Mounting evidence indicates that vitamin C has the potential to be a potent anti-cancer agent when administered intravenously and in high doses (high-dose IVC). Early phase clinical trials have confirmed safety and indicated efficacy of IVC in eradicating tumour cells of various cancer types. In recent years, the multi-targeting effects of vitamin C were unravelled, demonstrating a role as cancer-specific, pro-oxidative cytotoxic agent, anti-cancer epigenetic regulator and immune modulator, reversing epithelial-to-mesenchymal transition, inhibiting hypoxia and oncogenic kinase signalling and boosting immune response. Moreover, high-dose IVC is powerful as an adjuvant treatment for cancer, acting synergistically with many standard (chemo-) therapies, as well as a method for mitigating the toxic side-effects of chemotherapy. Despite the rationale and ample evidence, strong clinical data and phase III studies are lacking. Therefore, there is a need for more extensive awareness of the use of this highly promising, non-toxic cancer treatment in the clinical setting. In this review, we provide an elaborate overview of pre-clinical and clinical studies using high-dose IVC as anti-cancer agent, as well as a detailed evaluation of the main known molecular mechanisms involved. A special focus is put on global molecular profiling studies in this respect. In addition, an outlook on future implications of high-dose vitamin C in cancer treatment is presented and recommendations for further research are discussed.

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Minimally-invasive tools to assess tumour presence and burden may improve clinical management. FDG-PET (metabolic) imaging is the current gold standard for interim response assessment in patients with classical Hodgkin Lymphoma (cHL), but this technique cannot be repeated frequently. Here we show that microRNAs (miRNA) associated with tumour-secreted extracellular vesicles (EVs) in the circulation of cHL patients may improve response assessment. Small RNA sequencing and qRT-PCR reveal that the relative abundance of cHL-expressed miRNAs, miR-127-3p, miR-155-5p, miR-21-5p, miR-24-3p and let-7a-5p is up to hundred-fold increased in plasma EVs of cHL patients pre-treatment when compared to complete metabolic responders (CMR). Notably, in partial responders (PR) or treatment-refractory cases (n = 10) the EV-miRNA levels remain elevated. In comparison, tumour specific copy number variations (CNV) were detected in cell-free DNA of 8 out of 10 newly diagnosed cHL patients but not in patients with PR. Combining EV-miR-127-3p and/or EV-let-7a-5p levels, with serum TARC (a validated protein cHL biomarker), increases the accuracy for predicting PET-status (n = 129) to an area under the curve of 0.93 (CI: 0.87-0.99), 93.5% sensitivity, 83.8/85.0% specificity and a negative predictive value of 96%. Thus the level of tumour-associated miRNAs in plasma EVs is predictive of metabolic tumour activity in cHL patients. Our findings suggest that plasmaThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Combination therapies are used in the clinic to achieve cure, better efficacy and to circumvent resistant disease in patients. Initial assessment of the effect of such combinations, usually of two agents, is frequently performed using in vitro assays. In this review, we give a short summary of the types of analyses that were presented during the Preclinical and Early-phase Clinical Pharmacology Course of the Pharmacology and Molecular Mechanisms Group, European Organization for Research and Treatment on Cancer, that can be used to determine the efficacy of drug combinations. The effect of a combination treatment can be calculated using mathematical equations based on either the Loewe additivity or Bliss independence model, or a combination of both, such as Chou and Talalay's mediandrug effect model. Interactions can be additive, synergistic (more than additive), or antagonistic (less than additive). Software packages CalcuSyn (also available as CompuSyn) and Combenefit are designed to calculate the extent of the combined effects. Interestingly, the application of machinelearning methods in the prediction of combination treatments, which can include pharmacogenomic, genetic, metabolomic and proteomic profiles, might contribute to further refinement of combination regimens. However, more research is needed to apply appropriate rules of machine learning methods to ensure correct predictive models.Even as early as the 1960s, the majority of clinical treatments consisted of combination regimens. Combinations such as mechlorethamine, vincristine, procarbazine and prednisone (MOPP), and cyclophosphamide, hydroxydaunorubicin and oncovin with prednisone (CHOP) represented a breakthrough in the cure of lymphoma, while other combinations led to a high curation rate in childhood leukaemia (1). Depending on the type of combination used, the treatment rationale is to i) increase the efficacy of each separate drug without increasing toxicity, ii) add a drug which offers protection against toxicity, iii) bypass resistance development, or iv) target different subpopulations in a heterogeneous tumour. The initial clinical rationale was to achieve a better therapeutic effect (e.g. a complete response) than accomplished by each drug separately (e.g. only a partial response) (2). Historically, the selection of drugs to apply in combination therapies was based on the observation that each of the drugs showed antitumor activity against a certain tumour type, preferably with different toxicities of the two drugs. Doses and schedules were determined by trial and error. Soon thereafter, a complementary scientific approach was used to select combinations based on the mechanisms of action of each drug (3). An excellent example is the gemcitabine-cisplatin combination, which was initially developed by our group (4) (with the aim of preventing repair of DNA-platinum adducts) and is now standard therapy for tumours such as non-small cell lung cancer and bladder cancer. Another combination is 3303