Response and recurrence correlates in individuals treated with neoadjuvant anti-PD-1 therapy for resectable oral cavity squamous cell carcinoma

Sixue Liu, Hannah M Knochelmann, Shirley H Lomeli, Aayoung Hong, Mary Richardson, Zhentao Yang, Raymond J Lim, Yan Wang, Camelia Dumitras, Kostyantyn Krysan, Cynthia Timmers, Martin J Romeo, Carsten Krieg, Elizabeth C O'Quinn, Joshua D Horton, Steve M Dubinett, Chrystal M Paulos, David M Neskey, Roger S Lo, Sixue Liu, Hannah M Knochelmann, Shirley H Lomeli, Aayoung Hong, Mary Richardson, Zhentao Yang, Raymond J Lim, Yan Wang, Camelia Dumitras, Kostyantyn Krysan, Cynthia Timmers, Martin J Romeo, Carsten Krieg, Elizabeth C O'Quinn, Joshua D Horton, Steve M Dubinett, Chrystal M Paulos, David M Neskey, Roger S Lo

Abstract

Neoadjuvant PD-1 blockade may be efficacious in some individuals with high-risk, resectable oral cavity head and neck cancer. To explore correlates of response patterns to neoadjuvant nivolumab treatment and post-surgical recurrences, we analyzed longitudinal tumor and blood samples in a cohort of 12 individuals displaying 33% responsiveness. Pretreatment tumor-based detection of FLT4 mutations and PTEN signature enrichment favors response, and high tumor mutational burden improves recurrence-free survival. In contrast, preexisting and/or acquired mutations (in CDKN2A, YAP1, or JAK2) correlate with innate resistance and/or tumor recurrence. Immunologically, tumor response after therapy entails T cell receptor repertoire diversification in peripheral blood and intratumoral expansion of preexisting T cell clones. A high ratio of regulatory T to T helper 17 cells in pretreatment blood predicts low T cell receptor repertoire diversity in pretreatment blood, a low cytolytic T cell signature in pretreatment tumors, and innate resistance. Our study provides a molecular framework to advance neoadjuvant anti-PD-1 therapy for individuals with resectable head and neck cancer.

Trial registration: ClinicalTrials.gov NCT03021993.

Keywords: FLT4/PTEN/PPARG/CDKN2A/YAP1/JAK2; T cell repertoire; T regulatory to Th17 ratio; head-and-neck cancer/oral-cavity SCC; multiplex immunofluorescence; neoadjuvant anti-PD-1/L1 therapy; recurrence-free survival; response, resistance, and recurrence; tumor mutational burden; tumor phylogeny.

Conflict of interest statement

R.S.L. receives research or clinical trial support from Merck, Pfizer, BMS, and OncoSec. C.M.P. is a co-founder of Ares Immunotherapy.

© 2021 The Author(s).

Figures

Graphical abstract
Graphical abstract
Figure 1
Figure 1
Genomic correlates of innate tumor sensitivity versus resistance and survival in pretreatment tumors (A) TMBs in responders (n = 7) versus non-responders (n = 5); p value, Wilcoxon rank-sum test. Red dots, median values. (B and C) Kaplan-Meier curves of RFS (B) and OS (C) comparing tumors with high TMB (≥ median TMB, n = 6) versus tumors with a low TMB (CDKN2A detected in one responder and three non-responders. The CN of CDKN2A is labeled on top. (F) Infiltration levels of CD8+ T, TREG, and resting NK cells in FLT4WT (n = 508) versus FLT4Mut (n = 14) clinical HNSCC tumors from a public dataset in cBioPortal; p values, Wilcoxon rank-sum test. ∗p < 0.05, ∗∗∗p < 0.001. See also Figures S1 and S2, Tables S1–S4, and STAR Methods.
Figure 2
Figure 2
Evolution of post-operative recurrent tumors (A) Phylogenetic relationships of subject-specific normal tissue, pretreatment, and recurrent tumors in two responders (individuals 1 and 6) and one non-responder (individual 7). Phylogenetic distances between germline gDNA, most recent common tumor ancestor, pretreatment tumor, and recurrent tumor(s) reflect the number of SNVs and small indels. Select driver genes and their mutations are shown for each evolutionary trajectory. (B) Expression levels of PTEN and JAK2 in pretreatment and recurrent tumors of individual 1. (C) Representative immunofluorescent images merging (1) DAPI (nuclei), pan-cytokeratin (panCK), and PTEN or JAK2 signals from post-treatment and recurrent tumors (individual 1); (2) DAPI (nuclei), panCK, and YAP1 or MDM2 signals from post-treatment and two recurrent tumors (individual 6); and (3) DAPI (nuclei), panCK, and YAP1 signals from post-treatment and recurrent tumors of individual 7 as well as post-treatment tumors (controls) of individuals 9 and 10. Scale bars represent 50 microns, except for MDM2 images (20 μm). (D) Quantification of mIF across whole tissue sections comparing post-treatment versus recurrent tumors in individuals 1, 6, and 7. (E) Images representative of mIF quantifications in (D). Scale bar, 50 μm. See also Figure S1 and Tables S1–S4.
Figure 3
Figure 3
Transcriptomic features of response in pre- and post-treatment tumors (A) Heatmap showing the top gene sets differentially enriched in responsive versus non-responsive pretreatment tumors (n = 11; one pretreatment tumor was excluded because of RNA degradation of its matched post-treatment tumor). (B) Pearson correlation of enrichment scores between PTEN_DN and PPARG signatures in pretreatment tumors (n = 11). (C) Heatmap showing top gene sets differentially enriched in responsive versus non-responsive post-treatment tumors (n = 11). See also Figure S3 and Tables S2 and S3.
Figure 4
Figure 4
Post-treatment elevation in systemic TCR diversity and tumoral TCR clonality reflects responsiveness (A) Gini indices of TCRβ clones in tumors (left) and PBMCs (right) before or after neoadjuvant nivolumab treatment (red dots, average values; n = 3 per group). Pairwise comparisons by Student’s t test, ∗p 

