Analysis of 3D pathology samples using weakly supervised AI.

Human tissue, which is inherently three-dimensional (3D), is traditionally examined through standard-of-care histopathology as limited two-dimensional (2D) cross-sections that can insufficiently represent the tissue due to sampling bias. To holistically characterize histomorphology, 3D imaging modalities have been developed, but clinical translation is hampered by complex manual evaluation and lack of computational platforms to distill clinical insights from large, high-resolution datasets. We present TriPath, a deep-learning platform for processing tissue volumes and efficiently predicting clinical outcomes based on 3D morphological features. Recurrence risk-stratification models were trained on prostate cancer specimens imaged with open-top light-sheet microscopy or microcomputed tomography. By comprehensively capturing 3D morphologies, 3D volume-based prognostication achieves superior performance to traditional 2D slice-based approaches, including clinical/histopathological baselines from six certified genitourinary pathologists. Incorporating greater tissue volume improves prognostic performance and mitigates risk prediction variability from sampling bias, further emphasizing the value of capturing larger extents of heterogeneous morphology.

Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography.

Many COVID-19 patients infected by SARS-CoV-2 virus develop pneumonia (called novel coronavirus pneumonia, NCP) and rapidly progress to respiratory failure. However, rapid diagnosis and identification of high-risk patients for early intervention are challenging. Using a large computed tomography (CT) database from 3,777 patients, we developed an AI system that can diagnose NCP and differentiate it from other common pneumonia and normal controls. The AI system can assist radiologists and physicians in performing a quick diagnosis especially when the health system is overloaded. Significantly, our AI system identified important clinical markers that correlated with the NCP lesion properties. Together with the clinical data, our AI system was able to provide accurate clinical prognosis that can aid clinicians to consider appropriate early clinical management and allocate resources appropriately. We have made this AI system available globally to assist the clinicians to combat COVID-19.

Integrated Proteogenomic Characterization of HBV-Related Hepatocellular Carcinoma.

We performed the first proteogenomic characterization of hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) using paired tumor and adjacent liver tissues from 159 patients. Integrated proteogenomic analyses revealed consistency and discordance among multi-omics, activation status of key signaling pathways, and liver-specific metabolic reprogramming in HBV-related HCC. Proteomic profiling identified three subgroups associated with clinical and molecular attributes including patient survival, tumor thrombus, genetic profile, and the liver-specific proteome. These proteomic subgroups have distinct features in metabolic reprogramming, microenvironment dysregulation, cell proliferation, and potential therapeutics. Two prognostic biomarkers, PYCR2 and ADH1A, related to proteomic subgrouping and involved in HCC metabolic reprogramming, were identified. CTNNB1 and TP53 mutation-associated signaling and metabolic profiles were revealed, among which mutated CTNNB1-associated ALDOA phosphorylation was validated to promote glycolysis and cell proliferation. Our study provides a valuable resource that significantly expands the knowledge of HBV-related HCC and may eventually benefit clinical practice.

The Evolutionary Landscape of Localized Prostate Cancers Drives Clinical Aggression.

The majority of newly diagnosed prostate cancers are slow growing, with a long natural life history. Yet a subset can metastasize with lethal consequences. We reconstructed the phylogenies of 293 localized prostate tumors linked to clinical outcome data. Multiple subclones were detected in 59% of patients, and specific subclonal architectures associate with adverse clinicopathological features. Early tumor development is characterized by point mutations and deletions followed by later subclonal amplifications and changes in trinucleotide mutational signatures. Specific genes are selectively mutated prior to or following subclonal diversification, including MTOR, NKX3-1, and RB1. Patients with low-risk monoclonal tumors rarely relapse after primary therapy (7%), while those with high-risk polyclonal tumors frequently do (61%). The presence of multiple subclones in an index biopsy may be necessary, but not sufficient, for relapse of localized prostate cancer, suggesting that evolution-aware biomarkers should be studied in prospective studies of low-risk tumors suitable for active surveillance.

Tumor-Induced Generation of Splenic Erythroblast-like Ter-Cells Promotes Tumor Progression.

