Exhaled breath analysis for prediction of response to Immunotherapy

Jul 17, 2020

The emergence of cancer immunotherapy has revolutionized the treatment paradigm for advanced cancers across a wide range of tumor types and has brought hope to thousands of patients suffering from malignancies.

The underlying concept for the development of these agents is based on the fact that cancer cells commonly exploit different mechanisms to evade and disable immune cells (e.g. T-cells) that fight the disease. In some cases, to escape immune attack, cancer cells take advantage of the body’s own immunoregulatory pathways (immune checkpoints) which transmit inhibitory signals to diminish T-cell activity. Thus, blocking these negative regulators that limit anti-tumor responses could restore immune surveillance and its ability to recognize and combat tumors. Immune Checkpoint Inhibitors (ICI) such as those targeting cytotoxic T lymphocyte-associated protein 4 (CTLA-4) and the programmed cell death protein 1 (PD-1) / programmed cell death protein ligand 1 (PD-L1) axis are antibody-based inhibitors specifically designed to improve the immune system’s natural ability to recognize and kill cancer cells by releasing the brakes on the T-cells. Prolonged overall survival and durable responses upon treatment with ICIs have resulted in the integration of these agents into standard of care regimens of more than 10 types of cancers including melanoma, gastric cancer, renal cancer and non-small cell lung cancer (NSCLC) [i].

“This indicates that single biomarkers are probably not sufficient to capture the complexity and that there is an urgent need for a more comprehensive approach”

Accumulating evidence shows that despite encouraging responses in a subset of patients, most patients do not benefit from therapy with ICIs Therefore, there are ongoing efforts to find predictive blood or tumor biomarkers that could facilitate identifying patients with the highest probability of response to these agents. An optimized and rational patient selection will subsequently improve efficacy and reduce costs and life-threatening toxicities of an unnecessary treatment. PD-L1 expression on tumor cells has emerged as the first and most studied biomarker for predicting response to anti-PD1/PD-L1 therapy. However, results of different studies, including a new study that investigated all clinical trials (between 2011 and 2019) that led to approval of ICIs by Food and Drug Administration show that PD-L1 expression is not a robust biomarker to correctly distinguish between responders and non-responders to ICIs [ii]. Nevertheless, PD-L1 expression remains the biomarker of choice for some cancers such as NSCLC to optimize clinical decision-making for treatment with ICIs. Although the search for other potential predictive biomarkers such as tumor mutational burden [iii] and tumor-infiltrating lymphocytes [iv] are ongoing, it is becoming highly recognized that both primary and secondary resistance to immunotherapy is multifaceted. Meaning that being a responder or not depends on the interplay of multiple factors including but not limited to tumor intrinsic factors (e.g. PD-L1 expression), mutational burden and complex and dynamic cancer‐immune crosstalk [v]. This also indicates that single biomarkers are probably not sufficient to capture this complexity and that there is an urgent need for a more comprehensive approach.

Exhaled breath analysis for prediction of response to anti-PD1 in NSCLC

Recently, in a study published in Annals of Oncology, de Vries R, Muller M. et al. showed that exhaled breath analysis using electronic nose (eNose) technology could potentially prevent ineffective anti-PD1(pembrolizumab and nivolumab) treatment in 24% of NSCLC patients without erroneously withholding anyone effective treatment [vi]. A total of 143 patients with NSCLC were included in this prospective real-world study and at 3-month follow-up, their response to treatment was assessed according to the Response Evaluation Criteria in Solid Tumors (RECIST) criteria. With an accuracy as high as 90%, the eNose allowed discrimination at baseline (before starting anti-PD1 therapy) between responders and non-responders in both training and validation sets. The eNose also outperformed the currently used biomarker, PD-L1 (90% vs 66% accuracy).

The reason for achieving higher accuracies could arise from the fact that eNose technology applies pattern recognition algorithms and artificial intelligence for the discovery of multi-dimensional and composite biomarkers that are far more informative than single markers. The technology represents a metabolomics platform (breathomics) consisting of an array of cross-reactive sensors, each being sensitive to overlapping groups of volatile organic compounds (VOCs) [vii]. Capturing complex biology of the body with a non-invasive and easy-to-use technology meets the demands of modern medicine.

Whether the associations between VOCs and treatment response are a direct effect of metabolite production by the tumor cells, and/or the immunological or inflammatory host responses remains to be determined. However, this does not impact the clinical utility of a breath test for the prediction of response to anti-PD-1 therapy. In fact, the equations for calculating individual patient probabilities of responsiveness to anti-PD-1 therapy are provided by the researchers for future clinical application.

The results of this study will be further validated in large-scale studies. Bringing eNose technology to clinical practice will hopefully save individual patients from unnecessary delays and start treatment with a better alternative.


[i] Couzin-Frankel J. Breakthrough of the year 2013. Cancer immunotherapy. Science. 2013.

[ii] Davis AA et al. The role of PD-L1 expression as a predictive biomarker: an analysis of all US Food and Drug Administration (FDA) approvals of immune checkpoint inhibitors. J Immunother Cancer. 2019.

[iii] Alexandrov LB, et al. Signatures of mutational processes in human cancer. Nature. 2013.

[iv] Gibney GT, et al. Predictive biomarkers for checkpoint inhibitor-based immunotherapy. Lancet Oncol. 2016.

[v] Yonina R, et al. The future of cancer immunotherapy: microenvironment-targeting combinations. Cell Research volume. 2020.

[vi] de Vries R, Muller M et al. Prediction of response to anti-PD-1 therapy in patients with non-small-cell lung cancer by electronic nose analysis of exhaled breath. Annals of Oncology. 2019.

[vii] de Vries R, et al. Integration of electronic nose technology with spirometry: validation of a new approach for exhaled breath analysis. J Breath Research. 2015.

No Results Found

The page you requested could not be found. Try refining your search, or use the navigation above to locate the post.