Purpose: CD70 is a costimulatory molecule known to activate CD27-expressing T cells. CD27–CD70 interaction leads to the release of soluble CD27 (sCD27). Clear-cell renal cell carcinoma (ccRCC) expresses the highest levels of CD70 among all solid tumors; however, the clinical consequences of CD70 expression remain unclear. Experimental Design: Tumor tissue from 25 patients with ccRCC was assessed for the expression of CD27 and CD70 in situ using multiplex immunofluorescence. CD27+ T-cell phenotypes in tumors were analyzed by flow cytometry and their gene expression profile were analyzed by single-cell RNA sequencing then confirmed with public data. Baseline sCD27 was measured in 81 patients with renal cell carcinoma (RCC) treated with immunotherapy (35 for training cohort and 46 for validation cohort). Results: In the tumor microenvironment, CD27+ T cells interacted with CD70-expressing tumor cells. Compared with CD27− T cells, CD27+ T cells exhibited an apoptotic and dysfunctional signature. In patients with RCC, the intratumoral CD27–CD70 interaction was significantly correlated with the plasma sCD27 concentration. High sCD27 levels predicted poor overall survival in patients with RCC treated with anti–programmed cell death protein 1 in both the training and validation cohorts but not in patients treated with antiangiogenic therapy. Conclusions: In conclusion, we demonstrated that sCD27, a surrogate marker of T-cell dysfunction, is a predictive biomarker of resistance to immunotherapy in RCC. Given the frequent expression of CD70 and CD27 in solid tumors, our findings may be extended to other tumors.

Background: A high density of resident memory T cells (T_RM) in tumors correlates with improved clinical outcomes in immunotherapy-treated patients. In most clinical studies, T_RM are defined by the CD103 marker. However, it is clearly established that not all T_RM express CD103, but can be defined by other markers (CD49a, CD69, etc). The frequency of these subpopulations of T_RM expressing or not CD103 varies according to the location of the cancer. Little is known about their functionality and their predictive impact on response to immunotherapy. In preclinical models, only some subpopulations of T_RM are associated with cancer vaccine efficacy. Methods: Multiparametric cytometry analyses were used to demonstrate the presence of T_RM subpopulations in the lung in mice after vaccination and in fresh ex vivo human non-small cell lung cancer (NSCLC). An analysis of the T-cell repertoire of these T_RM was conducted to search for their relationships. Multiplex immunofluorescence techniques were used to quantify intratumor infiltration of T_RM subpopulations in two cohorts of patients with NSCLC. The impact on the clinical outcome of the T_RM tumor infiltration was also investigated. Results: We identified two main T_RM subpopulations in tumor-infiltrating lymphocytes derived from patients with NSCLC: one co-expressing CD103 and CD49a (double positive (DP)), and the other expressing only CD49a (simple positive (SP)); both exhibiting additional T_RM surface markers like CD69. Despite higher expression of inhibitory receptors, DP T_RM exhibited greater functionality compared with SP T_RM. Analysis of T-cell receptor (TCR) repertoire and expression of the stemness marker TCF1 revealed shared TCRs between populations, with the SP subset appearing more progenitor-like phenotype. In the training cohort, PD-L1 (Programmed Death-Ligand 1) and TCF1⁺CD8⁺T cells predict response to anti-PD-1. In patient with NSCLC validation cohorts, only DP T_RM predicted PD-1 blockade response. Multivariate analysis, including various biomarkers associated with responses to anti-PD-(L)1, such as total CD8, TCF1⁺CD8⁺T cells, and PD-L1, showed that only intratumoral infiltration by DP T_RM remained significant. Conclusions: This study highlights the non-equivalence of T_RM subpopulations. The population of T_RM co-expressing CD103 and CD49a appears to be the most functional and has the most significant capacity for predicting response to immunotherapy in multivariate analysis in patients with NSCLC.

Western Cambodia is recognized as the epicentre of emergence of Plasmodium falciparum multi-drug resistance. The emergence of artemisinin resistance has been observed in this area since 2008–2009 and molecular signatures associated to artemisinin resistance have been characterized in k13 gene. At present, one of the major threats faced, is the possible spread of Asian artemisinin resistant parasites over the world threatening millions of people and jeopardizing malaria elimination programme efforts. To anticipate the diffusion of artemisinin resistance, the identification of the P. falciparum population structure and the gene flow among the parasite population in Cambodia are essential. To this end, a mid-throughput PCR-LDR-FMA approach based on LUMINEX technology was developed to screen for genetic barcode in 533 blood samples collected in 2010–2011 from 16 health centres in malaria endemics areas in Cambodia. Based on successful typing of 282 samples, subpopulations were characterized along the borders of the country. Each 11-loci barcode provides evidence supporting allele distribution gradient related to subpopulations and gene flow. The 11-loci barcode successfully identifies recently emerging parasite subpopulations in western Cambodia that are associated with the C580Y dominant allele for artemisinin resistance in k13 gene. A subpopulation was identified in northern Cambodia that was associated to artemisinin (R539T resistant allele of k13 gene) and mefloquine resistance. The gene flow between these subpopulations might have driven the spread of artemisinin resistance over Cambodia.

