ABSTRACT Deep single-cell multi-omic profiling offers a promising approach to understand and overcome drug resistance in relapsed or refractory (rr) acute myeloid leukemia (AML). Here, we

combine single-cell ex vivo drug profiling (pharmacoscopy) with single-cell and bulk DNA, RNA, and protein analyses, alongside clinical data from 21 rrAML patients. Unsupervised data

integration reveals reduced ex vivo response to the Bcl-2 inhibitor venetoclax (VEN) in patients treated with both a hypomethylating agent (HMA) and VEN, compared to those pre-exposed to

chemotherapy or HMA alone. Integrative analysis identifies both known and unreported mechanisms of innate and treatment-related VEN resistance and suggests alternative treatments, like

AML research. SIMILAR CONTENT BEING VIEWED BY OTHERS A CHEMOGENOMIC APPROACH TO IDENTIFY PERSONALIZED THERAPY FOR PATIENTS WITH RELAPSE OR REFRACTORY ACUTE MYELOID LEUKEMIA: RESULTS OF A

PROSPECTIVE FEASIBILITY STUDY Article Open access 03 June 2020 LEUKEMIA STEMNESS AND CO-OCCURRING MUTATIONS DRIVE RESISTANCE TO IDH INHIBITORS IN ACUTE MYELOID LEUKEMIA Article Open access

10 May 2021 COMBINED INHIBITION OF XIAP AND BCL2 DRIVES MAXIMAL THERAPEUTIC EFFICACY IN GENETICALLY DIVERSE AGGRESSIVE ACUTE MYELOID LEUKEMIA Article 18 March 2021 INTRODUCTION The outcome

of patients with relapsed or refractory (rr) acute myeloid leukemia (AML) is poor with an estimated survival of <12 months1,2. Innate and acquired resistance to standard chemotherapy is

driven by a complex landscape of somatic mutations and clonal evolution3. Single-cell genomic and transcriptomic sequencing revealed a cellular hierarchy governed by leukemic stem cells

giving rise to a variety of aberrant progenitor cells with patient-specific gene expression and differentiation profiles3,4,5,6,7. Thus, heterogeneity exists even within clonal populations.

Combined with the scarcity of approved targeted therapies for most AML, this makes patient stratification based on genomic alterations alone challenging, and more comprehensive approaches

are needed to identify active drugs, particularly in rrAML. Functional precision medicine (FPM) strategies directly assess the therapeutic impact of drugs on primary patient material. A

variety of studies performing ex vivo drug testing have been performed8,9,10,11,12,13, and observational trials reported concordance with ex vivo and clinical responses13,14,15,16,17,18.

These findings enabled the transition to prospective interventional studies. Recently, FPM-guided treatment in AML and other hematologic malignancies was successfully implemented into

clinical decision-making19,20,21. In particular, using pharmacoscopy (PCY), a single-cell image-based platform, more than half of 56 patients with aggressive blood cancers treated according

to PCY recommendations achieved progression-free survival 1.3 times longer than on their previous treatment duration20,22. This finding was confirmed in a subsequent interventional trial

specifically focusing on AML patients with exhausted standard-of-care treatment options21. Integrating FPM data with molecular information has the potential to elucidate mechanisms

underlying drug sensitivity and resistance and to identify drug targets and vulnerabilities. Two recent AML studies11,23 integrated bulk RNA and genomic sequencing data from over 800

patients with ex vivo drug sensitivities. By applying computational deconvolution of bulk transcriptomics data, the maturation state of each AML was inferred and linked to outcome and ex

vivo drug sensitivity. In particular, the histone deacetylase (HDAC) inhibitor panobinostat and the B-cell lymphoma 2 (Bcl-2) inhibitor venetoclax showed completely opposite drug responses

based on the AML maturation state. In another study, bulk proteomic, transcriptomic, and genomic profiles integrated with ex vivo drug responses revealed an AML phenotype characterized by

high levels of mitochondrial proteins associated with poor outcome24. This proteomic signature was uncorrelated to the corresponding gene expression, highlighting the complementary nature of

the two measurements. Despite the promising results of integrative analyses, comprehensive studies combining single-cell resolved measurements of different molecular entities with

functional profiling are lacking. In this work, we perform in-depth molecular characterization of rrAML samples at the single-cell level and integrate the data with single-cell resolved ex

vivo drug responses. This approach reveals consistency among the molecular changes underlying both innate and acquired resistance to the Bcl-2 inhibitor (BCL2i) venetoclax (VEN) and suggests

alternative treatment options for BCL2i resistant patients. We provide a unique resource of single-cell resolved and clinically annotated multi-omic (DNA, RNA, protein) and functional (ex

vivo drug response) profiling of rrAML patients with clinical implications. RESULTS SINGLE-CELL MOLECULAR AND FUNCTIONAL PROFILING OF RRAML SAMPLES To gain insights into the molecular

determinants of the AML drug response landscape and identify actionable vulnerabilities, we integrated single-cell and bulk molecular measurements with ex vivo drug responses for rrAML

patients from the Tumor Profiler (TuPro) study25. Mononuclear cells (MNCs) isolated from blood or bone marrow biopsies were comprehensively characterized at the molecular and functional

level (Fig. 1a, Supplementary Data 1) using both clinically established workflows such as (digital) pathology and targeted next-generation sequencing (NGS), as well as experimental

technologies, referred to as TuPro technologies. Our genetic analysis encompassed mutational profiling at the bulk level (FoundationOne Heme assay, Supplementary Data 2), as well as analysis

of single-cell copy number variations (CNV, Supplementary Data 3). Moreover, we quantified RNA and protein levels in bulk (RNA-seq, Supplementary Data 4; proteotyping, Supplementary Data 5)

and with single-cell resolution (scRNA-seq, Supplementary Data 6; CyTOF, Supplementary Data 7). Finally, we assayed ex vivo responses to drug perturbations using two image-based screening

technologies: iterative indirect immunofluorescence imaging drug response profiling (4i DRP,26, Supplementary Data 8) focusing on short-time signaling responses, and pharmacoscopy

(PCY,13,20,21,27,28, Supplementary Data 9), which measures on-target reductions in AML blasts after 24 h of ex vivo treatment. Our cohort included 21 patients in total (Fig. 1a,

Supplementary Data 1), of which seven were profiled at multiple visits during the course of treatment. These longitudinal samples are further detailed in the Supplementary Case Studies and

Supplementary Figs. 1–6. For the majority of patients, both blood and bone marrow were collected. Emphasizing the feasibility of such multi-omic patient sample profiling, the majority of

patient visits (28/31) were analyzed by at least five technologies, and 10 visits (32%) were analyzed by all technologies (Fig. 1b). The turnaround time from sampling to completion of

analysis across all nodes was maximally 2 weeks, and 30/31 patient visits (97%) were discussed within 4 weeks of sampling in a pre-molecular tumor board (pre-MTB) consisting of members of

each technology node as well as clinical hemato-oncology experts. Because this trial was designed as an observational study with feasibility as the primary outcome, the pre-MTB results were

not used for actual treatment decisions. Nevertheless, the rapid availability of results underlines the feasibility of multi-modal tumor profiling for guiding treatment decisions. CyTOF,

PCY, and proteotyping analyzed blood and bone marrow, whereas all other technologies focused on a single sample per visit. The main reason for missing data was insufficient numbers of viable

cells for distribution across all technologies (“not received” in sheet 2 of Supplementary Data 1). The cohort spanned diverse WHO subtypes and treatment histories. Mutations commonly

affected _FLT3_ (7/21 patients)_, DNMT3A_ (8/21 patients), and _RUNX1_ (8/21 patients) (Fig. 1b), consistent with larger cohorts such as TCGA29 or BEAT-AML30. In addition, we observed a high

frequency (5/21 patients) of single-nucleotide changes with unknown somatic impact in _ROS1_, which are absent in TCGA or BEAT-AML. Large-scale copy number variations (CNVs) were rare, with

approximately half (12/26) of the samples analyzed by scDNA-seq not containing any CNVs (Fig. 1b). To assess whether different single-cell technologies were able to robustly quantify the

fraction of AML blasts per sample, we compared the blast content between different TuPro technologies (Supplementary Fig. 7a, Fig. 1c) and validated their consistency with the blast

fractions obtained by cytology (or histology if no cytology readout was available) performed in clinical routine diagnostics (Supplementary Fig. 7b, Supplementary Data 10). We found strong

agreement among the technologies for blast content, with an average Pearson correlation of 0.67 between TuPro methods, which increased to 0.84 when excluding blast content estimates derived

from scDNA-seq, in line with the large fraction of copy number-neutral tumors. All technologies were in agreement with pathology (_R_ ≥ 0.7, mean _R_ = 0.76). Furthermore, we assessed the

correlation of features measured on matched blood and bone marrow samples from the same patient visit for CyTOF and PCY (Fig. 1d). Blood and bone marrow samples taken at the same visit

showed highly similar protein levels and ex vivo drug responses. This similarity slightly decreased when comparing samples from the same patient across different time points, likely

reflecting the effect of treatment between visits. Nevertheless, it remained greater than that of unmatched samples. Taken together, these results show that our approach robustly measures

molecular and functional profiles in AML patient samples, enabling in-depth integrated analysis of drug responses and their molecular determinants. VENETOCLAX EXPOSURE SHAPES THE EX VIVO

