Comparison of the molecular and cellular phenotypes of common mouse syngeneic models with human tumors.

BACKGROUND: The clinical success of immune checkpoint inhibitors demonstrates that reactivation of the human immune system delivers durable responses for some patients and represents an exciting approach for cancer treatment. An important class of preclinical in vivo models for immuno-oncology is immunocompetent mice bearing mouse syngeneic tumors. To facilitate translation of preclinical studies into human, we characterized the genomic, transcriptomic, and protein expression of a panel of ten commonly used mouse tumor cell lines grown in vitro culture as well as in vivo tumors. RESULTS: Our studies identified a number of genetic and cellular phenotypic differences that distinguish commonly used mouse syngeneic models in our study from human cancers. Only a fraction of the somatic single nucleotide variants (SNVs) in these common mouse cell lines directly match SNVs in human actionable cancer genes. Some models derived from epithelial tumors have a more mesenchymal phenotype with relatively low T-lymphocyte infiltration compared to the corresponding human cancers. CT26, a colon tumor model, had the highest immunogenicity and was the model most responsive to CTLA4 inhibitor treatment, by contrast to the relatively low immunogenicity and response rate to checkpoint inhibitor therapies in human colon cancers. CONCLUSIONS: The relative immunogenicity of these ten syngeneic tumors does not resemble typical human tumors derived from the same tissue of origin. By characterizing the mouse syngeneic models and comparing with their human tumor counterparts, this study contributes to a framework that may help investigators select the model most relevant to study a particular immune-oncology mechanism, and may rationalize some of the challenges associated with translating preclinical findings to clinical studies.

TNER: a novel background error suppression method for mutation detection in circulating tumor DNA.

BACKGROUND: Ultra-deep next-generation sequencing of circulating tumor DNA (ctDNA) holds great promise as a tool for the early detection of cancer and for monitoring disease progression and therapeutic responses. However, the low abundance of ctDNA in the bloodstream coupled with technical errors introduced during library construction and sequencing complicates mutation detection. RESULTS: To achieve high accuracy of variant calling via better distinguishing low-frequency ctDNA mutations from background errors, we introduce TNER (Tri-Nucleotide Error Reducer), a novel background error suppression method that provides a robust estimation of background noise to reduce sequencing errors. The results on both simulated data and real data from healthy subjects demonstrate that the proposed algorithm consistently outperforms a current, state-of-the-art, position-specific error polishing model, particularly when the sample size of healthy subjects is small. CONCLUSIONS: TNER significantly enhances the specificity of downstream ctDNA mutation detection without sacrificing sensitivity. The tool is publicly available at https://github.com/ctDNA/TNER .

Outlier analysis of functional genomic profiles enriches for oncology targets and enables precision medicine.

BACKGROUND: Genome-scale functional genomic screens across large cell line panels provide a rich resource for discovering tumor vulnerabilities that can lead to the next generation of targeted therapies. Their data analysis typically has focused on identifying genes whose knockdown enhances response in various pre-defined genetic contexts, which are limited by biological complexities as well as the incompleteness of our knowledge. We thus introduce a complementary data mining strategy to identify genes with exceptional sensitivity in subsets, or outlier groups, of cell lines, allowing an unbiased analysis without any a priori assumption about the underlying biology of dependency. RESULTS: Genes with outlier features are strongly and specifically enriched with those known to be associated with cancer and relevant biological processes, despite no a priori knowledge being used to drive the analysis. Identification of exceptional responders (outliers) may not lead only to new candidates for therapeutic intervention, but also tumor indications and response biomarkers for companion precision medicine strategies. Several tumor suppressors have an outlier sensitivity pattern, supporting and generalizing the notion that tumor suppressors can play context-dependent oncogenic roles. CONCLUSIONS: The novel application of outlier analysis described here demonstrates a systematic and data-driven analytical strategy to decipher large-scale functional genomic data for oncology target and precision medicine discoveries.

