About

Ash A. Alizadeh, MD/PhD, is the Moghadam Family Professor of Medicine, Oncology, and Hematology (by courtesy) at Stanford University and leads the Cancer Genomics Program at Stanford Cancer Institute. His primary research interests involve the development and application of genome technologies and data science, including machine learning, to address problems in human disease, with a particular focus on cancer detection, classification, monitoring, and tumor immunology. Dr. Alizadeh's group studies cancer genomic profiles obtained from tumor tissues and noninvasive liquid biopsies, utilizing machine learning techniques to understand how cellular variation impacts cancer behavior and therapeutic response. His team has pioneered noninvasive cancer genomic techniques such as CAPP-Seq, PhasED-Seq, and EPIC-Seq, which analyze circulating nucleic acids for early cancer detection and monitoring, including predicting therapeutic responses. With a background that includes a BS in Biochemistry from UCLA, an MD from Stanford Medical School, and a PhD in Biophysics from Stanford, he has additional training at the NIH and HHMI. Dr. Alizadeh has received numerous awards and honors, is an elected member of the American Society for Clinical Investigation, and serves on various advisory boards and editorial committees. His clinical focus includes lymphoma, leukemia, and other hematologic malignancies, and he actively participates in clinical trials and research aimed at understanding tumor initiation, progression, and response to therapy through genomic biomarkers and bioinformatics.

Research topics

• Internal medicine
• Medicine
• Biology
• Genetics
• Cancer research
• Oncology
• Computer Science
• Computational biology
• Surgery
• Immunology
• Gastroenterology
• Bioinformatics

Selected publications

• Circulating Tumor DNA Assessment of Disease Response in Large B-Cell Lymphoma: Lisocabtagene Maraleucel Versus Autologous Stem Cell Transplantation Standard Therapy

  Journal of Clinical Oncology · 2026-04-27

  articleOpen access

  We report correlative circulating tumor DNA (ctDNA) analyses from TRANSFORM (ClinicalTrials.gov identifier: NCT03575351 ) evaluating lisocabtagene maraleucel (liso-cel) versus standard of care (salvage immunochemotherapy, high-dose chemotherapy, autologous stem cell transplantation [ASCT]) in second-line large B-cell lymphoma (LBCL). ctDNA association with efficacy was investigated at predefined time points (random assignment, day 43, day 64, and day 126 [3 months after liso-cel, approximately 2 months after ASCT]) for 136 patients using ultrasensitive PhasED-Seq. ctDNA clearance (measurable residual disease [MRD] neg ) predicted longer event-free survival (EFS) at all time points in both arms, with significantly more liso-cel–treated patients achieving MRD neg . Liso-cel demonstrated superior outcomes versus ASCT, including longer EFS, progression-free survival (PFS), and duration of response among patients in complete response (CR) and MRD neg . ctDNA re-emergence in patients with CR after ASCT confirmed its potential in predicting relapse. MRD neg remained significantly associated with EFS after adjusting for positron emission tomography (PET) response, while interaction testing revealed a significant interaction between PET status and treatment arm for EFS. Liso-cel achieved deeper, more durable molecular clearance by ctDNA, consistent with superior EFS and PFS versus ASCT for second-line LBCL treatment. ctDNA-MRD provided prognostic value beyond PET, supporting its role as a complementary biomarker for treatment response and relapse prediction.

  PublisherDOI

• Supplementary Tables S1-S15 from Tracing Founder Mutations in Circulating and Tissue-Resident Follicular Lymphoma Precursors

  2025-12-11

  articleOpen accessSenior author

  <p>Supplementary Table S1. Patient and control clinical characteristics Supplementary Table S2. Sequencing metrics for FL cases & controls Supplementary Table S3. Identified mutations in EPIC FL pre-diagnostic samples Supplementary Table S4. Identified mutations in control samples Supplementary Table S5. Statistical significance for detection in prediagnostic samples with paired tumor biopsy Supplementary Table S6. Identified mutations in FL tumors with detection in paired prediagnostic samples Supplementary Table S7. Paired tumor cohort histology & sample characteristics Supplementary Table S8. Identified mutations in paired biopsy samples Supplementary Table S9. Clonal VDJ rearrangements in paired biopsies Supplementary Table S10. Concordance analysis of paired biopsy samples Supplementary Table S11. Patient characteristics, marrow and sorting studies Supplementary Table S12. DNA and sequencing metrics for marrow and sorting studies Supplementary Table S13. Monoclonal antibodies and manufacturers Supplementary Table S14. Mutations detected in marrow and sorting studies Supplementary Table S15. Genes fully or partially covered in lymphoma-focused CAPP-Seq selector</p>

