Search

Background: The progress in Colorectal cancer (CRC) screening and management has resulted in an unprecedented caseload for histopathological diagnosis. While artificial intelligence (AI) presents a potential solution, the predominant emphasis on slide-level aggregation performance without thorough verification of cancer in each location, impedes both explainability and transparency. Effectively addressing these challenges is crucial to ensuring the reliability and efficacy of AI in histology applications. Method: In this study, we created an innovative AI algorithm using transfer learning from a polyp segmentation model in endoscopy. The algorithm precisely localized CRC targets within 0.25 mm2 grids from whole slide imaging (WSI). We assessed the CRC detection capabilities at this fine granularity and examined the influence of AI on the diagnostic behavior of pathologists. The evaluation utilized an extensive dataset comprising 858 consecutive patient cases with 1418 WSIs obtained from an external center. Results: Our results underscore a notable sensitivity of 90.25% and specificity of 96.60% at the grid level, accompanied by a commendable area under the curve (AUC) of 0.962. This translates to an impressive 99.39% sensitivity at the slide level, coupled with a negative likelihood ratio of <0.01, signifying the dependability of the AI system to preclude diagnostic considerations. The positive likelihood ratio of 26.54, surpassing 10 at the grid level, underscores the imperative for meticulous scrutiny of any AI-generated highlights. Consequently, all four participating pathologists demonstrated statistically significant diagnostic improvements with AI assistance. Conclusion: Our transfer learning approach has successfully yielded an algorithm that can be validated for CRC histological localizations in whole slide imaging. The outcome advocates for the integration of the AI system into histopathological diagnosis, serving either as a diagnostic exclusion application or a computer-aided detection (CADe) tool. This integration has the potential to alleviate the workload of pathologists and ultimately benefit patients. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) is one of the most common cancers worldwide and demands more effective treatments. We sought to identify tumor selective CRC antigens and their therapeutic potential for cytotoxic T-cell targeting by transcriptomic and immunohistochemical analysis. LY6G6D was identified as a tumor selectively expressed CRC antigen, mainly in the microsatellite stable (MSS) subtype. A specific anti LY6G6D/CD3 T cell engager (TcE) was generated and demonstrated potent tumor cell killing and T cell activation in vitro. Ex vivo treatment of primary patient-derived CRC tumor slice cultures with the LY6G6D/CD3 TcE led to IFNgγ secretion in LY6G6D positive tumor samples. In vivo, LY6G6D/CD3 TcE monotherapy demonstrated tumor regressions in pre-clinical mouse models of engrafted human CRC tumor cells and PBMCs. Lastly, 2D and 3D cocultures of LY6G6D positive and negative cells were used to explore the bystander killing of LY6G6D negative cells after specific activation of T cells by LY6G6D positive cells. LY6G6D/CD3 TcE treatment was shown to lyse target negative cells in the vicinity of target positive cells through a combined effect of IFNgγ, TNFa and Fas/FasL. In summary, LY6G6D was identified as a selectively expressed CRC antigen that can be utilized to potently re-direct and activate cytotoxic T-cells to lyse LY6G6D expressing CRC using a TcE. This effect can be spread to target negative neighboring tumor cells, potentially leading to improved therapeutic efficacy. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) is one of the most common cancers worldwide and demands more effective treatments. We sought to identify tumor selective CRC antigens and their therapeutic potential for cytotoxic T-cell targeting by transcriptomic and immunohistochemical analysis. LY6G6D was identified as a tumor selectively expressed CRC antigen, mainly in the microsatellite stable (MSS) subtype. A specific anti LY6G6D/CD3 T cell engager (TcE) was generated and demonstrated potent tumor cell killing and T cell activation in vitro. Ex vivo treatment of primary patient-derived CRC tumor slice cultures with the LY6G6D/CD3 TcE led to IFNγ secretion in LY6G6D positive tumor samples. In vivo, LY6G6D/CD3 TcE monotherapy demonstrated tumor regressions in pre-clinical mouse models of engrafted human CRC tumor cells and PBMCs. Lastly, 2D and 3D cocultures of LY6G6D positive and negative cells were used to explore the bystander killing of LY6G6D negative cells after specific activation of T cells by LY6G6D positive cells. LY6G6D/CD3 TcE treatment was shown to lyse target negative cells in the vicinity of target positive cells through a combined effect of IFNγ, TNFα and Fas/FasL. In summary, LY6G6D was identified as a selectively expressed CRC antigen that can be utilized to potently re-direct and activate cytotoxic T-cells to lyse LY6G6D expressing CRC using a TcE. This effect can be spread to target negative neighboring tumor cells, potentially leading to improved therapeutic efficacy.