Whole-genome sequencing identifies recurrent mutations in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the most deadly cancers worldwide and has no effective treatment, yet the molecular basis of hepatocarcinogenesis remains largely unknown. Here we report findings from a whole-genome sequencing (WGS) study of 88 matched HCC tumor/normal pairs, 81 of which are Hepatitis B virus (HBV) positive, seeking to identify genetically altered genes and pathways implicated in HBV-associated HCC. We find beta-catenin to be the most frequently mutated oncogene (15.9%) and TP53 the most frequently mutated tumor suppressor (35.2%). The Wnt/beta-catenin and JAK/STAT pathways, altered in 62.5% and 45.5% of cases, respectively, are likely to act as two major oncogenic drivers in HCC. This study also identifies several prevalent and potentially actionable mutations, including activating mutations of Janus kinase 1 (JAK1), in 9.1% of patients and provides a path toward therapeutic intervention of the disease.

Decoding complex patterns of genomic rearrangement in hepatocellular carcinoma.

Elucidating the molecular basis of hepatocellular carcinoma (HCC) is crucial to developing targeted diagnostics and therapies for this deadly disease. The landscape of somatic genomic rearrangements (GRs), which can lead to oncogenic gene fusions, remains poorly characterized in HCC. We have predicted 4314 GRs including large-scale insertions, deletions, inversions and translocations based on the whole-genome sequencing data for 88 primary HCC tumor/non-tumor tissues. We identified chromothripsis in 5 HCC genomes (5.7%) recurrently affecting chromosomal arms 1q and 8q. Albumin (ALB) was found to harbor GRs, deactivating mutations and deletions in 10% of cohort. Integrative analysis identified a pattern of paired intra-chromosomal translocations flanking focal amplifications and asymmetrical patterns of copy number variation flanking breakpoints of translocations. Furthermore, we predicted 260 gene fusions which frequently result in aberrant over-expression of the 3' genes in tumors and validated 18 gene fusions, including recurrent fusion (2/88) of ABCB11 and LRP2.

Patterns of somatic alterations between matched primary and metastatic colorectal tumors characterized by whole-genome sequencing.

Colorectal cancer (CRC) patients have poor prognosis after formation of distant metastasis. Understanding the molecular mechanisms by which genetic changes facilitate metastasis is critical for the development of targeted therapeutic strategies aimed at controlling disease progression while minimizing toxic side effects. A comprehensive portrait of somatic alterations in CRC and the changes between primary and metastatic tumors has yet to be developed. We performed whole genome sequencing of two primary CRC tumors and their matched liver metastases. By comparing to matched germline DNA, we catalogued somatic alterations at multiple scales, including single nucleotide variations, small insertions and deletions, copy number aberrations and structural variations in both the primary and matched metastasis. We found that the majority of these somatic alterations are present in both sites. Despite the overall similarity, several de novo alterations in the metastases were predicted to be deleterious, in genes including FBXW7, DCLK1 and FAT2, which might contribute to the initiation and progression of distant metastasis. Through careful examination of the mutation prevalence among tumor cells at each site, we also proposed distinct clonal evolution patterns between primary and metastatic tumors in the two cases. These results suggest that somatic alterations may play an important role in driving the development of colorectal cancer metastasis and present challenges and opportunities when considering the choice of treatment.

Genomic landscape of copy number aberrations enables the identification of oncogenic drivers in hepatocellular carcinoma.

