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  • 1
    In: Liver Cancer, S. Karger AG, Vol. 12, No. 3 ( 2023), p. 198-208
    Abstract: 〈 b 〉 〈 i 〉 Introduction: 〈 /i 〉 〈 /b 〉 Tumor-related liver failure (TRLF) is the most common cause of death in patients with intrahepatic cholangiocarcinoma (ICC). Though we previously showed that liver radiotherapy (L-RT) for locally advanced ICC is associated with less frequent TRLF and longer overall survival (OS), the role of L-RT for patients with extrahepatic metastatic disease (M1) remains undefined. We sought to compare outcomes for M1 ICC patients treated with and without L-RT. 〈 b 〉 〈 i 〉 Methods: 〈 /i 〉 〈 /b 〉 We reviewed ICC patients that found to have M1 disease at initial diagnosis at a single institution between 2010 and 2021 who received L-RT, matching them with an institutional cohort by propensity score and a National Cancer Database (NCDB) cohort by frequency technique. The median biologically effective dose was 97.5 Gy (interquartile range 80.5–97.9 Gy) for L-RT. Patients treated with other local therapies or supportive care alone were excluded. We analyzed survival with Cox proportional hazard modeling. 〈 b 〉 〈 i 〉 Results: 〈 /i 〉 〈 /b 〉 We identified 61 patients who received L-RT and 220 who received chemotherapy alone. At median follow-up of 11 months after diagnosis, median OS was 9 months (95% confidence interval [CI] 8–11) and 21 months (CI: 17–26) for patients receiving chemotherapy alone and L-RT, respectively. TRLF was the cause of death more often in the patients who received chemotherapy alone compared to those who received L-RT (82% vs. 47%; 〈 i 〉 p 〈 /i 〉 = 0.001). On multivariable propensity score-matched analysis, associations with lower risk of death included duration of upfront chemotherapy (hazard ratio [HR] 0.82; 〈 i 〉 p 〈 /i 〉 = 0.005) and receipt of L-RT (HR: 0.40; 〈 i 〉 p 〈 /i 〉 = 0.002). The median OS from diagnosis for NCDB chemotherapy alone cohort was shorter than that of the institutional L-RT cohort (9 vs. 22 months; 〈 i 〉 p 〈 /i 〉 & lt; 0.001). 〈 b 〉 〈 i 〉 Conclusion: 〈 /i 〉 〈 /b 〉 For M1 ICC, L-RT associated with a lower rate of death due to TRLF and longer OS versus those treated with chemotherapy alone. Prospective studies of L-RT in this setting are warranted.
    Type of Medium: Online Resource
    ISSN: 2235-1795 , 1664-5553
    Language: English
    Publisher: S. Karger AG
    Publication Date: 2023
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  • 2
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    American Society of Clinical Oncology (ASCO) ; 2019
    In:  Journal of Clinical Oncology Vol. 37, No. 8_suppl ( 2019-03-10), p. 64-64
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 37, No. 8_suppl ( 2019-03-10), p. 64-64
    Abstract: 64 Background: Stroma in the tumor microenvironment (TME) influences prognosis and response to therapy. Few mathematical models exist to prognosticate patients (pts), based on mRNA expressivity in the TME. Methods: Clinical outcomes data and mRNA-seq of 401 pts with muscle invasive urothelial carcinoma were obtained from TCGA. Expressivity of 191 genes enriched in cellular and structural elements of TME and clinical data were analyzed by Kaplan-Meier (KM) analysis, correlation analysis, and multivariate nonlinear regression assisted by machine learning to achieve confined optimization with model-data minimization among multiple distribution functions. Results: Prognostication was modeled with higher risk score (RS) representing worse prognosis in stage 2-4 (Table, stage 1 data not available from TCGA). P/G is the ratio of genes associated with poor (19 genes) to good (11) prognosis (refer to presentation). Based on RS, pts in each stage were clustered into 2 groups (high and low RS), showing 2 KM curves with p 〈 0.01 in each stage, confirming the validity of RS modeling. Analysis of immune profiles in these 2 groups shows that regardless of stage, expression of genes associated with Desmoplasia, Angiogenesis, and Epithelial-mesenchymal transition (DAE) is higher in high RS groups. Furthermore, expression of DEA genes in stage 4 correlated more strongly with poor prognosis than observed in stage 2-3 as evidenced by smaller p-value. Among stage 4 tumors, expression of genes related to IFN response, NK cells, and T1 helper cells is higher in low RS groups. In stage 2 and 3, genes related to immune activation and inhibition have no association with prognosis (p 〉 0.05). Conclusions: Machine learning-assisted mathematical modeling of RS and gene analysis show that genes related to immune activation are associated with better prognosis, while DAE genes correlate with poorer prognosis among advanced stages. RS enables prognostication of pts encountered in the clinic, given genomic profiles. [Table: see text]
