EFFECTS OF EVEN FUNCTIONAL GROUP DISTRIBUTION IN EMULSION STYRENE–BUTADIENE RUBBER PREPARED BY REVERSIBLE ADDITION–FRAGMENTATION CHAIN TRANSFER POLYMERIZATION ON THE PROPERTIES OF SILICA-FILLED COMPOUNDS

Recently, considerable attention has been paid to the development of new functionalized polymers to improve the fuel efficiency of vehicles by reducing the rolling resistance of tires to adhere to strict CO2 emission regulations. Accordingly, multifunctionalized (MF) reversible addition–fragmentation chain transfer (RAFT) emulsion styrene–butadiene rubbers (ESBR) were synthesized, in which chain-end and in-chain functionalization were performed simultaneously by introducing a third monomer (glycidyl methacrylate; GMA) using RAFT polymerization. Compared with GMA ESBR, in which GMA is introduced as a third monomer by conventional radical polymerization (CRP), there was an even distribution of GMA per chain in the MF-RAFT ESBR. After preparing the silica-filled compounds, vulcanizate structure analysis and mechanical property evaluation of the compounds were performed. The MF-RAFT ESBR prepared by RAFT polymerization exhibited superior in-chain functionalization efficiency compared with GMA ESBR prepared by CRP because of the even distribution of GMA and higher crosslink density. Consequently, MF-RAFT ESBR compound showed superior silica dispersion, abrasion resistance, and lower rolling resistance compared with the GMA ESBR compound.

Utilisation of village clinics in Southwest China: evidence from Yunnan Province

INTRODUCTION

Primary healthcare in rural China is underutilised, especially in village clinics in Southwest China. The aim of this study was to explore any relationships among the ethnicity of the healthcare provider, the clinical competence of the healthcare provider, and the utilisation of village clinics in Southwest China.

METHODS

This cross-sectional survey study involved 330 village healthcare providers from three prefectures in Yunnan Province in 2017. Multiple logistic regressions were adopted to investigate the utilisation of primary healthcare among different ethnic healthcare providers.

RESULTS

Primary healthcare utilisation was higher in village clinics where healthcare providers were Han Chinese than those where healthcare providers were ethnic minority (151 vs 101, P=0.008). The logistic regression analysis showed that clinical competence was positively associated with the utilisation of primary healthcare (odds ratio [OR]=1.49, 95% confidence interval [CI]=1.12-2.00; P=0.007) and that inadequate clinical competence of ethnic minority health workers may lead to a lag in the utilisation of primary healthcare (OR=0.45, 95% CI=0.23-0.89; P=0.022).

CONCLUSION

Our results confirm differences in the utilisation of primary healthcare in rural Yunnan Province among healthcare providers of different ethnicities. Appropriate enhancements of clinical competence could be conducive to improving the utilisation of primary healthcare, especially among ethnic minority healthcare providers.

General versus Brachial Plexus Block Anesthesia in Pain Management after Internal Fixation in Patients with Distal Radius Fracture: A Randomized Controlled Trial

Distal radius fractures (DRFs) are very common injuries associated with aging, and the number of fractures is increasing with the increase in the elderly population. General anesthesia or brachial plexus block (BPB) is required for fracture fixation, and acute postoperative pain control is necessary after operation. Early pain control can improve patient satisfaction and functional outcomes. In this study, we report the clinical differences in postoperative pain, according to the method of anesthesia (general anesthesia versus brachial plexus block). Volar plating was used to treat 72 patients older than 60 years who had comminuted DRF. Patients were randomized to either group A (36 patients), who underwent general anesthesia, or group B (36 patients), who underwent BPB. We compared these two groups prospectively for acute postoperative pain using a visual analog scale (VAS) at 2, 4, 6, 12, and 24 h after surgery. The VAS scores of each group were: 6.8 ± 2.5 in general anesthesia and 0.5 ± 2.3 in BPB at 2 h, postoperatively; 6.5 ± 2.4 in general anesthesia and 0.5 ± 2.4 in BPB anesthesia at 4 h, postoperatively; 5.2 ± 2.4 in general anesthesia and 1.5 ± 2.4 in BPB anesthesia at 6 h, postoperatively; 4.5 ± 2.5 in general anesthesia and 3.4 ± 2.7 in BPB anesthesia at 12 h, postoperatively; and 3.5 ± 2.5 in general anesthesia and 3.2 ± 2.7 in BPB anesthesia at 24 h, postoperatively. DRF patients with BPB anesthesia showed a lower VAS score than those subjected to general anesthesia in early postoperative period. As a result, the effect of BPB anesthesia on acute pain management after surgery was excellent, which resulted in a lower pain score compared with general anesthesia in DRF patients undergoing volar plating.

