Immuno-genomic profiling of biopsy specimens predicts neoadjuvant chemotherapy response in esophageal squamous cell carcinoma

Abstract

Esophageal squamous cell carcinoma (ESCC) is one of the most aggressive cancers and is primarily treated with platinum-based neoadjuvant chemotherapy (NAC). Some ESCCs respond well to NAC. However, biomarkers to predict NAC sensitivity and their response mechanism in ESCC remain unclear. We perform whole-genome sequencing and RNA sequencing analysis of 141 ESCC biopsy specimens before NAC treatment to generate a machine-learning-based diagnostic model to predict NAC reactivity in ESCC and analyzed the association between immunogenomic features and NAC response. Neutrophil infiltration may play an important role in ESCC response to NAC. We also demonstrate that specific copy-number alterations and copy-number signatures in the ESCC genome are significantly associated with NAC response. The interactions between the tumor genome and immune features of ESCC are likely to be a good indicator of therapeutic capability and a therapeutic target for ESCC, and machine learning prediction for NAC response is useful.

Keywords: Chemotherapy response; chemotherapy; copy-number variants; esophageal squamous cell carcinoma; machine learning; neutrophils.

Graphical abstract

Figure 1

Classification of ESCC based on immune signatures (A and B) GSEA of IL2-STAT5 signaling (A) and interferon-γ response (B) genes in ESCC RNA-seq data. (C) Unsupervised clustering of ESCC patients by six gene expression signatures related to immune cell fractions (NK cells, monocytes, B cells, CD8+ T cells, CD4+ T cells, and neutrophils). (D) Comparison of the responder rate: p = 0.030: neutrophils (N = 7/18) versus CD4 T cells (N = 20/27); and p = 0.032: neutrophils (N = 7/18) versus CD8 T cells (N = 19/26) by Fisher exact test. (E) OS (Left) and DFS (Right) in each immune cell group. ∗p < 0.05.

Figure 2

Anti-tumor efficacy of cisplatin and depletion of neutrophils in an SCC syngeneic mouse model (A) Scheme of the cisplatin and depletion of the neutrophils test set. Humanized mice were generated by injecting ASB-XIV cells into mice. (B and C) Tumor growth in each treatment group and summary (B) and comparison of tumor size on each day (C). (D) Tumor RNA expression analysis was performed in the control non-treated group (n = 5), the neutrophil-depleted group (n = 6), and the CDDP-treated group (n = 5). GSEA between the control non-treated group (and neutrophil-depleted groups (left) and CDDP-treated and -depleted groups (right). GSEA found that only Notch signal pathways were significantly enriched in tumors treated with neutrophil depletion more than those in the control and tumors treated with CDDP (p = 0.041 and 0.029, respectively). ∗p < 0.05 and ∗∗p < 0.01 by Dunn’s multiple comparisons test.

Figure 3

Somatic copy-number alterations in specific regions of ESCC (A and B) Comparison of chromosome 9p (A) and 12q (B) segment means between NAC responders (N = 55) and non-responders (N = 31). Chromosomes 9p and 12q are significantly smaller in NAC non-responders than in responders (p = 0.036 and 0.002 by Student’s test). ∗p < 0.05 and ∗∗p < 0.01 by Student’s test (C) Fifty-four recurrent focal CNA events were significantly different between the responders and non-responders. Locations with p values smaller than 0.01 are highlighted in red. (D) Pathways of gene groups of locations with p value ≤ 0.001 among the sub-locations shown in (C). ClueGO Cytoscape visualizes the interaction of gene clusters in a functionally grouped network using enrichment maps. Nodes in the same cluster are assigned the same node color, and the node size indicates the number of genes mapped to each GO term. Node labels are determined based on the common themes among the processes in the cluster. Additional information on the clusters can be found in Table S3.

Figure 4

Copy number signatures in ESCC Six copy-number signatures (ESCC-CNSig1, ESCC-CNSig2, ESCC-CNSig3, ESCC-CNSig4, ESCC-CNSig5, and ESCC-CNSig6) were identified using ESCC shallow and deep WGS data (n = 92). (A) Defining features of the CN signatures, showing each feature (segsize, bp10MB, osCN, changepoint, copy number, bpcharm) split into 36 constituent components, as defined in Macintyre et al. The mean value for each component is shown on the x axis, with the component weights shown on the y axis. Features are defined as follows: segment size (Mb); bp10MB, number of breakpoints (10 Mb−1); ocCN, region length with neighboring oscillating copy-number segments (Mb); changepoint, the difference in copy number between neighboring segments; copy number, the absolute copy number of a segment; bpcharm, breakpoints per chromosome arm. (B) Identified copy-number signatures of ESCC were compared with HGSOC copy-number signatures using cosine similarity scoring. (C) Comparison of copy-number signatures of NAC responders and non-responders. ESCC CNSig6 was significantly reduced in non-responders compared with responders (p = 0.034 by Mann-Whitney test). ∗p < 0.05.

Figure 5

Multi-parameter integrative modeling accurately predicts the therapeutic outcome (A) The diagnostic models to discriminate between responder and non-responder on the learning dataset. The decision tree had eight layers and eight nodes. The bar graphs show the respective number of patient responses at each node (class). (B) Seven features with the highest weighting scores. (C) Probability of each case (n = 32) being classified as responders or non-responders in the test set. This discriminating rule achieved 84.4% accuracy, 66.7% sensitivity, and 66.7% specificity. (D) The responsiveness model with the validation set (n = 20) indicates that the area under the curve is 81%.