Case report: Understanding the impact of persistent tissue-localization of SARS-CoV-2 on immune response activity via spatial transcriptomic analysis of two cancer patients with COVID-19 co-morbidity

Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected half a billion people, including vulnerable populations such as cancer patients. While increasing evidence supports the persistence of SARS-CoV-2 months after a negative nasopharyngeal swab test, the effects on long-term immune memory and cancer treatment are unclear. In this report, we examined post-COVID-19 tissue-localized immune responses in a hepatocellular carcinoma (HCC) patient and a colorectal cancer (CRC) patient. Using spatial whole-transcriptomic analysis, we demonstrated spatial profiles consistent with a lymphocyte-associated SARS-CoV-2 response (based on two public COVID-19 gene sets) in the tumors and adjacent normal tissues, despite intra-tumor heterogeneity. The use of RNAscope and multiplex immunohistochemistry revealed that the spatial localization of B cells was significantly associated with lymphocyte-associated SARS-CoV-2 responses within the spatial transcriptomic (ST) niches showing the highest levels of virus. Furthermore, single-cell RNA sequencing data obtained from previous (CRC) or new (HCC) ex vivo stimulation experiments showed that patient-specific SARS-CoV-2 memory B cells were the main contributors to this positive association. Finally, we evaluated the spatial associations between SARS-CoV-2-induced immunological effects and immunotherapy-related anti-tumor immune responses. Immuno-predictive scores (IMPRES) revealed consistent positive spatial correlations between T cells/cytotoxic lymphocytes and the predicted immune checkpoint blockade (ICB) response, particularly in the HCC tissues. However, the positive spatial correlation between B cells and IMPRES score was restricted to the high-virus ST niche. In addition, tumor immune dysfunction and exclusion (TIDE) analysis revealed marked T cell dysfunction and inflammation, alongside low T cell exclusion and M2 tumor-associated macrophage infiltration. Our results provide in situ evidence of SARS-CoV-2-generated persistent immunological memory, which could not only provide tissue protection against reinfection but may also modulate the tumor microenvironment, favoring ICB responsiveness. As the number of cancer patients with COVID-19 comorbidity continues to rise, improved understanding of the long-term immune response induced by SARS-CoV-2 and its impact on cancer treatment is much needed.

The promise and challenge of spatial omics in dissecting tumour microenvironment and the role of AI

“…examined the B-cell response within intratumoral tertiary lymphoid structures (TLS) in renal cell carcinoma, and found positive clinical outcomes associated with intratumoral TLS+ regions enriched with B-cell maturation and anti-tumorigenic antibody production ( 26 ); Lau et al. examined the long-term effects of SARS-CoV-2 in hepatocellular carcinoma and CRC, revealing persistent B-cell immune responses and improved in-silico IO-response scores in SARS-CoV-2-rich tissue regions ( 27 ). Another genome-wide ST method, Slide-seq (Applied Biotechnology Laboratory, UK), offers higher resolution (10 µm) read-outs with a comparable capture area (in mm-range), but is limited to FF tissues ( 30 , 63 , 64 ).…”

FAST: a fast and scalable factor analysis for spatially aware dimension reduction of multi-section spatial transcriptomics data

“…We applied ProFAST and the other methods to the analysis of four published datasets obtained via either Visium, Xenium, or Stereo-seq technologies (see "Methods"). The four datasets included a DLPFC dataset [33] and a hepatocellular carcinoma dataset [34] generated using 10× Visium, a breast cancer dataset [35] generated using 10× Xenium, and a mouse embryo dataset [12] generated using Stereo-seq. First, we examined the dimension reduction performance of ProFAST in comparison with SpatialPCA, PRECAST, DR-SC, scVI, PCA, multiBatchPCA, NMF, and LIGER, followed by the application of iSC-MEB to perform clustering analysis and align embeddings from multiple sections.…”

“…To study the mutational patterns in tumor and tumor-adjacent tissues, we further analyzed four sections from the HCC dataset [34] generated using 10× Visium platform, with two sections from tumors (HCC1 and HCC2) and two from tumor-adjacent tissues (HCC3 and HCC4) collected from a patient with HCC, containing a total of 9813 spatial locations with a median number of 3635 genes per location. Fig.…”

“…The HCC dataset used in this study was derived from two tissue sections, one from the tumor and the other from the tumor-adjacent regions of a patient with hepatocellular carcinoma [34]. The dataset consisted of 36,601 genes from more than 9,813 spatial locations.…”