Biallelic Inactivation Of TP53 Research Articles

Clinical Validation of a NGS Capture Panel to Identify Mutations, Copy Number Variations and Translocations in Patients with Multiple Myeloma

Clinical Validation of a NGS Capture Panel to Identify Mutations, Copy Number Variations and Translocations in Patients with Multiple Myeloma

Large-Scale Analysis of Relapsed/Refractory Multiple Myeloma Genome Reveals Increased Prevalence of High-Risk Molecular Features and Oncogenic Drivers

Introduction Multiple myeloma (MM) is a heterogeneous disease defined by genetic lesions including translocations, chromosomal copy number aberrations (CNA), and mutations. Large-scale analyses of genomic data from newly-diagnosed patients (ndMM) have defined key oncogenic drivers beyond previously known primary events. In relapsed/refractory MM (rrMM), analyses have been completed on small numbers of patient samples for mutational profiles; however, a large-scale analysis of genomic and transcriptomic data has not been performed. In this initial analysis, we describe the rrMM genomic landscape including prevalence of key translocations, oncogenic/tumor suppressor mutational drivers, and chromosomal CNA. Methods We generated whole genome sequencing (WGS) and whole transcriptome expression (RNA-seq) from 485 rrMM patient samples derived from clinical trials: NCT01712789/CC-4047-MM-010 (N=236), NCT02045017/CC-4047-MM-013 (N=17), NCT02773030/CC-220-MM-001 (N=45) and NCT01421524/CC-122-ST-001MM2 (N=10). Somatic single nucleotide variants (SNVs) and indels were derived from WGS from 308 samples using GATK/MuTect2 and annotated using ANNOVAR. Clonal and subclonal CNA were identified using Sclust. Translocations were determined using Manta. Oncogenic/tumor suppressor drivers were identified using cDriver, which utilizes recurrence and functional consequence and cancer cell fraction (CCF) of each mutation. Results Key translocations included: t(11;14) [76/308 (25%)], t(8;14) [79/308 (25%)], t(4;14) [44/308 (14%)], t(14;16) [24/308 (8%)], t(6;14) CCND3 [18/308 (6%)], t(14;20) [17/308 (5.5%)], and t(6;14) [IRF4 17/308 (5.5%)]. Hyperdiploidy was detected in 140/308 (46%) patients. Key chromosomal CNA included del14q 134/308 (44%), del13q 131/308 (43%), del8p 113/308 (37%), del17p 53/308 (17%), amplification of 1q (≥4 copies) 45/308 (15%), and del1q 31/308 (10%). Compared to samples from ndMM patients, we saw an increase of del17p (17% vs. 8%) and t(11;14) (25% vs. 15%) in rrMM. Further, out of the 53 del17p patients, 45 (85%) were high CCF (>0.55) versus 63/107 (59%) reported in ndMM (Thakurta et al, Blood. 2019) Double Hit patients (biallelic inactivation of TP53 or amplification of 1q on a background of ISS3) was detected in 12% patients which is significantly higher than ndMM (6%, p < 0.05) (Walker et al, Leukemia. 2018) We identified a total of 107 driver genes (FDR<0.05). Of these, 23 were known cancer genes according to the COSMIC Cancer Gene Census (CGC) of which 19 were Tier1 such as TP53, DNMT3A and SETD2. Further, 19/107 driver genes were previously identified in ndMM from the Myeloma Genome Project including IRF4, TRAF3, NFKB2 and FGFR3. The top ten ranking driver genes in rrMM were DIS3, FAM46C, IGLL5, KMT2B, TRAF3, SP140, MALRD1, TP53, L1TD1 and PRKCD. Among these, novel driver genes such as IGLL5 (N=19, 6.2%; medianCCF=1; Immunoglobulin Lambda-Like Polypeptide 5) were also identified. We examined if loss-of-function (LOF) and gain-of-function (GOF) variants result in different sets of drivers, thus identifying putative tumor suppressor genes (TSG) and oncogenes (ONC) respectively. We identified a total of 72 and 43 ONCs and TSGs, respectively (FDR < 0.05). The top ten ranking ONCs included driver genes such as UBR4 and IRF4. Among the top ten ranking TSGs were novel driver genes such as TDG and SMARCA4 as well as known ndMM driver genes such as UBR5 and CDKN1B. We confirmed that FGFR3 driver mutations were associated with t(4;14) and IRF4 were associated with t(11;14) (p < 0.05) in our rrMM population. Further analysis will be focused on association of significant mutations with CNA and translocation groups, impact on clinical outcomes in genetic subsets, delineating the effect of multiple lines of therapy on tumor clonality, genetic architecture of resistance patterns, and the role of oncogenic drivers. Conclusions We have established and analyzed the largest molecular rrMM dataset with associated clinical outcome data. These analyses are revealing the evolution of genetic drivers of resistance to therapy and will assist in identification of subsets of poor prognostic groups (eg, Double Hit) and new molecular subsets of rrMM where novel targeted therapies could be developed. Disclosures Towfic: Celgene Corporation: Employment, Equity Ownership. Ansari-Pour:Celgene Corporation: Consultancy. Ortiz:Celgene Corporation: Employment, Equity Ownership. Gooding:Celgene Corporation: Research Funding. Rozelle:Celgene Corporation: Other: Contractor for Celgene. Zadorozhny:Celgene Corporation: Other: Contractor for Celgene. Mavrommatis:Celgene Corporation: Employment, Equity Ownership. Lata:Celgene Corporation: Employment, Equity Ownership. Stong:Celgene Corporation: Employment, Equity Ownership. Kamalakaran:Celgene Corporation: Employment, Equity Ownership. Tsai:Celgene Corporation: Employment, Equity Ownership. Flynt:Celgene Corporation: Employment, Equity Ownership. Thakurta:Celgene: Employment, Equity Ownership.

