Biomarker correlates with response to NY-ESO-1 TCR T cells in patients with synovial sarcoma

Alexandra Gyurdieva, Stefan Zajic, Ya-Fang Chang, E Andres Houseman, Shan Zhong, Jaegil Kim, Michael Nathenson, Thomas Faitg, Mary Woessner, David C Turner, Aisha N Hasan, John Glod, Rosandra N Kaplan, Sandra P D'Angelo, Dejka M Araujo, Warren A Chow, Mihaela Druta, George D Demetri, Brian A Van Tine, Stephan A Grupp, Gregg D Fine, Ioanna Eleftheriadou, Alexandra Gyurdieva, Stefan Zajic, Ya-Fang Chang, E Andres Houseman, Shan Zhong, Jaegil Kim, Michael Nathenson, Thomas Faitg, Mary Woessner, David C Turner, Aisha N Hasan, John Glod, Rosandra N Kaplan, Sandra P D'Angelo, Dejka M Araujo, Warren A Chow, Mihaela Druta, George D Demetri, Brian A Van Tine, Stephan A Grupp, Gregg D Fine, Ioanna Eleftheriadou

Abstract

Autologous T cells transduced to express a high affinity T-cell receptor specific to NY-ESO-1 (letetresgene autoleucel, lete-cel) show promise in the treatment of metastatic synovial sarcoma, with 50% overall response rate. The efficacy of lete-cel treatment in 45 synovial sarcoma patients (NCT01343043) has been previously reported, however, biomarkers predictive of response and resistance remain to be better defined. This post-hoc analysis identifies associations of response to lete-cel with lymphodepleting chemotherapy regimen (LDR), product attributes, cell expansion, cytokines, and tumor gene expression. Responders have higher IL-15 levels pre-infusion (p = 0.011) and receive a higher number of transduced effector memory (CD45RA- CCR7-) CD8 + cells per kg (p = 0.039). Post-infusion, responders have increased IFNγ, IL-6, and peak cell expansion (p < 0.01, p < 0.01, and p = 0.016, respectively). Analysis of tumor samples post-treatment illustrates lete-cel infiltration and a decrease in expression of macrophage genes, suggesting remodeling of the tumor microenvironment. Here we report potential predictive and pharmacodynamic markers of lete-cel response that may inform LDR, cell dose, and strategies to enhance anticancer efficacy.

Conflict of interest statement

A.G. is an employee of and holds stocks/shares in GSK and holds stocks/shares in Amgen. St.Z., E.A.H., J.K., M.N., T.F., M.W., D.C.T. and I.E. are employees of and hold stocks/shares in GSK. Y.F.C. is an employee of GSK. Sh.Z. is a former employee of and holds stocks/shares in GS.K. A.N.H. is a former employee of and holds stocks/shares in GSK and receives patents and royalties from Atara Biotherapeutics. J.G. is protocol investigator for SARC and PBTC sponsored studies. R.N.K. and D.M.A. have no disclosures. S.P.D.A. participated on advisory boards for GSK, Nektar, Adaptimmune, and Merck; reports consulting fees from EMD Serono, Amgen, Nektar, Immune design, GSK, Incyte, Merck, Adaptimmune, and Immunocore, and received support for travel expense by Adaptimmune, EMD Serono, and Nektar. WAC reports grant and honoraria received from GSK. M.D. has received consulting fees from Adaptimmune and honoraria from Deciphera. GDD reports leadership roles with Blueprint Medicines, Merrimack Pharmaceuticals (ended Oct 2019), and Translate Bio (ended Sept 2021); stocks/options/shares in Blueprint Medicines, G1 Therapeutics, Caris Life Sciences, Erasca Pharmaceuticals, RELAY Therapeutics, Bessor Pharmaceuticals, CellCarta, IKENA Oncology, Kojin Therapeutics, and IDRX; paid consulting fees from Bayer, Pfizer, Novartis, Roche/Genentech, GSK, PharmaMar, Daiichi Sankyo, GSK, EMD-Serono, MEDSCAPE, Mirati, WCG/Arsenal Capital, MJ Hennessey/OncLive, C4 Therapeutics, Synlogic, McCann Health, G1 Therapeutics, Caris Life Sciences, RELAY Therapeutics, CellCarta, IKENA Oncology, Kojin Therapeutics and IDRX; royalties, patents or licenses from Novartis via Dana Farber for “use patent” of imatinib in GIST; and non-financial interests in AACR Science Policy and Government Affairs Committee and Alexandria Real Estate Equities summit conference series. BAVT received consulting fees from ADRx, Ayala Pharmaceuticals, Cytokinetics Inc, and Bayer; has honoraria from Adaptimmune Ltd, GSK, Bionest Partiners, and Intellisphere LLC; has received payment for expert testimony from Hinshaw & Culbertson LLP, Rodney Law, CRICO Risk Management Foundation, and Tracey & Fox Law Firm; has received research