WNT/β-catenin signaling in hepatocellular carcinoma: The aberrant activation, pathogenic roles, and therapeutic opportunities

Hepatocellular carcinoma (HCC) is a liver cancer, highly heterogeneous both at the histopathological and molecular levels. It arises from hepatocytes as the result of the accumulation of numerous genomic alterations in various signaling pathways, including canonical WNT/β-catenin, AKT/mTOR, MAPK pathways as well as signaling associated with telomere maintenance, p53/cell cycle regulation, epigenetic modifiers, and oxidative stress. The role of WNT/β-catenin signaling in liver homeostasis and regeneration is well established, whereas in development and progression of HCC is extensively studied. Herein, we review recent advances in our understanding of how WNT/β-catenin signaling facilitates the HCC development, acquisition of stemness features, metastasis, and resistance to treatment. We outline genetic and epigenetic alterations that lead to activated WNT/β-catenin signaling in HCC. We discuss the pivotal roles of CTNNB1 mutations, aberrantly expressed non-coding RNAs and complexity of crosstalk between WNT/β-catenin signaling and other signaling pathways as challenging or advantageous aspects of therapy development and molecular stratification of HCC patients for treatment.

Current advances in detecting genetic and epigenetic biomarkers of colorectal cancer

Colorectal carcinoma (CRC) is the third most common cancer in terms of diagnosis and the second in terms of mortality. Recent studies have shown that various proteins, extracellular vesicles (i.e., exosomes), specific genetic variants, gene transcripts, cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and altered epigenetic patterns, can be used to detect, and assess the prognosis of CRC. Over the last decade, a plethora of conventional methodologies (e.g., polymerase chain reaction [PCR], direct sequencing, enzyme-linked immunosorbent assay [ELISA], microarray, in situ hybridization) as well as advanced analytical methodologies (e.g., microfluidics, electrochemical biosensors, surface-enhanced Raman spectroscopy [SERS]) have been developed for analyzing genetic and epigenetic biomarkers using both optical and non-optical tools. Despite these methodologies, no gold standard detection method has yet been implemented that can analyze CRC with high specificity and sensitivity in an inexpensive, simple, and time-efficient manner. Moreover, until now, no study has critically reviewed the advantages and limitations of these methodologies. Here, an overview of the most used genetic and epigenetic biomarkers for CRC and their detection methods are discussed. Furthermore, a summary of the major biological, technical, and clinical challenges and advantages/limitations of existing techniques is also presented.

DARESOME enables concurrent profiling of multiple DNA modifications with restriction enzymes in single cells and cell-free DNA

5-Methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) are the most abundant DNA modifications that have important roles in gene regulation. Detailed studies of these different epigenetic marks aimed at understanding their combined effects and dynamic interconversion are, however, hampered by the inability of current methods to simultaneously measure both modifications, particularly in samples with limited quantities. We present DNA analysis by restriction enzyme for simultaneous detection of multiple epigenomic states (DARESOME), an assay based on modification-sensitive restriction digest and sequential tag ligation that can concurrently perform quantitative profiling of unmodified cytosine, 5mC, and 5hmC in CCGG sites genome-wide. DARESOME reveals the opposing roles of 5mC and 5hmC in gene expression regulation as well as their interconversion during aging in mouse brain. Implementation of DARESOME in single cells demonstrates pronounced 5hmC strand bias that reflects the semiconservative replication of DNA. Last, we showed that DARESOME enables integrative genomic, 5mC, and 5hmC profiling of cell-free DNA that uncovered multiomics cancer signatures in liquid biopsy.

