A 49-year-old Asian male who was diagnosed with hepatitis B virus (HBV)-positive HCC with no evidence of macrovascular involvement underwent curative segment VII liver resection (3.8 cm x 2.8 cm) 85 days after testing COVID-19 negative using RT-PCR swab test. A second patient, a 45-year-old Asian male diagnosed with invasive stage II T3N0 cecal adenocarcinoma (right-sided CRC), underwent laparoscopic right hemicolectomy 9 days after testing COVID-19 negative using RT-PCR swab test. Patient details have been reported previously. The two patients had mild symptoms of acute respiratory infection and were hospitalized for isolation purposes; in addition, they were unvaccinated and did not require oxygen therapy or any medical treatment for COVID-19. Importantly, the patients had not undergone any cancer treatment before COVID-19 infection or surgery, ruling out immunological effects caused by other therapy. While the CRC patient did not have other co-morbidities, the HCC patient had chronic HBV and past TB infections.
Initially, we conducted spatial transcriptomic (ST) profiling on paired specimens consisting of tumor and adjacent normal tissue from the HCC and CRC patients. This analysis was performed on fresh frozen sections using the Visium Spatial Gene Expression (10x Genomics) assay. The tissue sections placed on the capture areas of the Visium slide were fixed, stained with hematoxylin and eosin, and imaged. After tissue permeabilization, mRNA molecules were reverse transcribed to generate spatially barcoded cDNA molecules, which were then PCR-amplified and sequenced at 50,000 reads per capture spot. All bioinformatic analysis was performed on individual tissues using the sctransform-normalized data computed by R package Seurat (v4.0.3) (unless stated otherwise) ( Supplementary Figure 1A ).
Using the Visium ST data ( Supplementary Figure 2 ), we carried out spatial enrichment analysis (AddModuleScore function in Seurat) based on the SARS-CoV-2 immune response signatures from two public databases ( Supplementary Table 1 ). In the first database, Lee et al. identified lymphocyte-associated (CD8+ T cells and NK cells) and myeloid-associated (monocyte and dendritic cells) COVID-19 signatures, using influenza patients as a reference. In the second, Ren et al. determined three lymphocyte-associated (B cell, T cell, and NK cell) COVID-19 signatures and one myeloid-associated (neutrophil) signature, using COVID-19-negative peripheral blood mononuclear cells (PBMCs) as a reference. For the four lymphocyte signatures, spatial profiles were broadly consistent in all four tissues analyzed ( Figure 1 ); however, some differences in localization were observed for the SARS-CoV-2 response signatures associated with T cells and NK cells in the CRC-adjacent normal tissue ( Figures 1L, P ). By contrast, myeloid-associated SARS-CoV-2 responses did not demonstrate distinctive spatial localization, except in the HCC-adjacent normal tissue ( Supplementary Figure 3 , see panels  B  and  F ).
Independent of these SARS-CoV-2 immune responses, we identified four regions with distinct ST niches within each tissue using a spatially-aware clustering method (BayesSpace R package v1.2.0) ( Figures 2A-D ); the four clusters were selected arbitrarily to balance resolution and interpretability. Generation of uniform manifold approximation and projection (UMAP) plots was based on the top 30 principal components (RunPCA and RunUMAP functions in Seurat) of the ST niches ( Supplementary Figure 4 ). The distribution of SARS-CoV-2 immune responses was consistent in the ST niches in the HCC tissues but heterogenous in the CRC tissues ( Supplementary Figures 5  and  6 ).
