Fulton, Robert S.

Introduction: The NHLBI National MDS Study (NCT02775383) is a prospective cohort study conducted at 92 community hospitals and 29 academic centers. It enrolls patients undergoing work up for suspected MDS to understand the genetic, epigenetic, and biological factors associated with the initiation and progression of the disease. Previously untreated, cytopenic participants undergo both local and centralized pathology review and are assigned a diagnosis, including MDS, MDS/MPN, AML with blasts < 30%, and "Other". Emerging data suggests that Next Generation Sequencing (NGS), along with cytogenetics and clinical variables, may improve MDS diagnostic precision. Given that our study relies on central review (with additional tertiary pathology review used to adjudicate disagreements), we examined whether targeted gene sequencing data could be used to increase the agreement between local and central pathologic diagnosis of MDS vs. Other. Methods: Peripheral blood and bone marrow (BM) biopsy specimens from cytopenic patients, along with clinical history, CBC, and other results including karyotyping, FISH and pathology reports from local pathologists were reviewed by central pathologists. The updated 2016 WHO classifications were used to diagnose MDS. Targeted exon sequencing of 96 genes was performed using BM specimens. A subset of 648 individuals that were classified as MDS (n=212) or Other (n=436, including 90 CCUS and 89 individuals with other cancers) by pathology assessments were selected. A mean coverage of 1,317X was achieved and variants had a minimum variant allele frequency (VAF) of 2% (except FLT3). Variants for 596 subjects were manually reviewed to retain likely disease-causing variants to build a binary classifier (MDS vs. Other) using the maximum VAF per gene as input (Figure 1). Subjects diagnosed with MDS or Other by both central and local pathology were used for training, validation, and testing, and were considered "gold standard" (GS) cases (n=546). These subjects were split into 4 random groups with equal proportions of MDS cases. 75% of the GS cases were used to train and validate lasso-regularized logistic regression models using 3-fold cross validation. ROC curve analysis was carried out using the remaining 25% of GS cases (Test Set 1) on the best model to identify an optimal probability cut off point for classifying subjects as MDS. Model performance was then tested on 50 subjects for which the central and local pathology diagnosis disagreed (Test Set 2), as well as on 52 additional subjects irrespective of agreement (Test Set 3). Results : The best performing logistic regression model retained 7 genes as most informative in a discriminating diagnosis of MDS from Other based on their VAFs, in order of impact: TP53, SF3B1, U2AF1, ASXL1, TET2,STAG2, and SRSF2. We used this model to assign probabilities for each of the subjects in Test Set 1 and to estimate the performance using ROC analysis (Figure 1), resulting in a high area under the curve (AUC) of 0.89. We chose a probability cut-off of ≥0.17, being associated with a high percentage of correct classification of MDS with a sensitivity and specificity of 0.90 and 0.81, respectively. Among the cohort of 50 subjects with a discordant local and central pathology diagnosis (Test Set 2), the classifier accurately reassigned 37 subjects (accuracy = 74%) from the local to the central pathology. The blinded tertiary pathology reviewer agreed with central in all Test Set 2 cases. This included 24/34 MDS cases that had been labeled as Other by local pathology (positive predictive value [PPV]=0.89). 3/16 final pathology-classified Other cases were mis-classified as MDS by the local pathologist (negative predictive value [NPV] = 0.57). Next, we assessed the ability of the model to predict MDS vs. Other for 52 additional independent subjects using the third pathologist's diagnosis to break any ties (Test Set 3). The classifier correctly predicted 15/21 MDS cases (PPV=0.83) and misclassified 6/31 Others as MDS (NPV=0.82). The overall accuracy was 83%. Conclusions: We identified that VAFs for 7 genes can correctly re-classify subjects as either MDS or Other in 74% of cases that were misclassified between local and central pathology review. Further assessment on an independent cohort showed an accuracy of 83% of the model. Taken together, these data suggest that complementing pathology reviews with targeted sequencing of 7 genes could improve MDS diagnosis. Disclosures Lindsley: MedImmune: Research Funding; Jazz Pharmaceuticals: Consultancy, Research Funding; Bluebird Bio: Consultancy; Takeda Pharmaceuticals: Consultancy. Bejar:Aptose Biosciences: Current Employment; AbbVie/Genentech: Honoraria; Astex/Otsuka: Honoraria; Takeda: Honoraria, Research Funding; Celgene/BMS: Honoraria, Research