Identifying tumor-induced leukocyte subsets and their derived circulating factors has been instrumental in understanding cancer as a systemic disease. Nevertheless, how primary tumor-induced non-leukocyte populations in distal organs contribute to systemic spread remains poorly defined. Here, we report one population of tumor-inducible, erythroblast-like cells (Ter-cells) deriving from megakaryocyte-erythroid progenitor cells with a unique Ter-119+CD45-CD71+ phenotype. Ter-cells are enriched in the enlarged spleen of hosts bearing advanced tumors and facilitate tumor progression by secreting neurotrophic factor artemin into the blood. Transforming growth factor ß (TGF-ß) and Smad3 activation are important in Ter-cell generation. In vivo blockade of Ter-cell-derived artemin inhibits hepatocellular carcinoma (HCC) growth, and artemin deficiency abolishes Ter-cells' tumor-promoting ability. We confirm the presence of splenic artemin-positive Ter-cells in human HCC patients and show that significantly elevated serum artemin correlates with poor prognosis. We propose that Ter-cells and the secreted artemin play important roles in cancer progression with prognostic and therapeutic implications.

Tumor circadian clock strength influences metastatic potential and predicts patient prognosis in luminal A breast cancer.

Studies in shift workers and model organisms link circadian disruption to breast cancer. However, molecular circadian rhythms in noncancerous and cancerous human breast tissues and their clinical relevance are largely unknown. We reconstructed rhythms informatically, integrating locally collected, time-stamped biopsies with public datasets. For noncancerous breast tissue, inflammatory, epithelial-mesenchymal transition (EMT), and estrogen responsiveness pathways show circadian modulation. Among tumors, clock correlation analysis demonstrates subtype-specific changes in circadian organization. Luminal A organoids and informatic ordering of luminal A samples exhibit continued, albeit dampened and reprogrammed rhythms. However, CYCLOPS magnitude, a measure of global rhythm strength, varied widely among luminal A samples. Cycling of EMT pathway genes was markedly increased in high-magnitude luminal A tumors. Surprisingly, patients with high-magnitude tumors had reduced 5-y survival. Correspondingly, 3D luminal A cultures show reduced invasion following molecular clock disruption. This study links subtype-specific circadian disruption in breast cancer to EMT, metastatic potential, and prognosis.

A comprehensive patient-specific prediction model for temporomandibular joint osteoarthritis progression.

Temporomandibular joint osteoarthritis (TMJ OA) is a prevalent degenerative disease characterized by chronic pain and impaired jaw function. The complexity of TMJ OA has hindered the development of prognostic tools, posing a significant challenge in timely, patient-specific management. Addressing this gap, our research employs a comprehensive, multidimensional approach to advance TMJ OA prognostication. We conducted a prospective study with 106 subjects, 74 of whom were followed up after 2 to 3 y of conservative treatment. Central to our methodology is the development of an innovative, open-source predictive modeling framework, the Ensemble via Hierarchical Predictions through Nested cross-validation tool (EHPN). This framework synergistically integrates 18 feature selection, statistical, and machine learning methods to yield an accuracy of 0.87, with an area under the ROC curve of 0.72 and an F1 score of 0.82. Our study, beyond technical advancements, emphasizes the global impact of TMJ OA, recognizing its unique demographic occurrence. We highlight key factors influencing TMJ OA progression. Using SHAP analysis, we identified personalized prognostic predictors: lower values of headache, lower back pain, restless sleep, condyle high gray level-GL-run emphasis, articular fossa GL nonuniformity, and long-run low GL emphasis; and higher values of superior joint space, mouth opening, saliva Vascular-endothelium-growth-factor, Matrix-metalloproteinase-7, serum Epithelial-neutrophil-activating-peptide, and age indicate recovery likelihood. Our multidimensional and multimodal EHPN tool enhances clinicians' decision-making, offering a transformative translational infrastructure. The EHPN model stands as a significant contribution to precision medicine, offering a paradigm shift in the management of temporomandibular disorders and potentially influencing broader applications in personalized healthcare.

Accurate estimation of biological age and its application in disease prediction using a multimodal image Transformer system.

