David Allan Moore

Late-stage non-small cell lung cancer (NSCLC) is rarely curable ¹ , underscoring a need to intervene earlier in the disease process. Growing evidence suggests that antitumour T cell responses are mounted but become progressively dysregulated during early tumorigenesis ^2,3 . Tracking and targeting preinvasive T cell regulation may inform new approaches to detect and intercept lung cancer development. Here we explore how the T cell network is remodelled during NSCLC development via multi-omic, cross-tissue immune profiling in patients with preinvasive lung lesions surveilled by autofluorescence bronchoscopy and computed tomography (CT) imaging. Effector regulatory CD4 ⁺ T cells (eT _reg cells) expressing basic leucine zipper ATF-like transcription factor (BATF) accumulated in high-grade premalignant airway lesions and were clonally related to circulating eT _reg cells. Circulating eT _reg cells were increasingly elevated during preinvasive progression, enabling lung tumorigenesis to be tracked through analysis of peripheral blood. Emergence of this clonally coordinated eT _reg cell circuit defined rapid progression in patients with early-stage NSCLC detected during CT screening. In mice, carcinogen-driven lung tumorigenesis triggered an analogous preinvasive eT _reg cell axis across the blood, airways and draining lymph nodes (dLNs). This culminated in an expansion of lung BATF ⁺ T _reg cells and T _reg cell-rich peribronchial immature tertiary lymphoid structures (iTLSs). Immune interception via phosphoinositide 3-kinase-δ (PI3Kδ) inhibition abrogated formation of T _reg cell-rich iTLSs, reduced circulating and pulmonary T _reg cells, increased local conventional type 1 dendritic cells (cDC1s) and reduced lung tumour incidence and size. These data reveal a conserved eT _reg cell network that emerges across tissues during early pulmonary tumorigenesis and provide a theranostic framework to track and target preinvasive immune regulation for lung cancer interception.

Just as organismal evolution is driven by environmental changes, somatic evolution is similarly driven by changes in tissue environments, whether caused by the normal process of aging, by lifestyle choices, or by extrinsic exposures. We present deep mutational profiling using duplex sequencing of over 370 samples of non-malignant lung tissue from 202 subjects, showing how aging, smoking and different anti-cancer therapies are associated with selection for particular mutations in normal lung tissue. Mutations within known cancer driver genes exhibit selection in the normal tissue that differs from that for lung cancers, with a substantially expanded spectrum of sites under selection. Multiregional profiling of mutational landscapes reveals convergent evolution within each individual’s lung. Understanding the forces controlling clonal selection as we age and due to lifetime exposures could be critical for controlling multiple diseases of old age.

Limited understanding of the biological processes that govern metastatic dissemination hinders its prevention and treatment ¹ . Here, using 501 longitudinally collected primary and metastatic tumour samples from 24 patients with non-small cell lung cancer (NSCLC) enrolled in the TRACERx lung study and PEACE autopsy programme, we infer tumour evolution from diagnosis to death. With DNA-sequencing data encompassing 70% of the metastases that were radiologically detected before death and paired multi-region sampled primary tumours, we show that the genomes of metastases diverge markedly from those of their ancestral primary tumour, with additional driver alterations and genome doubling events occurring after metastatic dissemination. In 62.5% of patients, multiple primary tumour subclones disseminated, each founding a distinct metastasis. These metastases served as sources of onward spread: more than half of the metastases sampled were seeded by other metastases. The duration that metastases existed in situ influenced their likelihood of seeding further metastases. Most metastatic migrations started and ended in the same anatomical cavity. The few subclones that exited the thorax to seed metastases disseminated widely and were enriched for somatic copy-number alterations, suggesting that chromosomal instability may facilitate extrathoracic spread. This spatial and temporal evolutionary analysis sheds light on the extent of metastatic diversity and seeding in advanced NSCLC—which tends to be underestimated in single metastasis biopsies—and identifies genomic and clinical mediators of metastatic progression.

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.

The extent to which exogenous sources, including cancer treatment, contribute to somatic evolution in normal tissue remains unclear. Here we used high-depth duplex sequencing ¹ (more than 30,000× coverage) to analyse 168 cancer-free samples representing 16 organs from 22 patients with metastatic cancer enroled in the PEACE research autopsy study. In every sample, we identified somatic mutations (range 305–2,854 mutations) at low variant allele frequencies (median 0.0000323). We extracted 16 distinct single-base substitution mutational signatures, reflecting processes that have moulded the genomes of normal cells. We identified alcohol-induced mutation acquisition in liver, smoking-induced mutagenesis in lung and cardiac tissue, and multiple treatment-induced processes, which correlated with therapy type and duration. Exogenous sources, including treatment, underpinned, on average, more than 40% of mutations in liver but less than 10% of mutations in brain samples. Finally, we observed tissue-specific selection, with positive selection in tissues such as lung ( PTEN and PIK3CA ), liver ( NF2L2 ) and spleen ( BRAF and NOTCH2 ), and limited selection in others, such as brain and cardiac tissue. More than 25% of driver mutations in normal tissue exposed to systemic anti-cancer therapy, including in TP53 , could be attributed to treatment. Immunotherapy, although not associated with increased mutagenesis, was linked to driver mutations in PPM1D and TP53 , illustrating how non-mutagenic treatment can sculpt somatic evolution. Our study reveals the rich tapestry of mutational processes and driver mutations in normal tissue, and the profound effect of lifetime exposures, including cancer treatment, on somatic evolution.

Differentially expressed genes in TCGA LUSC and LUAD patients with high versus low S_intra/immune

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

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.