Computational Pathology for Prediction of Isocitrate Dehydrogenase Gene Mutation from Whole Slide Images in Adult Patients with Diffuse Glioma.

Isocitrate dehydrogenase gene (IDH) mutation is one of the most important molecular markers of glioma. Accurate detection of IDH status is a crucial step for integrated diagnosis of adult-type diffuse gliomas. Herein, a clustering-based hybrid of a convolutional neural network and a vision transformer deep learning model was developed to detect IDH mutation status from annotation-free hematoxylin and eosin-stained whole slide pathologic images of 2275 adult patients with diffuse gliomas. For comparison, a pure convolutional neural network, a pure vision transformer, and a classic multiple-instance learning model were also assessed. The hybrid model achieved an area under the receiver operating characteristic curve of 0.973 in the validation set and 0.953 in the external test set, outperforming the other models. The hybrid model's ability in IDH detection between difficult subgroups with different IDH status but shared histologic features, achieving areas under the receiver operating characteristic curve ranging from 0.850 to 0.985 in validation and test sets. These data suggest that the proposed hybrid model has a potential to be used as a computational pathology tool for preliminary rapid detection of IDH mutation from whole slide images in adult patients with diffuse gliomas.

The skin photoprotective effect of trilinolein: Induction of cellular autophagy via the AMPK-mTOR signaling pathway.

Trilinolein (TL) is an active substance contained in traditional Chinese herbs; modern studies have shown that trilinolein has anti-inflammatory and antioxidant effects on the body. This study delves into the photoprotective effect of trilinolein on UVB-irradiated Human Skin Fibroblast (HSF) cells and the underlying mechanisms. Our findings reveal that trilinolein had a photoprotective effect on HSF cells: trilinolein enhanced cellular autophagy, restored UVB-inhibited cell proliferative viability, and curbing UVB-induced reactive oxygen species (ROS) and apoptosis. Intriguingly, after inhibition of TL-induced autophagy via wortmannin, diminished trilinolein's photoprotective effects. Meanwhile, trilinolein was shown to modulate the AMPK-mTOR signaling pathway, thus enhance cellular autophagy in HSF cells, and this tendency was suppressed after the administration of compound C (AMPK inhibitor). In a mouse model of skin photodamage, trilinolein significantly mitigated photodamage extent through morphological and histopathological analyses. This study illuminates trilinolein could inhibit the photodamaging effects of UVB irradiation by regulating cellular autophagy through the AMPK-mTOR signaling pathway, suggesting its promising application in combating UV-induced skin disorders.

Imaging-proteomic analysis for prediction of neoadjuvant chemotherapy responses in patients with breast cancer.

BACKGROUND: Optimizing patient selection for neoadjuvant chemotherapy in patients with breast cancer remains an unmet clinical need. Quantitative features from medical imaging were reported to be predictive of treatment responses. However, the biologic meaning of these latent features is poorly understood, preventing the clinical use of such noninvasive imaging markers. The study aimed to develop a deep learning signature (DLS) from pretreatment magnetic resonance imaging (MRI) for predicting responses to neoadjuvant chemotherapy in patients with breast cancer and to further investigate the biologic meaning of the DLS by identifying its underlying pathways using paired MRI and proteomic sequencing data. METHODS: MRI-based DLS was constructed (radiogenomic training dataset, n = 105) and validated (radiogenomic validation dataset, n = 26) for the prediction of pathologic complete response (pCR) to neoadjuvant chemotherapy. Proteomic sequencing revealed biological functions facilitating pCR (n = 139). Their associations with DLS were uncovered by radiogenomic analysis. RESULTS: The DLS achieved a prediction accuracy of 0.923 with an AUC of 0.958, higher than the performance of the model trained by transfer learning. Cellular membrane formation, endocytosis, insulin-like growth factor binding, protein localization to membranes, and cytoskeleton-dependent trafficking were differentially regulated in patients showing pCR. Oncogenic signaling pathways, features correlated with human phenotypes, and features correlated with general biological processes were significantly correlated with DLS in both training and validation dataset (p.adj < 0.05). CONCLUSIONS: Our study offers a biologically interpretable DLS for the prediction of pCR to neoadjuvant chemotherapy in patients with breast cancer, which may guide personalized medication.

