Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data

BACKGROUND: Like microarray-based investigations, high-throughput proteomics techniques require machine learning algorithms to identify biomarkers that are informative for biological classification problems. Feature selection and classification algorithms need to be robust to noise and outliers in the data. RESULTS: We developed a recursive support vector machine (R-SVM) algorithm to select important genes/biomarkers for the classification of noisy data. We compared its performance to a similar, state-of-the-art method (SVM recursive feature elimination or SVM-RFE), paying special attention to the ability of recovering the true informative genes/biomarkers and the robustness to outliers in the data. Simulation experiments show that a 5 %-~20 % improvement over SVM-RFE can be achieved regard to these properties. The SVM-based methods are also compared with a conventional univariate method and their respective strengths and weaknesses are discussed. R-SVM was applied to two sets of SELDI-TOF-MS proteomics data, one from a human breast cancer study and the other from a study on rat liver cirrhosis. Important biomarkers found by the algorithm were validated by follow-up biological experiments. CONCLUSION: The proposed R-SVM method is suitable for analyzing noisy high-throughput proteomics and microarray data and it outperforms SVM-RFE in the robustness to noise and in the ability to recover informative features. The multivariate SVM-based method outperforms the univariate method in the classification performance, but univariate methods can reveal more of the differentially expressed features especially when there are correlations between the features

Responsible research impact: Ethics for making a difference

The need for ethical guidelines that support and empower researchers who aim to enhance the societal impact of research has become critical. Recognizing the growing emphasis on research impact by governments and funding bodies worldwide, this article investigates the often overlooked ethical dimensions of generating and evaluating research impact. We focus on ethical issues and practices that are specific to the process of intentionally working to develop societal impacts from research. We highlight the complexities and ethical dilemmas encountered when researchers engage with non-academic groups, such as policymakers, industries, and local communities. Through a combination of literature review and insights from participatory workshops, the article identifies key issues and offers a new ethical framework for responsible research impact. This framework aims to guide researchers and institutions through the process of limiting potential harm while delivering societal benefits in a way that is realistic and balanced. The aim is to establish ethical practices for engagement and impact, without making the process so onerous that researchers are less likely to undertake such activities. The article concludes with actionable recommendations for policymakers, research funders, research performing organizations, institutional review boards and/or ethics committees, and individual researchers. Making use of such recommendations can foster an ethically responsible approach to research impact across academic disciplines

Abstract 4823: Copy number gain and increased expression of BLM and FANCI is associated with sensitivity to genotoxic chemotherapy in triple negative breast and serous ovarian cancer

Abstract Introduction. Genotoxic chemotherapy such as platinum salts and anthracyclines induce DNA damage and are among the most widely used anti-cancer agents. Previous molecular studies have shown that BRCA1-associated and sporadic triple-negative breast cancers (TNBC) carry high levels of genomic abnormalities, suggesting these cancers may share similar defects in DNA repair, which may make them particularly sensitive to genotoxic chemotherapy. Results. To investigate whether specific genomic aberrations may affect cancer sensitivity to cisplatin, we generated tumor DNA copy number profiles of 21 and 24 TNBC patients who received pre-operative cisplatin-based chemotherapy in two separate clinical trials. Using the GISTIC algorithm (1), we found that only a single region on chromosome 15q26 showed consistent significant differential copy number in responders versus non-responders, being preferentially lost in non-responders, but preferentially gained in responders in both trials. To see if genes on 15q26 were associated with platinum sensitivity, we acquired gene expression data from the cisplatin TNBC trial (2), and from the carboplatin-only arm of an ovarian cancer trial (3). We then performed a leave-one-out analysis, and found 9 genes significantly associated with platinum response in at least 75% of all rounds in both cohorts. These included BLM and FANCI located in the 15q26 region, both showing higher expression in sensitive tumors, and known to be involved in related DNA repair processes. To investigate if BLM and FANCI were specifically associated with genotoxic chemotherapy sensitivity, we analyzed their expression in TNBCs from three neoadjuvant trials of epirubicin alone or taxane-containing combination therapy (5, 6) and in ovarian cancers from the taxane-only treatment arm(3). In the epirubicin trial, BLM and FANCI expression was again significantly associated with increased sensitivity to therapy. In contrast, there was no association between either BLM or FANCI expression and TNBC response to the taxane-containing regimen or ovarian cancer response to single agent taxane treatment. Conclusions. These data suggest that high expression of BLM and FANCI are associated with improved response to DNA damaging agents, but not with response to other types of chemotherapeutics. Furthermore, it suggests that the patient subpopulations that respond to drugs such as anthracyclines and taxanes are not overlapping, and that it will therefore be difficult to robustly identify predictors of single agent response based on multi-drug trials. 1. Beroukhim et al. Proc Natl Acad Sci U S A. 2007;104:20007-12. 2. Silver et al. Cancer. J Clin Oncol. 2010. 3. Ahmed et al. Cancer Cell. 2007;12:514-27. 4. Li et al. Nat Med. 2010;16:214-8. 5. Hess et al. J Clin Oncol. 2006;24:4236-44. 6. Popovici et al. Breast Cancer Res. 2010;12:R5. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 4823. doi:1538-7445.AM2012-4823</jats:p