Approximately 25% of colorectal cancer (CRC) patients experience systemic metastases, with the most frequent target organs being the liver and lung. Metabolic reprogramming has been recognized as one of the hallmarks of cancer. Here, metabolic and functional differences between two CRC cells with different metastatic organotropisms (metastatic KM12SM CRC cells to the liver and KM12L4a to the lung when injected in the spleen and in the tail vein of mice) were analysed in comparison to their parental non-metastatic isogenic KM12C cells, for a subsequent investigation of identified metabolic targets in CRC patients. Meta-analysis from proteomic and transcriptomic data deposited in databases, qPCR, WB, in vitro cell-based assays, and in vivo experiments were used to survey for metabolic alterations contributing to their different organotropism and for the subsequent analysis of identified metabolic markers in CRC patients. Although no changes in cell proliferation were observed between metastatic cells, KM12SM cells were highly dependent on oxidative phosphorylation at mitochondria, whereas KM12L4a cells were characterized by being more energetically efficient with lower basal respiration levels and a better redox management. Lipid metabolism-related targets were found altered in both cell lines, including LDLR, CD36, FABP4, SCD, AGPAT1, and FASN, which were also associated with the prognosis of CRC patients. Moreover, CD36 association with lung metastatic tropism of CRC cells was validated in vivo. Altogether, our results suggest that LDLR, CD36, FABP4, SCD, FASN, LPL, and APOA1 metabolic targets are associated with CRC metastatic tropism to the liver or lung. These features exemplify specific metabolic adaptations for invasive cancer cells which stem at the primary tumour.

Background: The progress in Colorectal cancer (CRC) screening and management has resulted in an unprecedented caseload for histopathological diagnosis. While artificial intelligence (AI) presents a potential solution, the predominant emphasis on slide-level aggregation performance without thorough verification of cancer in each location, impedes both explainability and transparency. Effectively addressing these challenges is crucial to ensuring the reliability and efficacy of AI in histology applications., Method: In this study, we created an innovative AI algorithm using transfer learning from a polyp segmentation model in endoscopy. The algorithm precisely localized CRC targets within 0.25 mm² grids from whole slide imaging (WSI). We assessed the CRC detection capabilities at this fine granularity and examined the influence of AI on the diagnostic behavior of pathologists. The evaluation utilized an extensive dataset comprising 858 consecutive patient cases with 1418 WSIs obtained from an external center., Results: Our results underscore a notable sensitivity of 90.25% and specificity of 96.60% at the grid level, accompanied by a commendable area under the curve (AUC) of 0.962. This translates to an impressive 99.39% sensitivity at the slide level, coupled with a negative likelihood ratio of <0.01, signifying the dependability of the AI system to preclude diagnostic considerations. The positive likelihood ratio of 26.54, surpassing 10 at the grid level, underscores the imperative for meticulous scrutiny of any AI-generated highlights. Consequently, all four participating pathologists demonstrated statistically significant diagnostic improvements with AI assistance., Conclusion: Our transfer learning approach has successfully yielded an algorithm that can be validated for CRC histological localizations in whole slide imaging. The outcome advocates for the integration of the AI system into histopathological diagnosis, serving either as a diagnostic exclusion application or a computer-aided detection (CADe) tool. This integration has the potential to alleviate the workload of pathologists and ultimately benefit patients., Competing Interests: Authors XX and JL declare as equity holders of Wision Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Yuan, Zhou, Xiao, Zhang, Chen, Liu, Liu, Bao and Tao.)

Approximately 25% of colorectal cancer (CRC) patients experience systemic metastases, with the most frequent target organs being the liver and lung. Metabolic reprogramming has been recognized as one of the hallmarks of cancer. Here, metabolic and functional differences between two CRC cells with different metastatic organotropisms (metastatic KM12SM CRC cells to the liver and KM12L4a to the lung when injected in the spleen and in the tail vein of mice) were analysed in comparison to their parental non-metastatic isogenic KM12C cells, for a subsequent investigation of identified metabolic targets in CRC patients. Meta-analysis from proteomic and transcriptomic data deposited in databases, qPCR, WB, in vitro cell-based assays, and in vivo experiments were used to survey for metabolic alterations contributing to their different organotropism and for the subsequent analysis of identified metabolic markers in CRC patients. Although no changes in cell proliferation were observed between metastatic cells, KM12SM cells were highly dependent on oxidative phosphorylation at mitochondria, whereas KM12L4a cells were characterized by being more energetically efficient with lower basal respiration levels and a better redox management. Lipid metabolism-related targets were found altered in both cell lines, including LDLR , CD36 , FABP4 , SCD , AGPAT1 , and FASN , which were also associated with the prognosis of CRC patients. Moreover, CD36 association with lung metastatic tropism of CRC cells was validated in vivo. Altogether, our results suggest that LDLR , CD36 , FABP4 , SCD , FASN , LPL , and APOA1 metabolic targets are associated with CRC metastatic tropism to the liver or lung. These features exemplify specific metabolic adaptations for invasive cancer cells which stem at the primary tumour., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling editor JI-G declared a shared parent affiliation with the author MF-A at the time of review., (Copyright © 2022 Montero-Calle, Gómez de Cedrón, Quijada-Freire, Solís-Fernández, López-Alonso, Espinosa-Salinas, Peláez-García, Fernández-Aceñero, Ramírez de Molina and Barderas.)