GNAO1 as a Novel Predictive Biomarker for Late Relapse in Hepatocellular Carcinoma

GNAO1, the alpha O1 subunit of G protein, was reported to be significantly downregulated in hepatocellular carcinoma (HCC), as well as being implicated in a variety of intracellular biological events; findings suggest that it may act as a tumor suppressor. Our goal was to further explore the expression of GNAO1 in HCC patients and its potential clinical significance. Oncomine and Kaplan–Meier plotter databases were used to assess the mRNA expression of GNAO1 in HCC tissues and patient survival time. Subsequently, immunohistochemistry (IHC) was used to measure GNAO1 protein level in tissue from 79 cases of HCC and paired adjacent tissues. The Kaplan–Meier survival analysis, Cox regression model, and prognostic nomogram were used to evaluate the prognostic role of GNAO1 in HCC. Results demonstrated that mRNA and protein expressions of GNAO1 were both lower in HCC tissues than in adjacent tissues (all p < 0.01 ). HCC patients with high expression of GNAO1 had better relapse-free survival (RFS) than those with low GNAO1 expression (all p < 0.05 ). A high expression of GNAO1, meanwhile, functioned as a good predictor of late relapse for HCC ( p < 0.05 ). The nomogram consisting of GNAO1 expression and the tumor-node-metastasis (TNM) model presented good ability in predicting the 3-year relapse for HCC (C-index = 0.614). In conclusion, GNAO1 was a reliable biomarker of relapse prediction for HCC.

Superiority of Leukemic Stem Cell-Based Minimal Residual Disease Assay to Traditional Multiparameter Flow Cytometry-Based Method for Relapse Prediction in AML Patients: A Prospective Study with Randomized Training and Validation Sets

Background: Minimal/measurable residual disease (MRD) determined by multiparameter flow cytometry (MFC) is an important variable for relapse prediction and treatment approach selection in patients with acute myeloid leukemia (AML). We aimed to investigate whether leukemia-stem cell (LSC)-based assay is superior to traditional MFC methods, including LAIP and D-F-N assays, for MRD evaluation in predicting clinical outcomes. Methods: In this cohort study, a total of 360 AML patients who received allogeneic stem cell transplantation (allo-SCT) between July 2018 and November 2019 were prospectively enrolled. The patients were randomized (1:1) and classified into a training set (n=180) and a validation set (n=180). Posttransplantation MRD were according to LSC based assay, mainly including a cocktail of CD7, CD11b, CD22, CD56, Tim-3, and CLL-1 on CD34 +CD38 - cells, and traditional assay determined by MFC, respectively. Findings: In the training set, patients were classified as LSC positive group (group A) and LSC negative group (group B) according to a cutoff value of CD34 +CD38 -cocktail + LSCs as 0.004%. Subjects in group A had a higher cumulative incidence of relapse (CIR, 42.7% vs. 2.6%, P&lt;0.001) and comparable non-relapse mortality (NRM, 0% vs. 8.1%, P=0.154) compared with cases in group B, leading to inferior leukemia-free survival (LFS, 57.3% vs. 89.3%, P&lt;0.001) and overall survival (OS, 70.8% vs. 90%, P=0.009) of cases in group A to group B. Multivariate analysis showed that positive LSCs after transplantation could independently predict CIR (P&lt;0.001), LFS (P&lt;0.001), and OS (P=0.021). The predictive value of positive LSCs following allo-HSCT for CIR (P&lt;0.001), LFS (P&lt;0.001), and OS (P=0.004) was further confirmed in the validation set. In the total case cohort, multivariate analysis also showed that positive LSCs after transplantation could independently predict CIR (HR=13.999, P&lt;0.001), LFS (HR=5.429, P&lt;0.001), and OS (HR=3.761, P=0.021). The total patients were classified into positive MRD and negative MRD groups according to the traditional MFC method. The results showed that the CIR of patients in the traditional MRD positive group was significantly higher than the CIR of patients in the traditional MRD negative group (44.9% vs. 7.3%, P&lt;0.001), leading to lower LFS (55.1% vs. 85.6%, P&lt;0.001) and OS (55.6% vs. 87.5%, P&lt;0.001). Compared with MRD detected by the traditional MFC method, using LSCs for MRD evaluation has high sensitivity (66.7% vs. 43%), a high C-index (0.76 vs. 0.69) and a high Youden index (0.58 vs. 0.37). The median time from CD34 +CD38cocktail + LSC positivity to relapse was longer than the median time from traditional MRD positivity to relapse by 141.5 days (range, 18-465 days) vs. 64.5 days (range, 13-144 days) (P=0.003). The median level between CD34 +CD38 -cocktail + LSCs and traditional MRD detected by MFC was 0.0072% (range, 0.0007%-3.742%) and 0.16% (range, 0.01%-3.75%) (P&lt;0.001). Interpretation: Our data suggest the superiority of LSC-based MRD assays such as higher sensitivity, low false negativity, and longer time for MRD positivity to relapse to traditional MFC MRD methods for outcome prediction in AML patients received allograft. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.

