Quantitative modeling and analysis of drug screening data for personalized cancer medicine

Despite recent progress in the field of molecular medicine, the treatment and cure of complex diseases such as cancer remains a challenge. Development of resistance to first-line chemotherapy is a common cause of current anticancer treatment failure. To deal with this problem, the personalized medicine (PM) approach has been adapted toward more targeted cancer research and management. The PM approach is based on each patient s genetic, epigenetic and drug response profiling, which is used to design the best treatment option for the given patient. As the PM approach is increasingly being adopted in clinical practice, there is an urgent need for computational models and data mining methods that allow fast processing and analysis of the massive relevant profiling datasets. High-throughput drug screening enables systematic profiling of cellular responses to a wide collection of oncology compounds and their combinations, hence providing an unbiased strategy for personalized drug treatment selection. However, screening experiments with patient-derived cell samples often results in high-dimensional data matrices, with inherent sources of noise. This complicates many downstream analyses, such as the detection of differential drug activity or understanding the mechanisms behind drug sensitivity and resistance in a given patient. To meet these challenges, a computational pipeline for drug response profiling was developed in this thesis. The pipeline was based on a novel metric to quantify drug response, called the drug sensitivity score (DSS). Further, by combining the normalized drug response profile of each cancer sample with a global drug-target interaction network, a target addiction score (TAS) was developed to de-convolute the selective protein targets and obtain knowledge on their functional importance. Finally, delta scoring was developed to quantify drug combination effects and to address the problem of the clonal evolution of cancer, which often leads to resistance to mono therapies. This novel computational pipeline improves understanding of cancer development and translates compound activities into informed treatment choices for clinicians. As exemplified in two case studies of adult acute myeloid leukemia (AML) and adult granulosa cell tumor (AGCT), the models developed here have the potential to significantly contribute to the effective analysis of data from individual cancer patients and from pan-cancer cell line panels. Hence, these models will play a substantial role in future personalized cancer treatment strategies and the selection of effective treatment options for individual cancer patients.N

Searching for Drug Synergy in Complex Dose–Response Landscapes Using an Interaction Potency Model

Corrigendum: Comput. Struct. Biotechnol. J. 2017, vol. 15, 387-387. DOI: 10.1016/j.csbj.2017.07.003Peer reviewe

Field Experiments on Jute Soil Stabilisers

The Central Road Research Institute has been constantly working in the area of geotextiles since early 1980s. A number of laboratory studies on evaluation of geotextiles for their suitability for road application, as well as, actual field trials have been carried out by the Institute. Recently, a project was taken up for the development and promotion of jute based geotextiles for road applications. A number of field trials have been carried out using jute based geotextiles for various applications. The paper presents summary of field experiments carried out to improve the soil behavior, improvement in the stability of road side slopes and the filtration function in fills behind a retaining wall using jute geotextiles

A case control study on s. uric acid and s. creatinine level in pre-eclampsia patients of a tertiary care hospital in Jabalpur district of Central India

Background: Pre-eclampsia is a multisystem disorder of pregnancy which is characterized by hypertension with proteinuria after 20 weeks of gestation in previously normotensive and non proteinuric pregnant women. Pre-eclampsia associated with intrauterine growth retardation, preterm birth, maternal and perinatal death. Serum creatinine and uric acid has been shown to play a significant role in the pathogenesis of the disease and often precede clinical manifestations. This study compares the serum creatinine and uric acid in pre -eclampsia case and normal pregnant women and to assess its role in pre-eclampsia.Methods: 158 patients of which 79 pre-eclampsia (cases) and 79 (controls) were selected randomly and were matched with their gestational age in patient who Attending ANC clinic at Department of Obstretics and Gynecology in March 2016 to August 2017. Lipid profile was estimated by the Randox imola is a compact fully automated clinical chemistry analyser.Results: Authors observed that pre-eclampsia is more common in young age pregnant women with low socioeconomic status with strenuous activities. The mean age was 24.51±3.707 years. The mean serum creatinine and urice acid value is analysed in pre-eclampia cases and compared with control group showing significantly increase (p<0.0001).Conclusions: Young age, nullyparity, low socio economic status specially labour occupation, with derangment of Serum creatinine in pregnant women were found to be more prone to develop pre-eclampsia. Proper history tacking, examination and estimation of serum creatinine and uric acid may be helpful for early diagnosis and management of pre–eclampsia in order to prevent fetal and maternal complications especially in nulliparous women

