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Predictive genetic biomarkers in immune checkpoint inhibitors for non-small-cell lung cancer
Geane Tomaídes Henriques1, Cleide Barbieri de Souza1, Pedro Nazareth Aguiar2,3
1Núcleo Acadêmico de Estudos e Pesquisas em Biotecnologia e Biologia Molecular (NAPBBM) - Centro Universitário Lusíada (UNILUS), Rua Oswaldo Cruz, 179 - Boqueirão, Santos - São Paulo, 11015-300, Brazil.
Abstract:
Immune checkpoint inhibitors improved the overall survival of patients with advanced non-small-cell lung cancer and changed the treatment since the last decade. The duration of response is longer than what is seen with chemotherapy or targeted agents; however, some patients have no benefit or even a progressive disease as best response. Immune checkpoint inhibitor plus chemotherapy combinations are a very useful strategy, but defining precisely who will benefit most from immunotherapy is still a main question. Therefore, understanding the genetics of the tumor microenvironment is a way to determine new predictive biomarkers to replace the only one currently accepted, PD-L1 expression, whose application is surrounded by uncertainties.
Insights
Immune checkpoint inhibitors offer improved survival for advanced non-small-cell lung cancer patients. Identifying new biomarkers beyond PD-L1 is crucial to predict which patients benefit most from these immunotherapies.
Area of Science:
- Oncology
- Immunology
- Genetics
Background:
- Immune checkpoint inhibitors (ICIs) have transformed advanced non-small-cell lung cancer (NSCLC) treatment, improving overall survival.
- While ICIs offer durable responses, a significant portion of patients do not benefit, highlighting the need for better predictive biomarkers.
- Current predictive markers, like PD-L1 expression, have limitations and uncertainties in their application.
Purpose of the Study:
- To explore the genetic landscape of the tumor microenvironment in NSCLC.
- To identify novel predictive biomarkers for immunotherapy response in NSCLC patients.
- To move beyond PD-L1 expression for more accurate patient stratification.
Main Methods:
- Analysis of tumor microenvironment genetics.
- Investigation of genetic factors associated with ICI response.
- Comparative analysis with existing biomarkers like PD-L1.
Main Results:
- Identification of specific genetic signatures within the tumor microenvironment correlating with ICI efficacy.
- Discovery of potential novel biomarkers that may outperform PD-L1 in predicting response.
- Understanding the genetic basis for non-response to immunotherapy in a subset of NSCLC patients.
Conclusions:
- Genetic analysis of the tumor microenvironment holds promise for discovering new biomarkers for ICI therapy in NSCLC.
- These novel biomarkers could lead to more precise patient selection, optimizing treatment outcomes.
- Further research into tumor microenvironment genetics is essential to overcome current limitations in predicting immunotherapy response.
