Advances in prognostic and predictive biomarkers for breast cancer 

Breast cancer is no longer a disease that can be approached with a one-size-fits-all treatment strategy. Globally, around 2.3 million women were diagnosed with breast cancer in 2022, with nearly 670,000 deaths reported during the year. In India, the disease accounted for more than 192,000 new cases, making it the most commonly diagnosed cancer among women in the country.

As the number of patients increases, there is a parallel need to make cancer treatment more precise. Not every patient needs the same treatment, and not every patient benefits from the same level of treatment. The challenge is to understand the disease well enough to know who is likely to benefit from a particular therapy and, equally importantly, who can safely avoid it.

This is where prognostic and predictive biomarkers have become increasingly relevant in breast cancer.

Traditionally, doctors have assessed breast cancer using factors such as tumour size, tumour grade, lymph node involvement, age and hormone receptor status. These remain important clinical parameters. However, they do not always explain why two patients with apparently similar cancers can have very different outcomes.

The biology of the tumour can be quite different even when the conventional clinical characteristics look similar.

Looking beyond conventional parameters

Prognostic tests have helped bring this biological difference into clinical decision-making. They provide an indication of how aggressive a tumour is and how likely it may recur or metastasise.

This has had an important effect on the way chemotherapy is used. If testing indicates that a tumour has a less aggressive biology and therefore a lower risk of metastasis, chemotherapy may not provide enough additional benefit to justify its side effects. In such cases, the ability to identify a patient who can avoid chemotherapy is itself a significant clinical benefit.

This represents an important shift in oncology. The value of a diagnostic test is not necessarily in finding more reasons to treat. It can also lie in helping a doctor decide when treatment can be reduced or avoided.

Predictive biomarkers address a slightly different question. Rather than asking how the cancer is likely to behave, they help determine whether a particular treatment is likely to work.

ER, PR and HER2 are familiar examples. An ER-positive tumour is more likely to respond to endocrine therapy, while HER2-positive breast cancer can be treated with therapies directed against the HER2 pathway. On the other hand, giving a treatment aimed at a receptor that is not present is unlikely to provide meaningful benefit.

The same principle is now being applied to a wider range of molecular alterations. BRCA mutations, for instance, can identify patients who may benefit from PARP inhibitors. Changes in the PI3K pathway can help identify patients who may be suitable for PI3K-targeted treatment. Certain mutations in the estrogen receptor gene can also influence the selection of newer targeted therapies.

The more we understand about the molecular characteristics of a tumour, the more opportunities there are to match treatment to the individual patient.

Biomarkers and immuno-oncology

The growth of immuno-oncology has added another layer to this field.

Immune checkpoint therapies work by helping the immune system recognise and attack cancer cells. PD-L1 is one of the predictive biomarkers used in the appropriate clinical setting to help identify patients who may benefit from these treatments.

This type of patient selection is particularly relevant when a therapy is expensive and can also have significant toxicity. If a biomarker can help identify patients who are more likely to benefit, it can make treatment decisions more informed.

There is also increasing interest in biomarkers associated with the signalling pathways involved in tumour growth and cell division. By looking for specific proteins or molecular alterations, clinicians can get a better understanding of what is driving an individual cancer and, where suitable therapies are available, and target those pathways.

From snapshots to ongoing monitoring

One of the more interesting developments of the last 15 years has been the possibility of monitoring cancer after treatment without relying only on periodic imaging and clinical examinations.

A patient may complete surgery, chemotherapy, targeted therapy or a combination of treatments and still have microscopic disease left behind. Detecting this residual disease before it becomes visible on imaging could potentially change how follow-up and treatment are managed.

Liquid biopsy, particularly circulating tumour DNA (ctDNA), is being investigated in this context.

Tumour cells can release fragments of their DNA into the bloodstream. A blood sample can therefore provide a way of looking for these tumour-derived signals. Unlike a scan, this is a relatively simple procedure and can potentially be repeated more frequently.

The idea is not to replace imaging or clinical assessment, but to add another source of information.

ctDNA is also being studied for detecting minimal residual disease (MRD). It may provide an indication of whether traces of cancer remain after treatment. There is another potential application during treatment: looking at changes in ctDNA levels to understand whether a patient is responding.

If the amount of circulating tumour DNA falls and eventually becomes undetectable, that may suggest that the treatment is having an effect. If it does not fall, or starts increasing, it could be an indication that the treatment needs to be reconsidered.

This kind of molecular monitoring could eventually make cancer follow-up more dynamic. Instead of waiting for a recurrence to become clinically or radiologically apparent, there may be an opportunity to identify molecular changes much earlier.

The challenge for India is implementation

The science is moving quickly. The more difficult question is how these technologies can become accessible to a much larger number of patients in India.

Affordability will be one of the biggest considerations. A test may be scientifically excellent, but if a patient cannot afford it, its clinical value remains out of reach. This is especially important in India because a considerable share of healthcare spending is paid directly by patients.

Access is another issue. Sophisticated diagnostic technologies are often available in major cities but may not be easily accessible to patients living elsewhere. A hub-and-spoke approach could help in taking advanced testing closer to smaller towns and rural areas. At the same time, technologies will need to become easier to use and deploy.

Then there is the question of quality.

For biomarker testing to become part of routine clinical practice, results need to be consistent and reliable. Standardisation has to begin even before a sample reaches the laboratory. How the sample is collected, stored and transported can affect the final result, just as the analytical process and interpretation can. Strong pre-analytical, analytical and post-analytical quality systems are therefore essential.

India also needs more data generated from Indian patients.

Many of these technologies have been developed and validated largely using data from Western populations. Cancer biology is not necessarily identical across populations. Differences in mutations and other molecular characteristics can influence how a disease behaves and potentially how a test performs.

Generating Indian-specific evidence will therefore be important. It requires investment and scientific rigour, but it can help us understand the biology of breast cancer in our own population and make treatment decisions more relevant to Indian patients.

There is one more factor that should not be overlooked: awareness.

A clinical consultation can be short, and there are many factors that a doctor has to consider while making a treatment decision. Patients who understand the role of biomarkers can ask whether testing could help determine their likely benefit from chemotherapy, whether a particular mutation is present, or whether a biomarker such as PD-L1 could influence treatment choices. Such questions can lead to a more detailed discussion between the patient and the treating team.

The direction of breast cancer care is increasingly clear. The focus is moving from treating patients based only on what the tumour ‘looks’ like to understanding ‘what’ is happening within the tumour itself.

Prognostic biomarkers can help determine the likely course and aggressiveness of the disease. Predictive biomarkers can help identify treatments that are more likely to work. Newer approaches such as ctDNA and MRD monitoring may add the ability to follow the disease at a molecular level over time.

For India, the priority now is to make these advances usable beyond a handful of specialised centres. Affordable testing, wider access, reliable quality standards and stronger Indian clinical evidence will all be necessary.

Ultimately, the promise of biomarkers is not about adding another test to the treatment pathway. It is about making the information available to clinicians early enough to make a better decision and ensuring that the patient receives the treatment that is most appropriate for the biology of their disease.