Resultados de investigación

Identification of genomic alterations that influence the immune response within the tumor microenvironment is mandatory in order to identify druggable vulnerabilities. In this article, by interrogating public genomic datasets we describe copy number variations (CNV) present in breast cancer (BC) tumors and corresponding subtypes, associated with different immune populations. We identified regulatory T-cells associated with the Basal-like subtype, and type 2 T-helper cells with HER2 positive and the luminal subtype. Using gene set enrichment analysis (GSEA) for the Type 2 T-helper cells, the most relevant processes included the ERBB2 signaling pathway and the Fibroblast Growth Factor Receptor (FGFR) signaling pathway, and for CD8+ T-cells, cellular response to growth hormone stimulus or the JAK-STAT signaling pathway. Amplification of ERBB2, GRB2, GRB7, and FGF receptor genes strongly correlated with the presence of type 2 T helper cells. Finally, only 8 genes were highly upregulated and present in the cellular membrane: MILR1, ACE, DCSTAMP, SLAMF8, CD160, IL2RA, ICAM2, and SLAMF6. In summary, we described immune populations associated with genomic alterations with different BC subtypes. We observed a clear presence of inhibitory cells, like Tregs or Th2 when specific chromosomic regions were amplified in basal-like or HER2 and luminal groups. Our data support further evaluation of specific therapeutic strategies in specific BC subtypes, like those targeting Tregs in the basal-like subtype., Peer reviewed

Identification of genomic alterations that influence the immune response within the tumor microenvironment is mandatory in order to identify druggable vulnerabilities. In this article, by interrogating public genomic datasets we describe copy number variations (CNV) present in breast cancer (BC) tumors and corresponding subtypes, associated with different immune populations. We identified regulatory T-cells associated with the Basal-like subtype, and type 2 T-helper cells with HER2 positive and the luminal subtype. Using gene set enrichment analysis (GSEA) for the Type 2 T-helper cells, the most relevant processes included the ERBB2 signaling pathway and the Fibroblast Growth Factor Receptor (FGFR) signaling pathway, and for CD8+ T-cells, cellular response to growth hormone stimulus or the JAK-STAT signaling pathway. Amplification of ERBB2, GRB2, GRB7, and FGF receptor genes strongly correlated with the presence of type 2 T helper cells. Finally, only 8 genes were highly upregulated and present in the cellular membrane: MILR1, ACE, DCSTAMP, SLAMF8, CD160, IL2RA, ICAM2, and SLAMF6. In summary, we described immune populations associated with genomic alterations with different BC subtypes. We observed a clear presence of inhibitory cells, like Tregs or Th2 when specific chromosomic regions were amplified in basal-like or HER2 and luminal groups. Our data support further evaluation of specific therapeutic strategies in specific BC subtypes, like those targeting Tregs in the basal-like subtype., Peer reviewed

Identification of genomic alterations that influence the immune response within the tumor microenvironment is mandatory in order to identify druggable vulnerabilities. In this article, by interrogating public genomic datasets we describe copy number variations (CNV) present in breast cancer (BC) tumors and corresponding subtypes, associated with different immune populations. We identified regulatory T-cells associated with the Basal-like subtype, and type 2 T-helper cells with HER2 positive and the luminal subtype. Using gene set enrichment analysis (GSEA) for the Type 2 T-helper cells, the most relevant processes included the ERBB2 signaling pathway and the Fibroblast Growth Factor Receptor (FGFR) signaling pathway, and for CD8+ T-cells, cellular response to growth hormone stimulus or the JAK-STAT signaling pathway. Amplification of ERBB2, GRB2, GRB7, and FGF receptor genes strongly correlated with the presence of type 2 T helper cells. Finally, only 8 genes were highly upregulated and present in the cellular membrane: MILR1, ACE, DCSTAMP, SLAMF8, CD160, IL2RA, ICAM2, and SLAMF6. In summary, we described immune populations associated with genomic alterations with different BC subtypes. We observed a clear presence of inhibitory cells, like Tregs or Th2 when specific chromosomic regions were amplified in basal-like or HER2 and luminal groups. Our data support further evaluation of specific therapeutic strategies in specific BC subtypes, like those targeting Tregs in the basal-like subtype., Peer reviewed