Molecular Dissection of Del(5q): Distinction between Primary and Secondary Del(5q) and Pathogenetic Implications

Del(5q) is the most common cytogenetic (CG) alteration in myeloid neoplasia (MN), and solo defines the 5q- syndrome. Despite significant progress in understanding the disease mechanisms resulting from del(5q), the pathogenesis behind this lesion, in particular its effect on leukemogenesis, is still not clear. The absence of corresponding somatic LOH in the del(5q) region suggests that the haploinsufficiency (HI) resulting from the deletion might be the key pathogenic factor. However, none of the investigated HI genes located on del(5q) can explain the 5q related growth advantage/transformation. High molecular diversity including somatic mutations in the retained allele, additional CG abnormalities, epigenetic factors (loss/gain of silencing), and LOH for germ line (GL) protective alleles may preclude the identification of the pathogenically essential lesions. We revisit the genomics of del(5q) by taking advantage of a large cohort of patients with molecular (WES, WGS, RNAseq) and clinical annotation. We analyzed a total of 400 samples (388 patients) with del(5q) and 825 diploid patients with MN. Patients were subgrouped into isolated del(5q) (iso-del5q; 49%) and del(5q) with additional lesions referred to as compound del(5q) (comp-del5q). Availability of paired GL WES results allowed us to reconstruct clonal hierarchy using precise bioanalytic allelic exclusion methods. Studies have postulated that del(5q) is a founder event; we now demonstrate that 52% of iso-del5q patients did have a dominant del(5q) event, but in 56% of comp-del5q and 28% of iso-del5q, del(5q) was subclonal. Co-dominant del(5q) with somatic mutations were also found (iso-del5q: 20% vs. comp-del5q: 11%). When del(5q) was dominant, patients had fewer associated mutations, while cases with secondary del(5q) had a poor prognosis due to ancestral TP53 mutations (MT) and accumulation of chromosomal breaks. We then focused on the expression level: 405 genes on 5q were interrogated, 188 within q14q34 (CDR-1: 41; CDR-2: 55). We defined HI as expression <25th %tile of the diploid expression. CSNK1A1 was deleted in 90% and HI in 77% of del(5q), whereas RPS14 was deleted in 89% but HI in 39% of del(5q) cases. Applying a more stringent definition (<68% of diploid), the most consistently HI genes were SIL1 in 61%, H2AFY in 58%, and CTNNA1 in 49% of cases vs. <5% of diploid cases. By this criterion, RPS14, ACSL6 and TGFBI were also HI in 19%, 17% and 16% of diploid cases. Del(5q) HI genes showed an enrichment of 49x in β-catenin phosphorylation cascade genes (P<.0001), 25x in Wnt signaling (P=.001) and 5x in cell cycle genes (P<.0001). When the entire profile was examined, TP53 and apoptotic genes also showed enrichment in upregulation (both P<.0001). We also genotyped del(5q) and a control diploid cohort for somatic mutations. Within the 5q CDR APC, RAD50, and CSNK1A1 were most frequently mutated (all hemizygous), particularlly in iso-del5q. No canonical DDX41 frame shift mutations (GL) or somatic mutations occurred in its ATP binding domains were found. However, there were DDX41MT (n=2) that coincided with del(5q) and one patient coincided with the CDR. Del(5q) patients had a distinct mutational profile of co-associated lesions with a higher frequency of TP53MT (P<.0001) and a significantly lower frequency of SF3B1, ASXL1, TET2, JAK2, SETBP1, U2AF1 and SRSF2 mutations (all P<.01) than diploid patients. TP53MT were enriched in comp-del5q in cases that frequently also demonstrated -7/7q- and 17p-. CSNK1A1 and TP53 are the two most common mutations in del(5q) patients. CSNK1A1MT were co-mutated with TP53 in only 15% of patients and CSNK1A1 HI did not enrich for TP53MT. Expression of p21 (a TP53 activation marker) was up-regulated in del(5q) except for cases with biallelic TP53 inactivation. RPS14 was HI in a fraction of del(5q) patients. Patients with the RPS14 deleted locus were more likely that those with HI to have an increase in p21 expression (P<.0001) but only in patients with advanced disease. In sum, our analysis of a comprehensive compendium of del(5q) genomics revises previous assumptions (del(5q) is not a uniform founder lesion, and the heterogeneity HI of genes across patients), discovers new HI candidate genes and precisely describes molecular relationships (e.g., del(5q) with TP53) and generated a minimalistic expression signature of del(5q). Disclosures Walter: MLL Munich Leukemia Laboratory: Employment. Hutter:MLL Munich Leukemia Laboratory: Employment. Díez-Campelo:Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene Corporation: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Nazha:Jazz Pharmacutical: Research Funding; MEI: Other: Data monitoring Committee; Novartis: Speakers Bureau; Incyte: Speakers Bureau; Tolero, Karyopharma: Honoraria; Abbvie: Consultancy; Daiichi Sankyo: Consultancy. Meggendorfer:MLL Munich Leukemia Laboratory: Employment. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. 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. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Maciejewski:Alexion: Consultancy; Novartis: Consultancy.

Validation of the Genetically-Defined DLBCL Subtypes and Generation of a Parsimonious Probabilistic Classifier