Funding; Daiichi-Sankyo: Honoraria; Forty-Seven/Gilead: Honoraria; Genoptix/NeoGenomics: Honoraria. DeZern:MEI: Consultancy; Astex: Research Funding; Abbvie: Consultancy; Celgene: Consultancy, Honoraria. Foran:H3Biosciences: Research Funding; Aptose: Research Funding; Kura Oncology: Research Funding; Trillium: Research Funding; Takeda: Research Funding; Revolution Medicine: Consultancy; Xencor: Research Funding; Agios: Honoraria, Research Funding; Aprea: Research Funding; Actinium: Research Funding; Servier: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; Abbvie: Research Funding; BMS: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Boehringer Ingelheim: Research Funding. Gore:Abbvie: Consultancy, Honoraria, Research Funding. Komrokji:Acceleron: Honoraria; Incyte: Honoraria; Abbvie: Honoraria; Agios: Speakers Bureau; BMS: Honoraria, Speakers Bureau; Jazz: Honoraria, Speakers Bureau; Geron: Honoraria; Novartis: Honoraria. Maciejewski:Alexion, BMS: Speakers Bureau; Novartis, Roche: Consultancy, Honoraria. Padron:Novartis: Honoraria; BMS: Research Funding; Incyte: Research Funding; Kura: Research Funding. Starczynowski:Captor Therapeutics: Consultancy; Tolero Therapeutics: Research Funding; Kurome Therapeutics: Consultancy, Current equity holder in private company, Research Funding. Sekeres:BMS: Consultancy; Takeda/Millenium: Consultancy; Pfizer: Consultancy.

Introduction: Myelodysplastic syndromes (MDS) are a heterogenous group of blood disorders defined by peripheral cytopenia(s), bone marrow failure, morphological dysplasia, and risk of progression. To understand the genetic, epigenetic and biological factors associated with the initiation and progression of MDS, NHLBI created the National MDS Study (NCT02775383). This is a prospective cohort study conducted at 92 community hospitals and 29 academic centers enrolling patients undergoing diagnostic work up for suspected MDS or MDS/myeloproliferative neoplasm (MPN) overlap syndrome. Eligible patients have yet to receive any therapy directed at their cytopenias. Previously untreated cytopenic participants underwent centralized histopathology and data review at the time of enrollment for assignment into distinct subcategories: MDS, MDS/MPN overlap, AML, and Other. Targeted exon sequencing of 96 genes was performed using marrow specimens from the first 300 consecutive individuals in the study. Here we report the genetic mutations for this cohort. Methods: NovaSeq 6000 was used for deep sequencing at a mean coverage of 1,286X and mean breadth (bases covered at ≥100X) of 99.8%. Reads were aligned against build GRCh38 using BWA-MEM, and VarScan2 was used to detect SNVs and INDELS. Variants were filtered for those with an allele base quality of >25 in combination with rule-based and manual review criteria. Subjects in the Other category without an identified malignancy were considered clonal cytopenias of undetermined significance (CCUS) when a mutation or a clonal cytogenetic change was present. Fisher's exact and Wilcoxon rank sum tests in combination with Bonferroni correction were applied to compare groups. Results : A total of 350 putative nonsynonymous pathogenic variants in 36 genes with an allele frequency of >.05 were identified across 150 patients (50%). At least one variant was noted in the following proportion of individuals: 61/72 (85%) with MDS, 13/13 (100%) with MDS/MPN, 15/17 (88%) with AML, and 61/198 (31%) in the Other category, of which 48 were CCUS and 13 were other cancers. Two CCUS patients only had a cytogenetic abnormality. Table 1 shows the distribution of variants in each subcategory of patients for the most commonly mutated genes in our cohort of 300 subjects. Mutations in these genes were enriched in specific groups: SF3B1, STAG2,TP53, and ASXL1 in MDS; TET2 in MDS/MPN; and IDH2 and TP53 in AML (one-sided p<0.0012). None were enriched in the Other group. Within the CCUS subset, 21 genes were mutated, with 37 of 50 (74%) patients having a mutation in TET2, ASXL1, SRSF2, SF3B1, or DNMT3A. The heatmap presented in Figure 1 summarizes variants by subject and allele frequency. Pair-wise comparisons of baseline characteristics of subjects between MDS, MDS/MPN, AML, or CCUS groups revealed no significant differences for age or sex. The CCUS group had significantly higher hemoglobins than the MDS group with median hemoglobin levels of 11.35 and 9.40 g/dL, respectively (p<0.0084). AML had significantly lower platelets compared to all other groups (median of 53 vs. > 110x109/L , respectively, p<0.0084). All groups significantly differed in their median ANC with AML having the lowest (0.8x109/L), followed by MDS (1.5x109/L), CCUS (2.45x109/L), and MDS/MPN overlap (5.95x109/L) (p<0.0084). There was no difference in the median number of variants per patient between groups or