Meeting News Coverage

Identification of immunotherapy biomarkers may prove 'very difficult'

NEW YORK — Although biomarkers will be helpful to determine which patients with melanoma will have the greatest benefit and least toxicity from immunotherapies, integrating all of the available information from the tumor microenvironment to predict potential outcome will likely be a challenge, according to a presentation at the HemOnc Today Melanoma and Cutaneous Malignancies meeting.

Mario Sznol, MD, professor of internal medicine at Yale University and the Yale Comprehensive Cancer Center, discussed recent data on predictive biomarkers for immunotherapy in melanoma and whether they are ready for clinical application.

There are a number of immunotherapies in melanoma — such interleukin (IL)-2, anti-CTLA–4, anti-PD–1, and the combination of ipilimumab (Yervoy, Bristol-Myers Squibb) and nivolumab (Opdivo, Bristol-Myers Squibb) —for which biomarkers would be helpful to produce maximum benefit but reduce toxicity, Sznol said.

“When we start thinking about biomarkers, we really need to think that we’re intervening in a very complex environment,” Sznol said. “When we just hit one spot, there are so many things that affect it, it is unrealistic that any single biomarker would provide the answer or predict for response with these complex drugs within a complex biology.”

There are many different types of potential predictive biomarkers, Sznol said. These include the presence and intratumoral location of CD8-positive T lymphocytes, the clonality and functional status of T cells, the genetics of the individual patient, circulating factors, immune suppressive cells in the microenvironment, host ‘competence’ such as the absolute lymphocyte count, and tumor burden.

For anti-CTLA–4 agents, researchers have demonstrated that patients with high C-reactive protein levels and low lymphocyte counts at baseline and week 7 are less likely to respond. However, doubling and persistent increases in CD4-positive and ICOS-positive T cells at week 12, as well as baseline FoxP3-positive T cells have been linked to a higher likelihood of response.

“With all of these, they are not absolute predictors,” Sznol said. “There are clearly people who don’t have a biomarker who respond. So what you use as a biomarker really depends on what therapies you have available to give to the patient.”

Snyder and colleagues evaluated data from patients with melanoma treated with a CTLA-4 blockade. Results, published in 2014 in The New England Journal of Medicine, demonstrated that responders had a different neoantigen signature compared with non-responders.

“It’s a very intriguing suggestion that the mutations you develop over time could influence your response to a drug like ipilimumab,” Sznol said. “You may already have a very strong immune response against viral and bacterial antigens that your mutations mimic, and you can induce a response in those patients. But we don’t fully understand this yet.”

For anti-PD–1 agents, early data from pembrolizumab (Keytruda, Merck) in 113 patients showed that PD-L1 expression was associated with a greater overall response rate compared with PD-L1 negativity (49% vs. 13%; P = .0007).

“These are very interesting results, suggesting that you enrich for response, but when you exclude biomarker-negative population, you can actually lose some patients who might respond,” Sznol said.

PD-L1 expression also is associated with prolonged PFS with nivolumab; however, it’s unclear whether PD-L1 is biomarker, or if it is a prognostic marker for outcome regardless of treatment, Sznol said.

Data from Robert and colleagues, published in 2014 in The New England Journal of Medicine, again demonstrated some PD-L1 negative patients responded to treatment. This may be due to false-negative results of the assay, or that PD-L1 is working in lymph nodes rather than the tumor microenvironment, Sznol said.

“We have a problem in that there is an enormous amount of information in the tumor microenvironment, and how to put all that information together and trying to decide how a single intervention is going to come out I think is going to be very difficult,” Sznol said. — by Alexandra Todak

Reference:

Daud AI. Abstract #CT104. Presented at: AACR Annual Meeting; April 5-9, 2014; San Diego.

Snyder A, et al. N Engl J Med. 2014;doi: 10.1056/NEJMoa1406498.

Sznol M. Predictive biomarkers for immunotherapy in melanoma: Ready for clinical application? Presented at: HemOnc Today Melanoma and Cutaneous Malignancies; April 10-11, 2015; New York.

Robert C, et al. N Engl J Med. 2014;doi:10.1056/NEJMoa1412082.

Disclosure: Sznol reports paid consultant roles with Anaeropharma, Astellas-Agensys, AstraZeneca/MedImmune, Bristol-Myers Squibb, Genentech/Roche, Immune Design, Immunova, Kyowa-Kirin, Lilly, Merus, Nektar, Novartis, Pfizer, Pierre-Fabre, and Seattle Genetics. He also reports scientific advisory board roles with Adaptive Biotechnologies, Amphivena, Lion Biotechnologies and Symphogen.

