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One of the major obstacles to sustained cancer treatment effectiveness is the development of medication resistance. Current therapies that block proteins associated with cancer progression often lose their efficacy due to acquired drug resistance, which is frequently driven by mutated or overexpressed protein targets. Proteolysis-targeting chimeras (PROTACs) offer an alternative therapeutic strategy by hijacking the cell's ubiquitin-proteasome system to degrade disease-causing proteins, presenting several potential advantages. Over the past few years, PROTACs have been developed to target various cancer-related proteins, offering new treatment options for patients with previously untreatable malignancies and serving as a foundation for next-generation therapeutics. One of the notable benefits of PROTACs is their ability to overcome certain resistance mechanisms that limit the effectiveness of conventional targeted therapies, as shown in several recent studies. Additionally, research teams are investigating how PROTACs can selectively degrade mutant proteins responsible for resistance to first-line cancer therapies. In the pursuit of novel and effective treatments, this review highlights recent advancements in the development of PROTACs aimed at overcoming cancer resistance. When it comes to drug design, heterocyclic scaffolds often serve as a foundational framework, offering opportunities for modification and optimization of novel molecules. Researchers are similarly exploring various heterocyclic derivatives as "warheads" in the design of PROTACs has been instrumental in pushing the boundaries of targeted protein degradation. As warheads, these heterocyclic compounds are responsible for recognizing and binding to the target protein, which ultimately leads to its degradation via the ubiquitin-proteasome system. This study aims to provide a comprehensive overview of cutting-edge strategies in PROTAC design, offering detailed insights into key concepts and methodologies for creating effective PROTACs. Special emphasis is placed on structure-based rational design, the development of novel warheads, and their critical in influencing biological activity., Competing Interests: Declaration of competing interest On behalf of all co-authors, I declare that we do not have any competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Masson SAS. All rights reserved.)

Aurora kinases (AURKs)-a family of serine/threonine protein kinases consisting of AURK-A, AURK-B, and AURK-C, are critical regulators of chromosomal segregation, centrosome maturation, and cytokinesis during the cell cycle. Each kinase is activated via phosphorylation at unique threonine residues: Thr288 (AURK-A), Thr232 (AURK-B), and Thr195 (AURK-C). Activation of AURK-A and AURK-B through phosphorylation triggers a series of downstream signaling pathways, including RalA, NF-κB, p53, PLK1, BRCA1/BRCA2, H2AX, and Kif2C, as well as multiple transmembrane kinase receptors. Dysregulation of these pathways has been implicated in cancer development and progression, positioning AURKs as pivotal targets for anticancer drug research. Inhibition of AURKs has demonstrated significant efficacy in tumor growth suppression and induction of cancer cell death, thereby focusing recent research on the development of potent AURK inhibitors. This review provides an in-depth exploration of AURK inhibitors, discussing their biological activities, structure-activity relationships, selectivity profiles, and mechanisms of action. Notably, compounds 6, 27, and 16 exhibit potent AURK-A inhibition with IC 50 values of 1.7 nM, 11.83 nM, and 15 nM, respectively. Similarly, compounds 28, 16, and 7 demonstrate strong AURK-B inhibitory activity, with IC 50 values of 10.5 nM, 12 nM, and 14.09 nM, respectively. This comprehensive overview aims to support medicinal chemists in developing more potent, selective, and safe AURK inhibitors as potential anticancer therapeutics., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Evading the cellular apoptosis mechanism by modulating multiple pathways poses a sturdy barrier to effective chemotherapy. Cancer cell adeptly resists the apoptosis signaling pathway by regulating anti and pro-apoptotic proteins to escape cell death. Nevertheless, bypassing the apoptotic pathway through necroptosis, an alternative programmed cell death process, maybe a potential therapeutic modality for apoptosis-resistant cells. However, synthetic mono-quinoxaline-based intercalator-induced cellular necroptosis as an anti-cancer perspective remains under-explored. To address this concern, we undertook the design and synthesis of quinoxaline-based small molecules (3a-3l). Our approach involved enhancing the π-surface of the mandatory benzyl moiety to augment its ability to induce DNA structural alteration via intercalation, thereby promoting cytotoxicity across various cancer cell lines (HCT116, HT-29, and HeLa). Notably, the potent compound 3a demonstrated the capacity to induce DNA damage in cancer cells, leading to the induction of ZBP1-mediated necroptosis in the RIP3-expressed cell line (HT-29), where Z-VAD effectively blocked apoptosis-mediated cell death. Interestingly, we observed that 3a induced RIP3-driven necroptosis in combination with DNA hypomethylating agents, even in the RIP3-silenced cell lines (HeLa and HCT116). Overall, our synthesized compound 3a emerged as a promising candidate against various cancers, particularly in apoptosis-compromised cells, through the induction of necroptosis., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Masson SAS.)