Aging in an individual refers to the temporal change, mostly decline, in the body's ability to meet physiological demands. Biological age (BA) is a biomarker of chronological aging and can be used to stratify populations to predict certain age-related chronic diseases. BA can be predicted from biomedical features such as brain MRI, retinal, or facial images, but the inherent heterogeneity in the aging process limits the usefulness of BA predicted from individual body systems. In this paper, we developed a multimodal Transformer-based architecture with cross-attention which was able to combine facial, tongue, and retinal images to estimate BA. We trained our model using facial, tongue, and retinal images from 11,223 healthy subjects and demonstrated that using a fusion of the three image modalities achieved the most accurate BA predictions. We validated our approach on a test population of 2,840 individuals with six chronic diseases and obtained significant difference between chronological age and BA (AgeDiff) than that of healthy subjects. We showed that AgeDiff has the potential to be utilized as a standalone biomarker or conjunctively alongside other known factors for risk stratification and progression prediction of chronic diseases. Our results therefore highlight the feasibility of using multimodal images to estimate and interrogate the aging process.

Construction of a prognostic model for lung adenocarcinoma based on m6A/m5C/m1A genes.

BACKGROUND: Developing a prognostic model for lung adenocarcinoma (LUAD) that utilizes m6A/m5C/m1A genes holds immense importance in providing precise prognosis predictions for individuals. METHODS: This study mined m6A/m5C/m1A-related differential genes in LUAD based on public databases, identified LUAD tumor subtypes based on these genes, and further built a risk prognostic model grounded in differential genes between subtypes. The immune status between high- and low-risk groups was investigated, and the distribution of feature genes in tumor immune cells was analyzed using single-cell analysis. Based on the expression levels of feature genes, a projection of chemotherapeutic and targeted drugs was made for individuals identified as high-risk. Ultimately, cell experiments were further verified. RESULTS: The 6-gene risk prognosis model based on differential genes between tumor subtypes had good predictive performance. Individuals classified as low-risk exhibited a higher (P < 0.05) abundance of infiltrating immune cells. Feature genes were mainly distributed in tumor immune cells like CD4+T cells, CD8+T cells, and regulatory T cells. Four drugs with relatively low IC50 values were found in the high-risk group: Elesclomol, Pyrimethamine, Saracatinib, and Temsirolimus. In addition, four drugs with significant positive correlation (P < 0.001) between IC50 values and feature gene expression were found, including Alectinib, Estramustine, Brigatinib, and Elesclomol. The low expression of key gene NTSR1 reduced the IC50 value of irinotecan. CONCLUSION: Based on the m6A/m5C/m1A-related genes in LUAD, LUAD patients were divided into 2 subtypes, and a m6A/m5C/m1A-related LUAD prognostic model was constructed to provide a reference for the prognosis prediction of LUAD.

Clinical and Prognostic Implications of Right Ventricular Uptake on Bone Scintigraphy in Transthyretin Amyloid Cardiomyopathy.

BACKGROUND: The extent of myocardial bone tracer uptake with technetium pyrophosphate, hydroxymethylene diphosphonate, and 3,3-diphosphono-1,2-propanodicarboxylate in transthyretin amyloid cardiomyopathy (ATTR-CM) might reflect cardiac amyloid burden and be associated with outcome. METHODS: Consecutive patients with ATTR-CM who underwent diagnostic bone tracer scintigraphy with acquisition of whole-body planar and cardiac single-photon emission computed tomography (SPECT) images from the National Amyloidosis Centre and 4 Italian centers were included. Cardiac uptake was defined according to the Perugini classification: 0=absent cardiac uptake; 1=mild uptake less than bone; 2=moderate uptake equal to bone; and 3=high uptake greater than bone. Extent of right ventricular (RV) uptake was defined as focal (basal segment of the RV free wall only) or diffuse (extending beyond basal segment) on the basis of SPECT imaging. The primary outcome was all-cause mortality. RESULTS: Among 1422 patients with ATTR-CM, RV uptake accompanying left ventricular uptake was identified by SPECT imaging in 100% of cases at diagnosis. Median follow-up in the whole cohort was 34 months (interquartile range, 21 to 50 months), and 494 patients died. By Kaplan-Meier analysis, diffuse RV uptake on SPECT imaging (n=936) was associated with higher all-cause mortality compared with focal (n=486) RV uptake (77.9% versus 22.1%; P<0.001), whereas Perugini grade was not associated with survival (P=0.27 in grade 2 versus grade 3). On multivariable analysis, after adjustment for age at diagnosis (hazard ratio [HR], 1.03 [95% CI, 1.02-1.04]; P<0.001), presence of the p.(V142I) TTR variant (HR, 1.42 [95% CI, 1.20-1.81]; P=0.004), National Amyloidosis Centre stage (each category, P<0.001), stroke volume index (HR, 0.99 [95% CI, 0.97-0.99]; P=0.043), E/e' (HR, 1.02 [95% CI, 1.007-1.03]; P=0.004), right atrial area index (HR, 1.05 [95% CI, 1.02-1.08]; P=0.001), and left ventricular global longitudinal strain (HR, 1.06 [95% CI, 1.03-1.09]; P<0.001), diffuse RV uptake on SPECT imaging (HR, 1.60 [95% CI, 1.26-2.04]; P<0.001) remained an independent predictor of all-cause mortality. The prognostic value of diffuse RV uptake was maintained across each National Amyloidosis Centre stage and in both wild-type and hereditary ATTR-CM (P<0.001 and P=0.02, respectively). CONCLUSIONS: Diffuse RV uptake of bone tracer on SPECT imaging is associated with poor outcomes in patients with ATTR-CM and is an independent prognostic marker at diagnosis.