funding from GSK, Merck, Pfizer, and Tracon; has received travel, accommodations, and expenses from GSK and Adaptimmune; has participated on advisory board meeting with Adaptimmune Ltd, Apexigen Inc, Boehringer Ingelheim, Daiichi Sankyo, Deciphera Pharmaceuticals Inc, Epizyme, GSK, Novartis, PTC Therapeutics, and Lilly, and is a board member for Polaris; and holds royalties or licenses for work performed with Accuronix Therapeutics. S.A.G. has received grants for study support from Novartis, Jazz Pharmaceuticals, Kite, Vertex, and Servier; has received consulting fees from Novartis, Roche, GSK, Vertex/CRISPR, CBMG, and Janssen/JnJ; has received payment for expert testimony from Irwin Mitchell and Jones Day; has patents managed according to the University of Pennsylvania patent policy; has participated on advisory boards for Novartis, Jazz Pharmaceuticals, Adaptimmune, Cellectis, Juno, Vertex, Allogene, and Cabaletta. G.D.F. is a former employee of GSK and has stock options in GSK and PACT Pharma, and holds stocks in Bluebird Bio, Agios, and BioMarin.

© 2022. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.

Figures

Fig. 1. Standard LDR of fludarabine and…
Fig. 1. Standard LDR of fludarabine and cyclophosphamide forms supportive environment for T cells and highlights impact of cell dose.
a, b Endogenous lymphocyte (a) and monocyte (b) cell counts across cohorts on day of infusion (n = 43 biologically independent samples). All responders except two in Cohorts 3 and 4 had <10 lymphocytes/μL prior to T-cell infusion. c,d, IL-15 levels prior to T-cell infusion across cohorts (c) and association with response (d) (n = 34 biologically independent samples). Values are log-transformed for consistency with other cytokine analyses. ANOVA and t-test were performed in c and d to be consistent with remaining cytokine analyses; Kruskal–Wallis p-value = 0.005 for (c) and Wilcoxon p-value = 0.050 for d. e Impact of transduced cell dose/kg on reduction in tumor size (n = 22 biologically independent samples) for patients in cohorts 1 and 2. Box plots depict median as horizontal line within box, with box bounds as the first and third quartiles. Dots represent individual data points. Lower whisker is the minimum value of the data within 1.5 times the interquartile range below the 25th percentile. Upper whisker is the maximum value of the data within 1.5 times the interquartile range above the 75th percentile. Two-sided p-values were calculated via bootstrapped median regression (10,000 bootstraps) to adjust for cohort or responder status (a, b), ANOVA (c), t-test (d), and standard test for Spearman correlation (e). CR complete response, IL interleukin, LDR lymphodepleting chemotherapy regimen, PD progressive disease, PR partial response, SD stable disease, SLD sum of longest diameter.
Fig. 2. Lete-cel expansion post-infusion associated with…
Fig. 2. Lete-cel expansion post-infusion associated with response, cell dose/kg, and LDR.
a Association of peak cell expansion (Cmax) with response (n = 43 biologically independent samples). b,c, Relationship of Cmax with weight-normalized cell dose with line of best fit in blue (b) and LDR (c) (n = 43 biologically independent samples). d Persistence of lete-cel at Week 4 post-infusion in all 4 cohorts, stratified by response (n = 35 biologically independent samples). e Relationship between Cmax and PFS (n = 43 biologically independent samples). PFS was defined as the time from T-cell infusion to the earliest documentation of disease progression or death from any cause or surgical resection or start of prohibited medication. Box plots depict median as horizontal line within box, with box bounds as the first and third quartiles. Dots represent individual data points. Lower whisker is the minimum value of the data within 1.5 times the interquartile range below the 25th percentile. Upper whisker is the maximum value of the data within 1.5 times the interquartile range above the 75th percentile. Two-sided p-values were calculated via Wilcoxon test (a, c, d) or linear (b) regression, and Cox proportional hazards model (e). CR complete response, Cy cyclophosphamide, LDR lymphodepleting chemotherapy regimen, PD progressive disease, PFS progression-free survival PR partial response, SD stable disease.