DRUG RESPONSE LANDSCAPE Drug responses in rrAML are governed by a variety of mechanisms, some of which are intrinsic to a patient’s disease, while others are induced by exposure to

treatments. In order to shed light on the drug response landscape of rrAML, we assessed ex vivo drug responses by PCY (Fig. 2a). Overall, we measured responses to 79 drugs or drug

combinations across 48 samples from 18 patients, and included the 38 samples with >5% blast content by pathology in the downstream analysis. We used a “PCY score” corresponding to a

concentration-aggregated relative reduction in AML blasts (relative blast fraction, RBF) as a readout13,20,21. Thus, the PCY score measures a “cellular on-target effect”, i.e., a specific

reduction in the target AML blast cell population. Treatments commonly used in AML, such as chemotherapeutic agents (cladribine, cytarabine, fludarabine) and BCL2i (VEN, navitoclax), were

among the drugs with the highest proportion of on-target effects (Supplementary Fig. 8a). In addition, crenolanib, afatinib, elesclomol, alvocidib, and omacetaxine mepesuccinate also

frequently scored on-target. Interestingly, we observed a subset of 11 samples from 5 patients with low sensitivity to the above-mentioned treatments (Fig. 2a, highlighted in red). These

samples were increasingly sensitive to a group of compounds including the receptor tyrosine kinase (RTK) inhibitors ponatinib, crizotinib, crenolanib, lapatinib, and afatinib as well as

volasertib, a polo-like kinase 1 (PLK1) inhibitor (Fig. 2a, Supplementary Fig. 8b, c). This finding indicates that resistance to specific drug classes may be associated with sensitivity to

others, consistent with previously observed opposite sensitivity patterns across samples for multiple drug classes in the BEAT-AML cohort11. To investigate the relationship between clinical

parameters and ex vivo drug responses and to identify clinically relevant patterns, we next associated PCY scores for each of the tested compounds with sample metadata (Fig. 2a, b).

Parameters with less than five patient observations were excluded from this analysis. The strongest association was identified between treatment with HMA-VEN right before or at the time of

sampling and the ex vivo response to VEN. VEN PCY scores were significantly higher in samples from patients who were not exposed to VEN at the time of sampling compared to those who were

(Fig. 2c). This suggests that we capture treatment-related resistance to VEN in our PCY assay. However, there was a large spread in the response strength (i.e., the percentage of reduction

in the blast fraction) within the non-exposed samples, suggesting that some are innately resistant to Bcl-2i. These observations were further confirmed by 4i DRP, an independent drug

screening measuring morphological and signaling responses after 8 h of ex vivo drug treatment. Across all features recorded by 4i DRP, absolute changes in response to short-term VEN exposure

correlated with the strength of response measured independently by PCY after 24 h (Supplementary Fig. 9a). This was particularly pronounced in early markers of cell death, including

elevated levels of cleaved caspase 3 and reduced nucleus area, as well as signaling by phospho-AKT and phospho-STAT3 (Fig. 2d, Supplementary Fig. 9b). MULTI-MODAL RECOVERY OF INNATE AND

ACQUIRED VEN RESISTANCE MECHANISMS Given the ex vivo VEN resistance observed in patients exposed to VEN at the time of sampling, we compared this treatment-related VEN resistance with the

innate VEN resistance observed ex vivo among the samples from VEN-naive patients who had never been treated with venetoclax (Fig. 3a, Supplementary Data 1). The primary target of VEN is

Bcl-2 (encoded by _BCL2_)31,32. We therefore first focused on the relationship between VEN resistance and Bcl-2 protein and RNA levels in AML blasts. Lower levels of Bcl-2 protein or RNA

were associated with VEN resistance irrespective of whether resistance was treatment-related or innate (Fig. 3b). To further investigate this relationship at the single-cell level, we

clustered the scRNA-seq profiles of 40,369 AML cells from all samples profiled by both PCY and scRNA-seq and visualized them using t-distributed stochastic neighbor embedding (t-SNE, Fig. 

3c). As expected, single cells from patients with innate and treatment-related resistance to VEN clustered together, and both showed decreased expression of _BCL2_, suggesting a shared

molecular state. Interestingly, some samples contained cells that spanned multiple phenotypes, indicative of intra-AML heterogeneity with distinct VEN sensitivity in different cell subsets

(Supplementary Fig. 10). Part of this heterogeneity could be explained by differences in blast maturation states, where in some patient samples, blasts covered the full spectrum from a

hematopoietic stem cell-like phenotype to a myeloid-like cell, while other samples were restricted to either only immature or only more differentiated phenotypes (Supplementary Fig. 10c).

Besides downregulation of _BCL2_, several mechanisms of VEN resistance in AML have been described and reviewed33,34,35,36,37. Common resistance mechanisms include mutations in _BCL2, TP53,

KRAS, NRAS_, or _FLT3_38,39. Altered apoptotic dependencies, changes in metabolism, and maturation state are additional known drivers of VEN resistance which are detectable by changes in

transcription (Supplementary Table 1, Fig. 3d, Supplementary Fig. 11a–c). Consistent with previous reports, we found decreased expression of _BCL2_ and _PMAIP1_ (NOXA) to be associated with

VEN resistance, and expression of _BIK_ showed a similar but weaker trend. _CD34_ expression was higher in VEN-sensitive samples, consistent with the notion that VEN primarily targets more

immature AML11,15,23. Finally, the strongest associations in our cohort were found for previously reported metabolic changes related to mitochondrial structure and function (upregulation of

_CLPB_40) and fatty acid metabolism (upregulation of _CD36_41). CD36 was measured both at the protein (CyTOF, proteotyping) and RNA (single-cell and bulk) levels, and its levels in AML

blasts were strongly associated with VEN resistance (Fig. 3e, Supplementary Fig. 11d, e). At the single-cell level, _CD36_ and _BCL2_ expression was nearly mutually exclusive, with only 2%

of cells expressing both genes (Fig. 3c, f). In summary, innate and treatment-related VEN resistance was accompanied by similar molecular features, and linked to known mechanisms of

treatment resistance, such as decreased levels of Bcl-2, more mature cellular state, and changes in mitochondrial metabolism reflected by upregulation of _CLPB_ and _CD36_. GLOBAL

TRANSCRIPTIONAL ANALYSIS LINKS OXIDATIVE PHOSPHORYLATION, PROLIFERATION, AND MITOCHONDRIAL METABOLISM TO VEN RESISTANCE After investigating previously reported mechanisms of VEN resistance

(Supplementary Table 1), we extended our analysis to the whole transcriptome in order to identify the gene expression networks underlying VEN resistance in an unbiased fashion. Consistent

with our previous findings, the associations between innate and treatment-related resistance were strongly correlated (Fig. 4a, Supplementary Fig. 12a–c). To link the individual genes to

pathways, we performed a gene set enrichment analysis (GSEA) using the average strength of associations with innate and acquired resistance to rank the gene list. This revealed an enrichment

of genes related to oxidative phosphorylation (OXPHOS), cell division, ribosome biogenesis, and mitochondrial organization among resistance-associated genes (Supplementary Fig. 12d). This

finding was further confirmed by investigating the top 100 genes associated with VEN resistance in the context of their protein-protein interaction network (based on the STRING database,

Fig. 4b). In addition, we calculated a pathway activation score (singscore42, reflecting the relative expression level of all genes in a pathway) for the terms “nuclear division” and

“OXPHOS”, and these pathway scores were negatively correlated with VEN PCY scores (Fig. 4c). Consistent with previous reports24, the abundance of proteins in the respiratory chain complex

followed a trend opposite to that observed at the level of RNA expression (Supplementary Fig. 12e, f). We next aimed to identify alternative treatments among the drugs tested by PCY that

might be effective for patients with VEN resistance. We utilized our ex vivo drug response data to find compounds that scored on-target in the majority of VEN-resistant samples

(Supplementary Fig. 12g). Among the top candidates, volasertib, an inhibitor of polo-like kinase 1 (PLK1) was on-target in six out of eight VEN-resistant samples (Fig. 4d), coinciding with a

negative correlation between _PLK1_ expression and VEN sensitivity (Fig. 4e). However, _PLK1_ expression alone did not fully explain volasertib response (Supplementary Fig. 12h), and half

of the VEN-sensitive samples were sensitive to volasertib despite low _PLK1_ expression, suggesting that there are additional mechanisms governing drug response. Taken together, integrating

functional and whole-transcriptome data uncovers similarity in the molecular mechanisms underlying both innate and treatment-related resistance to VEN, and identifies a trend towards

increased volasertib sensitivity in VEN resistance. TARGETING CD36 IN VEN-RESISTANT RRAML At the single-cell level, both protein and RNA levels of CD36 were strongly associated with VEN

resistance (Fig. 3d, e). CD36 is a membrane glycoprotein, which, among other functions, acts as a receptor and transporter of fatty acids43, and has been previously implicated in AML

sensitivity to venetoclax41,44,45,46,47. Consistently, we found high levels of CD36 to correlate with reduced overall survival of AML patients in the TCGA and BEAT-AML cohorts (Fig. 5a and

Supplementary Fig. 13). Re-analysis of data from two independent studies that performed bulk RNA-seq and ex vivo drug sensitivity testing to venetoclax further confirmed this association

(Fig. 5b, c). We hypothesized that antibody-mediated blocking of CD36 function may target VEN-resistant AML blasts. We therefore quantified the effect of the anti-CD36 clone FA6-152, which

blocks all functions of CD3648,49, on seven primary AML specimens by PCY (Fig. 5d, e). We included samples with low, medium, and high CD36 levels according to CyTOF measurements

(Supplementary Data 11). In two out of three CD36 high samples, we observed a decrease in AML cells after anti-CD36 exposure (Fig. 5f). This effect was most pronounced in one sample from

patient TP038 taken at the first visit and was dose-dependent, while CD36 blocking had no effect even at the highest dose in CD36-low samples such as TP031 baseline (Fig. 5g). The reduction

in AML cells was accompanied by increased cell-cell interactions (Figure 5h, i), which was again associated with CD36 expression levels in the tested samples. The majority of

cell-interaction clusters observed consisted of a mixture of cell types including AML cells, T-cells, monocytes, and others. To assess potential off-target effects of targeting CD36, we

further investigated which cell types express _CD36_ in our scRNA-seq dataset (Fig. 5j). Expression was highest in mature monocyte-like and erythroid cells, followed by myeloid-like AML.