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
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    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2019
    detail.hit.zdb_id: 2005181-5
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  • 3
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    Online Resource
    American Society of Clinical Oncology (ASCO) ; 2019
    In:  Journal of Clinical Oncology Vol. 37, No. 7_suppl ( 2019-03-01), p. 557-557
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 37, No. 7_suppl ( 2019-03-01), p. 557-557
    Abstract: 557 Background: Stroma in the tumor microenvironment (TME) influences prognosis and response to therapy. Few mathematical models exist to prognosticate patients (pts), based on mRNA expressivity in the TME. Methods: Clinical outcomes data and mRNA-seq of 533 pts with clear cell renal cancer were obtained from TCGA. Expressivity of 191 genes enriched in cellular and structural elements of TME and clinical data were analyzed via machine learning, multivariate nonlinear regression with confined optimization, and Kaplan-Meier (KM) analysis. Results: Prognostication was modeled with higher risk score (RS) representing worse prognosis in each stage (Table). P/G is the ratio of genes associated with poor (61 genes) to good (14) prognosis (refer to presentation). Based on RS, pts in each stage were clustered into 2 groups (high and low RS), showing 2 KM curves with p 〈 0.001 in each stage. Analysis of immune profiles in these 2 groups shows that in stage 1, expression of genes related to immune activation (IA) is not statistically different in high and low RS groups, but expression of genes related to immune inhibition (II) is higher in high RS group. In high RS groups of stage 2-4, IA genes are highly co-expressed with II genes. In high RS groups of all stages, expression of both IA and II genes increases as stage increases. In low RS groups, IA genes increase as stage increases, but II genes do not. Conclusions: Machine learning and mathematical modeling of RS and gene analysis show that IA genes are suppressed by high degree of II in high RS groups of advanced stages, contributing to worse prognosis. RS enables prognostication of pts encountered in the clinic, given genomic profiles. [Table: see text]
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
    RVK:
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    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2019
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  • 4
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    American Society of Clinical Oncology (ASCO) ; 2020
    In:  Journal of Clinical Oncology Vol. 38, No. 15_suppl ( 2020-05-20), p. e16765-e16765
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 38, No. 15_suppl ( 2020-05-20), p. e16765-e16765
    Abstract: e16765 Background: Even though the 8th edition AJCC cancer staging system for pancreatic cancer has validated with major clinicopathologic factors in multiple clinical cohorts, there is still an unmet need for integrative consideration using multi-omics data to stratify the patients with pancreatic cancer elaborately. Methods: We performed a comprehensive analysis and profiling using genomic, transcriptomic, and proteomic data from TCGA-PAAD and other translational cohorts (4 cohorts, n = 340). Molecular features and major subtypes were analyzed mutually with clinical and pathologic factors, especially the 8th AJCC staging system. Results: Aggressive molecular subtypes, basal-like and squamous subtype, were significantly associated with a higher nodal stage, but tumor size didn't show a clear association with molecular features. The activated stroma of pancreatic cancer microenvironment was significantly correlated with poor differentiation and large tumor size. The mutational pattern of KRAS and several transcriptomic pathways such as eptihelial-mesenchymal transition and DNA repair were differently presented in each clinical stage from the 8th AJCC TNM staging system. The optimal algorithm was identified to show significantly higher performance for the prediction for cancer relapse and cancer-specific survival in discovery and validation cohorts. The in silico prediction for molecular target agents and immunotherapy were performed for final clusters from optimal stratification system revealed from the integrative analysis. Conclusions: Our comprehensive multi-omics analysis reveals clear needs for the combination of clinical staging and molecular profiling and provides crucial evidence for precision strategy in patients with resectable pancreatic cancer.