Fig. 3. T-cell product enriched with activated,…
Fig. 3. T-cell product enriched with activated, effector memory CD8 cells is associated with response.
a Number of CD8 + Pentamer+ cells/kg infused per memory phenotype in non-responders vs responders (n = 36 biologically independent samples). b, c Association of number of infused cells/kg per memory phenotype with peak cell expansion (b) and peak IFNγ post-infusion (c) (n = 34 and 31 biologically independent samples for parts b and c respectively). Associations with EM cells are statistically significant with outlier at lowest infused cell counts removed—EM with Cmax (R = 0.42, p = 0.016) and EM with IFNγ (R = 0.41, p = 0.027). d Association of EM phenotype between apheresis and product (n = 34 biologically independent samples). Box plots depict median as horizontal line within box, with box bounds as the first and third quartiles. Dots represent individual data points. Lower whisker is the minimum value of the data within 1.5 times the interquartile range below the 25th percentile. Upper whisker is the maximum value of the data within 1.5 times the interquartile range above the 75th percentile. Nominal two-sided p-values based on the Wilcoxon rank sum test or log-rank test for PFS are presented and correlations are based on Spearman method. Line of best fit shown in blue for significant associations and gray area represents 95% confidence interval around the regression line. CM central memory (CD45RA-CCR7+), CR complete response, EM effector memory (CD45RA-CCR7-), Naïve (CD45RA + CCR7+), PFS progression-free survival, PD progressive disease, PR partial response, SD stable disease, TEMRA T effector memory RA (CD45RA + CCR7−), TSCM T stem cell memory (CD45RA + CCR7 + CD45RO-CD95 + CD127+).
Fig. 4. Characterization of transduced T cells…
Fig. 4. Characterization of transduced T cells post infusion.
a Association of % and number of CD 8+  Pentamer+EM cells/kg with PFS.PFS was defined as the time from T-cell infusion to the earliest documentation of disease progression or death from any cause or surgical resection or start of prohibited medication (n = 36 biologically independent samples). Blue and red shaded areas represent 95% confidence interval for the survival curve. b Levels of CD8 + Pentamer+ cells at Week 4. c Levels and composition of memory phenotypes of CD8 + Pentamer+ cells post-infusion. Product analyses consist of 36 patients. Number of patients for post-infusion analyses are listed on figures. Box plots depict median as horizontal line within box, with box bounds as the first and third quartiles. Dots represent individual data points. Lower whisker is the minimum value of the data within 1.5 times the interquartile range below the 25th percentile. Upper whisker is the maximum value of the data within 1.5 times the interquartile range above the 75th percentile. Nominal two-sided p-values based on the Wilcoxon rank sum test or log-rank test for PFS are presented and correlations are based on Spearman method. CM central memory (CD45RA-CCR7+), CR complete response, EM effector memory (CD45RA-CCR7-), Naïve (CD45RA + CCR7+), PFS progression-free survival, PD progressive disease, PR partial response, SD stable disease, TEMRA T effector memory RA (CD45RA + CCR7-), TSCM T stem cell memory (CD45RA + CCR7 + CD45RO-CD95 + CD127+).
Fig. 5. Increase in proinflammatory cytokines in…
Fig. 5. Increase in proinflammatory cytokines in responders post lete-cel infusion and correlation to peak cell expansion.
a Heatmap of Responder vs Nonresponder ratios in cytokine levels over time obtained from linear mixed effects (LME) model. T-cell infusion (TCI) occurred on Day 0. Nominal p-values from linear effects models were calculated via Wald test and t-distribution and are shown as one dot for 0.01 < p ≤ 0.05 and two dots for p ≤ 0.01. b Time courses of IFNγ, IL-6, IL-17A, and GM-CSF, cytokines differentially upregulated by responders. Geometric means and 95% confidence intervals from LME model (accounting for left-censoring where appropriate) are plotted. Dashed, horizontal lines represented lower limit of quantification. c Time course of soluble IL-2Rα levels showing increase in responders and correlation to peak cell expansion. Line of best fit shown in black and gray area represents 95% confidence bands, both from standard least-squares regression. Sample size varied across timepoints and cytokines, with an overall median of 32 patients (range, 15–38 depending on cytokine/timepoint). For c, Spearman correlation and corresponding two-sided p-value are presented. DY day, GM geometric mean, GM-CSF granulocyte-macrophage colony-stimulating factor, IFN interferon, IL interleukin, WK week.
