Publications

Selected Publications

• Uncovering Predictive Gene and Cellular Signatures for Checkpoint Immunotherapy Response through Machine Learning Analysis of Immune Single-Cell RNA-seq Data. Pinhasi, A., Yizhak, K. BioRxiv (2024)
  
• Single-cell meta-analysis of T cells reveals clonal dynamics of response to checkpoint immunotherapy. Shorer, O., Pinhasi, A., Yizhak, K. BioRxiv (2024)
  
• Metabolic predictors of response to immune checkpoint blockade therapy. Shorer, O., Yizhak, K. iScience (2023)
  
• Estimating tumor mutational burden from RNA-sequencing without a matched-normal sample. Katzir, R., Rodberg, N., Yizhak, K. Nature Communications (2022)  * Picked to feature in the Editor’s Highlights page
  
• RNA sequence analysis reveals macroscopic somatic clonal expansion across normal tissues. Yizhak, K., Aguet, F., Kim, , Hess^, J., Kubler, K., Grimsby J., Frazer, R., Zhang^, H., Haradhvala_, N., Rosebrock, D., Livitz, D., Li, X., Arich-Landkof, E., Shoresf, N., Stewart, C., Segre, A., Branton, P., Polak, P., Ardlie, K., Getz, G. Science (2019)
• Defining T cell states associated with response to checkpoint immunotherapy in melanoma. Sade-Feldman, M.^*, Yizhak, K.^*, Bjorgaard, S., Ray, J., De Boer, C., Jenkins, R., Lieb, D., Chen, J., Frederick, D., Barzily-Rokni, M., Freeman, S., Reuben, , Hoover, P., Villani, A., Ivanova, E., Portell, A., Lizotte, P., Aref, A., Eliane, J., Hammond, M., Vitzthum, H., Blackmon, S., Li, B., Gopalakrishnan, V., Reddy, S., Cooper, Z., Paweletz, C., Barbie, D., Stemmer-Rachamimov, A., Flaherty, K., Wargo, J., Boland, G., Sullivan, R., Getz, G., Hacohen, N. ^*Equal contribution. Cell (2018)
• A joint analysis of transcriptomic and metabolomic data uncovers enhanced enzyme-metabolite coupling in breast cancer. Auslander, N.,* Yizhak, K.,*, Weinstock, A., Budhu, A., Tang, W., Wang, X., Ambs, S., & Ruppin, E. ^*Equal contribution. Scientific Reports(2016)
• Diversion of aspartate in ASS1-deficient tumors fosters de novo pyrimidine synthesis. Rabinovich, L. Adler, Yizhak, K., A. Sarver, A. Silberman, S., Stettner, N., Sun, Q., Brandis, A., Helbing, D., Korman, S., Itzkovitz, S., Dimmock, D., Ulitsky, I., Nagamani, S., Ruppin, E. & Erez, A. Nature (2015)
• Modeling cancer metabolism on a genome-scale. Yizhak, K., Chaneton. B., Gottlieb E., & Ruppin E. Molecular Systems Biology (2015)
• Phenotype-based cell–specific metabolic modeling reveals metabolic liabilities in cancer. Yizhak, K., Gaude, E., Le Dévédec, S., Waldman, Y., Stein, G., van de Water, B., Frezza, C. & Ruppin, E. eLife (2014)
• A computational study of the Warburg effect identifies metabolic targets inhibiting cancer migration. Yizhak, K., Le Dévédec, S., Rogkoti, VM., Baenke, F., de Boer, VC., Schulze, A., Frezza, C., van de Water, B. & Ruppin E. Molecular Systems Biology (2014)
• Metabolically re-modeling the drug pipeline. Oberhardt, MA.^*, Yizhak, K.^*, Ruppin, E. ^*Equal contribution. Current Opinion in Pharmacology (2013)
• Model-based identification of drug targets that revert disrupted metabolism and its application to aging. Yizhak, K., Gabay, O., Cohen, H. & Ruppin, E. Nature Communications (2013)
• Metabolic modeling of endosymbiont genome reduction on a temporal scale. Yizhak, K., Tuller, T., Papp, B. & Ruppin, E. Molecular Systems Biology (2011)
• Integrating quantitative proteomics and metabolomics with a genome-scale metabolic network model. Yizhak, K., Benyamini, T., Liebermeister, W., Ruppin, E. & Shlomi, T. Bioinformatics (2010)

All Publications

• Metabolic predictors of response to immune checkpoint blockade therapy. Shorer, O., Yizhak, K. iScience (2023)

• Combined signals from tumor and immune cells predict outcomes of checkpoint inhibition in melanoma. S. Freeman, M. Sade-Feldman, J. Kim, C. Stewart, A. Gonye, A. Ravi, M. Arniella, I. Gushterova, T. LaSalle, E. Blaum, K. Yizhak…J. Wargo, K. Flaherty, G. Boland, R. Sullivan, M. Meyerson, G. Getz and N. Hacohen. Cell Reports Medicine, in press (2022).
• Estimating tumor mutational burden from RNA-sequencing without a matched-normal sample. Katzir, R., Yizhak, K. BioRxiv (2021)
• Alternative splicing of SLAMF6 in T cells creates a costimulatory isoform that counteracts the inhibitory effect of the full length receptor. Hajaj E., Zisman E., Tzaban S., Merims S., ... Sade-Feldman M., Tabach Y., Yizhak K., Navov A., Stephansky P., Hacohen N., Peretz T., Veillette A., Karni R., Eisenberg G., Lotem M. Cancer Immunology Research (2021)
  
