Donggeun Yoo

PURPOSE Biomarkers on the basis of tumor-infiltrating lymphocytes (TIL) are potentially valuable in predicting the effectiveness of immune checkpoint inhibitors (ICI). However, clinical application remains challenging because of methodologic limitations and laborious process involved in spatial analysis of TIL distribution in whole-slide images (WSI). METHODS We have developed an artificial intelligence (AI)–powered WSI analyzer of TIL in the tumor microenvironment that can define three immune phenotypes (IPs): inflamed, immune-excluded, and immune-desert. These IPs were correlated with tumor response to ICI and survival in two independent cohorts of patients with advanced non–small-cell lung cancer (NSCLC). RESULTS Inflamed IP correlated with enrichment in local immune cytolytic activity, higher response rate, and prolonged progression-free survival compared with patients with immune-excluded or immune-desert phenotypes. At the WSI level, there was significant positive correlation between tumor proportion score (TPS) as determined by the AI model and control TPS analyzed by pathologists ( P < .001). Overall, 44.0% of tumors were inflamed, 37.1% were immune-excluded, and 18.9% were immune-desert. Incidence of inflamed IP in patients with programmed death ligand-1 TPS at < 1%, 1%-49%, and ≥ 50% was 31.7%, 42.5%, and 56.8%, respectively. Median progression-free survival and overall survival were, respectively, 4.1 months and 24.8 months with inflamed IP, 2.2 months and 14.0 months with immune-excluded IP, and 2.4 months and 10.6 months with immune-desert IP. CONCLUSION The AI-powered spatial analysis of TIL correlated with tumor response and progression-free survival of ICI in advanced NSCLC. This is potentially a supplementary biomarker to TPS as determined by a pathologist.

Despite the importance of tumor-infiltrating lymphocytes (TIL) and PD-L1 expression to the immune checkpoint inhibitor (ICI) response, a comprehensive assessment of these biomarkers has not yet been conducted in neuroendocrine neoplasm (NEN). We collected 218 NENs from multiple organs, including 190 low/intermediate-grade NENs and 28 high-grade NENs. TIL distribution was derived from Lunit SCOPE IO, an artificial intelligence (AI)-powered hematoxylin and eosin (H&E) analyzer, as developed from 17,849 whole slide images. The proportion of intra-tumoral TIL-high cases was significantly higher in high-grade NEN (75.0% vs. 46.3%, p = 0.008). The proportion of PD-L1 combined positive score (CPS) ≥ 1 case was higher in high-grade NEN (85.7% vs. 33.2%, p < 0.001). The PD-L1 CPS ≥ 1 group showed higher intra-tumoral, stromal, and combined TIL densities, compared to the CPS < 1 group (7.13 vs. 2.95, p < 0.001; 200.9 vs. 120.5, p < 0.001; 86.7 vs. 56.1, p = 0.004). A significant correlation was observed between TIL density and PD-L1 CPS (r = 0.37, p < 0.001 for intra-tumoral TIL; r = 0.24, p = 0.002 for stromal TIL and combined TIL). AI-powered TIL analysis reveals that intra-tumoral TIL density is significantly higher in high-grade NEN, and PD-L1 CPS has a positive correlation with TIL densities, thus showing its value as predictive biomarkers for ICI response in NEN.

