From AI to the death of the default: what cancer science will look like in 2036

By 2036, the era of the 'generic patient' will be long gone, replaced by a research environment that prioritizes diversity and accuracy. The lab of the future will not merely use fewer mice, but will create a more inclusive research setting that faithfully reflects human biology. This will be achieved by combining high-dimensional patient datasets with genetically engineered mouse models, resulting in powerful data that can be utilized in digital twin models, organoids, and organ-on-chip studies.

This shift in research methodology is not just an ethical triumph; it is a clinical imperative. By accurately representing the population, researchers will be able to better understand and address the unique genetic makeup and lived experiences of each patient. This inclusivity will also involve collaboration with social sciences to ensure research addresses real-world problems faced by marginalized communities.

In 2036, the success of cancer research will be measured by the diversity at the highest levels of leadership, not just the number of students from underrepresented groups. The lab will be judged by the diverse representation of its Principal Investigators, aiming for a leadership team that mirrors the UK population in terms of ethnicity, gender, and socio-economic background.

Patients and the public will play a crucial role in shaping research programs, ensuring that the questions asked are as varied as the patients they serve. This inclusion will also help maintain public trust in scientific research and its benefits to society, which is vital for continued funding and support.

The integration of technology development and biomedical research is at the forefront of cancer discovery, with technology integration becoming a critical component. Collaborations between labs and platforms are becoming more widespread, leading to shared resources for technology and method development at national and international scales.

The biggest structural shift in cancer research is the focus on data generation and integration to address the disease at a systems-level. Spatial biology and multi-omics are emerging as transformative approaches, allowing researchers to understand cellular behavior and tissue architecture within intact tissue. In vitro platforms, such as organoids, human microphysiological systems, and organ-on-chip models, are gaining importance as they can recapitulate the complexity and dynamics of cancer more accurately than animal models.

These systems, which can be patient-specific, capture the tumor microenvironment, immune, vascular, and neural components, offering a more physiologically relevant and human-like model. This shift not only enhances discovery but also facilitates faster, patient-specific treatment options in the future. The FDA's Modernization Acts 2.0 and 3.0 have already recognized the potential of NAM-based regulatory submissions, making human-relevant preclinical data from these platforms a baseline expectation.