Search

Chimeric antigen receptor (CAR) T-cell therapy targeting CD19 has been a clinical breakthrough for pediatric B-cell acute lymphoblastic leukemia (B-ALL), and loss of the CD19 target antigen on leukemic cells represents a major mechanism of relapse. Previous studies have observed CD19 mutations specific to CD19− relapses, and we sought to clarify and strengthen this relationship using deep whole-exome sequencing in leukemic cells expanded in a patient-derived xenograft. By assessing pre-treatment and relapse cells from 13 patients treated with CAR T-cell therapy, 8 of whom developed CD19− relapse and 5 of whom developed CD19+ relapse, we demonstrate that relapse-specific single-nucleotide variants and small indels with high allele frequency combined with deletions in the CD19 gene in a manner specific to those patients with CD19− relapse. Before CAR T-cell infusion, one patient was found to harbor a pre-existing CD19 deletion in the context of genomic instability, which likely represented the first hit leading to the patient's subsequent CD19− relapse. Across patients, preexisting mutations and genomic instability were not significant predictors of subsequent CD19− relapse across patients, with sample size as a potential limiting factor. Together, our results clarify and strengthen the relationship between genomic events and CD19− relapse, demonstrating this intriguing mechanism of resistance to a targeted cancer immunotherapy.

Clinical information regarding the 13 patients involved in this study, including passage numbers of PDX samples.

Legends for supplementary figures and tables.

Chimeric antigen receptor (CAR) T-cell therapy targeting CD19 has been a clinical breakthrough for pediatric B-cell acute lymphoblastic leukemia (B-ALL), and loss of the CD19 target antigen on leukemic cells represents a major mechanism of relapse. Previous studies have observed CD19 mutations specific to CD19− relapses, and we sought to clarify and strengthen this relationship using deep whole-exome sequencing in leukemic cells expanded in a patient-derived xenograft. By assessing pre-treatment and relapse cells from 13 patients treated with CAR T-cell therapy, 8 of whom developed CD19− relapse and 5 of whom developed CD19+ relapse, we demonstrate that relapse-specific single-nucleotide variants and small indels with high allele frequency combined with deletions in the CD19 gene in a manner specific to those patients with CD19− relapse. Before CAR T-cell infusion, one patient was found to harbor a pre-existing CD19 deletion in the context of genomic instability, which likely represented the first hit leading to the patient's subsequent CD19− relapse. Across patients, preexisting mutations and genomic instability were not significant predictors of subsequent CD19− relapse across patients, with sample size as a potential limiting factor. Together, our results clarify and strengthen the relationship between genomic events and CD19− relapse, demonstrating this intriguing mechanism of resistance to a targeted cancer immunotherapy.

Details of SNVs and small indels detected from deep whole-exome sequencing in CD19 and selected genes associated with genomic instability.

Supplementary Figure 1-9.

The adoptive transfer of chimeric antigen receptor (CAR) T cells represents a breakthrough in clinical oncology, yet both between- and within-patient differences in autologously derived T cells are a major contributor to therapy failure. To interrogate the molecular determinants of clinical CAR T-cell persistence, we extensively characterized the premanufacture T cells of 71 patients with B-cell malignancies on trial to receive anti-CD19 CAR T-cell therapy. We performed RNA-sequencing analysis on sorted T-cell subsets from all 71 patients, followed by paired Cellular Indexing of Transcriptomes and Epitopes (CITE) sequencing and single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) on T cells from six of these patients. We found that chronic IFN signaling regulated by IRF7 was associated with poor CAR T-cell persistence across T-cell subsets, and that the TCF7 regulon not only associates with the favorable naïve T-cell state, but is maintained in effector T cells among patients with long-term CAR T-cell persistence. These findings provide key insights into the underlying molecular determinants of clinical CAR T-cell function.Significance:To improve clinical outcomes for CAR T-cell therapy, there is a need to understand the molecular determinants of CAR T-cell persistence. These data represent the largest clinically annotated molecular atlas in CAR T-cell therapy to date, and significantly advance our understanding of the mechanisms underlying therapeutic efficacy.This article is highlighted in the In This Issue feature, p. 2113

Supplementary Table S3. Gene sets for the TCF7 regulon and interferon response. For each gene set, three annotations are provided: the Ensembl gene id, the Entrez gene id, and HGNC symbol based on annotation from Ensembl version 94.

