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Table S3 and Fig. S1-S10

Beyond tertiary lymphoid structures, a significant number of immune-rich areas without germinal center-like structures are observed in non–small cell lung cancer. Here, we integrated transcriptomic data and digital pathology images to study the prognostic implications, spatial locations, and constitution of immune rich areas (immune hotspots) in a cohort of 935 patients with lung cancer from The Cancer Genome Atlas. A high intratumoral immune hotspot score, which measures the proportion of immune hotspots interfacing with tumor islands, was correlated with poor overall survival in lung squamous cell carcinoma but not in lung adenocarcinoma. Lung squamous cell carcinomas with high intratumoral immune hotspot scores were characterized by consistent upregulation of B-cell signatures. Spatial statistical analyses conducted on serial multiplex IHC slides further revealed that only 4.87% of peritumoral immune hotspots and 0.26% of intratumoral immune hotspots were tertiary lymphoid structures. Significantly lower densities of CD20+CXCR5+ and CD79b+ B cells and less diverse immune cell interactions were found in intratumoral immune hotspots compared with peritumoral immune hotspots. Furthermore, there was a negative correlation between the percentages of CD8+ T cells and T regulatory cells in intratumoral but not in peritumoral immune hotspots, with tertiary lymphoid structures excluded. These findings suggest that the intratumoral immune hotspots reflect an immunosuppressive niche compared with peritumoral immune hotspots, independent of the distribution of tertiary lymphoid structures. A balance toward increased intratumoral immune hotspots is indicative of a compromised antitumor immune response and poor outcome in lung squamous cell carcinoma.Significance:Intratumoral immune hotspots beyond tertiary lymphoid structures reflect an immunosuppressive microenvironment, different from peritumoral immune hotspots, warranting further study in the context of immunotherapies.

The relationship among B-cell, T-cell percentages and CD8+/CD4+FOXP3+ ratio in individual intratumoral IH and peritumoral IH without TLS or LAG. A, Percentages of B-cell subsets in individual intratumoral (n = 262) and peritumoral IH (n = 392) classified by CD8+/CD4+FOXP3+ ratio. The high and low groups were determined by the cut-off of median CD8+/CD4+FOXP3+ ratio in intratumoral and peritumoral IH, respectively. B, Percentages of CD8+ T cells and CD4+FOXP3+ Treg cells in individual intratumoral (n = 262) and peritumoral IH (n = 392) classified by the percentage of B cells. This B-cell percentage was calculated as the total count of all B-cell subsets divided by the total count of all 6 immune cell types. C, Percentages of CD8+ T cells in individual intratumoral (n = 262) and peritumoral IH (n = 392) classified by CD4+FOXP3+ Treg-cell percentages. Statistical significance was evaluated by the Wilcoxon rank sum test and adjusted by the Benjamini–Hochberg method.

Diversity of lymphocyte interactions in intratumoral and peritumoral IH without TLS or LAG. A, Examples of lymphocyte interactions in individual peritumoral and intratumoral IH. Lymphocytes identified on the serial IHC slides were connected by the Delaunay triangle graph. Lymphocyte subsets are represented by dots of different colors. Interactions significant in the univariate logistic regression are denoted by red, otherwise blue. B, Shannon diversity of lymphocyte densities and interactions in individual peritumoral (n = 392) and intratumoral IH (n = 262). Statistical significance was evaluated by the Wilcoxon signed-rank test. C, Univariate logistic regression predicting peritumoral and intratumoral IH with individual lymphocyte interactions as variables, n = 654. The coefficients of the significant variables were all negative, suggesting a higher frequency of the interactions in peritumoral IH compared with intratumoral IH. P values were adjusted by the Benjamini–Hochberg method. Regions identified as TLS or LAG were excluded from the above analyses.

Characteristics of the included patients and their association with the Sintra/immune, Sintra/cancer, and Sintra/tissue.

