immune landscapes predict chemotherapy resistance and ... · (pd-l1)-targeted immunotherapy occur...

Immune landscapes predict chemotherapy resistance and

immunotherapy response in acute myeloid leukemia

Jayakumar Vadakekolathu1, Mark D. Minden2, Tressa Hood3, Sarah E. Church3, Stephen

Reeder1, Heidi Altmann4, Amy H. Sullivan3, Elena Viboch3, Tasleema Patel5, Narmin

Ibrahimova2, Sarah E. Warren3, Andrea Arruda2, Yan Liang3, John Muth6, Marc Schmitz7,8,9,

Alessandra Cesano3, A. Graham Pockley1,10, Peter J.M. Valk11, Bob Löwenberg11, Martin

Bornhäuser4,8,9, Sarah K. Tasian5, Michael P. Rettig12, Jan Davidson-Moncada6, John F.

DiPersio12, Sergio Rutella1,10,*

1John van Geest Cancer Research Centre, Nottingham Trent University, Nottingham, United Kingdom 2Division of Medical Oncology and Hematology, Princess Margaret Cancer Centre, Toronto, Canada 3NanoString Technologies Inc., Seattle, WA, United States of America 4Department of Medicine, Universitätsklinikum Carl Gustav Carus, Dresden, Germany 5Department of Pediatrics, Division of Oncology and Centre for Childhood Cancer Research, Children’s Hospital of Philadelphia and University of Pennsylvania School of Medicine, PA, United States of America 6MacroGenics Inc., Rockville, MD, United States of America 7Institute of Immunology, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany 8National Center for Tumor Diseases (NCT), Partner Site Dresden, Dresden, Germany 9German Cancer Consortium (DKTK), Partner Site Dresden, and German Cancer Research Center (DKFZ), Heidelberg, Germany 10Centre for Health, Ageing and Understanding Disease (CHAUD), Nottingham Trent University, Nottingham, United Kingdom 11Department of Hematology, Erasmus University Medical Centre, Rotterdam, Netherlands 12Division of Oncology, Department of Internal Medicine, Washington University in St. Louis, St. Louis, MO, United States of America

Running title: Immune signatures predict therapeutic responses in AML

*Correspondence to: Professor Sergio Rutella, MD PhD FRCPath John van Geest Cancer Research Centre College of Science and Technology Nottingham Trent University - Clifton Campus Nottingham, NG11 8NS United Kingdom Tel.: +44 (0) 115 848 3205 E-mail: [email protected] Open Researcher and Contributor Identifier (ORCID) ID: 0000-0003-1970-7375

certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which was notthis version posted July 26, 2019. . https://doi.org/10.1101/702001doi: bioRxiv preprint

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Abstract

This study dissected the complexity of the immune architecture of acute myeloid leukemia

(AML) at high resolution and assessed its influence on therapeutic response. Using 387

primary bone marrow samples from three discovery cohorts of children and adults with AML,

we defined immune-infiltrated and immune-depleted disease subtypes and unraveled critical

differences in immune gene expression across age groups and disease stages. Importantly,

interferon (IFN)-γ-related mRNA profiles were predictive for both chemotherapy resistance

and response of primary refractory/relapsed AML to flotetuzumab immunotherapy. Our

compendium of microenvironmental gene and protein profiles sheds novel insights into the

immuno-biology of AML and will inform the delivery of personalized immunotherapies to IFN-

γ-dominant AML subtypes.


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Acute myeloid leukemia (AML) is a molecularly and clinically heterogeneous hematological

malignancy1. The discovery of the genomic landscape of AML, including the identification of

targetable mutations2, has propelled the development of novel anti-leukemic agents and is

enabling disease classification and patient stratification into favorable, intermediate or

adverse risk groups3. Despite success in many areas, AML is cured in only 35-40% of

patients <60 years of age and in 5-15% of patients >60 years of age. While chemotherapy

resistance is common, the majority of patients die of disease relapse. Investigation of new

molecularly-targeted and immuno-modulatory agents therefore remains a high priority for

both children and adults4.

Tumor phenotypes are dictated not only by the neoplastic cell component, but also by the

immunologic milieu within the tumor microenvironment (TME), which is equipped to subvert

host immune responses and hamper effector T-cell function5. In silico approaches have

been instrumental for the identification of immunogenomic features with therapeutic and

prognostic implications. In solid tumors, six immune subtypes have been described (wound

healing, interferon (IFN)-γ-dominant, inflammatory, lymphocyte-depleted, immunologically

quiet, and transforming growth factor (TGF)-β-dominant). These are characterized by

differences in macrophage or lymphocyte signatures, T helper type (Th)-1 to Th2 cell ratio,

extent of intratumoral heterogeneity and neoantigen load, aneuploidy, cell proliferation,

expression of immunomodulatory genes, and patient survival6.

Although immunotherapy may be an attractive modality to exploit in patients with AML7, the

ability to predict the groups of patients and the forms of leukemia that will respond to

immune targeting remains limited8-11. Clinical studies in patients with solid tumors have

shown that responses to anti-programmed cell death 1 (PD-1)/programmed death ligand 1

(PD-L1)-targeted immunotherapy occur most often in individuals with immune-inflamed

lesions that are characterized by pre-existing CD8+ T-cell responses, release of pro-

inflammatory and effector cytokines12-14, and an augmented T-cell receptor (TCR) clonal

diversity pre-treatment15,16. The above T-cell inflamed gene expression profile (GEP) is a


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measure of IFN-γ-responsive genes that are related to adaptive immune resistance (AIR)

mechanisms of immune escape such as indoleamine 2,3-dioxygenase-1 (IDO1) and PD-

L117, and is predictive of clinical benefit with pembrolizumab immunotherapy18,19. Although

IFN-γ plays a critical role in eliciting anti-tumor T-cell activity and enabling tumor rejection,

prolonged IFN-γ signaling under conditions of persistent antigen exposure has been shown

to activate a PD-L1-independent, STAT1-driven multigenic program which confers

resistance to radiotherapy and anti-cytotoxic T-lymphocyte antigen 4 (CTLA-4)

immunotherapy in mouse models of melanoma20.

Herein, we used targeted immune gene expression profiling (IGEP) and spatially-resolved

multiplexed digital spatial profiling (DSP) for the high-dimensional analysis of the

immunological contexture of a broad collection of bone marrow (BM) samples from patients

with AML and for the identification of molecular determinants of immunotherapeutic benefit.

We reveal unifying immune features and critical differences that define classes and

subclasses of TMEs and deliver predictions of chemotherapy resistance, survival and

immunotherapy response that are beyond the current capabilities of single molecular

markers.


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Results

Targeted immune gene expression profiling (IGEP) identifies immune subtypes of

AML.

We first analyzed unfractionated, archival BM samples from treatment-naïve patients with

non-promyelocytic AML (PMCC discovery series; n=290 cases; Table 1)21. We derived

immune scores from mRNA expression levels, similar to those of previous publications, and

devised an RNA-based, quantitative metric of immune infiltration22,23. As shown in Extended

Data Fig. 1A-B, patients with adverse cytogenetic features exhibited a shorter relapse-free

survival (RFS) and overall survival (OS) compared with patients with intermediate and

favorable cytogenetic risk, thus confirming the overall trends of well-established European

Leukemia-Net (ELN) categories24. A Pearson correlation matrix of immune gene sets

allowed us to identify co-expression patterns of pre-defined immune cell types and immune

biological activities. Immune signature modules in pre-treatment BM samples reflected the

co-expression of genes associated with 1) IFN-γ biology, 2) adaptive immune responses,

and 3) myeloid cell abundance (macrophages, neutrophils and dendritic cells (DCs); Fig.

1A). We then computed the sum of the individual scores in each signature module in Fig. 1A

and generated three immune scores (IFN-dominant, adaptive and myeloid) which

individually separated AML cases according to high and low expression values (Extended

Data Fig. 1C). When considered in aggregate, IFN-dominant, adaptive and myeloid scores

dichotomized BM samples into two immune subtypes, which will be herein termed immune-

infiltrated and immune-depleted (Fig. 1B)25. These immune subtypes of AML expressed

comparable levels of leukemia-associated antigens CD34, CD123 (IL3RA) and CD117 (KIT),

suggesting that targeted IGEP of bulk BM specimens largely captured elements of the

immunological TME rather than features of the tumor cell compartment (Extended Data Fig.

1D).

As shown in Fig. 1C, AML cases with immune-infiltrated profiles expressed significantly

higher levels of IFN-stimulated genes and T-cell recruiting factors (STAT1, CXCL10, IRF1),


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T-cell markers and cytolytic effectors (CD8A, CD8B, GZMB, PRF1), counter-regulatory

immune checkpoints and immunotherapy drug targets (IDO1, CTLA4, PD-L1 and BTLA),

and molecules involved in antigen processing and presentation (TAP1, TAP2, HLA-A, HLA-

B and HLA-C). Conceivably, high T-cell infiltration, MHC expression and PD-L1 levels in the

immune-infiltrated AML subtype reflected a pre-existing IFN-γ-driven adaptive immune

response which has previously been associated with suppressed anti-tumor immune

reactivity13,26, but also with immunotherapy responses in patients with solid tumors9,18,27 and

AML11. The expression of STAT1, a central component of the IFN-γ signaling pathway and

predictor of response to immune checkpoint blockade28, was more strongly correlated with

the presence of T-cell inhibitory receptors TNFRSF14 (a ligand for the immunoglobulin

superfamily members BTLA and CD160), PD-L1, HAVCR2 (Tim-3) and LAG-3, and with

IFN-stimulated genes MX1, IFIT1 and IRF1 in the immune-infiltrated relative to the immune-

depleted subtype, consistent with their coordinated regulation in an inflamed TME (Fig. 1D).

Finally, PD-L1, IFN-γ signaling, IFN downstream signaling and immunoproteasome mRNA

scores in the IFN-dominant gene module, but not adaptive and myeloid gene modules (data

not shown), individually separated patients into subgroups with different survival probabilities

(Extended Data Fig. 1E).

Highly multiplexed digital spatial profiling (DSP) unravels distinct T-cell neighbors in

immune-infiltrated and immune-depleted AML. Transcriptomic data do not provide

information on spatial relationships of tumor-infiltrating immune cells within the TME.

