Enhancement of endogenous midbrain neurogenesis by microneurotrophin BNN-20 after neural progenitor grafting in a mouse model of nigral degeneration

We have previously shown the neuroprotective and pro-neurogenic activity of microneurotrophin BNN-20 in the substantia nigra of the “weaver” mouse, a model of progressive nigrostriatal degeneration. Here, we extended our investigation in two clinically-relevant ways. First, we assessed the effects of BNN-20 on human induced pluripotent stem cell-derived neural progenitor cells and neurons derived from healthy and parkinsonian donors. Second, we assessed if BNN-20 can boost the outcome of mouse neural progenitor cell intranigral transplantations in weaver mice, at late stages of degeneration. We found that BNN-20 has limited direct effects on cultured human induced pluripotent stem cell-derived neural progenitor cells, marginally enhancing their differentiation towards neurons and partially reversing the pathological phenotype of dopaminergic neurons generated from parkinsonian donors. In agreement, we found no effects of BNN-20 on the mouse neural progenitor cells grafted in the substantia nigra of weaver mice. However, the graft strongly induced an endogenous neurogenic response throughout the midbrain, which was significantly enhanced by the administration of microneurotrophin BNN-20. Our results provide straightforward evidence of the existence of an endogenous midbrain neurogenic system that can be specifically strengthened by BNN-20. Interestingly, the lack of major similar activity on cultured human induced pluripotent stem cell-derived neural progenitors and their progeny reveals the in vivo specificity of the aforementioned pro-neurogenic effect

Multidimensional signals and analytic flexibility: Estimating degrees of freedom in human speech analyses

Recent empirical studies have highlighted the large degree of analytic flexibility in data analysis which can lead to substantially different conclusions based on the same data set. Thus, researchers have expressed their concerns that these researcher degrees of freedom might facilitate bias and can lead to claims that do not stand the test of time. Even greater flexibility is to be expected in fields in which the primary data lend themselves to a variety of possible operationalizations. The multidimensional, temporally extended nature of speech constitutes an ideal testing ground for assessing the variability in analytic approaches, which derives not only from aspects of statistical modeling, but also from decisions regarding the quantification of the measured behavior. In the present study, we gave the same speech production data set to 46 teams of researchers and asked them to answer the same research question, resulting insubstantial variability in reported effect sizes and their interpretation. Using Bayesian meta-analytic tools, we further find little to no evidence that the observed variability can be explained by analysts’ prior beliefs, expertise or the perceived quality of their analyses. In light of this idiosyncratic variability, we recommend that researchers more transparently share details of their analysis, strengthen the link between theoretical construct and quantitative system and calibrate their (un)certainty in their conclusions

The evolution of lung cancer and impact of subclonal selection in TRACERx

Lung cancer is the leading cause of cancer-associated mortality worldwide. Here we analysed 1,644 tumour regions sampled at surgery or during follow-up from the first 421 patients with non-small cell lung cancer prospectively enrolled into the TRACERx study. This project aims to decipher lung cancer evolution and address the primary study endpoint: determining the relationship between intratumour heterogeneity and clinical outcome. In lung adenocarcinoma, mutations in 22 out of 40 common cancer genes were under significant subclonal selection, including classical tumour initiators such as TP53 and KRAS. We defined evolutionary dependencies between drivers, mutational processes and whole genome doubling (WGD) events. Despite patients having a history of smoking, 8% of lung adenocarcinomas lacked evidence of tobacco-induced mutagenesis. These tumours also had similar detection rates for EGFR mutations and for RET, ROS1, ALK and MET oncogenic isoforms compared with tumours in never-smokers, which suggests that they have a similar aetiology and pathogenesis. Large subclonal expansions were associated with positive subclonal selection. Patients with tumours harbouring recent subclonal expansions, on the terminus of a phylogenetic branch, had significantly shorter disease-free survival. Subclonal WGD was detected in 19% of tumours, and 10% of tumours harboured multiple subclonal WGDs in parallel. Subclonal, but not truncal, WGD was associated with shorter disease-free survival. Copy number heterogeneity was associated with extrathoracic relapse within 1 year after surgery. These data demonstrate the importance of clonal expansion, WGD and copy number instability in determining the timing and patterns of relapse in non-small cell lung cancer and provide a comprehensive clinical cancer evolutionary data resource

The evolution of non-small cell lung cancer metastases in TRACERx

Metastatic disease is responsible for the majority of cancer-related deaths. We report the longitudinal evolutionary analysis of 126 non-small cell lung cancer (NSCLC) tumours from 421 prospectively recruited patients in TRACERx who developed metastatic disease, compared with a control cohort of 144 non-metastatic tumours. In 25% of cases, metastases diverged early, before the last clonal sweep in the primary tumour, and early divergence was enriched for patients who were smokers at the time of initial diagnosis. Simulations suggested that early metastatic divergence more frequently occurred at smaller tumour diameters (less than 8 mm). Single-region primary tumour sampling resulted in 83% of late divergence cases being misclassified as early, highlighting the importance of extensive primary tumour sampling. Polyclonal dissemination, which was associated with extrathoracic disease recurrence, was found in 32% of cases. Primary lymph node disease contributed to metastatic relapse in less than 20% of cases, representing a hallmark of metastatic potential rather than a route to subsequent recurrences/disease progression. Metastasis-seeding subclones exhibited subclonal expansions within primary tumours, probably reflecting positive selection. Our findings highlight the importance of selection in metastatic clone evolution within untreated primary tumours, the distinction between monoclonal versus polyclonal seeding in dictating site of recurrence, the limitations of current radiological screening approaches for early diverging tumours and the need to develop strategies to target metastasis-seeding subclones before relapse