Figure 5

Elevated ratio of T REG…

Figure 5

Elevated ratio of T REG to Th17 cells in peripheral blood as a…

Figure 5
Elevated ratio of TREG to Th17 cells in peripheral blood as a pretreatment marker of non-response (A) t-distribution stochastic neighbor embedding (t-SNE) map of live cell clusters and immune subpopulations in pre- and post-treatment PBMCs analyzed by CyTOF (n = 5 responders, n = 4 non-responders, n = 4 healthy donors). (B) Heatmap showing the expression values of immune phenotypic protein markers normalized to the maximum mean value across subpopulations. (C) Frequencies of CD4+ T cell subpopulations in the total T cell population in responders versus non-responders before or after neoadjuvant nivolumab therapy. p value, Student’s t test; ∗∗p < 0.01. (D) Ratios of frequencies of TREG versus Th17 cells. p value, Student’s t test; ∗p < 0.05. (E) Pearson correlations of the pretreatment PBMC TREG/Th17 cell ratios with pretreatment intratumoral levels of CD8+ T cells, cytolytic activity signature enrichment, effector T cell signature enrichment, IFNG-6 genes signature enrichment, PD-L1 expression, and Gini indices of TCRβ clonotypes in pretreatment PBMCs or post-treatment tumors. See also Figure S5 and Tables S2 and S3.
Figure 5
Figure 5
Elevated ratio of TREG to Th17 cells in peripheral blood as a pretreatment marker of non-response (A) t-distribution stochastic neighbor embedding (t-SNE) map of live cell clusters and immune subpopulations in pre- and post-treatment PBMCs analyzed by CyTOF (n = 5 responders, n = 4 non-responders, n = 4 healthy donors). (B) Heatmap showing the expression values of immune phenotypic protein markers normalized to the maximum mean value across subpopulations. (C) Frequencies of CD4+ T cell subpopulations in the total T cell population in responders versus non-responders before or after neoadjuvant nivolumab therapy. p value, Student’s t test; ∗∗p < 0.01. (D) Ratios of frequencies of TREG versus Th17 cells. p value, Student’s t test; ∗p < 0.05. (E) Pearson correlations of the pretreatment PBMC TREG/Th17 cell ratios with pretreatment intratumoral levels of CD8+ T cells, cytolytic activity signature enrichment, effector T cell signature enrichment, IFNG-6 genes signature enrichment, PD-L1 expression, and Gini indices of TCRβ clonotypes in pretreatment PBMCs or post-treatment tumors. See also Figure S5 and Tables S2 and S3.