A novel multi-biomarker combination predicting relapse from long-term remission after discontinuation of biological drugs in rheumatoid arthritis

Biological disease modifying anti-rheumatic drugs (bDMARDs) show dramatic treatment efficacy in rheumatoid arthritis (RA). Long-term use of bDMARDs, however, has disadvantages such as high costs and infection risk. Therefore, a methodology is needed to predict any future RA relapse. Herein, we report a novel multi-biomarker combination which predicts relapse after bDMARDs-withdrawal in patients in remission. Forty patients with RA in remission for more than 12 months were enrolled. bDMARDs were withdrawn and they were followed monthly for the next 24 months. Fourteen patients (35%) of 40 in the cohort remained in remission at 24 months, whereas 26 (65%) relapsed at various time-points. Serum samples obtained longitudinally from patients in remission were assessed for the relapse-prediction biomarkers and index from 73 cytokines by the exploratory multivariate ROC analysis. The relapse-prediction index calculated from the 5 cytokines, IL-34, CCL1, IL-1β, IL-2 and IL-19, strongly discriminated between patients who relapsed and those who stayed in remission. These findings could contribute to clinical decision-making as to the timing of when to discontinue bDMARDs in RA treatment.

A longitudinal study of gene expression in first-episode schizophrenia; exploring relapse mechanisms by co-expression analysis in peripheral blood

Little is known about the pathophysiological mechanisms of relapse in first-episode schizophrenia, which limits the study of potential biomarkers. To explore relapse mechanisms and identify potential biomarkers for relapse prediction, we analyzed gene expression in peripheral blood in a cohort of first-episode schizophrenia patients with less than 5 years of evolution who had been evaluated over a 3-year follow-up period. A total of 91 participants of the 2EPs project formed the sample for baseline gene expression analysis. Of these, 67 provided biological samples at follow-up (36 after 3 years and 31 at relapse). Gene expression was assessed using the Clariom S Human Array. Weighted gene co-expression network analysis was applied to identify modules of co-expressed genes and to analyze their preservation after 3 years of follow-up or at relapse. Among the 25 modules identified, one module was semi-conserved at relapse (DarkTurquoise) and was enriched with risk genes for schizophrenia, showing a dysregulation of the TCF4 gene network in the module. Two modules were semi-conserved both at relapse and after 3 years of follow-up (DarkRed and DarkGrey) and were found to be biologically associated with protein modification and protein location processes. Higher expression of DarkRed genes was associated with higher risk of suffering a relapse and early appearance of relapse (p = 0.045). Our findings suggest that a dysregulation of the TCF4 network could be an important step in the biological process that leads to relapse and suggest that genes related to the ubiquitin proteosome system could be potential biomarkers of relapse.

Recent Advances in Liquid Biopsy of Brain Cancers

Brain cancers are among the top causes of death worldwide. Although, the survival rates vary widely depending on the type of the tumor, early diagnosis could generally benefit in better prognosis outcomes of the brain cancer patients. Conventionally, neuroimaging and biopsy are the most widely used approaches in diagnosis, subtyping, and prognosis monitoring of brain cancers, while emerging liquid biopsy assays using peripheral blood or cerebrospinal fluid have demonstrated many favorable characteristics in this task, especially due to their minimally invasive and easiness in sampling nature. Here, we review the recent studies in the liquid biopsy of brain cancers. We discuss the methodologies and performances of various assays on diagnosis, tumor subtyping, relapse prediction as well as prognosis monitoring in brain cancers, which approaches have made a big step toward clinical benefits of brain cancer patients.

