Pancreatic cancer remains one of the most lethal of malignancies and a major health burden. We performed whole-genome sequencing and copy number variation (CNV) analysis of 100 pancreatic ductal adenocarcinomas (PDACs). Chromosomal rearrangements leading to gene disruption were prevalent, affecting genes known to be important in pancreatic cancer (TP53, SMAD4, CDKN2A, ARID1A and ROBO2) and new candidate drivers of pancreatic carcinogenesis (KDM6A and PREX2). Patterns of structural variation (variation in chromosomal structure) classified PDACs into 4 subtypes with potential clinical utility: the subtypes were termed stable, locally rearranged, scattered and unstable. A significant proportion harboured focal amplifications, many of which contained druggable oncogenes (ERBB2, MET, FGFR1, CDK6, PIK3R3 and PIK3CA), but at low individual patient prevalence. Genomic instability co-segregated with inactivation of DNA maintenance genes (BRCA1, BRCA2 or PALB2) and a mutational signature of DNA damage repair deficiency. Of 8 patients who received platinum therapy, 4 of 5 individuals with these measures of defective DNA maintenance responded.

Summary Accurate pathological diagnosis is crucial for optimal management of cancer patients. For the ~100 known central nervous system (CNS) tumour entities, standardization of the diagnostic process has been shown to be particularly challenging - with substantial inter-observer variability in the histopathological diagnosis of many tumour types. We herein present the development of a comprehensive approach for DNA methylation-based CNS tumour classification across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that availability of this method may have substantial impact on diagnostic precision compared with standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility we have designed a free online classifier tool (www.molecularneuropathology.org) requiring no additional onsite data processing. Our results provide a blueprint for the generation of machine learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology.

Tumors of glial origin consist of a core mass and a penumbra of invasive, single cells, decreasing in numbers towards the periphery and still detectable several centimeters away from the core lesion. Several decades ago, the diffuse nature of malignant gliomas was recognized by neurosurgeons when super-radical resections using hemispherectomies failed to eradicate these tumors. Local invasiveness eventually leads to regrowth of a recurrent tumor predominantly adjacent to the resection cavity, which is not significantly altered by radiation or chemotherapy. This raises the question of whether invasive glioma cells activate cellular programs that render these cells resistant to conventional treatments. Clinical and experimental data demonstrate that glioma invasion is determined by several independent mechanisms that facilitate the spread of these tumors along different anatomic and molecular structures. A common denominator of this cellular behavior may be cell motility. Gene-expression profiling showed upregulation of genes related to motility, and functional studies demonstrated that cell motility contributes to the invasive phenotype of malignant gliomas. There is accumulating evidence that invasive glioma cells show a decreased proliferation rate and a relative resistance to apoptosis, which may contribute to chemotherapy and radiation resistance. Interestingly, interference with cell motility by different strategies results in increased susceptibility to apoptosis, indicating that this dynamic relationship can potentially be exploited as an anti-invasive treatment paradigm. In this review, we discuss mechanisms of glioma invasion, characteristics of the invasive cell, and consequences of this cellular phenotype for surgical resection, oncologic treatments, and future perspectives for anti-invasive strategies.

L-Glutamine (Gln) functions physiologically to balance tissue requirements of carbon and nitrogen. It has been proposed that in cancer cells undergoing aerobic glycolysis, accelerated anabolism is sustained by Gln-derived carbons, which replenish the tricarboxylic acid (TCA) cycle (anaplerosis). However, it is shown here that in glioblastoma (GBM) cells, almost half of the Gln-derived glutamate (Glu) is secreted and does not enter the TCA cycle and, that inhibiting glutaminolysis does not affect proliferation. Moreover, Gln-starved cells are not rescued by TCA cycle replenishment. Instead, the conversion of Glu to Gln by Glutamine Synthetase (GS) (cataplerosis) confers Gln prototrophy, and fuels de novo purine biosynthesis. In both orthotopic GBM models and in patients, 13C-glucose tracing showed that GS produces Gln from TCA cycle-derived carbons. Finally, while it is contributed only marginally by the circulation, the Gln required for the growth of GBM tumours is either autonomously synthesized by GS-positive glioma cells, or supplied by astrocytes.