A pharmacogenomic method for individualized prediction of drug sensitivity

Small molecule-mediated targeting of microRNAs for drug discovery: Experiments, computational techniques, and disease implications

Small molecules have been providing medical breakthroughs for human diseases for more than a century. Recently, identifying small molecule inhibitors that target microRNAs (miRNAs) has gained importance, despite the challenges posed by labour-intensive screening experiments and the significant efforts required for medicinal chemistry optimization. Numerous experimentally-verified cases have demonstrated the potential of miRNA-targeted small molecule inhibitors for disease treatment. This new approach is grounded in their posttranscriptional regulation of the expression of disease-associated genes. Reversing dysregulated gene expression using this mechanism may help control dysfunctional pathways. Furthermore, the ongoing improvement of algorithms has allowed for the integration of computational strategies built on top of laboratory-based data, facilitating a more precise and rational design and discovery of lead compounds. To complement the use of extensive pharmacogenomics data in prioritising potential drugs, our previous work introduced a computational approach based on only molecular sequences. Moreover, various computational tools for predicting molecular interactions in biological networks using similarity-based inference techniques have been accumulated in established studies. However, there are a limited number of comprehensive reviews covering both computational and experimental drug discovery processes. In this review, we outline a cohesive overview of both biological and computational applications in miRNA-targeted drug discovery, along with their disease implications and clinical significance. Finally, utilizing drug-target interaction (DTIs) data from DrugBank, we showcase the effectiveness of deep learning for obtaining the physicochemical characterization of DTIs.

The impact of violating the independence assumption in meta-analysis on biomarker discovery

With rapid advancements in high-throughput sequencing technologies, massive amounts of "-omics" data are now available in almost every biomedical field. Due to variance in biological models and analytic methods, findings from clinical and biological studies are often not generalizable when tested in independent cohorts. Meta-analysis, a set of statistical tools to integrate independent studies addressing similar research questions, has been proposed to improve the accuracy and robustness of new biological insights. However, it is common practice among biomarker discovery studies using preclinical pharmacogenomic data to borrow molecular profiles of cancer cell lines from one study to another, creating dependence across studies. The impact of violating the independence assumption in meta-analyses is largely unknown. In this study, we review and compare different meta-analyses to estimate variations across studies along with biomarker discoveries using preclinical pharmacogenomics data. We further evaluate the performance of conventional meta-analysis where the dependence of the effects was ignored via simulation studies. Results show that, as the number of non-independent effects increased, relative mean squared error and lower coverage probability increased. Additionally, we also assess potential bias in the estimation of effects for established meta-analysis approaches when data are duplicated and the assumption of independence is violated. Using pharmacogenomics biomarker discovery, we find that treating dependent studies as independent can substantially increase the bias of meta-analyses. Importantly, we show that violating the independence assumption decreases the generalizability of the biomarker discovery process and increases false positive results, a key challenge in precision oncology.

Comparing the Predictivity of Human Placental Gene, microRNA, and CpG Methylation Signatures in Relation to Perinatal Outcomes

Molecular signatures are being increasingly integrated into predictive biology applications. However, there are limited studies comparing the overall predictivity of transcriptomic vs. epigenomic signatures in relation to perinatal outcomes. This study set out to evaluate mRNA and microRNA (miRNA) expression and cytosine-guanine dinucleotide (CpG) methylation signatures in human placental tissues and relate these to perinatal outcomes known to influence maternal/fetal health; namely, birth weight, placenta weight, placental damage, and placental inflammation. The following hypotheses were tested: (1) different molecular signatures will demonstrate varying levels of predictivity towards perinatal outcomes, and (2) these signatures will show disruptions from an example exposure (i.e., cadmium) known to elicit perinatal toxicity. Multi-omic placental profiles from 390 infants in the Extremely Low Gestational Age Newborns cohort were used to develop molecular signatures that predict each perinatal outcome. Epigenomic signatures (i.e., miRNA and CpG methylation) consistently demonstrated the highest levels of predictivity, with model performance metrics including R^2 (predicted vs. observed) values of 0.36-0.57 for continuous outcomes and balanced accuracy values of 0.49-0.77 for categorical outcomes. Top-ranking predictors included miRNAs involved in injury and inflammation. To demonstrate the utility of these predictive signatures in screening of potentially harmful exogenous insults, top-ranking miRNA predictors were analyzed in a separate pregnancy cohort and related to cadmium. Key predictive miRNAs demonstrated altered expression in association with cadmium exposure, including miR-210, known to impact placental cell growth, blood vessel development, and fetal weight. These findings inform future predictive biology applications, where additional benefit will be gained by including epigenetic markers.

Topsentinol L Trisulfate, a Marine Natural Product That Targets Basal-like and Claudin-Low Breast Cancers

Patients diagnosed with basal-like breast cancer suffer from poor prognosis and limited treatment options. There is an urgent need to identify new targets that can benefit patients with basal-like and claudin-low (BL-CL) breast cancers. We screened fractions from our Marine Invertebrate Compound Library (MICL) to identify compounds that specifically target BL-CL breast cancers. We identified a previously unreported trisulfated sterol, i.e., topsentinol L trisulfate (TLT), which exhibited increased efficacy against BL-CL breast cancers relative to luminal/HER2+ breast cancer. Biochemical investigation of the effects of TLT on BL-CL cell lines revealed its ability to inhibit activation of AMP-activated protein kinase (AMPK) and checkpoint kinase 1 (CHK1) and to promote activation of p38. The importance of targeting AMPK and CHK1 in BL-CL cell lines was validated by treating a panel of breast cancer cell lines with known small molecule inhibitors of AMPK (dorsomorphin) and CHK1 (Ly2603618) and recording the increased effectiveness against BL-CL breast cancers as compared with luminal/HER2+ breast cancer. Finally, we generated a drug response gene-expression signature and projected it against a human tumor panel of 12 different cancer types to identify other cancer types sensitive to the compound. The TLT sensitivity gene-expression signature identified breast and bladder cancer as the most sensitive to TLT, while glioblastoma multiforme was the least sensitive.

