Contact

  • Academic
    nphill22@stanford.edu
    University - Student Department: School of Medicine - IDP's - Cancer Biology Position: Graduate

All Publications

  • Field-effect-informed urine liquid biopsy for bladder cancer. Cell Shi, W. Y., Liu, K. J., Esfahani, M. S., Mach, K. E., Phillips, N. A., Almanza, D., Bajpai, R. K., Schroers-Martin, J. G., Trabanino, L., Lee, T. J., La, V., Rodriguez, G., Holton, G., Chen, S. B., Mullane, P., Wu, D. J., Nesselbush, M. C., Sugio, T., Cheng, J. C., Jabara, I., Hamilton, E. G., Alig, S. K., Liu, C. L., Peterson, D. J., Prado, K., Shkolyar, E., Thong, A., Shah, J. B., Gill, H., Kunder, C. A., Chan, E., Khaki, A. R., Skinner, E. C., Alizadeh, A. A., Liao, J. C., Diehn, M. 2026

    Abstract

    Only some non-muscle-invasive bladder cancer (NMIBC) patients benefit from intravesical Bacillus Calmette-Guérin (BCG), and predictive biomarkers remain lacking. While urine tumor DNA (utDNA) analysis is promising, mutations in tumor-adjacent normal urothelium, namely the field effect, limit specificity. Here, we show that the prevalence of somatic mutations in the urine increases with age. We introduce an improved utDNA minimal residual disease (MRD) approach that increases specificity by removing field-effect mutations. Applying this field-effect-informed MRD approach to 261 samples from NMIBC patients undergoing surgery and adjuvant BCG, we identify three molecular response classes, including surgical responders, BCG responders, and non-responders. Molecular predictors of response to the two treatments differ, with pre-existing immune activation and higher mutation burden enriched in BCG but not surgery responders. These findings highlight the potential of field-effect-informed liquid biopsy methods for guiding personalized therapy and uncovering biomarkers for individual components of multimodal treatments.

    View details for DOI 10.1016/j.cell.2025.12.054

    View details for PubMedID 41605210

  • Double-dose axicabtagene ciloleucel (Axi-Cel-2) as second-line therapy for high-risk relapsed or refractory large B-cell lymphoma (r/rLBCL): Interim results from a Phase 1b study Lee, D., Rodrigues, K., Rana, M., Kozy, K., Salazar, S., Jensen, A., Desai, M., Mallampet, J., Lohman, C., Huang, Y., Kang, X., Tian, F., Mutter, J., Phillips, N., Schweitzer, C., Cheema, F., Mikkilineni, L., Rezvani, A., Johnston, L., Shizuru, J., Meyer, E., Lowsky, R., Weng, W., Smith, M., Bharadwaj, S., Negrin, R., Sidana, S., Muffly, L., Sahaf, B., Natkunam, Y., Shen, R., Frank, M., Mackall, C., Good, Z., Alizadeh, A., Miklos, D., Dahiya, S. ELSEVIER. 2025: 671-672

    View details for DOI 10.1182/blood-2025-671

    View details for Web of Science ID 001659056200022

  • Non-invasive classification & molecular subtyping of mature lymphoid neoplasms by cell-free DNA profiling Mutter, J., Klanova, M., Phillips, N., Sugio, T., Esfahani, M., Schroers-Martin, J., Alig, S., Hamilton, M., Sworder, B., Tessoulin, B., Boegeholz, J., Rossi, C., Shi, S., Hosoya, H., Kato, K., Mehta-Shah, N., Russler-Germain, D., Olsen, M., Kang, X., Liu, C., Kurtz, D., le Gouill, S., Trneny, M., Diehn, M., Alizadeh, A. ELSEVIER. 2025: 5330-5331

