Clinical and pathological staging of the cancer at the nanoscale

MODEC: an unsupervised clustering method integrating omics data for identifying cancer subtypes

The identification of cancer subtypes can help researchers understand hidden genomic mechanisms, enhance diagnostic accuracy and improve clinical treatments. With the development of high-throughput techniques, researchers can access large amounts of data from multiple sources. Because of the high dimensionality and complexity of multiomics and clinical data, research into the integration of multiomics data is needed, and developing effective tools for such purposes remains a challenge for researchers. In this work, we proposed an entirely unsupervised clustering method without harnessing any prior knowledge (MODEC). We used manifold optimization and deep-learning techniques to integrate multiomics data for the identification of cancer subtypes and the analysis of significant clinical variables. Since there is nonlinearity in the gene-level datasets, we used manifold optimization methodology to extract essential information from the original omics data to obtain a low-dimensional latent subspace. Then, MODEC uses a deep learning-based clustering module to iteratively define cluster centroids and assign cluster labels to each sample by minimizing the Kullback-Leibler divergence loss. MODEC was applied to six public cancer datasets from The Cancer Genome Atlas database and outperformed eight competing methods in terms of the accuracy and reliability of the subtyping results. MODEC was extremely competitive in the identification of survival patterns and significant clinical features, which could help doctors monitor disease progression and provide more suitable treatment strategies.

Predicting Effectiveness of Imatinib Mesylate in Tumors Expressing Platelet-Derived Growth Factors (PDGF-AA, PDGF-BB), Stem Cell Factor Ligands and Their Respective Receptors (PDGFR-α, PDGFR-β, and c-kit)

INTRODUCTION

This research aims to optimize and predict the effectiveness of imatinib mesylate (imatinib) in tumors expressing platelet-derived growth factors (PDGF-AA, BB), kit/stem cell factor (SCF) ligands and their respective receptors (PDGFR-α, PDGFR-β, and c-kit).

MATERIAL AND METHODS

Samples of normal primary human T cells were incubated with graded concentrations of 1-5 μM imatinib. The energy yield by imatinib doses in those samples was identified in H-thymidine proliferation assay as described before in earlier studies. Tumor models of human pancreatic adenocarcinoma L3.6pl (PDGFAA/PDGFR-α-positive and KIT-negative), human male gonad Leydig tumor cells MA10 (PDGF-AA/PDGFR-α- positive and KIT-positive), human small-cell lung cancer [H209 (KIT-positive), NCI-H526 (PDGFR β-positive and KIT-positive), and NCI-H82 (PDGFR β-positive and KIT-negative)], and human neuroblastoma SMS-KCNR (PDGF-BB/PDGFR-β-positive and KIT-positive) in athymic nude mice were used. The antitumor activity of different doses of imatinib in different regimens in those xenografts was predicted as described before in earlier studies.

RESULTS

The energy yield by drug doses was perfectly logarithmic correlated (r = 1) with the drug dose. An efficient dose-energy model with perfect fit (R = 1) estimating the energy yield by imatinib doses has been established to administer the personalized dose. Predictions for the antitumor activity of imatinib in those xenografts using the dose-energy model and the histologic grade of the control animals were 100 % identical to those actually induced.

CONCLUSION

The effect of imatinib is transient and reversible, reduces tyrosine phosphorylation of tumor-derived PDGFR-α, PDGFR-β, and c-kit without affecting their levels of expression. A resumption of tumor growth nearly identical to the growth prior to therapy should be expected whenever the treatment is stopped. Tumors of PDGF-AA/PDGFR-α exhibit significant resistance to imatinib which requires administering imatinib three times a day, whereas resistance of tumors of PDGF-BB/PDGFR-β or KIT-positive is relatively lower which requires administering imatinib two times a day only to produce an actual inhibition 100 % identical to that predicted for tumor growth.

