595 Background: Stromal TIL are a well-recognized prognostic and predictive biomarker in breast cancer. There is a need for tools assisting visual assessment of TIL, to improve reproducibility as well as for convenience. This study aims to assess the clinical significance of AI-powered spatial TIL analysis in the prediction of pathologic complete response (pCR) after NAC in TNBC patients. Methods: H&E stained slides and clinical outcomes data were obtained from stage I – III TNBC patients treated with NAC in two centers in Korea. For spatial TIL analysis, we used Lunit SCOPE IO, an AI-powered H&E Whole-Slide Image (WSI) analyzer, which identifies and quantifies TIL within the cancer or stroma area. Lunit SCOPE IO was developed with a 13.5 x 109 micrometer2 area and 6.2 x 106 TIL from 17,849 H&E WSI of multiple cancer types, annotated by 104 board-certified pathologists. iTIL score and sTIL score were defined as area occupied by TIL in the intratumoral area (%) and the surrounding stroma (%), respectively. Immune phenotype (IP) of each slide was defined from spatial TIL calculation, as inflamed (high TIL density in tumor area), immune-excluded (high TIL density in stroma), or desert (low TIL density overall). Results: A total of 954 TNBC patients treated from 2006 to 2019 were included in this analysis. pCR (ypT0N0) was confirmed in 261 (27.4%) patients. The neoadjuvant regimens used were mostly anthracycline (97.8%) and taxane (75.1%) -based, with 116 (12.1%) patients receiving additional platinum and 41 (4.3%) patients treated as part of immune checkpoint inhibitor or PARP inhibitor clinical trials. The median iTIL score and sTIL score were 4.3% (IQR 3.2 – 5.8) and 8.1% (IQR 6.3 – 13.4), respectively. The mean iTIL score was significantly higher in patients who achieved pCR after NAC (5.8% vs. 4.5%, p < 0.001), and a similar difference was observed with sTIL score (12.1%.1 vs. 9.4%, p < 0.001). iTIL score was found to remain as an independent predictor of pCR along with cT stage and Ki-67 in the multivariable analysis (adjusted odds ratio 1.211 (95% CI 1.125 – 1.304) per 1 point (%) change in the score, p <0.001). By IP groups, 291 (30.5%) patients were classified as inflamed, 502 (52.6%) as excluded, and 161 (16.9%) as desert phenotype. The patients with inflamed phenotype were more likely to achieve pCR (44.7%) than other phenotypes (19.8%, p < 0.001). Conclusions: AI-powered spatial TIL analysis could assess TIL densities in the cancer area and surrounding stroma of TNBC, and TIL density scores and IP classification could predict pCR after NAC.

BackgroundEnrichment of tumor-infiltrating lymphocytes (TIL) in the tumor microenvironment (TME) is a reliable biomarker of immune checkpoint inhibitors (ICI) in non-small cell lung cancer (NSCLC). Phenotyping through computed tomography (CT) radiomics has the overcome the limitations of tissue-based assessment, including for TIL analysis. Here, we assess TIL enrichment objectively using an artificial intelligence-powered TIL analysis in hematoxylin and eosin (H&amp;E) image and analyze its association with quantitative radiomic features (RFs). Clinical significance of the selected RFs is then validated in the independent NSCLC patients who received ICI.MethodsIn the training cohort containing both tumor tissue samples and corresponding CT images obtained within 1 month, we extracted 86 RFs from the CT images. The TIL enrichment score (TILes) was defined as the fraction of tissue area with high intra-tumoral or stromal TIL density divided by the whole TME area, as measured on an H&amp;E slide. From the corresponding CT images, the least absolute shrinkage and selection operator model was then developed using features that were significantly associated with TIL enrichment. The CT model was applied to CT images from the validation cohort, which included NSCLC patients who received ICI monotherapy.ResultsA total of 220 NSCLC samples were included in the training cohort. After filtering the RFs, two features, gray level variance (coefficient 1.71 x 10-3) and large area low gray level emphasis (coefficient -2.48 x 10-5), were included in the model. The two features were both computed from the size-zone matrix, which has strength in reflecting intralesional texture heterogeneity. In the validation cohort, the patients with high predicted TILes (≥ median) had significantly prolonged progression-free survival compared to those with low predicted TILes (median 4.0 months [95% CI 2.2–5.7] versus 2.1 months [95% CI 1.6–3.1], p = 0.002). Patients who experienced a response to ICI or stable disease with ICI had higher predicted TILes compared with the patients who experienced progressive disease as the best response (p = 0.001, p = 0.036, respectively). Predicted TILes was significantly associated with progression-free survival independent of PD-L1 status.ConclusionsIn this CT radiomics model, predicted TILes was significantly associated with ICI outcomes in NSCLC patients. Analyzing TME through radiomics may overcome the limitations of tissue-based analysis and assist clinical decisions regarding ICI.

