Treatment Research for Multiple Myeloma (MM)

Treatment Research for Multiple Myeloma (MM) ABSTRACT INTRODUCTION Multiple myeloma (MM) is a hematological neoplasm that appears after transformation and uncontrolled proliferation of plasma cells. MM is characterized by a heterogeneous genetic aberrations and very different clinical outcomes (Avet-Loiseau, . Boyd KD). Although treatments for MM have increased by develop new and more sophisticated therapeutics drugs such an immunomodulatory drugs (IMiDs) and proteasome inhibitors (PIs)( Shaji K. Kumar, Raghupathy, Antonio Palumbo) the marked variability of responses indicate that larger studies will be required. Currently, identification of cytogenetic abnormalities is performed by conventional karyotyping and fluorescence in situ hybridization. However, these disorders are not sufficient to explain the malignant phenotype given that are also observed in premalignant states of MM such a monoclonal gammopathy of unknown significance (MGUS) or smoldering myeloma(SM)(Bergsagel, Hideshima). This finding justifies the need for a comprehensive screening of genetic abnormalities in MM patients, which has not been incorporated in the medical workup yet. Recently, the introduction of massive sequencing of patient genome using next-generation sequencing (NGS) technologies has considerably increased the understanding of the biological features of MM. Many works have described the complex and heterogeneous mutational profile of MM patients(bolli nuevo, walker). Whole Exome Sequency (WES) studies in newly diagnosis MM patients have confirmed that more than 50 genes are mutated in the first manifestation of disease (walker). However, only few genes have been detected recurrently mutated at diagnosis, including KRAS, NRAS, BRAF, DIS3, TP53 and FAM46C, and only TP53 mutations have been recurentelly associated with poor survival. In addition, other studies have assessed the clonal evolution over time, pointing out that systemic treatment with chemotherapy may affect the livelihood of some subclones more than others, and thus may influence the tumor evolution over time(Egan JB, bolli, Keats JJ, ) The introduction of targeted studies allows the detection of mutations even with very low allele frequencies at an affordable price, allowing the incorporation of extensive genetic studies to the clinical workup. In the last years, several groups have applied this approach in order to achieve a better patients stratification and prognosis prediction. Although many studies have highlighted the importance of the subclonal landscape in MM and many efforts have been undertaken to stratify patients and predicts their responses, there is no clear relation between sensitives or refractories clones to treatment, and more information about the prognostic impact of this subclonal profile in series of homogeneously treated MM patients is needed. A large number of clinical trial are being carried out with this aim, unify treatments in order to study more effectively the impact of genetic alterations in prognosis. In this work, newly diagnosis MM patient homogenously treated have been genetically characterized using a combination of the most recent techniques, including FISH and ultra-deep targeted sequencing. We applied the highest read depth described in the literature with the aim to detect minority subclones ignored to date. We also integrated these data with the clinical features to find out new patterns of behavior, relate them with survival and reveal new insight into the complexity of clonal and subclonal architecture of MM. Patient samples Samples were taken from the available 79 newly diagnosed MM patient enrolled in the clinical trial GEM10MAS65 (registered at www.clinicaltrials.gov as #NCT01237249). This is a phase III trial where patients older than 65 years were randomly assigned between two treatments arms: sequential melphalan/prednisone/Velcade (MPV) followed by Revlimid/low dose dexamethasone (Rd) versus alternating melphalan/prednisone/Velcade (MPV) with Revlimid/low dose dexamethasone (Rd). Progression free survival (PFS) and overall survival (OS) were measured from the starting date of the treatment. The median time to progression was 26.4 months with a median follow up of 31.5 months. Targeted sequencing and mutation calling Positive plasma cells CD138 were isolated from bone marrow aspirates and DNA was extracted using AllPrep DNA/RNA mini kit (Quiagen). Only 20 ng of DNA were used to prepare libraries using Ampliseq Library Kit 2.0. We also sequenced DNA from the 15 available CD138 negative fractions in order to filter out potential artifacts and corroborate detection sentivity. Samples were sequenced using Ion Torrent platform (IonProton, Thermofisher, Carlsbad, CA, USA) using the M3P gene panel (Mayo Clinic, Arizona). This panel