References

    1. Topalian S.L., Hodi F.S., Brahmer J.R., Gettinger S.N., Smith D.C., McDermott D.F., Powderly J.D., Carvajal R.D., Sosman J.A., Atkins M.B. Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. N. Engl. J. Med. 2012;366:2443–2454.
    1. Liu J., Blake S.J., Yong M.C., Harjunpää H., Ngiow S.F., Takeda K., Young A., O’Donnell J.S., Allen S., Smyth M.J., Teng M.W. Improved Efficacy of Neoadjuvant Compared to Adjuvant Immunotherapy to Eradicate Metastatic Disease. Cancer Discov. 2016;6:1382–1399.
    1. Blank C.U., Rozeman E.A., Fanchi L.F., Sikorska K., van de Wiel B., Kvistborg P., Krijgsman O., van den Braber M., Philips D., Broeks A. Neoadjuvant versus adjuvant ipilimumab plus nivolumab in macroscopic stage III melanoma. Nat. Med. 2018;24:1655–1661.
    1. Bray F., Ferlay J., Soerjomataram I., Siegel R.L., Torre L.A., Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 2018;68:394–424.
    1. Leemans C.R., Snijders P.J.F., Brakenhoff R.H. The molecular landscape of head and neck cancer. Nat. Rev. Cancer. 2018;18:269–282.
    1. Cooper J.S., Pajak T.F., Forastiere A.A., Jacobs J., Campbell B.H., Saxman S.B., Kish J.A., Kim H.E., Cmelak A.J., Rotman M., Radiation Therapy Oncology Group 9501/Intergroup Postoperative concurrent radiotherapy and chemotherapy for high-risk squamous-cell carcinoma of the head and neck. N. Engl. J. Med. 2004;350:1937–1944.
    1. Cancer Genome Atlas N., Cancer Genome Atlas Network Comprehensive genomic characterization of head and neck squamous cell carcinomas. Nature. 2015;517:576–582.
    1. Burtness B., Harrington K.J., Greil R., Soulières D., Tahara M., de Castro G., Jr., Psyrri A., Basté N., Neupane P., Bratland Å., KEYNOTE-048 Investigators Pembrolizumab alone or with chemotherapy versus cetuximab with chemotherapy for recurrent or metastatic squamous cell carcinoma of the head and neck (KEYNOTE-048): a randomised, open-label, phase 3 study. Lancet. 2019;394:1915–1928.
    1. Ferris R.L., Blumenschein G., Jr., Fayette J., Guigay J., Colevas A.D., Licitra L., Harrington K., Kasper S., Vokes E.E., Even C. Nivolumab for Recurrent Squamous-Cell Carcinoma of the Head and Neck. N. Engl. J. Med. 2016;375:1856–1867.
    1. Seiwert T.Y., Burtness B., Mehra R., Weiss J., Berger R., Eder J.P., Heath K., McClanahan T., Lunceford J., Gause C. Safety and clinical activity of pembrolizumab for treatment of recurrent or metastatic squamous cell carcinoma of the head and neck (KEYNOTE-012): an open-label, multicentre, phase 1b trial. Lancet Oncol. 2016;17:956–965.
    1. Schoenfeld J.D., Hanna G.J., Jo V.Y., Rawal B., Chen Y.H., Catalano P.S., Lako A., Ciantra Z., Weirather J.L., Criscitiello S. Neoadjuvant Nivolumab or Nivolumab Plus Ipilimumab in Untreated Oral Cavity Squamous Cell Carcinoma: A Phase 2 Open-Label Randomized Clinical Trial. JAMA Oncol. 2020;6:1563–1570.
    1. Uppaluri R., Campbell K.M., Egloff A.M., Zolkind P., Skidmore Z.L., Nussenbaum B., Paniello R.C., Rich J.T., Jackson R., Pipkorn P. Neoadjuvant and Adjuvant Pembrolizumab in Resectable Locally Advanced, Human Papillomavirus-Unrelated Head and Neck Cancer: A Multicenter, Phase II Trial. Clin. Cancer Res. 2020;26:5140–5152.
    1. Knochelmann H.M., Horton J.D., Liu S., Armeson K., Kaczmar J.M., Wyatt M., Richardson M., Lomeli S.H., Xiong Y., Graboyes E.M. Neoadjuvant presurgical PD-1 inhibition in oral cavity squamous cell carcinoma. Cell Reports Medicine. 2021;2:100426-1–100426-10.
    1. Chowell D., Morris L.G.T., Grigg C.M., Weber J.K., Samstein R.M., Makarov V., Kuo F., Kendall S.M., Requena D., Riaz N. Patient HLA class I genotype influences cancer response to checkpoint blockade immunotherapy. Science. 2018;359:582–587.