Sex-Biased Molecular Signature for Overall Survival of Liver Cancer Patients

Sex/gender disparity has been shown in the incidence and prognosis of many types of diseases, probably due to differences in genes, physiological conditions such as hormones, and lifestyle between the sexes. The mortality and survival rates of many cancers, especially liver cancer, differ between men and women. Due to the pronounced sex/gender disparity, considering sex/gender may be necessary for the diagnosis and treatment of liver cancer. By analyzing research articles through a PubMed literature search, the present review identified 12 genes which showed practical relevance to cancer and sex disparities. Among the 12 sex-specific genes, 7 genes (BAP1, CTNNB1, FOXA1, GSTO1, GSTP1, IL6, and SRPK1) showed sex-biased function in liver cancer. Here we summarized previous findings of cancer molecular signature including our own analysis, and showed that sex-biased molecular signature CTNNB1^High, IL6^High, RHOA^High and GLIPR1^Low may serve as a female-specific index for prediction and evaluation of OS in liver cancer patients. This review suggests a potential implication of sex-biased molecular signature in liver cancer, providing a useful information on diagnosis and prediction of disease progression based on gender.

Embryo-derived teratoma in vitro biological system reveals antitumor and embryotoxic activity of valproate

Antiepileptic/teratogen valproate (VPA) is a histone deacetylase inhibitor/epigenetic drug proposed for the antitumor therapy where it is generally crucial to target poorly or undifferentiated cells to prevent a recurrence. Transplanted rodent gastrulating embryos‐proper (primitive streak and three germ layers) are the source of teratoma/teratocarcinoma tumors. Human primitive‐streak remnants develop sacrococcygeal teratomas that may recur even when benign (well differentiated). To screen for unknown VPA impact on teratoma‐type tumors, we used original 2‐week embryo‐derived teratoma in vitro biological system completed by a spent media metabolome analysis. Gastrulating 9.5‐day‐old rat embryos‐proper were cultivated in Eagle's minimal essential medium (MEM) with 50% rat serum (controls) or with the addition of 2 mmVPA. Spent media metabolomes were analyzed by FTIR. Compared to controls, VPA acetylated histones; significantly diminished overall teratoma growth, impaired survival, increased the apoptotic index, and decreased proliferation index and incidence of differentiated tissues (e.g., neural tissue). Control teratomas continued to grow and differentiate for 14 days in isotransplants in vivo, but in vitro VPA‐treated teratomas resorbed. Principal component analysis of FTIR results showed that spent media metabolomes formed well‐separated clusters reflecting the treatment and day of cultivation. In metabolomes of VPA‐treated teratomas, we found elevation of previously described histone acetylation biomarkers [amide I α‐helix and A(CH₃)/A(CH₂)]) with apoptotic biomarkers within the amide I region for β‐sheets, and unordered and CH₂ vibrations of lipids. VPA may be proposed for therapy of the undifferentiated component of teratoma tumors and this biological system completed by metabolome analysis, for a faster dual screening of antitumor/embryotoxic agents.

Metabolomics reveals the effect of valproic acid on MCF-7 and MDA-MB-231 cells

1. Breast cancer is one of the most common malignancies in women worldwide. Metabolomics has been shown to be a promising strategy to elucidate the underlying pathogenesis of cancer and identify new targets for cancer diagnosis and therapy. Valproic acid (VPA), a histone deacetylase inhibitor, is a potential new drug in tumor therapy. This work used metabolomics to examine the effect of VPA on metabolism in breast cancer cells.2. Based on UPLC-MS/MS, we identified 3137 differential metabolites in human breast cancer MCF-7 cells and 2472 differential metabolites in human breast cancer MDA-MB-231 cells after VPA treatment.3. We selected 63 differential metabolites from MCF-7 samples and 61 differential metabolites from MDA-MB-231 cells with the more conspicuous changing trend. Furfural was up-regulated after VPA treatment in both cell lines. In both samples, VPA exerted an effect on the beta-alanine metabolism pathway and the taurine and hypotaurine metabolism pathway.4. This study identified the effect of VPA on metabolites and metabolic pathways in breast cancer cells, and these findings may contribute to the identification of new targets for breast cancer treatment.

Tumor-stroma interactions differentially alter drug sensitivity based on the origin of stromal cells

Due to tumor heterogeneity, most believe that effective treatments should be tailored to the features of an individual tumor or tumor subclass. It is still unclear, however, what information should be considered for optimal disease stratification, and most prior work focuses on tumor genomics. Here, we focus on the tumor microenvironment. Using a large-scale coculture assay optimized to measure drug-induced cell death, we identify tumor-stroma interactions that modulate drug sensitivity. Our data show that the chemo-insensitivity typically associated with aggressive subtypes of breast cancer is not observed if these cells are grown in 2D or 3D monoculture, but is manifested when these cells are cocultured with stromal cells, such as fibroblasts. Furthermore, we find that fibroblasts influence drug responses in two distinct and divergent manners, associated with the tissue from which the fibroblasts were harvested. These divergent phenotypes occur regardless of the drug tested and result from modulation of apoptotic priming within tumor cells. Our study highlights unexpected diversity in tumor-stroma interactions, and we reveal new principles that dictate how fibroblasts alter tumor drug responses.

Activity of distinct growth factor receptor network components in breast tumors uncovers two biologically relevant subtypes

BACKGROUND

The growth factor receptor network (GFRN) plays a significant role in driving key oncogenic processes. However, assessment of global GFRN activity is challenging due to complex crosstalk among GFRN components, or pathways, and the inability to study complex signaling networks in patient tumors. Here, pathway-specific genomic signatures were used to interrogate GFRN activity in breast tumors and the consequent phenotypic impact of GRFN activity patterns.

