TMEM30A loss-of-function mutations drive lymphomagenesis and confer therapeutically exploitable vulnerability in B-cell lymphoma.

Transmembrane protein 30A (TMEM30A) maintains the asymmetric distribution of phosphatidylserine, an integral component of the cell membrane and 'eat-me' signal recognized by macrophages. Integrative genomic and transcriptomic analysis of diffuse large B-cell lymphoma (DLBCL) from the British Columbia population-based registry uncovered recurrent biallelic TMEM30A loss-of-function mutations, which were associated with a favorable outcome and uniquely observed in DLBCL. Using TMEM30A-knockout systems, increased accumulation of chemotherapy drugs was observed in TMEM30A-knockout cell lines and TMEM30A-mutated primary cells, explaining the improved treatment outcome. Furthermore, we found increased tumor-associated macrophages and an enhanced effect of anti-CD47 blockade limiting tumor growth in TMEM30A-knockout models. By contrast, we show that TMEM30A loss-of-function increases B-cell signaling following antigen stimulation-a mechanism conferring selective advantage during B-cell lymphoma development. Our data highlight a multifaceted role for TMEM30A in B-cell lymphomagenesis, and characterize intrinsic and extrinsic vulnerabilities of cancer cells that can be therapeutically exploited.

Multiplexed LC-ESI-MRM-MS-based Assay for Identification of Coronary Artery Disease Biomarkers in Human Plasma.

PURPOSE: A highly-multiplexed LC-ESI-multiple reaction monitoring-MS-based assay is developed for the identification of coronary artery disease (CAD) biomarkers in human plasma. EXPERIMENTAL DESIGN: The assay is used to measure 107 stable isotope labeled peptide standards and native peptides from 64 putative biomarkers of cardiovascular diseases in tryptic digests of plasma from subjects with (n = 70) and without (n = 45) angiographic evidence of CAD and no subsequent cardiovascular mortality during follow-up. RESULTS: Extensive computational and statistical analysis reveals six plasma proteins associated with CAD, namely apolipoprotein CII, C reactive protein, CD5 antigen-like, fibronectin, inter alpha trypsin inhibitor heavy chain H1, and protein S. The identified proteins are combined into a LASSO-logistic score with high classification performance (cross-validated area under the curve = 0.74). When combined with a separate score computed from markers currently used in the clinic with similar performance, the area under the receiver operating curve increases to 0.84. Similar results are observed in an independent set of subjects (n = 87). CONCLUSIONS AND CLINICAL RELEVANCE: If externally validated, the assay and identified biomarkers can improve CAD risk stratification.

Predicting acute cardiac rejection from donor heart and pre-transplant recipient blood gene expression.

BACKGROUND: Acute rejection in cardiac transplant patients remains a contributory factor to limited survival of implanted hearts. Currently, there are no biomarkers in clinical use that can predict, at the time of transplantation, the likelihood of post-transplant acute cellular rejection. Such a development would be of great value in personalizing immunosuppressive treatment. METHODS: Recipient age, donor age, cold ischemic time, warm ischemic time, panel-reactive antibody, gender mismatch, blood type mismatch and human leukocyte antigens (HLA-A, -B and -DR) mismatch between recipients and donors were tested in 53 heart transplant patients for their power to predict post-transplant acute cellular rejection. Donor transplant biopsy and recipient pre-transplant blood were also examined for the presence of genomic biomarkers in 7 rejection and 11 non-rejection patients, using non-targeted data mining techniques. RESULTS: The biomarker based on the 8 clinical variables had an area under the receiver operating characteristic curve (AUC) of 0.53. The pre-transplant recipient blood gene-based panel did not yield better performance, but the donor heart tissue gene-based panel had an AUC = 0.78. A combination of 25 probe sets from the transplant donor biopsy and 18 probe sets from the pre-transplant recipient whole blood had an AUC = 0.90. Biologic pathways implicated include VEGF- and EGFR-signaling, and MAPK. CONCLUSIONS: Based on this study, the best predictive biomarker panel contains genes from recipient whole blood and donor myocardial tissue. This panel provides clinically relevant prediction power and, if validated, may personalize immunosuppressive treatment and rejection monitoring.

The use of multiplexed MRM for the discovery of biomarkers to differentiate iron-deficiency anemia from anemia of inflammation.

In this study we demonstrate the use of a multiplexed MRM-based assay to distinguish among normal (NL) and iron-metabolism disorder mouse models, particularly, iron-deficiency anemia (IDA), inflammation (INFL), and inflammation and anemia (INFL+IDA). Our initial panel of potential biomarkers was based on the analysis of 14 proteins expressed by candidate genes involved in iron transport and metabolism. Based on this study, we were able to identify a panel of 8 biomarker proteins: apolipoprotein A4 (APO4), transferrin, transferrin receptor 1, ceruloplasmin, haptoglobin, lactoferrin, hemopexin, and matrix metalloproteinase-8 (MMP8) that clearly distinguish among the normal and disease models. Within this set of proteins, transferrin showed the best individual classification accuracy over all samples (72%) and within the NL group (94%). Compared to the best single-protein biomarker, transferrin, the use of the composite 8-protein biomarker panel improved the classification accuracy from 94% to 100% in the NL group, from 50% to 72% in the INFL group, from 66% to 96% in the IDA group, and from 79% to 83% in the INFL+IDA group. Based on these findings, validation of the utility of this potentially important biomarker panel in human samples in an effort to differentiate IDA, inflammation, and combinations thereof, is now warranted. This article is part of a Special Section entitled: Understanding genome regulation and genetic diversity by mass spectrometry.