Hyperactivation of the Hedgehog (Hh) pathway, which controls refueling of multiple myeloma (MM) clones, might be critical to disease recurrence. Although several studies suggest the Hh pathway is activated in CD138- immature cells, differentiated CD138+ plasma cells might also be able to self-renew by producing themselves the Hh ligands. We studied the gene expression profiles of 126 newly diagnosed MM patients analyzed in both the CD138+ plasma cell fraction and CD138-CD19+ B-cell compartment. Results demonstrated that an Hh-gene signature was able to cluster patients in two subgroups characterized by the opposite Hh pathway expression in mature plasma cells and their precursors. Strikingly, patients characterized by Hh hyperactivation in plasma cells, but not in their B cells, displayed high genomic instability and an unfavorable outcome in terms of shorter progression-free survival (hazard ratio: 1.92; 95% confidence interval: 1.19-3.07) and overall survival (hazard ratio: 2.61; 95% confidence interval: 1.26-5.38). These results suggest that the mechanisms triggered by the Hh pathway ultimately led to identify a more indolent vs a more aggressive biological and clinical subtype of MM. Therefore, patient stratification according to their molecular background might help the fine-tuning of future clinical and therapeutic studies.

The iperactivation of Hedgehog (Hh) pathway, which controls the refuel of the tumor clone, might be critical to Multiple Myeloma (MM) recurrence. In order to dissect the role played by Hh pathway in different MM cells compartments, and to evaluate its clinical impact on patient outcomes, here we explore the transcriptomic and genomic profiles in both CD138+ plasma cells and CD138-19+ B progenitors cells obtained from newly diagnosed MM patients (pts), The study included a cohort of 126 pts, homogenously treated with bortezomib-based regimens and ASCT. DNA and RNA were obtained from both CD138+ plasma cell fraction and CD19+ B cells. Gene expression profiling (GEP) (HG U133 Plus 2.0) and genomic analysis (SNP 6.0) were performed on Affymetrix platform. Data were analysed by employing several software: dChip (GEP clustering), Ingenuity Pathway Analysis (GEP Enrichment) and Nexus (Copy number). By unsupervised hierarchical clustering, an Hh signature of 10 genes - SHH, IHH, DHH, SMO, PTCH1, PTCH2, SUFU, GLI1, GLI2 and GLI3 - was identified, and it was able to significantly cluster pts in two subgroups: cluster 1 (70 pts) and cluster 2 (56 pts. An overall significant activation of Hh pathway was shown in cluster 2, as compared to cluster 1. Of note, the Hh pathway was down regulated in CD19+ B cells obtained from pts included in cluster 2, while it was overexpressed in cluster 1 pts. Western blots on both cell fractions confirmed this opposite Hh genes behavior. A higher genomic instability (e.g. higher frequencies of both t(4;14) and del(17p)) was demonstrated in CD138+ cells from cluster 2 pts and, at least 5 known tumor suppressor genes, such as RB1, BRCA2, PDX1, FOXO1 and TP53 were affected. Conversely, cluster 1 pts were mainly characterized by hyperdiploid karyotypes. The more aggressive phenotype of cluster 2 pts was confirmed by an overall deregulation of cell adhesion processes (CD44, LIMS1, COL4A2, CTGF, COL1A1, FN1), increased proliferation (MYCBP, IL22, SDPR, SOX2, SOX6) and DNA repair mechanisms (SP1, SMARCD3, FOXA3). Hh pathway activation significantly influenced pts’ outcome, since those included in cluster 2 had a shorter PFS and OS compared to cluster 1. In fact, the 5-year PFS estimates were 31% vs 56% (p = 0.0062), whereas the OS probabilities were 66% and 83%, respectively (p = 0.0071). Of note, both hazard ratios for PFS and OS were doubled in pts included in cluster 2, as compared to pts included in cluster 1. Finally, multivariate analyses confirmed that being part of cluster 2 was an independent prognostic factor for both PFS and OS, along with del(17p) and ISS 3. Two alternate Hh-driven subtypes of MM might be identified at diagnosis, which correlated with pts outcomes. Stratification of pts according to their molecular background might help the fine-tuning of future clinical studies. Acknowledgements: FP7 NGS-PTL project, Progetto Regione-Università 2010/2012 L. Bolondi. Citation Format: Marina Martello, Daniel Remondini, Enrica Borsi, Mauro Procacci, Barbara Santacroce, Angela Flores Dico, Annalisa Pezzi, Elena Zamagni, Paola Tacchetti, Lucia Pantani, Giulia Marzocchi, Serena Rocchi, Katia Mancuso, Beatrice Anna Zannetti, Giovanni Martinelli, Michele Cavo, Carolina Terragna. The alternate activation of hedgehog pathway, either in CD138+ or in CD138-CD19+ multiple myeloma primary cells, impacts on disease outcome. [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 3189.