Supplementary tables 1-4 and figures 1-11

Supplementary Methods

Reproduction of all main results.

Purpose: The majority of ovarian carcinomas are of high-grade serous histology, which is associated with poor prognosis. Surgery and chemotherapy are the mainstay of treatment, and molecular characterization is necessary to lead the way to targeted therapeutic options. To this end, various computational methods for gene expression–based subtyping of high-grade serous ovarian carcinoma (HGSOC) have been proposed, but their overlap and robustness remain unknown.Experimental Design: We assess three major subtype classifiers by meta-analysis of publicly available expression data, and assess statistical criteria of subtype robustness and classifier concordance. We develop a consensus classifier that represents the subtype classifications of tumors based on the consensus of multiple methods, and outputs a confidence score. Using our compendium of expression data, we examine the possibility that a subset of tumors is unclassifiable based on currently proposed subtypes.Results: HGSOC subtyping classifiers exhibit moderate pairwise concordance across our data compendium (58.9%–70.9%; P < 10−5) and are associated with overall survival in a meta-analysis across datasets (P < 10−5). Current subtypes do not meet statistical criteria for robustness to reclustering across multiple datasets (prediction strength < 0.6). A new subtype classifier is trained on concordantly classified samples to yield a consensus classification of patient tumors that correlates with patient age, survival, tumor purity, and lymphocyte infiltration.Conclusions: A new consensus ovarian subtype classifier represents the consensus of methods and demonstrates the importance of classification approaches for cancer that do not require all tumors to be assigned to a distinct subtype. Clin Cancer Res; 24(20); 5037–47. ©2018 AACR.

Supplementary Figures S1-S8. Suppl. Fig. S1. Meta-analysis of CD73 and CD39 in ovarian cancer gene expression datasets. Suppl. Fig. S2. CD73 and CD8 staining in high grade serous ovarian cancer. Suppl. Fig. S3. Flow cytometry analysis of CD73 expression on 12 human ovarian cancer cell lines. Suppl. Fig. S4. Prognostic value of CD73 protein expression and intraepithelial CD8+ cell density. Suppl. Fig. S5. CD73 gene silencing in OV4485 and SKOV3 human ovarian cancer cells. Suppl. Fig. S6. CD39 and adenosine receptor expression in human ovarian cancer cells. Suppl. Fig. S7. CD73-expressing cancer-associated fibroblasts promote tumor growth in mice. Suppl. Fig. S8. Flow cytometry gating strategy and representative staining of ID8-OVA tumor cell suspension.

The cell surface nucleotidase CD73 is an immunosuppressive enzyme involved in tumor progression and metastasis. Although preclinical studies suggest that CD73 can be targeted for cancer treatment, the clinical impact of CD73 in ovarian cancer remains unclear. In this study, we investigated the prognostic value of CD73 in high-grade serous (HGS) ovarian cancer using gene and protein expression analyses. Our results demonstrate that high levels of CD73 are significantly associated with shorter disease-free survival and overall survival in patients with HGS ovarian cancer. Furthermore, high levels of CD73 expression in ovarian tumor cells abolished the good prognosis associated with intraepithelial CD8+ cells. Notably, CD73 gene expression was highest in the C1/stromal molecular subtype of HGS ovarian cancer and positively correlated with an epithelial-to-mesenchymal transition gene signature. Moreover, in vitro studies revealed that CD73 and extracellular adenosine enhance ovarian tumor cell growth as well as expression of antiapoptotic BCL-2 family members. Finally, in vivo coinjection of ID8 mouse ovarian tumor cells with mouse embryonic fibroblasts showed that CD73 expression in fibroblasts promotes tumor immune escape and thereby tumor growth. In conclusion, our study highlights a role for CD73 as a prognostic marker of patient survival and also as a candidate therapeutic target in HGS ovarian cancers. Cancer Res; 75(21); 4494–503. ©2015 AACR.

