Targeted sequencing in DLBCL, molecular subtypes, and outcomes: a Haematological Malignancy Research Network report.

Based on the profile of genetic alterations occurring in tumor samples from selected diffuse large B-cell lymphoma (DLBCL) patients, 2 recent whole-exome sequencing studies proposed partially overlapping classification systems. Using clustering techniques applied to targeted sequencing data derived from a large unselected population-based patient cohort with full clinical follow-up (n = 928), we investigated whether molecular subtypes can be robustly identified using methods potentially applicable in routine clinical practice. DNA extracted from DLBCL tumors diagnosed in patients residing in a catchment population of ∼4 million (14 centers) were sequenced with a targeted 293-gene hematological-malignancy panel. Bernoulli mixture-model clustering was applied and the resulting subtypes analyzed in relation to their clinical characteristics and outcomes. Five molecular subtypes were resolved, termed MYD88, BCL2, SOCS1/SGK1, TET2/SGK1, and NOTCH2, along with an unclassified group. The subtypes characterized by genetic alterations of BCL2, NOTCH2, and MYD88 recapitulated recent studies showing good, intermediate, and poor prognosis, respectively. The SOCS1/SGK1 subtype showed biological overlap with primary mediastinal B-cell lymphoma and conferred excellent prognosis. Although not identified as a distinct cluster, NOTCH1 mutation was associated with poor prognosis. The impact of TP53 mutation varied with genomic subtypes, conferring no effect in the NOTCH2 subtype and poor prognosis in the MYD88 subtype. Our findings confirm the existence of molecular subtypes of DLBCL, providing evidence that genomic tests have prognostic significance in non-selected DLBCL patients. The identification of both good and poor risk subtypes in patients treated with R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) clearly show the clinical value of the approach, confirming the need for a consensus classification.

The CellPhe toolkit for cell phenotyping using time-lapse imaging and pattern recognition.

With phenotypic heterogeneity in whole cell populations widely recognised, the demand for quantitative and temporal analysis approaches to characterise single cell morphology and dynamics has increased. We present CellPhe, a pattern recognition toolkit for the unbiased characterisation of cellular phenotypes within time-lapse videos. CellPhe imports tracking information from multiple segmentation and tracking algorithms to provide automated cell phenotyping from different imaging modalities, including fluorescence. To maximise data quality for downstream analysis, our toolkit includes automated recognition and removal of erroneous cell boundaries induced by inaccurate tracking and segmentation. We provide an extensive list of features extracted from individual cell time series, with custom feature selection to identify variables that provide greatest discrimination for the analysis in question. Using ensemble classification for accurate prediction of cellular phenotype and clustering algorithms for the characterisation of heterogeneous subsets, we validate and prove adaptability using different cell types and experimental conditions.

Molecular subclusters of follicular lymphoma: a report from the United Kingdom's Haematological Malignancy Research Network.

Follicular lymphoma (FL) is morphologically and clinically diverse, with mutations in epigenetic regulators alongside t(14;18) identified as disease-initiating events. Identification of additional mutational entities confirms this cancer's heterogeneity, but whether mutational data can be resolved into mechanistically distinct subsets remains an open question. Targeted sequencing was applied to an unselected population-based FL cohort (n = 548) with full clinical follow-up (n = 538), which included 96 diffuse large B-cell lymphoma (DLBCL) transformations. We investigated whether molecular subclusters of FL can be identified and whether mutational data provide predictive information relating to transformation. DNA extracted from FL samples was sequenced with a 293-gene panel representing genes frequently mutated in DLBCL and FL. Three clusters were resolved using mutational data alone, independent of translocation status: FL_aSHM, with high burden of aberrant somatic hypermutation (aSHM) targets; FL_STAT6, with high STAT6 & CREBBP mutation and low aSHM; and FL_Com, with the absence of features of other subtypes and enriched KMT2D mutation. Analysis of mutation signatures demonstrated differential enrichment of predicted mutation signatures between subgroups and a dominant preference in the FL_aSHM subgroup for G(C>T)T and G(C>T)C transitions consistent with previously defined aSHM-like patterns. Of transformed cases with paired samples, 17 of 26 had evidence of branching evolution. Poorer overall survival (OS) in the aSHM group (P = .04) was associated with older age; however, overall tumor genetics provided limited information to predict individual patient risk. Our approach identifies 3 molecular subclusters of FL linked to differences in underlying mechanistic pathways. These clusters, which may be further resolved by the inclusion of translocation status and wider mutation profiles, have implications for understanding pathogenesis as well as improving treatment strategies in the future.

Using echo state networks for classification: A case study in Parkinson's disease diagnosis.

Despite having notable advantages over established machine learning methods for time series analysis, reservoir computing methods, such as echo state networks (ESNs), have yet to be widely used for practical data mining applications. In this paper, we address this deficit with a case study that demonstrates how ESNs can be trained to predict disease labels when stimulated with movement data. Since there has been relatively little prior research into using ESNs for classification, we also consider a number of different approaches for realising input-output mappings. Our results show that ESNs can carry out effective classification and are competitive with existing approaches that have significantly longer training times, in addition to performing similarly with models employing conventional feature extraction strategies that require expert domain knowledge. This suggests that ESNs may prove beneficial in situations where predictive models must be trained rapidly and without the benefit of domain knowledge, for example on high-dimensional data produced by wearable medical technologies. This application area is emphasized with a case study of Parkinson's disease patients who have been recorded by wearable sensors while performing basic movement tasks.