Immune Escape in Glioblastoma Multiforme and the Adaptation of Immunotherapies for Treatment

Glioblastoma multiforme (GBM) is the most frequently occurring primary brain tumor and has a very poor prognosis, with only around 5% of patients surviving for a period of 5 years or more after diagnosis. Despite aggressive multimodal therapy, consisting mostly of a combination of surgery, radiotherapy, and temozolomide chemotherapy, tumors nearly always recur close to the site of resection. For the past 15 years, very little progress has been made with regards to improving patient survival. Although immunotherapy represents an attractive therapy modality due to the promising pre-clinical results observed, many of these potential immunotherapeutic approaches fail during clinical trials, and to date no immunotherapeutic treatments for GBM have been approved. As for many other difficult to treat cancers, GBM combines a lack of immunogenicity with few mutations and a highly immunosuppressive tumor microenvironment (TME). Unfortunately, both tumor and immune cells have been shown to contribute towards this immunosuppressive phenotype. In addition, current therapeutics also exacerbate this immunosuppression which might explain the failure of immunotherapy-based clinical trials in the GBM setting. Understanding how these mechanisms interact with one another, as well as how one can increase the anti-tumor immune response by addressing local immunosuppression will lead to better clinical results for immune-based therapeutics. Improving therapeutic delivery across the blood brain barrier also presents a challenge for immunotherapy and future therapies will need to consider this. This review highlights the immunosuppressive mechanisms employed by GBM cancers and examines potential immunotherapeutic treatments that can overcome these significant immunosuppressive hurdles

Cancer vaccines: adjuvant potency, importance of age, lifestyle, and treatments

Although the discovery and characterization of multiple tumor antigens have sparked the development of many antigen/derived cancer vaccines, many are poorly immunogenic and thus, lack clinical efficacy. Adjuvants are therefore incorporated into vaccine formulations to trigger strong and long-lasting immune responses. Adjuvants have generally been classified into two categories: those that ‘depot’ antigens (e.g. mineral salts such as aluminum hydroxide, emulsions, liposomes) and those that act as immunostimulants (Toll Like Receptor agonists, saponins, cytokines). In addition, several novel technologies using vector-based delivery of antigens have been used. Unfortunately, the immune system declines with age, a phenomenon known as immunosenescence, and this is characterized by functional changes in both innate and adaptive cellular immunity systems as well as in lymph node architecture. While many of the immune functions decline over time, others paradoxically increase. Indeed, aging is known to be associated with a low level of chronic inflammation—inflamm-aging. Given that the median age of cancer diagnosis is 66 years and that immunotherapeutic interventions such as cancer vaccines are currently given in combination with or after other forms of treatments which themselves have immune-modulating potential such as surgery, chemotherapy and radiotherapy, the choice of adjuvants requires careful consideration in order to achieve the maximum immune response in a compromised environment. In addition, more clinical trials need to be performed to carefully assess how less conventional form of immune adjuvants, such as exercise, diet and psychological care which have all be shown to influence immune responses can be incorporated to improve the efficacy of cancer vaccines. In this review, adjuvants will be discussed with respect to the above-mentioned important elements

Acute chest syndrome in children with sickle cell disease: Data from a national AIEOP cohort identify priority areas of intervention in a hub-and-spoke system

: Acute chest syndrome (ACS) is a frequent cause of hospitalization in sickle cell disease (SCD). Despite advances in acute care, many settings still lack knowledge about ACS best practices. After the AIEOP Guidelines were published in 2012, suggesting standardized management in Italy, a retrospective study was performed to assess the diagnostic and therapeutic pathways of ACS in children. From 2013 to 2018, 208 ACS episodes were presented by 122/583 kids in 11 centres. 73 were male, mean age 10.9 years, 85% African, 92% HbSS or Sβ°. In our hub-and-spoke system, a good adherence to Guidelines was documented, but discrepancies between reference centres and general hospitals were noted. Improvement is needed for timely transfer to reference centres, use of incentive spirometry, oxygen therapy and pain management

Myanmar Dengue Outbreak Associated with Displacement of Serotypes 2, 3, and 4 by Dengue 1

In 2001, Myanmar (Burma) had its largest outbreak of dengue—15,361 reported cases of dengue hemorrhagic fever/dengue shock syndrome (DHF/DSS), including 192 deaths. That year, 95% of dengue viruses isolated from patients were serotype 1 viruses belonging to two lineages that had diverged from an earlier, now extinct, lineage sometime before 1998. The ratio of DHF to DSS cases in 2001 was not significantly different from that in 2000, when 1,816 cases of DHF/DSS were reported and dengue 1 also was the most frequently isolated serotype. However, the 2001 ratio was significantly higher than that in 1998 (also an outbreak year) and in 1999, when all four serotypes were detected and serotypes 1, 2, and 3 were recovered in similar numbers. The large number of clinical cases in 2001 may have been due, in part, to a preponderance of infections with dengue 1 viruses

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment