ALBERT

Beschreibung: Introduction and Methods To investigate the mechanisms by which microenvironmental signals induce treatment resistance in chronic lymphocytic leukemia (CLL) we conducted a high-throughput perturbation assay combining pairwise 15 clinically relevant drugs with 18 stimulations mimicking the tumor microenvironment. We combined this with multi-layer omics data using whole exome sequencing, genome-wide DNA-methylation profiles and RNA sequencing to investigate molecular determinants of microenvironmental drug resistance. In parallel we assessed the in-vivo relevance of investigated pathways in 100 CLL infiltrated- and 100 healthy lymph node (LN) biopsies by staining for stimulus specific downstream pathway components using immunohistochemistry (IHC). Results We initially clustered drugs and microenvironmental stimuli separately based on the similarity of their response profiles across all CLL samples. We found drug response profiles to be correlated closely for drugs targeting the same signaling-pathway, e.g. the B-cell receptor inhibitors Ibrutinib (BTK) and PRT062607 (Syk) (Pearson correlation coefficient: r=0.78, 95% CI: 0.72 - 0.83). In contrast, responses to microenvironmental stimuli were less correlated, indicating diverse effects are caused by the tumor microenvironment (e.g. Interleukin 4 (IL-4) vs. Interleukin 2, Pearson correlation coefficient: r=0.17, 95% CI: 0.03 - 0.31). Twelve of the 18 tested stimulations enhanced survival of CLL cells, whereas two reduced the viability of CLL cells in-vitro (comparisons yielded BH adjusted t-test p-values

Beschreibung: Background: Signals provided by the microenvironment can modify and circumvent pathway activities that are therapeutically targeted by drugs. However, a systems-level understanding of how the microenvironment and the genetic and molecular alterations of the tumor interact with each other and contribute to drug resistance is lacking. Methods: To address this unmet need, we established an automated microscopy-based phenotyping platform that uses co-culture conditions mimicking the bone marrow environment. We cultured primary tumor cells from more than 100 leukemia patients (CLL, AML, MCL, T-PLL, HCL) with and without bone marrow stroma cell support in DMEM and 10% human serum and treated each condition with 57 drugs in 3 concentrations. After 72h of incubation, 22 000 images per patient were acquired and processed by our custom made image analysis pipeline. Our set-up allows us to increase sensitivity far beyond simple viability testing, as it reads out additional cell type specific features such as cell morphology, autophagy and cell-cell interactions. Results: Quality assessment revealed that in contrast to mono-culture conditions, assay plate edge effects can be avoided under stable stroma cell co-culture conditions. Correlation of replicated patient samples were comparable between mono- and co-cultures (R2〉0.75). In the absence of their native microenvironment, primary leukemia cells undergo spontaneous apoptosis ex-vivo. Viability at culture start was always 〉90% and dropped to a median of 51% (viability range: 17%-90%) after 72h in mono-cultures. Among CLL samples spontaneous apoptosis was not dependent on either IGHV mutation status or any major cytogenetic risk group. Bone marrow stroma cell co-culture conditions protected tumor cells from spontaneous apoptosis (p=8.2e-6, paired t-test). Patient samples with a high degree of spontaneous apoptosis benefited most from co-culture conditions (p=7.2e-10, Pearson correlation). To model interactions of stroma cell conditions and drug-induced apoptosis we established the following linear model: Viability ~ drug-effect + culture-model + drug-effect:culture-model. While activity of some drugs was significantly altered under co-culture conditions, we could also identify drugs with similar activity in mono- and co-cultures. For instance, the activity of common chemotherapeutics (fludarabine: p=0.002 at 0.6µM, cytarabine: p=0.001 at 1.5µM, ANOVA) or bromodomain inhibitors (I-BET-762: p=5.9e-5 at 4.5µM, JQ1: p=1.5e-8 at 1.5µM, ANOVA) was significantly reduced under co-culture conditions. In contrast, PI3K inhibitors idelalisib and duvelisib had a similar activity in mono-culture and stroma co-culture conditions and might represent a starting point to overcome stroma cell mediated drug resistance. In CLL, we identified IGHV mutation status and trisomy 12 as important determinants of response to kinase inhibitors. We confirmed these findings in stroma cell co-cultures, e.g. a better activity of B-cell receptor inhibitors in trisomy 12 and IGHV unmutated CLL. A systematic comparison of ex-vivo drug response pattern in mono- and co-cultures across 171 drug conditions will be presented. Conclusion: Our results suggest that high throughput co-culture drug testing can be robustly performed and provide an unprecedented understanding of how the stroma cell microenvironment and the genetic make-up of tumor cells contribute to drug resistance and sensitivity. Figure: Over 2 million microscopy images were acquired and analysed to assess drug resistance and sensitivity in a co-culture model of primary leukemia and bone marrow stroma cells. blue= Hoechst33342, green=Calcein AM, red=lysosomal dye NIR Figure 1. Figure 1. Disclosures No relevant conflicts of interest to declare.