  PublisherDOI

• Distinct genomic landscape & response dynamics of HIV-associated lymphomas identified by integrated noninvasive host and virome profiling

  Blood · 2025-11-03

  articleOpen accessSenior author

  Abstract Background: HIV infection increases lymphoma risk, with many cases linked to co-infection with latent Epstein-Barr Virus (EBV). Immunosuppression and oncogenic viruses contribute to aggressive cancers that often present at advanced stages and progress rapidly. Prior studies of HIV-associated lymphomas have generally been restricted to relatively small cohorts and pretreatment tumor specimens, and there is a lack of integrated methods for tracking viral dynamics alongside host tumor and immune responses. We characterized the genomic landscapes from a large HIV-positive (HIV+) lymphoma cohort as contrasted with HIV– counterparts, revealing how viral reactivation and immune function influence cancer development. Methods: We comprehensively profiled 305 blood samples from 122 patients, including 51 HIV+diffuse large B cell lymphoma (HIV+/DLBCL), 10 HIV+ Burkitt lymphoma (HIV+/BL), and 61 HIV–/DLBCL patients. HIV+ patients were treated with SC-EPOCH-RR (NCT000019253). Tumor EBV status was determined by Epstein-Barr-encoded small RNA (EBER) ISH. Samples were profiled by VirCAPP-Seq targeting 180 viral species (Garofalo, Blood 2019), CAPP-Seq targeting 186 B-cell lymphoma genes (Newman, Nat Med 2014), and a custom panel targeting HLA genes. Results: To capture virome dynamics, we applied unsupervised clustering to serial samples of HIV+ lymphomas using viral family abundances. We identified 3 distinct Viral abundance clusters (V1-3), each reflecting a unique spectrum of active species, with gradually decreasing viral abundances from V1 to V3. V1 reflected high abundance across anelloviruses (AV), polyomaviruses, EBV, and other herpesviruses. V2 had high abundance of AV, but low EBV abundance. V3 had low abundance across all viral families. Consistent with the degree of immunosuppression, pretreatment samples were enriched in V1. Patients achieving remission had a gradual clearance of diverse viruses during and after therapy, transitioning towards V2/V3 clusters over time. In contrast, patients experiencing progression (PD) or associated death remained enriched in V1 at time of PD. We genotyped EBV subtypes with adequate genomic coverage of HIV+ lymphoma alongside EBV+ cell lines, classical Hodgkin lymphomas, and post-transplant lymphoproliferative disorders. HIV+ lymphomas had higher prevalences of EBV type 2 and type 1/2 co-infections (p<0.05) compared to immunocompetent lymphomas. Hosts with evidence of HLA-A/B supertype homozygosity had a higher risk of death from PD (p<0.01). We also found that while antiretroviral treatment (ART) suppressed circulating HIV RNA as expected, HIV cfDNA was not affected by ART, likely measuring latent HIV infected reservoirs. Pretreatment mutant ctDNA levels correlated with cell-free EBV (cfEBV) levels in EBER+HIV+ lymphomas (p<0.01). EBER– cases exhibited more frequent and diverse somatic alterations, including evidence of genomic hypermutation from microsatellite instability. HIV+cfEBV-/DLBCL showed higher mutant frequencies of TP53 and epigenetic modifier genes, while HIV+cfEBV+/DLBCL had fewer distinguishing recurrent mutations. Compared to HIV–/DLBCL, HIV+/DLBCL showed more frequent and diverse TP53 alterations but fewer MYD88, CD79B, and PIM1 alterations. Classifying by LymphGen (Wright, Cancer Cell 2020), we identified a higher prevalence of unclassified cases (“Other”, p<0.05) and a lower prevalence of MCD than HIV-/DLBCL, with EBER+ cases more often unclassified than EBER– cases. We applied PhasED-Seq (Kurtz, Nat Biotechnol 2021) to monitor ctDNA minimal residual disease (MRD) during chemoimmunotherapy in serial HIV+ cases. The presence of MRD after 3 cycles of therapy strongly predicted disease-free survival (DFS) (p<0.05). Considering diverse clinical, genomic and viromic features, cfEBV level, IPI, and non-GCB histology each remained independently prognostic of DFS outcomes (p<0.05) in HIV+ lymphomas. Conclusions: Noninvasive genomic profiling is readily feasible and identifies distinct viral, immune, and somatic alteration signatures that characterize HIV-associated lymphomas and distinguish them from immunocompetent counterparts. Broad viral activity at diagnosis predicts poorer outcomes in HIV+ lymphomas, underscoring the need for controlling viral reactivation. The diversity of host and EBV subtypes and host MRD response dynamics holds promise not only for monitoring treatment efficacy but could also inform early lymphoma detection in people living with HIV.

