A temporal classification method based on behavior time series data in patients with behavioral variant of frontotemporal dementia and apathy.

Background Apathy is a common behavioral syndrome that occurs across neurological and psychiatric disorders. An influential theoretical framework defined apathy as the quantitative reduction of self-generated voluntary and purposeful behaviors. There is evidence in the literature of the multidimensional nature of apathy with cognitive, behavioral, and emotional dimensions. To date, apathy has been assessed using various scales and questionnaires. Alternative objective and ecological measurements of apathy are needed. New method We used the ECOCAPTURE protocol and an ethological approach to investigate behavior in bvFTD patients under ecological conditions (a waiting room) while they freely explored a novel environment. Data were collected by behavioral coding from 7-minute video using an ethogram and transformed into behavior time series data. We present an approach considering behavioral kinetics to assess behavior. We aimed to construct a new behavior analysis method, called ECOCAPTURE kinetics, using temporal classification for behavior time series data analysis. To develop our classifier, we retained a nonelastic Euclidian metric, combined with a convolutional approach. Results We applied the ECOCAPTURE kinetics method to a cohort of 20 bvFTD patients and 18 healthy controls. We showed that bvFTD patients can be classified according to their behavioral kinetics into three groups. Each subgroup was characterized by specific behavior disorders and neuropsychological profile. Comparison with Existing Method(s) The ECOCAPTURE kinetics method is different from those of the classical approach of measuring behavior, producing time budgets, frequency of behavior occurrences, or kinematic diagrams. Conclusions This approach can be extended to any behavioral study encoding time.

Integration of Genomic and Transcriptional Features in Pancreatic Cancer Reveals Increased Cell Cycle Progression in Metastases.

We integrated clinical, genomic, and transcriptomic data from 224 primaries and 95 metastases from 289 patients to characterize progression of pancreatic ductal adenocarcinoma (PDAC). Driver gene alterations and mutational and expression-based signatures were preserved, with truncations, inversions, and translocations most conserved. Cell cycle progression (CCP) increased with sequential inactivation of tumor suppressors, yet remained higher in metastases, perhaps driven by cell cycle regulatory gene variants. Half of the cases were hypoxic by expression markers, overlapping with molecular subtypes. Paired tumor heterogeneity showed cancer cell migration by Halstedian progression. Multiple PDACs arising synchronously and metachronously in the same pancreas were actually intra-parenchymal metastases, not independent primary tumors. Established clinical co-variates dominated survival analyses, although CCP and hypoxia may inform clinical practice.

A renewed model of pancreatic cancer evolution based on genomic rearrangement patterns.

Pancreatic cancer, a highly aggressive tumour type with uniformly poor prognosis, exemplifies the classically held view of stepwise cancer development. The current model of tumorigenesis, based on analyses of precursor lesions, termed pancreatic intraepithelial neoplasm (PanINs) lesions, makes two predictions: first, that pancreatic cancer develops through a particular sequence of genetic alterations (KRAS, followed by CDKN2A, then TP53 and SMAD4); and second, that the evolutionary trajectory of pancreatic cancer progression is gradual because each alteration is acquired independently. A shortcoming of this model is that clonally expanded precursor lesions do not always belong to the tumour lineage, indicating that the evolutionary trajectory of the tumour lineage and precursor lesions can be divergent. This prevailing model of tumorigenesis has contributed to the clinical notion that pancreatic cancer evolves slowly and presents at a late stage. However, the propensity for this disease to rapidly metastasize and the inability to improve patient outcomes, despite efforts aimed at early detection, suggest that pancreatic cancer progression is not gradual. Here, using newly developed informatics tools, we tracked changes in DNA copy number and their associated rearrangements in tumour-enriched genomes and found that pancreatic cancer tumorigenesis is neither gradual nor follows the accepted mutation order. Two-thirds of tumours harbour complex rearrangement patterns associated with mitotic errors, consistent with punctuated equilibrium as the principal evolutionary trajectory. In a subset of cases, the consequence of such errors is the simultaneous, rather than sequential, knockout of canonical preneoplastic genetic drivers that are likely to set-off invasive cancer growth. These findings challenge the current progression model of pancreatic cancer and provide insights into the mutational processes that give rise to these aggressive tumours.