Fig. 6. Tumor remodeling post lete-cel infusion.
Fig. 6. Tumor remodeling post lete-cel infusion.
a Macrophage markers CD163, CD68, and CD84 pre-infusion (n = 10 biologically independent samples) and at progression (n = 5 biologically independent samples). b CD163 expression in brown by IHC in patient (Subject ID 36) in baseline and at progression biopsy (Day 125) from the same lung lesion (representative region of tissue from an IHC run). c, d Average expression and heatmap of IFN downstream genes pre-infusion (n = 10 biologically independent samples). e Association between CD163 (left) and CD68 (right) and IFN downstream genes. Line of best fit shown in blue and gray area represents 95% confidence bands. Pre-infusion samples from seven archival screening samples (~1 year pre-infusion) and three fresh baseline samples (pre-lymphodepletion). At progression, samples consist of five samples. Box plots depict median as horizontal line within box, with box bounds as the first and third quartiles. Dots represent individual data points. Lower whisker is the minimum value of the data within 1.5 times the interquartile range below the 25th percentile. Upper whisker is the maximum value of the data within 1.5 times the interquartile range above the 75th percentile. Heatmap show z-scores per gene. Nominal two-sided p-values obtained from linear mixed effects models (a), limma models (c), and standard test for Spearman correlation coefficient (e). IFN interferon, IHC immunohistochemistry, NR non-responder, R responder.
Fig. 7. Decreased expression of HLA-A and…
Fig. 7. Decreased expression of HLA-A and antigen-presenting genes at progression.
a Change in gene expression of CTAG1 (NY-ESO-1) and HLA-A between pre-infusion (n = 10 biologically independent samples) and at progression (n = 5 biologically independent samples). b Average expression and heatmap of antigen presentation genes at pre-infusion (n = 10 biologically independent samples) and progression (n = 5 biologically independent samples). The following genes had background expression across all samples: KLRC2, KIR3DL2, KIR3DL1, KIR2DS1, and KR2DL1. c Persistence of lete-cel in blood of patient 32. d Characterization of patient 32 biopsy taken 919 days post-infusion. RNAScope results show CD3 cells in blue and lete-cel in red (left) (representative region of tissue from an IHC run). PDL1 and LAG3 staining in brown by IHC (middle and right images). Tumor samples were primarily from lung metastases. Pre-infusion samples from seven archival screening samples (~1 year pre-infusion) and three fresh baseline samples (pre-lymphodepletion). At progression, samples consist of five samples. Box plots depict median as horizontal line within box, with box bounds as the first and third quartiles. Dots represent individual data points. Lower whisker is the minimum value of the data within 1.5 times the interquartile range below the 25th percentile. Upper whisker is the maximum value of the data within 1.5 times the interquartile range above the 75th percentile. Heatmaps show z-scores per gene. Nominal two-sided p-values obtained from linear mixed effects models (a, b). HLA human leukocyte antigen, IHC immunohistochemistry, NR non-responder, R responder.

References

    1. Rohaan MW, Wilgenhof S, Haanen JBAG. Adoptive cellular therapies: the current landscape. Virchows Arch. 2019;474:449–461. doi: 10.1007/s00428-018-2484-0.
    1. Maude SL, et al. Tisagenlecleucel in children and young adults with B-Cell lymphoblastic leukemia. N. Engl. J. Med. 2018;378:439–448. doi: 10.1056/NEJMoa1709866.
    1. Chandran SS, Klebanoff CA. T cell receptor-based cancer immunotherapy: emerging efficacy and pathways of resistance. Immunological Rev. 2019;290:127–147. doi: 10.1111/imr.12772.
    1. Zhang J, Wang L. The emerging world of TCR-T cell trials against cancer: a systematic review. Technol. cancer Res. Treat. 2019;18:1533033819831068.
    1. Liu, Q., Cai, W., Zhang, W. & Li, Y. Cancer immunotherapy using T-cell receptor engineered T cell. Annals of Blood5 (2020).