Lymphocyte composition and the presence of TLS at intratumoral and peritumoral IH. A, Example regions showing annotations of TLS at the IH and pathologically defined lymphoid aggregates without a prominent germinal center (LAG) at the intratumoral IH. B, Lymphocyte subset densities at intratumoral (n = 10) and peritumoral IH (n = 10). Cell densities were measured as the cell counts divided by the total area of IHs. C, Pie chart displaying the average percentages of peritumoral IH and intratumoral IH identified as TLS, LAG, and non-TLS/LAG IH. D, Densities of TLS and LAG at intratumoral (n = 10) and peritumoral IH (n = 10). E, Densities of B- and T-cell subsets in TLS (n = 10), LAG (n = 10), and non-TLS/LAG IH (n = 10). Statistical significance was evaluated by the Wilcoxon signed-rank test, followed by the Benjamini–Hochberg method for P value adjustment. *, P < 0.05; **, P < 0.01. The median is marked by the horizontal line; the first and third quartiles are shown as box edges; whiskers indicate the range of error.

Lists of TCGA LUSC and LUAD cases in the discovery and validation cohort

Immune hotspots in LUSC. A, Illustrative examples of automatic cell classification results and hotspot distribution with zoomed-in regions showing areas enriched with immune cells and cancer–immune coclustering. Cancer hotspots (red) and IHs were identified based on the local clustering of cancer and lymphocytes. Intratumoral IH (green) was determined as regions enriched with both immune and cancer cells, while peritumoral IH (blue) were regions enriched with only immune cells. B, Schematic representations of tissue compartments including cancer hotspots, peritumoral IH, intratumoral IH based on automated spatial analysis, and pathologically defined TLSs and lymphoid aggregates without clear separation of T- and B-cell zones (LAG). The three spatial scores Sintra/immune, Sintra/cancer, and Sintra/tissue were determined as the proportion of intratumoral IH in the immune-rich area (intratumoral IH and peritumoral IH), cancer-rich area (intratumoral IH and cancer hotspots), and the entire tissue, respectively. C, Kaplan–Meier curves to illustrate the difference in OS in LUSC patients stratified by Sintra/immune, with the dichotomization optimized for the discovery cohort (n = 231) and assessed in the validation cohort (n = 231). D, Forest plots to show the prognostic value of Sintra/immune in multivariate models adjusted for stage, age, and smoking pack years in the validation cohort. E, Spearman correlation between lymphocyte percentage and Sintra/immune in the TCGA LUSC cohort (n = 462). F, Kaplan–Meier curve to illustrate the difference in OS in LUSC patients stratified by both Sintra/immune and lymphocyte percentage, with the stratification of the entire cohort shown (n = 462).

B-cell dominance of high-Sintra/immune group in LUSC. A, Gene Ontology (GO) biological processes found to be enriched in the DEGs according to patient groups stratified by Sintra/immune. The red line denotes the ratio of the number of DEGs in a pathway to the total number of genes in that pathway. FDR, false discovery rate. B, Volcano plot showing DEGs in the high-Sintra/immune group compared with the low group. Genes with log-fold change (logFC) smaller than 1 were excluded. Genes involved in immune responses are highlighted in yellow. C, Consistently upregulated B-cell signatures in high-Sintra/immune group across seven bioinformatics methods using the TCGA LUSC cohort, n = 459. Statistical significance was evaluated by the Wilcoxon rank sum test and adjusted by the Benjamini–Hochberg method.

Supplementary Figure 2: Localization of endogenous Akt and pAkt (pT308) in SKBR3 cells and FFPE human breast tumor tissues using TSA amplified immunofluorescence.

