Abstract 458: OGAP-verify protein database can identify and highlight mRNA and protein mis-matched oncology targets.

Abstract Many biotechnology and pharmaceutical companies rely on mRNA expression as a key to finding novel oncology targets for therapeutic intervention. One of the challenges with this approach is the mismatch that can occur quite frequently between mRNA expression and protein expression and abundance. While it is established that you need mRNA for protein expression, there can be a huge mismatch mainly due to factors such as translational and transcriptional delays, post-transcriptional modifications, degradations and structural or physical alterations. These discrepancies highlight the significant limitations of relying solely on mRNA data to predict protein targets. Here we provide specific examples demonstrating how our proprietary OGAP-Verify target discovery platforms uniquely identifying targets overlooked by RNA-based predictions using a variety of protocols including RNAseq, qPCR and in situ hybridization. A prime example in the public domain is Type 1 Collagen which, while not a membrane protein, exhibits significant mismatch between low RNA levels and high protein expression. Here, we present data of proprietary membrane specific targets which have significant RNA-Protein level mismatch as examples of the effectiveness of OGAP-Verify in identifying such novel oncology targets. OGAP-Verify has enabled OBT with a unique approach to identify membrane specific protein expression on patient tumors which has resulted in identifying a large number of new specific cancer targets that do not correlate with their RNA expression profiles. Immunohistochemistry (IHC) data shows direct correlation with OGAP-Verify proteomics results. Leveraging OGAP-Verify—the world’s largest cancer membrane protein expression database—OBT has exclusive access to unique oncology targets, including cancer-specific isoforms, unavailable elsewhere in the industry. Citation Format: Belinda Cairns, Andrew Woolley, Joseph Lewis, Daniel Davies, Lindsey Hudson, Somdatta Basu, Ben Thomas, Christian Rohlff. OGAP-verify protein database can identify and highlight mRNA and protein mis-matched oncology targets [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 458.

Authors

Institutions

Publication Details

Journal
Cancer Research
Published
2026-04-03
DOI
https://doi.org/10.1158/1538-7445.am2026-458
Primary Topic
Protein Degradation and Inhibitors
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Abstract 458: OGAP-verify protein database can identify and highlight mRNA and protein mis-matched oncology targets.

Lindsey Hudson, Joseph M. Lewis, Christian Rohlff, Dan Davies et al.
Cancer Research
Protein Degradation and Inhibitors
article

Abstract 458: OGAP-verify protein database can identify and highlight mRNA and protein mis-matched oncology targets.

Lindsey Hudson, Joseph M. Lewis, Christian Rohlff, Dan Davies, Belinda Cairns, Somdatta Basu, Ben Thomas, Andrew Woolley
article en

Abstract

Abstract Many biotechnology and pharmaceutical companies rely on mRNA expression as a key to finding novel oncology targets for therapeutic intervention. One of the challenges with this approach is the mismatch that can occur quite frequently between mRNA expression and protein expression and abundance. While it is established that you need mRNA for protein expression, there can be a huge mismatch mainly due to factors such as translational and transcriptional delays, post-transcriptional modifications, degradations and structural or physical alterations. These discrepancies highlight the significant limitations of relying solely on mRNA data to predict protein targets. Here we provide specific examples demonstrating how our proprietary OGAP-Verify target discovery platforms uniquely identifying targets overlooked by RNA-based predictions using a variety of protocols including RNAseq, qPCR and in situ hybridization. A prime example in the public domain is Type 1 Collagen which, while not a membrane protein, exhibits significant mismatch between low RNA levels and high protein expression. Here, we present data of proprietary membrane specific targets which have significant RNA-Protein level mismatch as examples of the effectiveness of OGAP-Verify in identifying such novel oncology targets. OGAP-Verify has enabled OBT with a unique approach to identify membrane specific protein expression on patient tumors which has resulted in identifying a large number of new specific cancer targets that do not correlate with their RNA expression profiles. Immunohistochemistry (IHC) data shows direct correlation with OGAP-Verify proteomics results. Leveraging OGAP-Verify—the world’s largest cancer membrane protein expression database—OBT has exclusive access to unique oncology targets, including cancer-specific isoforms, unavailable elsewhere in the industry. Citation Format: Belinda Cairns, Andrew Woolley, Joseph Lewis, Daniel Davies, Lindsey Hudson, Somdatta Basu, Ben Thomas, Christian Rohlff. OGAP-verify protein database can identify and highlight mRNA and protein mis-matched oncology targets [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 458.

Cancer ResearchVol. 86(7_Supplement)
Oxford Biotherapeutics (United Kingdom) (GB)
Industry, innovation and infrastructure
Openalex Percentile: Top 8%
Protein Degradation and Inhibitors
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.