Development and validation of DM-BMI in breast, tumor-adjacent and BC tissues.
A total of 221 breast tissues from non-BC cases (GEO database) were selected as the training cohort to develop the DNA-methylation-based BMI (DM-BMI) prediction model (Fig. 1). The median BMI and median age of training cohort were 28.24 (6.07–53.74) and 37 (17–82). 50 lasso regression models based on DNA methylation data of training cohort (301998 probes per sample) were constructed and the model with the minimum mean-squared error was selected based on the Lambda value (Fig.S1A). 42 probes were included and the coefficients of them were shown in Fig.S1B and Supplementary material 5. We used Spearman correlation coefficient and paired t-test to assess the predictive accuracy of the DM-BMI model. DM-BMI showed a significant correlation with BMI (Fig. 2A) and paired t-test revealed that there was no significant difference between DM-BMI and BMI (t =-0.384, df = 220, p-value = 0.702). Using a deconvolution algorithm, we evaluated the breast tissue composition and observed that the increased DM-BMI was significantly correlated with higher proportion of adipose tissue (Fig. 2B). These results showed the high accuracy of DM-BMI for BMI prediction.
Next, 44 normal breast cases (validation set 1) and 70 tumor-adjacent breast tissues (validation set 2) were enrolled for external validation (Fig. 1). The median BMI and median age of validation set 1 were 27.1 (14.6–62.7) and 44 (13–80). The median BMI and median age of validation set 2 were 28.65 (16.5–53.4) and 56 (29–84). Both in validation set 1 and 2, DM-BMI showed positive correlation with BMI (Fig. 2C and 2E) and paired t-test revealed that there was no significant difference between DM-BMI and BMI (t =-0.253, df = 43, p-value = 0.801,validation set 1;t =-1.87, df = 69, p-value = 0.066,validation set 2). Moreover, DM-BMI significantly correlated with adipose tissue proportion in both normal and tumor-adjacent breast tissues (Fig. 2D and 2F). The above results showed a high prediction accuracy of DM-BMI model in both normal and tumor-adjacent breast tissues.
Further, we assessed the DM-BMI of paired tumor and tumor-adjacent breast tissues in TCGA database. The tumor tissues exhibited a higher level of DM-BMI comparing to its paired tumor-adjacnt tissues (Fig. 2G). In BC tissues, DM-BMI positively correlated with adipose tissue proportion (Fig. 2H).
What Is Known About The 42 Bmi Predictors?
As DM-BMI was significantly correlated with the obesity status which has been suggested to risk factor for BC, we further assessed the relevance between BMI predictors and BC. As hyper-methylation of CpG Island at gene promoter regions often caused gene silencing, we first evaluated the distribution of BMI predictors. Among 22 pairs Chromosome (Chr), Chr1 and 16 are the most common region for BMI predictor distribution. 45.2% of BMI predictors located at the gene body regions while only 23.8% of them located at the promoter regions (TS1500 and TS200). Further, we observed that only a few part of BMI predictors located at CpG islands (Fig. 3A).
Next, we assessed the methylation variation of BMI predictors between tumor and tumor-adjacent breast tissues. 26 differentially methylated probes (DMP) were identified: 22 BMI predictors were hyper-methylated in tumor tissues comparing to the tumor-adjacent breast tissues and 4 were hypo-methylated in tumor (Fig. 3B). 3 of 42 BMI predictors correlated better OS for BC patients and 2 of them were located at gene-coding regions (Fig. 3C). In BC tissues, the correlation between methylation level of 42 BMI predictor and DM-BMI were evaluated and 11 of them significantly correlated with DM-BMI (correlation coefficient > 0.3 or < -0.3; Fig. 3D-E). By integrative analysis of DNA methylation and expression data, the negatively correlation between methylation level and gene expression were observed in 22/42 BMI predictors (Fig. 3E).
Functional and clinical characteristics analysis of DM-BMI related gene profile in BC.
