A pleiotropic variant in DNAJB4 is associated with multiple myeloma risk

Pleiotropy, which consists of a single gene or allelic variant affecting multiple unrelated traits, is common across cancers, with evidence for genome-wide significant loci shared across cancer and noncancer traits. This feature is particularly relevant in multiple myeloma (MM) because several susceptibility loci that have been identified to date are pleiotropic. Therefore, the aim of this study was to identify novel pleiotropic variants involved in MM risk using 28 684 independent single nucleotide polymorphisms (SNPs) from GWAS Catalog that reached a significant association (P < 5 × 10^-8 ) with their respective trait. The selected SNPs were analyzed in 2434 MM cases and 3446 controls from the International Lymphoma Epidemiology Consortium (InterLymph). The 10 SNPs showing the strongest associations with MM risk in InterLymph were selected for replication in an independent set of 1955 MM cases and 1549 controls from the International Multiple Myeloma rESEarch (IMMEnSE) consortium and 418 MM cases and 147 282 controls from the FinnGen project. The combined analysis of the three studies identified an association between DNAJB4-rs34517439-A and an increased risk of developing MM (OR = 1.22, 95%CI 1.13-1.32, P = 4.81 × 10^-7 ). rs34517439-A is associated with a modified expression of the FUBP1 gene, which encodes a multifunctional DNA and RNA-binding protein that it was observed to influence the regulation of various genes involved in cell cycle regulation, among which various oncogenes and oncosuppressors. In conclusion, with a pleiotropic scan approach we identified DNAJB4-rs34517439 as a potentially novel MM risk locus.

Does a Multiple Myeloma Polygenic Risk Score Predict Overall Survival of Myeloma Patients?

BACKGROUND

Genome-wide association studies (GWAS) of multiple myeloma (MM) in populations of European ancestry (EA) identified and confirmed 24 susceptibility loci. For other cancers (e.g. colorectum and melanoma), risk loci have also been associated with patient survival.

METHODS

We explored the possible association of all the known risk variants and their polygenic risk score (PRS) with MM overall survival (OS) in multiple populations of EA (IMMEnSE consortium, InterLymph consortium, CoMMpass and the German GWAS) for a total of 3748 MM cases. Cox proportional hazards regression was used to assess the association between each risk SNP with OS under the allelic and codominant models of inheritance. All analyses were adjusted for age, sex, country of origin (for IMMEnSE) or principal components (for the others) and disease stage (ISS). SNP associations were meta-analyzed.

RESULTS

SNP associations were meta-analyzed. From the meta-analysis, two MM risk SNPs were associated with OS (p<0.05), specifically POT1-AS1-rs2170352 (HR=1.37, 95% C.I.=1.09-1.73, p=0.007) and TNFRSF13B-rs4273077 (HR=1.19, 95% C.I.=1.01-1.41, p=0.04). The association between the combined 24 SNP MM-PRS and OS, however, was not significant.

CONCLUSIONS

Overall, our results did not support an association between the majority of MM risk SNPs and OS.

IMPACT

This is the first study to investigate the association between MM PRS and OS in MM.

Physicians' strategies for using family history data: having the data is not the same as using the data

Objective

To identify needs in a clinical decision support tool development by exploring how primary care providers currently collect and use family health history (FHH).

Design

Survey questionnaires and semi-structured interviews were administered to a mix of primary and specialty care clinicians within the University of Utah Health system (40 surveys, 12 interviews).

Results

Three key themes emerged regarding providers' collection and use of FHH: (1) Strategies for collecting FHH vary by level of effort; (2) Documentation practices extend beyond the electronic health record's dedicated FHH module; and (3) Providers desire feedback from genetic services consultation and are uncertain how to refer patients to genetic services.

Conclusion

Study findings highlight the varying degrees of engagement that providers have with collecting FHH. Improving the integration of FHH into workflow, and providing decision support, as well as links and tools to help providers better utilize genetic counseling may improve patient care.

