Prominent immune signatures of T cells are specifically associated with indolent B-cell lymphoproliferative disorders and predict prognosis

Immune signatures predict response to house dust mite subcutaneous immunotherapy in patients with allergic rhinitis

BACKGROUND

Identifying predictive biomarkers for allergen immunotherapy response is crucial for enhancing clinical efficacy. This study aims to identify such biomarkers in patients with allergic rhinitis (AR) undergoing subcutaneous immunotherapy (SCIT) for house dust mite allergy.

METHODS

The Tongji (discovery) cohort comprised 72 AR patients who completed 1-year SCIT follow-up. Circulating T and B cell subsets were characterized using multiplexed flow cytometry before SCIT. Serum immunoglobulin levels and combined symptom and medication score (CSMS) were assessed before and after 12-month SCIT. Responders, exhibiting ≥30% CSMS improvement, were identified. The random forest algorithm and logistic regression analysis were used to select biomarkers and establish predictive models for SCIT efficacy in the Tongji cohort, which was validated in another Wisco cohort with 43 AR patients.

RESULTS

Positive SCIT response correlated with higher baseline CSMS, allergen-specific IgE (sIgE)/total IgE (tIgE) ratio, and frequencies of Type 2 helper T cells, Type 2 follicular helper T (TFH2) cells, and CD23+ nonswitched memory B (BNSM) and switched memory B (BSM) cells, as well as lower follicular regulatory T (TFR) cell frequency and TFR/TFH2 cell ratio. The random forest algorithm identified sIgE/tIgE ratio, TFR/TFH2 cell ratio, and BNSM frequency as the key biomarkers discriminating responders from nonresponders in the Tongji cohort. Logistic regression analysis confirmed the predictive value of a combination model, including sIgE/tIgE ratio, TFR/TFH2 cell ratio, and CD23+ BSM frequency (AUC = 0.899 in Tongji; validated AUC = 0.893 in Wisco).

CONCLUSIONS

A T- and B-cell signature combination efficiently identified SCIT responders before treatment, enabling personalized approaches for AR patients.

Minimal residual disease status improved the response evaluation in patients with Waldenström's macroglobulinemia

Introduction

Minimal residual disease (MRD) has been recognized as an important prognostic factor of survival in patients with hematological malignancies. However, the prognostic value of MRD in Waldenström macroglobulinemia (WM) remains unexplored.

Methods

We analyzed 108 newly diagnosed WM patients receiving systematic therapy and assessed for MRD by multiparameter flow cytometry (MFC) using bone marrow samples.

Results

Of the total patients, 34 (31.5%) achieved undetectable MRD (uMRD). A hemoglobin level of >115 g/L (P=0.03), a serum albumin level of >35 g/L (P=0.01), a β2-MG level of ≤3 mg/L (P=0.03), and a low-risk International Prognostic Scoring System for WM (IPSSWM) stage (P<0.01) were associated with a higher rate of uMRD. Improvements in monoclonal immunoglobulin (P<0.01) and hemoglobin (P=0.03) levels were more evident in uMRD patients compared with that in MRD-positive patients. The 3-year progression-free survival (PFS) was better in uMRD patients compared with that in MRD-positive patients (96.2% vs. 52.8%; P=0.0012). Landmark analysis also showed that uMRD patients had better PFS compared with MRD-positive patients after 6 and 12 months. Patients who achieved partial response (PR) and uMRD had a 3-year PFS of 100%, which was significantly higher than that of patients with MRD-positive PR (62.6%, P=0.029). Multivariate analysis showed that MRD positivity was an independent factor of PFS (HR: 2.55, P=0.03). Moreover, the combination of the 6th International Workshop on WM assessment (IWWM-6 Criteria) and MRD assessment had a higher 3-year AUC compared with the IWWM-6 criteria alone (0.71 vs. 0.67).

Discussion

MRD status assessed by MFC is an independent prognostic factor for PFS in patients with WM, and its determination could improve the precision of response evaluation, especially in patients who achieved PR.

Identification of Pathway-Based Biomarkers with Crosstalk Analysis for Overall Survival Risk Prediction in Breast Cancer

Recently, many studies have investigated the role of gene-signature on the prognostic assessment of breast cancer (BC), however, the tumor heterogeneity and sequencing noise have limited the clinical usage of these models. Pathway-based approaches are more stable to the perturbation of certain gene expression. In this study, we constructed a prognostic classifier based on survival-related pathway crosstalk analysis. We estimated pathway’s deregulation scores (PDSs) for samples collected from public databases to select survival-related pathways. After pathway crosstalk analysis, we conducted K-means clustering analysis to cluster the patients into G1 and G2 subgroups. The survival outcome of the G2 subgroup was significantly worse than the G1 subgroup. Internal and external dataset exhibits high consistency with the training dataset. Significant differences were found between G2 and G1 subgroups on pathway activity, gene mutation, immune cell infiltration levels, and in particular immune cells/pathway’s activities were significantly negatively associated with BC patient’s outcomes. In conclusion, we established a novel classifier reflecting the overall survival risk of BC and successfully validated its clinical usage on multiple BC datasets, which could offer clinicians inspiration in formulating the clinical treatment plan.