Therefore, we used GeoMx® DSP to characterize the expression of 31 immuno-oncology

(IO) proteins in 10 fresh frozen paraffin embedded (FFPE) BM biopsies from treatment-naïve

patients with AML (SAL patient series) with varying degrees of T-cell infiltration (Extended

Data Fig. 2A). We selected 24 geometric regions of interest (ROIs) per BM sample using

fluorescent anti-CD3 (visualization marker for T cell-rich ROIs) and anti-CD123 antibodies

(visualization marker for myeloid blast-rich ROIs)29 (Extended Data Fig. 3-4). T-cell gene


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expression scores (calculated as detailed in Extended Data Fig. 2B) correlated with DSP

CD3 protein status (Fig. 2A and Extended Data Fig. 2C). Furthermore, mRNA and protein

levels for B cells, monocytes and Bcl-2 significantly and positively correlated, serving as a

validation for the mRNA-based immune scores (Extended Data Fig. 2D).

We then asked whether CD3-rich and CD3-poor BM samples (defined by a median split of

barcode counts) differed in terms of co-localization patterns of relevant IO proteins. As

shown in Extended Data Fig. 2A, CD3+ T cells in immune-infiltrated biopsies co-localized

with B cells, antigen processing and presentation-related proteins (β2-microglobulin),

negative immune checkpoint B7-H3 and β-catenin. In contrast, CD3+ T cells in immune-

depleted biopsies co-localized with markers of immunological memory (CD45RO) and T-cell

exhaustion (PD-1). When analyzing overall protein expression patterns (10 samples × 24

ROIs per sample × 31 proteins = 7,440 data points), we identified four protein signatures

(SIG), which were then further assessed in silico for correlations with clinical-biological

disease characteristics and potential prognostic value in The Cancer Genome Atlas (TCGA)-

AML cases (162 sequenced AML samples with putative copy-number alterations, mutations

and mRNA expression z-scores [threshold±2.0]). Abnormalities in SIG1, SIG2 and SIG4

genes (blue boxes in Fig. 2B) did not correlate with specific disease characteristics or

clinical outcomes (data not shown). In contrast, mRNA up-regulation, gene amplification,

deep deletion and mis-sense mutations in SIG3 genes, which were detected in 26% of

TCGA-AML cases (Fig. 2C), significantly correlated with TP53 mutation status, an

established adverse prognosticator in AML (p value from mutation enrichment analysis =

0.0285). Alterations in SIG3 genes, which included PD-L1, FoxP3, molecules associated

with cell cytotoxicity, PTEN and BCL2, were predominantly observed in patients with

immune-infiltrated mRNA profiles (Fig. 2D) and also correlated with higher number of

mutations (median=12 and 9 in AML cases with [n=41] or without [n=115] abnormalities in

SIG3 genes, respectively; p=0.021) and with adverse ELN cytogenetic features (χ2=25.03;

p<0.001), but not with other disease characteristics at presentation, including white blood


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cell (WBC) count, and percentage of AML blasts in pre-treatment blood and BM samples

(data not shown). Finally, patients with abnormalities in SIG3 genes experienced poor

clinical outcomes, as shown by the significantly lower RFS and OS rates (Fig. 2E).

Collectively, highly multiplexed in situ detection of IO proteins highlights critical differences in

T-cell infiltrated versus T-cell depleted AML subtypes and identifies protein signatures with

prognostic potential in T-cell infiltrated pre-treatment samples.

Interactions between immune subgroups, common cytogenetic alterations and

clinical factors. We next correlated immune signature scores with clinical and demographic

factors, including WBC count and blast cell count at diagnosis, ELN cytogenetic category

(available in 249 cases from the PMCC discovery series) and patient age. Leukemia burden

was significantly lower in the immune-infiltrated AML subtype (median WBC count at

diagnosis=10.0×103/μL, range 1.0-188.0, versus 25.8×103/μL, range 0.7-398.3, p<0.0001;

median percentage of BM blasts=50% versus 80%, p<0.0001, and median number of AML

blasts per μL of blood=3.7 versus 10.71, p<0.0001; Extended Data Fig. 5A-C). Although

immune signature scores were not correlated with the ELN cytogenetic risk category when

considered individually (data not shown), we observed a trend towards more AML cases

with ELN adverse cytogenetics in the immune-infiltrated subtype (32% versus 20%, p=0.021;

χ2 test for trend; Extended Data Fig. 5D). Finally, patients with high immune infiltration

tended to be of a more advanced age at diagnosis (median=58 years, range 23-79)

compared with patients with low immune infiltration (median=50 years, range 18-63,

p<0.0001; Extended Data Fig. 5E).

Immune subtypes improve survival prediction. The activation of immune pathways has

context-dependent prognostic impact that differs between tumor types6. We first assessed

the ability of the immune subtype to refine the accuracy of outcome prediction separately for

each ELN cytogenetic risk category. Among patients with favorable risk, RFS and OS times


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were significantly longer in individuals with an immune-infiltrated TME (Fig. 3A). In contrast,

clinical outcomes in ELN adverse risk cases were worse in individuals with an immune-

infiltrated TME. We also observed that the ELN classifier assisted outcome prediction only in

the immune-infiltrated subtype (Fig. 3B), allowing the identification of patient subgroups with

excellent survival estimates (87.5% RFS and 77.8% OS) or with very unsatisfactory

outcomes [10.4% RFS (log-rank χ2=15.07; p<0.0001) and 7.2% OS (log-rank χ2=25.75;

p<0.0001); Fig. 3B]. This finding is congruent with previous studies showing that specific

gene expression profiles correlate with longer OS in patients with favorable-risk AML, but not

in those with intermediate-risk or high-risk AML30. Similarly, leukemia stem cell-associated

gene signatures have been shown to provide prognostic separation within specific ELN risk

categories31.

Unexpectedly, our immunological classifier was unable to stratify survival in patients with

intermediate ELN risk (Fig. 3A). We therefore employed an immune gene signature-agnostic

approach to identify gene sets with prognostic impact in this specific patient category. By

performing Cox proportional hazards regression, we discovered a set of 21 differentially

expressed (DE) immune genes [false discovery rate (FDR)<0.05] between favorable and

adverse-risk AML (Supplemental Table 1), which were significantly associated with OS and

exhibited enrichment of gene ontologies (GO) and pathways related to T-cell activation, TCR

downstream signaling and regulation of cytokine production (Fig. 3C). Interestingly, patients

with intermediate-risk AML could be separated into subgroups with low and high gene

expression values, with the former being closely similar to ELN favorable-risk patients while

the latter resembled ELN adverse-risk patients (Fig. 3D). Importantly, RFS and OS

estimates were significantly poorer for intermediate-risk patients with high versus low

expression levels of the 21 DE genes (Fig. 3E).

Immune landscapes stratify AML patients in independent validation sets and differ

across age groups and disease stages. AML is a disease with age-dependent biological

specificities32,33. Furthermore, pediatric AML are inherently of low immunogenicity and are


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therefore less likely to respond to single-agent checkpoint inhibition34. To characterize the

immunological landscape of AML across age groups and longitudinally in patients who

initially achieve complete remission (CR) and then experience disease recurrence, we

profiled BM samples from a pediatric (CHOP series, n=39 cases) and an adult AML cohort

(SAL series, n=38 cases, 58 BM specimens in total). In line with findings in the PMCC

discovery series, we identified IFN-dominant, adaptive and myeloid mRNA profiles

(Extended Data Fig. 6A-D) which individually separated AML cases according to high and

low expression values and, when considered in aggregate, dichotomized AML cases into an

immune-infiltrated and immune-depleted subtype (Extended Data Fig. 6E). As summarized

in Extended Data Fig. 7A, comparison between children and adults with AML revealed a

set of DE immune genes (FDR<4.53×10-9) involved with cytokine and chemokine signaling,

as indicated by GO (Extended Data Fig. 8A and Supplemental Table 2) and protein

interaction network analysis (Extended Data Fig. 8B). Specifically, genes encoding pro-

inflammatory and pro-angiogenic chemokines, including IL8, CCL3L1, CCL3 and CXCL3,

were expressed at significantly higher levels in adult AML relative to childhood AML

(Extended Data Fig. 7B). When comparing matched BM samples from adult patients (SAL

series) at the time of diagnosis and achievement of CR after induction chemotherapy

(Extended Data Fig. 7C), we identified a set of DE genes (FDR<5.72×10-4) that were

enriched for GO biological processes related to the innate immune response, cytokine

signaling and toll-like receptor (TLR) cascade (Supplemental Table 3). As shown in

Extended Data Fig. 7C, FLT3, Bruton’s tyrosine kinase (BTK), IFN regulatory factor 3

(IRF3) and BMI1, which have been implicated in leukemia stem cell self-renewal and

inhibition of apoptosis, were expressed at significantly lower levels in AML cases in CR,

serving as a data reliability check. Finally, immune genes significantly associated with

relapsed AML (SAL series) largely captured CD8+ T-cell infiltration, elements of T-cell

biology, including T-cell receptor (TCR) downstream signaling (CD3z [CD247], CD3E),

leukocyte differentiation and immune regulation (Extended Data Fig. 7D and Supplemental

Table 4). The increased expression of surrogate markers of terminal T-cell differentiation,


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senescence and exhaustion [TBX21 (T-bet)35,36, TIGIT and CTLA4] in relapsed AML

suggests that BM-infiltrating cytotoxic T cells may fail to restrain leukemia growth (Extended

Data Fig. 8C). The DE genes between patient subgroups with newly diagnosed, CR and

relapsed AML in the SAL cohort, and between childhood and adult cases, were largely non-

overlapping, as shown in Extended Data Fig. 7E.

