The Evolution of Biomarkers in Modern Cancer Care

From early detection to targeted therapies, discover why biomarkers are vital tools in the fight against cancer.

Article

Published: June 26, 2025

speaking with

Samra Turajlic, PhD

Professor, Group Leader and Consultant Medical Oncologist at The Francis Crick Institute and The Royal Marsden Hospital.

Professor Samra Turajlic completed her undergraduate studies at Oxford University and her clinical training at University College London Medical School. She gained a PhD in 2013 from the Institute of Cancer Research in the field of melanoma genetics and targeted therapy resistance. In 2014, she was awarded a Cancer Research UK Clinician Scientist Fellowship to study cancer evolution at The Francis Crick Institute. She completed her training in medical oncology in 2015 and was appointed as a consultant medical oncologist in the skin and urology units at The Royal Marsden Hospital. She became an independent group leader at The Francis Crick Institute in 2019 and divides her time between the clinic and her lab. Turajlic is the chief investigator of translational studies into melanoma and kidney cancer (TRACERx), and her research goal is to develop an evolutionary understanding of cancer for patient benefit. She is a member of the National Cancer Research Institute Bladder and Renal Cancer Clinical Studies Group, European Society for Medical Oncology (ESMO) Faculty member for genitourinary cancers; and Scientific Committee member for ESMO, Molecular Analysis for Personalized Therapy, Kidney Cancer Association and EORTC-NCI-AACR Symposia. She is a Trustee of the Kidney Cancer Support Network and Melanoma Focus and a Senior Editor at Macmillan Cancer Support. Turajlic receives research funding from Cancer Research UK, Rosetrees Trust, The Royal Marsden Cancer Charity and The Royal Marsden/Institute for Cancer Research Biomedical Research Centre, among others.

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Cancer screening form in background in blue, with "Biomarkers in Focus" written across the foreground.

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Cancer biomarkers are biological indicators found in blood, body fluids or tissues that signal the presence of abnormal processes or diseases. In oncology, these markers offer more than just cancer identification – they provide critical insights into disease progression, recurrence risks and treatment outcomes. Tumor markers can serve as prognostic indicators, forecasting the disease’s course, whereas predictive biomarkers can help guide treatment responses.

Classified by proteins like enzymes, hormones and receptors, cancer biomarkers also emerge from genetic changes – mutations, amplifications and translocations – that help categorize cancer types and guide therapy. For effective clinical use, cancer biomarkers must be detectable through reliable, cost-efficient methods that improve patient outcomes.

The use of biomarkers in cancer care has expanded dramatically, offering new avenues for earlier diagnosis, improved treatment selection and more accurate prognostic predictions.

The evolution of cancer biomarkers in precision oncology

In the era of precision oncology, biomarkers are redefining how we detect, diagnose and treat cancer. Unlike traditional approaches, which often rely on population-level trends and non-specific clinical signs, biomarker-guided strategies enable a more individualized, predictive and dynamic model of cancer care.

“Biomarkers have fundamentally reshaped how we understand cancer,” Professor Samra Turajlic, group leader and consultant medical oncologist at The Francis Crick Institute and The Royal Marsden Hospital, told Technology Networks. “Historically, we treated cancers by site and appearance; now, we can probe the biological processes driving each tumor.”

Traditional diagnostic tools, such as imaging or histopathological evaluation, often detect cancer only after it has progressed to a detectable size or caused symptoms. In contrast, biomarkers – especially those detectable in blood or other biofluids – can signal the presence of malignancy before clinical symptoms appear. For example, a previous study used cell-free DNA methylation patterns for multi-cancer early detection, achieving a specificity of over 99% across more than 50 cancer types, with promising sensitivity even for stage I cancers.

“Biomarkers have allowed us to identify specific mutations, immune profiles or metabolic shifts that give us clues about how a cancer emerged, how it behaves and how it might respond to treatment,” said Turajlic. “Importantly, they’ve helped reveal that cancer isn’t a static disease; it evolves, and our biomarkers must evolve with it.”

One significant advantage of biomarkers is their ability to guide targeted therapy. Traditional treatments such as chemotherapy are often applied broadly, with varying success and considerable toxicity. Biomarkers, however, enable clinicians to match patients with therapies likely to be most effective for their specific tumor profile. The landmark use of epidermal growth factor receptor mutation diagnosis in guiding treatments for non-small cell lung cancer illustrates this: patients with these mutations respond dramatically better to tyrosine kinase inhibitors than to standard chemotherapy.

Similarly, the presence of PD-L1 expression or tumor mutational burden (TMB) has become instrumental in predicting response to immune checkpoint inhibitors. In the KEYNOTE-158 study, pembrolizumab showed durable responses in patients with high TMB across various tumor types, leading to the US Food and Drug Administration's (FDA) approval of the biomarker as a companion diagnostic.

Different types of cancer biomarkers

Biomarkers can be classified into diagnostic, predictive and prognostic categories, each with distinct roles in enhancing cancer management.

Diagnostic

Diagnostic biomarkers are crucial for identifying cancer in its early stages – when treatment is most effective. These biomarkers typically identify molecular changes associated with cancer cells, making it possible to detect cancer before symptoms appear.

Circulating tumor DNA (ctDNA) was identified by Diaz and colleagues as a promising diagnostic biomarker for the early detection of colorectal cancer. Their research demonstrated that ctDNA analysis could detect cancer-related mutations in plasma samples, offering a less invasive and highly sensitive method for early diagnosis, potentially replacing traditional tissue biopsies. This study laid the groundwork for ongoing efforts to integrate ctDNA-based tests into routine cancer screening.