Abstract 6172: Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes reveals immune-excluded phenotype is correlated with TGF-beta pathway related genomic aberrations

Aberrant transforming growth factor-beta(TGF-B) pathway in the tumor microenvironment has been highlighted as one of the core resistance pathways of immunotherapy, by excluding tumor-infiltrating lymphocytes (TIL) out of the tumor area. However, no studies have coupled immune phenotypes classified by spatial analysis of TIL in whole slide images (WSI) with TGF-B pathway analysis on a large-scale database. Here, we hypothesized that the immune-excluded phenotype classified by a deep-learning spatial analysis model, Lunit SCOPE IO, would be correlated with the aberrant TGF-B pathway in The Cancer Genome Atlas (TCGA) cohorts. Aberrant TGF-B pathway was measured by Trimmed Mean of M-values (TMM) normalized and transformed to log2 of counts-per-million of previously published gene sets of fibroblast-specific TGF-beta responsive gene signature, using edgeR packages from TCGA RNA-sequencing data (n=6,709) across the 23 cancer types. Lunit SCOPE IO was developed to identify immune phenotypes trained and validated from 3,166 multi-cancer H&E WSI with sections of 2.8e+9 mm2 tumor tissue containing 5.9e+6 TIL annotated by 52 board-certified pathologists. Lunit SCOPE IO classified immune phenotypes as immune-inflamed and -excluded according to the proportion of TIL density either highly conserved in cancer epithelium (CE) or cancer stroma (CS), respectively, and otherwise, classified as immune-desert with low TIL density in CE and CS. Aberrant TGF-B expression was highly enriched in multiple cancer types including pancreatic cancer, head and neck cancer, kidney clear cell carcinoma, lung squamous cell carcinoma, and breast cancer, in ascending order. TGF-B expression was increased in microsatellite-stable tumor samples (p = 7.4e-15) or samples with low tumor mutational burden (TMB, < 10/megabase, p = 4.9e-8), compared to those with microsatellite instability-high or high TMB, respectively. Interestingly, TGF-B expression was highly correlated with the proportion of cancer stroma in WSI (R = 0.315, p < 2.2e-16) and the proportion of immune-excluded phenotype (R = 0.115, p < 2.2e-16) across multiple cancer types. Tumor samples with SMAD4 mutations (n = 161, 2.4%) had significantly higher TGF-B expression (p = 0.0190), and a higher proportion of immune-excluded phenotype (p < 2.2e-16) in WSI, compared to wild-type SMAD4. Aberrant TGF-B pathway is clearly associated with increased proportion of cancer stroma, and excluded TIL, or immune-excluded phenotype in a large-scale pan-carcinoma analysis.

Citation Format: Gahee Park, Sanghoon Song, Hyung-Gyo Cho, Soo Ick Cho, Wonkyung Jung, Lunit AI team, Sergio Pereira, Donggeun Yoo, Kyunghyun Paeng, Chan-Young Ock. Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes reveals immune-excluded phenotype is correlated with TGF-beta pathway related genomic aberrations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6172.

Abstract 644: Deep learning-based H&E analyzer reveals distinct immune profiles and clinical outcomes among immune phenotypes in uterine corpus endometrial carcinoma

Introduction: Deep learning-based H&E analyzer can classify the tumor microenvironment as three immune phenotypes: the immune-inflamed, excluded and desert. Our previous study demonstrated a distinct transcriptomic and immunologic landscape amongst the phenotypes in non-small cell lung cancer (NSCLC). However, it has not been fully investigated in other cancers. Here, we explore the immune profiles and clinical outcomes between the three immune phenotypes in uterine corpus endometrial carcinoma (UCEC).

Methods: Tissue H&E slide images, sequencing data, and clinical data were utilized from The Cancer Genome Atlas (TCGA). Lunit-SCOPE IO was trained with multi-cancer 3,166 H&E whole slide images annotated by pathologists. Based on the proportion of tumor infiltrating lymphocytes (TIL) highly conserved either in cancer epithelium (CE) or cancer stroma (CS), Lunit-SCOPE IO classifies tumors as immune-inflamed and excluded, respectively. Also, it classifies tumors with low TIL density in CE and CS as immune-desert.

Results: Among 486 patients with UCEC, the frequency of immune-inflamed, excluded and desert was 174 (35.8%), 160 (32.9%), and 156 (32%), respectively. In the three subgroup comparison, immune-inflamed was associated with the best survival outcome and -excluded was associated with the worst survival outcome (Inflamed vs excluded, HR 0.30 95% CI 0.17-0.55, p<.001; desert vs excluded, HR 0.50 95% CI 0.30-0.84, p=0.009). Likewise, inflamed subtype showed better overall survival (HR 0.43, 95% CI 0.25-0.75, p=0.003) compared to others. In microsatellite instability high (MSI-H) tumors, we observed a similar tendency of improved overall survival in the tumors of inflamed subtype, both compared to the excluded subtype and to a combination of other subtypes. (Inflamed vs excluded, HR 0.18 95% CI 0.05-0.73, p=0.017; inflamed vs others, HR 0.21 95% CI 0.06-0.72, p=0.014). Immune-inflamed had significantly higher cytolytic activity (Inflamed 7.25 vs others 6.34, p<.001) and was associated with higher PD-L1 expression (Inflamed 19.03 vs others 10.7, p=0.003) and CTLA4 expression (Inflamed 60.62 vs others 31.5, p<.001). Immune-inflamed had a higher proportion of CD8 positive T cell (Inflamed 16.7% vs 12.8%, p<.001) and M1 macrophage (Inflamed 3.9% vs others 2.8%, p<.001) and a lower proportion of M2 macrophage (Inflamed 15% vs others 17.9%, p<.001).