IFN-related gene sets improve the prediction of therapy resistance. We then asked

whether the IFN-dominant gene module herein computed as the sum of IFN-γ signaling, IFN

downstream, immunoproteasome, myeloid inflammation, inflammatory chemokine, IL-10,

MAGE, PD-L1 and PD-L2 scores (Fig. 1 and Extended Data Fig. 1C) may assist the

prediction of therapeutic resistance, which we empirically defined as failure to achieve CR in

patients who survived at least 28 days (primary refractory AML) or as early relapse (<3

months) after achieving CR, as previously published by others37. When AML patients in the

PMCC cohort were dichotomized based on higher or lower than median IFN scores, a higher

percentage of patients with primary refractory disease was observed in the IFN-scorehigh

AML cases (65.4% versus 34.6%; p=0.0022, Fisher’s exact test), suggesting that

transcriptional programs orchestrated by microenvironmental IFN-γ might render AML blasts

resistant to chemotherapeutic agents20,38. In contrast, the frequency of primary refractory

cases was not different when comparing AML patients with higher or lower than median

adaptive module scores (29.6% versus 25.4%; p=NS) and myeloid module scores (26.7%

versus 28.1%; p=NS). In multivariate logistic regression analysis, the IFN-related module

scores significantly improved the ability of the ELN category to predict therapeutic resistance

(Fig. 4A-B; AUROC = 0.815 versus 0.702 with ELN risk only; model χ2=65.87 versus 33.43;

increased sensitivity compared to ENL risk only=15.6%; increased specificity=3%;

decreased false positive rate=11.5%; decreased false negative rate=4.9%), but not patient

survival (data not shown). Specifically, the myeloid inflammation score (p=0.003), IFN-γ

signaling score (p=0.014) and IFN downstream score (p=0.034) significantly contributed to

the model (Supplemental Table 5). Gene sets defining gene modules 2 and 3 in Fig. 1,


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reflective of adaptive immune responses and BM infiltration with cells of the myeloid lineage,

respectively, were not associated with either therapeutic resistance or patient survival (data

not shown).

In silico validation of therapy prediction by immune subtypes in the Beat AML and

HOVON cohorts. We next tested the predictive and prognostic power of immune scores in

silico using a broad collection of public transcriptomic data. We initially devised binary

logistic regression models utilizing RNA-sequencing data from 196 patients on the Beat AML

Master Trial® with clinical response information39. When considering disease type (primary

versus secondary), WBC count and patient age at diagnosis, the inclusion of genes

capturing IFN-γ-related biology significantly improved the predictive ability of the ELN risk

category (AUROC=0.921 versus 0.709 with ELN cytogenetic risk alone; model χ2=106.4

versus 29.6; increased specificity=4%; increased sensitivity=17%; decreased false positive

rate=39%; decreased false negative rate=18%; Fig. 4C).

Confirming our findings in the PMCC and Beat AML® cohorts, IFN-dominant, adaptive and

myeloid mRNA profiles, when used in aggregate, stratified patients in the HOVON database

(618 non-promyelocytic AML cases40) into subgroups with high and low immune infiltration

(Fig. 4D-E). Individuals with immune-infiltrated AML had lower leukemia burden (median

percentage of BM blasts=56% versus 71% in patients with immune-depleted AML;

p<0.0001) and tended to have more advanced age at diagnosis (median=51 years, range

15-74, versus 46 years, range 15-77; p=0.0067). A higher percentage of patients with IFN-

dominant AML failed to achieve CR in response to induction chemotherapy when compared

to non-IFN-dominant AML cases (27.2% versus 15.2%; p=0.0004, Fisher’s exact test). In

contrast, the occurrence of induction failure (IF) was not different when patients were

dichotomized based on higher or lower than median adaptive module scores (21.4% versus

21.0% IF rate; p=NS) or myeloid module scores (21.7% versus 20.7% IF rate; p=NS). Gene

set enrichment analysis (GSEA) with all transcripts in the HOVON dataset provided as input


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and ranked by the log2 fold-change between non-responders and responders confirmed the

over-expression of curated hallmark gene sets linked to IFN-γ responses and inflammatory

responses in chemotherapy-refractory patients (Fig. 4F; Supplemental Table 6). When

tested in a multinomial logistic regression model incorporating patient age, leukemia burden

and ELN cytogenetic risk (available in 615 HOVON cases)41, immune gene sets defining the

IFN-dominant module significantly and independently predicted whether patients responded

to induction chemotherapy and whether they experienced disease relapse (Supplemental

Table 7). In contrast, immune gene signatures were unable to assist the prediction of non-

leukemic deaths (Supplemental Table 7).

Mutations in tumor suppressor genes and transcription factors are enriched in

immune-infiltrated AML cases. It has recently been shown that genetic drivers of solid

malignancies dictate neutrophil and T-cell recruitment, thus affecting the immune milieu of

the tumor and assisting patient stratification42. We asked whether clonal driver mutations

may correlate with the immune subtypes that we identified herein. We therefore retrieved

TCGA AML RNA-sequencing data from cBioPortal (http://www.cbioportal.org/) and

computed immune cell type-specific and biological activity scores22. The mutational

spectrum of TCGA- AML cases, including co-occurrence and exclusivity of the most frequent

molecular lesions, is shown in Extended Data Fig. 9. Mis-sense mutations, mRNA up-

regulation, deep deletion and amplification in IFN downstream genes are summarized in

Extended Data Fig. 10. Patients with adverse-risk molecular lesions, including somatic

TP53 mutations and RUNX1 mutations, clustered in the immune-infiltrated subgroup

(Extended Data Fig. 10A). In particular, IFN-related gene sets, including the tumor

inflammation signature (TIS) score, were expressed at significantly higher levels in TCGA-

AML cases with TP53 and RUNX1 mutations relative to molecular lesions that confer

favorable or intermediate risk (Extended Data Fig. 11A-B). In contrast, the majority of

TCGA-AML cases with NPM1 mutations with or without FLT3-ITD (intermediate-risk and

favorable-risk cases, respectively) were classified as immune-depleted. When extending our


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in-silico analysis to the Beat AML® cohort (281 cases in total), 16 out of 17 (94%) TP53-

mutated AMLs expressed higher levels of genes implicated in downstream IFN signaling and

higher levels of CD8 transcripts and markers of cytotoxicity compared with TP53 wild-type

cases (Extended Data Fig. 11C-D).

IFN-γ-related gene expression profiles correlate with anti-leukemia responses after

flotetuzumab immunotherapy. Finally, we hypothesized that higher expression of IFN-γ-

related genes in immune-infiltrated AML cases, while underpinning chemotherapy

resistance, might identify patients with AML who derive benefit from immunotherapy with

flotetuzumab (FLZ; formerly MGD006)43, a CD3×CD123 DART®. Preliminary anti-leukemic

activity of FLZ at a target dose of ≥500ng/kg/day has been recently reported44,45. BM

samples collected prior to FLZ treatment from 30 adult patients with chemotherapy-

refractory or relapsed AML enrolled in the CP-MGD006-01 clinical trial (NCT#02152956)

were profiled using the PanCancer IO360™ gene expression assay. Patients’ characteristics

are summarized in Supplemental Table 8. BM samples from 92% of patients with evidence

of FLZ anti-leukemic activity (11 out of 12), which was defined as either CR, CR with partial

hematologic recovery (CRh), CR with incomplete hematologic recovery (CRi), partial

response or overall benefit (>30% reduction in BM and/or blood blasts), had an immune-

infiltrated TME relative to non-responders (Fig. 5A). Interestingly, the IFN-dominant module

score was significantly higher in patients with chemotherapy-refractory AML compared with

relapsed AML at time of FLZ treatment, and in individuals with evidence of anti-leukemic

activity compared to non-responders (Fig. 5B). Notably, the TIS score was a stronger

predictor of anti-leukemic responses to FLZ, with an AUROC value of 0.847 (Fig. 5B). On-

treatment BM samples (available in 19 patients at the end of cycle 1) displayed increased

antigen presentation and immune activation relative to baseline samples, as reflected by

higher TIS scores (6.47±0.22 versus 5.93±0.15, p=0.0006), antigen processing machinery

scores (5.67±0.16 versus 5.31±0.12, p=0.002), IFN-γ signaling scores (3.58±0.27 versus


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2.81±0.24, p=0.0004) and PD-L1 expression (3.43±0.28 versus 2.73±0.21, p=0.0062; Fig.

5C). Overall, these data substantiate a clinical benefit for AML patients with an immune-

infiltrated TME, support a local immune-modulatory effect of FLZ treatment and validate the

translational relevance of our findings.


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Discussion

Using large cohorts of subjects, the current study is the first to reveal underlying

transcriptomic features that stratify the TME of AML into immune subtypes and may assist

therapeutic predictions by defining patients who will potentially derive the greatest benefit

from immunotherapies11,46. We identified two subtypes of differentially-infiltrated tumors, an

observation that was validated in independent childhood and adult AML series, reinforcing

the notion that unique molecular features can distinguish AML across age groups33. In

agreement with commonly accepted criteria that AUROCs of 0.8-0.9 indicate good predictive

ability, the IFN-related gene sets identified in our study improved the prediction of

therapeutic resistance following conventional ‘3+7’ cytarabine and anthracycline

chemotherapy beyond that provided by the ELN cytogenetic risk category (AUC=0.815 in

PMCC cases [discovery series] and 0.870 in Beat AML® cases [in silico validation series])47.

In recent Southwestern Oncology Group (SWOG) and MD Anderson Cancer Center clinical

trials, pretreatment covariates such as cytogenetic risk and age only yielded AUROCs of

0.65 and 0.59 for therapeutic resistance, respectively37. Our models incorporating IFN-

related mRNA profiles also outperform a recently developed 29-gene and cytogenetic risk

predictor of chemotherapy resistance (AUROC=0.76)48. Intriguingly, an IFN-related DNA

damage resistance signature (IRDS) correlates with resistance to adjuvant chemotherapy

and with recurrence after radiotherapy in patients with breast cancer38, suggesting that

tumor cells over-expressing IRDS genes, including STAT1, ISG15 and IFIT1, as a result of

chronic activation of the IFN signaling pathway might receive pro-survival rather than

cytotoxic signals in response to DNA damage49.

The immune-infiltrated AML cases in the PMCC cohort were highly immune-suppressed, as

indicated by elevated expression of IFN-inducible negative immune checkpoints and

immunotherapy targets IDO1 and PD-L1. Furthermore, adults with relapsed AML in the SAL

series expressed higher levels of T-cell exhaustion molecules relative to matched pre-

treatment samples, suggesting the occurrence of escape from immune surveillance at the


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17

time of disease relapse50. In general, solid tumors with a substantial T-cell component and

displaying a type I immune response are associated with better OS and progression-free

survival estimates6. However, the highly proliferative IFN-γ-dominant solid tumors may

correlate with a less favorable survival, despite being infiltrated with CD8+ T cells and

harboring the highest type 1 macrophage signature scores6. By utilizing targeted IGEP, our

study highlights that the activation of IFN-related pathways and the relative abundance of

immune cell types, including the over-expression of T-cell markers and TCR signaling

intermediates in relapsed AML relative to disease onset, have negative prognostic

implications in AML; this is conceivably the result of a non-productive anti-leukemia immune

response and/or IFN-driven resistance to DNA damage induced by chemotherapeutic

agents20.