Excess Mortality and Loss of Life Expectancy After Myocardial Infarction: A Registry-Based Matched Cohort Study.

BACKGROUND: The effect of myocardial infarction (MI) on life expectancy is difficult to study because the prevalence of MI hinders direct comparison with the life expectancy of the general population. We sought to assess this in relation to age, sex, and left ventricular ejection fraction (LVEF) by comparing individuals with MI with matched comparators without previous MI. METHODS: We included patients with a first MI between 1991 and 2022 from the nationwide SWEDEHEART registry (Swedish Web-System for Enhancement and Development of Evidence-Based Care in Heart Disease Evaluated According to Recommended Therapies), each matched with up to 5 comparators on age, sex, and region of residence. Flexible parametric survival models were used to estimate excess mortality risk and mean loss of life expectancy (LOLE) depending on index year, age, sex, and LVEF, and adjusted for differences in characteristics. RESULTS: A total of 335 748 cases were matched to 1 625 396 comparators. A higher LOLE was observed in younger individuals, women, and those with reduced LVEF (<50%). In 2022, the unadjusted and adjusted mean LOLE spanned from 11.1 and 9.5 years in 50-year-old women with reduced LVEF to 5 and 3.7 months in 80-year-old men with preserved LVEF. Between 1992 and 2022, the adjusted mean LOLE decreased by 36% to 55%: from 4.4 to 2.0 years and from 3.3 to 1.9 years in 50-year-old women and men, respectively, and from 1.7 to 1.0 years and from 1.4 to 0.9 years in 80-year-old women and men, respectively. CONCLUSIONS: LOLE is higher in younger individuals, women, and those with reduced LVEF, but is attenuated when adjusting for comorbidities and risk factors. Advances in MI treatment during the past 30 years have almost halved LOLE, with no clear sign of leveling off to a plateau.

mPAP/CO Slope and Oxygen Uptake Add Prognostic Value in Aortic Stenosis.