An inexpensive semi-automated sample processing pipeline for cell-free RNA extraction

Despite advances in automated liquid handling and microfluidics, preparing samples for RNA sequencing at scale generally requires expensive equipment, which is beyond the reach of many academic laboratories. Manual sample preparation remains a slow, expensive and error-prone process. Here, we describe a low-cost, semi-automated pipeline to extract cell-free RNA using one of two commercially available, inexpensive and open-source robotic systems: the Opentrons OT1.0 or OT2.0. Like many RNA isolation protocols, ours can be decomposed into three subparts: RNA extraction, DNA digestion and RNA cleaning and concentration. RT-qPCR data using a synthetic spike-in confirms comparable RNA quality to the gold standard, manual sample processing. The semi-automated pipeline also shows improvement in sample throughput (+12×), time spent (-11×), cost (-3×) and biohazardous waste produced (-4×) compared with its manual counterpart. This protocol enables cell-free RNA extraction from 96 samples simultaneously in 4.5 h; in practice, this dramatically improves the time to results, as we recently demonstrated. Importantly, any laboratory already has most of the parts required (manual pipette and corresponding tips and kits for RNA isolation, cleaning and concentration) to build a semi-automated sample processing pipeline of their own and would only need to purchase or three-dimensionally print a few extra parts (US$5.5 K-12 K in total). This pipeline is also generalizable for many nucleic acid extraction applications, thereby increasing the scale of studies, which can be performed in small research laboratories.

Endometriosis in the era of precision medicine and impact on sexual and reproductive health across the lifespan and in diverse populations

Endometriosis is a common estrogen-dependent disorder wherein uterine lining tissue (endometrium) is found mainly in the pelvis where it causes inflammation, chronic pelvic pain, pain with intercourse and menses, and infertility. Recent evidence also supports a systemic inflammatory component that underlies associated co-morbidities, e.g., migraines and cardiovascular and autoimmune diseases. Genetics and environment contribute significantly to disease risk, and with the explosion of omics technologies, underlying mechanisms of symptoms are increasingly being elucidated, although novel and effective therapeutics for pain and infertility have lagged behind these advances. Moreover, there are stark disparities in diagnosis, access to care, and treatment among persons of color and transgender/nonbinary identity, socioeconomically disadvantaged populations, and adolescents, and a disturbing low awareness among health care providers, policymakers, and the lay public about endometriosis, which, if left undiagnosed and under-treated can lead to significant fibrosis, infertility, depression, and markedly diminished quality of life. This review summarizes endometriosis epidemiology, compelling evidence for its pathogenesis, mechanisms underlying its pathophysiology in the age of precision medicine, recent biomarker discovery, novel therapeutic approaches, and issues around reproductive justice for marginalized populations with this disorder spanning the past 100 years. As we enter the next revolution in health care and biomedical research, with rich molecular and clinical datasets, single-cell omics, and population-level data, endometriosis is well positioned to benefit from data-driven research leveraging computational and artificial intelligence approaches integrating data and predicting disease risk, diagnosis, response to medical and surgical therapies, and prognosis for recurrence.

Steadfast Toll Like Receptor 4 (TLR4) 5-Hydroxymethylcytosine Levels in Cell-Free DNA: A Promising Consistency Marker for Colorectal Cancer Patients

Cell-free DNA (cfDNA) from patient blood is emerging as a noninvasive diagnostic avenue for various cancers. We aimed to identify reliable biomarkers in cfDNA by investigating genes exhibiting significant differences between colorectal cancer and control samples. Our objective was to identify genes that showed a positive difference between cancer and control samples. To achieve this, we conducted an in silico analysis to identify genes that exhibit no significant variation in methylation between genomic DNA (gDNA) and cfDNA. We collected experimental data from publicly available repositories, which included 5-hydroxymethylcytosine (5hmC) profiles of gDNA and cfDNA samples from both cancer patients and healthy individuals. By comparing and overlapping these two groups, we identified 187 genes of interest, of which 53 genes had a positive difference among colon cancer patients and healthy individuals. Next, we performed an ANOVA test on these genes, resulting in the identification of 12 genes that showed statistically significant higher levels of 5hmC in cfDNA and gDNA from cancer patients compared to healthy individuals. Additionally, we compared the 5hmC status of these genes between cfDNA and gDNA from cancer patients. Interestingly, we found that the 5hmC of the toll like receptor 4 (TLR4) gene was not statistically different between cfDNA and gDNA from cancer patients, indicating consistency between cfDNA and gDNA. These findings have important implications, not only for experimental validation but also for the development of more sensitive and robust noninvasive methods to improve diagnostic, prognostic, and treatment options for colon cancer.