NEW YORK — Although biomarkers will be helpful to determine which patients with melanoma will have the greatest benefit and least toxicity from immunotherapies, integrating all of the available information from the tumor microenvironment to predict potential outcome will likely be a challenge, according to a presentation at the HemOnc Today Melanoma and Cutaneous Malignancies meeting.

Mario Sznol, MD, professor of internal medicine at Yale University and the Yale Comprehensive Cancer Center, discussed recent data on predictive biomarkers for immunotherapy in melanoma and whether they are ready for clinical application.

There are a number of immunotherapies in melanoma — such interleukin (IL)-2, anti-CTLA–4, anti-PD–1, and the combination of ipilimumab (Yervoy, Bristol-Myers Squibb) and nivolumab (Opdivo, Bristol-Myers Squibb) —for which biomarkers would be helpful to produce maximum benefit but reduce toxicity, Sznol said.

“When we start thinking about biomarkers, we really need to think that we’re intervening in a very complex environment,” Sznol said. “When we just hit one spot, there are so many things that affect it, it is unrealistic that any single biomarker would provide the answer or predict for response with these complex drugs within a complex biology.”

There are many different types of potential predictive biomarkers, Sznol said. These include the presence and intratumoral location of CD8-positive T lymphocytes, the clonality and functional status of T cells, the genetics of the individual patient, circulating factors, immune suppressive cells in the microenvironment, host ‘competence’ such as the absolute lymphocyte count, and tumor burden.

For anti-CTLA–4 agents, researchers have demonstrated that patients with high C-reactive protein levels and low lymphocyte counts at baseline and week 7 are less likely to respond. However, doubling and persistent increases in CD4-positive and ICOS-positive T cells at week 12, as well as baseline FoxP3-positive T cells have been linked to a higher likelihood of response.

“With all of these, they are not absolute predictors,” Sznol said. “There are clearly people who don’t have a biomarker who respond. So what you use as a biomarker really depends on what therapies you have available to give to the patient.”

Snyder and colleagues evaluated data from patients with melanoma treated with a CTLA-4 blockade. Results, published in 2014 in The New England Journal of Medicine, demonstrated that responders had a different neoantigen signature compared with non-responders.

“It’s a very intriguing suggestion that the mutations you develop over time could influence your response to a drug like ipilimumab,” Sznol said. “You may already have a very strong immune response against viral and bacterial antigens that your mutations mimic, and you can induce a response in those patients. But we don’t fully understand this yet.”

For anti-PD–1 agents, early data from pembrolizumab (Keytruda, Merck) in 113 patients showed that PD-L1 expression was associated with a greater overall response rate compared with PD-L1 negativity (49% vs. 13%; P = .0007).

“These are very interesting results, suggesting that you enrich for response, but when you exclude biomarker-negative population, you can actually lose some patients who might respond,” Sznol said.

PD-L1 expression also is associated with prolonged PFS with nivolumab; however, it’s unclear whether PD-L1 is biomarker, or if it is a prognostic marker for outcome regardless of treatment, Sznol said.

Data from Robert and colleagues, published in 2014 in The New England Journal of Medicine, again demonstrated some PD-L1 negative patients responded to treatment. This may be due to false-negative results of the assay, or that PD-L1 is working in lymph nodes rather than the tumor microenvironment, Sznol said.

“We have a problem in that there is an enormous amount of information in the tumor microenvironment, and how to put all that information together and trying to decide how a single intervention is going to come out I think is going to be very difficult,” Sznol said. — by Alexandra Todak

Reference:

Daud AI. Abstract #CT104. Presented at: AACR Annual Meeting; April 5-9, 2014; San Diego.

Snyder A, et al. N Engl J Med. 2014;doi: 10.1056/NEJMoa1406498.

Sznol M. Predictive biomarkers for immunotherapy in melanoma: Ready for clinical application? Presented at: HemOnc Today Melanoma and Cutaneous Malignancies; April 10-11, 2015; New York.

Robert C, et al. N Engl J Med. 2014;doi:10.1056/NEJMoa1412082.

Disclosure: Sznol reports paid consultant roles with Anaeropharma, Astellas-Agensys, AstraZeneca/MedImmune, Bristol-Myers Squibb, Genentech/Roche, Immune Design, Immunova, Kyowa-Kirin, Lilly, Merus, Nektar, Novartis, Pfizer, Pierre-Fabre, and Seattle Genetics. He also reports scientific advisory board roles with Adaptive Biotechnologies, Amphivena, Lion Biotechnologies and Symphogen.

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