Beyond tertiary lymphoid structures, a significant number of immune-rich areas without germinal center-like structures are observed in non–small cell lung cancer. Here, we integrated transcriptomic data and digital pathology images to study the prognostic implications, spatial locations, and constitution of immune rich areas (immune hotspots) in a cohort of 935 patients with lung cancer from The Cancer Genome Atlas. A high intratumoral immune hotspot score, which measures the proportion of immune hotspots interfacing with tumor islands, was correlated with poor overall survival in lung squamous cell carcinoma but not in lung adenocarcinoma. Lung squamous cell carcinomas with high intratumoral immune hotspot scores were characterized by consistent upregulation of B-cell signatures. Spatial statistical analyses conducted on serial multiplex IHC slides further revealed that only 4.87% of peritumoral immune hotspots and 0.26% of intratumoral immune hotspots were tertiary lymphoid structures. Significantly lower densities of CD20+CXCR5+ and CD79b+ B cells and less diverse immune cell interactions were found in intratumoral immune hotspots compared with peritumoral immune hotspots. Furthermore, there was a negative correlation between the percentages of CD8+ T cells and T regulatory cells in intratumoral but not in peritumoral immune hotspots, with tertiary lymphoid structures excluded. These findings suggest that the intratumoral immune hotspots reflect an immunosuppressive niche compared with peritumoral immune hotspots, independent of the distribution of tertiary lymphoid structures. A balance toward increased intratumoral immune hotspots is indicative of a compromised antitumor immune response and poor outcome in lung squamous cell carcinoma. Significance: Intratumoral immune hotspots beyond tertiary lymphoid structures reflect an immunosuppressive microenvironment, different from peritumoral immune hotspots, warranting further study in the context of immunotherapies.

Environmental carcinogenic exposures are major contributors to global disease burden yet how they promote cancer is unclear. Over 70 years ago, the concept of tumour promoting agents driving latent clones to expand was first proposed. In support of this model, recent evidence suggests that human tissue contains a patchwork of mutant clones, some of which harbour oncogenic mutations, and many environmental carcinogens lack a clear mutational signature. We hypothesised that the environmental carcinogen

Adaptive immunity recognizes and responds to tumors, although they are part of the immunological "self." T cells, both CD4+ and CD8+, play a key role in the process, and the specific set of receptors which recognize tumor antigens therefore has the potential to provide prognostic biomarkers for tracking tumor growth after cancer therapy, including immunotherapy. Most published data on the T cell repertoire continue to rely on commercial proprietary methods, which often do not allow access to the raw data, and are difficult to validate. We describe an open-source protocol for amplifying, sequencing, and analyzing T cell receptors which is economical, robust, sensitive, and versatile. The key experimental step is the ligation of a single-stranded oligonucleotide to the 3' end of the T cell receptor cDNA, which allows easy amplification of all possible rearrangements using only a single set of primers per locus, while simultaneously introducing a unique molecular identifier to label each starting cDNA molecule. After sequencing, this molecular identifier can be used to correct both sequence errors and the effects of differential PCR amplification efficiency, thus producing a more accurate measure of the true T cell receptor frequency within the sample. Samples are then tagged with unique pairs of indices, facilitating robotic scale-up and significantly reducing cross-sample contamination from index hopping. This method has been applied to the analysis of tumor-infiltrating lymphocytes and matched peripheral blood samples from patients with a variety of solid tumors.

Metastatic non-small cell lung cancer (NSCLC) results from a complex evolutionary process in which cancer cells migrate from a primary tumour to a new anatomical site. Immunotherapy is frequently used to treat NSCLC, but drug resistance is frequent (>50%) and metastasis remains the most common cause of death. Recent studies of primary and metastatic tumours suggest that anti-cancer treatments can impact cancer evolutionary dynamics by applying selective pressures that promote the development of treatment resistance in genetically distinct subpopulations of cancer cells, or tumour clones. However, the relationship between metastatic progression and the development of resistance to immunotherapy has not been fully explored. In particular, whether metastases become resistant to immunotherapy as a result of the migration of resistant cells or whether distinct metastatic sites converge in parallel toward an immunotherapy resistance phenotype irrespective of cell migration, is not known. In this work, we investigate the role of metastatic cell migration in the development of immunotherapy resistance using DNA sequencing data from primary and metastatic tumour samples from twelve immunotherapy-treated patients with NSCLC co-recruited to the national TRACERx and PEACE studies. Using computational approaches, we identified distinct tumour clones in primary and metastatic tumours and used these to reconstruct tumour phylogenies for each patient. By evaluating tumour clonal dynamics in relation to treatment, we classified tumour clones as either sensitive or resistant to immunotherapy. We reconstructed the metastatic migration histories of these clones using existing computational methods based on somatic single nucleotide variants. Our results suggest that heterogeneous mechanisms of immune evasion develop in parallel at distinct metastatic sites. The ongoing analysis of the genetic changes that distinguish resistant from sensitive clones, and migrating from non-migrating clones, will provide further detail regarding the impact of metastatic migrations on immunotherapy resistance. Using this approach to map the evolutionary history of metastatic NSCLC, we provide insight into the mechanism by which immunotherapy resistance develops at distinct metastatic sites. Citation Format: Sonya Hessey, Ariana Huebner, Oriol Pich, Abi Bunkum, Wing Liu, David A. Moore, Cristina Naceur-Lombardelli, Selvaraju Veeriah, Sophie Ward, Roberto Salgado, Nicholas McGranahan, Simone Zaccaria, Charles Swanton, Mariam Jamal-Hanjani. Tracking the emergence of immunotherapy resistance in lung cancer metastases [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2037.