    1. Stransky N., Egloff A.M., Tward A.D., Kostic A.D., Cibulskis K., Sivachenko A., Kryukov G.V., Lawrence M.S., Sougnez C., McKenna A. The mutational landscape of head and neck squamous cell carcinoma. Science. 2011;333:1157–1160.
    1. Shukla S.A., Rooney M.S., Rajasagi M., Tiao G., Dixon P.M., Lawrence M.S., Stevens J., Lane W.J., Dellagatta J.L., Steelman S. Comprehensive analysis of cancer-associated somatic mutations in class I HLA genes. Nat. Biotechnol. 2015;33:1152–1158.
    1. George S., Miao D., Demetri G.D., Adeegbe D., Rodig S.J., Shukla S., Lipschitz M., Amin-Mansour A., Raut C.P., Carter S.L. Loss of PTEN Is Associated with Resistance to Anti-PD-1 Checkpoint Blockade Therapy in Metastatic Uterine Leiomyosarcoma. Immunity. 2017;46:197–204.
    1. Peng W., Chen J.Q., Liu C., Malu S., Creasy C., Tetzlaff M.T., Xu C., McKenzie J.A., Zhang C., Liang X. Loss of PTEN promotes resistance to T cell-mediated immunotherapy. Cancer Discov. 2016;6:202–216.
    1. Zhao J., Chen A.X., Gartrell R.D., Silverman A.M., Aparicio L., Chu T., Bordbar D., Shan D., Samanamud J., Mahajan A. Immune and genomic correlates of response to anti-PD-1 immunotherapy in glioblastoma. Nat. Med. 2019;25:462–469.
    1. Hugo W., Shi H., Sun L., Piva M., Song C., Kong X., Moriceau G., Hong A., Dahlman K.B., Johnson D.B. Non-genomic and Immune Evolution of Melanoma Acquiring MAPKi Resistance. Cell. 2015;162:1271–1285.
    1. Kim M.H., Kim C.G., Kim S.K., Shin S.J., Choe E.A., Park S.H., Shin E.C., Kim J. YAP-Induced PD-L1 Expression Drives Immune Evasion in BRAFi-Resistant Melanoma. Cancer Immunol. Res. 2018;6:255–266.
    1. Yu M., Peng Z., Qin M., Liu Y., Wang J., Zhang C., Lin J., Dong T., Wang L., Li S. Interferon-γ induces tumor resistance to anti-PD-1 immunotherapy by promoting YAP phase separation. Mol. Cell. 2021;81:1216–1230.e9.
    1. Kato S., Goodman A., Walavalkar V., Barkauskas D.A., Sharabi A., Kurzrock R. Hyperprogressors after Immunotherapy: Analysis of Genomic Alterations Associated with Accelerated Growth Rate. Clin. Cancer Res. 2017;23:4242–4250.
    1. Fang D.D., Tang Q., Kong Y., Wang Q., Gu J., Fang X., Zou P., Rong T., Wang J., Yang D., Zhai Y. MDM2 inhibitor APG-115 synergizes with PD-1 blockade through enhancing antitumor immunity in the tumor microenvironment. J. Immunother. Cancer. 2019;7:327.
    1. Sahin I., Zhang S., Navaraj A., Zhou L., Dizon D., Safran H., El-Deiry W.S. AMG-232 sensitizes high MDM2-expressing tumor cells to T-cell-mediated killing. Cell Death Discov. 2020;6:57.
    1. Korpal M., Puyang X., Jeremy Wu Z., Seiler R., Furman C., Oo H.Z., Seiler M., Irwin S., Subramanian V., Julie Joshi J. Evasion of immunosurveillance by genomic alterations of PPARγ/RXRα in bladder cancer. Nat. Commun. 2017;8:103.
    1. Bonofiglio D., Gabriele S., Aquila S., Catalano S., Gentile M., Middea E., Giordano F., Andò S. Estrogen receptor alpha binds to peroxisome proliferator-activated receptor response element and negatively interferes with peroxisome proliferator-activated receptor gamma signaling in breast cancer cells. Clin. Cancer Res. 2005;11:6139–6147.
    1. Patel L., Pass I., Coxon P., Downes C.P., Smith S.A., Macphee C.H. Tumor suppressor and anti-inflammatory actions of PPARgamma agonists are mediated via upregulation of PTEN. Curr. Biol. 2001;11:764–768.
    1. Iizuka N., Oka M., Yamada-Okabe H., Mori N., Tamesa T., Okada T., Takemoto N., Sakamoto K., Hamada K., Ishitsuka H. Self-organizing-map-based molecular signature representing the development of hepatocellular carcinoma. FEBS Lett. 2005;579:1089–1100.
