Tumor microenvironment gene expression profiles associated to complete pathological response and disease progression in resectable NSCLC patients treated with neoadjuvant chemoimmunotherapy

Abstract

Background: Neoadjuvant chemoimmunotherapy for non-small cell lung cancer (NSCLC) has improved pathological responses and survival rates compared with chemotherapy alone, leading to Food and Drug Administration (FDA) approval of nivolumab plus chemotherapy for resectable stage IB-IIIA NSCLC (AJCC 7th edition) without ALK or EGFR alterations. Unfortunately, a considerable percentage of tumors do not completely respond to therapy, which has been associated with early disease progression. So far, it is impossible to predict these events due to lack of knowledge. In this study, we characterized the gene expression profile of tumor samples to identify new biomarkers and mechanisms behind tumor responses to neoadjuvant chemoimmunotherapy and disease recurrence after surgery.

Methods: Tumor bulk RNA sequencing was performed in 16 pretreatment and 36 post-treatment tissue samples from 41 patients with resectable stage IIIA NSCLC treated with neoadjuvant chemoimmunotherapy from NADIM trial. A panel targeting 395 genes related to immunological processes was used. Tumors were classified as complete pathological response (CPR) and non-CPR, based on the total absence of viable tumor cells in tumor bed and lymph nodes tested at surgery. Differential-expressed genes between groups and pathway enrichment analysis were assessed using DESeq2 and gene set enrichment analysis. CIBERSORTx was used to estimate the proportions of immune cell subtypes.

Results: CPR tumors had a stronger pre-established immune infiltrate at baseline than non-CPR, characterized by higher levels of IFNG, GZMB, NKG7, and M1 macrophages, all with a significant area under the receiver operating characteristic curve (ROC) >0.9 for CPR prediction. A greater effect of neoadjuvant therapy was also seen in CPR tumors with a reduction of tumor markers and IFNγ signaling after treatment. Additionally, the higher expression of several genes, including AKT1, BST2, OAS3, or CD8B; or higher dendritic cells and neutrophils proportions in post-treatment non-CPR samples, were associated with relapse after surgery. Also, high pretreatment PD-L1 and tumor mutational burden levels influenced the post-treatment immune landscape with the downregulation of proliferation markers and type I interferon signaling molecules in surgery samples.

Conclusions: Our results reinforce the differences between CPR and non-CPR responses, describing possible response and relapse immune mechanisms, opening the possibility of therapy personalization of immunotherapy-based regimens in the neoadjuvant setting of NSCLC.

Keywords: drug therapy, combination; gene expression profiling; immunotherapy; lung neoplasms; tumor biomarkers.

Figure 1

Differential immune landscape in pretreatment samples. (A) Volcano plot showing DEGs between CPR and non-CPR tumors. Red dots indicate upregulated genes in CPR tumors, while blue dots indicate upregulated genes in non-CPR tumors. (B) Expression levels in TPM of IFNG, GZMB and NKG7. (C) ROC curve for the prediction of complete pathological response using median TPM expression levels of IFNG, GZMB and NKG7, as cut-off. (D) Gene set enrichment analysis for pathological responses. Differentially upregulated pathways in non-CPR (left) and CPR tumors (right). (E) Absolute immune cell score and frequency of each immune cell subtype in CPR and non-CPR tumors determined by CIBERSORTx. P<0.002 was considered statistically significant after Bonferroni’s correction for multiple tests. (F) ROC curve for the prediction of complete pathological response using median proportion of M1 macrophages as cut-off. Each patient is represented by a black symbol. Comparisons were done between CPR (n=9) and non-CPR (n=5) groups. CPR, complete pathological response; DEGs, differential-expressed genes.

