Citations

Detection of mutations in the rpoB gene of Mycobacterium tuberculosis can be used as an accurate predictor of rifampin resistance in the majority of strains tested. Simple but highly accurate screening methods must be developed for the detection of these mutations. Either DNA sequence analysis or single-strand conformation polymorphism (SSCP) screening can be used to detect rpoB mutations, but these techniques either are expensive or yield results that may prove difficult to interpret when used in a clinical setting. This report describes the use of dideoxy fingerprinting (ddF) as a postamplification screening method to identify rifampin-resistant genotypes. The ddF protocol was performed on the amplified rpoB fragment with no preparatory steps, thus making ddF practical for laboratories equipped for polyacrylamide gel electrophoresis. When compared with the results of SSCP analysis, ddF results were more easily interpreted and contained more sequence-dependent information that facilitated differentiation of functionally significant and silent mutations. The ddF method was used for genotypic determination of rifampin susceptibility of 20 multidrug-resistant strains of M. tuberculosis. The results of this analysis were concordant with DNA sequence analysis and conventional clinical laboratory methods.

Recent analysis of the gene encoding the beta subunit of Mycobacterium tuberculosis RNA polymerase (rpoB) has demonstrated a small region that harbors the mutations most frequently associated with rifampin resistance. Earlier reports have described a high degree of sequence conservation of rpoB among mycobacteria other than M. tuberculosis and other GC-rich bacteria that can lead to false-positive amplification when applied directly to clinical specimens. We developed reagents for PCR amplification that are based on signature nucleotides discovered by comparative sequence analysis of the rpoB genes of organisms phylogenetically related to M. tuberculosis. The specificities of the reagents were challenged with 20 isolates of multiple-drug-resistant M. tuberculosis and more than 20 species of mycobacteria other than M. tuberculosis and other GC-rich organisms. A single-tube heminested PCR protocol was devised to obtain sensitivity equal to those of an IS6110-based PCR assay and culture in spiked sputum experiments. The assay correctly identified 21 of 24 (87.5%) culture-positive specimens, 13 of which were acid-fast smear-negative, in a panel of 51 clinical specimens. Three specimens that were false-positive initially were negative upon repeat testing when the assay was modified to eliminate the potential for aerosol carryover of the first-round amplification product during the open-tube addition of the second set of reaction reagents. This assay is the most sensitive and specific test to date for the direct detection of M. tuberculosis rpoB in clinical specimens. This rapid PCR-based assay can be used for the simultaneous identification of M. tuberculosis and its rifampin susceptibility genotype.

Pericytes are branched cells located in the wall of capillary blood vessels that are found throughout the body, embedded within the microvascular basement membrane and wrapping endothelial cells, with which they establish a strong physical contact. Pericytes regulate angiogenesis, vessel stabilization, and contribute to the formation of both the blood-brain and blood-retina barriers by Angiopoietin-1/Tie-2, platelet derived growth factor (PDGF) and transforming growth factor (TGF) signaling pathways, regulating pericyte-endothelial cell communication. Human pericytes that have been cultured for a long period give rise to multilineage progenitor cells and exhibit mesenchymal stem cell (MSC) features. We focused our attention on the roles of pericytes in brain and ocular diseases. In particular, pericyte involvement in brain ischemia, brain tumors, diabetic retinopathy, and uveal melanoma is described. Several molecules, such as adenosine and nitric oxide, are responsible for pericyte shrinkage during ischemia-reperfusion. Anti-inflammatory molecules, such as IL-10, TGFβ, and MHC-II, which are increased in glioblastoma-activated pericytes, are responsible for tumor growth. As regards the eye, pericytes play a role not only in ocular vessel stabilization, but also as a stem cell niche that contributes to regenerative processes in diabetic retinopathy. Moreover, pericytes participate in melanoma cell extravasation and the genetic ablation of the PDGF receptor reduces the number of pericytes and aberrant tumor microvessel formation with important implications for therapy efficacy. Thanks to their MSC features, pericytes could be considered excellent candidates to promote nervous tissue repair and for regenerative medicine.

