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Pan-cancer gene expression analysis of tissue microarray using EdgeSeq oncology biomarker panel and a
Koichiro Inaki1,2, Tomoko Shibutani1,2, Naoyuki Maeda1
1Translational Science Department I, Daiichi Sankyo Co., Ltd., Tokyo, Japan.
Abstract:
Molecular and protein biomarker profiling are key to oncology drug development. Antibody-drug conjugates (ADCs) directly deliver chemotherapeutic agents into tumor cells based on unique cancer cell biomarkers. A pan-cancer tissue microarray (TMA) data set and gene panel were validated and gene signature analyses were conducted on normal and cancer tissues to refine selection of ADC targets. Correlation of mRNA and protein levels, and human epidermal growth factor receptor (HER) expression patterns were assessed. An EdgeSeq biomarker panel (2862 genes) was used across 8531 samples (23 solid cancer types/subtypes; 16 normal tissues) with an established TMA data set, and immune cell and cell cycle gene signatures were analyzed. Discriminating gene expression signatures were defined based on pathological classification of cancer subtypes. Correlative analyses of HER2 and HER3 mRNA (EdgeSeq) and protein expression (immunohistochemistry [IHC]) were performed and compared with publicly available data (The Cancer Genome Atlas [TCGA]; Cancer Cell Line Encyclopedia [CCLE]). Gene expression patterns among cancer types in the TMA (EdgeSeq) and TCGA (RNA-seq) were similar. EdgeSeq gene signature analyses aligned with the majority of pathological cancer types/subtypes and identified cancer-specific gene expression patterns. TMA IHC H-scores for HER3 varied across cancer types/subtypes. In a few cancer types, HER3 mRNA and protein expression did not align, including lower liver hepatocellular carcinoma IHC H-score, compared with mRNA. Although all TNBC and ovarian cancer subtypes expressed mRNA, some had lower protein expression. This was seen in TMA and TCGA data sets, but not in CCLE. The EdgeSeq TMA data set can expand upon current biomarker data by including cancers not currently in TCGA. The primary analysis of EdgeSeq and IHC comparison suggested a unique protein-level regulation of HER3 in some tumor subtypes and highlights the importance of investigating protein levels of ADC targets in both tumor and normal tissues.
Insights
Biomarker profiling refines oncology drug development, particularly for antibody-drug conjugates (ADCs). This study highlights the importance of assessing both mRNA and protein levels for ADC targets, revealing unique protein regulation in some tumor subtypes.
Area of Science:
- Oncology
- Biomarker Discovery
- Molecular Diagnostics
Background:
- Antibody-drug conjugates (ADCs) are crucial for targeted cancer therapy, relying on specific biomarkers for efficacy.
- Accurate biomarker profiling, encompassing both gene and protein expression, is essential for optimizing ADC development.
- Understanding expression patterns in both normal and cancerous tissues is vital for target selection and safety.
Purpose of the Study:
- To refine the selection of antibody-drug conjugate (ADC) targets by analyzing molecular and protein biomarker profiles across a wide range of cancers.
- To correlate messenger RNA (mRNA) and protein expression levels for key targets, specifically human epidermal growth factor receptor (HER) family members.
- To evaluate the utility of a novel gene panel (EdgeSeq) and tissue microarray (TMA) data set for comprehensive biomarker analysis.
Main Methods:
- Utilized an EdgeSeq biomarker panel (2862 genes) on 8531 samples from 23 solid cancer types and 16 normal tissues, integrated with a TMA data set.
- Performed gene signature analyses, including immune cell and cell cycle signatures, to identify cancer-specific expression patterns.
- Correlated HER2 and HER3 mRNA expression (EdgeSeq) with protein expression (immunohistochemistry [IHC]) and compared findings with The Cancer Genome Atlas (TCGA) and Cancer Cell Line Encyclopedia (CCLE) data.
Main Results:
- Gene expression patterns analyzed by EdgeSeq showed similarities to TCGA RNA-seq data and aligned with pathological cancer classifications.
- Identified cancer-specific gene expression patterns and validated the EdgeSeq TMA data set's ability to include cancers not present in TCGA.
- Observed discrepancies between HER3 mRNA and protein levels in specific cancer subtypes (e.g., liver hepatocellular carcinoma, triple-negative breast cancer, ovarian cancer), indicating unique protein-level regulation.
Conclusions:
- The EdgeSeq TMA data set provides valuable insights for biomarker discovery, expanding current data repositories.
- Discrepancies in HER3 expression highlight the critical need to assess protein levels alongside mRNA for ADC target validation.
- Investigating protein-level regulation in both tumor and normal tissues is essential for the successful development of targeted oncology therapies like ADCs.