UNLABELLED: Cancer is a genetic disease with frequent somatic DNA alterations. Studying recurrent copy number aberrations (CNAs) in human cancers would enable the elucidation of disease mechanisms and the prioritization of candidate oncogenic drivers with causal roles in oncogenesis. We have comprehensively and systematically characterized CNAs and the accompanying gene expression changes in tumors and matched nontumor liver tissues from 286 hepatocellular carcinoma (HCC) patients. Our analysis identified 29 recurrently amplified and 22 recurrently deleted regions with a high level of copy number changes. These regions harbor established oncogenes and tumor suppressors, including CCND1 (cyclin D1), MET (hepatocyte growth factor receptor), CDKN2A (cyclin-dependent kinase inhibitor 2A) and CDKN2B (cyclin-dependent kinase inhibitor 2B), as well as many other genes not previously reported to be involved in liver carcinogenesis. Pathway analysis of cis-acting genes in the amplification and deletion peaks implicates alterations of core cancer pathways, including cell-cycle, p53 signaling, phosphoinositide 3-kinase signaling, mitogen-activated protein kinase signaling, Wnt signaling, and transforming growth factor beta signaling, in a large proportion of HCC patients. We further credentialed two candidate driver genes (BCL9 and MTDH) from the recurrent focal amplification peaks and showed that they play a significant role in HCC growth and survival. CONCLUSION: We have demonstrated that characterizing the CNA landscape in HCC will facilitate the understanding of disease mechanisms and the identification of oncogenic drivers that may serve as potential therapeutic targets for the treatment of this devastating disease.

Pancreatic Cancer Action Network's SPARK: A Cloud-Based Patient Health Data and Analytics Platform for Pancreatic Cancer.

PURPOSE: Pancreatic cancer currently holds the position of third deadliest cancer in the United States and the 5-year survival rate is among the lowest for major cancers at just 12%. Thus, continued research efforts to better understand the clinical and molecular underpinnings of pancreatic cancer are critical to developing both early detection methodologies as well as improved therapeutic options. This study introduces Pancreatic Cancer Action Network's (PanCAN's) SPARK, a cloud-based data and analytics platform that integrates patient health data from the PanCAN's research initiatives and aims to accelerate pancreatic cancer research by making real-world patient health data and analysis tools easier to access and use. MATERIALS AND METHODS: The SPARK platform integrates clinical, molecular, multiomic, imaging, and patient-reported data generated from PanCAN's research initiatives. The platform is built on a cloud-based infrastructure powered by Velsera. Cohort exploration and browser capabilities are built using Velsera ARIA, a specialized product for leveraging clinicogenomic data to build cohorts, query variant information, and drive downstream association analyses. Data science and analytic capabilities are also built into the platform allowing researchers to perform simple to complex analysis. RESULTS: Version 1 of the SPARK platform was released to pilot users, who represented diverse end users, including molecular biologists, clinicians, and bioinformaticians. Included in the pilot release of SPARK are deidentified clinical (including treatment and outcomes data), molecular, multiomic, and whole-slide pathology images for over 600 patients enrolled in PanCAN's Know Your Tumor molecular profiling service. CONCLUSION: The pilot release of the SPARK platform introduces qualified researchers to PanCAN real-world patient health data and analytical resources in a centralized location.

Longitudinal multi-omics study of palbociclib resistance in HR-positive/HER2-negative metastatic breast cancer.

BACKGROUND: Cyclin-dependent kinase 4/6 inhibitor (CDK4/6) therapy plus endocrine therapy (ET) is an effective treatment for patients with hormone receptor-positive/human epidermal receptor 2-negative metastatic breast cancer (HR+/HER2- MBC); however, resistance is common and poorly understood. A comprehensive genomic and transcriptomic analysis of pretreatment and post-treatment tumors from patients receiving palbociclib plus ET was performed to delineate molecular mechanisms of drug resistance. METHODS: Tissue was collected from 89 patients with HR+/HER2- MBC, including those with recurrent and/or metastatic disease, receiving palbociclib plus an aromatase inhibitor or fulvestrant at Samsung Medical Center and Seoul National University Hospital from 2017 to 2020. Tumor biopsy and blood samples obtained at pretreatment, on-treatment (6 weeks and/or 12 weeks), and post-progression underwent RNA sequencing and whole-exome sequencing. Cox regression analysis was performed to identify the clinical and genomic variables associated with progression-free survival. RESULTS: Novel markers associated with poor prognosis, including genomic scar features caused by homologous repair deficiency (HRD), estrogen response signatures, and four prognostic clusters with distinct molecular features were identified. Tumors with TP53 mutations co-occurring with a unique HRD-high cluster responded poorly to palbociclib plus ET. Comparisons of paired pre- and post-treatment samples revealed that tumors became enriched in APOBEC mutation signatures, and many switched to aggressive molecular subtypes with estrogen-independent characteristics. We identified frequent genomic alterations upon disease progression in RB1, ESR1, PTEN, and KMT2C. CONCLUSIONS: We identified novel molecular features associated with poor prognosis and molecular mechanisms that could be targeted to overcome resistance to CKD4/6 plus ET. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03401359. The trial was posted on 18 January 2018 and registered prospectively.

