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Dive into the research topics where Tavi Nathanson is active.

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Featured researches published by Tavi Nathanson.


PLOS Medicine | 2017

Contribution of systemic and somatic factors to clinical response and resistance to PD-L1 blockade in urothelial cancer: An exploratory multi-omic analysis

Alexandra Snyder; Tavi Nathanson; Samuel Funt; Arun Ahuja; Jacqueline Buros Novik; Matthew D. Hellmann; Eliza Chang; Bülent Arman Aksoy; Hikmat Al-Ahmadie; Erik Yusko; Marissa Vignali; Sharon Benzeno; Mariel Elena Boyd; Meredith Maisie Moran; Gopa Iyer; Harlan Robins; Elaine R. Mardis; Taha Merghoub; Jeff Hammerbacher; Jonathan E. Rosenberg; Dean F. Bajorin

Background Inhibition of programmed death-ligand 1 (PD-L1) with atezolizumab can induce durable clinical benefit (DCB) in patients with metastatic urothelial cancers, including complete remissions in patients with chemotherapy refractory disease. Although mutation load and PD-L1 immune cell (IC) staining have been associated with response, they lack sufficient sensitivity and specificity for clinical use. Thus, there is a need to evaluate the peripheral blood immune environment and to conduct detailed analyses of mutation load, predicted neoantigens, and immune cellular infiltration in tumors to enhance our understanding of the biologic underpinnings of response and resistance. Methods and findings The goals of this study were to (1) evaluate the association of mutation load and predicted neoantigen load with therapeutic benefit and (2) determine whether intratumoral and peripheral blood T cell receptor (TCR) clonality inform clinical outcomes in urothelial carcinoma treated with atezolizumab. We hypothesized that an elevated mutation load in combination with T cell clonal dominance among intratumoral lymphocytes prior to treatment or among peripheral T cells after treatment would be associated with effective tumor control upon treatment with anti-PD-L1 therapy. We performed whole exome sequencing (WES), RNA sequencing (RNA-seq), and T cell receptor sequencing (TCR-seq) of pretreatment tumor samples as well as TCR-seq of matched, serially collected peripheral blood, collected before and after treatment with atezolizumab. These parameters were assessed for correlation with DCB (defined as progression-free survival [PFS] >6 months), PFS, and overall survival (OS), both alone and in the context of clinical and intratumoral parameters known to be predictive of survival in this disease state. Patients with DCB displayed a higher proportion of tumor-infiltrating T lymphocytes (TIL) (n = 24, Mann-Whitney p = 0.047). Pretreatment peripheral blood TCR clonality below the median was associated with improved PFS (n = 29, log-rank p = 0.048) and OS (n = 29, log-rank p = 0.011). Patients with DCB also demonstrated more substantial expansion of tumor-associated TCR clones in the peripheral blood 3 weeks after starting treatment (n = 22, Mann-Whitney p = 0.022). The combination of high pretreatment peripheral blood TCR clonality with elevated PD-L1 IC staining in tumor tissue was strongly associated with poor clinical outcomes (n = 10, hazard ratio (HR) (mean) = 89.88, HR (median) = 23.41, 95% CI [2.43, 506.94], p(HR > 1) = 0.0014). Marked variations in mutation loads were seen with different somatic variant calling methodologies, which, in turn, impacted associations with clinical outcomes. Missense mutation load, predicted neoantigen load, and expressed neoantigen load did not demonstrate significant association with DCB (n = 25, Mann-Whitney p = 0.22, n = 25, Mann-Whitney p = 0.55, and n = 25, Mann-Whitney p = 0.29, respectively). Instead, we found evidence of time-varying effects of somatic mutation load on PFS in this cohort (n = 25, p = 0.044). A limitation of our study is its small sample size (n = 29), a subset of the patients treated on IMvigor 210 (NCT02108652). Given the number of exploratory analyses performed, we intend for these results to be hypothesis-generating. Conclusions These results demonstrate the complex nature of immune response to checkpoint blockade and the compelling need for greater interrogation and data integration of both host and tumor factors. Incorporating these variables in prospective studies will facilitate identification and treatment of resistant patients.