The heterogeneity of immune infiltration can also be determined by tumor cell-intrinsic

factors, including chemokine secretion51 and expression of cancer driver genes, all of which

affect response to immunotherapies42,52,53. Interestingly, we detected associations between

mutations in tumor suppressor and cancer driver genes and immune subtypes of AML and,

for the first time, we identified TP53 mutations as being strongly correlated with an IFN-γ

dominant TME and with prognostic protein signatures, including the expression of PD-L1,

FOXP3, cytotoxicity markers and PTEN, that were revealed by spatially-resolved, highly

multiplexed protein profiling. This observation is backed by a recent study showing higher

proportions of PD-L1-expressing CD8+ T cells, activation of IFN-γ-associated genes and

favorable responses to pembrolizumab immunotherapy in TP53-mutated lung cancers54. It is

tempting to speculate that immune-infiltrated, TP53-mutated AML cases, which have very

low response rates when treated with standard anthracycline-based and cytarabine-based

induction chemotherapy, could benefit from T cell-targeting approaches and/or

hypomethylating agents that potentially alter the immune surveillance of AML.55

Finally, IFN-γ-related gene expression programs in the AML TME, including the TIS score,

predicted response to immunotherapy with FLZ in 30 heavily pre-treated patients with


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18

relapsed/refractory AML on clinical trial CP-MGD006-01. FLZ treatment was associated with

increased expression of antigen processing machinery genes, enhanced IFN-γ signaling and

heightened PD-L1 expression. The latter finding provides a biological rationale for designing

clinical studies with sequential FLZ and checkpoint inhibitor blockade in AML patients in

remission with minimal residual disease. In conclusion, our work unveils the heterogeneity of

the immune landscape of AML and provides a novel precision medicine-based conceptual

framework for delivering T cell-targeting immunotherapy to subgroups of patients with IFN-γ-

dominant AML, who may be refractory to conventional cytotoxic chemotherapy but

responsive to T-cell engagers. The immunological stratification of pre-treatment BM samples

may therefore enable rapid risk prediction and selection of frontline therapeutic modalities11,

in conjunction with cytogenetic and mutational information.

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44. Uy, G.L., et al. Phase 1 cohort expansion of flotetuzumab, a CD123×CD3 bispecific Dart® protein in patients with relapsed/refractory acute myeloid leukemia (AML). Blood 132, 764-764 (2018).

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Methods

Methods, including statements of data availability and any associated accession codes and

references, are available online.

Disclosure of potential conflict of interest


https://doi.org/10.1101/702001

22

The authors have no potential conflicts of interest to disclose.

Authors’ Contributions

Concept and design: S. Rutella

Development of methodology: J. Vadakekolathu, T. Hood, S.E. Church, S. Reeder, S.E.

Warren, Y. Liang

Acquired, consented and managed patients; processed patient samples: M.D. Minden,

N. Ibrahimova, A. Arruda, J. Muth, P.J.M. Valk, B. Löwenberg, M. Bornhäuser, S.K. Tasian,

M.P. Rettig, J.F. DiPersio

Analysis and interpretation of data: J. Vadakekolathu, M.D. Minden, T. Hood, S.E.

Church, S. Reeder, A.H. Sullivan, E. Viboch, S.E. Warren, Y. Liang, M. Schmitz, A. Cesano,

P.J.M. Valk, B. Löwenberg, A.G. Pockley, M. Bornhäuser, S.K. Tasian, J. Davidson-

Moncada, J.F. DiPersio, S. Rutella

Clinical trial implementation: J.F. DiPersio was principal investigator at Washington

University in St. Louis, St. Louis, United States of America. B. Löwenberg was principal

investigator at Erasmus University Medical Centre, Rotterdam, Netherlands.

Writing of the manuscript: S. Rutella

Review and/or revision of the manuscript: J. Vadakekolathu, M.D. Minden, T. Hood, S.E.

Church, E. Viboch, S.E. Warren, M. Schmitz, A. Cesano, P.J.M. Valk, B. Löwenberg, A.G.

Pockley, M. Bornhäuser, S.K. Tasian, M.P. Rettig, J. Davidson-Moncada, J.F. DiPersio, S.

Rutella

Study supervision: S. Rutella

Acknowledgements

This work was supported by grants from the Qatar National Research Fund (NPRP8-2297-3-

494 to S. Rutella), the Roger Counter Foundation, United Kingdom (to A.G. Pockley and S.

Rutella), the John and Lucille van Geest Foundation (to A.G. Pockley and S. Rutella), the


https://doi.org/10.1101/702001

23

James Skillington Challenge for Leukemia (to A.G. Pockley), the NCI K08 CA184418 and

the Andrew McDonough B+ Foundation (to S.K. Tasian). The Study Alliance of Leukemia

(www.sal-aml.org) is gratefully acknowledged for providing primary patient material and

clinical data.

Online Methods

Patients’ demographics (discovery cohorts). Patient and disease characteristics are

detailed in Table 1. Primary patient specimens (non-promyelocytic AML) and associated

clinical data were obtained on research protocols approved by the Investigational Review

Boards of the Children’s Hospital of Philadelphia (CHOP), USA and Princess Margaret

Cancer Centre (PMCC), Canada and by the Ethics Committee of TU Dresden and

Studienallianz Leukämie (SAL), Germany.

Patients’ demographics (immunotherapy cohort). Thirty patients with primary refractory

(n=23) and relapsed AML (n=7) treated with flotetuzumab (FLZ) at the recommended phase

2 dose (500 ng/kg/day) on the CP-MGD006-01 clinical trial (NCT#02152956) were included

in this study. Patient and disease characteristics are detailed in Supplemental Table 8. BM

aspirates were collected at baseline (n=30) and after cycle 1 of FLZ (n=19) to evaluate the

temporal immunological effects associated with therapeutic response. Patients received a

lead-in dose of FLZ during week (W) 1, followed by 500 ng/kg/day during weeks 2-4 of cycle

1, and a 4-day on/3-day off schedule for cycle 2 and beyond. Disease status was assessed

by modified IWG criteria.

RNA isolation and processing.

Messenger RNA was isolated and processed as previously described41. For the PMCC, SAL

and CHOP patient cohorts, approximately 100 ng per sample of RNA extracted from 387

bulk BM aspirates from AML patients treated with curative intent were analyzed on the

nCounter™ FLEX analysis system using the PanCancer Immune [PCI] profiling panel (for

research use only and not for use in diagnostic procedures), which measures mRNA levels

of 740 genes representing 24 immune cell types, common checkpoint inhibitors, cancer

testis antigens and genes covering both the innate and adaptive immune response

(Extended Data Fig. 2B)25,56,57. BM samples from patients receiving FLZ immunotherapy

were analyzed using the PanCancer IO360™ mRNA panel (for research use only and not

for use in diagnostic procedures).

nCounter data quality control, data normalization and signature calculation.


https://doi.org/10.1101/702001

24

The reporter probe counts, i.e., the number of times the color-coded barcode for that gene is

detected, were tabulated in a comma separated value (CSV) format for data analysis with

the nSolver™ software package (version 4.0.62) and nSolver Advanced Analysis module

(version 2.0.115; NanoString Technologies). The captured transcript counts were normalized

to the geometric mean of the housekeeping reference genes included in the assay and the

code set’s internal positive controls.

The relative abundance of immune cell types and immuno-oncology biological signatures

were computed as previously published22,23. For samples run on the PCI profiling panel, we

also calculated an approximation of the Tumor Inflammation Signature (TIS) using 16 of the

18 functional genes and 5 of the 10 housekeeper genes that are present in the PCI profiling

panel18.

Gene ontology (GO) and gene set enrichment analysis (GSEA). Metascape.org was

used to enrich genes for GO biological processes and pathways. GSEA was performed

using the GSEA software v.3.0 (Broad Institute, Cambridge, USA)58. Hallmark IFN-γ

response and inflammatory response gene sets (M5913) were downloaded from the

Molecular Signature Database (MSigDB)22,23.

GeoMx™ Digital Spatial Profiling (DSP). Ten FFPE BM biopsies from patients with newly

diagnosed AML (SAL series) were profiled using the GeoMx DSP platform. Samples were

stained using 3 fluorescent visualization markers, CD3 (T cell), CD123 (myeloid blast),

SYTO™ 83 (nuclei), and 31 UV-cleavable oligo-labeled antibodies (Supplemental Table 9).

Stained slides were loaded on the DSP instrument and digitally scanned. Fluorescent scans

were used to select 24 geometric regions of interest (ROIs)27,59. The DSP instrument then

UV-illuminated selected ROIs to release conjugated oligos and the micro-capillary fluidics

system collected released oligos, which were counted on the nCounter system.

Data sources for in silico analyses. The first data series (E-MTAB-3444), hereafter

referred to as the HOVON series40, was retrieved from Array Express and encompassed

three independent cohorts of adults (≤60 years) with de novo AML. BM and blood samples

were collected at diagnosis and were analyzed on the Affymetrix Human Genome U133 Plus

2.0 Microarray40,60. Patients were treated with curative intent according to the Dutch-Belgian

Hematology-Oncology Cooperative Group and the Swiss Group for Clinical Cancer

Research (HOVON/SAKK) AML-04, -04A, -29, -32, -42, -42A, -43 or -92 protocols (available

at http://www.hovon.nl). Clinical annotations were provided by the authors. The second data

series, hereafter referred to as The Cancer Genome Atlas (TCGA) series, consisted of RNA-

sequencing data (Illumina HiSeq2000) from 162 adult AML patients with complete


https://doi.org/10.1101/702001

25

cytogenetic, immunophenotypic and clinical annotation who were enrolled on Cancer and

Leukemia Group B (CALGB) treatment protocols 8525, 8923, 9621, 9720, 10201 and

1980861. RNA and clinical data were retrieved from the TCGA data portal (https://tcga-

data.nci.nih.gov/tcga/tcgaDownload.jsp). The third data series (Beat AML) was retrieved

using the VIZOME user interface (http://www.vizome.org/aml/) and consisted of RNA-

sequencing data from primary specimens from 242 AML patients with detailed clinical

annotations, including diagnostic information, treatments, responses and outcomes treated

on the Beat AML Master Trial39. Patient and disease characteristics for in silico data sources

are summarized in Supplemental Table 10.