BACKGROUND: Recent guidelines redefined exercise pulmonary hypertension as a mean pulmonary artery pressure/cardiac output (mPAP/CO) slope >3 mm Hg·L-1·min-1. A peak systolic pulmonary artery pressure >60 mm Hg during exercise has been associated with an increased risk of cardiovascular death, heart failure rehospitalization, and aortic valve replacement in aortic valve stenosis. The prognostic value of the mPAP/CO slope in aortic valve stenosis remains unknown. METHODS: In this prospective cohort study, consecutive patients (n=143; age, 73±11 years) with an aortic valve area ≤1.5 cm2 underwent cardiopulmonary exercise testing with echocardiography. They were subsequently evaluated for the occurrence of cardiovascular events (ie, cardiovascular death, heart failure hospitalization, new-onset atrial fibrillation, and aortic valve replacement) during a follow-up period of 1 year. Findings were externally validated (validation cohort, n=141). RESULTS: One cardiovascular death, 32 aortic valve replacements, 9 new-onset atrial fibrillation episodes, and 4 heart failure hospitalizations occurred in the derivation cohort, whereas 5 cardiovascular deaths, 32 aortic valve replacements, 1 new-onset atrial fibrillation episode, and 10 heart failure hospitalizations were observed in the validation cohort. Peak aortic velocity (odds ratio [OR] per SD, 1.48; P=0.036), indexed left atrial volume (OR per SD, 2.15; P=0.001), E/e' at rest (OR per SD, 1.61; P=0.012), mPAP/CO slope (OR per SD, 2.01; P=0.002), and age-, sex-, and height-based predicted peak exercise oxygen uptake (OR per SD, 0.59; P=0.007) were independently associated with cardiovascular events at 1 year, whereas peak systolic pulmonary artery pressure was not (OR per SD, 1.28; P=0.219). Peak Vo2 (percent) and mPAP/CO slope provided incremental prognostic value in addition to indexed left atrial volume and aortic valve area (P<0.001). These results were confirmed in the validation cohort. CONCLUSIONS: In moderate and severe aortic valve stenosis, mPAP/CO slope and percent-predicted peak Vo2 were independent predictors of cardiovascular events, whereas peak systolic pulmonary artery pressure was not. In addition to aortic valve area and indexed left atrial volume, percent-predicted peak Vo2 and mPAP/CO slope cumulatively improved risk stratification.

Nonischemic or Dual Cardiomyopathy in Patients With Coronary Artery Disease.

BACKGROUND: Randomized trials in obstructive coronary artery disease (CAD) have largely shown no prognostic benefit from coronary revascularization. Although there are several potential reasons for the lack of benefit, an underexplored possible reason is the presence of coincidental nonischemic cardiomyopathy (NICM). We investigated the prevalence and prognostic significance of NICM in patients with CAD (CAD-NICM). METHODS: We conducted a registry study of consecutive patients with obstructive CAD on coronary angiography who underwent contrast-enhanced cardiovascular magnetic resonance imaging for the assessment of ventricular function and scar at 4 hospitals from 2004 to 2020. We identified the presence and cause of cardiomyopathy using cardiovascular magnetic resonance imaging and coronary angiography data, blinded to clinical outcomes. The primary outcome was a composite of all-cause death or heart failure hospitalization, and secondary outcomes were all-cause death, heart failure hospitalization, and cardiovascular death. RESULTS: Among 3023 patients (median age, 66 years; 76% men), 18.2% had no cardiomyopathy, 64.8% had ischemic cardiomyopathy (CAD+ICM), 9.3% had CAD+NICM, and 7.7% had dual cardiomyopathy (CAD+dualCM), defined as both ICM and NICM. Thus, 16.9% had CAD+NICM or dualCM. During a median follow-up of 4.8 years (interquartile range, 2.9, 7.6), 1116 patients experienced the primary outcome. In Cox multivariable analysis, CAD+NICM or dualCM was independently associated with a higher risk of the primary outcome compared with CAD+ICM (adjusted hazard ratio, 1.23 [95% CI, 1.06-1.43]; P=0.007) after adjustment for potential confounders. The risks of the secondary outcomes of all-cause death and heart failure hospitalization were also higher with CAD+NICM or dualCM (hazard ratio, 1.21 [95% CI, 1.02-1.43]; P=0.032; and hazard ratio, 1.37 [95% CI, 1.11-1.69]; P=0.003, respectively), whereas the risk of cardiovascular death did not differ from that of CAD+ICM (hazard ratio, 1.15 [95% CI, 0.89-1.48]; P=0.28). CONCLUSIONS: In patients with CAD referred for clinical cardiovascular magnetic resonance imaging, NICM or dualCM was identified in 1 of every 6 patients and was associated with worse long-term outcomes compared with ICM. In patients with obstructive CAD, coincidental NICM or dualCM may contribute to the lack of prognostic benefit from coronary revascularization.

Functional and clinical roles of stromal PDGF receptors in tumor biology.