TET (Ten-eleven translocation) family proteins: structure, biological functions and applications

Ten-eleven translocation (TET) family proteins (TETs), specifically, TET1, TET2 and TET3, can modify DNA by oxidizing 5-methylcytosine (5mC) iteratively to yield 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC), and 5-carboxycytosine (5caC), and then two of these intermediates (5fC and 5caC) can be excised and return to unmethylated cytosines by thymine-DNA glycosylase (TDG)-mediated base excision repair. Because DNA methylation and demethylation play an important role in numerous biological processes, including zygote formation, embryogenesis, spatial learning and immune homeostasis, the regulation of TETs functions is complicated, and dysregulation of their functions is implicated in many diseases such as myeloid malignancies. In addition, recent studies have demonstrated that TET2 is able to catalyze the hydroxymethylation of RNA to perform post-transcriptional regulation. Notably, catalytic-independent functions of TETs in certain biological contexts have been identified, further highlighting their multifunctional roles. Interestingly, by reactivating the expression of selected target genes, accumulated evidences support the potential therapeutic use of TETs-based DNA methylation editing tools in disorders associated with epigenetic silencing. In this review, we summarize recent key findings in TETs functions, activity regulators at various levels, technological advances in the detection of 5hmC, the main TETs oxidative product, and TETs emerging applications in epigenetic editing. Furthermore, we discuss existing challenges and future directions in this field.

Transcriptomic clock predicts vascular changes of prodromal diabetic retinopathy

Diabetic retinopathy is a common complication of long-term diabetes and that could lead to vision loss. Unfortunately, early diabetic retinopathy remains poorly understood. There is no effective way to prevent or treat early diabetic retinopathy until patients develop later stages of diabetic retinopathy. Elevated acellular capillary density is considered a reliable quantitative trait present in the early development of retinopathy. Hence, in this study, we interrogated whole retinal vascular transcriptomic changes via a Nile rat model to better understand the early pathogenesis of diabetic retinopathy. We uncovered the complexity of associations between acellular capillary density and the joint factors of blood glucose, diet, and sex, which was modeled through a Bayesian network. Using segmented regressions, we have identified different gene expression patterns and enriched Gene Ontology (GO) terms associated with acellular capillary density increasing. We developed a random forest regression model based on expression patterns of 14 genes to predict the acellular capillary density. Since acellular capillary density is a reliable quantitative trait in early diabetic retinopathy, and thus our model can be used as a transcriptomic clock to measure the severity of the progression of early retinopathy. We also identified NVP-TAE684, geldanamycin, and NVP-AUY922 as the top three potential drugs which can potentially attenuate the early DR. Although we need more in vivo studies in the future to support our re-purposed drugs, we have provided a data-driven approach to drug discovery.

Liquid biopsies for cancer: From bench to clinic

Over the past two decades, liquid biopsy has been increasingly used as a supplement, or even, a replacement to the traditional biopsy in clinical oncological practice, due to its noninvasive and early detectable properties. The detections can be based on a variety of features extracted from tumor‑derived entities, such as quantitative alterations, genetic changes, and epigenetic aberrations, and so on. So far, the clinical applications of cancer liquid biopsy mainly aimed at two aspects, prediction (early diagnosis, prognosis and recurrent evaluation, therapeutic response monitoring, etc.) and intervention. In spite of the rapid development and great contributions achieved, cancer liquid biopsy is still a field under investigation and deserves more clinical practice. To better open up future work, here we systematically reviewed and compared the latest progress of the most widely recognized circulating components, including circulating tumor cells, cell-free circulating DNA, noncoding RNA, and nucleosomes, from their discovery histories to clinical values. According to the features applied, we particularly divided the contents into two parts, beyond epigenetics and epigenetic-based. The latter was considered as the highlight along with a brief overview of the advances in both experimental and bioinformatic approaches, due to its unique advantages and relatively lack of documentation.