Dose-dependent effects of small-molecule antagonists on the genomic landscape of androgen receptor binding.

BACKGROUND: The androgen receptor plays a critical role throughout the progression of prostate cancer and is an important drug target for this disease. While chromatin immunoprecipitation coupled with massively parallel sequencing (ChIP-Seq) is becoming an essential tool for studying transcription and chromatin modification factors, it has rarely been employed in the context of drug discovery. RESULTS: Here we report changes in the genome-wide AR binding landscape due to dose-dependent inhibition by drug-like small molecules using ChIP-Seq. Integration of sequence analysis, transcriptome profiling, cell viability assays and xenograft tumor growth inhibition studies enabled us to establish a direct cistrome-activity relationship for two novel potent AR antagonists. By selectively occupying the strongest binding sites, AR signaling remains active even when androgen levels are low, as is characteristic of first-line androgen ablation therapy. Coupled cistrome and transcriptome profiling upon small molecule antagonism led to the identification of a core set of AR direct effector genes that are most likely to mediate the activities of targeted agents: unbiased pathway mapping revealed that AR is a key modulator of steroid metabolism by forming a tightly controlled feedback loop with other nuclear receptor family members and this oncogenic effect can be relieved by antagonist treatment. Furthermore, we found that AR also has an extensive role in negative gene regulation, with estrogen (related) receptor likely mediating its function as a transcriptional repressor. CONCLUSIONS: Our study provides a global and dynamic view of AR's regulatory program upon antagonism, which may serve as a molecular basis for deciphering and developing AR therapeutics.

Comparative biomarker analysis of PALOMA-2/3 trials for palbociclib.

While cyclin-dependent kinase 4/6 (CDK4/6) inhibitors, including palbociclib, combined with endocrine therapy (ET), are becoming the standard-of-care for hormone receptor-positive/human epidermal growth factor receptor 2‒negative metastatic breast cancer, further mechanistic insights are needed to maximize benefit from the treatment regimen. Herein, we conducted a systematic comparative analysis of gene expression/progression-free survival relationship from two phase 3 trials (PALOMA-2 [first-line] and PALOMA-3 [≥second-line]). In the ET-only arm, there was no inter-therapy line correlation. However, adding palbociclib resulted in concordant biomarkers independent of initial ET responsiveness, with shared sensitivity genes enriched in estrogen response and resistance genes over-represented by mTORC1 signaling and G2/M checkpoint. Biomarker patterns from the combination arm resembled patterns observed in ET in advanced treatment-naive patients, especially patients likely to be endocrine-responsive. Our findings suggest palbociclib may recondition endocrine-resistant tumors to ET, and may guide optimal therapeutic sequencing by partnering CDK4/6 inhibitors with different ETs. Pfizer (NCT01740427; NCT01942135).

P-cadherin expression as a prognostic biomarker in a 3992 case tissue microarray series of breast cancer.