Diffuse large B-cell lymphoma (DLBCL) is a clinically and molecularly heterogeneous disease with recognized transcriptional subtypes associated with normal cells of origin, activated B-cell (ABC) and germinal center B-cell (GCB) tumors. Emerging data suggested that additional heterogeneity existed, prompting us to comprehensively characterize genomic signatures of 304 newly diagnosed DLBCLs from patients treated with state-of-the-art therapy. We integrated recurrent mutations, somatic copy number alterations (SCNAs) and structural variants (SVs) and identified 5 genetically distinct DLBCL clusters (C1- C5 DLBCLs; Chapuy, Stewart, Dunford, et al. Nat Med 2018). Specifically, we identified two genetically distinct ABC subtypes, including favorable-risk C1 DLBCLs with features of extrafollicular origin and alterations also seen in transformed marginal zone lymphomas (NOTCH2 and NF-κB pathway member mutations and BCL6 SVs). Unfavorable-risk C5 ABC DLBCLs harbored frequent 18q/BCL2 copy gain and co-occurring CD79B and MYD88L265P mutations. We also identified two genetically distinct GCB subtypes, including unfavorable-risk C3 DLBCLs with frequent BCL2 SVs, mutations in chromatin-modifying enzymes (CREBBP, MLL2, EZH2) and BCR/PI3K signaling pathway members (including inactivating PTEN mutations and copy loss). Favorable-risk C4 GCB DLBCLs had frequent mutations in core and linker histones and signaling intermediates (SGK1, BRAF and STAT3). Additionally, we identified an ABC/GCB-independent subtype, C2 DLBCLs, characterized by frequent bi-allelic TP53 inactivation, 9p21.23/CDKN2A copy loss and associated genomic instability reflected in recurrent SCNAs, increased genome doublings and a distinct outcome following induction therapy. A next step in utilizing the characterized genetic substructure was to confirm it in an independent series and develop a molecular classifier that allows prospective identification of C1-C5 DLBCLs. To this end, we accessed whole exome sequencing, copy number and SV data from a recent cohort of newly diagnosed DLBCLs (39 tumor-normal pairs, 462 tumor-only samples; Schmitz et al. NEJM 2018). All samples were re-analyzed using our mutational and SCNA pipelines and our newly generated tumor-only algorithm (Chapuy, Stewart, Dunford, et al. Nat Med 2018) to avoid batch effects and harmonize the datasets. SVs were used as reported. Purity and ploidy were inferred using ABSOLUTE and samples with missing data or low purity were removed. For the combined cohort (579 samples), we assessed our previously characterized 158 genetic drivers (Chapuy, Stewart, Dunford, et al. Nat Med 2018) and confirmed equal distribution of their marginal frequencies (R=0.88, p=1.5e-51), excluding batch effects. Next, we applied non-negative matrix factorization (NNF) consensus clustering to the combined dataset (158 genetic drivers vs. 579 tumors) and confirmed the C1-C5 DLBCL genetic clusters. Notably, tumors from both series contributed at comparable frequencies to the respective C1-C5 DLBCLs. We also noted an enrichment of the alternative genetic labels from Schmitz et al. in 3 of our C1-C5 DLBCL subtypes (B2N in C1 DLBCLs, p<0.0001; EZB in C3 DLBCLs, p<0.0001; MCD in C5 DLBCLs, p<0.0001). These data confirmed the identity of the C1-C5 DLBCL clusters in an independent cohort. Next, we developed a molecular classifier that prospectively identified C1-C5 DLBCLs using a minimum number of easy-to-measure features. The NMF-defined classes of the combined cohort were used as gold-standard training and validation datasets. We tested different models for classification and selected an artificial neural network approach which provides accurate classification of individual samples and well-calibrated confidence metrics. To minimize potential overtraining, we developed a reduced input feature set of the 22 most discriminating features, constructed confidence metrics for each sample and trained an ensemble of Feed-Forward Neural Networks via 10-fold cross validation. With this approach, our classifier had 84% accuracy for the total set and 94% accuracy for the high-confidence samples (70% of all samples). The newly developed parsimonious classifier will allow prospective identification of the independently confirmed C1-C5 DLBCL subtypes in newly diagnosed patients, a necessity for clinical application. Disclosures Getz: Pharmacyclics: Research Funding; IBM: Research Funding; MuTect, ABSOLTUE, MutSig and POLYSOLVER: Patents & Royalties: MuTect, ABSOLTUE, MutSig and POLYSOLVER. Shipp:BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck & Co.: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; AstraZeneca: Honoraria, Membership on an entity's Board of Directors or advisory committees; Bayer: Research Funding; Gilead Sciences: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda Pharmaceuticals: Honoraria, Membership on an entity's Board of Directors or advisory committees.

Integration of Comprehensive Genomic Analysis and Functional Screening of Affected Molecular Pathways to Inform Cancer Therapy

ObjectiveTo select optimal therapies based on the detection of actionable genomic alterations in tumor samples is a major challenge in precision medicine. MethodsWe describe an effective process (opened December 1, 2017) that combines comprehensive genomic and transcriptomic tumor profiling, custom algorithms and visualization software for data integration, and preclinical 3-dimensiona ex vivo models for drug screening to assess response to therapeutic agents targeting specific genomic alterations. The process was applied to a patient with widely metastatic, weakly hormone receptor positive, HER2 nonamplified, infiltrating lobular breast cancer refractory to standard therapy. ResultsClinical testing of liver metastasis identified BRIP1, NF1, CDH1, RB1, and TP53 mutations pointing to potential therapies including PARP, MEK/RAF, and CDK inhibitors. The comprehensive genomic analysis identified 395 mutations and several structural rearrangements that resulted in loss of function of 36 genes. Meta-analysis revealed biallelic inactivation of TP53, CDH1, FOXA1, and NIN, whereas only one allele of NF1 and BRIP1 was mutated. A novel ERBB2 somatic mutation of undetermined significance (P702L), high expression of both mutated and wild-type ERBB2 transcripts, high expression of ERBB3, and a LITAF-BCAR4 fusion resulting in BCAR4 overexpression pointed toward ERBB-related therapies. Ex vivo analysis validated the ERBB-related therapies and invalidated therapies targeting mutations in BRIP1 and NF1. Systemic patient therapy with afatinib, a HER1/HER2/HER4 small molecule inhibitor, resulted in a near complete radiographic response by 3 months. ConclusionUnlike clinical testing, the combination of tumor profiling, data integration, and functional validation accurately assessed driver alterations and predicted effective treatment.

Large deletions (>10.9 MB) in 17p and bi-allelic TP53 inactivation events in newly-diagnosed multiple myeloma are associated with higher clonal cell fraction and poor prognosis

Deletions in Chromosome 17p (del17p) are associated with poor outcome in Multiple Myeloma (MM). We compiled a cohort of 139 whole-exome and whole-genome sequenced del17p newly diagnosed patient tumors and correlated the size of the deletion, as calculated by controlFreec, against progression-free and overall survival. Large (> 10.9MB) deletions in chromosome 17p that included TP53 region were significantly associated with poorer overall survival (mOS = 28.8 months vs. mOS = 45.0 months, pval= 0.047).