Conclusion: The three tissue phenomic subtypes showed distinct immune profiles and clinical outcomes, with immune-inflamed having the best overall survival outcome. In particular, non-inflamed group was associated with worse overall survival even in MSI-H tumors deemed to have more favorable prognosis compared to MSS tumors. Given the definite differences in the survival outcome, tissue H&E based tumor microenvironment classification may serve as a potential prognostic biomarker in UCEC.

Citation Format: Horyun Choi, Leeseul Kim, Jinah Kim, Yeun Ho Lee, Hyung-Gyo Cho, Na Hyun Kim, Gahyun Gim, Sanghoon Song, Gahee Park, Soo Ick Cho, Sergio Pereira, Donggeun Yoo, Kyunghyun Paeng, Chan-Young Ock, Young Kwang Chae. Deep learning-based H&E analyzer reveals distinct immune profiles and clinical outcomes among immune phenotypes in uterine corpus endometrial carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 644.

[Screening and preliminary validation of biomarkers in sputum-negative pulmonary tuberculosis based on positron emission tomography/computed tomography and transcriptomics]

Objective: To screen and perform preliminary clinical validation of biomarkers of activity based on positron emission tomography/computed tomography (PET-CT) and transcriptomics in sputum-negative pulmonary tuberculosis lesion tissue. Methods: Nine patients with sputum-negative pulmonary tuberculosis treated surgically at the Shanghai Public Health Clinical Center for Thoracic Surgery from January 1, 2017 to December 31, 2019 were retrospectively collected as the discovery group, including four males and five females, aged 20-57 years (mean 36 years). All of the patients underwent PET-CT scanning before surgery, and the resected specimens were postoperatively classified according to preoperative PET-CT. The resected specimens were divided into areas with increased fluorodeoxyglucose (FDG) metabolism (SUVmax>3) and areas with normal FDG metabolism (SUVmax ≤ 3) according to the preoperative PET-CT performance. After sample processing, total RNA was extracted from the tissues of different regions, and then whole gene transcriptome sequencing was performed. Bioinformatics analysis of the two sets of data was performed to discover the expression profiles of the differences in whole gene transcriptome data between the two regions and to screen for candidate biomarkers. Eighty patients with sputum-negative pulmonary tuberculosis admitted to Shanghai Public Health Clinical Center from January 1, 2019 to January 1, 2021 were retrospectively collected as the validation group, including 37 males and 43 females, aged 20-62 years, with an average age of 39 years. The validation group was divided into a group with increased SUV (n=40) and a group without lesions on CT imaging (n=40). Enzyme-linked immunosorbent assay (ELISA) was used to determine the protein levels of candidate biomarkers in the peripheral plasma of patients. The effect of biomarkers was assessed using subject operating characteristic (ROC) curves. Student's t-test was used to determine whether the difference in protein levels between the two groups was statistically significant. Results: Bioinformatics analysis revealed that the expression levels of C1QB, CCL19, CCL5 and HLA-DMB correlated with the metabolic activity of sputum-negative tuberculosis lesion tissue. Further screening and validation by the validation group confirmed that the difference in C1QB protein levels in the peripheral plasma of patients was statistically significant between the group with increased SUV and the group without lesions on CT imaging [(3.55±0.34) mg/L vs. (2.75±0.21) mg/L, t=4.12, P<0.001]. And the ROC curve showed that the area under the curve for C1QB protein levels was 0.731, which had potential clinical value. Conclusion: The C1QB protein level can be used to assess the activity of lesions in patients with sputum-negative tuberculosis and is a potential biomarker.

POS0811 CHARACTERISTICS OF INTESTINAL MICROBIOTA AND ITS RELATIONSHIP WITH LYMPHOCYTE SUBSETS AND CYTOKINES IN PATIENTS WITH VASCULITIS

Background

Vasculitis include a group of autoimmune inflammatory diseases with clinical heterogeneous characterized by inflammation of vascular wall, inflammation of perivascular tissues, and cell-like necrosis[1]. Disorder of gut microbiota, which plays a crucial role in regulating immune cells such as Th1, Th17 and Treg, is associated with other autoimmune diseases[2], and may also be involved in the pathogenesis of vasculitis.

Objectives

To investigate the changes of intestinal microbiota and its correlation with peripheral lymphocyte subsets and inflammatory factors levels in patients with vasculitis.

Methods

Combined with clinical manifestations and laboratory examination, 33 patients with vasculitis who met the 2012 revised International Chapel Hill Consensus Conference Nomenclature of Vasculitides[3] and 33 of age- and gender- matched healthy controls (HCs) were selected from the Second Hospital of Shanxi Medical University. The demographic characteristics, general laboratory indicators such as erythrocyte sedimentation rate (ESR), C-reaction protein (CRP), levels of peripheral lymphocyte subpopulations and serum cytokines detected by modified flow cytometry. Fecal microbiota detected by 16S rRNA gene sequencing and compiled and processed using Qiime2 and OTU-profiling tables were collected and analyzed in this study.

