Citations

Abstract Background Immunosuppression in glioblastoma (GBM) is an obstacle to effective immunotherapy. GBM-derived immunosuppressive monocytes are central to this. Programmed cell death ligand 1 (PD-L1) is an immune checkpoint molecule, expressed by GBM cells and GBM extracellular vesicles (EVs). We sought to determine the role of EV-associated PD-L1 in the formation of immunosuppressive monocytes. Methods Monocytes collected from healthy donors were conditioned with GBM-derived EVs to induce the formation of immunosuppressive monocytes, which were quantified via flow cytometry. Donor-matched T cells were subsequently co-cultured with EV-conditioned monocytes in order to assess effects on T-cell proliferation. PD-L1 constitutive overexpression or short hairpin RNA–mediated knockdown was used to determined the role of altered PD-L1 expression. Results GBM EVs interact with both T cells and monocytes but do not directly inhibit T-cell activation. However, GBM EVs induce immunosuppressive monocytes, including myeloid-derived suppressor cells (MDSCs) and nonclassical monocytes (NCMs). MDSCs and NCMs inhibit T-cell proliferation in vitro and are found within GBM in situ. EV PD-L1 expression induces NCMs but not MDSCs, and does not affect EV-conditioned monocytes T-cell inhibition. Conclusion These findings indicate that GBM EV-mediated immunosuppression occurs through induction of immunosuppressive monocytes rather than direct T-cell inhibition and that, while PD-L1 expression is important for the induction of specific immunosuppressive monocyte populations, immunosuppressive signaling mechanisms through EVs are complex and not limited to PD-L1.

