A new genomic framework to categorize pediatric acute myeloid leukemia

Recent studies on pediatric acute myeloid leukemia (pAML) have revealed pediatric-specific driver alterations, many of which are underrepresented in the current classification schemas. To comprehensively define the genomic landscape of pAML, we systematically categorized 887 pAML into 23 mutually distinct molecular categories, including new major entities such as UBTF or BCL11B, covering 91.4% of the cohort. These molecular categories were associated with unique expression profiles and mutational patterns. For instance, molecular categories characterized by specific HOXA or HOXB expression signatures showed distinct mutation patterns of RAS pathway genes, FLT3 or WT1, suggesting shared biological mechanisms. We show that molecular categories were strongly associated with clinical outcomes using two independent cohorts, leading to the establishment of a new prognostic framework for pAML based on these updated molecular categories and minimal residual disease. Together, this comprehensive diagnostic and prognostic framework forms the basis for future classification of pAML and treatment strategies.

Proposal of a new genomic framework for categorization of pediatric acute myeloid leukemia associated with prognosis

Recent studies on pediatric acute myeloid leukemia (pAML) have revealed pediatric-specific driver alterations, many of which are underrepresented in the current classification schemas. To comprehensively define the genomic landscape of pAML, we systematically categorized 895 pAML into 23 molecular categories that are mutually distinct from one another, including new entities such as UBTF or BCL11B, covering 91.4% of the cohort. These molecular categories were associated with unique expression profiles and mutational patterns. For instance, molecular categories characterized by specific HOXA or HOXB expression signatures showed distinct mutation patterns of RAS pathway genes, FLT3, or WT1, suggesting shared biological mechanisms. We show that molecular categories were strongly associated with clinical outcomes using two independent cohorts, leading to the establishment of a prognostic framework for pAML based on molecular categories and minimal residual disease. Together, this comprehensive diagnostic and prognostic framework forms the basis for future classification of pAML and treatment strategies.

Integrated Genomic Analysis Identifies UBTF Tandem Duplications as a Recurrent Lesion in Pediatric Acute Myeloid Leukemia

The genetics of relapsed pediatric acute myeloid leukemia (AML) has yet to be comprehensively defined. Here, we present the spectrum of genomic alterations in 136 relapsed pediatric AMLs. We identified recurrent exon 13 tandem duplications (TD) in upstream binding transcription factor (UBTF) in 9% of relapsed AML cases. UBTF-TD AMLs commonly have normal karyotype or trisomy 8 with cooccurring WT1 mutations or FLT3-ITD but not other known oncogenic fusions. These UBTF-TD events are stable during disease progression and are present in the founding clone. In addition, we observed that UBTF-TD AMLs account for approximately 4% of all de novo pediatric AMLs, are less common in adults, and are associated with poor outcomes and MRD positivity. Expression of UBTF-TD in primary hematopoietic cells is sufficient to enhance serial clonogenic activity and to drive a similar transcriptional program to UBTF-TD AMLs. Collectively, these clinical, genomic, and functional data establish UBTF-TD as a new recurrent mutation in AML.

SIGNIFICANCE

We defined the spectrum of mutations in relapsed pediatric AML and identified UBTF-TDs as a new recurrent genetic alteration. These duplications are more common in children and define a group of AMLs with intermediate-risk cytogenetic abnormalities, FLT3-ITD and WT1 alterations, and are associated with poor outcomes. See related commentary by Hasserjian and Nardi, p. 173. This article is highlighted in the In This Issue feature, p. 171.

Genomes for Kids: The scope of pathogenic mutations in pediatric cancer revealed by comprehensive DNA and RNA sequencing

Genomic studies of pediatric cancer have primarily focused on specific tumor types or high-risk disease. Here, we used a three-platform sequencing approach, including whole genome (WGS), exome, and RNA sequencing, to examine tumor and germline genomes from 309 prospectively identified children with newly diagnosed (85%) or relapsed/refractory (15%) cancers, unselected for tumor type. Eighty-six percent of patients harbored diagnostic (53%), prognostic (57%), therapeutically-relevant (25%), and/or cancer predisposing (18%) variants. Inclusion of WGS enabled detection of activating gene fusions and enhancer hijacks (36% and 8% of tumors, respectively), small intragenic deletions (15% of tumors) and mutational signatures revealing of pathogenic variant effects. Evaluation of paired tumor-normal data revealed relevance to tumor development for 55% of pathogenic germline variants. This study demonstrates the power of a three-platform approach that incorporates WGS to interrogate and interpret the full range of genomic variants across newly diagnosed as well as relapsed/refractory pediatric cancers.

