Pan-cancer proteogenomics characterization of tumor immunity.

Despite the successes of immunotherapy in cancer treatment over recent decades, less than <10%-20% cancer cases have demonstrated durable responses from immune checkpoint blockade. To enhance the efficacy of immunotherapies, combination therapies suppressing multiple immune evasion mechanisms are increasingly contemplated. To better understand immune cell surveillance and diverse immune evasion responses in tumor tissues, we comprehensively characterized the immune landscape of more than 1,000 tumors across ten different cancers using CPTAC pan-cancer proteogenomic data. We identified seven distinct immune subtypes based on integrative learning of cell type compositions and pathway activities. We then thoroughly categorized unique genomic, epigenetic, transcriptomic, and proteomic changes associated with each subtype. Further leveraging the deep phosphoproteomic data, we studied kinase activities in different immune subtypes, which revealed potential subtype-specific therapeutic targets. Insights from this work will facilitate the development of future immunotherapy strategies and enhance precision targeting with existing agents.

Airborne transmission of human-isolated avian H3N8 influenza virus between ferrets.

H3N8 avian influenza viruses (AIVs) in China caused two confirmed human infections in 2022, followed by a fatal case reported in 2023. H3N8 viruses are widespread in chicken flocks; however, the zoonotic features of H3N8 viruses are poorly understood. Here, we demonstrate that H3N8 viruses were able to infect and replicate efficiently in organotypic normal human bronchial epithelial (NHBE) cells and lung epithelial (Calu-3) cells. Human isolates of H3N8 virus were more virulent and caused severe pathology in mice and ferrets, relative to chicken isolates. Importantly, H3N8 virus isolated from a patient with severe pneumonia was transmissible between ferrets through respiratory droplets; it had acquired human-receptor-binding preference and amino acid substitution PB2-E627K necessary for airborne transmission. Human populations, even when vaccinated against human H3N2 virus, appear immunologically naive to emerging mammalian-adapted H3N8 AIVs and could be vulnerable to infection at epidemic or pandemic proportion.

Activation and Signaling Mechanism Revealed by Cannabinoid Receptor-Gi Complex Structures.

Human endocannabinoid systems modulate multiple physiological processes mainly through the activation of cannabinoid receptors CB1 and CB2. Their high sequence similarity, low agonist selectivity, and lack of activation and G protein-coupling knowledge have hindered the development of therapeutic applications. Importantly, missing structural information has significantly held back the development of promising CB2-selective agonist drugs for treating inflammatory and neuropathic pain without the psychoactivity of CB1. Here, we report the cryoelectron microscopy structures of synthetic cannabinoid-bound CB2 and CB1 in complex with Gi, as well as agonist-bound CB2 crystal structure. Of important scientific and therapeutic benefit, our results reveal a diverse activation and signaling mechanism, the structural basis of CB2-selective agonists design, and the unexpected interaction of cholesterol with CB1, suggestive of its endogenous allosteric modulating role.

Crystal Structure of the Human Cannabinoid Receptor CB2.

The cannabinoid receptor CB2 is predominately expressed in the immune system, and selective modulation of CB2 without the psychoactivity of CB1 has therapeutic potential in inflammatory, fibrotic, and neurodegenerative diseases. Here, we report the crystal structure of human CB2 in complex with a rationally designed antagonist, AM10257, at 2.8 Å resolution. The CB2-AM10257 structure reveals a distinctly different binding pose compared with CB1. However, the extracellular portion of the antagonist-bound CB2 shares a high degree of conformational similarity with the agonist-bound CB1, which led to the discovery of AM10257's unexpected opposing functional profile of CB2 antagonism versus CB1 agonism. Further structural analysis using mutagenesis studies and molecular docking revealed the molecular basis of their function and selectivity for CB2 and CB1. Additional analyses of our designed antagonist and agonist pairs provide important insight into the activation mechanism of CB2. The present findings should facilitate rational drug design toward precise modulation of the endocannabinoid system.

Identifying Epistasis in Cancer Genomes: A Delicate Affair.

Recent studies of the tumor genome seek to identify cancer pathways as groups of genes in which mutations are epistatic with one another or, specifically, "mutually exclusive." Here, we show that most mutations are mutually exclusive not due to pathway structure but to interactions with disease subtype and tumor mutation load. In particular, many cancer driver genes are mutated preferentially in tumors with few mutations overall, causing mutations in these cancer genes to appear mutually exclusive with numerous others. Researchers should view current epistasis maps with caution until we better understand the multiple cause-and-effect relationships among factors such as tumor subtype, positive selection for mutations, and gross tumor characteristics including mutational signatures and load.

Comprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer.

We report a comprehensive analysis of 412 muscle-invasive bladder cancers characterized by multiple TCGA analytical platforms. Fifty-eight genes were significantly mutated, and the overall mutational load was associated with APOBEC-signature mutagenesis. Clustering by mutation signature identified a high-mutation subset with 75% 5-year survival. mRNA expression clustering refined prior clustering analyses and identified a poor-survival "neuronal" subtype in which the majority of tumors lacked small cell or neuroendocrine histology. Clustering by mRNA, long non-coding RNA (lncRNA), and miRNA expression converged to identify subsets with differential epithelial-mesenchymal transition status, carcinoma in situ scores, histologic features, and survival. Our analyses identified 5 expression subtypes that may stratify response to different treatments.

Comprehensive and Integrated Genomic Characterization of Adult Soft Tissue Sarcomas.

Sarcomas are a broad family of mesenchymal malignancies exhibiting remarkable histologic diversity. We describe the multi-platform molecular landscape of 206 adult soft tissue sarcomas representing 6 major types. Along with novel insights into the biology of individual sarcoma types, we report three overarching findings: (1) unlike most epithelial malignancies, these sarcomas (excepting synovial sarcoma) are characterized predominantly by copy-number changes, with low mutational loads and only a few genes (TP53, ATRX, RB1) highly recurrently mutated across sarcoma types; (2) within sarcoma types, genomic and regulomic diversity of driver pathways defines molecular subtypes associated with patient outcome; and (3) the immune microenvironment, inferred from DNA methylation and mRNA profiles, associates with outcome and may inform clinical trials of immune checkpoint inhibitors. Overall, this large-scale analysis reveals previously unappreciated sarcoma-type-specific changes in copy number, methylation, RNA, and protein, providing insights into refining sarcoma therapy and relationships to other cancer types.

Breast Cancer Statistics, 2022.

This article is the American Cancer Society's update on female breast cancer statistics in the United States, including population-based data on incidence, mortality, survival, and mammography screening. Breast cancer incidence rates have risen in most of the past four decades; during the most recent data years (2010-2019), the rate increased by 0.5% annually, largely driven by localized-stage and hormone receptor-positive disease. In contrast, breast cancer mortality rates have declined steadily since their peak in 1989, albeit at a slower pace in recent years (1.3% annually from 2011 to 2020) than in the previous decade (1.9% annually from 2002 to 2011). In total, the death rate dropped by 43% during 1989-2020, translating to 460,000 fewer breast cancer deaths during that time. The death rate declined similarly for women of all racial/ethnic groups except American Indians/Alaska Natives, among whom the rates were stable. However, despite a lower incidence rate in Black versus White women (127.8 vs. 133.7 per 100,000), the racial disparity in breast cancer mortality remained unwavering, with the death rate 40% higher in Black women overall (27.6 vs. 19.7 deaths per 100,000 in 2016-2020) and two-fold higher among adult women younger than 50 years (12.1 vs. 6.5 deaths per 100,000). Black women have the lowest 5-year relative survival of any racial/ethnic group for every molecular subtype and stage of disease (except stage I), with the largest Black-White gaps in absolute terms for hormone receptor-positive/human epidermal growth factor receptor 2-negative disease (88% vs. 96%), hormone receptor-negative/human epidermal growth factor receptor 2-positive disease (78% vs. 86%), and stage III disease (64% vs. 77%). Progress against breast cancer mortality could be accelerated by mitigating racial disparities through increased access to high-quality screening and treatment via nationwide Medicaid expansion and partnerships between community stakeholders, advocacy organizations, and health systems.

A proteomic and phosphoproteomic landscape of KRAS mutant cancers identifies combination therapies.

KRAS mutant cancer, characterized by the activation of a plethora of phosphorylation signaling pathways, remains a major challenge for cancer therapy. Despite recent advancements, a comprehensive profile of the proteome and phosphoproteome is lacking. This study provides a proteomic and phosphoproteomic landscape of 43 KRAS mutant cancer cell lines across different tissue origins. By integrating transcriptomics, proteomics, and phosphoproteomics, we identify three subsets with distinct biological, clinical, and therapeutic characteristics. The integrative analysis of phosphoproteome and drug sensitivity information facilitates the identification of a set of drug combinations with therapeutic potentials. Among them, we demonstrate that the combination of DOT1L and SHP2 inhibitors is an effective treatment specific for subset 2 of KRAS mutant cancers, corresponding to a set of TCGA clinical tumors with the poorest prognosis. Together, this study provides a resource to better understand KRAS mutant cancer heterogeneity and identify new therapeutic possibilities.

