The immune microenvironment influences tumour evolution and can be both prognostic and predict response to immunotherapy1,2. However, measurements of tumour infiltrating lymphocytes (TILs) are limited by a shortage of appropriate data. Whole-exome sequencing (WES) of DNA is frequently performed to calculate tumour mutational burden and identify actionable mutations. Here we develop T cell exome TREC tool (T cell ExTRECT), a method for estimation of T cell fraction from WES samples using a signal from T cell receptor excision circle (TREC) loss during V(D)J recombination of the T cell receptor-α gene (TCRA (also known as TRA)). TCRA T cell fraction correlates with orthogonal TIL estimates and is agnostic to sample type. Blood TCRA T cell fraction is higher in females than in males and correlates with both tumour immune infiltrate and presence of bacterial sequencing reads. Tumour TCRA T cell fraction is prognostic in lung adenocarcinoma. Using a meta-analysis of tumours treated with immunotherapy, we show that tumour TCRA T cell fraction predicts immunotherapy response, providing value beyond measuring tumour mutational burden. Applying T cell ExTRECT to a multi-sample pan-cancer cohort reveals a high diversity of the degree of immune infiltration within tumours. Subclonal loss of 12q24.31–32, encompassing SPPL3, is associated with reduced TCRA T cell fraction. T cell ExTRECT provides a cost-effective technique to characterize immune infiltrate alongside somatic changes.
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The RNA-seq data, WES data and histopathology-derived TIL scores (in each case from the TRACERx study) generated, used or analysed during this study are not publicly available and restrictions apply to the availability of these data. Such RNA-seq, WES data and histopathology-derived TIL scores are available through the Cancer Research UK and University College London Cancer Trials Centre (email@example.com) for academic non-commercial research purposes upon reasonable request, and subject to review of a project proposal that will be evaluated by a TRACERx data access committee, entering into an appropriate data access agreement and subject to any applicable ethical approvals. Details of all other datasets obtained from third parties used in this study can be found in Extended Data Table 1. Clinical trial information (if applicable) is also available in the associated publications described in Extended Data Table 1.
The code used to produce TCRA T cell fraction scores is available for academic non-commercial research purposes at https://github.com/McGranahanLab/TcellExTRECT. All other code used in the analysis and to produce figures is available at https://github.com/McGranahanLab/T-cell-ExTRECT-figure-code-2021.
Rosenthal, R. et al. Neoantigen-directed immune escape in lung cancer evolution. Nature 567, 479–485 (2019).
Litchfield, K. et al. Meta-analysis of tumor- and T cell-intrinsic mechanisms of sensitization to checkpoint inhibition. Cell 184, 596–614 (2021).
Robert, C. et al. Ipilimumab plus dacarbazine for previously untreated metastatic melanoma. N. Engl. J. Med. 364, 2517–2526 (2011).
Schadendorf, D. et al. Pooled analysis of long-term survival data from phase II and phase III trials of ipilimumab in unresectable or metastatic melanoma. J. Clin. Oncol. 33, 1889–1894 (2015).
Goodman, A. M. et al. Tumor mutational burden as an independent predictor of response to immunotherapy in diverse cancers. Mol. Cancer Ther. 16, 2598–2608 (2017).
Van Loo, P. et al. Allele-specific copy number analysis of tumors. Proc. Natl Acad. Sci. USA 107, 16910–16915 (2010).
Favero, F. et al. Sequenza: allele-specific copy number and mutation profiles from tumor sequencing data. Ann. Oncol. 26, 64–70 (2015).
Shen, R. & Seshan, V. FACETS: Fraction and Allele-Specific Copy Number Estimates from Tumor Sequencing, Dept. Epidemiology and Biostatistics Working Paper Series Vol. 1 No. 50 (Memorial Sloan-Kettering Cancer Center, 2015).
Carter, S. L. et al. Absolute quantification of somatic DNA alterations in human cancer. Nat. Biotechnol. 30, 413–421 (2012).
López, S. et al. Interplay between whole-genome doubling and the accumulation of deleterious alterations in cancer evolution. Nat. Genet. 52, 283–293 (2020).
Ghandi, M. et al. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature 569, 503–508 (2019).
