Dr Gen Li, CEO and Founder, Phesi, looks at R&D trends for the year ahead.
Oncology indications – particularly breast, prostate and non-small-cell lung cancer – have led Phesi’s annual Most Studied Diseases list for the last four years, and we expect that trend to continue when we release our 2025 data in the new year. Even GLP-1s, which have dominated pharmaceutical headlines throughout 2025, are now being investigated in oncology.
There is, however, a deeper story beneath these headlines. Alongside the rise in oncology trials, the volume of contextualised patient data available to researchers has expanded at pace. In May 2025, an analysis of 167 million patient records showed that large, high-quality bodies of real-world data now exist across multiple cancer types. Colorectal cancer topped the list with almost six million patient records from more than 18,000 cohorts, followed by breast (4.8 million), lung (3 million), liver (2.3 million) and prostate (2.2 million).
Together, these growing datasets are giving researchers an increasingly granular understanding of individual patients, from genetic profiles to the impact of comorbidities. For example, recent work has revealed more information on the racial, ethnic and geographic characteristics of patients with specific types of cancer, such as EGFR NSCLC patients in Eastern Asia, or KRAS mutation in caucasians.
But this wealth of information will only translate into improved outcomes if sponsors can harness it effectively in trial design and execution.
The data opportunity
Historically, oncology studies have struggled with slow recruitment, inconsistent investigator site performance and variable data quality. One analysis found that one in five investigator sites enrolled only a single patient, and most participants came from a small minority of high-performing sites.
At the same time, the number of recruiting oncology investigator sites has increased by 49% in the past five years, bringing with it a growing pool of poorly performing sites. This leads to longer cycle times and weaker data integrity, since low-enrolling sites make measurement deviations harder to detect.
As we head into 2026, the industry is facing a rare convergence of pressures and possibilities.
Oncology research remains imprecise, yet the number of trials continues to rise. Real-world data is accumulating at unprecedented speed. And our understanding of cancer biology is deeper than it has ever been. For sponsors, this creates a clear opportunity to address long-standing inefficiencies and design more precise oncology trials in the year ahead.
Realising this opportunity will require bringing together contextualised patient data with sophisticated AI and clinical data analytics. Doing so will deliver meaningful benefits in three areas.
Patient insights: AI-powered digital patient profiles offer an accurate view of patient attributes, including demographics, comorbidities, concomitant medications and outcomes. By analysing these variables at scale, planners can identify the most appropriate patient cohorts for a study and avoid the mismatches that so often slow oncology trials.
Investigator site selection: Greater precision in site selection ensures that chosen investigators and physicians have the right expertise in the specific cancer subtype under investigation.
This reduces the risk of non-active and non-enrolling sites, improving enrolment velocity and strengthening data quality.
AI-driven capabilities enable sponsors to monitor the dynamic of global investigator sites with unprecedented precision, including their access to targeted patient populations, relevant medical expertise, and competition at country, site and investigator level.
Digital twins: By drawing on vast bodies of real-world patient data from identical or similar trials, digital twins provide a representation of typical patients in a specific population.
These models allow researchers to simulate, test and predict safety and efficacy outcomes before enrolling participants, supporting more informed protocol design, clearer interpretation of trial results and stronger submission packages that incorporate historic control data and reduce reliance on large control arms.
Looking ahead, the shift to an AI-powered, data-led precision model of oncology clinical development is both necessary and within reach. With the right data meeting the right science, 2026 is set to be the year when precision oncology trials truly come into their own – transforming development strategies, strengthening regulatory submissions and improving the probability of success across cancer research.