Cancer immunology research | 2017

Somatic Mutations and Neoepitope Homology in Melanomas Treated with CTLA-4 Blockade

Tavi Nathanson; Arun Ahuja; Alexander Rubinsteyn; Bülent Arman Aksoy; Matthew D. Hellmann; Diana Miao; Eliezer M. Van Allen; Taha Merghoub; Jedd D. Wolchok; Alexandra Snyder; Jeff Hammerbacher

This is a reanalysis of data described in Snyder et al., N Eng J Med 2014;371:2189–99, that also provides an open-source tool for comparing epitopes. No predictor of response to anti–CTLA-4 therapy was more accurate than mutation burden. Immune checkpoint inhibitors are promising treatments for patients with a variety of malignancies. Toward understanding the determinants of response to immune checkpoint inhibitors, it was previously demonstrated that the presence of somatic mutations is associated with benefit from checkpoint inhibition. A hypothesis was posited that neoantigen homology to pathogens may in part explain the link between somatic mutations and response. To further examine this hypothesis, we reanalyzed cancer exome data obtained from our previously published study of 64 melanoma patients treated with CTLA-4 blockade and a new dataset of RNA-Seq data from 24 of these patients. We found that the ability to accurately predict patient benefit did not increase as the analysis narrowed from somatic mutation burden, to inclusion of only those mutations predicted to be MHC class I neoantigens, to only including those neoantigens that were expressed or that had homology to pathogens. The only association between somatic mutation burden and response was found when examining samples obtained prior to treatment. Neoantigen and expressed neoantigen burden were also associated with response, but neither was more predictive than somatic mutation burden. Neither the previously described tetrapeptide signature nor an updated method to evaluate neoepitope homology to pathogens was more predictive than mutation burden. Cancer Immunol Res; 5(1); 84–91. ©2016 AACR.


Cancer Cell | 2018

Genomic Features of Response to Combination Immunotherapy in Patients with Advanced Non-Small-Cell Lung Cancer

Matthew D. Hellmann; Tavi Nathanson; Hira Rizvi; Benjamin C. Creelan; Francisco Sanchez-Vega; Arun Ahuja; Ai Ni; Jacki B. Novik; Levi Mangarin; Mohsen Abu-Akeel; Cailian Liu; Jennifer Sauter; Natasha Rekhtman; Eliza Chang; Margaret K. Callahan; Jamie E. Chaft; Martin H. Voss; Megan Tenet; Xuemei Li; Kelly Covello; Andrea Renninger; Patrik Vitazka; William J. Geese; Hossein Borghaei; Charles M. Rudin; Scott Antonia; Charles Swanton; Jeff Hammerbacher; Taha Merghoub; Nicholas McGranahan

Summary Combination immune checkpoint blockade has demonstrated promising benefit in lung cancer, but predictors of response to combination therapy are unknown. Using whole-exome sequencing to examine non-small-cell lung cancer (NSCLC) treated with PD-1 plus CTLA-4 blockade, we found that high tumor mutation burden (TMB) predicted improved objective response, durable benefit, and progression-free survival. TMB was independent of PD-L1 expression and the strongest feature associated with efficacy in multivariable analysis. The low response rate in TMB low NSCLCs demonstrates that combination immunotherapy does not overcome the negative predictive impact of low TMB. This study demonstrates the association between TMB and benefit to combination immunotherapy in NSCLC. TMB should be incorporated in future trials examining PD-(L)1 with CTLA-4 blockade in NSCLC.


bioRxiv | 2017

Contribution of systemic and somatic factors to clinical response and resistance in urothelial cancer: an exploratory multi-omic analysis

Alexandra Snyder; Tavi Nathanson; Samuel Funt; Arun Ahuja; Jacqueline Buros Novik; Matthew D. Hellmann; Eliza Chang; Bülent Arman Aksoy; Hikmat Al-Ahmadie; Erik Yusko; Marissa Vignali; Sharon Benzeno; Mariel Elena Boyd; Meredith Maisie Moran; Gopa Iyer; Harlan Robins; Elaine R. Mardis; Taha Merghoub; Jeff Hammerbacher; Jonathan E. Rosenberg; Dean F. Bajorin