Establishment and characterization of patient‐derived organoids from a young patient with cervical clear cell carcinoma

Cervical clear cell carcinoma (cCCC) constitutes an extremely rare subtype of cervical cancer. Consequently, its pathogenesis remains largely unknown, with no cell lines established from primary tumors. Here, we report the first establishment of cCCC organoids, from biopsy samples of a 23‐year‐old patient diagnosed with cCCC. By applying a protocol that we recently optimized for gynecological tumors, we were able to propagate a patient‐derived cell line (PDC) for more than 6 months as organoids. This PDC tolerated cryopreservation and proliferated either as spheroids or adherent cells, and developed xenografts in immunodeficient mice, ensuring robust utility as a cell line. Intriguingly, the resected tumor focally contained serous carcinoma (SC) in a tiny protruding lesion. Both organoids and derivative xenografts resembled the CCC component of the original tumor in histology, immunostaining profile, and genome‐wide copy number changes, including focal gain of MET. Genomic analysis revealed that both organoids and the CCC component harbored only a few mutations, of which 2 mutations were shared in common. In contrast, the SC component showed a mutator‐phenotype and prominent genome instability along with biallelic inactivation of TP53, but none of them were found in organoids or the CCC component. The PDC proved sensitive to major chemotherapeutic agents and MET inhibitors. These observations clearly indicated that the PDC, designated as YMC7, can be used as a novel cCCC cell line and provide novel insights into the pathogenesis of mixed cervical adenocarcinoma. As a valuable resource for rare cancer, it will likely contribute to investigations in many fields.

3195 Genomic Analysis of Primary Plasma Cell Leukemia reveals Complex Cytogenetic Alterations and High Risk Mutational Patterns

OBJECTIVES/SPECIFIC AIMS: 1) Determine the mutational landscape, including translocation, mutations and mutational signatures as well as copy number variations of pPCL and identify significant differences to non pPCL MM. 2) Determine whether genetic changes pertinent to pPCL could be explored as therapeutic targets to improve the dismal prognosis of this patient population. METHODS/STUDY POPULATION: Samples from overall 19 pPCL patients that presented to the Myeloma Center, UAMS between 2000-2018 were used for this study. We performed gene expression profiling (GEP; Affymetrix U133 Plus 2.0) of matched circulating peripheral PCs and bone marrow (BM) PCs from 13 patients. Whole exome sequencing (WES) was performed on purified CD138+ PCs from BM aspirates from 19 pPCL patients with a median depth of 61x. CD34+ sorted cells, taken at the time of stem cell harvest from the same 19 patients, were used as controls. Translocations and mutations were called using Manta and Strelka and annotated as previously reported. Copy number was determined by Sequenza. RESULTS/ANTICIPATED RESULTS: 1) GEP from the BM and circulating peripheral PCs showed that the expression patterns of the two samples from each individual clustered together, indicating that circulating PCs and BM PCs in pPCL result from the same clone and are biologically clearly related. 2) The clinical characteristics from the patient cohort used for WES analysis were as follows: median age was 58 years (range 36–77), females accounted for 74% (14/19), an elevated creatinine level was found in 78% (14/18) and an elevated LDH level in 71% (10/14). All patients presented with an ISS stage of III. Median OS of the whole dataset was poor at 22 months, which is consistent with OS from previously reported pPCL cohorts. 3) Primary Immunoglobulin translocations were common and identified in 63% (12/19) of patients, including MAF translocations, which are known to carry high risk in 42% (8/19) of patients [t(14;16), 32% and t(14;20), 10%] followed by t(11;14) (16%) and t(4;14) (10%). Furthermore, 32% (6/19) of patients had at least one MYC translocation, which are known to play a crucial role in disease progression. 4) The mutational burden of pPCL consisted of a median of 98 non-silent mutations per sample, suggesting that the mutational landscape of pPCL is highly complex and harbors more coding mutations than non-pPCL MM. 5) Driver mutations, that previously have been described in non-pPCL MM showed a different prevalence and distribution in pPCL, including KRAS and TP53 with 47% (9/19) and 37% (7/19) affected patients respectively compared to 21% and 5% in non-PCL MM. PIK3CA (5%), PRDM1 (10%), EP300 (10%) and NF1 (10%) were also enriched in the pPCL group compared to previously reported cases in non-pPCL MM. 6) Biallelic inactivation of TP53 – a feature of Double Hit myeloma - was found in 6/19 (32%) samples, indicating a predominance of high risk genomic features compared to non-pPCL MM. Furthermore, analysis of mutational signatures in pPCL showed that aberrant APOBEC activity was highly prevalent only in patients with a MAF translocation, but not in other translocation groups. DISCUSSION/SIGNIFICANCE OF IMPACT: In conclusion we present one of the first WES datasets on pPCL with the largest patient cohort reported to date and show that pPCL is a highly complex disease. The aggressive disease behavior can, at least in part, be explained by a high prevalence of MAF and MYC translocations, TP53 and KRAS mutations as well as bi-allelic inactivation of TP53. It is of interest that only KRAS but not NRAS mutations are highly enriched in pPCL. From all highly prevalent genomic alterations in pPCL, only KRAS mutations offer a potential for already available therapeutically targeting with MEK inhibitors, which should be further explored.

Poor overall survival in hyperhaploid multiple myeloma is defined by double-hit bi-allelic inactivation of TP53.

Hyperhaploid multiple myeloma is a rare numerical aberration group defined by a range of 24-34 chromosomes, which is associated with a poor prognosis with a 5-year survival rate of 23%. Hyperhaploid patient samples (n=8) were sequenced and copy number and mutations identified. Samples had a median of 13 monosomies (range 12-14), which in general were those not associated with trisomies in hyperdiploid samples. The chromosomes traditionally trisomic in hyperdiploid myeloma were disomic in hyperhaploid myeloma with retention of heterodisomy. We examined the hyperhaploid samples for frequently mutated genes and found that 8/8 (100%) hyperhaploid samples had a mutation in TP53, exceeding the overall rate of mutation in newly diagnosed patients (5.5%), indicating an oncogenic dependency in this group. All samples with TP53 mutation also had monosomy of chromosome 17, indicating bi-allelic inactivation of TP53. As such, this high risk group is part of double-hit myeloma.