Cellular Composition and 5hmC Signature Predict the Treatment Response of AML Patients to Azacitidine Combined with Chemotherapy

Azacitidine (AZA) is a DNA methyltransferase inhibitor and epigenetic modulator that can be an effective agent in combination with chemotherapy for patients with high-risk acute myeloid leukemia (AML). However, biological factors driving the therapeutic response of such hypomethylating agent (HMA)-based therapies remain unknown. Herein, the transcriptome and/or genome-wide 5-hydroxymethylcytosine (5hmC) is characterized for 41 patients with high-risk AML from a phase 1 clinical trial treated with AZA epigenetic priming followed by high-dose cytarabine and mitoxantrone (AZA-HiDAC-Mito). Digital cytometry reveals that responders have elevated Granulocyte-macrophage-progenitor-like (GMP-like) malignant cells displaying an active cell cycle program. Moreover, the enrichment of natural killer (NK) cells predicts a favorable outcome in patients receiving AZA-HiDAC-Mito therapy or other AZA-based therapies. Comparing 5hmC profiles before and after five-day treatment of AZA shows that AZA exposure induces dose-dependent 5hmC changes, in which the magnitude correlates with overall survival (p = 0.015). An extreme gradient boosting (XGBoost) machine learning model is developed to predict the treatment response based on 5hmC levels of 11 genes, achieving an area under the curve (AUC) of 0.860. These results suggest that cellular composition markedly impacts the treatment response, and showcase the prospect of 5hmC signatures in predicting the outcomes of HMA-based therapies in AML.

Disease Diagnosis Based on Nucleic Acid Modifications

Nucleic acid modifications include a wide range of epigenetic and epitranscriptomic factors and impact a wide range of nucleic acids due to their profound influence on biological inheritance, growth, and metabolism. The recently developed methods of mapping and characterizing these modifications have promoted their discovery as well as large-scale studies in eukaryotes, especially in humans. Because of these pioneering strategies, nucleic acid modifications have been shown to have a great impact on human disorders such as cancer. Therefore, whether nucleic acid modifications could become a new type of biomarker remains an open question. In this review, we briefly look back at classical nucleic acid modifications and then focus on the progress made in investigating these modifications as diagnostic biomarkers in clinical therapy and present our perspective on their development prospects.

Germline mechanisms of immunotherapy toxicities in the era of genome-wide association studies

Cancer immunotherapy has revolutionized the treatment of advanced cancers and is quickly becoming an option for early-stage disease. By reactivating the host immune system, immunotherapy harnesses patients' innate defenses to eradicate the tumor. By putatively similar mechanisms, immunotherapy can also substantially increase the risk of toxicities or immune-related adverse events (irAEs). Severe irAEs can lead to hospitalization, treatment discontinuation, lifelong immune complications, or even death. Many irAEs present with similar symptoms to heritable autoimmune diseases, suggesting that germline genetics may contribute to their onset. Recently, genome-wide association studies (GWAS) of irAEs have identified common germline associations and putative mechanisms, lending support to this hypothesis. A wide range of well-established GWAS methods can potentially be harnessed to understand the etiology of irAEs specifically and immunotherapy outcomes broadly. This review summarizes current findings regarding germline effects on immunotherapy outcomes and discusses opportunities and challenges for leveraging germline genetics to understand, predict, and treat irAEs.