Immature AML blasts expressed lower levels of _CD36_, consistent with the idea that the CD36-high AML cells may represent a more differentiated monocytic phenotype46 and are selectively

targeted by anti-CD36 antibodies. This notion is further supported by the increased CD36 expression observed in AML with (myelo-)monocytic and erythroid phenotypes in the BEAT-AML cohort

(Fig. 5k). DISCUSSION In this work, we present deep single-cell multiomics profiling of a cohort of 21 patients with rrAML that was characterized at the level of DNA, RNA, protein, and drug

response. Our dataset is a unique resource that complements existing datasets, which typically focus on measurements at the bulk level (TCGA, BEAT-AML11,29,30), and extends the repository of

available scRNA-seq data on AML4,50,51,52,53,54,55,56 with matching single-cell resolved and sample-matched DNA, protein, and functional data. We leveraged the TuPro dataset to investigate

clinical and molecular correlates of drug responses in rrAML. An ex vivo drug response landscape of rrAML measured by PCY highlighted diverse patterns of sensitivity and resistance. Commonly

used AML treatments such as cytarabine induced a reduction of AML blasts in a large fraction of samples, suggesting the presence of a chemosensitive blast population in rrAML. We further

observed a subset of samples with markedly different drug response patterns: While sensitivity to cytarabine, as well as cladribine, fludarabine, and VEN was decreased, these samples were

increasingly sensitive to different RTK inhibitors and the PLK1 inhibitor volasertib. This is in line with previous reports highlighting opposite drug sensitivity patterns in a

maturation-associated manner11,23. Consistently, we report a positive correlation between expression of the leukemic stem cell marker CD34 and venetoclax sensitivity. By analyzing

associations between clinical parameters and ex vivo response, we identified exposure to the Bcl-2i VEN at the time of sampling as one of the main drivers of ex vivo VEN resistance. In

addition, large variability in VEN ex vivo responses among VEN naive samples suggested the presence of innate resistance. In combination with hypomethylating agents, VEN has markedly changed

the perspective of patients ineligible for intensive chemotherapy and almost doubled overall survival38,39. In addition, it has become one of the most promising treatments for rrAML with

response rates of up to 70%, which is remarkable in this disease setting2,57,58,59,60. However, the development of treatment resistance is still common and treatment options for

VEN-resistant patients are limited. We, therefore, focused on understanding the mechanisms underlying innate and treatment-related VEN resistance by performing an in-depth integrative

analysis of the TuPro technologies. Our analysis highlighted similar mechanisms governing both avenues of treatment resistance. We first focused on known molecular changes involved in Bcl-2

inhibitor response33,34,35,36,37. Altered apoptotic dependency was reflected in decreased levels of Bcl-2 (protein and RNA), _PMAIP1_ (NOXA), and _BIK_. Changes in mitochondrial structure

and function affect the readiness to undergo apoptosis in AML cells, and deletion of _CLPB_, which promotes the formation of tighter mitochondrial cristae rescued VEN sensitivity40.

Consistent with these observations, high _CLPB_ expression was associated with innate and treatment-related VEN resistance in this cohort. In addition to changes in mitochondrial structure,

alterations in OXPHOS have also been implicated in VEN resistance44,45,61,62,63. In this cohort, increased expression of genes involved in OXPHOS was associated with VEN resistance, in

addition to elevated levels of the fatty acid receptor and transporter CD36. Switching from amino acids to fatty acids to fuel the TCA cycle and OXPHOS has previously been identified as a

mechanism by which AML cells circumvent the metabolic effects of Bcl-2 inhibition45, thereby leading to venetoclax resistance41. Based on these prior studies and our data, we hypothesize

that increased OXPHOS might be linked to increased availability of fatty acids (through CD36) to fuel the TCA cycle. In turn, high metabolic activity might enable increased proliferation

rates, which we also observe in VEN-resistant samples. CD36 has been implicated in multiple cancers, and high expression is generally associated with poor prognosis64. CD36 in solid tumors

is associated with increased proliferation, plasticity, epithelial-mesenchymal transition, and metastatic potential. Blocking CD36 in a mouse model of human oral carcinomas decreased

metastasis potential49. Anti-CD36 thus emerged as a promising target to prevent cancer spread and is currently being developed as a therapeutic agent65. In the context of AML, high

expression of CD36 correlates with poor prognosis in TCGA and BEAT-AML and was associated with chemoresistance, relapse, and extramedullary disease66 as well as resistance to VEN41,46,47.

Having observed a correlation between VEN ex vivo sensitivity and CD36 levels, we used antibody-mediated CD36 blocking to specifically target VEN-resistant AML cells. We observed reduced

numbers of AML cells upon exposure to anti-CD36, in conjunction with increased cell clumping. Cell clusters were composed of both AML cells, T-cells, and myeloid cells. Increased cell-cell

interaction likely indicates an immune activation phenotype triggered by the presence of antibody-coated cells. Thus, we hypothesize that CD36 treatment may target leukemic cells on multiple

levels. Firstly, by blocking the CD36-mediated uptake of fatty acids, it may impact the cells metabolic landscape and potentially lower OXPHOS and cell growth41,45. Second, it may guide the

immune-mediated destruction of CD36-expressing cells. Interestingly, blocking CD36 on T-cells has also been reported to decrease the immunosuppressive function of regulatory T-cells67,

which could further enhance this effect. Overall, our findings propose CD36 as a promising immunotherapeutic target for VEN-resistant rrAML. CD36 blockage showed promising activity in vivo

in a mouse model of human oral carcinoma49. In the future, our findings will need to be validated in an AML patient-derived xenograft model treated with an anti-CD36 blocking antibody in

order to improve the pre-clinical evaluation of this approach. Our data also highlighted a role for increased proliferation in VEN-resistant samples, which has been observed before in CLL68

but not AML. This proliferative signature was accompanied by increased expression of _PLK1_ and ex vivo sensitivity to the PLK1 inhibitor volasertib in 75% of the VEN-resistant samples.

Volasertib as a monotherapy or in combination with low-dose cytarabine or decitabine has shown promising activity as a treatment for AML patients who are not eligible for intensive

chemotherapy in phase 1/2 clinical trials69,70. However, this could not be confirmed in a follow-up phase 3 trial, and volasertib development has since been discontinued71. These findings

suggest that Volasertib could still be evaluated in the setting of VEN resistance, particularly in rrAML patients with high PLK1 expression. In conclusion, we here provide a valuable

resource of multi-modal molecular and functional profiling of rrAML at single-cell resolution. Analysis results were available within 2 weeks of sampling, highlighting the feasibility and

clinical applicability of our approach. The resulting integrative analysis revealed shared molecular signatures of innate and acquired resistance to venetoclax and identified emerging

treatment opportunities. METHODS STUDY DESIGN AND PARTICIPANTS The Swiss TumorProfiler (TuPro) was conducted as a prospective, non-randomized observational clinical study with feasibility as

the primary outcome. Treatments of this AML cohort during the study were solely based on current clinical guidelines and the decisions made by the treating physician and the patient. For

the hematology arm of the TuPro presented in this work, patients with acute myeloid leukemia according to WHO 2016 who were refractory to standard therapy or who developed a relapse were

eligible if they fulfilled the following additional criteria: * Age ≥ 18 years * Written informed consent according to national legal and regulatory requirements prior to any

project-specific procedures Patients, who fulfilled the following exclusion criteria were not considered for this project: * Any other serious underlying medical, psychiatric, psychological,

familial, or geographical condition, which in the judgment of the project leader may interfere with the project or affect patient compliance * Concurrent history of active malignancy in the

two past years prior to diagnosis (except basal and squamous cell carcinoma of the skin and in situ carcinoma of the cervix) * Concurrent severe and/or uncontrolled medical condition (e.g.,

uncontrolled diabetes, infection, hypertension, pulmonary disease) * Cardiac dysfunction (NYHA classification II-IV or unstable angina or unstable cardiac arrhythmias) * Legal incompetence