Systemic glucocorticoids are the principal treatment for acute graft-versus-host disease (GVHD), which remains the major cause of non-relapse mortality (NRM) after allogeneic hematopoietic cell transplantation (HCT). However, there are no validated biomarkers that measure a patient's response to glucocorticoid therapy, and thus response is evaluated by the change in clinical symptom severity. A major weakness in the predictive power of clinical responses is that changes to all organs are weighted equally even though the major driver of NRM is irreversible damage to the crypts of the GI tract. Recent studies from the Mount Sinai Acute GVHD International Consortium (MAGIC) have validated an algorithm probability (MAP) that combines serum concentrations of two biomarkers of GVHD (REG3α and ST2) to generate an estimated probability of 6 month NRM for individual patients. The MAP has been considered a "liquid biopsy" that estimates the damage caused by GVHD to crypts throughout the lower GI tract (Hartwell et al., JCI Insight, 2017; Major-Monfried et al., Blood, 2018). We hypothesized that the change in MAP between start of treatment and 28 days later could serve as a response biomarker for GVHD and might compare favorably to the change in clinical symptoms that measures response to GVHD treatment, which is widely used as a surrogate for long term survival and is the primary endpoint in most GVHD treatment trials (Martin et al., BBMT, 2009; MacMillan et al., Blood, 2010). We prospectively collected serum samples and clinical staging from 368 sequential HCT patients who received systemic treatment for acute GVHD in one of 20 MAGIC centers between January 2016 and February 2018. We measured the serum concentrations of REG3α and ST2 before and after systemic therapy for acute GVHD and computed MAPs, the changes in MAPs, and clinical responses for each patient. MAPs of patients who experienced 6 month NRM showed significantly greater increases than MAPs of patients who survived (p=0.0004). In patients whose MAPs at the start of treatment were low (Ann Arbor 1, MAP < 0.141) or intermediate (Ann Arbor 2, 0.141 ≤ MAP ≤ 0.290), 6 month NRM clustered among those who had the greatest increases in MAP after 28 days (Fig 1A,B). In patients with high MAPs at the start of treatment (Ann Arbor 3, MAP > 0.290), those who survived tended to have the largest decreases in MAP (Fig 1C). These changes in MAP suggested crossing a single threshold could predict risk of mortality. We found that patients whose MAPs rose above a threshold MAP of 0.290 (5% of Ann Arbor 1, 27% of Ann Arbor 2) had significantly worse survival compared to those who remained below it, whereas the large number patients with initially high MAPs that remained above the threshold (66% of Ann Arbor 3) had a large increases in mortality (Fig 2). When measured at day 28, the MAP was significantly more accurate in predicting NRM than the gold standard of the clinical response, with areas under the receiver operating characteristic curve (AUC) of 0.86 and 0.70, respectively (p<0.0001). An algorithm that combined clinical response with biomarkers generated the same AUC as the MAP alone (0.83 v 0.86, p = NS). We next tested whether the same MAP threshold of 0.290 could predict risk within clinical response subsets. A significant minority (10%) of clinical responders had high MAPs and experienced three-fold greater NRM than those with low MAPs (40% v 12%, p<0.0001) whereas the majority (57%) of non-responders had low MAPs and experienced almost three-fold lower NRM than those with high MAPs (24% v 65%, p<0.0001) (Fig 3). Thus the MAP provides important prognostic information over and above the change in clinical symptoms, further stratifying both responders and non-responders at four weeks of treatment. The MAP threshold classified patients both with and without significant lower GI symptoms because the MAP is a more specific measure of irreversible cryptic damage in patients with copious diarrhea and more sensitive in patients with less than 0.5 liters of daily diarrhea (Fig 4). We conclude that the MAP is, to our knowledge, the first validated laboratory test to serve as response biomarker for the treatment for acute GVHD and a more accurate predictor of survival than clinical response after four weeks of treatment. The MAP may serve as a novel endpoint and an important complement to changes in clinical symptom severity in future trials of GVHD treatment. Disclosures Srinagesh: National Institutes of Health: Research Funding. Ozbek:Viracor: Patents & Royalties: Biomarker Patent. Ayuk:Novartis: Honoraria, Other: Advisory Board, Research Funding. Aziz:Doris Duke Charitable Foundation: Research Funding. Defilipp:Incyte: Research Funding. Grupp:Novartis: Consultancy, Research Funding; Roche: Consultancy; GSK: Consultancy; CBMG: Consultancy; Novartis: Research Funding; Kite: Research Funding; Servier: Research Funding; Jazz: Other: study steering committees or scientific advisory boards; Adaptimmune: Other: study steering committees or scientific advisory boards; Cure Genetics: Consultancy; Humanigen: Consultancy. Hexner:novartis: Research Funding. Kitko:Mallinckrodt: Honoraria; Novartis: Consultancy, Honoraria. Mielke:EBMT/EHA: Other: Travel support; ISCT: Other: Travel support; Miltenyi: Consultancy, Honoraria, Other: Travel and speakers fee (via institution), Speakers Bureau; Jazz Pharma: Honoraria, Other: Travel support, Speakers Bureau; IACH: Other: Travel support; Kiadis Pharma: Consultancy, Honoraria, Other: Travel support (via institution), Speakers Bureau; DGHO: Other: Travel support; Bellicum: Consultancy, Honoraria, Other: Travel (via institution); GILEAD: Consultancy, Honoraria, Other: travel (via institution), Speakers Bureau; Celgene: Honoraria, Other: Travel support (via institution), Speakers Bureau. Merli:Sobi: Consultancy; Amgen: Honoraria; Novartis: Honoraria; Bellicum: Consultancy. Pulsipher:Amgen: Other: Lecture; Miltenyi: Research Funding; Bellicum: Consultancy; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Jazz: Other: Education for employees; CSL Behring: Membership on an entity's Board of Directors or advisory committees; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Medac: Honoraria. Qayed:Bristol-Myers Squibb: Honoraria. Reshef:Pfizer: Consultancy; Magenta: Consultancy; Kite: Consultancy, Research Funding; Atara: Consultancy, Research Funding; BMS: Consultancy; Pharmacyclics: Consultancy, Research Funding; Incyte: Consultancy, Research Funding; Celgene: Research Funding; Shire: Research Funding. Levine:Incyte: Consultancy, Research Funding; Biogen: Other: non-financial support; Viracor: Patents & Royalties: biomarker patent; Ironwood: Honoraria; bluebird bio: Consultancy; National Cancer Institute: Research Funding; Novartis: Honoraria; Kamada: Research Funding. Ferrara:National Institutes of Health: Research Funding; ViraCor: Consultancy; Incyte: Consultancy; Kamada: Consultancy; Mallinckrodt: Consultancy; Enlivex: Consultancy; Xenikos: Consultancy; CSL Behring: Consultancy.

