Gene Expression Ratios Lead to Accurate and Translatable Predictors of DR5 Agonism across Multiple Tumor Lineages

Expression of Androgen and Estrogen Receptors in the Human Lacrimal Gland

Lacrimal gland dysfunction causes dry eye disease (DED) due to decreased tear production. Aqueous-deficient DED is more prevalent in women, suggesting that sexual dimorphism of the human lacrimal gland could be a potential cause. Sex steroid hormones are a key factor in the development of sexual dimorphism. This study aimed to quantify estrogen receptor (ER) and androgen receptor (AR) expression in the human lacrimal gland and compare it between sexes. RNA was isolated from 35 human lacrimal gland tissue samples collected from 19 cornea donors. AR, ERα, and ERβ mRNA was identified in all samples, and their expression was quantified using qPCR. Immunohistochemical staining was performed on selected samples to evaluate protein expression of the receptors. ERα mRNA expression was significantly higher than the expression of AR and ERβ. No difference in sex steroid hormone (SSH) receptor mRNA expression was observed between sexes, and no correlation was observed with age. If ERα protein expression is found to be concordant with mRNA expression, it should be investigated further as a potential target for hormone therapy of DED. Further research is needed to elucidate the role of sex steroid hormone receptors in sex-related differences of lacrimal gland structure and disease.

Multimeric Anti-DR5 IgM Agonist Antibody IGM-8444 Is a Potent Inducer of Cancer Cell Apoptosis and Synergizes with Chemotherapy and BCL-2 Inhibitor ABT-199

Death receptor 5 (DR5) is an attractive target for cancer therapy due to its broad upregulated expression in multiple cancers and ability to directly induce apoptosis. Though anti-DR5 IgG antibodies have been evaluated in clinical trials, limited efficacy has been attributed to insufficient receptor crosslinking. IGM-8444 is an engineered, multivalent agonistic IgM antibody with 10 binding sites to DR5 that induces cancer cell apoptosis through efficient DR5 multimerization. IGM-8444 bound to DR5 with high avidity and was substantially more potent than an IgG with the same binding domains. IGM-8444 induced cytotoxicity in a broad panel of solid and hematologic cancer cell lines but did not kill primary human hepatocytes in vitro, a potential toxicity of DR5 agonists. In multiple xenograft tumor models, IGM-8444 monotherapy inhibited tumor growth, with strong and sustained tumor regression observed in a gastric PDX model. When combined with chemotherapy or the BCL-2 inhibitor ABT-199, IGM-8444 exhibited synergistic in vitro tumor cytotoxicity and enhanced in vivo efficacy, without augmenting in vitro hepatotoxicity. These results support the clinical development of IGM-8444 in solid and hematologic malignancies as a monotherapy and in combination with chemotherapy or BCL-2 inhibition.

A transcriptome-based classifier to determine molecular subtypes in medulloblastoma

Medulloblastoma is a highly heterogeneous pediatric brain tumor with five molecular subtypes, Sonic Hedgehog TP53-mutant, Sonic Hedgehog TP53-wildtype, WNT, Group 3, and Group 4, defined by the World Health Organization. The current mechanism for classification into these molecular subtypes is through the use of immunostaining, methylation, and/or genetics. We surveyed the literature and identified a number of RNA-Seq and microarray datasets in order to develop, train, test, and validate a robust classifier to identify medulloblastoma molecular subtypes through the use of transcriptomic profiling data. We have developed a GPL-3 licensed R package and a Shiny Application to enable users to quickly and robustly classify medulloblastoma samples using transcriptomic data. The classifier utilizes a large composite microarray dataset (15 individual datasets), an individual microarray study, and an RNA-Seq dataset, using gene ratios instead of gene expression measures as features for the model. Discriminating features were identified using the limma R package and samples were classified using an unweighted mean of normalized scores. We utilized two training datasets and applied the classifier in 15 separate datasets. We observed a minimum accuracy of 85.71% in the smallest dataset and a maximum of 100% accuracy in four datasets with an overall median accuracy of 97.8% across the 15 datasets, with the majority of misclassification occurring between the heterogeneous Group 3 and Group 4 subtypes. We anticipate this medulloblastoma transcriptomic subtype classifier will be broadly applicable to the cancer research and clinical communities.