In total, 21 patients were enrolled in this study over a period of 18 months starting in September 2019 and finishing at the end of March 2021. Follow-up samples were obtained for 7

patients. TP035 was a patient with myelodysplastic syndrome with excess of blasts with disease progression at the time of TuPro sampling. At the majority of patient visits, both blood and

bone marrow were sampled, resulting in a total of 57 samples collected and included in this study. Cohort characteristics are summarized in Supplementary Data 1. The research project was

carried out in accordance with the research plan and with principles enunciated in the current version of the Declaration of Helsinki (DoH), the Principles of Good Clinical Practice (GCP),

the Swiss Law, and Swiss regulatory authority’s requirements as applicable. All patients provided written informed consent to publish their anonymized clinical and molecular data as part of

this study. Ethical approval was granted by the Ethics Committee of the Canton of Zurich (CEC Zurich, BASEC-Nr: 2019-01326). SAMPLE PREPARATION Blood samples and/or bone marrow aspirates

were collected in BD vacutainer EDTA tubes and PBMC was immediately isolated by histopaque gradient purification (Histopaque-1077, Sigma-Aldrich). Live cell number was established using a

nucleocounter NC 200 using Via1 Cassettes (Chemometec) and the required number of cells was prepared for the different technology platforms. For Pharmacoscopy and scRNA sequencing, cells

were viably frozen in 10% DMSO in RPMI 1640 medium (Gibco). For Proteotyping, cells were snap frozen as a dry pellet in liquid nitrogen upon red blood cell lysis (10x Red Blood Cell Lysis

solution, Biolegend). For CyTOF, cells were stained for dead cells using 25 μM Pt194 (Standard BioTools) in a 1 min pulse before quenching with 10% FBS. Cells were then fixed with 1.6%

paraformaldehyde (Electron Microscopy Sciences) for 10 min at room temperature and stored at −80 °C. Ready-to-use samples were sent to the technology platforms in a coordinated way to be

processed simultaneously, with an expected 2-week turn-around time. PHARMACOSCOPY EXPERIMENTAL COMPOUNDS AND DRUG PLATES All compounds were purchased from Sigma-Aldrich as 5 mM stock

solutions in DMSO. 384-well flat-bottom plates (Perkin Elmer PhenoPlate) containing between 0.1 and 10 μM of drugs or drug combinations per well (Supplementary Data 12) were prepared using

an Echo liquid handling robot (Labcyte). Plates were sealed with aluminum foil and stored at −20 °C until used. EX VIVO DRUG RESPONSE ASSAY Pharmacoscopy (PCY) was performed as previously

described13,20,27,72. Briefly, mononuclear cells isolated from blood or bone marrow were resuspended in media (RPMI-1640 (Gibco) + 10% fetal bovine serum (Gibco)) at a density of around 0.1

million cells/ml. 50 μl of cell suspension (5000 cells) were then seeded into each well of a drug assay plate. Cells were incubated with compounds at 37 °C, 5% CO2 for 24 h. Afterward, the

media was aspirated and cells were fixed with 10% (v/v) formalin (Sigma-Aldrich F8775, 36–38% formaldehyde + 10–15% methanol in water) for 10 min at room temperature. Fixative was aspirated

and the cells were resuspended in PBS. To quench background fluorescence, the plate was put on an LED lamp overnight. Afterward, PBS was aspirated and 20 μl / well of 5% (v/v) FBS, 0.1%

(v/v) Triton-X, 5 μg/ml 4′,6-Diamidino-2-phenyl-indol-dihydrochlorid (DAPI) were added to the cells to block nonspecific antibody binding, permeabilize them and stain the nucleus. After 30 

min at room temperature, this solution was aspirated and 20 μl/well staining solution (10 mg/ml BSA in PBS, fluorescently labeled antibodies CD117 [AF488, BioLegend 313234], CD34 [PE,

BioLegend 343505], and CD33 [AF647, BioLegend 366626], each antibody at 1:300 dilution) was added. Cells were stained overnight at 4 °C. Afterwards, the staining solution was aspirated and

wells were covered with 70 μl of PBS. CD36 BLOCKING EXPERIMENT CD36 blocking experiment was performed analogous to ex vivo drug screening described above. Instead of small molecules, we

added between 0.01 and 1 μg/ml anti-CD36 FA6-152 (Abcam ab17044) or matching isotype control (Mouse IgG1 kappa, BioLegend 401401) per well. Cells were fixed and stained as described above,

with the only difference being that we swapped the CD33 antibody for a combination of CD3 (BioLegend 300416) and CD14 (BioLegend 325612). HIGH-THROUGHPUT CONFOCAL MICROSCOPY All plates were

imaged on an automated spinning-disk confocal microscope (PerkinElmer Opera Phenix), using 10x magnification and 9 images per well to cover the entire well area. We used five channels with

non-overlapping excitation/emission filters to image the following features: Channel 1 (transmission / 650–760 nm) for brightfield to capture general cell shape and texture, channel 2 (405 

nm/435–480 nm) for DAPI/nuclei, channel 3 (488 nm/500–550 nm) for CD117, channel 4 (561 nm/570–630 nm) for CD34, and channel 5 (640 nm/650–760 nm) for CD33. For the CD36 blocking experiment,

we used the same parameters but imaged at 20x magnification with 25 images per well. ANALYSIS IMAGE ANALYSIS AND FEATURE EXTRACTION Raw images were first analyzed using CellProfiler

v.2.2.073. Individual cells were detected based on maximum correlation thresholding of the DAPI signal. Staining intensities were extracted for the nucleus and a region of 6 pixels around

the nucleus that was used as a proxy for cytoplasmic intensity. For downstream analysis, intensities were log10 transformed and corrected for variation in local background as described by

Vladimer et al.74. FILTERING Cells with very low DAPI intensities or abnormally high or low nucleus area likely represent segmentation artifacts and were therefore removed from the analysis

by manual gating. In addition, outlier wells (very low or high total cell numbers, or aberrant staining patterns) were removed if the observed patterns could be attributed to pipetting

mistakes or the presence of large cell clumps by visual inspection. IDENTIFICATION OF AML CELLS AML cells were identified by thresholding the CD117 and CD34 intensities. Any cell positive

for at least one of these two markers was considered a blast. CALCULATION OF DRUG RESPONSE SCORES Drug response scores (1-relative blast fraction, RBF) per well were calculated as follows:

1-RBF = 1-(fraction of blast cells in drug condition/mean(fraction of blast cells in DMSO)) The responses were then averaged (mean) across replicate wells per condition (where a condition

describes one drug at one concentration), and responses per drug were further aggregated into a PCY score by taking the mean across these condition averages per drug. Thus, PCY score = mean

per drug(mean per condition(1-RBF)). CLUSTERING OF DRUG RESPONSES FOR VISUALIZATION For visualization in Fig. 2A, drug responses were clustered across drugs and samples using hierarchical

clustering as implemented by the Matlab _linkage_ function, using Euclidean distance and complete linkage. Only samples with > 5% blasts by pathology were included in this analysis.

ASSOCIATIONS WITH CLINICAL PARAMETERS Associations with clinical parameters were calculated for each individual drug. Samples were split by the levels of each clinical parameter, and each

level was tested separately against all the others. For statistical testing, a Wilcoxon rank-sum test was used (Matlab _ranksum_). _P_ values were corrected for multiple testing using the

Benjamini-Hochberg procedure. (Matlab _mafdr_ with BHFDR set to true). QUANTIFICATION OF CELL-CELL INTERACTIONS Cell-cell interactions in the CD36 blocking experiment were quantified from

the nucleus center positions obtained from CellProfiler. A cell was considered to be interacting if there was at least one other nucleus within 30 pixels from its own nucleus center. We used

the fraction of interacting cells among all cells per well as a readout. Due to the non-adherent nature of AML cells, cells tend to cluster at the edges of the well and in the corners as a

result of our washing and staining procedure. To avoid technical artifacts, we therefore restricted this analysis to nuclei that were at least 3000 pixels (900 μm) away from the well border.

SCRNA-SEQ EXPERIMENTAL Viability and cell count of the single-cell suspensions were assessed with a Cellometer K2 (Nexcelom Bioscience). Single cells were captured with the Chromium

Controller and scRNA-seq libraries were prepared with the Chromium Single Cell 3’ v3 Library & Gel Bead Kit (10x Genomics) according to the manufacturer’s instructions. QC assessment of

cDNAs and libraries was performed with Fragment Analyzers (Advanced Analytical, AATI). Libraries were sequenced with Illumina NextSeq 500 and NovaSeq 6000 systems according to the

manufacturer’s guidelines. ANALYSIS DATA PRE-PROCESSING A detailed description of the scRNA analysis pipeline used in this study was published previously75. Briefly, the analysis consists of

the following steps: (1) read data processing, quality control, and normalization, (2) cellular composition analysis including cell type classification and unsupervised clustering, (3)

differential gene expression analysis, (4) gene expression and pathway scoring. CELL TYPING Each cell in the dataset was assigned a label using the scROSHI76 workflow. Briefly, cells are

classified based on expression of cell-type specific genes (Supplementary Data 13) using a two-step procedure that takes into account the hierarchical nature of cell types. In the first

step, a “major cell type” (e.g., myeloid blast, T-cell, B-cell) is assigned. In the second step, this assignment is refined and cell subtypes are assigned within each major cell type. The

gene lists for the major cell types were generated based on unsupervised clustering and marker identification (Seurat: FindMarkers77,78) of the first three samples in this study (Sample IDs:

DOROFEG, UBADAFA, DOROBOF). The markers for non-malignant cell subtypes were obtained from CIBERSORT79, and the markers for AML subtypes were based on van Galen et al.4. BULKIFICATION For

cohort-level analyses, where the patient is the fundamental subject of observation, and in order to reduce complexity, read counts for each gene were aggregated (summed up) selectively

across groups of cells within a patient. Depending on the focus of the analysis, the grouping is performed on cells within a cell type or within a cluster of cells within a cell type. This

procedure is sometimes called selective bulkification. As a consequence of this aggregation, gene counts are not low and sparse as in single-cell data but large and frequent enough to be

analyzed with conventional bulk methods. For all sample-level analyses presented in this manuscript, we used selective bulkification across all cells typed as “AML” per sample. Thus, the

scRNA-seq expression represents the average expression in AML blasts per sample. AGGREGATION ACROSS PATIENTS AND CALCULATION OF TSNE For the t-SNEs shown in Fig. 3c, f, and Supplementary

Fig. 10a–c, raw counts of all cells classified as AML from all samples with >5% blast content were aggregated and normalized using scran (v.1.20.1)80. The normalized counts were then

batch-corrected using the _mnnCorrect_ function implemented in the Batchelor R package (v.1.8.0)81, with patient IDs as batches. Top 10% most variable genes were identified by the NBDrop

method implemented in the M3Drop R package (v.1.18.0) applied to the raw counts. The batch-corrected expression values of these most variable genes were used as input for t-SNE. The

embedding was calculated using _fitsne_ implemented in the R package snifter (v.1.2.0), which provides a wrapper to the FI-tSNE implementation from the Python package openTNSE82,83.

