Clemenceau, Jean R.

Abstract Background: Understanding the spatiotemporal dynamics of the tumor microenvironment (TME) during treatment is crucial for improving outcomes in pancreatic ductal adenocarcinoma (PDAC). This study presents a pre-planned spatiotemporal analysis from a phase II trial evaluating neoadjuvant mFOLFIRINOX in resectable PDAC, aimed at elucidating treatment resistance mechanisms and identifying predictive biomarkers. Methods: This multi-institutional, single-arm phase II trial enrolled treatment-naïve patients with histologically confirmed resectable PDAC (NCT05066802) who underwent six cycles of neoadjuvant mFOLFIRINOX followed by surgery. The primary endpoint was the R0 resection rate. Matched pre-treatment tumor biopsies and post-treatment surgical specimens were analyzed from selected responders (R) and non-responders (NR) using high-resolution spatial transcriptomics (10X Genomics Visium). This approach enabled comprehensive mapping of TME evolution and treatment-induced changes. Results: Between May 2020 and February 2022, 27 patients were enrolled. Of these, 24 patients underwent surgery after neoadjuvant treatment (resection rate: 88.9%), and 23 achieved R0 resection (85.2%), meeting the primary endpoint. However, 20 resected patients (83.3%) recurred. Spatiotemporal analysis was performed on matched samples from four R and four NR. In pre-treatment samples from NR group, clusters dominated by fibroblasts (enriched for ECM, EMT, and CAF phenotypes) and B cell subtypes were distinct compared to R group, which exhibited elevated antibody responses and robust T cell and macrophage activity. NR samples showed increased IgM expression but lacked corresponding Fc receptors, suggesting limited immune effector function. Post-treatment NR surgical specimens displayed elevated expression of MUC5AC and CEACAM6 in malignant ductal cells, alongside genes promoting tumor growth. Neoadjuvant mFOLFIRINOX increased fibroblast, while reducing immune cell components. NR tumors formed distinct "resistance niches" after chemotherapy, marked by ECM production, CAF activation, and RTK signaling. Conversely, R tumors showed enhanced epithelial organization with increased type-I hemidesmosome complexes facilitating epithelial adhesion to the basement membrane. Conclusion: Three months of neoadjuvant mFOLFIRINOX was feasible and tolerable for resectable PDAC but associated with high recurrence rate, emphasizing the need for better patient selection. Spatiotemporal analysis revealed critical differences in TME dynamics according to treatment response, including the emergence of resistance niches and distinct pre-treatment spatial signatures. These findings support the integration of spatial biology into future pancreatic cancer trials to improve stratification and therapy. Citation Format: Choong-kun Lee, Changjin Hong, Ho Kyoung Hwang, Min Hwan Kim, Jean Rene Clemenceau, Seock-Jin Chung, Isabel Barnfather, Sumanth Reddy Nakkireddy, Jeong Hwan Park, Ji-Youn Sung, Minji Kim, Yooho Choi, Inyeop Jang, Sunho Park, Sung Hyun Kim, Chang Moo Kang, Hye Jin Choi, Tae Hyun Hwang. Spatiotemporal evolution of the tumor microenvironment predicts treatment response in phase II trial of neoadjuvant chemotherapy for resectable pancreatic cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 771.