As other viral infections (e.g., HBV in the HCC patient) can induce similar immune responses, we further localized SARS-CoV-2 specific immune responses in tissues. This analysis was performed by quantifying SARS-CoV-2 spike protein (SP) counts on serial tissue sections, using RNAscope ( Figures 2E-H ; brightfield images presented in  Supplementary Figures 7 - 8 , raw counts given in  Supplementary Table 2 ;  Supplementary Materials ). The SARS-CoV-2 SP counts were spatially variable, especially in the CRC tumor tissue ( Figures 2I-L ). Despite the variability, the viral-high regions (defined as the ST niches exhibiting the highest average SARS-CoV-2 SP counts) were confirmed by mIHC performed on FFPE sections ( Supplementary Figure 9 ;  Supplementary Materials ). Both RNAscope and mIHC analyses were based on multiple high-quality regions of interest (ROIs; marked as boxes in  Figures 2E-H  and  Supplementary Figures 9A-D ) that were selected by a pathologist (JY). To compute ST niche-average counts, the images were manually overlaid onto the Visium tissue images, and the ROIs were assigned to the closest ST niches ( Supplementary Table 2 ; average SP and NP counts of ROIs assigned to individual ST niches are shown in  Figures 2I-L  and  Supplementary Figures 9E-H ). Furthermore, using a pathologist (JY)-trained machine-learning classifier in QuPath (v0.3.2; Supplementary Materials), the viral-high regions were predominantly identified as stromal or normal epithelial tissues ( Supplementary Figure 10 ).
Subsequently, we conducted immune cell type analysis using the microenvironment cell population (MCP)-counter (MCPcounter R package v1.2.0), whereby the presence of a cell type in the Visium spots was defined using a threshold of MCP-counter-estimated log2 expression > 0. The results revealed intra-tissue heterogeneity in terms of tissue coverage (% spots) and detectability ( Figure 2M ). B cells were detected homogeneously within viral-high regions in all tissues (although they were relatively sparse in the HCC-adjacent normal tissue), while other immune cells were either sparse or dispersed throughout the tissue ( Figure 2M ,  Supplementary Figures 11 - 12 ). To identify the immune cells that elicited the SARS-CoV-2 immunological effect, we examined the association between the presence of immune cells and public COVID-19 signature scores. Analysis of the COVID-19 signatures identified by Lee et al. showed that within viral-high regions, the lymphocyte-associated SARS-CoV-2 response was significantly positively associated with the spatial localization of B cells across HCC and CRC tissues (P < 0.005,  Figures 3A-D ); in contrast, the myeloid COVID-19 signature was only significantly associated with T cells in HCC tissues (P < 0.005,  Figures 3A, B ). Similar findings were obtained using the COVID-19 signatures from Ren et al.; the lymphocyte-specific SARS-CoV-2 responses (i.e., B, T, and NK-associated responses) were significantly associated with the spatial localization of B cells across HCC and CRC tissues (P < 0.005), but not CRC-adjacent normal tissue ( Supplementary Figure 13 ).
To further dissect the specificity of B cell subsets involved in the tissue-localized lymphocyte-based COVID-19 response, we characterized and annotated individual B cell phenotypes (memory [MBCs], naive, intermediate, and plasmablasts) by single-cell analysis of ex vivo SARS-CoV-2-stimulated patient-matched samples. Stimulated lymph node samples (CRC) had been obtained previously, while PBMCs from the HCC patient were stimulated following the same procedure prior to analysis (Supplementary Materials). Single-cell sequencing used Chromium Single Cell 5' and 3' Reagent Kits v2 and v3 (10x Genomics, San Francisco, CA, USA), while paired-end sequencing (2x150 bp) was performed using the NovaSeq 6000 platform (Illumina, Inc., San Diego, CA, USA) (Supplementary Materials; bioinformatic analysis is depicted in  Supplementary Figure 1B ). Marker genes for the B cell phenotypes were identified by comparing SARS-CoV-2-stimulated and unstimulated samples (using the FindMarkers function in Seurat;  Supplementary Table 3 ). The above-identified patient- and SARS-CoV-2-specific B cell phenotypes were then mapped to individual Visium spots using robust cell type decomposition (RCTD package v1.2.0), whereby the presence of a cell type was recorded if detected as a singlet or a doublet. Within the viral-high regions, plasmablasts and MBCs exhibited the highest spot coverage in HCC and CRC tissues, respectively ( Supplementary Figures 14 - 15 ); however, only the spatial localization of MBCs demonstrated a consistently positive association with lymphocyte-associated SARS-CoV-2 immunological effects ( Supplementary Figures 16A-H ). Notably, MBC infiltration (measured by spot coverage) was higher in the viral-high regions of CRC tissues, compared with the viral-high regions of HCC tissues ( Supplementary Figure 14 ).