e20520 Background: Recent data suggested immune infiltrates in TLS around tumor bed may be a favorable predictive factor for immunotherapy in various cancers. TLS have been suggested to be correlated with inflamed immune phenotype and relevant inflammatory gene signatures of adaptive immunity. To analyze this relationship, here, we developed an AI model to assess TLS objectively in H&amp;E WSI, and assessed its correlation with immune phenotype and immunologic signatures. Methods: H&amp;E images, relevant gene expression profiles and clinical data from The Cancer Genome Atlas (TCGA) lung cancer dataset (LUAD and LUSC, N = 913) were used for the analysis. Lunit SCOPE TLS, an AI-powered H&amp;E WSI analyzer, was developed with 3.59 x 10 9 μm 2 annotated area and 1,439 H&amp;E stained WSI of 18 cancer types to segment TLS within tumor microenvironment (TME). Based on spatial tumor-infiltrating lymphocyte (TIL) density, IPs were classified into inflamed IP (IIP) as high intratumoral TIL (iTIL), immune-excluded IP (IEP) as low iTIL and high sTIL, and immune-desert IP (IDP) as low TIL overall. The infiltration of immune cells, the activity of related pathways and biological processes, and the signature scores of interest gene sets were analyzed by using the CIBERSORT, DESeq2 and GSEA tools. Results: Of 913 samples, 69.7% (636/913) contain TLS. The median value of the proportion of TLS area within TME was 0.12%, which was applied for the cut point of TLS-high vs -low group. The proportion of TLS-high was not significantly different according to EGFR mutation (mutation vs wild type: 45.5% vs 50.2%), KRAS mutation (49.3% vs 50%), but numerically decreased in the merging set of other driver mutations including ALK, ROS1, RET, MET, NTRK1-3 (36% vs 50.3%, p = 0.223). Interestingly, TLS-high proportion was significantly different according to immune phenotype, as TLS were present in 57.6% of IIP (186/323), 49.8% of IEP (241/484), and 28.3% of IDP (30/106, p &lt; 0.001). TLS-high group was positively correlated with memory B cells (fold change [fc] 1.93, p &lt; 0.001), CD8+ T cells (fc 1.20, p &lt; 0.001), and M1 Macrophages (fc 1.17, p &lt; 0.001), and negatively correlated with neutrophils (fc 0.59, p &lt; 0.001) and M2 macrophages (fc 0.91, p = 0.006). This result was additionally supported by GSEA analysis of GO:BP or Hallmark gene sets which showed TLS-high was associated with B cell receptor signaling pathway (normalized enrichment score [NES] 3.16, p = 0.001), immunoglobulin production (NES 3.07, p = 0.002), and interferon-gamma response (NES 2.20, p = 0.002). Epithelial-mesenchymal transition was negatively associated with TLS-high (NES -1.54, p &lt; 0.001). Conclusions: TLS is associated with inflamed immune phenotype and infiltration of memory B cells as well as CD8+ T cells in non-small cell lung cancer.

<h3>Background</h3> Microscopic tumor fragmentation has shown association with the immune landscape of tumor microenvironment (TME).¹ The current study aimed to further investigate the effect of the tumor fragmentation index (TFI), defined by the number of tumor fragments per total tumor area, and fibroblast infiltration on inflammatory cytokines, as well as lymphocytes maturation in TME. <h3>Methods</h3> Tumor and stromal areas of The Cancer Genome Atlas (TCGA) H&amp;E whole-slide images (WSI, N = 7472) across the 23 carcinoma cancer types were segmented, and cell types including lymphocytes (LC), macrophages (MP), and fibroblasts (FB) were identified using Lunit SCOPE IO, an AI-powered WSI analyzer. Independent tumor masses were isolated by the connected component labeling algorithm. Tumor fragments which are too small in size (&lt; 968.2 μm²) or which are not in contact with the stromal area were filtered out, then TFI (count/mm²) was calculated. <h3>Results</h3> In pan-carcinoma dataset, the upper 75% of TFI was 35/mm², which was applied for the threshold of TFI-high vs TFI-low group. The proportion of TFI-high was enriched in pancreatic adenocarcinoma (85.2%), prostate adenocarcinoma (67.2%), breast cancer (64.4%), cholangiocarcinoma (52.8%), and lung adenocarcinoma (39.9%). In the whole dataset, FB density in TME was significantly different between TFI-high and TFI-low (median 1298 [interquartile range, 987–28379] vs 475 [241–760]), whereas LC density and MP density in TME were not (LC: 339 [170–698] vs 320 [138–703]; MP: 17 [9–38] vs 22 [10–48]). Gene expression profile analysis showed TFI-high had significantly decreased levels of <i>IFNG</i> (fold change, -29%), <i>IL1A</i> (-24%), and <i>IL17A</i> (-41%) compared to TFI-low had. In contrast, there were no significant differences in PD-1/PD-L1 and CTLA4/CD28 axes (-5% ~ +8%). Cibersort analysis² showed that gamma delta T cells (-52%), activated memory CD4 T cells (-50%), activated NK cells (-29%), CD8 T cells (-21%) demonstrated the most significant reductions in TFI-high. <h3>Conclusions</h3> Tumor with high fragmentation, or TFI-high is closely correlated with high fibroblast infiltration, low <i>IFNG</i> signature-related NK and T cells infiltration, but has minimal impact on overall densities of lymphocytes and macrophages, as well as PD-1/PD-L1 and CTLA4/CD28 axes. <h3>References</h3> Kim S, Song S, Park G, <i>et al.</i> Correlation of fragmented pattern of tumor mass captured by artificial intelligence (AI)-powered whole-slide image (WSI) analysis with biased fibroblast expansion over tumor growth and distinct mutational signatures. <i>Journal of Clinical Oncology</i>. 2023;<b>41</b>(16_suppl):e14657-e14657. doi: 10.1200/JCO.2023.41.16_suppl.e14657. Newman AM, Liu CL, Green MR, <i>et al</i>. Robust enumeration of cell subsets from tissue expression profiles. <i>Nature Methods</i>. 2015;<b>12</b>(5):453–457. doi: 10.1038/nmeth.3337.