Mutational Status of the TP53 Gene As a Predictor of Response and Survival in Patients With Chronic Lymphocytic Leukemia: Results From the LRF CLL4 Trial

PurposeTP53 mutations have been described in chronic lymphocytic leukemia (CLL) and have been associated with poor prognosis in retrospective studies. We aimed to address the frequency and prognostic value of TP53 abnormalities in patients with CLL in the context of a prospective randomized trial.Patients and MethodsWe analyzed 529 CLL samples from the LRF CLL4 (Leukaemia Research Foundation Chronic Lymphocytic Leukemia 4) trial (chlorambucil v fludarabine with or without cyclophosphamide) at the time of random assignment for mutations in the TP53 gene. TP53 mutation status was correlated with response and survival data.ResultsMutations of TP53 were found in 40 patients (7.6%), including 25 (76%) of 33 with 17p deletion and 13 (3%) of 487 without that deletion. There was no significant correlation between TP53 mutations and age, stage, IGHV gene mutations, CD38 and ZAP-70 expression, or any other chromosomal abnormality other than 17p deletion, in which concordance was high (96%). TP53 mutations were significantly associated with poorer overall response rates (27% v 83%; P <.001) and shorter progression-free survival (PFS) and overall survival (OS; 5-year PFS: 5% v 17%; 5-year OS: 20% v 59%; P <.001 for both). Multivariate analysis that included baseline clinical variables, treatment, and known adverse genetic factors confirmed that TP53 mutations have added prognostic value.ConclusionTP53 mutations are associated with impaired response and shorter survival in patients with CLL. Analysis of TP53 mutations should be performed in patients with CLL who have progressive disease before starting first-line treatment, and those with mutations should be selected for novel experimental therapies. J Clin Oncol 29: 2223-2229. (C) 2011 by American Society of Clinical Oncology

Abnormal Pedestrian Trajectory analysis based on arbitrary-length clustering

This papers examines the use of trajectory distance measures and clustering techniques to define normal
and abnormal trajectories in the context of pedestrian tracking in public spaces. In order to detect abnormal
trajectories, what is meant by a normal trajectory in a given scene is firstly defined. Then every trajectory
that deviates from this normality is classified as abnormal. By combining Dynamic Time Warping and a
modified K-Means algorithms for arbitrary-length data series, we have developed an algorithm for trajectory
clustering and abnormality detection. The final system performs with an overall accuracy of 83% and 75%
when tested in two different standard datasets.