The adoptive transfer of chimeric antigen receptor (CAR) T cells represents a breakthrough in clinical oncology, yet both between- and within-patient differences in autologously derived T cells are a major contributor to therapy failure. To interrogate the molecular determinants of clinical CAR T-cell persistence, we extensively characterized the premanufacture T cells of 71 patients with B-cell malignancies on trial to receive anti-CD19 CAR T-cell therapy. We performed RNA-sequencing analysis on sorted T-cell subsets from all 71 patients, followed by paired Cellular Indexing of Transcriptomes and Epitopes (CITE) sequencing and single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) on T cells from six of these patients. We found that chronic IFN signaling regulated by IRF7 was associated with poor CAR T-cell persistence across T-cell subsets, and that the TCF7 regulon not only associates with the favorable naïve T-cell state, but is maintained in effector T cells among patients with long-term CAR T-cell persistence. These findings provide key insights into the underlying molecular determinants of clinical CAR T-cell function. Significance: To improve clinical outcomes for CAR T-cell therapy, there is a need to understand the molecular determinants of CAR T-cell persistence. These data represent the largest clinically annotated molecular atlas in CAR T-cell therapy to date, and significantly advance our understanding of the mechanisms underlying therapeutic efficacy. This article is highlighted in the In This Issue feature, p. 2113

The adoptive transfer of T lymphocytes reprogrammed to target tumor cells has demonstrated significant potential in various malignancies. However, little is known about the long-term potential and the clonal stability of the infused cells. Here, we studied the longest persisting CD19 redirected chimeric antigen receptor (CAR) T cells to date in two chronic lymphocytic leukemia (CLL) patients who achieved a complete remission in 2010. CAR T-cells were still detectable up to 10+ years post-infusion, with sustained remission in both patients. Surprisingly, a prominent, highly activated CD4+ population developed in both patients during the years post-infusion, dominating the CAR T-cell population at the late time points. This transition was reflected in the stabilization of the clonal make-up of CAR T-cells with a repertoire dominated by few clones. Single cell multi-omics profiling via Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-Seq) with TCR sequencing of CAR T-cells obtained 9.3 years post-infusion demonstrated that these long-persisting CD4+ CAR T-cells exhibited cytotoxic characteristics along with strong evidence of ongoing functional activation and proliferation. Our data provide novel insight into the CAR T-cell characteristics associated with long-term remission in leukemia.

The adoptive transfer of T lymphocytes reprogrammed to target tumour cells has demonstrated potential for treatment of various cancers

Purpose: Systems biology approaches can identify critical targets in complex cancer signaling networks to inform new therapy combinations that may overcome conventional treatment resistance. Experimental Design: We performed integrated analysis of 1,046 childhood B-ALL cases and developed a data-driven network controllability-based approach to identify synergistic key regulator targets in Philadelphia chromosome–like B-acute lymphoblastic leukemia (Ph-like B-ALL), a common high-risk leukemia subtype associated with hyperactive signal transduction and chemoresistance. Results: We identified 14 dysregulated network nodes in Ph-like ALL involved in aberrant JAK/STAT, Ras/MAPK, and apoptosis pathways and other critical processes. Genetic cotargeting of the synergistic key regulator pair STAT5B and BCL2-associated athanogene 1 (BAG1) significantly reduced leukemia cell viability in vitro. Pharmacologic inhibition with dual small molecule inhibitor therapy targeting this pair of key nodes further demonstrated enhanced antileukemia efficacy of combining the BCL-2 inhibitor venetoclax with the tyrosine kinase inhibitors ruxolitinib or dasatinib in vitro in human Ph-like ALL cell lines and in vivo in multiple childhood Ph-like ALL patient-derived xenograft models. Consistent with network controllability theory, co-inhibitor treatment also shifted the transcriptomic state of Ph-like ALL cells to become less like kinase-activated BCR-ABL1–rearranged (Ph+) B-ALL and more similar to prognostically favorable childhood B-ALL subtypes. Conclusions: Our study represents a powerful conceptual framework for combinatorial drug discovery based on systematic interrogation of synergistic vulnerability pathways with pharmacologic inhibitor validation in preclinical human leukemia models.

