Enhancing Cognitive Performance Prediction through White Matter Hyperintensity Connectivity Assessment: A Multicenter Lesion Network Mapping Analysis of 3,485 Memory Clinic Patients.

Introduction: White matter hyperintensities of presumed vascular origin (WMH) are associated with cognitive impairment and are a key imaging marker in evaluating cognitive health. However, WMH volume alone does not fully account for the extent of cognitive deficits and the mechanisms linking WMH to these deficits remain unclear. We propose that lesion network mapping (LNM), enables to infer if brain networks are connected to lesions, and could be a promising technique for enhancing our understanding of the role of WMH in cognitive disorders. Our study employed this approach to test the following hypotheses: (1) LNM-informed markers surpass WMH volumes in predicting cognitive performance, and (2) WMH contributing to cognitive impairment map to specific brain networks. Methods & results: We analyzed cross-sectional data of 3,485 patients from 10 memory clinic cohorts within the Meta VCI Map Consortium, using harmonized test results in 4 cognitive domains and WMH segmentations. WMH segmentations were registered to a standard space and mapped onto existing normative structural and functional brain connectome data. We employed LNM to quantify WMH connectivity across 480 atlas-based gray and white matter regions of interest (ROI), resulting in ROI-level structural and functional LNM scores. The capacity of total and regional WMH volumes and LNM scores in predicting cognitive function was compared using ridge regression models in a nested cross-validation. LNM scores predicted performance in three cognitive domains (attention and executive function, information processing speed, and verbal memory) significantly better than WMH volumes. LNM scores did not improve prediction for language functions. ROI-level analysis revealed that higher LNM scores, representing greater disruptive effects of WMH on regional connectivity, in gray and white matter regions of the dorsal and ventral attention networks were associated with lower cognitive performance. Conclusion: Measures of WMH-related brain network connectivity significantly improve the prediction of current cognitive performance in memory clinic patients compared to WMH volume as a traditional imaging marker of cerebrovascular disease. This highlights the crucial role of network effects, particularly in attentionrelated brain regions, improving our understanding of vascular contributions to cognitive impairment. Moving forward, refining WMH information with connectivity data could contribute to patient-tailored therapeutic interventions and facilitate the identification of subgroups at risk of cognitive disorders.

Detection and localization of early- and late-stage cancers using platelet RNA.

Cancer patients benefit from early tumor detection since treatment outcomes are more favorable for less advanced cancers. Platelets are involved in cancer progression and are considered a promising biosource for cancer detection, as they alter their RNA content upon local and systemic cues. We show that tumor-educated platelet (TEP) RNA-based blood tests enable the detection of 18 cancer types. With 99% specificity in asymptomatic controls, thromboSeq correctly detected the presence of cancer in two-thirds of 1,096 blood samples from stage I-IV cancer patients and in half of 352 stage I-III tumors. Symptomatic controls, including inflammatory and cardiovascular diseases, and benign tumors had increased false-positive test results with an average specificity of 78%. Moreover, thromboSeq determined the tumor site of origin in five different tumor types correctly in over 80% of the cancer patients. These results highlight the potential properties of TEP-derived RNA panels to supplement current approaches for blood-based cancer screening.

Multi-omics analyses of MEN1 missense mutations identify disruption of menin-MLL and menin-JunD interactions as critical requirements for molecular pathogenicity.

BACKGROUND: Loss-of-function mutations of the multiple endocrine neoplasia type 1 (MEN1) gene are causal to the MEN1 tumor syndrome, but they are also commonly found in sporadic pancreatic neuroendocrine tumors and other types of cancers. The MEN1 gene product, menin, is involved in transcriptional and chromatin regulation, most prominently as an integral component of KMT2A/MLL1 and KMT2B/MLL2 containing COMPASS-like histone H3K4 methyltransferase complexes. In a mutually exclusive fashion, menin also interacts with the JunD subunit of the AP-1 and ATF/CREB transcription factors. RESULTS: Here, we applied and in silico screening approach for 253 disease-related MEN1 missense mutations in order to select a set of nine menin mutations in surface-exposed residues. The protein interactomes of these mutants were assessed by quantitative mass spectrometry, which indicated that seven of the nine mutants disrupt interactions with both MLL1/MLL2 and JunD complexes. Interestingly, we identified three missense mutations, R52G, E255K and E359K, which predominantly reduce the MLL1 and MLL2 interactions when compared with JunD. This observation was supported by a pronounced loss of binding of the R52G, E255K and E359K mutant proteins at unique MLL1 genomic binding sites with less effect on unique JunD sites. CONCLUSIONS: Our results underline the effects of MEN1 gene mutations in both familial and sporadic tumors of endocrine origin on the interactions of menin with the MLL1 and MLL2 histone H3K4 methyltransferase complexes and with JunD-containing transcription factors. Menin binding pocket mutants R52G, E255K and E359K have differential effects on MLL1/MLL2 and JunD interactions, which translate into differential genomic binding patterns. Our findings encourage future studies addressing the pathophysiological relevance of the separate MLL1/MLL2- and JunD-dependent functions of menin mutants in MEN1 disease model systems.