The role of real‑world evidence (RWE) in oncology

The treatment of various cancer types has changed drastically in the last 20 years. A relevant driver for this is molecular analytics, which enables identification of specific tumour characteristics and allows physicians to tailor the cancer therapy to the tumour. For instance, an over-expression of the human epidermal growth factor receptor 2 (HER2) is frequently detected in breast cancer cases, but interestingly, it has also been identified in cases of ovarian, stomach, lung, and uterine cancer ​(Iqbal N, 2014)​. Similar discoveries have been made for other molecular targets. However, there are still considerable knowledge gaps in the use and impact of these therapies, which is where real-world data can become useful.

The expanding knowledge of cancer is changing the way cancer treatment is approached. Traditional treatment modalities for advanced cancer such as chemotherapy and radiotherapy have demonstrated their effectiveness in slowing tumour progression. However, there is still room for further improvement in preventing tumour recurrence, and targeted therapies are becoming more and more mainstream. Thanks in part to technological breakthroughs in sequencing, which have illuminated the mutational landscape of various cancers, ​(Waarts MR, 2022)​ the pharmaceutical industry has been able to initiate a paradigm shift towards mutation-driven approaches.

An illustrative example of this is the first discovery of BCR-ABL fusion gene in chronic myelogenous leukemia (CML) and the subsequent development of the BCR-ABL inhibitor imatinib in 2001. It significantly improved the 10-year survival rate of CML patients from less than 50% to approximately 80% ​(Hagop Kantarjian, 2002)​. Since then, numerous targeted therapies have been approved, with many more agents under investigation. These therapies aim to disrupt the aberrant signaling pathways resulting from oncogenic gene mutations, utilising various mechanisms such as protein inhibition and interference with downstream signaling pathways.

Despite the success of targeted therapies, they are, unfortunately, far from perfect. The leading limitation of targeted cancer therapies is that cancer cells can be intrinsically irresponsive or acquire resistance after a period of treatment because of either the mutation of target molecules or tumour cells gaining survival advantages from a new mechanism, independent of the target ​(Lei ZN, 2020)​. Major current challenges in this context include identifying the patients who benefit from a specific therapy, and predicting when therapy resistance will develop in patients. These important knowledge gaps need to be resolved, but the lab and clinical trials have not been able to crack the code.

Real-world data could play a key role in uncovering patterns and generating hypotheses. By ​​analysing longitudinal data on patients’ responses to targeted therapies, clinicians and researchers can identify commonalities among patients who develop resistance, including specific genetic mutations or signalling pathway alternations. This information can guide the development of strategies to overcome resistance and further improve treatment outcomes of cancer patients.

Furthermore, knowledge extracted from real-world data has the potential to further enable personalised treatment approaches, such as selecting the most appropriate therapy based on individual patient profiles or adjusting treatment regiments in real time to optimise outcomes.

Finally, real-world data can also provide valuable feedback on the long-term effectiveness and safety of targeted therapies. By tracking patient responses over time, clinicians can identify early signs of treatment failure or disease progression and intervene accordingly. As well as facilitate comparative effectiveness research, where clinicians can access the relative benefits of different treatment options in real-world practice and make informed decisions about therapy selection.