Pharmacogenomic Testing to Guide Personalized Cancer Medicine Decisions in Private Oncology Practice: A Case Study

Treatment of evolving cancers will require dynamic decision support

Cancer research has traditionally focused on developing new agents, but an underexplored question is that of the dose and frequency of existing drugs. Based on the modus operandi established in the early days of chemotherapies, most drugs are administered according to predetermined schedules that seek to deliver the maximum tolerated dose and are only adjusted for toxicity. However, we believe that the complex, evolving nature of cancer requires a more dynamic and personalized approach. Chronicling the milestones of the field, we show that the impact of schedule choice crucially depends on processes driving treatment response and failure. As such, cancer heterogeneity and evolution dictate that a one-size-fits-all solution is unlikely-instead, each patient should be mapped to the strategy that best matches their current disease characteristics and treatment objectives (i.e. their 'tumorscape'). To achieve this level of personalization, we need mathematical modeling. In this perspective, we propose a five-step 'Adaptive Dosing Adjusted for Personalized Tumorscapes (ADAPT)' paradigm to integrate data and understanding across scales and derive dynamic and personalized schedules. We conclude with promising examples of model-guided schedule personalization and a call to action to address key outstanding challenges surrounding data collection, model development, and integration.

Engineering of gemcitabine coated nano-graphene oxide sheets for efficient near-infrared radiation mediated in vivo lung cancer photothermal therapy

Gemcitabine (GEM) and its derivatives of deoxycytosine is a promising anticancer candidate which is effective for the treatment of various cancers including lung cancer via cascade targetting Erk/Mek/Raf/Ras pathway and blocking the proliferation of the tumor cells. In this present work, we have described reduced graphene oxide (rGO) in the presence of anticancer utilizing ascorbic acid as reducing agents for lung cancer treatment. GEM reduced graphene oxide (termed as GEM-rGO) has resulted in a smooth and transparent morphological surface, which was confirmed by various spectroscopical investigations. The anticancer drug-loaded rGO has displayed remarkable cytotoxic activities against a panel of lung cancer cell lines when compared to the untreated lung cancer cells. Further, we examined the morphological observation of the cancer cell death was monitored through the fluorescence microscopic examinations. In addition, the cell deaths of the lung cancer cells were observed by the flow cytometry analyses. In addition, the non-toxic nature of potent GEM-rGO and GEM-rGO + NIR was confirmed by in vivo systemic toxicity analysis. Besides, the higher safety feature of the GEM-rGO and GEM-rGO + NIR was evidenced by histological analyses of the mice organs. The subcutaneous injection of GEM-rGO and GEM-rGO + NIR into mice bearing A549 xenografts more effectively inhibited the tumor than the free GEM. Based on the outcomes, we can summarise that the GEM reduced graphene oxide (GEM-rGO) can be used as a promising drug candidate for the treatment of lung cancer in the future.