Deconvolution of clinical variance in CAR-T cell pharmacology and response.

Chimeric antigen receptor T cell (CAR-T) expansion and persistence vary widely among patients and predict both efficacy and toxicity. However, the mechanisms underlying clinical outcomes and patient variability are poorly defined. In this study, we developed a mathematical description of T cell responses wherein transitions among memory, effector and exhausted T cell states are coordinately regulated by tumor antigen engagement. The model is trained using clinical data from CAR-T products in different hematological malignancies and identifies cell-intrinsic differences in the turnover rate of memory cells and cytotoxic potency of effectors as the primary determinants of clinical response. Using a machine learning workflow, we demonstrate that product-intrinsic differences can accurately predict patient outcomes based on pre-infusion transcriptomes, and additional pharmacological variance arises from cellular interactions with patient tumors. We found that transcriptional signatures outperform T cell immunophenotyping as predictive of clinical response for two CD19-targeted CAR-T products in three indications, enabling a new phase of predictive CAR-T product development.

Identification and stratification of systemic lupus erythematosus patients into two transcriptionally distinct clusters based on IFN-I signature.

OBJECTIVE: Despite the significant advancement in the understanding of the pathophysiology of systemic lupus erythematosus (SLE) variable clinical response to newer therapies remain a major concern, especially for patients with lupus nephritis and neuropsychiatric systemic lupus erythematosus (NPSLE). We performed this study with an objective to comprehensively characterize Indian SLE patients with renal and neuropsychiatric manifestation with respect to their gene signature, cytokine profile and immune cell phenotypes. METHODS: We characterized 68 Indian SLE subjects with diverse clinical profiles and disease activity and tried to identify differentially expressed genes and enriched pathways. To understand the temporal profile, same patients were followed at 6 and 12-months intervals. Additionally, auto-antibody profile, levels of various chemokines, cytokines and the proportion of different immune cells and their activation status were captured in these subjects. RESULTS: Multiple IFN-related pathways were enriched with significant increase in IFN-I gene signature in SLE patients as compared to normal healthy volunteers (NHV). We identified two transcriptionally distinct clusters within the same cohort of SLE patients with differential immune cell activation status, auto-antibody as well as plasma chemokines and cytokines profile. CONCLUSIONS: Identification of two distinct clusters of patients based on IFN-I signature provided new insights into the heterogeneity of underlying disease pathogenesis of Indian SLE cohort. Importantly, patient within those clusters retain their distinct expression dynamics of IFN-I signature over the time course of one year despite change in disease activity. This study will guide clinicians and researchers while designing future clinical trials on Indian SLE cohort.

Transporter Activity Changes in Nonalcoholic Steatohepatitis: Assessment with Plasma Coproporphyrin I and III.

Expression and functional changes in the organic anion transporting polypeptide (OATP)-multidrug resistance-associated protein (MRP) axis of transporters are well reported in patients with nonalcoholic steatohepatitis (NASH). These changes can impact plasma and tissue disposition of endo- and exogenous compounds. The transporter alterations are often assessed by administration of a xenobiotic or by transporter proteomic analysis from liver biopsies. Using gene expression, proteomics, and endogenous biomarkers, we show that the gene expression and activity of OATP and MRP transporters are associated with disease progression and recovery in humans and in preclinical animal models of NASH. Decreased OATP and increased MRP3/4 gene expression in two cohorts of patients with steatosis and NASH, as well as gene and protein expression in multiple NASH rodent models, have been established. Coproporphyrin I and III (CP I and III) were established as substrates of MRP4. CP I plasma concentration increased significantly in four animal models of NASH, indicating the transporter changes. Up to a 60-fold increase in CP I plasma concentration was observed in the mouse bile duct-ligated model compared with sham controls. In the choline-deficient amino acid-defined high-fat diet (CDAHFD) model, CP I plasma concentrations increased by >3-fold compared with chow diet-fed mice. In contrast, CP III plasma concentrations remain unaltered in the CDAHFD model, although they increased in the other three NASH models. These results suggest that tracking CP I plasma concentrations can provide transporter modulation information at a functional level in NASH animal models and in patients. SIGNIFICANCE STATEMENT: Our analysis demonstrates that multidrug resistance-associated protein 4 (MRP4) transporter gene expression tracks with nonalcoholic steatohepatitis (NASH) progression and intervention in patients. Additionally, we show that coproporphyrin I and III (CP I and III) are substrates of MRP4. CP I plasma and liver concentrations increase in different diet- and surgery-induced rodent NASH models, likely explained by both gene- and protein-level changes in transporters. CP I and III are therefore potential plasma-based biomarkers that can track NASH progression in preclinical models and in humans.

Considerations for Optimization of High-Throughput Sequencing Bioinformatics Pipelines for Virus Detection.

High-throughput sequencing (HTS) has demonstrated capabilities for broad virus detection based upon discovery of known and novel viruses in a variety of samples, including clinical, environmental, and biological. An important goal for HTS applications in biologics is to establish parameter settings that can afford adequate sensitivity at an acceptable computational cost (computation time, computer memory, storage, expense or/and efficiency), at critical steps in the bioinformatics pipeline, including initial data quality assessment, trimming/cleaning, and assembly (to reduce data volume and increase likelihood of appropriate sequence identification). Additionally, the quality and reliability of the results depend on the availability of a complete and curated viral database for obtaining accurate results; selection of sequence alignment programs and their configuration, that retains specificity for broad virus detection with reduced false-positive signals; removal of host sequences without loss of endogenous viral sequences of interest; and use of a meaningful reporting format, which can retain critical information of the analysis for presentation of readily interpretable data and actionable results. Furthermore, after alignment, both automated and manual evaluation may be needed to verify the results and help assign a potential risk level to residual, unmapped reads. We hope that the collective considerations discussed in this paper aid toward optimization of data analysis pipelines for virus detection by HTS.