Single-cell omics assays have become essential tools for identifying and characterizing cell types and states of complex tissues. While each single-modality assay reveals distinctive features about the sequenced cells, true multi-omics assays are still in early stage of development. This notion signifies the importance of computationally integrating single-cell omics data that are conducted on various samples across various modalities. In addition, the advent of multiplexed molecular imaging assays has given rise to a need for computational methods for integrative analysis of single-cell imaging and omics data. Here, we present GLUER (inteGrative anaLysis of mUlti-omics at single-cEll Resolution), a flexible tool for integration of single-cell multi-omics data and imaging data. Using multiple true multi-omics data sets as the ground truth, we demonstrate that GLUER achieved significant improvement over existing methods in terms of the accuracy of matching cells across different data modalities resulting in ameliorating downstream analyses such as clustering and trajectory inference. We further demonstrate the broad utility of GLUER for integrating single-cell transcriptomics data with imaging-based spatial proteomics and transcriptomics data. Finally, we extend GLUER to leverage true cell-pair labels when available in true multi-omics data, and show that this approach improves co-embedding and clustering results. With the rapid accumulation of single-cell multi-omics and imaging data, integrated data holds the promise of furthering our understanding of the role of heterogeneity in development and disease.

Joint analysis of genomic and transcriptomic data reveals diverse noncoding mutation landscapes in five types of pediatric cancers., Interpreting the function of noncoding mutations in cancer genomes remains a major challenge. Here, we developed a computational framework to identify putative causal noncoding mutations of all classes by joint analysis of mutation and gene expression data. We identified thousands of SNVs/small indels and structural variants as putative causal mutations in five major pediatric cancers. We experimentally validated the oncogenic role of CHD4 overexpression via enhancer hijacking in B-ALL. We observed a general exclusivity of coding and noncoding mutations affecting the same genes and pathways. We showed that integrated mutation profiles can help define novel patient subtypes with different clinical outcomes. Our study introduces a general strategy to systematically identify and characterize the full spectrum of noncoding mutations in cancers.

Purpose: The majority of ovarian carcinomas are of high-grade serous histology, which is associated with poor prognosis. Surgery and chemotherapy are the mainstay of treatment, and molecular characterization is necessary to lead the way to targeted therapeutic options. To this end, various computational methods for gene expression–based subtyping of high-grade serous ovarian carcinoma (HGSOC) have been proposed, but their overlap and robustness remain unknown. Experimental Design: We assess three major subtype classifiers by meta-analysis of publicly available expression data, and assess statistical criteria of subtype robustness and classifier concordance. We develop a consensus classifier that represents the subtype classifications of tumors based on the consensus of multiple methods, and outputs a confidence score. Using our compendium of expression data, we examine the possibility that a subset of tumors is unclassifiable based on currently proposed subtypes. Results: HGSOC subtyping classifiers exhibit moderate pairwise concordance across our data compendium (58.9%–70.9%; P < 10−5) and are associated with overall survival in a meta-analysis across datasets (P < 10−5). Current subtypes do not meet statistical criteria for robustness to reclustering across multiple datasets (prediction strength < 0.6). A new subtype classifier is trained on concordantly classified samples to yield a consensus classification of patient tumors that correlates with patient age, survival, tumor purity, and lymphocyte infiltration. Conclusions: A new consensus ovarian subtype classifier represents the consensus of methods and demonstrates the importance of classification approaches for cancer that do not require all tumors to be assigned to a distinct subtype. Clin Cancer Res; 24(20); 5037–47. ©2018 AACR.

The adoptive transfer of genetically engineered chimeric antigen receptor (CAR) T cells has opened a new frontier in cancer therapy. Unlike the paradigm of targeted therapies, the efficacy of CAR T-cell therapy depends not only on the choice of target but also on a complex interplay of tumor, immune, and stromal cell communication. This presents both challenges and opportunities from a discovery standpoint. Whereas cancer consortia have traditionally focused on the genomic, transcriptomic, epigenomic, and proteomic landscape of cancer cells, there is an increasing need to expand studies to analyze the interactions between tumor, immune, and stromal cell populations in their relevant anatomical and functional compartments. Here, we focus on the promising application of systems biology to address key challenges in CAR T-cell therapy, from understanding the mechanisms of therapeutic resistance in hematologic and solid tumors to addressing important clinical challenges in biomarker discovery and therapeutic toxicity. We propose a systems biology view of key clinical objectives in CAR T-cell therapy and suggest a path forward for a biomedical discovery process that leverages modern technological approaches in systems biology.