Abstract Patients with acute myeloid leukemia (AML) harboring FLT3 internal tandem duplications (ITDs) have poor outcomes, in particular AML with a high (≥0.5) mutant/wild-type allelic ratio (AR). The 2017 European LeukemiaNet (ELN) recommendations defined 4 distinct FLT3-ITD genotypes based on the ITD AR and the NPM1 mutational status. In this retrospective exploratory study, we investigated the prognostic and predictive impact of the NPM1/FLT3-ITD genotypes categorized according to the 2017 ELN risk groups in patients randomized within the RATIFY trial, which evaluated the addition of midostaurin to standard chemotherapy. The 4 NPM1/FLT3-ITD genotypes differed significantly with regard to clinical and concurrent genetic features. Complete ELN risk categorization could be done in 318 of 549 trial patients with FLT3-ITD AML. Significant factors for response after 1 or 2 induction cycles were ELN risk group and white blood cell (WBC) counts; treatment with midostaurin had no influence. Overall survival (OS) differed significantly among ELN risk groups, with estimated 5-year OS probabilities of 0.63, 0.43, and 0.33 for favorable-, intermediate-, and adverse-risk groups, respectively (P < .001). A multivariate Cox model for OS using allogeneic hematopoietic cell transplantation (HCT) in first complete remission as a time-dependent variable revealed treatment with midostaurin, allogeneic HCT, ELN favorable-risk group, and lower WBC counts as significant favorable factors. In this model, there was a consistent beneficial effect of midostaurin across ELN risk groups.

Introduction: Despite advances in treatment strategy, multiple myeloma (MM) remains a challenging, incurable disease as patients (pts) often relapse. Here we describe preclinical data of a novel cereblon (CRBN) E3 ligase modulatory compound (CELMoD), iberdomide (IBER; CC-220), as well as clinical and translational data from a current phase 1/2 clinical trial (NCT02773030). These data support a differentiated profile for IBER from immunomodulatory agents (IMiD agents), and development of IBER as a foundation of combination therapy for the treatment of relapsed/refractory MM (RRMM). Methods: Preclinical analyses were performed on pomalidomide (POM)-sensitive and acquired-resistant MM cell lines and peripheral blood mononuclear cells from healthy volunteers. Eligible pts enrolled on the clinical trial had RRMM and had received ≥ 2 prior regimens, containing at least an IMiD agent and proteasome inhibitor (PI), and had experienced disease progression within 60 days of last MM therapy. Escalating doses of IBER were given on Days 1-21, in combination with dexamethasone (DEX; 40 mg; 20 mg in pts aged > 75 years) on Days 1, 8, 15, and 22, of each 28-day cycle. Immunohistochemistry (IHC) data on CRBN, Ikaros, Aiolos, and ZFP91 expression was obtained from pt bone marrow specimens at screening and at Cycle 2, Day 15. Immune profiling was evaluated by flow cytometry of peripheral blood at Cycle 1, Day 1 and Cycle 2, Day 15. Primary objectives were to evaluate maximum tolerated dose (MTD), recommended phase 2 dose (RP2D), safety, and preliminary efficacy. Results: Preclinically, IBER exhibited potent tumoricidal anti-MM and immunostimulatory activity. IBER was more potent than lenalidomide (LEN) or POM in overcoming the immunosuppressive activity of bortezomib (BORT), inducing a more than twofold increase in T cell proliferation in the presence of BORT. IBER also enhanced the antibody-dependent cell-mediated cytotoxicity and complement-dependent cytotoxicity of daratumumab (DARA), leading to deeper responses than combinations with LEN or POM. Further differentiating from both LEN and POM, IBER showed activity alone and in combination with BORT or DARA in POM-resistant MM cell lines. As of June 28, 2019, 69 pts with RRMM had received IBER + DEX. Median age was 65 years (range 33-80), and median number of prior regimens was 5 (range 2-12). Prior therapies included autologous stem cell transplantation (80%), LEN (100%), POM (70%), PIs (100%), DARA (71%) and anti-BCMA (6%), including CAR T cells. Clinical