• B cells and tertiary lymphoid structures promote immunotherapy response. Helmink, B., Reddy. S., Gao, J., Zhang, S., Basar, R., Thakur, R., Yizhak, K., Sade-Feldman, S.,… Hacohen, N., Rezvani, K., Sharma, P., Tetzlaff, M., Wang, L., Wargo, J. Nature (2020)
• PD-1 blockade in subprimed CD8 cells induces dysfunctional PD-1+ CD38 hi cells and anti-PD-1 resistance. Verma, V., Shrimali, R., Ahmad, S., Dai, W., Wang, H., Lu, S., Nandre, R., Gaur, P., Lopez, J., Sade-Feldman, M., Yizhak, K., Bjorgaard, S., Flaherty, K., Wargo, J., Boland, G., Sullivan, R., Getz, G., Hammond, S., Tan, M., Qi, J., Wong, P., Merghoub, T., Wolchok, J., Hacohen, N., Janik, J., Mkrtichyan, M., Gupta, S., Khleif, S. Nature Immunology (2019)
• RNA sequence analysis reveals macroscopic somatic clonal expansion across normal tissues. Yizhak, K., Aguet, F., Kim, , Hess^, J., Kubler, K., Grimsby J., Frazer, R., Zhang^, H., Haradhvala_, N., Rosebrock, D., Livitz, D., Li, X., Arich-Landkof, E., Shoresf, N., Stewart, C., Segre, A., Branton, P., Polak, P., Ardlie, K., Getz, G. Science (2019)
• Defining T cell states associated with response to checkpoint immunotherapy in melanoma. Sade-Feldman, M.^*, Yizhak, K.^*, Bjorgaard, S., Ray, J., De Boer, C., Jenkins, R., Lieb, D., Chen, J., Frederick, D., Barzily-Rokni, M., Freeman, S., Reuben, , Hoover, P., Villani, A., Ivanova, E., Portell, A., Lizotte, P., Aref, A., Eliane, J., Hammond, M., Vitzthum, H., Blackmon, S., Li, B., Gopalakrishnan, V., Reddy, S., Cooper, Z., Paweletz, C., Barbie, D., Stemmer-Rachamimov, A., Flaherty, K., Wargo, J., Boland, G., Sullivan, R., Getz, G., Hacohen, N. ^*Equal contribution. Cell (2018)
• Developmental and oncogenic programs in H3K27M gliomas dissected by single-cell RNA-seq. Filbin, MG., Tirosh, I., Hovestadt, V., Shaw, ML., Escalante, LE., Mathewson, ND., Neftel, C., Frank, N., Pelton, K., Hebert, CM., Haberler, C., Yizhak, K., Gojo, J., Egervari, K., Mount, C., van Galen, P., Bonal, DM., Nguyen, QD., Beck, A., Sinai, C., Czech, T., Dorfer, C., Goumnerova, L., Lavarino, C., Carcaboso, AM., Mora, J., Mylvaganam, R., Luo, CC., Peyrl, A., Popović, M., Azizi, A., Batchelor, TT., Frosch, MP., Martinez-Lage, M., Kieran, MW., Bandopadhayay, P., Beroukhim, R., Fritsch, G., Getz, G., Rozenblatt-Rosen, O., Wucherpfennig, KW., Louis, DN., Monje, M., Slavc, I., Ligon, KL., Golub, TR., Regev, A., Bernstein, BE., Suvà, ML. Science (2018)
• Single-Cell Transcriptomic Analysis of Primary and Metastatic Tumor Ecosystems in Head and Neck Cancer. Puram, SV., Tirosh, I., Parikh, AS., Patel, AP., Yizhak, K., Gillespie, S., Rodman, C., Luo, CL., Mroz, EA., Emerick, KS., Deschler, DG., Varvares, MA., Mylvaganam, R., Rozenblatt-Rosen, O., Rocco, JW., Faquin, WC., Lin, DT., Regev, A., Bernstein, BE. Cell (2017)
• An integrated computational and experimental study uncovers FUT9 as a metabolic driver of colorectal cancer. Auslander, N., Cunningham, CE., Toosi, BM., McEwen, EJ., Yizhak, K., Vizeacoumar, FS., Parameswaran, S., Gonen, N., Freywald, T., Bhanumathy, KK., Freywald, A., Vizeacoumar, FJ., Ruppin, E. Molecular Systems Biology (2017)
• Resistance to checkpoint blockade therapy through inactivation of antigen presentation. Sade-Feldman, M., Jiao, YJ., Chen, JH., Rooney, MS., Barzily-Rokni, M., Eliane, JP., Bjorgaard, SL., Hammond, MR., Vitzthum, H., Blackmon, SM., Frederick, DT., Hazar-Rethinam, M., Nadres, BA., Van Seventer, EE., Shukla, SA., Yizhak, K., Ray, JP., Rosebrock, D., Livitz, D., Adalsteinsson, V., Getz, G., Duncan, LM., Li, B., Corcoran, RB., Lawrence, DP., Stemmer-Rachamimov, A., Boland, GM., Landau, DA., Flaherty, KT., Sullivan, RJ., Hacohen, N. Nature Communications (2017)
• Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq. Venteicher, AS., Tirosh. I., Hebert, C., Yizhak, K., Neftel, C., Filbin, MG., Hovestadt, V., Escalante, LE., Shaw, ML., Rodman, C., Gillespie, SM., Dionne, D., Luo, CC., Ravichandran, H., Mylvaganam, R., Mount, C., Onozato, ML., Nahed, BV., Wakimoto, H., Curry, WT., Iafrate, AJ., Rivera, MN., Frosch, MP., Golub, TR., Brastianos, PK., Getz, G., Patel, AP., Monje, M., Cahill, DP., Rozenblatt-Rosen, O., Louis, DN., Bernstein, BE., Regev, A., Suvà, ML. Science (2017)
• Essential genes embody increased mutational robustness to compensate for the lack of backup genetic redundancy. Cohen, O., Oberhardt, M., Yizhak, K., Ruppin, E. PLoS One (2016)
• Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma. Tirosh, I., Venteicher, A., Hebert, C., Escalante, L., Patel, A., Yizhak, K., Fisher, J., Rodman, C., Mount, C., Filbin, M., Neftel, C., Desai, N., Nyman, J., Izar, B., Luo, C., Francis, J., Patel, A., Onozato, M., Riggi, N., Livak, K., Gennert, D., Nahed, B., Curry, W., Martuza, R., Mylvaganam, R., Lafrate, A., Frosch, M., Golub, T., River, M., Getz, G., Rosen, O., Cahill,D., Monje, M., Bernstein, B., Louis, D., Regev, A., Suva, M. Nature (2016)
• A joint analysis of transcriptomic and metabolomic data uncovers enhanced enzyme-metabolite coupling in breast cancer. Auslander, N.,* Yizhak, K.,*, Weinstock, A., Budhu, A., Tang, W., Wang, X., Ambs, S., & Ruppin, E. ^*Equal contribution. Scientific Reports(2016)
• Diversion of aspartate in ASS1-deficient tumors fosters de novo pyrimidine synthesis. Rabinovich, L. Adler, Yizhak, K., A. Sarver, A. Silberman, S., Stettner, N., Sun, Q., Brandis, A., Helbing, D., Korman, S., Itzkovitz, S., Dimmock, D., Ulitsky, I., Nagamani, S., Ruppin, E. & Erez, A. Nature (2015)
• Modeling cancer metabolism on a genome-scale. Yizhak, K., Chaneton. B., Gottlieb E., & Ruppin E. Molecular Systems Biology (2015)
• Phenotype-based cell–specific metabolic modeling reveals metabolic liabilities in cancer. Yizhak, K., Gaude, E., Le Dévédec, S., Waldman, Y., Stein, G., van de Water, B., Frezza, C. & Ruppin, E. eLife (2014)
• Integrating transcriptomics with metabolic modeling predicts biomarkers and drug targets for Alzheimer’s disease. Stempler, S., Yizhak, K., & Ruppin, E. PLoS One (2014)
• A computational study of the Warburg effect identifies metabolic targets inhibiting cancer migration. Yizhak, K., Le Dévédec, S., Rogkoti, VM., Baenke, F., de Boer, VC., Schulze, A., Frezza, C., van de Water, B. & Ruppin E. Molecular Systems Biology (2014)
• Maximal sum of metabolic exchanges predicts cellular growth rate. Zarecki, R., Oberhardt, M., Yizhak, K., Wagner, A., Segal, E., Freilich, S., Henry, C., Gophna, U. & Ruppin, E. PLoS One (2014)
• Metabolically re-modeling the drug pipeline. Oberhardt, MA.^*, Yizhak, K.^*, Ruppin, E. ^*Equal contribution. Current Opinion in Pharmacology (2013)
• Model-based identification of drug targets that revert disrupted metabolism and its application to aging. Yizhak, K., Gabay, O., Cohen, H. & Ruppin, E. Nature Communications (2013)
• p53 promotes the expression of gluconeogenesis-related genes and enhances hepatic glucose production. Goldstein, I., Yizhak, K., Madar, S., Ruppin, E. & Rotter, V. Cancer & Metabolism (2013)
• Metabolic modeling of endosymbiont genome reduction on a temporal scale. Yizhak, K., Tuller, T., Papp, B. & Ruppin, E. Molecular Systems Biology (2011)
• Integrating quantitative proteomics and metabolomics with a genome-scale metabolic network model. Yizhak, K., Benyamini, T., Liebermeister, W., Ruppin, E. & Shlomi, T. Bioinformatics (2010)