Comparative Analysis of Calcineurin Inhibitor-Based Methotrexate and Mycophenolate Mofetil-Containing Regimens for Prevention of Graft-versus-Host Disease after Reduced-Intensity Conditioning Allogeneic Transplantation

The combination of a calcineurin inhibitor (CNI) such as tacrolimus (TAC) or cyclosporine (CYSP) with methotrexate (MTX) or with mycophenolate mofetil (MMF) has been commonly used for graft-versus-host disease (GVHD) prophylaxis after reduced-intensity conditioning (RIC) allogeneic hematopoietic cell transplantation (alloHCT), but there are limited data comparing efficacy of the 2 regimens. We evaluated 1564 adult patients who underwent RIC alloHCT for acute myelogenous leukemia (AML) and acute lymphoblastic leukemia (ALL), chronic myelogenous leukemia (CML), and myelodysplastic syndrome (MDS) from 2000 to 2013 using HLA-identical sibling (matched related donor [MRD]) or unrelated donor (URD) peripheral blood graft and received CYSP or TAC with MTX or MMF for GVHD prophylaxis. Primary outcomes of the study were acute and chronic GVHD and overall survival (OS). The study divided the patient population into 4 cohorts based on regimen: MMF-TAC, MMF-CYSP, MTX-TAC, and MTX-CYSP. In the URD group, MMF-CYSP was associated with increased risk of grade II to IV acute GVHD (relative risk [RR], 1.78; P <.001) and grade III to IV acute GVHD (RR, 1.93; P =.006) compared with MTX-TAC. In the URD group, use of MMF-TAC (versus MTX-TAC) lead to higher nonrelapse mortality. (hazard ratio, 1.48; P =.008). In either group, no there was no difference in chronic GVHD, disease-free survival, and OS among the GVHD prophylaxis regimens. For RIC alloHCT using MRD, there are no differences in outcomes based on GVHD prophylaxis. However, with URD RIC alloHCT, MMF-CYSP was inferior to MTX-based regimens for acute GVHD prevention, but all the regimens were equivalent in terms of chronic GVHD and OS. Prospective studies, targeting URD recipients are needed to confirm these results

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

Risk Factors for Graft-versus-Host Disease in Haploidentical Hematopoietic Cell Transplantation Using Post-Transplant Cyclophosphamide

Post-transplant cyclophosphamide (PTCy) has significantly increased the successful use of haploidentical donors with a relatively low incidence of graft-versus-host disease (GVHD). Given its increasing use, we sought to determine risk factors for GVHD after haploidentical hematopoietic cell transplantation (haplo-HCT) using PTCy. Data from the Center for International Blood and Marrow Transplant Research on adult patients with acute myeloid leukemia, acute lymphoblastic leukemia, myelodysplastic syndrome, or chronic myeloid leukemia who underwent PTCy-based haplo-HCT (2013 to 2016) were analyzed and categorized into 4 groups based on myeloablative (MA) or reduced-intensity conditioning (RIC) and bone marrow (BM) or peripheral blood (PB) graft source. In total, 646 patients were identified (MA-BM = 79, MA-PB = 183, RIC-BM = 192, RIC-PB = 192). The incidence of grade 2 to 4 acute GVHD at 6 months was highest in MA-PB (44%), followed by RIC-PB (36%), MA-BM (36%), and RIC-BM (30%) (P =.002). The incidence of chronic GVHD at 1 year was 40%, 34%, 24%, and 20%, respectively (P <.001). In multivariable analysis, there was no impact of stem cell source or conditioning regimen on grade 2 to 4 acute GVHD; however, older donor age (30 to 49 versus <29 years) was significantly associated with higher rates of grade 2 to 4 acute GVHD (hazard ratio [HR], 1.53; 95% confidence interval [CI], 1.11 to 2.12; P =.01). In contrast, PB compared to BM as a stem cell source was a significant risk factor for the development of chronic GVHD (HR, 1.70; 95% CI, 1.11 to 2.62; P =.01) in the RIC setting. There were no differences in relapse or overall survival between groups. Donor age and graft source are risk factors for acute and chronic GVHD, respectively, after PTCy-based haplo-HCT. Our results indicate that in RIC haplo-HCT, the risk of chronic GVHD is higher with PB stem cells, without any difference in relapse or overall survival

COSNet : a cost sensitive neural network for semi-supervised learning in graphs

The semi-supervised problem of learning node labels in graphs consists, given a partial graph labeling, in inferring the unknown labels of the unlabeled vertices. Several machine learning algorithms have been proposed for solving this problem, including Hopfield networks and label propagation methods; however, some issues have been only partially considered, e.g. the preservation of the prior knowledge and the unbalance between positive and negative labels. To address these items, we propose a Hopfield-based cost sensitive neural network algorithm (COSNet). The method factorizes the solution of the problem in two parts: 1) the sub- network composed by the labelled vertices is considered, and the net- work parameters are estimated through a supervised algorithm; 2) the estimated parameters are extended to the subnetwork composed of the unlabeled vertices, and the attractor reached by the dynamics of this subnetwork allows to predict the labeling of the unlabeled vertices. The proposed method embeds in the neural algorithm the \u201da priori\u201d knowl- edge coded in the labelled part of the graph, and separates node labels and neuron states, allowing to differentially weight positive and nega- tive node labels. Moreover, COSNet introduces an efficient cost-sensitive strategy which allows to learn the near-optimal parameters of the net- work in order to take into account the unbalance between positive and negative node labels. Finally, the dynamics of the network is restricted to its unlabeled part, preserving the minimization of the overall objective function and significantly reducing the time complexity of the learning algorithm. COSNet has been applied to the genome-wide prediction of gene function in a model organism. The results, compared with those ob- tained by other semi-supervised label propagation algorithms and super- vised machine learning methods, show the effectiveness of the proposed approach

Driver Fusions and Their Implications in the Development and Treatment of Human Cancers.