activity was observed across all dose levels with an overall response rate (ORR) of 29%, clinical benefit rate of 45%, and disease control rate of 80%. ORR was 30%, 28% and 29% in IMiD agent-refractory, DARA-refractory and Quad-class (IMiD agents/PIs/steroids/CD-38 antibodies)-refractory pts, respectively. IBER + DEX showed a favorable safety profile, with grade 3-4 neutropenia, infections, and thrombocytopenia, occurring in 29%, 25%, and 12% of pts, respectively. To date, IBER doses range from 0.3 to 1.3 mg and the MTD/RP2D has not yet been reached. Pharmacodynamic immunophenotyping changes were dose-dependent. Treatment with IBER (at doses > 0.75mg) + DEX doubled the percentage of proliferating T cells and NK cells (P < 0.001 for each) and increased activated and effector memory CD8+ and CD4+ T cells by > 50% (P < 0.01 for each subset). Pharmacodynamic changes in pt bone marrow by quantitative IHC scoring showed significant degradation of substrates (Ikaros, P = 0.006; Aiolos, P < 0.001; ZFP91, P < 0.001) and downregulation of c-myc (P = 0.027), including in pts refractory to POM and with low CRBN expression. Conclusions: IBER + DEX has shown notable clinical activity and favorable tolerability in heavily pretreated pts with RRMM, including pts refractory to prior IMiD therapy. Preclinical and translational data suggest a differentiated profile of IBER from IMiD agents in regard to activity in POM-resistant models, potency in combination with BORT and DARA, and by inducing changes in CD4+ T cells that were not observed in pts treated with POM from prior studies. Taken together, these data support a differentiated profile for IBER from IMiD agents, and the clinical development of IBER as a foundation of combination therapy for the treatment of RRMM. This study is ongoing, and includes cohorts evaluating the combinations of IBER + DEX with DARA, BORT, and carfilzomib. Updated results will be presented at the meeting. Disclosures Lonial: Karyopharm: Consultancy; GSK: Consultancy; Takeda: Consultancy, Research Funding; Celgene Corporation: Consultancy, Research Funding; Amgen: Consultancy; Genentech: Consultancy; Janssen: Consultancy, Research Funding; BMS: Consultancy. Amatangelo:Celgene Corporation: Employment, Equity Ownership. Popat:Celgene Corporation: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: travel, accommodations, expenses; AbbVie: Consultancy, Membership on an entity's Board of Directors or advisory committees; GSK: Consultancy, Honoraria; Takeda: Honoraria, Other: travel, accommodations, expenses; Janssen: Honoraria, Other: travel support to meetings. Minnema:Gilead: Honoraria; Celgene Corporation: Honoraria, Research Funding; Amgen: Honoraria; Servier: Honoraria; Jansen Cilag: Honoraria. Zonder:Alnylam: Consultancy, Membership on an entity's Board of Directors or advisory committees; BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Oncopeptides: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Intellia: Consultancy, Membership on an entity's Board of Directors or advisory committees; Caelum: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene Corporation: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Larsen:Janssen Oncology: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees. Oriol Rocafiguera:Celgene Corporation: Consultancy, Speakers Bureau; Janssen: Consultancy; Amgen: Consultancy, Speakers Bureau. Rodriguez Otero:Celgene Corporation: Consultancy, Honoraria, Speakers Bureau; Janssen: Consultancy, Honoraria; Takeda: Consultancy; BMS: Honoraria; Kite Pharma: Consultancy. Badros:Celgene Corporation: Consultancy; Amgen: Consultancy. Siegel:Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Bristol-Myers Squibb Company: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. Jagannath:Bristol-Myers Squibb: Consultancy; Janssen Pharmaceuticals: Consultancy; Karyopharm Therapeutics: Consultancy; AbbVie: Consultancy; Merck & Co.: Consultancy; Celgene Corporation: Consultancy. Bringhen:Bristol-Myers Squibb: Honoraria; Celgene Corporation: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy. Gironella:Amgen: Honoraria; Janssen: Honoraria; Celgene Corporation: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Kaiser:Takeda, Janssen, Celgene, Amgen: Honoraria, Other: Travel Expenses; Abbvie, Celgene, Takeda, Janssen, Amgen, Abbvie, Karyopharm: Consultancy; Celgene, Janssen: Research Funding. Cook:Janssen, Takeda, Sanofi, Karyopharm, Celgene: Consultancy, Honoraria, Speakers Bureau; Amgen, Bristol-Myers Squib, GlycoMimetics, Seattle Genetics, Sanofi: Honoraria; Celgene, Janssen-Cilag, Takeda: Honoraria, Research Funding. Gamberi:Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Sanofi: Consultancy, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria; Celgene Corporation: Consultancy, Membership on an entity's Board of Directors or advisory committees. Sonneveld:SkylineDx: Research Funding; Takeda: Honoraria, Research Funding; Karyopharm: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; BMS: Honoraria; Amgen: Honoraria, Research Funding. Nguyen:Celgene Corporation: Employment, Equity Ownership. Chen:Celgene Corporation: Employment, Equity Ownership. Homan:Celgene Corporation: Employment. Bjorklund:Celgene Corporation: Employment, Equity Ownership. Wang:Celgene Corporation: Employment, Equity Ownership. Pierceall:Celgene Corporation: Employment, Equity Ownership. Bensmaine:Celgene Corporation: Employment, Equity Ownership. Thakurta:Celgene: Employment, Equity Ownership. Peluso:Celgene Corporation: Employment. Van De Donk:Janssen Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene Corporation: Membership on an entity's Board of Directors or advisory committees, Research Funding; Servier: Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees; Roche: Membership on an entity's Board of Directors or advisory committees; AMGEN: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bayer: Membership on an entity's Board of Directors or advisory committees.

Introduction: While the molecular target of immunomodulators such as pomalidomide (POM) and lenalidomide (LEN) has been identified, the mechanisms underlying therapeutic resistance remain incompletely understood. The uniformly emerging resistance to therapy over time in the absence of identifiable cereblon pathway mutations in the majority of patients raises questions about alternative mechanisms including aberrant gene expression. Methods: We performed gene expression profiling using an Affymetrix GeneChip Human Genome U133 Plus 2.0 microarray on CD138+ bone marrow cells from patients with relapsed / refractory (RRMM) and newly diagnosed (NDMM) multiple myeloma prior to initiating treatment. Patients were treated on two phase II clinical trial protocols (MC0789: POM ± dexamethasone in RRMM; MC0884: LEN ± dexamethasone in NDMM) between 2007 and 2012. We categorized patients based on their IMWG response as non-responders (SD) and responders (VGPR+). We selected 15 responders and 15 non-responders from MC0789 (n = 30) and compared overall survival, gene expression patterns, and involved cellular pathways between the two groups. We selected 5 responders and 5 non-responders from MC0884 (n = 10) for targeted validation of differentially expressed candidate genes. After data quality control and normalization of gene expression values, differential gene expression was estimated using limma. Statistical significance was adjusted for multiple testing in the discovery set using a false discovery rate-based approach for genome-wide experiments (q-value). We used Gene Ontology and PANTHER pathway analysis for functional annotation of differentially expressed genes. Overall survival estimates were calculated using the Kaplan-Meier method. Computation and visualization were performed in R. Results: Median age at treatment initiation on MC0789 was 65 years (40 - 82), 65% of the patients were male. Pomalidomide resistance was associated with an increase in mortality (median overall survival 1.6 versus 6.4 years, p = 0.009, Kaplan-Meier plot). There were 1076 differentially regulated genes between responders and non-responders (521 up- and 555 down-regulated, q < 0.050 for all genes, volcano plot). Expression of