METHODS

Novel pathway signatures were generated in human primary mammary epithelial cells by overexpressing key genes from GFRN pathways (HER2, IGF1R, AKT1, EGFR, KRAS (G12V), RAF1, BAD). The pathway analysis toolkit Adaptive Signature Selection and InteGratioN (ASSIGN) was used to estimate pathway activity for GFRN components in 1119 breast tumors from The Cancer Genome Atlas (TCGA) and across 55 breast cancer cell lines from the Integrative Cancer Biology Program (ICBP43). These signatures were investigated for their relationship to pro- and anti-apoptotic protein expression and drug response in breast cancer cell lines.

RESULTS

Application of these signatures to breast tumor gene expression data identified two novel discrete phenotypes characterized by concordant, aberrant activation of either the HER2, IGF1R, and AKT pathways ("the survival phenotype") or the EGFR, KRAS (G12V), RAF1, and BAD pathways ("the growth phenotype"). These phenotypes described a significant amount of the variability in the total expression data across breast cancer tumors and characterized distinctive patterns in apoptosis evasion and drug response. The growth phenotype expressed lower levels of BIM and higher levels of MCL-1 proteins. Further, the growth phenotype was more sensitive to common chemotherapies and targeted therapies directed at EGFR and MEK. Alternatively, the survival phenotype was more sensitive to drugs inhibiting HER2, PI3K, AKT, and mTOR, but more resistant to chemotherapies.

CONCLUSIONS

Gene expression profiling revealed a bifurcation pattern in GFRN activity represented by two discrete phenotypes. These phenotypes correlate to unique mechanisms of apoptosis and drug response and have the potential of pinpointing targetable aberration(s) for more effective breast cancer treatments.

Window-of-Opportunity Study of Valproic Acid in Breast Cancer Testing a Gene Expression Biomarker

Purpose

The anticancer activity of valproic acid (VPA) is attributed to the inhibition of histone deacetylase. We previously published the genomically derived sensitivity signature for VPA (GDSS-VPA), a gene expression biomarker that predicts breast cancer sensitivity to VPA in vitro and in vivo. We conducted a window-of-opportunity study that examined the tolerability of VPA and the ability of the GDSS-VPA to predict biologic changes in breast tumors after treatment with VPA.

Patients and Methods

Eligible women had untreated breast cancer with breast tumors larger than 1.5 cm. After a biopsy, women were given VPA for 7 to 12 days, increasing from 30 mg/kg/d orally divided into two doses per day to a maximum of 50 mg/kg/d. After VPA treatment, serum VPA level was measured and then breast surgery or biopsy was performed. Tumor proliferation was assessed by using Ki-67 immunohistochemistry. Histone acetylation of peripheral blood mononuclear cells was assessed by Western blot. Dynamic contrast-enhanced magnetic resonance imaging scans were performed before and after VPA treatment.

Results

Thirty women were evaluable. The median age was 54 years (range, 31-73 years). Fifty-two percent of women tolerated VPA at 50 mg/kg/d, but 10% missed more than two doses as a result of adverse events. Grade 3 adverse events included vomiting and diarrhea (one patient) and fatigue (one patient). The end serum VPA level correlated with a change in histone acetylation of peripheral blood mononuclear cells (ρ = 0.451; P = .024). Fifty percent of women (three of six) with triple-negative breast cancer had a Ki-67 reduction of at least 10% compared with 17% of other women. Women whose tumors had higher GDSS-VPA were more likely to have a Ki-67 decrease of at least 10% (area under the curve, 0.66).

Conclusion

VPA was well tolerated and there was a significant correlation between serum VPA levels and histone acetylation. VPA treatment caused a decrease in proliferation of breast tumors. The genomic biomarker correlated with decreased proliferation. Inhibition of histone deacetylase is a valid strategy for drug development in triple-negative breast cancer using gene expression biomarkers.

Entropy-based divergent and convergent modular pattern reveals additive and synergistic anticerebral ischemia mechanisms

Module-based network analysis of diverse pharmacological mechanisms is critical to systematically understand combination therapies and disease outcomes. We first constructed drug-target ischemic networks in baicalin, jasminoidin, ursodeoxycholic acid, and their combinations baicalin and jasminoidin as well as jasminoidin and ursodeoxycholic acid groups and identified modules using the entropy-based clustering algorithm. The modules 11, 7, 4, 8 and 3 were identified as baicalin, jasminoidin, ursodeoxycholic acid, baicalin and jasminoidin and jasminoidin and ursodeoxycholic acid-emerged responsive modules, while 12, 8, 15, 17 and 9 were identified as disappeared responsive modules based on variation of topological similarity, respectively. No overlapping differential biological processes were enriched between baicalin and jasminoidin and jasminoidin and ursodeoxycholic acid pure emerged responsive modules, but two were enriched by their co-disappeared responsive modules including nucleotide-excision repair and epithelial structure maintenance. We found an additive effect of baicalin and jasminoidin in a divergent pattern and a synergistic effect of jasminoidin and ursodeoxycholic acid in a convergent pattern on "central hit strategy" of regulating inflammation against cerebral ischemia. The proposed module-based approach may provide us a holistic view to understand multiple pharmacological mechanisms associated with differential phenotypes from the standpoint of modular pharmacology.

Gene signatures of estrogen and progesterone receptor pathways predict the prognosis of colorectal cancer

The associations of estrogen receptor (ER) and progesterone receptor (PR) pathways with the prognosis of colorectal cancer (CRC) are still controversial. The aim of this study was to readdress these issues by introducing a gene signature-based approach to semiquantitate pathway activity. In this approach, the ER and PR pathway activities in CRC were computed based on the expression profiles of the signature genes of ER and PR pathways, respectively. The results showed that the ER pathway activity was progressively significantly decreased from normal colorectal mucosa, colorectal adenoma to CRC. ER pathway signaling was a favorable factor for the presence of microsatellite stability (MSS) in CRC in seven cohorts tested, while was an unfavorable factor for cancer recurrence in all four CRC cohorts tested (n = 1122; overall HR: 0.311, 95% CI: 0.199-0.488, P < 0.001). Subset stratification in stage II patients showed that ER pathway remained significantly inversely associated with recurrence. PR pathway was also suppressed in colorectal tumors and inversely associated with recurrence of CRC, but to a much lesser extent than ER pathway. Moreover, the inverse association of PR pathway with cancer recurrence was more likely observed in CRC with high ER pathway activity, suggesting the interactions between the two pathways. PR pathway was not associated with MSS in CRC, but it was more significant than ER pathway associated with advance cancer stages and cancer response to adjuvant chemotherapy. These results suggested the potential application of the gene signatures of ER and PR pathways, especially the former, as novel markers for prognosis and management of CRC.