    1. Iliopoulos D., Hirsch H.A., Wang G., Struhl K. Inducible formation of breast cancer stem cells and their dynamic equilibrium with non-stem cancer cells via IL6 secretion. Proc. Natl. Acad. Sci. USA. 2011;108:1397–1402.
    1. Bolen C.R., McCord R., Huet S., Frampton G.M., Bourgon R., Jardin F., Dartigues P., Punnoose E.A., Szafer-Glusman E., Xerri L. Mutation load and an effector T-cell gene signature may distinguish immunologically distinct and clinically relevant lymphoma subsets. Blood Adv. 2017;1:1884–1890.
    1. Ayers M., Lunceford J., Nebozhyn M., Murphy E., Loboda A., Kaufman D.R., Albright A., Cheng J.D., Kang S.P., Shankaran V. IFN-γ-related mRNA profile predicts clinical response to PD-1 blockade. J. Clin. Invest. 2017;127:2930–2940.
    1. Johnson B.J., Costelloe E.O., Fitzpatrick D.R., Haanen J.B., Schumacher T.N., Brown L.E., Kelso A. Single-cell perforin and granzyme expression reveals the anatomical localization of effector CD8+ T cells in influenza virus-infected mice. Proc. Natl. Acad. Sci. USA. 2003;100:2657–2662.
    1. Kagamu H., Kitano S., Yamaguchi O., Yoshimura K., Horimoto K., Kitazawa M., Fukui K., Shiono A., Mouri A., Nishihara F. CD4(+) T-cell Immunity in the Peripheral Blood Correlates with Response to Anti-PD-1 Therapy. Cancer Immunol. Res. 2020;8:334–344.
    1. Hugo W., Zaretsky J.M., Sun L., Song C., Moreno B.H., Hu-Lieskovan S., Berent-Maoz B., Pang J., Chmielowski B., Cherry G. Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma. Cell. 2016;165:35–44.
    1. Rizvi N.A., Hellmann M.D., Snyder A., Kvistborg P., Makarov V., Havel J.J., Lee W., Yuan J., Wong P., Ho T.S. Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science. 2015;348:124–128.
    1. Tumeh P.C., Harview C.L., Yearley J.H., Shintaku I.P., Taylor E.J., Robert L., Chmielowski B., Spasic M., Henry G., Ciobanu V. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature. 2014;515:568–571.
    1. Yu J., Yan J., Guo Q., Chi Z., Tang B., Zheng B., Yu J., Yin T., Cheng Z., Wu X. Genetic Aberrations in the CDK4 Pathway Are Associated with Innate Resistance to PD-1 Blockade in Chinese Patients with Non-Cutaneous Melanoma. Clin. Cancer Res. 2019;25:6511–6523.
    1. Brenner E., Schörg B.F., Ahmetlić F., Wieder T., Hilke F.J., Simon N., Schroeder C., Demidov G., Riedel T., Fehrenbacher B. Cancer immune control needs senescence induction by interferon-dependent cell cycle regulator pathways in tumours. Nat. Commun. 2020;11:1335.
    1. Mlecnik B., Bindea G., Kirilovsky A., Angell H.K., Obenauf A.C., Tosolini M., Church S.E., Maby P., Vasaturo A., Angelova M. The tumor microenvironment and Immunoscore are critical determinants of dissemination to distant metastasis. Sci. Transl. Med. 2016;8:327ra26.
    1. Cancer Genome Atlas Network Genomic Classification of Cutaneous Melanoma. Cell. 2015;161:1681–1696.
    1. Lund A.W., Wagner M., Fankhauser M., Steinskog E.S., Broggi M.A., Spranger S., Gajewski T.F., Alitalo K., Eikesdal H.P., Wiig H., Swartz M.A. Lymphatic vessels regulate immune microenvironments in human and murine melanoma. J. Clin. Invest. 2016;126:3389–3402.
    1. Korbecki J., Bobiński R., Dutka M. Self-regulation of the inflammatory response by peroxisome proliferator-activated receptors. Inflamm. Res. 2019;68:443–458.
    1. Hogan S.A., Courtier A., Cheng P.F., Jaberg-Bentele N.F., Goldinger S.M., Manuel M., Perez S., Plantier N., Mouret J.F., Nguyen-Kim T.D.L. Peripheral Blood TCR Repertoire Profiling May Facilitate Patient Stratification for Immunotherapy against Melanoma. Cancer Immunol. Res. 2019;7:77–85.