Can endometrial volume assessment predict the endometrial receptivity on the day of hCG trigger in patients of fresh IVF cycles: a prospective observational study

Background: Objective of present study was to evaluate the role of three dimensional (3D) endometrial volume measurement on the day of hCG trigger in predicting the endometrial receptivity. The present study is a prospective observational study conducted at assisted reproductive centre of a tertiary care hospital.Methods: Endometrial volume was evaluated by three-dimensional ultrasound in 90 patients undergoing first cycle of IVF on hCG trigger day and was correlated with endometrial receptivity.Results: Out of 90 patients studied 12 patients achieved pregnancy. A significant difference was found in mean endometrial volume on hCG trigger day among pregnant (5.33±2.14 cm3) women compared to non-pregnant women (4.17±1.72cm3). Using Receiver operating characteristics (ROC) analysis the cutoff value for endometrial volume on hCG trigger day was 3.50 cm3 corresponding to sensitivity 75% and specificity 37.2%. Conclusions: The endometrial volume on hCG trigger day was significantly higher in pregnant women as compared to non-pregnant

Identification of selective cytotoxic and synthetic lethal drug responses in triple negative breast cancer cells

Background: Triple negative breast cancer (TNBC) is a highly heterogeneous and aggressive type of cancer that lacks effective targeted therapy. Despite detailed molecular profiling, no targeted therapy has been established. Hence, with the aim of gaining deeper understanding of the functional differences of TNBC subtypes and how that may relate to potential novel therapeutic strategies, we studied comprehensive anticancer-agent responses among a panel of TNBC cell lines. Method: The responses of 301 approved and investigational oncology compounds were measured in 16 TNBC cell lines applying a functional profiling approach. To go beyond the standard drug viability effect profiling, which has been used in most chemosensitivity studies, we utilized a multiplexed readout for both cell viability and cytotoxicity, allowing us to differentiate between cytostatic and cytotoxic responses. Results: Our approach revealed that most single-agent anti-cancer compounds that showed activity for the viability readout had no or little cytotoxic effects. Major compound classes that exhibited this type of response included anti-mitotics, mTOR, CDK, and metabolic inhibitors, as well as many agents selectively inhibiting oncogene-activated pathways. However, within the broad viability-acting classes of compounds, there were often subsets of cell lines that responded by cell death, suggesting that these cells are particularly vulnerable to the tested substance. In those cases we could identify differential levels of protein markers associated with cytotoxic responses. For example, PAI-1, MAPK phosphatase and Notch-3 levels associated with cytotoxic responses to mitotic and proteasome inhibitors, suggesting that these might serve as markers of response also in clinical settings. Furthermore, the cytotoxicity readout highlighted selective synergistic and synthetic lethal drug combinations that were missed by the cell viability readouts. For instance, the MEK inhibitor trametinib synergized with PARP inhibitors. Similarly, combination of two non-cytotoxic compounds, the rapamycin analog everolimus and an ATP-competitive mTOR inhibitor dactolisib, showed synthetic lethality in several mTOR-addicted cell lines. Conclusions: Taken together, by studying the combination of cytotoxic and cytostatic drug responses, we identified a deeper spectrum of cellular responses both to single agents and combinations that may be highly relevant for identifying precision medicine approaches in TNBC as well as in other types of cancers.Peer reviewe

Breeze : an integrated quality control and data analysis application for high-throughput drug screening