Dual Epitope Targeting and Enhanced Hexamerization by DR5 Antibodies as a Novel Approach to Induce Potent Antitumor Activity Through DR5 Agonism

Higher-order death receptor 5 (DR5) clustering can induce tumor cell death; however, therapeutic compounds targeting DR5 have achieved limited clinical efficacy. We describe HexaBody-DR5/DR5, an equimolar mixture of two DR5-specific IgG1 antibodies with an Fc-domain mutation that augments antibody hexamerization after cell surface target binding. The two antibodies do not compete for binding to DR5 as demonstrated using binding competition studies, and binding to distinct epitopes in the DR5 extracellular domain was confirmed by crystallography. The unique combination of dual epitope targeting and increased IgG hexamerization resulted in potent DR5 agonist activity by inducing efficient DR5 outside-in signaling and caspase-mediated cell death. Preclinical studies in vitro and in vivo demonstrated that maximal DR5 agonist activity could be achieved independent of Fc gamma receptor-mediated antibody crosslinking. Most optimal agonism was observed in the presence of complement complex C1, although without inducing complement-dependent cytotoxicity. It is hypothesized that C1 may stabilize IgG hexamers that are formed after binding of HexaBody-DR5/DR5 to DR5 on the plasma membrane, thereby strengthening DR5 clustering and subsequent outside-in signaling. We observed potent antitumor activity in vitro and in vivo in large panels of patient-derived xenograft models representing various solid cancers. The results of our preclinical studies provided the basis for an ongoing clinical trial exploring the activity of HexaBody-DR5/DR5 (GEN1029) in patients with malignant solid tumors.

NRF2-Driven KEAP1 Transcription in Human Lung Cancer

Constitutive NRF2 activation by disrupted KEAP1-NRF2 interaction has been reported in a variety of human cancers. However, studies focusing on NRF2-driven KEAP1 expression under human cancer contexts are still uncommon. We examined mRNA expression correlation between NRF2 and KEAP1 in multiple human cancers. We measured KEAP1 mRNA and protein alterations in response to the activation or silencing of NRF2. We queried chromatin immunoprecipitation sequencing (ChIP-seq) datasets to identify NRF2 binding to KEAP1 promoters in human cells. We used reporter assay and CRISPR editing to assess KEAP1 promoter activity and mRNA abundance change. To determine specimen implication of the feedback pattern, we used gene expression ratio to predict NRF2 signal disruption as well as patients' prognosis. Correlation analysis showed KEAP1 mRNA expression was in positive association with NRF2 in multiple squamous cell cancers. The positive correlations were consistent across all squamous cell lung cancer cohorts, but not in adenocarcinomas. In human lung cells, NRF2 interventions significantly altered KEAP1 mRNA and protein expressions. ChIP-quantitative PCR (ChIP-qPCR) and sequencing data demonstrated consistent NRF2 occupancy to KEAP1 promoter. Deleting NRF2 binding site significantly reduced baseline and inducible KEAP1 promoter activity and KEAP1 mRNA expression. By incorporating tumor tissue KEAP1 mRNA expressions in estimating NRF2 signaling disruptions, we found increased TXN/KEAP1 mRNA ratio in cases with NRF2 gain or KEAP1 loss and decreased NRF2/KEAP1 mRNA ratio in cases with NRF2-KEAP1 somatic mutations. In TCGA PanCancer datasets, we also identified that cases with loss-of-function mutations in NRF2 pathway recurrently appeared above the NRF2-KEAP1 mRNA expression regression lines. Moreover, compared with previous NRF2 signatures, the ratio-based strategy showed better predictive performance in survival analysis with multiple squamous cell lung cancer cohort validations. IMPLICATIONS: NRF2-driven KEAP1 transcription is a crucial component of NRF2 signaling modulation. This hidden circuit will provide in-depth insight into novel cancer prevention and therapeutic strategies.

MicroRNA Ratios Distinguish Melanomas from Nevi

The use of microRNAs as biomarkers has been proposed for many diseases, including the diagnosis of melanoma. Although hundreds of microRNAs have been identified as differentially expressed in melanomas as compared to benign melanocytic lesions, a limited consensus has been achieved across studies, constraining the effective use of these potentially useful markers. In this study, we applied a machine learning-based pipeline to a dataset consisting of genetic features, clinical features, and next-generation microRNA sequencing from micro-dissected formalin-fixed paraffin embedded melanomas and their adjacent benign precursor nevi. We identified patient age and tumor cellularity as variables that frequently confound the measured expression of potentially diagnostic microRNAs. By employing the ratios of microRNAs that were either enriched or depleted in melanoma compared to the nevi as a normalization strategy, we developed a model that classified all the available published cohorts with an area under the receiver operating characteristic curve of 0.98. External validation on an independent cohort classified lesions with 81% sensitivity and 88% specificity and was uninfluenced by the tumor content of the sample or patient age.

Human against Machine? Machine Learning Identifies MicroRNA Ratios as Biomarkers for Melanoma

Identification of quantitative molecular biomarkers to distinguish melanoma from nevi is highly desirable. Expressions of microRNAs (miRNAs) are promising candidates but lack consensus in many studies. Torres et al. (2020) utilized a machine learning pipeline to identify miRNA ratios as strong biomarkers. Results indicate that machine learning, although powerful, requires human input to identify high quality biomarker signatures.