The surgical resection of solid tumours can be enhanced by fluorescence-guided imaging. However, variable tumour uptake and incomplete clearance of fluorescent dyes reduces the accuracy of distinguishing tumour from normal tissue via conventional fluorescence intensity-based imaging. Here we show that, after systemic injection of the near-infrared dye indocyanine green in patients with various types of solid tumour, the fluorescence lifetime (FLT) of tumour tissue is longer than the FLT of non-cancerous tissue. This tumour-specific shift in FLT can be used to distinguish tumours from normal tissue with an accuracy of over 97% across tumour types, and can be visualized at the cellular level using microscopy and in larger specimens through wide-field imaging. Unlike fluorescence intensity, which depends on imaging-system parameters, tissue depth and the amount of dye taken up by tumours, FLT is a photophysical property that is largely independent of these factors. FLT imaging with indocyanine green may improve the accuracy of cancer surgeries., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

The inhibitory receptor PD-1 suppresses T cell activation by recruiting the phosphatase SHP-2. However, mice with a T-cell-specific deletion of SHP-2 do not have improved antitumor immunity. Here we showed that mice with conditional targeting of SHP-2 in myeloid cells, but not in T cells, had diminished tumor growth. RNA sequencing (RNA-seq) followed by gene set enrichment analysis indicated the presence of polymorphonuclear myeloid-derived suppressor cells and tumor-associated macrophages (TAMs) with enriched gene expression profiles of enhanced differentiation, activation and expression of immunostimulatory molecules. In mice with conditional targeting of PD-1 in myeloid cells, which also displayed diminished tumor growth, TAMs had gene expression profiles enriched for myeloid differentiation, activation and leukocyte-mediated immunity displaying >50% overlap with enriched profiles of SHP-2-deficient TAMs. In bone marrow, GM-CSF induced the phosphorylation of PD-1 and recruitment of PD-1-SHP-2 to the GM-CSF receptor. Deletion of SHP-2 or PD-1 enhanced GM-CSF-mediated phosphorylation of the transcription factors HOXA10 and IRF8, which regulate myeloid differentiation and monocytic-moDC lineage commitment, respectively. Thus, SHP-2 and PD-1-SHP-2 signaling restrained myelocyte differentiation resulting in a myeloid landscape that suppressed antitumor immunity., (© 2022. The Author(s).)

Receptor tyrosine kinase (RTK) regulates multiple pathways, including Mitogenactivated protein kinases (MAPKs), PI3/AKT, JAK/STAT pathway, etc. which has a significant role in the progression and metastasis of tumor. As RTK activation regulates numerous essential bodily processes, including cell proliferation and division, RTK dysregulation has been identified in many types of cancers. Targeting RTK is a significant challenge in cancer due to the abnormal upregulation and downregulation of RTK receptors subfamily EGFR, FGFR, PDGFR, VEGFR, and HGFR in the progression of cancer, which is governed by multiple RTK receptor signalling pathways and impacts treatment response and disease progression. In this review, an extensive focus has been carried out on the normal and abnormal signalling pathways of EGFR, FGFR, PDGFR, VEGFR, and HGFR and their association with cancer initiation and progression. These are explored as potential therapeutic cancer targets and therefore, the inhibitors were evaluated alone and merged with additional therapies in clinical trials aimed at combating global cancer., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Purpose: Fluorescence molecular imaging, using cancer-targeted near infrared (NIR) fluorescent probes, offers the promise of accurate tumor delineation during surgeries and the detection of cancer specific molecular expression in vivo. However, nonspecific probe accumulation in normal tissue results in poor tumor fluorescence contrast, precluding widespread clinical adoption of novel imaging agents. Here we present the first clinical evidence that fluorescence lifetime (FLT) imaging can provide tumor specificity at the cellular level in patients systemically injected with panitumumab-IRDye800CW, an EGFR-targeted NIR fluorescent probe., Experimental Design: We performed wide-field and microscopic FLT imaging of resection specimens from patients injected with panitumumab-IRDye800CW under an FDA directed clinical trial., Results: We show that the FLT within EGFR-overexpressing cancer cells is significantly longer than the FLT of normal tissue, providing high sensitivity (>98%) and specificity (>98%) for tumor versus normal tissue classification, despite the presence of significant nonspecific probe accumulation. We further show microscopic evidence that the mean tissue FLT is spatially correlated (r > 0.85) with tumor-specific EGFR expression in tissue and is consistent across multiple patients. These tumor cell-specific FLT changes can be detected through thick biological tissue, allowing highly specific tumor detection and noninvasive monitoring of tumor EFGR expression in vivo., Conclusions: Our data indicate that FLT imaging is a promising approach for enhancing tumor contrast using an antibody-targeted NIR probe with a proven safety profile in humans, suggesting a strong potential for clinical applications in image guided surgery, cancer diagnostics, and staging., (©2022 American Association for Cancer Research.)