Abstract Purpose: Black women are disproportionately affected by aggressive breast cancers compared to white women, suggesting underlying biological differences in normal breast tissue. This study aims to identify molecular disparities in normal breast tissue between premenopausal Black and white women that may contribute to increased breast cancer risk. Experimental Procedures: Visium spatial transcriptomic analysis was performed on a pilot cohort of normal breast tissue samples from premenopausal Black and white women. Tissue sections were spatially profiled, and capture spots were classified based on predominant cell types: epithelial, stromal, or endothelial. Differential gene expression analysis between racial groups was conducted for each cell type classification. Multiplex immunofluorescence (mIF) was used to validate selected markers in the pilot cohort. Meta-analysis compared differentially expressed genes with published gene sets associated with breast cancer subtypes. Results: Significant racial differences in gene expression profiles were identified in both epithelial and stromal spots. In epithelial spots, genes upregulated in Black women significantly overlapped with over 20 published gene sets associated with estrogen receptor-negative (ER−) breast cancer, including key genes involved in Wnt signaling inhibition (e.g., WIF1) and basal epithelial markers (e.g., KRT14, KRT15). This suggests alterations in pathways linked to more aggressive breast cancer phenotypes. In stromal spots, differentially expressed genes related to extracellular matrix remodeling and fibroblast activation (e.g., PI16, DPT, EFEMP1) were observed, indicating potential differences in stromal-epithelial interactions. Ongoing deconvolution and spatial analyses are being performed to further elucidate these findings. Preliminary mIF validation confirmed increased expression of PI16 in samples from Black women. Validation of additional markers is underway in a larger cohort of premenopausal Black and white women. Conclusions: Spatial transcriptomic profiling of normal breast tissue reveals distinct molecular differences between Black and white women, particularly in pathways associated with aggressive breast cancer subtypes and stromal interactions. These findings suggest that inherent variations in the normal breast microenvironment may contribute to the increased risk and aggressiveness of breast cancer in Black women. Further validation and analysis may identify potential biomarkers for risk assessment and inform targeted prevention strategies. Citation Format: Camila Pacheco Erak, Nicole Cruz-Reyes, Pria Westerman, Joshua Ogony, Jean R. Clemenceau, Tae Hyun Hwang, Sarah McLaughlin, Derek Radisky. Spatial transcriptomic profiling of normal breast tissue reveals potential mechanisms underlying increased breast cancer risk in Black women [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 765.

Abstract The extracellular matrix (ECM) plays a critical role in cancer progression, with its biophysical properties influencing cancer behavior. ECM stiffness can induce long-lasting adaptations in cancer cells, promoting sustained invasion through a process known as “mechanical memory” (MM). Glioblastoma (GBM) exhibits spatial heterogeneity in stiffness, with stiffer ECM found at the tumor periphery where invasive cells reside and promote recurrence. We hypothesize that increased stiffness at the tumor periphery primes GBM cells for sustained invasion through MM. METHODS Cell lines were established from image-guided human GBM samples from tumor margin and core (n=10 patients, 20 cell lines). Cells from random locations were conditioned in physiologically relevant extracellular matrices for two weeks. Human organotypic assay (n=3), Migration Assays, Atomic Force Microscopy (AFM), Immunocytochemistry, Western Blot, and RNA-sequencing (n=6) were performed. RESULTS GBM cells at higher stiff surfaces displayed increased migration (p<0.01). Magnetic Resonance Elastography (n=9) confirmed GBM heterogeneity, with softer cores (0.2-0.5kPa) transitioning to stiffer margins (>2kPa). Organotypic scaffolds mimicking the tumor margin promoted enhanced migration of GFP-labeled GBM cells. Margin cells and cells conditioned on stiffer matrices demonstrated higher migratory potential compared to their core and soft-conditioned counterparts (p<0.05). These trends persist over time and across multiple passages. ECM stiffness exposure causes nanomechanical, cytoskeletal, and transcriptional adaptations. AFM shows that cells from the margin (n=6) are softer (p<0.01) and more deformable (p<0.01). RNAseq identified upregulation of MM-specific genes in GBM margin cells. The mechanosensitive transcription factor YAP1 emerged as a key driver of MM. Notably, YAP1 is targetable by the FDA-approved drug Verteporfin. YAP1 expression was elevated in margin and stiff-conditioned cells (p<0.05). Stiff-conditioned cells and margin cells displayed increased sensitivity to Verteporfin treatment (p<0.05), with treatment significantly reducing margin migration compared to the core (p<0.001). CONCLUSION Stiffer GBM margins promote the formation of aggressive phenotypes through MM. Targeting YAP1 with Verteporfin, a readily available drug, presents a promising strategy to halt GBM invasion and improve patient outcomes.