Functional Validation of ABCA3 as a Miltefosine Transporter in Human Macrophages: IMPACT ON INTRACELLULAR SURVIVAL OF LEISHMANIA (VIANNIA) PANAMENSIS

Within its mammalian host, Leishmania resides and replicates as an intracellular parasite. The direct activity of antileishmanials must therefore depend on intracellular drug transport, metabolism, and accumulation within the host cell. In this study, we explored the role of human macrophage transporters in the intracellular accumulation and antileishmanial activity of miltefosine (MLF), the only oral drug available for the treatment of visceral and cutaneous leishmaniasis (CL). Membrane transporter gene expression in primary human macrophages infected in vitro with Leishmania Viannia panamensis and exposed to MLF showed modulation of ABC and solute liquid carrier transporters gene transcripts. Among these, ABCA3, a lipid transporter, was significantly induced after exposure to MLF, and this induction was confirmed in primary macrophages from CL patients. Functional validation of MLF as a substrate for ABCA3 was performed by shRNA gene knockdown (KD) in THP-1 monocytes. Intracellular accumulation of radiolabeled MLF was significantly higher in ABCA3(KD) macrophages. ABCA3(KD) resulted in increased cytotoxicity induced by MLF exposure. ABCA3 gene expression inversely correlated with intracellular MLF content in primary macrophages from CL patients. ABCA3(KD) reduced parasite survival during macrophage infection with an L. V. panamensis strain exhibiting low in vitro susceptibility to MLF. Confocal microscopy showed ABCA3 to be located in the cell membrane of resting macrophages and in intracellular compartments in L. V. panamensis-infected cells. These results provide evidence of ABCA3 as an MLF efflux transporter in human macrophages and support its role in the direct antileishmanial effect of this alkylphosphocholine drug.

Deciphering Signaling Pathway Networks to Understand the Molecular Mechanisms of Metformin Action

A drug exerts its effects typically through a signal transduction cascade, which is non-linear and involves intertwined networks of multiple signaling pathways. Construction of such a signaling pathway network (SPNetwork) can enable identification of novel drug targets and deep understanding of drug action. However, it is challenging to synopsize critical components of these interwoven pathways into one network. To tackle this issue, we developed a novel computational framework, the Drug-specific Signaling Pathway Network (DSPathNet). The DSPathNet amalgamates the prior drug knowledge and drug-induced gene expression via random walk algorithms. Using the drug metformin, we illustrated this framework and obtained one metformin-specific SPNetwork containing 477 nodes and 1,366 edges. To evaluate this network, we performed the gene set enrichment analysis using the disease genes of type 2 diabetes (T2D) and cancer, one T2D genome-wide association study (GWAS) dataset, three cancer GWAS datasets, and one GWAS dataset of cancer patients with T2D on metformin. The results showed that the metformin network was significantly enriched with disease genes for both T2D and cancer, and that the network also included genes that may be associated with metformin-associated cancer survival. Furthermore, from the metformin SPNetwork and common genes to T2D and cancer, we generated a subnetwork to highlight the molecule crosstalk between T2D and cancer. The follow-up network analyses and literature mining revealed that seven genes (CDKN1A, ESR1, MAX, MYC, PPARGC1A, SP1, and STK11) and one novel MYC-centered pathway with CDKN1A, SP1, and STK11 might play important roles in metformin’s antidiabetic and anticancer effects. Some results are supported by previous studies. In summary, our study 1) develops a novel framework to construct drug-specific signal transduction networks; 2) provides insights into the molecular mode of metformin; 3) serves a model for exploring signaling pathways to facilitate understanding of drug action, disease pathogenesis, and identification of drug targets.

A deep understanding of a drug’s mechanisms of actions is essential not only in the discovery of new treatments but also in minimizing adverse effects. Here, we develop a computational framework, the Drug-specific Signaling Pathway Network (DSPathNet), to reconstruct a comprehensive signaling pathway network (SPNetwork) impacted by a particular drug. To illustrate this computational approach, we used metformin, an anti-diabetic drug, as an example. Starting from collecting the metformin-related upstream genes and inferring the metformin-related downstream genes, we built one metformin-specific SPNetwork via random walk based algorithms. Our evaluation of the metformin-specific SPNetwork by using disease genes and genotyping data from genome-wide association studies showed that our DSPathNet approach was efficient to synopsize drug’s key components and their relationship involved in the type 2 diabetes and cancer, even the metformin anticancer activity. This work presents a novel computational framework for constructing individual drug-specific signal transduction networks. Furthermore, its successful application to the drug metformin provides some valuable insights into the mode of metformin action, which will facilitate our understanding of the molecular mechanisms underlying drug treatments, disease pathogenesis, and identification of novel drug targets and repurposed drugs.

A network flow-based method to predict anticancer drug sensitivity

Predicting anticancer drug sensitivity can enhance the ability to individualize patient treatment, thus making development of cancer therapies more effective and safe. In this paper, we present a new network flow-based method, which utilizes the topological structure of pathways, for predicting anticancer drug sensitivities. Mutations and copy number alterations of cancer-related genes are assumed to change the pathway activity, and pathway activity difference before and after drug treatment is used as a measure of drug response. In our model, Contributions from different genetic alterations are considered as free parameters, which are optimized by the drug response data from the Cancer Genome Project (CGP). 10-fold cross validation on CGP data set showed that our model achieved comparable prediction results with existing elastic net model using much less input features.