Transcriptional enhanced associate domain (TEAD) is a family of transcription factors that plays a significant role during embryonic developmental processes, and its dysregulation is responsible for tumour progression. TEAD is considered as druggable targets in various diseases, namely cancer, cardiovascular diseases and neurodegenerative disorders. Previous structural studies revealed the importance of the central hydrophobic pocket of TEAD as a potential target for small-molecule inhibitors and demonstrated flufenamic acid (FLU) (a COX-2 enzyme inhibitor) to bind and inhibit TEAD2 functions. However, to date, no drug candidates that bind specifically to TEAD2 with high selectivity and efficacy have been developed or proposed. Within this framework, we present here a case study where we have identified potential TEAD2 inhibitor candidates by integrating multiple computational approaches. Among the candidates, the top two ranked compounds ZINC95969481 (LG1) which is a fused pyrazole derivative and ZINC05203789 (LG2), a fluorene derivative resulted in much favourable binding energy scores than the reference ligand, FLU. The drug likeliness of the best compounds was also evaluated in silico to ensure the bioavailability of these compounds particularly LG1 as compared to FLU thus providing a strong rationale for their development as leads against TEAD. Molecular dynamics simulations results highlighted the role of key residues contributing to favourable interactions in TEAD2-LG1 complex with much favourable interaction and binding free energy values with respect to the reference compound. Altogether, this study provides a starting platform to be more exploited by future experimental research towards the development of inhibitors against TEAD, a persuasive strategy for therapeutic intervention in cancer treatment., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Motivation: Anti-cancer drug sensitivity prediction using deep learning models for individual cell line is a significant challenge in personalized medicine. Recently developed REFINED (REpresentation of Features as Images with NEighborhood Dependencies) CNN (Convolutional Neural Network)-based models have shown promising results in improving drug sensitivity prediction. The primary idea behind REFINED-CNN is representing high dimensional vectors as compact images with spatial correlations that can benefit from CNN architectures. However, the mapping from a high dimensional vector to a compact 2D image depends on the a priori choice of the distance metric and projection scheme with limited empirical procedures guiding these choices., Results: In this article, we consider an ensemble of REFINED-CNN built under different choices of distance metrics and/or projection schemes that can improve upon a single projection based REFINED-CNN model. Results, illustrated using NCI60 and NCI-ALMANAC databases, demonstrate that the ensemble approaches can provide significant improvement in prediction performance as compared to individual models. We also develop the theoretical framework for combining different distance metrics to arrive at a single 2D mapping. Results demonstrated that distance-averaged REFINED-CNN produced comparable performance as obtained from stacking REFINED-CNN ensemble but with significantly lower computational cost., Availability and Implementation: The source code, scripts, and data used in the paper have been deposited in GitHub (https://github.com/omidbazgirTTU/IntegratedREFINED)., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2021. Published by Oxford University Press.)

