Evaluation of AlphaFold2 Structures for Hit Identification across Multiple Scenarios.

The introduction of AlphaFold2 (AF2) has sparked significant enthusiasm and generated extensive discussion within the scientific community, particularly among drug discovery researchers. Although previous studies have addressed the performance of AF2 structures in virtual screening (VS), a more comprehensive investigation is still necessary considering the paramount importance of structural accuracy in drug design. In this study, we evaluate the performance of AF2 structures in VS across three common drug discovery scenarios: targets with holo, apo, and AF2 structures; targets with only apo and AF2 structures; and targets exclusively with AF2 structures. We utilized both the traditional physics-based Glide and the deep-learning-based scoring function RTMscore to rank the compounds in the DUD-E, DEKOIS 2.0, and DECOY data sets. The results demonstrate that, overall, the performance of VS on AF2 structures is comparable to that on apo structures but notably inferior to that on holo structures across diverse scenarios. Moreover, when a target has solely AF2 structure, selecting the holo structure of the target from different subtypes within the same protein family produces comparable results with the AF2 structure for VS on the data set of the AF2 structures, and significantly better results than the AF2 structures on its own data set. This indicates that utilizing AF2 structures for docking-based VS may not yield most satisfactory outcomes, even when solely AF2 structures are available. Moreover, we rule out the possibility that the variations in VS performance between the binding pockets of AF2 and holo structures arise from the differences in their biological assembly composition.

The Transcriptional Landscape of Immune-Response 3'-UTR Alternative Polyadenylation in Melanoma.

The prognosis of patients with malignant melanoma has been improved in recent decades due to advancements in immunotherapy. However, a considerable proportion of patients are refractory to treatment, particularly at advanced stages. This underscores the necessity of developing a new strategy to improve it. Alternative polyadenylation (APA), as a marker of crucial posttranscriptional regulation, has emerged as a major new type of epigenetic marker involved in tumorigenesis. However, the potential roles of APA in shaping the tumor microenvironment (TME) are largely unexplored. Herein, we collected two cohorts comprising melanoma patients who received immune checkpoint inhibitor (ICI) immunotherapy to quantify transcriptome-wide discrepancies in APA. We observed a global change in 3'-UTRs between responders and non-responders, which might involve DNA damage response, angiogenesis, PI3K-AKT signaling pathways, etc. Ten putative master APA regulatory factors for those APA events were detected via a network analysis. Notably, we established an immune response-related APA scoring system (IRAPAss), which exhibited a great performance of predicting immunotherapy response in multiple cohorts. Furthermore, we examined the correlation of APA with TME at the single-cell level using four single-cell immune profiles of tumor-infiltrating lymphocytes (TILs), which revealed an overall discrepancy in 3'-UTR length across diverse T cell populations, probably contributing to immunoregulation in melanoma. In conclusion, our study provides a transcriptional landscape of APA implicated in immunoregulation, which might lay the foundation for developing a new strategy for improving immunotherapy response for melanoma patients by targeting APA.

A pathway-based mutation signature to predict the clinical outcomes and response to CTLA-4 inhibitors in melanoma.

Immune checkpoint inhibitor (ICI) therapy has become a powerful clinical strategy for treating melanoma. The relationship between somatic mutations and the clinical benefits of immunotherapy has been widely recognized. However, the gene-based predictive biomarkers are less stable due to the heterogeneity of cancer at the individual gene level. Recent studies have suggested that the accumulation of gene mutations in biological pathways may activate antitumor immune responses. Herein, a novel pathway mutation signature (PMS) was constructed to predict the survival and efficacy of ICI therapy. In a dataset of melanoma patients treated with anti-CTLA-4, we mapped the mutated genes into the pathways and then identified seven significant mutation pathways associated with survival and immunotherapy response, which were used to construct the PMS model. According to the PMS model, the patients in the PMS-high group showed better overall survival (hazard ratio (HR) = 0.37; log-rank test, p < 0.0001) and progression-free survival (HR = 0.52; log-rank test, p = 0.014) than those in the PMS-low group. The PMS-high patients also showed a significantly higher objective response rate to anti-CTLA-4 therapy than the PMS-low patients (Fisher's exact test, p = 0.0055), and the predictive power of the PMS model was superior to that of TMB. Finally, the prognostic and predictive value of the PMS model was validated in two independent validation sets. Our study demonstrated that the PMS model can be considered a potential biomarker to predict the clinical outcomes and response to anti-CTLA-4 therapy in melanoma patients.

iATMEcell: identification of abnormal tumor microenvironment cells to predict the clinical outcomes in cancer based on cell-cell crosstalk network.