Results

Compared with HCs, the richness and diversity of intestinal flora in patients with vasculitis tended to decrease with a statistically significant difference in β diversity (P = 0.025, Figure 1 A and B). More specifically, vasculitis patients had a lower frequency of Firmicutes while higher Proteobacteria and Bacteroidota at the phylum level (P < 0.001, Figure 1C). In vasculitis patients, the relative abundances of 23 bacteria differed from HCs at the genus level was all decreased, including Gemella, Anaeroglobus, Campylobacter, Fournierella, et al (P < 0.001, Figure 1D and E). More importantly, the relative abundance of Muribaculaceae were positively correlated with the absolute count of Th2 and the proportions of Th1 and CD4+T cells and negatively correlated with CRP and ESR, while relative abundance of [Eubacterium]_ventriosum were positively associated with the absolute number of Treg cells and negatively correlated with the percentages of Th2 and CD8+T cells (Figure 1F).

Figure 1.

Differences in α diversity (A), β diversity (B), phylum (C), genus (D), and microbial composition (E) between vasculitis patients and HC and correlation analysis between differential microflora and clinical data in patients with vasculitis (F).

Conclusion

Disturbance of intestinal flora, mainly manifested by decreased diversity and richness, may be involved in the occurrence and development of vasculitis by inducing disroders in lymphocyte subsets and cytokines. Consequently, further studies on the immune mechanisms and influencing factors of intestinal flora may provide new ideas for the diagnosis and treatment of the disease for vasculitis patients.

References

[1]Aierken X, Zhu Q, Wu T, et al. Increased Urinary CD163 Levels in Systemic Vasculitis with Renal Involvement[J]. Biomed Res Int, 2021, 2021: 6637235. DOI: 10.1155/2021/6637235.

[2]Zhang X, Zhang D, Jia H, et al. The oral and gut microbiomes are perturbed in rheumatoid arthritis and partly normalized after treatment[J]. Nat Med, 2015, 21(8): 895-905. DOI: 10.1038/nm.3914.

[3]Jennette JC, Falk RJ, Bacon PA, et al. 2012 revised International Chapel Hill Consensus Conference Nomenclature of Vasculitides[J]. Arthritis Rheum, 2013, 65(1): 1-11. DOI: 10.1002/art.37715.

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No.82001740).

Disclosure of Interests

None declared

AB0202 GUT MICROBIOTA DYSBIOSIS WERE CLOSELY CORRELATED WITH LYMPHOCYTE SUBSETS AND CYTOKINES IN PATIENTS WITH INFLAMMATORY ARTHRITIS

Background

Inflammatory arthritis includes a group of chronic conditions, particularly rheumatoid arthritis (RA), ankylosing spondylitis (AS) and psoriatic arthritis (PsA)[1].Growing evidences link gut microbiota dysbiosis with the development of inflammatory arthritis[2].

Objectives

The aim of this study was to discover the characters of microbiota in inflammatory arthritis patients and compare the relationship between the microbiota and peripheral lymphocyte subsets and cytokines.

Methods

Fecal samples were collected from 73 arthritis patients (13 PsA, 30 AS, 30 RA patients) and 140 sex- and age-matched healthy controls (HCs). The gut microbiota was studied by sequencing the V3-V4 variable regions of bacterial 16S rRNA genes by the Illumina Miseq PE300 system. Peripheral lymphocyte subsets in these participants were assessed by flow cytometry. Measures of disease activity such as erythrocyte sedimentation rate (ESR), C-reactive protein (CRP) were recorded. Alpha and Beta diversity was assessed using results from QIIME2 and gut microbiome profiles were compared using linear discriminant analysis (LDA) effect size (LEfSe). R (version 4.0.1) was used for comparative statistics, using pearson correlation analysis to assess the correlation between the relative abundance of genus in the sample and clinical parameters.

Results

Compared with HCs, the richness of gut microbiota (ACE and Chao 1) was significantly lower (p < 0.05) in arthritis patients, and bacterial diversity including Shannon and Simpson indices (p < 0.001) was also significant in arthritis decreased (Figure 1A). β-diversity analysis based on Bray-curtis distance revealed significant differences in microbial communities between arthritis and HCs (Figure 1B, r=0.098, p=0.001, ANOSIM). In addition, compared with HCs at the genus level, 9 bacterial groups were significantly different in PsA (p < 0.05), 19 bacterial groups in AS (p < 0.05), and 17 bacterial groups in RA(p < 0.05) (Figure 1C). There was a significant positive correlation between CD4+T and Prevotella(p<0.01), T and Prevotella(p<0.05), Blautia(p<0.05) as well as Megamonas(p<0.05), Th17 and Prevotella(p<0.01), CD8+T and Megamonas(p<0.01), Th1 and Megamonas(p<0.05), Prevotella(p<0.01),Coprococcus(p<0.05), B and Erysipelotricbaceae_UCG-003(p<0.01), and Erysipelotricbaceae_UCG-003(p<0.01), Anaerostipes(p<0.01), CRP and Fusobacterium(p<0.05) as well as Roseburia(p<0.05). There were negative correlations between T and Erysipelotricbaceae_UCG-003 (p<0.05),CD8+T and Fusobacterium(p<0.01), CD4+T and Fusobacterium(p<0.05), NK and Fusicatenibacter(p<0.05).

