Highlights d scRNA-seq reveals the complex interplay among cells within the evolving tumor d T cells recruited from lymph nodes are activated and clonally expand in situ d Temporally regulated, functionally distinct stromal populations exist d Cross-compartment interactions can be identified using the CellPhoneDB database

Highlights d Molecular subtypes and genetics shape immune landscape in hematological malignancies d Cytotoxic T/NK cell infiltration in MDS-like AML with TP53 mutations and ABC DLBCL d Methylation changes suppress HLA genes in AML and induce cancer antigens in myeloma d Cancer type-specific targets such as VISTA in myeloid and CD70 in lymphoid cancers

Adaptive immune system uses T cell receptors (TCRs) to recognize pathogens and to consequently initiate immune responses. TCRs can be sequenced from individuals and methods analyzing the specificity of the TCRs can help us better understand individuals’ immune status in different disorders. For this task, we have developed TCRGP, a novel Gaussian process method that predicts if TCRs recognize specified epitopes. TCRGP can utilize the amino acid sequences of the complementarity determining regions (CDRs) from TCRα and TCRβ chains and learn which CDRs are important in recognizing different epitopes. Our comprehensive evaluation with epitope-specific TCR sequencing data shows that TCRGP achieves on average higher prediction accuracy in terms of AUROC score than existing state-of-the-art methods in epitope-specificity predictions. We also propose a novel analysis approach for combined single-cell RNA and TCRαβ (scRNA+TCRαβ) sequencing data by quantifying epitope-specific TCRs with TCRGP and identify HBV-epitope specific T cells and their transcriptomic states in hepatocellular carcinoma patients.

We report the immunogenomic landscape of >10,000 hematological malignancies by integrating large-scale genomic, epigenomic, and transcriptomic datasets in this article. During its preparation, we submitted an incorrect version of Figure 1A, in which the numbers of the cases in the Hemap dataset were incorrect (1,288 and 4,293 lymphoma and leukemia samples, respectively; the correct numbers are 1,300 and 4,281). Similarly, in Figure S1A, the number of cell lines in CCLE dataset was incorrect (CHL n = 9 changed to n = 8, and unknown n = 7 is now included). The number of cases reported in the first paragraph of the Results section has also been corrected to reflect these revisions (''We used 7,092 samples from 36 hematological malignancies, with 770 healthy donor hematological cell populations and 610 cell lines as controls [Pö lö nen et al., 2019], to comprehensively analyze immunological properties in hematological cancer transcriptomes [Figures 1A and S1A; Table S1]''). These errors do not affect any of the data or conclusions in the article, and the figures have been revised in the online and printed versions of the paper, which differ from the version originally published online on July 9, 2020. We apologize for any confusion these errors may have caused.

T cell receptors (TCRs) can recognize various pathogens and consequently start immune responses. TCRs can be sequenced from individuals and methods that can analyze the specificity of the TCRs can help us better understand the individual's immune status in different diseases. We have developed TCRGP, a novel Gaussian process (GP) method that can predict if TCRs recognize certain epitopes. This method can utilize different CDR sequences from both TCRα and TCRβ chains from single-cell data and learn which CDRs are important in recognizing the different epitopes. We have experimented with one previously presented and one new data set and show that TCRGP outperforms other state-of-the-art methods in predicting the epitope specificity of TCRs on both data sets. The software implementation and data sets are available at https://github.com/emmijokinen/TCRGP.