Levy, E. et al. Immune DNA signature of T-cell infiltration in breast tumor exomes. Sci. Rep. 6, 30064 (2016).
Jamal-Hanjani, M. et al. Tracking the evolution of non–small-cell lung cancer. N. Engl. J. Med. 376, 2109–2121 (2017).
Danaher, P. et al. Pan-cancer adaptive immune resistance as defined by the tumor inflammation signature (TIS): results from The Cancer Genome Atlas (TCGA). J. Immunother. Cancer 6, 1–17 (2018).
Davoli, T., Uno, H., Wooten, E. C. & Elledge, S. J. Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy. Science 355, eaaf8399 (2017).
Aran, D., Hu, Z. & Butte, A. J. xCell: Digitally portraying the tissue cellular heterogeneity landscape. Genome Biol. 18, 1–14 (2017).
Li, T. et al. TIMER: A web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Res. 77, e108–e110 (2017).
Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).
Racle, J., de Jonge, K., Baumgaertner, P., Speiser, D. E. & Gfeller, D. Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data. eLife 6, e26476 (2017).
Cancer Genome Atlas Research Network. Comprehensive genomic characterization of squamous cell lung cancers. Nature 489, 519–525 (2012).
Cancer Genome Atlas Research Network. Comprehensive molecular profiling of lung adenocarcinoma. Nature 511, 543–550 (2014).
Yokoyama, A. et al. Age-related remodelling of oesophageal epithelia by mutated cancer drivers. Nature 565, 312–317 (2019).
Poore, G. D. et al. Microbiome analyses of blood and tissues suggest cancer diagnostic approach. Nature 579, 567–574 (2020).
Watkins, T. B. K. et al. Pervasive chromosomal instability and karyotype order in tumour evolution. Nature 587, 126–132 (2020).
Jongsma, M. L. M. et al. The SPPL3-defined glycosphingolipid repertoire orchestrates HLA class I-mediated immune responses. Immunity 54, 132-150.e9 (2021).
AbdulJabbar, K. et al. Geospatial immune variability illuminates differential evolution of lung adenocarcinoma. Nat. Med. 26, 1054–1062 (2020).
Schwartz, L. H. et al. RECIST 1.1—update and clarification: from the RECIST committee. Eur. J. Cancer 62, 132–137 (2016).
Conforti, F. et al. Sex-based dimorphism of anticancer immune response and molecular mechanisms of immune evasion. Clin. Cancer Res. 27, https://doi.org/10.1158/1078-0432.CCR-21-0136 (2021).
Capone, I., Marchetti, P., Ascierto, P. A., Malorni, W. & Gabriele, L. Sexual dimorphism of immune responses: a new perspective in cancer immunotherapy. Front. Immunol. 9, 552 (2018).
van der Spek, J., Groenwold, R. H. H., van der Burg, M. & van Montfrans, J. M. TREC based newborn screening for severe combined immunodeficiency disease: a systematic review. J. Clin. Immunol. 35, 416–430 (2015).
Kuchenbecker, L. et al. IMSEQ-A fast and error aware approach to immunogenetic sequence analysis. Bioinformatics 31, 2963–2971 (2015).
Wood, D. E. & Salzberg, S. L. Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol. 15, R46 (2014).
Middleton, G. et al. The National Lung Matrix Trial of personalized therapy in lung cancer. Nature 583, 807–812 (2020).
Wang, K. et al. PennCNV: an integrated hidden Markov model designed for high-resolution copy number variation detection in whole-genome SNP genotyping data. Genome Res. 17, 1665–1674 (2007).
Brastianos, P. K. et al. Genomic characterization of brain metastases reveals branched evolution and potential therapeutic targets. Cancer Discov. 5, 1164–1177 (2015).
Gerlinger, M. et al. Genomic architecture and evolution of clear cell renal cell carcinomas defined by multiregion sequencing. Nat. Genet. 46, 225–233 (2014).
Gerlinger, M. et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl. J. Med. 366, 883–892 (2012).
Harbst, K. et al. Multiregion whole-exome sequencing uncovers the genetic evolution and mutational heterogeneity of early-stage metastatic melanoma. Cancer Res. 76, 4765–4774 (2016).
Lamy, P. et al. Paired exome analysis reveals clonal evolution and potential therapeutic targets in urothelial carcinoma. Cancer Res. 76, 5894–5906 (2016).