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INTRODUCTION. Although remarkable advances have been reported in Multiple Myeloma (MM) therapy, mainly due to the introduction of novel agents, the disease remains incurable in most of the patients. The iperactivation of self-renewal mechanisms, like Hedgehog (Hh) pathway, which controls the refuel of the tumor clone, might be critical to disease recurrence. Whilst several studies suggestthatHh pathway is activated in the putative CD138- Myeloma Propagating Cells (MPCs), it is likely that also terminally differentiated CD138+ plasma cells might contribute to drug resistance, by reverting to an immature phenotype. AIM. In order to dissect the role played by Hh pathway in different MM cells compartments, and to evaluate the impact of Hh pathway expression on patientsÕ clinical outcomes, a high-throughput molecular characterization was employed to explore the transcriptomic and genomic profiles in both CD138+ plasma cells and CD138-19+ B cells progenitors obtained from newly diagnosed MM patients. PATIENTS AND METHODS. The study included a cohort of 126 patients, homogenously treated with bortezomib-based regimens and ASCT, who were randomly included in a training set and a test set. For each patient, the CD138+ plasma cell fraction was isolated by immunomagnetic beads method; CD19+ B cells were isolated in 18 patients. Gene expression profiling (GEP) (HG U133 Plus 2.0 chip) and genomic analysis (SNP 6.0 chip) were performed on Affymetrix platform. dChip analysis software was used to perform GEP clustering. Expression data were analyzed by Ingenuity Pathway Analysis software and were validated by Western Blot assays. Copy number analysis was carried out using Nexus Copy Number software. RESULTS. The expression of Hh pathway genes resulted deregulated in both CD138+ and CD19+ cells, as compared to their normal counterparts. By unsupervised hierarchical clustering, an Hh signature of 10 genes - SHH, IHH, DHH, SMO, PTCH1, PTCH2, SUFU, GLI1, GLI2 and GLI3 - was identified, and was able to significantly cluster patients in two subgroups: cluster 1 included 39 patients while 37 were included in cluster 2. Clustering robustness was validated in an independent cohort of 50 patients (test set), of whom 31 were assigned to cluster 1 and 19 to cluster 2. An overall significant activation of Hh pathway was shown in cluster 2, as compared to cluster 1. Of note, the Hh pathway was down regulated in CD19+ B cells obtained from patients included in cluster 2, while it was overexpressed in cluster 1 patients. Western blots on both cell fractions confirmed this opposite Hh genes behavior. Peculiar genomic and transcriptomic profiles characterized patients included in clusters 1 and 2: indeed, a higher genomic instability (e.g. higher frequencies of both t(4;14) and del(17p)) was demonstrated in CD138+ plasma cells from cluster 2 patients and, at least 5 known tumor suppressor genes, such as RB1, BRCA2, PDX1, FOXO1 and TP53 were included in deleted regions. Conversely, cluster 1 patients were mainly characterized by hyperdiploid karyotypes. The more aggressive phenotype of cluster 2 patients was confirmed by an overall deregulation of cell adhesion processes (CD44, LIMS1, COL4A2, CTGF, COL1A1, FN1), increased proliferation (MYCBP, IL22, SDPR, SOX2, SOX6) and impaired DNA repair mechanisms (SP1, SMARCD3, FOXA3). Hh pathway activation significantly influenced patientsÕ outcome, since those included in cluster 2 had a shorter PFS and OS compared to cluster 1. In fact, the 5-year PFS estimates were 31% vs 56% (p=0.0062), whereas the OS probabilities were 66% and 83%, respectively (p=0.0071) (Fig.1,2). Of note, both hazard ratios for PFS and OS were doubled in patients included in cluster 2, as compared to patients included in cluster 1. Finally, multivariate analyses confirmed that being included in cluster 2 was an independent prognostic factor for both PFS and OS, along with del(17p) and ISS 3 (Tab. 1). CONCLUSION. Sorts of Òying -yang Ó effect of Hh pathway between mature CD138+ plasma cells and immature CD138-CD19+ MPCs could be hypothesized, where two alternate Hh-driven subtypes of MM at diagnosis correlated well with patientsÕ outcomes. Stratification of patients according to their molecular background might help the fine-tuning of future clinical studies. Supported by Regione-Universita 2010-12 (L. Bolondi), FP7 NGS-PTL project. Disclosures Zamagni: Celgene Corporation: Honoraria, Speakers Bureau; Janssen Pharmaceuticals: Honoraria, Speakers Bureau; Amgen: Honoraria, Speakers Bureau. Martinelli:AMGEN: Consultancy; BMS: Consultancy, Speakers Bureau; Ariad: Consultancy; Novartis: Consultancy, Speakers Bureau; MSD: Consultancy; Pfizer: Consultancy; ROCHE: Consultancy. Cavo:Janssen-Cilag, Celgene, Amgen, BMS: Honoraria.