Conclusion

The gut microbiota of patients with inflammatory arthritis differs from HC and also varies among individual arthritis, which was closely related to lymphocyte subsets.

References

[1]Wu X. Innate Lymphocytes in Inflammatory Arthritis[J]. Front Immunol, 2020, 11: 565275.DOI: 10.3389/fimmu.2020.565275

[2]Breban M. Gut microbiota and inflammatory joint diseases[J]. Joint Bone Spine, 2016, 83(6): 645-649.DOI: 10.1016/j.jbspin.2016.04.005

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 82001740).

Disclosure of Interests

None declared

Artificial intelligence-powered pathology image analysis merged with spatial transcriptomics reveals distinct TIGIT expression in the immune-excluded tumor-infiltrating lymphocytes

2570

Background: TIGIT is a promising emerging immunotherapeutic target. However, the specific sources of TIGIT expression within the tumor microenvironment are largely unknown. Here, we present an AI-powered spatial tumor-infiltrating lymphocyte (TIL) analyzer, Lunit SCOPE IO, to integrate image analysis from whole slide images with single-cell molecular profiling. Methods: We used The Cancer Genome Atlas (TCGA) RNA expression data across 23 cancer types (n=6,930). Lunit SCOPE IO was developed, trained, and validated based on >17k H&E whole-slide images, to segment cancer area (CA) and cancer-associated stroma (CS) and to detect tumor cells and TILs. The intra-tumoral TIL, stromal TIL, and tumor cell purity (TCP) in the CA+CS area were calculated. The public spatial transcriptomics (ST) dataset for breast cancer was downloaded from the 10X Visium web page. Lunit SCOPE IO was applied to the associated H&E WSIs to match distinct TIGIT expression to single cells identified in the WSIs. Results: TIGIT was highly expressed in TGCT (3.45±0.11; median±SEM), LUAD (3.07±0.05), and HNSC (2.89±0.06), and was highly enriched in samples with microsatellite instability-high or tumor mutational burden-high (≥ 10/Mb) compared to those without them (fold change = 1.30, p < 0.001). At a macroscopic, bulk-level in the TCGA dataset, TIGIT expression was positively correlated with intra-tumoral TIL density (R=0.37, p<0.001) and stromal TIL density (R=0.42, p<0.001), but it was negatively correlated with TCP (R=-0.27, p<0.001). Lunit SCOPE IO analyzed the images from ST analysis and calculated intra-tumoral TIL, stromal TIL, and TCP of each region of interest, containing 2 (IQR 0-7) cells. Interestingly, at a microscopic, cell-level, TIGIT expression was still higher in areas of enriched stromal TIL (P < 0.001) and lower in tumor cell-dense areas, but it was not significantly correlated with enriched intra-tumoral TIL areas, meaning that TIGIT expression is likely derived from the excluded TILs in the CS area. Conclusions: Interactive analysis of spatial transcriptomics with AI-powered pathology image analysis revealed that TIGIT expression in the tumor microenvironment is exclusive to confined areas with stromal TIL enrichment, reflecting the exclusion of TIL from the tumor nest. [Table: see text]

Artificial intelligence (AI)–powered spatial analysis of tumor-infiltrating lymphocytes (TIL) for prediction of response to neoadjuvant chemotherapy (NAC) in triple-negative breast cancer (TNBC)

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.

Artificial intelligence-powered human epidermal growth factor receptor 2 (HER2) analyzer in breast cancer as an assistance tool for pathologists to reduce interobserver variation

e12543

Background: Human epidermal growth factor receptor 2 (HER2) expression is a predictive marker for HER2-targeted therapy in breast cancer patients. Interobserver variation in the interpretation of HER2 levels exists among pathologists, thus a method to increase the consistency of evaluation is needed. This study aimed to evaluate the performance of the artificial intelligence (AI)-based Lunit SCOPE HER2 in assisting pathologists to evaluate HER2 expression levels in breast cancer. Methods: Lunit SCOPE HER2 was developed with a 1.04 x 1010 μm2 area and 7.31 x 105 tumor cells from 1,133 HER2 immunohistochemistry stained whole-slide images (WSI) of breast cancer, annotated by 113 board-certified pathologists. The AI model was developed based on a semantic segmentation algorithm, which consists of two atrous spatial pyramid pooling blocks for tissue level classification and for tumor cell level classification. To validate the model, a total of 209 HER2 WSIs diagnosed with breast cancer were obtained from Kyung Hee University Hospital in Korea and were assigned as an external validation set. Three board-certified pathologists evaluated slide level HER2 expression (3+, 2+, 1+, and 0) twice, first without AI assistance and second, with it. The second reading was performed for WSIs where the pathologist's reading showed discrepancy with the AI model. Results: In the external validation set, all pathologists scored the same HER2 grade in 103 WSIs (49.3%), and the Fleiss kappa value was 0.512. The HER2 grade from the AI model and pathologists was the same in 151 WSIs (72.2%), and the weighted kappa value was 0.844. The pathologists re-evaluate 43, 63, and 83 WSIs, respectively. After AI assistance, all pathologists scored the same HER2 grade in 156 WSIs (74.6%), and the Fleiss kappa value increased to 0.762 (Table). Conclusions: This study demonstrates that an AI-powered HER2 analyzer can help achieve consistent HER2 expression level evaluation in breast cancer by reducing interobserver variability. Thus, the AI model can be applied as an assistance tool for pathologists in HER2 grade evaluation.[Table: see text]

