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Intratumour heterogeneity (ITH) fuels lung cancer evolution, which leads to immune evasion and resistance to therapy 1 . Here, using paired whole-exome and RNA sequencing data, we investigate intratumour transcriptomic diversity in 354 non-small cell lung cancer tumours from 347 out of the first 421 patients prospectively recruited into the TRACERx study 2 , 3 . Analyses of 947 tumour regions, representing both primary and metastatic disease, alongside 96 tumour-adjacent normal tissue samples implicate the transcriptome as a major source of phenotypic variation. Gene expression levels and ITH relate to patterns of positive and negative selection during tumour evolution. We observe frequent copy number-independent allele-specific expression that is linked to epigenomic dysfunction. Allele-specific expression can also result in genomic–transcriptomic parallel evolution, which converges on cancer gene disruption. We extract signatures of RNA single-base substitutions and link their aetiology to the activity of the RNA-editing enzymes ADAR and APOBEC3A, thereby revealing otherwise undetected ongoing APOBEC activity in tumours. Characterizing the transcriptomes of primary–metastatic tumour pairs, we combine multiple machine-learning approaches that leverage genomic and transcriptomic variables to link metastasis-seeding potential to the evolutionary context of mutations and increased proliferation within primary tumour regions. These results highlight the interplay between the genome and transcriptome in influencing ITH, lung cancer evolution and metastasis. Computational and machine-learning approaches that integrate genomic and transcriptomic variation from paired primary and metastatic non-small cell lung cancer samples from the TRACERx cohort reveal the role of transcriptional events in tumour evolution.

Cancer-associated cachexia (CAC) is a major contributor to morbidity and mortality in individuals with non-small cell lung cancer. Key features of CAC include alterations in body composition and body weight. Here, we explore the association between body composition and body weight with survival and delineate potential biological processes and mediators that contribute to the development of CAC. Computed tomography-based body composition analysis of 651 individuals in the TRACERx (TRAcking non-small cell lung Cancer Evolution through therapy (Rx)) study suggested that individuals in the bottom 20th percentile of the distribution of skeletal muscle or adipose tissue area at the time of lung cancer diagnosis, had significantly shorter lung cancer-specific survival and overall survival. This finding was validated in 420 individuals in the independent Boston Lung Cancer Study. Individuals classified as having developed CAC according to one or more features at relapse encompassing loss of adipose or muscle tissue, or body mass index-adjusted weight loss were found to have distinct tumor genomic and transcriptomic profiles compared with individuals who did not develop such features. Primary non-small cell lung cancers from individuals who developed CAC were characterized by enrichment of inflammatory signaling and epithelial–mesenchymal transitional pathways, and differentially expressed genes upregulated in these tumors included cancer-testis antigen MAGEA6 and matrix metalloproteinases, such as ADAMTS3 . In an exploratory proteomic analysis of circulating putative mediators of cachexia performed in a subset of 110 individuals from TRACERx, a significant association between circulating GDF15 and loss of body weight, skeletal muscle and adipose tissue was identified at relapse, supporting the potential therapeutic relevance of targeting GDF15 in the management of CAC. Results of the TRACERx study shed new light into the association between body composition and body weight with survival in individuals with non-small cell lung cancer, and delineate potential biological processes and mediators contributing to the development of cancer-associated cachexia.

Lung adenocarcinomas (LUADs) display a broad histological spectrum from low-grade lepidic tumors through to mid-grade acinar and papillary and high-grade solid, cribriform and micropapillary tumors. How morphology reflects tumor evolution and disease progression is poorly understood. Whole-exome sequencing data generated from 805 primary tumor regions and 121 paired metastatic samples across 248 LUADs from the TRACERx 421 cohort, together with RNA-sequencing data from 463 primary tumor regions, were integrated with detailed whole-tumor and regional histopathological analysis. Tumors with predominantly high-grade patterns showed increased chromosomal complexity, with higher burden of loss of heterozygosity and subclonal somatic copy number alterations. Individual regions in predominantly high-grade pattern tumors exhibited higher proliferation and lower clonal diversity, potentially reflecting large recent subclonal expansions. Co-occurrence of truncal loss of chromosomes 3p and 3q was enriched in predominantly low-/mid-grade tumors, while purely undifferentiated solid-pattern tumors had a higher frequency of truncal arm or focal 3q gains and SMARCA4 gene alterations compared with mixed-pattern tumors with a solid component, suggesting distinct evolutionary trajectories. Clonal evolution analysis revealed that tumors tend to evolve toward higher-grade patterns. The presence of micropapillary pattern and ‘tumor spread through air spaces’ were associated with intrathoracic recurrence, in contrast to the presence of solid/cribriform patterns, necrosis and preoperative circulating tumor DNA detection, which were associated with extra-thoracic recurrence. These data provide insights into the relationship between LUAD morphology, the underlying evolutionary genomic landscape, and clinical and anatomical relapse risk. Analyses of the TRACERx study unveil the relationship between tissue morphology, the underlying evolutionary genomic landscape, and clinical and anatomical relapse risk of lung adenocarcinomas.