    1. Nagarsheth, N. B. et al. TCR-engineered T cells targeting E7 for patients with metastatic HPV-associated epithelial cancers. Nat Med. 10.1038/s41591-020-01225-1 (2021).
    1. D’Angelo SP, et al. Antitumor activity associated with prolonged persistence of adoptively transferred NY-ESO-1 (c259)T cells in synovial sarcoma. Cancer Disco. 2018;8:944–957. doi: 10.1158/-17-1417.
    1. Ramachandran I, et al. Systemic and local immunity following adoptive transfer of NY-ESO-1 SPEAR T cells in synovial sarcoma. J. Immunother. Cancer. 2019;7:276. doi: 10.1186/s40425-019-0762-2.
    1. Robbins PF, et al. A pilot trial using lymphocytes genetically engineered with an NY-ESO-1-reactive T-cell receptor: long-term follow-up and correlates with response. Clin. Cancer Res. 2015;21:1019–1027. doi: 10.1158/1078-0432.CCR-14-2708.
    1. Milone MC, Bhoj VG. The pharmacology of T cell therapies. Mol. Ther. Methods Clin. Dev. 2018;8:210–221. doi: 10.1016/j.omtm.2018.01.010.
    1. Nielsen TO, Poulin NM, Ladanyi M. Synovial sarcoma: recent discoveries as a roadmap to new avenues for therapy. Cancer Disco. 2015;5:124–134. doi: 10.1158/-14-1246.
    1. Hale, R., Sandakly, S., Shipley, J. & Walters, Z. Epigenetic targets in synovial sarcoma: a mini-review. Front. Oncol.10.3389/fonc.2019.01078 (2019).
    1. Lai JP, Rosenberg AZ, Miettinen MM, Lee CC. NY-ESO-1 expression in sarcomas: a diagnostic marker and immunotherapy target. Oncoimmunology. 2012;1:1409–1410. doi: 10.4161/onci.21059.
    1. Dallos M, Tap WD, D’Angelo SP. Current status of engineered T-cell therapy for synovial sarcoma. Immunotherapy. 2016;8:1073–1080. doi: 10.2217/imt-2016-0026.
    1. Pollack SM, et al. T-cell infiltration and clonality correlate with programmed cell death protein 1 and programmed death-ligand 1 expression in patients with soft tissue sarcomas. Cancer. 2017;123:3291–3304. doi: 10.1002/cncr.30726.
    1. Dancsok AR, et al. Tumor-associated macrophages and macrophage-related immune checkpoint expression in sarcomas. Oncoimmunology. 2020;9:1747340–1747340. doi: 10.1080/2162402X.2020.1747340.
    1. Chen DS, Mellman I. Elements of cancer immunity and the cancer–immune set point. Nature. 2017;541:321–330. doi: 10.1038/nature21349.
    1. Oike N, et al. Prognostic impact of the tumor immune microenvironment in synovial sarcoma. Cancer Sci. 2018;109:3043–3054. doi: 10.1111/cas.13769.
    1. Chalmers ZR, et al. Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden. Genome Med. 2017;9:34. doi: 10.1186/s13073-017-0424-2.
    1. Cancer Genome Atlas Research Network Comprehensive and integrated genomic characterization of adult soft tissue sarcomas. Cell. 2017;171:950–965.e928. doi: 10.1016/j.cell.2017.10.014.
    1. Tawbi HA, et al. Pembrolizumab in advanced soft-tissue sarcoma and bone sarcoma (SARC028): a multicentre, two-cohort, single-arm, open-label, phase 2 trial. Lancet Oncol. 2017;18:1493–1501. doi: 10.1016/S1470-2045(17)30624-1.
    1. Maki RG, et al. A pilot study of anti-CTLA4 antibody ipilimumab in patients with synovial sarcoma. Sarcoma. 2013;2013:168145–168145. doi: 10.1155/2013/168145.
    1. Hirayama AV, et al. The response to lymphodepletion impacts PFS in patients with aggressive non-Hodgkin lymphoma treated with CD19 CAR T cells. Blood. 2019;133:1876–1887. doi: 10.1182/blood-2018-11-887067.