    View details for DOI 10.1182/blood-2025-5330

    View details for Web of Science ID 001665082900030

  • CAR19 therapy drives expansion of clonal hematopoiesis and associated cytopenias. Research square Hamilton, M. P., Phillips, N., Noordenbos, T., Boegeholz, J., Sugio, T., Sworder, B. J., Alig, S. K., Good, Z., Schroers-Martin, J. G., Tamaresis, J., Esfahani, M. S., Lu, Y., Olsen, M., Liu, C. L., Ehlinger, Z., Desai, M., Muffly, L., Negrin, R. S., Arai, S., Johnston, L., Lowsky, R., Meyer, E., Rezvani, A., Shizuru, J., Weng, W. K., Shiraz, P., Sidana, S., Bharadwaj, S., Smith, M., Dahiya, S., Sahaf, B., Frank, M. J., Mackall, C. L., Diehn, M., Kurtz, D. M., Miklos, D. B., Alizadeh, A. A. 2025

    Abstract

    CD19-directed chimeric antigen receptor T-cell therapy (CAR19) improves survival in patients with relapsed/refractory large B-cell lymphoma (rrLBCL) compared to immunochemotherapy with intent for autologous hematopoietic cell transplantation (HCT). However, major toxicities of CAR19 therapy include prolonged cytopenias, infection, and secondary hematologic malignancies. To investigate the mechanisms underlying these toxicities we studied a cohort of lymphoma patients receiving CAR19. CAR19-treated patients exhibited impaired immune reconstitution and increased infection compared to propensity-matched HCT-treated controls. Bone marrow analysis revealed prolonged post-CAR cytopenias is associated with clonal cytopenias of undetermined significance (CCUS) and is characterized by interferon-mediated inflammation. Despite durable lymphoma remissions, clonal hematopoiesis (CH) commonly expanded following CAR19 infusion and was associated with impaired immune reconstitution and the development of treatment related myeloid malignancy (tMN). The molecular composition and clinical outcomes of post-CAR tMN were comparable to those of post-HCT tMN. Single-cell DNA analysis revealed that most post-CAR CH clones harbored a single independent mutation and that CAR integration into T cells with CH mutations may drive persistence. These findings broadly implicate CH mutation burden and CH expansion in the development of post-CAR cytopenias and malignancies as well as mechanistically suggest these expansions occur in a background of marrow inflammation. Together, our results provide insight into the origins of key CAR19-associated toxicities, including infection and tMN.

    View details for DOI 10.21203/rs.3.rs-7746241/v1

    View details for PubMedID 41282159

    View details for PubMedCentralID PMC12633523

  • Therapeutic targeting of the nuclear pore complex with molecular glue degraders in pancreatic cancer Yuan, L., Ji, W., Dwyer, B. G., Lu, J., Bian, J., Colombo, G. M., Martinez, M. J., Fernandez, D., Phillips, N. A., Tang, M. T., Zhou, C. W., Jones, H. M., Calla, N., Huancas, C., Eckart, M., Tran, J., Qiu, T., Doench, J. G., Rees, M. G., Roth, J. A., Cameron, M. D., Charville, G. W., Kuo, C. J., Dixon, S. J., Zhang, T., Hinshaw, S. M., Gray, N. S., Corsello, S. M. AMER ASSOC CANCER RESEARCH. 2025

    View details for DOI 10.1158/1538-7445.PANCREATIC25-B003

    View details for Web of Science ID 001588114400040

  • Defining the antitumor mechanism of action of a clinical-stage compound as a selective degrader of the nuclear pore complex. Cancer discovery Yuan, L., Ji, W., Dwyer, B. G., Lu, J., Bian, J., Colombo, G. M., Martinez, M. J., Fernandez, D., Phillips, N. A., Tang, M. T., Zhou, C. W., Quispe Calla, N. E., Guzman Huancas, C., Eckart, M., Tran, J., Jones, H. M., Qiu, T., Doench, J. G., Rees, M. G., Roth, J. A., Cameron, M. D., Charville, G. W., Kuo, C. J., Dixon, S. J., Zhang, T., Hinshaw, S. M., Gray, N. S., Corsello, S. M. 2025

