Jie-Yi Shi, Xiaodong Wang, Guang-Yu Ding, Zhou Dong, Jing Han, Zehui Guan, Li-Jie Ma, Yuxuan Zheng, Lei Zhang, Guan-Zhen Yu, Xiao-Ying Wang, Zhen-Bin Ding, Ai-Wu Ke, Haoqing Yang, Liming Wang, Lirong Ai, Ya Cao, Jian Zhou, Jia Fan, Xiyang Liu, Qiang Gao <<<

OBJECTIVE: Tumour pathology contains rich information, including tissue structure and cell morphology, that reflects disease progression and patient survival. However, phenotypic information is subtle and complex, making the discovery of prognostic indicators from pathological images challenging.DESIGN: An interpretable, weakly supervised deep learning framework incorporating prior knowledge was proposed to analyse hepatocellular carcinoma (HCC) and explore new prognostic phenotypes on pathological whole-slide images (WSIs) from the Zhongshan cohort of 1125 HCC patients (2451 WSIs) and TCGA cohort of 320 HCC patients (320 WSIs). A 'tumour risk score (TRS)' was established to evaluate patient outcomes, and then risk activation mapping (RAM) was applied to visualise the pathological phenotypes of TRS. The multi-omics data of The Cancer Genome Atlas(TCGA) HCC were used to assess the potential pathogenesis underlying TRS.RESULTS: Survival analysis revealed that TRS was an independent prognosticator in both the Zhongshan cohort (p<0.0001) and TCGA cohort (p=0.0003). The predictive ability of TRS was superior to and independent of clinical staging systems, and TRS could evenly stratify patients into up to five groups with significantly different prognoses. Notably, sinusoidal capillarisation, prominent nucleoli and karyotheca, the nucleus/cytoplasm ratio and infiltrating inflammatory cells were identified as the main underlying features of TRS. The multi-omics data of TCGA HCC hint at the relevance of TRS to tumour immune infiltration and genetic alterations such as the FAT3 and RYR2 mutations.CONCLUSION: Our deep learning framework is an effective and labour-saving method for decoding pathological images, providing a valuable means for HCC risk stratification and precise patient treatment. <<<

OBJECTIVES: Bacteriome and virome alterations are associated with colorectal cancer (CRC). Nevertheless, the gut fungal microbiota in CRC remains largely unexplored. We aimed to characterise enteric mycobiome in CRC.DESIGN: Faecal shotgun metagenomic sequences of 184 patients with CRC, 197 patients with adenoma and 204 control subjects from Hong Kong were analysed (discovery cohort: 73 patients with CRC and 92 control subjects; validation cohort: 111 patients with CRC, 197 patients with adenoma and 112 controls from Hong Kong). CRC-associated fungal markers and ecological changes were also validated in additional independent cohorts of 90 patients with CRC, 42 patients with adenoma and 66 control subjects of published repository sequences from Germany and France. Assignment of taxonomies was performed by exact k-mer alignment against an integrated microbial reference genome database.RESULTS: Principal component analysis revealed separate clusters for CRC and control (p<0.0001), with distinct mycobiomes in early-stage and late-stage CRC (p=0.0048). Basidiomycota:Ascomycota ratio was higher in CRC (p=0.0042), with increase in Malasseziomycetes (p<0.0001) and decrease in Saccharomycetes (p<0.0001) and Pneumocystidomycetes (p=0.0017). Abundances of 14 fungal biomarkers distinguished CRC from controls with an area under the receiver-operating characteristic curve (AUC) of 0.93 and validated AUCs of 0.82 and 0.74 in independent Chinese cohort V1 and European cohort V2, respectively. Further ecological analysis revealed higher numbers of co-occurring fungal intrakingdom and co-exclusive bacterial-fungal correlations in CRC (p<0.0001). Moreover, co-occurrence interactions between fungi and bacteria, mostly contributed by fungal Ascomycota and bacterial Proteobacteria in control, were reverted to co-exclusive interplay in CRC (p=0.00045).CONCLUSIONS: This study revealed CRC-associated mycobiome dysbiosis characterised by altered fungal composition and ecology, signifying that the gut mycobiome might play a role in CRC. <<<

Ting Li, Yue-Tao Tan, Yan-Xing Chen, Xiao-Jun Zheng, Wen Wang, Kun Liao, Hai-Yu Mo, Junzhong Lin, Wei Yang, Hai-Long Piao, Rui-Hua Xu, Huai-Qiang Ju <<<

OBJECTIVE: Methionine metabolism is involved in a myriad of cellular functions, including methylation reactions and redox maintenance. Nevertheless, it remains unclear whether methionine metabolism, RNA methylation and antitumour immunity are molecularly intertwined.DESIGN: The antitumour immunity effect of methionine-restricted diet (MRD) feeding was assessed in murine models. The mechanisms of methionine and YTH domain-containing family protein 1 (YTHDF1) in tumour immune escape were determined in vitro and in vivo. The synergistic effects of MRD or YTHDF1 depletion with PD-1 blockade were also investigated.RESULTS: We found that dietary methionine restriction reduced tumour growth and enhanced antitumour immunity by increasing the number and cytotoxicity of tumour-infiltrating CD8+ T cells in different mouse models. Mechanistically, the S-adenosylmethionine derived from methionine metabolism promoted the N6-methyladenosine (m6A) methylation and translation of immune checkpoints, including PD-L1 and V-domain Ig suppressor of T cell activation (VISTA), in tumour cells. Furthermore, MRD or m6A-specific binding protein YTHDF1 depletion inhibited tumour growth by restoring the infiltration of CD8+ T cells, and synergised with PD-1 blockade for better tumour control. Clinically, YTHDF1 expression correlated with poor prognosis and immunotherapy outcomes for cancer patients.CONCLUSIONS: Methionine and YTHDF1 play a critical role in anticancer immunity through regulating the functions of T cells. Targeting methionine metabolism or YTHDF1 could be a potential new strategy for cancer immunotherapy. <<<