Identification of genomic alterations that influence the immune response within the tumor microenvironment is mandatory in order to identify druggable vulnerabilities. In this article, by interrogating public genomic datasets we describe copy number variations (CNV) present in breast cancer (BC) tumors and corresponding subtypes, associated with different immune populations. We identified regulatory T-cells associated with the Basal-like subtype, and type 2 T-helper cells with HER2 positive and the luminal subtype. Using gene set enrichment analysis (GSEA) for the Type 2 T-helper cells, the most relevant processes included the ERBB2 signaling pathway and the Fibroblast Growth Factor Receptor (FGFR) signaling pathway, and for CD8+ T-cells, cellular response to growth hormone stimulus or the JAK-STAT signaling pathway. Amplification of ERBB2, GRB2, GRB7, and FGF receptor genes strongly correlated with the presence of type 2 T helper cells. Finally, only 8 genes were highly upregulated and present in the cellular membrane: MILR1, ACE, DCSTAMP, SLAMF8, CD160, IL2RA, ICAM2, and SLAMF6. In summary, we described immune populations associated with genomic alterations with different BC subtypes. We observed a clear presence of inhibitory cells, like Tregs or Th2 when specific chromosomic regions were amplified in basal-like or HER2 and luminal groups. Our data support further evaluation of specific therapeutic strategies in specific BC subtypes, like those targeting Tregs in the basal-like subtype., Peer reviewed

Identification of genomic alterations that influence the immune response within the tumor microenvironment is mandatory in order to identify druggable vulnerabilities. In this article, by interrogating public genomic datasets we describe copy number variations (CNV) present in breast cancer (BC) tumors and corresponding subtypes, associated with different immune populations. We identified regulatory T-cells associated with the Basal-like subtype, and type 2 T-helper cells with HER2 positive and the luminal subtype. Using gene set enrichment analysis (GSEA) for the Type 2 T-helper cells, the most relevant processes included the ERBB2 signaling pathway and the Fibroblast Growth Factor Receptor (FGFR) signaling pathway, and for CD8+ T-cells, cellular response to growth hormone stimulus or the JAK-STAT signaling pathway. Amplification of ERBB2, GRB2, GRB7, and FGF receptor genes strongly correlated with the presence of type 2 T helper cells. Finally, only 8 genes were highly upregulated and present in the cellular membrane: MILR1, ACE, DCSTAMP, SLAMF8, CD160, IL2RA, ICAM2, and SLAMF6. In summary, we described immune populations associated with genomic alterations with different BC subtypes. We observed a clear presence of inhibitory cells, like Tregs or Th2 when specific chromosomic regions were amplified in basal-like or HER2 and luminal groups. Our data support further evaluation of specific therapeutic strategies in specific BC subtypes, like those targeting Tregs in the basal-like subtype., Peer reviewed

S1: Combining sample aliquots stained with a CD45 antibody conjugated to 8 different fluorochromes into only two antibody combinations ready to be measured in the flow cytometer. Table S1: Panels of fluorochrome-conjugated antibody reagents used in this study. Table S2: Sources and specificities of the monoclonal antibody reagents used in this study. Table S3: Detailed immunophenotypic features of T-cell subsets showing extreme TRBC1+ percentages within the more mature polyclonal and monoclonal Tαβ-cell populations expressing a specific TCRVβ family. Figure S1: Distribution of T cells expressing different TCRVβ families among total Tαβ cells and their Tαβ CD8+ and Tαβ CD4+ cell subsets and their maturation-associated stages of CD28− effector memory and terminal effector cells as identified in the blood of healthy donors (n = 6)., Peer reviewed

SUPPLEMENTAL MATERIAL 1.- Description of genetic analyses performed in the IMCL-2015 study 2.- Treatment discontinuation events and relationship with IR combination 3.- Supplemental Table S1 . Histological review according to the tissue sample 4.- Supplemental Table S2. Clinical and biological features according to TP53 mutational status 5.- Supplemental Table S3. Clinical and biological features according to nnMCL or cMCL molecular subtypes 6.- Supplemental Table S4. Responses after 12 cycles of IR combination according to genomic complexity 7.- Supplemental Table S5. Molecular response in peripheral blood and bone marrow at 6,12, 18 and 24 months 8.- Supplemental Figure S1. PFS from the time of ibrutinib stop in patients in response and sustained undetectable MRD 9.- Supplemental Figure S2. PFS according to MRD status at PB (Cycle 12) 10.- Supplemental Figure S3. A) OS by MIPI and B) OS by TP53 mutational status 2 Supplemental Figure Legends Supplemental Figure S1. PFS from the time of ibrutinib stop in patients in response and sustained undetectable MRD PFS, progression-free survival; MRD, minimal residual disease Supplemental Figure S2. PFS according to MRD status at PB (Cycle 12) (P=.063) PFS, progression-free survival ;MRD, minimal residual disease; PB, peripheral blood Supplemental Figure S3. A) OS by MIPI (P= .007) and B) OS by TP53 mutational status (P= .0002) OS, overall survival; MIPI, Mantle cell lymphoma International Prognostic Index; wt, wild-type; mut, mutated, Peer reviewed

