Metabolic insights into tumor pathogenesis: Unveiling pan‐cancer metabolism and the potential of untargeted metabolomics

Wang, Taorui; Gao, Yuanxu

doi:10.1002/mef2.59

Cited by 4 publications

(1 citation statement)

References 199 publications

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“…Consequently, the need for effective multi-modal fusion methods has become more prominent. The incorporation of deep learning techniques for the fusion of diverse data types opens new avenues for discovering cancer biomarkers and enhancing clinical decision-making, ultimately aiding in patient stratification and advancing personalized healthcare [ 11 , 12 , 13 ]. Notably, few studies have explored the use of AI for a comprehensive evaluation of recurrence and metastasis risk, incorporating a triad of data sources: molecular data, pathological slides, and clinical information.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based multi-modal data integration enhancing breast cancer disease-free survival prediction

Wang,

Lin,

et al. 2024

Precision Clinical Medicine

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Background The prognosis of breast cancer is often unfavorable, emphasizing the need for early metastasis risk detection and accurate treatment predictions. This study aimed to develop a novel multi-modal deep learning model using preoperative data to predict disease-free survival (DFS). Methods We retrospectively collected pathology imaging, molecular and clinical data from The Cancer Genome Atlas and one independent institution in China. We developed a novel Deep Learning Clinical Medicine Based Pathological Gene Multi-modal (DeepClinMed-PGM) model for DFS prediction, integrating clinicopathological data with molecular insights. The patients included the training cohort (n = 741), internal validation cohort (n = 184), and external testing cohort (n = 95). Result Integrating multi-modal data into the DeepClinMed-PGM model significantly improved area under the receiver operating characteristic curve (AUC) values. In the training cohort, AUC values for 1-, 3-, and 5-year DFS predictions increased to 0.979, 0.957, and 0.871, while in the external testing cohort, the values reached 0.851, 0.878, and 0.938 for 1-, 2-, and 3-year DFS predictions, respectively. The DeepClinMed-PGM's robust discriminative capabilities were consistently evident across various cohorts, including the training cohort [hazard ratio (HR) 0.027, 95% confidence interval (CI) 0.0016–0.046, P < 0.0001], the internal validation cohort (HR 0.117, 95% CI 0.041–0.334, P < 0.0001), and the external cohort (HR 0.061, 95% CI 0.017–0.218, P < 0.0001). Additionally, the DeepClinMed-PGM model demonstrated C-index values of 0.925, 0.823, and 0.864 within the three cohorts, respectively. Conclusion This study introduces an approach to breast cancer prognosis, integrating imaging and molecular and clinical data for enhanced predictive accuracy, offering promise for personalized treatment strategies.

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Section: Introductionmentioning

confidence: 99%

Deep learning-based multi-modal data integration enhancing breast cancer disease-free survival prediction

Wang,

Lin,

et al. 2024

Precision Clinical Medicine

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Multimodal data fusion AI model uncovers tumor microenvironment immunotyping heterogeneity and enhanced risk stratification of breast cancer

Yu,

Cai,

Lin

et al. 2024

MedComm

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Breast cancer is the leading cancer among women, with a significant number experiencing recurrence and metastasis, thereby reducing survival rates. This study focuses on the role of long noncoding RNAs (lncRNAs) in breast cancer immunotherapy response. We conducted an analysis involving 1027 patients from Sun Yat‐sen Memorial Hospital, Sun Yat‐sen University, and The Cancer Genome Atlas, utilizing RNA sequencing and pathology whole‐slide images. We employed unsupervised clustering to identify distinct lncRNA expression patterns and developed an AI‐based pathology model using convolutional neural networks to predict immune–metabolic subtypes. Additionally, we created a multimodal model integrating lncRNA data, immune‐cell scores, clinical information, and pathology images for prognostic prediction. Our findings revealed four unique immune–metabolic subtypes, and the AI model demonstrated high predictive accuracy, highlighting the significant impact of lncRNAs on antitumor immunity and metabolic states within the tumor microenvironment. The AI‐based pathology model, DeepClinMed‐IM, exhibited high accuracy in predicting these subtypes. Additionally, the multimodal model, DeepClinMed‐PGM, integrating pathology images, lncRNA data, immune‐cell scores, and clinical information, showed superior prognostic performance. In conclusion, these AI models provide a robust foundation for precise prognostication and the identification of potential candidates for immunotherapy, advancing breast cancer research and treatment strategies.

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Multiomics biomarkers were not superior to clinical variables for pan-cancer screening

Smelik,

Zhao,

Mansour Aly

et al. 2024

Commun Med

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Background Cancer screening tests are considered pivotal for early diagnosis and survival. However, the efficacy of these tests for improving survival has recently been questioned. This study aims to test if cancer screening could be improved by biomarkers in peripheral blood based on multi-omics data. Methods We utilize multi-omics data from 500,000 participants in the UK Biobank. Machine learning is applied to search for proteins, metabolites, genetic variants, or clinical variables to diagnose cancers collectively and individually. Results Here we show that the overall performance of the potential blood biomarkers do not outperform clinical variables for collective diagnosis. However, we observe promising results for individual cancers in close proximity to peripheral blood, with an Area Under the Curve (AUC) greater than 0.8. Conclusions Our findings suggest that the identification of blood biomarkers for cancer might be complicated by variable overlap between molecular changes in tumor tissues and peripheral blood. This explanation is supported by local proteomics analyses of different tumors, which all show high AUCs, greater than 0.9. Thus, multi-omics biomarkers for the diagnosis of individual cancers may potentially be effective, but not for groups of cancers.

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Metabolic insights into tumor pathogenesis: Unveiling pan‐cancer metabolism and the potential of untargeted metabolomics

Cited by 4 publications

References 199 publications

Deep learning-based multi-modal data integration enhancing breast cancer disease-free survival prediction

Deep learning-based multi-modal data integration enhancing breast cancer disease-free survival prediction

Multimodal data fusion AI model uncovers tumor microenvironment immunotyping heterogeneity and enhanced risk stratification of breast cancer

Multiomics biomarkers were not superior to clinical variables for pan-cancer screening

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