Subtyping and prognostic model construction based on vesicle-mediated transport-related genes in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) is impacted by various environmental and genetic variables. Dysregulation of vesicle-mediated transport-related genes (VMTRGs) has been observed in many malignancies, but their effect on prognosis in CRC remains unclear. METHODS: CRC samples were clustered into varying subtypes per differential expression of VMTRGs. R package was utilized to explore differences in survival, immune, and drug sensitivity among different disease subtypes. According to differentially expressed genes (DEGs) between subtypes, regression analysis was employed to build a riskscore model and identify independent prognostic factors. The model was validated through a Gene Expression Omnibus (GEO) dataset. Immune landscape, immunophenoscore (IPS), and Tumor Immune Dysfunction and Exclusion (TIDE) scores for different risk groups were calculated. RESULTS: Two subtypes of CRC were identified based on VMTRGs, which showed significant differences in survival rates, immune cell infiltration abundance, immune functional activation levels, and immune checkpoint expression levels. Cluster2 exhibited higher sensitivity to anti-tumor drugs such as Nilotinib, Cisplatin, and Oxaliplatin compared to Cluster1. DEGs were mainly enriched in biological processes such as epidermis development, epidermal cell differentiation, and receptor-ligand activity, and signaling pathways like pancreatic secretion. The constructed 13-gene riskscore model demonstrated good predictive ability for CRC patients' prognosis. Furthermore, differences in immune landscape, IPS, and TIDE scores were observed among different risk groups. CONCLUSION: This study successfully obtained two CRC subtypes with distinct survival statuses and immune levels based on differential expression of VMTRGs. A 13-gene risk model was constructed. The findings had important implications for prognosis and treatment of CRC.

Breast cancer statistics, 2019.

This article is the American Cancer Society's biennial update on female breast cancer statistics in the United States, including data on incidence, mortality, survival, and screening. Over the most recent 5-year period (2012-2016), the breast cancer incidence rate increased slightly by 0.3% per year, largely because of rising rates of local stage and hormone receptor-positive disease. In contrast, the breast cancer death rate continues to decline, dropping 40% from 1989 to 2017 and translating to 375,900 breast cancer deaths averted. Notably, the pace of the decline has slowed from an annual decrease of 1.9% during 1998 through 2011 to 1.3% during 2011 through 2017, largely driven by the trend in white women. Consequently, the black-white disparity in breast cancer mortality has remained stable since 2011 after widening over the past 3 decades. Nevertheless, the death rate remains 40% higher in blacks (28.4 vs 20.3 deaths per 100,000) despite a lower incidence rate (126.7 vs 130.8); this disparity is magnified among black women aged <50 years, who have a death rate double that of whites. In the most recent 5-year period (2013-2017), the death rate declined in Hispanics (2.1% per year), blacks (1.5%), whites (1.0%), and Asians/Pacific Islanders (0.8%) but was stable in American Indians/Alaska Natives. However, by state, breast cancer mortality rates are no longer declining in Nebraska overall; in Colorado and Wisconsin in black women; and in Nebraska, Texas, and Virginia in white women. Breast cancer was the leading cause of cancer death in women (surpassing lung cancer) in four Southern and two Midwestern states among blacks and in Utah among whites during 2016-2017. Declines in breast cancer mortality could be accelerated by expanding access to high-quality prevention, early detection, and treatment services to all women.

Blockade of a novel MAP4K4-LATS2-SASH1-YAP1 cascade inhibits tumorigenesis and metastasis in luminal breast cancer.