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
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    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2020
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  • 5
    In: Cancer, Wiley, Vol. 128, No. 13 ( 2022-07), p. 2529-2539
    Abstract: In this analysis of 27,571 patients in the United States with unresected intrahepatic cholangiocarcinoma diagnosed between 2004 and 2018, use of liver radiotherapy remained constant despite growing evidence supporting that its use is associated with longer survival. Among patients who received liver radiotherapy, higher doses have been increasingly used; patients who received higher doses had longer survival than those receiving lower doses (median 23.7 vs 12.8 months, respectively).
    Type of Medium: Online Resource
    ISSN: 0008-543X , 1097-0142
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2022
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    detail.hit.zdb_id: 2599218-1
    detail.hit.zdb_id: 2594979-2
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  • 6
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    American Association for Cancer Research (AACR) ; 2022
    In:  Cancer Research Vol. 82, No. 12_Supplement ( 2022-06-15), p. 1721-1721
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 82, No. 12_Supplement ( 2022-06-15), p. 1721-1721
    Abstract: Background: We previously demonstrated that the analysis of the tumor microenvironment (TME) in histopathology images via tissue segmentation [1] and cell density in lymphocyte-rich area [2] impacts prognosis and treatment in hepatocellular carcinoma (HCC). Few biomarker models exist to prognosticate patients with HCC via the automated analysis of TME at the cellular level. Methods: Clinical outcomes data and histopathology images of 351 patients with HCC were obtained from TCGA. We advanced a deep learning-based algorithm to analyze the tumor volume and spatial distribution of nuclei in TME. This was based on combination of two models: the PAIP2019 dataset was used for DenseNet-based HCC segmentation, which showed the performance of 0.8582 on the F1-score metric [3]; HoverNet-based cell detection model, which showed the performance of 0.654 on the binary PQ metric, annotated lymphocytes, macrophages, and neutrophils on the MonuSac dataset [4] . Results: The HCC segmentation model divided the TME into tumoral, marginal, and peritumoral areas by image processing. The marginal and peritumoral areas were defined as inner 50 um area and outer 100 um area from the estimated tumoral boundary, respectively. The ratios of neutrophils, lymphocytes, macrophages to the total cell count on marginal and peritumoral areas were calculated through integration of HCC segmentation and cell detection models. The proportions of leukocytes were subjected into Cox proportional hazard analysis. The results of Cox proportional hazard analysis calculated the proportions of macrophages and lymphocytes to other cells in the TME. The macrophage proportion on the peritumoral area was a significant prognostic indicator showing Log(hazard ratio) (-2.42 ± 2.14, p=0.026). The lymphocyte proportion on both areas of the peritumor and margins showed significant Log(hazard ratio) (-1.70 ± 1.61, p=0.042). Conclusions: The retrospective analysis of the TME using deep learning-assisted algorithm combining tissue segmentation and cell detection models reveals that the ratio of lymphocytes and macrophages in the peri-tumoral areas of HCC TME significantly impact prognosis. Further analyses in the prospective studies may provide more information about cellular biomarkers. [1] Kim et al. Cancer Res 2020 (80) (16 Supp) 2631 [2] Park et al. Journal of Clinical Oncology 39, no. 15_suppl (May 20, 2021) 4107-4107 [3] Kim, Yoo Jung, Jang, Hyungjoon, Lee, Kyoungbun et al. Medical Image Analysis 67 (2021): 101854. [4] Verma, Ruchika. IEEE Transactions on Medical Imaging 39 (2020): 1380-1391. [5] Graham, Simon. Medical Image Analysis 58 (2019): 101563. Citation Format: Hongseok Lee, Kyungdoc Kim, Guhyun Kang, Kyu-Hwan Jung, Sunyoung S. Lee. Spatial distribution of immune cells as quantitative prognosis indicator in hepatocellular 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 1721.