High-throughput screening (HTS) enables systematic testing of thousands of chemical compounds for potential use as investigational and therapeutic agents. HTS experiments are often conducted in multi-well plates that inherently bear technical and experimental sources of error. Thus, HTS data processing requires the use of robust quality control procedures before analysis and interpretation. Here, we have implemented an open-source analysis application, Breeze, an integrated quality control and data analysis application for HTS data. Furthermore, Breeze enables a reliable way to identify individual drug sensitivity and resistance patterns in cell lines or patient-derived samples for functional precision medicine applications. The Breeze application provides a complete solution for data quality assessment, dose-response curve fitting and quantification of the drug responses along with interactive visualization of the results.Peer reviewe

Targeting Apoptosis Pathways With BCL2 and MDM2 Inhibitors in Adult B-cell Acute Lymphoblastic Leukemia : s

In adult patients, the treatment outcome of acute lymphoblastic leukemia (ALL) remains suboptimal. Here, we used an ex vivo drug testing platform and comprehensive molecular profiling to discover new drug candidates for B-ALL. We analyzed sensitivity of 18 primary B-ALL adult patient samples to 64 drugs in a physiological concentration range. Whole-transcriptome sequencing and publicly available expression data were used to examine gene expression biomarkers for observed drug responses. Apoptotic modulators targeting BCL2 and MDM2 were highly effective. Philadelphia chromosome-negative (Ph-) samples were sensitive to both BCL2/BCL-W/BCL-XL-targeting agent navitoclax and BCL2-selective venetoclax, whereas Ph-positive (Ph+) samples were more sensitive to navitoclax. Expression of BCL2 was downregulated and BCL-W and BCL-XL upregulated in Ph+ ALL compared with Ph- samples, providing elucidation for the observed difference in drug responses. A majority of the samples were sensitive to MDM2 inhibitor idasanutlin. The regulatory protein MDM2 suppresses the function of tumor suppressor p53, leading to impaired apoptosis. In B-ALL, the expression of MDM2 was increased compared with other hematological malignancies. In B-ALL cell lines, a combination of BCL2 and MDM2 inhibitor was synergistic. In summary, antiapoptotic proteins including BCL2 and MDM2 comprise promising targets for future drug studies in B-ALL.Peer reviewe

From drug response profiling to target addiction scoring in cancer cell models

Deconvoluting the molecular target signals behind observed drug response phenotypes is an important part of phenotype-based drug discovery and repurposing efforts. We demonstrate here how our network-based deconvolution approach, named target addiction score (TAS), provides insights into the functional importance of druggable protein targets in cell-based drug sensitivity testing experiments. Using cancer cell line profiling data sets, we constructed a functional classification across 107 cancer cell models, based on their common and unique target addiction signatures. The pan-cancer addiction correlations could not be explained by the tissue of origin, and only correlated in part with molecular and genomic signatures of the heterogeneous cancer cells. The TAS-based cancer cell classification was also shown to be robust to drug response data resampling, as well as predictive of the transcriptomic patterns in an independent set of cancer cells that shared similar addiction signatures with the 107 cancers. The critical protein targets identified by the integrated approach were also shown to have clinically relevant mutation frequencies in patients with various cancer subtypes, including not only well-established pan-cancer genes, such as PTEN tumor suppressor, but also a number of targets that are less frequently mutated in specific cancer types, including ABL1 oncoprotein in acute myeloid leukemia. An application to leukemia patient primary cell models demonstrated how the target deconvolution approach offers functional insights into patient-specific addiction patterns, such as those indicative of their receptor-type tyrosine-protein kinase FLT3 internal tandem duplication (FLT3-ITD) status and co-addiction partners, which may lead to clinically actionable, personalized drug treatment developments. To promote its application to the future drug testing studies, we have made available an open-source implementation of the TAS calculation in the form of a stand-alone R package.Peer reviewe

KIT pathway upregulation predicts dasatinib efficacy in acute myeloid leukemia

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