Loss of mitosis regulation is a common feature of malignant cells that leads to aberrant cell division with inaccurate chromosome segregation. The mitotic checkpoint is responsible for faithful transmission of genetic material to the progeny. Defects in this checkpoint, such as mutations and changes in gene expression, lead to abnormal chromosome content or aneuploidy that may facilitate cancer development. Furthermore, a defective checkpoint response is indicated in the development of drug resistance to microtubule poisons that are used in treatment of various blood and solid cancers for several decades. Mitotic slippage and senescence are important cell fates that occur even with an active mitotic checkpoint and are held responsible for the resistance. However, contradictory findings in both the scenarios of carcinogenesis and drug resistance have aroused questions on whether mitotic checkpoint defects are truly responsible for these dismal outcomes. Here, we discuss the possible contribution of the faulty checkpoint signaling in cancer development and drug resistance, followed by the latest research on this pathway for better outcomes in cancer treatment.

Deep learning with Convolutional Neural Networks has shown great promise in image-based classification and enhancement but is often unsuitable for predictive modeling using features without spatial correlations. We present a feature representation approach termed REFINED (REpresentation of Features as Images with NEighborhood Dependencies) to arrange high-dimensional vectors in a compact image form conducible for CNN-based deep learning. We consider the similarities between features to generate a concise feature map in the form of a two-dimensional image by minimizing the pairwise distance values following a Bayesian Metric Multidimensional Scaling Approach. We hypothesize that this approach enables embedded feature extraction and, integrated with CNN-based deep learning, can boost the predictive accuracy. We illustrate the superior predictive capabilities of the proposed framework as compared to state-of-the-art methodologies in drug sensitivity prediction scenarios using synthetic datasets, drug chemical descriptors as predictors from NCI60, and both transcriptomic information and drug descriptors as predictors from GDSC.

The histone deacetylase (HDAC) enzymes have emerged as an important class of molecular targets in cancer therapy, with five inhibitors in clinical use. Recently, it has been shown that a lack of selectivity between the 11 Zn-dependent HDAC isoforms may lead to unwanted side-effects. In this paper, we show that piano stool Ru complexes can act as HDAC inhibitors, and variation in the capping arene leads to differences in HDAC isoform selectivity.

Light-activated molecular nanomachines (MNMs) can be used to drill holes into prokaryotic (bacterial) cell walls and the membrane of eukaryotic cells, including mammalian cancer cells, by their fast rotational movement, leading to cell death. We examined how these MNMs function in multicellular organisms and investigated their use for treatment and eradication of specific diseases by causing damage to certain tissues and small organisms. Three model eukaryotic species, Caenorhabditis elegans , Daphnia pulex , and Mus musculus (mouse), were evaluated. These organisms were exposed to light-activated fast-rotating MNMs and their physiological and pathological changes were studied in detail. Slow rotating MNMs were used to control for the effects of rotation rate. We demonstrate that fast-rotating MNMs caused depigmentation and 70% mortality in C. elegans while reducing the movement as well as heart rate and causing tissue damage in Daphnia . Topically applied light-activated MNMs on mouse skin caused ulceration and microlesions in the epithelial tissue, allowing MNMs to localize into deeper epidermal tissue. Overall, this study shows that the nanomechanical action of light-activated MNMs is effective against multicellular organisms, disrupting cell membranes and damaging tissue in vivo . Customized MNMs that target specific tissues for therapy combined with spatial and temporal control could have broad clinical applications in a variety of benign and malignant disease states including treatment of cancer, parasites, bacteria, and diseased tissues.

Purpose: Imaging techniques for highly specific detection of cancer cells in vivo can have applications ranging from preclinical drug discovery studies to clinical cancer diagnosis and surgical therapy. Although fluorescence imaging using cancer-targeted antibodies has shown promise, nonspecific probe accumulation in tissue results in significant background fluorescence, reducing detection sensitivity using traditional intensity-based continuous-wave (CW) fluorescence imaging. Here we demonstrate that fluorescence lifetime (FLT) imaging can provide significant tumor contrast enhancement over CW intensity in preclinical models of human breast cancer., Experimental Design: Mice bearing MDA-MB-231 tumors were injected with anti-EGFR antibody conjugated to the fluorescent dye IRDye 800CW (anti-EGFR-800). Time domain fluorescence imaging was performed in vivo and in situ up to 48 hours after dye injection., Results: Mice injected with anti-EGFR-800 showed a significantly longer FLT (0.7 ± 0.03 ns) compared with the FLT of nonspecific probe uptake in liver (0.63 ± 0.05 ns), providing a dramatic improvement in sensitivity and specificity compared with CW intensity. IgG antibody-conjugated IRDye 800CW did not show an increased FLT compared with normal tissue, suggesting that the FLT increase of anti-EGFR-800 in tumors was associated with receptor expression. Using serial surgery, we show that FLT allows the detection of smaller residual tumors in the surgical bed than possible using CW intensity., Conclusions: Our data suggest that FLT can significantly enhance tumor contrast using fluorescently labeled antibodies, thereby accelerating the efficient clinical application of these probes for margin assessment in image-guided surgery and for highly specific detection of tumor receptors in vivo ., (©2019 American Association for Cancer Research.)