Interactions between Tumor microenvironment (TME) cells shape the unique growth environment, sustaining tumor growth and causing the immune escape of tumor cells. Nonetheless, no studies have reported a systematic analysis of cellular interactions in the identification of cancer-related TME cells. Here, we proposed a novel network-based computational method, named as iATMEcell, to identify the abnormal TME cells associated with the biological outcome of interest based on a cell-cell crosstalk network. In the method, iATMEcell first manually collected TME cell types from multiple published studies and obtained their corresponding gene signatures. Then, a weighted cell-cell crosstalk network was constructed in the context of a specific cancer bulk tissue transcriptome data, where the weight between cells reflects both their biological function similarity and the transcriptional dysregulated activities of gene signatures shared by them. Finally, it used a network propagation algorithm to identify significantly dysregulated TME cells. Using the cancer genome atlas (TCGA) Bladder Urothelial Carcinoma training set and two independent validation sets, we illustrated that iATMEcell could identify significant abnormal cells associated with patient survival and immunotherapy response. iATMEcell was further applied to a pan-cancer analysis, which revealed that four common abnormal immune cells play important roles in the patient prognosis across multiple cancer types. Collectively, we demonstrated that iATMEcell could identify potentially abnormal TME cells based on a cell-cell crosstalk network, which provided a new insight into understanding the effect of TME cells in cancer. iATMEcell is developed as an R package, which is freely available on GitHub (https://github.com/hanjunwei-lab/iATMEcell).

Identification of comutation in signaling pathways to predict the clinical outcomes of immunotherapy.

BACKGROUND: Immune checkpoint blockades (ICBs) have emerged as a promising treatment for cancer. Recently, tumour mutational burden (TMB) and neoantigen load (NAL) have been proposed to be potential biomarkers to predict the efficacy of ICB; however, they were limited by difficulties in defining the cut-off values and inconsistent detection platforms. Therefore, it is critical to identify more effective predictive biomarkers for screening patients who will potentially benefit from immunotherapy. In this study, we aimed to identify comutated signaling pathways to predict the clinical outcomes of immunotherapy. METHODS: Here, we comprehensively analysed the signaling pathway mutation status of 9763 samples across 33 different cancer types from The Cancer Genome Atlas (TCGA) by mapping the somatic mutations to the pathways. We then explored the comutated pathways that were associated with increased TMB and NAL by using receiver operating characteristic (ROC) curve analysis and multiple linear regressions. RESULTS: Our results revealed that comutation of the Spliceosome (Sp) pathway and Hedgehog (He) signaling pathway (defined as SpHe-comut+) could be used as a predictor of increased TMB and NAL and was associated with increased levels of immune-related signatures. In seven independent immunotherapy cohorts, we validated that SpHe-comut+ patients exhibited a longer overall survival (OS) or progression-free survival (PFS) and a higher objective response rate (ORR) than SpHe-comut- patients. Moreover, a combination of SpHe-comut status with PD-L1 expression further improved the predictive value for ICB therapy. CONCLUSION: Overall, SpHe-comut+ was demonstrated to be an effective predictor of immunotherapeutic benefit in seven independent immunotherapy cohorts and may serve as a potential and convenient biomarker for the clinical application of ICB therapy.

Tumor Microenvironment Characterization in Breast Cancer Identifies Prognostic Pathway Signatures.

