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Evolution of Neoantigen Landscape during Immune Checkpoint Blockade in Non–Small Cell Lung Cancer

Valsamo Anagnostou, Kellie N. Smith, Patrick M. Forde, Noushin Niknafs, Rohit Bhattacharya, James White, Theresa Zhang, Vilmos Adleff, Jillian Phallen, Neha Wali, Carolyn Hruban, Violeta B. Guthrie, Kristen Rodgers, Jarushka Naidoo, Hyunseok Kang, William Sharfman, Christos Georgiades, Franco Verde, Peter Illei, Qing Kay Li, Edward Gabrielson, Malcolm V. Brock, Cynthia A. Zahnow, Stephen B. Baylin, Robert B. Scharpf, Julie R. Brahmer, Rachel Karchin, Drew M. Pardoll and Victor E. Velculescu
Valsamo Anagnostou
1The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland.
2The Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, Maryland.
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Kellie N. Smith
1The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland.
2The Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, Maryland.
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Patrick M. Forde
1The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland.
2The Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, Maryland.
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Noushin Niknafs
3Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland.
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Rohit Bhattacharya
3Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland.
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James White
1The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland.
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Theresa Zhang
4Personal Genome Diagnostics, Baltimore, Maryland.
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Vilmos Adleff
1The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland.
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Jillian Phallen
1The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland.
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Neha Wali
1The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland.
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Carolyn Hruban
1The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland.
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Violeta B. Guthrie
3Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland.
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Kristen Rodgers
5Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland.
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Jarushka Naidoo
1The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland.
2The Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, Maryland.
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Hyunseok Kang
1The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland.
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William Sharfman
1The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland.
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Christos Georgiades
6Department of Radiology and Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland.
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Franco Verde
7Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland.
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Peter Illei
1The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland.
8Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland.
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Qing Kay Li
8Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland.
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Edward Gabrielson
1The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland.
8Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland.
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Malcolm V. Brock
1The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland.
5Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland.
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Cynthia A. Zahnow
1The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland.
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Stephen B. Baylin
1The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland.
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Robert B. Scharpf
1The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland.
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Julie R. Brahmer
1The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland.
2The Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, Maryland.
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Rachel Karchin
3Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland.
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Drew M. Pardoll
1The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland.
2The Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, Maryland.
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Victor E. Velculescu
1The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland.
2The Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, Maryland.
3Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland.
8Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland.
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  • For correspondence: velculescu@jhmi.edu
DOI: 10.1158/2159-8290.CD-16-0828 Published March 2017
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    Figure 1.

    Overview of next-generation sequencing, neoantigen prediction, and functional T-cell analyses. Whole-exome sequencing was performed on the pretreatment and postprogression tumor and matched normal samples. Exome data were applied in a neoantigen prediction pipeline that evaluates antigen processing, MHC binding, and gene expression to generate neoantigens specific to the patient's HLA haplotype. Truncal neoantigens were identified by correcting for tumor purity and ploidy, and the TCR repertoire was evaluated at baseline, at the time of response, and upon emergence of resistance. Putative eliminated neoantigens at the time of resistance were used to generate peptides and stimulate autologous T cells, followed by TCR next-generation sequencing. PBL, peripheral blood lymphocyte.

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    Figure 2.

    Mutation cellularity analyses for eliminated mutations in pretreatment and postprogression tumor samples. Mutation cellularities at baseline (T1) and upon progression (T2) were estimated with the SCHISM pipeline; a cellularity of 0 was observed for 18, 10, 7, and 6 sequence alterations in resistant T2 tumors for CGLU116, CGLU117, CGLU127, and CGLU161, respectively (A). These somatic mutations were lost either by LOH or by subclonal elimination at the time of emergence of therapeutic resistance to immune checkpoint blockade. Somatic mutations in SLC26A7, PGAP1, HELB, and ANKRD12 that are associated with functionally validated neoantigens were detected in the pretreatment tumors but not in the resistant tumor or matched normal DNA; MAF denotes the mutant allele frequency (B).

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    Figure 3.

    Emergence of resistance to immune checkpoint blockade is associated with elimination of mutation-associated neoantigens by LOH and a more diverse T-cell repertoire independent of PD-L1 expression. A, CT images of patient CGLU117 at baseline, at the time of therapeutic response, and at time of acquired resistance. Pretreatment CT image of the abdomen demonstrates a right adrenal mass (T1, circled). Radiologic tumor regression is noted after 2 months of treatment, followed by disease relapse at 4 months from treatment initiation with a markedly increased right adrenal metastasis (T2, circled). Third follow-up CT demonstrates further disease progression in the adrenal lesion. Tumor burden kinetics for target lesions by RECIST criteria are shown in B. Peripheral T-cell expansion of a subset of intratumoral clones was noted to peak at the time of response and decrease to baseline levels at the time of resistance (C). Productive TCR frequency denotes the frequency of a specific rearrangement that can produce a functional protein receptor among all productive rearrangements. D and E, B allele frequency graphs for chromosome 17. A value of 0.5 indicates a heterozygous genotype, whereas allelic imbalance is observed as a deviation from 0.5. The region that undergoes LOH in the resistant tumor (E, orange box) contains three mutation-associated neoantigens that are thus eliminated. No differences in CD8+ T-cell density (F and G) or PD-L1 expression (H and I) were observed between baseline and resistant tumors.

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    Figure 4.

