Abstract: Chromosomal rearrangements are a frequent cause of oncogene deregulation in human malignancies. Overexpression of EVI1 is found in a subgroup of acute myeloid leukemia (AML) with 3q26 chromosomal rearrangements, which is often therapy resistant. In AMLs harboring a t(3;8)(q26;q24), we observed the translocation of a MYC super-enhancer ( MYC SE) to the EVI1 locus. We generated an in vitro model mimicking a patient-based t(3;8)(q26;q24) using CRISPR-Cas9 technology and demonstrated hyperactivation of EVI1 by the hijacked MYC SE. This MYC SE contains multiple enhancer modules, of which only one recruits transcription factors active in early hematopoiesis. This enhancer module is critical for EVI1 overexpression as well as enhancer-promoter interaction. Multiple CTCF binding regions in the MYC SE facilitate this enhancer-promoter interaction, which also involves a CTCF binding site upstream of the EVI1 promoter. We hypothesize that this CTCF site acts as an enhancer-docking site in t(3;8) AML. Genomic analyses of other 3q26-rearranged AML patient cells point to a common mechanism by which EVI1 uses this docking site to hijack enhancers active in early hematopoiesis.

Abstract: Background: Paroxysmal nocturnal hemoglobinuria (PNH) is a hemolytic anemia associated with severe thrombophilia and characterized by complement-mediated lysis of erythrocytes lacking glycosylphosphatidylinositol (GPI)-anchored proteins. In the majority of cases, GPI deficiency is caused by somatic mutations in the PIGA gene. Presence of PNH clones is associated with acquired aplastic anemia (AA) and can be found in patients with myelodysplastic syndrome (MDS) or rarely other myeloid neoplasms (MN). Flow cytometric analysis for deficiency of GPI-anchored proteins on multiple cell lineages detects PNH clones, and PIGA mutational analysis is not mandatory to establish the diagnosis. In contrast, molecular genetic analysis of targeted gene panels is widely used in the diagnostic workup of MN. We hypothesized that the inclusion of PIGA into the myeloid gene panel could identify obscure cases with PNH clones irrespective of the initial clinical suspicion. Aim: To assess the significance of incidental findings of mutations in PIGA in the diagnostic workup of MN. Methods: 20,320 consecutive patients undergoing sequencing analysis for a confirmed or suspected MN were analyzed for the presence of mutations in the PIGA gene. Patients with confirmed PNH analyzed only for PIGA were not included, and cases with previously known PIGA mutations were excluded from further analysis. DNA was isolated from peripheral blood (PB) or bone marrow, and sequencing was performed on NovaSeq after Illumina DNA Prep for Enrichment library preparation (Illumina, San Diego, CA) and hybrid capture of a 41 gene panel including the complete coding sequence of PIGA (IDT Inc., Coralville, IA); data was analyzed with Pisces and Pindel (BaseSpace, Illumina). Flow cytometry was performed on granulocytes, monocytes, and erythrocytes in PB using antibodies against GPI-anchored proteins (CD14, CD24, CD55, and CD59), fluorescein-labeled proaerolysin (FLAER) staining, and Navios cytometers; analysis was done using Kaluza software (both Beckman Coulter, Miami, FL). Results: PIGA mutations were newly identified in 67 patients (0.3%) undergoing targeted sequencing within the diagnostic workup of MN. 30 patients were excluded from further analysis as the gene panel had been requested for a MN associated with previously diagnosed PNH. From the remaining patients, PB for flow cytometry analysis could be obtained from 20 patients. Flow cytometry confirmed the presence of a PNH clone in 17 (85%) of these patients (median clone size: 41% for granulocytes, 54.5% for monocytes, and 12% for erythrocytes). In 3 patients (15%) with unexpected PIGA mutations, flow cytometry detected no PNH clone. The type of PIGA mutations differed significantly in those cases: Patients in whom a PNH clone was confirmed, showed protein-truncating frame-shift (41%) or nonsense (6%) mutations, splice site mutations (18%), or multiple mutations (35%) including at least one protein-truncating mutation at a median variant allele frequency (VAF) of 15.4% (range 2.0% to 50.1%). In contrast, patients without a PNH clone showed only singular missense mutations of PIGA with a VAF of 3.6% to 5.3% (Figure 1). Final diagnoses in patients with confirmed clones were sole PNH (n=9), or PNH clone associated with MDS (n=4), AA (n=3), and AML (n=1), and additional mutations in other genes were observed in 9 cases. While the initial clinical presentation included the differential diagnosis of PNH in some of the patients, flow cytometry was requested as a direct result of the PIGA mutation in 4 cases with an accompanying MN and in 3 patients without - the later showing a median latency of 6.5 years from the initial clinical presentation to the diagnosis. Con clusions: The inclusion of PIGA into a standardized targeted sequencing panel for MN helps to identify patients with PNH clones irrespective of the initial clinical suspicion but is not sufficient to rule out PNH. Protein-truncating PIGA mutations are highly specific for PNH clones whereas singular missense mutations may not necessarily effect GPI biosynthesis. Our data indicate that the incidental finding of a PIGA mutation in sequencing analysis shall entail flow cytometry of GPI-anchored proteins in PB. The potential clinical sequelae and the availability of specific treatment options such as complement inhibitors warrant the thorough exclusion of PNH in the diagnostic workup of suspected MN. Figure 1 Figure 1. Disclosures Hoermann: Novartis: Honoraria. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Kern: MLL Munich Leukemia Laboratory: Other: Part ownership.