Recent years have seen an increase in the availability of pharmacogenomic databases such as Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) that provide genomic and functional characterization information for multiple cell lines. Studies have alluded to the fact that specific characterizations may be inconsistent between different databases. Analysis of the potential discrepancies in the different databases is highly significant, as these sources are frequently used to analyze and validate methodologies for personalized cancer therapies. In this article, we review the recent developments in investigating the correspondence between different pharmacogenomics databases and discuss the potential factors that require attention when incorporating these sources in any modeling analysis. Furthermore, we explored the consistency among these databases using copulas that can capture nonlinear dependencies between two sets of data., (© The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Summary: Biological processes are characterized by a variety of different genomic feature sets. However, often times when building models, portions of these features are missing for a subset of the dataset. We provide a modeling framework to effectively integrate this type of heterogeneous data to improve prediction accuracy. To test our methodology, we have stacked data from the Cancer Cell Line Encyclopedia to increase the accuracy of drug sensitivity prediction. The package addresses the dynamic regime of information integration involving sequential addition of features and samples., Availability and Implementation: The framework has been implemented as a R package Sstack, which can be downloaded from https://cran.r-project.org/web/packages/Sstack/index.html, where further explanation of the package is available., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Accurately predicting sensitivity of tumor cells to anti-cancer drugs based on genetic characterizations is a significant challenge for personalized cancer therapy. This chapter provides a computational procedure to design predictive models from individual genomic characterizations and combine them to arrive at an integrated predictive model. Integrated modeling employs the complementary information from heterogeneous genetic characterizations to improve the prediction error as well as lowering the error confidence interval.

Background: In precision medicine, scarcity of suitable biological data often hinders the design of an appropriate predictive model. In this regard, large scale pharmacogenomics studies, like CCLE and GDSC hold the promise to mitigate the issue. However, one cannot directly employ data from multiple sources together due to the existing distribution shift in data. One way to solve this problem is to utilize the transfer learning methodologies tailored to fit in this specific context., Results: In this paper, we present two novel approaches for incorporating information from a secondary database for improving the prediction in a target database. The first approach is based on latent variable cost optimization and the second approach considers polynomial mapping between the two databases. Utilizing CCLE and GDSC databases, we illustrate that the proposed approaches accomplish a better prediction of drug sensitivities for different scenarios as compared to the existing approaches., Conclusion: We have compared the performance of the proposed predictive models with database-specific individual models as well as existing transfer learning approaches. We note that our proposed approaches exhibit superior performance compared to the abovementioned alternative techniques for predicting sensitivity for different anti-cancer compounds, particularly the nonlinear mapping model shows the best overall performance.