Abstract Glioblastoma remains as one of the deadliest malignancies with most cases likely to recur. Recent work has focused on characterizing not only glioma cells, but the tumor microenvironment via single-cell and spatial transcriptomic technologies. This has improved our understanding of the high heterogeneity of this disease and described potential tumor domains. However, there is still a need to further understand the changes that occur between the primary and the recurrent lesion. We propose that a spatial, multi-omic characterization of matched primary and recurrent glioblastoma can yield insights into recurrence mechanisms occurring throughout the tumor microenvironment. We collected 22 IDH-WT glioblastoma samples from 12 patients (11 primary, 11 recurrent, 10 matched pairs) from the Mayo Clinic Neuroscience Biobank. We performed single-nucleus RNA sequencing (15 samples, 10 patients, 5 matched pairs) and proteomics (16 samples, 6 matched pairs) from fresh-frozen tissue, as well as spatial transcriptomics with 10x Visium (14 samples, 8 patients, 6 matched pairs) and 10x Xenium in-situ molecular imaging (6 samples, 2 matched pairs). Using spatial transcriptomics data, we identified regional subsets present in different proportions and distributions between primary and recurrent glioblastoma. These clusters were characterized with gene set and gene ontology analysis, identifying regions enriched with known glioma subtypes and cellular activity. Spatial-based cluster genes were consistent with significantly differentially expressed proteins. We used single-nucleus RNAseq and in-situ molecular imaging data from matched tissue samples to further interrogate relevant cell populations and location in the tumor micro-environment. We identified regions with enriched extra cellular matrix interaction and remodeling signatures to be highly expressed in recurrent glioblastoma. Primary glioblastoma was enriched with oligodendrocyte-like regions with high cell communication and extracellular vesicle signatures. This study will further our understanding of the changes in the mechanisms at play in the complex ecosystem of glioblastoma as it recurs. Citation Format: Jean R. Clemenceau, Paola Suarez-Meade, Alfredo Quiñones-Hinojosa, Tae Hyun Hwang. Spatial and multi-omic interrogation of the primary and recurrent glioblastoma microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5482.

Abstract Lung cancer is the leading cause of cancer-related deaths worldwide. Existing therapeutic options have limited efficacy, underscoring the critical need for the identification of new actionable therapeutic targets. We previously identified the Transmembrane Serine Protease TMPRSS11B as a novel gene that promotes the transformation of human bronchial epithelial cells in vitro and induces tumorigenesis in vivo. TMPRSS11B promotes extracellular release of Basigin, an obligate chaperone of the lactate monocarboxylate transporters MCT1/4, enhancing lactate export and glycolytic metabolism, and thereby promoting tumorigenesis. Importantly, TMPRSS11B is frequently overexpressed in human lung squamous cell cancers (LSCC) and high expression is associated with poor patient survival. Moreover, as a cell surface protein and enzyme, TMPRSS11B represents a tractable target for therapeutic intervention. To investigate whether TMPRSS11B activity impacts the host immune system and the tumor microenvironment (TME), we evaluated the effect of Tmprss11b depletion in a syngeneic LSCC mouse model, KLN205. Tmprss11b loss of function in KLN205 LSCC cells significantly reduced tumor burden in immunocompetent mice and triggered an accumulation of CD4+ T cells. We are currently extending these studies by investigating the effect of Tmprss11b loss of function using CRISPR/Cas9 editing in the Rosa26LSL-Sox2-IRES-GFP; Nkx2-1 fl/fl; Lkb1fl/fl (SNL) mouse model of LSCC. Tumor burden analysis and pH-sensitive fluorescent imaging are ongoing to assess changes in lactate accumulation. Moreover, RNA FISH analysis and spatial transcriptomics revealed that Tmprss11b expression is highly enriched in lung squamous tumors compared to lung adenocarcinomas and normal lung in SNL mice. Spatial analysis of the transcriptome is currently ongoing to assess the effects of Tmprss11b expression on the TME. Collectively, these studies will elucidate the molecular mechanisms through which TMPRSS11B promotes tumor growth and alters the tumor microenvironment in LSCC. Citation Format: Hari Shankar Sunil, Barrett L. Updegraff, Jingfei Zhu, Lisa Thomas, Bret M. Evers, Jean R. Clemenceau, Isabel Barnfather, John D. Minna, Ralph J. Deberardinis, Trudy G. Oliver, Tae Hyun Hwang, Kathryn A. O'Donnell. Transmembrane serine protease TMPRSS11B promotes tumorigenesis in lung squamous cell carcinoma through enhanced lactate export and modulation of the tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1793.

