Identifying patterns in amyotrophic lateral sclerosis progression from sparse longitudinal data

Ramamoorthy, Divya; Severson, Kristen A.; Ghosh, Soumya; Baxi, Emily G.; Coyne, Alyssa N.; Mosmiller, Elizabeth; Hayes, Lindsey; Cerezo, Aianna; Ahmad, Omar; Roy, Promit; Zeiler, Steven R.; Krakauer, John W.; Li, Jonathan; Donde, Aneesh; Huynh, Nhan; Adam, Miriam; Wassie, Brook T.; Lenail, Alex; Patel-Murray, Natasha L.; Raghav, Yogindra; Sachs, Karen; Kozareva, Velina; Tsitkov, Stanislav; Ehrenberger, Tobias; Kaye, Julia; Lima, Leandro; Wyman, Stacia K.; Vertudes, Edward; Amirani, Naufa; Raja, Krishna Kumar; Thomas, Reuben; Lim, Ryan G.; Miramontes, Ricardo; Wu, Jie; Vaibhav, Vineet; Matlock, Andrea; Venkatraman, Vidya; Holewenski, Ronald; Sundararaman, Niveda; Pandey, Rakhi; Manalo, Danica-Mae; Frank, Aaron; Ornelas, Loren; Panther, Lindsey; Gomez, Emilda; Galvez, Erick; Pérez, Daniel; Meepe, Imara; Lei, Susan; Pinedo, Louis; Liu, Chunyan; Moran, Ruby; Sareen, Dhruv; Landin, Barry; Agurto, Carla; Cecchi, Guillermo A.; Norel, Raquel; Thrower, Sara L.; Luppino, Sarah; Farrar, Alanna; Pothier, Lindsay; Yu, Hong; Sinani, Ervin; Vigneswaran, Prasha; Farr, S. Michelle; Mandefro, Berhan; Trost, Hannah; Bañuelos, Maria G.; García, Verónica Vázquez; Workman, Michael; Ho, Ritchie; Baloh, Robert H.; Roggenbuck, Jennifer; Harms, Matthew B.; Prina, Carolyn; Heintzman, Sarah; Kolb, Stephen J.; Stocksdale, Jennifer; Wang, Keona; Morgan, Todd M.; Heitzman, Daragh; Jamil, Arish; Jockel-Balsarotti, Jennifer; Karanja, Elizabeth; Markway, Jesse; McCallum, Molly; Miller, Tim; Joslin, Ben; Alibazoglu, Deniz; Beavers, Jay C.; Bellard, Mary; Bruce, Elizabeth J.; Maragakis, Nicholas J.; Cudkowicz, Merit; Thompson, Terri G.; Finkbeiner, Steven; Thompson, Leslie M.; Eyk, Jennifer E. Van; Svendsen, Clive N.; Rothstein, Jeffrey D.; Glass, Jonathan D.; Fournier, Christina; Sherman, Alexander; Lunetta, Christian; Walk, David; Hayat, Ghazala; Wymer, James P.; Gwathmey, Kelly; Olney, Nicholas; Ajroud‐Driss, Senda; Heiman‐Patterson, Terry; Arcila‐Londono, Ximena; Faulconer, Kenneth; Sanani, Ervin; Berger, Alex; Mirochnick, Julia; Herrington, Todd M.; Berry, James; Ng, Kenney; Fraenkel, Ernest

doi:10.1038/s43588-022-00299-w

Cited by 12 publications

(3 citation statements)

References 53 publications

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“…Mutations, even in cancer, are generally infrequent when the entire genome or exome is taken into consideration [ 219 , 220 ]. This sparsity extends to other sources of biological data [ 220 , 221 , 222 , 223 , 224 , 225 ]. Most machine-learning models have difficulty learning and picking up patterns for prediction based on sparse data, which can lead to overfitting [ 219 , 226 , 227 , 228 , 229 , 230 ].…”

Section: Major Challenges For Clinical Utility Of Complex and Data-dr...mentioning

confidence: 77%

Computational Methods Summarizing Mutational Patterns in Cancer: Promise and Limitations for Clinical Applications

Patterson

Elbasir

Tian

et al. 2023

Cancers

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Since the rise of next-generation sequencing technologies, the catalogue of mutations in cancer has been continuously expanding. To address the complexity of the cancer-genomic landscape and extract meaningful insights, numerous computational approaches have been developed over the last two decades. In this review, we survey the current leading computational methods to derive intricate mutational patterns in the context of clinical relevance. We begin with mutation signatures, explaining first how mutation signatures were developed and then examining the utility of studies using mutation signatures to correlate environmental effects on the cancer genome. Next, we examine current clinical research that employs mutation signatures and discuss the potential use cases and challenges of mutation signatures in clinical decision-making. We then examine computational studies developing tools to investigate complex patterns of mutations beyond the context of mutational signatures. We survey methods to identify cancer-driver genes, from single-driver studies to pathway and network analyses. In addition, we review methods inferring complex combinations of mutations for clinical tasks and using mutations integrated with multi-omics data to better predict cancer phenotypes. We examine the use of these tools for either discovery or prediction, including prediction of tumor origin, treatment outcomes, prognosis, and cancer typing. We further discuss the main limitations preventing widespread clinical integration of computational tools for the diagnosis and treatment of cancer. We end by proposing solutions to address these challenges using recent advances in machine learning.