Novel components in the noncanonical Hippo pathway that mediate the growth, metastasis, and drug resistance of breast cancer (BC) cells need to be identified. Here, we showed that expression of SAM and SH3 domain-containing protein 1 (SASH1) is negatively correlated with expression of mitogen-activated protein kinase kinase kinase kinase 4 (MAP4K4) in a subpopulation of patients with luminal-subtype BC. Downregulated SASH1 and upregulated MAP4K4 synergistically regulated the proliferation, migration, and invasion of luminal-subtype BC cells. The expression of LATS2, SASH1, and YAP1 and the phosphorylation of YAP1 were negatively regulated by MAP4K4, and LATS2 then phosphorylated SASH1 to form a novel MAP4K4-LATS2-SASH1-YAP1 cascade. Dephosphorylation of Yes1 associated transcriptional regulator (YAP1), YAP1/TAZ nuclear translocation, and downstream transcriptional regulation of YAP1 were promoted by the combined effects of ectopic MAP4K4 expression and SASH1 silencing. Targeted inhibition of MAP4K4 blocked proliferation, cell migration, and ER signaling both in vitro and in vivo. Our findings reveal a novel MAP4K4-LATS2-SASH1-YAP1 phosphorylation cascade, a noncanonical Hippo pathway that mediates ER signaling, tumorigenesis, and metastasis in breast cancer. Targeted intervention with this noncanonical Hippo pathway may constitute a novel alternative therapeutic approach for endocrine-resistant BC.

Dissecting super-enhancer heterogeneity: time to re-examine cancer subtypes?

The heterogeneity of transcriptional regulations by super-enhancers (SEs) is poorly understood in human cancers. Herein, we summarize a bioinformatics workflow for genome-wide SE profiling and identification of subtype-specific SEs and regulatory networks. Dissecting SE heterogeneity provides new insights into cancer biology and alternative therapeutic strategies for cancer precision medicine.

Deep multi-view contrastive learning for cancer subtype identification.

Cancer heterogeneity has posed great challenges in exploring precise therapeutic strategies for cancer treatment. The identification of cancer subtypes aims to detect patients with distinct molecular profiles and thus could provide new clues on effective clinical therapies. While great efforts have been made, it remains challenging to develop powerful computational methods that can efficiently integrate multi-omics datasets for the task. In this paper, we propose a novel self-supervised learning model called Deep Multi-view Contrastive Learning (DMCL) for cancer subtype identification. Specifically, by incorporating the reconstruction loss, contrastive loss and clustering loss into a unified framework, our model simultaneously encodes the sample discriminative information into the extracted feature representations and well preserves the sample cluster structures in the embedded space. Moreover, DMCL is an end-to-end framework where the cancer subtypes could be directly obtained from the model outputs. We compare DMCL with eight alternatives ranging from classic cancer subtype identification methods to recently developed state-of-the-art systems on 10 widely used cancer multi-omics datasets as well as an integrated dataset, and the experimental results validate the superior performance of our method. We further conduct a case study on liver cancer and the analysis results indicate that different subtypes might have different responses to the selected chemotherapeutic drugs.

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

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

PhenoDriver: interpretable framework for studying personalized phenotype-associated driver genes in breast cancer.

Identifying personalized cancer driver genes and further revealing their oncogenic mechanisms is critical for understanding the mechanisms of cell transformation and aiding clinical diagnosis. Almost all existing methods primarily focus on identifying driver genes at the cohort or individual level but fail to further uncover their underlying oncogenic mechanisms. To fill this gap, we present an interpretable framework, PhenoDriver, to identify personalized cancer driver genes, elucidate their roles in cancer development and uncover the association between driver genes and clinical phenotypic alterations. By analyzing 988 breast cancer patients, we demonstrate the outstanding performance of PhenoDriver in identifying breast cancer driver genes at the cohort level compared to other state-of-the-art methods. Otherwise, our PhenoDriver can also effectively identify driver genes with both recurrent and rare mutations in individual patients. We further explore and reveal the oncogenic mechanisms of some known and unknown breast cancer driver genes (e.g. TP53, MAP3K1, HTT, etc.) identified by PhenoDriver, and construct their subnetworks for regulating clinical abnormal phenotypes. Notably, most of our findings are consistent with existing biological knowledge. Based on the personalized driver profiles, we discover two existing and one unreported breast cancer subtypes and uncover their molecular mechanisms. These results intensify our understanding for breast cancer mechanisms, guide therapeutic decisions and assist in the development of targeted anticancer therapies.

A denoised multi-omics integration framework for cancer subtype classification and survival prediction.

The availability of high-throughput sequencing data creates opportunities to comprehensively understand human diseases as well as challenges to train machine learning models using such high dimensions of data. Here, we propose a denoised multi-omics integration framework, which contains a distribution-based feature denoising algorithm, Feature Selection with Distribution (FSD), for dimension reduction and a multi-omics integration framework, Attention Multi-Omics Integration (AttentionMOI) to predict cancer prognosis and identify cancer subtypes. We demonstrated that FSD improved model performance either using single omic data or multi-omics data in 15 The Cancer Genome Atlas Program (TCGA) cancers for survival prediction and kidney cancer subtype identification. And our integration framework AttentionMOI outperformed machine learning models and current multi-omics integration algorithms with high dimensions of features. Furthermore, FSD identified features that were associated to cancer prognosis and could be considered as biomarkers.