Polyketides (PKs) and nonribosomal peptides (NRPs) are profoundly important natural products, forming the foundations of many therapeutic regimes. Decades of research have revealed over 11,000 PK and NRP structures, and genome sequencing is uncovering new PK and NRP gene clusters at an unprecedented rate. However, only ∼10% of PK and NRPs are currently associated with gene clusters, and it is unclear how many of these orphan gene clusters encode previously isolated molecules. Therefore, to efficiently guide the discovery of new molecules, we must first systematically de-orphan emergent gene clusters from genomes. Here we provide to our knowledge the first comprehensive retro-biosynthetic program, generalized retro-biosynthetic assembly prediction engine (GRAPE), for PK and NRP families and introduce a computational pipeline, global alignment for natural products cheminformatics (GARLIC), to uncover how observed biosynthetic gene clusters relate to known molecules, leading to the identification of gene clusters that encode new molecules.

Antibiotics are essential for numerous medical procedures, including the treatment of bacterial infections, but their widespread use has led to the accumulation of resistance, prompting calls for the discovery of antibacterial agents with new targets. A majority of clinically approved antibacterial scaffolds are derived from microbial natural products, but these valuable molecules are not well annotated or organized, limiting the efficacy of modern informatic analyses. Here, we provide a comprehensive resource defining the targets, chemical origins and families of the natural antibacterial collective through a retrobiosynthetic algorithm. From this we also detail the directed mining of biosynthetic scaffolds and resistance determinants to reveal structures with a high likelihood of having previously unknown modes of action. Implementing this pipeline led to investigations of the telomycin family of natural products from Streptomyces canus, revealing that these bactericidal molecules possess a new antibacterial mode of action dependent on the bacterial phospholipid cardiolipin.

Chimeric Antigen Receptor (CAR) T-cell therapy has been a major success for B-cell Acute Lymphoblastic Leukemia (B-ALL), yet recent clinical trials suggest that approximately 20% of patients do not respond to therapy and 40% of initial responders relapse within a year. We hypothesize that intrinsic T-cell factors are a major contributor to early therapeutic failure to CAR T-cell therapy. In particular, since most protocols do not select for T-cell subtypes, the heterogeneity in T-cell composition likely plays a major role in CAR T-cell engineering and confounds functional and clinical correlative studies. In order to characterize the molecular phenotypes of T-cells underlying therapeutic response, we performed subtype-specific transcriptomic profiling of clinical T-cell populations in 71 patients treated with CAR T-cell therapy at the Children's Hospital of Philadelphia. We obtained clinical T-cells via clinical leukapheresis prior to CAR T-cell engineering, used FACS to separate T-cells into five subtypes per patient (Naive, Stem Cell Memory, Central Memory, Effector Memory, and Effector), and performed RNA-Sequencing on all 355 samples. To our knowledge, this is the largest and most comprehensive transcriptomic profiling of clinical T-cells in CAR T-cell therapy to date. We found that a higher proportion of CCR7+CD62L+ naive and early memory T-cell phenotypes associates with longer clinical T-cell persistence (HR: 3.25 [1.37-7.89], P = 0.006), and that combining proportions of these populations with CCR7-CD62L-CD45RO+CD95+ effector memory T-cells has the greatest prognostic value (HR: 4.82 [1.89-12.32], P < 0.001). Apoptotic signaling pathways were enriched in effector and effector memory T-cell lineages (FDR < 0.001), suggesting that programmed cell death limits the contribution of these phenotypes to long-term CAR T-cell persistence. Maintenance of naive and early memory T-cell phenotypes associates positively with expression of transcription factors TCF7 and LEF1 (FDR < 0.001) and negatively with TBX21, EOMES, and ZEB2 (FDR < 0.001). Within T-cell subtypes, mixed-effects regression and integrative network analysis identified genes and network modules that discriminate between clinical responders and non-responders. We found that higher expression of CRTAM and SLAMF6 in naive and stem cell memory T-cells associates positively with clinical CAR T-cell persistence (FDR = 0.0088-0.019), suggesting that these lineage-determining factors direct T-cell fates toward effective CAR T-cell lineages. Finally, we identify an enrichment of type I interferon response pathways among patients with poor CAR T-cell persistence (F-test P < 0.001), suggesting a role of the chronic interferon response network in T-cell dysfunction. Together, these data shed light on the critical role of pre-infusion T-cell populations in CAR T-cell therapy, and may inform clinical prognosis and the development of improved CAR T-cell therapies. Citation Format: Gregory M. Chen, Changya Chen, Rajat K. Das, Yang-Yang Ding, Bing He, Hannah Kim, David M. Barrett, Kai Tan. A subtype-specific T-cell transcriptomic atlas reveals determinants of T-cell dysfunction in CAR T-cell therapy resistance [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4236.