Translating the transcriptome into tools for the early detection and prevention of lung cancer

Despite advances in the management of lung cancer, this disease remains a significant global health burden with survival rates that have not significantly improved in decades. The mortality reduction achieved by low-dose helical CT (LDCT) screening of select high-risk patients is challenged by the high false positive rate of this screening modality and the potential for morbidity associated with follow-up diagnostic evaluation in patients with high risk for iatrogenic complications. The diagnostic dilemma of the indeterminate nodule incidentally identified on diagnostic or screening CT has created a need for reliable biomarkers capable of distinguishing benign from malignant disease. Furthermore, there is an urgent need to develop molecular biomarkers to supplement clinical risk models in order to identify patients at highest risk for having an early stage lung cancer that may derive the greatest benefit from LDCT screening, as well as identifying patients at high-risk for developing lung cancer that may be candidates for emerging chemopreventive strategies. Evolving bioinformatic techniques and the application of these algorithms to analyse the transcriptomic changes associated with lung cancer promise translational discoveries that can bridge these large clinical gaps. The identification of lung cancer associated transcriptomic alterations in readily accessible tissue sampling sites offers the potential to develop early diagnostic and risk stratification strategies applicable to large populations. This review summarises the challenges associated with the early detection, screening and chemoprevention of lung cancer with an emphasis on how genomic information encapsulated by the transcriptome can facilitate future innovations in these clinical settings.

Modeling precision treatment of breast cancer

Background

First-generation molecular profiles for human breast cancers have enabled the identification of features that can predict therapeutic response; however, little is known about how the various data types can best be combined to yield optimal predictors. Collections of breast cancer cell lines mirror many aspects of breast cancer molecular pathobiology, and measurements of their omic and biological therapeutic responses are well-suited for development of strategies to identify the most predictive molecular feature sets.

Results

We used least squares-support vector machines and random forest algorithms to identify molecular features associated with responses of a collection of 70 breast cancer cell lines to 90 experimental or approved therapeutic agents. The datasets analyzed included measurements of copy number aberrations, mutations, gene and isoform expression, promoter methylation and protein expression. Transcriptional subtype contributed strongly to response predictors for 25% of compounds, and adding other molecular data types improved prediction for 65%. No single molecular dataset consistently out-performed the others, suggesting that therapeutic response is mediated at multiple levels in the genome. Response predictors were developed and applied to TCGA data, and were found to be present in subsets of those patient samples.

Conclusions

These results suggest that matching patients to treatments based on transcriptional subtype will improve response rates, and inclusion of additional features from other profiling data types may provide additional benefit. Further, we suggest a systems biology strategy for guiding clinical trials so that patient cohorts most likely to respond to new therapies may be more efficiently identified.

Prediction of dynamical drug sensitivity and resistance by module network rewiring-analysis based on transcriptional profiling

Revealing functional reorganization or module rewiring between modules at network levels during drug treatment is important to systematically understand therapies and drug responses. The present article proposed a novel model of module network rewiring to characterize functional reorganization of a complex biological system, and described a new framework named as module network rewiring-analysis (MNR) for systematically studying dynamical drug sensitivity and resistance during drug treatment. MNR was used to investigate functional reorganization or rewiring on the module network, rather than molecular network or individual molecules. Our experiments on expression data of patients with Hepatitis C virus infection receiving Interferon therapy demonstrated that consistent module genes derived by MNR could be directly used to reveal new genotypes relevant to drug sensitivity, unlike the other differential analyses of gene expressions. Our results showed that functional connections and reconnections among consistent modules bridged by biological paths were necessary for achieving effective responses of a drug. The hierarchical structures of the temporal module network can be considered as spatio-temporal biomarkers to monitor the efficacy, efficiency, toxicity, and resistance of the therapy. Our study indicates that MNR is a useful tool to identify module biomarkers and further predict dynamical drug sensitivity and resistance, characterize complex dynamic processes for therapy response, and provide biologically systematic clues for pharmacogenomic applications.

From transient transcriptome responses to disturbed neurodevelopment: role of histone acetylation and methylation as epigenetic switch between reversible and irreversible drug effects

The superordinate principles governing the transcriptome response of differentiating cells exposed to drugs are still unclear. Often, it is assumed that toxicogenomics data reflect the immediate mode of action (MoA) of drugs. Alternatively, transcriptome changes could describe altered differentiation states as indirect consequence of drug exposure. We used here the developmental toxicants valproate and trichostatin A to address this question. Neurally differentiating human embryonic stem cells were treated for 6 days. Histone acetylation (primary MoA) increased quickly and returned to baseline after 48 h. Histone H3 lysine methylation at the promoter of the neurodevelopmental regulators PAX6 or OTX2 was increasingly altered over time. Methylation changes remained persistent and correlated with neurodevelopmental defects and with effects on PAX6 gene expression, also when the drug was washed out after 3-4 days. We hypothesized that drug exposures altering only acetylation would lead to reversible transcriptome changes (indicating MoA), and challenges that altered methylation would lead to irreversible developmental disturbances. Data from pulse-chase experiments corroborated this assumption. Short drug treatment triggered reversible transcriptome changes; longer exposure disrupted neurodevelopment. The disturbed differentiation was reflected by an altered transcriptome pattern, and the observed changes were similar when the drug was washed out during the last 48 h. We conclude that transcriptome data after prolonged chemical stress of differentiating cells mainly reflect the altered developmental stage of the model system and not the drug MoA. We suggest that brief exposures, followed by immediate analysis, are more suitable for information on immediate drug responses and the toxicity MoA.

