Background</p> <p>DNA methylation plays an important role in the process of tumorigenesis. Identifying differentially methylated genes or CpG islands (CGIs) associated with genes between two tumor subtypes is thus an important biological question. The methylation status of all CGIs in the whole genome can be assayed with differential methylation hybridization (DMH) microarrays. However, patient samples or cell lines are heterogeneous, so their methylation pattern may be very different. In addition, neighboring probes at each CGI are correlated. How these factors affect the analysis of DMH data is unknown.</p> <p>Results</p> <p>We propose a new method for identifying differentially methylated (DM) genes by identifying the associated DM CGI(s). At each CGI, we implement four different mixed effect and generalized least square models to identify DM genes between two groups. We compare four models with a simple least square regression model to study the impact of incorporating random effects and correlations.</p> <p>Conclusions</p> <p>We demonstrate that the inclusion (or exclusion) of random effects and the choice of correlation structures can significantly affect the results of the data analysis. We also assess the false discovery rate of different models using CGIs associated with housekeeping genes.</p