Abstract: Although genomic alterations drive the pathogenesis of acute myeloid leukemia (AML), traditional classifications are largely based on morphology, and prototypic genetic founder lesions define only a small proportion of AML patients. The historical subdivision of primary/de novo AML and secondary AML has shown to variably correlate with genetic patterns. The combinatorial complexity and heterogeneity of AML genomic architecture may have thus far precluded genomic-based subclassification to identify distinct molecularly defined subtypes more reflective of shared pathogenesis. We integrated cytogenetic and gene sequencing data from a multicenter cohort of 6788 AML patients that were analyzed using standard and machine learning methods to generate a novel AML molecular subclassification with biologic correlates corresponding to underlying pathogenesis. Standard supervised analyses resulted in modest cross-validation accuracy when attempting to use molecular patterns to predict traditional pathomorphologic AML classifications. We performed unsupervised analysis by applying the Bayesian latent class method that identified 4 unique genomic clusters of distinct prognoses. Invariant genomic features driving each cluster were extracted and resulted in 97% cross-validation accuracy when used for genomic subclassification. Subclasses of AML defined by molecular signatures overlapped current pathomorphologic and clinically defined AML subtypes. We internally and externally validated our results and share an open-access molecular classification scheme for AML patients. Although the heterogeneity inherent in the genomic changes across nearly 7000 AML patients was too vast for traditional prediction methods, machine learning methods allowed for the definition of novel genomic AML subclasses, indicating that traditional pathomorphologic definitions may be less reflective of overlapping pathogenesis.

Abstract: Nucleophosmin (NPM1) mutations in acute myeloid leukemia (AML) affect exon 12, but also sporadically affect exons 9 and 11, causing changes at the protein C-terminal end (tryptophan loss, nuclear export signal [NES] motif creation) that lead to aberrant cytoplasmic NPM1 (NPM1c+), detectable by immunohistochemistry. Combining immunohistochemistry and molecular analyses in 929 patients with AML, we found non–exon 12 NPM1 mutations in 5 (1.3%) of 387 NPM1c+ cases. Besides mutations in exons 9 (n = 1) and 11 (n = 1), novel exon 5 mutations were discovered (n = 3). Another exon 5 mutation was identified in an additional 141 patients with AML selected for wild-type NPM1 exon 12. Three NPM1 rearrangements (NPM1/RPP30, NPM1/SETBP1, NPM1/CCDC28A) were detected and characterized among 13 979 AML samples screened by cytogenetic/fluorescence in situ hybridization and RNA sequencing. Functional studies demonstrated that in AML cases, new NPM1 proteins harbored an efficient extra NES, either newly created or already present in the fusion partner, ensuring its cytoplasmic accumulation. Our findings support NPM1 cytoplasmic relocation as critical for leukemogenesis and reinforce the role of immunohistochemistry in predicting AML-associated NPM1 genetic lesions. This study highlights the need to develop new assays for molecular diagnosis and monitoring of NPM1-mutated AML.

