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- That ESCC PDX models recapitulate patient disease preclinically, allowing the investigation of translational biomarkers of response
- How a Mouse Clinical Trial across a cohort of PDX models identified a subset of responders to cetuximab monotherapy
- That gene amplification and expression analysis indicated that high EGFR copy number, high EGFR mRNA expression, and IHC score of 2–3 associate with cetuximab response, indicating EGFR may function as a single predictive biomarker for cetuximab response in ESCC
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- New methods making the design and analysis of MCTs more rational, flexible, and powerful, enabling MCTs to be a better tool in oncology R&D
- The relationship between mouse number and measurement accuracy for categorical and continuous efficacy endpoints
- Case studies demonstrating the advantages of linear mixed models (LMMs) in discovering biomarkers and exploring drug mechanisms of action
- How MCTs can explain discrepancies in clinical trial results
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- Barcode deep NGS-based profiling, a more efficient and robust method for authenticating and detecting contamination in mouse and human samples compared to conventional STR and SNP analysis methods
- How barcode deep NGS-based profiling enables large-scale authentication for the maintenance of high quality biobanks
- How barcode deep NGS-based profiling can determine gender, ethnicity, detect viral infection, and mycoplasma contamination
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- How the genomic analysis of clinical samples has the potential to guide patient stratification, exemplified here for the Chinese ccRCC population
- That 3 classes of ccRCC patient subgroups were identified, based on gene expression
- Specific differences in gene mutations, e.g. PBRM1 mutation in the Chinese cohort investigated was much lower than for Caucasian cohort assessed by TCGA
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- How bioinformatics including biomarker discovery and validation, advanced study design and analysis, model selection, and evaluation of combination effects can add significant value to your drug candidate
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- How to de-risk therapeutic development by identifying robust and sensitive biomarkers early in drug development through an integrated platform
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- How to maximize your therapeutic effect while limiting toxicity through cytokine and chemokine profiling
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- How to significantly enhance your preclinical data by using next generation sequencing and customized data analysis
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- How to gain in-depth biological insight into your drug MoA and PD through robust immunophenotyping
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- Use in vitro screening to generate biomarker sets, identify primary indications, and guide in vivo model selection
- Corroborate clinical observations using preclinical in vivo models, such as unique patient-derived xenografts (PDX), by exploring drug MoA
- Translate preclinical biomarkers into the clinic, enabling the accurate identification and stratification of clinical trial participants