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e16214 Background: Immune checkpoint inhibitors (ICIs) have shown promising treatment outcomes for various types of tumors. However, in neuroendocrine tumors and carcinomas (NET/NEC), ICI has proven to be applicable for only limited cases. In addition, little is known about the immunoprofile of NET/NEC. Here we investigate the landscape of tumor-infiltrating lymphocytes (TIL) using artificial intelligence (AI)-powered H&E whole-slide image (WSI) analyzer to elucidate the tumor microenvironment of NET/NEC. Methods: A total of 240 H&E stained pathologic slides diagnosed with NET/NEC were obtained from Ajou University Medical Center in Korea (from January 2020 to December 2021). For spatial TIL analysis, we used Lunit SCOPE IO, an AI-powered H&E WSI analyzer, which identifies and quantifies TIL within the cancer or stroma area. The AI was developed with a 13.5 x 109 μm2 area and 6.2 x 106 TIL from 17,849 H&E WSI of multiple cancer types, annotated by 104 board-certified pathologists. Intra-tumoral TIL, stromal TIL, and combined (cancer + stroma) TIL density were defined as the TIL count divided by the area of interest respectively. NET with histological grade 1 and 2 were labeled as low grade and NET with histological grade 3 and together with NEC were labeled as high grade. Primary origins of the NET/NEC were grouped by colorectum, stomach, small intestine, hepatopancreatobiliary, lung, and other organs (including anus, appendix, breast, cervix, and larynx). Results: Total slides classified as low grade and high grade were 211 and 29, respectively; 175 samples were from colorectal, 19 from stomach, 16 from small intestine, 16 from hepatopancreaticobiliary, seven from lung, and seven from other organs. The median intra-tumoral TIL, stromal TIL, and combined TIL density were 4.2/mm2 (IQR 1.718 - 11.478), 139.1/mm2 (IQR 75.4 - 313.9), and 62.4/mm2 (IQR 36.3 - 162.6), respectively. The median intra-tumoral TIL density was significantly higher in patients with high grade NET/NEC compared with low grade (11.9/mm2 [IQR 4.51 - 30.9] vs 3.45/mm2 [IQR 1.63 - 9.81], p < 0.001). However, statistical differences in stromal TIL density and combined TIL density were not observed between low grade and high grade NET/NEC. The highest intra-tumoral TIL density in the group classified according to primary origins was lung (n = 7, median: 16.5/mm2, IQR 5.01 - 34.1) and was followed by stomach (n = 19, median: 11.8/mm2, IQR 8.64 - 20.8), and small intestine (n = 16, median: 7.23/mm2, IQR 4.12 - 25.2). Conclusions: AI-powered TIL analysis reveals that the intra-tumoral TIL density is significantly higher in high grade NET/NEC than low grade NET. Our findings align with recent evidence that ICIs are effective against large cell NEC and small cell carcinoma.Therefore, AI-powered TIL analysis should be investigated as a predictive biomarker for ICI response in NET/NEC.

Background Newer strategies of targeting HER2 such as novel HER2-targeted agents and combination with immune checkpoint inhibitors (ICIs) call for development of newer biomarkers based on deeper understanding of biology. To understand the immune microenvironment of HER2-expressing tumors, we performed spatial analysis of TIL in association with HER2 status, in the The Cancer Genome Atlas (TCGA) pan-carcinoma set. Methods Hematoxylin and eosin (H&E)-stained slides, copy number alterations, and mRNA expression levels of HER2 for 7,322 patients across 22 cancer types and immunohistochemistry (IHC) results for 622 breast cancer patients were obtained from the TCGA data set. Spatial analysis of TIL distribution was done by an artificial intelligence-powered H&E analyzer, Lunit SCOPE IO. Intratumoral TIL (iTIL) and stromal TIL (sTIL) densities were defined as the number of TILs in each 1mm2 grid of cancer area and stromal area. The HER2 amplified cancers were classified as per annotation of TCGA. Receiver operating characteristic (ROC) analysis was used to determine the optimal thresholds of mRNA expression showing maximal value of sensitivity and specificity, to discriminate HER2-expressed (! 1+) against HER2-negative (0) using IHC results. These thresholds were subsequently used in pan-cancer analysis, to define HER2-expressed vs. HER2-negative. Results Overall, iTIL and sTIL densities of HER2 amplified cancers were lower than those of non-amplified cancers in the TCGA data set. Also, the iTIL and sTIL densities were mostly decreased in HER2-expressed cancers by the RNA-seq-based threshold. However, when breaking down into individual cancer types, TIL densities increased in endometrial cancer (UCEC) and ovarian cancer (OV). In UCEC (n = 148), iTIL density increased from 74.5±75.6 (mean±standard deviation) in HER2-negative to 167.17±322.30 in HER2-expressed and sTIL density increased from 649.2±517.2 to 875.2±1172.0, respectively. In OV (n = 70), although iTIL density was slightly increased from 50.5±53.6 to 52.9±63.5, sTIL density was notably increased from 243.6±215.6 to 540.9±563.9, respectively. Among the HER2-expressed cancers, the iTIL and sTIL levels were higher in those expressing PD-L1 or having high-tumor mutational burden (TMB). The differences were notable in urothelial carcinoma (TMB, PD-L1); breast cancer, cervical cancer, lung cancer, and thyroid cancer (PD-L1). Conclusions HER2 amplification or expression was associated with lower immune infiltration in the pan-cancer cohort, consistent with previous data. 1 Further investigation in individual tumor types may further identify possible responders for combination therapy with HER2-targeted agents and ICIs.