Objectives: We aimed to develop a reliable and valid measure to assess public beliefs in mythical causes of cancer: the Cancer Awareness Measure-MYthical Causes Scale (CAM-MYCS)., Design and Setting: Cancer myth items were generated from a literature review, social media and interviews (n=16). The CAM-MYCS was prepared by reducing items using (a) an online sample (n=527) with exploratory factor analysis and (b) cancer experts with Delhpi methodology (n=13). To assess test-retest reliability and sensitivity to change, students (n=91) completed the CAM-MYCS at baseline and 1 week after exposure to information on lifestyle-related cancer causes or control information. Construct validity was tested by comparing CAM-MYCS scores between cancer experts (n=25) and students (n=91). Factor structure and internal reliability were investigated in a national sample (n=1993)., Results: Out of 42 items generated, 12 were retained based on factor loadings, prevalence of endorsement and expert consensus. CAM-MYCS scores improved (fewer myths endorsed) among students exposed to information on cancer causes compared with the control group (p<0.001) and showed high test-retest reliability (r=0.90, p<0.001). Cancer experts reported higher CAM-MYCS scores (fewer myths endorsed) than students (p<0.001). The factor structure of the CAM-MYCS was confirmed in the national sample and internal reliability was high (α=0.86). Inclusion of the CAM-MYCS alongside items assessing knowledge of actual cancer causes did not affect responses., Conclusions: The CAM-MYCS tool is a reliable and valid tool assessing beliefs in mythical causes of cancer, and it can be used alongside items assessing known causes of cancer., Competing Interests: Competing interests: LS has received honoraria for talks, an unrestricted research grant and travel expenses to attend meetings and workshops by pharmaceutical companies that make smoking cessation products (Pfizer, Johnson&Johnson) and has acted as paid reviewer for grant awarding bodies and as a paid consultant for health care companies. Other research has been funded by the government, a community-interested company (National Centre for Smoking Cessation) and charitable sources. SS is an academic advisor for LUTO., (© Author(s) (or their employer(s)) 2018. Re-use permitted under CC BY. Published by BMJ.)

Microtubule associated serine/threonine kinase (MASTL) is an important Ser/Thr kinase belonging to the family of AGC kinases. It is the human orthologue of Greatwall kinase (Gwl) that plays a significant role in mitotic progression and cell cycle regulation. Upregulation of MASTL in various cancers and its association with poor patient survival establishes it as an important drug target in cancer therapy. Nevertheless, the target remains unexplored with the paucity of studies focused on identification of inhibitors against MASTL, which emphasizes the relevance of our present study. We explored various drug databases and performed virtual screening of compounds from both natural and synthetic sources. A list of promising compounds displaying high binding characteristics towards MASTL protein is reported. Among the natural compounds, we found a 6-hydroxynaphthalene derivative ZINC85597499 to display best binding energy value of -9.32 kcal/mol. While among synthetic compounds, a thieno-pyrimidinone based tricyclic derivative ZINC53845290 compound exhibited best binding affinity of value -7.85 kcal/mol. MASTL interactions with these two compounds were further explored using molecular dynamics simulations. Altogether, this study identifies potential inhibitors of human Gwl kinase from both natural and synthetic origin and calls for studying these compounds as potential drugs for cancer therapy.

Summary: IntegratedMRF is an open-source R implementation for integrating drug response predictions from various genomic characterizations using univariate or multivariate random forests that includes various options for error estimation techniques. The integrated framework was developed following superior performance of random forest based methods in NCI-DREAM drug sensitivity prediction challenge. The computational framework can be applied to estimate mean and confidence interval of drug response prediction errors based on ensemble approaches with various combinations of genetic and epigenetic characterizations as inputs. The multivariate random forest implementation included in the package incorporates the correlations between output responses in the modeling and has been shown to perform better than existing approaches when the drug responses are correlated. Detailed analysis of the provided features is included in the Supplementary Material ., Availability and Implementation: The framework has been implemented as a package IntegratedMRF , which can be downloaded from https://cran.r-project.org/web/packages/IntegratedMRF/index.html , where further explanation of the package is available., Contact: ranadip.pal@ttu.edu., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com)

Background: Design of personalized targeted therapies involve modeling of patient sensitivity to various drugs and drug combinations. Majority of studies evaluate the sensitivity of tumor cells to targeted drugs without modeling the effect of the drugs on normal cells. In this article, we consider the individual modeling of drug responses to tumor and normal cells and utilize them to design targeted combination therapies that maximize sensitivity over tumor cells and minimize toxicity over normal cells., Results: The problem is formulated as maximizing sensitivity over tumor cell models while maintaining sensitivity below a threshold over normal cell models. We utilize the constrained structure of tumor proliferation models to design an accelerated lexicographic search algorithm for generating the optimal solution. For comparison purposes, we also designed two suboptimal search algorithms based on evolutionary algorithms and hill-climbing based techniques. Results over synthetic models and models generated from Genomics of Drug Sensitivity in Cancer database shows the ability of the proposed algorithms to arrive at optimal or close to optimal solutions in significantly lower number of steps as compared to exhaustive search. We also present the theoretical analysis of the expected number of comparisons required for the proposed Lexicographic search that compare favorably with the observed number of computations., Conclusions: The proposed algorithms provide a framework for design of combination therapy that tackles tumor heterogeneity while satisfying toxicity constraints.