CRBN was 1.5-fold down-regulated in non-responders (q = 0.005). Supervised hierarchical clustering of the top 500 differentially expressed genes demonstrated distinct patterns in pomalidomide-resistant disease (heatmap). Gene ontology analysis revealed protein synthesis as one of the most enriched biological processes (bar graph). Pathway analysis showed a 6-fold enrichment (FDR = 0.007) of the ubiquitin proteasome pathway in pomalidomide-resistant disease. Differentially expressed genes involved key protein degradation pathways, epigenetic modifiers, and transcription factors. Targeted validation in MC0884 revealed 13 common genes with at least 1.5-fold differential expression (5 up- and 8 down-regulated), 12 of which have previously been implicated in the regulation of apoptosis, tumor glucose metabolism, Rho and Wnt signaling, miRNA-driven resistance to chemotherapy, and ubiquitin-dependent protein degradation (Table and Sankey diagram). The most up-regulated gene in non-responders was MYRIP, a gene coding for a vesicle trafficking protein associated with platinum resistance and suppression of pro-apoptotic BCL-2 family members in solid malignancies. The most down-regulated gene was FRZB, a gene coding for a negative regulator of Wnt signaling, previously implicated in the progression of monoclonal gammopathy of undetermined significance to multiple myeloma. Conclusions: Overall survival of patients with pomalidomide-resistant RRMM remains poor. Pomalidomide resistance was associated with differential gene expression in several potentially targetable cellular pathways beyond the known drug target cereblon. Targeted validation of candidate genes revealed common cellular pathways in immunomodulator-resistant disease. Elucidating the exact molecular mechanisms underlying immunomodulator resistance is of considerable interest for biomarker development and the identification of novel therapeutic targets and warrants further exploration. Figure Disclosures Lacy: Celgene: Research Funding. Dispenzieri:Celgene: Research Funding; Alnylam: Research Funding; Intellia: Consultancy; Janssen: Consultancy; Pfizer: Research Funding; Akcea: Consultancy; Takeda: Research Funding. Kumar:Takeda: Research Funding; Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding.

Introduction: Recent advancements in the treatment of multiple myeloma (MM) have centered on engaging the immune system to target multiple myeloma cells. Although these therapies are being combined with immunomodulatory imide drugs (IMiDs) and corticosteroids, the individual contributions of these drugs on the immune system of MM patients has not been examined in the upfront setting. In this study, we examined the peripheral blood immunophenotypes of newly diagnosed multiple myeloma (NDMM) patients receiving the IMiD lenalidomide with or without the corticosteroid dexamethasone. Methods: To characterize immunophenotypes, we utilized flow cytometry to profile white blood cell populations from 35 patients enrolled in a clinical trial testing the efficacy of lenalidomide with and without dexamethasone in NDMM (NCT00772915). In this trial, all patients were initiated on single-agent lenalidomide. Dexamethasone was initiated in patients that did not meet desirable responses or for disease progression. At each cycle, peripheral blood was stained with a 17-marker antibody panel against several immune lineages and functional surface markers. We grouped patients into two groups: 1) lenalidomide alone or 2) lenalidomide with dexamethasone according to their treatment regimen at each cycle timepoint. Results: First we confirmed anti-myeloma cell activity for both groups by measuring a steady decline in circulating plasma cells in both groups. Examining peripheral blood immunophenotypes showed an expected decrease in T cells and a smaller decrease in B cells in both groups of patients. Closer inspection of B cell populations revealed a switch towards a more immature B cell phenotype in both treatment groups. This was measured as a switch from CD19-CD20+ cells to CD19+CD20- B cells. Inspection of T cell subsets