Investigating PSMA-Targeted Radioligand Therapy Efficacy as a Function of Cellular PSMA Levels and Intratumoral PSMA Heterogeneity.

PURPOSE: Prostate-specific membrane antigen (PSMA) targeting radioligands deliver radiation to PSMA-expressing cells. However, the relationship between PSMA levels and intralesion heterogeneity of PSMA expression, and cytotoxic radiation by radioligand therapy (RLT) is unknown. Here we investigate RLT efficacy as function of PSMA levels/cell, and the fraction of PSMA+ cells in a tumor. EXPERIMENTAL DESIGN: RM1 cells expressing different levels of PSMA (PSMA-, PSMA+, PSMA++, PSMA+++; study 1) or a mix of PSMA+ and PSMA- RM1 (study 2, 4) or PC-3/PC-3-PIP (study 3) cells at various ratios were injected into mice. Mice received 177Lu- (studies 1-3) or 225Ac- (study 4) PSMA617. Tumor growth was monitored. Two days post-RLT, tumors were resected in a subset of mice. Radioligand uptake and DNA damage were quantified. RESULTS: 177Lu-PSMA617 efficacy increased with increasing PSMA levels (study 1) and fractions of PSMA positive cells (studies 2, 3) in both, the RM1 and PC-3-PIP models. In tumors resected 2 days post-RLT, PSMA expression correlated with 177Lu-PSMA617 uptake and the degree of DNA damage. Compared with 177Lu-PSMA617, 225Ac-PSMA617 improved overall antitumor effectiveness and tended to enhance the differences in therapeutic efficacy between experimental groups. CONCLUSIONS: In the current models, both the degree of PSMA expression and the fraction of PSMA+ cells correlate with 177Lu-/225Ac-PSMA617 tumor uptake and DNA damage, and thus, RLT efficacy. Low or heterogeneous PSMA expression represents a resistance mechanism to RLT.See related commentary by Ravi Kumar and Hofman, p. 2774.

Functional connectivity in the brain estimated by analysis of gamma events.

It is known that gamma activity is generated by local networks. In this paper we introduced a new approach for estimation of functional connectivity between neuronal networks by measuring temporal relations between peaks of gamma event amplitudes. We have shown in freely moving rats that gamma events recorded between electrodes 1.5 mm apart in the majority of cases, are generated by different neuronal modules interfering with each other. The map of functional connectivity between brain areas during the resting state, created based on gamma event temporal relationships is in agreement with anatomical connections and with maps described by fMRI methods during the resting state. The transition from the resting state to exploratory activity is accompanied by decreased functional connectivity between most brain areas. Our data suggest that functional connectivity between interhemispheric areas depends on GABAergic transmission, while intrahemispheric functional connectivity is kainate receptor dependent. This approach presents opportunities for merging electrographic and fMRI data on brain functional connectivity in normal and pathological conditions.

Non-linear classification of heart rate parameters as a biomarker for epileptogenesis.

PURPOSE: To characterize a biomarker for epileptogenesis based on cardiac interbeat interval characteristics. METHODS: Electrocardiograph (ECG) and electroencephalogram (EEG) signals were recorded from freely moving rats (n = 23) before status epilepticus (SE) induced by i.p. pilocarpine (PILO) injection as baseline, and on days 1, 3 and 7 after SE. We assessed several features from cardiac interbeat intervals, including linear, non-linear and frequency parameters of interbeat intervals, and power spectra of interpolated intervals during epileptogenesis. After thresholding, the altered values were applied to a non-linear classifier. The non-linear classifier divided animals into two groups; with and without epilepsy, based on all collected data. RESULTS: We found that none of the single altered parameters in cardiac activity emerged as a sole biomarker for epileptogenesis. However, the non-linear classifier distinguished animals that later developed from those and did not develop epilepsy. The non-linear classification was performed on preliminary findings from 23 animals; six did not develop epilepsy and the rest did. The average positive predictive value (precision rate) was 78%. This was calculated based on the average sensitivity and specificity, which were 80.6% and 35.2% respectively, for the 100 classification passes. We also showed that these numbers would have increased as the number of subjects increased. CONCLUSION: Changes to the brain caused by status epilepticus that lead to epileptogenesis have systemic effects, and alter cardiac activity. A non-linear classifier performed on several extracted features of cardiac interbeat intervals may be useful as a biomarker to identify animals with low and high probability of developing epilepsy after status epilepticus.

Absence of CNTNAP2 leads to epilepsy, neuronal migration abnormalities, and core autism-related deficits.

Although many genes predisposing to autism spectrum disorders (ASD) have been identified, the biological mechanism(s) remain unclear. Mouse models based on human disease-causing mutations provide the potential for understanding gene function and novel treatment development. Here, we characterize a mouse knockout of the Cntnap2 gene, which is strongly associated with ASD and allied neurodevelopmental disorders. Cntnap2(-/-) mice show deficits in the three core ASD behavioral domains, as well as hyperactivity and epileptic seizures, as have been reported in humans with CNTNAP2 mutations. Neuropathological and physiological analyses of these mice before the onset of seizures reveal neuronal migration abnormalities, reduced number of interneurons, and abnormal neuronal network activity. In addition, treatment with the FDA-approved drug risperidone ameliorates the targeted repetitive behaviors in the mutant mice. These data demonstrate a functional role for CNTNAP2 in brain development and provide a new tool for mechanistic and therapeutic research in ASD.