Myeloid neoplasms with erythroid or megakaryocytic differentiation include pure erythroid leukemia (PEL), myelodysplastic syndrome (MDS) with erythroid features, and acute megakaryoblastic leukemia (FAB M7) and are characterized by poor prognosis and limited treatment options. Here, we investigate the drug sensitivity landscape of these rare malignancies. We show that acute myeloid leukemia (AML) cells with erythroid or megakaryocytic differentiation depend on the anti-apoptotic protein BCL-XL, rather than BCL-2, using combined ex vivo drug sensitivity testing, genetic perturbation, and transcriptomic profiling. High-throughput screening of > 500 compounds identified the BCL-XL-selective inhibitor A-1331852 and navitoclax as highly effective against erythroid/megakaryoblastic leukemia cell lines. In contrast, these AML subtypes were resistant to the BCL-2 inhibitor venetoclax used clinically in the treatment of AML. Consistently, genome-scale CRISPR-Cas9 and RNAi screening data demonstrated striking essentiality of BCL2L1 encoding BCL-XL, but not BCL2 or MCL1, for the survival of erythroid/megakaryoblastic leukemia cell lines. Single-cell and bulk transcriptomics of patient samples with erythroid and megakaryoblastic leukemias identified high BCL2L1 expression compared to other subtypes of AML and other hematological malignancies, where BCL2 and MCL1 were more prominent. BCL-XL inhibition effectively killed blasts in AML patient samples with erythroid or megakaryocytic differentiation ex vivo and reduced tumor burden in a mouse erythroleukemia xenograft model. Combining BCL-XL inhibitor with the JAK inhibitor ruxolitinib showed synergistic and durable responses in cell lines. Our results suggest targeting BCL-XL as a potential therapy option in erythroid/megakaryoblastic leukemias and highlight an AML subgroup with potentially reduced sensitivity to venetoclax-based treatments.

The BCL2 inhibitor venetoclax has revolutionized the treatment of acute myeloid leukemia (AML) patients not benefitting from intensive chemotherapy. Nevertheless, treatment failure remains a challenge, and predictive markers are needed, particularly for relapsed or refractory (R/R) AML. Ex vivo drug sensitivity testing may correlate with outcomes, but its prospective predictive value remains unexplored. Here we report the results of the first stage of the prospective Phase 2 VenEx trial evaluating the utility and predictiveness of venetoclax sensitivity testing using different cell culture conditions and cell viability assays in patients receiving venetoclax-azacitidine (NCT04267081). Participants with de novo AML ineligible for intensive chemotherapy, R/R AML, or secondary AML were included. The primary endpoint was the treatment response in ex vivo sensitive participants and the key secondary endpoints were the correlation of sensitivity with responses and survival. Venetoclax sensitivity testing was successful in 38/39 participants. Experimental conditions significantly influenced predictive accuracy. Blast-specific venetoclax sensitivity measured in conditioned medium most accurately correlated with treatment outcomes; 88% of sensitive participants achieved treatment response. Median survival was significantly longer for ex vivo sensitive participants (14. 6 months for s ensitive, 3. 5 for insensitive, p < 0 . 001). T his analysis illustrates the feasibility of integrating drug-response profiling into clinical practice and demonstrates excellent predictivity.

Deregulation of the Janus kinase/signal transducers and activators of transcription (JAK/STAT) signaling pathway is found in cancer with STAT5A/B controlling leukemic cell survival and disease progression. As mutations in STAT5B, but not STAT5A, have been frequently described in hematopoietic tumors, we used BCR/ABL as model systems to investigate the contribution of STAT5A or STAT5B for leukemogenesis. The absence of STAT5A decreased cell survival and colony formation. Even more drastic effects were observed in the absence of STAT5B. STAT5B-deficient cells formed BCR/ABL+ colonies or stable cell lines at low frequency. The rarely evolving Stat5b−/− cell lines expressed enhanced levels of BCR/ABL oncoprotein compared to wild-type cells. In line, Stat5b−/− leukemic cells induced leukemia with a significantly prolonged disease onset, whereas Stat5a−/− cells rapidly caused a fatal disease superimposable to wild-type cells. RNA-sequencing (RNA-seq) profiling revealed a marked enhancement of interferon (IFN)-α and IFN-γ signatures in Stat5b−/− cells. Inhibition of IFN responses rescued BCR/ABL+ colony formation of Stat5b−/−-deficient cells. A downregulated IFN response was also observed in patients suffering from leukemia carrying STAT5B mutations. Our data define STAT5B as major STAT5 isoform driving BCR/ABL+ leukemia. STAT5B enables transformation by suppressing IFN-α/γ, thereby facilitating leukemogenesis. Our findings might help explain the high frequency of STAT5B mutations in hematopoietic tumors.