Transcriptomic profiling in canines and humans reveals cancer specific gene modules and biological mechanisms common to both species.

Understanding relationships between spontaneous cancer in companion (pet) canines and humans can facilitate biomarker and drug development in both species. Towards this end we developed an experimental-bioinformatic protocol that analyzes canine transcriptomics data in the context of existing human data to evaluate comparative relevance of canine to human cancer. We used this protocol to characterize five canine cancers: melanoma, osteosarcoma, pulmonary carcinoma, B- and T-cell lymphoma, in 60 dogs. We applied an unsupervised, iterative clustering method that yielded five co-expression modules and found that each cancer exhibited a unique module expression profile. We constructed cancer models based on the co-expression modules and used the models to successfully classify the canine data. These canine-derived models also successfully classified human tumors representing the same cancers, indicating shared cancer biology between canines and humans. Annotation of the module genes identified cancer specific pathways relevant to cells-of-origin and tumor biology. For example, annotations associated with melanin production (PMEL, GPNMB, and BACE2), synthesis of bone material (COL5A2, COL6A3, and COL12A1), synthesis of pulmonary surfactant (CTSH, LPCAT1, and NAPSA), ribosomal proteins (RPL8, RPS7, and RPLP0), and epigenetic regulation (EDEM1, PTK2B, and JAK1) were unique to melanoma, osteosarcoma, pulmonary carcinoma, B- and T-cell lymphoma, respectively. In total, 152 biomarker candidates were selected from highly expressing modules for each cancer type. Many of these biomarker candidates are under-explored as drug discovery targets and warrant further study. The demonstrated transferability of classification models from canines to humans enforces the idea that tumor biology, biomarker targets, and associated therapeutics, discovered in canines, may translate to human medicine.

Cross-Platform Bayesian Optimization System for Autonomous Biological Assay Development.

Current high-throughput screening assay optimization is often a manual and time-consuming process, even when utilizing design-of-experiment approaches. A cross-platform, Cloud-based Bayesian optimization-based algorithm was developed as part of the National Center for Advancing Translational Sciences (NCATS) ASPIRE (A Specialized Platform for Innovative Research Exploration) Initiative to accelerate preclinical drug discovery. A cell-free assay for papain enzymatic activity was used as proof of concept for biological assay development and system operationalization. Compared with a brute-force approach that sequentially tested all 294 assay conditions to find the global optimum, the Bayesian optimization algorithm could find suitable conditions for optimal assay performance by testing 21 assay conditions on average, with up to 20 conditions being tested simultaneously, as confirmed by repeated simulation. The algorithm could achieve a sevenfold reduction in costs for lab supplies and high-throughput experimentation runtime, all while being controlled from a remote site through a secure connection. Based on this proof of concept, this technology is expected to be applied to more complex biological assays and automated chemistry reaction screening at NCATS, and should be transferable to other institutions.

A Perspective on Innovating the Chemistry Lab Bench.

Innovating on the design and function of the chemical bench remains a quintessential challenge of the ages. It requires a deep understanding of the important role chemistry plays in scientific discovery as well a first principles approach to addressing the gaps in how work gets done at the bench. This perspective examines how one might explore designing and creating a sustainable new standard for advancing automated chemistry bench itself. We propose how this might be done by leveraging recent advances in laboratory automation whereby integrating the latest synthetic, analytical and information technologies, and AI/ML algorithms within a standardized framework, maximizes the value of the data generated and the broader utility of such systems. Although the context of this perspective focuses on the design of advancing molecule of potential therapeutic value, it would not be a stretch to contemplate how such systems could be applied to other applied disciplines like advanced materials, foodstuffs, or agricultural product development.

Translational in vitro research: integrating 3D drug discovery and development processes into the drug development pipeline.

As we witness steady progress towards the development of robust, scalable, and reproducible 3D tissue models for preclinical drug testing, there is a need for systematic physiological and pharmacological validation and benchmarking. Ongoing and future studies should generate evidence as to whether 3D tissue models are more predictive, help reduce the risk of failure rate, and can be used for decision making in the drug discovery and development pipeline. Here, we discuss the importance of harmonizing the validation of these models based on throughput capacity and physiological complexity as a requirement to establish their true translational capacity. We also outline our strategy for a novel 3D-tailored holistic drug discovery concept rather than piecemeal integration of 3D models into the current process.

Twenty-First Century Diseases: Commonly Rare and Rarely Common?

Alzheimer's drugs are failing at a rate of 99.6%, and success rate for drugs designed to help patients with this form of dementia is 47 times less than for drugs designed to help patients with cancers ( www.scientificamerican.com/article/why-alzheimer-s-drugs-keep-failing/2014 ). How can it be so difficult to produce a valuable drug for Alzheimer's disease? Each human has a unique genetic and epigenetic makeup, thus endowing individuals with a highly unique complement of genes, polymorphisms, mutations, RNAs, proteins, lipids, and complex sugars, resulting in distinct genome, proteome, metabolome, and also microbiome identity. This editorial is taking into account the uniqueness of each individual and surrounding environment, and stresses the point that a more accurate definition of a "common" disorder could be simply the amalgamation of a myriad of "rare" diseases. These rare diseases are being grouped together because they share a rather constant complement of common features and, indeed, generally respond to empirically developed treatments, leading to a positive outcome consistently. We make the case that it is highly unlikely that such treatments, despite their statistical success measured with large cohorts using standardized clinical research, will be effective on all patients until we increase the depth and fidelity of our understanding of the individual "rare" diseases that are grouped together in the "buckets" of common illnesses. Antioxid. Redox Signal. 27, 511-516.

Exploring Drug Dosing Regimens In Vitro Using Real-Time 3D Spheroid Tumor Growth Assays.