    1. Postow M.A., Manuel M., Wong P., Yuan J., Dong Z., Liu C., Perez S., Tanneau I., Noel M., Courtier A. Peripheral T cell receptor diversity is associated with clinical outcomes following ipilimumab treatment in metastatic melanoma. J. Immunother. Cancer. 2015;3:23.
    1. Naidus E., Bouquet J., Oh D.Y., Looney T.J., Yang H., Fong L., Standifer N.E., Zhang L. Early changes in the circulating T cells are associated with clinical outcomes after PD-L1 blockade by durvalumab in advanced NSCLC patients. Cancer Immunol. Immunother. 2021;70:2095–2102.
    1. Tan C.L., Kuchroo J.R., Sage P.T., Liang D., Francisco L.M., Buck J., Thaker Y.R., Zhang Q., McArdel S.L., Juneja V.R. PD-1 restraint of regulatory T cell suppressive activity is critical for immune tolerance. J. Exp. Med. 2021;218:e20182232.
    1. Forde P.M., Chaft J.E., Pardoll D.M. Neoadjuvant PD-1 Blockade in Resectable Lung Cancer. N. Engl. J. Med. 2018;379:e14.
    1. Hong A., Piva M., Liu S., Hugo W., Lomeli S.H., Zoete V., Randolph C.E., Yang Z., Wang Y., Lee J.J. Durable Suppression of Acquired MEK Inhibitor Resistance in Cancer by Sequestering MEK from ERK and Promoting Antitumor T-cell Immunity. Cancer Discov. 2021;11:714–735.
    1. Moriceau G., Hugo W., Hong A., Shi H., Kong X., Yu C.C., Koya R.C., Samatar A.A., Khanlou N., Braun J. Tunable-combinatorial mechanisms of acquired resistance limit the efficacy of BRAF/MEK cotargeting but result in melanoma drug addiction. Cancer Cell. 2015;27:240–256.
    1. Shi H., Hugo W., Kong X., Hong A., Koya R.C., Moriceau G., Chodon T., Guo R., Johnson D.B., Dahlman K.B. Acquired Resistance and Clonal Evolution in Melanoma during BRAF Inhibitor Therapy. Cancer Discov. 2014;4:80–93.
    1. Shi H., Moriceau G., Kong X., Lee M.K., Lee H., Koya R.C., Ng C., Chodon T., Scolyer R.A., Dahlman K.B. Melanoma whole-exome sequencing identifies (V600E)B-RAF amplification-mediated acquired B-RAF inhibitor resistance. Nat. Commun. 2012;3:724.
    1. Ramos A.H., Lichtenstein L., Gupta M., Lawrence M.S., Pugh T.J., Saksena G., Meyerson M., Getz G. Oncotator: cancer variant annotation tool. Hum. Mutat. 2015;36:E2423–E2429.
    1. Favero F., Joshi T., Marquard A.M., Birkbak N.J., Krzystanek M., Li Q., Szallasi Z., Eklund A.C. Sequenza: allele-specific copy number and mutation profiles from tumor sequencing data. Ann. Oncol. 2015;26:64–70.
    1. Kim D., Langmead B., Salzberg S.L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods. 2015;12:357–360.
    1. Anders S., Pyl P.T., Huber W. HTSeq–a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31:166–169.
    1. Hänzelmann S., Castelo R., Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics. 2013;14:7.
    1. Newman A.M., Steen C.B., Liu C.L., Gentles A.J., Chaudhuri A.A., Scherer F., Khodadoust M.S., Esfahani M.S., Luca B.A., Steiner D. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat. Biotechnol. 2019;37:773–782.
    1. Kotecha N., Krutzik P.O., Irish J.M. Web-based analysis and publication of flow cytometry experiments. Curr. Protoc. Cytom. 2010 Chapter 10, Unit 10.17.
    1. Chen H., Lau M.C., Wong M.T., Newell E.W., Poidinger M., Chen J. Cytofkit: A Bioconductor Package for an Integrated Mass Cytometry Data Analysis Pipeline. PLoS Comput. Biol. 2016;12:e1005112.
    1. Nazarov V.I., Pogorelyy M.V., Komech E.A., Zvyagin I.V., Bolotin D.A., Shugay M., Chudakov D.M., Lebedev Y.B., Mamedov I.Z. tcR: an R package for T cell receptor repertoire advanced data analysis. BMC Bioinformatics. 2015;16:175.

Source: PubMed

Sponsoren und Mitarbeiter

Krankheiten

Drogeninterventionen

3
Abonnieren