Breast cancer is one of the most common female malignancies worldwide. Due to its early metastases formation and a high degree of malignancy, the 10 year-survival rate of metastatic breast cancer does not exceed 30%. Thus, more precise biomarkers are urgently needed. In our study, we first estimated the tumor microenvironment (TME) infiltration using the xCell algorithm. Based on TME infiltration, the three main TME clusters were identified using consensus clustering. Our results showed that the three main TME clusters cause significant differences in survival rates and TME infiltration patterns (log-rank test, p = 0.006). Then, multiple machine learning algorithms were used to develop a nine-pathway-based TME-related risk model to predict the prognosis of breast cancer (BRCA) patients (the immune-related pathway-based risk score, defined as IPRS). Based on the IPRS, BRCA patients were divided into two subgroups, and patients in the IPRS-low group presented significantly better overall survival (OS) rates than the IPRS-high group (log-rank test, p < 0.0001). Correlation analysis revealed that the IPRS-low group was characterized by increases in immune-related scores (cytolytic activity (CYT), major histocompatibility complex (MHC), T cell-inflamed immune gene expression profile (GEP), ESTIMATE, immune, and stromal scores) while exhibiting decreases in tumor purity, suggesting IPRS-low patients may have a strong immune response. Additionally, the gene-set enrichment analysis (GSEA) result confirmed that the IPRS-low patients were significantly enriched in several immune-associated signaling pathways. Furthermore, multivariate Cox analysis revealed that the IPRS was an independent prognostic biomarker after adjustment by clinicopathologic characteristics. The prognostic value of the IPRS model was further validated in three external validation cohorts. Altogether, our findings demonstrated that the IPRS was a powerful predictor to screen out certain populations with better prognosis in breast cancer and may serve as a potential biomarker guiding clinical treatment decisions.

A novel pathway mutation perturbation score predicts the clinical outcomes of immunotherapy.

The link between tumor genetic variations and immunotherapy benefits has been widely recognized. Recent studies suggested that the key biological pathways activated by accumulated genetic mutations may act as an effective biomarker for predicting the efficacy of immune checkpoint inhibitor (ICI) therapy. Here, we developed a novel individual Pathway Mutation Perturbation (iPMP) method that measures the pathway mutation perturbation level by combining evidence of the cumulative effect of mutated genes with the position of mutated genes in the pathways. In iPMP, somatic mutations on a single sample were first mapped to genes in a single pathway to infer the pathway mutation perturbation score (PMPscore), and then, an integrated PMPscore profile was produced, which can be used in place of the original mutation dataset to identify associations with clinical outcomes. To illustrate the effect of iPMP, we applied it to a melanoma cohort treated with ICIs and identified seven significant perturbation pathways, which jointly constructed a pathway-based signature. With the signature, patients were classified into two subgroups with significant distinctive overall survival and objective response rate to immunotherapy. Moreover, the pathway-based signature was consistently validated in two independent melanoma cohorts. We further applied iPMP to two non-small cell lung cancer cohorts and also obtained good performance. Altogether, the iPMP method could be used to identify the significant mutation perturbation pathways for constructing the pathway-based biomarker to predict the clinical outcomes of immunotherapy. The iPMP method has been implemented as a freely available R-based package (https://CRAN.R-project.org/package=PMAPscore).

Pan-cancer analysis reveals sex-specific signatures in the tumor microenvironment.

The processes of cancer initiation, progression, and response to therapy are affected by the sex of cancer patients. Immunotherapy responses largely depend on the tumor microenvironment (TME), but how sex may shape some TME features, remains unknown. Here, we analyzed immune infiltration signatures across 19 cancer types from 1771 male and 1137 female patients in The Cancer Genome Atlas to evaluate how sex may affect the tumor mutational burden (TMB), immune scores, stromal scores, tumor purity, immune cells, immune checkpoint genes, and functional pathways in the TME. Pan-cancer analyses showed higher TMB and tumor purity scores, as well as lower immune and stromal scores in male patients as compared to female patients. Lung adenocarcinoma, lung squamous carcinoma, kidney papillary carcinoma, and head and neck squamous carcinoma showed the most significant sex biases in terms of infiltrating immune cells, immune checkpoint gene expression, and functional pathways. We further focused on lung adenocarcinoma samples in order to identify and validate sex-specific immune cell biomarkers with prognostic potential. Overall, sex may affect the tumor microenvironment, and sex-specific TME biomarkers may help tailor cancer immunotherapy in certain cancer types.

An Exploration of Subgroups of Neuropsychiatric Symptoms in Mild Cognitive Impairment and Their Risks of Conversion to Dementia or Death.