Neuropathologist-level integrated classification of adult-type diffuse gliomas using deep learning from whole-slide pathological images.

Current diagnosis of glioma types requires combining both histological features and molecular characteristics, which is an expensive and time-consuming procedure. Determining the tumor types directly from whole-slide images (WSIs) is of great value for glioma diagnosis. This study presents an integrated diagnosis model for automatic classification of diffuse gliomas from annotation-free standard WSIs. Our model is developed on a training cohort (n = 1362) and a validation cohort (n = 340), and tested on an internal testing cohort (n = 289) and two external cohorts (n = 305 and 328, respectively). The model can learn imaging features containing both pathological morphology and underlying biological clues to achieve the integrated diagnosis. Our model achieves high performance with area under receiver operator curve all above 0.90 in classifying major tumor types, in identifying tumor grades within type, and especially in distinguishing tumor genotypes with shared histological features. This integrated diagnosis model has the potential to be used in clinical scenarios for automated and unbiased classification of adult-type diffuse gliomas.

Kangfuxin liquid reduces the ultraviolet B-induced photodamage of HaCaT cells by regulating autophagy.

Kangfuxin liquid (KFX), an extract of the American cockroach, has been clinically proven to be effective in various skin damage disorders, but there are no reports on its use in photodamage. We explored the effect of KFX on ultraviolet B (UVB)-induced photodamage and whether its mechanism was related to autophagy. We found that KFX treatment reduced UVB-induced reactive oxygen species production and improved the vitality of cells inhibited by UVB irradiation. The expression of LC3 (A/B), which was inhibited after UVB irradiation, could be rescued by KFX treatment. Furthermore, KFX may upregulate the level of cellular autophagy by regulating the AMPK-mTOR signaling pathway. When the autophagy inhibitor wortmannin was used to inhibit autophagy, the protective effect of KFX on cells was diminished or even disappeared. Our study suggests that KFX may resist UVB-mediated oxidative stress damage of HaCaT through the induction of autophagy.

Radiomic features from multiparametric magnetic resonance imaging predict molecular subgroups of pediatric low-grade gliomas.

BACKGROUND: We aimed to develop machine learning models for prediction of molecular subgroups (low-risk group and intermediate/high-risk group) and molecular marker (KIAA1549-BRAF fusion) of pediatric low-grade gliomas (PLGGs) based on radiomic features extracted from multiparametric MRI. METHODS: 61 patients with PLGGs were included in this retrospective study, which were divided into a training set and an internal validation set at a ratio of 2:1 based on the molecular subgroups or the molecular marker. The patients were classified into low-risk and intermediate/high-risk groups, BRAF fusion positive and negative groups, respectively. We extracted 5929 radiomic features from multiparametric MRI. Thereafter, we removed redundant features, trained random forest models on the training set for predicting the molecular subgroups or the molecular marker, and validated their performance on the internal validation set. The performance of the prediction model was verified by 3-fold cross-validation. RESULTS: We constructed the classification model differentiating low-risk PLGGs from intermediate/high-risk PLGGs using 4 relevant features, with an AUC of 0.833 and an accuracy of 76.2% in the internal validation set. In the prediction model for predicting KIAA1549-BRAF fusion using 4 relevant features, an AUC of 0.818 and an accuracy of 81.0% were achieved in the internal validation set. CONCLUSIONS: The current study demonstrates that MRI radiomics is able to predict molecular subgroups of PLGGs and KIAA1549-BRAF fusion with satisfying sensitivity. TRIAL REGISTRATION: This study was retrospectively registered at clinicaltrials.gov (NCT04217018).

A radio-pathologic integrated model for prediction of lymph node metastasis stage in patients with gastric cancer.