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Section: Major Challenges For Clinical Utility Of Complex and Data-dr...mentioning

confidence: 77%

Computational Methods Summarizing Mutational Patterns in Cancer: Promise and Limitations for Clinical Applications

Patterson

Elbasir

Tian

et al. 2023

Cancers

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“…The pre-post analysis of the efficacy outcome measures assumes linearity in the deterioration. However, while ALS clinical trials assume linear deterioration in ALSFRS-R over time [6,34], there is also evidence suggesting that ALS may progress in a non-linear fashion [43,44]. This study did not assess transplant engraftment; therefore we have no data about the survival of AstroRx ® in patients and the effectiveness of immunosuppression to prevent rejection.…”

Section: Study Limitationsmentioning

confidence: 98%

Safety and efficacy of first-in-man intrathecal injection of human astrocytes (AstroRx®) in ALS patients: phase I/IIa clinical trial results

et al. 2023

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Background Malfunction of astrocytes is implicated as one of the pathological factors of ALS. Thus, intrathecal injection of healthy astrocytes in ALS can potentially compensate for the diseased astrocytes. AstroRx® is an allogeneic cell-based product, composed of healthy and functional human astrocytes derived from embryonic stem cells. AstroRx® was shown to clear excessive glutamate, reduce oxidative stress, secrete various neuroprotective factors, and act as an immunomodulator. Intrathecal injection of AstroRx® to animal models of ALS slowed disease progression and extended survival. Here we report the result of a first-in-human clinical study evaluating intrathecal injection of AstroRx® in ALS patients. Methods We conducted a phase I/IIa, open-label, dose-escalating clinical trial to evaluate the safety, tolerability, and therapeutic effects of intrathecal injection of AstroRx® in patients with ALS. Five patients were injected intrathecally with a single dose of 100 × 106 AstroRx® cells and 5 patients with 250 × 106 cells (low and high dose, respectively). Safety and efficacy assessments were recorded for 3 months pre-treatment (run-in period) and 12 months post-treatment (follow-up period). Results A single administration of AstroRx® at either low or high doses was safe and well tolerated. No adverse events (AEs) related to AstroRx® itself were reported. Transient AEs related to the Intrathecal (IT) procedure were all mild to moderate. The study demonstrated a clinically meaningful effect that was maintained over the first 3 months after treatment, as measured by the pre-post slope change in ALSFRS-R. In the 100 × 106 AstroRx® arm, the ALSFRS-R rate of deterioration was attenuated from − 0.88/month pre-treatment to − 0.30/month in the first 3 months post-treatment (p = 0.039). In the 250 × 106 AstroRx® arm, the ALSFRS-R slope decreased from − 1.43/month to − 0.78/month (p = 0.0023). The effect was even more profound in a rapid progressor subgroup of 5 patients. No statistically significant change was measured in muscle strength using hand-held dynamometry and slow vital capacity continued to deteriorate during the study. Conclusions Overall, these findings suggest that a single IT administration of AstroRx® to ALS patients at a dose of 100 × 106 or 250 × 106 cells is safe. A signal of beneficial clinical effect was observed for the first 3 months following cell injection. These results support further investigation of repeated intrathecal administrations of AstroRx®, e.g., every 3 months. Trial Registration: NCT03482050.

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“…MoGP identifies clusters of participants with similar rate and pattern of decline for a given outcome measure. It has been described for clustering participants based on ALSFRS-R decline (47).…”

Section: Le Analysismentioning

confidence: 99%

Listener effort quantifies clinically meaningful progression of dysarthria in people living with amyotrophic lateral sclerosis

Bingham,

Norel,

Roitberg

et al. 2024

Preprint

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Amyotrophic lateral sclerosis (ALS) is a neurodegenerative motor neuron disease that causes progressive muscle weakness. Progressive bulbar dysfunction causes dysarthria and thus social isolation, reducing quality of life. The Everything ALS Speech Study obtained longitudinal clinical information and speech recordings from 292 participants. In a subset of 120 participants, we measured speaking rate (SR) and listener effort (LE), a measure of dysarthria severity rated by speech pathologists from recordings. LE intra- and inter-rater reliability was very high (ICC 0.88 to 0.92). LE correlated with other measures of dysarthria at baseline. LE changed over time in participants with ALS (slope 0.77 pts/month; p<0.001) but not controls (slope 0.005 pts/month; p=0.807). The slope of LE progression was similar in all participants with ALS who had bulbar dysfunction at baseline, regardless of ALS site of onset. LE could be a remotely collected clinically meaningful clinical outcome assessment for ALS clinical trials.

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Identifying patterns in amyotrophic lateral sclerosis progression from sparse longitudinal data

Cited by 12 publications

References 53 publications

Computational Methods Summarizing Mutational Patterns in Cancer: Promise and Limitations for Clinical Applications

Computational Methods Summarizing Mutational Patterns in Cancer: Promise and Limitations for Clinical Applications

Safety and efficacy of first-in-man intrathecal injection of human astrocytes (AstroRx®) in ALS patients: phase I/IIa clinical trial results

Listener effort quantifies clinically meaningful progression of dysarthria in people living with amyotrophic lateral sclerosis

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