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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.

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 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.

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.

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.

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.

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.