Search

Extrachromosomal DNA (ecDNA) is a common mode of oncogene amplification but is challenging to analyze. Here, we adapt CRISPR-CATCH, in vitro CRISPR-Cas9 treatment and pulsed field gel electrophoresis of agarose-entrapped genomic DNA, previously developed for bacterial chromosome segments, to isolate megabase-sized human ecDNAs. We demonstrate strong enrichment of ecDNA molecules containing EGFR, FGFR2 and MYC from human cancer cells and NRAS ecDNA from human metastatic melanoma with acquired therapeutic resistance. Targeted enrichment of ecDNA versus chromosomal DNA enabled phasing of genetic variants, identified the presence of an EGFRvIII mutation exclusively on ecDNAs and supported an excision model of ecDNA genesis in a glioblastoma model. CRISPR-CATCH followed by nanopore sequencing enabled single-molecule ecDNA methylation profiling and revealed hypomethylation of the EGFR promoter on ecDNAs. We distinguished heterogeneous ecDNA species within the same sample by size and sequence with base-pair resolution and discovered functionally specialized ecDNAs that amplify select enhancers or oncogene-coding sequences.

Understanding the evolutionary pathways to metastasis and resistance to immune-checkpoint inhibitors (ICI) in melanoma is critical for improving outcomes. Here, we present the most comprehensive intrapatient metastatic melanoma dataset assembled to date as part of the Posthumous Evaluation of Advanced Cancer Environment (PEACE) research autopsy program, including 222 exome sequencing, 493 panel-sequenced, 161 RNA sequencing, and 22 single-cell whole-genome sequencing samples from 14 ICI-treated patients. We observed frequent whole-genome doubling and widespread loss of heterozygosity, often involving antigen-presentation machinery. We found KIT extrachromosomal DNA may have contributed to the lack of response to KIT inhibitors of a KIT-driven melanoma. At the lesion-level, MYC amplifications were enriched in ICI nonresponders. Single-cell sequencing revealed polyclonal seeding of metastases originating from clones with different ploidy in one patient. Finally, we observed that brain metastases that diverged early in molecular evolution emerge late in disease. Overall, our study illustrates the diverse evolutionary landscape of advanced melanoma.Significance:Despite treatment advances, melanoma remains a deadly disease at stage IV. Through research autopsy and dense sampling of metastases combined with extensive multiomic profiling, our study elucidates the many mechanisms that melanomas use to evade treatment and the immune system, whether through mutations, widespread copy-number alterations, or extrachromosomal DNA.

Supplementary figure 1: Cohort overview. Number of samples sequenced with whole exome, panel or whole RNA sequencing. Supplementary figure 2: Phylogeny and WGD events in CRUKP1047. Supplementary figure 3: Ploidy and SCNA burden. Supplementary figure 4: Overview of each case. Supplementary figure 5: MEDICC2 copy number sample trees. Supplementary figure 6: MEDICC tree of all exome samples demonstrating that samples cluster together by patient, and not by melanoma subtype. Supplementary figure 7: SCNA frequency of cutaneous (a), acral (b) and melanoma of unknown primary (MUP, c). Supplementary figure 8: Correlation between liver copy number distance to other sites and time of emergence after stage IV diagnosis. Supplementary figure 9: Examination of tumour heterogeneity of alterations to antigen-presentation machinery genes, with site and patient annotation. Supplementary figure 10: Boxplots indicating the proportion of losses in the cohort for each segment. Supplementary figure 11: Balance of expression between nonsynonymous mutations that were not predicted to be neoantigens and clonal predicted neoantigens. Supplementary figure 12: Barplot of TIL infiltration score frequencies, determined by pathologist assessment of histology, across all samples. Supplementary figure 13: Number of samples per patient that are classified as either none-low in terms of TILs or moderate-heavy. Supplementary figure 14: Histogram of purity for samples with RNA-seq data. Supplementary figure 15: TME deconvolution. Supplementary figure 16: The effect of metastatic site on transcriptional profiles. Supplementary figure 17: Association of PHF3 copy number with expression. Supplementary figure 18: Overview of gene fusions identified in RNA-seq data. Supplementary figure 19: Comparison of ploidy estimates from panel sequencing data, exome data and FISH. Supplementary figure 20: Comparison of ploidy estimates in panel, exome, FISH and single cell data. Supplementary figure 21: FACs sort plot for CRUKP2567 diaphragmatic metastasis. Supplementary figure 22: Ploidy and wGII values from single cell sequencing of FACS-sorted tumour cells. Supplementary figure 23: Copy number profiles on chromosome 5 for bulk samples from primary and DI_2_R2, a diaphragmatic metastasis. Supplementary figure 24: Histogram of purity of samples for which RNA-seq was performed faceted by patient. Supplementary figure 25: Histogram of purity of samples for which RNA-seq was performed faceted by tissue site.

