Integrative clustering reveals a novel split in the luminal A subtype of breast cancer with impact on outcome

Background: Breast cancer is a heterogeneous disease at the clinical and molecular level. In this study we integrate classifications extracted from five different molecular levels in order to identify integrated subtypes. Methods: Tumor tissue from 425 patients with primary breast cancer from the Oslo2 study was cut and blended, and divided into fractions for DNA, RNA and protein isolation and metabolomics, allowing the acquisition of representative and comparable molecular data. Patients were stratified into groups based on their tumor characteristics from five different molecular levels, using various clustering methods. Finally, all previously identified and newly determined subgroups were combined in a multilevel classification using a "cluster-of-clusters" approach with consensus clustering. Results: Based on DNA copy number data, tumors were categorized into three groups according to the complex arm aberration index. mRNA expression profiles divided tumors into five molecular subgroups according to PAM50 subtyping, and clustering based on microRNA expression revealed four subgroups. Reverse-phase protein array data divided tumors into five subgroups. Hierarchical clustering of tumor metabolic profiles revealed three clusters. Combining DNA copy number and mRNA expression classified tumors into seven clusters based on pathway activity levels, and tumors were classified into ten subtypes using integrative clustering. The final consensus clustering that incorporated all aforementioned subtypes revealed six major groups. Five corresponded well with the mRNA subtypes, while a sixth group resulted from a split of the luminal A subtype; these tumors belonged to distinct microRNA clusters. Gain-of-function studies using MCF-7 cells showed that microRNAs differentially expressed between the luminal A clusters were important for cancer cell survival. These microRNAs were used to validate the split in luminal A tumors in four independent breast cancer cohorts. In two cohorts the microRNAs divided tumors into subgroups with significantly different outcomes, and in another a trend was observed. Conclusions: The six integrated subtypes identified confirm the heterogeneity of breast cancer and show that finer subdivisions of subtypes are evident. Increasing knowledge of the heterogeneity of the luminal A subtype may add pivotal information to guide therapeutic choices, evidently bringing us closer to improved treatment for this largest subgroup of breast cancer.Peer reviewe

Penggunaan Media Gambar Dalam Meningkatkan Kemampuan Membaca Permulaan Siswa Kelas I SDN Uwedaka Kecamatan Pagimana Kabupaten Banggai

Pokok permasalahan dalam penelitian ini adalah rendahnya tingkat kemampuan membaca permulaan siswa kelas I SDN Uwedaka dalam pembelajaran Bahasa Indonesia. Tujuan Penelitian adalah untuk meningkatkan kemampuan membaca permulaan siswa kelas I SDN Uwedaka Kecamatan Pagimana Kabupaten Banggai. Berdasarkan hasil observasi yang didapatkan masih terdapat beberapa siswa yang sama sekali belum bisa membaca. Pembelajaran membaca permulaan di SDN Uwedaka selama ini hanya menggunakan media pembelajaran yang konvensional yaitu dengan menggunakan papan tulis, pembelajaran yang hanya berpusat pada guru, penggunaan media dalam pembelajaran sebagai alat bantu masih sangat terbatas, hal ini menyebabkan kemampuan membaca permulaan yang masih rendah dan terlihat hampir 65% siswa masih mengalami kesulitan membaca dalam proses belajar mengajar. Metode yang digunakan adalah metode deskriptif kualitatif dan kuantitatif. Data kualitatif didapatkan dari hasil tes dan observasi siswa dan guru. data kuantitatif didapatkan dari hasil tes belajar. Desain penelitian ini mengacu pada desain oleh Kemmis dan Mc Taggart yang terdiri dari empat tahapan, yaitu perencanaan, pelaksanaan tindakan, observasi dan refleksi. Data dikumpulkan melalui penilaian proses dan penilaian hasil setiap akhir tindakan. Penelitian ini dilakukan dalam dua siklus. Pada siklus I diperoleh nilai rata-rata siswa yaitu sebesar 67 dengan ketuntasan belajar klasikal sebesar 40% serta daya serap 66,6%. Pada siklus II, nilai rata-rata meningkat menjadi 83 dengan ketuntasan klasikal sebesar 100% serta daya serap klasikal sebesar 83,3%. Bersarkan hasil penelitian maka dapat disimpulkan bahwa penggunaan media gambar dapat meningkatkan kemampuan membaca permulaan terhadap siswa kelas I SDN Uwedaka Kecamatan Pagimana Kabupaten Banggai