Human chorionic gonadotropin (hCG), a hormone essential for pregnancy, is also ectopically expressed by a variety of cancers and is associated with poor prognosis; molecular mechanisms which may contribute to tumor progression remain ill-defined. Exogenous hCG enhanced the viability of human colorectal and lung cancer cells and promoted the growth of syngeneic tumors in mice. It induced the synthesis of VEGF, IL-8, matrix metalloprotease (MMP)-2 and MMP-9, and increased invasiveness in an MMP-dependent manner. While inducing the secretion of the tumor-associated extra-cellular matrix proteoglycan versican from tumor cells, hCG consequently caused the TLR-2-mediated generation of the inflammatory, tumor-associated cytokines TNF-α and IL-6 from peripheral blood adherent cells. The molecule up-modulated the Treg-associated transcription factor FOXP3 in tumor cells and increased the secretion of TGFβ and IL-10, thereby inhibiting T cell proliferation and inducing the differentiation FOXP3 - CD4 + CD25 - cells into functional FOXP3 + CD4 + CD25 + suppressor cells. Co-culture of hCG-treated tumor cells with mature bone-marrow derived dendritic cells induced the generation of active indoleamine deoxygenase. While anti-hCG antibodies restricted the growth of implanted tumor cells in nude mice, immunization of immune competent mice with a βhCG-TT conjugate supplemented with Mycobacterium indicus pranii provided synergistic survival benefit in animals implanted with syngeneic, hCG-responsive tumor cells. These studies elucidate the pathways by which hCG can promote tumorigenesis, providing further rationale for anti-hCG vaccination in the treatment of gonadotropin-sensitive tumors. © 2016 Wiley Periodicals, Inc., (© 2016 Wiley Periodicals, Inc.)

Modeling sensitivity to drugs based on genetic characterizations is a significant challenge in the area of systems medicine. Ensemble based approaches such as Random Forests have been shown to perform well in both individual sensitivity prediction studies and team science based prediction challenges. However, Random Forests generate a deterministic predictive model for each drug based on the genetic characterization of the cell lines and ignores the relationship between different drug sensitivities during model generation. This application motivates the need for generation of multivariate ensemble learning techniques that can increase prediction accuracy and improve variable importance ranking by incorporating the relationships between different output responses. In this article, we propose a novel cost criterion that captures the dissimilarity in the output response structure between the training data and node samples as the difference in the two empirical copulas. We illustrate that copulas are suitable for capturing the multivariate structure of output responses independent of the marginal distributions and the copula based multivariate random forest framework can provide higher accuracy prediction and improved variable selection. The proposed framework has been validated on genomics of drug sensitivity for cancer and cancer cell line encyclopedia database.

Background: Human chorionic gonadotropin (hCG) has essential roles in pregnancy. Reports linking hCG in non-trophoblastic tumors with poor patient prognosis has spurred interest in patho-physiological roles the hormone might play., Methods: The ability of hCG to prevent tumor cell death and sustain viability in the presence of chemotherapeutic drugs was assessed and potential synergies with TLR ligands explored. hCG-induced up-modulation of genes involved in chemoresistance was documented and targets validated by siRNA knock-down. Whether hCG could drive collaboration between tumor cells and macrophages in the production of IL-6 and consequent chemoresistance was assessed. The effects of concurrent anti-hCG immunization and chemotherapy on the growth of syngeneic murine tumors were evaluated., Results: hCG maintained basal levels of cytokine secretion by tumor cells exposed to chemotherapeutic drugs, and enhanced viability and proliferation; pre-treatment with hCG also decreased apoptosis, as assessed by Annexin-V binding and the cleavage of caspase 3. While co-incubation with hCG along with several TLR ligands mediated heightened chemo-resistance, TLR-2/6 and TLR-9 ligands increased the phosphorylation of JNK, and TLR-2 and TLR-8 ligands the phosphorylation of ERK in presence of hCG and curcumin, providing evidence of tri-molecular synergy. The hormone increased the transcription and/or expression of molecular intermediates (SURVIVIN, HIF-1α, PARP-1, Bcl-2, c-FLIP, KLK-10, XIAP, c-IAP-1) associated with chemo-resistance and increased levels of stress modulators (PON2, HO-1, HSP27 and NRF-2). siRNAs to SURVIVIN, NRF-2, HO-1 and HIF-1α attenuated hCG-mediated chemo-resistance. hCG-conditioned tumor cell supernatants induced heightened secretion of IL-6 and TNF-α from peripheral blood adherent cells and secreted IL-6 imparted chemo-resistance to naïve tumor cells. Co-administration of curcumin along with an anti-hCG vaccine (hCGβ conjugated to Tetanus Toxoid (TT)) to mice carrying syngeneic tumors resulted in significantly enhanced benefits on animal survival; synergy was demonstrated between anti-hCG antibodies and curcumin in the reduction of tumor cell viability., Conclusions: The data suggest that hCG, via direct as well as collaborative effects with TLR ligands and accessory cell-secreted cytokines, mediates chemo-resistance in gonadotropin-sensitive tumors and outlines the potential benefits of combination therapy.