  PublisherOA PDFDOI

• Molecular determinants of therapeutic response to nivolumab in Relapsed/Refractory PTCL: An integrated multiomic study of A phase II investigator-initiated trial

  Blood · 2025-11-03

  articleOpen access

  Abstract Background: Peripheral T-cell lymphomas (PTCL) display heterogeneous responses to immune checkpoint inhibitors, including rare durable responses. The basis for such variability remains unclear. Previously, we identified elevated expression of immune checkpoint genes (e.g., CD274 [PD-L1]) in ~50% of PTCL-NOS, associated with an M2 macrophage-like gene expression signature (Sugio et al., Blood Adv 2018). We hypothesized that a subset of relapsed/refractory (r/r) PTCL patients may benefit from PD-1 blockade, with molecular profiles helping to identify them using tumor and liquid biopsies. Methods: We conducted a Phase II investigator-initiated trial of Nivolumab monotherapy (240mg every 2 weeks) in r/r PTCL patients who failed ≥2 prior therapies (WJHS-NHL02, UMIN000034499). The primary endpoint was overall response rate (ORR); secondary endpoints included CR rate, PFS, and OS. Molecular profiling included tumor RNA-seq, whole exome sequencing, and plasma cfDNA analyses (CAPP-Seq for SNVs/indels [Newman et al., Nat Med 2016], CANARy for CNAs [Chabon et al., Nature 2020], EPIC-Seq for inferred expression [Esfahani et al., NBT 2022], SABER/QUARTZ for TCR analysis [Shukla et al., ASH2022, Sugio et al., ASH2024]). We also performed Imaging Mass Cytometry (IMC) to characterize spatial proteomic profiles of one patient with an exceptionally long response using Hyperion. Results: Of 20 enrolled patients, 19 received Nivolumab. Median age was 68.5 years; 21% were female. Histologies included PTCL-NOS (n=4), ENKTL (n=6), AITL (n=4), ALCL (n=5; 20% ALK+), and EATL (n=1). Grade 3/4 SAEs occurred in 9 patients, including infectious disease (21%). One patient developed breast cancer and discontinued treatment despite long-term lymphoma response. The ORR was 10.3% (2/19: 1 PTCL-NOS, 1 AITL) with no CRs, and one patient had SD. Three patients were not evaluable for response due to insufficient imaging follow-up. Three patients achieving PR/SD had durable responses >2 years. All of 4 evaluable ENKTL cases had PD. The median OS and PFS were 5.7 and 2.0 months, respectively. Responders showed enrichment for specific molecular features, including PD-L1/2 mutations, higher total mutation burden (TMB), and distinct T-cell receptor (TCR) dynamics. One PTCL-NOS patient with early PR had a CD274 intron 5 mutation and immune-inflammatory pathway alterations; IMC revealed a focal infiltration of PD-L1+ macrophage and CD8+ T cells with a well-demarcated boundary. A second patient with EATL experiencing >2 years of durable SD had mutations in CD274 3'UTR, PDCD2LG1 (PD-L2), and PTPRD. Another patient achieving PR had typical AITL-associated mutations. Responders also had significantly greater diversity of TCR in cfDNA (cfTCR). Specifically, among 13 non-ENKTL patients with dominant tumor TCR clonotypes in tumor tissues, 4 lacked detectable monoclonal expansions of these clonotypes in cfTCR, of whom 3 (75%) achieved >2 years PFS. The patients experiencing Nivolumab-induced early ctDNA reductions had higher levels of diversity in cfTCRs, which were non-tumor derived. All patients with >2 years of PFS showed either complete clearance of ctDNA MRD or a >90% reduction. One patient who discontinued Nivolumab due to breast cancer maintained her long-term PR with sustained absence of ctDNA MRD. Among the 3 patients who were unevaluable radiographically, two showed notable ctDNA reductions (85% and 25%) at the time of discontinuation, whereas all other PD cases showed increased ctDNA levels. ctDNA responders had significantly higher TMB (p = 0.02) and ctDNA levels (p = 0.04) at baseline. RNA-seq of diagnostic tumor tissue showed higher expression of macrophage-related genes in PR patients. EPIC-Seq of pre-treatment cfDNA revealed elevated expression of macrophage, B-cell, and dendritic cell-related genes, as well as PD-L1. These findings suggest enhanced turnover of both tumor and non–tumor immune cells in patients with PR. Conclusions: Although the pre-specified efficacy threshold (ORR 30%) was not met, Nivolumab monotherapy induced durable disease controls in 3 (15.8%) patients with r/r PTCL, including 2 (10%) patients with PRs. Durable disease controls were associated with PD-L1/2 and PTPRD mutations, higher TMB, and increased cfTCR diversity. We also observed augmented turnover of both tumor cells and non-tumor immune cells in responders. cfDNA profiling may help identify candidates for PD-1 blockade in PTCL and should be integrated into future trials.