spreads out over 77 genes frequently mutated in MM, which are related to critical pathophysiological pathways, associated to drug resistance or targetable with molecular drugs [paper mayo kortum etal]. Quality filter and alignments was performed using Torrent Suit software (Life Technologies) Single nucleotide variants were calling and annotated using Ion Reporter software applying in-house modifications in call variants process. Variants listed in Single Nucleotide Polimorph ism database (dbSNP, http://www.ncbi.nlm.nih.gov/SNP/) were excluded from samples without germline available, as well as variants that were detected in multiple samples. In addition, to test the ability of the workflow previously described, we applied a novel bioinformatics pipeline developed by Spanish National Cancer Research Centre (CNIO). All reported mutations were detected by both bioinformatics approaches. The integrative genomic viewer (IGV) was used to visualize the read alignments, single variants and correct sequencing errors due to homopolymer regions. Statistical analysis All statistical analysis was performed using the statistical environment R. Correlation coefficients between mutated genes and cytogenetic aberrations was assessed and plotted using corrplot (https://cran.r-project.org/web/packages/corrplot/). Differences in survival were tested using the log-rank test. Cox proportional hazard regression was employed to obtain hazards ratios (HR) and evaluated at 5% of significance level. A second approach called LASSO (least absolute shrinkage and selection operator) was performed to detect relevant variables among clinical, cytogenetic and mutated genes.   Further details can be found in Data Supplement. RESULTS Mutated genes and altered pathways (cambiar tà ­tulo) We sequenced 79 tumor samples with a mean coverage depth of 1600X. The minimum coverage of the detected variants was 60X and the average coverage 370X. We identified 170 nonsynonymous missense/nonsense/stoploss single variants, 81 of them (48%) were predicted pathogenic by Sift and Polyphen and 61 (36%)   have been described in COSMIC data base.   85% of patients harbored at least 1 mutation with a median of 2.1 mutations per patient. We detected mutations in 53 genes (Figure1), although 6 genes accounted the 39% of the total number of mutations; KRAS= 21.5%, DIS3= 19%, NRAS= 16.5%, BRAF= 10.1%, TP53= 8.8% and ATM= 7.6% of the patients.  Ã‚   48% of patients (38/79) presented at least one mutation in genes envolved in RAS/MAPK pathway, being the most frequently mutated pathway. 72 and 100% of variants in KRAS and NRAS respectively were detected in the hotspot codons 12, 13 and 61, and the targetable V600E BRAF mutation was detected in 1 patient. (Figure 2 supplemental?). NFKB p athway was the second most frequently mutated in our cohort, accounting for the 15% of all mutations distributed in 25 % of the patients (19/79). This pathway included TRAF3 (5 mutations, one nonsense and 4 missense) and TRAF2 (3 missense mutation) all of them predicted pathogenic by Sift and Polyphen. Other pathways importantly altered in the cohort were MYC in 11% of patients (9/79), cereblon and ciclyn both in 9% of patients (7/79). Multiple mutations within the same gene were observed in 11 patients: 4 of whom harbored 2 and 3 mutations within DIS3 (patient 1-24= Glu501Lys and Phe120Leu at 8 and 53 % of VRF, patient 2-236= Asp487His and Asp479Glu at 4 and 21 of VRF respectively, patient 3 321= Tyr753Asn and Glu126Lys at 2 and 58 % of VRF respectively and patient 4-42 = Arg820Trp, Gly249Glu and at 14, 20 and 24% of VRF respectively). Other 2 patients showed 2 and 3 mutation in KRAS (patient 5-168= Gly13Asp and Gln61His at 9 and 13 %of VRF and patient 6-269 = Tyr71Asp, UTR3 in exon 6 and Gln61Glu at 3, 15 and 37 % of VRF respectively; 2 patients with 2 mutation in NRAS(patient 7-177= .Gln61Lys   and Gly12Ala at 5 and 12% of VRF respectively and patient 8-257=   Gln61Glu   and Gly12Ala at 5 and 6% of VRF respectively), one patient with 3 mutations in MAX (patient 9-190= Arg36Lys , Arg35Leu and Glu32Val at 10, 20 and 26 %of VRF), one patient with 3 mutations in TRAF3 (patient 10-40 = Lys453Asn, His136Tyr   and Phe445Leu 8, 11 and1 3% of VRF) and one patient with 2 mutation in TP53 (patient 11-40 = Asp208Val   and Glu204Ter at 35 and 36% of VRF respectively). Variant Read Frequency study The VRF found in our cohort were diverse.   We detected 50% of variants (85/170) below 25% of VRF and 27% (46/170) below 10% (Figure 2). KRAS (n=5) DIS3 (n=5), BRAF (n=4), NRAS (n=4), and TP53 (n=3) were the genes that harbored the largest number of low frequency mutations (Figure 2). KRAS, NRAS, BRAF and TP53 mutations were, in all cases, lower than 50% of VRF while DIS3 showed mutations in a broad range (from 2 to 85%)(Fig3). ). Most of DIS3 mutations with VRF