PDGF receptors play pivotal roles in both developmental and physiological processes through the regulation of mesenchymal cells involved in paracrine instructive interactions with epithelial or endothelial cells. Tumor biology studies, alongside analyses of patient tissue samples, provide strong indications that the PDGF signaling pathways are also critical in various types of human cancer. This review summarizes experimental findings and correlative studies, which have explored the biological mechanisms and clinical relevance of PDGFRs in mesenchymal cells of the tumor microenvironment. Collectively, these studies support the overall concept that the PDGF system is a critical regulator of tumor growth, metastasis, and drug efficacy, suggesting yet unexploited targeting opportunities. The inter-patient variability in stromal PDGFR expression, as being linked to prognosis and treatment responses, not only indicates the need for stratified approaches in upcoming therapeutic investigations but also implies the potential for the development of PDGFRs as biomarkers of clinical utility, interestingly also in settings outside PDGFR-directed treatments.

Interpretable artificial intelligence model for accurate identification of medical conditions using immune repertoire.

Underlying medical conditions, such as cancer, kidney disease and heart failure, are associated with a higher risk for severe COVID-19. Accurate classification of COVID-19 patients with underlying medical conditions is critical for personalized treatment decision and prognosis estimation. In this study, we propose an interpretable artificial intelligence model termed VDJMiner to mine the underlying medical conditions and predict the prognosis of COVID-19 patients according to their immune repertoires. In a cohort of more than 1400 COVID-19 patients, VDJMiner accurately identifies multiple underlying medical conditions, including cancers, chronic kidney disease, autoimmune disease, diabetes, congestive heart failure, coronary artery disease, asthma and chronic obstructive pulmonary disease, with an average area under the receiver operating characteristic curve (AUC) of 0.961. Meanwhile, in this same cohort, VDJMiner achieves an AUC of 0.922 in predicting severe COVID-19. Moreover, VDJMiner achieves an accuracy of 0.857 in predicting the response of COVID-19 patients to tocilizumab treatment on the leave-one-out test. Additionally, VDJMiner interpretively mines and scores V(D)J gene segments of the T-cell receptors that are associated with the disease. The identified associations between single-cell V(D)J gene segments and COVID-19 are highly consistent with previous studies. The source code of VDJMiner is publicly accessible at https://github.com/TencentAILabHealthcare/VDJMiner. The web server of VDJMiner is available at https://gene.ai.tencent.com/VDJMiner/.

ProgCAE: a deep learning-based method that integrates multi-omics data to predict cancer subtypes.

Determining cancer subtypes and estimating patient prognosis are crucial for cancer research. The massive amount of multi-omics data generated by high-throughput sequencing technology is an important resource for cancer prognosis. Deep learning methods can integrate such data to accurately identify more cancer subtypes. We propose a prognostic model based on a convolutional autoencoder (ProgCAE) that can predict cancer subtypes associated with survival using multi-omics data. We demonstrated that ProgCAE predicted subtypes of 12 cancer types with significant survival differences and outperformed traditional statistical methods for predicting the survival of most patients with cancer. Supervised classifiers can be constructed based on subtypes predicted by robust ProgCAE.

The repertoire of copy number alteration signatures in human cancer.

Copy number alterations (CNAs) are a predominant source of genetic alterations in human cancer and play an important role in cancer progression. However comprehensive understanding of the mutational processes and signatures of CNA is still lacking. Here we developed a mechanism-agnostic method to categorize CNA based on various fragment properties, which reflect the consequences of mutagenic processes and can be extracted from different types of data, including whole genome sequencing (WGS) and single nucleotide polymorphism (SNP) array. The 14 signatures of CNA have been extracted from 2778 pan-cancer analysis of whole genomes WGS samples, and further validated with 10 851 the cancer genome atlas SNP array dataset. Novel patterns of CNA have been revealed through this study. The activities of some CNA signatures consistently predict cancer patients' prognosis. This study provides a repertoire for understanding the signatures of CNA in cancer, with potential implications for cancer prognosis, evolution and etiology.

HBV-infected hepatocellular carcinoma can be robustly classified into three clinically relevant subgroups by a novel analytical protocol.