Background We introduce BPG, a framework for generating publication-quality, highly-customizable plots in the R statistical environment. Results This open-source package includes multiple methods of displaying high-dimensional datasets and facilitates generation of complex multi-panel figures, making it suitable for complex datasets. A web-based interactive tool allows online figure customization, from which R code can be downloaded for integration with computational pipelines. Conclusion BPG provides a new approach for linking interactive and scripted data visualization and is available at http://labs.oicr.on.ca/boutros-lab/software/bpg or via CRAN at https://cran.r-project.org/web/packages/BoutrosLab.plotting.general Electronic supplementary material The online version of this article (10.1186/s12859-019-2610-2) contains supplementary material, which is available to authorized users.

Chimeric antigen receptor (CAR) T-cell therapy has been a major breakthrough in B-cell acute lymphoblastic leukemia (B-ALL), yet therapeutic resistance remains an important clinical challenge. Given that approximately 20% of patients do not respond to therapy and 40% of initial responders relapse within a year, there is a pressing need to understand the basis of therapy resistance in order to identify prognostic biomarkers and improve the therapeutic modality. We hypothesize that intrinsic T-cell factors are a major contributor to early therapeutic failure to CAR T-cell therapy. Most protocols do not select for specific T-cell subtypes, leading to the use of heterogeneous T-cell populations for CAR T-cell engineering. In order to characterize the molecular phenotypes of T cells underlying therapeutic response, we performed subtype-specific transcriptomic profiling of clinical T-cell populations in patients subsequently treated with anti-CD19 CAR T-cell therapy. We identified 71 patients with B-ALL treated with CAR T-cell therapy at the Children’s Hospital of Philadelphia, and obtained T cells via clinical leukapheresis prior to CAR T-cell engineering. We used FACS to separate T cells into five subtypes per patient (Naive, Stem Cell Memory, Central Memory, Effector Memory, and Effector), and performed bulk RNA-sequencing on all 355 samples. To our knowledge, this is the largest and most comprehensive transcriptomic profiling of clinical T cells in CAR T-cell therapy to date. We find that a higher proportion of CCR7+CD62L+ naive and early memory T-cell phenotypes associates with longer clinical T-cell persistence (HR=3.25 [1.37-7.89], p=0.006), and that combining proportions of these populations with CCR7−CD62L−CD45RO+CD95+ effector memory T cells has the greatest prognostic value (HR: 4.82 [1.89-12.32]; p This abstract is also being presented as Poster A65. Citation Format: Gregory M. Chen, Changya Chen, Rajat K. Das, Yang-Yang Ding, Bing He, Hannah Kim, David M. Barrett, Kai Tan. Development of a transcriptomic T-cell atlas highlights the differential role of T-cell subpopulations in CAR T-cell therapy resistance [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2019 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2020;8(3 Suppl):Abstract nr PR3.