S. Table 1: List of PEACE Consortium members. S. Table 2: Sample database used for the study for Panel, Exome and RNASeq samples. S. Table 3: Overview of lines of treatment given to each patient. S. Table 4: Lesions and patients included in the lesion-level response to ICI analysis. S. Table 5: Hallmark genesets significantly upregulated in normal tissue vs tumor tissue. Table shows p-values for tissue type, purity; T value for tissue type, purity; q-value for tissue type, purity (FDR corrected). S. Table 6: Hallmark genesets significantly upregulated in tumor tissue vs normal tissue. Table shows p-values for tissue type, purity; T value for tissue type, purity; q-value for tissue type, purity (FDR corrected). S. Table 7: Genes included in the target panel. S. Table 8: Tree topology comparisons between manually constructed trees and pairtree-constructed trees.

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

Table S3 and Fig. S1-S10

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.

MOV file 2346K, Video showing dynamic flow adhesion assays of ZR75.1 shNTC and shST6 cells on a HUVEC monolayer

Assessing APOBEC3 gene expression using immunohistochemical and bioinformatic approaches.

APOBEC3 gene expression during senescence and cell cycle progression.

PDF file 149K, In vivo validation of 3 hits identified in the screen (Mre11a, Fen1, Wwc1)

Supplementary Table 1: Overview of data resource. Supplementary Table 2: List of antibodies used for immunofluorescence. Supplementary Table 3: List of antibodies used for western blotting. Supplementary Table 4. List of primers used for PCR.

Generation and characterization of A3A and A3B knockouts in TIIP cells.

A complete list of investigators in the Tracking Non-Small Cell Lung Cancer Evolution through Therapy (TRACERx) Consortium

Correlation between APOBEC3 expression and different measures of CIN.

PDF file 107K, Inventory of supplementary material. Supplementary Table S1 listing primers and siRNAs. Legends for Supplementary Fig. S1 - S7 Legends for Supplementary Movies S1 and S2 Supplementary Figures S1 - S6 legends Supplementary Movies S1 and S2 legends

APOBEC3 enzymes are cytosine deaminases implicated in cancer. Precisely when APOBEC3 expression is induced during cancer development remains to be defined. Here we show that specific APOBEC3 genes are upregulated in breast ductal carcinoma in situ, and in preinvasive lung cancer lesions coincident with cellular proliferation. We observe evidence of APOBEC3-mediated subclonal mutagenesis propagated from TRACERx preinvasive to invasive non–small cell lung cancer (NSCLC) lesions. We find that APOBEC3B exacerbates DNA replication stress and chromosomal instability through incomplete replication of genomic DNA, manifested by accumulation of mitotic ultrafine bridges and 53BP1 nuclear bodies in the G1 phase of the cell cycle. Analysis of TRACERx NSCLC clinical samples and mouse lung cancer models revealed APOBEC3B expression driving replication stress and chromosome missegregation. We propose that APOBEC3 is functionally implicated in the onset of chromosomal instability and somatic mutational heterogeneity in preinvasive disease, providing fuel for selection early in cancer evolution.Significance:This study reveals the dynamics and drivers of APOBEC3 gene expression in preinvasive disease and the exacerbation of cellular diversity by APOBEC3B through DNA replication stress to promote chromosomal instability early in cancer evolution.This article is highlighted in the In This Issue feature, p. 2355