We evaluated the potential impact of a persistent SARS-CoV-2-induced B cell immune response on spontaneous or immunotherapy-induced anti-tumor activity in situ by determining the immuno-predictive scores (IMPRES). IMPRES, a transcriptomic biomarker for ICB, was computed by summing the binary outcomes (gene 1 > gene 2, using spot-level log-normalized counts) of 15 pairs of immune checkpoint genes ( Supplementary Table 4 ) and normalizing by the available pairs. In individual tissues, the spatial localization of B cells exhibited variable associations (positive and negative) with IMPRES scores across the ST niches. However, within viral-high regions, spots with detection of B cells showed consistently higher IMPRES scores for all four tissues ( Figure 4 ; viral-high regions are indicated by orange stars/red arrows). By contrast, spots with detection of T cells and cytotoxic lymphocytes (representing key effector cells in immunotherapy) demonstrated consistently higher IMPRES scores across the ST niches, although they were undetected in some CRC regions. Although the differences in scores were not significant, they were indicative of overall trends. CD8+ T cells were generally sparse and not detected in any of the viral-high regions of the four tissues. Other immune cells, including NK cells, myeloid dendritic cells, monocytic lineage cells, and neutrophils, were either not positively associated with IMPRES scores or were undetected in the viral-high regions.
We also evaluated another transcriptomic predictor of ICB, the tumor immune dysfunction and exclusion (TIDE) framework, which simultaneously measures T cell dysfunction and exclusion as key mechanisms of tumor immune evasion. TIDE analysis was conducted on the web platform , along with the analysis of individual immune components. Specifically, T cell-dysfunction/exclusion, interferon gamma (IFNG), Merck18 (T-cell inflammatory), microsatellite instability (MSI), and tumor-associated macrophages (TAMs; M2 subtype) were analyzed based on ST niche-average log-normalized counts (normalized by log-normalized counts of all other ST niches as a reference). While spatially heterogeneous TIDE scores were observed across the ST niches, TIDE-predicted ICB responsiveness (TIDE scores < 0) was detected in the viral-high regions in both HCC and CRC tumor tissues ( Supplementary Figure 17A ). Notably, compared with other ST niches, the viral-high regions in both HCC and CRC tumor tissues harbored the highest T cell dysfunction, IFNG, and Merck18 signatures ( Supplementary Figure 17B ), alongside the lowest T cell exclusion, microsatellite instability (MSI), and TAM M2 signatures ( Supplementary Figure 17C ). In addition, the viral-high region in HCC tumor tissue harbored the highest expression of the checkpoint molecule CD274 (PD-L1) and the lowest immune cytolytic activity; however, such immune exhaustion was not replicated in the viral-high region of CRC tumor tissue, which might be due to the shorter time elapsed since infection ( Supplementary Figure 18 ). Immune cytolytic activity scores were obtained by averaging the sctransform-normalized values of two key cytolytic effectors, granzyme A and perforin. Mapping of HBV transcripts (based on 73 HBV variants downloaded from NCBI GenBank;  Supplementary Table 5 ) onto ST niches revealed that HBV was detected homogeneously across the HCC tumor tissue but was relatively sparse within the viral-high region; in addition, HBV was not detected in the adjacent normal HCC tissue ( Supplementary Figure 19 ).
All analyses were conducted in R (version 4.1.0). Associations were evaluated by the non-parametric Kruskal-Wallis test. Statistical significance was judged using a two-sided alpha significance level of 0.05.