Immunotherapy containing regimens have become the standard of care first-line therapy for non-small cell lung cancer (NSCLC) in the metastatic setting and are being explored in the adjuvant and neo-adjuvant setting. However, as intratumor heterogeneity (ITH) is pervasive in cancer we investigated the impact of ITH on established and putative biomarkers predictive of immunotherapy response. We have used whole exome sequencing, RNA sequencing and PDL1 immunohistochemistry (IHC) derived from the multiregion treatment naïve TRACERx cohort to investigate this. Whole exome sequencing data was available for 432 primary tumours from 421 patients with greater than 1500 unique tumour regions. Tumour mutational burden (TMB) was calculated using best practice guidelines from the TMB harmonisation project. Using the FDA approved threshold of ≥ 10 mutations per megabase to define high TMB we found that 41% of patients with high TMB had at least one region with In conclusion we demonstrate significant heterogeneity of immune biomarkers in NSCLC and in particular a lack of robustness of expression-based predictors of response to immunotherapy. This highlights the need for biomarkers that are robust to intra-tumour heterogeneity and sampling bias or the need for more representative tumour sampling techniques. Citation Format: Crispin T. Hiley, Kevin Litchfield, Oriol Pich, David Moore, Cristina Naceur-Lombardelli, Selvaraju Veeriah, Maise Al Bakir, Simranpreet Summan, Kristiana Grigoriadis, Carlos Martinez Ruiz, Clare Puttick, Katey Enfield, Sophia Ward, Alexander Frankell, Dhruva Biswas, Rachel Rosenthal, Nicolai J. Birkbak, Mariam Jamal-Hanjani, Nicholas McGranahan, Charles Swanton, TRACERx Consortium. Heterogeneity of immunotherapy biomarkers in the TRACERx non-small cell lung cancer multi-region lung cancer cohort study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 645.

Patient-derived xenograft (PDX) models have become important models in cancer biology. They are thought to mimic tumor biology more closely than traditional cell lines as a consequence of their in vivo heterocellularity and cell-matrix interactions, their 3D architecture and their relatively recent derivation. The genomic fidelity of PDXs is integral to their applications in both basic research and in translational medicine. We derived multi-region PDX models from patients enrolled in TRACERx - a study of the evolutionary dynamics of non-small cell lung cancer (NSCLC) that uses a multi-region deep whole-exome sequencing (WES) approach - using more extensive spatial sampling to better understand the histological and genomic fidelity of the PDX approach. We transplanted regional NSCLC tumor tissue subcutaneously into immunocompromised NSG mice. 134 regions were attempted, resulting in 60 passagable xenografts (44.8%). Of these, 44 regional xenografts (32.8% of all regions) were NSCLC-derived, while 16 (11.9% of all regions) were B-cell lymphoproliferative disease. Multi-region sampling revealed heterogeneous success of PDX derivation between regions within individual patient tumors. There was correlation between PDX derivation and tumor purity by WES. Overall, NSCLC PDX models were established for 22 patients (50%; range 1-5 regional models per patient; Table 1). Histologically, broad similarity was observed between PDX models and corresponding patient tumor regions, although in a small number of models, variability was noted between the patient tumor and the corresponding PDX model, or during PDX passaging. PDXs were subjected to WES for comparison with matched patient tumor regions. Overall, our study demonstrates the feasibility of systematic multi-region PDX derivation and suggests that multiple spatial sampling of tumors could improve PDX take rates and generate PDXs that represent the intratumor diversity of heterogenous NSCLCs. Table 1. Engraftment rates of multiple spatial regions from non-small cell lung cancer tumors. Attempted NSCLC PDX B-Lymphoproliferations No Xenograft: Regions No Xenograft: Any Region LUAD 48 (21) 12 (7) 9 (7) 27 (10) LUSC 61 (17) 20 (11) 6 (5) 35 (4) Other 25 (6) 12 (4) 1 (1) 12 (1) TOTAL 134 (44) 44 (22) 16 (13) 74 (15) Citation Format: Robert E. Hynds, David R. Pearce, Ayse U. Akarca, Sophia Ward, Ariana Huebner, Oriol Pich, Gareth A. Wilson, Kate H. Gowers, Rebecca Towns, Assma Ben Aissa, Selvaraju Veeriah, Sergio A. Quezada, Mariam Jamal-Hanjani, Sam M. Janes, Nicholas McGranahan, David A. Moore, Teresa Marafioti, Charles Swanton. Multi-region patient-derived xenograft models from non-small cell lung cancer patients enrolled in lung TRACERx [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3096.