SummarySickle cell disease (SCD) has a distinct pattern of transfusional iron overload (IO) when compared to transfusion‐dependent β‐thalassaemia major (TDT). We conducted a single institution prospective study to evaluate plasma biomarkers of iron regulation and inflammation in patients with SCD with IO (SCD IO cases, n = 22) and without IO (SCD non‐IO cases, n = 11), and non‐SCD controls (n = 13). Hepcidin was found to be inappropriately low, as evidenced by a significantly higher median hepcidin/ferritin ratio in non‐SCD controls compared to SCD IO cases (0·3 vs. 0·02, P < 0·0001) and SCD non‐IO cases (0·3 vs. 0·02, P < 0·0001), suggesting that certain inhibitory mechanism (s) work to suppress hepcidin in SCD. As opposed to the SCD non‐IO state, where hepcidin shows a strong significant positive correlation with ferritin (Spearman ρ = 0·7, P = 0·02), this correlation was lost when IO occurs (Spearman ρ = −0·2, P = 0·4). Although a direct non‐linear correlation between erythroferrone (ERFE) and hepcidin did not reach statistical significance both in the IO (Spearman ρ = −0·4, P = 0·08) and non‐IO state (Spearman ρ = −0·6, P = 0·07), patients with highest ERFE had low hepcidin levels, suggesting that ERFE contributes to hepcidin regulation in some patients. Our results suggest a multifactorial mechanism of hepcidin regulation in SCD.

Hepatocellular carcinoma (HCC) is among the leading causes of worldwide cancer‐related morbidity and mortality. Poor prognosis of HCC is attributed primarily to tumor presentation at an advanced stage when there is no effective treatment to achieve the long term survival of patients. Currently available tests such as alpha‐fetoprotein have limited accuracy as a diagnostic or prognostic biomarker for HCC. Liver biopsy provides tissue that can reveal tumor biology but it is not used routinely due to its invasiveness and risk of tumor seeding, especially in early‐stage patients. Liver biopsy is also limited in revealing comprehensive tumor biology due to intratumoral heterogeneity. There is a clear need for new biomarkers to improve HCC detection, prognostication, prediction of treatment response, and disease monitoring with treatment. Liquid biopsy could be an effective method of early detection and management of HCC. Circulating tumor cells (CTCs) are cancer cells in circulation derived from the original tumor or metastatic foci, and their measurement by liquid biopsy represents a great potential in facilitating the implementation of precision medicine in patients with HCC. CTCs can be detected by a simple peripheral blood draw and potentially show global features of tumor characteristics. Various CTC detection platforms using immunoaffinity and biophysical properties have been developed to identify and capture CTCs with high efficiency. Quantitative abundance of CTCs, as well as biological characteristics and genomic heterogeneity among the CTCs, can predict disease prognosis and response to therapy in patients with HCC. This review article will discuss the currently available technologies for CTC detection and isolation, their utility in the clinical management of HCC patients, their limitations, and future directions of research.

TGF-β–dependent metabolic dysregulation contributes to lung fibrosis.

Abstract Transforming growth factor‐beta (TGFβ) is an enigmatic protein with various roles in healthy tissue homeostasis/development as well as the development or progression of cancer, wound healing, fibrotic disorders, and immune modulation, to name a few. As TGFβ is causal to various fibroproliferative disorders featuring localized or systemic tissue/organ fibrosis as well as the activated stroma observed in various malignancies, characterizing the pathways and players mediating its action is fundamental. In the current study, we found that TGFβ induces the expression of the immunoinhibitory molecule Programed death‐ligand 1 (PD‐L1) in human and murine fibroblasts in a Smad2/3‐ and YAP/TAZ‐dependent manner. Furthermore, PD‐L1 knockdown decreased the TGFβ‐dependent induction of extracellular matrix proteins, including collagen Iα1 (colIα1) and alpha‐smooth muscle actin (α‐SMA), and cell migration/wound healing. In addition to an endogenous role for PD‐L1 in profibrotic TGFβ signaling, TGFβ stimulated‐human lung fibroblast‐derived PD‐L1 into extracellular vesicles (EVs) capable of inhibiting T cell proliferation in response to T cell receptor stimulation and mediating fibroblast cell migration. These findings provide new insights and potential targets for a variety of fibrotic and malignant diseases.