Figure S1. CONSORT diagram. aCrossed over to receive single-agent ibrutinib after PD (n = 35). bOf the 29 patients in the placebo-rituximab arm who discontinued treatment due to investigator decision, 24 patients discontinued due to study unblinding per Data Monitoring Committee recommendation. cDiscontinued rituximab before the completion of 8 infusions. Figure S2. Progression-free survival benefit across prespecified subgroups by prior treatment status. CI, confidence interval; IgG, immunoglobulin G; Hgb, hemoglobin; HR, hazard ratio; RTX, rituximab; IPSSWM, International Prognostic Scoring System for Waldenström’s Macroglobulinemia. Figure S3. Serum immunoglobulin M (IgM) improvement over time. Figure S4. Overall survival. Not censored at crossover. aCounterfactual survival times in crossover patients (n = 35) were calculated using the two-stage Accelerated Failure Time model (Latimer NR, et al: Med Decis Mak. 2014;34(3):387-402). CI, confidence interval; HR, hazard ratio. TABLE S1. Baseline Demographics and Clinical Characteristics by Genotype TABLE S2. Reasons for Ibrutinib Discontinuation. TABLE S3. Key Concomitant Medications of Interest TABLE S4. Adverse events leading to ibrutinib dose reductions., Peer reviewed

S1. Sample preparation protocol for evaluation of OneFlow kits (BD). Figure S1. Impact of different BSA concentrations and different pH of the washing buffer on the relative percentage distribution of different cell populations identifiable in normal PB samples with LST. Figure S2. Impact of different BSA concentrations and different pH of the washing buffer on the expression levels of individual LST markers as assessed on different cell populations in normal PB samples. Table S1. Detailed information on the antibody reagents used in the current study, including the antibody titer used per test. Table S2. Absolute and relative percentage changes of particular cell populations, debris and doublets detectable in BM samples stained with ALOT tube at day 0 and after 24-h storage. Table S3. Ratios of percentage of debris, doublets, and pathological/clonal B-cells in particular BM and PB samples stained with LST tube at different time points. Table S4. Ratios of percentage of debris, doublets, and pathological/clonal B-cells in particular BM and PB samples stained with the first tube of B-CLPD panel at different time points. Table S5. Ratios of percentage of debris, doublets, and pathological/clonal plasma cells in particular BM and PB samples stained with PCD panel at different time points. Table S6. MFI values of LST markers evaluated on relevant cell populations obtained at different pH and BSA concentrations. Table S7. Expression of markers on relevant PB cell populations stained with LST tube at different pH on averaging the results for different BSA concentrations. Table S8. Selected additional markers that were not directly studied in the current study that may be influenced by the preparation-associated variables., Peer reviewed

Figure A.1: Selection of Comparator Arms for ITC Analyses Figure A.2: Results of sensitivity analyses with OIs removed for OS at all (A) and first (B) index dates Figure A.3: Results of sensitivity analyses with LocoMMotion removed for OS at all (A) and first (B) index dates, and PF at first index dates (C) Table A.1: Characteristics of Data Sources for PCT arms in ITCs Table A.2: Published ITC Results and Augmented Results Included in Meta-analyses (All Index Dates) Table A.3: Published ITC Results and Augmented Results Included in Meta-analyses (First Index Dates) Table A.4: Baseline Covariates After Adjustment (mITT Populations; All Index Dates) Table A.5: Baseline Covariates After Adjustment (mITT Populations; First Index Dates) Table A.6: Outcome Definitions in ITC Analyses, [Objective]: In the absence of head-to-head trials, indirect treatment comparisons (ITCs) between ciltacabtagene autoleucel (cilta-cel; in CARTITUDE-1) and treatments used in real-world clinical practice (physician’s choice of treatment [PCT]), were previously conducted. We conducted multiple meta-analyses using available ITC data to consolidate the effectiveness of cilta-cel versus PCT for patients with triple-class exposed relapsed or refractory multiple myeloma (RRMM). [Methods]: Five ITCs were assessed for similarity to ensure robust comparisons using meta-analysis. Effectiveness outcomes were overall survival (OS), progression-free survival (PFS), time to next treatment (TTNT), and overall response rate (ORR). A robust variance estimator was used to account for the use of CARTITUDE-1 in each pairwise ITC. Analyses were conducted in both treated and enrolled populations of CARTITUDE-1. [Results]: Four ITCs were combined for evaluation of OS. Results were statistically significantly in favor of cilta-cel versus PCT in treated patients (hazard ratio [HR]: 0.24, 95% confidence interval [CI]: 0.22–0.26). Three ITCs were combined for evaluation of PFS and TTNT. Cilta-cel reduced the risk of progression and receiving a subsequent treatment by 80% (HR: 0.20 [95% CI: 0.06, 0.70]) and 83% (HR: 0.17 [95% CI: 0.12, 0.26]), respectively. Three ITCs were combined for evaluation of ORR. Cilta-cel increased the odds of achieving an overall response by 86-times versus PCT in treated patients. Findings were consistent in the enrolled populations and across sensitivity analyses. [Conclusions]: Evaluating multiple indirect comparisons, cilta-cel demonstrated a significantly superior advantage over PCT, highlighting its effectiveness as a therapy in patients with triple-class exposed RRMM., Peer reviewed