Nearest template prediction: a single-sample-based flexible class prediction with confidence assessment

Spatial single-cell protein landscape reveals vimentinhigh macrophages as immune-suppressive in the microenvironment of hepatocellular carcinoma

Tumor microenvironment heterogeneity in hepatocellular carcinoma (HCC) on a spatial single-cell resolution is unclear. Here, we conducted co-detection by indexing to profile the spatial heterogeneity of 401 HCC samples with 36 biomarkers. By parsing the spatial tumor ecosystem of liver cancer, we identified spatial patterns with distinct prognosis and genomic and molecular features, and unveiled the progressive role of vimentin (VIM)high macrophages. Integration analysis with eight independent cohorts demonstrated that the spatial co-occurrence of VIMhigh macrophages and regulatory T cells promotes tumor progression and favors immunotherapy. Functional studies further demonstrated that VIMhigh macrophages enhance the immune-suppressive activity of regulatory T cells by mechanistically increasing the secretion of interleukin-1β. Our data provide deep insights into the heterogeneity of tumor microenvironment architecture and unveil the critical role of VIMhigh macrophages during HCC progression, which holds potential for personalized cancer prevention and drug discovery and reinforces the need to resolve spatial-informed features for cancer treatment.

Integrated Proteomic and Glycoproteomic Analysis Reveals Heterogeneity and Molecular Signatures of Brain Metastases from Lung Adenocarcinomas

Brain metastasis is a major cause of poor prognosis and death in lung adenocarcinoma (LUAD); however, the understanding of therapeutic strategies and mechanisms for brain metastases from LUAD (BM-LUAD) remains notably limited, especially at the proteomics levels. To address this issue, we conducted integrated proteomic and glycoproteomic analyses on 49 BM-LUAD tumors, revealing two distinct subtypes of the disease: BM-S1 and BM-S2. Whole exome sequencing analysis revealed that somatic mutations in STK11 and KEAP1, as well as copy number deletions on chr19p13.3, such as STK11, UQCR11, and SLC25A23, were more frequently detected in BM-S2. In BM-S1 tumors, we observed significant infiltration of GFAP+ astrocytes, as evidenced by elevated levels of GFAP, GABRA2, GABRG1 and GAP43 proteins and an enrichment of astrocytic signatures in both our proteomic data and external spatial transcriptomic data. Conversely, BM-S2 tumors demonstrated higher levels of PD-1 immune cell infiltration, supported by the upregulation of PD-1 and LAG-3 genes. These findings suggest distinct microenvironmental adaptations required by the different BM-LUAD subtypes. Additionally, we observed unique glycosylation patterns between the subtypes, with increased fucosylation in BM-S1 and enhanced sialylation in BM-S2, primarily affected by glycosylation enzymes such as FUT9, B4GALT1, and ST6GAL1. Specifically, in BM-S2, these sialylation modifications are predominantly localized to the lysosomes, underscoring the critical role of N-glycosylation in the tumor progression of BM-LUAD. Overall, our study not only provides a comprehensive multi-omic data resource but also offers valuable biological insights into BM-LUAD, highlighting potential mechanisms and therapeutic targets for further investigation.

The association of appendectomy with prognosis and tumor-associated macrophages in patients with colorectal cancer

The vermiform appendix plays an important role in colorectal immunity and the homeostasis of the gut microbiome. We aimed to evaluate the prognostic value of prior appendectomy for patients with colorectal cancer (CRC). This study revealed that prior appendectomy is an independent risk factor for the prognosis of patients with CRC, based on a multicentral CRC cohort. We further demonstrated that appendectomy induced a poor prognosis of CRC through the depletion of M1 macrophage cells in AOM-induced mice, which was confirmed in age-, sex-, and location-matched patients' cohorts and orthotopic model models with the CT26 cell line. Poor responses to anti-PD-1 immunotherapy were detected in patients with CRC with appendectomy, and cetuximab is an effective treatment for patients with appendectomy-associated colorectal cancer (APD-CRC) to improve their prognosis. Our study will provide a reference for developing treatment plans for a considerable number of patients with APD-CRC, which is of great clinical significance.

Discovery of therapeutic targets in cancer using chromatin accessibility and transcriptomic data

Most cancer types lack targeted therapeutic options, and when first-line targeted therapies are available, treatment resistance is a huge challenge. Recent technological advances enable the use of assay for transposase-accessible chromatin with sequencing (ATAC-seq) and RNA sequencing (RNA-seq) on patient tissue in a high-throughput manner. Here, we present a computational approach that leverages these datasets to identify drug targets based on tumor lineage. We constructed gene regulatory networks for 371 patients of 22 cancer types using machine learning approaches trained with three-dimensional genomic data for enhancer-to-promoter contacts. Next, we identified the key transcription factors (TFs) in these networks, which are used to find therapeutic vulnerabilities, by direct targeting of either TFs or the proteins that they interact with. We validated four candidates identified for neuroendocrine, liver, and renal cancers, which have a dismal prognosis with current therapeutic options.

Molecular precision medicine: Multi-omics-based stratification model for acute myeloid leukemia

Acute myeloid leukemia (AML), as the most common malignancy of the hematopoietic system, poses challenges in treatment efficacy, relapse, and drug resistance. In this study, we have utilized 151 RNA sequencing datasets, 194 DNA methylation datasets, and 200 somatic mutation datasets from the AML cohort in the TCGA database to develop a multi-omics stratification model. This model enables comparison of prognosis, clinical features, gene mutations, immune microenvironment and drug sensitivity across subgroups. External validation datasets have been sourced from the GEO database, which includes 562 mRNA datasets and 136 miRNA datasets from 984 adult AML patients. Through multi-omics-based stratification model, we classified 126 AML patients into 4 clusters (CS). CS4 had the best prognosis, with the youngest age, highest M3 subtype proportion, fewest copy number alterations, and common mutations in WT1, FLT3, and KIT genes. It showed sensitivity to HDAC inhibitors and BCL-2 inhibitors. Both the M3 subtype and CS4 were identified as independent protective factors for survival. Conversely, CS3 had the worst prognosis due to older age, high copy number alterations, and frequent mutations in RUNX1, DNMT3A, and TP53 genes. Additionally, it showed higher proportions of cytotoxic cells and Tregs, suggesting potential sensitivity to mTOR inhibitors. CS1 had a better prognosis than CS2, with more copy number alterations, while CS2 had higher monocyte proportions. CS1 showed good sensitivity to cytarabine, while CS2 was sensitive to RXR agonists. Both CS1 and CS2, which predominantly featured mutations in FLT3, NPM1, and DNMT3A genes, benefited from FLT3 inhibitors. Using the Kappa test, our stratification model underwent robust validation in the miRNA and mRNA external validation datasets. With advancements in sequencing technology and machine learning algorithms, AML is poised to transition towards multi-omics precision medicine in the future. We aspire for our study to offer new perspectives on multi-drug combination clinical trials and multi-targeted precision medicine for AML.

Integrating multi-omics and machine learning survival frameworks to build a prognostic model based on immune function and cell death patterns in a lung adenocarcinoma cohort

Introduction

The programmed cell death (PCD) plays a key role in the development and progression of lung adenocarcinoma. In addition, immune-related genes also play a crucial role in cancer progression and patient prognosis. However, further studies are needed to investigate the prognostic significance of the interaction between immune-related genes and cell death in LUAD.

Methods

In this study, 10 clustering algorithms were applied to perform molecular typing based on cell death-related genes, immune-related genes, methylation data and somatic mutation data. And a powerful computational framework was used to investigate the relationship between immune genes and cell death patterns in LUAD patients. A total of 10 commonly used machine learning algorithms were collected and subsequently combined into 101 unique combinations, and we constructed an immune-associated programmed cell death model (PIGRS) using the machine learning model that exhibited the best performance. Finally, based on a series of in vitro experiments used to explore the role of PSME3 in LUAD.

Results

We used 10 clustering algorithms and multi-omics data to categorize TCGA-LUAD patients into three subtypes. patients with the CS3 subtype had the best prognosis, whereas patients with the CS1 and CS2 subtypes had a poorer prognosis. PIGRS, a combination of 15 high-impact genes, showed strong prognostic performance for LUAD patients. PIGRS has a very strong prognostic efficacy compared to our collection. In conclusion, we found that PSME3 has been little studied in lung adenocarcinoma and may be a novel prognostic factor in lung adenocarcinoma.

Discussion

Three LUAD subtypes with different molecular features and clinical significance were successfully identified by bioinformatic analysis, and PIGRS was constructed using a powerful machine learning framework. and investigated PSME3, which may affect apoptosis in lung adenocarcinoma cells through the PI3K/AKT/Bcl-2 signaling pathway.