A novel checkpoint mechanism regulating the G1/S transition

Ultraviolet irradiation of fission yeast cells in G1 phase induced a delay in chromatin binding of replication initiation factors and, consistently, a transient delay in S-phase entry. The cell cycle delay was totally dependent on the Gcn2 kinase, a sensor of the nutritional status, and was accompanied by phosphorylation of the translation initiation factor eIF2α and by a general depression of translation. However, the G1-specific synthesis of factors required for DNA replication was not reduced by ultraviolet radiation. The cell cycle delay represents a novel checkpoint with a novel mechanism of action that is not activated by ionizing radiation

Time series analysis of neoadjuvant chemotherapy and bevacizumab-treated breast carcinomas reveals a systemic shift in genomic aberrations

Background Chemotherapeutic agents such as anthracyclines and taxanes are commonly used in the neoadjuvant setting. Bevacizumab is an antibody which binds to vascular endothelial growth factor A (VEGFA) and inhibits its receptor interaction, thus obstructing the formation of new blood vessels. Methods A phase II randomized clinical trial of 123 patients with Her2-negative breast cancer was conducted, with patients treated with neoadjuvant chemotherapy (fluorouracil (5FU)/epirubicin/cyclophosphamide (FEC) and taxane), with or without bevacizumab. Serial biopsies were obtained at time of diagnosis, after 12 weeks of treatment with FEC ± bevacizumab, and after 25 weeks of treatment with taxane ± bevacizumab. A time course study was designed to investigate the genomic landscape at the three time points when tumor DNA alterations, tumor percentage, genomic instability, and tumor clonality were assessed. Substantial differences were observed with some tumors changing mainly between diagnosis and at 12 weeks, others between 12 and 25 weeks, and still others changing in both time periods. Results In both treatment arms, good responders (GR) and non-responders (NR) displayed significant difference in genomic instability index (GII) at time of diagnosis. In the combination arm, copy number alterations at 25 loci at the time of diagnosis were significantly different between the GR and NR. An inverse aberration pattern was also observed between the two extreme response groups at 6p22-p12 for patients in the combination arm. Signs of subclonal reduction were observed, with some aberrations disappearing and others being retained during treatment. Increase in subclonal amplification was observed at 6p21.1, a locus which contains the VEGFA gene for the protein which are targeted by the study drug bevacizumab. Of the 13 pre-treatment samples that had a gain at VEGFA, 12 were responders. Significant decrease of frequency of subclones carrying gains at 17q21.32-q22 was observed at 12 weeks, with the peak occurring at TMEM100, an ALK1 receptor signaling-dependent gene essential for vasculogenesis. This implies that cells bearing amplifications of VEGFA and TMEM100 are particularly sensitive to this treatment regime. Conclusions Taken together, these results suggest that heterogeneity and subclonal architecture influence the response to targeted treatment in combination with chemotherapy, with possible implications for clinical decision-making and monitoring of treatment efficacy. Trial registration NCT00773695 . Registered 15 October 200

Integrated analysis reveals microRNA networks coordinately expressed with key proteins in breast cancer

Background The role played by microRNAs in the deregulation of protein expression in breast cancer is only partly understood. To gain insight, the combined effect of microRNA and mRNA expression on protein expression was investigated in three independent data sets. Methods Protein expression was modeled as a multilinear function of powers of mRNA and microRNA expression. The model was first applied to mRNA and protein expression for 105 selected cancer-associated genes and to genome-wide microRNA expression from 283 breast tumors. The model considered both the effect of one microRNA at a time and all microRNAs combined. In the latter case the Lasso penalized regression method was applied to detect the simultaneous effect of multiple microRNAs. Results An interactome map for breast cancer representing all direct and indirect associations between the expression of microRNAs and proteins was derived. A pattern of extensive coordination between microRNA and protein expression in breast cancer emerges, with multiple clusters of microRNAs being associated with multiple clusters of proteins. Results were subsequently validated in two independent breast cancer data sets. A number of the microRNA-protein associations were functionally validated in a breast cancer cell line. Conclusions A comprehensive map is derived for the co-expression in breast cancer of microRNAs and 105 proteins with known roles in cancer, after filtering out the in-cis effect of mRNA expression. The analysis suggests that group action by several microRNAs to deregulate the expression of proteins is a common modus operandi in breast cancer