Abstract: Background: Cytomorphology is the gold standard for quick assessment of peripheral blood (PB) and bone marrow samples in hematological neoplasms and is used to orchestrate specific diagnostics. Artificial Intelligence (AI) promises to provide an unbiased way of interrogating blood smear data as reproducibility varies across labs. This is a prospective clinical study (ClinicalTrials.gov Identifier: NCT04466059) conducted on our approach outlined at ASH 2020. Aim: Use an AI model to classify cell images to produce differential counts of PB smears side-by-side to routine diagnostics. Methods: We enrolled 10,082 patient samples which were sent to our lab between 01/2021 and 07/2021 for cytomorphology with a suspected hematologic neoplasm. Blood smears were differentiated by highly skilled technicians (median 5y in lab) and all were reviewed by hematologists. In parallel, all samples were scanned on a MetaSystems (Altlussheim, Germany) Metafer Scanning System (Zeiss (Oberkochen, Germany) Axio Imager.Z2 microscope, automatic slide feeder). Areas of interest were defined and leukocyte positions were flagged by pre-scan in 10x magnification followed by high resolution scan in 40x to generate cell images for analysis. We set up a supervised Machine Learning model based on ImageNet-pretrained Xception using Amazon Sagemaker (AS) and trained it on 8,425 carefully annotated color images to identify 21 predefined classes (including 1 garbage class). Overall accuracy of this model against hold-out-set (10%) was 96%. The algorithm consumes 144x144pixel cell images and produces probability scores (PS) for each class in every image. Results: For routine diagnostics in median 100 cells/sample (range 82 - 103) were differentiated manually, overall 988,130. The automated process gathered 500 cell images/sample (range 101 - 500), overall 4,937,389. Average capture times for 500 cells: 4:37 min. Cropped images were uploaded to a cloud storage and exposed to an AS endpoint to initiate classification and the computation of a PS for each of the predefined 21 classes in the model. For the study we only considered images with a probability of at least 90% (n=3,781,670/4,937,389) and excluded normoblasts, smudge cells and images identified as garbage (together n=2,120,258). Final diagnosis included: no lymphoma detectable (2,186), MDS (1,152), AML (369), in these 11 APL, MPN (658), CLL (558), other mature B-cell neoplasms (377), CML (326), multiple myeloma (155), but also rare entities such as hairy cell leukemia variant (2) or PPBL (3). Comparing the benign normal cells in peripheral blood we identified (all values normalized) segmented neutrophils (manual (M): 516,648=52% vs AI: 882,538=53%), eosinophils (M: 24,860=2.52% vs. AI: 55,699=3.36%), basophils (M: 7159=0,72% vs. AI: 11,957=0,72%), monocytes (M: 74,113=7.5% vs. AI: 110,126=6.64%), lymphocytes (M: 313,518=31.7% vs. AI: 399,249=24%). Pathogenic blasts were detected in 16,048 (0.97%) images by AI (M: 16,290=1.65%). In routine diagnostics 536 cases with blast cells, including "questionable blasts" were identified. The AI identified 493 (91%) of these cases. At least one atypical/malignant lymphocyte was found in 2,323 samples manually, out of which the AI identified 2,279 (98%). In few cases manual differentiation relies on the number of pathogenic cells from an immunophenotyping analysis, which the AI does not had. During the course of the study by chance we identified at least 3 instances, were the AI detected pathogenic cells (blasts, atypical promyelocytes (APL) or bilobulated promyelocytes (APL-v)) which were initially missed manually (in some case WBC below .5 G/l) or flagged during subsequent immunophenotyping/molecular genetic analysis. Upon manually revisiting the smear, we could verify the presence of the AI-anticipated cells, revealing the higher sensitivity of the 5 time increase in cells/sample investigated by AI and power of algorithms. Conclusion: We present data of a prospective, blinded clinical study comparing blood smear analysis between humans and AI head-to-head. The concordance is extremely high with 95% for pathogenic cases. Misclassified cells are used for retraining to continuously improve the model and benefit from large datasets even for rare cell types. The model's cloud based implementation makes it easy to connect scanning devices for automated, unbiased classification. Disclosures Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Kern: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership.