Context-Dependent Distinct Roles of SOX9 in Combined Hepatocellular Carcinoma-Cholangiocarcinoma

Combined hepatocellular carcinoma-cholangiocarcinoma (cHCC-CCA) is a challenging primary liver cancer subtype with limited treatment options and a devastating prognosis. Recent studies have underscored the context-dependent roles of SOX9 in liver cancer formation in a preventive manner. Here, we revealed that liver-specific developmental Sox9 elimination using Alb-Cre;Sox9(flox/flox) (LKO) and CRISPR/Cas9-based tumor-specific acute Sox9 elimination (CKO) in SB-HDTVI-based Akt-YAP1 (AY) and Akt-NRAS (AN) cHCC-CCA models showed contrasting responses. LKO abrogates the AY CCA region while stimulating poorly differentiated HCC proliferation, whereas CKO prevents AY and AN cHCC-CCA development irrespective of tumor cell fate. Additionally, AN, but not AY, tumor formation partially depends on the Sox9-Dnmt1 cascade. SOX9 is dispensable for AY-mediated, HC-derived, LPC-like immature CCA formation but is required for their maintenance and transformation into mature CCA. Therapeutic Sox9 elimination using the OPN-CreERT2 strain combined with inducible Sox9 iKO specifically reduces AY but not AN cHCC-CCA tumors. This necessitates the careful consideration of genetic liver cancer studies using developmental Cre and somatic mutants, particularly for genes involved in liver development. Our findings suggest that SOX9 elimination may hold promise as a therapeutic approach for a subset of cHCC-CCA and highlight the need for further investigation to translate these preclinical insights into personalized clinical applications.

An atlas of the human liver diurnal transcriptome and its perturbation by hepatitis C virus infection

Chronic liver disease and cancer are global health challenges. The role of the circadian clock as a regulator of liver physiology and disease is well established in rodents, however, the identity and epigenetic regulation of rhythmically expressed genes in human disease is less well studied. Here we unravel the rhythmic transcriptome and epigenome of human hepatocytes using male human liver chimeric mice. We identify a large number of rhythmically expressed protein coding genes in human hepatocytes of male chimeric mice, which includes key transcription factors, chromatin modifiers, and critical enzymes. We show that hepatitis C virus (HCV) infection, a major cause of liver disease and cancer, perturbs the transcriptome by altering the rhythmicity of the expression of more than 1000 genes, and affects the epigenome, leading to an activation of critical pathways mediating metabolic alterations, fibrosis, and cancer. HCV-perturbed rhythmic pathways remain dysregulated in patients with advanced liver disease. Collectively, these data support a role for virus-induced perturbation of the hepatic rhythmic transcriptome and pathways in cancer development and may provide opportunities for cancer prevention and biomarkers to predict HCC risk.

Overexpression of ESYT3 improves radioimmune responses through activating cGAS-STING pathway in lung adenocarcinoma

BACKGROUND

Radiotherapy can modulate systemic antitumor immunity, while immune status in the tumor microenvironment also influences the efficacy of radiotherapy, but relevant molecular mechanisms are poorly understood in lung adenocarcinoma (LUAD).

METHODS

In this study, we innovatively proposed a radiotherapy response classification for LUAD, and discovered ESYT3 served as a tumor suppressor and radioimmune response sensitizer. ESYT3 expression was measured both in radioresistant and radiosensitive LUAD tissues and cells. The influence of ESYT3 on radiotherapy sensitivity and resistance was then investigated. Interaction between ESYT3 and STING was evaluated through multiple immunofluorescent staining and coimmunoprecipitation, and downstream molecules were further analyzed. In vivo models were constructed to assess the combination treatment efficacy of ESYT3 overexpression with radiotherapy.

RESULTS

We found that radioresistant subtype presented immunosuppressive state and activation of DNA damage repair pathways than radiosensitive subtype. ESYT3 expression was remarkably attenuated both in radioresistant LUAD tissues and cells. Clinically, low ESYT3 expression was linked with radioresistance. Overexpression of ESYT3 enabled to alleviate radioresistance, and sensitize LUAD cells to DNA damage induced by irradiation. Mechanically, ESYT3 directly interacted with STING, and activated cGAS-STING signaling, subsequently increasing the generation of type I IFNs as well as downstream chemokines CCL5 and CXCL10, thus improving radioimmune responses. The combination treatment of ESYT3 overexpression with radiotherapy had a synergistic anticancer effect in vitro and in vivo.

CONCLUSIONS

In summary, low ESYT3 expression confers resistance to radiotherapy in LUAD, and its overexpression can improve radioimmune responses through activating cGAS-STING-dependent pathway, thus providing an alternative combination therapeutic strategy for LUAD patients.

Epigenetic heterogeneity hotspots in human liver disease progression

BACKGROUND AND AIMS

Disruption of the epigenome is a hallmark of human disease, including liver cirrhosis and HCC. While genetic heterogeneity is an established effector of pathologic phenotypes, epigenetic heterogeneity is less well understood. Environmental exposures alter the liver-specific DNA methylation landscape and influence the onset of liver cancer. Given that currently available treatments are unable to target frequently mutated genes in HCC, there is an unmet need for novel therapeutics to prevent or reverse liver damage leading to hepatic tumorigenesis, which the epigenome may provide.

APPROACH AND RESULTS

We performed genome-wide profiling of DNA methylation, copy number, and gene expression from multiple liver regions from 31 patients with liver disease to examine their crosstalk and define the individual and combinatorial contributions of these processes to liver disease progression. We identified epigenetic heterogeneity hotspots that are conserved across patients. Elevated epigenetic heterogeneity is associated with increased gene expression heterogeneity. Cirrhotic regions comprise 2 distinct cohorts-one exclusively epigenetic, and the other where epigenetic and copy number variations collaborate. Epigenetic heterogeneity hotspots are enriched for genes central to liver function (eg, HNF1A ) and known tumor suppressors (eg, RASSF1A ). These hotspots encompass genes including ACSL1 , ACSL5 , MAT1A , and ELFN1 , which have phenotypic effects in functional screens, supporting their relevance to hepatocarcinogenesis. Moreover, epigenetic heterogeneity hotspots are linked to clinical measures of outcome.

CONCLUSIONS

Substantial epigenetic heterogeneity arises early in liver disease development, targeting key pathways in the progression and initiation of both cirrhosis and HCC. Integration of epigenetic and transcriptional heterogeneity unveils putative epigenetic regulators of hepatocarcinogenesis.

Molecular subtypes of ischemic heart disease based on circadian rhythm

Coronary atherosclerotic heart disease (CAD) is among the most prevalent chronic diseases globally. Circadian rhythm disruption (CRD) is closely associated with the progression of various diseases. However, the precise role of CRD in the development of CAD remains to be elucidated. The Circadian rhythm disruption score (CRDscore) was employed to quantitatively assess the level of CRD in CAD samples. Our investigation revealed a significant association between high CRDscore and adverse prognosis in CAD patients, along with a substantial correlation with CAD progression. Remarkably distinct CRDscore distributions were also identified among various subtypes. In summary, we have pioneered the revelation of the relationship between CRD and CAD at the single-cell level and established reliable markers for the development, treatment, and prognosis of CAD. A deeper understanding of these mechanisms may offer new possibilities for incorporating "the therapy of coronary heart disease based circadian rhythm" into personalized medical treatment regimens.

Identification of CXCL13 as a Promising Biomarker for Immune Checkpoint Blockade Therapy and PARP Inhibitor Therapy in Ovarian Cancer

Ovarian cancer has poor response rates to immune checkpoint blockade (ICB) therapy, despite the use of genomic sequencing to identify molecular targets. Homologous recombination deficiency (HRD) is a conventional indicator of genomic instability (GI) and has been used as a marker for targeted therapies. Indicators reflecting HRD status have shown potential in predicting the efficacy of ICB treatment. Public databases, including TCGA, ICGC, and GEO, were used to obtain data. HRD scores, neoantigen load, and TMB were obtained from the TCGA cohort. Candidate biomarkers were validated in multiple databases, such as the Imvigor210 immunotherapy cohort and the open-source single-cell sequencing database. Immunohistochemistry was performed to further validate the results in independent cohorts. CXCL10, CXCL11, and CXCL13 were found to be significantly upregulated in HRD tumors and exhibited prognostic value. A comprehensive analysis of the tumor immune microenvironment (TIME) revealed that CXCL13 expression positively correlated with neoantigen load and immune cell infiltration. In addition, single-cell sequencing data and clinical trial results supported the utility of CXCL13 as a biomarker for ICB therapy. Not only does CXCL13 serve as a biomarker reflecting HRD status, but it also introduces a potentially novel perspective on prognostic biomarkers for ICB in ovarian cancer.

Deciphering the role of zinc homeostasis in the tumor microenvironment and prognosis of prostate cancer

BACKGROUND

Dysregulation of zinc homeostasis is widely recognized as a hallmark feature of prostate cancer (PCa) based on the compelling clinical and experimental evidence. Nevertheless, the implications of zinc dyshomeostasis in PCa remains largely unexplored.

METHODS

In this research, the zinc homeostasis pattern subtype (ZHPS) was constructed according to the profile of zinc homeostasis genes. The identified subtypes were assessed for their immune functions, mutational landscapes, biological peculiarities and drug susceptibility. Subsequently, we developed the optimal signature, known as the zinc homeostasis-related risk score (ZHRRS), using the approach won out in multifariously machine learning algorithms. Eventually, clinical specimens, Bayesian network inference and single-cell sequencing were used to excavate the underlying mechanisms of MT1A in PCa.

RESULTS

The zinc dyshomeostasis subgroup, ZHPS2, possessed a markedly worse prognosis than ZHPS1. Moreover, ZHPS2 demonstrated a more conspicuous genomic instability and better therapeutic responses to docetaxel and olaparib than ZHPS1. Compared with traditional clinicopathological characteristics and 35 published signatures, ZHRRS displayed a significantly improved accuracy in prognosis prediction. The diagnostic value of MT1A in PCa was substantiated through analysis of clinical samples. Additionally, we inferred and established the regulatory network of MT1A to elucidate its biological mechanisms.

