Enriching for correct prediction of biological processes using a combination of diverse classifiers

<p>Abstract</p> <p>Background</p> <p>Machine learning models (classifiers) for classifying genes to biological processes each have their own unique characteristics in what genes can be classified and to what biological processes. No single learning model is qualitatively superior to any other model and overall precision for each model tends to be low. The classification results for each classifier can be complementary and synergistic suggesting the benefit of a combination of algorithms, but often the prediction probability outputs of various learning models are neither comparable nor compatible for combining. A means to compare outputs regardless of the model and data used and combine the results into an improved comprehensive model is needed.</p> <p>Results</p> <p>Gene expression patterns from NCI's panel of 60 cell lines were used to train a Random Forest, a Support Vector Machine and a Neural Network model, plus two over-sampled models for classifying genes to biological processes. Each model produced unique characteristics in the classification results. We introduce the Precision Index measure (PIN) from the maximum posterior probability that allows assessing, comparing and combining multiple classifiers. The class specific precision measure (PIC) is introduced and used to select a subset of predictions across all classes and all classifiers with high precision. We developed a single classifier that combines the PINs from these five models in prediction and found that the PIN Combined Classifier (PINCom) significantly increased the number of correctly predicted genes over any single classifier. The PINCom applied to test genes that were not used in training also showed substantial improvement over any single model.</p> <p>Conclusions</p> <p>This paper introduces novel and effective ways of assessing predictions by their precision and recall plus a method that combines several machine learning models and capitalizes on synergy and complementation in class selection, resulting in higher precision and recall. Different machine learning models yielded incongruent results each of which were successfully combined into one superior model using the PIN measure we developed. Validation of the boosted predictions for gene functions showed the genes to be accurately predicted.</p

Assessment of ABT-263 activity across a cancer cell line collection leads to a potent combination therapy for small-cell lung cancer

BH3 mimetics such as ABT-263 induce apoptosis in a subset of cancer models. However, these drugs have shown limited clinical efficacy as single agents in small-cell lung cancer (SCLC) and other solid tumor malignancies, and rational combination strategies remain underexplored. To develop a novel therapeutic approach, we examined the efficacy of ABT-263 across >500 cancer cell lines, including 311 for which we had matched expression data for select genes. We found that high expression of the proapoptotic gene Bcl2-interacting mediator of cell death (BIM) predicts sensitivity to ABT-263. In particular, SCLC cell lines possessed greater BIM transcript levels than most other solid tumors and are among the most sensitive to ABT-263. However, a subset of relatively resistant SCLC cell lines has concomitant high expression of the antiapoptotic myeloid cell leukemia 1 (MCL-1). Whereas ABT-263 released BIM from complexes with BCL-2 and BCL-XL, high expression of MCL-1 sequestered BIM released from BCL-2 and BCL-XL, thereby abrogating apoptosis. We found that SCLCs were sensitized to ABT-263 via TORC1/2 inhibition, which led to reduced MCL-1 protein levels, thereby facilitating BIM-mediated apoptosis. AZD8055 and ABT-263 together induced marked apoptosis in vitro, as well as tumor regressions in multiple SCLC xenograft models. In a Tp53; Rb1 deletion genetically engineered mouse model of SCLC, the combination of ABT-263 and AZD8055 significantly repressed tumor growth and induced tumor regressions compared with either drug alone. Furthermore, in a SCLC patient-derived xenograft model that was resistant to ABT-263 alone, the addition of AZD8055 induced potent tumor regression. Therefore, addition of a TORC1/2 inhibitor offers a therapeutic strategy to markedly improve ABT-263 activity in SCLC.United States. Dept. of Defense (Grant W81-XWH-13-1-0323)National Cancer Institute (U.S.) (Cancer Center Support Grant P30-CA14051

Integration of Human Papillomavirus Genomes in Head and Neck Cancer: Is It Time to Consider a Paradigm Shift?

Human papillomaviruses (HPV) are detected in 70–80% of oropharyngeal cancers in the developed world, the incidence of which has reached epidemic proportions. The current paradigm regarding the status of the viral genome in these cancers is that there are three situations: one where the viral genome remains episomal, one where the viral genome integrates into the host genome and a third where there is a mixture of both integrated and episomal HPV genomes. Our recent work suggests that this third category has been mischaracterized as having integrated HPV genomes; evidence indicates that this category consists of virus–human hybrid episomes. Most of these hybrid episomes are consistent with being maintained by replication from HPV origin. We discuss our evidence to support this new paradigm, how such genomes can arise, and more importantly the implications for the clinical management of HPV positive head and neck cancers following accurate determination of the viral genome status

Regulation of human genome expression and RNA splicing by human papillomavirus 16 E2 protein

Human papillomavirus 16 (HPV16) is causative in human cancer. The E2 protein regulates transcription from and replication of the viral genome; the role of E2 in regulating the host genome has been less well studied. We have expressed HPV16 E2 (E2) stably in U2OS cells; these cells tolerate E2 expression well and gene expression analysis identified 74 genes showing differential expression specific to E2. Analysis of published gene expression data sets during cervical cancer progression identified 20 of the genes as being altered in a similar direction as the E2 specific genes. In addition, E2 altered the splicing of many genes implicated in cancer and cell motility. The E2 expressing cells showed no alteration in cell growth but were altered in cell motility, consistent with the E2 induced altered splicing predicted to affect this cellular function. The results present a model system for investigating E2 regulation of the host genome

Restoring the DREAM Complex Inhibits the Proliferation of High-Risk HPV Positive Human Cells

High-risk (HR) human papillomaviruses are known causative agents in 5% of human cancers including cervical, ano-genital and head and neck carcinomas. In part, HR-HPV causes cancer by targeting host-cell tumor suppressors including retinoblastoma protein (pRb) and RB-like proteins p107 and p130. HR-HPV E7 uses a LxCxE motif to bind RB proteins, impairing their ability to control cell-cycle dependent transcription. E7 disrupts DREAM (Dimerization partner, RB-like, E2F and MuvB), a transcriptional repressor complex that can include p130 or p107, but not pRb, which regulates genes required for cell cycle progression. However, it is not known whether disruption of DREAM plays a significant role in HPV-driven tumorigenesis. In the DREAM complex, LIN52 is an adaptor that binds directly to p130 via an E7-like LxSxE motif. Replacement of the LxSxE sequence in LIN52 with LxCxE (LIN52-S20C) increases p130 binding and partially restores DREAM assembly in HPV-positive keratinocytes and human cervical cancer cells, inhibiting proliferation. Our findings demonstrate that disruption of the DREAM complex by E7 is an important process promoting cellular proliferation by HR-HPV. Restoration of the DREAM complex in HR-HPV positive cells may therefore have therapeutic benefits in HR-HPV positive cancers