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Tumor cell lines and cultures are widely used in biomedical research. They are excellent model systems for analysis of oncological mechanisms and understanding the biology of tumor cells. Cell cultures are used to develop and test new anticancer drugs, radiosensitizers and radiotherapy methods. Clinical application of tumor cell cultures is directly related to development of personalized medicine. Using tumor cell culture in a particular patient, physicians can select treatment considering molecular genetic characteristics of patient and tumor. In addition, it is possible to choose the optimal drug or radiotherapy regimen with obvious effectiveness in certain cell culture. This review describes the advantages of such an approach.

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In recent years, mathematical models have developed into an important tool for cancer research, combining quantitative analysis and natural processes. We have focused on Chronic Lymphocytic Leukemia (CLL), since it is one of the most common adult leukemias, which remains incurable. As the first step toward the mathematical prediction of in vivo drug efficacy, we first found that logistic growth best described the proliferation of fluorescently labeled murine A20 leukemic cells injected in immunocompetent Balb/c mice. Then, we tested the cytotoxic efficacy of Ibrutinib (Ibr) and Cytarabine (Cyt) in A20-bearing mice. The results afforded calculation of the killing rate of the A20 cells as a function of therapy. The experimental data were compared with the simulation model to validate the latter's applicability. On the basis of these results, we developed a new ordinary differential equations (ODEs) model and provided its sensitivity and stability analysis. There was excellent accordance between numerical simulations of the model and results from in vivo experiments. We found that simulations of our model could predict that the combination of Cyt and Ibr would lead to approximately 95% killing of A20 cells. In its current format, the model can be used as a tool for mathematical prediction of in vivo drug efficacy, and could form the basis of software for prediction of personalized chemotherapy.

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Background: Addition of oxaliplatin to adjuvant 5-FU has significantly improved the disease-free survival and served as the first line adjuvant chemotherapy in advanced colorectal cancer (CRC) patients. However, a fraction of patients remains refractory to oxaliplatin-based treatment. It is urgent to establish a preclinical platform to predict the responsiveness toward oxaliplatin in CRC patients as well as to improve the efficacy in the resistant patients. Methods: A living biobank of organoid lines were established from advanced CRC patients. Oxaliplatin sensitivity was assessed in patient-derived tumor organoids (PDOs) in vitro and in PDO-xenografted tumors in mice. Based on in vitro oxaliplatin IC50 values, PDOs were classified into either oxaliplatin-resistant (OR) or oxaliplatin-sensitive (OS) PDOs. The outcomes of patients undergone oxaliplatin-based treatment was followed. RNA-sequencing and bioinformatics tools were performed for molecular profiling of OR and OS PDOs. Oxaliplatin response signatures were submitted to Connectivity Map algorithm to identify perturbagens that may antagonize oxaliplatin resistance. Results: Oxaliplatin sensitivity in PDOs was shown to correlate to oxaliplatin-mediated inhibition on PDO xenograft tumors in mice, and parallelled clinical outcomes of CRC patients who received FOLFOX treatment. Molecular profiling of transcriptomes revealed oxaliplatin-resistant and -sensitive PDOs as two separate entities, each being characterized with distinct hallmarks and gene sets. Using Leave-One-Out Cross Validation algorithm and Logistic Regression model, 18 gene signatures were identified as predictive biomarkers for oxaliplatin response. Candidate drugs identified by oxaliplatin response signature-based strategies, including inhibitors targeting c-ABL and Notch pathway, DNA/RNA synthesis inhibitors, and HDAC inhibitors, were demonstrated to potently and effectively increase oxaliplatin sensitivity in the resistant PDOs. Conclusions: PDOs are useful in informing decision-making on oxaliplatin-based chemotherapy and in designing personalized chemotherapy in CRC patients.

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While combination of rituximab, cyclophosphamide, doxorubicin, vincristine and prednisone (R-CHOP) cures most patients with diffuse large B-cell lymphoma (DLBCL), those with high-risk international prognostic index disease have inferior survival. Enzastaurin as a potent inhibitor of PKC-ß and PI3K/AKT pathway suppressor has been tested in many clinical trials including two key studies in DLBCL: Phase III maintenance study (Preventing Relapse in Lymphoma Using Daily Enzastaurin [PRELUDE]) and a first-line Phase II study (S028). DNA extracted from PRELUDE patients' blood samples was retrospectively genotyped identifying a novel genetic biomarker, DGM1 that showed high correlation with response to enzastaurin. A similar finding observed in the S028 study suggested that addition of enzastaurin to R-CHOP may significantly improve outcomes as frontline therapy for high-risk DGM1 positive DLBCL patients. ENGINE is a global, multicenter, placebo-controlled and randomized study to compare the effect of R-CHOP/enzastaurin as frontline treatment in high-risk DLBCL patients. The primary end point for this study is overall survival in patients who are DGM1 positive. Clinical Trial Registration Identifier: NCT03263026.

