Tissue-Based Biomarker for Kidney Cancer
TECHNOLOGY NUMBERS: 2022-046, 2022-045
OVERVIEW
Tissue-based biomarker tests predicting recurrence and progression risks in kidney cancer.
- Identifies immediate post-diagnosis and post-surgery risks using genetic patterns, outperforming standard diagnostic methods.
- Risk-sorting patients in pathology labs, cancer clinics, and clinical trials.
BACKGROUND
Kidney cancer, especially clear cell renal cell carcinoma, often comes back or gets worse even after surgery, making improved risk-prediction essential. Historically, doctors relied on standard clinical assessments, but these current approaches fail to identify who is at risk right after diagnosis and who might need extra attention after surgery, unlike advanced methods. Despite the growing adoption of genetic information to guide cancer care and expanding insurance coverage, injury and recurrence rates remain a challenge, creating strong demand for tools that deliver detailed, personalized risk insight. Hospitals, pathology labs, cancer clinics, and drug companies need improved ways to predict outcomes, match aggressive treatments and follow-up to those most likely to benefit, optimize resource efficiency, and design better clinical trials. This highlights an urgent unmet clinical need for reliable tissue-based biomarker tests capable of accurately categorizing patient risk across various stages of care.
INNOVATION
This technology consists of two advanced tissue-based biomarker tests analyzing gene patterns in tumor samples to guide patient care. The first technology utilizes modern gene-sequencing methods to measure tumor cell change—specifically the likelihood to move and spread—scoring patients to group them into distinct risk categories for progression and serious outcomes. The second technology analyzes gene activity to identify a unique signature predicting whether cancer returns after kidney removal surgery, flagging patients needing post-surgical therapy. Together or individually, these tests provide actionable, personalized insights into cancer risk at different treatment stages. In real-world applications, they enable doctors to target aggressive treatments and follow-up effectively within pathology labs, cancer clinics, and pharmaceutical trials. Furthermore, by optimizing patient stratification and healthcare resource efficiency, these tools enhance personalized treatment strategies, potentially expanding into additional oncology domains where gene-expression profiling guides complex clinical decision-making.
ADDITIONAL INFORMATION
INTELLECTUAL PROPERTY
Pending