    Type of Medium: Online Resource
    ISSN: 1538-7445
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2022
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  • 7
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    American Association for Cancer Research (AACR) ; 2018
    In:  Cancer Research Vol. 78, No. 13_Supplement ( 2018-07-01), p. 2095-2095
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 78, No. 13_Supplement ( 2018-07-01), p. 2095-2095
    Abstract: Objective: A characteristic histologic feature of pancreatic adenocarcinoma (PAD) and cholangiocarcinoma (COL) is extensive desmoplasia (DP) alongside leukocytes and stromal cells (SCs). Advances in mRNA-sequencing have enhanced our understanding of cancer biology in relation to selective changes in expressivity amongst SCs. DP changes secondary to aberrant expression in SCs creates a barrier to absorption and penetration of therapeutic drugs, but few models exist to analyze the spatial and architectural elements composing the complex tumor microenvironment (TME) in association with mRNA levels. Methods: The histopathology images (H & E stain) and mRNA-seq of 178 PAD and 36 COL patients (pts) were obtained from the Cancer Genome Atlas (TCGA) and analyzed with the deep learning (DL) algorithm, which characterizes histological features in comparison to the corresponding mRNA-seq, allowing for rapid automated analysis of large quantities of data. Ninety genes enriched in leukocytes (CD8+ T cells, B cells, CD4+ regulatory T cells, macrophages, neutrophils, NK cells, and plasmacytoid dendritic cells), 7 genes for cytolytic activities (GZMA, PRF1, GZMH, GZMK, NKG7, CD3E, and CD247), and 5 genes involved with fibroblastic and DP changes (PDFGRA, ACTA2, COL1A1, COL1A2, and PDPN) were analyzed. For each pt, mRNA levels of select genes were analyzed against histologic features, including degree of DP reaction, number of leukocytes, and degree of leukocyte clustering and isolation from tumor cells. Results: DL analysis demonstrates that the number of fibroblasts and degree of DP correlates with and predicts the mRNA expression of genes associated with fibroblastic and DP changes. The mRNA level of CXCL12 correlates with the degree of leukocyte clustering and spatial isolation in PAD and COL. The 5 genes associated with DP and fibrosis do not have a linear relationship with CXCL12 mRNA levels (R2 & lt;0.1) in COL and (R2=0.2196 to 0.6279) in PAD. Cytolytic activity, measured by the mRNA levels from 7 genes, does not correlate with CXCL12 expression (R2 & lt;0.1) in COL, and (R2=0.3530 to 0.6060) in PAD. Conclusion: A DL model enables automated analysis and mapping of DP changes within stromal and malignant cells, revealing the spatial and architectural relationship in the TME with varying gene expression. This demonstrates that the degree of leukocyte clustering and isolation from tumor cells correlates with CXCL12 mRNA levels in PAD and COL. CXCL12 expressivity appears to be a contributing factor, limiting access of leukocytes to tumor cells and diminishing an important mechanism combating tumor progression. Varying degrees of DP and cytolytic activities of immune cells within the TME were also observed in association with CXCL12 expression in PAD and COL. Further biomarker-driven prospective studies in the context of immunotherapy and anti-fibrosis are warranted. Citation Format: Sunyoung S. Lee, Jin Cheon Kim, Seongwon Lee, Jilliam Dolan, Andrew Baird. Automated mapping and analysis of stromal cells in tumor microenvironment in pancreatic adenocarcinoma andcholangiocarcinoma using deep learning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2095.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2018
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  • 8
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    Online Resource
    American Society of Clinical Oncology (ASCO) ; 2020
    In:  Journal of Clinical Oncology Vol. 38, No. 15_suppl ( 2020-05-20), p. e16658-e16658
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 38, No. 15_suppl ( 2020-05-20), p. e16658-e16658