A Multicenter Study To Evaluate the Performance of High-Throughput Sequencing for Virus Detection.

The capability of high-throughput sequencing (HTS) for detection of known and unknown viruses makes it a powerful tool for broad microbial investigations, such as evaluation of novel cell substrates that may be used for the development of new biological products. However, like any new assay, regulatory applications of HTS need method standardization. Therefore, our three laboratories initiated a study to evaluate performance of HTS for potential detection of viral adventitious agents by spiking model viruses in different cellular matrices to mimic putative materials for manufacturing of biologics. Four model viruses were selected based upon different physical and biochemical properties and commercial availability: human respiratory syncytial virus (RSV), Epstein-Barr virus (EBV), feline leukemia virus (FeLV), and human reovirus (REO). Additionally, porcine circovirus (PCV) was tested by one laboratory. Independent samples were prepared for HTS by spiking intact viruses or extracted viral nucleic acids, singly or mixed, into different HeLa cell matrices (resuspended whole cells, cell lysate, or total cellular RNA). Data were obtained using different sequencing platforms (Roche 454, Illumina HiSeq1500 or HiSeq2500). Bioinformatic analyses were performed independently by each laboratory using available tools, pipelines, and databases. The results showed that comparable virus detection was obtained in the three laboratories regardless of sample processing, library preparation, sequencing platform, and bioinformatic analysis: between 0.1 and 3 viral genome copies per cell were detected for all of the model viruses used. This study highlights the potential for using HTS for sensitive detection of adventitious viruses in complex biological samples containing cellular background. IMPORTANCE Recent high-throughput sequencing (HTS) investigations have resulted in unexpected discoveries of known and novel viruses in a variety of sample types, including research materials, clinical materials, and biological products. Therefore, HTS can be a powerful tool for supplementing current methods for demonstrating the absence of adventitious or unwanted viruses in biological products, particularly when using a new cell line. However, HTS is a complex technology with different platforms, which needs standardization for evaluation of biologics. This collaborative study was undertaken to investigate detection of different virus types using two different HTS platforms. The results of the independently performed studies demonstrated a similar sensitivity of virus detection, regardless of the different sample preparation and processing procedures and bioinformatic analyses done in the three laboratories. Comparable HTS detection of different virus types supports future development of reference virus materials for standardization and validation of different HTS platforms.

Systems approach for the selection of micro-RNAs as therapeutic biomarkers of anti-EGFR monoclonal antibody treatment in colorectal cancer.

miRNA plays an important role in tumourgenesis by regulating expression of oncogenes and tumour suppressors. Thus affects cell proliferation and differentiation, apoptosis, invasion and angiogenesis. miRNAs are potential biomarkers for diagnosis, prognosis and therapies of different forms of cancer. However, relationship between response of cancer patients towards targeted therapy and the resulting modifications of the miRNA transcriptome in the context of pathway regulation is poorly understood. With ever-increasing pathways and miRNA-mRNA interaction databases, freely available mRNA and miRNA expression data in multiple cancer therapy have produced an unprecedented opportunity to decipher the role of miRNAs in early prediction of therapeutic efficacy in diseases. Efficient translation of -omics data and accumulated knowledge to clinical decision-making are of paramount scientific and public health interest. Well-structured translational algorithms are needed to bridge the gap from databases to decisions. Herein, we present a novel SMARTmiR algorithm to prospectively predict the role of miRNA as therapeutic biomarker for an anti-EGFR monoclonal antibody i.e. cetuximab treatment in colorectal cancer.

Molecular biomarkers in clinical development: what could we learn from the clinical trial registry?

AIM: Objective of this research is to assess whether the trend of stratified medicine widely discussed in scientific literature is translated into real clinical trials registered in ClinicalTrials.gov . METHODS: By semi-automatic screening of over 150,000 trials, we filtered trials with stratified biomarker to analyze their therapeutic focus, major drivers and elucidated the impact of stratified biomarker programs on trial duration and completion. RESULTS: >5% of trials are using molecular biomarker for stratification; duration of such trials is longer. 21% of them are done in late stages and Oncology is the major focus. CONCLUSION: Trials with stratified biomarker in drug development has quadrupled in last decade but represents a small part of all interventional trials reflecting multiple co-developmental challenges of therapeutic compounds and companion diagnostics.

Challenges and opportunities for oncology biomarker discovery.

Recent success of companion diagnostics along with the increasing regulatory pressure for better identification of the target population has created an unprecedented incentive for drug discovery companies to invest in novel strategies for biomarker discovery. In parallel with the rapid advancement and clinical adoption of high-throughput technologies, a number of knowledge management and systems biology approaches have been developed to analyze an ever increasing collection of OMICs data. This review discusses current biomarker discovery technologies highlighting challenges and opportunities of knowledge capturing and presenting a perspective of the future integrative modeling approaches as an emerging trend in biomarker prediction.