Abstract 642: Genomes for Kids: Comprehensive DNA and RNA sequencing defining the scope of actionable mutations in pediatric cancer

Clinical genomic studies of pediatric cancer have primarily focused on specific tumor types or high-risk disease. In the Genomes for Kids study (NCT02530658) we used a three-platform sequencing approach, including whole genome (WGS), whole exome (WES) and RNA sequencing, to examine tumor and paired germline genomes from prospectively identified children with cancer. The goal of the study was to assess the potential of comprehensive next generation sequencing to elucidate the molecular mechanisms underlying tumor formation and investigate the potential of this information to influence clinical decision-making.The cohort, with a median age of 6 yrs, range 0 - 26 yrs, included 301 patients with newly diagnosed (85%) or relapsed/refractory (15%) cancers, unselected for tumor type or stage. Patients with hematologic malignancies accounted for 41% of cases, 31% had CNS tumors, and 28% had other non-CNS solid tumors. This cohort also included 18 patients with very rare tumor types, defined here as occurring in less than 2 cases per million person per year.Two hundred fifty three patients (84%) had sufficient tumor for three-platform sequencing and all 301 had adequate paired germline samples. Following analysis, 86% of patients harbored diagnostic (53%), prognostic (57%), therapeutically relevant (25%), and/or cancer predisposing (18%) variants. The inclusion of WGS enabled detection of oncogenic gene fusions, as well as 22 cases in which oncogenes were activated through enhancer hijacking, a particularly frequent occurrence in hematologic malignancies. In addition, WGS effectively detected clinically relevant small intragenic deletions (15% of tumors) and a variety of mutational signatures, which were not detectable through analysis of whole exome data. Evaluation of 56 pathogenic germline variants in the context of paired tumor sequence data helped establish the disease relevance of several genes that are not typically associated with the cancer type in question, providing critical insights on a case-by-case basis. Examples include a pathogenic germline variant in MUTYH in a patient with retinoblastoma whose tumor exhibited a mutation signature associated with reactive oxygen species indicative of loss of MUTYH function; and conversely, a likely pathogenic variant in PMS2 in a rare brain cancer, which did not exhibit a mutation signature associated with microsatellite instability. This study successfully demonstrated the power of this three-platform approach to interrogate and interpret the full range of genomic variants across newly diagnosed as well as relapsed/refractory pediatric cancers. As a result of these findings, we have incorporated this three-platform approach into our routine real-time clinical service at St. Jude Children's Hospital.

Citation Format: David A. Wheeler, Scott Newman, Joy Nakitandwe, Chimene A. Kesserwan, Elizabeth M. Azzato, Michael C. Rusch, Sheila Shurtleff, Armita Bahrami, Brent Orr, Jeffery M. Klco, Dale J. Hedges, Kayla V. Hamilton, Scott G. Foy, Michael N. Edmonson, Andrew Thrasher, Jiali Gu, Lynn W. Harrison, Lu Wang, Roya Mostafavi, Manish Kubal, Jamie Maciaszek, Michael Clay, Annastasia Ouma, Antonina Silkov, Yanling Liu, Zhaojie Zhang, Yu Liu, Samuel W. Brady, Xin Zhou, Mark Wilkinson, Delaram Rahbarinia, Jay Knight, Jian Wang, Charles G. Mullighan, Rose B. McGee, Emily A. Quinn, Elsie L. Gerhardt, Leslie M. Taylor, Regina Nuccio, Jessica M. Valdez, Stacy J. Hines-Dowell, Alberto Pappo, Giles Robinson, Liza-Marie Johnson, Ching-Hon Pui, David W. Ellison, James R. Downing, Jinghui Zhang, Kim E. Nichols. Genomes for Kids: Comprehensive DNA and RNA sequencing defining the scope of actionable mutations in pediatric cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 642.