A three step network based approach (TSNBA) to finding disease molecular signature and key regulators: a case study of IL-1 and TNF-alpha stimulated inflammation

A disease molecular signature is a set of biomolecular features that are prognostic of clinical phenotypes and indicative of underlying pathology. It is of great importance to develop computational approaches for finding more relevant molecular signatures. Based upon the hypothesis that various components in a molecular signature are more likely to share similar patterns, we introduced a novel three step network based approach (TSNBA) to identify the molecular signature and key pathological regulators. Protein-protein interaction (PPI) network and ranking algorithm were integrated in the first step to find pathology related proteins with high accuracy. It was followed by the second step to further screen with co-expression patterns for better pathology enrichment. Context likelihood of relatedness (CLR) algorithm was used in the third step to infer gene regulatory networks and identify key transcription regulators. We applied this approach to study IL-1 (interleukin-1) and TNF-alpha (tumor necrosis factor-alpha) stimulated inflammation. TSNBA identified inflammatory signature with high accuracy and outperformed 5 competing methods namely fold change, degree, interconnectivity, neighborhood score and network propagation based approaches. The best molecular signature, with 80% (40/50) confirmed inflammatory genes, was used to predict inflammation related genes. As a result, 8 out of 10 predicted inflammation genes that were not included in the benchmark Entrez Gene database were validated by literature evidence. Furthermore, 23 of the 32 predicted inflammation regulators were validated by literature evidence. The rest 9 were also validated with TF (transcription factor) binding site analysis. In conclusion, we developed an efficient strategy for disease molecular signature finding and key pathological regulator identification.

Patient-derived xenografts of triple-negative breast cancer reproduce molecular features of patient tumors and respond to mTOR inhibition

INTRODUCTION

Triple-negative breast cancer (TNBC) is aggressive and lacks targeted therapies. Phosphatidylinositide 3-kinase (PI3K)/mammalian target of rapamycin (mTOR) pathways are frequently activated in TNBC patient tumors at the genome, gene expression and protein levels, and mTOR inhibitors have been shown to inhibit growth in TNBC cell lines. We describe a panel of patient-derived xenografts representing multiple TNBC subtypes and use them to test preclinical drug efficacy of two mTOR inhibitors, sirolimus (rapamycin) and temsirolimus (CCI-779).

METHODS

We generated a panel of seven patient-derived orthotopic xenografts from six primary TNBC tumors and one metastasis. Patient tumors and corresponding xenografts were compared by histology, immunohistochemistry, array comparative genomic hybridization (aCGH) and phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit alpha (PIK3CA) sequencing; TNBC subtypes were determined. Using a previously published logistic regression approach, we generated a rapamycin response signature from Connectivity Map gene expression data and used it to predict rapamycin sensitivity in 1,401 human breast cancers of different intrinsic subtypes, prompting in vivo testing of mTOR inhibitors and doxorubicin in our TNBC xenografts.

RESULTS

Patient-derived xenografts recapitulated histology, biomarker expression and global genomic features of patient tumors. Two primary tumors had PIK3CA coding mutations, and five of six primary tumors showed flanking intron single nucleotide polymorphisms (SNPs) with conservation of sequence variations between primary tumors and xenografts, even on subsequent xenograft passages. Gene expression profiling showed that our models represent at least four of six TNBC subtypes. The rapamycin response signature predicted sensitivity for 94% of basal-like breast cancers in a large dataset. Drug testing of mTOR inhibitors in our xenografts showed 77 to 99% growth inhibition, significantly more than doxorubicin; protein phosphorylation studies indicated constitutive activation of the mTOR pathway that decreased with treatment. However, no tumor was completely eradicated.

CONCLUSIONS

A panel of patient-derived xenograft models covering a spectrum of TNBC subtypes was generated that histologically and genomically matched original patient tumors. Consistent with in silico predictions, mTOR inhibitor testing in our TNBC xenografts showed significant tumor growth inhibition in all, suggesting that mTOR inhibitors can be effective in TNBC, but will require use with additional therapies, warranting investigation of optimal drug combinations.

Engineered cell-based therapies: a vanguard of design-driven medicine

Engineered cell-based therapies are uniquely capable of performing sophisticated therapeutic functions in vivo, and this strategy is yielding promising clinical benefits for treating cancer. In this review, we discuss key opportunities and challenges for engineering customized cellular functions using cell-based therapy for cancer as a representative case study. We examine the historical development of chimeric antigen receptor (CAR) therapies as an illustration of the engineering design cycle. We also consider the potential roles that the complementary disciplines of systems biology and synthetic biology may play in realizing safe and effective treatments for a broad range of patients and diseases. In particular, we discuss how systems biology may facilitate both fundamental research and clinical translation, and we describe how the emerging field of synthetic biology is providing novel modalities for building customized cellular functions to overcome existing clinical barriers. Together, these approaches provide a powerful set of conceptual and experimental tools for transforming information into understanding, and for translating understanding into novel therapeutics to establish a new framework for design-driven medicine.

BRCA-Monet: a breast cancer specific drug treatment mode-of-action network for treatment effective prediction using large scale microarray database

BACKGROUND

Connectivity map (cMap) is a recent developed dataset and algorithm for uncovering and understanding the treatment effect of small molecules on different cancer cell lines. It is widely used but there are still remaining challenges for accurate predictions.

METHOD

Here, we propose BRCA-MoNet, a network of drug mode of action (MoA) specific to breast cancer, which is constructed based on the cMap dataset. A drug signature selection algorithm fitting the characteristic of cMap data, a quality control scheme as well as a novel query algorithm based on BRCA-MoNet are developed for more effective prediction of drug effects.

RESULT

BRCA-MoNet was applied to three independent data sets obtained from the GEO database: Estrodial treated MCF7 cell line, BMS-754807 treated MCF7 cell line, and a breast cancer patient microarray dataset. In the first case, BRCA-MoNet could identify drug MoAs likely to share same and reverse treatment effect. In the second case, the result demonstrated the potential of BRCA-MoNet to reposition drugs and predict treatment effects for drugs not in cMap data. In the third case, a possible procedure of personalized drug selection is showcased.

CONCLUSIONS

The results clearly demonstrated that the proposed BRCA-MoNet approach can provide increased prediction power to cMap and thus will be useful for identification of new therapeutic candidates.

