Nutritional status and response to immunotherapy in lung cancer patients

In the past years, lung cancer has become the single largest cause of cancer deaths in developed countries. Because it remains subclinical for a long time and the symptoms are commonly non-specific, patients are usually diagnosed at advanced stages of disease. This situation limits treatment options, but immune checkpoint inhibitors are an effective treatment for many patients. This type of drug works by counteracting the mechanisms that tumors develop to evade the immune system control and elicit proliferation and action of cytotoxic T-cells to kill tumor cells. Despite the effectiveness of this therapy once the immune system action is re-established, more than half of the lung cancer patients receiving it do not respond to therapy. As the gut microbiome and the nutritional status of patients have been extensively related to outcomes in cancer and to the integrity of the immune system, they can have a prognostic value to predict the response a given patient will have to cancer treatment and to evaluate their candidacy for the therapy. In addition, the patients’ diet modulates both their gut microbiome and nutritional status. To date, combining data from gut microbiome, nutritional status and dietary intake has not been attempted to find combinations of these features that may predict response to immune checkpoint inhibitors in lung cancer patients.The aim of this study was to generate a data model that integrated relevant diet, nutritional status and microbiome factors to predict immune checkpoint blockade response in lung cancer patients. To establish the features of body weight, body composition, dietary intake and gut microbiome, related to cancer outcomes (survival and response to therapy), a dataset from Valencia, Spain (n=69) was analyzed and the same analyses were performed using an independent cohort of 266 non-small cell lung cancer patients from Montreal, as a pre-modeling step. Comparisons among the two cohorts were performed and the final variables to include in the modeling phase were chosen. Modeling was carried out using the Montreal dataset only and using Cox-proportional hazard regression as the statistical approach and Archetypal Analysis as the machine-learning approach.After adjusting for EGFR-mutation status, ECOG status and PD-L1 expression in tumor cells, body mass index and skeletal muscle mass were found to be non-independent predictors of progression-free survival, with higher values being protective against lung cancer progression risk (Body mass index: HR=0.95, 95%CI=[0.924–0.987], p<0.01; skeletal muscle: HR=0.99, 95%CI=[0.992–0.999], p=0.01). Further data modeling using archetypal analysis revealed patterns for progression after immune checkpoint blockade administration that incorporated the relative abundance of five bacterial species, body composition variables, body mass index and macronutrients intake. Three patterns (archetypes) were found that explain subgroups of patients with different characteristics across all the variables included related to their progression after therapy administration. In the pre-modeling phase and in the archetypal analysis higher values of body mass index, skeletal muscle and higher relative abundance of Monoglobus pectinilyticus associated with better response to therapy, whilst higher relative abundance of Streptococcus spp. associated with non-response.This study showed that combinations of gut microbiome, dietary intake and nutritional status features along with other clinical variables can be used to assess response to immune checkpoint blockade therapy in lung cancer

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