ALBERT

Description: 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.

Description: Gene expression profiles enable global analysis that can interrogate the activity patterns of various cellular pathways across biological conditions. Indeed this approach has generated data across numerous patient populations over the past decades allowing molecular stratification of disease, including hematological and lymphoid malignancies. An emerging theme from cancer genomics studies is the remarkable similarity between specific cancers of different lineages. For example, particular subtypes of bladder cancer resemble breast and lung tumors despite their different tissue of origin. Whether the same may hold true across hematological and lymphoid cancers is currently unknown. In normal development, cells commit to their lineage by activation of densely interconnected transcription factors (TFs), or TF modules, in a series of decision points at which a choice is made between alternative lineage fates. Their mutual exclusivity can be used for discovery of key genes of both normal and malignant hematopoiesis. Importantly, TF translocations represent frequent genetic events in hematological cancer. We harnessed computational methods to organize and characterize samples from 9544 distinct hematological and lymphoid cancer patients, healthy donors and pre-malignant stages generating a pan-cancer resource for interrogating their molecular states. A central part of the resource is a curated transcriptome dataset that we provide across 37 different disease subtypes as an interactive online resource (http://compbio.uta.fi/hemap/). The dimensionality reduction method known as t-Distributed Stochastic Neighbor Embedding (t-SNE) achieved optimal placement of highly similar samples at close proximity in two dimensions, enabling a biologically meaningful visualization of the data set as well as comparative analysis based on gene signatures, drug target expression and regulatory network state. For patient stratification, unsupervised clustering in t-SNE space yielded comparable performance to robust and reproducible classifiers. We further demonstrate with multilevel data from The Cancer Genome Atlas that new samples can be included in context of the existing patient profiles. Data integration highlights the molecular architecture that relates to the clinical and genetic features of the samples studied, revealing new insight on molecular phenotypes that distinguish AML samples that lack a subtype based on current clinical stratification. Finally, we used the resource to provide a roadmap for candidate drug therapies and quantify the regulatory network alterations across hematological malignancies. The divergence of cancer regulatory networks from the reference healthy cell states and mutually exclusive patterns of TF expression that are specific to the different malignancies pave the way towards therapies targeting the cancer epigenome and characterization of downstream targets of TF-fusions or aberrant enhancer usage, as exemplified with independent validation data at the IRX1 and ERG loci. Disclosures No relevant conflicts of interest to declare.

Description: 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.