CONCLUSIONS

The ZHPS classifier and ZHRRS model hold great potential as clinical applications for improving outcomes of PCa patients.

PIPET: predicting relevant subpopulations in single-cell data using phenotypic information from bulk data

Single-cell RNA sequencing has revealed cellular heterogeneity in complex tissues, notably benefiting research on diseases such as cancer. However, the integration of single-cell data from small samples with extensive clinical features in bulk data remains underexplored. In this study, we introduce PIPET, an algorithmic method for predicting relevant subpopulations in single-cell data based on multivariate phenotypic information from bulk data. PIPET generates feature vectors for each phenotype from differentially expressed genes in bulk data and then identifies relevant cellular subpopulations by assessing the similarity between single-cell data and these vectors. Subsequently, phenotype-related cell states can be analyzed based on these subpopulations. In simulated datasets, PIPET showed robust performance in predicting multiclassification cellular subpopulations. Application of PIPET to lung adenocarcinoma single-cell RNA sequencing data revealed cellular subpopulations with poor survival and associations with TP53 mutations. Similarly, in breast cancer single-cell data, PIPET identified cellular subpopulations associated with the PAM50 clinical subtypes and triple-negative breast cancer subtypes. Overall, PIPET effectively identified relevant cellular subpopulations in single-cell data, guided by phenotypic information from bulk data. This approach comprehensively delineates the molecular characteristics of each cellular subpopulation, offering insights into disease-related subpopulations and guiding personalized treatment strategies.

Therapeutic potential of SOX9 dysruption in Combined Hepatocellular Carcinoma-Cholangiocarcinoma

Combined hepatocellular carcinoma-cholangiocarcinoma (cHCC-CCA) represents a challenging subtype of primary liver cancer with limited treatment options and a poor prognosis. Recently, we and others have highlighted the context-dependent roles of the biliary-specific transcription factor SOX9 in the pathogenesis of liver cancers using various Cre applications in Sox9 (flox/flox) strains, to achieve elimination for exon 2 and 3 of the Sox9 gene locus as a preventive manner. Here, we reveal the contrasting responses of developmental Sox9 elimination using Alb-Cre;Sox9 (flox/flox) ( Sox9 LKO) versus CRISPR/Cas9 -based tumor specific acute Sox9 CKO in SB-HDTVI-based Akt-YAP1 and Akt-NRAS cHCC-CCA formation. Sox9 LKO specifically abrogates the Akt-YAP1 CCA region while robustly stimulating the proliferation of remaining poorly differentiated HCC pertaining liver progenitor cell characteristics, whereas Sox9 CKO potently prevents Akt-YAP1 and Akt-NRAS cHCC-CCA development irrespective of fate of tumor cells compared to respective controls. Additionally, we find that Akt-NRAS , but not Akt-YAP1 , tumor formation is partially dependent on the Sox9-Dnmt1 cascade. Pathologically, SOX9 is indispensable for Akt-YAP1 -mediated HC-to-BEC/CCA reprogramming but required for the maintenance of CCA nodules. Lastly, therapeutic elimination of Sox9 using the OPN-CreERT2 strain combined with an inducible CRISPR/Cas9 -based Sox9 iKO significantly reduces Akt-YAP1 cHCC-CCA tumor burden, similar to Sox9 CKO. Thus, we contrast the outcomes of acute Sox9 deletion with developmental Sox9 knockout models, emphasizing the importance of considering adaptation mechanisms in therapeutic strategies. This necessitates the careful consideration of genetic liver cancer studies using developmental Cre and somatic mutant lines, particularly for genes involved in hepatic commitment during development. Our findings suggest that SOX9 elimination may hold promise as a therapeutic approach for cHCC-CCA and underscore the need for further investigation to translate these preclinical insights into clinical applications.

Transcriptomic Profiling of Plasma Extracellular Vesicles Enables Reliable Annotation of the Cancer-Specific Transcriptome and Molecular Subtype

Longitudinal monitoring of patients with advanced cancers is crucial to evaluate both disease burden and treatment response. Current liquid biopsy approaches mostly rely on the detection of DNA-based biomarkers. However, plasma RNA analysis can unleash tremendous opportunities for tumor state interrogation and molecular subtyping. Through the application of deep learning algorithms to the deconvolved transcriptomes of RNA within plasma extracellular vesicles (evRNA), we successfully predicted consensus molecular subtypes in patients with metastatic colorectal cancer. Analysis of plasma evRNA also enabled monitoring of changes in transcriptomic subtype under treatment selection pressure and identification of molecular pathways associated with recurrence. This approach also revealed expressed gene fusions and neoepitopes from evRNA. These results demonstrate the feasibility of using transcriptomic-based liquid biopsy platforms for precision oncology approaches, spanning from the longitudinal monitoring of tumor subtype changes to the identification of expressed fusions and neoantigens as cancer-specific therapeutic targets, sans the need for tissue-based sampling.

SIGNIFICANCE

The development of an approach to interrogate molecular subtypes, cancer-associated pathways, and differentially expressed genes through RNA sequencing of plasma extracellular vesicles lays the foundation for liquid biopsy-based longitudinal monitoring of patient tumor transcriptomes.

The analysis of transcriptomic signature of TNBC-searching for the potential RNA-based predictive biomarkers to determine the chemotherapy sensitivity

Neoadjuvant chemotherapy is the foundation treatment for triple-negative breast cancer (TNBC) and frequently results in pathological complete response (pCR). However, there are large differences in clinical response and survival after neoadjuvant chemotherapy of TNBC patients. The aim was to identify genes whose expression significantly associates with the efficacy of neoadjuvant chemotherapy in patients with TNBC. Transcriptomes of 46 formalin-fixed paraffin-embedded (FFPE) tumor samples from TNBC patients were analyzed by RNA-seq by comparing 26 TNBCs with pCR versus 20 TNBCs with pathological partial remission (pPR). Subsequently, we narrowed down the list of genes to those that strongly correlated with drug sensitivity of 63 breast cancer cell lines based on Dependency Map Consortium data re-analysis. Furthermore, the list of genes was limited to those presenting specific expression in breast tumor cells as revealed in three large published single-cell RNA-seq breast cancer datasets. Finally, we analyzed which of the selected genes were significantly associated with overall survival (OS) in TNBC TCGA dataset. A total of 105 genes were significantly differentially expressed in comparison between pPR versus pCR. As revealed by PLSR analysis in breast cancer cell lines, out of 105 deregulated genes, 42 were associated with sensitivity to docetaxel, doxorubicin, paclitaxel, and/or cyclophosphamide. We found that 24 out of 42 sensitivity-associated genes displayed intermediate or strong expression in breast malignant cells using single-cell RNAseq re-analysis. Finally, 10 out of 24 genes were significantly associated with overall survival in TNBC TCGA dataset. Our RNA-seq-based findings suggest that there might be transcriptomic signature consisted of 24 genes specifically expressed in tumor malignant cells for predicting neoadjuvant response in FFPE samples from TNBC patients prior to treatment initiation. Additionally, nine out of 24 genes were potential survival predictors in TNBC. This group of 24 genes should be further investigated for its potential to be translated into a predictive test(s).

Spatial proximity of tumor-immune interactions predicts patient outcome in hepatocellular carcinoma

BACKGROUND AND AIMS

The fitness and viability of a tumor ecosystem are influenced by the spatial organization of its cells. We aimed to study the structure, architecture, and cell-cell dynamics of the heterogeneous liver cancer tumor microenvironment using spatially resolved multiplexed imaging.

APPROACH AND RESULTS

We performed co-detection by indexing multiplexed immunofluorescence imaging on 68 HCC biopsies from Thai patients [(Thailand Initiative in Genomics and Expression Research for Liver Cancer (TIGER-LC)] as a discovery cohort, and then validated the results in an additional 190 HCC biopsies from Chinese patients [Liver Cancer Institute (LCI)]. We segmented and annotated 117,270 and 465,632 cells from the TIGER-LC and LCI cohorts, respectively. We observed 4 patient groups of TIGER-LC (IC1, IC2, IC3, and IC4) with distinct tumor-immune cellular interaction patterns. In addition, patients from IC2 and IC4 had much better overall survival than those from IC1 and IC3. Noticeably, tumor and CD8 + T-cell interactions were strongly enriched in IC2, the group with the best patient outcomes. The close proximity between the tumor and CD8 + T cells was a strong predictor of patient outcome in both the TIGER-LC and the LCI cohorts. Bulk transcriptomic data from 51 of the 68 HCC cases were used to determine tumor-specific gene expression features of our classified subtypes. Moreover, we observed that the presence of immune spatial neighborhoods in HCC as a measure of overall immune infiltration is linked to better patient prognosis.

CONCLUSIONS

Highly multiplexed imaging analysis of liver cancer reveals tumor-immune cellular heterogeneity within spatial contexts, such as tumor and CD8 + T-cell interactions, which may predict patient survival.

Comprehensive profiling of enhancer RNA in stage II/III colorectal cancer defines two prognostic subtypes with implications for immunotherapy

BACKGROUND

Recently, enhancer RNAs (eRNAs) have garnered attention as pivotal biomarkers for the onset and progression of cancer. However, the landscape of eRNAs and the implications of eRNA-based molecular subtypes in stage II/III colorectal cancer (CRC) remain largely unexplored.