The Mutational Landscape of Primary Plasma Cell Leukemia

The Mutational Landscape of Primary Plasma Cell Leukemia

Poor Overall Survival in Hyperhaploid Multiple Myeloma Is Defined By Double-Hit Bi-Allelic Inactivation of TP53

Introduction Hyperhaploid multiple myeloma is a rare numerical aberration group defined by a range of 24-34 chromosomes. We have previously shown that hyperhaploid myeloma is associated with a poor prognosis with a 5-year survival rate of 23.1%, compared to 64% for hyperdiploid myeloma, and 80.4% for those with a normal karyotype. It is known that hyperhaploid myeloma frequently has monosomy of chromosome 17, making it a high risk group, but no data are currently available on the mutational status of this interesting sub-group, or how the copy number difference arises.Methods We analyzed data from whole genome, whole exome, and targeted panel sequencing from 1141 newly diagnosed myeloma patients. Internal samples were selected for whole exome or targeted sequencing based on previous karyotype information, or were identified in the process of other sequencing studies. The CoMMpass dataset was screened for the presence of hyperhaploidy. Hyperhaploid samples without prior karyotype information were identified by conflicting copy number profile and B allele frequency information, where the samples had incorrectly been normalized to a diploid copy number. These samples were re-normalized to a haploid copy number. Copy number, B allele frequency, and mutations of key genes were examined.Results In the entire dataset 9 hyperhaploid samples were identified, of which 2 came from the CoMMpass dataset. From those with gene expression array data, 5/7 were GEP70 high risk and all belonged to the D1 hyperdiploid gene expression subgroup. Samples had a median of 13 monosomies (range 9-14), which in general were those not associated with trisomies in hyperdiploid samples. The chromosomes traditionally trisomic in hyperdiploid myeloma were disomic in hyperhaploid myeloma. We examined the B allele frequency of these disomic chromosomes and saw that they all retained heterodisomy. Retention of heterodisomy indicates that the method of generating hyperhaploidy is through deletion of the monosomic chromosomes, rather than reverting to a haploid genome followed by duplication of some chromosomes. Retention of heterodisomy was also seen on chromosome 18, which is not normally trisomic in hyperdiploid samples, indicating that heterodisomy of chromosome 18 may be essential for a viable plasma cell clone.We examined the hyperhaploid samples for frequently mutated genes and found that 8/9 (88.8%) of hyperhaploid samples had a mutation in TP53. This rate of mutation far exceeds the overall rate of mutation in newly diagnosed patients (5.5%), indicating an oncogenic dependency in this group. The sample without mutation of TP53 had only 9 monosomies, fewer than the other samples (12-14 monosomies), indicating there may be a prognostic difference that is dependent on the total chromosome count. All samples with TP53 mutation also had monosomy of chromosome 17, indicating bi-allelic inactivation of TP53. The variant allele frequency of the TP53 mutations was high (median=0.94), indicating that bi-allelic inactivation was a clonal event. No other significant mutations were found, including those that encode chromosome segregation or kinetochore proteins.Conclusions We have previously described bi-allelic inactivation of TP53 as Double Hit myeloma, and here we identify that hyperhaploid myeloma belongs to this poor prognosis group. The method of generating the hyperhaploid clone is through deletion of chromosomes, which may happen in a way that is similar to gain of chromosomes in hyperdiploid myeloma. These Double Hit patients may be good candidates for new therapies, but using next generation sequencing techniques researchers must be careful when normalizing data to correctly identify them as hyperhaploid rather than hyperdiploid, using copy number and B allele frequency data. DisclosuresDavies:MMRF: Honoraria; TRM Oncology: Honoraria; Janssen: Consultancy, Honoraria; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Abbvie: Consultancy; ASH: Honoraria. Morgan:Takeda: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria; Janssen: Research Funding; Celgene: Consultancy, Honoraria, Research Funding.

Molecular Resistance Mechanisms in Multiple Myeloma

Mechanisms of drug resistance in Multiple Myeloma (MM) are poorly understood. Mutations and/or changes in the protein expression of the CRBN pathway and proteasome subunits have been identified to induce resistance to IMiDs and PIs. However, only few patients are affected by these alterations. To determine the specific genomic fingerprint of MM relapse we selected 57 MM patients from the CoMMpass trial (version IA11) that have genomic data of paired samples available (diagnosis/relapse). 35 of them have also sequential FISH-seq data. We focused on acquired mutations in first relapse and filtered all mutations and genetic alterations already present at diagnosis. Doing so, we found 1.274 mutations, representing an average of 23 new mutations/patient (range; 2-76). Of interest, 66% of the acquired mutations were present in a sub-clonal level (Variant read frequency (VRF) < 25%).Most common mutations include known hotspots of the RAS pathway (NRAS 12%, KRAS 7% and BRAF 4%). Notably, all 7 NRAS mutations in relapse were located at Q61K, suggesting a functional role of disease progression for this specific and known hotspot location. In total 5 of 35 cases (14%) with FISH-seq data developed a 17p13 deletion in relapse. Of these, three patients acquired a bi-allelic alteration in addition to a preexisting TP53 mutation and one developed a biallelic inactivation of TP53 (VRF = 100%), through parallel acquisition of del17p and TP53 mutation. Gain of 1q21 was observed in relapse in 5 of 35 (14%) cases, and one 1q gain was lost from diagnosis to relapse. Two cases (4%) presented mutations in IMiD treatment related genes, with two mutations in the CRBN pathway. One harbored a missense mutation in the Lenalidomide (LEN) degron sequence of IKZF3 (G159A) (VRF = 36%), known to be essential for the IMiD action in vitro, 45 months after continuous exposition to LEN . The other case presented two subclonal frameshift mutations in CUL4B (VRF = 5% and 32%), detected after more than three years of LEN containing therapy. We functionally validated in vitro LEN resistance through CRISPR/Cas9 knockout of CUL4B, suggesting a resistance inducing effect of the acquired CUL4B mutations. Six cases (11%) harbored acquired mutations in proteasome subunit genes (PSMC2, PSMC6, PSMD8, PSME4, PSMB9 (two mutations)), all of them had undergone prior proteasome inhibitor (PI) containing therapy. We validated earlier the 19S protein subunits PSMC6 and PSMC2 (KO and/or point mutations) as inducers of PI resistance in vitro, thus we hypothesize contribution to resistance induction / disease progression through these 19s mutations.Remarkably ubiquitin (E3, E2 and SUBs) and histone related genes (histones and histone methylases and deacetylases) were found mutated in 51% and 19% of the relapsed patients. Genes for drug transporters (ATP-binding cassette (ABC) and Solute Carrier (SLC) transporters) were hit in 32% of cases and genes for mucins (previously related with genotoxic agents and immunotherapy resistance) in 19%. Notably, RRBP1 presented 10 mutations in 6 patients (11%) with the mutations clustering within 30 amino-acids (aa) of exons 9 and 10 and 3 hotspots (2 patients each) in aa Q426P, K430R and Q436P. RRBP1 is involved in the binding of the ribosome to the endoplasmic reticulum (ER) and is related with the unfolded protein response and ER stress via GRP78. All the patients with RRBP1 mutations were pretreated with PI inhibitors and exhibited worst survival outcome affecting PFS (Pval<0.001) and OS (Pval=0.0016) in this limited dataset. The mutations were detected on average 433 days (range: 258-568) after diagnosis. Five of the 6 patients died on average 180 days after RRBP1 mutation detection (range: 18-446) further suggesting high risk features of such acquired mutations.In summary, we observe clonal selection of known high-risk related alterations like TP53 mutations, 17p deletions or 1q13 in early relapse data of the CoMMpass trial. Furthermore we identify RRBP1 mutations as a new acquired high-risk biomarker of MM. Alterations are specifically related to subclonal selection by therapy, thus we suggest that the definition of high-risk disease in MM needs to be revisited and should also include clonal selection processes under anti-tumor therapy. DisclosuresNo relevant conflicts of interest to declare.

