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  • 1
    In: Cancer Cell, Elsevier BV, Vol. 38, No. 3 ( 2020-09), p. 424-428
    Type of Medium: Online Resource
    ISSN: 1535-6108
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2020
    detail.hit.zdb_id: 2074034-7
    detail.hit.zdb_id: 2078448-X
    SSG: 12
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  • 2
    In: Cell Reports, Elsevier BV, Vol. 8, No. 6 ( 2014-09), p. 1943-1956
    Type of Medium: Online Resource
    ISSN: 2211-1247
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2014
    detail.hit.zdb_id: 2649101-1
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  • 3
    In: Leukemia, Springer Science and Business Media LLC, Vol. 32, No. 7 ( 2018-7), p. 1643-1656
    Type of Medium: Online Resource
    ISSN: 0887-6924 , 1476-5551
    RVK:
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2018
    detail.hit.zdb_id: 2008023-2
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  • 4
    In: Blood, American Society of Hematology, Vol. 132, No. Supplement 1 ( 2018-11-29), p. 2596-2596
    Abstract: Understanding factors that shape the immune landscape across hematological malignancies is essential for immunotherapy development. How cancer-cell intrinsic genomic and epigenetic alterations influence immune signatures in hematological malignancies is not known. Here, we integrated over 8,000 transcriptomes of hematologic cancers and multilevel genomic datasets to investigate associations of immune states to cancer molecular subtypes, genetic and epigenetic alterations, and clinical outcomes. We utilized a resource of over 8,000 transcriptomes collected across 36 hematologic malignancies and normal hematopoietic cells (Hemap), together with multi-omics datasets of acute myeloid leukemia (AML) and diffuse large B-cell lymphoma (DLBCL) from The Cancer Genome Atlas and other sources (Figure). In addition to gene expression data, we integrated somatic DNA alterations, methylation data, multiplex immunohistochemistry (mIHC), and flow cytometry to comprehensively map immune-associated features and validate the robustness of the findings. To characterize the composition of the cytolytic immune infiltrate from bulk transcriptomes, we defined a signature of genes most specifically expressed in cytotoxic CD8+ T lymphocytes and natural killer (NK) cells termed cytolytic score. We found significant heterogeneity in the cytotoxic lymphocyte infiltration signature across hematologic malignancies. Highest cytolytic infiltrate was detected in lymphomas and correlated with IFN-γ and myeloid cell infiltration signatures including CXCL9-11 and IDO1, distinguishing the lymphoma microenvironment from leukemias. In addition to transcriptomic microenvironmental properties, specific genetic alterations were associated with cytotoxic lymphocyte infiltration. In DLBCL, driver alterations enriched in the germinal center B-cell like (GCB) molecular subtype including BCL2 translocations and KMT2D were linked to an immune-cold transcriptomic phenotype. In contrast, DTX1 alterations defined immune-infiltrated lymphomas within the GCB molecular subtype. In AML, TP53 mutations and complex karyotype were enriched in a distinct tSNE-based transcriptomic cluster characterized by increased immune infiltration in the bone marrow (BM). Given the importance of effective antigen presentation for adaptive anti-tumor immune responses, we aimed to understand the transcriptional regulation of HLA genes and co-stimulatory and co-inhibitory signaling in subtypes of hematological malignancies. Downregulation of the antigen-presenting HLA II genes was associated with CpG methylation of the promoter region of the HLA class II master regulator CIITA in distinct transcriptomic clusters of AML harboring PML-RARA or NPM1 alterations. Expression of genes encoding immune checkpoint molecules was strongly influenced by the cell-of-origin and microenvironment of each cancer type. We identified novel associations of inhibitory immune checkpoint molecules to disease subtypes, such as VISTA/PD1-H enriched in myeloid malignancies including AML, CML, and MDS, validated by mIHC performed on BM biopsies. Furthermore, variation in the expression of several genes encoding immune checkpoints was associated with somatic mutations (e.g. CD70 in DLBCL), copy-number alterations (e.g. MICB in DLBCL), and DNA methylation (e.g. PDL1 and PDCD1LG2 in AML). Finally, we integrated GTEx gene expression data across tissues to define cancer-germline antigens (CGAs) with an immune privileged tissue expression pattern. CGAs were frequently expressed in multiple myeloma and DLBCL compared to other hematologic malignancies. CGA expression was associated with cytogenetic alterations and increased MYC activity signature in myeloma and CD58 and KLHL6 mutations in DLBCL. In addition, CGA expression in myeloma and DLBCL was linked to reduced antigen gene promoter methylation and decreased survival. In summary, our findings demonstrate that molecular subtypes of hematological malignancies harbor distinct immunological signatures influenced by genetic and epigenetic alterations. Integrating genetic, epigenetic, and transcriptomic data may facilitate the development of precision immune intervention strategies in hematological malignancies. Figure. Figure. Disclosures Leppa: Bayer: Research Funding; Celgene: Consultancy; Roche: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Research Funding; Takeda: Consultancy, Research Funding. Mustjoki:Bristol-Myers Squibb: Honoraria, Research Funding; Celgene: Honoraria; Pfizer: Honoraria, Research Funding; Ariad: Research Funding; Novartis: Honoraria, Research Funding.