Understanding the evolutionary pathways to metastasis and resistance to immune checkpoint inhibitors (ICI) in melanoma is critical for improving outcomes. Here we present the most comprehensive intra-patient metastatic melanoma dataset assembled to date as part of the PEACE research autopsy programme, including 222 exome, 493 panel-sequenced, 161 RNA-seq, and 22 single-cell whole-genome sequencing samples from 14 ICI-treated patients. We observed frequent whole-genome doubling and widespread loss of heterozygosity, often involving antigen presentation machinery. We found KIT extrachromosomal DNA may have contributed to the lack of response to KIT inhibitors of a KIT-driven melanoma. At the lesion-level, MYC amplifications were enriched in ICI non-responders. Single-cell sequencing revealed polyclonal seeding of metastases originating from clones with different ploidy in one of the patients. Finally, we observed that brain metastases that diverged early in molecular evolution emerge late in disease. Overall, our study illustrates the diverse evolutionary landscape of advanced melanoma. Here we present the processed data for this study, including mutation calls, segment-level copy number calls, ploidy calls, neoantigen calls, pyclone calls, transcriptomic count data, lesion-level response data, single-cell copy number calls, and sample database.

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.

Objectives There is an increasing number of driver fusions in NSCLC which are amenable to targeted therapy. Panel testing for fusions is increasingly appropriate but can be costly and requires adequate good quality biopsy material. In light of the typical mutual exclusivity of driver events in NSCLC, the objective of this study was to trial a novel testing pathway, supported by industrial collaboration, in which only patients negative for driver mutations on DNA-NGS were submitted for fusion panel analysis. Materials and methods Over 18 months, all patients from a single centre with non-squamous NSCLC were submitted for DNA-NGS, plus ALK and ROS1 immunohistochemistry +/− FISH. Those which were negative for a driver mutation were then recalled for RNA panel testing. Results 307 samples were referred for DNA-NGS mutation analysis, of which, 10% of cases were unsuitable for or failed DNA-NGS analysis. Driver mutations were detected in 61% (167/275) of all those successfully tested. Of those without a driver mutation and with some remaining tissue available, 28% had insufficient tissue/extracted RNA or failed RNA-NGS. Of those successfully tested, 24% (17/72) had a fusion gene detected involving either ALK, ROS, MET, RET, FGFR or EGFR. Overall, 66% (184/277) of patients had a driver event detected through the combination of DNA and RNA panels. Conclusion Sequential DNA and RNA based molecular profiling increased the efficacy of detecting fusion driven NSCLCs. Continued optimisation of tissue procurement, handling and the diagnostic pathways for gene fusion analysis is necessary to reduce analysis failure rates and improve detection rate for treatment with the next generation of small molecule inhibitors.

Over the past 10 years, lung cancer clinical and translational research has been characterised by exponential progress, exemplified by the introduction of molecularly targeted therapies, immunotherapy and chemo-immunotherapy combinations to stage III and IV non-small cell lung cancer. Along with squamous and small cell lung cancers, large cell neuroendocrine carcinoma (LCNEC) now represents an area of unmet need, particularly hampered by the lack of an encompassing pathological definition that can facilitate real-world and clinical trial progress. The steps we have proposed in this article represent an iterative and rational path forward towards clinical breakthroughs that can be modelled on success in other lung cancer pathologies.

