Purpose: In the era of DNA-guided personalized cancer treatment, it is essential to conduct predictive analysis on the tissue that matters. Here, we analyzed genetic differences between primary colorectal adenocarcinomas (CRC) and their respective hepatic metastasis.Experimental Design: The primary CRC and the subsequent hepatic metastasis of 21 patients with CRC were analyzed using targeted deep-sequencing of DNA isolated from formalin-fixed, paraffin-embedded archived material.Results: We have interrogated the genetic constitution of a designed "Cancer Mini-Genome" consisting of all exons of 1,264 genes associated with pathways relevant to cancer. In total, 6,696 known and 1,305 novel variations were identified in 1,174 and 667 genes, respectively, including 817 variants that potentially altered protein function. On average, 83 (SD ¼ 69) potentially function-impairing variations were gained in the metastasis and 70 (SD ¼ 48) variations were lost, showing that the primary tumor and hepatic metastasis are genetically significantly different. Besides novel and known variations in genes such as KRAS, BRAF, KDR, FLT1, PTEN, and PI3KCA, aberrations in the up/downstream genes of EGFR/PI3K/VEGF-pathways and other pathways (mTOR, TGFb, etc.) were also detected, potentially influencing therapeutic responsiveness. Chemotherapy between removal of the primary tumor and the metastasis (N ¼ 11) did not further increase the amount of genetic variation.Conclusion: Our study indicates that the genetic characteristics of the hepatic metastases are different from those of the primary CRC tumor. As a consequence, the choice of treatment in studies investigating targeted therapies should ideally be based on the genetic properties of the metastasis rather than on those of the primary tumor.

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Brain network characteristics’ potential to serve as a neurological and psychiatric pathology biomarker has been hampered by the so-called thresholding problem. The minimum spanning tree (MST) is increasingly applied to overcome this problem. It is yet unknown whether this approach leads to more consistent findings across studies and converging outcomes of either disease-specific biomarkers or transdiagnostic effects. We performed a systematic review on MST analysis in neurophysiological and neuroimaging studies (N = 43) to study consistency of MST-metrics between different network sizes, and assessed disease specificity and transdiagnostic sensitivity of MST-metrics for neurological and psychiatric conditions. Analysis of data from control groups (12 studies) showed that MST leaf fraction but not diameter decreased with increasing network size. Studies showed a broad range in metric values, suggesting that specific processing pipelines affect MST topology. Contradicting findings remain in the inconclusive literature of MST brain network studies, but some trends were seen: 1) a more line-like organization characterizes neurodegenerative disorders across pathologies, and is associated with symptom severity and disease progression; 2) neurophysiological studies in epilepsy show frequency band specific MST alterations that normalize after successful treatment; and 3) less efficient MST topology in alpha band is found across disorders associated with attention impairments.

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