Data to establish the optimal standard regimen and predicting the response to docetaxel therapy

This paper contains data to establish the optimal standard regimen and predicting the response to docetaxel therapy (Moawad, 2014) [1]. Docetaxel has been in use for over a decade without demonstrating data indicates a predictable response in the treatment of cancer. Data of puzzling response to docetaxel therapy was due to its cell cycle specific effect. Although several administered schedules were investigated, the relative therapeutic advantage of high versus low doses has not been identified yet. Also the antitumor target of docetaxel has not yet been identified to optimize therapy by predicting the response of patients prior to therapy to provide a protection against treatment failure. In the present paper, we demonstrate the data used to optimize docetaxel therapy and investigate the possibility of predicting for the first time the antitumor target of docetaxel.

Optimal standard regimen and predicting response to docetaxel therapy

The purpose of this research is optimizing and predicting the potent activity of docetaxel through an efficient regimen to settle down a new protocol for the treatment of cancer. Effectiveness of docetaxel was examined in vivo in several mouse models engrafted either subcutaneously or intravenously with several types of cell lines. The effects of 147-5040mg/L of docetaxel in treatments of different regimens in those xenograft growths were monitored and quantified to identify energy of those doses as described before in earlier studies. Mock processes were performed on untreated groups of mice for controls. Docetaxel had significant influence on all sizes of treated tumors compared to the control animals. The longer the induced tumor doubling time intraday to more than half the time period from the start of therapy to the time of delivery of the dose, the higher the energy of docetaxel doses and hence the effectiveness of the treatment and vice versa. The energy yield by drug doses in optimal standard regimens was perfectly power correlated (r=1) with the drug dose. An efficient dose-energy model with a perfect fit (R(2)=1) estimating the energy yield by docetaxel doses in optimal standard regimens has been established to administer the personalized dose. Administration of docetaxel doses should be patient-specific and sufficient for the suggested regimen. Time periods from the start of therapy to the time of dose delivery of the efficient regimen are shorter than twice the tumor doubling time intraday on time of dose delivery. Patients with tumors of lower mitotic index may particularly benefit more from optimal standard regimens, whereas metronomic regimens would be more efficient in patients with tumors of higher mitotic index that need lower doses of docetaxel.

Identifying the optimal dose of ritonavir in the treatment of malignancies

Identifying the optimal dose of ritonavir therapy overcomes the chemical resistance may exhibit in some cases due to poor prognosis of imprecise staging. Dose modeling was performed by analyzing previously published data of ritonavir cancer growth inhibition in vitro and in vivo. In-vitro 3H-Thymidine-based cell proliferation assay was performed on samples of the GL15 cell line incubated with 0, 1, 10 and 100 μ M of ritonavir. Proliferation inhibition was quantified to identify energy of the used doses as described before in earlier studies. Models involving in-vivo growth of established breast cancer tumor (MDA-MB-231), KSIMM tumor and EL4-T cell thymomas in mice were used. The effects of 40 mg/kg/day for 52 days, 30 mg/kg/day for 15 days and 8.8 mg/mouse/day for about 1 week of ritonavir in those xenograft growths respectively were monitored and quantified to identify energy of those doses as described before in earlier studies. Ritonavir demonstrated an in-vitro reduction in proliferation rate in dose dependent manner. The energy of the in-vitro influences following ritonavir therapy were perfectly correlated (r = 1) with ritonavir dose, allowed to establish an efficient energy-model with a perfect fit (R2=1) describes the energy yield by ritonavir doses, enables to administer the appropriate dose. Ritonavir had also a significant influence in-vivo on all sizes of treated tumors compared to the control animals such that the energy yield by the administered drug as derived from the energy-model was 100% identical to the induced influence in tumor energy. The in-vitro determination of inhibition to proliferation by ritonavir doses is useful to characterize the response of cancer to ritonavir therapy targeting patient-personalized cancer medicine. The molecular method of response determination by 3H-TDR incorporation and ritonavir dose-energy model are reliable to avoid chemo-resistance by identifying the optimal dosing regimens and schedules prior therapy allowing the use of much lower dose of ritonavir and thus decreases the drug side effects and risks of relapse.

