Spatial transcriptomics reveals gene expression characteristics in invasive micropapillary carcinoma of the breast

Abstract

Invasive micropapillary carcinoma (IMPC) is a special histological subtype of breast cancer, featured with extremely high rates of lymphovascular invasion and lymph node metastasis. Based on a previous series of studies, our team proposed the hypothesis of “clustered metastasis of IMPC tumor cells”. However, the transcriptomics characteristics underlying its metastasis are unknown, especially in spatial transcriptomics (ST). In this paper, we perform ST sequencing on four freshly frozen IMPC samples. We draw the transcriptomic maps of IMPC for the first time and reveal its extensive heterogeneity, associated with metabolic reprogramming. We also find that IMPC subpopulations with abnormal metabolism are arranged in different spatial areas, and higher levels of lipid metabolism are observed in all IMPC hierarchical clusters. Moreover, we find that the stromal regions show varieties of gene expression programs, and this difference depends on their distance from IMPC regions. Furthermore, a total of seven IMPC hierarchical clusters of four samples share a common higher expression level of the SREBF1 gene. Immunohistochemistry results further show that high SREBF1 protein expression is associated with lymph node metastasis and poor survival in IMPC patients. Together, these findings provide a valuable resource for exploring the inter- and intra-tumoral heterogeneity of IMPC and identify a new marker, SREBF1, which may facilitate accurate diagnosis and treatment of this disease.

Whole-Exome Sequencing Reveals the Genomic Features of the Micropapillary Component in Ground-Glass Opacities

“…The intratumoral heterogeneity (ITH) patterns of the paired components in each sample were quantified to estimate the component’s genetic correlations. As expected, the MPP component had higher ITH levels compared with those of the non-MPP component, consistent with the aggressive profile of the MPP component, suggesting an expansionary predominance of this component in early progression [ 19 , 20 ] ( Figure 1 I), as reported previously [ 21 , 22 ]. These results suggest that, although there is some homology between MPP and non-MPP components, both emerged and retained their respective adaptive mutations during their development, with a more complex genomic landscape in the MPP region relative to the other components.…”

Spatial transcriptomics technology in cancer research

“…Through spot deconvolution and prior cell marker genes, researchers can estimate the cell composition of spots and further divide the ST sections into several spatially different areas i.e., tumor area, tumor leading-edge area and para-tumor area. Several pilot studies have shown ST-based pathological annotations displayed comparable or even higher accuracy than that from pathologists ( 38 , 82 , 110 , 112 , 123 ) ( Table 2 ). Moreover, ST can distinguish cancer subtypes as well ( 108 ).…”

Tumor heterogeneity reshapes the tumor microenvironment to influence drug resistance

“…This indicates that this newly developed technology could help people successfully reveal the unexplored landscape of heterogeneity. Then, it was widely used in other types of tumors, including melanoma 81 , neuroblastoma 82 , gastric cancer 83 , and breast cancer 83 . In the effort of invasive micropapillary carcinoma (IMPC) studies, spatial transcriptomics analysis provides a valuable resource for exploring the inter- and intratumoral heterogeneity of IMPC and identifies a new marker, SREBF1, which facilitates accurate diagnosis and treatment of the disease 83 .…”

“…Then, it was widely used in other types of tumors, including melanoma 81 , neuroblastoma 82 , gastric cancer 83 , and breast cancer 83 . In the effort of invasive micropapillary carcinoma (IMPC) studies, spatial transcriptomics analysis provides a valuable resource for exploring the inter- and intratumoral heterogeneity of IMPC and identifies a new marker, SREBF1, which facilitates accurate diagnosis and treatment of the disease 83 . The spatial heterogeneity analysis-guided treatment decision approach has also been further validated in other types of tumors 84 , 85 .…”