Statistical analyses. Descriptive statistics included calculation of mean, median, SD, and

proportions to summarize study outcomes. Comparisons were performed with the Mann-

Whitney U test for paired or unpaired data (two-sided), as appropriate, or with the ANOVA

with correction for multiple comparisons. IBM SPSS Statistics (version 24) and GraphPad

Prism (version 8) were used for statistical analyses. A two-tailed p value <0.05 was

considered to reflect statistically significant differences. The log-rank (Mantel-Cox) test was

used to compare survival distributions.

Therapeutic resistance was defined as failure to achieve complete remission (CR) despite

not experiencing early treatment-related mortality (within 28 days of chemotherapy initiation;

primary refractory cases) or as early relapse (<3 months) after achieving CR37. Overall

survival (OS) was computed from the date of diagnosis to the date of death. Relapse-Free-

Survival (RFS) was measured from the date of first CR to the date of relapse or death.

Subjects lost to follow-up were censored at their date of last known contact.

Binary logistic regression and multinomial logistic regression were used to ascertain the

relative contribution of immune subtypes and other pretreatment covariates selected a priori

based on known clinical relevance (ELN risk group, FLT3-ITD status, NPM1 mutational

status, patient age at diagnosis and primary versus secondary AML) toward the predicted

likelihood of response to induction chemotherapy, AML relapse and patient death41.

Variables measured after the initiation of induction chemotherapy were excluded, since the

goal of the current study was to identify AML patients at higher risk of treatment failure prior

to starting therapy.

Data availability. Gene expression data have been deposited in NCBI's Gene Expression

Omnibus62 and are accessible through GEO Series accession number GSE134589

(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE134589). Processed input data

and basic association analyses will be made available from the corresponding author on

request for the purpose of conducting legitimate scientific research.


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26

References

18. Ayers, M., et al. IFN-gamma-related mRNA profile predicts clinical response to PD-1 blockade. J Clin Invest 127, 2930-2940 (2017).

22. Danaher, P., Warren, S. & Cesano, A. Development of gene expression signatures characterizing the tumor-immune interaction. J Clin Oncol 36, 205-205 (2018).

23. Danaher, P., et al. Gene expression markers of Tumor Infiltrating Leukocytes. J Immunother Cancer 5, 18 (2017).

25. Vadakekolathu, J., et al. Immune gene expression profiling in children and adults with acute myeloid leukemia identifies distinct phenotypic patterns. Blood 130, 3942-3942 (2017).

27. Blank, C.U., et al. Neoadjuvant versus adjuvant ipilimumab plus nivolumab in macroscopic stage III melanoma. Nat Med 24, 1655-1661 (2018).

37. Walter, R.B., et al. Prediction of early death after induction therapy for newly diagnosed acute myeloid leukemia with pretreatment risk scores: a novel paradigm for treatment assignment. J Clin Oncol 29, 4417-4423 (2011).

39. Tyner, J.W., et al. Functional genomic landscape of acute myeloid leukaemia. Nature 562, 526-531 (2018).

40. Valk, P.J., et al. Prognostically useful gene-expression profiles in acute myeloid leukemia. N Engl J Med 350, 1617-1628 (2004).

41. Wagner, S., et al. A parsimonious 3-gene signature predicts clinical outcomes in an acute myeloid leukemia multicohort study. Blood Adv 3, 1330-1346 (2019).

47. Walter, R.B., et al. Resistance prediction in AML: analysis of 4601 patients from MRC/NCRI, HOVON/SAKK, SWOG and MD Anderson Cancer Center. Leukemia 29, 312-320 (2015).

56. Sugio, T., et al. Microenvironmental immune cell signatures dictate clinical outcomes for PTCL-NOS. Blood Adv 2, 2242-2252 (2018).

57. Payton, J.E., et al. High throughput digital quantification of mRNA abundance in primary human acute myeloid leukemia samples. J Clin Invest 119, 1714-1726 (2009).

58. Subramanian, A., et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102, 15545-15550 (2005).

59. Rutella, S., et al. Capturing the complexity of the immune microenvironment of acute myeloid leukemia with 3D biology technology. J Clin Oncol 36, 50-50 (2018).

60. Stavropoulou, V., et al. MLL-AF9 expression in hematopoietic stem cells drives a highly invasive AML expressing EMT-related genes linked to poor outcome. Cancer Cell 30, 43-58 (2016).


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61. Ley, T.J., et al. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N Engl J Med 368, 2059-2074 (2013).

62. Edgar, R., Domrachev, M. & Lash, A.E. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 30, 207-210 (2002).

63. Lauten, M., et al. Prediction of outcome by early bone marrow response in childhood acute lymphoblastic leukemia treated in the ALL-BFM 95 trial: differential effects in precursor B-cell and T-cell leukemia. Haematologica 97, 1048-1056 (2012).

64. Metsalu, T. & Vilo, J. ClustVis: a web tool for visualizing clustering of multivariate data using Principal Component Analysis and heatmap. Nucleic Acids Res 43, W566-570 (2015).


https://doi.org/10.1101/702001

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Table 1. AML cohorts selected for targeted immune gene expression profiling

Patient series PMCC CHOP SAL # of patients 290 39 38 Age (0-39), n 76 39 11 Age (40-59), n 126 0 15 Age (>60), n 88 0 12 Median age (years, range) 52 (18-81) 10 (0.1-20) 52.5 (23-75)

WBC count at presentation 19.15 × 103/µL (0.7-399)

N.A. 60.75 × 103/µL (0.84-320.2)

Cytogenetic risk group, n ELN favorable

ELN intermediate ELN adverse

N.A.

35

155 59 41

8

24 7 -

7

27 2 2

Disease status at time of BM sampling, n

Diagnosis Complete remission

Relapse

290 0 0

39 0 0

38 11 9

# of BM samples analyzed 290 39 58 # of BM samples analyzed using the GeoMx® DSP platform 0 0 10

De novo/secondary/therapy-related

244/46/0 38/1/0 31/5/2

Response to induction chemotherapy

Yes No

N.A.

210 80 -

29 (M1*) 4 (M2/M3*)

6

33 2 3

Relapse Yes No

Induction failure N.A.

132 118 39 1

24 13 2 -

17 18 0 3

Median follow-up time (months) 101.23 39.9 16.89 Median relapse-free survival (months) 19.1 14.7 18.3

Median overall survival (months) 21.37 44.8 25.67

Legend: N.A. = Not available; PMCC = Princess Margaret Cancer Centre; CHOP =Children’s Hospital of Philadelphia; SAL = Studienallianz Leukämie; ELN = European Leukemia Net; BM = bone marrow; DSP = digital spatial profiling; N.A. = not available. Median follow-up time was calculated using the reverse Kaplan-Meier method, with the event indicator reversed so that the outcome of interest becomes censored. *M1, M2 and M3 BM remission status was defined as <5%, 5% to 24% and >25% AML blasts after induction chemotherapy, respectively63.


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29

Figure legends.

Fig. 1: Immune gene sets stratify bone marrow samples from patients with newly

diagnosed AML (PMCC cohort). A) Unsupervised hierarchical clustering (Euclidean

distance, complete linkage) of the correlation matrix of immune and biological activity

signatures identifies co-expression patterns (blue boxes) of immune gene sets (correlation

value color-coded per the legend; Pearson correlation coefficient >0.45) in the bone marrow

(BM) microenvironment of patients with AML, namely, IFN-dominant (1), adaptive (2) and

myeloid (3) gene modules. Immune cell type23 and signature scores22 were calculated from

mRNA levels as pre-defined linear combinations (weighted averages) of biologically relevant

gene sets. Morpheus, an online tool developed at the Broad Institute (MA, USA) was used

for data analysis and visualization. B) IFN-dominant, adaptive and myeloid scores in

aggregate stratify patients with newly diagnosed AML into two distinct clusters, which are

referred in this study as immune-infiltrated and immune-depleted25. ClustVis, an online tool

for clustering of multivariate data, was used for data analysis and visualization64. C) Violin

plots summarizing the expression of IFN-stimulated genes (ISGs), T-cell and cytotoxicity

markers, negative immune checkpoints, genes implicated in antigen processing and

presentation, and immunotherapy targets in AML cases with an immune-infiltrated and

immune-depleted tumor microenvironment (TME). Data were compared with the Mann-

Whitney U test for unpaired determinations (two-sided). *P<0.05; ***p<0.0001. D)

Correlation between STAT1, ISGs [IRF1, MX1, IFIT1, TNFRSF14, PD-L1 (CD274)],

surrogate markers for cytotoxic T cells (CD8A, GZMA) and negative immune checkpoints

[LAG3, HAVCR2 (Tim-3)] under conditions of high and low immune infiltration.

Fig. 2: Multiplexed protein detection with GeoMx™ Digital Spatial Profiling (DSP)

identifies prognostic signatures in immune-infiltrated AML. A) CD3 expression (green

fluorescence) in representative regions of interest (ROIs) from a bone marrow (BM) trephine

biopsy obtained at time of AML diagnosis (SAL series). B) Correlation matrix of protein


https://doi.org/10.1101/702001

30

expression in BM biopsies from 10 patients with newly diagnosed AML (SAL series). Protein

expression data were subjected to unsupervised hierarchical clustering. Heat-maps were

built using Morpheus (Broad Institute, MA) with blue boxes denoting four distinct protein co-

expression patterns (Pearson correlation coefficient >0.45) or signatures (SIG). C)

Abnormalities in SIG3 proteins were detected in 26% of TCGA-AML cases (42 of 162

sequenced BM samples). Data were retrieved, analyzed and visualized using cBioPortal. D)

Heat-map of immune cell type-specific scores and biological activity scores in TCGA-AML

cases with and without abnormalities of SIG3 genes. ClustVis, an online tool for clustering of

multivariate data, was used for data analysis and visualization64. E) Kaplan-Meier estimates

of relapse-free survival (RFS) and overall survival (OS) in TCGA-AML patients with (red line)

and without (blue line) abnormalities of SIG3 genes. HR = hazard ratio. Survival curves were

compared using a log-rank (Mantel-Cox) test.