POS0745 GUT DYSBIOSIS ASSOCIATED WITH PERIPHERAL LYMPHOCYTES AND CYTOKINES IN PATIENTS WITH SJÖGREN’S SYNDROME

Background

Primary Sjögren’s syndrome (pSS) is a systemic autoimmune disease characterized by disorders of lymphocyte subpopulations with various cytokines and auto-antibodies1. Growing evidences suggest that gut microbiome dysbiosis may contribute to the development of pSS2.

Objectives

To investigate the alterations to the gut microbiome and the correlation with peripheral lymphocytes and serum cytokines as well as inflammatory factors in pSS patients.

Methods

A total of 101 pSS patients and 101 age- and sex- matched healthy controls (HCs) were enrolled in this study from The Second Hospital of Shanxi Medical University (Taiyuan, Shanxi, China). Patients fulfilled the 2019 ACR/EULAR classification criteria. We conducted 16S rRNA gene sequencing using fecal microbiota samples and analyzed the peripheral lymphocyte subsets by flow cytometry. Serum cytokines, erythrocyte sedimentation rate (ESR), C-reaction protein (CRP), unstimulated and stimulated whole saliva (UWS and SWS) secretion rate was also collected, respectively. Sequence data were compiled and processed using Qiime2 and OTU-profiling tables were constructed. Correlations between different taxa and gut microbiome, as well as clinical variables, were calculated by Spearman’s rank test.

Results

Patients with pSS exhibited a significant reduction in the richness and diversity of gut microbiota compared with those of HCs (Figure 1A-B, p < 0.05). Detailly, at the phylum level, pSS patients had a lower frequency of Firmicutes while higher Proteobacteria (Figure 1C, p < 0.05). Compared with HCs, 11 species of flora were discovered to be distinctly different at the genus level (p < 0.05). Patients presented fewer Faecalibacterium and Roseburia but more Lactobacillus (Figure 1D, p < 0.05). Lactobacillus negatively correlated with T cells (r=-0.407), CD8+T (r=-0.417) and Th2 (r=-0.323). There was a significant positive correlation between Faecalibacterium and IL-2(r=0.312), IFN-γ(r=0.338), TNF-α levels(r=0.322) (Figure 1E, p < 0.05). As for clinical disease measures, IL-6 increases were in line with ESR and CRP, while IL-2 levels inversely related to CRP. Additional UWS secretion rate and SWS secretion rate had negative correlation with ESR (Figure 1F, p < 0.05).

Conclusion

The structural disorder of gut microbiota was distinct in pSS which were associated with peripheral lymphocyte subsets and cytokines. Disorders of gut microbiota and immune systems may contribute to the occurrence and development of pSS.

References

[1]Mariette X, Criswell LA. Primary Sjogren’s Syndrome. N Engl J Med 2018;378(10):931-39. doi: 10.1056/NEJMcp1702514

[2]Trujillo-Vargas CM, Schaefer L, Alam J, et al. The gut-eye-lacrimal gland-microbiome axis in Sjogren Syndrome. Ocul Surf 2020;18(2):335-44. doi: 10.1016/j.jtos.2019.10.006

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 82001740).

Disclosure of Interests

None declared

POS0330 DIFFERENCES IN GUT MICROBIOTA ASSOCIATED WITH LYMPHOCYTE SUBSETS, CYTOKINES AND DISEASE ACTIVITY IN ANKYLOSING SPONDYLITIS

Background

Ankylosing spondylitis (AS), a common chronic inflammatory disease, is a prototype of spondyloarthritis affecting sacroiliac joints and spine with or without peripheral arthritis and other systemic symptoms[1]. Environmental factors, especially microorganisms have been suggested to implicate with AS pathogenesis[2].

Objectives

Utilizing 16S rRNA genes sequencing on the feces of untreated AS patients and healthy controls (HCs), our study aimed to provide an in-depth understanding of AS gut microbiota and identifying a feasible diagnostic strategy for AS.

Methods

Fecal samples were collected from 62 AS patients and 62 age-and-gender- matched HCs. Microbial genome was extracted from approximately 250mg fresh fecal samples from all participants using QIAamp PowerFecal DNA Kit (Qiagen). The V3-V4 variable regions of bacterial 16S rRNA genes were sequenced with the Illumina Miseq PE300 system. QIIME2 based pipeline was used to process the raw sequence data. Alpha and beta diversities were assessed using result from QIIME2, and comparisons of gut microbiome profile were performed using linear discriminant analysis (LDA) effect size (LEfSe) to examine differences between AS and HCs. R (version 4. 0.1) was used for comparative statistics, and pearson’s correlation was used to assess the correlations between the relative abundances of bacterial genera and clinical parameters; correlations with p<0.05 were considered significant.

