Stanford Advisors


All Publications


  • MITI minimum information guidelines for highly multiplexed tissue images. Nature methods Schapiro, D., Yapp, C., Sokolov, A., Reynolds, S. M., Chen, Y., Sudar, D., Xie, Y., Muhlich, J., Arias-Camison, R., Arena, S., Taylor, A. J., Nikolov, M., Tyler, M., Lin, J., Burlingame, E. A., Human Tumor Atlas Network, Chang, Y. H., Farhi, S. L., Thorsson, V., Venkatamohan, N., Drewes, J. L., Pe'er, D., Gutman, D. A., Herrmann, M. D., Gehlenborg, N., Bankhead, P., Roland, J. T., Herndon, J. M., Snyder, M. P., Angelo, M., Nolan, G., Swedlow, J. R., Schultz, N., Merrick, D. T., Mazzili, S. A., Cerami, E., Rodig, S. J., Santagata, S., Sorger, P. K., Abravanel, D. L., Achilefu, S., Ademuyiwa, F. O., Adey, A. C., Aft, R., Ahn, K. J., Alikarami, F., Alon, S., Ashenberg, O., Baker, E., Baker, G. J., Bandyopadhyay, S., Bayguinov, P., Beane, J., Becker, W., Bernt, K., Betts, C. B., Bletz, J., Blosser, T., Boire, A., Boland, G. M., Boyden, E. S., Bucher, E., Bueno, R., Cai, Q., Cambuli, F., Campbell, J., Cao, S., Caravan, W., Chaligne, R., Chan, J. M., Chasnoff, S., Chatterjee, D., Chen, A. A., Chen, C., Chen, C., Chen, B., Chen, F., Chen, S., Chheda, M. G., Chin, K., Cho, H., Chun, J., Cisneros, L., Coffey, R. J., Cohen, O., Colditz, G. A., Cole, K. A., Collins, N., Cotter, D., Coussens, L. M., Coy, S., Creason, A. L., Cui, Y., Zhou, D. C., Curtis, C., Davies, S. R., Bruijn, I., Delorey, T. M., Demir, E., Denardo, D., Diep, D., Ding, L., DiPersio, J., Dubinett, S. M., Eberlein, T. J., Eddy, J. A., Esplin, E. D., Factor, R. E., Fatahalian, K., Feiler, H. S., Fernandez, J., Fields, A., Fields, R. C., Fitzpatrick, J. A., Ford, J. M., Franklin, J., Fulton, B., Gaglia, G., Galdieri, L., Ganesh, K., Gao, J., Gaudio, B. L., Getz, G., Gibbs, D. L., Gillanders, W. E., Goecks, J., Goodwin, D., Gray, J. W., Greenleaf, W., Grimm, L. J., Gu, Q., Guerriero, J. L., Guha, T., Guimaraes, A. R., Gutierrez, B., Hacohen, N., Hanson, C. R., Harris, C. R., Hawkins, W. G., Heiser, C. N., Hoffer, J., Hollmann, T. J., Hsieh, J. J., Huang, J., Hunger, S. P., Hwang, E., Iacobuzio-Donahue, C., Iglesia, M. D., Islam, M., Izar, B., Jacobson, C. A., Janes, S., Jayasinghe, R. G., Jeudi, T., Johnson, B. E., Johnson, B. E., Ju, T., Kadara, H., Karnoub, E., Karpova, A., Khan, A., Kibbe, W., Kim, A. H., King, L. M., Kozlowski, E., Krishnamoorthy, P., Krueger, R., Kundaje, A., Ladabaum, U., Laquindanum, R., Lau, C., Lau, K. S., LeBoeuf, N. R., Lee, H., Lenburg, M., Leshchiner, I., Levy, R., Li, Y., Lian, C. G., Liang, W., Lim, K., Lin, Y., Liu, D., Liu, Q., Liu, R., Lo, J., Lo, P., Longabaugh, W. J., Longacre, T., Luckett, K., Ma, C., Maher, C., Maier, A., Makowski, D., Maley, C., Maliga, Z., Manoj, P., Maris, J. M., Markham, N., Marks, J. R., Martinez, D., Mashl, J., Masilionis, I., Massague, J., Mazurowski, M. A., McKinley, E. T., McMichael, J., Meyerson, M., Mills, G. B., Mitri, Z. I., Moorman, A., Mudd, J., Murphy, G. F., Deen, N. N., Navin, N. E., Nawy, T., Ness, R. M., Nevins, S., Nirmal, A. J., Novikov, E., Oh, S. T., Oldridge, D. A., Owzar, K., Pant, S. M., Park, W., Patti, G. J., Paul, K., Pelletier, R., Persson, D., Petty, C., Pfister, H., Polyak, K., Puram, S. V., Qiu, Q., Villalonga, A. Q., Ramirez, M. A., Rashid, R., Reeb, A. N., Reid, M. E., Remsik, J., Riesterer, J. L., Risom, T., Ritch, C. C., Rolong, A., Rudin, C. M., Ryser, M. D., Sato, K., Sears, C. L., Semenov, Y. R., Shen, J., Shoghi, K. I., Shrubsole, M. J., Shyr, Y., Sibley, A. B., Simmons, A. J., Sinha, A., Sivagnanam, S., Song, S., Southar-Smith, A., Spira, A. E., Cyr, J. S., Stefankiewicz, S., Storrs, E. P., Stover, E. H., Strand, S. H., Straub, C., Street, C., Su, T., Surrey, L. F., Suver, C., Tan, K., Terekhanova, N. V., Ternes, L., Thadi, A., Thomas, G., Tibshirani, R., Umeda, S., Uzun, Y., Vallius, T., Van Allen, E. R., Vandekar, S., Vega, P. N., Veis, D. J., Vennam, S., Verma, A., Vigneau, S., Wagle, N., Wahl, R., Walle, T., Wang, L., Warchol, S., Washington, M. K., Watson, C., Weimer, A. K., Wendl, M. C., West, R. B., White, S., Windon, A. L., Wu, H., Wu, C., Wu, Y., Wyczalkowski, M. A., Xu, J., Yao, L., Yu, W., Zhang, K., Zhu, X. 2022; 19 (3): 262-267