    Abstract: e16658 Background: Stromal elements in the tumor microenvironment (TME) impact prognosis and response to therapy. Advances in mRNA-sequencing improved understanding of gene expressivity, but few models exist to model prognosis in association with mRNA expression. Methods: Clinical data and mRNA-seq of 256 patients (pts) with hepatocellular carcinoma (HCC) were obtained from TCGA. The expressivity of 191 genes enriched in cellular and structural components of the TME and clinical data were analyzed using machine learning, multivariable COX model, and Kaplan-Meier (KM) analysis to model risk score (RS) for prediction of prognosis. Results: Prognostication was modeled with higher risk score (RS) representing worse prognosis. Gene expression associated with poor (P) and good (G) in stage 1 and 2 HCC was identified (refer to presentation). RS (stage 1) = 5.997 - 0.589 × (Age at diagnosis −7.979E-06 ) - 4.818 × (P/G −0.009 ); RS (stage 2) = -5.704 - 0.780 × (Age at diagnosis −9.383E-06 ) + 7.228 × (P/G −0.004 ). Based on RS, pts were clustered into 2 groups in each stage - high and low RS groups, showing two KM curves with P 〈 0.05, HR = 3.213 (95% CI 2.212 – 4.347) in stage 1; HR = 2.733 (95% CI 2.131 – 3.426) in stage 2, confirming the validity of RS modeling. Analysis of immune profiles in high and low RS groups shows that expression of genes associated with immunosuppressive factors, desmoplastic reaction, neutrophils, and co-inhibitory factors of T-cells are higher in high RS group in both stages (p 〈 0.05). Conclusions: Machine learning-assisted mathematical modeling of RS and gene analysis identified TME-related genes and gene groups that are strongly associated with worse prognosis in stage 1 and 2 of HCC. RS could potentially prognosticate pts in the clinic with available genomic profiles.
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
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    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2020
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  • 9
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    Online Resource
    American Society of Clinical Oncology (ASCO) ; 2019
    In:  Journal of Clinical Oncology Vol. 37, No. 15_suppl ( 2019-05-20), p. 8544-8544
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 37, No. 15_suppl ( 2019-05-20), p. 8544-8544
    Abstract: 8544 Background: The tumor microenvironment (TME) influences prognosis and response to therapy. The correlation between immune profiles in the TME and cancer DNA mutations is not well established. Methods: Clinical outcomes data, mRNA-seq, and DNA mutation of 480 patients (pts) with lung adenocarcinoma (LAD) were obtained from TCGA. Pts were clustered into 4 groups using unsupervised machine learning, based on mRNA expression of genes related to antigen presentation (AP) and cytolytic activity (CA): group (G) 1 with high AP and CA (52 pts); G2, high AP, low CA (82); G3, low AP, high CA (66); G4, low AP and CA (280). Analysis of the immune landscape was performed using mRNA-seq of 191 genes enriched in cellular and structural elements of TME. DNA mutations were analyzed using the R package ggpubr and correlated in G1-G4. Results: Pts in G1 have high expression of genes related to immune activation (IA) and decreased expression of immune suppression (IS) and have the best prognosis. Pts in G2 have intermediate prognosis with decreased IA genes and intermediate expression of genes related to IS and immune checkpoints. Pts in G3 have the worst prognosis with very high expression of genes related to immune checkpoints, desmoplasia, T cell co-inhibition, and IS. They also have low CD39 expression implying low cancer antigen-driven T cells. Pts in G4 have intermediate prognosis with highly depressed IA genes. Out of 70,199 non-synonymous mutations, the top 50 mutated genes in each pt group were identified: 36, 26, 31, and 17 DNA mutations were only found in G1, G2, G3, and G4 (refer to presentation). EGFR mutation was only found in G2; KRAS in G2/4; TP53 in G2/3/4. Conclusions: Our correlation analysis of mRNA-seq and DNA mutation shows that the immune landscape of TME can predict DNA mutations and prognosis. It further demonstrates a close connection between DNA mutations and changes in TME mRNA expressivity which appear to have valuable prognostication potential in the clinical setting with now widely available genomic testing.