revealed that patients receiving single-agent lenalidomide had a sustained decrease in the levels of CD4+ T cells and increase in the levels of CD8+ T cells. This was seen in both naïve and regulatory T cells evidenced by a decrease in the CD4/CD8 ratio among CD28+ T cells as well as CD25+ T cells. Importantly, this alteration did not lead to sustained alterations in the overall level of CD25+ or CD28+ T cells, and the addition of dexamethasone reverses this trend. In addition to the effects seen on T and B cell numbers, we detected expansions of NK cell populations in patients receiving lenalidomide alone. This expansion is detected as an overall increase in CD56+ mononuclear cells with the majority of cells being CD56+CD3- cells. Conclusions: Our data show that lenalidomide and dexamethasone therapy have shared but distinct effects on peripheral blood immunophenotypes in NDMM. Both drugs alter B cells numbers and populations leading to an expansion of CD19+CD20- B cells. However, lenalidomide alone decreases the CD4/CD8 T cell ratio; and, lenalidomide more strongly expands NK cell populations. The addition of dexamethasone reverses this trend and leads to a restoration of the CD4/CD8 ratio. This suggests that lenalidomide without dexamethasone might be counterproductive in immunotherapies intended to recruit CD4+ T cells. Conversely, lenalidomide alone could increase the efficacy of immunotherapies dependent on NK cell recruitment such as antibody-dependent cellular cytotoxicity (ADCC). This information may benefit future investigations of immune responses in MM patients and improve the adoption of immunotherapies to MM patients. Figure Disclosures Kumar: Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Takeda: Research Funding.

Introduction: High-risk multiple myeloma can be defined by the presence of specific cytogenetic abnormalities (structural) or by characteristic changes in bone marrow and peripheral blood biomarkers (functional). While both entities are characterized by therapeutic resistance, frequent disease relapses, and adverse survival outcomes, the underlying molecular mechanisms remain incompletely understood. Methods: We performed gene expression profiling (GEP) using an Affymetrix GeneChip Human Genome U133 Plus 2.0 microarray on CD138+ bone marrow cells from 137 patients diagnosed with multiple myeloma between 2004 and 2012. All patients underwent Fluorescence In-situ Hybridization (FISH) evaluation, plasma cell labeling, International Staging System (ISS) risk stratification, and GEP prior to initiating treatment with novel agents. The presence of del(17p), t(4;14), t(14;16), and t(14;20) on FISH, a plasma cell labeling index (PCLI) > 2%, and ISS stage III were considered high-risk abnormalities: FISH-HR (n = 15, structural high-risk, at least one high-risk FISH lesion), PCLI-HR (n = 20; functional high-risk, PCLI > 2%), and ISS-HR (n = 12; functional high-risk, ISS stage III). For each HR group we sampled standard risk (SR) controls in a 4:1 ratio. After data quality control and normalization, differential gene expression was estimated using limma. Statistical significance was adjusted for multiple comparisons using a false discovery rate-based approach for genome-wide experiments (q-value). We employed PANTHER pathway analysis for the differentially expressed genes in each HR group. We implemented a simple gene expression score (GES) by calculating the sum of quartiles of the normalized gene expression values for genes differentially expressed in more than one HR group (GES = ΣUP(quartile - 1) + ΣDN(4 - quartile)) and externally validated its prognostic significance (UAMS TT2 / TT3, GSE24080). Survival outcomes were analyzed using the methods described by Kaplan, Meier, and Cox. Computation and visualization were performed in R. Results: Median age at diagnosis was 63 years (32 - 87), 53% of the patients were male. High-risk disease was associated with inferior overall survival, regardless of the used definition (left Kaplan-Meier plots): FISH-HR (HR 4.3, 95% CI 1.9 - 9.8, p < 0.001), PCLI-HR (HR 2.7, 95% CI 1.4 - 5.3, p = 