Figure 2

Differential immune landscape in post-treatment samples. (A) Volcano plot showing the DEGs between CPR and non-CPR tumors in post-treatment samples. Red dots indicate upregulated genes in CPR tumors, while blue dots indicate upregulated genes in non-CPR tumors. (B) Expression levels in TPM of CCNB2, CDKN3 and ISG15. (C) ROC curve for the assessment of complete pathological response using the median TPM expression levels of CCNB2, CDKN3 and ISG15 as cut-off. (D) Gene set enrichment analysis for pathological responses. Differentially upregulated pathways in non-CPR and CPR post-treatment tumors. (E) Frequency of follicular helper T cells in tumors determined by CIBERSORTx analysis. P<0.002 was considered statistically significant after Bonferroni’s correction for multiple tests. Each patient is represented by a black symbol. Comparisons were done between CPR (n=22) and non-CPR (n=14) groups. CPR, complete pathological response; DEGs, differential-expressed genes.

Figure 3

Immune expression signature associated with disease progression in patients with non-CPR tumors. (A) Volcano plot showing the DEGs between post-treatment tumors from patients with disease progression (n=5) versus no disease progression (n=9). Red dots indicate upregulated genes in tumors from patients with disease progression, while blue dots indicate upregulated genes in tumors from patients with non-disease progression. (B) Kaplan-Meier plots of progression-free survival and overall survival for patients with high (n=4) and low (n=10) expression of AKT (p=0.033 and p=0.003, respectively). (C) Kaplan-Meier plots of progression-free survival and overall survival for patients with high (n=4) and low (n=19) proportion of activated dendritic cells (p=0.019 and p=0.052). (D) Kaplan-Meier plots of progression-free survival and overall survival for patients with high (n=4) and low (n=10) proportion of neutrophils (p=0.033 and p=0.003). CPR, complete pathological response; DEGs, differential-expressed genes.

Figure 4

Changes in the immune landscape during treatment. (A) Volcano plot showing the DEGs between paired post-treatment and pretreatment CPR tumor samples (n=7). Red dots indicate upregulated genes, while blue dots indicate downregulated genes in post-treatment samples. (B) Differentially upregulated (upper panel) and downregulated pathways (lower panel) in post-treatment CPR tumor samples. (C) Frequency of immune cell subtypes obtained with CIBERSORTx analysis in pretreatment and post-treatment timepoints from CPR tumors. (D) Volcano plot showing the DEGs between paired post- and pretreatment non-CPR tumor samples (n=4). Red dots indicate upregulated genes, while blue dots indicate downregulated genes in post-treatment samples. (E) Differentially upregulated pathways in post-treatment non-CPR tumor samples. (F) Frequency of immune cell subtypes in non-CPR tumors and comparison between pretreatment and post-treatment timepoints. CPR, complete pathological response; DEGs, differential-expressed genes.

Figure 5

Immune landscape regarding PD-L1 and TMB status in pretreatment samples. (A) Volcano plot of DEGs in pretreatment samples between PD-L1 high (≥25%, n=9) and PD-L1 low (<25%, n=6) tumors. Red dots indicate upregulated genes, while blue dots indicate downregulated genes in PD-L1 high samples. (B) Differentially upregulated pathways in pretreatment samples of TMB high and PD-L1 high tumors. (C) Volcano plot of DEGs in pretreatment samples between TMB high (≥5.89, n=7) and TMB low (<5.89, n=7) tumors. Red dots indicate upregulated genes, while blue dots indicate downregulated genes in TMB high samples. (D) Frequency of regulatory T cells measured with CIBERSORTx in TMB high compared with TMB low tumors. (E) Volcano plot of DEGs in post-treatment samples whose pretreatment tissue showed PD-L1 high (≥25%, n=11) or PD-L1 low (<25%, n=11) expression. Red dots indicate upregulated genes, while blue dots indicate downregulated genes in PD-L1 high samples. (F) Volcano plot of DEGs in post-treatment samples whose pretreatment tissue had TMB high (≥5.89, n=12) or TMB low (<5.89, n=10). Red dots indicate upregulated genes, while blue dots indicate downregulated genes in TMB high samples. (G) Differentially upregulated pathways in post-treatment samples of pretreatment PD-L1 high tumors. (H) Differentially upregulated pathways in post-treatment samples of pretreatment TMB low tumors. (I) Frequency of follicular helper T cells and M2 macrophages in post-treatment samples of high or low PD-L1 expression at pretreatment. Each patient is represented by a black dot. DEGs, differentially expressed genes; TMB, tumor mutational burden.