Abstract Traditional cancer research methodologies predominantly focus on 2D tumor modeling, including spatial profiling and AI-driven analysis at the 2D level. To advance beyond these limitations, we implemented Holotomography (HT), a 3D, label-free optical imaging method. This technique, leveraging refractive index contrasts, allows for enhanced visualization of tumor tissues, organoids, and cells in three dimensions. Using serial H&E sections and unstained slides, we created detailed 3D tumor models in gastric cancer, providing insights into tumor, immune, and stromal composition, as well as cell-cell interactions and extracellular matrix characteristics. AI algorithms further enhanced our 3D tumor modeling, offering novel perspectives on the initiation and progression of tumor, immune, and stromal components at the subcellular level. Analyzing gastric tumors treated with immune checkpoint inhibitors before, during, and after treatment revealed dynamic changes in spatial composition over time, offering unique insights into the evolution of the 3D tumor ecosystem. Additionally, by integrating spatial transcriptomic and in situ molecular imaging data, we developed a novel Multimodal 3D Tumor Model. This model not only uncovers subcellular-level molecular characteristics but also elucidates cellular interactions during treatment, suggesting potential biomarkers and therapeutic approaches. Lastly, by constructing 3D cellular interaction networks incorporating true 3D interactions, we provided an in-depth view of cellular communication within the tumor microenvironment. This pioneering approach, representing the first Multimodal 3D Tumor Model at the subcellular level, offers novel insights into gastric cancer's spatial evolution, potentially revolutionizing our understanding and treatment of gastric cancer. Citation Format: Yoon Ho Choi, Minji Kim, Katya Redekop, Inyeop Jang, Jean R. Clemenceau, Isabel Barnfather, Sumanth Reddy Nakkireddy, Changjin Hong, Sunho Park, Ji-Youn Sung, Jeong Hwan Park, Shitara Kohei, Aoki Yu, Sung Hak Lee, Sam Wang, Jaeho Cheong, Tae Hyun Hwang. AI-driven multimodal 3D tumor modeling with spatial molecular insights for predicting ICI response in gastric cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7400.

Abstract Differences in clonality, genomic and epigenetic alterations are known to occur between primary Glioblastoma (pGBM) and recurrent (rGBM), but this has been insufficient to yield a promising therapeutic approach. Recent studies show that the GBM tumor microenvironment (TME), composed of reactive glial cells, microglial subtypes, neuronal activity, myeloid cells, lymphocytes, etc., plays a crucial role in the invasiveness and evolution of the glioma cells. Nevertheless, how the spatial architecture and interplay among these cells facilitates GBM resurgence is largely unknown. To this point, we developed a pipeline for characterizing the TME changes using single nucleus and spatial transcriptomics (ST). We retrospectively collected matched, fresh-frozen, IDH-wildtype pGBM and rGBM samples (n=14, 7 patients) from the Mayo Clinic Neuroscience Biobank. Single nucleus RNA sequencing was performed using the 10x Chromium platform. Matched FFPE blocks from 8 samples were collected from the Mayo Clinic pathology department for 4 of the 7 patients. A region of interest was identified from the corresponding histopathology slide by a pathologist, avoiding areas of necrosis and hemorrhage. The region was collected from the block using a 5mm biopsy punch. The tissue was re-paraffinized, and a 5µm thick section was used to perform ST with the 10x Visium for FFPE platform. We performed transcriptomic and image texture-based clustering of capture dots along with differential gene expression analysis. Based on gene expression, we identified and characterized conserved regions across samples. We used single nucleus data to infer cell composition. Spatial correlation and autocorrelation statistics were calculated on features, such as gene expression, gene set scores, cell type prediction, and antibody capture. The features’ relative spatial changes were compared between pGBM and rGBM. Our analyses showed high heterogeneity within and across samples, but we found higher molecular and textural heterogeneity in rGBM