User-controlled pipelines for feature integration and head and neck radiation therapy outcome predictions

Purpose: Precision cancer medicine is dependent on accurate prediction of disease and treatment outcome, requiring integration of clinical, imaging and interventional knowledge. User controlled pipelines are capable of feature integration with varied levels of human interaction. In this work we present two pipelines designed to combine clinical, radiomic (quantified imaging), and RTx-omic (quantified radiation therapy (RT) plan) information for prediction of locoregional failure (LRF) in head and neck cancer (H&amp;N).Methods: Pipelines were designed to extract information and model patient outcomes based on clinical features, computed tomography (CT) imaging, and planned RT dose volumes. We predict H&amp;N LRF using: 1) a highly user-driven pipeline that leverages modular design and machine learning for feature extraction and model development; and 2) a pipeline with minimal user input that utilizes deep learning convolutional neural networks to extract and combine CT imaging, RT dose and clinical features for model development.Results: Clinical features with logistic regression in our highly user-driven pipeline had the highest precision recall area under the curve (PR-AUC) of 0.66 (0.33-0.93), where a PR-AUC = 0.11 is considered random.CONCLUSIONS: Our work demonstrates the potential to aggregate features from multiple specialties for conditional-outcome predictions using pipelines with varied levels of human interaction. Most importantly, our results provide insights into the importance of data curation and quality, as well as user, data and methodology bias awareness as it pertains to result interpretation in user controlled pipelines.</p

The Primate-specific Protein TBC1D3 Is Required for Optimal Macropinocytosis in a Novel ARF6-dependent Pathway

The generation of novel genes and proteins throughout evolution has been proposed to occur as a result of whole genome and gene duplications, exon shuffling, and retrotransposition events. The analysis of such genes might thus shed light into the functional complexity associated with highly evolved species. One such case is represented by TBC1D3, a primate-specific gene, harboring a TBC domain. Because TBC domains encode Rab-specific GAP activities, TBC-containing proteins are predicted to play a major role in endocytosis and intracellular traffic. Here, we show that the TBC1D3 gene originated late in evolution, likely through a duplication of the RNTRE locus, and underwent gene amplification during primate speciation. Despite possessing a TBC domain, TBC1D3 is apparently devoid of Rab-GAP activity. However, TBC1D3 regulates the optimal rate of epidermal growth factor–mediated macropinocytosis by participating in a novel pathway involving ARF6 and RAB5. In addition, TBC1D3 binds and colocalize to GGA3, an ARF6-effector, in an ARF6-dependent manner, and synergize with it in promoting macropinocytosis, suggesting that the two proteins act together in this process. Accordingly, GGA3 siRNA-mediated ablation impaired TBC1D3-induced macropinocytosis. We thus uncover a novel signaling pathway that appeared after primate speciation. Within this pathway, a TBC1D3:GGA3 complex contributes to optimal propagation of signals, ultimately facilitating the macropinocytic process

OpenKBP-Opt: an international and reproducible evaluation of 76 knowledge-based planning pipelines

Objective: To establish an open framework for developing plan optimization models for knowledge-based planning (KBP). Approach: Our framework includes radiotherapy treatment data (i.e. reference plans) for 100 patients with head-and-neck cancer who were treated with intensity-modulated radiotherapy. That data also includes high-quality dose predictions from 19 KBP models that were developed by different research groups using out-of-sample data during the OpenKBP Grand Challenge. The dose predictions were input to four fluence-based dose mimicking models to form 76 unique KBP pipelines that generated 7600 plans (76 pipelines × 100 patients). The predictions and KBP-generated plans were compared to the reference plans via: the dose score, which is the average mean absolute voxel-by-voxel difference in dose; the deviation in dose-volume histogram (DVH) points; and the frequency of clinical planning criteria satisfaction. We also performed a theoretical investigation to justify our dose mimicking models. Main results: The range in rank order correlation of the dose score between predictions and their KBP pipelines was 0.50-0.62, which indicates that the quality of the predictions was generally positively correlated with the quality of the plans. Additionally, compared to the input predictions, the KBP-generated plans performed significantly better (P\u3c 0.05; one-sided Wilcoxon test) on 18 of 23 DVH points. Similarly, each optimization model generated plans that satisfied a higher percentage of criteria than the reference plans, which satisfied 3.5% more criteria than the set of all dose predictions. Lastly, our theoretical investigation demonstrated that the dose mimicking models generated plans that are also optimal for an inverse planning model. Significance: This was the largest international effort to date for evaluating the combination of KBP prediction and optimization models. We found that the best performing models significantly outperformed the reference dose and dose predictions. In the interest of reproducibility, our data and code is freely available

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field