Routine Clustering of Mobile Sensor Data Facilitates Psychotic Relapse Prediction in Schizophrenia Patients (Preprint)

BACKGROUND Behavioral representations obtained from mobile sensing data could be helpful for the prediction of an oncoming psychotic relapse in schizophrenia patients and delivery of timely interventions to mitigate such relapse. OBJECTIVE In this work, we aim to develop clustering models to obtain behavioral representations from continuous multimodal mobile sensing data towards relapse prediction tasks. The identified clusters could represent different routine behavioral trends related to daily living of patients as well as atypical behavioral trends associated with impending relapse. METHODS We used the mobile sensing data obtained in the CrossCheck project for our analysis. Continuous data from six different mobile sensing-based modalities (e.g. ambient light, sound/conversation, acceleration etc.) obtained from a total of 63 schizophrenia patients, each monitored for up to a year, were used for the clustering models and relapse prediction evaluation. Two clustering models, Gaussian Mixture Model (GMM) and Partition Around Medoids (PAM), were used to obtain behavioral representations from the mobile sensing data. These models have different notions of similarity between behaviors as represented by the mobile sensing data and thus provide differing behavioral characterizations. The features obtained from the clustering models were used to train and evaluate a personalized relapse prediction model using Balanced Random Forest. The personalization was done by identifying optimal features for a given patient based on a personalization subset consisting of other patients who are of similar age. RESULTS The clusters identified using the GMM and PAM models were found to represent different behavioral patterns (such as clusters representing sedentary days, active but with low communications days, etc.). While GMM based models better characterized routine behaviors by discovering dense clusters with low cluster spread, some other identified clusters had a larger cluster spread likely indicating heterogeneous behavioral characterizations. PAM model based clusters on the other hand had lower variability of cluster spread, indicating more homogeneous behavioral characterization in the obtained clusters. Significant changes near the relapse periods were seen in the obtained behavioral representation features from the clustering models. The clustering model based features, together with other features characterizing the mobile sensing data, resulted in an F2 score of 0.24 for the relapse prediction task in a leave-one-patient-out evaluation setting. This obtained F2 score is significantly higher than a random classification baseline with an average F2 score of 0.042. CONCLUSIONS Mobile sensing can capture behavioral trends using different sensing modalities. Clustering of the daily mobile sensing data may help discover routine as well as atypical behavioral trends. In this work, we used GMM and PAM-based cluster models to obtain behavioral trends in schizophrenia patients. The features derived from the cluster models were found to be predictive for detecting an oncoming psychotic relapse. Such relapse prediction models can be helpful to enable timely interventions.

Reference-free transcriptome signatures for prostate cancer prognosis

Background RNA-seq data are increasingly used to derive prognostic signatures for cancer outcome prediction. A limitation of current predictors is their reliance on reference gene annotations, which amounts to ignoring large numbers of non-canonical RNAs produced in disease tissues. A recently introduced kind of transcriptome classifier operates entirely in a reference-free manner, relying on k-mers extracted from patient RNA-seq data. Methods In this paper, we set out to compare conventional and reference-free signatures in risk and relapse prediction of prostate cancer. To compare the two approaches as fairly as possible, we set up a common procedure that takes as input either a k-mer count matrix or a gene expression matrix, extracts a signature and evaluates this signature in an independent dataset. Results We find that both gene-based and k-mer based classifiers had similarly high performances for risk prediction and a markedly lower performance for relapse prediction. Interestingly, the reference-free signatures included a set of sequences mapping to novel lncRNAs or variable regions of cancer driver genes that were not part of gene-based signatures. Conclusions Reference-free classifiers are thus a promising strategy for the identification of novel prognostic RNA biomarkers.