Beschreibung: Introduction Ex-vivo drug response profiling (DRP) represents a promising strategy to better understand the tumor's pathway dependencies, genotype-phenotype associations and the underlying molecular networks. However, little is known about the predictive value of drug response profiling and its utility for individualized treatment recommendations. Methods We report the first analysis of our ongoing prospective non-interventional SMARTrial (Systematic and Mechanism-based Approach to Rational Treatment Trial of Blood Cancer; ClinicalTrials.gov: NCT03488641). This trial aims to demonstrate the feasibility of high-throughput DRP in the clinical routine setting and the ability to predict in-vivo outcome by DRP. Ex-vivo responses to 107 different drugs including chemotherapeutic agents and FDA-approved or clinically investigated small molecules were assessed by an ATP-based assay after 48 hours of incubation. Results were processed by our customized analysis pipeline, enabling immediate reporting of DRP results. Patients with hematological malignancies and scheduled treatment were eligible if sufficient tumor cells were available from either lymph node biopsies, the bone marrow or peripheral blood. Results 58 patients out of the target study population of 80 patients have been recruited (AML, n = 24; B-cell lymphomas, n = 28; T-cell-lymphoma, n = 5; ALL, n = 1) as of July 24th, 2019. The median follow-up was 6 months. The primary endpoint, defined as proportion of successfully completed DRP tests within 7 days, is used to evaluate the feasibility of DRP in the clinical routine. In our study cohort, 93% of all recruited patients met this primary endpoint. We will describe our optimized workflow for drug testing and data analysis, which enables us to provide DRP reports in the clinical routine. First, we analyzed if the DRP reflected expected genotype-drug response associations and systematically regressed mutational data on DRP phenotypes. This analysis exemplarily revealed that FLT3-ITD-mutated AML cells with a mutation ratio 〉 0.8 respond significantly stronger to FLT3 inhibitors than FLT3-ITD-mutated AML cells with a mutation ratio 〈 0.8 or wildtype AML cells (Quizartinib, t-test, p = 0.022; Gilteritinib, t-test, p 〈 0.001), confirming that the assay can uncover clinically meaningful vulnerabilities of cancer cells. 89.3% of all evaluable patients received treatments covered by our ex-vivo DRP assay, which facilitates the correlation of in-vivo and ex-vivo drug response. The majority of patients were scheduled for chemotherapy treatment (65%) or had received chemotherapy before (32%). In-vivo responses after scheduled treatment were categorized as response (Resp), incomplete response (IResp) or progressive disease (PD). We compared ex-vivo drug responses between these different in-vivo response categories. Cancer cells of patients with PD were significantly less sensitive to ex-vivo treatment with chemotherapy (mean of all ex-vivo tested chemotherapeutics) than patients with Resp (t-test, PD vs. Resp, p = 0.007; PD vs. IResp, p = 0.054). While the anthracycline daunorubicin (t-test, p = 0.034) and the purin analogon fludarabine (t-test, p = 0.004) were significantly more active in samples from patients with a clinical Resp than in samples from patients with PD, we did not observe this trend for vinca alkaloids (e.g. vincristine, vindesine) or antifolates (e.g. pralatrexate). Interestingly, individual patients exhibited an exceptional ex-vivo sensitivity to anti-folates, despite an almost complete insensitivity to all other chemotherapeutic drugs. For a patient with refractory Burkitt Lymphoma, we observed such a strong ex-vivo sensitivity to pralatrexate despite a general ex-vivo insensitivity to most drugs. This patient had progressed on treatment with DA-EPOCH and DHAP, but responded well to methotrexate, enabling him to undergo consolidating allogeneic stem cell transplantation. Conclusion Ex-vivo drug response profiling prior to treatment can be successfully implemented in the clinical routine. Our preliminary results suggest that DRP can predict in-vivo treatment resistance and support treatment guidance for individualized treatment. Disclosures Dreger: AbbVie, Gilead, Novartis, Riemser, Roche: Speakers Bureau; Neovii, Riemser: Research Funding; MSD: Membership on an entity's Board of Directors or advisory committees, Other: Sponsoring of Symposia; AbbVie, AstraZeneca, Gilead, Janssen, Novartis, Riemser, Roche: Consultancy.