The longitudinal transcriptional response to neoadjuvant chemotherapy with and without bevacizumab in breast cancer

Purpose: Chemotherapy-induced alterations to gene expression are due to transcriptional reprogramming of tumor cells or subclonal adaptations to treatment. The effect on whole-transcriptome mRNA expression was investigated in a randomized phase II clinical trial to assess the effect of neoadjuvant chemotherapy with the addition of bevacizumab. Experimental Design: Tumor biopsies and whole-transcriptome mRNA profiles were obtained at three fixed time points with 66 patients in each arm. Altogether, 358 specimens from 132 patients were available, representing the transcriptional state before treatment start, at 12 weeks and after treatment (25 weeks). Pathologic complete response (pCR) in breast and axillary nodes was the primary endpoint. Results: pCR was observed in 15 patients (23%) receiving bevacizumab and chemotherapy and 8 patients (12%) receiving only chemotherapy. In the estrogen receptor–positive patients, 11 of 54 (20%) treated with bevacizumab and chemotherapy achieved pCR, while only 3 of 57 (5%) treated with chemotherapy reached pCR. In patients with estrogen receptor–positive tumors treated with combination therapy, an elevated immune activity was associated with good response. Proliferation was reduced after treatment in both treatment arms and most pronounced in the combination therapy arm, where the reduction in proliferation accelerated during treatment. Transcriptional alterations during therapy were subtype specific, and the effect of adding bevacizumab was most evident for luminal-B tumors. Conclusions: Clinical response and gene expression response differed between patients receiving combination therapy and chemotherapy alone. The results may guide identification of patients likely to benefit from antiangiogenic therapy. Clin Cancer Res; 23(16); 4662–70. ©2017 AACR

Metabolic clusters of breast cancer in relation to gene- and protein expression subtypes

Background The heterogeneous biology of breast cancer leads to high diversity in prognosis and response to treatment, even for patients with similar clinical diagnosis, histology, and stage of disease. Identifying mechanisms contributing to this heterogeneity may reveal new cancer targets or clinically relevant subgroups for treatment stratification. In this study, we have merged metabolite, protein, and gene expression data from breast cancer patients to examine the heterogeneity at a molecular level. Methods The study included primary tumor samples from 228 non-treated breast cancer patients. High-resolution magic-angle spinning magnetic resonance spectroscopy (HR MAS MRS) was performed to extract the tumors metabolic profiles further used for hierarchical cluster analysis resulting in three significantly different metabolic clusters (Mc1, Mc2, and Mc3). The clusters were further combined with gene and protein expression data. Results Our result revealed distinct differences in the metabolic profile of the three metabolic clusters. Among the most interesting differences, Mc1 had the highest levels of glycerophosphocholine (GPC) and phosphocholine (PCho), Mc2 had the highest levels of glucose, and Mc3 had the highest levels of lactate and alanine. Integrated pathway analysis of metabolite and gene expression data uncovered differences in glycolysis/gluconeogenesis and glycerophospholipid metabolism between the clusters. All three clusters had significant differences in the distribution of protein subtypes classified by the expression of breast cancer-related proteins. Genes related to collagens and extracellular matrix were downregulated in Mc1 and consequently upregulated in Mc2 and Mc3, underpinning the differences in protein subtypes within the metabolic clusters. Genetic subtypes were evenly distributed among the three metabolic clusters and could therefore contribute to additional explanation of breast cancer heterogeneity. Conclusions Three naturally occurring metabolic clusters of breast cancer were detected among primary tumors from non-treated breast cancer patients. The clusters expressed differences in breast cancer-related protein as well as genes related to extracellular matrix and metabolic pathways known to be aberrant in cancer. Analyses of metabolic activity combined with gene and protein expression provide new information about the heterogeneity of breast tumors and, importantly, the metabolic differences infer that the clusters may be susceptible to different metabolically targeted drugs