Induction of multiple sclerosis and response to tyrosine kinase inhibitors

The goal of this work is to determine the role of the autoimmune cells in multiple sclerosis (MS) induction and the immunomodulatory mechanism of therapy with tyrosine kinase inhibitors (TKIs) in MS attenuation. Samples (5 × 10(5) cells per well) of C6 and primary rat astrocytes were stimulated with 10 ng/mL of platelet-derived growth factor (PDGFbb) as a positive control forming a mouse model of MS. PDGFbb was added to the astrocytes in the absence or presence of 0.1 and 1 μM of imatinib. Proliferation of C6 and primary rat astrocytes samples were assessed for samples staging by the addition of 1 μCi of (3)H-thymidine per well. Samples of RAW 264.7 cells were stimulated for 48 h with 10 ng/mL of PDGFbb in the absence or presence of 0.1 and 1 μM of sorafenib. Tumour necrotic factor (TNF) levels in culture supernatants from RAW 264.7 cells were measured by ELISA. The histologic grade (HG) and the level of TNF of the mouse model of MS was 1/5 and 5 times respectively of those in the control one to clarify that MS induction is due to a major decrease in HG inversely proportional to the accompanied increase in TNF level perpetuating local inflammation and demyelination in MS lesion. The addition of 0.1 and 1 μM doses of imatinib increased HG of the mouse model of MS by 6 and 11 times respectively while 0.1 and 1 μM doses of sorafenib decreased TNF level to be 1/2 and 1/5 of that in the mouse model of MS respectively restoring normal rate of TNF level of normal lesion to show that HGand TNF level would be strongly inversely correlated (r = -0.99) in attenuating MS effectively by TKIs therapy but not in an inverse proportion as in MS induction.

Administering the Optimum Dose of l-Arginine in Regional Tumor Therapy

The purpose of this study is optimizing the l-arginine (l-Arg) doses on the basis of chemical structure in regional accessible tumor therapy to settle down a new protocol for the treatment of cancer. (3)H-thymidine-based cell proliferation assay was performed in vitro on tumor cell lines of fibrosarcoma (FS), lymphosarcoma-ascitic and on normal cell line of NIH 3T3 after treatment with different concentrations of l-Arg in phosphate buffered saline (PBS). The cultures were harvested after 22 h and the incorporated radioactivity was counted to identify their histologic grades as described in earlier studies. In vivo therapy of murine tumors was conducted where FS cells injected subcutaneously at ventro-lateral position of mice. Various drug delivery schedules were injected into the centre of tumor base, once a day for 4 days. Tumor diameter and survivals were monitored where the day of sacrifice was considered for monitoring the survival period. By identifying the histologic grades of the treated cultures in vitro and in vivo by different concentrations of l-Arg, the corresponding energy of such concentrations were determined. An efficient model with a good fit (R(2) = 0.98) was established to describe the energy yield by l-Arg dose. The equivalence between the tumor histologic grade and energy of the l-Arg dose delivered in saline (PBS) environment is the optimum condition for regional tumor therapy achieves higher survival rate. The selective cytotoxicity to tumor cells with minimal damage to normal cells by l-Arg due to its chemical structure suggests to be considered the most promising drug for regional therapy of the accessible tumors like breast cancers of early stage with no distant metastasis.

Optimizing bioethanol production by regulating yeast growth energy

The goal of this work is to optimize production of bio-ethanol by fermentation through regulating yeast growth energy (YGE), and provide the mechanism of ethanol production from food-waste leachate (FWL) using yeast (S. cerevisiae) as inoculums to be predictable and controllable. The wide range of reduced sugar concentration (RSC) which is commonly administered from low (35 g per liter) to very high (100 g per liter) is responsible for costs increasing besides risks of FWL contamination and death of yeast cells. A mathematical model is presented to describe yeast growth energy (YGE) due to RSC doses along with predicting the amounts of ethanol yield by each dose to identify the optimum one. Simulations of the presented model showed that YGE, energy intake (EI), and their produced ethanol energy (PEE) are always balanced during fermentation process according to the law of conservation of energy. For a better fermentation rate in a continuous process and a large-scale production; YGE should be less than half of EI and more than its quarter (i.e. [Formula: see text]) which keeps the residual energy less than YGE to avoid risks of osmotic stresses or aging of cells allowing the survival of all yeast cells as long as possible to maximize ethanol production and decrease productivity costs.