Weight change, genetics and antiepileptic drugs

Weight gain caused by antiepileptic drugs (AEDs) constitutes a serious problem in the management of people with epilepsy. AEDs associated with weight gain include sodium valproate, pregabalin and vigabatrin. Excessive weight gain can lead to non-compliance with treatment and to an exacerbation of obesity-related conditions. The mechanisms by which AEDs cause weight gain are not fully understood. It is likely that weight change induced by some AEDs has a genetic underpinning, and recent developments in DNA sequencing technology should speed the understanding, prediction and thus prevention of serious weight change associated with AEDs. This review focuses on the biology of obesity in the context of AEDs. Future directions in the investigations of the mechanism of weight change associated with these drugs and the use of such knowledge in tailoring the treatment of specific patient groups are explored.

Personalized management of chronic obstructive pulmonary disease via transcriptomic profiling of the airway and lung

Chronic obstructive pulmonary disease (COPD) is a clinically heterogeneous disease composed of variable degrees of airflow obstruction, emphysematous destruction, and small airway wall thickening. The natural history of this disease, although generally characterized by continued decline in lung function, is also highly variable. Novel transcriptomic approaches to study the airway and lung tissue in COPD hold the potential to improve our understanding of the molecular mechanisms underlying this heterogeneity and identify molecular subtypes of disease that have similar clinical manifestations. This new understanding can be leveraged to develop targeted COPD therapies and ultimately personalize treatment of COPD based on each patient's specific molecular subphenotype.

Genomic pathway analysis reveals that EZH2 and HDAC4 represent mutually exclusive epigenetic pathways across human cancers

Background

Alterations in epigenetic marks, including methylation or acetylation, are common in human cancers. For many epigenetic pathways, however, direct measures of activity are unknown, making their role in various cancers difficult to assess. Gene expression signatures facilitate the examination of patterns of epigenetic pathway activation across and within human cancer types allowing better understanding of the relationships between these pathways.

Methods

We used Bayesian regression to generate gene expression signatures from normal epithelial cells before and after epigenetic pathway activation. Signatures were applied to datasets from TCGA, GEO, CaArray, ArrayExpress, and the cancer cell line encyclopedia. For TCGA data, signature results were correlated with copy number variation and DNA methylation changes. GSEA was used to identify biologic pathways related to the signatures.

Results

We developed and validated signatures reflecting downstream effects of enhancer of zeste homolog 2(EZH2), histone deacetylase(HDAC) 1, HDAC4, sirtuin 1(SIRT1), and DNA methyltransferase 2(DNMT2). By applying these signatures to data from cancer cell lines and tumors in large public repositories, we identify those cancers that have the highest and lowest activation of each of these pathways. Highest EZH2 activation is seen in neuroblastoma, hepatocellular carcinoma, small cell lung cancer, and melanoma, while highest HDAC activity is seen in pharyngeal cancer, kidney cancer, and pancreatic cancer. Across all datasets studied, activation of both EZH2 and HDAC4 is significantly underrepresented. Using breast cancer and glioblastoma as examples to examine intrinsic subtypes of particular cancers, EZH2 activation was highest in luminal breast cancers and proneural glioblastomas, while HDAC4 activation was highest in basal breast cancer and mesenchymal glioblastoma. EZH2 and HDAC4 activation are associated with particular chromosome abnormalities: EZH2 activation with aberrations in genes from the TGF and phosphatidylinositol pathways and HDAC4 activation with aberrations in inflammatory and chemokine related genes.

Conclusion

Gene expression patterns can reveal the activation level of epigenetic pathways. Epigenetic pathways define biologically relevant subsets of human cancers. EZH2 activation and HDAC4 activation correlate with growth factor signaling and inflammation, respectively, and represent two distinct states for cancer cells. This understanding may allow us to identify targetable drivers in these cancer subsets.

Cancer systems biology in the genome sequencing era: part 2, evolutionary dynamics of tumor clonal networks and drug resistance

A tumor often consists of multiple cell subpopulations (clones). Current chemo-treatments often target one clone of a tumor. Although the drug kills that clone, other clones overtake it and the tumor recurs. Genome sequencing and computational analysis allows to computational dissection of clones from tumors, while singe-cell genome sequencing including RNA-Seq allows profiling of these clones. This opens a new window for treating a tumor as a system in which clones are evolving. Future cancer systems biology studies should consider a tumor as an evolving system with multiple clones. Therefore, topics discussed in Part 2 of this review include evolutionary dynamics of clonal networks, early-warning signals (e.g., genome duplication events) for formation of fast-growing clones, dissecting tumor heterogeneity, and modeling of clone-clone-stroma interactions for drug resistance. The ultimate goal of the future systems biology analysis is to obtain a 'whole-system' understanding of a tumor and therefore provides a more efficient and personalized management strategies for cancer patients.

Patient-derived models of human breast cancer: protocols for in vitro and in vivo applications in tumor biology and translational medicine

Research models that replicate the diverse genetic and molecular landscape of breast cancer are critical for developing the next-generation therapeutic entities that can target specific cancer subtypes. Patient-derived tumorgrafts, generated by transplanting primary human tumor samples into immune-compromised mice, are a valuable method to model the clinical diversity of breast cancer in mice, and are a potential resource in personalized medicine. Primary tumorgrafts also enable in vivo testing of therapeutics and make possible the use of patient cancer tissue for in vitro screens. Described in this unit are a variety of protocols including tissue collection, biospecimen tracking, tissue processing, transplantation, and three-dimensional culturing of xenografted tissue, which enable use of bona fide uncultured human tissue in designing and validating cancer therapies.

Comparison and validation of genomic predictors for anticancer drug sensitivity

BACKGROUND

An enduring challenge in personalized medicine lies in selecting the right drug for each individual patient. While testing of drugs on patients in large trials is the only way to assess their clinical efficacy and toxicity, we dramatically lack resources to test the hundreds of drugs currently under development. Therefore the use of preclinical model systems has been intensively investigated as this approach enables response to hundreds of drugs to be tested in multiple cell lines in parallel.