Abstract 263: Novel transcriptomic framework captures prognostic and predictive markers in CLL

Chronic lymphocytic leukemia (CLL) is a hematological malignancy where malignant B-cells arise in the bone marrow and circulate in the blood. The disease course in CLL is heterogeneous. Some patients require immediate, aggressive treatment, while others do not require treatment for many years. There are a variety of prognostic and predictive markers for CLL, and research for new markers is ongoing. Our goal is to define multiple, informative, transcriptome variables for use in epidemiology and clinical studies, providing an agnostic framework to represent the many sources of heterogeneity that exist across CLL patients. Data cleaning and normalization were designed with parametric modeling in mind. After pre-processing, principal component analysis (PCA) was used to define orthogonal, quantitative components, referred to as spectra, to parameterize the transcriptome space. Each patient receives a set of quantitative values; one for each of the variables. Each of these variables is a multi-gene expression biomarker. Bulk RNA-sequencing was performed on treatment naïve CD19+/CD5+ sorted B-cells on the HiSeq4000 or NovaSeq platforms. Transcript-based read counts were generated from FASTQ files using Salmon. High-quality genes were selected, read counts internally normalized, and corrected for batch effects using ComBat. Pre-processing resulted in a final set of 8,895 quality-controlled, autosomal, protein-coding genes. PCA resulted in 13 spectra representing 55.7% of the total variance across all 202 CLL transcriptomes. To assess how well our novel CLL spectra framework captured known molecular marks for prognosis, we investigated associations between spectra with IGHV mutational status (determined using MiXCR) and CD49d expression. In multivariable analysis, the model including all spectra significantly predicted IGHV mutational status (p&lt;2.0x10-20). A highly significant model was also found for quantitative CD49d expression (which was not a gene in the framework, p&lt;7.4x10-32). Using matched germline DNA and tumor DNA sequencing we identified somatic CNVs and mutations using GATK and Strelka. We then assessed how well our novel framework captured these DNA characteristics. Significant spectra-based models were found that predicted common CLL CNVs (11q23 del, 13q14 del, 17p13 del and trisomy 12) as well as a complex karyotype (&gt;2 large CNVs) phenotype. Presence of ATM, NOTCH1, and TP53 protein-altering mutations was also independently captured in the framework. An agnostic framework of quantitative spectra (transcriptome variables) was able to identify known expression-based and genomic tumor features. This indicates that spectra provide a flexible intrinsic framework to represent tumor characteristics. Spectra are independent and designed to be used as predictor variables, alongside other covariates, in outcome modeling and have the potential to improve both epidemiology and clinical studies.

Citation Format: Julie Ellen Feusier, Rosalie G. Waller, Michael J. Madsen, Brian Avery, Nicola J. Camp. Novel transcriptomic framework captures prognostic and predictive markers in CLL [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 263.

Expression quantitative trait loci of genes predicting outcome are associated with survival of multiple myeloma patients

Gene expression profiling can be used for predicting survival in multiple myeloma (MM) and identifying patients who will benefit from particular types of therapy. Some germline single nucleotide polymorphisms (SNPs) act as expression quantitative trait loci (eQTLs) showing strong associations with gene expression levels. We performed an association study to test whether eQTLs of genes reported to be associated with prognosis of MM patients are directly associated with measures of adverse outcome. Using the genotype-tissue expression portal, we identified a total of 16 candidate genes with at least one eQTL SNP associated with their expression with P < 10^-7 either in EBV-transformed B-lymphocytes or whole blood. We genotyped the resulting 22 SNPs in 1327 MM cases from the International Multiple Myeloma rESEarch (IMMEnSE) consortium and examined their association with overall survival (OS) and progression-free survival (PFS), adjusting for age, sex, country of origin and disease stage. Three polymorphisms in two genes (TBRG4-rs1992292, TBRG4-rs2287535 and ENTPD1-rs2153913) showed associations with OS at P < .05, with the former two also associated with PFS. The associations of two polymorphisms in TBRG4 with OS were replicated in 1277 MM cases from the International Lymphoma Epidemiology (InterLymph) Consortium. A meta-analysis of the data from IMMEnSE and InterLymph (2579 cases) showed that TBRG4-rs1992292 is associated with OS (hazard ratio = 1.14, 95% confidence interval 1.04-1.26, P = .007). In conclusion, we found biologically a plausible association between a SNP in TBRG4 and OS of MM patients.

