CUP

This project aims to improve care, service delivery, and patient experience for people with Cancer of Unknown Primary (CUP) by addressing a critical gap in understanding the lived experiences of patients and carers. By exploring how individuals navigate diagnostic uncertainty, fragmented care pathways, and interactions with healthcare professionals, the study will identify key points of distress, unmet need, and communication breakdown. The findings will support the development of more person-centred and compassionate care, including clearer communication, improved service coordination, and better recognition of the psychological and emotional impact of CUP. By highlighting the challenges of uncertainty and lack of diagnostic clarity, the project will inform more tailored support for both patients and carers. A unique strength of this study is the inclusion of a professional illustrator to visually capture focus group discussions, supporting the narrative inquiry approach and aiding the interpretation of shared meanings within the data. These visual summaries will enhance engagement, inclusivity, and accessibility, ensuring that complex experiences are communicated clearly to patients, carers, clinicians, and wider audiences. Through strong PPIE involvement and accessible dissemination, patient and carer voices will directly inform service improvements and future care design. Overall, this project will help ensure CUP care is more coordinated, communicative, inclusive, and responsive to patient and carer needs, ultimately improving experience, wellbeing, and trust in healthcare services.

This study will enroll patients with metastatic malignancies. Tumor samples (fresh or formalin-fixed paraffin-embedded tissue specimens) will undergo RNA extraction and next-generation sequencing (RNA-seq). Once the raw data is obtained, the system will analyze the transcriptomic feature values (cancer-specific RNA transcripts and tissue-specific RNA transcripts) expressed in the tumor tissue samples to further predict tissue origin using a machine learning model. The output includes probabilities and confidence intervals for tissue origin.