STATISTICAL METHODS FOR THE ANALYSIS OF CANCER GENOME SEQUENCING DATA

The purpose of cancer genome sequencing studies is to determine the nature and types of alterations present in a typical cancer and to discover genes mutated at high frequencies. In this article we discuss statistical methods for the analysis of data generated in these studies. We place special emphasis on a two-stage study design introduced by Sjoblom et al.[1]. In this context, we describe statistical methods for constructing scores that can be used to prioritize candidate genes for further investigation and to assess the statistical signicance of the candidates thus identfied

‘The longest suicide vote in history’: the Labour Party leadership election of 2015

The Labour leadership contest of 2015 resulted in the election of the veteran Left-wing backbencher, Jeremy Corbyn, who clearly defeated the early favourite, Andy Burnham. Yet Corbyn enjoyed very little support among Labour MPs, and his victory plunged the PLP into turmoil, particularly as he was widely viewed as incapable of leading the Party to victory in the 2020 general election. Given that, much of the established academic literature on Party leadership contests emphasises the ability to foster unity, and thereby render a party electable, as two of the key criteria for electing a new leader, coupled with overall competence, important questions are raised about how and why the Labour Party chose someone to lead them who clearly does not meet these criteria. We will argue that whilst these are the natural priorities of MPs when electing a new leader, in Corbyn’s case, much of the extra-parliamentary Labour Party was more concerned about ideological conviction and purity of principles, regardless of how far these diverged from public opinion. This was especially true of those who signed-up to the Labour Party following the 2015 general election defeat. Indeed, many of these only did so after Corbyn had become a candidate. This clearly suggests a serious tension between maximising intra-party democracy and ensuring the electability of the parliamentary party itself

Personalized Pathway Enrichment Map of Putative Cancer Genes from Next Generation Sequencing Data

BACKGROUND: Pathway analysis of a set of genes represents an important area in large-scale omic data analysis. However, the application of traditional pathway enrichment methods to next-generation sequencing (NGS) data is prone to several potential biases, including genomic/genetic factors (e.g., the particular disease and gene length) and environmental factors (e.g., personal life-style and frequency and dosage of exposure to mutagens). Therefore, novel methods are urgently needed for these new data types, especially for individual-specific genome data. METHODOLOGY: In this study, we proposed a novel method for the pathway analysis of NGS mutation data by explicitly taking into account the gene-wise mutation rate. We estimated the gene-wise mutation rate based on the individual-specific background mutation rate along with the gene length. Taking the mutation rate as a weight for each gene, our weighted resampling strategy builds the null distribution for each pathway while matching the gene length patterns. The empirical P value obtained then provides an adjusted statistical evaluation. PRINCIPAL FINDINGS/CONCLUSIONS: We demonstrated our weighted resampling method to a lung adenocarcinomas dataset and a glioblastoma dataset, and compared it to other widely applied methods. By explicitly adjusting gene-length, the weighted resampling method performs as well as the standard methods for significant pathways with strong evidence. Importantly, our method could effectively reject many marginally significant pathways detected by standard methods, including several long-gene-based, cancer-unrelated pathways. We further demonstrated that by reducing such biases, pathway crosstalk for each individual and pathway co-mutation map across multiple individuals can be objectively explored and evaluated. This method performs pathway analysis in a sample-centered fashion, and provides an alternative way for accurate analysis of cancer-personalized genomes. It can be extended to other types of genomic data (genotyping and methylation) that have similar bias problems

