RPPA-based proteomics recognizes distinct epigenetic signatures in chronic lymphocytic leukemia with clinical consequences

Proteomics for optimizing therapy in acute myeloid leukemia: venetoclax plus hypomethylating agents versus conventional chemotherapy

The use of Hypomethylating agents combined with Venetoclax (VH) for the treatment of Acute Myeloid Leukemia (AML) has greatly improved outcomes in recent years. However not all patients benefit from the VH regimen and a way to rationally select between VH and Conventional Chemotherapy (CC) for individual AML patients is needed. Here, we developed a proteomic-based triaging strategy using Reverse-phase Protein Arrays (RPPA) to optimize therapy selection. We evaluated the expression of 411 proteins in 810 newly diagnosed adult AML patients, identifying 109 prognostic proteins, that divided into five patient expression profiles, which are useful for optimizing therapy selection. Furthermore, using machine learning algorithms, we determined a set of 14 proteins, among those 109, that were able to accurately recommend therapy, making it feasible for clinical application. Next, we identified a group of patients who did not benefit from either VH or CC and proposed target-based approaches to improve outcomes. Finally, we calculated that the clinical use of our proteomic strategy would have led to a change in therapy for 30% of patients, resulting in a 43% improvement in OS, resulting in around 2600 more cures from AML per year in the United States.

Angiogenesis-related gene signatures reveal the prognosis of cervical cancer based on single cell sequencing and co-expression network analysis

Cervical cancer ranks first in female reproductive tract tumors in terms of morbidity and mortality. Yet the curative effect of patients with persistent, recurrent or metastatic cervical cancer remains unsatisfactory. Although antitumor angiogenic drugs have been recommended as the first-line treatment options for cervical cancer, there are no comprehensive prognostic indicators for cervical cancer based on angiogenic signature genes. In this study, we aimed to develop a model to assess the prognosis of cervical cancer based on angiogenesis-related (AG) signature genes, and to provide some reference for the comprehensive treatment of cervical cancer in the clinical setting. First we screened the AG gene set from GeneCard website, and then performed angiogenesis-related scores (AGS) per cell from single cell sequencing dataset GSE168652, followed by performing weighted gene co-expression network analysis (WGCNA) for cervical cancer patients according to angiogenesis phenotype. Thus, we established a prognostic model based on AGS by taking the intersection of WGCNA angiogenic module gene and differential gene (DEGs) of GSE168652. The GSE44001 was selected as an external validation set, followed by performing ROC curve analysis to assess its accuracy. The results showed that we successfully constructed a prognostic model related to the AG genes. Patients in the high-AGS group in both the train, test and the validation sets had a worse prognosis than those in the low-AGS group, had lower expression of most immune checkpoint-associated genes and lower tumor mutational burden as well. Patients in the low-AGS group were more sensitive to AMG.706, Bosutinib, and Lenalidomide while Imatinib, Pazopanib, and Sorafenib were more recommended to patients in the high-AGS group. Finally, TXNDC12 and ZC3H13, which have high hazard ratio and poor prognosis in the model, were highly expressed in cervical cancer cell lines and tissue. Meanwhile, the results showed that TXNDC12 promoted the migration of cervical cancer cells and the tubule-forming ability of endothelial cells. In conclusion, our model based on genes with AG features can effectively assess the prognosis of cervical cancer, and can also provide reference for clinicians to choose immune-related treatments.

Proteogenomics refines the molecular classification of chronic lymphocytic leukemia

Cancer heterogeneity at the proteome level may explain differences in therapy response and prognosis beyond the currently established genomic and transcriptomic-based diagnostics. The relevance of proteomics for disease classifications remains to be established in clinically heterogeneous cancer entities such as chronic lymphocytic leukemia (CLL). Here, we characterize the proteome and transcriptome alongside genetic and ex-vivo drug response profiling in a clinically annotated CLL discovery cohort (n = 68). Unsupervised clustering of the proteome data reveals six subgroups. Five of these proteomic groups are associated with genetic features, while one group is only detectable at the proteome level. This new group is characterized by accelerated disease progression, high spliceosomal protein abundances associated with aberrant splicing, and low B cell receptor signaling protein abundances (ASB-CLL). Classifiers developed to identify ASB-CLL based on its characteristic proteome or splicing signature in two independent cohorts (n = 165, n = 169) confirm that ASB-CLL comprises about 20% of CLL patients. The inferior overall survival in ASB-CLL is also independent of both TP53- and IGHV mutation status. Our multi-omics analysis refines the classification of CLL and highlights the potential of proteomics to improve cancer patient stratification beyond genetic and transcriptomic profiling.

Proteomic profiling based classification of CLL provides prognostication for modern therapy and identifies novel therapeutic targets

Protein expression for 384 total and post-translationally modified proteins was assessed in 871 CLL and MSBL patients and was integrated with clinical data to identify strategies for improving diagnostics and therapy, making this the largest CLL proteomics study to date. Proteomics identified six recurrent signatures that were highly prognostic of survival and time to first or second treatment at three levels: individual proteins, when grouped into 40 functionally related groups (PFGs), and systemically in signatures (SGs). A novel SG characterized by hairy cell leukemia like proteomics but poor therapy response was discovered. SG membership superseded other prognostic factors (Rai Staging, IGHV Status) and were prognostic for response to modern (BTK inhibition) and older CLL therapies. SGs and PFGs membership provided novel drug targets and defined optimal candidates for Watch and Wait vs. early intervention. Collectively proteomics demonstrates promise for improving classification, therapeutic strategy selection, and identifying novel therapeutic targets.