Pollard, B. | Search bioRxiv

bioRxiv2024

Optimizing transcriptome-based synthetic lethality predictions to improve precision oncology in early-stage breast cancer: BC-SELECT

BackgroundPatients with non-metastatic triple-negative (TNBC) or HER2-positive (HER2+) breast cancer do not have tools to predict treatment response or prioritize options prior to starting therapy. Pathologic complete response (pCR) is a proxy that requires neoadjuvant treatment. BC-SELECT leverages genetic interactions (e.g. synthetic lethality (SL)/rescue (SR)) to predict treatment response via pCR from breast cancer gene expression data. BC-SELECT involves (1) "gene-pair identification" that identifies clinically relevant partner genes from large-scale datasets for a given targeted therapy or immunotherapy target, and (2) "training / parameter tuning" using clinical trials to predict treatment response. We evaluated BC-SELECTs ability to predict pCR for trastuzumab, poly (ADP-ribose) polymerase (PARP) inhibitors, and immunotherapy, with tuning supported by three trials (n=86) and validation on nine unseen trials (n=722). ResultsBC-SELECT significantly predicted pCR in 6/9 trials, including all PARP inhibitors (ROC-AUCs 0.6-0.8; Odds Ratio (OR): 1.9-3.5) and immunotherapy (ROC-AUCs 0.7-0.8; OR: 3.2-7.1). BC-SELECT significantly distinguished between pCR and residual disease in 8/9 trials. ConclusionsNo current standard-of-care, pre-treatment approaches predict benefit in non-metastatic TNBC and HER2+ breast cancer. BC-SELECT leverages genetic interactions to predict treatment response from gene expression data. Our findings support development of BC-SELECT towards prioritizing PARP inhibitors and immunotherapy in non-metastatic breast cancer.

bioinformatics↗

bioRxiv2017

Gravitational Signals Underlie Tonic Muscle Activity During Goal-Directed Reaching

Human reaching movements require complex muscle activations to produce the forces necessary to move the limb in a controlled manner. How gravity and the complex kinetic properties of the limb contribute to the generation of the muscle activation pattern by the central nervous system (CNS) is a long-standing question in neuroscience. To address this question, muscle activity is often subdivided into static and phasic components. The former is thought to be related to posture maintenance and transitions between postures. The latter represents the remainder of muscle activity and is thought to be related to active movement production and the compensation for the kinetic properties of the limb. In the present study, we directly addressed how this subdivision of muscle activity into static and phasic components is related to the corresponding components of active muscle torques. Eight healthy subjects pointed in virtual reality to visual targets arranged to create a standard center-out reaching task in three dimensions. Muscle activity and motion capture data were synchronously collected during the movements. The motion capture data were used to calculate gravitational and dynamic components of active muscle torques using a dynamic model of the arm with 5 degrees of freedom. Principal Component Analysis (PCA) was then applied to muscle activity and the torque components, separately, to reduce the dimensionality of the data. Muscle activity was also reconstructed from gravitational and dynamic torque components. Results show that the gravitational and dynamic components of muscle torque represent a significant amount of variance in muscle activity. This method could be used to identify static and phasic components of muscle activity using muscle torques. The contribution of both components to the overall muscle activity was largely equal, unlike their relative contribution to active muscle torques, which may reflect a neural control strategy.

neuroscience↗