Liver cancer is the third leading cause of cancer-related death worldwide, and hepatocellular carcinoma (HCC) accounts for a relatively large proportion of all primary liver malignancies. Among the several known risk factors, hepatitis B virus (HBV) infection is one of the important causes of HCC. In this study, we demonstrated that the HBV-infected HCC patients could be robustly classified into three clinically relevant subgroups, i.e. Cluster1, Cluster2 and Cluster3, based on consistent differentially expressed mRNAs and proteins, which showed better generalization. The proposed three subgroups showed different molecular characteristics, immune microenvironment and prognostic survival characteristics. The Cluster1 subgroup had near-normal levels of metabolism-related proteins, low proliferation activity and good immune infiltration, which were associated with its good liver function, smaller tumor size, good prognosis, low alpha-fetoprotein (AFP) levels and lower clinical stage. In contrast, the Cluster3 subgroup had the lowest levels of metabolism-related proteins, which corresponded with its severe liver dysfunction. Also, high proliferation activity and poor immune microenvironment in Cluster3 subgroup were associated with its poor prognosis, larger tumor size, high AFP levels, high incidence of tumor thrombus and higher clinical stage. The characteristics of the Cluster2 subgroup were between the Cluster1 and Cluster3 groups. In addition, MCM2-7, RFC2-5, MSH2, MSH6, SMC2, SMC4, NCPAG and TOP2A proteins were significantly upregulated in the Cluster3 subgroup. Meanwhile, abnormally high phosphorylation levels of these proteins were associated with high levels of DNA repair, telomere maintenance and proliferative features. Therefore, these proteins could be identified as potential diagnostic and prognostic markers. In general, our research provided a novel analytical protocol and insights for the robust classification, treatment and prevention of HBV-infected HCC.

Multiomics dynamic learning enables personalized diagnosis and prognosis for pancancer and cancer subtypes.

Artificial intelligence (AI) approaches in cancer analysis typically utilize a 'one-size-fits-all' methodology characterizing average patient responses. This manner neglects the diverse conditions in the pancancer and cancer subtypes of individual patients, resulting in suboptimal outcomes in diagnosis and treatment. To overcome this limitation, we shift from a blanket application of statistics to a focus on the explicit recognition of patient-specific abnormalities. Our objective is to use multiomics data to empower clinicians with personalized molecular descriptions that allow for customized diagnosis and interventions. Here, we propose a highly trustworthy multiomics learning (HTML) framework that employs multiomics self-adaptive dynamic learning to process each sample with data-dependent architectures and computational flows, ensuring personalized and trustworthy patient-centering of cancer diagnosis and prognosis. Extensive testing on a 33-type pancancer dataset and 12 cancer subtype datasets underscored the superior performance of HTML compared with static-architecture-based methods. Our findings also highlighting the potential of HTML in elucidating complex biological pathogenesis and paving the way for improved patient-specific care in cancer treatment.

ReProMSig: an integrative platform for development and application of reproducible multivariable models for cancer prognosis supporting guideline-based transparent reporting.

Adequate reporting is essential for evaluating the performance and clinical utility of a prognostic prediction model. Previous studies indicated a prevalence of incomplete or suboptimal reporting in translational and clinical studies involving development of multivariable prediction models for prognosis, which limited the potential applications of these models. While reporting templates introduced by the established guidelines provide an invaluable framework for reporting prognostic studies uniformly, there is a widespread lack of qualified adherence, which may be due to miscellaneous challenges in manual reporting of extensive model details, especially in the era of precision medicine. Here, we present ReProMSig (Reproducible Prognosis Molecular Signature), a web-based integrative platform providing the analysis framework for development, validation and application of a multivariable prediction model for cancer prognosis, using clinicopathological features and/or molecular profiles. ReProMSig platform supports transparent reporting by presenting both methodology details and analysis results in a strictly structured reporting file, following the guideline checklist with minimal manual input needed. The generated reporting file can be published together with a developed prediction model, to allow thorough interrogation and external validation, as well as online application for prospective cases. We demonstrated the utilities of ReProMSig by development of prognostic molecular signatures for stage II and III colorectal cancer respectively, in comparison with a published signature reproduced by ReProMSig. Together, ReProMSig provides an integrated framework for development, evaluation and application of prognostic/predictive biomarkers for cancer in a more transparent and reproducible way, which would be a useful resource for health care professionals and biomedical researchers.