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• Figures S1-S8 from Integrating ctDNA Analysis and Radiomics for Dynamic Risk Assessment in Localized Lung Cancer

  2025-08-04

  preprintOpen access

  <p>Figures showing the CONSORT diagram, clonal hematopoiesis prevalence, the SNV score model, mid-chemoradiation ctDNA analysis, radiomic model robustness, pre-chemoradiation prognostic factors, association of clonal hematopoiesis with outcomes, and association of the CIRI-LCRT model with progression-free survival.</p>

  PublisherOA PDFDOI

• Treatment Kinetics and Local Response during and after Bridging Radiotherapy prior to CAR-T Cell Therapy for Lymphomas

  International Journal of Radiation Oncology*Biology*Physics · 2025-09-01

  article
  PublisherDOI

• Non-invasive Mantle Cell Lymphoma risk stratification by inference of tumor proliferation from cfDNA

  Blood · 2025-11-03

  articleOpen accessSenior author

  Abstract Background: Variability in tumor proliferation is a key prognostic factor in Mantle Cell Lymphoma (MCL). However, its measurement by gene expression profiling of frozen tumor tissue specimens (Rosenwald et al. Cancer Cell) or Ki-67 index of fixed tumor tissues (Katzenberger et al.Blood) have each faced limitations. The MCL35 gene expression signature is a validated independent prognostic biomarker for risk stratification in MCL, relying on tumor RNA profiling to accurately quantify variability in tumor proliferation on routinely fixed tissues (Scott et al. JCO). However, its clinical use can be limited by the requirement for invasive lymph node or tumor tissue biopsies, their attendant risks and associated costs, as well as specific technical challenges when profiling bone marrow specimens and leukemic MCL samples. While liquid biopsies might offer a promising alternative, no approach has yet bridged the molecular gap between blood- versus tissue-derived prognostic signatures for faithfully measuring MCL-proliferation associated risk noninvasively. Here, we tackle this challenge. Methods: We profiled 161 total samples from 117 patients from the Phase 3 randomized LYMA trial (Le Gouill et al. NEJM) relying on R-DHAP/ASCT for first remission induction in treatment-naïve MCL. Cases were selected based on availability of baseline paired blood and/or tissue specimens at diagnosis. We profiled 88 matched-tumor plasma specimens from 44 patients; for 73 patients, either isolated tumor specimens (n=56) or plasma specimens (n=17) were available. Tumor specimens (n=100; 68% FFPE, 32% Frozen) were expression profiled by RNA-Seq. Plasma specimens were profiled by EPIC-Seq (Esfahani et al. Nat Biotech) using a customized lymphoma-specific panel including key proliferation associated MCL genes (Mutter et al. Blood). We developed a novel deep generative ML model to better infer transcriptomic expression from cfDNA fragmentomic features. Trained on both paired and unpaired tumor and plasma samples from MCL patients (60% training, 40% test), this new generative ML model (termed iEPIC) learns a domain transfer function aiming to reconstruct RNA-like profiles from raw blood plasma EPIC-Seq Promoter Fragmentation Entropy (PFE) measurements. We computed MCL35 scores from these predicted profiles using optimized coefficients. Model performance was benchmarked against ground-truth