Predictive

Predictive biomarkers help clinicians predict how patients will respond to specific therapies, allowing for more personalized treatment plans. These biomarkers provide information on the likelihood that a patient will respond to a particular drug or treatment regimen, thus optimizing therapeutic outcomes.

A well-known example of a predictive biomarker is the use of the HER2 gene amplification status in breast cancer. A pivotal study by Slamon and colleagues demonstrated that patients with HER2-positive breast cancer (those with overexpression of the HER2 protein) were more likely to benefit from trastuzumab (Herceptin^TM), a targeted therapy. This finding led to the FDA's approval of trastuzumab for HER2-positive breast cancer, revolutionizing treatment and improving survival rates. Testing for HER2 status is now a routine part of breast cancer diagnosis and treatment planning

Prognostic

Prognostic biomarkers provide insight into a patient’s likely disease course, irrespective of treatment, helping clinicians assess the aggressiveness of the cancer and predict patient outcomes. These biomarkers are essential for determining the best course of action, particularly when choosing between treatment regimens or deciding whether active surveillance is appropriate.

A notable example of prognostic biomarker use is the Oncotype DX test for breast cancer – a gene expression test that analyzes a panel of 21 genes to assess the risk of recurrence in early-stage breast cancer. The TAILORx trial demonstrated that patients with low Oncotype DX scores could safely avoid chemotherapy, sparing them from unnecessary side effects without compromising their survival. The study significantly influenced how breast cancer treatment is personalized, demonstrating how prognostic biomarkers can improve decision-making.

Challenges in identifying and validating reliable cancer biomarkers

Identifying and validating reliable cancer biomarkers is a complex and multi-faceted challenge that continues to hinder advancements in personalized oncology.

“Cancer is profoundly heterogeneous, not just between patients, but within a single patient’s tumor,” Turajlic explained. “This means that a single biopsy might not capture the full biological landscape. That’s compounded by sample variability and assay heterogeneity; different labs, platforms and protocols can yield different results even when analyzing the same biomarker in the same patient.”

Even more challenging is the ability to differentiate between benign and malignant conditions using a single marker or a set of biomarkers. Research has shown that the genetic and molecular diversity between tumors is a key obstacle in identifying biomarkers universally applicable across different cancer subtypes.

“There’s a tendency to treat biomarkers as static indicators, when, in fact, tumors evolve – especially under the selective pressure of treatment. Without dynamic, longitudinal measures, we risk missing the biological shifts that are most clinically relevant.”

Even after a potential biomarker is identified, validating its clinical utility is a lengthy and rigorous process. It must demonstrate not only that it can accurately detect or predict disease but also that it can improve patient outcomes. This requires large-scale clinical trials to confirm the biomarker’s sensitivity, specificity and reproducibility in real-world settings. Furthermore, biomarkers must meet regulatory standards set by bodies, such as the FDA, a process that can be slow and resource-intensive.

Turajlic explained that this often leads to a slow and uneven translation into clinical research, often due to underestimating the complexity of the disease and overestimating what a single biomarker can tell us.

“Most current technologies provide a static view, usually from a single biopsy at one time point. But cancer is not static. That’s a huge limitation.”

Progressing the future of cancer biomarker research

Despite the promising strides in cancer biomarker research, several challenges must be addressed to maximize their clinical impact. Future research must focus on overcoming the complexities posed by intra-tumoral heterogeneity, which complicates the identification of universally applicable biomarkers. Developing biomarkers that are consistently present across various types of cancer and stages, as well as distinguishing between malignant and benign conditions, will be crucial for improving diagnostic accuracy.

Turajlic and her team are looking to progress the use of biomarkers in cancer research by conducting the Multiomic Analysis of Immunotherapy Features Evidencing Success and Toxicity (MANIFEST) project – for which Turajlic is the project’s lead.

“The MANIFEST project is a national effort to build a sustainable, National Health Service-embedded platform for deep genotyping and phenotyping, specifically focused on understanding what drives patient response, adverse effects and resistance to immunotherapy,” she explained.

As part of the MANIFEST project, Turajlic and the team are collecting rich, longitudinal data from patients, including multiomic profiles from tissue, blood and stool, so they can identify biological signatures that predict both efficacy and toxicity of immunotherapy.

“One of our central aims is to move away from single biomarkers and instead build integrated models that consider multiple biological layers and time points. We’re trying to move towards composite, multiomic biomarkers where we integrate genetic, transcriptomic, proteomic and clinical data into a more holistic signature that reflects both the tumor and its environment. That’s what will ultimately enable more tailored and effective treatment strategies,” Turajlic detailed.

Moreover, advancements in technologies like liquid biopsies and single-cell analysis hold great promise in revolutionizing biomarker detection. Non-invasive methods for continuous monitoring of tumor evolution, such as ctDNA analysis and minimal residual disease detection, will play a key role in early detection, monitoring and personalized treatment strategies.

“We're now seeing progress with single-cell sequencing, spatial transcriptomics and liquid biopsy, all of which give us a more dynamic picture,” she said. “These technologies can help us track clonal evolution, monitor treatment response and identify emerging resistance.

“The challenge now is integrating these technologies into routine care in a way that’s scalable, cost-effective, and clinically meaningful. That’s where collaboration between researchers, clinicians and data scientists becomes crucial,” Turajlic concluded.

Meet the Author

Isabel Ely, PhD

Science Writer

Isabel is a Science Writer and Editor at Technology Networks . She holds a BSc in exercise and sport science from the University of Exeter, a MRes in medicine and health and a PhD in medicine from the University of Nottingham. Her doctoral research explored the role of dietary protein and exercise in optimizing muscle health as we age.

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