BACKGROUND: Accurate prediction of lymph node metastasis stage (LNMs) facilitates precision therapy for gastric cancer. We aimed to develop and validate a deep learning-based radio-pathologic model to predict the LNM stage in patients with gastric cancer by integrating CT images and histopathological whole-slide images (WSIs). METHODS: A total of 252 patients were enrolled and randomly divided into a training set (n = 202) and a testing set (n = 50). Both pretreatment contrast-enhanced abdominal CT and WSI of biopsy specimens were collected for each patient. The deep radiologic and pathologic features were extracted from CT and WSI using ResNet-50 and Vision Transformer (ViT) network, respectively. By fusing both radiologic and pathologic features, a radio-pathologic integrated model was constructed to predict the five LNM stages. For comparison, four single-modality models using CT images or WSIs were also constructed, respectively. All models were trained on the training set and validated on the testing set. RESULTS: The radio-pathologic integrated mode achieved an overall accuracy of 84.0% and a kappa coefficient of 0.795 on the testing set. The areas under the curves (AUCs) of the integrated model in predicting the five LNM stages were 0.978 (95% Confidence Interval (CI 0.917-1.000), 0.946 (95% CI 0.867-1.000), 0.890 (95% CI 0.718-1.000), 0.971 (95% CI 0.920-1.000), and 0.982 (95% CI 0.911-1.000), respectively. Moreover, the integrated model achieved an AUC of 0.978 (95% CI 0.912-1.000) in predicting the binary status of nodal metastasis. CONCLUSION: Our study suggests that radio-pathologic integrated model that combined both macroscale radiologic image and microscale pathologic image can better predict lymph node metastasis stage in patients with gastric cancer.

Single-cell RNA-seq Revealed that Altered Tumor Infiltrating Lymphocytes in Cirrhotic Liver Indicate Development of Hepatocellular Carcinoma.

Background and Aims: Cirrhosis is the precursor lesion for most hepatocellular carcinoma (HCC) cases. However, no biomarker effectively predicted HCC initiation before diagnosis by imaging. We aimed to investigate the hallmarks of immune microenvironments in healthy, cirrhotic livers and HCC tumor tissues and to identify immune biomarkers of cirrhosis-HCC transition. Methods: Expression matrices of single-cell RNA sequencing studies were downloaded and integrated with Seurat package vignettes. Clustering was performed to analyze the immune cell compositions of different sample types. Results: The cirrhotic liver and HCC tumors had distinct immune microenvironments, but the immune landscape of cirrhotic livers was not markedly modified compared with healthy livers. Two subsets of B cells and three subsets of T cells were identified in the samples. Among the T cells, naïve T cells were more prominent in the cirrhotic and healthy liver samples than in the HCC samples. In contrast, the neutrophil count was lower in cirrhotic livers. Two macrophage clusters were identified, one that actively interacted with T cells and B cells and was enriched in cirrhotic blood compared with HCC blood samples. Conclusions: Decreased naïve T cell infiltration and increased neutrophil infiltration in the liver may indicate the development of HCC in cirrhotic patients. Alterations in blood-resident immune cells may also be a sign of HCC development in cirrhotic patients. The dynamics of the immune cell subsets may serve as novel biomarkers to predict the transition from cirrhosis to HCC.

Handelin protects human skin keratinocytes against ultraviolet B-induced photodamage via autophagy activation by regulating the AMPK-mTOR signaling pathway.

Handelin is a natural ingredient extracted from Chrysanthemum boreale flowers that has been shown to decrease stress-related cell death, prolong lifespan, and promote anti-photoaging. However, whether handelin inhibits ultraviolet (UV) B stress-induced photodamage remains unclear. In the present study, we investigate whether handelin has protective properties on skin keratinocytes under UVB irradiation. Human immortalized keratinocytes (HaCaT keratinocytes) were pretreated with handelin for 12 h before UVB irradiation. The results indicated that handelin protects keratinocytes against UVB-induced photodamage by activating autophagy. However, the photoprotective effect of handelin was suppressed by an autophagic inhibitor (wortmannin) or the transfection of keratinocytes with a small interfering RNA targeting ATG5. Notably, handelin reduced mammalian target of rapamycin (mTOR) activity in UVB-irradiated cells in a manner similar to that shown by the mTOR inhibitor rapamycin. Adenosine monophosphate-activated protein kinase (AMPK) activity was also induced by handelin in UVB-damaged keratinocytes. Finally, certain effects of handelin, including autophagy induction, mTOR activity inhibition, AMPK activation, and reduction of cytotoxicity, were suppressed by an AMPK inhibitor (compound C). Our data suggest that handelin effectively prevents photodamage by protecting skin keratinocytes against UVB-induced cytotoxicity via the regulation of AMPK/mTOR-mediated autophagy. These findings provide novel insights that can aid the development of therapeutic agents against UVB-induced keratinocyte photodamage.

