Tumor-Immune Partitioning and Clustering (TIPC) algorithm reveals distinct signatures of tumor-immune cell interactions within the tumor microenvironment

Abstract

Growing evidence supports the importance of understanding tumor-immune spatial relationship in the tumor microenvironment in order to achieve precision cancer therapy.However, existing methods, based on oversimplistic cell-to-cell proximity, are largely confounded by immune cell density and are ineffective in capturing tumor-immune spatial patterns. Here we developed a novel computational algorithm, termed Tumor-Immune Partitioning and Clustering (TIPC), to offer an effective solution for spatially informed tumor subtyping. Our method could measure the extent of immune cell partitioning between tumor epithelial and stromal areas as well as the degree of immune cell clustering. Using a U.S. nation-wide colorectal cancer database, we showed that TIPC could determine tumor subtypes with unique tumor-immune spatial patterns that were significantly associated with patient survival and key tumor molecular features. We also demonstrated that TIPC was robust to parameter settings and readily applicable to different immune cell types. The capability of TIPC in delineating clinically relevant patient subtypes that encapsulate tumor-immune spatial relationship, immune density, and tumor morphology is expected to shed light on underlying immune mechanisms. Hence, TIPC can be a useful bioinformatics tool for effective characterization of the spatial composition of the tumor-immune microenvironment to inform precision immunotherapy.

Association of Fusobacterium nucleatum with Specific T-cell Subsets in the Colorectal Carcinoma Microenvironment

“…We constructed tissue microarrays of colorectal cancer cases with sufficient tissue materials, including up to four tumor cores from each case in a tissue microarray block (26). As described previously (27,28), 4 mm sections from tissue microarray blocks were sequentially stained using the following antibody and fluorophore combinations, in order: anti-CD3 antibody (clone F7.2.38, Dako; Agilent Technologies)/Opal-520, anti-FOXP3 (clone 206D, BioLegend)/Opal-540, anti-CD45RO (one of PTPRC isoforms, clone UCHL1, Dako)/ Opal-650, anti-CD8 (clone C8/144B, Dako)/Opal-570, anti-CD4 (clone 4B12, Dako)/Opal-690, and anti-KRT (keratin, pan-cytokeratins; clone AE1/AE3, Dako and clone C11, Cell Signaling Technology)/Opal-620 (Supplementary Fig. S1; with standardized protein nomenclature recommended by a panel of experts; ref.…”

“…Tumor microsatellite instability (MSI) status was analyzed using PCR for 10 microsatellite markers (D2S123, D5S346, D17S250, BAT25, BAT26, BAT40, D18S55, D18S56, D18S67, and D18S487), and MSI-high was defined as presence of instability in ≥30% of the markers, as described previously (24,28,32). Using bisulfite-treated DNA, methylation status of eight CpG island methylator phenotype (CIMP)-specific promoters (CACNA1G, CDKN2A, CRABP1, IGF2, MLH1, NEUROG1, RUNX3, and SOCS1) and long interspersed nucleotide element-1 (LINE-1) was determined as described previously (24,28,32). CIMP-high was defined as ≥6 methylated promoters of eight promoters, and CIMP-low/negative as 0-5 methylated promoters, as described previously (24,28,32).…”

“…Using bisulfite-treated DNA, methylation status of eight CpG island methylator phenotype (CIMP)-specific promoters (CACNA1G, CDKN2A, CRABP1, IGF2, MLH1, NEUROG1, RUNX3, and SOCS1) and long interspersed nucleotide element-1 (LINE-1) was determined as described previously (24,28,32). CIMP-high was defined as ≥6 methylated promoters of eight promoters, and CIMP-low/negative as 0-5 methylated promoters, as described previously (24,28,32). PCR and pyrosequencing were performed for KRAS (codons 12, 13, 61, and 146), BRAF (codon 600), and PIK3CA (exons 9 and 20), as described previously (23,24,28,33).…”

“…CIMP-high was defined as ≥6 methylated promoters of eight promoters, and CIMP-low/negative as 0-5 methylated promoters, as described previously (24,28,32). PCR and pyrosequencing were performed for KRAS (codons 12, 13, 61, and 146), BRAF (codon 600), and PIK3CA (exons 9 and 20), as described previously (23,24,28,33). Whole-exome sequencing was performed using DNA from tumor and matched normal tissue pairs, as described previously (34).…”

“…Using NetMHCpan (version 2.4; ref. 35), we predicted the binding affinities of all possible 9-and 10-mer mutant peptides to the corresponding human leukocyte antigen alleles inferred by the POLYSOLVER algorithm, as described previously (28,34). On the basis of all colorectal cancers with available whole-exome sequencing data, neoantigen load and exome-wide tumor mutational burden were divided into quartiles (Q1-Q4).…”