correlation with age (rs=0.11, p=0.18). The maximum variant allele frequency (maxVAF) per patient was highest in the MDS/MPN group (median = 0.42, range = 0.38-0.91) and lowest in the CCUS group (median = 0.37, range =0.06-0.98) with the MDS/MPN group having a significantly higher maxVAF compared to the MDS and the CCUS groups (p<0.0084). The proportions of subjects with mutations was similar for those who had abnormal (92% [34/37]) and normal (91% [80/88]) cytogenetics. Conclusions: Incorporation of gene-panel sequencing in the comprehensive evaluation of 300 adult cytopenic patients identified half of the cohort with potentially pathogenic variants. Ultimately, a diagnosis of CCUS was possible in 48 of 183 subjects (26%) not diagnosed with MDS, MDS/MPN overlap syndrome, AML, other cancers or clonal cytogenetics. This study continues to serially bank samples from patients with CCUS, in addition to MDS, MDS/MPN, and ICUS, with the goal to better understand the natural history of these diseases and their progression. Disclosures Lindsley: Jazz Pharmaceuticals: Research Funding; Takeda Pharmaceuticals: Consultancy; Medlmmune: Research Funding. Bejar:Celgene: Consultancy; Takeda Pharmaceuticals: Research Funding; AbbVie/Genentech: Consultancy, Honoraria; Astex/Otsuka: Consultancy; Modus Outcomes: Consultancy; Daiichi-Sankyo: Consultancy. Al Baghdadi:Celgene: Consultancy, Honoraria; Heron: Consultancy, Honoraria; Tracon: Equity Ownership; Epizyme: Equity Ownership; Bristol Myer Squibb: Consultancy, Honoraria; Sunesis: Equity Ownership; Portola: Equity Ownership; Heron therapeutics: Equity Ownership; Cardinal health: Consultancy, Honoraria; Bristol Myer Squibb: Equity Ownership; Celgene: Equity Ownership; Spectrum pharmaceutical: Equity Ownership; Astrazeneca: Equity Ownership; Seattle genetics: Equity Ownership; Roche: Consultancy, Honoraria. DeZern:Astex Pharmaceuticals, Inc.: Consultancy; Celgene: Consultancy. Foran:Agios: Honoraria, Research Funding. Gore:Celgene Corporation: Consultancy, Research Funding. Komrokji:DSI: Consultancy; Agios: Consultancy; Novartis: Speakers Bureau; JAZZ: Speakers Bureau; pfizer: Consultancy; celgene: Consultancy; Incyte: Consultancy; JAZZ: Consultancy. Maciejewski:Alexion: Consultancy; Novartis: Consultancy. Starczynowski:Kurome Therapeutics: Consultancy. Sekeres:Millenium: Membership on an entity's Board of Directors or advisory committees; Syros: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees.

Abstract Background: Multiple myeloma (MM) is a hematologic cancer characterized by a diversity of genetic lesions-translocations, copy number alterations (CNAs), and single nucleotide variants (SNVs). The prognostic value of translocations and of CNAs has been well established. Determining the clinical significance of SNVs, which are recurrently mutated at much lower frequencies, and how this significance is impacted by translocations and CNAs requires additional, large-scale correlative studies. Such studies can be facilitated by cost-effective targeted sequencing approaches. Hence, we designed a single-platform targeted sequencing approach capable of detecting all three variant types. Methods: We designed oligonucleotide probes complementary to the coding regions of 467 genes and to the IgH and MYC loci, allowing a probe to closely match at most 5 regions within the genome. Genes were selected if they were expressed in an independent RNA-seq MM data set and harbored germline SNP-filtered variants that: (1) occurred with frequency >3%, (2) were clustered in hotspots, (3) occurred in recurrently mutated "cancer genes" (as annotated in COSMIC or MutSig), or (4) occurred in genes involved in DNA repair and/or B-cell biology. IgH and MYC tiling was unbiased (with respect to annotated features within the loci) and spanned from 50 kilobasepairs (kbps) upstream of both regions to 50 kbps downstream of IgH and 100 kbps downstream of MYC. Results: We performed targeted sequencing of 96 CD138-enriched samples derived from MM patients, as well as matched peripheral blood leukocyte normal controls. Sequencing depth (mean 107X) was commensurate with that of available exome sequencing data from these samples (mean 71X). Samples harbored a mean of 25 non-silent variants, including those in known MM-associated genes: NRAS (24%), KRAS (22%), FAM46C (17%), TP53 (10%), DIS3 (8%), and BRAF (3%). Variants detected by both platforms showed a strong correlation (r^2 = 0.8). The capture array detected activating, oncogenic variants in NRAS Q61K (n=3 patients) and KRAS G12C/D/R/V (n=5) that were not detected in exome data. Additionally, we found non-silent, capture-specific variants in MTOR (3%) and in two transcription-related genes that have been previously implicated in cancer: ZFHX4 (5%) and CHD3 (5%). To assess the potential role of deep