Alterations of the retinoblastoma gene in metastatic breast cancer

Germline mutations affecting the retinoblastoma gene (RB1) predispose to inherited retinoblastomas but also other malignancies, including breast cancer. While somatic RB1 mutations have been detected in different malignancies, information about the potential role of RB1 mutations in breast cancer is limited. Recently, we discovered RB1 mutations to be associated with resistance to anthracyclines/mitomycin in primary breast cancer. The present work is the first report evaluating RB1 mutation and epigenetic status in metastatic breast cancer. Among 148 breast cancer samples analyzed by MLPA, four samples harbored intragenic deletions/duplications: Thus, exons 1–2 were deleted in two tumors and exons 21–23 in one tumor, while one sample harbored duplication of exons 18–23. The entire RB1 gene was duplicated in two tumors and multiple amplifications were revealed in one sample. Reduced copy number was observed in 17 samples (11.5%). No point mutation or promoter hypermethylation was discovered (n = 38 and 114 tumors analyzed, respectively). Interestingly, among seven tumors expressing lack of response to epirubicin, two samples harbored alterations in RB1, contrasting none out of 16 tumors with stable disease or an objective response (P = 0.08). In summary, the frequency of RB1 alterations in metastatic lesions was not increased when compared to primary breast cancer, indicating that RB1 alterations do not play a major role in metastatic development. While a non-significant association suggesting RB1 alterations to be linked to therapy resistance was observed, our data do not suggest a major role for RB1 alterations explaining acquired drug resistance

Mouse models of cancers: opportunities to address heterogeneity of human cancer and evaluate therapeutic strategies

The heterogeneity of human breast cancer has been well described at the morphological, molecular, and genomic levels. This heterogeneity presents one of the greatest obstacles in the effective treatment of breast cancer since the distinct forms of breast cancer that reflect distinct mechanisms of disease will require distinct therapies. Although mouse models of cancer have traditionally been used to simplify the study of human disease, we suggest that there are opportunities to also model the complexity and heterogeneity of human cancer. Here, we illustrate the similarities of mouse models to the human condition in the heterogeneity of both pathologies and gene expression. We then provide an illustration of the potential of gene expression analysis methods when used in conjunction with current treatment options to model individualized therapeutic regimes

Treatment adherence with the easypod™ growth hormone electronic auto-injector and patient acceptance: survey results from 824 children and their parents

<p>Abstract</p> <p>Background</p> <p>Accurately monitoring adherence to treatment with recombinant human growth hormone (r-hGH) enables appropriate intervention in cases of poor adherence. The electronic r-hGH auto-injector, easypod™, automatically records the patient's adherence to treatment. This study evaluated adherence to treatment of children who started using the auto-injector and assessed opinions about the device.</p> <p>Methods</p> <p>A multicentre, multinational, observational 3-month survey in which children received r-hGH as part of their normal care. Physicians reviewed the recorded dose history and children (with or without parental assistance) completed a questionnaire-based survey. Children missing ≤2 injections per month (92% of injections given) were considered adherent to treatment. Adherence was compared between GH treatment-naïve and treatment-experienced children.</p> <p>Results</p> <p>Of 834 recruited participants, 824 were evaluated. The median (range) age was 11 (1-18) years. From the recorded dose history, 87.5% of children were adherent to treatment over the 3-month period. Recorded adherence was higher in treatment-naïve (89.7%, n = 445/496) than in treatment-experienced children (81.7%, n = 152/186) [Fisher's exact test FI(X) = 7.577; <it>p </it>= 0.0062]. According to self-reported data, 90.2% (607/673) of children were adherent over 3 months; 51.5% (421/817) missed ≥1 injection over this period (mainly due to forgetfulness). Concordance between reported and recorded adherence was 84.3%, with a trend towards self-reported adherence being higher than recorded adherence. Most children liked the auto-injector: over 80% gave the top two responses from five options for ease of use (720/779), speed (684/805) and comfort (716/804). Although 38.5% (300/780) of children reported pain on injection, over half of children (210/363) considered the pain to be less or much less than expected. Given the choice, 91.8% (732/797) of children/parents would continue using the device.</p> <p>Conclusions</p> <p>easypod™ provides an accurate method of monitoring adherence to treatment with r-hGH. In children who received treatment with r-hGH using easypod™, short-term adherence is good, and significantly higher in treatment-naïve children compared with experienced children. Children/parents rate the device highly. The high level of acceptability of the device is reflected by a desire to continue using it by over 90% of the children in the survey.</p