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

During cancer evolution, constituent tumor cells compete under dynamic selection pressures. Phenotypic variation can be observed as intratumor heterogeneity, which is propagated by genome instability leading to mutations, somatic copy-number alterations, and epigenomic changes. TRACERx was set up in 2014 to observe the relationship between intratumor heterogeneity and patient outcome. By integrating multiregion sequencing of primary tumors with longitudinal sampling of a prospectively recruited patient cohort, cancer evolution can be tracked from early- to late-stage disease and through therapy. Here we review some of the key features of the studies and look to the future of the field. Significance: Cancers evolve and adapt to environmental challenges such as immune surveillance and treatment pressures. The TRACERx studies track cancer evolution in a clinical setting, through primary disease to recurrence. Through multiregion and longitudinal sampling, evolutionary processes have been detailed in the tumor and the immune microenvironment in non–small cell lung cancer and clear-cell renal cell carcinoma. TRACERx has revealed the potential therapeutic utility of targeting clonal neoantigens and ctDNA detection in the adjuvant setting as a minimal residual disease detection tool primed for translation into clinical trials.

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.

The translational challenges in the field of precision oncology are in part related to the biological complexity and diversity of this disease. Technological advances in genomics have facilitated large sequencing efforts and discoveries that have further supported this notion. In this review, we reflect on the impact of these discoveries on our understanding of several concepts: cancer initiation, cancer prevention, early detection, adjuvant therapy and minimal residual disease monitoring, cancer drug resistance, and cancer evolution in metastasis. We discuss key areas of focus for improving cancer outcomes, from biological insights to clinical application, and suggest where the development of these technologies will lead us. Finally, we discuss practical challenges to the wider adoption of molecular profiling in the clinic and the need for robust translational infrastructure.

Background: The vast majority of cancer deaths can be attributed to tumor metastases. Disseminated tumor cells (DTCs) are thought to act as a reservoir of tumor clones which can lie dormant before causing overt metastases. Their presence has been shown to be a poor prognostic indicator in several tumor types. Despite their importance, the timing and source of DTCs is unclear across most solid cancers and even less is known about them in late-stage disease. The Posthumous Evaluation of the Advanced Cancer Environment (PEACE) post-mortem study enables extensive sampling of malignant lesions but also normal tissues, thereby allowing study of DTCs in patients with advanced cancer. Methods: We performed research autopsy sampling of macroscopically normal bone, lung and liver in 3 patients with advanced clear cell renal cell carcinoma (RCC). In addition, we performed bone marrow aspiration at autopsy from vertebral bodies and the ilium using an anterior approach. Mechanical disaggregation followed by collagenase digestion was used to generate single cell suspensions. Detection of tumor cells was carried out using antibodies against known tumor or epithelial markers carbonic anhydrase IX (CAIX). Epithelial cell adhesion molecule (EpCAM) was also used for bone marrow samples as native bone marrow cells do not express this marker. These sub-populations were then isolated using fluorescence activated cell sorting and DNA was extracted and amplified from single cells. Results: We performed and optimised collagenase-based digestion on all solid tissues collected. The percentage of viable cells generated varied significantly between subjects and tissues with bone marrow samples more viable than lung and liver. Rare and distinct populations of CAIX+ and EpCAM+ cells were detected in normal tissues between 0.5-2%. DNA amplification was successful on single cells isolated from normal lung and liver but not from bone tissue. Pilot shallow coverage whole genome sequencing revealed genomically aberrant cells ranging from single chromosome arm losses to widespread copy number aberrations. Conclusion: We demonstrate feasibility of sequencing single cells from autopsy study subjects. Rare populations of single cells with markers of clear cell RCC were detected and isolated both in normal tissues of patients with advanced disease. Genomic analyses of these cells will lead to insights into their relationship to the primary tumor and overt metastatic lesions. Citation Format: Haixi Yan, Cristina Cotobal Martin, Christie English, Annelien Verfaillie, Jonas Demeulemeester, Andrew Rowan, Annika Fendler, Anne-Laure Cattin, Sucheta Mahapatra, Lewis Au, Scott Shepherd, Ben Shum, Charlotte Spencer, Zayd Tippu, Ula Mahadeva, Anna Green, Eleanor Carlyle, Cristina Naceur-Lombardelli, PEACE consortium, Mariam Jamal-Hanjani, Charles Swanton, Samra Turajlic, Peter Van Loo. Detection of disseminated tumor cells in advanced clear cell renal cell carcinoma through research autopsy [abstract]. In: Proceedings of the AACR Special Conference: Cancer Metastasis; 2022 Nov 14-17; Portland, OR. Philadelphia (PA): AACR; Cancer Res 2022;83(2 Suppl_2):Abstract nr A011.