TP53 mutations are associated with mutated MYD88 and CXCR4, and confer an adverse outcome in Waldenström macroglobulinaemia.

Little is known about TP53 mutations in Waldenström Macroglobulinaemia (WM). We evaluated 265 WM patients for TP53 mutations by next-generation sequencing, and validated the findings by Sanger sequencing. TP53 mutations were identified and validated in 6 (2·6%) patients that impacted the DNA-binding domain. All six were MYD88- and CXCR4-mutated. Ibrutinib showed activity in patients carrying all three mutations. With a median follow-up of 18months, 2 (33%) with biallelic TP53 inactivation died of progressive disease. TP53 mutations are rare in WM, and associate with MYD88 and CXCR4 mutations. WM patients with TP53 mutations show response to ibrutinib.

Abstract IA28: The genomic landscape of SCLC and other neuroendocrine lung tumors

Abstract Small cell lung cancer (SCLC) accounts for 15% of all lung cancers and belongs to the family of pulmonary neuroendocrine tumors. This tumor subgroup additionally includes large-cell neuroendocrine carcinomas (LCNEC) and pulmonary carcinoids (PCA), which occur in 3% and 2% of all lung cancer patients, respectively. Carcinoids are usually clinically benign, whereas SCLC and LCNEC are characterized as high-grade malignant tumors. Clinical and histologic studies have suggested similarities between SCLC and LCNECs: Both lung tumor subtypes are associated with heavy smoking and reveal an aggressive tumor growth resulting in a poor overall survival. We have performed comprehensive and multilevel genomic profiling on SCLC and LCNECs, including genome, exome, and transcriptome sequencing. SCLC and LCNEC tumors reveal high mutation rates with an average of 9.5 and 8.6 mutations per megabase, respectively. Our sequencing studies have shown that SCLC tumors are characterized by bi-allelic inactivation of TP53 and RB1 in almost 100% of the cases. LCNECs, on the other hand, are composed of two mutually exclusive subgroups, which we categorized as “type I LCNECs” and “type II LCNECs.” Type I LCNECs harbor TP53 and STK11 or KEAP1 alterations. Despite sharing genomic alterations with lung adenocarcinomas and squamous cell carcinomas, type I LCNECs exhibit a high neuroendocrine profile with close similarity to SCLC tumors. Type II LCNECs harbor loss of TP53 and RB1, thus showing genetic resemblance to SCLC. However, these tumors were revealed to be different from the majority of SCLC with reduced levels of neuroendocrine markers, an upregulated immune signaling profile, and high activity of the NOTCH pathway. Conclusively, our studies show marked biologic differences between high-grade neuroendocrine lung tumors and we emphasize the precise distinction of LCNEC subgroups from SCLC. Citation Format: Julie George. The genomic landscape of SCLC and other neuroendocrine lung tumors [abstract]. In: Proceedings of the Fifth AACR-IASLC International Joint Conference: Lung Cancer Translational Science from the Bench to the Clinic; Jan 8-11, 2018; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(17_Suppl):Abstract nr IA28.

A high-risk, Double-Hit, group of newly diagnosed myeloma identified by genomic analysis

Patients with newly diagnosed multiple myeloma (NDMM) with high-risk disease are in need of new treatment strategies to improve the outcomes. Multiple clinical, cytogenetic, or gene expression features have been used to identify high-risk patients, each of which has significant weaknesses. Inclusion of molecular features into risk stratification could resolve the current challenges. In a genome-wide analysis of the largest set of molecular and clinical data established to date from NDMM, as part of the Myeloma Genome Project, we have defined DNA drivers of aggressive clinical behavior. Whole-genome and exome data from 1273 NDMM patients identified genetic factors that contribute significantly to progression free survival (PFS) and overall survival (OS) (cumulative R2 = 18.4% and 25.2%, respectively). Integrating DNA drivers and clinical data into a Cox model using 784 patients with ISS, age, PFS, OS, and genomic data, the model has a cumlative R2 of 34.3% for PFS and 46.5% for OS. A high-risk subgroup was defined by recursive partitioning using either a) bi-allelic TP53 inactivation or b) amplification (≥4 copies) of CKS1B (1q21) on the background of International Staging System III, comprising 6.1% of the population (median PFS = 15.4 months; OS = 20.7 months) that was validated in an independent dataset. Double-Hit patients have a dire prognosis despite modern therapies and should be considered for novel therapeutic approaches.