samples compared to pGBM. We found higher androgen response activity (p < 0.001) presents diffusely (I: 0.09) throughout rGBM tissue but localized to mesenchymal-like regions in pGBM (I: 0.15). KRAS signaling signature was decreased in pGBM (p < 0.01) and increased in rGBM (p < 0.01). This increase was significantly overexpressed in cluster 0 for rGBM (p=0.02), which is an area characterized by neural progenitor signature. No significant difference in KRAS or EGFR magnitude or distribution of expression was present for either GBM type, indicating an independent mechanism for pGBM. We established a pipeline for assessing the spatio-temporal changes of the GBM TME. We found evidence indicating differences in the type of growth signaling, the cells bing targeted, and their distribution across the TME between pGBM and rGBM. Our work suggests how ST can help us better understand the intricate dynamics of GBM. Citation Format: Jean R. Clemenceau, Paola Suarez Meade, Mark E. Jentoft, Alfredo Quiñones-Hinojosa, Tae Hyun Hwang. Spatial transcriptomic landscape of the primary and recurrent glioblastoma tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 144.

Abstract Background: Spatial molecular analysis can provide functional characteristics of cellular components and their interactions present in the tumor immune microenvironment (TME), thus improving our understanding of the cancer ecosystem at a systemic level. We investigated the spatial and functional characteristics of the tumor, stroma, and immune cells, and their roles in the TME in facilitating cancer development, progression, and metastasis in pancreatic cancer. Methods: We performed spatial profiles of multiple regional TMEs of 13 pancreatic cancers (7 pancreatic adenocarcinoma, 3 pancreatic neuroendocrine, and 3 acinar cell carcinomas) using NanoString GeoMX at the whole transcriptome level (18,676 genes). We collected a total of 23 tissue samples containing matched primary tumors, adjacent normal pancreatic tissue, or normal/metastatic lymph node. Tumor, stroma, and immune cells are stained by PanCK, αSMA, and CD45 markers, respectively. We selected 234 regions of interest (ROIs) in the tumor core, border, adjacent normal pancreas, and lymph node to investigate spatial patterns of different cells and their cellular interactions in neighborhoods from paired primary and metastatic tumors. Results: TMEs in different ROIs are spatially correlated within a tumor but showed distinct phenotypes across tumors and tumor types. Each cell type in the ROIs has a unique role to drive cancer evolution in TMEs. Diverse types of cancer-associated fibroblasts (CAFs) are present in primary and metastatic TME, while CAFs in tumor border and metastatic lymph nodes showed mainly enriched with antigen-presenting CAF (apCAF) subtypes with higher extracellular matrix (ECM) and collagen fibrils components. Similarly, we found diverse functional phenotypes of the immune cell in TMEs. The immune response signaling pathway is activated in the stroma-infiltrated immune cells, but, in fact, not in the tumor-infiltrated immune cells. Instead, higher dysfunctional T cell and growth factors are present in the stroma-infiltrated immune cells from primary and metastatic tumors, but not in stromal immune normal pancreatic and lymph node tissues. The tumor-infiltrated immune cells showed suppressive immune activities by increasing TGFβ receptor signaling. We also discovered CCL5-driven metastatic patterns in TMEs from primary tumors and metastatic lymph nodes. Lastly, we concluded that these functional phenotypes of cells in TMEs are preserved in a certain pancreatic cancer type, suggesting the origin and evolution of different pancreatic tumors could be driven by diverse cellular and functional phenotypes of cells in TMEs. Conclusion: Our comprehensive analysis results demonstrate that the spatial analysis could provide insights into the spatial patterns that drive cancer evolution in pancreatic TMEs, thus helping understand the cellular microenvironment governing pancreatic cancer metastasis. Citation Format: Changjin Hong, Jean R. Clemenceau, Rondell P. Graham, Tae Hyun Hwang. Spatial transcriptomic landscape of primary and lymph node metastatic pancreatic cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2362.

