Austin Christofferson

Comprehensive molecular profiling of 718 Multiple Myelomas reveals significant differences in mutation frequencies between African and European descent cases

Multiple Myeloma (MM) is a plasma cell malignancy with significantly greater incidence and mortality rates among African Americans (AA) compared to Caucasians (CA). The overall goal of this study is to elucidate differences in molecular alterations in MM as a function of self-reported race and genetic ancestry. Our study utilized somatic whole exome, RNA-sequencing, and correlated clinical data from 718 MM patients from the Multiple Myeloma Research Foundation CoMMpass study Interim Analysis 9. Somatic mutational analyses based upon self-reported race corrected for ancestry revealed significant differences in mutation frequency between groups. Of interest, BCL7A, BRWD3, and AUTS2 demonstrate significantly higher mutation frequencies among AA cases. These genes are all involved in translocations in B-cell malignancies. Moreover, we detected a significant difference in mutation frequency of TP53 and IRF4 with frequencies higher among CA cases. Our study provides rationale for interrogating diverse tumor cohorts to best understand tumor genomics across populations.

Baseline mutational patterns and sustained MRD negativity in patients with high-risk smoldering myeloma

Early results of a prospective phase 2 clinical trial of carfilzomib, lenalidomide, and dexamethasone followed by lenalidomide maintenance in high-risk smoldering myeloma showed promising results that were previously published. Here, we provide novel insights into the genetic landscape of high-risk smoldering myeloma and information on sustained minimal residual disease (MRD) negativity with an expanded cohort of patients. Eighteen patients with high-risk smoldering myeloma were enrolled between 29 May 2012, and 14 January 2014. We included patients with newly diagnosed multiple myeloma enrolled in a parallel trial who received the same therapy (reference group). The overall response rate was 100%. With median potential follow-up of 43.3 months, 10 (63%) remain in MRD negativity, and the estimated 4-year progression-free and overall survival rates are 71% and 100%, respectively. Importantly, we report differences in mutational patterns in patients with high-risk smoldering myeloma and newly diagnosed multiple myeloma, reflected in a lower frequency of mutations in significant myeloma genes (6.6% vs 45%) and NFKB pathway genes (6.6% vs 25%). Treatment with carfilzomib, lenalidomide, and dexamethasone followed by lenalidomide maintenance was associated with a 100% response rate and 63% MRD negativity with a safety profile consistent with previous reports for this regimen. This study had a small numbers of participants, but there seemed to be important differences in the genetic landscape of patients with high-risk smoldering myeloma and those with newly diagnosed multiple myeloma, suggestive of a more treatment-responsive biology in early disease.

Whole Genome Library Construction for Next Generation Sequencing.

With the rapid evolution of genomics technologies over the past decade, whole genome sequencing (WGS) has become an increasingly accessible tool in biomedical research. WGS applications include analysis of genomic DNA from single individuals, multiple related family members, and tumor/normal samples from the same patient in the context of oncology. A number of different modalities are available for performing WGS; this chapter focuses on wet lab library construction procedures for complex short insert WGS libraries using the KAPA Hyper Prep Kit (Kapa Biosystems), and includes a discussion of appropriate quality control measures for sequencing on the Illumina HiSeq2000 platform. Additional modifications to the protocol for long insert WGS library construction, to assess structural alterations and copy number changes, are also described.

Abstract A32: Comprehensive analysis of molecular pathogenesis of multiple myeloma by genetic ancestry