Molecular subtypes of diffuse large B cell lymphoma are associated with distinct pathogenic mechanisms and outcomes.

Diffuse large B cell lymphoma (DLBCL), the most common lymphoid malignancy in adults, is a clinically and genetically heterogeneous disease that is further classified into transcriptionally defined activated B cell (ABC) and germinal center B cell (GCB) subtypes. We carried out a comprehensive genetic analysis of 304 primary DLBCLs and identified low-frequency alterations, captured recurrent mutations, somatic copy number alterations, and structural variants, and defined coordinate signatures in patients with available outcome data. We integrated these genetic drivers using consensus clustering and identified five robust DLBCL subsets, including a previously unrecognized group of low-risk ABC-DLBCLs of extrafollicular/marginal zone origin; two distinct subsets of GCB-DLBCLs with different outcomes and targetable alterations; and an ABC/GCB-independent group with biallelic inactivation of TP53, CDKN2A loss, and associated genomic instability. The genetic features of the newly characterized subsets, their mutational signatures, and the temporal ordering of identified alterations provide new insights into DLBCL pathogenesis. The coordinate genetic signatures also predict outcome independent of the clinical International Prognostic Index and suggest new combination treatment strategies. More broadly, our results provide a roadmap for an actionable DLBCL classification.

Integrative genomic profiling of large-cell neuroendocrine carcinomas reveals distinct subtypes of high-grade neuroendocrine lung tumors

Pulmonary large-cell neuroendocrine carcinomas (LCNECs) have similarities with other lung cancers, but their precise relationship has remained unclear. Here we perform a comprehensive genomic (n = 60) and transcriptomic (n = 69) analysis of 75 LCNECs and identify two molecular subgroups: “type I LCNECs” with bi-allelic TP53 and STK11/KEAP1 alterations (37%), and “type II LCNECs” enriched for bi-allelic inactivation of TP53 and RB1 (42%). Despite sharing genomic alterations with adenocarcinomas and squamous cell carcinomas, no transcriptional relationship was found; instead LCNECs form distinct transcriptional subgroups with closest similarity to SCLC. While type I LCNECs and SCLCs exhibit a neuroendocrine profile with ASCL1high/DLL3high/NOTCHlow, type II LCNECs bear TP53 and RB1 alterations and differ from most SCLC tumors with reduced neuroendocrine markers, a pattern of ASCL1low/DLL3low/NOTCHhigh, and an upregulation of immune-related pathways. In conclusion, LCNECs comprise two molecularly defined subgroups, and distinguishing them from SCLC may allow stratified targeted treatment of high-grade neuroendocrine lung tumors.

New Insights into the Molecular Characteristics of Pulmonary Carcinoids and Large Cell Neuroendocrine Carcinomas, and the Impact on Their Clinical Management.

Carcinoids and large cell neuroendocrine carcinomas (LCNECs) are rare neuroendocrine lung tumors. Here we provide an overview of the most updated data on the molecular characteristics of these diseases. Recent genomic studies showed that carcinoids generally contain a low mutational burden and few recurrently mutated genes. Most of the reported mutations occur in chromatin-remodeling genes (e.g., menin 1 gene [MEN1]), and few affect genes of the phosphoinositide 3-kinase (PI3K)-AKT-mechanistic target of rapamycin gene pathway. Aggressive disease has been related to chromothripsis, DNA-repair gene mutations, loss of orthopedia homeobox/CD44, and upregulation of ret proto-oncogene gene (RET) gene expression. In the case of LCNECs, which present with a high mutation burden, two major molecular subtypes have been identified: one with biallelic inactivation of tumor protein p53 gene (TP53) and retinoblastoma gene (RB1), a hallmark of SCLC; and the other one with biallelic inactivation of TP53 and serine/threonine kinase 11 gene (STK11)/kelch like ECH associated protein 1 gene (KEAP1), genes that are frequently mutated in NSCLC. These data, together with the identification of common mutations in the different components of combined LCNEC tumors, provide further evidence of the close molecular relation of LCNEC with other lung tumor types. In terms of therapeutic options, future studies should explore the association between mechanistic target of rapamycin pathway mutations and response to mechanistic target of rapamycin inhibitors in carcinoids. For LCNEC, preliminary data suggest that the two molecular subtypes might have a predictive value for chemotherapy response, but this observation needs to be validated in randomized prospective clinical trials. Finally, delta like Notch canonical ligand 3 inhibitors and immunotherapy may provide alternative options for patient-tailored therapy in LCNEC.

Abstract 122: Comparative analysis of small cell lung cancer and other pulmonary neuroendocrine tumors