The modulation of subpopulations of pro-angiogenic monocytes (VEGFR-1⁺CD14 and Tie2⁺CD14) was analyzed in an ancillary study from the prospective PazopanIb versus Sunitinib patient preferenCE Study (PISCES) (NCT01064310), where metastatic renal cell carcinoma (mRCC) patients were treated with two anti-angiogenic drugs, either sunitinib or pazopanib. Blood samples from 86 patients were collected prospectively at baseline (T1), and at 10 weeks (T2) and 20 weeks (T3) after starting anti-angiogenic therapy. Various subpopulations of myeloid cells (monocytes, VEGFR-1⁺CD14 and Tie2⁺CD14 cells) decreased during treatment. When patients were divided into two subgroups with a decrease (defined as a >20% reduction from baseline value) (group 1) or not (group 2) at T3 for VEGFR-1⁺CD14 cells, group 1 patients presented a median PFS and OS of 24 months and 37 months, respectively, compared with a median PFS of 9 months (p = 0.032) and a median OS of 16 months (p = 0.033) in group 2 patients. The reduction in Tie2⁺CD14 at T3 predicted a benefit in OS at 18 months after therapy (p = 0.04). In conclusion, in this prospective clinical trial, a significant decrease in subpopulations of pro-angiogenic monocytes was associated with clinical response to anti-angiogenic drugs in patients with mRCC.

The development of high-throughput genomic technologies associated with recent genetic perturbation techniques such as short hairpin RNA (shRNA), gene trapping, or gene editing (CRISPR/Cas9) has made it possible to obtain large perturbation data sets. These data sets are invaluable sources of information regarding the function of genes, and they offer unique opportunities to reverse engineer gene regulatory networks in specific cell types. Modular response analysis (MRA) is a well-accepted mathematical modeling method that is precisely aimed at such network inference tasks, but its use has been limited to rather small biological systems so far. In this study, we show that MRA can be employed on large systems with almost 1,000 network components. In particular, we show that MRA performance surpasses general-purpose mutual information-based algorithms. Part of these competitive results was obtained by the application of a novel heuristic that pruned MRA-inferred interactionsa posteriori. We also exploited a block structure in MRA linear algebra to parallelize large system resolutions.

In 2011–2012, Northern Vietnam experienced its first large scale hand foot and mouth disease (HFMD) epidemic. In 2011, a major HFMD epidemic was also reported in South Vietnam with fatal cases. This 2011–2012 outbreak was the first one to occur in North Vietnam providing grounds to study the etiology, origin and dynamic of the disease. We report here the analysis of the VP1 gene of strains isolated throughout North Vietnam during the 2011–2012 outbreak and before. The VP1 gene of 106 EV-A71 isolates from North Vietnam and 2 from Central Vietnam were sequenced. Sequence alignments were analyzed at the nucleic acid and protein level. Gene polymorphism was also analyzed. A Factorial Correspondence Analysis was performed to correlate amino acid mutations with clinical parameters. The sequences were distributed into four phylogenetic clusters. Three clusters corresponded to the subgenogroup C4 and the last one corresponded to the subgenogroup C5. Each cluster displayed different polymorphism characteristics. Proteins were highly conserved but three sites bearing only Isoleucine (I) or Valine (V) were characterized. The isoleucine/valine variability matched the clusters. Spatiotemporal analysis of the I/V variants showed that all variants which emerged in 2011 and then in 2012 were not the same but were all present in the region prior to the 2011–2012 outbreak. Some correlation was found between certain I/V variants and ethnicity and severity. The 2011–2012 outbreak was not caused by an exogenous strain coming from South Vietnam or elsewhere but by strains already present and circulating at low level in North Vietnam. However, what triggered the outbreak remains unclear. A selective pressure is applied on I/V variants which matches the genetic clusters. I/V variants were shown on other viruses to correlate with pathogenicity. This should be investigated in EV-A71. I/V variants are an easy and efficient way to survey and identify circulating EV-A71 strains.