Fig. 3: Clinical correlates of immune profiles in patients with newly diagnosed AML

(PMCC cohort). A) Stratification of patient survival within each ELN cytogenetic risk

category by immune subtype (immune-infiltrated and immune-depleted). Kaplan-Meier

estimates of RFS and OS are shown. Survival curves were compared using a log-rank

(Mantel-Cox) test. *P<0.05; **P<0.01. HR=hazard ratio; CI=confidence interval. B)

Cytogenetically-defined categories stratify survival in patients with immune-infiltrated AML.

Kaplan-Meier estimates of RFS and OS are shown. Survival curves were compared using a

log-rank (Mantel-Cox) test. ***P<0.001. C) Differentially expressed (DE) genes (n=21)

between favorable and adverse-risk AML were mapped to gene ontology (GO) biological

processes and pathways using Metascape.org. D) Expression of the 21 DE genes across

the PMCC discovery cohort (unsupervised hierarchical clustering; Euclidean distance;

complete linkage). FDR=false discovery rate. Morpheus, an online tool developed at the

Broad Institute (MA, USA), was used for data analysis and visualization. E) Kaplan-Meier

estimate of RFS and OS in patients with ELN intermediate-risk AML stratified by the 21-gene

classifier. Survival curves were compared using a log-rank (Mantel-Cox) test. HR=hazard


https://doi.org/10.1101/702001

31

ratio; CI=confidence interval. **P<0.01; ***P<0.001.

Fig. 4: IFN-related mRNA profiles predict therapeutic resistance. A) Binary logistic

regression predicting therapeutic response from IFN-related scores and conventional

prognosticators, i.e., ELN cytogenetic risk category, WBC count at diagnosis, disease type

(primary versus secondary AML), and patient age at diagnosis (PMCC discovery cohort).

AUROC = area under receiver operating characteristic. The dotted line indicates currently

accepted thresholds (>0.80) of AUROC with good predictive ability in AML47. B) AUROC

curves measuring the predictive ability of ELN cytogenetic risk and IFN-related scores for

therapeutic response (PMCC discovery cohort). SE = standard error; CI = confidence

interval. AUROC=1.0 would denote perfect prediction and AUROC=0.5 would denote no

predictive ability. C) AUROC curves measuring the predictive ability of ELN cytogenetic risk

and IFN-related scores for therapeutic response in Beat AML trial specimens (validation

cohort). SE = standard error; CI = confidence interval. D) Unsupervised hierarchical

clustering (Euclidean distance, complete linkage) of the correlation matrix of immune and

biological activity signatures identifies co-expression patterns of immune gene sets

(correlation value color-coded per the legend; Pearson correlation coefficient >0.45; blue

boxes) in the bone marrow (BM) microenvironment of AML patients in the HOVON series

(n=618 cases with therapy response and ELN cytogenetic risk information). E) IFN-

dominant, adaptive and myeloid scores in aggregate stratify patients in the HOVON series

into immune subtypes (immune-infiltrated and immune-depleted). F) Gene set enrichment

analysis (GSEA) plots representing the normalized enrichment score (NES) of hallmark IFN-

γ-response genes (n=186), inflammatory response genes (n=189) and a subset of

overlapping genes (n=36) between IFN-γ and inflammatory gene sets in AML patients in the

HOVON series who failed to respond to induction chemotherapy. Gene sets were

downloaded from the Molecular Signature Database (MSigDB) and are listed in

Supplemental Table 6. Each run was performed with 1,000 permutations. FDR=false


https://doi.org/10.1101/702001

32

discovery rate.

Fig. 5: Immune subtypes associate with response to flotetuzumab immunotherapy. A)

Unsupervised hierarchical clustering (Euclidean distance, complete linkage) of immune and

biological activity signatures in the bone marrow (BM) microenvironment of patients with

relapsed/refractory AML (n=30) receiving flotetuzumab (FLZ) immunotherapy in the CP-

MGD006-01 clinical trial (NCT#02152956). Anti-leukemic response was defined as either

complete remission (CR), CR with incomplete hematologic recovery (CRi), CR with partial

hematologic recovery (CRh), partial remission (PR) or “other benefit” (OB; >30% decrease in

BM blasts). Non-responders were individuals with either treatment failure (TF), stable

disease (SD) or progressive disease (PD). Chemotherapy refractoriness was defined as ≥2

induction attempts or 1st CR with initial CR duration <6 months. B) IFN module score and

TIS score in baseline BM samples from patients with primary refractory and relapsed AML.

Red dots denote patients with evidence of FLZ anti-leukemic activity. Horizontal lines

indicate median values. Comparisons were performed with the Mann-Whitney U test for

unpaired data (two-sided). **P<0.01. C) Area under receiver operating characteristic

(AUROC) curves measuring the predictive ability of the IFN-module score and tumor

inflammation signature (TIS) for therapeutic response to FLZ. CI=confidence interval. D)

Immune activation in the TME during FLZ treatment (matched BM samples from 19

patients). Red dots denote patients with evidence of FLZ anti-leukemic activity. Horizontal

lines indicate median values. Comparisons were performed with the Mann-Whitney U test

for paired data (two-sided). Pre = baseline. C1 = cycle 1. **P<0.01. ***P<0.001.


https://doi.org/10.1101/702001

Fig. 1

A

Adaptive MyeloidIFN dominant

Pearson >0.45

Immune-depletedImmune-infiltrated

C

Immune checkpoints and immunotherapy targets

T-cell and cytotoxicity markers

IFN-stimulated genes

Antigen processing and presentationB

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id dendrogram_cut

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D

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Infiltrated Depleted0

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5

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5

10

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8

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11

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12

13

14

15

16

17

HLA-A

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12

14

16

18

HLA-B

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✱✱✱


10

12

14

16

HLA-C

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5

10

15

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5

10

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CD8A

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4

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8

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10

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5

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8

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Immune-infiltrated Immune-depleted

p=0.0001 p=0.001

p=NS p=NS


https://doi.org/10.1101/702001

Altered in 42 (26%) of 162 sequenced patients (TCGA)C

Pearson correlation coefficient >0.45-1.00 1.000.00

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id

ROI 17ROI 7

0 30 60 90 1200

25

50

75

100

Relapse-free survival (months)

Perc

ent s

urvi

val

Alterations in query genes (median RFS=13.4 mo.)

No alterations in query genes (median RFS=24.1 mo.)

P=0.006HR=1.63 (95% CI 0.91-2.97); Χ2=3.36

Number at riskQuery genes not altered

Query genes altered 11340

338

154

33

11

0 30 60 90 1200

25

50

75

100

Overall survival (months)

Perc

ent s

urvi

val

Alterations in query genes (median OS=11 mo.)No alterations in query genes (median OS=22.3 mo.)

P=0.0127HR=1.7 (95% CI 1.05-2.74); Χ2=6.21

Number at riskQuery genes not altered

Query genes altered 12041

4510

194

43

11

Fig. 2

A

DepletedInfiltrated

E

B7-H3CD44CD68Ki-67CD14VISTA

b2MCD3CD19CD4

CD45ROCD8APD-1

AKTGZMBSTAT3Bcl-2PTENFoxP3PD-L1

b-cateninCD45P-AKT

P-STAT3

Risk (Cyto)

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4%

6%

9%

2.5%

5%

6%

4%

Genetic Alteration Missense Mutation (unknown significance) Amplification Deep Deletion mRNA High mRNA Low No alterations

Risk (Cyto) Good Intermediate N.D. Poor D

TCGA-AML seriesSAL series


https://doi.org/10.1101/702001

Fig. 3

D

BA

Imm

une-

infil

trat

edIm

mun

e-de

plet

ed

PMCC series

0 24 48 72 96 120 144 168 1920

20

40

60

80

100

Relapse-free survival time (months)

Per

cent

sur

viva

l

ELN intermediate (median RFS=30.23 mo.)ELN adverse (median RFS=18.67 mo.)

ELN favorable (median RFS=19.1 mo.)

ns

Log-rank χ2=0.3

44.8%

47.5%

40.0%

0 24 48 72 96 120 144 168 1920

20

40

60

80

100


Per

cent

sur

viva

l

ELN intermediate (median RFS=24.47 mo.)ELN adverse (median RFS=6.47 mo.)

ELN favorable (median RFS=undefined)

✱✱✱

Log-rank χ2=15.07

87.5%

44.6%

10.4%

0 24 48 72 96 120 144 168 1920

20

40

60

80

100

Overall survival time (months)

Perc

ent s

urvi

val

ELN intermediate (median OS=24.9 mo.)ELN adverse (median OS=9.48 mo.)

ELN favorable (median OS=undefined)

✱✱✱

Log-rank χ2=25.75

77.7%

32.5%

7.1%

0 24 48 72 96 120 144 168 1920

20

40

60

80

100


Perc

ent s

urvi

val

ELN adverse (median OS=15.23 mo.)ELN intermediate (median OS=28.47 mo.)ELN favorable (median OS=29.56 mo.)

ns

Log-rank χ2=4.86

30.0%

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ELN

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ELN

inte

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0 24 48 72 96 120 144 1680

20

40

60

80

100


Per

cent

sur

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l

Depleted (median RFS=19.1 mo.)Infiltrated (median RFS=undefined)

nsHR=0.19 (95% CI=0.06-0.65)

87.5%

47.5%

0 24 48 72 96 120 144 168 1920

20

40

60

80

100


Per

cent

sur

viva

l

Depleted (median RFS=30.23 mo.)

Infiltrated (median RFS=24.47 mo.)

44.6%

44.8%

0 24 48 72 96 120 1440

20

40

60

80

100


Per

cent

sur

viva

l

Depleted (median RFS=18.67 mo.)Infiltrated (median RFS=6.47 mo.)

✱✱HR=2.73 (95% CI=1.14-6.52)

40%

10%

0 24 48 72 96 120 144 1680

20

40

60

80

100


Perc

ent s

urvi

val

Depleted (median OS=29.56 mo.)Infiltrated (median OS=undefined)

nsHR=0.34 (95% CI=0.12-0.99)

46.1%

77.7%

0 24 48 72 96 120 144 168 1920

20

40

60

80

100


Perc

ent s

urvi

val

Depleted (median OS=28.47 mo.)Infiltrated (median OS=27.93 mo.)

30.0%

32.5%

0 24 48 72 96 120 1440

20

40

60

80

100


Perc

ent s

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val

Depleted (median OS=15.23 mo.)Infiltrated (median OS=9.48 mo.)

