Chemotherapy Induced Sensory Neuropathy Depends on Non-Linear Interactions with Cancer
Housley, Stephen N.
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For the constellation of neurological disorders known as chemotherapy induced neuropathy, mechanistic understanding, and treatment remain deficient. In project one, I leveraged a multi-scale experimental approach to provide the first evidence that chronic sensory neuropathy depends on non-linear interactions between cancer and chemotherapy. Global transcriptional profiling of dorsal root ganglia revealed amplified differential expression, notably in regulators of neuronal excitability, metabolism and inflammatory responses, all of which were unpredictable from effects observed with either chemotherapy or cancer alone. Systemic interactions between cancer and chemotherapy also determined the extent of deficits in sensory encoding in vivo and ion channel protein expression by single mechanosensory neurons, with the potassium ion channel Kv3.3 emerging as candidate mechanisms explaining sensory neuron dysfunction. The sufficiency of this novel molecular mechanism was tested in an in silico biophysical model of mechanosensory function. Finally, validated measures of sensorimotor behavior in awake behaving animals confirmed that dysfunction after chronic chemotherapy treatment is exacerbated by cancer. Notably, errors in precise fore-limb placement emerged as a novel behavioral deficit unpredicted by our previous study of chemotherapy alone. These original findings identify novel contributors to peripheral neuropathy, and emphasize the fundamental dependence of neuropathy on the systemic interaction between chemotherapy and cancer across multiple levels of biological control. In project two, I extend study to multiple classes of mechanosensory neurons that are necessary for generating the information content (population code) needed for proprioception. I first tested the hypothesis that exacerbated neuronal dysfunction is conserved across multiple classes of mechanosensory neurons. Results revealed co-suppression of specific signaling parameters across all neuronal classes. To understand the consequences of corrupt population code, I employed a long-short-term memory neural network (LSTM), a deep-learning algorithm, to test how decoding of spatiotemporal features of movement are altered after chemotherapy treatment of cancer. Results indicate that spiking activity from the population of neurons in animals with cancer, treated by chemotherapy contain significantly less information about key features of movement including, e.g. timing, magnitudes, and velocity. I then modeled the central nervous systems (CNS) capacity to compensate for this information loss. Even under optimal learning conditions, the inability to fully restore predictive power suggests that the CNS would not be able to compensate and restore full function. Our results support our proposal that lasting deficits in mobility and perception experienced by cancer survivors can originate from sensory information that is corrupted and un-interpretable by CNS neurons or networks. Collectively, I present the first evidence that chronic cancer neuropathy cannot be explained by the effects of chemotherapy alone but instead depend on non-linear interactions with cancer. This understanding is a prerequisite for designing future studies and for developing effective treatments or preventative measures.