Abstract PO-01: Transcriptional dimensions provide a framework for describing tumor heterogeneity in CLL

Our goal is to define multiple informative transcriptome variables that capture sources of heterogeneity in chronic lymphocytic leukemia (CLL) cells, for flexible modeling in epidemiology and clinical studies. CD19+/CD5+ B-cells were sorted from whole blood of 227 CLL patients and RNA was sequenced on the HiSeq4000 or NovaSeq platforms as part of the ORIEN Avatar Initiative. Transcript-based read counts were generated from FASTQ files using Salmon. High-quality genes were selected and read counts internally normalized and corrected for batch effects using ComBat. Preprocessing resulted in a final set of 8,716 quality-controlled, autosomal, protein-coding genes. PCA was performed and, using a scree test, we selected 14 dimensions that represented 55.9% of the total variance across the CLL patients' transcriptomes. Fourteen quantitative, orthogonal CLL dimension variables were calculated for all 227 patients. By design, these CLL dimensions capture transcriptome variance and provide novel multi-gene expression biomarkers. We assessed whether these CLL transcriptome dimensions captured known clinically relevant molecular differences. First, we investigated associations with IGVH mutational status (determined using MiXCR). CLL dimension variables 1, 5, 6, and 8 predicted IGHV mutational status (p=4.6x10-16). Next, we investigated associations with ZAP70 and CD38 biomarkers, calculated by their expression in the RNA sequencing data using a separate pipeline and correcting for batch effects by ComBat (neither gene was in the 8,716 genes retained for PCA). CLL dimension variables 3, 5, 6, 7, and 8 significantly predicted Zap70 expression (p=1.6x10-38). CLL dimension variables 2, 3, 5, and 6 significantly predicted CD38 expression (p=3.1x10-31). Transcriptome dimension variables provide a flexible intrinsic framework to describe heterogeneity across CLL patients. We have shown that our transcriptome dimensions capture IGHV mutational status and ZAP70 and CD38 expression, all biomarkers for prognosis. Future work will include exploring the ability of the 14 dimensions to capture other known important molecular markers for CLL, including somatic deletion of 17p deletion, somatic mutational patterns, microsatellite instability, and previously described expression-based subgroups. Transcriptome dimensions are designed for utility as predictor variables, alongside other covariates, in parametric modeling, and have the potential to improve both epidemiology and clinical studies.

Citation Format: Julie E. Feusier, Rosalie G. Waller, Michael J. Madsen, Brian Avery, Nicola J. Camp. Transcriptional dimensions provide a framework for describing tumor heterogeneity in CLL [abstract]. In: Proceedings of the AACR Virtual Meeting: Advances in Malignant Lymphoma; 2020 Aug 17-19. Philadelphia (PA): AACR; Blood Cancer Discov 2020;1(3_Suppl):Abstract nr PO-01.

Abstract A40: Characterization of quantitative gene-expression dimensions in myeloma tumors

Background: Clinical management and research of multiple myeloma (MM), a cancer of plasma cells, is limited by tumor heterogeneity. A standard approach to deconstruct tumor heterogeneity is to use hierarchical clustering techniques to determine mutually exclusive categorical subtypes. However, categorical subtypes may fail to capture potential important variations that cross tumor subtypes. An alternate approach is to determine orthogonal, quantitative tumor dimensions where each dimension is an independent tumor characteristic. We hypothesize that using a quantitative framework for tumor heterogeneity in MM will uncover biologically relevant components to tumors and may reflect specific molecular liabilities and therapeutic vulnerabilities. Further, these quantitative characteristics may be more homogeneous genetically and useful for germline gene mapping.

Objective: Identify orthogonal, quantitative dimensions in MM tumors using gene expression.

Data: RNA sequencing on treatment-naïve, CD138 sorted tumor cells from 768 individuals. Publicly available from the MM Research Foundation’s Clinical Outcomes on MM Genetic Profiles Assessment (CoMMpass) Interim Analysis 12a. SALMON transcripts per million adjusted expression estimates on 16,870 protein coding genes.

Analyses: Multistage singular value decomposition (SVD) to 1) select representative genes, and 2) characterize the orthogonal, quantitative tumor dimensions. Stage 1: Genes that contribute most to the initial SVD will be selected as representative genes. Stage 2: SVD on selected genes to identify quantitative gene expression tumor dimensions. Each dimension is a linear combination of the representative genes. Future work will associate the quantitative dimensions with demographic, clinical, and genetic (germline and somatic) characteristics, in addition to response to treatment using penalized linear regression modeling.

Conclusions: We present a new approach for the characterization of MM tumors using a more sophisticated quantitative framework that will facilitate more flexibility for subsequent statistical modeling. Improved measures for tumors have the potential to provide increased power for identification of association between tumor characteristics and genetic (germline or somatic) characteristics with ultimate potential for genetic counseling, insights into mechanism, risk stratification, response to treatment, and new candidates for precision therapeutics.

Citation Format: Rosalie G. Waller, Michael J. Madsen, John Gardner, Douglas Sborov, Nicola J. Camp. Characterization of quantitative gene-expression dimensions in myeloma tumors [abstract]. In: Proceedings of the AACR Special Conference on Modernizing Population Sciences in the Digital Age; 2019 Feb 19-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(9 Suppl):Abstract nr A40.

Novel displays of patient information in critical care settings: a systematic review

OBJECTIVE

Clinician information overload is prevalent in critical care settings. Improved visualization of patient information may help clinicians cope with information overload, increase efficiency, and improve quality. We compared the effect of information display interventions with usual care on patient care outcomes.

MATERIALS AND METHODS

We conducted a systematic review including experimental and quasi-experimental studies of information display interventions conducted in critical care and anesthesiology settings. Citations from January 1990 to June 2018 were searched in PubMed and IEEE Xplore. Reviewers worked independently to screen articles, evaluate quality, and abstract primary outcomes and display features.

RESULTS

Of 6742 studies identified, 22 studies evaluating 17 information displays met the study inclusion criteria. Information display categories included comprehensive integrated displays (3 displays), multipatient dashboards (7 displays), physiologic and laboratory monitoring (5 displays), and expert systems (2 displays). Significant improvement on primary outcomes over usual care was reported in 12 studies for 9 unique displays. Improvement was found mostly with comprehensive integrated displays (4 of 6 studies) and multipatient dashboards (5 of 7 studies). Only 1 of 5 randomized controlled trials had a positive effect in the primary outcome.

CONCLUSION

We found weak evidence suggesting comprehensive integrated displays improve provider efficiency and process outcomes, and multipatient dashboards improve compliance with care protocols and patient outcomes. Randomized controlled trials of physiologic and laboratory monitoring displays did not show improvement in primary outcomes, despite positive results in simulated settings. Important research translation gaps from laboratory to actual critical care settings exist.

Critical care information display approaches and design frameworks: A systematic review and meta-analysis

OBJECTIVE

To systematically review original user evaluations of patient information displays relevant to critical care and understand the impact of design frameworks and information presentation approaches on decision-making, efficiency, workload, and preferences of clinicians.

METHODS

We included studies that evaluated information displays designed to support real-time care decisions in critical care or anesthesiology using simulated tasks. We searched PubMed and IEEExplore from 1/1/1990 to 6/30/2018. The search strategy was developed iteratively with calibration against known references. Inclusion screening was completed independently by two authors. Extraction of display features, design processes, and evaluation method was completed by one and verified by a second author.

RESULTS

Fifty-six manuscripts evaluating 32 critical care and 22 anesthesia displays were included. Primary outcome metrics included clinician accuracy and efficiency in recognizing, diagnosing, and treating problems. Implementing user-centered design (UCD) processes, especially iterative evaluation and redesign, resulted in positive impact in outcomes such as accuracy and efficiency. Innovative display approaches that led to improved human-system performance in critical care included: (1) improving the integration and organization of information, (2) improving the representation of trend information, and (3) implementing graphical approaches to make relationships between data visible.

CONCLUSION

Our review affirms the value of key principles of UCD. Improved information presentation can facilitate faster information interpretation and more accurate diagnoses and treatment. Improvements to information organization and support for rapid interpretation of time-based relationships between related quantitative data is warranted. Designers and developers are encouraged to involve users in formal iterative design and evaluation activities in the design of electronic health records (EHRs), clinical informatics applications, and clinical devices.

