Pediatric versus Adult Medulloblastoma: Towards a Definition That Goes beyond Age

Medulloblastoma is a rare malignant brain tumor that predominantly affects children but also occurs in adults. The incidence declines significantly after age 15, and distinct tumor molecular features are seen across the age spectrum. Standard of care treatment consists of maximal safe surgical resection followed by adjuvant radiation and/or chemotherapy. Adjuvant treatment decisions are based on individual patient risk factors and have been informed by decades of prospective clinical trials. These trials have historically relied on arbitrary age cutoffs for inclusion (age 16, 18, or 21, for example), while trials that include adult patients or stratify patients by molecular features of disease have been rare. The aim of this literature review is to review the history of clinical trials in medulloblastoma, with an emphasis on selection criteria, and argue in favor of rational and inclusive trials based on molecular features of disease as opposed to chronological age. We performed a scoping literature review for medulloblastoma and clinical trials and include a summary of those results. We also discuss some of the significant advances made in understanding the molecular biology of medulloblastoma within the past decade, most notably the identification of four distinct subgroups based on gene expression profiling. We will also cite the recent experiences of childhood leukemia and the emergence of tissue-agnostic therapies as examples of successes of rationally designed, inclusive trials translating to improved clinical outcomes for patients across the age spectrum. Despite the prior trial history and recent molecular advances outcomes remain poor for ~30% of medulloblastoma patients. We believe that defining patients by the specific molecular alterations their tumors harbor is the best way to ensure they can access potentially efficacious therapies on clinical trials.

SIOP PNET5 MB Trial: History and Concept of a Molecularly Stratified Clinical Trial of Risk-Adapted Therapies for Standard-Risk Medulloblastoma

Background. SIOP PNET5 MB was initiated in 2014 as the first European trial using clinical, histological, and molecular parameters to stratify treatments for children and adolescents with standard-risk medulloblastoma. Methods. Stratification by upfront assessment of molecular parameters requires the timely submission of adequate tumour tissue. In the standard-risk phase-III cohort, defined by the absence of high-risk criteria (M0, R0), pathological (non-LCA), and molecular biomarkers (MYCN amplification in SHH–MB or MYC amplification), a randomized intensification by carboplatin concomitant with radiotherapy is investigated. In the LR stratum for localized WNT-activated medulloblastoma and age <16 years, a reduction of craniospinal radiotherapy dose to 18 Gy and a reduced maintenance chemotherapy are investigated. Two additional strata (WNT-HR, SHH-TP53) were implemented during the trial. Results. SIOP PNET5 MB is actively recruiting. The availability of adequate tumour tissue for upfront real-time biological assessments to assess inclusion criteria has proven feasible. Conclusion. SIOP PNET5 MB has demonstrated that implementation of biological parameters for stratification is feasible in a prospective multicentre setting, and may improve risk-adapted treatment. Comprehensive research studies may allow assessment of additional parameters, e.g., novel medulloblastoma subtypes, and identification and validation of biomarkers for the further refinement of risk-adapted treatment in the future.

Drug repurposing for COVID-19 using graph neural network and harmonizing multiple evidence

Since the 2019 novel coronavirus disease (COVID-19) outbreak in 2019 and the pandemic continues for more than one year, a vast amount of drug research has been conducted and few of them got FDA approval. Our objective is to prioritize repurposable drugs using a pipeline that systematically integrates the interaction between COVID-19 and drugs, deep graph neural networks, and in vitro/population-based validations. We first collected all available drugs (n = 3635) related to COVID-19 patient treatment through CTDbase. We built a COVID-19 knowledge graph based on the interactions among virus baits, host genes, pathways, drugs, and phenotypes. A deep graph neural network approach was used to derive the candidate drug’s representation based on the biological interactions. We prioritized the candidate drugs using clinical trial history, and then validated them with their genetic profiles, in vitro experimental efficacy, and population-based treatment effect. We highlight the top 22 drugs including Azithromycin, Atorvastatin, Aspirin, Acetaminophen, and Albuterol. We further pinpointed drug combinations that may synergistically target COVID-19. In summary, we demonstrated that the integration of extensive interactions, deep neural networks, and multiple evidence can facilitate the rapid identification of candidate drugs for COVID-19 treatment.