subclonal variants and our ability to detect them, we performed additional sequencing (mean 565X) on six of the tumor/normal pairs. This revealed 14 manually-reviewed, non-silent variants that were not detected by the initial targeted sequencing. These had a mean variant allele frequency of 2.8% and included mutations in DNMT3A and FAM46C. At least one of these 14 variants occurred in five of the six re-sequenced samples. This highlights the importance of this additional depth, which will be used in future studies. Our approach successfully detected CNAs near expected frequencies, including hyperdiploidy (52%), del(13) (43%), and gain of 1q (35%). Similarly, it inferred IgH translocations at expected frequencies: t(4;14) (14%), t(6;14) (3%), t(11;14) (15%), and t(14;20) (1%). As expected, translocations occur predominantly within the IgH constant region, but also frequently 5' (i.e., telomeric) of the IGHM switch region, and occasionally within the V and D regions. We detected MYC -associated translocations, whose frequencies have been the subject of debate, at 10% (n=9 patients), with five involving IgH, three having both partners in or near MYC, and one having both types. Finally, our platform detected novel IgH translocations with partners near DERL3 (n=2), MYCN (n=1), and FLT3 (n=1). Additional evidence suggests that DERL3 and MYCN may be targets of IgH-induced overexpression: of 84 RNA-seq patient samples, six exhibited outlying expression of DERL3, including one sample in which we detected the translocation in corresponding DNA, and one exhibited outlying expression of MYCN. Conclusion: Our MM-specific targeted sequencing strategy is capable of detecting deeply subclonal SNVs, in addition to CNAs and IgH and MYC translocations. Though additional validation is required, particularly with respect to translocation detection, we anticipate that such technology will soon enable clinical testing on a single sequencing platform. Disclosures Vij: Celgene, Onyx, Takeda, Novartis, BMS, Sanofi, Janssen, Merck: Consultancy; Takeda, Onyx: Research Funding.

Abstract Background: Estrogen receptors are over-expressed in around 70% of breast cancer cases. The genetic changes that occur during aromatase inhibitor (AI) treatment are not well understood and may differ depending upon the patient's response phenotype. Methods: We performed whole genome sequencing (WGS) of matched blood, pre-treatment, and post-treatment biopsy samples from 22 estrogen receptor positive breast cancer patients treated with neoadjuvant aromatase inhibitors. For 5 cases, we performed the whole genome sequencing (WGS) on patients’ matched normal, two pre AI-treatment, and two post AI-treatment DNA isolates from biopsy samples. We validated all putative coding and non-coding somatic mutations using deep sequencing. By comparing the validated somatic mutations from pre- and post- AI treatment biopsy samples, we were able to determine the alterations in the tumor genomes. In every case we defined the clonal architecture of each pair of pre-treatment and post-treatment biopsy samples by comparing the variant allele frequencies from thousands of validated somatic mutations. Results: Comparisons of the two pre AI-treatment biopsy samples from the same patient indicates that the variant allele frequencies of mutations showed high concordances in all 5 cases, 0.74 to 0.95 range of correlation coefficient. Only a small percentage of somatic mutations were detected in one pre-treatment sample and not the other (4.65% overall). In comparing the somatic variations between pre-treatment and matched post-treatment biopsy samples in 22 cases, we found that patients with good clinical response to AI treatment retained known driver mutations only in their pre-treatment tumors. Conversely, those patients with poor clinical response presented new driver mutations in their post-treatment samples. Furthermore, the variant allele frequency for most mutated genes decreased in post AI treatment samples for patients with good AI treatment response; on the contrary, the variant allele frequency increased for patients with poor clinical response. Conclusions: From WGS of matched normal, pre-treatment, and post-treatment biopsy samples, we identified new driver genes mutated in patients with poor clinical response, while patients with good clinical response had lost mutated driver genes in their post-treatment biopsy samples. The genetic landscape revealed by WGS of pre-treatment and post-treatment biopsy samples reveals mutational repertoires are remodeled by AI therapy. This finding suggests deep sequencing of AI treated samples will be necessary to reveal the complete complement of mutations present in a patient's tumor. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr LB-423. doi:1538-7445.AM2012-LB-423