Gene fusions represent an important class of somatic alterations in cancer. We systematically investigated fusions in 9,624 tumors across 33 cancer types using multiple fusion calling tools. We identified a total of 25,664 fusions, with a 63% validation rate. Integration of gene expression, copy number, and fusion annotation data revealed that fusions involving oncogenes tend to exhibit increased expression, whereas fusions involving tumor suppressors have the opposite effect. For fusions involving kinases, we found 1,275 with an intact kinase domain, the proportion of which varied significantly across cancer types. Our study suggests that fusions drive the development of 16.5% of cancer cases and function as the sole driver in more than 1% of them. Finally, we identified druggable fusions involving genes such as TMPRSS2, RET, FGFR3, ALK, and ESR1 in 6.0% of cases, and we predicted immunogenic peptides, suggesting that fusions may provide leads for targeted drug and immune therapy

The Cancer Genome Atlas Comprehensive Molecular Characterization of Renal Cell Carcinoma

Renal cell carcinoma(RCC) is not a single disease, but several histologically defined cancers with different genetic drivers, clinical courses, and therapeutic responses. The current study evaluated 843 RCC from the three major histologic subtypes, including 488 clear cell RCC, 274 papillary RCC, and 81 chromophobe RCC. Comprehensive genomic and phenotypic analysis of the RCC subtypes reveals distinctive features of each subtype that provide the foundation for the development of subtype-specific therapeutic and management strategies for patients affected with these cancers. Somatic alteration of BAP1, PBRM1, and PTEN and altered metabolic pathways correlated with subtype-specific decreased survival, while CDKN2A alteration, increased DNA hypermethylation, and increases in the immune-related Th2 gene expression signature correlated with decreased survival within all major histologic subtypes. CIMP-RCC demonstrated an increased immune signature, and a uniform and distinct metabolic expression pattern identified a subset of metabolically divergent (MD) ChRCC that associated with extremely poor survival

Somatic Mutational Landscape of Splicing Factor Genes and Their Functional Consequences across 33 Cancer Types

Hotspot mutations in splicing factor genes have been recently reported at high frequency in hematological malignancies, suggesting the importance of RNA splicing in cancer. We analyzed whole-exome sequencing data across 33 tumor types in The Cancer Genome Atlas (TCGA), and we identified 119 splicing factor genes with significant non-silent mutation patterns, including mutation over-representation, recurrent loss of function (tumor suppressor-like), or hotspot mutation profile (oncogene-like). Furthermore, RNA sequencing analysis revealed altered splicing events associated with selected splicing factor mutations. In addition, we were able to identify common gene pathway profiles associated with the presence of these mutations. Our analysis suggests that somatic alteration of genes involved in the RNA-splicing process is common in cancer and may represent an underappreciated hallmark of tumorigenesis

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

Although the MYC oncogene has been implicated in cancer, a systematic assessment of alterations of MYC, related transcription factors, and co-regulatory proteins, forming the proximal MYC network (PMN), across human cancers is lacking. Using computational approaches, we define genomic and proteomic features associated with MYC and the PMN across the 33 cancers of The Cancer Genome Atlas. Pan-cancer, 28% of all samples had at least one of the MYC paralogs amplified. In contrast, the MYC antagonists MGA and MNT were the most frequently mutated or deleted members, proposing a role as tumor suppressors. MYC alterations were mutually exclusive with PIK3CA, PTEN, APC, or BRAF alterations, suggesting that MYC is a distinct oncogenic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such as immune response and growth factor signaling; chromatin, translation, and DNA replication/repair were conserved pan-cancer. This analysis reveals insights into MYC biology and is a reference for biomarkers and therapeutics for cancers with alterations of MYC or the PMN. We present a computational study determining the frequency and extent of alterations of the MYC network across the 33 human cancers of TCGA. These data, together with MYC, positively correlated pathways as well as mutually exclusive cancer genes, will be a resource for understanding MYC-driven cancers and designing of therapeutics

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

Long noncoding RNAs (lncRNAs) are commonly dysregulated in tumors, but only a handful are known to play pathophysiological roles in cancer. We inferred lncRNAs that dysregulate cancer pathways, oncogenes, and tumor suppressors (cancer genes) by modeling their effects on the activity of transcription factors, RNA-binding proteins, and microRNAs in 5,185 TCGA tumors and 1,019 ENCODE assays. Our predictions included hundreds of candidate onco- and tumor-suppressor lncRNAs (cancer lncRNAs) whose somatic alterations account for the dysregulation of dozens of cancer genes and pathways in each of 14 tumor contexts. To demonstrate proof of concept, we showed that perturbations targeting OIP5-AS1 (an inferred tumor suppressor) and TUG1 and WT1-AS (inferred onco-lncRNAs) dysregulated cancer genes and altered proliferation of breast and gynecologic cancer cells. Our analysis indicates that, although most lncRNAs are dysregulated in a tumor-specific manner, some, including OIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergistically dysregulate cancer pathways in multiple tumor contexts. Chiu et al. present a pan-cancer analysis of lncRNA regulatory interactions. They suggest that the dysregulation of hundreds of lncRNAs target and alter the expression of cancer genes and pathways in each tumor context. This implies that hundreds of lncRNAs can alter tumor phenotypes in each tumor context