Germline Lysine-Specific Demethylase 1 (LSD1/KDM1A) Mutations Confer Susceptibility to Multiple Myeloma

Given the frequent and largely incurable occurrence of multiple myeloma, identification of germline genetic mutations that predispose cells to multiple myeloma may provide insight into disease etiology and the developmental mechanisms of its cell of origin, the plasma cell (PC). Here, we identified familial and early-onset multiple myeloma kindreds with truncating mutations in lysine-specific demethylase 1 (LSD1/KDM1A), an epigenetic transcriptional repressor that primarily demethylates histone H3 on lysine 4 and regulates hematopoietic stem cell self-renewal. In addition, we found higher rates of germline truncating and predicted deleterious missense KDM1A mutations in patients with multiple myeloma unselected for family history compared with controls. Both monoclonal gammopathy of undetermined significance (MGUS) and multiple myeloma cells have significantly lower KDM1A transcript levels compared with normal PCs. Transcriptome analysis of multiple myeloma cells from KDM1A mutation carriers shows enrichment of pathways and MYC target genes previously associated with myeloma pathogenesis. In mice, antigen challenge followed by pharmacologic inhibition of KDM1A promoted PC expansion, enhanced secondary immune response, elicited appearance of serum paraprotein, and mediated upregulation of MYC transcriptional targets. These changes are consistent with the development of MGUS. Collectively, our findings show that KDM1A is the first autosomal-dominant multiple myeloma germline predisposition gene providing new insights into its mechanistic roles as a tumor suppressor during post-germinal center B-cell differentiation.Significance: KDM1A is the first germline autosomal dominant predisposition gene identified in multiple myeloma and provides new insights into multiple myeloma etiology and the mechanistic role of KDM1A as a tumor suppressor during post-germinal center B-cell differentiation. Cancer Res; 78(10); 2747-59. ©2018 AACR.

Novel pedigree analysis implicates DNA repair and chromatin remodeling in multiple myeloma risk

The high-risk pedigree (HRP) design is an established strategy to discover rare, highly-penetrant, Mendelian-like causal variants. Its success, however, in complex traits has been modest, largely due to challenges of genetic heterogeneity and complex inheritance models. We describe a HRP strategy that addresses intra-familial heterogeneity, and identifies inherited segments important for mapping regulatory risk. We apply this new Shared Genomic Segment (SGS) method in 11 extended, Utah, multiple myeloma (MM) HRPs, and subsequent exome sequencing in SGS regions of interest in 1063 MM / MGUS (monoclonal gammopathy of undetermined significance-a precursor to MM) cases and 964 controls from a jointly-called collaborative resource, including cases from the initial 11 HRPs. One genome-wide significant 1.8 Mb shared segment was found at 6q16. Exome sequencing in this region revealed predicted deleterious variants in USP45 (p.Gln691* and p.Gln621Glu), a gene known to influence DNA repair through endonuclease regulation. Additionally, a 1.2 Mb segment at 1p36.11 is inherited in two Utah HRPs, with coding variants identified in ARID1A (p.Ser90Gly and p.Met890Val), a key gene in the SWI/SNF chromatin remodeling complex. Our results provide compelling statistical and genetic evidence for segregating risk variants for MM. In addition, we demonstrate a novel strategy to use large HRPs for risk-variant discovery more generally in complex traits.

Discordant Haplotype Sequencing Identifies Functional Variants at the 2q33 Breast Cancer Risk Locus

The findings from genome-wide association studies hold enormous potential for novel insight into disease mechanisms. A major challenge in the field is to map these low-risk association signals to their underlying functional sequence variants (FSV). Simple sequence study designs are insufficient, as the vast numbers of statistically comparable variants and a limited knowledge of noncoding regulatory elements complicate prioritization. Furthermore, large sample sizes are typically required for adequate power to identify the initial association signals. One important question is whether similar sample sizes need to be sequenced to identify the FSVs. Here, we present a proof-of-principle example of an extreme discordant design to map FSVs within the 2q33 low-risk breast cancer locus. Our approach employed DNA sequencing of a small number of discordant haplotypes to efficiently identify candidate FSVs. Our results were consistent with those from a 2,000-fold larger, traditional imputation-based fine-mapping study. To prioritize further, we used expression-quantitative trait locus analysis of RNA sequencing from breast tissues, gene regulation annotations from the ENCODE consortium, and functional assays for differential enhancer activities. Notably, we implicate three regulatory variants at 2q33 that target CASP8 (rs3769823, rs3769821 in CASP8, and rs10197246 in ALS2CR12) as functionally relevant. We conclude that nested discordant haplotype sequencing is a promising approach to aid mapping of low-risk association loci. The ability to include more efficient sequencing designs into mapping efforts presents an opportunity for the field to capitalize on the potential of association loci and accelerate translation of association signals to their underlying FSVs. Cancer Res; 76(7); 1916-25. ©2016 AACR.

A Solid Ovarian Teratoma

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