Background: Inhibition of programmed death-ligand one (PD-L1) with atezolizumab can induce durable clinical benefit (DCB) in patients with metastatic urothelial cancers, including complete remissions in patients with chemotherapy refractory disease. Although mutation load and PD-L1 immune cell (IC) staining have been associated with response, they lack sufficient sensitivity and specificity for clinical use. Thus, there is a need to evaluate the peripheral blood immune environment and to conduct detailed analyses of mutation load, predicted neoantigens and immune cellular infiltration in tumors to enhance our understanding of the biologic underpinnings of response and resistance. Methods and Findings: We performed whole exome sequencing (WES), RNA sequencing (RNA-seq), and T cell receptor sequencing (TCR-seq) of pre-treatment tumor samples as well as TCR sequencing of matched, serially collected peripheral blood pre- and post-treatment with atezolizumab. These parameters were assessed for correlation with DCB (defined as progression free survival (PFS) > 6 months) and overall survival (OS), both alone and in the context of clinical and intratumoral parameters known to be predictive of survival in this disease state. Patients with DCB displayed a higher proportion of tumor infiltrating T lymphocytes (TIL) (n=24, Mann-Whitney p=0.047). Pre-treatment peripheral blood TCR clonality below the median was associated with improved PFS (n=29, log-rank p=0.048) and OS (n=29, log-rank p=0.011). Patients with DCB also demonstrated more substantial expansion of tumor-associated TCR clones in the peripheral blood 3 weeks after starting treatment (n=22, Mann-Whitney p=0.022). The combination of high pre-treatment peripheral blood TCR clonality with elevated PD-L1 IC staining in tumor tissue was strongly associated with poor clinical outcomes (n=10, HR=86.22, 95% CI (2.55, 491.65)). Marked variations in mutation loads were seen with different somatic variant calling methodologies, which in turn impacted associations with clinical outcomes. Missense mutation load, predicted neoantigen load and expressed neoantigen load did not demonstrate significant association with DCB (n=25, Mann-Whitney p=0.22, n=25, Mann-Whitney p=0.55, and n=25, Mann-Whitney p=0.29 respectively). Instead, we found evidence of time-varying effects of somatic mutation load on progression-free survival in this cohort (n=25, p=0.044). Conclusions: These results demonstrate the complex nature of immune response to checkpoint blockade and the compelling need for greater interrogation and data integration of both host and tumor factors. Incorporating these variables in prospective studies will facilitate identification and treatment of resistant patients.Background: Inhibition of programmed death-ligand one (PD-L1) with atezolizumab can induce durable clinical benefit (DCB) in patients with metastatic urothelial cancers, including complete remissions in patients with chemotherapy refractory disease. Although mutation load and PD-L1 immune cell (IC) staining have been associated with response, they lack sufficient sensitivity and specificity for clinical use. Thus, there is a need to evaluate the peripheral blood immune environment and to conduct detailed analyses of mutation load, predicted neoantigens and immune cellular infiltration in tumors to enhance our understanding of the biologic underpinnings of response and resistance. Methods and Findings: The goals of this study were to (1) evaluate the association of mutation load and predicted neoantigen load with therapeutic benefit, and (2) determine whether intratumoral and peripheral blood T cell receptor (TCR) clonality inform clinical outcomes in urothelial carcinoma treated with atezolizumab. We hypothesized that an elevated mutation load in combination with T cell clonal dominance among intratumoral lymphocytes prior to treatment or among peripheral T cells after treatment would be associated with effective tumor control upon treatment with anti-PD-L1 therapy. We performed whole exome sequencing (WES), RNA sequencing (RNA-seq), and T cell receptor sequencing (TCR-seq) of pre-treatment tumor samples as well as TCR sequencing of matched, serially collected peripheral blood collected before and after treatment with atezolizumab. These parameters were assessed for correlation with DCB (defined as progression free survival (PFS) > 6 months), PFS, and overall survival (OS), both alone and in the context of clinical and intratumoral parameters known to be predictive of survival in this disease state. Patients with DCB displayed a higher proportion of tumor infiltrating T lymphocytes (TIL) (n=24, Mann-Whitney p=0.047). Pre-treatment peripheral blood TCR clonality below the median was associated with improved PFS (n=29, log-rank p=0.048) and OS (n=29, log-rank p=0.011). Patients with DCB also demonstrated more substantial expansion of tumor-associated TCR clones in the peripheral blood 3 weeks after starting treatment (n=22, Mann-Whitney p=0.022). The combination of high pre-treatment peripheral blood TCR clonality with elevated PD-L1 IC staining in tumor tissue was strongly associated with poor clinical outcomes (n=10, HR (mean)=89.88, HR (median)=23.41, 95% CI (2.43, 506.94), p(HR>1)=0.0014). Marked variations in mutation loads were seen with different somatic variant calling methodologies, which in turn impacted associations with clinical outcomes. Missense mutation load, predicted neoantigen load and expressed neoantigen load did not demonstrate significant association with DCB (n=25, Mann-Whitney p=0.22, n=25, Mann-Whitney p=0.55, and n=25, Mann-Whitney p=0.29 respectively). Instead, we found evidence of time-varying effects of somatic mutation load on progression-free survival in this cohort (n=25, p=0.044). A limitation of our study is its small sample size (n=29), a subset of the patients treated on IMvigor 210 (NCT02108652). Given the number of exploratory analyses performed, we intend for these results to be hypothesis-generating. Conclusions: These results demonstrate the complex nature of immune response to checkpoint blockade and the compelling need for greater interrogation and data integration of both host and tumor factors. Incorporating these variables in prospective studies will facilitate identification and treatment of resistant patients.