Therapies for patients with coexisting heart failure with reduced ejection fraction and non-alcoholic fatty liver disease

Heart failure with reduced ejection fraction (HFrEF) and nonalcoholic fatty liver disease (NAFLD) are two common comorbidities that share similar pathophysiological mechanisms. There is a growing interest in the potential of targeted therapies to improve outcomes in patients with coexisting HFrEF and NAFLD. This manuscript reviews current and potential therapies for patients with coexisting HFrEF and NAFLD. Pharmacological therapies, including angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, mineralocorticoids receptor antagonist, and sodium-glucose cotransporter-2 inhibitors, have been shown to reduce fibrosis and fat deposits in the liver. However, there are currently no data showing the beneficial effects of sacubitril/valsartan, ivabradine, hydralazine, isosorbide nitrates, digoxin, or beta blockers on NAFLD in patients with HFrEF. This study highlights the importance of considering HFrEF and NAFLD when developing treatment plans for patients with these comorbidities. Further research is needed in patients with coexisting HFrEF and NAFLD, with an emphasis on novel therapies and the importance of a multidisciplinary approach for managing these complex comorbidities.

Network neighborhood operates as a drug repositioning method for cancer treatment

Computational drug repositioning approaches are important, as they cost less compared to the traditional drug development processes. This study proposes a novel network-based drug repositioning approach, which computes similarities between disease-causing genes and drug-affected genes in a network topology to suggest candidate drugs with highest similarity scores. This new method aims to identify better treatment options by integrating systems biology approaches. It uses a protein-protein interaction network that is the main topology to compute a similarity score between candidate drugs and disease-causing genes. The disease-causing genes were mapped on this network structure. Transcriptome profiles of drug candidates were taken from the LINCS project and mapped individually on the network structure. The similarity of these two networks was calculated by different network neighborhood metrics, including Adamic-Adar, PageRank and neighborhood scoring. The proposed approach identifies the best candidates by choosing the drugs with significant similarity scores. The method was experimented on melanoma, colorectal, and prostate cancers. Several candidate drugs were predicted by applying AUC values of 0.6 or higher. Some of the predictions were approved by clinical phase trials or other in-vivo studies found in literature. The proposed drug repositioning approach would suggest better treatment options with integration of functional information between genes and transcriptome level effects of drug perturbations and diseases.

PATH-SURVEYOR: pathway level survival enquiry for immuno-oncology and drug repurposing

Pathway-level survival analysis offers the opportunity to examine molecular pathways and immune signatures that influence patient outcomes. However, available survival analysis algorithms are limited in pathway-level function and lack a streamlined analytical process. Here we present a comprehensive pathway-level survival analysis suite, PATH-SURVEYOR, which includes a Shiny user interface with extensive features for systematic exploration of pathways and covariates in a Cox proportional-hazard model. Moreover, our framework offers an integrative strategy for performing Hazard Ratio ranked Gene Set Enrichment Analysis and pathway clustering. As an example, we applied our tool in a combined cohort of melanoma patients treated with checkpoint inhibition (ICI) and identified several immune populations and biomarkers predictive of ICI efficacy. We also analyzed gene expression data of pediatric acute myeloid leukemia (AML) and performed an inverse association of drug targets with the patient's clinical endpoint. Our analysis derived several drug targets in high-risk KMT2A-fusion-positive patients, which were then validated in AML cell lines in the Genomics of Drug Sensitivity database. Altogether, the tool offers a comprehensive suite for pathway-level survival analysis and a user interface for exploring drug targets, molecular features, and immune populations at different resolutions.

Transcriptomics-driven drug repositioning for the treatment of diabetic foot ulcer

Diabetic foot ulcers (DFUs) are a common complication of diabetes and can lead to severe disability and even amputation. Despite advances in treatment, there is currently no cure for DFUs and available drugs for treatment are limited. This study aimed to identify new candidate drugs and repurpose existing drugs to treat DFUs based on transcriptomics analysis. A total of 31 differentially expressed genes (DEGs) were identified and used to prioritize the biological risk genes for DFUs. Further investigation using the database DGIdb revealed 12 druggable target genes among 50 biological DFU risk genes, corresponding to 31 drugs. Interestingly, we highlighted that two drugs (urokinase and lidocaine) are under clinical investigation for DFU and 29 drugs are potential candidates to be repurposed for DFU therapy. The top 5 potential biomarkers for DFU from our findings are IL6ST, CXCL9, IL1R1, CXCR2, and IL10. This study highlights IL1R1 as a highly promising biomarker for DFU due to its high systemic score in functional annotations, that can be targeted with an existing drug, Anakinra. Our study proposed that the integration of transcriptomic and bioinformatic-based approaches has the potential to drive drug repurposing for DFUs. Further research will further examine the mechanisms by which targeting IL1R1 can be used to treat DFU.