    Type of Medium: Online Resource
    ISSN: 0006-4971 , 1528-0020
    RVK:
    RVK:
    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2018
    detail.hit.zdb_id: 1468538-3
    detail.hit.zdb_id: 80069-7
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  • 5
    In: SSRN Electronic Journal, Elsevier BV
    Type of Medium: Online Resource
    ISSN: 1556-5068
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2019
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  • 6
    In: Cancer Discovery, American Association for Cancer Research (AACR), Vol. 12, No. 2 ( 2022-02-01), p. 388-401
    Abstract: We generated ex vivo drug-response and multiomics profiling data for a prospective series of 252 samples from 186 patients with acute myeloid leukemia (AML). A functional precision medicine tumor board (FPMTB) integrated clinical, molecular, and functional data for application in clinical treatment decisions. Actionable drugs were found for 97% of patients with AML, and the recommendations were clinically implemented in 37 relapsed or refractory patients. We report a 59% objective response rate for the individually tailored therapies, including 13 complete responses, as well as bridging five patients with AML to allogeneic hematopoietic stem cell transplantation. Data integration across all cases enabled the identification of drug response biomarkers, such as the association of IL15 overexpression with resistance to FLT3 inhibitors. Integration of molecular profiling and large-scale drug response data across many patients will enable continuous improvement of the FPMTB recommendations, providing a paradigm for individualized implementation of functional precision cancer medicine. Significance: Oncogenomics data can guide clinical treatment decisions, but often such data are neither actionable nor predictive. Functional ex vivo drug testing contributes significant additional, clinically actionable therapeutic insights for individual patients with AML. Such data can be generated in four days, enabling rapid translation through FPMTB. See related commentary by Letai, p. 290. This article is highlighted in the In This Issue feature, p. 275
    Type of Medium: Online Resource
    ISSN: 2159-8274 , 2159-8290
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2022
    detail.hit.zdb_id: 2607892-2
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  • 7
    In: Blood, American Society of Hematology, Vol. 132, No. Supplement 1 ( 2018-11-29), p. 2774-2774
    Abstract: BACKGROUND A complex interaction between blasts and surrounding cells in the acute myeloid leukemia (AML) bone marrow (BM) microenvironment sustains blast proliferation and confers chemoresistance. T- and NK-cells have been shown to be dysfunctional in AML, which might be associated with immune evasion and poor prognosis. Here, we present a comprehensive analysis of the immune contexture of the AML BM at diagnosis and study its interaction with clinicopathological variables. METHODS Diagnostic BM biopsies (n=69) were collected from AML patients treated in the Helsinki University Hospital during 2005-2017 and age and gender-matched controls (n=12) to construct tissue microarrays (TMA). Using 8-plex immunohistochemistry (mIHC) and computerized image analysis, we determined cell abundance and immunophenotypic states of millions of immune cells. Immunoprofiles were integrated with a total of 120 clinicopathological variables including cytogenetics and molecular genetics, ELN (European Leukemia Net) risk classification, disease burden parameters, and patient demographics. RESULTS Unsupervised hierarchical clustering of the immunologic contexture defined by mIHC analysis grouped AML patients distinctly from control subjects (Fig 1a). By extracting significant differences (Mann-Whitney U test, q 〈 0.05) and annotating immunologic markers as either anti-cancer or immunosuppression drivers based on literature, we observed an interesting polarization of increased immunosuppression in AML compared to control BM (Fig 1b). In AML patients, lower fraction of granzyme B expressing (GrB+) cells was noted both in CD3+CD4+ helper T-cells (10.9% vs 24.3%, q=0.002, in AML vs control BM) and CD3+CD8+ cytotoxic T-cells (23.5% vs 33.5%, q=0.02). Moreover, we observed pronounced T-cell inhibition features, such as higher proportion of regulatory T-cells (1.5% vs 0.0% FOXP3+ of helper T-cells, q 〈 0.001), and lower expression of class I HLA in BM cells (89.1% vs 100.0%, q 〈 0.001). Putative exhausted PD1+ T-cells were also markedly enriched in the AML BM (15.7% vs 1.7% PD1+ of helper T-cells and 13.2% vs 2.0% PD1+ of cytotoxic T-cells, q 〈 0.001). Among the high interpatient heterogeneity, we discovered two main immune profiles. Cluster 1 was characterized with higher proportion (Log2 fold change 〉 0.5, q 〈 0.05) of cytotoxic T-cells and expression of CD57, CD27, and CD25 in T-cells, as