The characterization of cancer genomes has provided insight into somatically altered genes across tumors, transformed our understanding of cancer biology, and enabled tailoring of therapeutic strategies. However, the function of most cancer alleles remains mysterious, and many cancer features transcend their genomes. Consequently, tumor genomic characterization does not influence therapy for most patients. Approaches to understand the function and circuitry of cancer genes provide complementary approaches to elucidate both oncogene and non-oncogene dependencies. Emerging work indicates that the diversity of therapeutic targets engendered by non-oncogene dependencies is much larger than the list of recurrently mutated genes. Here we describe a framework for this expanded list of cancer targets, providing novel opportunities for clinical translation.

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A prostate computer-aided diagnosis (CAD) based on random forest to detect prostate cancer using a combination of spatial, intensity, and texture features extracted from three sequences, T2W, ADC, and B2000 images, is proposed. The random forest training considers instance-level weighting for equal treatment of small and large cancerous lesions as well as small and large prostate backgrounds. Two other approaches, based on an AutoContext pipeline intended to make better use of sequence-specific patterns, were considered. One pipeline uses random forest on individual sequences while the other uses an image filter described to produce probability map-like images. These were compared to a previously published CAD approach based on support vector machine (SVM) evaluated on the same data. The random forest, features, sampling strategy, and instance-level weighting improve prostate cancer detection performance [area under the curve (AUC) 0.93] in comparison to SVM (AUC 0.86) on the same test data. Using a simple image filtering technique as a first-stage detector to highlight likely regions of prostate cancer helps with learning stability over using a learning-based approach owing to visibility and ambiguity of annotations in each sequence.

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Deep brain stimulation (DBS) is an effective surgical therapy for Parkinson’s disease (PD). However, limitations of the DBS systems have led to great interest in adaptive neuromodulation systems that can dynamically adjust stimulation parameters to meet concurrent therapeutic demand. Constant high-frequency motor cortex stimulation has not been remarkably efficacious, which has led to greater focus on modulation of subcortical targets. Understanding of the importance of timing in both cortical and subcortical stimulation has generated an interest in developing more refined, parsimonious stimulation techniques based on critical oscillatory activities of the brain. Concurrently, much effort has been put into identifying biomarkers of both parkinsonian and physiological patterns of neuronal activities to drive next generation of adaptive brain stimulation systems. One such biomarker is beta-gamma phase amplitude coupling (PAC) that is detected in the motor cortex. PAC is strongly correlated with parkinsonian specific motor signs and symptoms and respond to therapies in a dose-dependent manner. PAC may represent the overall state of the parkinsonian motor network and have less instantaneously dynamic fluctuation during movement. These findings raise the possibility of novel neuromodulation paradigms that are potentially less invasiveness than DBS. Successful application of PAC in neuromodulation may necessitate phase-dependent stimulation technique, which aims to deliver precisely timed stimulation pulses to a specific phase to predictably modulate to selectively modulate pathological network activities and behavior in real time. Overcoming current technical challenges can lead to deeper understanding of the parkinsonian pathophysiology and development of novel neuromodulatory therapies with potentially less side-effects and higher therapeutic efficacy.

Spinal cord injury (SCI) is a devastating disease with limited effective treatment options. Animal paradigms are vital for understanding the pathogenesis of SCI and testing potential therapeutics. The porcine model of SCI is increasingly favored because of its greater similarity to humans. However, its adoption is limited by the complexities of care and range of testing parameters. Researchers need to consider swine selection, injury method, post-operative care, rehabilitation, behavioral outcomes, and histology metrics. Therefore, we systematically reviewed full-text English-language articles to evaluate study characteristics used in developing a porcine model and summarize the interventions that have been tested using this paradigm. A total of 63 studies were included, with 33 examining SCI pathogenesis and 30 testing interventions. Studies had an average sample size of 15 pigs with an average weight of 26 kg, and most used female swine with injury to the thoracic cord. Injury was most commonly induced by weight drop with compression. The porcine model is amenable to testing various interventions, including mean arterial pressure augmentation ( n = 7), electrical stimulation ( n = 6), stem cell therapy ( n = 5), hypothermia ( n = 2), biomaterials ( n = 2), gene therapy ( n = 2), steroids ( n = 1), and nanoparticles ( n = 1). It is also notable for its clinical translatability and is emerging as a valuable pre-clinical study tool. This systematic review can serve as a guideline for researchers implementing and testing the porcine SCI model.

BACKGROUND: Growing evidence suggests that piriform cortex resection during anterior temporal lobectomy is important for achieving good seizure outcome in mesial temporal lobe epilepsy (mTLE). However, the relationship between seizure outcome and piriform cortex ablation during MR-guided laser interstitial thermal therapy (MRgLITT) remains unclear. OBJECTIVE: To determine whether ablation of piriform cortex was associated with seizure outcome in patients with mTLE undergoing MRgLITT. METHODS: We performed preablation and postablation volumetric analyses of hippocampus, amygdala, piriform cortex, and ablation volumes in patients with mTLE who underwent MRgLITT at our institution from 2014 to 2019. RESULTS: Thirty nine patients with mTLE were analyzed. In univariate logistic regression, percent piriform cortex ablation was associated with International League Against Epilepsy (ILAE) class 1 at 6 months (odds ratio [OR] 1.051, 95% CI [1.001-1.117], P = .045), whereas ablation volume, percent amygdala ablation, and percent hippocampus ablation were not (P > .05). At 1 year, ablation volume was associated with ILAE class 1 (OR 1.608, 95% CI [1.071-2.571], P = .021) while percent piriform cortex ablation became a trend (OR 1.050, 95% CI [0.994-1.109], P = .054), and both percent hippocampus ablation and percent amygdala ablation were not significantly associated with ILAE class 1 (P > .05). In multivariable logistic regression, only percent piriform cortex ablation was a significant predictor of seizure freedom at 6 months (OR 1.085,], P = .019) and at 1 year (OR 1.074, 95% CI [1.003-1.178], P = .041). CONCLUSION: Piriform cortex ablation volume is associated with seizure outcome in patients with mTLE undergoing MRgLITT. The piriform cortex should be considered a high yield ablation target to achieve good seizure outcome.