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Personalized chemotherapy with the use of biomarkers helps to maximize clinical efficiency. Therefore, the present study aimed to identify a potential method for identifying biomarkers in esophageal cancer. A total of 49 freshly resected tumor tissues and 72 paraffin-embedded specimens from patients with esophageal cancer were obtained. mRNA expression levels of ERCC1, BRCA1, TUBB3, FBW7, RRM1, MDM2, TS and TOP1 were measured quantitative reverse transcription polymerase chain reaction (RT-qPCR). In vitro chemosensitivity to cisplatin, docetaxel, gemcitabine, etoposide, fluorouracil and irinotecan were tested using histoculture drug response assay (HDRA). BRCA1 mRNA levels were positively correlated with resistance to cisplatin (P=0.027) and sensitivity to docetaxel (P=0.002). TS mRNA levels were inversely correlated with fluorouracil sensitivity (P=0.044), and TOP1 mRNA expression was positively correlated with irinotecan sensitivity (P=0.008). In addition, high BRCA1 mRNA levels correlated with decreased median overall survival (mOS; P<0.001) and response rate (RR; P=0.002) in cisplatin-fluorouracil chemotherapy group and also correlated with increased mOS (P<0.001) and RR (P=0.023) in docetaxel-fluorouracil chemotherapy group. Overall, these results suggested that HDRA combined with RT-qPCR may serve as an effective method for screening biomarkers in personalized chemotherapy for esophageal cancer.

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Background: It is expected that about 65,000 new patients will be diagnosed with head and neck cancer in 2017 in the United States. Patients with recurrent or advanced or metastatic head and neck do not have good survival due to aggressive and recurrent nature of this cancer. Moreover, cumulative and residual toxicities from previous and ongoing treatments significantly impede quality of remaining part of their life. Currently available chemotherapeutic regimens for this group are derived from the treatments used for the potentially curable disease. These regimens and associated toxicity are obviously not the best matches for the treatment with palliative intent. We here present a retrospective study where we used dose-adjusted chemotherapy specifically for palliative treatment this sub-group of head and neck cancer patients. Methods: Study population was identified from the University of Florida, and IRB approval was obtained. We used currently available and approved chemotherapeutic agents (including Taxols, Platins, 5-Fluorouracil and Epidermal Growth Factor Receptor inhibitors) for treatment of head and neck cancer but dose-adjusted at approximate 50% dose of currently recommended doses. We then gave personalized doses for a prolonged period by titrating doses based on response and tolerability of each patient. Data was collected for treatment, response, side effects, and outcomes. KM analysis was performed for survival data. Results: Total of 32 patients were included in this study with a median age of 65.2 years and a median follow-up of 10.1 months. 62.5% (n = 20) had locally advanced disease and rest had metastatic disease. 37.5% (n = 12) had new disease while rest had recurrent cancer. Of 32 patients, 14 patients received TPF based while 18 patients received PFE based chemotherapy. Total of 270 chemotherapy cycles were delivered among these 32 patients. They received a median of 9 cycles (range 3­14) over a median of 6.2 months (range 1.8­21.1). With this treatment approach, we noted median progression-free survival of 14.0 months and median overall survival of 15.7 months. Notable grade 3 toxicities were generalized fatigue in 12.5% (n = 4), nausea/vomiting in 6.3% (n = 2), diarrhea in in 6.3% (n = 2), mouth soreness in 6.3% (n = 2), rash in 3.1% (n = 1), neutropenia in 18% (n = 6) and anemia in 15.6% (n = 5) while notable grade 4 toxicities were neutropenia and anaphylaxis in 3.1% (n = 1) patient each (AU)

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Personalized chemotherapy is based on the specific genetic profile of individual patients and is replacing the traditional 'one size fits all' medicine. Breast cancer 1 (BRCA1) plays a central role in the chemotherapy-induced DNA damage response. It has been repeatedly demonstrated that BRCA1 mRNA levels were negatively associated with cisplatin sensitivity, but positively associated with docetaxel sensitivity in patients with gastric cancer in experimental and clinical studies. This feature leads to customized chemotherapy based on the BRCA1 mRNA expression level and results in a high efficacy of treatment. The present study describes the case of a 77-year-old patient with metastatic gastric cancer who was treated with personalized chemotherapy based on quantitative BRCA1 mRNA expression level. This study and the available literature data suggest that the expression level of BRCA1 mRNA is dynamic to BRCA1-based chemotherapy. More importantly, de novo assessment of BRCA1 status is a preferable option for ciscisplatin- or docetaxel-resistant patients, since the expression levels of BRCA1 mRNA in certain patients may alter significantly following treatment. Therefore, BRCA1 expression should be assessed for predicting differential chemosensitivity and tailoring chemotherapy in gastric cancer.