    1. Kochenderfer JN, et al. Lymphoma remissions caused by anti-CD19 chimeric antigen receptor T cells are associated with high serum interleukin-15 Levels. J. Clin. Oncol.: Off. J. Am. Soc. Clin. Oncol. 2017;35:1803–1813. doi: 10.1200/JCO.2016.71.3024.
    1. Muranski P, et al. Increased intensity lymphodepletion and adoptive immunotherapy–how far can we go? Nat. Clin. Pract. Oncol. 2006;3:668–681. doi: 10.1038/ncponc0666.
    1. Gattinoni L, et al. Removal of homeostatic cytokine sinks by lymphodepletion enhances the efficacy of adoptively transferred tumor-specific CD8+ T cells. J. Exp. Med. 2005;202:907–912. doi: 10.1084/jem.20050732.
    1. Ramos CA, et al. Anti-CD30 CAR-T cell therapy in relapsed and refractory Hodgkin lymphoma. J. Clin. Oncol. 2020;38:3794–3804. doi: 10.1200/JCO.20.01342.
    1. Hegde M, et al. Tumor response and endogenous immune reactivity after administration of HER2 CAR T cells in a child with metastatic rhabdomyosarcoma. Nat. Commun. 2020;11:3549. doi: 10.1038/s41467-020-17175-8.
    1. Wooldridge L, et al. Tricks with tetramers: how to get the most from multimeric peptide-MHC. Immunology. 2009;126:147–164. doi: 10.1111/j.1365-2567.2008.02848.x.
    1. Boulch, M. et al. A cross-talk between CAR T cell subsets and the tumor microenvironment is essential for sustained cytotoxic activity. Sci. Immunol. 10.1126/sciimmunol.abd4344 (2021).
    1. D’Angelo SP, et al. Prevalence of tumor-infiltrating lymphocytes and PD-L1 expression in the soft tissue sarcoma microenvironment. Hum. Pathol. 2015;46:357–365. doi: 10.1016/j.humpath.2014.11.001.
    1. Nabeshima A, et al. Tumour-associated macrophages correlate with poor prognosis in myxoid liposarcoma and promote cell motility and invasion via the HB-EGF-EGFR-PI3K/Akt pathways. Br. J. Cancer. 2015;112:547–555. doi: 10.1038/bjc.2014.637.
    1. Lee AJ, Ashkar AA. The dual nature of type I and type II interferons. Front. Immunol. 2018;9:2061. doi: 10.3389/fimmu.2018.02061.
    1. Di Franco S, Turdo A, Todaro M, Stassi G. Role of type I and II interferons in colorectal cancer and melanoma. Front. Immunol. 2017;8:878. doi: 10.3389/fimmu.2017.00878.
    1. Zhang S, et al. Systemic interferon-γ increases MHC class I expression and T-cell infiltration in cold tumors: results of a phase 0 clinical trial. Cancer Immunol. Res. 2019;7:1237–1243. doi: 10.1158/2326-6066.CIR-18-0940.
    1. Borden EC. Interferons α and β in cancer: therapeutic opportunities from new insights. Nat. Rev. Drug Discov. 2019;18:219–234. doi: 10.1038/s41573-018-0011-2.
    1. Raj S, Miller LD, Triozzi PL. Addressing the adult soft tissue sarcoma microenvironment with intratumoral immunotherapy. Sarcoma. 2018;2018:9305294. doi: 10.1155/2018/9305294.
    1. Fraietta JA, et al. Determinants of response and resistance to CD19 chimeric antigen receptor (CAR) T cell therapy of chronic lymphocytic leukemia. Nat. Med. 2018;24:563–571. doi: 10.1038/s41591-018-0010-1.
    1. Raje N, et al. Anti-BCMA CAR T-cell therapy bb2121 in relapsed or refractory multiple myeloma. N. Engl. J. Med. 2019;380:1726–1737. doi: 10.1056/NEJMoa1817226.
    1. Neelapu SS, et al. Axicabtagene ciloleucel CAR T-cell therapy in refractory large B-cell lymphoma. N. Engl. J. Med. 2017;377:2531–2544. doi: 10.1056/NEJMoa1707447.
    1. Tietze JK, et al. The proportion of circulating CD45RO+CD8+ memory T cells is correlated with clinical response in melanoma patients treated with ipilimumab. Eur. J. Cancer. 2017;75:268–279. doi: 10.1016/j.ejca.2016.12.031.