tumor-derived MCL35 scores and evaluated for clinical validity in survival stratification and prediction of early progression (POD24). Results: When considering tumor gene expression profiles by RNA-Seq (n=100), higher MCL35 scores were significantly associated with blastoid morphology (p<0.001) and tumor Ki67% index (p<0.001), as expected. Higher tumor MCL35 scores were also significantly associated with both inferior PFS and OS, whether as a continuous (p=0.007 [PFS], p=0.004 [OS]) or categorical (p=0.01 [PFS], p=0.01 [OS]) variable. Multivariate Cox models confirmed the superior prognostic value of MCL35 after adjusting for other key prognostic variables including MIPI and Ki67. When considering inferred expression profiles from plasma cfDNA using EPIC-Seq, the new iEPIC GEP model significantly improved single-gene level correlations between noninvasive plasma measurements from cfDNA and invasive tumor tissue measurements from RNA. This improvement included correlation gains in key mitotic proliferation genes, such as MKI67 (p<0.01), TOP2A (p<0.01), and FOXM1 (p<0.01). When extended to the full MCL35 proliferation signature, iEPIC achieved ~0.7 Pearson correlation against tumor RNA-Seq, allowing improvements in precision, recall, and F1 score of noninvasive measurements by ~23%. Our noninvasive iEPIC GEP model also significantly stratified patients across Ki67 groups (p < 0.02), similar to tumor RNA-Seq (p<0.002). For prediction of POD24 status, this noninvasive iEPIC GEP model achieved reasonably high performance (AUC=0.73), as compared with invasive MCL35 from tumor RNA-Seq (AUC=0.83). Conclusions: We describe a novel non-invasive approach for computing MCL tumor proliferation score directly from blood-based cfDNA, toward enabling risk stratification in MCL without tissue biopsies. This model maintains gene-level biological fidelity and could be deployed in settings where tissue is unavailable or serial monitoring is required. We expect that this innovation could yield a significant advance toward liquid biopsy-driven precision medicine for patients with MCL.

  PublisherOA PDFDOI

• Distinct cell state ecosystems for nodular lymphocyte-predominant Hodgkin lymphoma

  Nature Communications · 2025-09-26

  articleOpen access

  Nodular lymphocyte-predominant Hodgkin lymphoma (NLPHL) is a rare cancer, and few studies have comprehensively investigated the immune microenvironment and rare lymphocyte-predominant (LP) cells. Here we develop a NLPHL specific lymphocyte-predominant ecotype (LPE) model to identify 34 distinct cell states across 14 cell types that co-occur within 3 LPEs for 171 cases. LPE1 and LPE2 were characterized by immunosuppressive microenvironments with high expression of B2M on LP cells, CD8 T-cell exhaustion, immune checkpoint genes expressed by follicular T-cells, and an improved freedom from progression compared to LPE3 in training (n = 109, with 65% LPE1/2) and validation cohorts (n = 62, with 61% LPE1/2). We validate the co-occurrence and co-localization of cell states using spatial transcriptomics. Protein expression of HLA-I and HLA-II on LP cells and SSTR2 on dendritic cells was predictive of LPE1 (C-statistic=0.69), LPE2 (C-statistic=0.79), and LPE3 (C-statistic=0.60). This study establishes a clinically relevant biologic categorization for NLPHL.