    Neoantigen-specific TCR expansion in stimulated T-cell cultures. Peptides generated from the eliminated mutation-associated neoantigen candidates were synthesized and used to pulse autologous peripheral T cells for patient CGLU116. T cells were stimulated with respective mutant and wild-type peptides and cultured for 10 days, followed by next-generation TCR sequencing of expanded T-cell cultures. Reactive TCR clonotypes were matched to clones found in infiltrating tumor lymphocytes. Neoantigen-specific TCR reactivity was observed for the mutant peptides associated with mutant HELB987P>S (SASPLSVV; A), SLC26A7117R>Q (ISANAVEQIV; B), and PGAP1903Y>F (AFGSAHLFR and VIAFGSAHLFR; C) compared with their wild-type counterparts. An oligoclonal TCR expansion was observed for both mutant (STPSASPLSV) and wild-type (STPSASPLPVV) peptides associated with a single-base substitution in HELB (D). Adjusted P values are given for pairwise comparisons between productive frequencies in peptide-stimulated versus unstimulated T cells. Solid bars represent mutant peptides, and bars with diagonal pattern denote wild-type peptides.

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    • Supplementary Figure Legends - Supplementary Figure Legends
    • Supplementary Figures S1 - S16 - Supplementary Figure S1. Computed Tomographic (CT) findings in patient CGLU116. Supplementary Figure S2. CT findings in patient CGLU127. Supplementary Figure S3. CT findings in patient CGLU161. Supplementary Figure S4. Tumor burden kinetics. Supplementary Figure S5. Neoantigen-specific TCR expansion in stimulated T cell cultures for patient CGLU127. Supplementary Figure S6. Neoantigen-specific TCR expansion in stimulated T cell cultures for patient CGLU161. Supplementary Figure S7. Loss of heterozygosity analyses for patient CGLU116. Supplementary Figure S8. Loss of heterozygosity analyses for patient CGLU117. Supplementary Figure S9. Loss of heterozygosity analyses for patient CGLU127. Supplementary Figure S10. Loss of heterozygosity analyses for patient CGLU161. Supplementary Figure S11. TCR clonality analyses for patients CGLU127 and CGLU161. Supplementary Figure S12. TCR clonality analyses for a responder and a non-responder to PD-1 blockade. Supplementary Figure S13. PD-L1 expression in responsive and resistant tumors. Supplementary Figure S14. Eliminated mutations for case CGHN2. Supplementary Figure S15. Comparison of five methods for estimation of tumor purity. Supplementary Figure S16. CD8+ T cell density in resistant tumors.
    • Supplementary Tables S1 - S18 - Supplementary Table S1. Summary of Patient and Sample Characteristics. Supplementary Table S2. Summary of Next-Generation Sequencing Analyses. Supplementary Table S3. Somatic Sequence Alterations*. Supplementary Table S4. Somatic Copy Number Alterations. Supplementary Table S5. Neoantigen Predictions. Supplementary Table S6. Characteristics of a Subset of Eliminated Candidate Neoantigens in the NSCLC patients*. Supplementary Table S7. Summary of Functionally Validated Eliminated, Gained, and Retained cMANAs. Supplementary Table S8. Eliminated MANA-specific T Cell Clonotypes in CGLU116, CGLU127 and CGLU161. Supplementary Table S9. Retained and Gained MANA-specific T Cell Clonotypes in CGLU116. Supplementary Table S10. Allelic Imbalance Analysis and Cellularity Estimates for CGLU116. Supplementary Table S11. Allelic Imbalance Analysis and Cellularity Estimates for CGLU117. Supplementary Table S12. Allelic Imbalance Analysis and Cellularity Estimates for CGLU127. Supplementary Table S13. Allelic Imbalance Analysis and Cellularity Estimates for CGLU161. Supplementary Table S14. Summary of Eliminated Neoantigens in NSCLC Cases. Supplementary Table S15. TCR-beta Sequencing Analysis. Supplementary Table S16. PD-L1 and CD8 Immunohistochemistry. Supplementary Table S17. Regions of Allelic Imbalance. Supplementary Table S18. Tumor Purity and Ploidy Estimates.
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Cancer Discovery: 7 (3)
March 2017
Volume 7, Issue 3
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Evolution of Neoantigen Landscape during Immune Checkpoint Blockade in Non–Small Cell Lung Cancer
Valsamo Anagnostou, Kellie N. Smith, Patrick M. Forde, Noushin Niknafs, Rohit Bhattacharya, James White, Theresa Zhang, Vilmos Adleff, Jillian Phallen, Neha Wali, Carolyn Hruban, Violeta B. Guthrie, Kristen Rodgers, Jarushka Naidoo, Hyunseok Kang, William Sharfman, Christos Georgiades, Franco Verde, Peter Illei, Qing Kay Li, Edward Gabrielson, Malcolm V. Brock, Cynthia A. Zahnow, Stephen B. Baylin, Robert B. Scharpf, Julie R. Brahmer, Rachel Karchin, Drew M. Pardoll and Victor E. Velculescu
Cancer Discov March 1 2017 (7) (3) 264-276; DOI: 10.1158/2159-8290.CD-16-0828

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Evolution of Neoantigen Landscape during Immune Checkpoint Blockade in Non–Small Cell Lung Cancer
Valsamo Anagnostou, Kellie N. Smith, Patrick M. Forde, Noushin Niknafs, Rohit Bhattacharya, James White, Theresa Zhang, Vilmos Adleff, Jillian Phallen, Neha Wali, Carolyn Hruban, Violeta B. Guthrie, Kristen Rodgers, Jarushka Naidoo, Hyunseok Kang, William Sharfman, Christos Georgiades, Franco Verde, Peter Illei, Qing Kay Li, Edward Gabrielson, Malcolm V. Brock, Cynthia A. Zahnow, Stephen B. Baylin, Robert B. Scharpf, Julie R. Brahmer, Rachel Karchin, Drew M. Pardoll and Victor E. Velculescu
Cancer Discov March 1 2017 (7) (3) 264-276; DOI: 10.1158/2159-8290.CD-16-0828
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