✱

HR=1.90 (95% CI=1.07-3.37)

7.1%

19.3%

row min row max

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ELN_Cytogenetic_RiskCCL23CT45A1MRC1IL2RACARD11STAT4MAFNFATC1PNMA1CD34F2RL1TNFRSF18ARG2IL15CD55DPP4LILRA4RAG1PRM1CYFIP2APP

id

ELN intermediate risk

ELN favorable risk ELN adverse riskHigh expression(21 DE genes; FDR<0.05)

Low expression(21 DE genes; FDR<0.05)

C

0 24 48 72 96 120 144 168 1920

25

50

75

100


Perc

ent s

urvi

val

ELN int.-21 genelow (median OS=90.3 mo.)ELN int.-21 genehigh (median OS=18.5 mo.)

✱✱✱

Log-rank χ2=19.66; HR=0.42 (95%CI 0.28-0.62)

46%

17%

0 2 4 6 8 10 12

Regulation of leukocyte activation (GO:0002694)Cytokine production (GO:0001816)

Positive regulation of T cell activation (GO:0050870)PID CD8 TCR downstream pathway (M272)

Regulation of tumor necrosis factor production (GO:0032680)T cell apoptotic process (GO:0070231)

Cytokine-mediated signaling pathway (GO:0019221)Fc receptor signaling pathway (GO:0038093)

Leukocyte migration (GO:0050900)Viral entry into host cell (GO:0046718)

-log10(P)E

0 24 48 72 96 120 144 168 1920

25

50

75

100


Per

cent

sur

viva

l

ELN int.-21 genelow (median RFS=undefined)ELN int.-21 genehigh (median RFS=13.73 mo.)

Log-rank χ2=10.56; HR=0.46 (95%CI 0.28-0.75)

✱✱

58%

29%


https://doi.org/10.1101/702001

Fig. 4

0.4 0.6 0.8 1.0

Cytogenetic risk

WBC

Secondary AML

Age

IFN-related profiles

AUROC

A PMCC series B

HOVON series

AdaptiveMyeloid IFNdominant

-1.00 1.000.00

Myle

oid.

infla

mm

atio

nIn

flam

mat

ory.c

hem

okin

esM

AGEs

IL10

IFN.

gam

ma

IFN.

down

stre

amPD

L1Im

mun

opro

teas

ome

PDL2

Apop

tosis

ARG

1M

ast c

ells

IDO

1B

cells

Exha

uste

d CD

8Cy

toto

xicity

Cyto

toxic

cel

lsTh

1 ce

llsT

cells

NK c

ells

CD8

T ce

llsTI

GIT

Lym

phoi

dTI

STr

egCT

LA4

PD1

Mye

loid

Mac

roph

ages

Neut

roph

ilsDC

sCD

45TG

F-be

taB7

H3

idMyleoid.inflammationInflammatory.chemokinesMAGEsIL10IFN.gammaIFN.downstreamPDL1ImmunoproteasomePDL2ApoptosisARG1Mast cellsIDO1B cellsExhausted CD8CytotoxicityCytotoxic cellsTh1 cellsT cellsNK cellsCD8 T cellsTIGITLymphoidTISTregCTLA4PD1MyeloidMacrophagesNeutrophilsDCsCD45TGF-betaB7H3

id

-1.00 1.000.00

MHC

2Im

mun

opro

teas

ome

Infla

mm

ator

y.che

mok

ines

Mye

loid

.infla

mm

atio

nM

yelo

id c

ells

Mac

roph

ages

Neut

roph

ilsCD

45TG

F.be

taAp

opto

sisM

ast c

ells

IFN.

gam

ma

APM

IFN.

down

stre

amTI

SB

cells

Exha

uste

d CD

8Cy

toto

xicity

Cyto

toxic

cel

lsCD

8 T

cells

T ce

llsLy

mph

oid

cells

Th1

cells

TIG

ITNK

cel

lsDC

sCT

LA4

MAG

EsPD

L1Tr

egPD

1ID

O1

IL10

ARG

1PD

L2B7

-H3

idMHC2ImmunoproteasomeInflammatory.chemokinesMyeloid.inflammationMyeloid cellsMacrophagesNeutrophilsCD45TGF.betaApoptosisMast cellsIFN.gammaAPMIFN.downstreamTISB cellsExhausted CD8CytotoxicityCytotoxic cellsCD8 T cellsT cellsLymphoid cellsTh1 cellsTIGITNK cellsDCsCTLA4MAGEsPDL1TregPD1IDO1IL10ARG1PDL2B7-H3

id

D

1

2

3Immune-depletedImmune-infiltrated

E

FFDR=0.02FDR=0.053FDR=0.052

IFN-gResponse (MSigDB)

150(44.2%)

153(45.1%)

36(10.6%)

InflammatoryResponse (MSigDB)

Beat-AML series

VariableIFN scoresCytogenetic risk

AUROC0.9210.709

SE0.040.021

95% CI0.88-0.9610.629-0.788

C

20 40 60 80

Cytogenetic risk

WBC

Secondary AML

Age

IFN scores

Wald χ2

VariableIFN scoresCytogenetic risk

AUROC0.8150.702

SE0.0310.038

95% CI0.755-0.8760.628-0.776


https://doi.org/10.1101/702001

Fig. 5


B

TIS score

D

A

AUROC = 0.806(95% CI = 0.65-0.96)

P=0.005

IFN module score

AUROC = 0.847(95% CI = 0.70-0.99)

P=0.001

Pre Post-C14

5

6

7

8

9

TIS

sco

re

✱✱✱

Pre Post-C14

5

6

7

8

AP

M s

core

✱✱

Pre Post-C10

2

4

6

8

IFN

-γ s

igna

ling

scor

e

✱✱✱

Pre Post-C10

2

4

6

8

PD

-L1

scor

e

✱✱

Refr. Rel.0

10

20

30

40

50

IFN

mod

ule

scor

e

✱✱

Refr. Rel.4

5

6

7

8

TIS

sco

re

✱✱

Anti-leukemic activityNo response

Anti-leukemic activityNo response

C


https://doi.org/10.1101/702001

Extended Data Fig. 1

IFN-dominant score

Adaptive score

Myeloid score

C

A

D

0 24 48 72 96 120 144 168 1920

25

50

75

100


Per

cent

sur

viva

l

ELN favorable (median=undefined)ELN intermediate (median=25.97 mo.)ELN adverse (median=8.9 mo.)

χ2=7.75

✱

Number at risk ELN favorable: 34 17 17 17 10 9 8 1 1ELN intermediate: 142 61 48 44 26 16 12 3 1ELN adverse: 37 11 9 6 4 2 1 1 1

0 24 48 72 96 120 144 168 1920

25

50

75

100


Perc

ent s

urvi

val

ELN favorable (median=undefined)ELN intermediate (median=25.87 mo.)ELN adverse (median=12.73 mo.)

χ2=25.11

✱✱✱

Number at risk ELN favorable: 35 22 20 20 20 12 8 1 1ELN intermediate: 155 83 62 52 14 31 12 3 1ELN adverse: 59 18 13 7 5 4 1 1 1

PMCC series

Total=290

Favorable RiskIntermediate RiskAdverse RiskNA

B

0 24 48 72 96 120 144 168 192 2160

25

50

75

100


Per

cent

sur

viva

l

Immunoproteasome scoreHighest 25% (median RFS=10.93 mo.)Lowest 25% (median RFS=undefined)

χ2=8.77

✱✱

51%

26%

E

0

5

10

15

Nor

mal

ized

gen

eex

pres

sion

(log

2)

Immune-depleted Immune-infiltrated

CD33 CD34 CD123 CD117

✱✱✱ ns nsns

0 24 48 72 96 120 144 168 192 2160

25

50

75

100


Per

cent

sur

viva

l

IFN-γ signaling score

Lowest 25% (median RFS=undefined)Highest 25% (median RFS=15.87 mo.)

χ2=3.87

✱

0 24 48 72 96 120 144 168 192 2160

25

50

75

100


Perc

ent s

urvi

val

IFN downstream score

Lowest 25% (median OS=13.9 mo.)Highest 25% (median OS=21.23 mo.)

✱

χ2=4.53

0 24 48 72 96 120 144 168 192 2160

25

50

75

100


Per

cent

sur

viva

l

PD-L1 scoreHighest 25% (median RFS=11.3 mo.)Lowest 25% (median RFS=35.03 mo.)

✱

χ2=5.08


https://doi.org/10.1101/702001

>median <median0

1000

2000

3000

4000

CD3high and CD3low BMs

CD

3 ba

rcod

es

-1.00 1.000.00


dendrogram_cut1.001.002.002.003.003.00

CD19

CD3

CD45

B2M

Beta

-Cat

enin

CD4

CD14

B7-H

3CD

44PD

1CD

45RO

CD8A

AKT

PTEN

PD-L

1P-

AKT

CD68

GZM

BKi

67VI

STA

FoxP

3CD

20Bc

l-2ST

AT3

pS

TAT3

CD56

Pan-

Cyto

kera

tin

iddendrogram_cut

CD19CD3CD45B2MBeta-CateninCD4CD14B7-H3CD44PD1CD45ROCD8A

AKTPTENPD-L1P-AKTCD68GZMBKi67VISTAFoxP3CD20Bcl-2STAT3 pSTAT3

CD56Pan-Cytokeratin

id dendrogram_cut

-1.00 1.000.00

dendrogram_cut1.002.00

dendrogram_cut1.001.002.002.00

B2M

CD19

CD3

B7-H

3CD

44Be

ta-C

aten

inCD

45CD

4CD

45RO

CD8A

PD1

CD14

CD68

VIST

ACD

20Pa

n-Cy

toke

ratin

CD56

Bcl-2

PTEN

FoxP

3PD

-L1

AKT

GZM

BST

AT3

Ki

67P-

AKT

pSTA

T3

iddendrogram_cut

B2MCD19CD3B7-H3CD44Beta-CateninCD45CD4CD45ROCD8APD1CD14CD68VISTACD20Pan-Cytokeratin