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Various predictive diagnostic tests are highly demanded to guide optimal treatments for individual patients, as individual patients with the same disease such as cancer frequently exhibit dramatically different therapeutic responses to multiple available treatment options. A large number of clinical trials have thus been performed to test the predictive ability and utility of various therapeutic biomarker tests. However, in these trial designs the conventional optimization criteria such as positive predictive value or negative predictive value cannot reflect each patient's true chance of success associated with continuous predictive biomarker scores. We have developed a novel statistical concept, point success rate (PSR), to overcome deficiencies in these conventional methods for optimizing biomarker-based clinical trials. We demonstrate statistical superiority as well as clinical improvement by a PSR-based treatment selection both with simulated and breast cancer patient data.

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Irinotecan hydrochloride is a camptothecin derivative that exerts antitumor activity against a variety of tumors. SN-38 produced in the body by carboxylesterase is the active metabolite of irinotecan. After irinotecan was introduced for the treatment of metastatic colorectal cancer (CRC) at the end of the last century, survival has improved dramatically. Irinotecan is now combined with 5-fluorouracil, oxaliplatin and several molecularly-targeted anticancer drugs, resulting in the extension of overall survival to longer than 30 mo. Severe, occasionally life-threatening toxicity occurs sporadically, even in patients in relatively good condition who have a low risk of chemotherapy-induced toxicity, often causing the failure of irinotecan-based chemotherapy. Clinical pharmacological studies have revealed that such severe toxicity is related to exposure to SN-38 and genetic polymorphisms in UDP-glucuronosyltransferase 1A1 gene. The large inter- and intra-patient variability in systemic exposure to SN-38 is determined not only by genetic factors but also by physiological and environmental factors. This review first summarizes the roles of irinotecan in chemotherapy for metastatic CRC and then discusses the optimal dosing of irinotecan based on the aforementioned factors affecting systemic exposure to SN-38, with the ultimate goal of achieving personalized irinotecan-based chemotherapy.

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Paclitaxel (PTX) is a widely used chemotherapeutic drug effective against numerous cancers. To elucidate cellular pathways targeted by PTX and identify novel mechanisms of PTX resistance, we used a SILAC based quantitative proteomic approach to evaluate global changes of cellular protein abundance in HeLa cells. We identified 347 proteins involved in a number of biological processes including spindle assembly, mitotic exit, and extracellular adhesion whose abundance changes upon PTX treatment. Notably, the tumor suppressor PDCD4 involved in translation suppression was down-regulated by PTX. We demonstrated that PDCD4 is a cell-cycle regulated protein and that changes in its abundance are sufficient to alter PTX sensitivity in multiple human cancer cell lines. Immunoprecipitation of PDCD4-RNA complexes and RT-PCR revealed that PDCD4 mediated PTX sensitivity acts through its interaction with mRNA of UBE2S, a ubiquitin K11 linkage conjugating enzyme critical for mitotic exit. Lastly, high levels of PDCD4 in lung cancer tissues are positively correlated with the longer overall survival time of the examined lung cancer patients with PTX involved adjuvant therapy. Therefore, our proteomic screen for paclitaxel targets not only provided novel insight into the cellular resistance to paclitaxel via the PDCD4-mitotic exit regulation axis, but also offered a predictive biomarker for paclitaxel-based personalized chemotherapy in the treatment of lung cancer.

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We have developed novel phenotypic fluorescent three-dimensional co-culture platforms that efficiently and economically screen anti-angiogenic/anti-metastatic drugs on a high-throughput scale. Individual cell populations can be identified and isolated for protein/gene expression profiling studies and cellular movement/interactions can be tracked by time-lapse cinematography. More importantly, these platforms closely parallel the in vivo angiogenic and metastatic outcomes of a given tumor xenograft in the nude mouse model but, unlike in vivo models, our co-culture platforms produce comparable results in five to nine days. Potentially, by incorporating cancer patient biopsies, the co-culture platforms should greatly improve the effectiveness and efficiency of personalized chemotherapy.