METHODS

Comprehensive profiling of eRNAs was conducted on a public stage II/III CRC cohort with total RNA-seq data. We used unsupervised clustering of prognostic eRNAs to establish an eRNA-based subtyping system. Further evaluations included molecular characteristics, immune infiltration, clinical outcomes, and drug responses. Finally, we validated the eRNA-based subtyping system in The Cancer Genome Atlas (TCGA) CRC cohort.

RESULTS

We identified a total of 6453 expressed eRNAs, among which 237 were prognostic. A global upregulation of eRNAs was observed in microsatellite-stable (MSS) CRCs when compared to microsatellite instability-high (MSI-H) CRCs. Through consensus clustering, two novel molecular subtypes, termed Cluster 1(C1) and Cluster 2(C2), were further identified. C1, associated with the activation of epithelial-mesenchymal transition (EMT), hypoxia, and KRAS signaling pathways, showed poorer prognosis. C2, correlated with the canonical CRC subtype, exhibited superior survival outcomes. In addition, C1 showed enrichment with immune infiltration and more sensitivity to immune checkpoint inhibitors.

CONCLUSION

Our study unravels the molecular heterogeneity of stage II/III CRC at the eRNA level and highlights the potential applications of the novel eRNA-based subtyping system in predicting prognosis and guiding immunotherapy.

Prognostic PET [11C]-acetate uptake is associated with hypoxia gene expression in patients with late-stage hepatocellular carcinoma - a bench to bed study

BACKGROUND

Positron Emission Tomography (PET) with combined [18F]-FDG and [11C]-acetate (dual-tracer) is used for the management of hepatocellular carcinoma (HCC) patients, although its prognostic value and underlying molecular mechanism remain poorly understood. We hypothesized that radiotracer uptake might be associated with tumor hypoxia and validated our findings in public and local human HCC cohorts.

METHODS

Twelve orthotopic HCC xenografts were established using MHCC97L cells in female nude mice, with 5 having undergone hepatic artery ligation (HAL) to create tumor hypoxia in vivo. Tumors in both Control and HAL-treated xenografts were imaged with [11C]-acetate and [18F]-FDG PET-MR and RNA sequencing was performed on the resected tumors. Semiquantitative analysis of PET findings was then performed, and the findings were then validated on the Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) cohort and patients from our institution.

RESULTS

HAL-treated mice showed lower [11C]-acetate (HAL-treated vs. Control, tumor-to-liver SUV ratio (SUVTLR): 2.14[2.05-2.21] vs 3.11[2.75-5.43], p = 0.02) but not [18F]-FDG (HAL-treated vs. Control, SUVTLR: 3.73[3.12-4.35] vs 3.86[3.7-5.29], p = 0.83) tumor uptakes. Gene expression analysis showed the PET phenotype is associated with upregulation of hallmark hypoxia signature. The prognostic value of the hypoxia gene signature was tested on the TCGA-LIHC cohort with upregulation of hypoxia gene signature associated with poorer overall survival (OS) in late-stage (stage III and IV) HCC patients (n = 66, OS 2.05 vs 1.67 years, p = 0.046). Using a local cohort of late-stage HCC patients who underwent dual-tracer PET-CT, tumors without [11C]-acetate uptake are associated with poorer prognosis (n = 51, OS 0.25 versus 1.21 years, p < 0.0001) and multivariable analyses showed [11C]-acetate tumor uptake as an independent predictor of OS (HR 0.17 95%C 0.06-0.42, p < 0.0001).

CONCLUSIONS

[11C]-acetate uptake is associated with alteration of tumor hypoxia gene expression and poorer prognosis in patients with advanced HCC.

Cancer cell genetics shaping of the tumor microenvironment reveals myeloid cell-centric exploitable vulnerabilities in hepatocellular carcinoma

Myeloid cells are abundant and plastic immune cell subsets in the liver, to which pro-tumorigenic, inflammatory and immunosuppressive roles have been assigned in the course of tumorigenesis. Yet several aspects underlying their dynamic alterations in hepatocellular carcinoma (HCC) progression remain elusive, including the impact of distinct genetic mutations in shaping a cancer-permissive tumor microenvironment (TME). Here, in newly generated, clinically-relevant somatic female HCC mouse models, we identify cancer genetics' specific and stage-dependent alterations of the liver TME associated with distinct histopathological and malignant HCC features. Mitogen-activated protein kinase (MAPK)-activated, NrasG12D-driven tumors exhibit a mixed phenotype of prominent inflammation and immunosuppression in a T cell-excluded TME. Mechanistically, we report a NrasG12D cancer cell-driven, MEK-ERK1/2-SP1-dependent GM-CSF secretion enabling the accumulation of immunosuppressive and proinflammatory monocyte-derived Ly6Clow cells. GM-CSF blockade curbs the accumulation of these cells, reduces inflammation, induces cancer cell death and prolongs animal survival. Furthermore, GM-CSF neutralization synergizes with a vascular endothelial growth factor (VEGF) inhibitor to restrain HCC outgrowth. These findings underscore the profound alterations of the myeloid TME consequential to MAPK pathway activation intensity and the potential of GM-CSF inhibition as a myeloid-centric therapy tailored to subsets of HCC patients.

Crosstalk of cell death pathways unveils an autophagy-related gene AOC3 as a critical prognostic marker in colorectal cancer

The intricate crosstalk of various cell death forms was recently implicated in cancers, laying a foundation for exploring the association between cell death and cancers. Recent evidence has demonstrated that biological networks outperform snapshot gene expression profiles at discovering promising biomarkers or heterogenous molecular subtypes across different cancer types. In order to investigate the behavioral patterns of cell death-related interaction perturbation in colorectal cancer (CRC), this study constructed the interaction-perturbation network with 11 cell death pathways and delineated four cell death network (CDN) derived heterogeneous subtypes (CDN1-4) with distinct molecular characteristics and clinical outcomes. Specifically, we identified a subtype (CDN4) endowed with high autophagy activity and the worst prognosis. Furthermore, AOC3 was identified as a potential autophagy-related biomarker, which demonstrated exceptional predictive performance for CDN4 and significant prognostic value. Overall, this study sheds light on the complex interplay of various cell death forms and reveals an autophagy-related gene AOC3 as a critical prognostic marker in CRC.

Molecular classification reveals the sensitivity of lung adenocarcinoma to radiotherapy and immunotherapy: multi-omics clustering based on similarity network fusion

BACKGROUND

Due to individual differences in tumors and immune systems, the response rate to immunotherapy is low in lung adenocarcinoma (LUAD) patients. Combinations with other therapeutic strategies improve the efficacy of immunotherapy in LUAD patients. Although radioimmunotherapy has been demonstrated to effectively suppress tumors, the underlying mechanisms still need to be investigated.

METHODS

Total RNA from LUAD cells was sequenced before and after radiotherapy to identify differentially expressed radiation-associated genes. The similarity network fusion (SNF) algorithm was applied for molecular classification based on radiation-related genes, immune-related genes, methylation data, and somatic mutation data. The changes in gene expression, prognosis, immune cell infiltration, radiosensitivity, chemosensitivity, and sensitivity to immunotherapy were assessed for each subtype.

RESULTS

We used the SNF algorithm and multi-omics data to divide TCGA-LUAD patients into three subtypes. Patients with the CS3 subtype had the best prognosis, while those with the CS1 and CS2 subtypes had poorer prognoses. Among the strains tested, CS2 exhibited the most elevated immune cell infiltration and expression of immune checkpoint genes, while CS1 exhibited the least. Patients in the CS2 subgroup were more likely to respond to PD-1 immunotherapy. The CS2 patients were most sensitive to docetaxel and cisplatin, while the CS1 patients were most sensitive to paclitaxel. Experimental validation of signature genes in the CS2 subtype showed that inhibiting the expression of RHCG and TRPA1 could enhance the sensitivity of lung cancer cells to radiation.

CONCLUSIONS

In summary, this study identified a risk classifier based on multi-omics data that can guide treatment selection for LUAD patients.

Whole-transcriptome defines novel glucose metabolic subtypes in colorectal cancer

Colorectal cancer (CRC) is the most prevalent malignancy of the digestive system. Glucose metabolism plays a crucial role in CRC development. However, the heterogeneity of glucose metabolic patterns in CRC is not well characterized. Here, we classified CRC into specific glucose metabolic subtypes and identified the key regulators. 2228 carbohydrate metabolism-related genes were screened out from the GeneCards database, 202 of them were identified as prognosis genes in the TCGA database. Based on the expression patterns of the 202 genes, three metabolic subtypes were obtained by the non-negative matrix factorization clustering method. The C1 subtype had the worst survival outcome and was characterized with higher immune cell infiltration and more activation in extracellular matrix pathways than the other two subtypes. The C2 subtype was the most prevalent in CRC and was characterized by low immune cell infiltration. The C3 subtype had the smallest number of individuals and had a better prognosis, with higher levels of NRF2 and TP53 pathway expression. Secreted frizzled-related protein 2 (SFRP2) and thrombospondin-2 (THBS2) were confirmed as biomarkers for the C1 subtype. Their expression levels were elevated in high glucose condition, while their knockdown inhibited migration and invasion of HCT 116 cells. The analysis of therapeutic potential found that the C1 subtype was more sensitive to immune and PI3K-Akt pathway inhibitors than the other subtypes. To sum up, this study revealed a novel glucose-related CRC subtype, characterized by SFRP2 and THBS2, with poor prognosis but possible therapeutic benefits from immune and targeted therapies.