    Abstract

    Cancer cells are acutely dependent on nuclear transport due to elevated transcriptional activity, suggesting an unrealized opportunity for selective therapeutic inhibition of the nuclear pore complex. Through large-scale phenotypic profiling of cancer cell lines, genome-scale functional genomic modifier screens, and mass spectrometry-based proteomics, we discovered that the clinical drug PRLX-93936 is a molecular glue that binds and reprograms the TRIM21 ubiquitin ligase to degrade the nuclear pore complex. Upon compound-induced TRIM21 recruitment, the nuclear pore is ubiquitylated and degraded, resulting in the loss of short-lived cytoplasmic mRNA transcripts and induction of cancer cell apoptosis. Direct compound binding to TRIM21 was confirmed via surface plasmon resonance and x-ray crystallography, while compound-induced TRIM21-nucleoporin complex formation was demonstrated through multiple orthogonal approaches in cells and in vitro. Phenotype-guided optimization yielded compounds with 10-fold greater potency and drug-like properties with robust pharmacokinetics and efficacy against pancreatic cancer xenografts and patient-derived organoids.

    View details for DOI 10.1158/2159-8290.CD-25-0271

    View details for PubMedID 40891634

  • Beyond Trial and Error: A Mathematical Model Wrangles DLBCL Heterogeneity Toward Optimizing Combination Therapy. Blood cancer discovery Goldstein, J. S., Phillips, N. A., Alizadeh, A. A. 2025: OF1-OF4

    Abstract

    In this issue of Blood Cancer Discovery, Pomeroy and Palmer present a new mathematical model, incorporating both inter- and intra-patient heterogeneity, to accurately predict outcomes for first-line combination therapies in diffuse large B-cell lymphoma. Their quantitative framework opens a range of possibilities for optimizing trial design and lymphoma therapy with potential to expedite clinical development. See related article by Pomeroy and Palmer, p. XX.

    View details for DOI 10.1158/2643-3230.BCD-25-0071

    View details for PubMedID 40232087

  • Single Institution Analysis of Lymphoma Treatment Related Post-CAR Myeloid Neoplasms Hamilton, M., Phillips, N., Lee, D., Ehlinger, Z., Kramer, A., Srinagesh, H., Good, Z., Kang, X., Olsen, M., Liu, C., Tian, F., Sworder, B. J., Hosoya, H., Schroers-Martin, J. G., Bharadwaj, S., Arai, S., Johnston, L., Lowsky, R., Meyer, E. H., Negrin, R. S., Rezvani, A. R., Shizuru, J., Mikkilineni, L., Shiraz, P., Sidana, S., Weng, W., Kennedy, V. E., Smith, M., Muffly, L., Kurtz, D., Dahiya, S., Frank, M. J., Mackall, C. L., Sahaf, B., Miklos, D. B., Alizadeh, A. A. ELSEVIER. 2024: 96-97

    View details for DOI 10.1182/blood-2024-205378

    View details for Web of Science ID 001410860600045

  • Withdrawal of 'Precision Neoantigen Discovery Using Large-scale Immunopeptidomes and Composite Modeling of MHC Peptide Presentation'. Molecular & cellular proteomics : MCP Pyke, R. M., Mellacheruvu, D., Dea, S., Abbott, C. W., Zhang, S. V., Phillips, N. A., Harris, J., Bartha, G., Desai, S., McClory, R., West, J., Snyder, M. P., Chen, R., Boyle, S. M. 2023; 22 (4): 100511

    View details for DOI 10.1016/j.mcpro.2023.100511

    View details for PubMedID 37019059

  • Precision neoantigen discovery using large-scale immunopeptidomes and composite modeling of MHC peptide presentation. Molecular & cellular proteomics : MCP Pyke, R. M., Mellacheruvu, D., Dea, S., Abbott, C., Zhang, S. V., Phillips, N. A., Harris, J., Bartha, G., Desai, S., McClory, R., West, J., Snyder, M. P., Chen, R., Boyle, S. M. 2023: 100506