PURPOSE OF REVIEW Thoracic malignancies are amongst the most lethal of all cancers. Cancer cachexia lacks unanimously accepted diagnostic criteria, and therefore is referenced to as a conceptual framework whereby cancer cachexia is ‘an ongoing loss of skeletal muscle mass (termed sarcopenia), with or without loss of fat mass that cannot be reversed by conventional nutritional support and leads to progressive functional impairment’. This review summarises the current evidence base in this field, including imaging techniques currently used to define sarcopenia, inflammatory and metabolic changes associated with the syndrome and ongoing research into potential treatment strategies. RECENT FINDINGS Sarcopenia is a key component of the cancer cachexia syndrome. It is common in patients with both early-stage and advanced NSCLC. Patients with sarcopenia have more treatment-related side effects and poorer overall survival compared with nonsarcopenic patients. SUMMARY Early identification of cancer cachexia may facilitate stratification of patients most-at-risk and initiation of emerging anticachexia treatments. If these are proven to be effective, this strategy has the potential to improve tolerance to anti-cancer therapies, improving the quality of life, and perhaps the survival, of patients with thoracic malignancies.

Murine tissues harbor signature γδ T cell compartments with profound yet differential impacts on carcinogenesis. Conversely, human tissue-resident γδ cells are less well defined. In the present study, we show that human lung tissues harbor a resident Vδ1 γδ T cell population. Moreover, we demonstrate that Vδ1 T cells with resident memory and effector memory phenotypes were enriched in lung tumors compared with nontumor lung tissues. Intratumoral Vδ1 T cells possessed stem-like features and were skewed toward cytolysis and helper T cell type 1 function, akin to intratumoral natural killer and CD8

Stage I non-small cell lung cancer (NSCLC) presents diverse outcomes. To identify molecular features leading to tumor recurrence in early-stage NSCLC, we perform multiregional whole-exome sequencing (WES), RNA sequencing, and plasma-targeted circulating tumor DNA (ctDNA) detection analysis between recurrent and recurrent-free stage I NSCLC patients (CHN-P cohort) who had undergone R0 resection with a median 5-year follow-up time. Integrated analysis indicates that the multidimensional clinical and genomic model can stratify the prognosis of stage I NSCLC in both CHN-P and EUR-T cohorts and correlates with positive pre-surgical deep next generation sequencing (NGS) ctDNA detection. Increased genomic instability related to DNA interstrand crosslinks and double-strand break repair processes is significantly associated with early tumor relapse. This study reveals important molecular insights into stage I NSCLC and may inform clinical postoperative treatment and follow-up strategies.

Approximately 50% of patients with early-stage non-small-cell lung cancer (NSCLC) who undergo surgery with curative intent will relapse within 5 years1,2. Detection of circulating tumor cells (CTCs) at the time of surgery may represent a tool to identify patients at higher risk of recurrence for whom more frequent monitoring is advised. Here we asked whether CellSearch-detected pulmonary venous CTCs (PV-CTCs) at surgical resection of early-stage NSCLC represent subclones responsible for subsequent disease relapse. PV-CTCs were detected in 48% of 100 patients enrolled into the TRACERx study3, were associated with lung-cancer-specific relapse and remained an independent predictor of relapse in multivariate analysis adjusted for tumor stage. In a case study, genomic profiling of single PV-CTCs collected at surgery revealed higher mutation overlap with metastasis detected 10 months later (91%) than with the primary tumor (79%), suggesting that early-disseminating PV-CTCs were responsible for disease relapse. Together, PV-CTC enumeration and genomic profiling highlight the potential of PV-CTCs as early predictors of NSCLC recurrence after surgery. However, the limited sensitivity of PV-CTCs in predicting relapse suggests that further studies using a larger, independent cohort are warranted to confirm and better define the potential clinical utility of PV-CTCs in early-stage NSCLC.