bioRxiv | 2017

Cycledash: a web application for the interactive analysis and exploration of variants

Isaac Hodes; Dan Vanderkam; Tavi Nathanson; B. Arman Aksoy; Jaclyn Perrone; Jeffrey Hammerbacher

As genomics begins to influence clinical care, validating the result of a somatic variant calling pipeline has become increasingly important. Cycledash is a web application which facilitates the validation and analysis of somatic variants, bringing together and streamlining existing tools and workflows for examining and verifying the existence of variants in patient samples. Availability The source code is freely available at https://github.com/hammerlab/cycledash under the Apache 2 License. A public demo (username cycledash password cycledash123) is available at http://cycledash.hammerlab.org. The web application is tested to work in recent version of Firefox, Safari, and Chrome, and the server applications will run on Linux and OS X systems. Contact [email protected]


F1000Research | 2016

Personalized neoantigen vaccination with synthetic long peptides

John P. Finnigan; Alex Rubinsteyn; Tavi Nathanson; Nicholas Akers; Nina Bhardwaj; Jeff Hammerbacher; Bojan Losic; Eric E. Schadt

§ All non-synonymous somatic variants identified via WES translated into a 40+ character long string corresponding to the amino acid sequence of the mutated genetic region. § Every string is broken into multiple 8-11 character long overlapping substrings, each of which is assessed for pMHC binding affinity, and immunogenicity using NetMHCcons. § Mutated amino acid sequences are ranked based on the sum of putative epitope scores contained in their sequence, and 31-mer vaccine peptides are chosen from the twenty highest scoring sequences via sliding window optimization. § Among all 31-mer sliding windows with equivalent mutant epitope content (candidate vaccine peptides), we select the vaccine peptide with the least number of wildtype epitopes. § The mutation in each vaccine peptide must be at least 5 residues from either the beginning or end of the peptide sequence. References


F1000Research | 2015

Neoantigen homology and predicting response to immune checkpoint blockade in cancer

Arun Ahuja; Tavi Nathanson; Alex Rubinsteyn; Alexandra Snyder; Matt Hellman; Timothy A. Chan; Taha Merghoub; Jedd D. Wolchok; Jeff Hammerbacher

● Pathogen Homology ○ Predicted neoantigens were aligned with T-cell positive peptides from IEDB of the same length, considering positions 3 through n-1 (n = length) ○ Peptide alignment was scored with the PMBEC matrix [3] and a gap penalty of min(PMBEC). For example, the following entry had a score of 1.4: Immune checkpoint inhibitors are promising cancer treatments for a variety of malignancies, but accurate prediction of clinical response remains an active area of research.


Archive | 2016

varcode v0.4.15

Alex Rubinsteyn; Tavi Nathanson; Lee-kai Wang; Tim O'Donnell; Eliza Chang; B. Arman Aksoy; Arun Ahuja


Archive | 2016

topiary: Version 0.0.15

Alex Rubinsteyn; Tim O'Donnell; Tavi Nathanson


Archive | 2016

varcode: Version 0.4.2

Alex Rubinsteyn; Tavi Nathanson; Tim O'Donnell; Eliza Chang; B. Arman Aksoy; leekaiinthesky; Arun Ahuja

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Arun Ahuja

Icahn School of Medicine at Mount Sinai

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Alex Rubinsteyn

Icahn School of Medicine at Mount Sinai

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Jeff Hammerbacher

Icahn School of Medicine at Mount Sinai

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Eliza Chang

Icahn School of Medicine at Mount Sinai

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Taha Merghoub

Memorial Sloan Kettering Cancer Center

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Alexandra Snyder

Memorial Sloan Kettering Cancer Center

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Matthew D. Hellmann

Memorial Sloan Kettering Cancer Center

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B. Arman Aksoy

Icahn School of Medicine at Mount Sinai

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Bülent Arman Aksoy

Memorial Sloan Kettering Cancer Center

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Bojan Losic

Icahn School of Medicine at Mount Sinai

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