Repurposing of neprilysin inhibitor 'sacubitrilat' as an anti-cancer drug by modulating epigenetic and apoptotic regulators

Modifications in the epigenetic landscape have been considered a hallmark of cancer. Histone deacetylation is one of the crucial epigenetic modulations associated with the aggressive progression of various cancer subtypes. Herein, we have repurposed the neprilysin inhibitor sacubitrilat as a potent anticancer agent using in-silico protein-ligand interaction profiler (PLIP) analysis, molecular docking, and in vitro studies. The screening of PLIP profiles between vorinostat/panobinostat and HDACs/LTA4H followed by molecular docking resulted in five (Sacubitrilat, B65, BDS, BIR, and NPV) FDA-approved, experimental and investigational drugs. Sacubitrilat has demonstrated promising anticancer activity against colorectal cancer (SW-480) and triple-negative breast cancer (MDA-MB-231) cells, with IC₅₀ values of 14.07 μg/mL and 23.02 μg/mL, respectively. FACS analysis revealed that sacubitrilat arrests the cell cycle at the G0/G1 phase and induces apoptotic-mediated cell death in SW-480 cells. In addition, sacubitrilat inhibited HDAC isoforms at the transcriptomic level by 0.7-0.9 fold and at the proteomic level by 0.5-0.6 fold as compared to the control. Sacubitrilat increased the protein expression of tumor-suppressor (p53) and pro-apoptotic makers (Bax and Bid) by 0.2-2.5 fold while decreasing the expression of anti-apoptotic Bcl2 and Nrf2 proteins by 0.2-0.5 fold with respect to control. The observed cleaved PARP product indicates that sacubitrilat induces apoptotic-mediated cell death. This study may pave the way to identify the anticancer potential of sacubitrilat and can be explored in human clinical trials.

Computational drug repositioning for the identification of new agents to sensitize drug-resistant breast tumors across treatments and receptor subtypes

Introduction

Drug resistance is a major obstacle in cancer treatment and can involve a variety of different factors. Identifying effective therapies for drug resistant tumors is integral for improving patient outcomes.

Methods

In this study, we applied a computational drug repositioning approach to identify potential agents to sensitize primary drug resistant breast cancers. We extracted drug resistance profiles from the I-SPY 2 TRIAL, a neoadjuvant trial for early stage breast cancer, by comparing gene expression profiles of responder and non-responder patients stratified into treatments within HR/HER2 receptor subtypes, yielding 17 treatment-subtype pairs. We then used a rank-based pattern-matching strategy to identify compounds in the Connectivity Map, a database of cell line derived drug perturbation profiles, that can reverse these signatures in a breast cancer cell line. We hypothesize that reversing these drug resistance signatures will sensitize tumors to treatment and prolong survival.

Results

We found that few individual genes are shared among the drug resistance profiles of different agents. At the pathway level, however, we found enrichment of immune pathways in the responders in 8 treatments within the HR+HER2+, HR+HER2-, and HR-HER2- receptor subtypes. We also found enrichment of estrogen response pathways in the non-responders in 10 treatments primarily within the hormone receptor positive subtypes. Although most of our drug predictions are unique to treatment arms and receptor subtypes, our drug repositioning pipeline identified the estrogen receptor antagonist fulvestrant as a compound that can potentially reverse resistance across 13/17 of the treatments and receptor subtypes including HR+ and triple negative. While fulvestrant showed limited efficacy when tested in a panel of 5 paclitaxel resistant breast cancer cell lines, it did increase drug response in combination with paclitaxel in HCC-1937, a triple negative breast cancer cell line.