INTRODUCTIONVarious computational methods for gene expression-based subtyping of high-grade serous (HGS) ovarian cancer have been proposed. This resulted in the identification of molecular subtypes that are based on different datasets and were differentially validated, making it difficult to achieve consensus on which definitions to use in follow-up studies. We assess three major subtype classifiers for their robustness and association to outcome by a meta-analysis of publicly available expression data, and provide a classifier that represents their consensus.METHODSWe use a compendium of 15 microarray datasets consisting of 1,774 HGS ovarian tumors to assess 1) concordance between published subtyping algorithms, 2) robustness of those algorithms to re-clustering across datasets, and 3) association of subtypes with overall survival. A consensus classifier is trained on concordantly classified samples, and validated by leave-one-dataset-out validation.RESULTSEach subtyping classifier identified subsets significantly differing in overall survival, but were not robust to re-fitting in independent datasets and grouped only approximately one third of patients concordantly into four subtypes. We propose a consensus classifier to identify the minority of unambiguously classifiable tumors across multiple gene expression platforms, using a 100-gene signature. The resulting consensus subtypes correlate with patient age, survival, tumor purity, and lymphocyte infiltration.CONCLUSIONSOur analysis demonstrates that most HGS ovarian cancers are not able to be subtyped. A minority of tumors can be classified and our proposed consensus classifier consolidates and improves on the robustness of three previously proposed subtype classifiers. It provides reliable stratification of patients with HGS ovarian tumors of clearly defined subtype, and will assist in studying the role of polyclonality in the majority of tumors that are not robustly classifiable.

Key Points Question Does the language of medicine in academic journals indicate whether the culture of clinical investigation has shifted toward patient centeredness? Findings In this qualitative study of medical language of 302 293 articles from 5 premier medical journals, use in the last 40 years has changed to reflect a shift from individuals to populations, a separation of patient and disease, and an increase in patient-centric titles. Meaning Whereas medical language previously emphasized treatments and disease processes, the trend during the last 40 years has been to separate patients from their disease and to emphasize the patient rather than characterize patients by their disease., This qualitative study of medical research language assesses changes in 5 medical journals and broader changes in the culture of clinical investigation during the last 40 years., Importance The language of medical research appears to be intrinsically tied to the culture of medical research and provides a unique window into broader trends in the culture of medicine. Objective To analyze medical language from 5 premier medical journals and investigate broader changes in the culture of clinical investigation during the last 40 years. Design, Setting, and Participants In this qualitative study using a data-driven analysis, 302 293 PubMed records were extracted from JAMA, The Lancet, Annals of Internal Medicine, the BMJ, and New England Journal of Medicine from January 1, 1976, through December 31, 2015, to identify key trends in medical language. A frequency analysis was applied across the 40-year time frame in JAMA to assess the major trends in all publication types. Patient-centered language was analyzed in clinical trials in the flanking time periods (1976-1980 and 2011-2015) across the 5 journals. Data were analyzed from November 16, 2016, through November 9, 2018. Main Outcomes and Measures Increasing or decreasing frequency of words (monograms) and word pairs (bigrams) and the proportion of patient-centric words in journal article titles. Results In JAMA, 50 277 articles of all publication types were included. In the frequency analysis, the most increased terms were reflective of the language of epidemiological research. The bigram analysis revealed a decline in causal language (−2.42/100 000 words to −2.03/100 000 words; false discovery rate [FDR]

A wealth of transcriptomic and clinical data on solid tumours are under-utilized due to unharmonized data storage and format. We have developed the MetaGxData package compendium, which includes manually-curated and standardized clinical, pathological, survival, and treatment metadata across breast, ovarian, and pancreatic cancer data. MetaGxData is the largest compendium of curated transcriptomic data for these cancer types to date, spanning 86 datasets and encompassing 15,249 samples. Open access to standardized metadata across cancer types promotes use of their transcriptomic and clinical data in a variety of cross-tumour analyses, including identification of common biomarkers, establishing common patterns of co-expression networks, and assessing the validity of prognostic signatures. Here, we demonstrate that MetaGxData is a flexible framework that facilitates meta-analyses by using it to identify common prognostic genes in ovarian and breast cancer. Furthermore, we use the data compendium to create the first gene signature that is prognostic in a meta-analysis across 3 cancers. These findings demonstrate the potential of MetaGxData to serve as an important resource in oncology research and provide a foundation for future development of cancer-specific compendia.

