Project description:This model is based on:
Reinforcement learning-based control of tumor growth under anti-angiogenic therapy
Authors: Parisa Yazdjerdi, Nader Meskin, Mohammad Al-Naemi, Ala-Eddin Al Moustafa, Levente Kovacs
Abstract:
Background and objectives: In recent decades, cancer has become one of the most fatal and destructive diseases which is threatening humans life. Accordingly, different types of cancer treatment are studied with the main aim to have the best treatment with minimum side effects. Anti-angiogenic is a molecular targeted therapy which can be coupled with chemotherapy and radiotherapy. Although this method does not eliminate the whole tumor, but it can keep the tumor size in a given state by preventing the formation of new blood vessels. In this paper, a novel model-free method based on reinforcement learning (RL) framework is used to design a closed-loop control of anti-angiogenic drug dosing administration.
Methods: A Q-learning algorithm is developed for the drug dosing closed-loop control. This controller is designed using two different values of the maximum drug dosage to reduce the tumor volume up to a desired value. The mathematical model of tumor growth under anti-angiogenic inhibitor is used to simulate a real patient.
Results: The effectiveness of the proposed method is shown through in silico simulation and its robustness to patient parameters variation is demonstrated. It is demonstrated that the tumor reaches its minimal volume in 84 days with maximum drug inlet of 30 mg/kg/day. Also, it is shown that the designed controller is robust with respect to ± 20% of tumor growth parameters changes.
Conclusion: The proposed closed-loop reinforcement learning-based controller for cancer treatment using anti-angiogenic inhibitor provides an effective and novel result such that with a clinically valid and safe dosage of drug, the volume reduces up to 1mm3 in a reasonable short period compared to the literature.
Project description:Ovarian cancer is a highly aggressive female tract cancer. Anti-VEGF therapy is widely used for the treatment of ovarian cancer, however it has shown moderate impact on patient prognosis. The elucidation of mechanism of drug resistance is highly beneficial for the advances in ovarian cancer treatment. Here, we investigated gene expression profile of HM-1 tumor which were treated with or without anti-VEGF antibody (B20-4.1.1) using transcriptome array to identify special molecular features of ovarian tumor after anti-VEGF treatment.
Project description:We have defined the mechanism of action of lurbinectedin, a marine-derived drug exhibiting a potent anti-tumorigenic activity across several cancer cell lines and tumor xenografts. This drug currently undergoing clinical evaluation in ovarian, breast and small-cell lung cancer patients inhibits the transcription process through (1) its binding to CG rich sequences, mainly located around the promoter of protein coding genes; (2) the irreversible stalling of elongating RNA polymerase II (Pol II) on the DNA template and its specific degradation by the ubiquitin/proteasome machinery and (3) the generation of DNA breaks. The finding that inhibition of Pol II phosphorylation prevents its degradation and the formation of DNA breaks after drug treatment underscores the connection between transcription elongation and DNA repair. Our results not only help to better understand the high specificity of this drug in cancer therapy but also improve our understanding of an important transcription regulation mechanism.
Project description:Isocitrate Dehydrogenase 1 (IDH1) is the most commonly mutated metabolic gene across human cancers. Mutant IDH1 (mIDH1) generates the oncometabolite (R)-2-hydroxyglutarate, disrupting enzymes involved in epigenetics and other processes. A hallmark of IDH1-mutant solid tumors is T cell exclusion, whereas mIDH1 inhibition in preclinical models restores anti-tumor immunity. Here, we define a cell-autonomous mechanism of mIDH1-driven immune evasion. IDH1-mutant solid tumors show striking, selective hypermethylation and silencing of the cytoplasmic dsDNA sensor, CGAS, compromising innate immune signaling. mIDH1 inhibition restores DNA demethylation, derepressing CGAS and transposable element (TE) subclasses. dsDNA produced by TE-reverse transcriptase activates cGAS, triggering viral mimicry and stimulating anti-tumor immunity. Thus, we demonstrate that mIDH1 epigenetically suppresses innate immunity and link endogenous reverse transcriptase activity to the mechanism of action of an FDA-approved oncology drug.
Project description:Cell lines have been used for drug discovery as useful models of cancers; however, they do not recapitulate cancers faithfully, especially in the points of rapid growth rate and microenvironment independency. Consequently, the majority of conventional anti-cancer drugs are less sensitive to slow growing cells and do not target microenvironmental support, although most primary cancer cells grow slower than cell lines and depend on microenvironmental support. Here, we developed a novel high throughput drug screening system using patient-derived xenograft (PDX) cells of lymphoma that maintained primary cancer cell phenotype more than cell lines. The library containing 2613 known pharmacologically active substance and off-patent drugs were screened by this system. We could find many compounds showing higher cytotoxicity than conventional anti-tumor drugs. Especially, pyruvinium pamoate showed the highest activity, and its strong anti-tumor effect was confirmed also in vivo. We extensively investigated its mechanism of action and found that it inhibited glutathione supply from stromal cells to lymphoma cells, implying the importance of the stromal protection from ox 1 idative stress for lymphoma cell survival and a new therapeutic strategy for lymphoma. Our system introduces a primary cancer cell phenotype into cell-based phenotype screening and sheds new light on anti-cancer drug development. Global gene expression profiles of PDX cells showed high similarity to those of original primary cells. The correlation coefficient of gene expression profiles between PDX cells and the originalprimary cells was 0.814-0.890.
Project description:To investigate the anti-tumor effect of Rosiglitazone+Trametinib in basal bladder cancer, we tested the drug combination in BBN-induced basal tumor mouse model in vivo and BBN-derived tumor cell line BBN963 in vitro.
Project description:Drug target identification is a critical step towards the understanding of the mechanism of action of a drug, which will help to improve the current therapeutic regime and to expand the drug’s therapeutic potential. However, current in vitro affinity chromatography-based and in vivo activity- based protein profiling (ABPP) approaches generally face difficulties discriminating specific drug targets from non-specific ones. Here we describe a novel approach combining isobaric tag for relative and absolute quantitation (iTRAQ) with Clickable ABPP, named ICABPP, to specifically and comprehensively identify the protein targets of andrographolide (Andro), a natural product with known anti-inflammation and anti-cancer effects, in live cancer cells. We identified a spectrum of specific targets of Andro, which furthered our understanding of the mechanism of action of the drug. We found that Andro has a potential novel application as the tumor metastasis inhibitor, which was validated through cell migration and invasion assays. Moreover, we have unveiled the target binding mechanism of Andro with a combination of drug analogue synthesis, protein engineering and mass spectrometry-based approaches and determined the drug-binding sites of two protein targets, NF-kappaB and actin.