0.004), and ISS-HR (HR 2.8, 95% CI 1.2 - 6.5, p = 0.015). There were 59 (FISH-HR), 424 (ISS-HR), and 507 (PCLI-HR) differentially expressed genes (q < 0.050 for all genes, volcano plots). PCLI-HR and FISH-HR demonstrated a predominance of transcriptional up-regulation while ISS-HR had a balanced gene expression profile with a similar number of genes being up- and down-regulated. The involved cellular pathways were different across the HR groups except for anti-apoptotic signaling (bar graphs). All HR groups had distinct gene expression profiles with no complete overlap between all HR groups. There were 71 genes with overlap between two HR groups (69 up-regulated, 2 down-regulated, Venn diagrams). The median GES was 97 (18 - 206, higher numbers indicating higher expression of up-regulated and lower numbers of down-regulated high-risk genes) in 559 patients treated on UAMS TT2 / TT3 (GSE24080). Tertiles of the GES were associated with event-free survival (HR 1.4, 95% CI 1.2 - 1.6, p < 0.001) and remained independently prognostic after adjusting for age, sex, and ISS stage (HR 1.3, 95% CI 1.1 - 1.5, p < 0.001). Conclusions: High-risk multiple myeloma remains associated with inferior overall survival, regardless of the used definition (structural or functional). The subtypes of high-risk disease have distinct gene expression profiles and involve different cellular pathways, providing important clues to the underlying biology. A 71 gene signature derived from the different high-risk subtypes was of prognostic significance in a clinical trial population after adjusting for known prognostic factors. Figure Disclosures Lacy: Celgene: Research Funding. Dispenzieri:Akcea: Consultancy; Intellia: Consultancy; Janssen: Consultancy; Pfizer: Research Funding; Takeda: Research Funding; Celgene: Research Funding; Alnylam: Research Funding. Stewart:Takeda: Consultancy; Seattle Genetics: Consultancy; Roche: Consultancy; Ono: Consultancy; Celgene: Consultancy, Research Funding; Ionis: Consultancy; Janssen: Consultancy, Research Funding; Oncopeptides: Consultancy; Amgen: Consultancy, Research Funding; Bristol Myers-Squibb: Consultancy. Bergsagel:Celgene: Consultancy; Ionis Pharmaceuticals: Consultancy; Janssen Pharmaceuticals: Consultancy. Kumar:Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Takeda: Research Funding.

Abstract Recurrent glioblastoma is challenging to distinguish from so called “treatment effect” on routine clinical imaging. Further, within tumor heterogeneity reveals that some regions can be histologically dominated by tumor progression whilst others can be dominated by secondary effects of treatment response. Apparent tumor progression on MRI can be very difficult to manage clinically as it is unclear the degree to which the imaging changes are actually tumor progression vs response to treatment (including inflammatory response and necrosis). In this analysis, we study a unique cohort of patients for whom image localized-biopsies reveal heterogeneity in response vs progression. Our dataset included 70 biopsy samples from 32 patients with GBM each histolopatholgically characterized for tumor abundance vs immune infiltrate. Six multiparametric MRI contrasts were available, including T1, T1gd, T2, FLAIR, SWI, and ADC. Images were co-registered. Radiomic (statistical + texture) features were extracted from the region of six image contrasts locally matched with each biopsy sample. Machine learning models were built to predict each biomarker using radiomic features. Leave-one-out cross validation was used to evaluate the prediction accuracy. Radiomic features were found to be informative to the prediction of biomarkers. ANOVA tests show significant improvement of using radiomic features compared with the null model. The prediction accuracy was higher when considering the biomarkers on a binary scale using the median as the cutoff than on a numerical scale. Spatially-informed radiomics models for tumor progression vs treatment effect are possible and can play an instrumental role in navigating confounding imaging changed common during treatment progression.