Abstract: Background: The current routine genetic work-up in hematological malignancies includes chromosome banding analysis (CBA) to detect complete or partial chromosomal deletions and fusions, and the identification of point mutations and small deletions or insertions by sequencing panels (max. length ~50 bp). Deletions of individual genes (e.g. IKZF1 in ALL) are only detected by specifically designed molecular tools. Therefore, those microdeletions might be overlooked by the current gold standard despite their clinical relevance. We established a bioinformatic pipeline to screen for microdeletions in whole genome sequencing (WGS) data of myeloid malignancies. Aim: (1) Screen for recurrent microdeletions in myeloid malignancies with a normal karyotype, and (2) characterize a patient specific profile of microdeletions in genes with known clinical and/or prognostic relevance. Patients and Methods: We analyzed 1356 cases (M/F: 778/578) of myeloid malignancies with a normal karyotype according to CBA (aCML: n=47; AML: n=251; CMML: n=165, mastocytosis: n=90; MDS: n=415, MDS/MPN-RS-T: n=69; MDS/MPN-U: n=42; MPN: n=250; PNH: n=27) using WGS. Median age was 71 [20-94] years. Amplification-free WGS was performed on the NovaSeq or HiSeq system with a median coverage of 103x (Illumina, San Diego, CA). Reads were aligned to the human reference genome (GRCh37, Ensembl annotation, Isaac aligner) and somatic copy number variant (CNV) discovery was performed with GATK (v 4.0.2.1), following best practice guidelines. Only gene overlapping CNV calls were considered for analysis (gene coordinates biomaRt (v 2.42.1), GRCh37 Ensembl). Results: On average, 38 genes per patient were partially or completely deleted and the size of the deletions ranged from 0.9 kb to 32 Mb (median 399 kb). The microdeletions affected a broad list of genes, but no gene was present in & gt;5% of myeloid malignancies. As technical validation, we used 36 B-ALL samples (normal karyotype) and identified the known deletions of IKZF1 (42%); PAX5 (25%) and CDKN2A/CDKN2B (22%) with expected incidences. We focused on a patient-by-patient analysis of genes (n=47) with known clinical relevance in myeloid malignancies. We identified deleted genes in 46 out of 1356 patients (3.4%). In aCML 13% of patients had one of the above-mentioned genes deleted (6/47), in mastocytosis only 1% (1/90). The most frequently deleted genes were TET2 (20/1356, 1.5%) and RUNX1 (9/1356, 0.7%). Other deletions also affected transcription factors (e.g. GATA2) or epigenetic regulators (e.g. DNMT3A, figure 1). No deletion of splicing factors, RAS genes or cohesion complex regulators was observed. We found only two deletions of kinases, which are predominantly affected by activating mutations (both FLT3). Instead, the deletions in 41 patients involved genes with a known loss-of-function mutation profile in myeloid malignancies. This corresponds to 89% (41/46) of patients with microdeletions or 3% (41/1356) of all analyzed patients with myeloid malignancies. Microdeletions are thus another genetic element that can lead to loss of gene activity. Deletions and mutations are either alternative genetic mechanisms or co-operate as double hits to affect the same gene. We found additional mutations present in 18 of the 46 patients with microdeletions (39%, figure 1). The majority of these (n=14) involved TET2. TET2 mutations had a median variant allele frequency of 82% [9-100%] indicative of a mutation on the non-deleted allele. For the remaining genes (incl. RUNX1), deletions are predominantly an alternative genetic mechanism to mutations. For validation of WGS results we applied interphase FISH and identified 6/9 RUNX1 deletions. The remaining three microdeletions were only detectable by WGS and too small to be identified by FISH. Conclusions: (1) WGS data unrevealed a plethora of microdeletions, which can be an alternative genetic mechanism to mutations, but are not detected with today's standard diagnostic tools. (2) In the light of increasingly personalized therapy and diagnostics, all genetic mechanisms should be considered, which impact the function of clinically relevant genes. (3) Bioinformatic pipelines for WGS as a potential diagnostic tool in the near future should address microdeletions in genes with relevance for patients' diagnosis, prognosis and hopefully targeted treatment. Figure 1 Figure 1. Disclosures Kern: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership.