METHODS

Two large-scale pharmacogenomic studies recently screened multiple anticancer drugs on over 1000 cell lines. We propose to combine these datasets to build and robustly validate genomic predictors of drug response. We compared five different approaches for building predictors of increasing complexity. We assessed their performance in cross-validation and in two large validation sets, one containing the same cell lines present in the training set and another dataset composed of cell lines that have never been used during the training phase.

RESULTS

Sixteen drugs were found in common between the datasets. We were able to validate multivariate predictors for three out of the 16 tested drugs, namely irinotecan, PD-0325901, and PLX4720. Moreover, we observed that response to 17-AAG, an inhibitor of Hsp90, could be efficiently predicted by the expression level of a single gene, NQO1.

CONCLUSION

These results suggest that genomic predictors could be robustly validated for specific drugs. If successfully validated in patients' tumor cells, and subsequently in clinical trials, they could act as companion tests for the corresponding drugs and play an important role in personalized medicine.

SIRT1 pathway dysregulation in the smoke-exposed airway epithelium and lung tumor tissue

Cigarette smoke produces a molecular field of injury in epithelial cells lining the respiratory tract. However, the specific signaling pathways that are altered in the airway of smokers and the signaling processes responsible for the transition from smoking-induced airway damage to lung cancer remain unknown. In this study, we use a genomic approach to study the signaling processes associated with tobacco smoke exposure and lung cancer. First, we developed and validated pathway-specific gene expression signatures in bronchial airway epithelium that reflect activation of signaling pathways relevant to tobacco exposure, including ATM, BCL2, GPX1, NOS2, IKBKB, and SIRT1. Using these profiles and four independent gene expression datasets, we found that SIRT1 activity is significantly upregulated in cytologically normal bronchial airway epithelial cells from active smokers compared with nonsmokers. In contrast, this activity is strikingly downregulated in non-small cell lung cancer. This pattern of signaling modulation was unique to SIRT1, and downregulation of SIRT1 activity is confined to tumors from smokers. Decreased activity of SIRT1 was validated using genomic analyses of mouse models of lung cancer and biochemical testing of SIRT1 activity in patient lung tumors. Together, our findings indicate a role of SIRT1 in response to smoke and a potential role in repressing lung cancer. Furthermore, our findings suggest that the airway gene expression signatures derived in this study can provide novel insights into signaling pathways altered in the "field of injury" induced by tobacco smoke and thus may impact strategies for prevention of tobacco-related lung cancer.

A single-sample microarray normalization method to facilitate personalized-medicine workflows

Gene-expression microarrays allow researchers to characterize biological phenomena in a high-throughput fashion but are subject to technological biases and inevitable variabilities that arise during sample collection and processing. Normalization techniques aim to correct such biases. Most existing methods require multiple samples to be processed in aggregate; consequently, each sample's output is influenced by other samples processed jointly. However, in personalized-medicine workflows, samples may arrive serially, so renormalizing all samples upon each new arrival would be impractical. We have developed Single Channel Array Normalization (SCAN), a single-sample technique that models the effects of probe-nucleotide composition on fluorescence intensity and corrects for such effects, dramatically increasing the signal-to-noise ratio within individual samples while decreasing variation across samples. In various benchmark comparisons, we show that SCAN performs as well as or better than competing methods yet has no dependence on external reference samples and can be applied to any single-channel microarray platform.

Molecular signatures from omics data: from chaos to consensus

In the past 15 years, new "omics" technologies have made it possible to obtain high-resolution molecular snapshots of organisms, tissues, and even individual cells at various disease states and experimental conditions. It is hoped that these developments will usher in a new era of personalized medicine in which an individual's molecular measurements are used to diagnose disease, guide therapy, and perform other tasks more accurately and effectively than is possible using standard approaches. There now exists a vast literature of reported "molecular signatures". However, despite some notable exceptions, many of these signatures have suffered from limited reproducibility in independent datasets, insufficient sensitivity or specificity to meet clinical needs, or other challenges. In this paper, we discuss the process of molecular signature discovery on the basis of omics data. In particular, we highlight potential pitfalls in the discovery process, as well as strategies that can be used to increase the odds of successful discovery. Despite the difficulties that have plagued the field of molecular signature discovery, we remain optimistic about the potential to harness the vast amounts of available omics data in order to substantially impact clinical practice.

Managing drug resistance in cancer: lessons from HIV therapy

Drug resistance is a common cause of treatment failure for HIV infection and cancer. The high mutation rate of HIV leads to genetic heterogeneity among viral populations and provides the seed from which drug-resistant clones emerge in response to therapy. Similarly, most cancers are characterized by extensive genetic, epigenetic, transcriptional and cellular diversity, and drug-resistant cancer cells outgrow their non-resistant peers in a process of somatic evolution. Patient-specific combination of antiviral drugs has emerged as a powerful approach for treating drug-resistant HIV infection, using genotype-based predictions to identify the best matched combination therapy among several hundred possible combinations of HIV drugs. In this Opinion article, we argue that HIV therapy provides a 'blueprint' for designing and validating patient-specific combination therapies in cancer.

Genomic medicine and lung diseases

The recent explosion of genomic data and technology points to opportunities to redefine lung diseases at the molecular level; to apply integrated genomic approaches to elucidate mechanisms of lung pathophysiology; and to improve early detection, diagnosis, and treatment of lung diseases. Research is needed to translate genomic discoveries into clinical applications, such as detecting preclinical disease, predicting patient outcomes, guiding treatment choices, and most of all identifying potential therapeutic targets for lung diseases. The Division of Lung Diseases in the National Heart, Lung, and Blood Institute convened a workshop, "Genomic Medicine and Lung Diseases," to discuss the potential for integrated genomics and systems approaches to advance 21st century pulmonary medicine and to evaluate the most promising opportunities for this next phase of genomics research to yield clinical benefit. Workshop sessions included (1) molecular phenotypes, molecular biomarkers, and therapeutics; (2) new technology and opportunity; (3) integrative genomics; (4) molecular anatomy of the lung; (5) novel data and information platforms; and (6) recommendations for exceptional research opportunities in lung genomics research.