Multi-omic profiling of clear cell renal cell carcinoma identifies metabolic reprogramming associated with disease progression

Clear cell renal cell carcinoma (ccRCC) is a complex disease with remarkable immune and metabolic heterogeneity. Here we perform genomic, transcriptomic, proteomic, metabolomic and spatial transcriptomic and metabolomic analyses on 100 patients with ccRCC from the Tongji Hospital RCC (TJ-RCC) cohort. Our analysis identifies four ccRCC subtypes including De-clear cell differentiated (DCCD)-ccRCC, a subtype with distinctive metabolic features. DCCD cancer cells are characterized by fewer lipid droplets, reduced metabolic activity, enhanced nutrient uptake capability and a high proliferation rate, leading to poor prognosis. Using single-cell and spatial trajectory analysis, we demonstrate that DCCD is a common mode of ccRCC progression. Even among stage I patients, DCCD is associated with worse outcomes and higher recurrence rate, suggesting that it cannot be cured by nephrectomy alone. Our study also suggests a treatment strategy based on subtype-specific immune cell infiltration that could guide the clinical management of ccRCC.

Signature construction and molecular subtype identification based on liver-specific genes for prediction of prognosis, immune activity, and anti-cancer drug sensitivity in hepatocellular carcinoma

BACKGROUND

Liver specific genes (LSGs) are crucial for hepatocyte differentiation and maintaining normal liver function. A deep understanding of LSGs and their heterogeneity in hepatocellular carcinoma (HCC) is necessary to provide clues for HCC diagnosis, prognosis, and treatment.

METHODS

The bulk and single-cell RNA-seq data of HCC were downloaded from TCGA, ICGC, and GEO databases. Through unsupervised cluster analysis, LSGs-based HCC subtypes were identified in TCGA-HCC samples. The prognostic effects of the subtypes were investigated with survival analyses. With GSVA and Wilcoxon test, the LSGs score, stemness score, aging score, immune score and stromal score of the samples were estimated and compared. The HCC subtype-specific genes were identified. The subtypes and their differences were validated in ICGC-HCC samples. LASSO regression analysis was used for key gene selection and risk model construction for HCC overall survival. The model performance was estimated and validated. The key genes were validated for their heterogeneities in HCC cell lines with quantitative real-time PCR and at single-cell level. Their dysregulations were investigated at protein level. Their correlations with HCC response to anti-cancer drugs were estimated in HCC cell lines.

RESULTS

We identified three LSGs-based HCC subtypes with different prognosis, tumor stemness, and aging level. The C1 subtype with low LSGs score and high immune score presented a poor survival, while the C2 subtype with high LSGs score and immune score indicated an enduring survival. Although no significant survival difference between C2 and C3 HCCs was shown, the C2 HCCs presented higher immune score and stroma score. The HCC subtypes and their differences were confirmed in ICGC-HCC dataset. A five-gene prognostic signature for HCC survival was constructed. Its good performance was shown in both the training and validation datasets. The five genes presented significant heterogeneities in different HCC cell lines and hepatocyte subclusters. Their dysregulations were confirmed at protein level. Furthermore, their significant associations with HCC sensitivities to anti-cancer drugs were shown.

CONCLUSIONS

LSGs-based HCC subtype classification and the five-gene risk model might provide useful clues not only for HCC stratification and risk prediction, but also for the development of more personalized therapies for effective HCC treatment.

Ensemble methods of rank-based trees for single sample classification with gene expression profiles

Building Single Sample Predictors (SSPs) from gene expression profiles presents challenges, notably due to the lack of calibration across diverse gene expression measurement technologies. However, recent research indicates the viability of classifying phenotypes based on the order of expression of multiple genes. Existing SSP methods often rely on Top Scoring Pairs (TSP), which are platform-independent and easy to interpret through the concept of "relative expression reversals". Nevertheless, TSP methods face limitations in classifying complex patterns involving comparisons of more than two gene expressions. To overcome these constraints, we introduce a novel approach that extends TSP rules by constructing rank-based trees capable of encompassing extensive gene-gene comparisons. This method is bolstered by incorporating two ensemble strategies, boosting and random forest, to mitigate the risk of overfitting. Our implementation of ensemble rank-based trees employs boosting with LogitBoost cost and random forests, addressing both binary and multi-class classification problems. In a comparative analysis across 12 cancer gene expression datasets, our proposed methods demonstrate superior performance over both the k-TSP classifier and nearest template prediction methods. We have further refined our approach to facilitate variable selection and the generation of clear, precise decision rules from rank-based trees, enhancing interpretability. The cumulative evidence from our research underscores the significant potential of ensemble rank-based trees in advancing disease classification via gene expression data, offering a robust, interpretable, and scalable solution. Our software is available at https://CRAN.R-project.org/package=ranktreeEnsemble .

Cellular Ligand-Receptor Perturbations Unravel MEIS2 as a Key Factor for the Aggressive Progression and Prognosis in Stage II/III Colorectal Cancer

Approximately 10-15% of stage II and 25-30% of stage III colorectal cancer (CRC) patients experience recurrence within 5 years after surgery, and existing taxonomies are insufficient to meet the needs of clinical precision treatment. Thus, robust biomarkers and precise management were urgently required to stratify stage II and III CRC and identify potential patients who will benefit from postoperative adjuvant therapy. Alongside, interactions of ligand-receptor pairs point to an emerging direction in tumor signaling with far-reaching implications for CRC, while their impact on tumor subtyping has not been elucidated. Herein, based on multiple large-sample multicenter cohorts and perturbations of the ligand-receptor interaction network, four well-characterized ligand-receptor-driven subtypes (LRDS) were established and further validated. These molecular taxonomies perform with unique heterogeneity in terms of molecular characteristics, immune and mutational landscapes, and clinical features. Specifically, MEIS2, a key LRDS4 factor, performs significant associations with proliferation, invasion, migration, and dismal prognosis of stage II/III CRC, revealing promising directions for prognostic assessment and individualized treatment of CRC patients. Overall, our study sheds novel insights into the implications of intercellular communication on stage II/III CRC from a ligand-receptor interactome perspective and revealed MEIS2 as a key factor in the aggressive progression and prognosis for stage II/III CRC.

Multiomics analysis reveals metabolic subtypes and identifies diacylglycerol kinase α (DGKA) as a potential therapeutic target for intrahepatic cholangiocarcinoma

BACKGROUND

Intrahepatic cholangiocarcinoma (iCCA) is a highly heterogeneous and lethal hepatobiliary tumor with few therapeutic strategies. The metabolic reprogramming of tumor cells plays an essential role in the development of tumors, while the metabolic molecular classification of iCCA is largely unknown. Here, we performed an integrated multiomics analysis and metabolic classification to depict differences in metabolic characteristics of iCCA patients, hoping to provide a novel perspective to understand and treat iCCA.

METHODS

We performed integrated multiomics analysis in 116 iCCA samples, including whole-exome sequencing, bulk RNA-sequencing and proteome analysis. Based on the non-negative matrix factorization method and the protein abundance of metabolic genes in human genome-scale metabolic models, the metabolic subtype of iCCA was determined. Survival and prognostic gene analyses were used to compare overall survival (OS) differences between metabolic subtypes. Cell proliferation analysis, 5-ethynyl-2'-deoxyuridine (EdU) assay, colony formation assay, RNA-sequencing and Western blotting were performed to investigate the molecular mechanisms of diacylglycerol kinase α (DGKA) in iCCA cells.

RESULTS

Three metabolic subtypes (S1-S3) with subtype-specific biomarkers of iCCA were identified. These metabolic subtypes presented with distinct prognoses, metabolic features, immune microenvironments, and genetic alterations. The S2 subtype with the worst survival showed the activation of some special metabolic processes, immune-suppressed microenvironment and Kirsten rat sarcoma viral oncogene homolog (KRAS)/AT-rich interactive domain 1A (ARID1A) mutations. Among the S2 subtype-specific upregulated proteins, DGKA was further identified as a potential drug target for iCCA, which promoted cell proliferation by enhancing phosphatidic acid (PA) metabolism and activating mitogen-activated protein kinase (MAPK) signaling.

CONCLUSION

Via multiomics analyses, we identified three metabolic subtypes of iCCA, revealing that the S2 subtype exhibited the poorest survival outcomes. We further identified DGKA as a potential target for the S2 subtype.

A transcriptome based molecular classification scheme for cholangiocarcinoma and subtype-derived prognostic biomarker

Previous studies on the molecular classification of cholangiocarcinoma (CCA) focused on certain anatomical sites, and disregarded tissue contamination biases in transcriptomic profiles. We aim to provide universal molecular classification scheme and prognostic biomarker of CCAs across anatomical locations. Comprehensive bioinformatics analysis is performed on transcriptomic data from 438 CCA cases across various anatomical locations. After excluding CCA tumors showing normal tissue expression patterns, we identify two universal molecular subtypes across anatomical subtypes, explore the molecular, clinical, and microenvironmental features of each class. Subsequently, a 30-gene classifier and a biomarker (called "CORE-37") are developed to predict the molecular subtype of CCA and prognosis, respectively. Two subtypes display distinct molecular characteristics and survival outcomes. Key findings are validated in external cohorts regardless of the stage and anatomical location. Our study provides a CCA classification scheme that complements the conventional anatomy-based classification and presents a promising prognostic biomarker for clinical application.