Drugs targeting specific kinases are becoming common in cancer research and are a basis for personalized cancer therapy. Some of these drugs have the capacity to target multiple kinases. Promiscuous kinase inhibitors can be effective but the "off-target" effects can bring in toxicity for the patient. Thus the success of targeted cancer therapies with nominal harmful side effects is dependent on administering a single or multiple combinations of kinase inhibitors that targets the minimum number of kinases required to inhibit the tumor pathways. This requires a framework to predict the tumor sensitivities of a drug or drug combination based on the knowledge of the kinase inhibitors of a drug. In this article, we present a novel approach to predict the tumor sensitivities of a drug based on the generation of deterministic and stochastic Kinase Inhibition Maps. We build sensitivity maps or truth tables for a cell line from experimentally generated tumor sensitivities to kinase inhibitor drugs and use them to predict the sensitivity of a new drug or drug combinations based on known kinase inhibitor targets. We test our algorithms on a dataset of a dog osteosarcoma cell line with 317 possible kinase inhibitor targets after application of 36 targeted drugs. Our proposed algorithms are able to predict the sensitivities with high accuracy based on the given kinase inhibitor targets.

The present article reviews the historical and popular uses of garlic, its antioxidant, haematological, antimicrobial, hepatoprotective and antineoplastic properties and its potential toxicity (from sulfoxide). Garlic has been suggested to affect several cardiovascular risk factors. It has also been shown that garlic and its organic allyl sulfur components are effective inhibitors of the cancer process. Since garlic and its constituents can suppress carcinogen formation, bioactivation and tumour proliferation, it is imperative that biomarkers be established to identify which individuals might benefit most. Garlic powder, aged garlic and garlic oil have demonstrated antiplatelet and anticoagulant effects by interfering with cyclo-oxygenase-mediated thromboxane synthesis. Garlic has also been found to have synergistic effects against Helicobacter pylori with a proton pump inhibitor. The active compound allicin may affect atherosclerosis not only by acting as an antioxidant, but also by other mechanisms, such as lipoprotein modification and inhibition of LDL uptake and degradation by macrophages. Freshly prepared garlic homogenate protects against isoniazid+rifampicin-induced liver injury in experimental animal models. Several mechanisms are likely to account for this protection.

Motivation: An early use of gene-expression data coming from microarrays was to discover non-linear multivariate intergene relationships. Pursuing this direction, the motivation for this paper is 2-fold: (1) to discover and elucidate multivariate logical predictive relations among gene expressions in a dataset arising from radiation studies using the NCI 60 Anti-Cancer Drug Screen (ACDS) cell lines; and (2) to demonstrate how these logical relations based on coarse quantization reflect corresponding relations in the continuous data., Results: Using the coefficient of determination, a large number of logical relationships have been discovered among genes in the NCI 60 ACDS cell lines. Moreover, these relationships can be seen directly in the original continuous data, and many are robust relative to the thresholds used to obtain the logical data from the continuous data. A key observation is that a number of intergene relationships appear to be considerably stronger when p53 is functional as compared to when it is not, which is consistent with earlier findings in the literature., Availability: The appendix is available at http://gsp.tamu.edu/Publications/supplement.htm, Contact: edward@ee.tamu.edu.