  PublisherOA PDFDOI

• Remission Assessment by Circulating Tumor DNA in Large B-Cell Lymphoma

  Journal of Clinical Oncology · 2025-08-13 · 21 citations

  articleOpen accessSenior author

  PURPOSE Large B-cell lymphomas (LBCLs) are curable, but patients with residual disease after therapy invariably experience progression. Ultrasensitive methods to detect circulating tumor DNA (ctDNA) as minimal residual disease (MRD) may improve the determination of remission. METHODS We integrated data from five prospective studies of frontline anthracycline-based chemotherapy in patients with LBCL. Tumor-specific phased variants were identified from pretreatment samples and monitored at landmark time points. Serial plasma specimens were blindly analyzed for detectable ctDNA as MRD. MRD status was compared with conventional response criteria for prognosis of progression-free survival (PFS). RESULTS We studied ctDNA-MRD in 137 patients by monitoring 409 plasma specimens over time. Detectable ctDNA rates decreased during therapy with 55% and 78% of patients achieving undetectable ctDNA after two cycles and at the end of therapy, respectively. After a median follow-up of 37 months, the 2-year PFS for patients with detectable versus undetectable ctDNA after two cycles was 67% versus 96% ( P = .0025; hazard ratio [HR], 6.9) and after therapy was 29% versus 97% ( P < .0001; HR, 28.7), respectively. Ninety-two (94%) patients with undetectable ctDNA at the end of therapy remained alive without progression, while 19 (68%) patients with detectable ctDNA progressed or died. MRD status at the end of therapy had greater prognostic utility than conventional lymphoma response criteria using positron emission tomography (PET) scans (HR, 3.6 for positive PET and 28.3 for detectable ctDNA). CONCLUSION Ultrasensitive ctDNA detection after frontline LBCL therapy is more prognostic than conventional radiographic response criteria. A refined definition of remission with ctDNA-MRD may improve clinical and psychological outcomes for patients with LBCL.

  PublisherDOI

• A unified spatial & molecular atlas of anatomically restricted large B-cell lymphomas defines stereotyped cell ecosytems