Perplexity was set to 50 and exaggeration to 1. All other parameters were left at their defaults. CLUSTERING OF SINGLE-CELL EXPRESSION PROFILES Single-cell clusters shown in Supplementary

Fig. 10 were obtained by spectral clustering using Rphenograph (v.0.99.1). The batch-corrected expression values were used as input, and the number of nearest neighbors _k_ = 50 was used.

All other parameters were left at their default values. CALCULATION OF PATHWAY SCORES Pathway activation scores were calculated from bulkified expression profiles using a rank-based method

as implemented in the singscore R package42, with all parameters left at their default values. This method works by first ranking all genes per sample by increasing expression level. A

directed pathway score (assuming that a pathway is more active the higher the corresponding genes are expressed) for a given gene set is then defined as the average, mean-centered rank of

all genes in that gene set. Pathway scores thus reflect the relative mean percentile rank of the target gene sets within each sample, and a sample with a high score has higher relative

expression of the genes in a given pathway. SCDNA-SEQ EXPERIMENTAL Viability and cell count of the single-cell suspensions were assessed with a Cellometer K2 (Nexcelom Bioscience). Single

cells were captured with the Chromium Controller and scDNA-seq libraries were prepared with the Chromium Single Cell DNA Library & Gel Bead Kit (10x Genomics) according to the

manufacturer’s instructions. QC assessment of sequencing libraries was performed with Fragment Analyzers (Advanced Analytical, AATI). Libraries were sequenced with Illumina’s NovaSeq 6000

system according to the manufacturer’s guidelines. ANALYSIS In total, 26 clinical samples were processed from 18 patients. After each sample was sequenced, the barcoded reads were mapped and

assigned to their individual cells. The reads are binned into 20 kb genomic regions which are corrected for GC content and mappability using the CellRanger software. The adjusted number of

reads in each bin reflects the underlying copy number state of that genomic region. To identify copy number aberrations, we first detect potential breakpoints by pooling information related

to differences in read counts across all cells. After segmenting the genome in this fashion, cells are clustered into clones and the copy number profiles of the clones along with their

phylogenetic relationships are inferred. These analyses were performed using a version of the SCICoNE software84. Close to half (12/26) of the AML samples have no detected CNAs. Amongst the

non-diploid cells, the downstream analysis identified 34 clones among the remaining 14 samples. BULK RNA-SEQ EXPERIMENTAL Total RNA was isolated from cell suspensions using the Quick-RNA

Microprep Kit (Zymo Research) including DNase I treatment according to the manufacturer’s instructions. The quality of the isolated RNA was checked with a 4200 TapeStation device (Agilent).

RNA-seq libraries were prepared using TruSeq Stranded Total RNA Library Prep Gold reagent kits (Illumina) according to Illumina’s guidelines. QC of sequencing libraries was performed using

Fragment Analyzer systems (Advanced Analytical, AATI). Libraries were pooled and sequenced PE100 on an Illumina NovaSeq 6000 system to target 100 million reads per library. ANALYSIS

Alignment to the reference genome (GRCh38) was performed with STAR85. The raw expression counts were obtained using simple counting, an in-house pipeline accessible through github:gromics

(at commit #67e1f4e). Counts were normalized for differences in library size using relative log expression as implemented in edgeR’s calcNormFactor, with the method parameter set to ‘RLE’.

Note that this is equivalent to the default normalization employed by DESeq2. For downstream analysis, a pseudocount of 1 was added to the normalized counts and they were log2 transformed.

CYTOF EXPERIMENTAL PBMCs isolated from BM or peripheral blood were stained for viability with 25 μM cisplatin (Enzo Life Sciences) in a 1 min pulse before quenching with 10% FBS. Up to 10

patient samples were barcoded and mixed with a pre-barcoded set of reference cells as described previously in order to allow for batch correction86. Pooled barcoded cells were incubated for

10 min at 4 °C with FcR blocking reagent (Miltenyi Biotec) and were subsequently split in two aliquots and stained with 100 µL of an AML antibody panel per 107 cells (Supplementary Data 14).

Upon staining, cells were washed three times in Cell Staining Media (CSM, PBS 0.5% BSA and 2 mM EDTA). To enable cell detection, cells were stained overnight at 4 °C with the DNA

intercalator Ir191/193 (0.5 µM, Standard BioTools). Samples were then prepared for CyTOF acquisition by washing the cells successively in CSM, PBS, and water. Cells were then diluted to 0.5 

× 106 cells/mL in Cell Acquisition Solution (Standard BioTools) containing 10% EQ™ Four Element Calibration Beads (Standard BioTools) and acquired on a Helios upgraded CyTOF 2 (Standard

BioTools). ANALYSIS DATA PRE-PROCESSING AND CELL TYPE CLASSIFICATION FCS files were preprocessed as described previously to identify individual patient samples based on single-cell

debarcoding and to perform spillover correction, batch correction, and live cell identification86,87. The main cell types present in each sample, including B-cells, T/NK-cells, myeloid

cells, granulocytes, platelets, and AML cells were identified using a random forest classifier trained on manually gated cell populations in Cytobank. Specifically, CD34-positive AML cells

were defined as CD34 high CD45 low. We also observed a population of cells that was negative for most of the tested markers. This population may represent an atypical type of AML cell and

was labeled as “putative AML” (see Supplementary Fig. 8a). Because this population likely includes both malignant and nonmalignant myeloid progenitors, we also considered those cells as AML,

however, we only included samples with > 5% blasts by pathology in the downstream analyses. COHORT-LEVEL ANALYSES For comparisons of marker intensities across the full cohort, the levels

of each protein were averaged across all cells of a certain cell type per sample. All comparisons shown in this work were performed using the average marker intensities across all AML cells

(CD34 positive and putative AML). PROTEOTYPING EXPERIMENTAL Samples for proteotyping were generated using the PreOmics iST kit (PreOmics). In short, cells were lysed, digested, and cleaned

up according to the manufacturer’s recommendations. Lysis was supported but an additional sonication step using three 30 s sonication pulses in a VialTweeter (Dr. Hielscher). The tryptic

digest was performed for 3 h. Samples were resuspended at a concentration of 1 μg/μl for MS measurements. As control and reference between the different sample batches leukemia cell lines

NB-4,CMK-11-5, and MV-4-11 were mixed in a ratio of 1:1:1 and then digested and prepared as described above. 34 patient samples (_n_ = 1) together with 13 controls were measured on an

Orbitrap Lumos mass spectrometer (Thermo Fisher Scientific) or alternatively on a Q Exactive HF-X mass spectrometer (Thermo Fisher Scientific) equipped with an Easy-nLC 1200 (Thermo Fisher

Scientific). Peptides were separated on a C18 50 cm EASY-Spray™ HPLC column (2 µm, 100 Å, 75 µm i.d.(ES903, Thermo Fisher Scientific)). Mobile phase A consisted of HPLC-grade water with 0.1%

formic acid, and mobile phase B consisted of HPLC-grade ACN (80%) with HPLC-grade water and 0.1% (v/v) formic acid. Peptides were eluted at a flow rate of 200 nl/min using a non-linear

gradient from 4% to 52% mobile phase B in 117 min. For data-independent acquisition (DIA) on the HF-X, DIA isolation windows were set to 15 m/z, and a mass range of m/z 400-1210 was covered.

A total of 54 DIA scan windows were recorded at a resolution of 30,000 with an AGC target value set to 1e688,89. For DIA on the Lumos, DIA isolation windows were variable and a mass range

of m/z 350–1650 was covered. Resolution was set to 30,000 with a normalized AGC target of 2000%. HCD fragmentation was set to 24%, 27%, 30% stepped (Lumos), or 28% normalized (HF-X)

collision. Full MS spectra were recorded at a resolution of 120,000 with a normalized AGC target of 250% and a maximum injection time of 60 ms (Lumos) or an AGC target of 3e6 and maximum

injection time of 50 ms (HF-X). ANALYSIS DIA data were analyzed using Spectronaut v13 (Biognosys). MS1 values were used for peptide quantification, peptide quantity was set to sum. Data were

filtered using Qvalue with a precursor Qvalue cut-off of 0.001 and a protein Qvalue cut-off of 0.01 FDR. PTM localization was set to True and the probability cutoff to 0.75. Interference

correction was performed. Quantitation was set to Qvalue identified, missing values were imputed with the missing values derived from the experiment-wide distribution of quantities. Data

were searched against a Uniprot human library (release September 2019) together with standards and common contaminants in standard DIA with an in-house generated spectral library containing

cell lines, patients, and healthy samples. Protease cleavage was defined as Trypsin (full) with a maximum of two miscelavages. C-Carbamidomethylation was set as static, M-Oxidation, and

N-terminal acetylation as dynamic modifications. The minimal peptide length threshold was six amino acids. Mass tolerance for precursors was 10 ppm and fragment ion mass tolerance was set to

