Tumor microenvironment
3D rendering of the tumor microenvironment [Source: Marcin Klapczynski/Getty Images]

A Johns Hopkins University team of engineers have published new findings in Cancer Research detailed the use of Raman spectroscopy as a non-invasive tool to better understand the effects of immunotherapies on tumors. The method employs machine learning to aid in mapping the biochemical composition of tumors, and changes in the tumor microenvironment, from immunotherapy.

“Immunotherapy really works like magic and has fundamentally changed the way we view how cancer can be managed,” notes Isham Barman, Ph.D., a Johns Hopkins associate professor in mechanical engineering and co-author of the study. “However, only around 25% of patients derive benefit from it, so there’s an urgent need to identify predictive biomarkers to determine who should receive the treatment.”

Raman spectroscopy uses light to determine the molecular composition of materials. The team probed colon cancer tumors in mice treated with the two types of immune checkpoint inhibitors used in immunotherapy, as well as a control group of untreated mice.

“Cancer immunotherapy provides durable clinical benefit in only a small fraction of patients, and identifying these patients is difficult due to a lack of reliable biomarkers for prediction and evaluation of treatment response. Here we demonstrate the first application of label-free Raman spectroscopy for elucidating biomolecular changes induced by anti-CTLA-4 and anti-PD-L1 immune checkpoint inhibitors (ICI) in the tumor microenvironment (TME) of colorectal tumor xenografts,” write the investigators.

“Multivariate curve resolution-alternating least squares (MCR-ALS) decomposition of Raman spectral datasets revealed early changes in lipid, nucleic acid, and collagen content following therapy. Support vector machine classifiers and random forests analysis provided excellent prediction accuracies for response to both ICIs and delineated spectral markers specific to each therapy, consistent with their differential mechanisms of action.

“Corroborated by proteomics analysis, our observation of biomolecular changes in the TME should catalyze detailed investigations for translating such markers and label-free Raman spectroscopy for clinical monitoring of immunotherapy response in cancer patients.”

Optimized for biomedical applications

Raman spectroscopy has only recently been optimized for biomedical applications. “This is the first study that shows the ability of this optical technique to identify early response or resistance to immunotherapy,” points out Santosh Paidi, PhD, one of the lead authors who worked on the research as a mechanical engineering doctoral student at Johns Hopkins.

One of the benefits of Raman spectroscopy is that it provides exquisite molecular specificity, says Paidi, who is now a postdoctoral fellow at the University of California, Berkeley. “You get a precise molecular signature.”

The method is also well-suited for exploring the compositional changes of the tumor microenvironment, rather than the cancer cells only, according to the scientists.

“Rather than homing in on a few suspected molecules, we’re interested in getting a more holistic picture of the tumor microenvironment. That’s because the tumor is not just the malignant cell. The microenvironment contains a complex combination of the tumor stroma, blood vessels, infiltrating inflammatory cells, and a variety of associated tissue cells,” explains Barman. “Our idea is to take this approach and systematize it so it can be used by doctors to determine whether immunotherapy will be beneficial for the patient.”

The team used the Raman data—approximately 7,500 spectral data points from 25 tumors—to train an algorithm to determine a range of features that were induced by immunotherapy.

“Our question was can we differentiate between the three groups, and then what are the specific spectral features that are allowing us to differentiate between them,” continues Barman.

The team used data from different mice to build a machine learning classifier and test its performance. The goal was to mimic the biological variability the algorithm would encounter when presented with new data.

“You need to prove beyond a doubt that the differences that you’re seeing are immune checkpoint inhibitor-induced as opposed to just differences between two individuals,” notes Barman.

The results were promising, the team reported. “We were able to establish that collagen levels, lipid levels, and nucleic acid levels, as well as their spatial distribution in the tumor, change significantly when each immune checkpoint inhibitor therapy is given,” says Barman.

The differences were subtle but statistically significant and consistent with proteomics analysis conducted on the samples, pointing to the power of the technique for providing early signs of how a tumor is responding to treatment.

More research is needed, but the team believes their work will pave the way to develop a method for predicting whether a patient will respond positively to immunotherapy.

“Combined with machine learning, Raman spectroscopy has the potential to transform clinical methods for predicting therapy response,” predicts Paidi.

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