Unleashing the potential of gene signatures as prognostic and predictive tools: A step closer to personalized medicine in hepatocellular carcinoma (HCC)

Hepatocellular carcinoma (HCC) is one of the growing malignancies globally, affecting a myriad of people and causing numerous cancer-related deaths. Despite therapeutic improvements in treatment strategies over the past decades, HCC still remains one of the leading causes of person-years of life lost. Numerous studies have been conducted to assess the characteristics of HCC with the aim of predicting its prognosis and responsiveness to treatment. However, the identified biomarkers have shown limited sensitivity, and the translation of these findings into clinical practice has faced challenges. The development of sequencing techniques has facilitated the exploration of a wide range of genes, leading to the emergence of gene signatures. Although several studies assessed differentially expressed genes in normal and HCC tissues to find the unique gene signature with prognostic value, to date, no study has reviewed the task, and to the best of our knowledge, this review represents the first comprehensive analysis of relevant studies in HCC. Most gene signatures focused on immune-related genes, while others investigated genes related to metabolism, autophagy, and apoptosis. Even though no identical gene signatures were found, NDRG1, SPP1, BIRC5, and NR0B1 were the most extensively studied genes with prognostic value. Finally, despite challenges such as the lack of consistent patterns in gene signatures, we believe that comprehensive analysis of pertinent gene signatures will bring us a step closer to personalized medicine in HCC, where treatment strategies can be tailored to individual patients based on their unique molecular profiles.

Tumor biology, immune infiltration and liver function define seven hepatocellular carcinoma subtypes linked to distinct drivers, survival and drug response

BACKGROUND & AIMS

Tumor heterogeneity is jointly determined by the components of the tumor ecosystem (TES) including tumor cells, immune cells, stromal cells, and non-cellular components. We aimed to identify subtypes using TES-related genes and determine subtype specific drivers and treatments for hepatocellular carcinoma (HCC).

METHODS

We collected 68 genesets depicting tumor biology, immune infiltration, and liver function, totaling 2831 genes, and collected mRNA profiles and clinical data for over 6000 tumors from 65 datasets in the GEO, TCGA, ICGC, and several other databases. We designed a three-step clustering pipeline to identify subtypes. The microenvironment, genomic alteration, and drug response differences were systematically compared among subtypes.

RESULTS

Seven subtypes (TES-1/2/3/4/5/6/7) were revealed in 159 tumors from the CHCC-HBV cohort. We constructed a single sample classifier using paired genes (sscpgsTES). TES subtypes were significantly associated with multiple clinical variables including etiology, and survival in 14 of 17 cohorts and the meta-cohort. TES-1 had the poorest prognosis and highest proliferation level. Both TES-2 and TES-7 were immune-enriched, however, TES-2 had a significantly worse prognosis, and hypoxic and immunosuppressive microenvironment. TES-4 had activated Wnt pathway, driven by CTNNB1 mutation. Good prognosis TES-6 exhibited the best differentiation. TES-5 and TES-3 were considered as novel subclasses by comparing with ten previous subtyping systems. TES-5 tumors had high AFP but good overall survival, and ∼45% of them harbored AXIN1 mutation. TES-3 was immune and stromal desert, may be driven by high copy number alteration burden, and had the poorest response to immune checkpoint inhibitor. TES-1 and TES-2 had significantly lower response to transarterial chemoembolization, but they showed significantly higher sensitivity to compound YM-155.

CONCLUSIONS

Tumor ecosystem subtypes expand existing HCC subtyping systems, have distinct drivers, prognosis, and treatment vulnerabilities.

Genome-wide profiling of transcription factor activity in primary liver cancer using single-cell ATAC sequencing

Primary liver cancer (PLC) consists of two main histological subtypes; hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (iCCA). The role of transcription factors (TFs) in malignant hepatobiliary lineage commitment between HCC and iCCA remains underexplored. Here, we present genome-wide profiling of transcription regulatory elements of 16 PLC patients using single-cell assay for transposase accessible chromatin sequencing. Single-cell open chromatin profiles reflect the compositional diversity of liver cancer, identifying both malignant and microenvironment component cells. TF motif enrichment levels of 31 TFs strongly discriminate HCC from iCCA tumors. These TFs are members of the nuclear/retinoid receptor, POU, or ETS motif families. POU factors are associated with prognostic features in iCCA. Overall, nuclear receptors, ETS and POU TF motif families delineate transcription regulation between HCC and iCCA tumors, which may be relevant to development and selection of PLC subtype-specific therapeutics.

The cellular trajectories of tumor-associated macrophages decipher the heterogeneity of pancreatic cancer

Emerging evidence indicates that the interactions and dynamic changes among tumor-associated macrophages (TAMs) are pivotal in molding the tumor microenvironment (TME), thereby influencing diverse clinical outcomes. However, the potential clinical ramifications of these evolutionary shifts in tumor-associated macrophages within pancreatic adenocarcinoma (PAAD) remain largely unexamined. Single-cell RNA sequencing (scRNA-seq) data were retrieved from the Tumor Immune Single-cell Hub. The Seurat and Monocle algorithms were employed to elucidate the progression of TAMs, using non-negative matrix factorization (NMF) to determine molecular classifications. Subsequently, the prognosis, biological characteristics, genomic modifications, and immune landscape across various clusters were interpreted. Furthermore, the sensitivity of potential therapeutic drugs between subtypes was predicted. Cellular experiments were conducted to explore the function of the NR1H3 gene in pancreatic cancer. These experiments encompassed gene knockdown, proliferation assessment, clone formation evaluation, transwell examination, and apoptosis analysis. Trajectory gene expression analysis of tumor-associated macrophages identified three disparate clusters, each associated with different clinical outcomes Compared to clusters C1 and C2, cluster C3 is seemingly at a less advanced pathological stage and associates with a relatively favorable prognosis. Further investigation revealed pronounced genetic instability in cluster C2, whereas cluster C3 demonstrated notable genetic stability. Cluster C1, characterized as "immune-hot," exhibits an abundance of immune cells and elevated immune checkpoint expression, suggesting its suitability for immunotherapy. Furthermore, several potential therapeutic agents have been pinpointed, potentially facilitating the clinical application of these insights. Cell assays indicated that NR1H3 knockdown markedly induced apoptosis and suppressed clonogenesis, migration, and proliferation of pancreatic cancer cells in the PTAU-8988 and PANC-1 cell lines. Overall, our study discerned three clusters with unique characteristics, defined by the evolution of TAMs. We propose customized therapeutic strategies for patients within these specific clusters to improve clinical outcomes and optimize clinical management.

Silencing of genes by promoter hypermethylation shapes tumor microenvironment and resistance to immunotherapy in clear-cell renal cell carcinomas

The efficacy of immune checkpoint inhibitors varies in clear-cell renal cell carcinoma (ccRCC), with notable primary resistance among patients. Here, we integrate epigenetic (DNA methylation) and transcriptome data to identify a ccRCC subtype characterized by cancer-specific promoter hypermethylation and epigenetic silencing of Polycomb targets. We develop and validate an index of methylation-based epigenetic silencing (iMES) that predicts primary resistance to immune checkpoint inhibition (ICI) in the BIONIKK trial. High iMES is associated with VEGF pathway silencing, endothelial cell depletion, immune activation/suppression, EZH2 activation, BAP1/SETD2 deficiency, and resistance to ICI. Combination therapy with hypomethylating agents or tyrosine kinase inhibitors may benefit patients with high iMES. Intriguingly, tumors with low iMES exhibit increased endothelial cells and improved ICI response, suggesting the importance of angiogenesis in ICI treatment. We also develop a transcriptome-based analogous system for extended applicability of iMES. Our study underscores the interplay between epigenetic alterations and tumor microenvironment in determining immunotherapy response.

Anti-tumor activity of all-trans retinoic acid in gastric-cancer: gene-networks and molecular mechanisms

BACKGROUND

Gastric-cancer is a heterogeneous type of neoplastic disease and it lacks appropriate therapeutic options. There is an urgent need for the development of innovative pharmacological strategies, particularly in consideration of the potential stratified/personalized treatment of this tumor. All-Trans Retinoic-acid (ATRA) is one of the active metabolites of vitamin-A. This natural compound is the first example of clinically approved cyto-differentiating agent, being used in the treatment of acute promyelocytic leukemia. ATRA may have significant therapeutic potential also in the context of solid tumors, including gastric-cancer. The present study provides pre-clinical evidence supporting the use of ATRA in the treatment of gastric-cancer using high-throughput approaches.

METHODS

We evaluated the anti-proliferative action of ATRA in 27 gastric-cancer cell-lines and tissue-slice cultures from 13 gastric-cancer patients. We performed RNA-sequencing studies in 13 cell-lines exposed to ATRA. We used these and the gastric-cancer RNA-sequencing data of the TCGA/CCLE datasets to conduct multiple computational analyses.

RESULTS

Profiling of our large panel of gastric-cancer cell-lines for their quantitative response to the anti-proliferative effects of ATRA indicate that approximately half of the cell-lines are characterized by sensitivity to the retinoid. The constitutive transcriptomic profiles of these cell-lines permitted the construction of a model consisting of 42 genes, whose expression correlates with ATRA-sensitivity.  The model predicts that 45% of the TCGA gastric-cancers are sensitive to ATRA. RNA-sequencing studies performed in retinoid-treated gastric-cancer cell-lines provide insights into the gene-networks underlying ATRA anti-tumor activity. In addition, our data demonstrate that ATRA exerts significant immune-modulatory effects, which seem to be largely controlled by IRF1 up-regulation. Finally, we provide evidence of a feed-back loop between IRF1 and DHRS3, another gene which is up-regulated by ATRA.

