SummaryAmyotrophic lateral sclerosis (ALS) is a progressive, fatal neurodegenerative disease characterized by motor neuron cell death. However, not all motor neurons are equally susceptible. Most of what we know about the surviving motor neurons comes from gene expression profiling; less is known about their functional traits. We found that resistant motor neurons cultured from SOD1 ALS mouse models have enhanced axonal outgrowth and dendritic branching. They also have an increase in the number and size of actin-based structures like growth cones and filopodia. These phenotypes occur in cells cultured from presymptomatic mice and mutant SOD1 models that do not develop ALS but not in embryonic motor neurons. Enhanced outgrowth and upregulation of filopodia can be induced in wild-type adult cells by expressing mutant SOD1. These results demonstrate that mutant SOD1 can enhance the regenerative capability of ALS-resistant motor neurons. Capitalizing on this mechanism could lead to new therapeutic strategies.

In the originally published version of this article, the authors concluded that the observed effects of SOD1 mutation were elicited in adult cells only. This statement was meant to summarize the results of this article. In light of other studies that have examined the morphology of motor neurons at early post-natal stages, the text referring to the effects of mutant SOD1 expression in adult cells has been modified. An expanded discussion and references have also been added to the manuscript. Specifically, these sentences: ''Enhanced regeneration of mutant SOD1 motor neurons only occurs in adult cells and is independent of ALS onset.'' ''Increased outgrowth only occurs in adult neurons and is independent of ALS symptoms.'' ''Figure 2. Enhanced outgrowth and branching of adult motor neurons from mutant SOD1 mice is specific to adult cells and independent of ALS onset.'' now read ''Enhanced regeneration of adult mutant SOD1 motor neurons is independent of ALS onset.'' ''Increased outgrowth in adult motor neurons is independent of ALS symptoms.'' ''Figure 2. Enhanced outgrowth and branching of adult motor neurons from mutant SOD1 mice is independent of ALS onset.'' The sentence ''In fact, the enhanced outgrowth and branching phenotypes only becomes apparent after the mouse is two months old (Figure 2D).'' has been deleted. Finally, the authors have expanded the discussion and added the references reported below.

Amyotrophic lateral sclerosis (ALS) is a progressive and fatal neurodegenerative disease characterized by motor neuron cell death and subsequent paralysis of voluntary muscles. Although ALS specifically affects motor neurons, some cells are resistant to disease progression. Most ALS studies have focused on the cellular mechanisms that cause loss of motor neuron viability. Less is known about the surviving neurons, and most of that information has come from gene expression profiling. In this study, we functionally characterize the surviving spinal motor neurons by culturing them from SOD1 ALS mouse models at various stages of disease progression. Surprisingly, we found that in comparison to non-transgenic controls, ALS resistant motor neurons from adult SOD1 G93A mice have enhanced axonal outgrowth and dendritic branching. Further, the enhanced outgrowth occurs before the mice become symptomatic, but increases with disease progression. Motor neurons from SOD1 G93A mice also display an increase in the number and size of actin-based structures such as growth cones and filopodia. The increased outgrowth and branching phenotype is predominantly cellintrinsic and can be induced in motor neurons from non-transgenic mice by exogenous expression of SOD1 G93A . These results indicate that expression of mutant SOD1 in ALS-resistant adult motor neurons can enhance their regenerative capability via a mechanism that is not directly correlated with the onset of ALS symptoms. Understanding the positive effects that mutant SOD1 has on motor neuron regeneration could lead to new therapeutic strategies that capitalize on this mechanism.