    1. Gide TN, et al. Distinct immune cell populations define response to Anti-PD-1 monotherapy and Anti-PD-1/Anti-CTLA-4 combined therapy. Cancer Cell. 2019;35:238–255.e236. doi: 10.1016/j.ccell.2019.01.003.
    1. Deng Q, et al. Characteristics of anti-CD19 CAR T cell infusion products associated with efficacy and toxicity in patients with large B cell lymphomas. Nat. Med. 2020;26:1878–1887. doi: 10.1038/s41591-020-1061-7.
    1. Locke FL, et al. Tumor burden, inflammation, and product attributes determine outcomes of axicabtagene ciloleucel in large B-cell lymphoma. Blood Adv. 2020;4:4898–4911. doi: 10.1182/bloodadvances.2020002394.
    1. Krishna S, et al. Stem-like CD8 T cells mediate response of adoptive cell immunotherapy against human cancer. Science. 2020;370:1328–1334. doi: 10.1126/science.abb9847.
    1. Mahnke YD, Brodie TM, Sallusto F, Roederer M, Lugli E. The who’s who of T-cell differentiation: human memory T-cell subsets. Eur. J. Immunol. 2013;43:2797–2809. doi: 10.1002/eji.201343751.
    1. Berger C, et al. Adoptive transfer of effector CD8+ T cells derived from central memory cells establishes persistent T cell memory in primates. J. Clin. Invest. 2008;118:294–305. doi: 10.1172/JCI32103.
    1. Morotti M, et al. Promises and challenges of adoptive T-cell therapies for solid tumours. Br. J. Cancer. 2021;124:1759–1776. doi: 10.1038/s41416-021-01353-6.
    1. Britten CM, Shalabi A, Hoos A. Industrializing engineered autologous T cells as medicines for solid tumours. Nat. Rev. Drug Disco. 2021;20:476–488. doi: 10.1038/s41573-021-00175-8.
    1. Anderson KG, Stromnes IM, Greenberg PD. Obstacles posed by the tumor microenvironment to T cell activity: a case for synergistic therapies. Cancer Cell. 2017;31:311–325. doi: 10.1016/j.ccell.2017.02.008.
    1. Jerby-Arnon L, et al. Opposing immune and genetic mechanisms shape oncogenic programs in synovial sarcoma. Nat. Med. 2021;27:289–300. doi: 10.1038/s41591-020-01212-6.
    1. Jain MD, et al. Tumor interferon signaling and suppressive myeloid cells are associated with CAR T-cell failure in large B-cell lymphoma. Blood. 2021;137:2621–2633. doi: 10.1182/blood.2020007445.
    1. Shah NN, Fry TJ. Mechanisms of resistance to CAR T cell therapy. Nat. Rev. Clin. Oncol. 2019;16:372–385.
    1. Cheng J, et al. Understanding the mechanisms of resistance to CAR T-cell therapy in malignancies. Front. Oncol. 2019;9:1237. doi: 10.3389/fonc.2019.01237.
    1. Tran E, et al. T-cell transfer therapy targeting mutant KRAS in cancer. N. Engl. J. Med. 2016;375:2255–2262. doi: 10.1056/NEJMoa1609279.
    1. Vaida F, Liu L. Fast implementation for normal mixed effects models with censored response. J. Comput Graph Stat. 2009;18:797–817. doi: 10.1198/jcgs.2009.07130.
    1. Kenward MG, Roger JH. Small sample inference for fixed effects from restricted maximum likelihood. Biometrics. 1997;53:983–997. doi: 10.2307/2533558.
    1. Smyth GK. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol. 2004;3:Article3. doi: 10.2202/1544-6115.1027.
    1. Chang WH, Lai AG. Pan-cancer genomic amplifications underlie a WNT hyperactivation phenotype associated with stem cell-like features leading to poor prognosis. Transl. Res.: J. Lab. Clin. Med. 2019;208:47–62. doi: 10.1016/j.trsl.2019.02.008.
    1. Korkut A, et al. A pan-cancer analysis reveals high-frequency genetic alterations in mediators of signaling by the TGF-β superfamily. Cell Syst. 2018;7:422–437.e427. doi: 10.1016/j.cels.2018.08.010.
    1. Edgar R, Domrachev M, Lash AE. Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002;30:207–210. doi: 10.1093/nar/30.1.207.

Source: PubMed

3
Prenumerera