Imaging phenotypes from MRI for the prediction of glioma immune subtypes from RNA sequencing: A multicenter study.

Tumor subtyping based on its immune landscape may guide precision immunotherapy. The aims of this study were to identify immune subtypes of adult diffuse gliomas with RNA sequencing data, and to noninvasively predict this subtype using a biologically interpretable radiomic signature from MRI. A subtype discovery dataset (n = 210) from a public database and two radiogenomic datasets (n = 130 and 55, respectively) from two local hospitals were included. Brain tumor microenvironment-specific signatures were constructed from RNA sequencing to identify the immune types. A radiomic signature was built from MRI to predict the identified immune subtypes. The pathways underlying the radiomic signature were identified to annotate their biological meanings. The reproducibility of the findings was verified externally in multicenter datasets. Three distinctive immune subtypes were identified, including an inflamed subtype marked by elevated hypoxia-induced immunosuppression, a "cold" subtype that exhibited scarce immune infiltration with downregulated antigen presentation, and an intermediate subtype that showed medium immune infiltration. A 10-feature radiomic signature was developed to predict immune subtypes, achieving an AUC of 0.924 in the validation dataset. The radiomic features correlated with biological functions underpinning immune suppression, which substantiated the hypothesis that molecular changes can be reflected by radiomic features. The immune subtypes, predictive radiomic signature, and radiomics-correlated biological pathways were validated externally. Our data suggest that adult-type diffuse gliomas harbor three distinctive immune subtypes that can be predicted by MRI radiomic features with clear biological significance. The immune subtypes, radiomic signature, and radiogenomic links can be replicated externally.

Image-based deep learning identifies glioblastoma risk groups with genomic and transcriptomic heterogeneity: a multi-center study.

OBJECTIVES: To develop and validate a deep learning imaging signature (DLIS) for risk stratification in patients with multiforme (GBM), and to investigate the biological pathways and genetic alterations underlying the DLIS. METHODS: The DLIS was developed from multi-parametric MRI based on a training set (n = 600) and validated on an internal validation set (n = 164), an external test set 1 (n = 100), an external test set 2 (n = 161), and a public TCIA set (n = 88). A co-profiling framework based on a radiogenomics analysis dataset (n = 127) using multiscale high-dimensional data, including imaging, transcriptome, and genome, was established to uncover the biological pathways and genetic alterations underpinning the DLIS. RESULTS: The DLIS was associated with survival (log-rank p < 0.001) and was an independent predictor (p < 0.001). The integrated nomogram incorporating the DLIS achieved improved C indices than the clinicomolecular nomogram (net reclassification improvement 0.39, p < 0.001). DLIS significantly correlated with core pathways of GBM (apoptosis and cell cycle-related P53 and RB pathways, and cell proliferation-related RTK pathway), as well as key genetic alterations (del_CDNK2A). The prognostic value of DLIS-correlated genes was externally confirmed on TCGA/CGGA sets (p < 0.01). CONCLUSIONS: Our study offers a biologically interpretable deep learning predictor of survival outcomes in patients with GBM, which is crucial for better understanding GBM patient's prognosis and guiding individualized treatment. KEY POINTS: • MRI-based deep learning imaging signature (DLIS) stratifies GBM into risk groups with distinct molecular characteristics. • DLIS is associated with P53, RB, and RTK pathways and del_CDNK2A mutation. • The prognostic value of DLIS-correlated pathway genes is externally demonstrated.

Advantages and Limitations of Monitoring Circulating Tumor DNA Levels to Predict the Prognosis of Patients Diagnosed With Gastric Cancer.

Next-generation sequencing-based genomic profiling facilitates biomarker detection by cell-free DNA (cfDNA) liquid biopsy. However, the efficiency of mutation calling and the prognostic value of cfDNA biomarkers are disputed. We investigated 24 patients with gastric cancer in this study, using a 605-gene sequencing panel to sequence their plasma cfDNA and tumor tissue DNA. The mutation concordance between plasma cfDNA and tumor tissue DNA was 70.6% in stage IV gastric cancer and 30.2% in stage III gastric cancer, indicating insufficient mutation detection rates in stage III and early-stage cancer. When compared with total cfDNA load and blood tumor mutation burden (bTMB), the variant allele frequencies (VAF) of commonly mutated genes are highly accurate in representing disease burden. Further, VAF are a better prognostic indicator compared with serum biomarkers including carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA19-9), cancer antigen 125 (CA125), and alpha-fetoprotein (AFP). The use of cfDNA in molecular profiling of patients allows prediction of patient survival and clinical response, as well as the development of personalized therapy regimens.