Abstract 2289: Empowering point-and-click genomic analysis with large pediatric genomic reference data on St. Jude Cloud

Next-generation sequencing-based genomic profiling is now a mainstay of pediatric oncology research and clinical testing. Correlating genomic features of patient cancer genomes with curated data extracted from large reference cohorts is critical for identifying molecular subtypes and underlying mutagenesis processes. To facilitate such investigation, we developed two user-friendly workflows on St. Jude Cloud, a data sharing ecosystem hosting genomic data for >10,000 pediatric cancer patients and survivors. These workflows leverage St. Jude Cloud comprehensive pediatric cancer genomic data, including 1,616 RNA-seq of 135 cancer subtypes and 958 whole genome sequencing (WGS) of 35 subtypes, to enable user analysis of their data in the context of St. Jude Cloud cohorts without a need to download large datasets.

The RNA-Seq Expression Classification workflow enables a user to compare their patient RNA-Seq gene expression data with blood (832), brain (456), and solid tumor (319) pediatric cancer reference cohorts and PDX models (45), enabling subtype classification using t-Distributed Stochastic Neighbor Embedding (t-SNE). Reference cohorts include curated subtype-defining somatic alterations integrating genomic variant data with expression profile. Resulting interactive t-SNE plots can be explored and annotated - with options to highlight cancer subtypes or samples and display sample information (age of onset, clinical diagnosis, molecular driver). To demonstrate, we analyze PAWNXH, a Children's Oncology Group AML sample with a novel ZBTB7A-NUTM1 fusion and find it clusters with AML samples harboring KMT2A re-arrangements suggesting a potential mechanism of pathogenesis. Integrating PDX samples enables model selection for functional experiments by connecting patient subtypes with mouse models.

The Mutational Signatures workflow identifies and quantifies COSMIC mutational signatures in user-uploaded somatic VCF files for comparison to reference pediatric cancer cohorts. The interactive interface enables rapid identification of signatures within the query cohort and facilitates comparison to the reference using a cohort-level summary view. Identified signatures may also be explored at the sample-level for both query and reference cohorts, enabling the user to identify samples with signatures of interest for further analysis. We show an example comparison of mutational signatures identified in pediatric and adult AML samples.

These workflows enable users to leverage curated pediatric cancer data to make discoveries in their own samples. Enabling point-and-click analysis in St. Jude Cloud removes the barrier for non-computational researchers and eliminates the need to download large reference datasets for local analysis. Both workflows utilize post-processed rather than raw genomic data, reducing transfer costs for uploading user data to the cloud.

Citation Format: Andrew Thrasher, Michael Macias, Alexander M. Gout, Delaram Rahbarinia, Xin Zhou, Samuel W. Brady, Clay McLeod, Michael C. Rusch, Xiaolong Chen, Soheil Meshinchi, Michael A. Dyer, Suzanne J. Baker, Martine F. Roussel, Jinghui Zhang. Empowering point-and-click genomic analysis with large pediatric genomic reference data on St. Jude Cloud [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2289.

St. Jude Cloud: A Pediatric Cancer Genomic Data-Sharing Ecosystem

Effective data sharing is key to accelerating research to improve diagnostic precision, treatment efficacy, and long-term survival in pediatric cancer and other childhood catastrophic diseases. We present St. Jude Cloud (https://www.stjude.cloud), a cloud-based data-sharing ecosystem for accessing, analyzing, and visualizing genomic data from >10,000 pediatric patients with cancer and long-term survivors, and >800 pediatric sickle cell patients. Harmonized genomic data totaling 1.25 petabytes are freely available, including 12,104 whole genomes, 7,697 whole exomes, and 2,202 transcriptomes. The resource is expanding rapidly, with regular data uploads from St. Jude's prospective clinical genomics programs. Three interconnected apps within the ecosystem-Genomics Platform, Pediatric Cancer Knowledgebase, and Visualization Community-enable simultaneously performing advanced data analysis in the cloud and enhancing the Pediatric Cancer knowledgebase. We demonstrate the value of the ecosystem through use cases that classify 135 pediatric cancer subtypes by gene expression profiling and map mutational signatures across 35 pediatric cancer subtypes. SIGNIFICANCE: To advance research and treatment of pediatric cancer, we developed St. Jude Cloud, a data-sharing ecosystem for accessing >1.2 petabytes of raw genomic data from >10,000 pediatric patients and survivors, innovative analysis workflows, integrative multiomics visualizations, and a knowledgebase of published data contributed by the global pediatric cancer community.This article is highlighted in the In This Issue feature, p. 995.