Genomic–transcriptomic evolution in lung cancer and metastasis

Intratumour heterogeneity (ITH) fuels lung cancer evolution, which leads to immune evasion and resistance to therapy. Here, using paired whole-exome and RNA sequencing data, we investigate intratumour transcriptomic diversity in 354 non-small cell lung cancer tumours from 347 out of the first 421 patients prospectively recruited into the TRACERx study. Analyses of 947 tumour regions, representing both primary and metastatic disease, alongside 96 tumour-adjacent normal tissue samples implicate the transcriptome as a major source of phenotypic variation. Gene expression levels and ITH relate to patterns of positive and negative selection during tumour evolution. We observe frequent copy number-independent allele-specific expression that is linked to epigenomic dysfunction. Allele-specific expression can also result in genomic–transcriptomic parallel evolution, which converges on cancer gene disruption. We extract signatures of RNA single-base substitutions and link their aetiology to the activity of the RNA-editing enzymes ADAR and APOBEC3A, thereby revealing otherwise undetected ongoing APOBEC activity in tumours. Characterizing the transcriptomes of primary–metastatic tumour pairs, we combine multiple machine-learning approaches that leverage genomic and transcriptomic variables to link metastasis-seeding potential to the evolutionary context of mutations and increased proliferation within primary tumour regions. These results highlight the interplay between the genome and transcriptome in influencing ITH, lung cancer evolution and metastasis

Antibodies against endogenous retroviruses promote lung cancer immunotherapy

B cells are frequently found in the margins of solid tumours as organized follicles in ectopic lymphoid organs called tertiary lymphoid structures (TLS). Although TLS have been found to correlate with improved patient survival and response to immune checkpoint blockade (ICB), the underlying mechanisms of this association remain elusive. Here we investigate lung-resident B cell responses in patients from the TRACERx 421 (Tracking Non-Small-Cell Lung Cancer Evolution Through Therapy) and other lung cancer cohorts, and in a recently established immunogenic mouse model for lung adenocarcinoma. We find that both human and mouse lung adenocarcinomas elicit local germinal centre responses and tumour-binding antibodies, and further identify endogenous retrovirus (ERV) envelope glycoproteins as a dominant anti-tumour antibody target. ERV-targeting B cell responses are amplified by ICB in both humans and mice, and by targeted inhibition of KRAS(G12C) in the mouse model. ERV-reactive antibodies exert anti-tumour activity that extends survival in the mouse model, and ERV expression predicts the outcome of ICB in human lung adenocarcinoma. Finally, we find that effective immunotherapy in the mouse model requires CXCL13-dependent TLS formation. Conversely, therapeutic CXCL13 treatment potentiates anti-tumour immunity and synergizes with ICB. Our findings provide a possible mechanistic basis for the association of TLS with immunotherapy response

eMatch: An Android Application for Finding Friends in Your Location

The work presented in this paper is twofold. We first outline the architectural design, the functional requirements, and the user interface of eMatch, an Android application which was inspired by the idea of fighting the loneliness we all witness in large cities. eMatch has the goal of connecting people with common interests that happen to be in the same geographical area. We then propose EgoSimilar, a new algorithm which computes the similarity between users and is implemented in eMatch. The algorithm is compared against two other well-known and widely used similarity computation methods and is shown to outperform them in terms of the most significant metrics used in our study

Semantic similarity computation and word sense induction using hidden sets multidimensional scaling

Summarization: In this thesis, motivated by evidences in psycholinguistics and cognition, we propose an unsupervised language-agnostic Distributional Semantic Model (DSM), that utilize web harvested data, for the problem of semantic similarity estimation. Semantic similarity can be applied to numerous tasks of Natural Language Processing (NLP), such as affective text analysis and paraphrasing. In the first part of the thesis, the construction of typical DSMs following the well-established Vector Space Model, is presented. More specifically, we describe the creation of corpora by harvesting web documents following a query-based approach, as well as state-of-the-art DSMs used for the computation of semantic similarity from the corpora. Next, we propose a novel hierarchical distributed semantic model (DSM), that is inspired by evidence in psycholinguistics and cognition, and consists of low-dimensional manifolds built on semantic neighborhoods. Each manifold is sparsely encoded and mapped into a low-dimensional space. Global operations are decomposed into local operations in multiple sub-spaces; results from these local operations are fused to come up with semantic relatedness estimates. Manifold DSM are constructed starting from a pairwise word-level semantic similarity matrix. The proposed model is evaluated against state-of-the-art/baseline DSMs on semantic similarity estimation task, where the similarity metrics are evaluated against human similarity ratings. The proposed model significantly improve performance comparing to the baseline approaches for the task of semantic similarity estimation between words. Furthermore the proposed model is evaluated in a taxonomy task achieving achieving state-of-the-art results. Finally, motivated by evidence of cognitive organization of concepts based on the degree of concreteness, we present the performance of proposed DSM for abstract and concrete nouns

Root cause analysis of miscommunication hotspots in spoken dialogue systems

Summarization: A major challenge in Spoken Dialogue Systems (SDS) is the detection of problematic communication (hotspots), as well as the classification of these hotspots into different types (root cause analysis). In this work, we focus on two classes of root cause, namely, erroneous speech recognition vs. other (e.g., dialogue strategy). Specifically, we propose an automatic algorithm for detecting hotspots and classifying root causes in two subsequent steps. Regarding hotspot detection, various lexico-semantic features are used for capturing repetition patterns along with affective features. Lexico-semantic and repetition features are also employed for root cause analysis. Both algorithms are evaluated with respect to the Let's Go dataset (bus information system). In terms of classification unweighted average recall, performance of 80% and 70% is achieved for hotspot detection and root cause analysis, respectively.Παρουσιάστηκε στο: 17th Annual Conference of the International Speech Communication Associatio