P-cadherin is a calcium-dependent cell-cell adhesion glycoprotein. P-cadherin expression is restricted to the myoepithelial cells in normal breast tissue, and aberrant staining has also been described in invasive tumors. Several small studies have reported P-cadherin as a marker of poor outcome in breast cancer patients but its prognostic significance in relation to other variables has not been established in a large series of breast cancers. A tissue microarray was constructed from 3992 cases of invasive breast carcinoma, and P-cadherin expression was evaluated using immunohistochemistry. Median follow-up was 12.5 years. The immunohistochemistry-based definitions of cancer subtypes were luminal (ER+ or PR+/HER2-), luminal/HER2+ (ER+ or PR+/HER2+), HER2+ (ER-/PR-/HER2+), and basal (ER-/PR-/HER2-/CK5/6+ or EGFR+). Clinical covariate and biomarker associations were assessed using contingency tables, and Pearson's χ(2) or Fisher's exact test. Survival associations were assessed using Kaplan-Meier plots, logrank and Breslow tests, and Cox proportional hazards regression analysis. P-cadherin was expressed in 34.8% (1290/3710, 50% cut point) of cases. P-cadherin staining was strongly associated with HER2+ and basal carcinoma subtypes (P<0.0005). P-cadherin-positive patients showed significantly poorer short-term (0-10 years) overall survival, disease-specific survival, distant relapse-free interval, and locoregional relapse-free interval in univariable models (P<0.05). In multivariable Cox models containing standard clinical covariates and cancer subtypes, P-cadherin did not show independent prognostic value. P-cadherin expression was positively associated with histological grade, chemotherapy, Ki-67, EGFR, CK5/6, p53, YB-1, and HER2 expression (P<0.002), and negatively associated with age at diagnosis, ER, PR, and Bcl-2 expression (P<0.0005). This study shows the value of P-cadherin as a marker of poor prognosis. The large sample size of this series clarifies contradictory findings of many smaller studies. P-cadherin positivity is associated with high-grade tumor subtypes and well-established markers of poor prognosis, and may represent a promising antibody therapeutic target.

Preclinical Evaluation of 89Zr-Df-IAB22M2C PET as an Imaging Biomarker for the Development of the GUCY2C-CD3 Bispecific PF-07062119 as a T Cell Engaging Therapy.

PURPOSE: A sensitive and specific imaging biomarker to monitor immune activation and quantify pharmacodynamic responses would be useful for development of immunomodulating anti-cancer agents. PF-07062119 is a T cell engaging bispecific antibody that binds to CD3 and guanylyl cyclase C, a protein that is over-expressed by colorectal cancers. Here, we used 89Zr-Df-IAB22M2C (89Zr-Df-Crefmirlimab), a human CD8-specific minibody to monitor CD8+ T cell infiltration into tumors by positron emission tomography. We investigated the ability of 89Zr-Df-IAB22M2C to track anti-tumor activity induced by PF-07062119 in a human CRC adoptive transfer mouse model (with injected activated/expanded human T cells), as well as the correlation of tumor radiotracer uptake with CD8+ immunohistochemical staining. PROCEDURES: NOD SCID gamma mice bearing human CRC LS1034 tumors were treated with four different doses of PF-07062119, or a non-targeted CD3 BsAb control, and imaged with 89Zr-Df-IAB22M2C PET at days 4 and 9. Following PET/CT imaging, mice were euthanized and dissected for ex vivo distribution analysis of 89Zr-Df-IAB22M2C in tissues on days 4 and 9, with additional data collected on day 6 (supplementary). Data were analyzed and reported as standard uptake value and %ID/g for in vivo imaging and ex vivo tissue distribution. In addition, tumor tissues were evaluated by immunohistochemistry for CD8+ T cells. RESULTS: The results demonstrated substantial mean uptake of 89Zr-Df-IAB22M2C (%ID/g) in PF-07062119-treated tumors, with significant increases in comparison to non-targeted BsAb-treated controls, as well as PF-07062119 dose-dependent responses over time of treatment. A moderate correlation was observed between tumor tissue radioactivity uptake and CD8+ cell density, demonstrating the value of the imaging agent for non-invasive assessment of intra-tumoral CD8+ T cells and the mechanism of action for PF-07062119. CONCLUSION: Immune-imaging technologies for quantitative cellular measures would be a valuable biomarker in immunotherapeutic clinical development. We demonstrated a qualification of 89Zr-IAB22M2C PET to evaluate PD responses (mice) to a novel immunotherapeutic.