Further, we explored the biological significance of DM-BMI in breast cancer tissues. The survival correlation of DM-BMI was evaluated in BCs and subgroup of BCs with cancer therapy (chemotherapy, endocrine-therapy, anti-HER2 therapy and radiation-therapy). DM-BMI was consistently correlated with higher mortality risk in the whole cohort of BC patients and subgroups of patients with chemotherapy, endocrine-therapy or radiation-therapy, respectively (Table 1). Tissues from patients with postmenopausal status and TP53-mutation exhibited a significantly higher level of DM-BMI (Fig. 4A-B). Otherwise, an increasing level of DM-BMI was correlated with increased proportion of ERBB2 and MYC amplification (Fig. 4C-D).
Table 1. Survival analysis of DM-BMI in BC with systemic therapy
|
Subgroup
|
HR (High vs Low)
|
95%CI
|
P value
|
Overall
|
1.046329459
|
1.005-1.089
|
0.027924058
|
Chemotherapy
|
1.100065801
|
1.029-1.176
|
0.00529122
|
Hormone therapy
|
1.087123098
|
1.009-1.171
|
0.027857745
|
Anti-HER2 therapy
|
1.048120675
|
0.832-1.320
|
0.689677033
|
Radiation therapy
|
1.0775886
|
1.003-1.158
|
0.041331982
|
Previous studies showed that adipokines produced by obese adipose tissues drives obesity-mediated inflammation and BC progression. In BC tissues, proportion of adipose tissue was positively correlated with DM-BMI. Expression of 6 pro-inflammatory adipokines were positively correlated DM-BMI while expression of 2 anti-inflammatory adipokines were negatively correlated DM-BMI (Fig. 4E). These results indicated that obesity increased the expression of pro-inflammatory adipokines in BC tissues.
Further, we assessed the DM-BMI (obesity) related gene expression profile and mRNA expression of 10032 gene significantly correlated with DM-BMI. To evaluate the biological effect of obesity on BC, we performed GSEA analysis of DM-BMI related genes using KEGG and GO database. GO analysis showed that gens positively correlated DM-BMI were significantly involved in antigen process and presentation, immune cell activation, MHC protein binding, immune receptor activity in BC (Fig. 4F). KEGG consistently showed that DM-BMI related genes were significantly enriched in tumor-immunity related pathway (including: antigen processing and presentation, NK cell mediated cytotoxicity, T cell differentiation, and PD-1 checkpoint pathway) (Fig. 4G). These results indicated that the obesity-related gene profile involved in the regulation of immune response in BC.
DM-BMI correlated with T cell infiltration and ICI response markers in BC.
We evaluated the correlation between obesity and immune response to BC. Gene mutation which changes protein-coding sequence and leads to the expression of abnormal proteins was suggested to be the driving factor for cancer development. Also, the abnormal protein derived from tumor mutation might arise immune response. In BC tissues, we observed a positive correlation between DM-BMI and TMB. Using Estimate algorithm, we evaluated the degree of immune cell infiltration in TCGA BC tissues. Interestingly, we found a positive correlation between DM-BMI and Estimate-immune score while no significant correlation was observed between DM-BMI and Estimate-Stromal score (Fig. 5B). Next, we calculated the relative abundance of 22 immune cell types in TCGA BC tissues. Among them, the content of CD8-T, CD4 memory activated-T, T follicular helper and regulatory T cells (Treg) were positively correlated with DM-BMI indicating the more intense T cell mediated immune response in BC tissues with increased DM-BMI (Fig. 5C). As the representative of immunotherapy, the ICI therapy suppressed BC progression by activating T cell mediated immune response. Thus, we examined whether DM-BMI predicted the tumor response to ICI. 5 markers for ICI response and 4 markers for ICI resistance were selected to evaluate the tumor response. In BC tissues, DM-BMI positively correlated with IFNG, IFNG.GS, CD274, CD8 and APS indicating that BC tissues with increased DM-BMI exhibited a better response to ICI (Fig. 5D). Moreover, DM-BMI was negatively correlated with two ICI resistance markers (CAFs and TAM-M2). All these results indicated that BC tissue at obesity status might exhibited a more intense response to ICI based on markers of ICI response.