Stanford Advisors
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Ash Alizadeh, Postdoctoral Faculty Sponsor
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Ash Alizadeh, Postdoctoral Research Mentor
All Publications
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The effect of ibrutinib on the myeloid cell compartment in CNS lymphoma.
Leukemia
2025
View details for DOI 10.1038/s41375-025-02600-y
View details for PubMedID 40210767
View details for PubMedCentralID 3795457
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Modulation of the Tumor Microenvironment By CCL19 in Primary CNS Lymphomas
ELSEVIER. 2024: 856-857
View details for DOI 10.1182/blood-2024-199257
View details for Web of Science ID 001412608600049
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Distinct Hodgkin lymphoma subtypes defined by noninvasive genomic profiling.
Nature
2023
Abstract
The scarcity of malignant Hodgkin and Reed-Sternberg (HRS) cells hamper tissue-based comprehensive genomic profiling of classic Hodgkin lymphoma (cHL). Liquid biopsies, in contrast, show promise for molecular profiling of cHL due to relatively high circulating tumor DNA (ctDNA) levels1-4. Here, we show that the plasma representation of mutations exceeds the bulk tumor representation in most cases, making cHL particularly amenable to noninvasive profiling. Leveraging single-cell transcriptional profiles of cHL tumors, we demonstrate HRS ctDNA shedding to be shaped by DNASE1L3, whose increased tumor microenvironment-derived expression drives high ctDNA concentrations. Using this insight, we comprehensively profile 366 patients, revealing two distinct cHL genomic subtypes with characteristic clinical and prognostic correlates, as well as distinct transcriptional and immunological profiles. Furthermore, we identify a novel class of truncating IL4R-mutations that are dependent on IL13 signaling and therapeutically targetable with IL4R blocking antibodies. Finally, using PhasED-Seq5 we demonstrate the clinical value of pre- and on-treatment ctDNA levels for longitudinally refining cHL risk prediction, and for detection of radiographically occult minimal residual disease. Collectively, these results support the utility of noninvasive strategies for genotyping and dynamic monitoring of cHL as well as capturing molecularly distinct subtypes with diagnostic, prognostic, and therapeutic potential.
View details for DOI 10.1038/s41586-023-06903-x
View details for PubMedID 38081297
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Genomic, Transcriptional, and Immunological Validation of Distinct Molecular Subtypes of Classic Hodgkin Lymphoma through Tissue-Based and Noninvasive Methods
AMER SOC HEMATOLOGY. 2023
View details for DOI 10.1182/blood-2023-186810
View details for Web of Science ID 001159306700178
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Longitudinal Noninvasive Surveillance & Fragmentomic Characterization of Follicular Lymphoma
AMER SOC HEMATOLOGY. 2023
View details for DOI 10.1182/blood-2023-187116
View details for Web of Science ID 001159306702041
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Inferred Gene Expression By Cell-Free DNA Profiling Allows Noninvasive Lymphoma Classification
AMER SOC HEMATOLOGY. 2023
View details for DOI 10.1182/blood-2023-186853
View details for Web of Science ID 001159306701002
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An Integrated Multimodal Framework for Noninvasive TCL Disease Detection and Monitoring
AMER SOC HEMATOLOGY. 2023
View details for DOI 10.1182/blood-2023-180492
View details for Web of Science ID 001159306706091
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Improved early outcome prediction by MRI-based 3D tumor volume assessment in patients with CNS lymphomas.
Neuro-oncology
2023
Abstract
BACKGROUND: Central nervous system lymphomas (CNSL) display remarkable clinical heterogeneity, yet accurate prediction of outcomes remains challenging. The IPCG criteria are widely used in routine practice for the assessment of treatment response. However, the value of the IPCG criteria for ultimate outcome prediction is largely unclear, mainly due to the uncertainty in delineating complete from partial responses during and after treatment.METHODS: We explored various MRI features including semi-automated 3D tumor volume measurements at different disease milestones and their association with survival in 93 CNSL patients undergoing curative-intent treatment.RESULTS: At diagnosis, patients with more than three lymphoma lesions, periventricular involvement, and high 3D tumor volumes showed significantly unfavorable PFS and OS. At first interim MRI during treatment, the IPCG criteria failed to discriminate outcomes in responding patients. Therefore, we randomized these patients into training and validation cohorts to investigate whether 3D tumor volumetry could improve outcome prediction. We identified a 3D tumor volume reduction of ≥97% as the optimal threshold for risk stratification (=3D early response, 3D_ER). Applied to the validation cohort, patients achieving 3D_ER had significantly superior outcomes. In multivariate analyses, 3D_ER was independently prognostic of PFS and OS. Finally, we leveraged prognostic information from 3D MRI features and circulating biomarkers to build a composite metric that further improved outcome prediction in CNSL.CONCLUSIONS: We developed semi-automated 3D tumor volume measurements as strong and independent early predictors of clinical outcomes in CNSL patients. These radiologic features could help improve risk stratification and help guide future treatment approaches.
View details for DOI 10.1093/neuonc/noad177
View details for PubMedID 37713267
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Circulating Tumor DNA Profiling for Detection, Risk Stratification, and Classification of Brain Lymphomas.
Journal of clinical oncology : official journal of the American Society of Clinical Oncology
2022: JCO2200826
Abstract
Clinical outcomes of patients with CNS lymphomas (CNSLs) are remarkably heterogeneous, yet identification of patients at high risk for treatment failure is challenging. Furthermore, CNSL diagnosis often remains unconfirmed because of contraindications for invasive stereotactic biopsies. Therefore, improved biomarkers are needed to better stratify patients into risk groups, predict treatment response, and noninvasively identify CNSL.We explored the value of circulating tumor DNA (ctDNA) for early outcome prediction, measurable residual disease monitoring, and surgery-free CNSL identification by applying ultrasensitive targeted next-generation sequencing to a total of 306 tumor, plasma, and CSF specimens from 136 patients with brain cancers, including 92 patients with CNSL.Before therapy, ctDNA was detectable in 78% of plasma and 100% of CSF samples. Patients with positive ctDNA in pretreatment plasma had significantly shorter progression-free survival (PFS, P < .0001, log-rank test) and overall survival (OS, P = .0001, log-rank test). In multivariate analyses including established clinical and radiographic risk factors, pretreatment plasma ctDNA concentrations were independently prognostic of clinical outcomes (PFS HR, 1.4; 95% CI, 1.0 to 1.9; P = .03; OS HR, 1.6; 95% CI, 1.1 to 2.2; P = .006). Moreover, measurable residual disease detection by plasma ctDNA monitoring during treatment identified patients with particularly poor prognosis following curative-intent immunochemotherapy (PFS, P = .0002; OS, P = .004, log-rank test). Finally, we developed a proof-of-principle machine learning approach for biopsy-free CNSL identification from ctDNA, showing sensitivities of 59% (CSF) and 25% (plasma) with high positive predictive value.We demonstrate robust and ultrasensitive detection of ctDNA at various disease milestones in CNSL. Our findings highlight the role of ctDNA as a noninvasive biomarker and its potential value for personalized risk stratification and treatment guidance in patients with CNSL.
View details for DOI 10.1200/JCO.22.00826
View details for PubMedID 36542815