Neuroblastoma is a childhood cancer of the developing peripheral nervous system and is uniquely characterized by a broad spectrum of phenotypes ranging from spontaneous tumor regression to relentless progression. The 5-year survival rate for children in the low- and intermediate-risk groups is over 95%; however, children with high-risk disease have a 40-50% likelihood of survival despite aggressive multimodal therapy. High-risk neuroblastomas harbor relatively few protein coding mutations at diagnosis, with the oncogenic mechanisms underlying MYCN amplification and ALK activation remaining obscure. We therefore hypothesized that characterizing high-risk neuroblastoma noncoding genetic variation will define gene regulatory mechanisms driving tumor progression, and that somatically acquired noncoding mutations alter critical neurodevelopmental gene regulatory regions. Here, we have integrated neuroblastoma cell line histone ChIP-Seq (N = 10), ATAC-Seq (N = 12), and RNA-Seq (N = 39) with high-risk patient (N = 105) whole-genome mutation data to define the landscape of noncoding somatic events dysregulating enhancers and promoters in high-risk neuroblastoma. We found a significantly higher number of mutations per patient in high-risk and stage 4 patients, compared to other risk groups (Wilcox Whigh-low = 129.5, p = 1.47e-07; Whigh-int = 70, p = 3.66e-08) and stages (WStage4-Stage3 = 86, p = 2.68e-3; WStage4-Stage4s= 65.5, p = 1.21e-12). We found a median of 1,809 somatic noncoding single nucleotide mutations per patient, and a subset of these recurrently mutate sequences underlying super-enhancer peaks of 102 genes. An additional subset lie within CCCTC-binding factor (CTCF) motifs (a median of 5 mutations per patient), suggesting potential dysregulation of chromosome looping in neuroblastoma. Understanding noncoding drivers of oncogenic gene expression and translating these basic science discoveries to the clinic is a critical step towards changing the devastating course of this disease. Citation Format: Jo Lynne Harenza, Gregory Chen, John M. Maris. Characterization of somatically acquired regulatory mutations in high-risk neuroblastoma [abstract]. In: Proceedings of the AACR Special Conference: Pediatric Cancer Research: From Basic Science to the Clinic; 2017 Dec 3-6; Atlanta, Georgia. Philadelphia (PA): AACR; Cancer Res 2018;78(19 Suppl):Abstract nr B24.

Herein we provide a detailed molecular analysis of the spatial heterogeneity of clinically localized, multifocal prostate cancer to delineate new oncogenes or tumor suppressors. We initially determined the copy number aberration (CNA) profiles of 74 patients with index tumors of Gleason score 7. Of these, 5 patients were subjected to whole-genome sequencing using DNA quantities achievable in diagnostic biopsies, with detailed spatial sampling of 23 distinct tumor regions to assess intraprostatic heterogeneity in focal genomics. Multifocal tumors are highly heterogeneous for single-nucleotide variants (SNVs), CNAs and genomic rearrangements. We identified and validated a new recurrent amplification of MYCL, which is associated with TP53 deletion and unique profiles of DNA damage and transcriptional dysregulation. Moreover, we demonstrate divergent tumor evolution in multifocal cancer and, in some cases, tumors of independent clonal origin. These data represent the first systematic relation of intraprostatic genomic heterogeneity to predicted clinical outcome and inform the development of novel biomarkers that reflect individual prognosis.

The cell surface nucleotidase CD73 is an immunosuppressive enzyme involved in tumor progression and metastasis. Although preclinical studies suggest that CD73 can be targeted for cancer treatment, the clinical impact of CD73 in ovarian cancer remains unclear. In this study, we investigated the prognostic value of CD73 in high-grade serous (HGS) ovarian cancer using gene and protein expression analyses. Our results demonstrate that high levels of CD73 are significantly associated with shorter disease-free survival and overall survival in patients with HGS ovarian cancer. Furthermore, high levels of CD73 expression in ovarian tumor cells abolished the good prognosis associated with intraepithelial CD8+ cells. Notably, CD73 gene expression was highest in the C1/stromal molecular subtype of HGS ovarian cancer and positively correlated with an epithelial-to-mesenchymal transition gene signature. Moreover, in vitro studies revealed that CD73 and extracellular adenosine enhance ovarian tumor cell growth as well as expression of antiapoptotic BCL-2 family members. Finally, in vivo coinjection of ID8 mouse ovarian tumor cells with mouse embryonic fibroblasts showed that CD73 expression in fibroblasts promotes tumor immune escape and thereby tumor growth. In conclusion, our study highlights a role for CD73 as a prognostic marker of patient survival and also as a candidate therapeutic target in HGS ovarian cancers. Cancer Res; 75(21); 4494–503. ©2015 AACR.