    View details for DOI 10.1038/s41592-022-01415-4

    View details for PubMedID 35277708

  • Master lineage transcription factors anchor trans mega transcriptional complexes at highly accessible enhancer sites to promote long-range chromatin clustering and transcription of distal target genes. Nucleic acids research White, S. M., Snyder, M. P., Yi, C. 2021

    Abstract

    The term 'super enhancers' (SE) has been widely used to describe stretches of closely localized enhancers that are occupied collectively by large numbers of transcription factors (TFs) and co-factors, and control the transcription of highly-expressed genes. Through integrated analysis of >600 DNase-seq, ChIP-seq, GRO-seq, STARR-seq, RNA-seq, Hi-C and ChIA-PET data in five human cancer cell lines, we identified a new class of autonomous SEs (aSEs) that are excluded from classic SE calls by the widely used Rank Ordering of Super-Enhancers (ROSE) method. TF footprint analysis revealed that compared to classic SEs and regular enhancers, aSEs are tightly bound by a dense array of master lineage TFs, which serve as anchors to recruit additional TFs and co-factors in trans. In addition, aSEs are preferentially enriched for Cohesins, which likely involve in stabilizing long-distance interactions between aSEs and their distal target genes. Finally, we showed that aSEs can be reliably predicted using a single DNase-seq data or combined with Mediator and/or P300 ChIP-seq. Overall, our study demonstrates that aSEs represent a unique class of functionally important enhancer elements that distally regulate the transcription of highly expressed genes.

    View details for DOI 10.1093/nar/gkab1105

    View details for PubMedID 34850122

  • A Yap-Myc-Sox2-p53 Regulatory Network Dictates Metabolic Homeostasis and Differentiation in Kras-Driven Pancreatic Ductal Adenocarcinomas DEVELOPMENTAL CELL Murakami, S., Nemazanyy, I., White, S. M., Chen, H., Nguyen, C. K., Graham, G. T., Saur, D., Pende, M., Yi, C. 2019; 51 (1): 113-+

