Cis inhibition of NOTCH1 through JAGGED1 sustains embryonic hematopoietic stem cell fate.

Hematopoietic stem cells (HSCs) develop from the hemogenic endothelium (HE) in the aorta- gonads-and mesonephros (AGM) region and reside within Intra-aortic hematopoietic clusters (IAHC) along with hematopoietic progenitors (HPC). The signalling mechanisms that distinguish HSCs from HPCs are unknown. Notch signaling is essential for arterial specification, IAHC formation and HSC activity, but current studies on how Notch segregates these different fates are inconsistent. We now demonstrate that Notch activity is highest in a subset of, GFI1 + , HSC-primed HE cells, and is gradually lost with HSC maturation. We uncover that the HSC phenotype is maintained due to increasing levels of NOTCH1 and JAG1 interactions on the surface of the same cell (cis) that renders the NOTCH1 receptor from being activated. Forced activation of the NOTCH1 receptor in IAHC activates a hematopoietic differentiation program. Our results indicate that NOTCH1-JAG1 cis-inhibition preserves the HSC phenotype in the hematopoietic clusters of the embryonic aorta.

Biomarkers of tumor-reactive CD4+ and CD8+ TILs associate with improved prognosis in endometrial cancer.

BACKGROUND: Despite the growing interest in immunotherapeutic interventions for endometrial cancer (EC), the prevalence, phenotype, specificity and prognostic value of tumor infiltrating lymphocytes (TILs) in this tumor type remains unclear. METHODS: To better understand the role of TILs in EC, we analyzed the phenotypic traits of CD8+ and CD4+ EC-resident T cells from 47 primary tumors by high-dimensional flow cytometry. In addition, CD8+ and CD4+ TIL subpopulations were isolated based on the differential expression of programmed cell death protein-1 (PD-1) (negative, dim and high) and CD39 (positive or negative) by fluorescence activated cell sorting (FACS), expanded in vitro, and screened for autologous tumor recognition. We further investigated whether phenotypic markers preferentially expressed on CD8+ and CD4+ tumor-reactive TIL subsets were associated with the four distinct molecular subtypes of EC, tumor mutational burden and patient survival. RESULTS: We found that CD8+TILs expressing high levels of PD-1 (PD-1hi) co-expressed CD39, TIM-3, HLA-DR and CXCL13, as compared with TILs lacking or displaying intermediate levels of PD-1 expression (PD-1- and PD-1dim, respectively). Autologous tumor reactivity of sorted and in vitro expanded CD8+ TILs demonstrated that the CD8+PD-1dimCD39+ and PD-1hiCD39+ T cell subsets both contained tumor-reactive TILs and that a higher level of PD-1 expression was associated with increased CD39 and a superior frequency of tumor reactivity. With respect to CD4+ T conventional (Tconv) TILs, co-expression of inhibitory and activation markers was more apparent on PD-1hi compared with PD-1- or PD-1dim T cells, and in fact, it was the CD4+PD-1hi subpopulation that accumulated the antitumor T cells irrespective of CD39 expression. Most importantly, detection of CD8+PD-1hiCD39+ and CD4+PD-1hi tumor-reactive T-cell subsets, but also markers specifically expressed by these subpopulations of TILs, that is, PD-1hi, CD39, CXCL13 and CD103 by CD8+ TILs and PD-1hi and CXCL13 by CD4+ Tconv TILs, correlated with prolonged survival of patients with EC. CONCLUSIONS: Our results demonstrate that EC are frequently infiltrated by tumor-reactive TILs, and that expression of PD-1hi and CD39 or PD-1hi can be used to select and expand CD8+ and CD4+ tumor-reactive TILs, respectively. In addition, biomarkers preferentially expressed on tumor-reactive TILs, rather than the frequency of CD3+, CD8+ and CD4+ lymphocytes, hold prognostic value suggesting their protective role in antitumor immunity.

Comparison of algorithms for the detection of cancer drivers at subgene resolution.

Understanding genetic events that lead to cancer initiation and progression remains one of the biggest challenges in cancer biology. Traditionally, most algorithms for cancer-driver identification look for genes that have more mutations than expected from the average background mutation rate. However, there is now a wide variety of methods that look for nonrandom distribution of mutations within proteins as a signal for the driving role of mutations in cancer. Here we classify and review such subgene-resolution algorithms, compare their findings on four distinct cancer data sets from The Cancer Genome Atlas and discuss how predictions from these algorithms can be interpreted in the emerging paradigms that challenge the simple dichotomy between driver and passenger genes.