CD56Bcl-2PTENFoxP3PD-L1AKTGZMBSTAT3 Ki67P-AKTpSTAT3

id dendrogram_cut

-1.00 1.000.00


dendrogram_cut1.001.002.002.003.003.00

B7-H

3B2

MCD

45CD

19CD

44pS

TAT3

P-AK

TKi

67CD

20

CD4

CD68

Beta

-Cat

enin

CD8A

AKT

GZM

BST

AT3

CD

56Bc

l-2PT

ENCD

45RO

CD3

PD1

CD14

VIST

AFo

xP3

PD-L

1

Pan-

Cyto

kera

tin

iddendrogram_cut

B7-H3B2MCD45CD19CD44pSTAT3P-AKTKi67CD20

CD4CD68Beta-CateninCD8AAKTGZMBSTAT3 CD56Bcl-2PTENCD45ROCD3PD1CD14VISTAFoxP3PD-L1

Pan-Cytokeratin

id dendrogram_cut

-1.00 1.000.00

dendrogram_cut1.002.00

dendrogram_cut1.001.002.002.00

B2M

CD19

CD3

B7-H

3CD

44Be

ta-C

aten

inCD

45CD

4CD

45RO

CD8A

PD1

CD14

CD68

VIST

ACD

20Pa

n-Cy

toke

ratin

CD56

Bcl-2

PTEN

FoxP

3PD

-L1

AKT

GZM

BST

AT3

Ki

67P-

AKT

pSTA

T3

iddendrogram_cut

B2MCD19CD3B7-H3CD44Beta-CateninCD45CD4CD45ROCD8APD1CD14CD68VISTACD20Pan-Cytokeratin

CD56Bcl-2PTENFoxP3PD-L1AKTGZMBSTAT3 Ki67P-AKTpSTAT3

id dendrogram_cut

CD3 neighboursCD4, CD8, CD19B7-H3b2Mb-catenin

CD3 neighboursPD1

CD45RO

A

B

CD3high CD3low5

6

7

8

9

Exh

aust

ed C

D8

scor

e

0.0317

As determined by DSP

CD3high CD3low0

2

4

6

8

10

T-ce

ll sc

ore

0.0079


CD3high CD3low4

5

6

7

8

Cyt

otox

icity

sco

re

0.0952


CD3high CD3low0

2

4

6

8

CD

8 sc

ore

0.0317


C

SAL series

6 9 12 150

2000

4000

6000

8000

CD68-mRNA (log2)

CD

68 (b

arco

de c

ount

s) 0.03580.4426

6 7 8 9 100

5000

10000

15000

20000

Bcl2-mRNA (log2)

Bcl

-2 (b

arco

de c

ount

s) 0.01390.5517

0 5 10 150

1000

2000

3000

4000

CD14-mRNA (log2)

CD

14 (b

arco

de c

ount

s) 0.01560.5393

4 6 8 10 120

500

1000

1500

2000

CD19-mRNA (log2)

CD

19 (b

arco

de c

ount

s) 0.00260.6987

D



https://doi.org/10.1101/702001

ROI 8ROI 7 ROI 12ROI 11ROI 10ROI 9

ROI 1 ROI 2 ROI 6ROI 5ROI 4ROI 3

ROI 13 ROI 18ROI 17ROI 16ROI 15ROI 14

ROI 24ROI 23ROI 22ROI 21ROI 20ROI 19

Id: #22585Median CD3 barcode count=1,510


SAL series


https://doi.org/10.1101/702001

ROI 13

ROI 24ROI 23ROI 22ROI 21ROI 20ROI 19

ROI 18ROI 17ROI 16ROI 15ROI 14

ROI 1 ROI 2

ROI 8ROI 7

ROI 6ROI 5ROI 4ROI 3

ROI 12ROI 11ROI 10ROI 9

Id: #33140Median CD3 barcode count=242


SAL series


https://doi.org/10.1101/702001


A


100

200

300

400

500

WB

C c

ount

(×10

3 /µL

)

✱✱✱


20

40

60

80

100

Age

(yea

rs)

✱✱✱


30

60

90

120

BM

bla

sts

(%)

✱✱✱


100

200

300

400

500

PB b

last

s (×

103 /µ

L)

✱✱✱

ELN fav. ELN int. ELN adv.0

30

60

90

120

Num

ber

of p

atie

nts

InfiltratedDepleted

✱

B C

D E

PMCC series


https://doi.org/10.1101/702001

2. Adaptive 3.Myeloid1. IFN dominant

A

B

SAL

serie

s

2. Adaptive3. Myeloid

1. IFN dominant

-1.00 1.000.00

Imm

unop

rote

asom

eAp

opto

sisM

ast c

ells

Exha

uste

d CD

8 ce

llsID

O1

CD45

Mye

loid

cel

lsM

acro

phag

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utro

phils

Infla

mm

ator

y.che

mok

ines

DCs

Mye

loid

.infla

mm

atio

nIL

10IF

N.ga

mm

aPD

L2B7

-H3

TGF.

beta

Cyto

toxic

ityCy

toto

xic c

ells

T ce

llsB

cells

Lym

phoi

d ce

llsCD

8 T

cells

TIS

NK c

ells

Th1

cells

CTLA

4TI

GIT

IFN.

down

stre

amPD

L1M

AGEs

Treg

cel

lsPD

1AR

G1

idImmunoproteasomeApoptosisMast cellsExhausted CD8 cellsIDO1CD45Myeloid cellsMacrophagesNeutrophilsInflammatory.chemokinesDCsMyeloid.inflammationIL10IFN.gammaPDL2B7-H3TGF.betaCytotoxicityCytotoxic cellsT cellsB cellsLymphoid cellsCD8 T cellsTISNK cellsTh1 cellsCTLA4TIGITIFN.downstreamPDL1MAGEsTreg cellsPD1ARG1

id

3

2

1

CH

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Infla

mm

ator

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mok

ines

Imm

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N.ga

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wnst

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eg c

ells

DCs

IL10

Apop

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cells

Cyto

toxic

ityCy

toto

xic c

ells

Th1

cells

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Lym

phoi

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yelo

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acro

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mat

ion

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beta

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idInflammatory.chemokinesImmunoproteasomePDL2IFN.gammaIFN.downstreamPDL1MAGEsTreg cellsDCsIL10ApoptosisARG1B cellsCytotoxicityCytotoxic cellsTh1 cellsNK cellsLymphoid cellsTIGITTISCD8 T cellsT cellsCTLA4PD1Exhausted CD8 cellsIDO1CD45Myeloid cellsNeutrophilsMacrophagesMyeloid.inflammationTGF.betaB7-H3Mast cells

id

2

3

1

CHOP series SAL seriesMyeloid scores

Adaptive scores

IFN scores

C D


CHOP series SAL series

E


https://doi.org/10.1101/702001

A

C

Onset CR7

8

9

10

11

BTK

mR

NA

(log 2)

✱✱✱

Onset CR

4

6

8

10

IRF3

mR

NA

(log 2)

✱✱✱

Onset CR0

5

10

15

FLT3

mR

NA

(log 2)

✱✱✱

Onset CR6

8

10

12

14

BMI1

mR

NA

(log 2)

✱✱✱

Adult Pediatric0

5

10

15

20

IL-8

mR

NA

(log

2)

✱✱✱

Adult Pediatric0

4

8

12

16

CCL3

mR

NA

(log

2)

✱✱✱

Adult Pediatric3

6

9

12

15

18

CCL3L1

mR

NA

(log

2)

✱✱✱

Adult Pediatric0

3

6

9

12

15

18

CXCL3

mR

NA

(log

2)

✱✱✱

Onset Relapse0

3

6

9

12

CD8A

mR

NA

(log 2)

✱✱✱

Onset Relapse0

5

10

15

CD3E

mR

NA

(log 2)

✱✱✱

Onset Relapse3

7

11

15

GNLY

mR

NA

(log 2)

✱✱

Onset Relapse0

5

10

15

CD

3z (C

D24

7) m

RN

A (lo

g 2) ✱✱

BDE in adults

versus children

19(34.5%)

15(27.3%)

1(1.8%)

DE at disease onsetversus CR

DE in relapsed AMLversus disease onset

0(0%)

16(29.1%)

0(0%)

4(7.3%)

DLog2 (fold change)

-Log

10 (p

valu

e)

Log2 (fold change)

-Log

10 (p

valu

e)

Log2 (fold change)

-Log

10 (p

valu

e)



https://doi.org/10.1101/702001

BPr

otei

n in

tera

ctio

n ne

twor

k(D

E be

twee

n ad

ult a

nd c

hild

hood

AM

L)Pr

otei

n in

tera

ctio

n ne

twor

k(D

E be

twee

n AM

L on

set a

nd C

R)

Prot

ein

inte

ract

ion

netw

ork

(DE

betw

een

AML

onse

t and

rela

pse)

A

Gene ontology enrichment (disease onset versus complete remission)

Gene ontology enrichment (children versus adults)

Gene ontology enrichment (disease onset versus relapse)

Onset Relapse2

4

6

8

10

T-bet m

RN

A (lo

g 2)

✱✱

Onset Relapse2

4

6

8

10

TIG

IT m

RN

A (lo

g 2)

✱✱

Onset Relapse

2

4

6

8

10

12

CTL

A4

mR

NA

(log 2)

✱✱

C



https://doi.org/10.1101/702001

7 9 0 0

Truncating Missense In-frame Other

A

B

TCGA-AML series



https://doi.org/10.1101/702001

IFN downstream genes and TP53 mutational status

TCGA-AML series



https://doi.org/10.1101/702001

B

D

A

C

TCGA-AML series

Beat-AML series

Mut.TP53

Mut.RUNX1

FLT3-ITD Mut. NPM1w FLT3-ITD

Mut.NPM1

Other2

4

6

8

IFN

dow

nstr

eam

sco

re

✱✱✱

Mut.TP53

Mut.RUNX1


Mut.NPM1

Other0

2

4

6

Infla

mm

ator

y ch

emok

ine

scor

e

✱✱✱

Mut.TP53

Mut.RUNX1


Mut.NPM1

Other0

2

4

6

8

IFN

-γ s

core

✱✱✱

Mut.TP53

Mut.RUNX1


Mut.NPM1

Other2

4

6

8

TIS

scor

e

✱✱✱

TP53-mut. TP53-wt0

5

10

15

IFIT3

mR

NA

(log

2)

✱✱

TP53-mut. TP53-wt0

5

10

15IFIT2

mR

NA

(log

2)✱✱

TP53-mut. TP53-wt-2

0

2

4

6

8

CD3G

mR

NA

(log

2)

✱

TP53-mut. TP53-wt0

2

4

6

8

10

GZM

B mRNA

✱




https://doi.org/10.1101/702001

immune landscapes predict chemotherapy resistance and ... · (pd-l1)-targeted immunotherapy occur...

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