    Abstract

    Employing inducible genetically engineered and orthotopic mouse models, we demonstrate a key role for transcriptional regulator Yap in maintenance of Kras-mutant pancreatic tumors. Integrated transcriptional and metabolomics analysis reveals that Yap transcribes Myc and cooperates with Myc to maintain global transcription of metabolic genes. Yap loss triggers acute metabolic stress, which causes tumor regression while inducing epigenetic reprogramming and Sox2 upregulation in a subset of pancreatic neoplastic cells. Sox2 restores Myc expression and metabolic homeostasis in Yap-deficient neoplastic ductal cells, which gradually re-differentiate into acinar-like cells, partially restoring pancreatic parenchyma in vivo. Both the short-term and long-term effects of Yap loss in inducing cell death and re-differentiation, respectively, are blunted in advanced, poorly differentiated p53-mutant pancreatic tumors. Collectively, these findings reveal a highly dynamic and interdependent metabolic, transcriptional, and epigenetic regulatory network governed by Yap, Myc, Sox2, and p53 that dictates pancreatic tumor metabolism, growth, survival, and differentiation.

    View details for DOI 10.1016/j.devcel.2019.07.022

    View details for Web of Science ID 000489167900012

    View details for PubMedID 31447265

    View details for PubMedCentralID PMC6783361

  • YAP/TAZ Inhibition Induces Metabolic and Signaling Rewiring Resulting in Targetable Vulnerabilities in NF2-Deficient Tumor Cells DEVELOPMENTAL CELL White, S. M., Avantaggiati, M., Nemazanyy, I., Di Poto, C., Yang, Y., Pende, M., Gibney, G. T., Ressom, H. W., Field, J., Atkins, M. B., Yi, C. 2019; 49 (3): 425-+

    Abstract

    Merlin/NF2 is a bona fide tumor suppressor whose mutations underlie inherited tumor syndrome neurofibromatosis type 2 (NF2), as well as various sporadic cancers including kidney cancer. Multiple Merlin/NF2 effector pathways including the Hippo-YAP/TAZ pathway have been identified. However, the molecular mechanisms underpinning the growth and survival of NF2-mutant tumors remain poorly understood. Using an inducible orthotopic kidney tumor model, we demonstrate that YAP/TAZ silencing is sufficient to induce regression of pre-established NF2-deficient tumors. Mechanistically, YAP/TAZ depletion diminishes glycolysis-dependent growth and increases mitochondrial respiration and reactive oxygen species (ROS) buildup, resulting in oxidative-stress-induced cell death when challenged by nutrient stress. Furthermore, we identify lysosome-mediated cAMP-PKA/EPAC-dependent activation of RAF-MEK-ERK signaling as a resistance mechanism to YAP/TAZ inhibition. Finally, unbiased analysis of TCGA primary kidney tumor transcriptomes confirms a positive correlation of a YAP/TAZ signature with glycolysis and inverse correlations with oxidative phosphorylation and lysosomal gene expression, supporting the clinical relevance of our findings.

    View details for DOI 10.1016/j.devcel.2019.04.014

    View details for Web of Science ID 000466934100016

    View details for PubMedID 31063758

    View details for PubMedCentralID PMC6524954

  • The complex entanglement of Hippo-Yap/Taz signaling in tumor immunity ONCOGENE White, S. M., Murakami, S., Yi, C. 2019; 38 (16): 2899-2909

    Abstract

    The Hippo-Yap/Taz pathway, originally identified as a central developmental regulator of organ size, has been found perturbed in many types of human tumors, and linked to tumor growth, survival, evasion, metastasis, stemness, and drug resistance. Beside these tumor-cell-intrinsic functions, Hippo signaling also plays important immune-regulatory roles. In this review, we will summarize and discuss recent breakthroughs in our understanding of how various components of the Hippo-Yap/Taz pathway influence the tumor immune microenvironment, including their effects on the tumor secretome and immune infiltrates, their roles in regulating crosstalk between tumor cells and T cells, and finally their intrinsic functions in various types of innate and adaptive immune cells. While further research is needed to integrate and reconcile existing findings and to discern the overall effects of Hippo signaling on tumor immunity, it is clear that Hippo signaling functions as a key bridge connecting tumor cells with both the adaptive and innate immune systems. Thus, all future therapeutic development against the Hippo-Yap/Taz pathway should take into account their multi-faceted roles in regulating tumor immunity in addition to their growth-regulatory functions. Given that immune therapies have become the mainstay of cancer treatment, it is also important to pursue how to manipulate Hippo signaling to boost response or overcome resistance to existing immune therapies.