Impulsivity relates to multi-trial choice strategy in probabilistic reversal learning

Impulsivity is defined as a trait-like tendency to engage in rash actions that are poorly thought out or expressed in an untimely manner. Previous research has found that impulsivity relates to deficits in decision making, in particular when it necessitates executive control or reward outcomes. Reinforcement learning (RL) relies on the ability to integrate valenced outcomes to make good decisions, and has recently be shown to often recruit executive function; as such, it is unsurprising that impulsivity has been studied in the context of RL. However, how impulsivity relates to the mechanisms of RL remains unclear. We aimed to investigate the relationship between impulsivity and learning in a reward-driven learning task with probabilistic feedback and reversal known to recruit executive function. Based on prior literature in clinical populations, we predicted that higher impulsivity would be associated with poorer performance on the task, driven by more frequent switching following unrewarded outcomes. Our results did not support this prediction, but more advanced, trial-history dependent analyses revealed specific effects of impulsivity on switching behavior following consecutive unrewarded trials. Computational modeling captured group-level behavior, but not impulsivity results. Our results support previous findings highlighting the importance of sensitivity to negative outcomes in understanding how impulsivity relates to learning, but indicate that this may stem from more complex strategies than usually considered in computational models of learning. This should be an important target for future research.

Limitations of a proposed correction for slow drifts in decision criterion

Trial history biases in decision-making tasks are thought to reflect systematic updates of decision variables, therefore their precise nature informs conclusions about underlying heuristic strategies and learning processes. However, random drifts in decision variables can corrupt this inference by mimicking the signatures of systematic updates. Hence, identifying the trial-by-trial evolution of decision variables requires methods that can robustly account for such drifts. Recent studies (Lak 20, Mendonça 20) have made important advances in this direction, by proposing a convenient method to correct for the influence of slow drifts in decision criterion, a key decision variable. Here we apply this correction to a variety of updating scenarios, and evaluate its performance. We show that the correction fails for a wide range of commonly assumed systematic updating strategies, distorting one's inference away from the veridical strategies towards a narrow subset. To address these limitations, we propose a model-based approach for disambiguating systematic updates from random drifts, and demonstrate its success on real and synthetic datasets. We show that this approach accurately recovers the latent trajectory of drifts in decision criterion as well as the generative systematic updates from simulated data. Our results offer recommendations for methods to account for the interactions between history biases and slow drifts, and highlight the advantages of incorporating assumptions about the generative process directly into models of decision-making.

Purkinje Cell Activity During Suppression of Voluntary Eye Movements in Rhesus Macaques

Conscious control of actions helps us to reach our goals by suppressing responses to distracting external stimuli. The cerebellum has been suggested to complement cerebral control of inhibition of targeted movements (conscious control), though by what means, remains unclear. By measuring Purkinje cell (PC) responses during antisaccades, we show that the cerebellum not only plays a role in the execution of eye movements, but also in during the volitional inhibition thereof. We found that simple spike (SS) modulation during instruction and execution of prosaccades and antisaccades was prominent in PCs of both medial and lateral cerebellum, showing distinct, time-ordered sequences, but each with different sensitivities for execution and trial-history. SS activity in both regions modulated bidirectionally, with both facilitation (increasing SS firing) and suppression (decreasing SS firing) PCs showing firing-rate changes associated with instruction and execution, respectively. These findings show that different cerebellar regions can contribute to behavioral control and inhibition, but with different propensities, enriching the cerebellar machinery in executive control.

Expectations about motion direction affect perception and anticipatory smooth pursuit differently

We show that expectations about motion direction that are based on long-term trial history affect perception and anticipatory pursuit differently. Whereas anticipatory pursuit direction was coherent with the expected motion direction (attraction bias), perception was biased opposite to the expected direction (repulsion bias). These opposite biases potentially reveal different ways in which perception and action utilize prior information and support the idea of different information processing for perception and pursuit.

Drug Repurposing for COVID-19 using Graph Neural Network with Genetic, Mechanistic, and Epidemiological Validation

Amid the pandemic of 2019 novel coronavirus disease (COVID-19) infected by SARS-CoV-2, a vast amount of drug research for prevention and treatment has been quickly conducted, but these efforts have been unsuccessful thus far. Our objective is to prioritize repurposable drugs using a drug repurposing pipeline that systematically integrates multiple SARS-CoV-2 and drug interactions, deep graph neural networks, and in-vitro/population-based validations. We first collected all the available drugs (n= 3,635) involved in COVID-19 patient treatment through CTDbase. We built a SARS-CoV-2 knowledge graph based on the interactions among virus baits, host genes, pathways, drugs, and phenotypes. A deep graph neural network approach was used to derive the candidate drug’s representation based on the biological interactions. We prioritized the candidate drugs using clinical trial history, and then validated them with their genetic profiles, in vitro experimental efficacy, and electronic health records. We highlight the top 22 drugs including Azithromycin, Atorvastatin, Aspirin, Acetaminophen, and Albuterol. We further pinpointed drug combinations that may synergistically target COVID-19. In summary, we demonstrated that the integration of extensive interactions, deep neural networks, and rigorous validation can facilitate the rapid identification of candidate drugs for COVID-19 treatment. This paper had been uploaded to arXiv : https://arxiv.org/abs/2009.10931