对乳腺癌患者全血中全长mRNA进行测序以选择抗体药物偶联物治疗

Identifying biomarkers for selection of therapeutics is a critical unmet need. For many existing treatments, particularly antibody-drug conjugates, there are limited or only partially effective options for determining efficacy. For example, breast cancer therapy selection requires accurate quantification of tumour HER2 expression, but current HER2 testing methods, primarily based on IHC and ISH, lack the precision to distinguish HER2 status in HER2 low and ultra-low patients. When determining HER2 ADC eligibility, the lack of reliable HER2 quantification can lead to under- or over-treatment. Developing an accurate and reliable HER2 quantification method will enable safer and more effective HER2 ADC utilization.We have developed a novel platform that leverages long-read sequencing to capture full-length mRNA and generate isoform-level expression profiles. We processed 30 tumor biopsies and 50 whole blood samples from breast cancer patients, as well as blood samples from 50 control patients. By integrating the tumor and blood transcriptome data, we characterized the expression of 61,537 genes from the Ensembl reference, including 1,223 cancer-related genes, such as HER2 (based on COSMIC Cancer Gene Census, established ADC targets and HER2-expression related gene sets) to identify novel splice junctions and other features with the potential to act as diagnostic biomarkers for ADC targets including HER2. We identified 17,114 novel splice junctions from novel RNA isoforms (not previously reported in the Ensembl reference human transcriptome annotation) across 5,423 genes that were found at 5-50% prevalence within our cancer patient cohort. This included 1,381 novel splice junctions found within 357 of the cancer-related genes, including 12 novel splice junctions in HER2 representing potential alternative receptor structures. Comparative analysis of combined feature sets in samples with different HER2 statuses demonstrate potential for developing a liquid biopsy method of characterising cancer receptor profiles for informing suitable ADC therapies. Our novel approach utilizing full-length RNA sequencing enables more comprehensive characterization and precise quantification of tumor-associated expression of HER2 and other cancer-related genes, providing new insights into transcript diversity and expression patterns that are critical for improving diagnostic accuracy, refining treatment selection, and ultimately enhancing patient outcomes.