Conclusion

We applied a computational drug repurposing approach to identify potential agents to sensitize drug resistant breast cancers in the I-SPY 2 TRIAL. We identified fulvestrant as a potential drug hit and showed that it increased response in a paclitaxel-resistant triple negative breast cancer cell line, HCC-1937, when treated in combination with paclitaxel.

Thymic lymphoma detection in RORγ knockout mice using 5-hydroxymethylcytosine profiling of circulating cell-free DNA

A high incidence of thymic lymphoma has been noted in mice deficient of retinoid-related orphan receptor γ2 (RORγ2), which is required for differentiation of naïve CD4⁺ T cells into T_H17 cells. Using a RORγ homozygous knockout (KO) mouse model of thymic lymphoma, we characterized this tumor progression and investigated the utility of 5-hydroxymethylcytosine (5hmC) signatures as a non-invasive circulating biomarker for early prediction of malignancy. No evidence for malignancy was noted in the wild-type mice, while primary thymic lymphoma with multi-organ metastasis was observed microscopically in 97% of the homozygous RORγ KO mice. The severity of thymic lymphoma was not age-dependent in the KO mice of 2 to 4 months old. Differential enrichment of 5hmC in thymic DNA and plasma cell-free DNA (cfDNA) was compared across different stages of tumor progression. Random forest modeling of plasma cfDNA achieved good predictivity (AUC = 0.74) in distinguishing early non-metastatic thymic lymphoma compared to cancer-free controls, while perfect predictivity was achieved with advanced multi-organ metastatic disease (AUC = 1.00). Lymphoid-specific genes involved in thymocyte selection during T cell development (Themis, Tox) were differentially enriched in both plasma and thymic tissue. This could help in differentiating thymic lymphoma from other tumors commonly detected in rodent carcinogenicity studies used in pharmaceutical drug development to inform human malignancy risk. Overall, these results provide a proof-of-concept for using circulating cfDNA profiles in rodent carcinogenicity studies for early risk assessment of novel pharmaceutical targets.

Rational design, synthesis and structural characterization of peptides and peptidomimetics to target Hsp90/Cdc37 interaction for treating hepatocellular carcinoma

Heat shock protein 90 (Hsp90) and cell division cycle 37 (Cdc37) work together as a molecular chaperone complex to regulate the activity of a multitude of client protein kinases. These kinases belong to a wide array of intracellular signaling networks that mediate multiple cellular processes including proliferation. As a result, Hsp90 and Cdc37 represent innovative therapeutic targets in various cancers (such as leukemia, multiple myeloma, and hepatocellular carcinoma (HCC)) in which their expression levels are elevated. Conventional small molecule Hsp90 inhibitors act by blocking the conserved adenosine triphosphate (ATP) binding site. However, by targeting less conserved sites in a more specific manner, peptides and peptidomimetics (modified peptides) hold potential as more efficacious and less toxic alternatives to the conventional small molecule inhibitors. Using a rational approach, we herein developed bioactive peptides targeting Hsp90/Cdc37 interaction. A six amino acid linear peptide derived from Cdc37, KTGDEK, was designed to target Hsp90. We used in silico computational docking to first define its mode of interaction, and binding orientation, and then conjugated the peptide with a cell penetrating peptide, TAT, and a fluorescent dye to confirm its ability to colocalize with Hsp90 in HCC cells. Based on the parent linear sequence, we developed a peptidomimetics library of pre-cyclic and cyclic derivatives. These peptidomimetics were evaluated for their binding affinity to Hsp90, and bioactivity in HCC cell lines. Among them, a pre-cyclic peptidomimetic demonstrates high binding affinity and bioactivity in HCC cells, causing reduced cell proliferation that is associated with induction of cell apoptosis, and down-regulation of phosphorylated MEK1/2. Overall, this generalized approach of rational design, structural optimization, and cellular validation of 'drug-like' peptidomimetics against Hsp90/Cdc37 offers a feasible and promising way to design novel therapeutic agents for malignancies and other diseases that are dependent on this molecular chaperone complex.