  Blood · 2025-11-03

  articleOpen access

  Abstract Background Large B-cell lymphomas (LBCLs) are clinically and biologically heterogeneous, yet specific extranodal variants can display strikingly stereotyped features reflecting their anatomical predilections. For instance, LBCLs arising in immune-privileged sites—such as primary central nervous system (PCNSL) or testis (PTL)—commonly harbor NF-κB pathway lesions and an activated B-cell phenotype, correlating with poor outcomes. In contrast, primary bone LBCLs (PBL), usually linked to favorable outcomes, often harbor immune modulatory and epigenetic mutations and a germinal center B-cell phenotype. These patterns suggest that anatomical origin may serve as a meaningful basis for LBCL subtype classification, potentially reflecting local immune responses to each tumor. To clarify these relationships, here we describe a Unified Spatial and Molecular Atlas of Anatomical Restricted LBCLs. By integrating genomic and transcriptomic data with spatial proteomic measurements and clinical outcomes, we provide a comprehensive, multimodal resource. This atlas enables robust evaluation of established classifiers across anatomical subtypes and lays the groundwork for identifying novel, site-specific therapeutic vulnerabilities in LBCL. Method We studied archival diagnostic formalin-fixed paraffin-embedded (FFPE) biopsy specimens from well-annotated LBCL cases at Leiden University Medical Center (n=68): 22 PCNSL, 9 PTL, 21 PBL, and 18 localized nodal LBCL. The same FFPE blocks were sourced for DNA, RNA and spatial proteomics. Genomic profiling was done with custom amplicon sequencing (111 genes) and transcriptome profiling with a custom probeset (800 genes, nCounter). Spatial proteomics employed imaging mass cytometry (41-marker panel, Hyperion). We developed the computational pipeline “Unhuddle” for refined single-cell profiling. Absolute cell abundances were quantified as cells/mm² and stable case level clustering was achieved with hierarchical clustering. Case classifications were performed by existing algorithms that rely on tumor somatic genotype [LymphGen (Wright et al. 2020) and DLBclass (Chapuy et al. 2025)], and microenvironment decomposition from transcriptomic profiles [LymphoMAP (Li et al. 2025), Cluster (Ciavarella et al. 2018), LME (Kotlov et al. 2021), EcoTyper (Steen et al. 2021)]. Results: We identified 3 stereotyped ‘spatial protein’ cell ecosystems across anatomically defined LBCL tumors, including Cytotoxic Predominant, Complex Immune, and Immune Depleted patterns. Nodal DLBCL and PBL were enriched for the Complex Immune ecosystem, which were conspicuously absent from PCNSL and PTL tumors (OR=52, p<0.001). In contrast, PCNSL tumors were enriched for Cytotoxic Predominant ecosystem (OR=6, p=0.01). The Immune Depleted showed no specific enrichment by anatomical site. Each spatial protein ecosystem strongly aligned with distinct corresponding gene expression and genomic signatures. Tumors with the Complex Immune ecosystem had better overall survival outcomes, and were enriched for the LN LymphoMAP (OR=3, p=0.02), ‘Hot’ Ciavarella cluster (OR=7, p<0.001), Mesenchymal LME (OR=10, p<0.001), and C3 DLBclass (OR=4, p<0.01). In contrast, tumors with Cytotoxic Predominant ecosystem were enriched for TEX LymphoMAP (OR=8, p<0.01), Inflammatory LME (OR=8, p<0.01), and DLBclass C4 (OR=4, p<0.05). Tumors with Immune Depleted ecosystem were enriched for FMAC LymphoMAP (OR=5, p=0.01), ‘Cold’ Ciavarella cluster (OR=4, p=0.01), Depleted LME (OR=9, p<0.001), and C5 DLBclass (OR=4, p=0.02). DLBclass C5 is more closely associated with anatomical site than with spatial protein ecosystem (Cramer's V: 0.5 vs 0.3, p=0.04). Conclusions: This atlas provides a direct link between genomic and transcriptomic profiles and their corresponding spatial immune ecosystems in LBCLs. PTL and PCNSL are defined by immune exclusion or cytotoxic exhaustion, explaining their poorer survival. In contrast, complex immune ecosystem confers a survival benefit and is almost exclusively found in bone and nodal LBCL. Tumor genomic profiles align more strongly with anatomical location than with the immune ecosystem, indicating that immune contexture can sometimes override underlying risk conferred by the tumor genome. The spatial proteomic profiling framework we describe can provide a ground truth for immune ecosystems previously inferred from genomic or transcriptomic data and adds crucial biological and clinical nuance for defining site-specific vulnerabilities in LBCL.

  PublisherOA PDFDOI

Recent grants

• Noninvasive monitoring of lung cancer patients treated with radiotherapy

  NIH · $1.8M · 2015–2021

• Analysis of urine tumor nucleic acids for detection and personalized surveillance of bladder cancer

  NIH · $3.0M · 2020–2026

• Molecularly-based outcome and toxicity prediction after radiotherapy for lung cancer

  NIH · $3.1M · 2020–2026

• Modeling the Molecular Determinants of Induced Anti-Tumor Immune Responses in Mantle Cell Lymphoma

  NIH · $3.1M · 2015–2020

• A Noninvasive Integrated Genomic Approach for Early Cancer Detection and Risk Stratification after Transplantation

  NIH · $3.0M · 2019–2024

Frequent coauthors

• Maximilian Diehn

  588 shared

• David M. Kurtz

  National Institute of Environmental Health Sciences

  324 shared

• Chih Long Liu

  Stanford University

  256 shared

• Aaron M. Newman

  240 shared

• Ronald Levy

  Stanford University

  233 shared

• Joseph G. Schroers‐Martin

  Menlo School

  219 shared

• Henning Stehr

  200 shared

• Jacob J. Chabon

  Stanford University

  192 shared

Education

• M.D.

  Stanford University

• Ph.D.

  Stanford University

Awards & honors

• Scholar Award from the American Society of Hematology (ASH)
• Leukemia & Lymphoma Society (LLS) Award
• V-Foundation Award
• American Red Cross Award
• Damon Runyon Cancer Research Foundation Award