Case Report: Cetuximab in Combination With Chemotherapy for the Treatment of Multifocal Hepatic Metastases From Colorectal Cancer Guided by Genetic Tests.

Hepatic metastases were reported in up to 70% of colorectal cancer patients, among which multifocal hepatic metastasis represents one of the complications that lead to poor prognosis. The majority of the patients carrying multifocal hepatic metastases required pharmaceutical treatments to reduce the tumor size prior to surgical resection. However, the clinical responses to pharmaceutical agents were difficult to predict due to the heterogeneous nature of the multifocal tumors. Here, we report a case with multifocal hepatic metastases from colorectal cancer that was resistant to the primary chemotherapy and Bevacizumab plus chemotherapy, but responded to the combined therapy of Cetuximab and FOLFOX. Genetic tests had revealed that the tumor was highly metastatic due to the mutations of the WNT signaling pathway, and the metastatic tumors might be sensitive to Cetuximab. Consistent with the molecular characterizations, the metastatic tumors continue to emerge after chemotherapy, and rapidly relapsed in great numbers after liver resection. However, the combined therapy of Cetuximab and FOLFOX guided by the genetic tests significantly reduced the size and number of metastatic tumors. To conclude, deciphering the mutation profiles of multifocal metastatic tumors may guide the determination of treatment tactics, which may benefit the patients with non-resectable advanced carcinoma.

The Genomic Characteristics of ALK Fusion Positive Tumors in Chinese NSCLC Patients.

Anaplastic lymphoma kinase (ALK) fusion events account for ~3-7% genetic alterations in patients with non-small cell lung cancer (NSCLC). In this study, we identified the ALK fusion patterns and a novel ALK fusion partner in 44 ALK positive NSCLC patients using a customized HapOncoCDx panel, and identified ALK fusion partners. The most common partner is EML4, forming the variant 1 (v1, E13:A20, 18/44), variant 2 (v2, E20:A20, 5/44), and variant 3 (v3, E6:A20, 13/44). Moreover, we detected a new ALK fusion partner HMBOX1. At the mutation level, TP53 is the most frequently mutated gene (24%), followed by ALK (12%) and STED2 (12%). The median tumor mutation burden (TMB) of these samples is 2.29 mutations/Mb, ranging from 0.76 mut/Mb to 16.79 muts/Mb. We further elaborately portrayed the TP53 mutation sites on the peptide sequence of the encoded protein by lollipop. The mutational signature and copy number alterations (CNAs) of the samples were also analyzed. The CNA events were found in 13 (13/44) patients, and the most commonly amplified genes were MDM2 (n = 4/13) and TERT (n = 4/13). Together, these results may guide personalized clinical management of patients with ALK fusion in the era of precision medicine.

Genetic Biomarkers For Hepatocellular Carcinoma In The Era Of Precision Medicine.

Being one of the most lethal cancers that exhibit high levels of heterogeneity, hepatocellular carcinoma (HCC) is associated with diverse oncogenic pathways underpinned by varied driver genes. HCC can be induced by different etiological factors including virus infection, toxin exposure or metabolic disorders. Consequently, patients may display varied genetic profiles, and may respond differently to the treatments involving inhibition of target pathways. These DNA/RNA mutations, copy number variations, chromatin structural changes, aberrant expression of non-coding RNAs and epigenetic modifications were considered as biomarkers in the application of precision medication. To explore how genetic testing could contribute to early diagnosis, prognosis, treatment and postoperative monitoring of HCC, we conducted a systematic review of genetic markers associated with different pathologies. Moreover, we summarized on-going clinical trials for HCC treatment, including the trials for multiple kinase inhibitors and immune checkpoint blockade (ICB). The efficacy of ICB treatment in HCC is not as good as what was observed in lung cancer and melanoma, which might be due to the heterogeneity of the microenvironment of the liver.

The cell-wide web coordinates cellular processes by directing site-specific Ca2+ flux across cytoplasmic nanocourses.