well as higher expression of PD-L1 in the BM. Moreover, lower expression (Log2 fold change 〈 0.5, q 〈 0.05) of OX40 and CD45RO in T-cells and proportion of CD11c+BDCA1+ (type 1 myeloid dendritic cells) were observed. Patients of Cluster 1 were associated with longer event-free survival (EFS; HR 1.9, p=0.049) as well as lower age (median 53.6 vs 64.4 years, p=0.001). No connection between immunologic clusters and FLT3 or NPM1 genotype, complex karyotype, ELN risk class, blast proportion or leukocyte count was found. To support clinical decision-making in ELN 2017 intermediate-risk patients, we developed a risk stratification model focusing on this particular subgroup (n=28) using L1-penalized Cox regression. Patient age over 60 years (HR 8.1, CI95% 2.5-26.6 p 〈 0.001) and low proportion of CD45+CD2+CD3- NK-cells (HR 0.92, CI95% 0.85-0.99 p=0.03) predicted worse EFS. In intermediate-risk patients (n=68) of a separate validation cohort (n=145) analyzed with flow cytometry, low NK cell proportion and high age predicted worse EFS (HR 2.7 CI95% 1.6-4.6 p 〈 0.001) and OS (HR 3.9 CI95% 2.1-7.3 p 〈 0.001) after adjusting with induction therapy protocol. Lower NK-cell proportion was associated with FLT3-ITD genotype (0.45% vs 1.1% NK-cells/all cells in FLT3-ITD+ vs FLT3-ITD- AML patients, p=0.01), and higher than median PB leukocyte (WBC) count (0.50% vs 1.9% NK-cells/all cells in patients with PB WBC ≥7.8x10E9/L vs 〈 7.8x10E9/L, p 〈 0.001). CONCLUSIONS Using TMA cytometrics with mIHC and automated image analysis for detailed characterization of the immune contexture, we discovered pronounced immune exhaustion and suppression in AML BM. An aging-related immune profile was identified and was associated with poor prognosis. Furthermore, survival prediction of intermediate-risk patients might be enhanced by considering patient age and NK-cell proportion. Taken together, immunophenotyping of AML patients might improve risk stratification and identify a subgroup benefiting from immunomodulatory treatments. Disclosures Pallaud: Novartis: Employment. Marques Ramos:Novartis: Employment. Porkka:Novartis: Honoraria, Research Funding; Celgene: Honoraria, Research Funding. Mustjoki:Ariad: Research Funding; Novartis: Honoraria, Research Funding; Celgene: Honoraria; Bristol-Myers Squibb: Honoraria, Research Funding; Pfizer: Honoraria, Research Funding.
    Type of Medium: Online Resource
    ISSN: 0006-4971 , 1528-0020
    RVK:
    RVK:
    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2018
    detail.hit.zdb_id: 1468538-3
    detail.hit.zdb_id: 80069-7
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  • 8
    In: Blood, American Society of Hematology, Vol. 128, No. 22 ( 2016-12-02), p. 1725-1725
    Abstract: INTRODUCTION The recent success of checkpoint blockade immunotherapies in diverse solid tumors has prompted the evaluation of these treatments in hematologic malignancies such as acute myeloid leukemia (AML). It is critical to identify the patient and disease subsets that could respond to such therapies. Infiltration of tumors by cytotoxic T lymphocytes (CTLs) has been associated with better prognosis and responses to checkpoint inhibition. We hypothesized that the presence of a substantial fraction of activated CTLs and natural killer (NK) cells in the blood and bone marrow samples of hematologic tumors could indicate a preexisting active immune response potentially targeting the tumor cells. Moreover, the density of the immune infiltrate could shape and be shaped by the expression of cancer-germline and leukemia-associated antigens (LAAs), antigen-presenting machinery (APM) and immunosuppressive genes by the tumor cells. Here, we examined these immunological properties of hematological tumors in large-scale gene expression datasets to identify immunologically active patient subsets. METHODS Curated set of 9,544 transcriptomes collected across 36 hematological malignancies (HEMAP), including 1,858 AML cases was utilized to identify subsets of patients with existing, potentially tumor-directed immune responses. Additional multi-omics datasets of 173 AML patients from The Cancer Genome Atlas (TCGA) were integrated to gain insight into the genetic landscape of immunologically active patients. Cytolytic activity (geometric mean of GZMA (granzyme A) and PRF1 (perforin) transcript levels, Rooney et al., Cell 2015) was used as a marker of immunologic activity. Cytolytic activity was correlated to the expression levels of all transcripts, gene sets from collections such as MSigDB and manually curated gene sets representing the APM (HLA-A, -B, -C, B2M), 145 known cancer-germline antigens as well as established LAAs such as WT1 and PRTN3. Furthermore, we used an in silico flow cytometry approach, CIBERSORT (Newman et al., Nat Methods 2015), to infer the relative fractions of 22 immune cell subpopulations from the gene expression data to dissect