CONDITIONAL LEAST SQUARES ESTIMATION OF THE PARAMETERS OF HIGHER ORDER RANDOM ENVIRONMENT INAR MODElS

Two different random environment INAR models of higher order, precisely RrNGINARmax(p) and RrNGINAR1(p), are presented as a new approach to modeling non-stationary nonnegative integer-valued autoregressive processes. The interpretation of these models is given in order to better understand the circumstances of their application to random environment counting processes. The estimation statistics, defined using the Conditional Least Squares (CLS) method, is introduced and the properties are tested on the replicated simulated data obtained by RrNGINAR models with different parameter values. The obtained CLS estimates are presented and discussed

Time to first recurrence, pattern of recurrence, and survival after recurrence in endometrial cancer according to the molecular classification.

OBJECTIVE Despite its generally favorable prognosis at primary diagnosis, recurrence of endometrial cancer remains an important clinical challenge. The aim of this study was to analyze the value of molecular classification in recurrent endometrial cancer. METHODS This study included patients with recurrent endometrial cancer who underwent primary surgical treatment between 2004 and 2015 at the Karolinska University Hospital, Sweden and the Bern University Hospital, Switzerland (KImBer cohort) with molecular classification of the primary tumor. RESULTS Out of 594 molecularly classified endometrial cancer patients, 101 patients experienced recurrence, consisting of 2 POLEmut, 33 MMRd, 30 p53abn, and 36 NSMP tumors. Mean age at recurrence was 71 years and mean follow-up was 54 months. Overall, median time to first recurrence was 16 months (95% CI 12-20); with the shortest median time in MMRd patients, with 13 months (95% CI 5-21). The pattern of recurrence was distinct among molecular subgroups: MMRd tumors experienced more locoregional, while p53abn cases showed more abdominal recurrences (P = .042). Median survival after recurrence was best for MMRd cases (43 months, 95% CI 11-76), compared to 39 months (95% CI 21-57) and 10 months (95% CI 7-13) for the NSMP and p53abn cases respectively (log-rank, P = .001). CONCLUSION Molecular classification is a significant indicator of survival after recurrence in endometrial cancer patients, and patterns of recurrence differ by molecular subgroups. While MMRd endometrial cancer show more locoregional recurrence and the best survival rates after recurrence, p53abn patients experience abdominal recurrence more often and had the worst prognosis of all recurrent patients

Edge-centric queries stream management based on an ensemble model

The Internet of things (IoT) involves numerous devices that can interact with each other or with their environment to collect and process data. The collected data streams are guided to the cloud for further processing and the production of analytics. However, any processing in the cloud, even if it is supported by improved computational resources, suffers from an increased latency. The data should travel to the cloud infrastructure as well as the provided analytics back to end users or devices. For minimizing the latency, we can perform data processing at the edge of the network, i.e., at the edge nodes. The aim is to deliver analytics and build knowledge close to end users and devices minimizing the required time for realizing responses. Edge nodes are transformed into distributed processing points where analytics queries can be served. In this paper, we deal with the problem of allocating queries, defined for producing knowledge, to a number of edge nodes. The aim is to further reduce the latency by allocating queries to nodes that exhibit low load (the current and the estimated); thus, they can provide the final response in the minimum time. However, before the allocation, we should decide the computational burden that a query will cause. The allocation is concluded by the assistance of an ensemble similarity scheme responsible to deliver the complexity class for each query. The complexity class, thus, can be matched against the current load of every edge node. We discuss our scheme, and through a large set of simulations and the adoption of benchmarking queries, we reveal the potentials of the proposed model supported by numerical results

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe

Comprehensive analysis of chromothripsis in 2,658 human cancers using whole-genome sequencing

Chromothripsis is a mutational phenomenon characterized by massive, clustered genomic rearrangements that occurs in cancer and other diseases. Recent studies in selected cancer types have suggested that chromothripsis may be more common than initially inferred from low-resolution copy-number data. Here, as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), we analyze patterns of chromothripsis across 2,658 tumors from 38 cancer types using whole-genome sequencing data. We find that chromothripsis events are pervasive across cancers, with a frequency of more than 50% in several cancer types. Whereas canonical chromothripsis profiles display oscillations between two copy-number states, a considerable fraction of events involve multiple chromosomes and additional structural alterations. In addition to non-homologous end joining, we detect signatures of replication-associated processes and templated insertions. Chromothripsis contributes to oncogene amplification and to inactivation of genes such as mismatch-repair-related genes. These findings show that chromothripsis is a major process that drives genome evolution in human cancer

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Sonographic, demographic characteristics, and the Proactive Molecular Risk Classifier for Endometrial cancer (ProMisE) in the prediction of tumor recurrence or progression.

OBJECTIVES To identify and assess demographic, sonographic and Proactive Molecular Risk Classifier for Endometrial cancer (ProMisE) prognostic factors for recurrence or progression in endometrial cancer (EC). METHODS We prospectively included 339 women with EC, undergoing expert transvaginal ultrasound before surgery. Tumors were classified according to FIGO, and ProMisE (MMR-D, POLE EDM, p53wt and p53abn). ProMisE subtypes were compared regarding demographic, sonographic characteristics, recurrence or progression, and survival. Cox regression was used to identify prognostic factors associated with recurrence or progression, with univariable models to study crude associations and multivariable models to study adjusted associations. Logistic regression and ROC curves analysis was used to assess the predictive ability of the prognostic factors, regarding recurrence or progression within three years, and to compared their predictive ability to that of the European Society for Medical Oncology (ESMO) classification. In separate sub analysis, tumors were stratified by p53 status (present/absent) and ultrasound tumor size (< 2 cm/≥ 2 cm). RESULTS Median follow-up time was 58 (IQR, 48-71, range 0-102) months. Recurrence/progression occurred in 51/339 (15%), in MMR-D 14%, POLE EDM 8%, p53wt 9%, and p53abn 46%. The multivariable 'ProMisE model' (ProMisE subtype, age, waist circumference, ultrasound tumor extension and ultrasound tumor size) (AUC 0.89, 95% CI 0.85-0.93) predicted recurrence/progression with comparable ability to the multivariable 'histotype and grade model' (histotype and grade, age, waist circumference, ultrasound tumor extension and ultrasound tumor size) (AUC 0.88, 95% CI 0.83-0.92) and with higher ability than both the preoperative (AUC 0.74, 95% CI 0.67-0.82), p <0.01), and postoperative (AUC 0.79, 95% CI 0.72-0.86), p <0.01) ESMO classification. The 48% with the combination of non-p53abn subtype and tumor size <2cm had a very low risk (1.8%) of recurrence/progression. CONCLUSION A combination of demographic, sonographic and ProMisE prognostic factors had higher ability to predict recurrence or progression than the ESMO classification, supporting their use in preoperative risk stratification. The p53 status combined with ultrasound tumor size has the potential to preoperatively identify a large group of women with a very low risk of recurrence or progression. This article is protected by copyright. All rights reserved