Murine tissues harbour signature, resident γδ T cell compartments with profound yet differential impacts on carcinogenesis. γδ T cell knockout mice have heightened susceptibility to carcinogenesis. However, it is now clear in the murine setting that IFNγ-producing γδ T cells reject tumours whilst IL-17-producing γδ T cells promote them. Nonetheless, many human γδ T cell compartments are distinct from that in mice and vice versa. The extent to which human tissues and tumours harbour resident γδ T cells, their effector function and their role in cancer is less clear. Although a large scale in silico study of 5000+ patients with cancer found that intratumoural γδ T cells were the most important correlate of survival, smaller studies have found that these cells to be associated with either survival or progression. Many historical studies have however been limited by the availability of technologies to rigorously identify, isolate, and examine tissue-resident γδ T cells. To address these issues, we present data from stage I-III non-small cell lung cancers and paired non-tumour (NT) tissue obtained at primary surgery from 25 patients enrolled in the TRACERx Study. Using flow cytometry and quantitative T cell receptor sequencing, we demonstrate that NT lung tissues harbour a resident population of Vδ1 γδ T cells, entirely distinct to blood. Compared with NT lung tissues, resident-memory and effector-memory Vδ1 T cells are enriched in tumours. RNA sequencing revealed that intratumoural Vδ1 T cells are skewed towards cytolysis and T-helper-1 functions, akin to intratumoural NK and CD8+ T cells. Importantly, we found no evidence of T-helper-17 skew that has been implicated in tumour promotion in murine models. Ongoing remission after surgery was significantly associated with the presence of CD103+ tissue-resident Vδ1 T cells in non-malignant lung tissues and the presence of CD45RA-/CD27+ effector-memory Vδ1 T cells in tumours. Moreover, patients with a greater proportion of intratumoural Vδ1 T cell clones shared with paired NT tissues were more likely to remain in remission, consistent with the cells’ proposed immunosurveillance function in steady state epithelial tissues. Whilst immunotherapies modulating αβ T cells have been successful for some patients, including those with non-small cell lung cancer, clinical trials of γδ T cells have so far demonstrated poor efficacy in solid cancers. These trials have hitherto exclusively utilised Vδ2 T cells, a subset which is found predominantly in peripheral blood and more commonly associated, albeit still rarely, with IL-17 production. The first-in-human clinical trial of Vδ1 T cell immunotherapy has just opened for patients with acute myeloid leukaemia. Thus, our findings have immediate translational relevance and support the utilisation of these as-yet-untapped Vδ1 T cells in solid cancer immunotherapy. Citation Format: Yin Wu, Dhruva Biswas, Ieva Usaite, Angelova Mihaela, Stefan Boeing, Takahiro Karasaki, Selvaraju Veeriah, Justyna Czyzewska-Khan, James Reading, Andrew Georgiou, Maise Al-Bakir, Nicholas McGranahan, Mariam Jamal-Hanjani, Allan Hackshaw, TRACERx Consortium, Sergio Quezada, Adrian Hayday, Charles Swanton. V-delta-1 T cells are resident in the human lung and associate with survival in patients with non-small cell lung cancer in the TRACERx Study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5636.