Abstract: Background: TP53 is altered in ~50% of human cancers. Alterations mainly include mutations and/or deletions, but also copy-neutral loss of heterozygosity (CN-LOH) was reported. Frequently, both TP53 alleles are altered (by mutation + deletion, mutation + CN-LOH or ≥2 mutations), leading to a "double hit" event. Aim: Analysis of TP53 aberrations using WGS in 4646 cases with 29 different hematological malignancies, comparing (1) the frequencies of TP53 alterations, (2) occurrence of single hit vs. double hit, (3) correlation with complex karyotype and (4) impact on survival. Methods: Whole-genome sequencing (WGS) was performed for all 4,646 patients (median coverage 100x). 151bp paired-end reads were generated on NovaSeq 6000 and HiSeqX machines (Illumina, San Diego, CA). As no sample specific normal tissue was available, a so-called Tumor/Unmatched normal (TUN) workflow was used to reduce technical artefacts and germline calls. All reported p-values are two-sided and were considered significant at p & lt;0.05. Results: In the total cohort of 4,646 cases, in 582 (13%) at least one alteration (alt) involving TP53 was detected (comprising mutations (mut), deletions (del) and CN-LOH (LOH); Fig 1A,B). Cases were categorized as follows: cases with (1) 1 TP53 mut only (n=166), (2) del only (n=100), (3) LOH only (n=15), constituting the single hit events. Further, (4) cases with mut+del (might include 1 or more mut, n=211), cases with mut+LOH (≥1 mut, n=41), cases with ≥2 mut only (without del or LOH, n=49), resulting in double hit events (Fig 1B). Regarding the respective entities, high frequencies of TP53 alt were mainly detected in lymphoid malignancies (e.g. HGBL, MPAL, vHZL, MZL, MCL), whereas they were infrequent or absent in many myeloid malignancies (e.g. aCML, MPN, CMML, CML, MLN_eo; Fig 1A). For further analysis, only entities in which & gt;10 cases showed TP53 alt events were used. Comparison of single hit vs. double hit revealed that T-NHL, MM, MPN and MDS predominantly showed a single hit, whereas the double hit was frequent in MPAL, MZL, MDS/MPN-U, CLL and MCL cases (Fig 1A). However, the type of double hit differed between myeloid and lymphoid malignancies, as myeloid neoplasms showed a high frequency of cases with ≥2 mut only, whereas in many lymphoid malignancies the double hit was predominantly generated by mut+del (Fig 1A,C). All TP53-associated events (mut, del, LOH and the respective combinations) were found to be associated with a complex karyotype in the total cohort (LOH: 14% complex karyotype in cases without TP53 alt vs. 59% in cases with TP53 alt, p & lt;0.001). This association was also detected for most of the selected entities (exceptions: MZL, T-NHL). Regarding overall survival (OS), in the total cohort, all events involving TP53 impact on OS (TP53 alt: 22 months vs. 84 months, p & lt;0.001; TP53 mut: 20 vs. 82 months, p & lt;0.001; TP53 del: 20 vs. 79 months, p & lt;0.001; TP53 LOH: 20 vs. 75 months, p & lt;0.001). Moreover, although the single hit already impacts on OS, the double hit leads to an even inferior outcome (no hit vs. single hit vs. double hit: 84 vs. 39 vs. 14 months, p & lt;0.001). In the selected entities, an influence of TP53 alt on OS was detected for all malignancies except HGBL, MZL and T-NHL, for which also the presence of a double hit did not show an effect on OS. For the majority of the other entities, the double hit leads to a shorter OS than the single hit (as observed for the total cohort), with the exceptions of MCL and MPAL: in these entities, the single hit did not impact on OS, but only a double hit is associated with inferior outcome. Conclusions: (1) Frequency of TP53 alterations and of double hit vs. single hit differs markedly between entities. (2) The kind of TP53 complexity differs between both lineages (double hit in myeloid neoplasms: often ≥2 mut only; in lymphoid malignancies: predominantly mut+del). (3) In 7% (41/582) of cases with TP53 alt, CN-LOH transforms a single hit into a double hit. (4) In the total cohort and in the majority of selected entities (except MZL and T-NHL), TP53 alt are associated with complex karyotype. (5) In the total cohort, all events involving TP53 impact on OS; cases with double hit show an inferior outcome compared to single hit. (6) Regarding OS, the selected entities can be divided into three categories: (i) no influence of TP53 alt (HGBL, MZL, T-NHL); (ii) double hit required for impact on OS (MCL, MPAL); (iii) influence of both single hit and double hit with inferior outcome of double hit (all other). Figure 1 Figure 1. Disclosures Kern: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership.

