Cancer patients have primarily been limited to one option for care — chemotherapy — which attacks almost any cell that replicates quickly. Recently, however, predictive biomarkers have opened the door to matching patients with targeted treatments based on their unique disease biology. As objective, quantifiable measures of biological processes, biomarkers can reveal details of a patient’s disease and its progression, as well as how they might respond to treatment.

Tumor mutation burden (TMB) is a measurement of the number of mutations carried by tumor cells and an emerging area of focus in biomarker research. By comparing DNA sequences from a patient’s healthy tissues and tumor cells, and using a number of complex algorithms, scientists can determine the number of acquired somatic mutations present in tumors but not in normal tissues. “It’s important because that’s a marker in the cancer itself. By measuring TMB, the cancer cell now has a unique fingerprint of natural and mutated proteins,” says Saurabh Saha, head of translational medicine at Bristol-Myers Squibb.

Decoding the Potential of TMB

Unlike most cancer biomarkers for immunotherapies, which are specific to certain immune proteins expressed by the tumor, TMB is derived solely from mutations. Given that cancer arises from mutations, a high TMB might sound more alarming than a lower one, but that is not necessarily true. Researchers have found that some tumors with a higher number of mutations may be more susceptible to an immune response.

TMB is measured using DNA sequencing to determine the number of acquired mutations in the tumor. Credit: KTSDESIGN/SCIENCE PHOTO LIBRARY/Getty Images

“When we see cells start to mutate, a fraction of the mutations present in the tumor cells will code for proteins that are presented on the surface of those cells, known as neoantigens. These make the immune system more likely to recognize the tumor as foreign,” says Joe Szustakowski, head of translational bioinformatics at Bristol-Myers Squibb.

Work by John Wherry, director of the Institute for Immunology at the University of Pennsylvania, and his colleagues shows that TMB correlates with the number of immune cells — specifically T cells — that reach a tumor. Tumors with more mutations may be more responsive to immunotherapy, Wherry says, “because the immune system sees them as foreign and engages many T cells.”

TMB may also provide useful insights on why certain cancers respond in certain ways to I-O treatment. “Sunlight or smoking drive mutations that tend to be responsive to [immunotherapy],” Wherry says. “Other tumor types, like pancreatic or colon cancer, have fewer mutations, which may be why they’re less likely to respond to therapy.”

However, with more than three billion letters in the human genome, researchers are posed with an interesting challenge: How do we standardize a biomarker that is influenced by so many different factors?   

Setting the Standards

To be clinically effective, TMB needs to have consistent standards for measurement and performance, but as Steve Averbuch, head of precision medicine at Bristol-Myers Squibb, notes, that presents a challenge. “It requires a rather detailed effort and complex technology to establish the reporting standards and demonstrate accuracy and reproducibility for a diagnostic.”

In 2013, Gad Getz, director of the cancer genome computational analysis group at the Broad Institute of MIT and Harvard, and a large team of scientists reported more than 1,000-fold variation in the amount of mutations found in cancers. Such variability makes it difficult to standardize the use of TMB. As Averbuch says, “We need to assess TMB in populations of patients for relevance and predictive value to assign a cutoff number that defines what ‘high’ means to predict response.” Although the field has started to make progress, there is still much more research to be done.

To overcome this challenge, scientists need data. Bristol-Myers Squibb scientists collect as much data as they can from consented samples donated by both healthy patients and ones with cancer, which allows them to look for correlations between the mutations — both the number and specific types — and the kind of cancer. “We also compare whether any of the mutations correlate with a patient’s response to a particular drug,” Szustakowski says. Combining that information could reveal a “high” TMB measurement for a specific cancer in a particular patient, which could help clinicians identify the best possible treatments.

“Currently, there is no standard way to measure or report TMB, but we’re optimistic that this will be possible in the future,” says Szustakowski. Reporting data in a standard format makes any biomarker more useful, because it allows comparisons across studies.

“It will be very exciting to see how we progress,” says Jean-Marie Bruey, head of oncology biomarkers at Bristol-Myers Squibb. “TMB could not only serve as a diagnostic, but also as a tool for monitoring a tumor over the course of treatment, allowing doctors to identify new mutations and adapt to them. We’re not there yet, but that may be the future of precision medicine.”

The Future of TMB

Some steps toward that future are already underway. For example, the U.S. Food and Drug Administration recently approved a companion diagnostic for several cancer medications — showing which therapy may work for a patient’s specific cancer.

Also, while TMB could represent a large step in the journey toward precision medicine, it is not the last one. In the future, biomarkers will likely consist of multiple elements, including TMB, and other features such as inflammatory gene expression.

“We’re at a point in cancer research where a number of interesting biomarkers are emerging that can help play a role in defining the patients that will best benefit from treatment,” says Saha. “We’re just scratching the surface at this point, but our work is bringing us closer to making personalized medicine a reality in cancer treatment.”