INTRODUCTION In newly diagnosed Multiple Myeloma (MM) patients (pts), Copy Number (CN) losses of chromosome 17p13, carrying the TP53 tumor-suppressor gene, are strong predictors of poor outcomes. On the contrary, the prognostic relevance of TP53 mutations at the onset of the disease is less clear, due to the very limited frequency of clonal lesions, as revealed by Sanger sequencing. To address this poorly investigated issue, we used an ultra-deep sequencing (UDS) approach to characterize the TP53 structural architecture in both newly diagnosed and relapsed MM pts and to assess the prognostic role and evolution over time of small TP53 mutated sub-clones. SAMPLES AND METHODS A cohort of 99 newly diagnosed MM pts treated up-front with bortezomib-based regimens and autologous stem cell transplantation, was included in this molecular study. In 29 cases, samples were collected both at diagnosis and at relapse(s). DNA was obtained from CD138+ highly purified plasma cells. TP53 gene mutational status was analysed by using an amplicon-targeted UDS approach (GSJ, 454 Roche Life Sciences). In order to discriminate between low frequency sub-clonal TP53 variants and sequencing errors, sequencing raw data were filtered according to cut-off values based on different ranges of sequences' coverage depth. Additional filters were also applied, based on both quality and biological cut-offs, to obtain a final confident list of variants. Analysis of CN alterations (CNAs) was performed by SNPs array and results analysed with ChAS software. RESULTS With a median coverage of 1386X, a list of 129 correctly called TP53 variants (either missense, or nonsense or splice ones), including 20 INDELs, was detected. Only deleterious and N/A variants (according to SIFT classification) were included in the list. Most newly diagnosed MM pts (55%) carried at least one TP53 sub-clonal variant (on average 1.08 variants per pts), with 45/99 (45%) carrying non-mutated TP53. Pts carrying TP53 sub-clonal variants bared also TP53 CN hemizygous losses (20%), CKS1B gains (56%) and cdkn2c losses (14%). According to TP53 sub-clonal mutational load, pts were stratified in two sub-groups, including 28 pts with ≥2 (high load) and 71 with <2 variants (low load), respectively. Eleven out of 129 variants were recurrent (RVs), as being detected in at least 3% of pts, with Variants Allele Frequencies (VAFs) ranging from 0.24 to 70.1% (median 0,53%); RVs were observed in 29 pts. The clinical impact of the TP53 sub-clonal mutational load, as well as of variants recurrence, was evaluated in 90/99 MM (median follow up = 70 months). Results of statistical analysis are summarized in Table 1. Pts carrying either high TP53 sub-clonal mutational load or RVs had significantly shorter OS and OS after relapse, as compared to the others, while no difference between these two groups was seen regarding PFS and TTP. Multivariate analysis showed that high TP53 mutational load, as well as the presence of TP53 RVs, both resulted independent factors adversely affecting OS and OS after relapse (Table 2). Of note, none of the detected genomic aberrations significantly influenced the response to front-line induction therapy. The distribution of both TP53 sub-clonal variants and genomic CNAs was overall modified in samples collected at relapse(s): 90% of relapsed pts carried at least one sub-clonal variant (on average 1.63 variants per pts) with 3/29 (10%) relapsed MM carrying non-mutated TP53. Moreover, 5 different sub-clonal lesions proved a linear increment of both TP53 VAFs (from 29.4% to 54.6%; from 7.8% to 12.4%; from 0.5% to 4.3%) and TP53 CN loss smooth signal (from 7% to 89% and from 50% to 100%), as evaluated in longitudinally collected samples. CONCLUSIONS The UDS analysis of TP53 coding sequence in newly diagnosed MM highlighted for the first time a high rate of variants, recurring with a wide range of frequencies among samples. The increased number of TP53 sub-clonal variants per pts in samples collected at relapse(s), compared to that seen at the onset of the disease, suggests a sub-clonal dynamics over time. This finding might explain the adverse impact of high TP53 sub-clonal mutational load and TP53 RVs on OS, due to a shorter OS after relapse. Acknowledgments: Roche Diagnostics for applicationsupport in the realization of this project. Disclosures Zamagni: Celgene Corporation: Honoraria, Speakers Bureau; Janssen Pharmaceuticals: Honoraria, Speakers Bureau; Amgen: Honoraria, Speakers Bureau. Martinelli:Novartis: Consultancy, Speakers Bureau; BMS: Consultancy, Speakers Bureau; Ariad: Consultancy; AMGEN: Consultancy; ROCHE: Consultancy; Pfizer: Consultancy; MSD: Consultancy. Cavo:Janssen-Cilag, Celgene, Amgen, BMS: Honoraria.