Ca2+ coordinates diverse cellular processes, yet how function-specific signals arise is enigmatic. We describe a cell-wide network of distinct cytoplasmic nanocourses with the nucleus at its centre, demarcated by sarcoplasmic reticulum (SR) junctions (≤400 nm across) that restrict Ca2+ diffusion and by nanocourse-specific Ca2+-pumps that facilitate signal segregation. Ryanodine receptor subtype 1 (RyR1) supports relaxation of arterial myocytes by unloading Ca2+ into peripheral nanocourses delimited by plasmalemma-SR junctions, fed by sarco/endoplasmic reticulum Ca2+ ATPase 2b (SERCA2b). Conversely, stimulus-specified increases in Ca2+ flux through RyR2/3 clusters selects for rapid propagation of Ca2+ signals throughout deeper extraperinuclear nanocourses and thus myocyte contraction. Nuclear envelope invaginations incorporating SERCA1 in their outer nuclear membranes demarcate further diverse networks of cytoplasmic nanocourses that receive Ca2+ signals through discrete RyR1 clusters, impacting gene expression through epigenetic marks segregated by their associated invaginations. Critically, this circuit is not hardwired and remodels for different outputs during cell proliferation.

mTORC1 controls lysosomal Ca2+ release through the two-pore channel TPC2.

Two-pore segment channel 2 (TPC2) is a ubiquitously expressed, lysosomally targeted ion channel that aids in terminating autophagy and is inhibited upon its association with mechanistic target of rapamycin (mTOR). It is controversial whether TPC2 mediates lysosomal Ca2+ release or selectively conducts Na+ and whether the binding of nicotinic acid adenine dinucleotide phosphate (NAADP) or phosphatidylinositol 3,5-bisphosphate [PI(3,5)P2] is required for the activity of this ion channel. We show that TPC2 is required for intracellular Ca2+ signaling in response to NAADP or to mTOR inhibition by rapamycin. In pulmonary arterial myocytes, rapamycin and NAADP evoked global Ca2+ transients that were blocked by depletion of lysosomal Ca2+ stores. Preincubation of cells with high concentrations of rapamycin resulted in desensitization and blocked NAADP-evoked Ca2+ signals. Moreover, rapamycin and NAADP did not evoke discernable Ca2+ transients in myocytes derived from Tpcn2 knockout mice, which showed normal responses to other Ca2+-mobilizing signals. In HEK293 cells stably overexpressing human TPC2, shRNA-mediated knockdown of mTOR blocked rapamycin- and NAADP-evoked Ca2+ signals. Confocal imaging of a genetically encoded Ca2+ indicator fused to TPC2 demonstrated that rapamycin-evoked Ca2+ signals localized to lysosomes and were in close proximity to TPC2. Therefore, inactivation of mTOR may activate TPC2 and consequently lysosomal Ca2+ release.

From contraction to gene expression: nanojunctions of the sarco/endoplasmic reticulum deliver site- and function-specific calcium signals.

Calcium signals determine, for example, smooth muscle contraction and changes in gene expression. How calcium signals select for these processes is enigmatic. We build on the "panjunctional sarcoplasmic reticulum" hypothesis, describing our view that different calcium pumps and release channels, with different kinetics and affinities for calcium, are strategically positioned within nanojunctions of the SR and help demarcate their respective cytoplasmic nanodomains. SERCA2b and RyR1 are preferentially targeted to the sarcoplasmic reticulum (SR) proximal to the plasma membrane (PM), i.e., to the superficial buffer barrier formed by PM-SR nanojunctions, and support vasodilation. In marked contrast, SERCA2a may be entirely restricted to the deep, perinuclear SR and may supply calcium to this sub-compartment in support of vasoconstriction. RyR3 is also preferentially targeted to the perinuclear SR, where its clusters associate with lysosome-SR nanojunctions. The distribution of RyR2 is more widespread and extends from this region to the wider cell. Therefore, perinuclear RyR3s most likely support the initiation of global calcium waves at L-SR junctions, which subsequently propagate by calcium-induced calcium release via RyR2 in order to elicit contraction. Data also suggest that unique SERCA and RyR are preferentially targeted to invaginations of the nuclear membrane. Site- and function-specific calcium signals may thus arise to modulate stimulus-response coupling and transcriptional cascades.