CONCLUSIONS

ATRA is endowed with significant therapeutic potential in the stratified/personalized treatment gastric-cancer. Our data represent the fundaments for the design of clinical trials focusing on the use of ATRA in the personalized treatment of this heterogeneous tumor. Our gene-expression model will permit the development of a predictive tool for the selection of ATRA-sensitive gastric-cancer patients. The immune-regulatory responses activated by ATRA suggest that the retinoid and immune-checkpoint inhibitors constitute rational combinations for the management of gastric-cancer.

Racial Disparities in MiT Family Translocation Renal Cell Carcinoma

Racial disparities have been documented in the biology and outcome of certain renal cell carcinomas (RCCs) among Black patients. However, little is known about racial differences in MiT family translocation RCC (TRCC). To investigate this issue, we performed a case-control study using data from The Cancer Genome Atlas (TCGA) and the Chinese OrigiMed2020 cohort. A total of 676 patients with RCC (14 Asian, 113 Black, and 525 White) were identified in TCGA, and TRCC was defined as RCC with TFE3/TFEB translocation or TFEB amplification, leading to 21 patients with TRCC (2 Asian, 8 Black, 10 White, and 1 unknown). Asian (2 of 14 [14.3%] vs 10 of 525 [1.9%]; P = .036) and Black (8 of 113 [7.1%] vs 1.9%; P = .007) patients with RCC showed significantly higher prevalence of TRCC compared with White patients with RCC. The overall mortality rate of TRCC was slightly higher in Asian and Black patients compared with White patients (HR: 6.05, P = .069). OrigiMed2020 Chinese patients with RCC had a significantly higher proportion of TRCC with TFE3 fusions than TCGA White patients with RCC (13 of 250 [5.2%] vs 7 of 525 [1.3%]; P = .003). Black patients with TRCC were more likely to exhibit the proliferative subtype than White patients (6 of 8 [75%] vs 2 of 9 [22.2%]; P = .057) for those who had RNA-seq profiles. We present evidence of higher prevalence of TRCC in Asian and Black patients with RCC compared with White patients and show that these tumors in Asian and Black patients have distinct transcriptional signatures and are associated with poor outcomes.

Oncogenic pathways refine a new perspective on the classification of hepatocellular carcinoma

BACKGROUND

Genetic alterations in oncogenic pathways are critical for cancer initiation, development, and treatment resistance. However, studies are limited regarding pathways correlated with prognosis, sorafenib, and transcatheter arterial chemoembolization (TACE) in hepatocellular carcinoma (HCC).

METHODS

In this study, 1928 patients from 11 independent datasets and a clinical in-house cohort were screened to explore the relationships among canonical pathway alterations in HCC patients. The molecular mechanisms, biological functions, immune landscape, and clinical outcomes among three heterogeneous phenotypes were further explored.

RESULTS

We charted the detailed landscape of pathway alterations in the TCGA-LIHC cohort, screened three pivotal pathways (p53, PI3K, and WNT), identified co-occurrence patterns and mutual exclusively, and stratified patients into three altered-pathway dominant phenotypes (ADPs). P53|PI3K ADP characterized by genomic instability (e.g., highest TMB, FGA, FGG, and FGL) indicated an unfavorable prognosis. While, patients in WNT ADP suggested a median prognosis, enhanced immune activation, and sensitivity to PD-L1 therapy. Remarkably, sorafenib and TACE exhibited efficacy for patients in WNT ADP and low frequent alteration phenotype (LFP). Additionally, ADP could work independently of common clinical traits (e.g., AJCC stage) and previous molecular classifications (e.g., iCluster, serum biomarkers).

CONCLUSIONS

ADP provides a new perspective for identifying patients at high risk of recurrence and could optimize precision treatment to improve the clinical outcomes in HCC.

Machine learning-based establishment and validation of age-related patterns for predicting prognosis in non-small cell lung cancer within the context of the tumor microenvironment

Lung cancer (LC) is a leading cause of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) accounting for over 80% of cases. The impact of aging on clinical outcomes in NSCLC remains poorly understood, particularly with respect to the immune response. In this study, we explored the effects of aging on NSCLC using 307 genes associated with human aging from the Human Ageing Genomic Resources. We identified 53 aging-associated genes that significantly correlate with overall survival of NSCLC patients, including the clinically validated gene BUB1B. Furthermore, we developed an aging-associated enrichment score to categorize patients based on their aging subtypes and evaluated their prognostic and therapeutic response values in LC. Our analyses yielded two aging-associated subtypes with unique profiles in the tumor microenvironment, demonstrating varying responses to immunotherapy. Consensus clustering based on transcriptome profiles provided insights into the effects of aging on NSCLC and highlighted the potential of personalized therapeutic approaches tailored to aging subtypes. Our findings provide a new target and theoretical support for personalized therapeutic approaches in patients with NSCLC, offering insights into the potential impact of aging on cancer outcomes.

Multiomics characterization and verification of clear cell renal cell carcinoma molecular subtypes to guide precise chemotherapy and immunotherapy

Clear cell renal cell carcinoma (ccRCC) is a heterogeneous tumor with different genetic and molecular alterations. Schemes for ccRCC classification system based on multiomics are urgent, to promote further biological insights. Two hundred and fifty-five ccRCC patients with paired data of clinical information, transcriptome expression profiles, copy number alterations, DNA methylation, and somatic mutations were collected for identification. Bioinformatic analyses were performed based on our team's recently developed R package "MOVICS." With 10 state-of-the-art algorithms, we identified the multiomics subtypes (MoSs) for ccRCC patients. MoS1 is an immune exhausted subtype, presented the poorest prognosis, and might be caused by an exhausted immune microenvironment, activated hypoxia features, but can benefit from PI3K/AKT inhibitors. MoS2 is an immune "cold" subtype, which represented more mutation of VHL and PBRM1, favorable prognosis, and is more suitable for sunitinib therapy. MoS3 is the immune "hot" subtype, and can benefit from the anti-PD-1 immunotherapy. We successfully verified the different molecular features of the three MoSs in external cohorts GSE22541, GSE40435, and GSE53573. Patients that received Nivolumab therapy helped us to confirm that MoS3 is suitable for anti-PD-1 therapy. E-MTAB-3267 cohort also supported the fact that MoS2 patients can respond more to sunitinib treatment. We also confirm that SETD2 is a tumor suppressor in ccRCC, along with the decreased SETD2 protein level in advanced tumor stage, and knock-down of SETD2 leads to the promotion of cell proliferation, migration, and invasion. In summary, we provide novel insights into ccRCC molecular subtypes based on robust clustering algorithms via multiomics data, and encourage future precise treatment of ccRCC patients.

Inflammatory response-based prognostication and personalized therapy decisions in clear cell renal cell cancer to aid precision oncology

OBJECTIVE

The impact of inflammatory response on tumor development and therapeutic response is of significant importance in clear cell renal cell carcinoma (ccRCC). The customization of specialized prognostication approaches and the exploration of supplementary treatment options hold critical clinical implications in relation to the inflammatory response.

METHODS

In the present study, unsupervised clustering was implemented on TCGA-KIRC tumors using transcriptome profiles of inflammatory response genes, which was then validated in two ccRCC datasets (E-MATB-1980 and ICGC) and two immunotherapy datasets (IMvigor210 and Liu et al.) via SubMap and NTP algorithms. Combining co-expression and LASSO analyses, inflammatory response-based scoring system was defined, which was evaluated in pan-cancer.

RESULTS

Three reproducible inflammatory response subtypes (named IR1, IR2 and IR3) were determined and independently verified, each exhibiting distinct molecular, clinical, and immunological characteristics. Among these subtypes, IR2 had the best OS outcomes, followed by IR3 and IR1. In terms of anti-angiogenic agents, sunitinib may be appropriate for IR1 patients, while axitinib and pazopanib may be suitable for IR2 patients, and sorafenib for IR3 patients. Additionally, IR1 patients might benefit from anti-CTLA4 therapy. A scoring system called IRscore was defined for individual ccRCC patients. Patients with high IRscore presented a lower response rate to anti-PD-L1 therapy and worse prognostic outcomes. Pan-cancer analysis demonstrated the immunological features and prognostic relevance of the IRscore.

CONCLUSION

Altogether, characterization of inflammatory response subtypes and IRscore provides a roadmap for patient risk stratification and personalized treatment decisions, not only in ccRCC, but also in pan-cancer.

Lenvatinib activates anti-tumor immunity by suppressing immunoinhibitory infiltrates in the tumor microenvironment of advanced hepatocellular carcinoma

BACKGROUND

Lenvatinib, a multiple receptor tyrosine kinase inhibitor, might exert antitumor effects via tumor immune modulation. However, changes in the tumor immune microenvironment induced by lenvatinib are poorly understood. We investigated the effect of lenvatinib on immune features in clinical samples from patients with hepatocellular carcinoma.