0.02 Da. The minimum number of unique peptides for protein identification was one. Batch correction on the Spectronaut peptide output was performed using proBatch90. CALCULATION OF PATHWAY

SCORES Pathway activation scores were calculated from blast-content corrected protein abundance profiles using the same method as described above for scRNA-seq (singscore42). 4I DRP

EXPERIMENTAL Prior to cell seeding, 384-well plates were coated for 2 h at room temperature (RT) with a coating mix (1:1 Poly-L-Lysine (Sigma Aldrich, P4832-50ML), 1:100 Fibronectin (Sigma

Aldric, F0895-2MG) in PBS). The coating mix was removed using an EL406 Washer-Dispense (WD) and the 384-well plate was left 1 h at RT to dry. Depending on the abundance of live cells in the

patient samples, 3500–7500 cells were seeded per well of a 384-well plate using the WD’s peripump cassette in culture medium (RPMI without L-Glutamine (Sigma Aldrich, R0883), 10% FCS (Sigma

Aldrich), 1 mM Sodium Pyruvate (Thermo Fisher Scientific, 11360070), 2 mM L-Glutamine (Sigma Aldrich, G7513), Anti/Anti 100x (Gibco CatNr: 15240-096)). The cells were incubated overnight at

37 °C and 5% CO2. The next day, drugs were added using a BRAVO liquid handling robot to the seeded cells at a final concentration of 5 µM and 0.5% DMSO. Vehicle-treated control cells were

only incubated with 0.5% DMSO. The drug-treated cells were incubated at 37 °C and 5% CO2 for a further 8 h. After elapsing of the incubation time, the cells were fixed at 4% Paraformaldehyde

(Electron Microscopy Sciences) in PBS for 15 min. After which, they were washed 4 times with PBS and permeabilized at 0.5% Triton X-100 (Sigma Aldrich) in PBS for 15 min. Next, the cells

were washed 4 times with double distilled water (ddH2O). Next, iterative indirect immunofluorescence imaging (4i) was performed on the samples to acquire the following molecular markers:

CD33, CD34, CD117, CD45, Histone H3 Lysine 4 methylation (H3K4Met), proliferating cell nuclear antigen (PCNA), phospho-ERK, phospho-STAT3, phospho-AKT, phospho- ribosomal protein S6 kinase 1

(pS6K1), c-Myc, tubulin, cleaved caspase 3 (Cl.CASP3), and SC-35 (Supplementary Data 8). For a detailed 4i protocol, see refs. 26,91. Sample blocking, primary and secondary incubation were

performed for 1 h. The nucleus of cells was stained using DAPI (every cycle). The cell out staining was performed as described by Battich et al.92. Imaging was performed using a Cytiva IN

Cell 6000 at 20X magnification with a Nikon Plan Apo (0.95 NA), correction collar 0.11-0.23 CFI/Lambda objective. 16 fields of view were imaged per well, with 3 z-sections per field. The

Z-sections were collapsed to one image by Maximum Intensity Projection. Laser lines used for the study: 406 nm, 488 nm, 568 nm, 625 nm. ANALYSIS Image processing and image analysis tasks on

the 4i images were performed using TissueMaps (https://github.com/pelkmanslab/TissueMAPS). The tasks include image alignment of 4i cycles based on the DAPI signal, correction of the

illumination bias, nuclear and cell segmentation, measurements of nuclear and cell morphology as well as measurements of intensity moments of the different fluorescence signals in nucleus

and cell objects. To link the 4i DRP features to PCY-based ex vivo responses, single-cell resolved measurements in AML blasts (CD34 or CD117 positive) were averaged across drug-containing

wells and normalized to DMSO control. Association with PCY ex vivo responses was performed using a linear regression model with the PCY response to VEN as a predictor and each

DMSO-normalized 4i DRP feature in the VEN condition as the response variable. INTEGRATIVE ANALYSIS For all integrative analyses, we excluded samples with <5% blast content by pathology

(see Supplementary Data 1, column “Flagged for low blast content”). Sex-stratified analysis was not performed due to the limited size of the cohort. ASSOCIATION OF INNATE AND

TREATMENT-RELATED VEN RESISTANCE WITH MOLECULAR MEASUREMENTS To quantify the association of treatment-related VEN resistance with patient-level gene and protein expression, we first z-scored

the expression data derived from CyTOF, scRNA, bulk RNA, and prototyping. Then, we performed one two-tailed Welch’s t-test (R _t.test_, var.equal set to false) per gene/protein, comparing

samples with exposure to VEN at the time of sampling to those without. The effect size was calculated as the difference in mean RNA / protein level between VEN naive and VEN exposed samples.

To assess the relationship between innate resistance and protein/RNA expression levels, we subset the cohort to only contain VEN naive samples. Then, we performed a linear regression model

per protein/gene, with the z-scored protein/RNA levels as the response variable and ex vivo response scores to VEN as a predictor. The slope of the regression line was then used to measure

effect size. In order to have effect sizes on a similar scale as for the association with acquired resistance, we scaled the ex vivo response scores to between 0 and 1. In all of those

comparisons, samples from patients who had received venetoclax in earlier treatment lines (not directly at the time of sampling) were excluded. GENE SET ENRICHMENT ANALYSIS (GSEA) FOR VEN

RESISTANCE-ASSOCIATED GENES GSEA was performed on GO Terms using the gseGO function from the clusterProfiler R package93,94. We restricted the analysis to the GO:Biological process (BP) gene

set category and the eps parameter was set to 0. All other parameters were left at their default values. Genes were ranked by their average association with innate and acquired VEN

resistance. Top terms were selected based on the adjusted _P_ value of the enrichment and sorted by normalized enrichment score (NES) for visualization. VISUALIZING THE STRING NETWORK OF TOP

ASSOCIATED GENES A complete network of human protein interactions was obtained from STRING, using the STRINGdb R package (v. 2.4.2, database version 11.5)95 A score threshold of 500 was

used. This network was then filtered to only retain the top 100 genes associated with VEN resistance. As for GSEA, genes were ranked by their mean association with innate and acquired VEN

resistance. For visualization, only genes connected to at least one other gene were retained. In addition, clusters with less than 3 genes were omitted. ANALYSIS OF PUBLICLY AVAILABLE

DATASETS TCGA: ASSOCIATION OF _CD36_ EXPRESSION AND OVERALL SURVIVAL Gene expression data (“rna_seq.augmented_star_gene_counts.tsv”) from the TCGA-LAML cohort was downloaded from the GDC

data portal (https://portal.gdc.cancer.gov/projects/TCGA-LAML, accessed 15th September 2023). Raw counts were normalized and transformed using the normTransform function in DESEq2

(v.1.32.0)96. The cohort was stratified into low and high _CD36_ expression using an automatically determined threshold as implemented in the _surv_cutpoint_ function from the survminer R

package (v.0.4.9). Overall survival was obtained from the TCGA clinical data (“days_to_death”). Patients with vital status “Alive” were marked as censored at the time of last follow-up

(“days_to_last_follow_up”). Survival analysis was performed using the R package survival (v3.2-11), using a log-rank test as implemented in the _survfit_ function, and the result was

visualized using survminer’s _ggsurvplot_. BEAT-AML: CORRELATION OF CD36 EXPRESSION WITH VEN EX VIVO RESPONSE _CD36_ gene expression, clinical data, and venetoclax ex vivo responses were

downloaded from the BEAT-AML web interface (http://www.vizome.org/aml2/, accessed 12. September 2023). Association between _CD36_ expression and venetoclax ex vivo response (AUC) was

assessed using a linear regression model. BEAT-AML: ASSOCIATION OF _CD36_ EXPRESSION AND OVERALL SURVIVAL Survival data and harmonized gene expression data across all 4 waves of BEAT-AML

were downloaded from https://biodev.github.io/BeatAML2/ (accessed 24. May 2024). The cohort was stratified into low and high _CD36_ expression using an automatically determined threshold as

implemented in the _surv_cutpoint_ function from the survminer R package (v.0.4.9). Survival analysis was performed using the R package survival (v3.2-11), using a log-rank test as

implemented in the _survfit_ function, and the result was visualized using survminer’s _ggsurvplot_. REPORTING SUMMARY Further information on research design is available in the Nature

Portfolio Reporting Summary linked to this article. DATA AVAILABILITY The publicly available data used in this study are available from BEAT-AML under http://www.vizome.org/aml2 and

https://biodev.github.io/BeatAML2/11; TCGA under https://portal.gdc.cancer.gov/projects/TCGA-LAML29; and the STRING database under https://string-db.org/ or via the stringdb R package (v.

2.4.2)95. The raw sequencing data (bulk- and single cell RNA-seq, single-cell DNA-seq) generated in this study have been deposited in the European Genome-Phenome Archive (EGA) under the

accession codes EGAD50000000822, EGAD50000000823 and EGAD50000000824. The data is available under restricted access due to patient privacy concerns. Access can be requested by contacting the

Tumor Profiler Center (TPC) leadership (https://tumorprofilercenter.ch/contacts). Data access will be granted to registered users listed on the data access agreement with the TPC within

four weeks of receipt of the Data Access Agreement, provided that the applicant provides all necessary ethics committee approval and supporting documents needed to meet the requirements of

the agreement. The user institution agrees to destroy or discard the data once it is no longer used for the project, and in cases where data must be archived, it must be deleted within 10

years of the project’s completion. If data has not been archived, it must be deleted no later than 2 years following the completion of the project. An extension to this period can be

provided upon request to the TPC leadership. The raw proteomics data generated in this study have been deposited in MassIVE under the accession MSV000092970. The processed data for scRNA-seq

and CyTOF are available from Zenodo under https://doi.org/10.5281/zenodo.13837019. The remaining data are available within the Article, Supplementary Information, or Source Data file. 