Savas, P. et al. The subclonal architecture of metastatic breast cancer: results from a prospective community-based rapid autopsy program “CASCADE”. PLoS Med. 13, e1002204 (2016).
Suzuki, H. et al. Mutational landscape and clonal architecture in grade II and III gliomas. Nat. Genet. 47, 458–468 (2015).
Turajlic, S. et al. Deterministic evolutionary trajectories influence primary tumor growth: TRACERx renal. Cell 173, 595-610.e11 (2018).
Messaoudene, M. et al. T-cell bispecific antibodies in node-positive breast cancer: novel therapeutic avenue for MHC class I loss variants. Ann. Oncol. 30, 934–944 (2019).
Snyder, A. et al. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N. Engl. J. Med. 371, 2189–2199 (2014).
Van Allen, E. M. et al. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science 352, 207–212 (2016).
Hugo, W. et al. Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma. Cell 165, 35–44 (2016).
Riaz, N. et al. Tumor and microenvironment evolution during immunotherapy with nivolumab. Cell 171, 934-949.e15 (2017).
Cristescu, R. et al. Pan-tumor genomic biomarkers for PD-1 checkpoint blockade-based immunotherapy. Science 362, eaar3593 (2018).
Snyder, A. et al. Contribution of systemic and somatic factors to clinical response and resistance to PD-L1 blockade in urothelial cancer: an exploratory multi-omic analysis. PLoS Med. 14, 1–24 (2017).
Mariathasan, S. et al. TGFβ attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature 554, 544–548 (2018).
McDermott, D. F. et al. Clinical activity and molecular correlates of response to atezolizumab alone or in combination with bevacizumab versus sunitinib in renal cell carcinoma. Nat. Med. 24, 749–757 (2018).
Rizvi, N. A. et al. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 348, 124–128 (2015).
Le, D. T. et al. PD-1 blockade in tumors with mismatch repair deficiency. N. Engl. J. Med. 372, 2509–2520 (2015).
Shim, J. H. et al. HLA-corrected tumor mutation burden and homologous recombination deficiency for the prediction of response to PD-(L)1 blockade in advanced non-small-cell lung cancer patients. Ann. Oncol. 31, 902–911 (2020).
Hendry, S. et al. Assessing tumor-infiltrating lymphocytes in solid tumors. Adv. Anat. Pathol. 24, 235–251 (2017).
Denkert, C. et al. Standardized evaluation of tumor-infiltrating lymphocytes in breast cancer: Results of the ring studies of the international immuno-oncology biomarker working group. Mod. Pathol. 29, 1155–1164 (2016).
R.B. is supported by the NIHR BRC at University College London Hospitals. K.L. is funded by the UK Medical Research Council (MR/P014712/1 and MR/V033077/1), Rosetrees Trust and Cotswold Trust (A2437) and Cancer Research UK (C69256/A30194). T.B.K.W. is supported by the Francis Crick Institute, which receives its core funding from Cancer Research UK (FC001169), the UK Medical Research Council (FC001169) and the Wellcome Trust (FC001169) as well as the Marie Curie ITN Project PLOIDYNET (FP7-PEOPLE-2013, 607722), Breast Cancer Research Foundation (BCRF), Royal Society Research Professorships Enhancement Award (RP/EA/180007) and the Foulkes Foundation. E.L.L. receives funding from NovoNordisk Foundation (ID 16584). R.R. is supported by Royal Society Research Professorships Enhancement Award (RP/EA/180007). C.M.-R. is supported by Rosetrees. C.T.H. is supported by the NIHR BRC at University College London Hospitals. M.J.-H. has received funding from Cancer Research UK, National Institute for Health Research, Rosetrees Trust, UKI NETs and NIHR University College London Hospitals Biomedical Research Centre. N.M. is a Sir Henry Dale Fellow, jointly funded by the Wellcome Trust and the Royal Society (Grant Number 211179/Z/18/Z) and also receives funding from Cancer Research UK, Rosetrees and the NIHR BRC at University College London Hospitals and the CRUK University College London Experimental Cancer Medicine Centre. C.S. is a Royal Society Napier Research Professor. His work was supported by the Francis Crick Institute, which receives its core funding from Cancer Research UK (FC001169), the UK Medical Research Council (FC001169) and the Wellcome Trust (FC001169). C.S. is funded by Cancer Research UK (TRACERx, PEACE and CRUK Cancer Immunotherapy Catalyst Network), Cancer Research UK Lung Cancer Centre of Excellence, the Rosetrees Trust, Butterfield and Stoneygate Trusts, NovoNordisk Foundation (ID16584), Royal Society Research Professorships Enhancement Award (RP/EA/180007), the NIHR BRC at University College London Hospitals, the CRUK-UCL Centre, Experimental Cancer Medicine Centre and the Breast Cancer Research Foundation (BCRF). This research is supported