Beschreibung: Background: Chronic lymphocytic leukemia (CLL) is the most common adult leukemia in the western world and shows a very heterogeneous clinical course. While the genetic landscape of CLL has been well characterized during recent years it can only partially explain the underlying biology of this heterogeneity. Proteogenomics could offer a valuable tool to fill this gap and improve the understanding of CLL biology. Methods: Here, we performed a large proteogenomic analysis (n=263) of three clinically annotated CLL cohorts: For the discovery cohort (Germany_1: n=68) we performed in-depth HiRIEF LC-MS based proteomics (more than 9000 proteins quantified) alongside genome-, transcriptome and ex-vivo drug response-profiling with 43 clinically established drugs. The proteome of two additional validation cohorts (Germany_2: n=44, Sweden_1: n=89), were characterized by data-independent acquisition (DIA) mass spectrometry. Results: To connect the CLL genotype with the molecular phenotype, we investigated associations between recurrent genetic alterations of CLL, mRNA expression and protein abundance. We found that trisomy 12, IGHV status and SF3B1 mutations had the greatest impact on protein abundances. CLL specific recurrent chromosomal deletions and gains (trisomy 12, del17p, del13q, del11q, gain8q24) consistently impacted on gene expression and protein abundance through gene dosage effects. We explored functional consequences of these gene dosage effects and found that the additional copy of chromosome 12 increased the abundance of central B-cell receptor (BCR) protein complexes through cis- and trans-effects, which could explain the increased response of trisomy 12 patient samples to BCR inhibition. Somatic mutations of TP53, ATM and XPO1 were associated with less, but specific and biologically-relevant protein abundance changes. p53 for instance, was the most upregulated protein in TP53 mutated samples, owing to the known stabilisation of mutant p53. This effect was not detectable on transcript level. ATM and XPO1 protein abundances were significantly lower in ATM and XPO1 mutated cases, indicating loss-of-function phenotypes of these mutations. To understand global similarities and differences between CLL patients on the proteomic level, we performed unsupervised clustering and identified clinically meaningful subgroups. Unsupervised clustering of the proteomics data identified six subgroups with contrasting clinical behaviour. TP53 mutations, IGHV status, trisomy 12 and their interactions explained five subgroups. These results show that quantitative mass spectrometry-based proteomics distinguished clinically relevant subgroups of CLL. Most importantly, we identified a previously unappreciated subgroup of CLL, comprising 20% of all cases, which could be uncovered by proteomic profiling and showed no association with frequent genetic or transcriptional alterations. This new CLL subgroup was characterized by accelerated disease progression, SF3B1 mutation-independent splicing alterations, metabolomic reprogramming and increased vulnerability to inhibitors of metabolic enzymes and the proteasome. Surprisingly, major BCR signaling proteins were downregulated in this subgroup, suggesting less dependence on BCR activity. In accordance with this observation, an unsupervised analysis revealed that low levels of many BCR signaling proteins (e.g. PLCG2 and PIK3CD) were associated with short time to next treatment. The existence of this subgroup could be confirmed in the validation cohorts. Finally, we performed an unsupervised multi-omics factor analysis (MOFA) across all omics data sets in parallel. This unsupervised analysis confirmed the existence of the above identified CLL subgroups and an important role of SF3B1 mutation-independent splicing alterations in CLL. Conclusion: Our integrative multi-omics analysis provides the first comprehensive overview of the interplay between genetic variants, the transcriptome, and the proteome, along with functional consequences for drug response and clinical outcome in CLL. Importantly, we identified a new subgroup with accelerated disease progression, a distinct proteomic signature and a clinically exploitable drug sensitivity profile. Figure Disclosures Mueller-Tidow: BiolineRx: Research Funding; Daiichi Sankyo: Research Funding; Pfizer: Membership on an entity's Board of Directors or advisory committees, Research Funding; BMBF: Research Funding; Wilhelm-Sander-Stiftung: Research Funding; Jose-Carreras-Siftung: Research Funding; Bayer AG: Research Funding; Deutsche Krebshilfe: Research Funding; Deutsche Forschungsgemeinschaft: Research Funding; Janssen-Cilag Gmbh: Membership on an entity's Board of Directors or advisory committees. Dreger:Neovii: Research Funding; Roche: Consultancy, Speakers Bureau; Riemser: Consultancy, Research Funding, Speakers Bureau; Novartis: Consultancy, Speakers Bureau; Janssen: Consultancy; Gilead: Consultancy, Speakers Bureau; AstraZeneca: Consultancy; AbbVie: Consultancy, Speakers Bureau. Stilgenbauer:Pharmacyclics: Consultancy, Honoraria, Other, Research Funding; Novartis: Consultancy, Honoraria, Other, Research Funding; Mundipharma: Consultancy, Honoraria, Other, Research Funding; Janssen-Cilag: Consultancy, Honoraria, Other: travel support, Research Funding; GlaxoSmithKline: Consultancy, Honoraria, Other: travel support, Research Funding; Gilead: Consultancy, Honoraria, Other: travel support, Research Funding; Genzyme: Consultancy, Honoraria, Other: travel support, Research Funding; Genentech: Consultancy, Honoraria, Other: travel support, Research Funding; F. Hoffmann-LaRoche: Consultancy, Honoraria, Other: travel support, Research Funding; Celgene: Consultancy, Honoraria, Other: travel support, Research Funding; Boehringer-Ingelheim: Consultancy, Honoraria, Other: travel support, Research Funding; Amgen: Consultancy, Honoraria, Other: travel support, Research Funding; AbbVie: Consultancy, Honoraria, Other: travel support, Research Funding. Tausch:Roche: Consultancy, Honoraria, Research Funding; AbbVie: Consultancy, Honoraria, Research Funding; Janssen-Cilag: Consultancy, Honoraria, Research Funding. Dietrich:Roche: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees; KITE: Membership on an entity's Board of Directors or advisory committees.