DataSheet_1_Construction of a molecular inflammatory predictive model with histone modification-related genes and identification of CAMK2D as a potential response signature to infliximab in ulcerative colitis.docx

BackgroundUlcerative colitis (UC) is a lifelong inflammatory disease affecting the rectum and colon with numerous treatment options that require an individualized treatment plan. Histone modifications regulate chromosome structure and gene expression, resulting in effects on inflammatory and immune responses. However, the relationship between histone modification-related genes and UC remains unclear.MethodsTranscriptomic data from GSE59071 and GSE66407 were obtained from the Gene Expression Omnibus (GEO), encompassing colonic biopsy expression profiles of UC patients in inflamed and non-inflamed status. Differentially expressed gene (DEG) analyses, functional enrichment analyses, weighted gene co-expression network analysis (WGCNA), and random forest were performed to identify histone modification-related core genes associated with UC inflammation. Features were screened through the least absolute shrinkage and selection operator (LASSO) and support vector machine‐recursive feature elimination (SVM‐RFE), establishing a molecular inflammatory predictive model using logistic regression. The model was validated in the GSE107499 dataset, and the performance of the features was assessed using receiver operating characteristic (ROC) and calibration curves. Immunohistochemistry (IHC) staining of colonic biopsy tissues from UC patients treated with infliximab was used to further confirm the clinical application value. Univariate logistic regression on GSE14580 highlighted features linked to infliximab response.ResultsA total of 253 histone modification-related DEGs were identified between inflammatory and non-inflammatory patients with UC. Seven key genes (IL-1β, MSL3, HDAC7, IRF4, CAMK2D, AUTS2, and PADI2) were selected using WGCNA and random forest. Through univariate logistic regression, three core genes (CAMK2D, AUTS2, and IL-1β) were further incorporated to construct the molecular inflammatory predictive model. The area under the curve (AUC) of the model was 0.943 in the independent validation dataset. A significant association between CAMK2D protein expression and infliximab response was observed, which was validated in another independent verification set of GSE14580 from the GEO database.ConclusionThe molecular inflammatory predictive model based on CAMK2D, AUTS2, and IL-1β could reliably distinguish the mucosal inflammatory status of UC patients. We further revealed that CAMK2D was a predictive marker of infliximab response. These findings are expected to provide a new evidence base for personalized treatment and management strategies for UC patients.</p

Deconvolution of the Gene Expression Profiles of Valuable Banked Blood Specimens for Studying the Prognostic Values of Altered Peripheral Immune Cell Proportions in Cancer Patients

<div><p>Background</p><p>The altered composition of immune cells in peripheral blood has been reported to be associated with cancer patient survival. However, analysis of the composition of peripheral immune cells are often limited in retrospective survival studies employing banked blood specimens with long-term follow-up because the application of flow cytometry to such specimens is problematic. The aim of this study was to demonstrate the feasibility of deconvolving blood-based gene expression profiles (GEPs) to estimate the proportions of immune cells and determine their prognostic values for cancer patients.</p><p>Methods and Results</p><p>Here, using GEPs from peripheral blood mononuclear cells (PBMC) of 108 non-small cell lung cancer (NSCLC) patients, we deconvolved the immune cell proportions and analyzed their association with patient survival. Univariate Kaplan-Meier analysis showed that a low proportion of T cells was significantly associated with poor patient survival, as was the proportion of T helper cells; however, only the proportion of T cells was independently prognostic for patients by a multivariate Cox regression analysis (hazard ratio = 2.23; 95% CI, 1.01–4.92; <i>p</i> = .048). Considering that altered peripheral blood compositions can reflect altered immune responses within the tumor microenvironment, based on a tissue-based GEPs of NSCLC patients, we demonstrated a significant association between poor patient survival and the low level of antigen presentation, which play a critical role in T cell proliferation.</p><p>Conclusions</p><p>These results demonstrate that it is feasible to deconvolve GEPs from banked blood specimens for retrospective survival analysis of alterations of immune cell composition, and suggest the proportion of T cells in PBMC which might reflect the antigen presentation level within the tumor microenvironment can be a prognostic marker for NSCLC patients.</p></div

Survival analysis of NSCLC patients based on the tissue dataset.

<p><b>Note:</b></p>a<p>The log-rank <i>p</i> value was derived from the Kaplan–Meier method using the log-rank test;</p>b<p>the <i>p</i> value was derived from the Cox regression model; HR, hazard ratio; CI, confidence interval;</p><p>*L, the group with low <i>MHC</i> gene expression pattern; H, the group with high <i>MHC</i> gene expression pattern.</p

Survival analysis of patients with NSCLC based on the PBMC dataset.

<p>Note:</p>a<p>The log-rank <i>p</i> value was derived from the Kaplan-Meier method using the log-rank test;</p>b<p>the <i>p</i> value was derived from the Cox regression model; HR, hazard ratio; CI, confidence interval;</p><p>* The cell proportions estimated using marker genes in the IRIS, L, group with a lower cell proportion (</p><p>** The cell proportions estimated using marker genes in the HaemAtlas.</p

The <i>MHC</i> genes signature identifies two groups with different death and relapse risks.

