Cancer biomarker Market
Indicators of the presence of a cancerous tumour, its stage, and possible prognosis, cancer biomarkers help in the early detection, diagnosis, monitoring, and personalized therapy for the patient. For example, there are CEA, SCC, NSE, Cyfra 21-1, TPA (lung cancer); PSA (prostate cancer); and CA15-3, EGFR, BRCA1/2, Cytokeratin-14 (breast cancer). This section covers the latest developments.
Key Drivers
Rising cancer incidence is driven by the global ageing population. There is rising interest in personalized medicine aimed at tailoring the most effective treatments to individual patients based on their molecular profiles. Technological advancements in genomics and proteomics, liquid biopsies, and the integration of AI/machine learning with other technologies are game changers. There is also funding, regulatory and public health support for early detection and cancer screening programs.
Challenges
The development, validation and clinical implementation of biomarkers is resource-intensive and costly, and this is a challenge for low-resource settings. There are low success rates in clinical trials for novel biomarkers due to issues with reproducibility, specificity/sensitivity, and other regulatory hurdles. Advanced diagnostic tools require large-scale clinical validation, and standardization along with equitable access and comprehensive diagnostics for reproducibility.
What diagnostic options are rising most rapidly in demand?
- Liquid Biopsies
Liquid biopsies are far less invasive than tissue biopsies and allow for monitoring over time. Detecting tumour DNA (ctDNA), circulating tumour cells (CTCs), circulating tumour RNA, or exosomes in blood (or other fluids) is an option. Liquid biopsies are increasingly used for early detection, tracking treatment response, and detecting recurrence and sometimes for prognosis.
- Multi-Omics Profiling
Integrating genomics + transcriptomics + proteomics + metabolomics + epigenomics to get a fuller picture of tumour biology. These help in discovering new biomarkers and improving the predictive power of existing ones.
- AI / Machine Learning & Computational Biomarkers
The focus is on developing algorithms that can process and interpret large and complex datasets (omics, imaging, clinical), aiming to identify and classify patterns that may escape human attention. For instance, AI can be used to score HER2 expression in breast cancer more accurately.
- Point-of-Care (POC) & Portable Diagnostics
These are diagnostic devices that can be used closer to the patient, especially in clinics and rural zones, providing rapid results with little to no infrastructure. As an instance, in India, an immunosensor device designed for osteosarcoma (which detects osteopontin) is portable, inexpensive, and ideal for remote and under-resourced settings.
- Multiplex Panels & Companion Diagnostics
These are diagnostic tools that measure several clinical biomarkers simultaneously to increase the precision of a diagnosis and the information provided (eg, subtype, risk, prognosis, etc.). Companion diagnostics are particularly useful in immuno-oncology with PD-L1 testing, MSI status, etc., to determine eligibility for checkpoint inhibitors and other targeted therapies.
- Epigenetic Biomarkers
These biomarkers are the result of DNA methylation, histone modification, or other gene regulatory alterations, pre- or post-disease formation, and sometimes, before a disease becomes morphologically evident. Due to the lack of other core indicators, many are eager to put down standard clinical practices built around the integration of epigenetic markers for screening and diagnostics.
Which Diagnostic Option is Most Preferred/Poised for the Highest Growth?
When consolidating the trends and forecasts, liquid biopsy with multi-omics, augmented with AI/ML, is predicted to become the most preferred/ fastest growing diagnostic option for the following reasons:
- More patient-friendly than traditional biopsies, non-invasive, and lower risk.
- Enables dynamic, over time, monitoring for treatment response and recurrence.
- Decreasing costs associated with sequencing and assays.
- AI/ML enhances sensitivity and specificity, and multiple biomarker types (genetic, epigenetic, and proteomic) can be utilized.
- To increase access in lower/middle-income countries, point-of-care and resource-lean versions of these technologies are in the pipeline.
Summary
The cancer biomarker diagnostics market is advancing quickly. Among the various choices, the combination of liquid biopsies, multi-omics, and AI-driven multiplex diagnostics is the most sought after. This combination provides non-invasiveness, early detection, monitoring, and personalized diagnostics. The rapidly expanding market, particularly within diagnostic applications, is fueled by technologically advanced sequencing, computational methods, and epigenetics, creating solutions that were unattainable just a few years ago.