MIMESIS: minimal DNA-methylation signatures to quantify and classify tumor signals in tissue and cell-free DNA samples.

DNA-methylation alterations are common in cancer and display unique characteristics that make them ideal markers for tumor quantification and classification. Here we present MIMESIS, a computational framework exploiting minimal DNA-methylation signatures composed by a few dozen informative DNA-methylation sites to quantify and classify tumor signals in tissue and cell-free DNA samples. Extensive analyses of multiple independent and heterogenous datasets including >7200 samples demonstrate the capability of MIMESIS to provide precise estimations of tumor content and to enable accurate classification of tumor type and molecular subtype. To assess our framework for clinical applications, we designed a MIMESIS-informed assay incorporating the minimal signatures for breast cancer. Using both artificial samples and clinical serial cell-free DNA samples from patients with metastatic breast cancer, we show that our approach provides accurate estimations of tumor content, sensitive detection of tumor signal and the ability to capture clinically relevant molecular subtype in patients' circulation. This study provides evidence that our extremely parsimonious approach can be used to develop cost-effective and highly scalable DNA-methylation assays that could support and facilitate the implementation of precision oncology in clinical practice.

A partial epithelial-mesenchymal transition signature for highly aggressive colorectal cancer cells that survive under nutrient restriction.

Partial epithelial-mesenchymal transition (p-EMT) has recently been identified as a hybrid state consisting of cells with both epithelial and mesenchymal characteristics and is associated with the migration, metastasis, and chemoresistance of cancer cells. Here, we describe the induction of p-EMT in starved colorectal cancer (CRC) cells and identify a p-EMT gene signature that can predict prognosis. Functional characterisation of starvation-induced p-EMT in HCT116, DLD1, and HT29 cells showed changes in proliferation, morphology, and drug sensitivity, supported by in vivo studies using the chorioallantoic membrane model. An EMT-specific quantitative polymerase chain reaction (qPCR) array was used to screen for deregulated genes, leading to the establishment of an in silico gene signature that was correlated with poor disease-free survival in CRC patients along with the CRC consensus molecular subtype CMS4. Among the significantly deregulated p-EMT genes, a triple-gene signature consisting of SERPINE1, SOX10, and epidermal growth factor receptor (EGFR) was identified. Starvation-induced p-EMT was characterised by increased migratory potential and chemoresistance, as well as E-cadherin processing and internalisation. Both gene signature and E-cadherin alterations could be reversed by the proteasomal inhibitor MG132. Spatially resolving EGFR expression with high-resolution immunofluorescence imaging identified a proliferation stop in starved CRC cells caused by EGFR internalisation. In conclusion, we have gained insight into a previously undiscovered EMT mechanism that may become relevant when tumour cells are under nutrient stress, as seen in early stages of metastasis. Targeting this process of tumour cell dissemination might help to prevent EMT and overcome drug resistance. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Serum Proteomics Identifies Biomarkers Associated With the Pathogenesis of Idiopathic Pulmonary Fibrosis.

The heterogeneity of idiopathic pulmonary fibrosis (IPF) limits its diagnosis and treatment. The association between the pathophysiological features and the serum protein signatures of IPF currently remains unclear. The present study analyzed the specific proteins and patterns associated with the clinical parameters of IPF based on a serum proteomic dataset by data-independent acquisition using MS. Differentiated proteins in sera distinguished patients with IPF into three subgroups in signal pathways and overall survival. Aging-associated signatures by weighted gene correlation network analysis coincidently provided clear and direct evidence that aging is a critical risk factor for IPF rather than a single biomarker. Expression of LDHA and CCT6A, which was associated with glucose metabolic reprogramming, was correlated with high serum lactic acid content in patients with IPF. Cross-model analysis and machine learning showed that a combinatorial biomarker accurately distinguished patients with IPF from healthy individuals with an area under the curve of 0.848 (95% CI = 0.684-0.941) and validated from another cohort and ELISA assay. This serum proteomic profile provides rigorous evidence that enables an understanding of the heterogeneity of IPF and protein alterations that could help in its diagnosis and treatment decisions.