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.

Lung cancer is the leading cause of cancer-associated mortality worldwide, and genomic intratumour heterogeneity as a result of cancer evolution has been linked to therapeutic resistance and poor outcomes. TRACERx is a prospective longitudinal cancer evolution programme which aims to investigate the relationship between intratumour heterogeneity and clinical outcomes, and to map the evolutionary pathways adopted in lung cancer from early to late stage disease. Here we present our findings from mutational, copy number and mutational signature analyses in over 420 tumours, and provide insights into the shared patterns of tumour evolution and their impact on prognosis. We demonstrate a significant association between somatic copy number alteration (SCNA) intratumour heterogeneity, defined as the percentage of subclonal SCNAs as previously reported, and poor outcomes. Furthermore, we find that subclonal genome doubling, a driver of SCNA intratumour heterogeneity, is associated with poor outcome independent of clinical variables, such as tumour stage. Citation Format: Mariam Jamal-Hanjani. Lung cancer evolution and its prognostic impact [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 SY39-03.

Background: The adaptive immune system plays an important role in tumor evolution. A key source of cytotoxic T cell response in cancer is neoantigens, cancer cell specific mutations that result in mutant peptides that elicit a T cell mediated immune response. However, a mutation can only engender a neoantigen if the associated mutant peptide is presented to T cells by HLA molecules, and, as such, transcriptional repression or loss of the HLA genes can have important implications for immune evasion. Methods: We elucidate allele specific genomic and transcriptomic disruption to class I and II HLA genes. In whole exome sequencing (WES) data, our new tool (LOHHLA2.0) assesses loss of heterozygosity (LOH) status and somatic mutations. In RNA sequencing (RNAseq) data, LOHHLA2.0 quantifies allele specific expression and transcriptional repression referencing matched tumor adjacent normal samples. We applied LOHHLA2.0 to the TRACERx421 dataset, including 1554 WES tumor regions from 421 patients and 941 RNAseq regions from 357 tumor patients, 96 of which also had RNAseq from tumor adjacent normal samples. Results: We find that our method is more accurate than existing tools (RSEM) at calling gene level expression, e.g. in HLA-DPB1, RSEM under-calls HLA expression by a factor of two. 36% of TRACERx421 primary tumors harbored HLA LOH of at least one class I HLA gene, validating our previous findings in the TRACERx100 cohort. Strikingly, we found that 74% (71/96) of primary tumors with matched tumor adjacent normal tissue exhibited transcriptional repression of one or more class I HLA alleles, and 81% (78/96) exhibited class II allele transcriptional repression. Class I HLA transcriptional repression was more likely to occur subclonally than LOH. In a subset of tumors, we observed convergence upon disruption of the same allele through alternative mechanisms; with genomic loss in one tumor region and transcriptional repression in another region of the same tumor. Across the tumor regions, we found a concordance between HLA expression and immune infiltrate levels, with immune hot tumors exhibiting higher HLA class I expression. Conclusions: In this study, we find significant disruption to class I and II HLA expression adding to the diversity of immune evasion processes evident in early stage treatment naive NSCLC. Citation Format: Clare Puttick, Oriol Pich, Michelle Leung, Carlos Martinez-Ruiz, Robert Bentham, Rachel Rosenthal, Sonya Hessey, James R. Black, Emilia L. Lim, Katey Enfield, Emma Colliver, Krijn Dijkstra, Crispin T. Hiley, Takahiro Karasaki, Ariana Huebner, Maise Al Bakir, Thomas B. Watkins, Alexander M. Frankell, Simone Zaccaria, Mariam Jamal-Hanjani, Nicholas McGranahan, Charles Swanton. Pervasive allele specific transcriptional repression of the class I and II HLA genes in TRACERx non-small cell lung cancer [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 1394.