METHODS

Fifty-one patients with advanced hepatocellular carcinoma who received lenvatinib monotherapy as first-line treatment were enrolled. We collected blood sample (n = 51) and tumor tissue (n, baseline/four weeks after treatment initiation/post-progression = 50/8/12). DNA, RNA, and proteins extracted from the tissues were subjected to multi-omics analysis, and patients were classified into two groups according to baseline immune status. Each group was investigated in terms of the dynamics of tumor signaling. We also longitudinally analyzed circulating immune proteins and chemokines in peripheral blood.

RESULTS

Here we show that lenvatinib has similar anti-tumor efficacy with objective response rate and progression-free survival in both Immune-Hot and Immune-Cold subtypes. Immune signatures associated with T-cell functions and interferon responses are enriched in the early phase of treatment, while signatures associated with immunoinhibitory cells are downregulated along with efficient vascular endothelial growth factor receptor and fibroblast growth factor receptor blockades. These findings are supported by imaging mass cytometry, T-cell receptor repertoire analysis and kinetics of circulating proteins. We also identify interleukin-8 and angiopoietin-2 as possible targets of intervention to overcome resistance to existing immunotherapies.

CONCLUSIONS

Our findings show the ability of lenvatinib to modulate tumor immunity in clinical samples of hepatocellular carcinoma.

Immune perturbation network identifies an EMT subtype with chromosomal instability and tumor immune-desert microenvironment

Most gastric cancer (GC) subtypes are identified through transcriptional profiling overlooking dynamic changes and interactions in gene expression. Based on the background network of global immune genes, we constructed sample-specific edge-perturbation matrices and identified four molecular network subtypes of GC (MNG). MNG-1 displayed the best prognosis and vigorous cell cycle activity. MNG-2 was enriched by immune-hot phenotype with the potential for immunotherapy response. MNG-3 and MNG-4 were identified with epithelial-mesenchymal transition (EMT) peculiarity and worse prognosis, termed EMT subtypes. MNG-3 was characterized by low mutational burden and stromal cells and considered a replica of previous subtypes associated with poor prognosis. Notably, MNG-4 was considered a previously undefined subtype with a dismal prognosis, characterized by chromosomal instability and immune-desert microenvironment. This subtype tended to metastasize and was resistant to respond to immunotherapy. Pharmacogenomics analysis showed three therapeutic agents (NVP-BEZ235, LY2606368, and rutin) were potential interventions for MNG-4.

A molecular classification system for estimating radiotherapy response and anticancer immunity for individual breast cancer patients

Objective

Radiotherapy is a cornerstone of breast cancer therapy, but radiotherapy resistance is a major clinical challenge. Herein, we show a molecular classification approach for estimating individual responses to radiotherapy.

Methods

Consensus clustering was adopted to classify radiotherapy-sensitive and -resistant clusters in the TCGA-BRCA cohort based upon prognostic differentially expressed radiotherapy response-related genes (DERRGs). The stability of the classification was proven in the GSE58812 cohort via NTP method and the reliability was further verified by quantitative RT-PCR analyses of DERRGs. A Riskscore system was generated through Least absolute shrinkage and selection operator (LASSO) analysis, and verified in the GSE58812 and GSE17705. Treatment response and anticancer immunity were evaluated via multiple well-established computational approaches.

Results

We classified breast cancer patients as radiotherapy-sensitive and -resistant clusters, namely C1 and C2, also verified by quantitative RT-PCR analyses of DERRGs. Two clusters presented heterogeneous clinical traits, with poorer prognosis, older age, more advanced T, and more dead status in the C2. The C1 tumors had higher activity of reactive oxygen species and response to X-ray, proving better radiotherapeutic response. Stronger anticancer immunity was found in the C1 tumors that had rich immune cell infiltration, similar expression profiling to patients who responded to anti-PD-1, and activated immunogenic cell death and ferroptosis. The Riskscore was proposed for improving patient prognosis. High Riskscore samples had lower radiotherapeutic response and stronger DNA damage repair as well as poor anticancer immunity, while low Riskscore samples were more sensitive to docetaxel, doxorubicin, and paclitaxel.

Conclusion

Our findings propose a novel radiotherapy response classification system based upon molecular profiles for estimating radiosensitivity for individual breast cancer patients, and elucidate a methodological advancement for synergy of radiotherapy with ICB.

Genome-scale CRISPR-Cas9 screening stratifies pancreatic cancer with distinct outcomes and immunotherapeutic efficacy

Pancreatic cancer (PC) was featured by dramatic heterogeneity and dismal outcomes. An ideal classification strategy capable of achieving risk stratification and individualized treatment is urgently needed to significantly improve prognosis. In this study, using the 105 prognostic cancer essential genes identified by genome-scale CRISPR-Cas9 screening and univariate Cox analysis, we established and verified three heterogeneous subtypes via non-negative matrix factorization (NMF) and nearest template prediction (NTP) algorithms in the TCGA-PAAD cohort (176 samples) and four multi-center cohorts (233 samples), respectively. Among them, C1 with the worst prognosis was enriched in immune-related pathways, possessed superior infiltration abundance of immune cells and immune checkpoint molecules expression, and might be most sensitive to immunotherapy. C3, owing a moderate prognosis, might be featured by proliferative biological function, and despite its highest immunogenicity, the defects in antigen processing and presentation ability coupled with barren immune environment render it ineffective for immunotherapy, while it had potential sensitivity to paclitaxel and methotrexate. Besides, C2 harbored the best prognosis and was characterized by metabolism-related functions. These results could deepen our understanding of PC molecular heterogeneity and provide a trustworthy reference for prognostic stratification management and precision medicine in clinical practice.

Combination of an ACLY inhibitor with a GLP-1R agonist exerts additive benefits on nonalcoholic steatohepatitis and hepatic fibrosis in mice

Increased liver de novo lipogenesis (DNL) is a hallmark of nonalcoholic steatohepatitis (NASH). A key enzyme controlling DNL upregulated in NASH is ATP citrate lyase (ACLY). In mice, inhibition of ACLY reduces liver steatosis, ballooning, and fibrosis and inhibits activation of hepatic stellate cells. Glucagon-like peptide-1 receptor (GLP-1R) agonists lower body mass, insulin resistance, and steatosis without improving fibrosis. Here, we find that combining an inhibitor of liver ACLY, bempedoic acid, and the GLP-1R agonist liraglutide reduces liver steatosis, hepatocellular ballooning, and hepatic fibrosis in a mouse model of NASH. Liver RNA analyses revealed additive downregulation of pathways that are predictive of NASH resolution, reductions in the expression of prognostically significant genes compared with clinical NASH samples, and a predicted gene signature profile that supports fibrosis resolution. These findings support further investigation of this combinatorial therapy to treat obesity, insulin resistance, hypercholesterolemia, steatohepatitis, and fibrosis in people with NASH.

Metabolic classification suggests the GLUT1/ALDOB/G6PD axis as a therapeutic target in chemotherapy-resistant pancreatic cancer

Metabolic reprogramming is known as an emerging mechanism of chemotherapy resistance, but the metabolic signatures of pancreatic ductal adenocarcinomas (PDACs) remain unclear. Here, we characterize the metabolomic profile of PDAC organoids and classify them into glucomet-PDAC (high glucose metabolism levels) and lipomet-PDAC (high lipid metabolism levels). Glucomet-PDACs are more resistant to chemotherapy than lipomet-PDACs, and patients with glucomet-PDAC have a worse prognosis. Integrated analyses reveal that the GLUT1/aldolase B (ALDOB)/glucose-6-phosphate dehydrogenase (G6PD) axis induces chemotherapy resistance by remodeling glucose metabolism in glucomet-PDAC. Increased glycolytic flux, G6PD activity, and pyrimidine biosynthesis are identified in glucomet-PDAC with high GLUT1 and low ALDOB expression, and these phenotypes could be reversed by inhibiting GLUT1 expression or by increasing ALDOB expression. Pharmacological inhibition of GLUT1 or G6PD enhances the chemotherapy response of glucomet-PDAC. Our findings uncover potential metabolic heterogeneity related to differences in chemotherapy sensitivity in PDAC and develop a promising pharmacological strategy for patients with chemotherapy-resistant glucomet-PDAC through the combination of chemotherapy and GLUT1/ALDOB/G6PD axis inhibitors.

An omics data analysis method based on feature linear relationship and graph convolutional network

Biological networks are known to be highly modular, and the dysfunction of network modules may cause diseases. Defining the key modules from the omics data and establishing the classification model is helpful in promoting the research of disease diagnosis and prognosis. However, for applying modules in downstream analysis such as disease states discrimination, most methods only utilize the node information, and ignore the node interactions or topological information, which may lead to false positives and limit the model performance. In this study, we propose an omics data analysis method based on feature linear relationship and graph convolutional network (LCNet). In LCNet, we adopt a way of applying the difference of feature linear relationships during disease development to characterize physiological and pathological changes and construct the differential linear relation network, which is simple and interpretable from the perspective of feature linear relationship. A greedy strategy is developed for searching the highly interactive modules with a strong discrimination ability. To fully utilize the information of the detected modules, the personalized sub-graphs for each sample based on the modules are defined, and the graph convolutional network (GCN) classifiers are trained to predict the sample labels. The experimental results on public datasets show the superiority of LCNet in classification performance. For Breast Cancer metabolic data, the identified metabolites by LCNet involve important pathways. Thus, LCNet can identify the module biomarkers by feature linear relationship and a greedy strategy, and label samples by personalized sub-graphs and GCN. It provides a new manner of utilizing node (molecule) information and topological information in the defined modules for better disease classification.