Abstract: Background High telomerase activity represents a critical feature of hematopoietic stem cells. Excessive shortening of telomere length (TL) due to replicative stress may be - in analogy to many solid tumors - a hallmark of myeloid neoplasia (MN). Also, telomeric footprints in leukemic genomes may vary between various subtypes corresponding to the differentiation arrest at various stages of hematopoietic ontogeny or specific molecular defects. Critical TL shortening has been associated with genomic instability and accelerated acquisition of genomic lesions leading to a more aggressive phenotype. These processes have not been systematically studied in MN, especially AML. Aim By taking advantage of next-generation sequencing to assay both molecular features and TL within large cohorts of patients, we tested the hypothesis that TL shortening is excessive in highly proliferative MN, but that distinct invariant differences characterize genetic subtypes. Methods Our cohort included AML (N=734), MDS (N=701), healthy controls (HC) (N=11) and PNH (N=102) serving as clonal non-malignant controls. All patients were diagnosed according to WHO standards before being subjected to transcriptome (WTS) and genome (WGS, 100x) sequencing. To retrieve TL characteristics and telomere repeat heterogeneity from WGS data, we used TelomereHunter (TH). In parallel, we performed C-Circle assays. Patients were annotated for clinical features and analyzed for genetic/transcriptomic patterns. Results For a subset of patients for whom corresponding benign lymphocyte DNA was available a significant TL shortening in blasts vs control lymphocytes (A; P=.0023) was detected. While age correlation was established in controls, despite a trend, in MN age did not significantly affect TL (B) and thus subsequent comparisons were not adjusted for age. Next, we studied a cohort of patients with AML, MDS, PNH and HC and found that TL shortening was an overarching finding in AML, MDS and PNH as compared to HC (C). Since no matched DNA was available as reference, we examined the distribution of TL across different age cohorts, AML patients divided according to age cohorts harbored TL in a similar range (D, P=.057). Classic morphologic (E) or cytogenetic subtypes AML exhibited no difference. Similarly, no differences were found between high and low risk MDS patients (not shown). The variability of TL ranges suggested that there may be molecular factors which affect individual TL. When we compared TL grouped according to frequent mutations, only TP53 mutations were associated with longer TL (F, P & lt;.0001). A significant positive correlation (G, P=.021) between TL and TP53 clonal burden was found; samples with the longest vs shortest TL showed significantly higher TP53 VAF (H, P=.0229). In analogy, the presence of multiple TP53 mutations (putative biallelic inactivation) showed longer TL than single hits but no association was found between the nature of mutations and TL (I, J, K). Availability of WTS data allowed us to assess the telomerase activity using the EXTEND score (ES) which has been shown to assess telomerase activity. Indeed, the ES was correlated with TL (L) and TP53 mutant status was associated with a higher ES compared to WT samples (M, P & lt;.0001). Similarly, because of the compensatory upregulation of TP53 in mutant cases, we have also found that TP53 mRNA levels correlated with ES (N P & lt;.0001). Another explanation of TL increase could be the occurrence of alternative lengthening (ALT). TH software allows for estimation of the abundance of specific telomeric repeats. Singleton analysis showed that increase in telomere repeats variants (TTTGGG, O, P=.003) was related to mutations in TP53 arguing against the involvement of ALT. The final confirmation that TL extension was not due to ALT was provided by C-Circle assays. When C-Circle assays were performed for samples with a high/low TL and mutant/WT TP53, none of the subgroups was identified as ALT + (P). Conclusion We stipulate that TL measurements using NGS will be helpful to investigate pathophysiological features associated with TL shortening. Availability of therapies targeting the telomere machinery (Imetelstat) may offer an opportunity for personalized therapy beyond MPN, its current indication. It remains to be tested whether long TL associated with TP53 mutations can serve as marker of sensitivity or resistance to these agents. Figure 1 Figure 1. Disclosures Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Kern: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Maciejewski: Regeneron: Consultancy; Bristol Myers Squibb/Celgene: Consultancy; Alexion: Consultancy; Novartis: Consultancy.