Statistical method on nonrandom clustering with application to somatic mutations in cancer.

BACKGROUND: Human cancer is caused by the accumulation of tumor-specific mutations in oncogenes and tumor suppressors that confer a selective growth advantage to cells. As a consequence of genomic instability and high levels of proliferation, many passenger mutations that do not contribute to the cancer phenotype arise alongside mutations that drive oncogenesis. While several approaches have been developed to separate driver mutations from passengers, few approaches can specifically identify activating driver mutations in oncogenes, which are more amenable for pharmacological intervention. RESULTS: We propose a new statistical method for detecting activating mutations in cancer by identifying nonrandom clusters of amino acid mutations in protein sequences. A probability model is derived using order statistics assuming that the location of amino acid mutations on a protein follows a uniform distribution. Our statistical measure is the differences between pair-wise order statistics, which is equivalent to the size of an amino acid mutation cluster, and the probabilities are derived from exact and approximate distributions of the statistical measure. Using data in the Catalog of Somatic Mutations in Cancer (COSMIC) database, we have demonstrated that our method detects well-known clusters of activating mutations in KRAS, BRAF, PI3K, and beta-catenin. The method can also identify new cancer targets as well as gain-of-function mutations in tumor suppressors. CONCLUSIONS: Our proposed method is useful to discover activating driver mutations in cancer by identifying nonrandom clusters of somatic amino acid mutations in protein sequences.

Chemotherapy induces dynamic immune responses in breast cancers that impact treatment outcome.

To elucidate the effects of neoadjuvant chemotherapy (NAC), we conduct whole transcriptome profiling coupled with histopathology analyses of a longitudinal breast cancer cohort of 146 patients including 110 pairs of serial tumor biopsies collected before treatment, after the first cycle of treatment and at the time of surgery. Here, we show that cytotoxic chemotherapies induce dynamic changes in the tumor immune microenvironment that vary by subtype and pathologic response. Just one cycle of treatment induces an immune stimulatory microenvironment harboring more tumor infiltrating lymphocytes (TILs) and up-regulation of inflammatory signatures predictive of response to anti-PD1 therapies while residual tumors are immune suppressed at end-of-treatment compared to the baseline. Increases in TILs and CD8+ T cell proportions in response to NAC are independently associated with pathologic complete response. Further, on-treatment immune response is more predictive of treatment outcome than immune features in paired baseline samples although these are strongly correlated.

Combining CDK4/6 inhibition with taxanes enhances anti-tumor efficacy by sustained impairment of pRB-E2F pathways in squamous cell lung cancer.

The CDK4/6 inhibitor palbociclib reduces tumor growth by decreasing retinoblastoma (RB) protein phosphorylation and inducing cell cycle arrest at the G1/S phase transition. Palbociclib in combination with anti-hormonal therapy brings significant benefit to breast cancer patients. In this study, novel combination approaches and underlying molecular/cellular mechanisms for palbociclib were explored in squamous cell lung cancer (SqCLC), the second most common subtype of non-small cell lung cancer. While approximate 20% lung patients benefit from immunotherapy, most SqCLC patients who receive platinum-doublet chemotherapy as first-line treatment, which often includes a taxane, are still in need of more effective combination therapies. Our results demonstrated enhanced cytotoxicity and anti-tumor effect with palbociclib plus taxanes at clinically achievable doses in multiple SqCLC models with diverse cancer genetic backgrounds. Comprehensive gene expression analysis revealed a sustained disruption of pRB-E2F signaling by combination that was accompanied with enhanced regulation of pleiotropic biological effects. These included several novel mechanisms such as abrogation of G2/M and mitotic spindle assembly checkpoints, as well as impaired induction of hypoxia-inducible factor 1 alpha (HIF-1α). The decrease in HIF-1α modulated a couple key angiogenic and anti-angiogenic factors, resulting in an enhanced anti-angiogenic effect. This preclinical work suggests a new therapeutic opportunity for palbociclib in lung and other cancers currently treated with taxane based chemotherapy as standard of care.