Introduction: Multiple Myeloma (MM) is a complex malignancy of plasma cells triggered by immunoglobulin gene rearrangements and well-described hyperdyploidy, accounting for slightly more than 10% of all hematologic cancers. MM is the most common hematologic malignancy in African American population with conspicuous racial disparities in both, mortality rates and incidence in cancer compared to European American. This observation evolved into a central hypothesis that MM has distinct biological differences across different ethnicities with yet unidentified race specific markers of tumor heterogeneity. Clear understanding of these molecular differences among ethnic minorities with MM will fulfill a major unmet medical need and eliminate racial disparity. Methods: We acquired data from the Multiple Myeloma Research Foundation (MMRF) initiated comprehensive longitudinal study (CoMMpass) with an overall goal to profile 1,000 multiple myeloma patients at diagnosis, with multiple follow-up points throughout the course of the disease. To generate population subgroups based on genetic ancestry, we used a population stratification principle component analysis (PCA) and STRUCTURE to stratify myeloma patients by Ancestry Informative Markers. These well-established methods have allowed us to avoid confounders associated with self-reporting, and thus stratify the myeloma samples by genetic ancestry mapped along with anchor populations developed by 1000 genome project. We then assessed mutational frequencies as a function of PCA for each ancestry group using complex bioinformatics algorithms. Results: We confirmed known commonly mutated genes in MM including KRAS, NRAS, FAM46C, and DIS3. Among the most striking and novel observations in our preliminary analysis of CoMMpass data using genetic ancestry and PCA was a significant difference in the frequency of nonsilent mutations in TP53, with a frequency of 7.1% (33/464) in patients clustering within the European ancestry compared to none (0/142) in African ancestry populations. Further analysis of enrichment of differentially mutated key factors within the TP53 pathway showed ATM as another gene with a significantly (p= 0.019) higher mutation frequency in EA PCA 4.7% (22/464) compared to AA PCA 0.7% (1/142). Analysis of clinical outcomes data showed poorer overall survival in patients harboring TP53 alterations. Furthermore, a comprehensive mutation analysis across samples identified a novel candidate PTCHD3 (p = 7.07E-06) with a significantly higher mutation occurrence in patients of African ancestry. Moreover, the frequency of copy number alterations known to be associated with poor prognosis revealed notable, but not significant (p=0.259) lower frequency of 1q gain in tumors from African compared to European descent. Lastly, we also observed a significant (p=0.0157) two-fold increase in early age of onset of MM in patients of African descent compared to those of European descent. Conclusion: CoMMpass has constructed a fruitful discovery environment at nexus of high-resolution next generation deep sequencing with detailed clinical data allowing to elucidate potential ancestral drivers of MM paving the way to personalized treatments. Ultimately, these data may help us further delineate the influence of percent admixture on biological factors that drive differences in incidence and outcomes among multi-ethnic MM patients. Citation Format: Zarko Manojlovic, Austin Christofferson, Gil Speyer, Seungchan Kim, Winnie Liang, Mary Derome, Daniel Auclair, David Craig, Jonathan Keats, John Carpten. Comprehensive analysis of molecular pathogenesis of multiple myeloma by genetic ancestry [abstract]. In: Proceedings of the AACR International Conference: New Frontiers in Cancer Research; 2017 Jan 18-22; Cape Town, South Africa. Philadelphia (PA): AACR; Cancer Res 2017;77(22 Suppl):Abstract nr A32.

Abstract 5271: Optimization and detection of focal somatic copy number variants in whole genome, whole exome and panel sequencing for tumor/normal matched pairs and tumor only analysis

Often in the clinical setting, tumor samples may be derived from historical tissue or from fresh frozen tissue and it may not be feasible to obtain normal tissue from the individual. Comparing unmatched tissues from different individuals possess a unique challenge of addressing common copy number variations not faced when comparing matched samples. In addition to the challenges of comparing unmatched samples, DNA extracted from samples preserved as formalin-fixed, paraffin-embedded (FFPE) tissue is often highly degraded and when compared to more intact DNA extracted from different source (e.g. blood) can increase the inherent noise in the system. Here we analytically characterize the accuracy and performance of algorithmic approaches for separating germline inherited copy number variation from somatic copy number changes, focusing on both filtering approaches and use of pooled reference samples sequenced under similar conditions. Example approaches include filtering known common copy number variation within 1000 Genomes Phase 3 and Database of Genomic Variants (DGV) Gold Standard. Additionally, we utilized a tumor/reference pool based analysis where a reference pool was constructed by equimolar pooling of multiple individuals. Determination of fold changes between tumor and reference was calculated by determining physical coverage of read pair fragments in 100 bases increments. Next, to address differences in sequencing performance between tumor and reference, the read depth data for each sample is collapsed/averaged into a lower resolution according to user-selected parameters (e.g. distance between points and read depth). Normalized log2 fold-changes between tumor and reference samples are then calculated and an adjustable smoothing window is applied. In addition, we utilize tumor allele frequencies of known heterozygous germline SNPs identified within the normal to both evaluate potential false positives and correct biases. Lastly, a segmentation algorithm is applied to summarize the individual log2 fold-changes into intervals with a constant copy number state. We will present the advantages and limitations of these approaches both when a germline normal is available and when tumor only analysis is necessary. Citation Format: Jessica Aldrich, Jonathan J. Keats, Austin Christofferson, Winnie S. Liang, John D. Carpten, Lisa Baumbach-Reardon, David W. Craig. Optimization and detection of focal somatic copy number variants in whole genome, whole exome and panel sequencing for tumor/normal matched pairs and tumor only analysis. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5271.