As the global burden of cancer increases every year, oncology professionals are seeking targeted, precise and sensitive methods to identify tumours and monitor their growth. For over a decade, many have used serum tumour markers (STM) as part of their diagnostic toolkit.

To better understand how STMs are currently used and what the future holds for this approach, Lab Insights spoke to two leading Chinese healthcare professionals: Prof Liu Ji Wei, Director of the Oncology Department at Da Lian Medical University, and Prof Cui Wei, Director of Laboratory Medicine at the Chinese Academy of Medical Science National Cancer Centre.

Today’s clinical utility of serum tumour markers

The clinical perspective: Prof Liu Ji Wei

In his clinical practice, Prof Liu uses STMs to evaluate responses to chemotherapy or targeted cancer therapy, monitor recurrence, provide an accurate differential diagnosis, and stage cancers. Since the majority of patients that he sees have stage 4 metastatic or recurrent cancer after surgery, STMs are useful for highly specific and sensitive cancer detection and monitoring.

One of the benefits of STMs, according to Prof Liu, is their accessibility, as they are non-invasive. He also notes that they can be highly informative with respect to imaging techniques in some situations. For example, a drop in CEA values can indicate that a treatment is working, whereas imaging methods like CT scanning or MRI may overlook tumours that are outside a field of view or too small to detect.

STMs can also provide critical information before targeted therapy or immunotherapy can be initiated. While STMs alone are not the sole or even the key driver in choosing these approaches, they can be combined with data on tumour mutational burden, microsatellite instability or other indicators to guide treatment selection. Looking ahead, Prof Liu sees the future of such testing involving markers that indicate potential immunotherapy-induced toxicity, which will be critical for ensuring successful treatment outcomes.

The lab perspective: Prof Cui Wei

Prof Cui’s clinical laboratory routinely tests serum and plasma samples for over 20 tumour markers commonly seen in cancers, including lung, ovarian and prostate cancer. To provide a comprehensive and accurate perspective on a patient’s tumour landscape, her laboratory combines STM tests with molecular, radiological, imaging and histological investigations.

In non small-cell lung cancer (NSCLC), for example, she looks for STMs such as NSE, CYFRA 21-1, ProGRP, CEA and SCC, alongside mutations in key genes like EGFR. In ovarian cancer, HE4 and CA125 testing is done. Since STMs typically require small sample input volumes, they can be advantageous for cancer patients who often need multiple tests.

Additionally, Prof Cui uses STM panel testing to discriminate between tumour subtypes, evaluate cancer risks and monitor disease progression. She also combines STM testing with newer assays such as those for circulating tumour DNA, or with novel molecular tools, to diagnose and monitor therapeutic response, detect residual lesions and target drug-resistant mutations.

The future of serum tumour markers and cancer care

AI, algorithms and the future of STMs

While the spectrum of uses for STMs is currently limited, many believe that innovations in data science, artificial intelligence and machine learning will expand their clinical utility. Drawing together multivariate datasets, new algorithms and analytic tools allows clinicians to select more specific or suitable treatments based on a patient’s STMs.

Machine learning techniques can now distinguish between the early stages of cancer and non-tumourous, controlled growths. Intelligent algorithms can also incorporate imaging, molecular testing, tumour markers and even patient factors such as their medical and family histories. Such integrations may soon be able to pinpoint the most meaningful STMs for clinicians to use at critical decision points in the cancer care pathway.

“A decade ago, Chinese researchers explored the categorisation of lung cancers according to disease stage by combining the detection of 5 lung cancer STMs with big data algorithms,” notes Prof Cui. “Now, we can integrate new, intelligent algorithms and multidisciplinary data in sophisticated platforms to better understand patient data.”

Prof Cui notes that most labs are only just beginning to implement AI, machine learning and intelligent algorithms in this way. She acknowledges that more research is needed to seamlessly connect algorithms to patient care, but she is optimistic that this process will make STMs more actionable and useful in a wider variety of cancers.

Lab-clinician communication remains essential

Despite these advances, Prof Cui points out that no technology can simultaneously analyse, interpret and clinically explain oncology data. The Chinese Society of Laboratory Medicine understands that the value of STMs is maximised only if genetic screening data are interpreted correctly, and has progressively focused on improving communication between laboratories and doctors.

Prof Cui believes that laboratory clinicians should collaborate with clinicians in patient care, such as by proactively providing data interpretations and treatment suggestions directly to clinicians. When parsing high-STM results, her clinical colleagues may sometimes need to be educated on a test’s limitations. For example, some clinicians may not be aware that STMs can manifest even in benign or normal physiological states, such as higher levels of CA125, CEA and AFP in early pregnancy.

As stakeholders come together to establish algorithms and disease models, more regional and global cooperation will be needed to realise the full potential of STMs for cancer care. Such cooperation is starting to take root in China, and will likely drive progress that has impact far beyond the country’s borders.