Antisense inhibition of 11betahydroxysteroid dehydrogenase type 1 improves diabetes in a novel cortisone-induced diabetic KK mouse model.

The inhibition of 11betahydroxysteroid dehydrogenase 1 (11betaHSD1), an enzyme that catalyzes the conversion of inactive cortisone to active cortisol, is an attractive target to treat diabetes by suppressing hepatic gluconeogenesis. To test this hypothesis, we developed a novel glucocorticoid-induced diabetic KK mouse model and used 11betaHSD1 antisense oligonucleotide (ASO) as an inhibitory tool. KK mice were treated with 25 or 50mg/kg/day of 11betaHSD1 ASO for 28 days. On day 25, cortisone pellets were surgically implanted to induce diabetes. In the ASO-treated mice, plasma blood glucose levels were significantly reduced by up to 54%. In parallel, cortisol and other diabetes endpoints were also significantly reduced. Hepatic 11betaHSD1 mRNA was suppressed by up to 84% with a concomitant respective decrease of up to 49% in the expression of PEPCK. The results suggest that inhibition of 11betaHSD1 activity reduces the availability of cortisol to activate the glucocorticoid receptor, down regulates gluconeogenesis and thus reduces plasma glucose levels in cortisone-induced diabetic KK mice.

Demonstration of proof of mechanism and pharmacokinetics and pharmacodynamic relationship with 4'-cyano-biphenyl-4-sulfonic acid (6-amino-pyridin-2-yl)-amide (PF-915275), an inhibitor of 11 -hydroxysteroid dehydrogenase type 1, in cynomolgus monkeys.

Glucocorticoids, through activation of the glucocorticoid receptor (GR), regulate hepatic gluconeogenesis. Elevated hepatic expression and activity of 11beta-hydroxysteroid dehydrogenase type 1 (11betaHSD1) play a key role in ligand-induced activation of the GR through the production of cortisol. Evidence from genetically modified mice suggests that inhibition of 11betaHSD1 might be a therapeutic approach to treat the metabolic syndrome. We have identified a potent 11betaHSD1 inhibitor, 4'-cyano-biphenyl-4-sulfonic acid (6-amino-pyridin-2-yl)-amide (PF-915275), that is selective for the primate and human enzymes. The objective of this study was to demonstrate target inhibition with PF-915275 and to quantify the relationship between target inhibition and drug exposure in monkeys. We characterized the ability of PF-915275 to inhibit the conversion of prednisone, a synthetic cortisone analog that can be distinguished from the endogenous substrate cortisone, enabling a direct measure of substrate to product conversion without the complication of feedback. Adult cynomolgus monkeys were administered either vehicle or various doses of PF-915275 followed by a 10-mg/kg dose of prednisone. Prednisone conversion to prednisolone and the concentrations of PF-915275 were measured by liquid chromatography/tandem mass spectrometry. PF-915275 dose-dependently inhibited 11betaHSD1-mediated conversion of prednisone to prednisolone, with a maximum of 87% inhibition at a 3-mg/kg dose. An exposure-response relationship was demonstrated, with an estimated EC(50) of 391 nM (total) and 17 nM (free). Insulin levels were also reduced in a dose-related manner. These results should enable the development of a biomarker for evaluating target modulation in humans that will aid in identifying 11betaHSD1 inhibitors to treat diabetes and other related metabolic diseases.

Complexity and simplicity of ligand-macromolecule interactions: the energy landscape perspective.

The energy landscape approach has contributed to recent progress in understanding the complexity and simplicity of ligand-macromolecule interactions. Significant advances in computational structure prediction of ligand-protein complexes have been made using approaches that include the effects of protein flexibility and incorporate a hierarchy of energy functions. The results suggest that the complexity of structure prediction in molecular recognition may be determined by low-resolution properties of the underlying binding energy landscapes and by the nature of the energy funnels near the native structures of the complexes.