Transformation of Different Human Breast Epithelial Cell Types Leads to Distinct Tumor Phenotypes

SummaryWe investigated the influence of normal cell phenotype on the neoplastic phenotype by comparing tumors derived from two different normal human mammary epithelial cell populations, one of which was isolated using a new culture medium. Transformation of these two cell populations with the same set of genetic elements yielded cells that formed tumor xenografts exhibiting major differences in histopathology, tumorigenicity, and metastatic behavior. While one cell type (HMECs) yielded squamous cell carcinomas, the other cell type (BPECs) yielded tumors closely resembling human breast adenocarcinomas. Transformed BPECs gave rise to lung metastases and were up to 104-fold more tumorigenic than transformed HMECs, which are nonmetastatic. Hence, the pre-existing differences between BPECs and HMECs strongly influence the phenotypes of their transformed derivatives

Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data

Abstract Background Like microarray-based investigations, high-throughput proteomics techniques require machine learning algorithms to identify biomarkers that are informative for biological classification problems. Feature selection and classification algorithms need to be robust to noise and outliers in the data. Results We developed a recursive support vector machine (R-SVM) algorithm to select important genes/biomarkers for the classification of noisy data. We compared its performance to a similar, state-of-the-art method (SVM recursive feature elimination or SVM-RFE), paying special attention to the ability of recovering the true informative genes/biomarkers and the robustness to outliers in the data. Simulation experiments show that a 5 %-~20 % improvement over SVM-RFE can be achieved regard to these properties. The SVM-based methods are also compared with a conventional univariate method and their respective strengths and weaknesses are discussed. R-SVM was applied to two sets of SELDI-TOF-MS proteomics data, one from a human breast cancer study and the other from a study on rat liver cirrhosis. Important biomarkers found by the algorithm were validated by follow-up biological experiments. Conclusion The proposed R-SVM method is suitable for analyzing noisy high-throughput proteomics and microarray data and it outperforms SVM-RFE in the robustness to noise and in the ability to recover informative features. The multivariate SVM-based method outperforms the univariate method in the classification performance, but univariate methods can reveal more of the differentially expressed features especially when there are correlations between the features.</p

Mst1 is an interacting protein that mediates PHLPPs\u27 induced apoptosis

PHLPP1 and PHLPP2 phosphatases exert their tumor-suppressing functions by dephosphorylation and inactivation of Akt in several breast cancer and glioblastoma cells. However, Akt, or other known targets of PHLPPs that include PKC and ERK, may not fully elucidate the physiological role of the multifunctional phosphatases, especially their powerful apoptosis induction function. Here, we show that PHLPPs induce apoptosis in cancer cells independent of the known targets of PHLPPs. We identified Mst1 as a binding partner that interacts with PHLPPs both in vivo and in vitro. PHLPPs dephosphorylate Mst1 on the T387 inhibitory site, which activate Mst1 and its downstream effectors p38 and JNK to induce apoptosis. The same T387 site can be phosphorylated by Akt. Thus, PHLPP, Akt, and Mst1 constitute an autoinhibitory triangle that controls the fine balance of apoptosis and proliferation that is cell type and context dependent