the immune cell composition of the samples. RESULTS Cytolytic activity showed high correlation with other transcripts expressed in activated CTLs and NK cells (e.g. GZMB, GNLY, KLRB1, CD8A, CD2; Spearman's R ≥ 0.7) as well as lymphocyte activation-related gene sets across both the HEMAP and the TCGA AML datasets, validating it as a robust and specific metric of active cellular immunity. When correlated to the CIBERSORT immune cell populations, cytolytic activity was positively associated with CD8 T cells and showed a negative correlation to the proportion of M2 macrophages. High levels of cancer-germline antigens were associated with decreased expression of components of the APM and low cytolytic activity, suggesting HLA downregulation as a mechanism of immune evasion by cancer-germline antigen-expressing tumor cells. We observed extensive heterogeneity in the cytolytic activity between different diseases and subtypes within the same disease, most prominently in AML. In AML patients, complex karyotype and unfavorable prognosis were correlated with high cytolytic activity, indicating biological similarity of the immune-infiltrated tumors. Furthermore, TP53 mutations, genome fragmentation and immune checkpoint transcripts such as CD274 (PD-L1), PDCD1LG2 (PD-L2), CTLA4 and LAG3 were enriched within the complex karyotype cluster in the TCGA AML dataset. In contrast, mutations in NPM1 and FLT3 showed a modest but significant negative correlation to cytolytic activity. CIBERSORT analysis revealed that AML cases with low cytolytic activity preferentially had enrichment of an eosinophilic phenotype in addition to increased M2 polarization of macrophages. CONCLUSIONS Using large-scale transcriptomics approaches, we were able to identify patient subsets with variable levels of immune cytolytic activity within hematologic malignancies. Furthermore, we identified connections between the cytotoxic immune response and genetic properties of AML tumors. These observations have potential clinical implications, as the choice of patients to clinical trials receiving immune checkpoint blockade immunotherapies would require careful consideration in light of the observed immunological heterogeneity. Disclosures Mustjoki: Bristol-Myers Squibb: Honoraria, Research Funding; Pfizer: Honoraria, Research Funding; Novartis: Honoraria, Research Funding; Ariad: Research Funding.
    Type of Medium: Online Resource
    ISSN: 0006-4971 , 1528-0020
    RVK:
    RVK:
    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2016
    detail.hit.zdb_id: 1468538-3
    detail.hit.zdb_id: 80069-7
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  • 9
    In: Nucleic Acids Research, Oxford University Press (OUP), Vol. 47, No. 13 ( 2019-07-26), p. e76-e76
    Abstract: Existing large gene expression data repositories hold enormous potential to elucidate disease mechanisms, characterize changes in cellular pathways, and to stratify patients based on molecular profiles. To achieve this goal, integrative resources and tools are needed that allow comparison of results across datasets and data types. We propose an intuitive approach for data-driven stratifications of molecular profiles and benchmark our methodology using the dimensionality reduction algorithm t-distributed stochastic neighbor embedding (t-SNE) with multi-study and multi-platform data on hematological malignancies. Our approach enables assessing the contribution of biological versus technical variation to sample clustering, direct incorporation of additional datasets to the same low dimensional representation, comparison of molecular disease subtypes identified from separate t-SNE representations, and characterization of the obtained clusters based on pathway databases and additional data. In this manner, we performed an integrative analysis across multi-omics acute myeloid leukemia studies. Our approach indicated new molecular subtypes with differential survival and drug responsiveness among samples lacking fusion genes, including a novel myelodysplastic syndrome-like cluster and a cluster characterized with CEBPA mutations and differential activity of the S-adenosylmethionine-dependent DNA methylation pathway. In summary, integration across multiple studies can help to identify novel molecular disease subtypes and generate insight into disease biology.
    Type of Medium: Online Resource
    ISSN: 0305-1048 , 1362-4962
    RVK:
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2019
    detail.hit.zdb_id: 1472175-2
    SSG: 12
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  • 10
    In: Cancer Cell, Elsevier BV, Vol. 38, No. 3 ( 2020-09), p. 380-399.e13
    Type of Medium: Online Resource
    ISSN: 1535-6108
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2020
    detail.hit.zdb_id: 2074034-7
    detail.hit.zdb_id: 2078448-X
    SSG: 12
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