    View details for DOI 10.1038/s41388-018-0649-6

    View details for Web of Science ID 000465167600002

    View details for PubMedID 30617303

    View details for PubMedCentralID PMC7567008

  • Rac1-Mediated DNA Damage and Inflammation Promote Nf2 Tumorigenesis but Also Limit Cell-Cycle Progression DEVELOPMENTAL CELL Shi, Y., Bollam, S. R., White, S. M., Laughlin, S. Z., Graham, G. T., Wadhwa, M., Chen, H., Nguyen, C., Vitte, J., Giovannini, M., Toretsky, J., Yi, C. 2016; 39 (4): 452-465

    Abstract

    Merlin encoded by the Nf2 gene is a bona fide tumor suppressor that has been implicated in regulation of both the Hippo-Yap and Rac1-Pak1 pathways. Using genetically engineered murine liver models, we show that co-deletion of Rac1 with Nf2 blocks tumor initiation but paradoxically exacerbates hepatomegaly induced by Nf2 loss, which can be suppressed either by treatment with pro-oxidants or by co-deletion of Yap. Our results suggest that while Yap acts as the central driver of proliferation during Nf2 tumorigenesis, Rac1 primarily functions as an inflammation switch by inducing reactive oxygen species that, on one hand, induce nuclear factor κB signaling and expression of inflammatory cytokines, and on the other activate p53 checkpoint and senescence programs dampening the cyclin D1-pRb-E2F1 pathway. Interestingly, senescence markers are associated with benign NF2 tumors but not with malignant NF2 mutant mesotheliomas, suggesting that senescence may underlie the benign nature of most NF2 tumors.

    View details for DOI 10.1016/j.devcel.2016.09.027

    View details for Web of Science ID 000389162800010

    View details for PubMedID 27818180

    View details for PubMedCentralID PMC5519326

  • An ensemble model of QSAR tools for regulatory risk assessment JOURNAL OF CHEMINFORMATICS Pradeep, P., Povinelli, R. J., White, S., Merrill, S. J. 2016; 8: 48

    Abstract

    Quantitative structure activity relationships (QSARs) are theoretical models that relate a quantitative measure of chemical structure to a physical property or a biological effect. QSAR predictions can be used for chemical risk assessment for protection of human and environmental health, which makes them interesting to regulators, especially in the absence of experimental data. For compatibility with regulatory use, QSAR models should be transparent, reproducible and optimized to minimize the number of false negatives. In silico QSAR tools are gaining wide acceptance as a faster alternative to otherwise time-consuming clinical and animal testing methods. However, different QSAR tools often make conflicting predictions for a given chemical and may also vary in their predictive performance across different chemical datasets. In a regulatory context, conflicting predictions raise interpretation, validation and adequacy concerns. To address these concerns, ensemble learning techniques in the machine learning paradigm can be used to integrate predictions from multiple tools. By leveraging various underlying QSAR algorithms and training datasets, the resulting consensus prediction should yield better overall predictive ability. We present a novel ensemble QSAR model using Bayesian classification. The model allows for varying a cut-off parameter that allows for a selection in the desirable trade-off between model sensitivity and specificity. The predictive performance of the ensemble model is compared with four in silico tools (Toxtree, Lazar, OECD Toolbox, and Danish QSAR) to predict carcinogenicity for a dataset of air toxins (332 chemicals) and a subset of the gold carcinogenic potency database (480 chemicals). Leave-one-out cross validation results show that the ensemble model achieves the best trade-off between sensitivity and specificity (accuracy: 83.8 % and 80.4 %, and balanced accuracy: 80.6 % and 80.8 %) and highest inter-rater agreement [kappa (κ): 0.63 and 0.62] for both the datasets. The ROC curves demonstrate the utility of the cut-off feature in the predictive ability of the ensemble model. This feature provides an additional control to the regulators in grading a chemical based on the severity of the toxic endpoint under study.

    View details for DOI 10.1186/s13321-016-0164-0

    View details for Web of Science ID 000383831100001

    View details for PubMedID 28316646

    View details for PubMedCentralID PMC5034616