Purpose: xDECIDE is a clinical decision support system, accessed through a web portal and powered by a "Human-AI Team", that offers oncology healthcare providers a set of treatment options personalized for their cancer patients, and provides outcomes tracking through an observational research protocol. This article describes the xDECIDE process and the AI-assisted technologies that ingest semi-structured electronic medical records to identify and then standardize clinico-genomic features, generate a structured personal health record (PHR), and produce ranked treatment options based on clinical evidence, expert insights, and the real world evidence generated within the system itself. Method: Patients may directly enroll in the IRB-approved pan-cancer XCELSIOR registry (NCT03793088). Patient consent permits data aggregation, continuous learning from clinical outcomes, and sharing of limited datasets within the research team. Assisted by numerous AI-based technologies, the xDECIDE team aggregates and processes patients' electronic medical records, and applies multiple levels of natural language processing (NLP) and machine learning to generate a structured case summary and a standardized list of patient features. Next a ranked list of treatment options is created by an ensemble of AI-based models, called xCORE. The output of xCORE is reviewed by molecular pharmacologists and expert oncologists in a virtual tumor board (VTB). Finally a report is produced that includes a ranked list of treatment options and supporting scientific and medical rationales. Treating physicians can use an interactive portal to view all aspects of these data and associated reports, and to continuously monitor their patients' information. The xDECIDE system, including xCORE, is self-improving; feedback improves aspects of the process through machine learning, knowledge ingestion, and outcomes-directed process improvement. Results: At the time of writing, over 2,000 patients have enrolled in XCELSIOR, including over 650 with CNS cancers, over 300 with pancreatic cancer, and over 100 each with ovarian, colorectal, and breast cancers. Over 150 VTBs of CNS cancer patients and ~100 VTBs of pancreatic cancer patients have been performed. In the course of these discussions, ~450 therapeutic options have been discussed and over 2,000 consensus rationales have been delivered. Further, over 500 treatment rationale statements ("rules") have been encoded to improve algorithm decision making between similar therapeutics or regimens in the context of individual patient features. We have recently deployed the xCORE AI-based treatment ranking algorithm for validation in real-world patient populations. At present approximately 15 patients are processed each week via the full xDECIDE process, including xCORE, under the continuing oversight of experts and VTBs. Conclusion: Clinical decision support through xDECIDE is available for oncologists to utilize in their standard practice of medicine by enrolling a patient in the XCELSIOR trial and accessing xDECIDE through its web portal. This system can help to identify potentially effective treatment options individualized for each patient, based on sophisticated integration of real world evidence, human expert knowledge and opinion, and scientific and clinical publications and databases.

xCures operates a direct-to-patient, real-world evidence platform for decentralized clinical research. The platform leverages a nationwide observational research protocol (XCELSIOR, NCT03793088) to aggregate, normalize, and analyze N-of-1 clinical outcomes to continuously learn from and inform treatment decisions. Individual data elements are extracted directly from medical documents such as clinic notes, and radiology, genomics, and pathology reports. The data elements are standardized to established biomedical ontologies and stored in a validated and part 11 compliant electronic database, suitable for statistical analyses and regulatory filings. This permits comparison of patient outcomes across institutions and removes the burden of data entry from oncologists and their staff. As an extremely efficient real-world data solution, we have utilized this platform to accelerate both academic- and commercial-sponsored clinical research, prospectively integrating diagnostics and algorithms with interventional treatments. For each patient that participates in XCELSIOR, artificial intelligence-powered clinical decision support algorithms suggest testing and treatment options. These options and supporting treatment rationales are sourced from key opinion leaders, tumor boards, clinical researchers, practicing oncologists, and published literature, and ranked using the real-world outcomes data from the registry. At the conference, we will present an overview of this real-time learning infrastructure and report on clinical case studies for pharma and non-profit groups including over 75 virtual tumor boards and real-world evidence generated from over 150 patients with CNS cancers that we have helped in partnership with Cancer Commons and The Musella Foundation for Brain Tumor Research and Education. Outcomes analyses stratified by therapeutic interventions and biomarkers will be reported, including frequency of adverse events, time to treatment failure, time to disease progression, and overall survival. Interventions include standard-of-care chemotherapies as well as therapies accessed by clinical trial, expanded access, and off-label prescription.