Results

AS for alpha-diversity, ACE and Chao1 indices were lower in AS compared with those HCs(Figure 1A, p<0.05), though no significant differences observed in Shannon and Simpson index. Bray curtis distance-based beta-diversity analysis revealed significant differences in the microbial community between AS and HCs (Figure 1B, p=0.003, ANOSIM). Fecal microbial communities in AS differed significantly from those in HCs, driven by higher abundances of Escherichia-Shigella, Turicibacter, Enterococcus, et al. and a lower abundance of Agathobacter, Roseburia, Eubacterium_eligens_group, et al (Figure 1C, p<0.05). There was a significant positive correlation between ESR and Klebsiella, Butyricicoccus, Roseburia, CRP and Faecalibacterium, Muribaculaceae, ASDAS-CRP score and Faecalibacterium, Ruminococcus, total lymphocyte cells and Agathobacter, Ruminococcus, T cell and Agathobacter, CD4+T cell and Agathobacter, B cell and Agathobacter, Streptococcus, Th1 and Prevotella, CAG−352, Th2 and Agathobacter, Th17 and Prevotella, Agathobacter, IL-2 and Agathobacter, IL-4 and Agathobacter, IL-6 and Lachnospiraceae_UCG−004, Muribaculaceae, IL-17 and Eubacterium_hallii_group, IFN-gama and Phascolarctobacterium.There were negative correlations between total lymphocytes and Escherichia−Shigella, CD4+T cell and Enterobacteriaceae, Th2 cell and Escherichia−Shigella, IL-10 and CAG−352, Ruminococcus (Figure 2, p<0.05).

Figure 1.

Feature of gut microbiota in AS patients and HCs. (A) Alpha-diversity assessed by richness (Chao1, ACE) and diversity (Shannon, Simpson), Median estimates compared across cohorts. (B) PCoA plot based on the Bray curtis distance of gut microbiota samples from AS patients vs. HC group(p=0.003, ANOSIM). (C) Panel demonstrated the average relative abundance of different genus in AS and HCs. (D) Distribution of gut microbiota at genus level.

Figure 2.

Correlations between the relative abundance of significantly different bacteria and clinical variables. *p<0.05, **p < 0.01, ***p <0 .001, ****p < 0.0001.

Conclusion

Human gut microbiome in patients with AS differed from that of the HCs. Characters of bacteria communities were associated with disease activity.

References

[1]Simone D, Al Mossawi M H, Bowness P. Progress in our understanding of the pathogenesis of ankylosing spondylitis [J]. Rheumatology (Oxford), 2018, 57(suppl_6): vi4-vi9.

[2]Zhou C, Zhao H, Xiao X Y, et al. Metagenomic profiling of the pro-inflammatory gut microbiota in ankylosing spondylitis [J]. J Autoimmun, 2020, 107(102360.

Acknowledgements

This project was supported by the National Natural Science Foundation of China (No. 82001740).

Disclosure of Interests

None declared

AB0492 INTESTINAL MICROBIOLOGICAL DISORDER CLOSELY ASSOCIATED WITH PERIPHERAL LYMPHOCYTE SUBSETS AND CYTOKINES IN SYSTEMIC LUPUS ERYTHEMATOSUS

Background

Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by widespread inflammation and tissue damage in multiple organs[1]. Microbiome is one of environmental factors that has been suggested to contribute to the occurrence and development of SLE[2].

Objectives

This study aims to the understanding of the pathogenesis of SLE from the perspective of intestinal microorganisms and investigate the associations between flora and peripheral lymphocyte subpopulations and cytokines in SLE patients.

Methods

Fecal samples were collected from 96 patients with SLE, and 96 sex-and age-matched healthy controls (HCs). The gut microbiota were investigated via 16s rRNA sequencing and the peripheral T lymphocyte subsets of these participants were assessed by flow cytometry. Indicators of disease activity such as erythrocyte sedimentation rate (ESR), C-reaction protein (CRP), complement C3 and C4 were recorded. Differential abundance analysis was carried out using the edgeR algorithm. The Wilcoxon rank-sum test was used to compare alpha diversity indices, bacterial abundances, and the F/B ratio between groups. R (version 4.0.1) was used for comparative statistics, and pearson’s correlation analysis was used to assess the correlations between the relative abundances of bacterial genera and serum levels of ESR, CRP, C3 and C4 in the samples; correlations with p < 0.05 were considered significant.