<p>(A) Heat map of the <i>MHC</i> genes in the NSCLC patients. For the tissue expression dataset for NSCLC, the patients were classified into two <i>MHC</i>-related groups (<i>MHC</i> High and <i>MHC</i> Low) based on the expression intensities of 18 detected <i>MHC</i> genes listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100934#pone.0100934.s002" target="_blank">Table S2</a>, using two-means clustering. The expression intensities of the <i>MHC</i> genes characterize the level of antigen presentation by DCs in the tumor microenvironment. (B) Kaplan-Meier curve for patients stratified into two <i>MHC</i>-related groups. The <i>p</i> value was calculated using the log-rank test. (C) The distribution of relapsing patients in the <i>MHC</i>-related groups. The chi-square test was used to compare the correlation between the level of <i>MHC</i> gene expression and relapse. Two patients were excluded because information about their relapse was unknown.</p

Clinical characteristics of the NSCLC patients in the PBMC dataset.

<p><b>Notes:</b> “AD” and “SCC” represent adenocarcinoma and squamous cell carcinoma, respectively; “C” and “AA” represent Caucasian and African American; “COPD” represents chronic obstructive pulmonary disease.</p

Kaplan-Meier curves for patients stratified by the proportion of T cells.

<p>The patients were stratified into two groups based on the median of the T cell proportion among the NSCLC patients: Low (less than the median) and High (greater than or equal to the median). The median overall survival (OS) was assessed using the Kaplan-Meier analysis. <i>p</i> was calculated using the log-rank test.</p

DataSheet_1_Classification of multiple primary lung cancer in patients with multifocal lung cancer: assessment of a machine learning approach using multidimensional genomic data.docx

BackgroundMultiple primary lung cancer (MPLC) is an increasingly well-known clinical phenomenon. However, its molecular characterizations are poorly understood, and still lacks of effective method to distinguish it from intrapulmonary metastasis (IM). Herein, we propose an identification model based on molecular multidimensional analysis in order to accurately optimize treatment.MethodsA total of 112 Chinese lung cancers harboring at least two tumors (n = 270) were enrolled. We retrospectively selected 74 patients with 121 tumor pairs and randomly divided the tumor pairs into a training cohort and a test cohort in a 7:3 ratio. A novel model was established in training cohort, optimized for MPLC identification using comprehensive genomic profiling analyzed by a broad panel with 808 cancer-related genes, and evaluated in the test cohort and a prospective validation cohort of 38 patients with 112 tumors.ResultsWe found differences in molecular characterizations between the two diseases and rigorously selected the characterizations to build an identification model. We evaluated the performance of the classifier using the test cohort data and observed an 89.5% percent agreement (PA) for MPLC and a 100.0% percent agreement for IM. The model showed an excellent area under the curve (AUC) of 0.947 and a 91.3% overall accuracy. Similarly, the assay achieved a considerable performance in the independent validation set with an AUC of 0.938 and an MPLC predictive value of 100%. More importantly, the MPLC predictive value of the classification achieved 100% in both the test set and validation cohort. Compared to our previous mutation-based method, the classifier showed better κ consistencies with clinical classification among all 112 patients (0.84 vs. 0.65, p ConclusionThese data provide novel evidence of MPLC-specific genomic characteristics and demonstrate that our one-step molecular classifier can accurately classify multifocal lung tumors as MPLC or IM, which suggested that broad panel NGS may be a useful tool for assisting with differential diagnoses.</p

Table_1_Classification of multiple primary lung cancer in patients with multifocal lung cancer: assessment of a machine learning approach using multidimensional genomic data.xls

BackgroundMultiple primary lung cancer (MPLC) is an increasingly well-known clinical phenomenon. However, its molecular characterizations are poorly understood, and still lacks of effective method to distinguish it from intrapulmonary metastasis (IM). Herein, we propose an identification model based on molecular multidimensional analysis in order to accurately optimize treatment.MethodsA total of 112 Chinese lung cancers harboring at least two tumors (n = 270) were enrolled. We retrospectively selected 74 patients with 121 tumor pairs and randomly divided the tumor pairs into a training cohort and a test cohort in a 7:3 ratio. A novel model was established in training cohort, optimized for MPLC identification using comprehensive genomic profiling analyzed by a broad panel with 808 cancer-related genes, and evaluated in the test cohort and a prospective validation cohort of 38 patients with 112 tumors.ResultsWe found differences in molecular characterizations between the two diseases and rigorously selected the characterizations to build an identification model. We evaluated the performance of the classifier using the test cohort data and observed an 89.5% percent agreement (PA) for MPLC and a 100.0% percent agreement for IM. The model showed an excellent area under the curve (AUC) of 0.947 and a 91.3% overall accuracy. Similarly, the assay achieved a considerable performance in the independent validation set with an AUC of 0.938 and an MPLC predictive value of 100%. More importantly, the MPLC predictive value of the classification achieved 100% in both the test set and validation cohort. Compared to our previous mutation-based method, the classifier showed better κ consistencies with clinical classification among all 112 patients (0.84 vs. 0.65, p ConclusionThese data provide novel evidence of MPLC-specific genomic characteristics and demonstrate that our one-step molecular classifier can accurately classify multifocal lung tumors as MPLC or IM, which suggested that broad panel NGS may be a useful tool for assisting with differential diagnoses.</p

Kaplan-Meier curves for patients stratified by the proportion of T cell subsets.

<p>The patients were stratified into two groups according to the median of (A) Th cell proportion and (B) CTL cell proportion in PBMC among the NSCLC patients.</p