by a Stand Up To Cancer-LUNGevity-American Lung Association Lung Cancer Interception Dream Team Translational Research Grant (SU2C-AACR-DT23-17). Stand Up To Cancer is a program of the Entertainment Industry Foundation. Research grants are administered by the American Association for Cancer Research, the Scientific Partner of SU2C. C.S. also receives funding from the European Research Council (ERC) under the European Union’s Seventh Framework Programme (FP7/2007-2013) Consolidator Grant (FP7-THESEUS-617844), European Commission ITN (FP7-PloidyNet 607722), an ERC Advanced Grant (PROTEUS) from the European Research Council under the European Union’s Horizon 2020 research and innovation programme (835297) and Chromavision from the European Union’s Horizon 2020 research and innovation programme (665233). The TRACERx study (Clinicaltrials.gov no: NCT01888601) is sponsored by University College London (UCL/12/0279) and has been approved by an independent Research Ethics Committee (13/LO/1546). TRACERx is funded by Cancer Research UK (C11496/A17786) and coordinated through the Cancer Research UK and UCL Cancer Trials Centre. The results published here are based in part on data generated by The Cancer Genome Atlas pilot project established by the NCI and the National Human Genome Research Institute. The data were retrieved through the database of Genotypes and Phenotypes (dbGaP) authorization (accession number phs000178.v9.p8). Information about TCGA and the constituent investigators and institutions of the TCGA research network can be found at http://cancergenome.nih.gov/. This project was enabled through access to the MRC eMedLab Medical Bioinformatics infrastructure, supported by the Medical Research Council (MR/L016311/1). In particular, we acknowledge the support of the High-Performance Computing at the Francis Crick Institute as well as the UCL Department of Computer Science Cluster and the support team. In addition, this work was supported by the CRUK City of London Centre Award (C7893/A26233). We thank all investigators, funders and industry partners that supported the generation of the data within this study, as well as patients for their participation. Specifically, we thank Merck & Co, Genentech and Bristol-Myers Squibb for generating the industry datasets used in this study, and E. Van Allen, L. Diaz, T. A. Chan, L. A. Garraway, R. S. Lo, D. F. Bajorin, D. Schadendorf, T. Powles, S.-H. Lee, A. Ribas and S. Ogawa for academic datasets. This work has been funded by Merck and Co. We gratefully acknowledge the patients and their families, the investigators and site personnel who participated in the KEYNOTE-001, -006, -012 and -028 studies.
D.A.M. reports speaker fees from AstraZeneca. M.A.B. has consulted for Achilles Therapeutics. R.R. has consulted for and has stock options in Achilles Therapeutics. K.L. has a patent on indel burden and CPI response pending and speaker fees from Roche tissue diagnostics, research funding from CRUK TDL/Ono/LifeArc alliance, and a consulting role with Monopteros Therapeutics. C.T.H. has received speaker fees from AstraZeneca. M.J.-H. is a member of the Scientific Advisory Board and Steering Committee for Achilles Therapeutics. N.M. has stock options in and has consulted for Achilles Therapeutics and holds a European patent in determining HLA LOH (PCT/GB2018/052004). C.S. acknowledges grant support from Pfizer, AstraZeneca, Bristol Myers Squibb, Roche-Ventana, Boehringer-Ingelheim, Archer Dx Inc. (collaboration in minimal residual disease sequencing technologies) and Ono Pharmaceutical; is an AstraZeneca Advisory Board Member and Chief Investigator for the MeRmaiD1 clinical trial; has consulted for Amgen, Pfizer, Novartis, GlaxoSmithKline, MSD, Bristol Myers Squibb, AstraZeneca, Illumina, Genentech, Roche-Ventana, GRAIL, Medicxi, Bicycle Therapeutics, Metabomed and the Sarah Cannon Research Institute; has stock options in Apogen Biotechnologies, Epic Bioscience and GRAIL; and has stock options and is co-founder of Achilles Therapeutics. C.S. holds patents relating to assay technology to detect tumour recurrence (PCT/GB2017/053289); to targeting neoantigens (PCT/EP2016/059401), identifying patent response to immune checkpoint blockade (PCT/EP2016/071471), determining HLA LOH (PCT/GB2018/052004), predicting survival rates of patients with cancer (PCT/GB2020/050221), to treating cancer by targeting Insertion/deletion mutations (PCT/GB2018/051893); identifying insertion/deletion mutation targets (PCT/GB2018/051892); methods for lung cancer detection (PCT/US2017/028013); and identifying responders to cancer treatment (PCT/GB2018/051912).
