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Harnessing RWE for novel cancer therapy development

We summarise a recent article, published in Cell, that explored the benefits and challenges of using real-world evidence (RWE) in developing novel therapies for cancer.


The use of RWE, particularly that obtained from electronic health records (EHRs), has received a lot of attention as a tool to enhance cancer treatment. Nonetheless, its use in the study of cancer is not new. It has previously provided valuable insights into patient subgroups with poor prognosis and cancer incidence. Now, emerging RWE is becoming more accessible and has finer resolution with respect to treatment outcomes. Additionally, it can now provide insights into patient experience, such as tracking access to care. Most importantly, emerging data can link the use of specific treatment regimens with efficacy and safety risks, define factors associated with clinical decision making and also identify underserved patient populations.

In terms of cancer care, RWE is becoming increasingly important in developing novel therapies. Researchers can leverage a wide range of information from RWE. This ranges from selection of clinical trial sites to optimising the likelihood of achieving therapeutic benefits. Drug developers can use RWE-based studies to evaluate available therapies and their outcomes. Moreover, well-curated sources and advances in data capture can also enable longitudinal follow-up of patients. This in turn can identify unmet medical needs and improve the speed of evaluating novel therapies.

The identification of highly responsive patients is essential for developing novel therapies. Genomic testing of patients is expanding and can provide new opportunities for retrospective associations of tumour genomics and outcomes. Real-world matched clinical and genomic datasets of patients can provide the basis for interpretation of outcomes with novel targeted therapies in a given population.


There are several challenges that hinder the practical use of RWE in drug development. The major limiting factor is access to suitable data. While academic institutions and commercial data sources can play key roles in developing well curated patient cohorts, operational challenges still exist. These include inconsistent data entry, fragmentation of care, and complex and diverse EHR platforms. Valuable information, such as medical history and baseline disease characteristics, are not often captured in a structured format. Advances in natural language processing can help improve knowledge extraction from unstructured notes. Nonetheless, harmonisation of data across research collaborations is still essential.

In drug development, there are numerous challenges to be aware of when applying RWE. A core strength of RWE is that it allows the study of larger cohorts with longer follow-up duration periods compared to RCTs. However, RWE data is not as standardised as that in RCTs. Therefore, it may be subject to potential bias, which limits its comparability. Moreover, data used in retrospective RWE cohorts is not flexible and does not capture important factors in patient care. For example, it does not capture the essence of a physician’s decision to treat patients.


Multidisciplinary efforts will be key to leverage RWE in developing new cancer therapies. There are emerging approaches, such as the successful imputation of patient performance status from RWE, that are helping to harmonise RWD with clinical trial data capture. In addition, data sparseness and incompatibility are being addressed through a consensus-based set of core RWE data elements for cancer. Novel analytical approaches are bringing together diverse data types to expand novel insights gained from RWE. The development of high-quality data coupled with machine learning can help find areas of therapeutic need, enhance clinical trial interpretation and even identify novel targets. Furthermore, harnessing the potential of RWE will require close cooperation between key stakeholders across pharma, commercial providers, academic institutions and government. If effective, these collaborations will advance interoperability and data harmonisation, while maintaining data privacy and enabling clinically informative analyses.

Image credit: By gstudioimagen –

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