Results

The alpha estimators of richness (ACE and Chao 1) were significantly reduced in SLE feces samples compared with those of HCs (p < 0.0001). Bacterial diversity estimators, including the Shannon (p < 0.001) and Simpson’s (p < 0.01) indices, were also significantly lower in SLE (Figure 1A-D). The microbial community structures of the SLE and HCs could be separated by unweighted UnFrac-based principal coordinates analysis (PCoA) (R = 0.186, and p = 0.001; Figure 1E). Significant differences in gut microbiota composition between SLE and HCs were found using the edgeR algorithm. Compared with HCs, 24 species of flora were discovered to be distinctly different(p < 0.05). Moreover, there was a significant positive correlation between Tregs and Corynebacterium(p < 0.05), CD8+T and Corynebacterium (p < 0.05), CD4+T and Corynebacterium (p < 0.05), T and Corynebacterium (p < 0.05), Th1 and Escherichia−Shigella (p < 0.01), Th2 and Dielma (P<0.001) as well as Eubacterium eligens group (p < 0.05), NK and Faecalibacterium (p < 0.01). as well as Corynebacterium (p < 0.001), IL-6 and Coprococcus (p < 0.05), IL-10 and Eubacterium eligens group (p < 0.001) as well as Veillonella (p < 0.05). and Lachnospira (p < 0.01). As for clinical disease measures, there were positive correlations between CRP and Eubacterium ventriosum (p < 0.05). and Coprococcus (p < 0.05), C4 and the abundance of Corynebacterium (p < 0.05) (Figure 1F).

Conclusion

Patients with gut dysbiosis that mainly characterized by reduced the diversity and impaired abundance of the intestinal flora. Abnormality of T cell subsets and cytokines, especially the level of CD4+T, CD8+T, NK, Treg, Th, IL-6 and IL-10 cells contributes to the occurrence and progression of SLE, which may be related to the disturbance of gut microbiota. The discovery of the associated intestinal microbiota of SLE may provide a new idea for treatment.

References

[1]Fava A, Petri M. Systemic lupus erythematosus: diagnosis and clinical management. J Autoimmun. (2019) 96:1–13. 10.1016/j.jaut.2018.11.001

[2]He Z, Shao T, Li H, Xie Z, Wen C: Alterations of the gut microbiome in Chinese patients with systemic lupus erythematosus. Gut pathogens 2016, 8:64.

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 82001740).

Disclosure of Interests

None declared

POS0556 ALTERATION OF THE GUT MICROBIOTA IN CHINESE POPULATION WITH RHEUMATOID ARTHRITIS

Background

Rheumatoid arthritis (RA) is an aggressive immune-mediated joint disease characterized by synovial proliferation and inflammation, cartilage destruction, and joint destruction. Growing evidences suggests a chronic inflammatory response induced by gut microbiome critically contribute to the development of rheumatoid arthritis.

Objectives

The aim of this study was to evaluate and quantify differences in the composition of gut microbiota in RA patients and investigate the associations between flora and clinical variables in RA patients.

Methods

Fecal samples from 145 RA patients and 145 age- and gender- matched healthy controls (HCs) were collected for bacterial 16S rRNA genes sequencing. The alpha-diversity, beta-diversity and the microbial composition (at the phylum and genus level) analysis of the gut microbiome were used to define the difference of gut microbiota profiles between RA patients and HCs. The peripheral lymphocytes of these patients were assessed by flow cytometry, and inflammatory biomarkers (ESR, CRP), auto-antibodies(ACPA, MCV) and cytokines measured by ELISA were recorded. Correlations between different taxa and clinical variables, were calculated by Spearman’s rank test.

Results

Consistent with trends observed for diversity, patients with RA had a lower richness compared with those of HCs (p < 0.01, Figure 1a), suggesting gut microbiome was markedly less diverse in composition in RA. Bray curtis distance-based beta diversity analysis revealed significant differences in the microbial community between RA and HCs (ANOSIM, R2=0.061, p=0.001, Figure 1b). Ten selected taxonomic biomarkers at different phylogenetic levels showed great discriminant ability, with Log10 LDA score > 4.0 (Figure 1e-g). Detailly, at the phylum level, RA patients had a lower frequency of Firmicutes while higher Proteobacteria. RA patients presented fewer Faecalibacterium but more Escherichia_Shigella at the genus level (Figure 1c-d). PICRUSt analysis found that in the KEGG pathways, the microbial gene functions related to Propanoate metabolism were higher in the fecal microbiome of RA patients (Figure 1h). Escherichia_Shigella positively correlated with ACPA antibodies (r=0.176, p < 0.05) and IL-4 (r=0.204, p < 0.05, Figure 1i), wheras Faecalibacterium as a probiotic showed no significant correlation with our clinical measures.

Figure 1.

Conclusion

Specific gut microbiota played an important role in the pathogenesis of RA, which may aid in the diagnosis or determination of the susceptibility of individuals to RA via detection of the gut microbiome.

References

[1]de Oliveira GLV, Leite AZ, Higuchi BS, et al. Intestinal dysbiosis and probiotic applications in autoimmune diseases. Immunology 2017;152(1):1-12. doi: 10.1111/imm.12765

[2]Chen J, Wright K, Davis JM, et al. An expansion of rare lineage intestinal microbes characterizes rheumatoid arthritis. Genome Med 2016;8(1):43. doi: 10.1186/s13073-016-0299-7

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 82001740).

Disclosure of Interests

None declared.