Abstract Purpose: We sought to identify biomarkers that predict overall survival (OS) and response to immune checkpoint inhibitors (ICI) for patients with gastric cancer. Experimental Design: This was a retrospective study of multiple independent cohorts of patients with gastric cancer. The association between tumor ACTA2 expression and OS and ICI response were determined in patients whose tumors were analyzed with bulk mRNA sequencing. Single-cell RNA sequencing (scRNA-seq) and digital spatial profiling data were used to compare tumors from patients with gastric cancer who did and did not respond to ICI. Results: Increasing tumor ACTA2 expression was independently associated with worse OS in a 567-patient discovery cohort [HR, 1.28 per unit increase; 95% confidence interval (CI), 1.02–1.62]. This finding was validated in three independent cohorts (n = 974; HR, 1.52 per unit increase; 95% CI, 1.34–1.73). Of the 108 patients treated with ICI, 56% of patients with low ACTA2 expression responded to ICI versus 25% of patients with high ACTA2 expression (P = 0.004). In an analysis of a publicly available scRNA-seq dataset of 5 microsatellite instability-high patients treated with ICI, the patient who responded to ICI had lower tumor stromal ACTA2 expression than the 4 nonresponders. Digital spatial profiling of tumor samples from 4 ICI responders and 5 ICI nonresponders revealed that responders may have lower ACTA2 expression in α-SMA–positive cancer-associated fibroblasts (CAF) than nonresponders (median: 5.00 vs. 5.50). Conclusions: ACTA2 expression is associated with survival and ICI response in patients with gastric cancer. ACTA2 expression in CAFs, but not in other cellular compartments, appears to be associated with ICI response.

Abstract Histological distribution and density of tumor infiltrating lymphocytes (TILs) are known to be informative to cancer patients’ outcome. However, due to the heterogeneity and huge size of digitized pathology slides, it has been a challenging problem to quantify TILs-related features in whole slide images (WSI). This study aimed to explore the impact of spatial distribution of TILs quantified by deep learning (DL) approaches based on digitalized WSI stained with hematoxylin and eosin in patients with colorectal cancer (CRC). The cancer and TILs regions are first separately identified by deep learning models in the WSI. The TILs densities at tumor invasive margins are then quantified by image analysis algorithms and used for prognosis of progression-free survival (PFS) in CRC patients. The prognostic impact of spatial distributions of TILs in CRC patients was explored in the Yonsei cohort (n=180) and validated in the TCGA cohort (n=268). Two experienced pathologists manually measured TILs at the most invasive margin as 0-3 by the Klintrup-Mäkinen (KM) grading method and compared to DL approaches. On multivariate analysis, TILs densities within 200µm of the invasive margin (f_im200) was remained as the most significant prognostic factor for PFS (HR 0.004 [95% CI, 0.0001-0.1502], p=.002) in the Yonsei cohort. On multivariate analysis using the TCGA dataset, f_im200 retained prognostic significance for PFS (HR 0.031, [95% CI 0.001-0.645], p=.024). Inter-observer agreement of manual KM grading was insignificant in both cohorts. The survival analysis by pathologists’ manual KM grading showed statistically significant different PFS from the TCGA cohort, but not the Yonsei cohort. In summary, automated quantification of TILs at the invasive margin showed a prognostic utility to predict PFS, and could provide reproducible TILs density measurement in CRC patients. Citation Format: Hongming Xu, Yoon Jin Cha, Jean Clemenceau, Jinhwan Choi, Sung Hak Lee, Jeonghyun Kang, Tae Hyun Hwang. Spatial analysis of tumor infiltrating lymphocytes in histological images using deep learning predicts progression-free survival in colorectal cancer [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 642.