Genetic variants of CTLA4 are associated with clinical outcome of patients with multiple myeloma

Mitochondria-associated programmed cell death: elucidating prognostic biomarkers, immune checkpoints, and therapeutic avenues in multiple myeloma

Background

Multiple myeloma (MM) is a hematological malignancy characterized by the abnormal proliferation of plasma cells. Mitochondrial dysfunction and dysregulated programmed cell death (PCD) pathways have been implicated in MM pathogenesis. However, the precise roles of mitochondria-related genes (MRGs) and PCD-related genes (PCDRGs) in MM prognosis remain unclear.

Methods

Transcriptomic data from MM patients and healthy controls were analyzed to identify differentially expressed genes (DEGs). Candidate genes were selected by intersecting DEGs with curated lists of MRGs and PCDRGs. Univariate Cox, least absolute shrinkage and selection operator (LASSO), multivariate Cox, and stepwise regression analyses identified prognostic genes among the candidates. A risk model was constructed from these genes, and patients were stratified into high- and low-risk groups for survival analysis. Independent prognostic factors were incorporated into a nomogram to predict MM patient outcomes. Model performance was evaluated using calibration curves, receiver operating characteristic (ROC) analysis, and decision curve analysis (DCA). Finally, associations between prognostic genes and immune cell infiltration/drug responses were explored.

Results

2,192 DEGs were detected between MM and control samples. 30 candidate genes were identified at the intersection of DEGs, 1,136 MRGs, and 1,548 PCDRGs. TRIAP1, TOMM7, PINK1, CHCHD10, PPIF, BCL2L1, and NDUFA13 were selected as prognostic genes. The risk model stratified patients into high- and low-risk groups with significantly different survival probabilities. Age, gender, ISS stage, and risk score were independent prognostic factors. The nomogram displayed good calibration and discriminative ability (AUC) in predicting survival, with clinical utility demonstrated by DCA. 9 immune cell types showed differential infiltration between MM and controls, with significant associations to risk scores and specific prognostic genes. 57 drugs, including nelarabine and vorinostat, were predicted to interact with the prognostic genes. Ultimately, qPCR in clinical samples from MM patients and healthy donors validated the expression levels of the seven key prognostic genes, corroborating the bioinformatic findings.

Conclusion

Seven genes (TRIAP1, TOMM7, PINK1, CHCHD10, PPIF, BCL2L1, NDUFA13) involved in mitochondrial function and PCD pathways were identified as prognostic markers in MM. These findings provide insights into MM biology and prognosis, highlighting potential therapeutic targets.

Identifying the prognostic significance of mitophagy-associated genes in multiple myeloma: a novel risk model construction

Multiple myeloma (MM) is a highly heterogeneous hematological malignancy that is currently incurable. Individualized therapeutic approaches based on accurate risk assessment are essential for improving the prognosis of MM patients. Nevertheless, current prognostic models for MM exhibit certain limitations and prognosis heterogeneity still an unresolved issue. Recent studies have highlighted the pivotal involvement of mitochondrial autophagy in the development and drug sensitivity of MM. This study seeks to conduct an integrative analysis of the prognostic significance and immune microenvironment of mitophagy-related signature in MM, with the aim of constructing a novel predictive risk model. GSE4581 and GSE47552 datasets were acquired from the Gene Expression Omnibus database. MM-differentially expressed genes (DEGs) were identified by limma between MM samples and normal samples in GSE47552. Mitophagy key module genes were obtained by weighted gene co-expression network analysis in the Cancer Genome Atlas (TCGA)-MM dataset. Mitophagy DEGs were identified by the overlap genes between MM-DEGs and mitophagy key module genes. Prognostic genes were selected through univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analysis, and a risk model was subsequently constructed based on these prognostic genes. Subsequently, the MM samples were stratified into high- and low-risk groups based on their median risk scores. The validity of the risk model was further evaluated using the GSE4581 dataset. Moreover, a nomogram was developed using the independent prognostic factors identified from the risk score and various clinical indicators. Additionally, analyses were conducted on immune infiltration, immune scores, immune checkpoint, and chemotherapy drug sensitivity. The 17 mitophagy DEGs were obtained by intersection of 803 MM-DEGs and 1084 mitophagy key module genes. Five prognostic genes (CDC6, PRIM1, SNRPB, TOP2A, and ZNF486) were selected via LASSO and univariate cox regression analyses. The predictive performance of the risk model, which was constructed based on the five prognostic genes, demonstrated favorable results in both TCGA-MM and GSE4581 datasets as indicated by the receiver operating characteristic (ROC) curves. In addition, calibration curve, ROC curve, and decision curve analysis curve corroborated that the nomogram exhibited superior predictive accuracy for MM. Furthermore, immune analysis results indicated a significant difference in stromal scores of two risk groups categorized on median risk scores. And four immune checkpoints (CD274, CTLA4, LAG3, and PDCD1LG2) showed significant differences in different risk groups. The analysis of chemotherapy drug sensitivity revealed that etoposide and doxorubicin, which target TOP2A, exhibited superior treatment outcomes in the high-risk group. A novel prognostic model for MM was developed and validated, demonstrating significant potential in predicting patient outcomes and providing valuable guidance for personalized immunotherapy counseling.

CD200 genotype is associated with clinical outcome of patients with multiple myeloma

Immune dysfunction in patients with MM affects both the innate and adaptive immune system. Molecules involved in the immune response pathways are essential to determine the ability of cancer cells to escape from the immune system surveillance. However, few data are available concerning the role of immune checkpoint molecules in predicting the myeloma control and immunological scape as mechanism of disease progression. We retrospectively analyzed the clinical impact of the CD200 genotype (rs1131199 and rs2272022) in 291 patients with newly diagnosed MM. Patients with a CD200 rs1131199 GG genotype showed a median overall survival (OS) significantly lower than those with CC+CG genotype (67.8 months versus 94.4 months respectively; p: 0.022) maintaining significance in the multivariate analysis. This effect was specially detected in patients not receiving an autologous stem cell transplant (auto-SCT) (p < 0.001). In these patients the rs1131199 GG genotype negatively influenced in the mortality not related with the progression of MM (p: 0.02) mainly due to infections events.

Checkpoint inhibition in hematologic malignancies

Checkpoint inhibitor therapy has emerged as an effective therapeutic strategy for many types of malignancies, especially in solid tumors. Within the last two decades, numerous monoclonal antibody drugs targeting the CTLA-4 and PD-1/PD-L1 checkpoint pathways have seen FDA approval. Within hematologic malignancies, Hodgkin Lymphoma has seen the greatest clinical benefits thus far with more recent data showing efficacy in the front-line setting. As our understanding of checkpoint inhibition expands, using these pathways as a therapeutic target has shown some utility in the treatment of other hematologic malignancies as well, primarily in the relapsed/refractory settings. Checkpoint inhibition also appears to have a role as a synergistic agent to augment clinical responses to other forms of therapy such as hematopoietic stem cell transplant. Moreover, alternative checkpoint molecules that bypass the well-studied CTLA-4 and PD-1/PD-L1 pathways have emerged as exciting new therapeutic targets. Most excitingly is the use of anti-CD47 blockade in the treatment of high risk MDS and TP-53 mutated AML. Overall, there has been tremendous progress in understanding the benefits of checkpoint inhibition in hematologic malignancies, but further studies are needed in all areas to best utilize these agents. This is a review of the most recent developments and progress in Immune Checkpoint Inhibition in Hematologic Malignancies in the last decade.