Two-dimensional monolayer cell proliferation assays for cancer drug discovery have made the implementation of large-scale screens feasible but only seem to reflect a simplified view that oncogenes or tumor suppressor genes are the genetic drivers of cancer cell proliferation. However, there is now increased evidence that the cellular and physiological context in which these oncogenic events occur play a key role in how they drive tumor growth in vivo and, therefore, in how tumors respond to drug treatments. In vitro 3D spheroid tumor models are being developed to better mimic the physiology of tumors in vivo, in an attempt to improve the predictability and efficiency of drug discovery for the treatment of cancer. Here we describe the establishment of a real-time 3D spheroid growth, 384-well screening assay. The cells used in this study constitutively expressed green fluorescent protein (GFP), which enabled the real-time monitoring of spheroid formation and the effect of chemotherapeutic agents on spheroid size at different time points of sphere growth and drug treatment. This real-time 3D spheroid assay platform represents a first step toward the replication in vitro of drug dosing regimens being investigated in vivo. We hope that further development of this assay platform will allow the investigation of drug dosing regimens, efficacy, and resistance before preclinical and clinical studies.

A multicenter pilot study examining the role of circulating tumor cells as a blood-based tumor marker in patients with extensive small-cell lung cancer.

BACKGROUND: Small-cell lung cancer (SCLC), a variant of lung cancer marked by early metastases, accounts for 13% of all lung cancers diagnosed in US. Despite high response rates to treatment, it is an aggressive disease with a median survival of 9-11 months for patients with extensive stage (EX-SCLC). Detection of circulating tumor cells (CTCs) is a novel laboratory technique currently in use to determine response to therapy and to predict prognosis in breast, colorectal, and prostate cancer. We initiated a pilot study to analyze the role of CTCs as a biomarker of response and relapse in patients with EX-SCLC. METHODS: We collected blood samples from chemotherapy naïve patients with EX-SCLC prior to initiation of therapy, after completion of systemic therapy, and follow-up every 6-8 weeks and at relapse. The number of CTCs was determined using the cell search system in a central laboratory. The study was conducted in four different sites, and it was reviewed and approved by respective research review committees and IRBs. RESULTS: We enrolled 26 patients with EX-SCLC, 1 was excluded due to ineligibility, all were treated with platinum and etoposide. We observed partial response in 16 patients, stable disease in 3 patients, 1 patient with disease progression, and 6 patients were not assessed (5 deceased, 1 not available). The overall median number of CTCs in 24 patients measured at baseline and post-tx was 75 (range 0-3430) and 2 (range 0-526), respectively. A significant reduction in CTCs from baseline to post-treatment was identified for 15 subjects; the median reduction was 97.4% (range -100 to +100%, p < 0.001). Higher baseline CTCs and percentage change in post-treatment CTCs were associated with decreased survival. CONCLUSION: We demonstrated that it is feasible to detect CTCs in EX-SCLC. If validated in other prospective studies, CTCs could be a useful biomarker in the management of EX-SCLC by predicting patients' clinical responses to therapy.

A high-throughput screening assay for determining cellular levels of total tau protein.

The microtubule-associated protein (MAP) tau has been implicated in the pathology of numerous neurodegenerative diseases. In the past decade, the hyperphosphorylated and aggregated states of tau protein have been important targets in the drug discovery field for the potential treatment of Alzheimer's disease. Although several compounds have been reported to reduce the hyperphosphorylated state of tau or impact the stabilization of tau, their therapeutic activities are remain to be validated. Recently, reduction of total cellular tau protein has emerged as an alternate intervention point for drug development and a potential treatment of tauopathies. We have developed and optimized homogenous assays, using the AlphaLISA and HTRF assay technologies, for the quantification of total cellular tau protein levels in the SH-SY5Y neuroblastoma cell line. The signal-to-basal ratios were 375 and 5.3, and the Z' factors were 0.67 and 0.60 for the AlphaLISA and HTRF tau assays, respectively. The clear advantages of these homogeneous tau assays over conventional total tau assays, such as ELISA and Western blot, are the elimination of plate wash steps and miniaturization of the assay into 1536-well plate format for the ultra-high-throughput screening of large compound libraries.

Activity of anticancer agents in a three-dimensional cell culture model.

Cell-monolayer-based assays for chemotherapeutic drug discovery have proven to be highly artificial compared with physiological systems. The objective of this study was to culture cancer cells in a simple 3-dimensional (3D) collagen gel model to study the antiproliferative activity of known lung cancer drugs. The validity of our 3D model was tested by measuring the activity of 10 lung cancer drugs (Paclitaxel, Alimta, Zactima, Doxorubicin, Vinorelbine, Gemcitabine, 17AAg, Cisplatin, and 2 experimental drugs from the University of Kansas [KU174 and KU363]) in 2 lung cancer cell lines (A549 and H358) and comparing the activity in a traditional 2-dimensional (2D) in vitro cellular assay. Both potency and efficacy of these drugs were calculated to evaluate the activity of the drugs. Our results demonstrate that the activity of these drugs showed significant differences when tested in 3D cultures, which varied with individual drugs and the cell line used for testing. For example, the cytotoxicity of Paclitaxel, KU174, Alimta, Zacitma, Doxorubicin, Vinorelbine, KU363, and 17AAg was significantly changed when tested in the 3D model, whereas the potency of Cisplatin and Gemcitabine in H358 cell line remained unaffected. A similar pattern, with some differences, was observed in A549 cells and is discussed in detail in this article. The observed differences in potency and efficacy of the cancer drugs in 3D models suggest that the biological implications of screening configurations should be taken into account to select superior cancer drug candidates in preclinical studies.