OBJECTIVE: To explore the heterogeneity of neuropsychiatric symptom (NPS) complexes in individuals with mild cognitive impairment (MCI) and assess the relative risks of converting to dementia or dying. DESIGN: Latent class analysis using 7,971 participants with MCI. SETTING: Participants in the Uniform Data Set (UDS) from 39 NIH Alzheimer's Disease Centers. PARTICIPANTS: Persons with a diagnosis of MCI at initial visit from each center and with either a Mini-Mental State Examination (MMSE) score of 22 or greater or an equivalent education-adjusted Montreal Cognitive Assessment (MoCA) score of 16 or greater. MEASUREMENTS: Neuropsychiatric Inventory Questionnaire (NPI-Q) administered at initial visit. RESULTS: In addition to a subgroup with mild or no NPS (relative frequency, 50%), three empirically-based subgroups of NPS were identified: 1) an "affect" or "negative mood" subgroup (27%) with depression, anxiety, apathy, nighttime disturbance, and change in appetite; 2) a "hyperactive" subgroup (14%) with agitation, irritability, and disinhibition; and 3) a "psychotic with additional severe NPS" subgroup (9%) with the highest risk of delusions and hallucinations, as well as highest risk of all other NPS. Each of these three subgroups had significantly higher risk of converting to dementia than the "mild NPS" class, with the "psychotic with additional severe NPS" subgroup possessing a 64% greater risk. The subgroups did not differ in their risks of death without dementia. CONCLUSION: Our findings of three NPS subgroups in MCI characterized by affect, hyperactive, or psychotic features are largely consistent with a previous 3-factor model of NPS found in a demented population. The consistency of these findings across studies and samples, coupled with our results on the associated risks of converting to dementia, suggests that the NPS structure is robust, and warrants further consideration in classification models of MCI.

CNA2Subpathway: identification of dysregulated subpathway driven by copy number alterations in cancer.

Biological pathways reflect the key cellular mechanisms that dictate disease states, drug response and altered cellular function. The local areas of pathways are defined as subpathways (SPs), whose dysfunction has been reported to be associated with the occurrence and development of cancer. With the development of high-throughput sequencing technology, identifying dysfunctional SPs by using multi-omics data has become possible. Moreover, the SPs are not isolated in the biological system but interact with each other. Here, we propose a network-based calculated method, CNA2Subpathway, to identify dysfunctional SPs is driven by somatic copy number alterations (CNAs) in cancer through integrating pathway topology information, multi-omics data and SP crosstalk. This provides a novel way of SP analysis by using the SP interactions in the system biological level. Using data sets from breast cancer and head and neck cancer, we validate the effectiveness of CNA2Subpathway in identifying cancer-relevant SPs driven by the somatic CNAs, which are also shown to be associated with cancer immune and prognosis of patients. We further compare our results with five pathway or SP analysis methods based on CNA and gene expression data without considering SP crosstalk. With these analyses, we show that CNA2Subpathway could help to uncover dysfunctional SPs underlying cancer via the use of SP crosstalk. CNA2Subpathway is developed as an R-based tool, which is freely available on GitHub (https://github.com/hanjunwei-lab/CNA2Subpathway).

Development and Validation of a Three-Gene Prognostic Signature Based on Tumor Microenvironment for Gastric Cancer.

Gastric cancer (GC), which has high morbidity and low survival rate, is one of the most common malignant tumors in the world. The increasing evidences show that the tumor microenvironment (TME) is related to the occurrence and progression of tumors and the prognosis of patients. In this study, we aimed to develop a TME-based prognostic signature for GC. We first identified the differentially expressed genes (DEGs) related to the TME using the Wilcoxon rank-sum test in a training set of GC. Univariate Cox regression analysis was used to identify prognostic-related DEGs. To decrease the overfitting, we performed the least absolute shrinkage and selection operator (LASSO) regression to reduce the number of signature genes and obtained three genes (LPPR4, ADAM12, NOX4). Next, the multivariate Cox regression was performed to construct the risk score model, and a three-gene prognostic signature was developed. According to the signature, patients were classified into high-risk and low-risk groups with significantly different survival. The signature was then applied to three independent validated sets and obtained the same results. We conducted the time-dependent Receiver Operating Characteristic (ROC) curve analysis to evaluate our signature. We further evaluated the differential immune characters between high-risk and low-risk patients to reveal the potential immune mechanism of the impact on the prognosis of the model. Overall, we identified a three-gene prognostic signature based on TME to predict the prognosis of patients with GC and facilitate the development of a precise treatment strategy.