Abstract Small cell lung cancer (SCLC) is accounting for 15% of all lung cancer cases and belongs to the family of pulmonary neuroendocrine tumors. This tumor entity additionally includes large-cell neuroendocrine carcinomas (LCNEC) and pulmonary carcinoids (PCA), which occur in 3% and 2% of all lung cancer patients, respectively. Whereas lung carcinoids are clinically benign, SCLC and LCNEC are characterized as high-grade malignant tumors, which are associated with heavy smoking and a poor 5-year survival rate of less than 5%. We aimed to characterize the genomic alterations in neuroendocrine lung tumors and performed whole genome, whole exome and transcriptome sequencing on up to 110 SCLC cases, over 40 pulmonary carcinoids and over 50 LCNEC tumors. SCLC and LCNEC tumors reveal high mutation rates with an average of 9.5 and 8.6 mutations per megabase, respectively. Our sequencing studies have shown that SCLC tumors are characterized by bi-allelic inactivation of TP53 and RB1 in almost 100% of the cases. Recurrent significant genomic alterations affected among others TP73 (13%) and NOTCH family genes (25%). Further studies in a pre-clinical mouse model confirmed NOTCH as tumor suppressor and as a regulator of neuroendocrine differentiation in SCLC (George et al., 2015). LCNEC tumors showed TP53 and RB1 alterations in up to 20% of the cases. Additionally, LCNECs were found with STK11 and KEAP1 mutations, which occurred mutually exclusive to RB1 alterations (P&amp;lt;0.05). In light of these distinct mutational characteristics, we analyzed the transcriptome sequencing data of neuroendocrine lung tumors in comparison to lung adenocarcinomas (LUAD) and lung squamous cell carcinomas (LUSQ). An unsupervised clustering approach led to the identification of five expression clusters: LUAD, LUSQ and PCA formed distinct transcriptional classes; the majority of SCLC and LCNEC tumors clustered into two subgroups. While a few LCNEC tumors clustered with LUAD, LUSQ and PCA, RB1-mutated LCNEC tumors predominantly clustered with SCLC samples (P &amp;lt;0.05). KEAP1 and STK11 mutated LCNECs did not segregate with a particular histological tumor type. Neuroendocrine lung tumors confirmed high expression levels of neuroendocrine markers. A detailed comparison of LCNEC and SCLC tumors revealed that the majority of the SCLC and a few LCNEC tumors were found in a transcriptional group that showed high expression of the neuroendocrine lineage transcription factor ASCL1, gastrin releasing peptide (GRP) and DLL3, which was less pronounced in transcriptional subsets formed by the majority of LCNEC tumors. Patient tumors that clustered to the transcriptional class marked by higher ASCL1 expression were identified to have a worse overall survival (P&amp;lt;0.05). In summary, these findings point to transcriptional differences in SCLC and LCNEC tumors and emphasize the precise analysis of these histological tumor types with respect to their molecular biology and therapeutic treatment option. Citation Format: Julie George, Lynnette Fernandez-Cuesta, Vonn Walter, Neil Hayes, Roman Thomas. Comparative analysis of small cell lung cancer and other pulmonary neuroendocrine tumors. [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 122.

Comprehensive Genetic Characterization of 17p Deleted CLL Identifies Predictors of Overall Survival

Many studies have shown that deletion at chromosome 17p targeting the TP53 gene, or del(17p), is associated with poor prognosis in chronic lymphocytic leukemia (CLL). Despite this, not all del(17p) CLL progresses rapidly to treatment and has short survival. We hypothesized that other coexisting genetic aberrations may contribute to the poor clinical outcome and heterogeneity of del(17p) cases. To assess this, we analyzed copy number alterations (CNAs) using Affymetrix SNP array data from 200 CLL patients (55 with del(17p)), and somatic mutation profile by whole exome sequencing (WES; Illumina) in 168 patients (53 with del(17p)). Ninety-nine patients were studied with both SNP arrays and WES, 39 of whom had del(17p).Analysis of copy number showed that del(17p) CLL had a median of 11 CNA events, mostly copy number losses, compared to 2 events in WT CLL (p=1.1E-12). Both the number of CNA events (p=7.9E-10) and the total length of copy number gain (p=1.6E-4) or loss (p=1.3E-8) were associated with shorter overall survival (OS), even when controlling for co-existing del(17p). Using GISTIC analysis, we discovered three significantly deleted regions specific to del(17p), namely large chromosomal deletions at 3p, 4p, and 9p. These novel recurrent deletions were rarely seen in wild type CLL and the presence of any of these deletions with del(17p) was strongly associated with shorter OS compared to del(17p) alone. We were able to evaluate complex karyotype (CKT) determined by stimulated metaphase cytogenetics in a subset of the cohort, and found that 17/27 (63%) evaluable del(17p) CLLs had CKT, while only 13/58 (22%) WT CLLs did (p=0.0005). Analysis of OS showed that del(17p) and CKT together had worse OS than either individually, although individually they each conferred OS worse than WT (p<0.0001).Next we tested whether the number and size of CNAs predicts progression to treatment in 138 patients who were treatment naïve at sampling (n=23 for del(17p)), 65 of whom progressed to treatment after sampling (n=16 for del(17p)). We found that increasing number of CNA events (p=9E-6), total length of losses (p=4E-4), and total length of gains (p=2E-4) were all predictive of need for future treatment in WT as well as del(17p) CLL. Those treatment naive del(17p) patients who remained untreated had a median of 4 CNAs (n=7), compared to 12.5 for those who went on to treatment (p=0.013).Turning our attention to somatic mutation analysis, del(17p) CLLs had higher numbers of total somatic mutations (21 vs 18, p=0.0046), and nonsynonymous mutations (16 vs 13, p=0.0059) than WT CLL, with no difference in subclonal mutations (12 vs 10, p=0.34). Increasing number of total mutations (p=0.0017) and nonsynonymous mutations (p=0.0003) were both associated with shorter OS, even when controlling for 17p deletion. No significant correlation was observed between number of CNAs and number of somatic mutations in a given CLL, suggesting different mechanisms involved in their causation. In the del(17p) CLLs, as expected, TP53 was the most commonly mutated gene, seen in 43/53 (81%) patients. Interestingly, most TP53 mutations (78%) were clonal, suggesting they occurred early in CLL development, while SF3b1 assessed for comparison was most commonly subclonal (68%). Patients with subclonal TP53 mutations had longer OS than patients with clonal TP53 mutations (p=0.041).We also explored the outcome of patients with loss of both TP53 alleles via 17p deletion and TP53 mutation. We found that biallelic loss was associated with more CNAs (median = 15 vs 9) and longer total length of CNAs than monoallelic loss. Patients with biallelic loss of TP53 showed a trend towards worse OS than the limited cohort with monoallelic loss (p=0.07).In addition to TP53 and other previously reported CLL drivers such as NOTCH1 (n=8, q=8E-5) and DDX3X (n=4, q=0.03), we report RPS15 (n=6, q=6E-5) and GPS2 (n=3, q=0.03) as novel significantly mutated genes in del(17p) CLL but not in WT CLL. All RPS15 mutations were clustered in a 15-amino acid region in the far carboxyl terminus of the gene, suggesting conserved functions. RNA sequencing (n=2) confirmed that the mutant alleles of RPS15 were expressed in the CLLs. We conclude that the genetic profile of del(17p) CLL differs significantly from WT CLL and show that worse OS in del(17p) CLL is associated with complex karyotype and biallelic inactivation of TP53 as well as increasing number of somatic mutations and novel CNAs. DisclosuresNo relevant conflicts of interest to declare.