Abstract: The prevailing theory in del(5q) is that haploinsuffciency (HI) stemming from deletion and not simply LOH (loss of heterozygosity) is the culprit in clonal evolution. To date no haploinsufficient gene has been found to be the leukemogenic factor conveying growth advantage, but various other genes have been found to be important for phenotypic features or for propensity to acquire subsequent specific lesions. RPS14 is an example of such a gene, particularly in patients (pts) with isolated del(5q), responsible for macrocytic anemia and erythroid dysplasia and a propensity for acquisition of TP53 mutations. We hypothesized that RPS14 downmodulation and its consequences may be more common than del(5q) and it is frequent pathophysiologic feature in MDS. We first analyzed the genomic and expression profile of 170 pts with del(5q) and 825 diploid for 5q. We developed a new analytic pipeline to identify the most HI genes present in a large number of del(5q) pts. Genes within CDR (common deleted region) were classified as HI from the linear model fit if (i) clonality vs. gene expression slope from the isolated del(5q) was negative and FDR & lt;.05; and (ii) effect of del(5q) at 50% clonality vs. other cases was negative and FDR & lt;.05. A total of 62 genes met these criteria for linear-model based genes HI status, with a further 5 genes dropping due to low expression. Gene expression for these 57 HI genes among del(5q) samples was adjusted to 50%-clonality using the slopes from the estimated linear model to remove clonal heterogeneity. After applying model-based sparse clustering approach on all cohort, we obtained 7 clusters (Figure 1). As expected, del(5q) cases clustered together and showed consistent HI of 5q marker gene expression. Cluster-1 (n=146) included almost all del(5q) cases, except for 8 "mis-categorized" patients. It was characterized by low risk MDS (LR-MDS), presence of anemia/neutropenia and low mutational burden, with TP53 being the most commonly mutated gene and the only cluster with CSNK1A1 mutations. The remaining non-del(5q) patients were grouped in 6 clusters. Diploid cluster-2 (n=133) featured a normal karyotype, frequent ASXL1 and TET2 mutations, and profound down-modulation of RPS14 in all the patients included in the cluster (vs. other diploid pts). While the median RPS14 expression in cluster-1 (del(5q) cluster, with 50% adjusted clonality) was 7.29 (range 4.68-8.82 Log 2CPM), cluster-2 exhibited a median RPS14 expression of 6.12 Log 2CPM (range: 4.91-7.31 Log 2CPM). Clusters-3, -4, -5 (n=138, 90, 94, respectively) included most of the high risk MDS (HR-MDS). Cluster-3 was enriched for thrombocytopenia and SRSF2 mutations; cluster-4 for anemia, thrombocytopenia and ASXL1 and SRSF2 mutations. Cluster-5 was characterized by pancytopenia and frequent ASXL1 mutations and CK (complex karyotype). Cluster-6 (n=66) and -7 (n=233) contained the majority of non-del(5q) LR-MDS. When we analyzed the RPS14 expression in these clusters based on the RPS14 expression in cluster 2 we found 13% (n=18), 21% (n=19), 9% (n=8), 14% (n=9), 7% (n=16) of low RPS14 expressors in cluster-3, -4, -5, -6, -7, respectively. Cluster-2 showed a similar percentage of patients with anemia, and thrombocytopenia vs. Cluster-1 (69 vs. 50%, 23 vs. 30%; respectively). The mutational profile included a higher frequency of mutations for SRSF2 (29 vs. 0%), NRAS/KRAS (22% vs. 4%), ASXL1 (40 vs. 15%), TET2 (35 vs. 15%), and JAK2 (17 vs. 6%). These results indicate a more proliferative molecular spectrum of RPS14 downregulated cluster-2 than del(5q)-cluster-1, but RPS14 downmodulation did not lead to acquisition of TP53 mutations (4% vs. 76%). Considering all non-del(5q) RPS14 low expressors (n=186), only 3% of the cases had TP53 mutations. Since TP53 and CSNK1A1 mutations were characteristic of cluster-1 we studied interactions with HI RPS14. HI RPS14 in del(5q) and diploid low expressors showed a decreased expression of CDKN1A (P & lt;.001) in comparison to the non-HI or low RPS14. We also found that CSNK1A1 mutations were not found outside of del(5q) pts, CSNK1A1 low expressors coincided with RPS14 low expressors. In conclusion, RPS14 expression defect is more widespread than del(5q) in MDS. However, only del(5q) RPS14 HI pts are prone to harbor TP53 and CSNK1A1 mutations; a group of diploid pts with low RPS14 and CSNK1A1 expressions might mimic some del5q features and could potentially respond to similar treatments. Figure 1 Figure 1. Disclosures Diez-Campelo: Takeda Oncology: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. Carraway: AbbVie: Other: Independent review committee; Jazz: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Takeda: Other: Independent review committee; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Agios: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Astex: Other: Independent review committee; Stemline: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene, a Bristol Myers Squibb company: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Maciejewski: Bristol Myers Squibb/Celgene: Consultancy; Regeneron: Consultancy; Novartis: Consultancy; Alexion: Consultancy.