Peer review information Nature thanks Florian Markowetz and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
a, Outline of quantification of the TCRA T cell fraction utilising V(D)J recombination and TRECs. top: Schematic demonstrating how RDR signals are used to detect SCNA gain or loss events in a standard tumour and matched control sample analysis. In this analysis cells consist of three distinct cell types: tumour cells, T cells and all other stromal cells. bottom: Schematic of how this same process works when focussing on the TCRA gene in relation to V(D)J recombination and TRECs, the lower right panel indicates an increased number of breakpoints detected in the TRACERx100 dataset within the TCRA gene relative to surrounding areas of 14q, suggesting that the TREC signal is captured. b, c, Plots showing examples of RDR in two TRACERx100 samples demonstrating either increased levels of T cell content in blood compared to matched tumour (b) or increased levels of T cell content in tumour compared to matched blood (c). VDV segments refer to variable segments in both the TCRα and TCRδ locus. d, TCRA T cell fraction (non-GC corrected) value for FFPE and fresh frozen samples for bladder and melanoma tumours within the CPI1000+ cohort (bladder: n = 228, melanoma: n = 297, two sided Wilcoxon rank-sum (Mann-Whitney U) test used, boxplot shows lower quartile, median and upper quartile values). e, Summary of linear model for prediction of non-GC corrected TCRA T cell fraction from histology and FFPE sample status within the CPI cohort. f, Pie charts of calculated TCRA T cell fraction from WES of either T cell-derived cell lines or non-T cell derived cell lines, all HCT116 cell lines had calculated fractions < 1 e-15. g, Overview of samples in the TRACERx100 cohort. e, Association of the CDR3 V(D)J read score based on the iDNA method to TCRA T cell fraction in TRACERx100, error bands represent the 95% confidence interval of the fitted linear model.
a, Simulated log RDR from a sample consisting of 24% T cells, 75% tumour, and 1% non-T cell stroma (TCRA copy number = 1). b, Calculated TCRA T cell fraction versus actual T cell fraction value for simulated data c, Difference between calculated naive T cell fraction and actual fraction for range of tumour purities and local tumour copy number states at the TCRA locus. d, Difference between TCRA T cell fraction and actual fraction for a range of local tumour copy number to the TCRA locus and tumour purities. e,. Downsampling of 5 TRACERx100 samples to different depths. f, Downsampling of simulated data to different depth levels. g, Downsampling of the 5 TRACERx100 samples that with the highest CDR3 read counts to different depths and the resulting CDR3 read counts.
a, Association of blood TCRA T cell fraction to histology in TRACERx100 (n = 93 LUAD and LUSC patients). b, Predictors of blood TCRA T cell fraction in TCGA LUAD and LUSC cohort (left panel: n = 1017, middle panel: n = 976, right panel: n = 714). c, Overview of samples in the TCGA LUAD and LUSC cohort. d, Summary of mean TCRA T cell fraction in PNE cohort. e, Overview plot of PNE cohort containing multi-sample microdissected tissue paired with normal blood samples. f, Summary of linear model for predicting blood TCRA T cell fraction, PNE infiltration defined as TCRA T cell fraction > 0.001, ESCC = Oesophageal squamous cell carcinoma, HGD = high grade dysplasia. g, Linear model for TCRA T cell fraction in PNE samples from genomic factors. h, Association of microbial reads from Kraken with TCRA T cell fraction in tumour samples (n = 880). i, -Log10 p-values for 59 microbial species tested for association with TCRA T cell fraction in blood and tumour sample in LUAD and LUSC. Red line represents the significance threshold at P = 0.000423. j, The significant hit Willamsia in LUAD tumours, red dots represent samples where reads were detected while blue represent samples with no reads detected (n = 501). k, The significant hit Paeniclostridium in LUSC tumours (n = 379). All Wilcoxon tests refer to Wilcoxon rank-sum (Mann-Whitney U) tests and are two sided. Boxplots represent lower quartile, median and upper quartile.