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Tumor Profiler Study is jointly funded by a public-private partnership involving F. Hoffmann-La Roche Ltd., ETH Zurich (Strategic focus area Personalized Health and Related Technologies,

PHRT), University of Zurich, University Hospital Zurich, and University Hospital Basel. Moreover, we acknowledge the support of the Clinical Trials Center (CTC) Zurich and the Clinical

Trials Unit (CTU) Basel. NGS was carried out at the Genomics Facility Basel of the University of Basel and the Department of Biosystems Science and Engineering, ETH Zurich. We gratefully

acknowledge the excellent project management support by the following individuals: Andreas Strauss, Thomas Solbach, Lina Reypens, Vanessa Mareike Johnen, and Roland Steinacher. The results

presented in this study are in part based upon data generated by the TCGA Research Network (https://www.cancer.gov/tcga) and the BEAT-AML trial (http://www.vizome.org/aml2/). Some Figure

parts were created with BioRender (Fig. 1a: BioRender.com/f53q390, Fig. 2a: BioRender.com/o06x626, Fig. 5d: BioRender.com/e30e859, Supplementary Figs. 1–6: BioRender.com/c06t989). AUTHOR

INFORMATION Author notes * Nora C. Toussaint Present address: Swiss Data Science Center, ETH Zürich, Zurich, Switzerland * These authors contributed equally: Rebekka Wegmann, Ximena Bonilla,

Ruben Casanova, Stéphane Chevrier, Ricardo Coelho, Cinzia Esposito, Joanna Ficek-Pascual, Sandra Goetze, Gabriele Gut, Francis Jacob, Andrea Jacobs, Jack Kuipers, Ulrike Lischetti, Julien

Mena, Emanuela S. Milani, Michael Prummer, Jacobo Sarabia Del Castillo, Franziska Singer, Sujana Sivapatham, Nora C. Toussaint, Oliver Vilinovszki, Mattheus H. E. Wildschut. * These authors

jointly supervised this work: Rudolf Aebersold, Marina Bacac, Niko Beerenwinkel, Christian Beisel, Bernd Bodenmiller, Viola Heinzelmann-Schwarz, Viktor H. Koelzer, Mitchell P. Levesque,

Holger Moch, Lucas Pelkmans, Gunnar Rätsch, Markus Tolnay, Andreas Wicki, Bernd Wollscheid, Markus G. Manz, Berend Snijder, Alexandre P. A. Theocharides. AUTHORS AND AFFILIATIONS * Institute

of Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich, Switzerland Rebekka Wegmann, Julien Mena, Mattheus H. E. Wildschut, Rudolf Aebersold & Berend Snijder *

Department of Computer Science, ETH Zurich, Zurich, Switzerland Ximena Bonilla, Joanna Ficek-Pascual & Gunnar Rätsch * Department of Quantitative Biomedicine, University of Zurich,

Zurich, Switzerland Ruben Casanova, Stéphane Chevrier, Andrea Jacobs, Sujana Sivapatham, Bernd Bodenmiller, Stefanie Engler & Bernd Bodenmiller * Department of Biomedicine, University

Hospital Basel and University of Basel, Basel, Switzerland Ricardo Coelho, Francis Jacob, Ulrike Lischetti, André Fedier, Viola Heinzelmann-Schwarz, Flavio C. Lombardo, Monica Nunez Lopez, 

Salvatore Piscuoglio, Natalie Rimmer, Mathilde Ritter & Viola Heinzelmann-Schwarz * Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland Cinzia Esposito, 

Gabriele Gut, Jacobo Sarabia Del Castillo & Lucas Pelkmans * Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland Sandra Goetze, Emanuela S. Milani, Mattheus H.

E. Wildschut, Silvana I. Albert, Jonas Albinus, Sandra Goetze, Martin Mehnert, Wenguang Shao, Audrey Van Drogen, Fabian Wendt, Bernd Bodenmiller & Bernd Wollscheid * ETH PHRT Swiss

Multi-Omics Center (SMOC), Lausanne, Switzerland Sandra Goetze & Katharina Jahn * SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland Sandra Goetze, Michael Prummer, Franziska

Singer, Nora C. Toussaint, Faisal S. Al-Quaddoomi, Sonali Andani, Niko Beerenwinkel, Anne Bertolini, Lars Bosshard, Natalia Chicherova, Joanna Ficek-Pascual, Linda Grob, Alva R. James, André

Kahles, Abdullah Kahraman, Jack Kuipers, Michael Prummer, Laurie Prélot, María L. Rosano-González, Gunnar Rätsch, Franziska Singer, Berend Snijder, Vipin T. Sreedharan, Stefan Stark, Daniel

J. Stekhoven, Tinu M. Thomas, Mustafa A. Tuncel, Shuqing Yu, Niko Beerenwinkel, Gunnar Rätsch & Berend Snijder * Department of Biosystems Science and Engineering, ETH Zurich, Zurich,

Switzerland Jack Kuipers, Niko Beerenwinkel & Christian Beisel * NEXUS Personalized Health Technologies, ETH Zurich, Zurich, Switzerland Michael Prummer, Franziska Singer & Nora C.

Toussaint * Department of Medical Oncology and Hematology, University Hospital Zurich, Zurich, Switzerland Oliver Vilinovszki, Mattheus H. E. Wildschut, Tharshika Thavayogarajah, Nicola

Miglino, Mattheus H. E. Wildschut, Andreas Wicki, Markus G. Manz & Alexandre P. A. Theocharides * Harvard Medical School and Dana-Farber Cancer Institute, Boston, USA Disha Malani *

Roche Pharmaceutical Research and Early Development, Roche Innovation Center Zurich, Zurich, Switzerland Marina Bacac * Department of Pathology and Molecular Pathology, University Hospital

Zurich, Zurich, Switzerland Sonali Andani, Anja L. Frei, Abdullah Kahraman, Viktor H. Koelzer, Holger Moch, Marta Nowak, Bettina Sobottka, Norbert Wey, Martin Zoche, Viktor H. Koelzer & 

Holger Moch * University of Zurich, Faculty of Medicine, Zurich, Switzerland Viktor H. Koelzer, Nicola Miglino, Viktor H. Koelzer, Holger Moch & Andreas Wicki * Department of

Dermatology, University Hospital Zurich, Zurich, Switzerland Melike Ak, Reinhard Dummer, Anja Irmisch, Mitchell Levesque, Marina Tusup, Johanna Ziegler & Mitchell P. Levesque * AI Center

at ETH Zurich, Zurich, Switzerland Joanna Ficek-Pascual, Gunnar Rätsch & Gunnar Rätsch * Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland

Markus Tolnay * Department of Biology, ETH Zurich, Institute of Molecular Systems Biology, Zurich, Switzerland Rudolf Aebersold, Julien Mena, Berend Snijder, Rebekka Wegmann & Mattheus

H. E. Wildschut * NEXUS Personalized Health Technologies, ETH Zurich, Zurich, Switzerland Faisal S. Al-Quaddoomi, Anne Bertolini, Lars Bosshard, Natalia Chicherova, Linda Grob, Michael

Prummer, María L. Rosano-González, Franziska Singer, Vipin T. Sreedharan, Daniel J. Stekhoven & Shuqing Yu * University Hospital Basel, Institute of Medical Genetics and Pathology,

Basel, Switzerland Ilaria Alborelli, Daniel Baumhoer, Byron Calgua, Philip M. Jermann, Viktor H. Koelzer, Simone Muenst, Charlotte K. Y. Ng, Stefan Nicolet, Salvatore Piscuoglio, Markus

Tolnay & Tatjana Vlajnic * Department of Computer Science, ETH Zurich, Institute of Machine Learning, Zurich, Switzerland Sonali Andani, Ximena Bonilla, Natalie R. Davidson, Joanna

Ficek-Pascual, Alexander Immer, Alva R. James, André Kahles, Kjong-Van Lehmann, Philipp Markolin, Laurie Prélot, Gunnar Rätsch, Stefan Stark, Tanmay Tanna, Tinu M. Thomas & Marc

Zimmermann * University Hospital Zurich, Biomedical Informatics, Zurich, Switzerland Sonali Andani, Joanna Ficek-Pascual, Alva R. James, André Kahles, Laurie Prélot, Gunnar Rätsch, Stefan

Stark & Tinu M. Thomas * F. Hoffmann-La Roche Ltd, Basel, Switzerland Per-Olof Attinger, Maya D’Costa, Tim M. Jaeger, Sebastian Lugert & Severin Schwan * Roche Pharmaceutical

Research and Early Development, Roche Innovation Center Zurich, Schlieren, Switzerland Marina Bacac, Sylvia Herter, Tamara Huesser & Vinko Tosevski * University of Zurich, VP Medicine,

Zurich, Switzerland Beatrice Beck-Schimmer * Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland Niko Beerenwinkel, Christian Beisel, Monica-Andreea Drăgan, 

Katja Eschbach, Pedro F. Ferreira, Jack Kuipers, Natascha Santacroce, Tobias Schär, Tanmay Tanna & Mustafa A. Tuncel * University Hospital Zurich, Clinical Trials Center, Zurich,

Switzerland Lara Bernasconi, Gabriela Senti & Sandra Weber * ETH Zurich, Institute of Molecular Health Sciences, Zurich, Switzerland Bernd Bodenmiller * University of Zurich, Institute

of Molecular Life Sciences, Zurich, Switzerland Esther Danenberg, Cinzia Esposito, Gabriele Gut, Rene Holtackers, Lucas Pelkmans & Jacobo Sarabia del Castillo * Roche Pharmaceutical

Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland Martin Erkens, Christian Rommel & Petra C. Schwalie * Life Science Zurich Graduate School, Biomedicine

PhD Program, Zurich, Switzerland Joanna Ficek-Pascual * Roche Diagnostics GmbH, Penzberg, Germany Bruno Frey & Gerd Maass * Department of Chemistry and Applied Biosciences, ETH Zurich,

Zurich, Switzerland Detlef Günther * Cantonal Hospital Baselland, Medical University Clinic, Liestal, Switzerland Pirmin Haeuptle * University Hospital Basel, Gynecological Cancer Center,

Basel, Switzerland Viola Heinzelmann-Schwarz * Max Planck ETH Center for Learning Systems, Tübingen, Germany Alexander Immer * FHNW, School of Life Sciences, Institute of Chemistry and

Bioanalytics, Muttenz, Switzerland Abdullah Kahraman * University Hospital Basel, Basel, Switzerland Werner Kuebler * Department of Information- and Communication Technology, University

Hospital Basel, Basel, Switzerland Christian P. Kunze * University Hospital Basel, Brustzentrum, Basel, Switzerland Christian Kurzeder * Cancer Research Center Cologne-Essen, University

Hospital Cologne, Cologne, Germany Kjong-Van Lehmann * Center for Integrated Oncology Aachen (CIO-A), Aachen, Germany Kjong-Van Lehmann * Joint Research Center Computational Biomedicine,

University Hospital RWTH Aachen, Aachen, Germany Kjong-Van Lehmann * Department of Gynecology, University Hospital Zurich, Zurich, Switzerland Julian M. Metzler * University of Zurich,

Services and Support for Science IT, Zurich, Switzerland Riccardo Murri * Department of BioMedical Research, University of Bern, Bern, Switzerland Charlotte K. Y. Ng * Department of Biology,

ETH Zurich, Zurich, Switzerland Patrick G. A. Pedrioli & Gunnar Rätsch * Roche Pharmaceutical Research and Early Development, Roche Innovation Center Munich, Roche Diagnostics GmbH,

Penzberg, Germany Ramona Schlenker * ETH Zurich, Swiss Data Science Center, Zurich, Switzerland Nora C. Toussaint * University Hospital Basel, Brustzentrum & Tumorzentrum, Basel,

Switzerland Marcus Vetter * University Hospital Basel and University of Basel, Department of Surgery, Brustzentrum, Basel, Switzerland Walter P. Weber * Department of Neurology, University

Hospital and University of Zurich, Zurich, Switzerland Michael Weller * University Hospital Zurich, Zurich, Switzerland Gregor Zuend Authors * Rebekka Wegmann View author publications You

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Scholar * Alexandre P. A. Theocharides View author publications You can also search for this author inPubMed Google Scholar CONSORTIA THE TUMORPROFILER CONSORTIUM * Rudolf Aebersold * , 

Melike Ak * , Faisal S. Al-Quaddoomi * , Silvana I. Albert * , Jonas Albinus * , Ilaria Alborelli * , Sonali Andani * , Per-Olof Attinger * , Marina Bacac * , Daniel Baumhoer * , Beatrice

Beck-Schimmer * , Niko Beerenwinkel * , Christian Beisel * , Lara Bernasconi * , Anne Bertolini * , Bernd Bodenmiller * , Ximena Bonilla * , Lars Bosshard * , Byron Calgua * , Ruben Casanova

* , Stéphane Chevrier * , Natalia Chicherova * , Ricardo Coelho * , Maya D’Costa * , Esther Danenberg * , Natalie R. Davidson * , Monica-Andreea Drăgan * , Reinhard Dummer * , Stefanie

Engler * , Martin Erkens * , Katja Eschbach * , Cinzia Esposito * , André Fedier * , Pedro F. Ferreira * , Joanna Ficek-Pascual * , Anja L. Frei * , Bruno Frey * , Sandra Goetze * , Linda

Grob * , Gabriele Gut * , Detlef Günther * , Pirmin Haeuptle * , Viola Heinzelmann-Schwarz * , Sylvia Herter * , Rene Holtackers * , Tamara Huesser * , Alexander Immer * , Anja Irmisch * , 

Francis Jacob * , Andrea Jacobs * , Tim M. Jaeger * , Katharina Jahn * , Alva R. James * , Philip M. Jermann * , André Kahles * , Abdullah Kahraman * , Viktor H. Koelzer * , Werner Kuebler *

, Jack Kuipers * , Christian P. Kunze * , Christian Kurzeder * , Kjong-Van Lehmann * , Mitchell Levesque * , Ulrike Lischetti * , Flavio C. Lombardo * , Sebastian Lugert * , Gerd Maass * , 

Markus G. Manz * , Philipp Markolin * , Martin Mehnert * , Julien Mena * , Julian M. Metzler * , Nicola Miglino * , Emanuela S. Milani * , Holger Moch * , Simone Muenst * , Riccardo Murri *

, Charlotte K. Y. Ng * , Stefan Nicolet * , Marta Nowak * , Monica Nunez Lopez * , Patrick G. A. Pedrioli * , Lucas Pelkmans * , Salvatore Piscuoglio * , Michael Prummer * , Laurie Prélot *

, Natalie Rimmer * , Mathilde Ritter * , Christian Rommel * , María L. Rosano-González * , Gunnar Rätsch * , Natascha Santacroce * , Jacobo Sarabia del Castillo * , Ramona Schlenker * , 

Petra C. Schwalie * , Severin Schwan * , Tobias Schär * , Gabriela Senti * , Wenguang Shao * , Franziska Singer * , Sujana Sivapatham * , Berend Snijder * , Bettina Sobottka * , Vipin T.

Sreedharan * , Stefan Stark * , Daniel J. Stekhoven * , Tanmay Tanna * , Alexandre P. A. Theocharides * , Tinu M. Thomas * , Markus Tolnay * , Vinko Tosevski * , Nora C. Toussaint * , 

Mustafa A. Tuncel * , Marina Tusup * , Audrey Van Drogen * , Marcus Vetter * , Tatjana Vlajnic * , Sandra Weber * , Walter P. Weber * , Rebekka Wegmann * , Michael Weller * , Fabian Wendt *

, Norbert Wey * , Andreas Wicki * , Mattheus H. E. Wildschut * , Bernd Wollscheid * , Shuqing Yu * , Johanna Ziegler * , Marc Zimmermann * , Martin Zoche *  & Gregor Zuend CONTRIBUTIONS

Conceptualization: T.P.C.; Formal analysis: R.W., X.B., S.C., J.F.P., F.S., N.C.T., J.M., E.S.M., R.Ca., S.G., J.K., C.E., J.S., M.P., U.L., G.G.; Investigation: R.W., X.B., S.C., J.M.,

E.S.M., R.Ca., F.J., R.Co., S.G., C.E., J.S., A.J., S.S., O.V., M.W., T.T., D.M.; Resources: T.P.C.; Writing—Original Draft: R.W., B.S., A.P.T.; Writing—Review and Editing: R.W., X.B., S.C.,

J.F.P., F.S., N.C.T., J.M., E.S.M., R.Ca., S.G., J.K., C.E., J.S., M.P., U.L., G.G., O.V., M.W., T.T., R.A., M.B., N.B., C.B., B.B., V.H.S., V.H.K., H.M., M.P.L., L.P., G.R., M.T., A.W.,

B.W., M.G.M., B.S., A.P.T.; Visualization: R.W.; Supervision: R.A., M.B., N.B., C.B., B.B., V.H.S., V.H.K., H.M., M.P.L., L.P., G.R., M.T., A.W., B.W., M.G.M., B.S., A.P.T.; Funding

Acquisition: T.P.C. CORRESPONDING AUTHORS Correspondence to Markus G. Manz, Berend Snijder or Alexandre P. A. Theocharides. ETHICS DECLARATIONS COMPETING INTERESTS B.B. has founded and is a

shareholder and member of the board of Navignostics, a precision oncology spin-off from the University of Zurich. B.B. reports personal consultancy fees as Advisory board member from

Standard BioTools. B.S. was a scientific co-founder of Allcyte, which has been acquired by Exscientia. B.S. is a shareholder of Exscientia and a co-inventor on US patent application

15/514,045 relevant to the study. L.P. and G.G. are listed as inventors on patents related to the 4i technology (WO 2019/207004; WO 2020/008071). G.R. is listed on a patent application

related to single-cell analyses (European Patent Application No. 20170724.7), and G.R. is cofounder and on the Scientific Advisory Board of Computomics GmbH. H.M. is on advisory boards for

Bayer, Astra Zeneca, Janssen, Roche, and Merck. M.P.L. is scientific advisor and board member of Oncobit and has received unrelated research funding from Roche, Novartis, Scailyte, Molecular

Partners, and Bacoba. V.H.K. is an invited speaker for Sharing Progress in Cancer Care (SPCC) and Indica Labs; advisory board of Takeda; sponsored research agreements with Roche and IAG all

unrelated to the current study; listed as innovator on patent applications in computational pathology. The Tumor Profiler study is jointly funded by a public-private partnership involving

F. Hoffmann-La Roche Ltd., ETH Zurich, University of Zurich, University Hospital Zurich, and University Hospital Basel. The remaining authors declare no competing interests. PEER REVIEW PEER

REVIEW INFORMATION _Nature Communications_ thanks Yoonkyu Lee, Zohar Sachs, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file

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