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
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    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2019
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  • 10
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    Online Resource
    American Society of Clinical Oncology (ASCO) ; 2022
    In:  Journal of Clinical Oncology Vol. 40, No. 16_suppl ( 2022-06-01), p. 4119-4119
    In: Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 40, No. 16_suppl ( 2022-06-01), p. 4119-4119
    Abstract: 4119 Background: Tumor microenvironment (TME) is known to impact prognosis in hepatocellular carcinoma (HCC). Although digital pathology and artificial intelligence have been adopted in modern medicine and oncology, few quantitative biomarkers have been identified to predict the prognosis and guide treatment for HCC via an automated analysis of TME at the cellular level. Methods: Histopathological images and clinical data of 365 cases with HCC were obtained from TCGA (The Cancer Genome Atlas), and 60 of HCC pathology images and cancer lesion annotations were collected from PAIP2019 [1]. DenseNet-based HCC segmentation model (F1-score, 0.904) and Hover-Net-based cell detection model (F1-score, 0.914) were developed using PAIP2019 and MoNuSac datasets, respectively [2,3,4] . Each histopathology image of TME was segmented via the segmentation model into two areas: 1) non-tumoral regions that include the stroma; 2) tumoral regions where HCC cells are concentrated. The cell detection model recognized individual cells on images, specified lymphocytes, and calculated ratios of lymphocyte to total cell count (RLTCC) in segmented regions. RLTCC was then correlated with clinical survival outcomes, HCC primary risk factors, and RNA expression profiles. Results: RLTCC in tumoral regions was not significantly associated with prognosis. Patient groups with higher RLTCC in non-tumoral regions (RLTCC in NT) showed better overall survival (OS) than those with lower RLTCC in NT regardless of HCC risk factors (median OS 45.7 vs 18.6 months; log hazard ratio of -1.6 ± 1.1, p=0.006). These patients had significantly enriched expression of genes (p 〈 0.05) related to cancer antigen presentation (higher gene expression by +33.7%), recognition of cancer cells by T-cell (+32.0%), T-cell priming and activation (+32.2%), immune cell localization to tumors (+31.9%), and killing of cancer cells (+24.7%). Those with HCC etiology of hepatitis B and C had more patients in the higher RLTCC in NT (17/21 patients, 81.0%; 23/29, 79.3%, respectively). In comparison, those with alcohol consumption showed equal distribution (26/53, 49.1%). The RLTCC in NT in hepatitis B/C groups was statistically higher than alcohol consumption group (p 〈 0.05). Conclusions: A digital prognostic biomarker, RLTCC in NT of TME was identified as a significant prognostic indicator, and it was shown to correlate with RNA gene expression related to T-cell mediated cancer immunity. A retrospective analysis of clinical response from systemic therapy in relation to digital biomarkers is underway and will be reported. References: [1] Kim et al. Med. Image Anal. 67 (2021). [2] Riasatian, Abtin, et al. Med. Image Anal. 70 (2021). [3] Graham, Simon, et al. Med. Image Anal. 58 (2019). [4] Verma, Ruchika, et al. IEEE Trans Med Imaging 39.1380-1391 (2020).
    Type of Medium: Online Resource
    ISSN: 0732-183X , 1527-7755
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    Language: English
    Publisher: American Society of Clinical Oncology (ASCO)
    Publication Date: 2022
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