a, Overview of immune heterogeneity across multi-sample pan-cancer cohort with tumour samples ranked by TCRA T cell fraction, upper panel: histogram of entire cohort, lower panel: tumour sample grouped by patients with solid horizontal lines joining regions from the same patient, each line includes 2 or more tumour region and dashed red line is at the mean TCRA T cell fraction in the cohort (0.11). b, Overview of patients in the multi-sample pan-cancer cohort. c, Lower panel: number of tumours in pan-cancer multi-sample cohort with subclonal gains (dark red) or losses (dark blue) across the genome, horizontal lines signify the samples which have more than 30 tumours (Methods) with subclonal gains or losses. Upper panel: - log10(p-value) of the 160 cytoband regions tested for association between TCRA T cell fraction and subclonal gains (dark red points) or losses (dark blue points). Red horizontal line marks significance threshold, only one region is significant, a loss event on chromosome 12q24.31-32. d, Volcano plot for the RNA-seq analysis in the TRACERx100 cohort between samples with 12q24.31-32 loss and samples without, genes within the locus are labeled, dotted lines at fold change of 0.25 and adjusted P = 0.05.
a, Kaplan-Meier curves for the multi-sample TRACERx100 cohort for LUAD (top) and LUSC (bottom) divided by the number of cold samples in the tumour. Immune-hot and immune-cold samples were defined by using the median of all the tumour samples (0.0736) as a threshold. In each Kaplan-Meier curve the included patients were restricted to those with total samples greater than the number of immune-cold samples used in defining the threshold. b, Kaplan-Meier curves for overall and progression free survival in the TCGA LUAD cohort, dividing the cohort into immune-hot and immune-cold groups using the mean of the TCGA LUAD cohort (0.109) as a threshold. c, Kaplan-Meier curves for the TCGA LUSC, and TCGA LUAD & LUSC cohorts for overall and progression free survival using the mean of the TCGA LUAD cohort (0.109) as a threshold for distinguishing hot and cold tumours. d, Log2(Hazard ratios) from Kaplan-Meier plots for the TCGA separating the tumour samples into immune-hot and immune-cold based on different thresholds from 0 to 0.16 in steps of 0.0025 for overall and progression free survival. e, Hazard ratios of separate Cox regression models relating disease free survival to different multi-sample measures related to the TCRA T cell fraction in the entire TRACERx100 cohort as well as the LUAD and LUSC patients separately. TCRA divergence score is defined as the maximum divided by the upper 95% confidence interval of the minimum. f, Hazard ratios of separate Cox regression models for TCRA T cell fraction for the TCGA LUAD and LUSC cohort for both overall survival (OS) and progression free survival (PFS).
a, Cohort overview of the CPI1000+ dataset. b, Overview of samples in the CPI1000+ cohort excluding Snyder et al.49 and those with prior CPI treatment. c, ROC plot of GLM models for predicting CPI response (blue: clonal TMB, red: clonal TMB + TCRA T cell fraction, green: clonal TMB + CD8A expression). d, Cohort overview of the CPI lung dataset, red lines in upper panel reflect the median TCRA T cell fraction in patients with (0.10) or without (0.0070) a response to CPI, note that Tumour TCRA T cell fraction particularly in non-responders is often zero. e, Overview of patients in the CPI Lung cohort.
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Bentham, R., Litchfield, K., Watkins, T.B.K. et al. Using DNA sequencing data to quantify T cell fraction and therapy response. Nature 597, 555–560 (2021). https://doi.org/10.1038/s41586-021-03894-5
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