    Abstract

    Major histocompatibility complex (MHC)-bound peptides that originate from tumor-specific genetic alterations, known as neoantigens, are an important class of anti-cancer therapeutic targets. Accurately predicting peptide presentation by MHC complexes is a key aspect of discovering therapeutically relevant neoantigens. Technological improvements in mass-spectrometry-based immunopeptidomics and advanced modeling techniques have vastly improved MHC presentation prediction over the past two decades. However, improvement in the sensitivity and specificity of prediction algorithms is needed for clinical applications such as the development of personalized cancer vaccines, the discovery of biomarkers for response to checkpoint blockade and the quantification of autoimmune risk in gene therapies. Toward this end, we generated allele-specific immunopeptidomics data using 25 mono-allelic cell lines and created Systematic HLA Epitope Ranking Pan Algorithm (SHERPA™), a pan-allelic MHC-peptide algorithm for predicting MHC-peptide binding and presentation. In contrast to previously published large-scale mono-allelic data, we used an HLA-null K562 parental cell line and a stable transfection of HLA alleles to better emulate native presentation. Our dataset includes five previously unprofiled alleles that expand MHC binding pocket diversity in the training data and extend allelic coverage in underprofiled populations. To improve generalizability, SHERPA systematically integrates 128 mono-allelic and 384 multi-allelic samples with publicly available immunoproteomics data and binding assay data. Using this dataset, we developed two features that empirically estimate the propensities of genes and specific regions within gene bodies to engender immunopeptides to represent antigen processing. Using a composite model constructed with gradient boosting decision trees, multi-allelic deconvolution and 2.15 million peptides encompassing 167 alleles, we achieved a 1.44 fold improvement of positive predictive value compared to existing tools when evaluated on independent mono-allelic datasets and a 1.17 fold improvement when evaluating on tumor samples. With a high degree of accuracy, SHERPA has the potential to enable precision neoantigen discovery for future clinical applications.

    View details for DOI 10.1016/j.mcpro.2023.100506

    View details for PubMedID 36796642

  • Precision neoantigen discovery using large-scale immunopeptidomes and composite modeling of MHC peptide presentation. Molecular & cellular proteomics : MCP Pyke, R. M., Mellacheruvu, D., Dea, S., Abbott, C., Zhang, S. V., Phillips, N. A., Harris, J., Bartha, G., Desai, S., McClory, R., West, J., Snyder, M. P., Chen, R., Boyle, S. M. 2021: 100111

    Abstract

    Major histocompatibility complex (MHC)-bound peptides that originate from tumor-specific genetic alterations, known as neoantigens, are an important class of anti-cancer therapeutic targets. Accurately predicting peptide presentation by MHC complexes is a key aspect of discovering therapeutically relevant neoantigens. Technological improvements in mass-spectrometry-based immunopeptidomics and advanced modeling techniques have vastly improved MHC presentation prediction over the past two decades. However, improvement in the sensitivity and specificity of prediction algorithms is needed for clinical applications such as the development of personalized cancer vaccines, the discovery of biomarkers for response to checkpoint blockade and the quantification of autoimmune risk in gene therapies. Toward this end, we generated allele-specific immunopeptidomics data using 25 mono-allelic cell lines and created Systematic HLA Epitope Ranking Pan Algorithm (SHERPA), a pan-allelic MHC-peptide algorithm for predicting MHC-peptide binding and presentation. In contrast to previously published large-scale mono-allelic data, we used an HLA-null K562 parental cell line and a stable transfection of HLA alleles to better emulate native presentation. Our dataset includes five previously unprofiled alleles that expand MHC binding pocket diversity in the training data and extend allelic coverage in under profiled populations. To improve generalizability, SHERPA systematically integrates 128 mono-allelic and 384 multi-allelic samples with publicly available immunoproteomics data and binding assay data. Using this dataset, we developed two features that empirically estimate the propensities of genes and specific regions within gene bodies to engender immunopeptides to represent antigen processing. Using a composite model constructed with gradient boosting decision trees, multi-allelic deconvolution and 2.15 million peptides encompassing 167 alleles, we achieved a 1.44 fold improvement of positive predictive value compared to existing tools when evaluated on independent mono-allelic datasets and a 1.15 fold improvement when evaluating on tumor samples. With a high degree of accuracy, SHERPA has the potential to enable precision neoantigen discovery for future clinical applications.

    View details for DOI 10.1016/j.mcpro.2021.100111

    View details for PubMedID 34126241