Background: Veno-venous extracorporeal membrane oxygenation (VV-ECMO) is a critical intervention for patients with severe lung failure, especially acute respiratory distress syndrome (ARDS). The weaning process from ECMO relies largely on expert opinion due to a lack of evidence-based guidelines. The ventilatory ratio (VR), which correlates with dead space and mortality in ARDS, is calculated as [minute ventilation (mL/min) x arterial pCO2 (mmHg)]/[predicted body weight × 100 × 37.5]. Objectives: The aim of this study was to determine whether the VR alone can serve as a reliable predictor of safe or unsafe liberation from VV-ECMO in critically ill patients. Methods: A multicenter retrospective analysis was conducted, involving ARDS patients undergoing VV-ECMO weaning at Massachusetts General Hospital (January 2016 – December 2020) and at the University Hospital Aachen (January 2012–December 2021). Safe liberation was defined as no need for ECMO recannulation within 48 h after decannulation. Clinical parameters were obtained for both centers at the same time point: 30 min after the start of the SGOT (sweep gas off trial). Results: Of the patients studied, 83.3% (70/84) were successfully weaned from VV-ECMO. The VR emerged as a significant predictor of unsafe liberation (OR per unit increase: 0.38; CI: 0.17–0.81; p = 0.01). Patients who could not be safely liberated had longer ICU and hospital stays, with a trend towards higher mortality (38% vs. 13%; p = 0.05). Conclusions: The VR may be a valuable predictor for safe liberation from VV-ECMO in ARDS patients, with higher VR values associated with an elevated risk of unsuccessful weaning and adverse clinical outcomes.

(1) Background: Liver metastases (LM) are the leading cause of death in colorectal cancer (CRC) patients. Despite advancements, relapse rates remain high and current prognostic nomograms lack accuracy. Our objective is to develop an interpretable neoadjuvant algorithm based on mathematical models to accurately predict individual risk, ensuring mathematical transparency and auditability. (2) Methods: We retrospectively evaluated 86 CRC patients with LM treated with neoadjuvant systemic therapy followed by complete surgical resection. A comprehensive analysis of 155 individual patient variables was performed. Logistic regression (LR) was utilized to develop the predictive model for relapse risk through significance testing and ANOVA analysis. Due to data limitations, gradient boosting machine (GBM) and synthetic data were also used. (3) Results: The model was based on data from 74 patients (12 were excluded). After a median follow-up of 58 months, 5-year relapse-free survival (RFS) rate was 33% and 5-year overall survival (OS) rate was 60.7%. Fifteen key variables were used to train the GBM model, which showed promising accuracy (0.82), sensitivity (0.59), and specificity (0.96) in predicting relapse. Similar results were obtained when external validation was performed as well. (4) Conclusions: This model offers an alternative for predicting individual relapse risk, aiding in personalized adjuvant therapy and follow-up strategies.

Capecitabine and temozolomide chemotherapy was used in 65 patients with grade 1/2 neuroendocrine tumors (NETs). 46 patients (70.8%) had pancreatic NETs (pNETs). Response rate was 47.7%, with two complete responses (3.1%), 29 partial responses (44.6%) and 27 patients (41.5%) achieved stable disease. Median progression-free survival was 16.1 months (95% CI: 10.7-21.6) and overall survival was 38.3 months (95% CI: 24.6-51.9). Differences in progression-free survival and overall survival between pNETs and non-pNETs were not found. Nine (13.8%) patients experienced grade 3/4 toxicities, mainly thrombocytopenia (10.8%) and neutropenia (7.7%). This is the largest reported series of NETs treated with capecitabine and temozolomide in daily practice and shows that this combination is a promising treatment option for both grade 1/2 pNETs and non-pNETs.

Sequencing of tumors is now routine and guides personalized cancer therapy. Mutant allele fractions (MAFs, or the ‘mutation dose’) of a driver gene may reveal the genomic structure of tumors and influence response to targeted therapies. We performed a comprehensive analysis of MAFs of driver alterations in unpaired primary and metastatic colorectal cancer (CRC) at our institution from 2010 to 2015 and studied their potential clinical relevance. Of 763 CRC samples, 622 had detailed annotation on overall survival in the metastatic setting (OSmet) and 89 received targeted agents matched to KRAS (MEK inhibitors), BRAF (BRAF inhibitors), or PIK3CA mutations (PI3K pathway inhibitors). MAFs of each variant were normalized for tumor purity in the sample (adjMAFs). We found lower adjMAFs for BRAFV600E and PIK3CA than for KRAS, NRAS, and BRAF non‐V600 variants. TP53 and BRAFV600E adjMAFs were higher in metastases as compared to primary tumors, and high KRAS adjMAFs were found in CRC metastases of patients with KRAS wild‐type primary tumors previously exposed to EGFR antibodies. Patients with RAS‐ or BRAFV600E‐mutated tumors, irrespective of adjMAFs, had worse OSmet. There was no significant association between adjMAFs and time to progression on targeted therapies matched to KRAS, BRAF, or PIK3CA mutations, potentially related to the limited antitumor activity of the employed drugs (overall response rate of 4.5%). In conclusion, the lower BRAFV600E and PIK3CA adjMAFs in subsets of primary CRC tumors indicate subclonality of these driver genes. Differences in adjMAFs between metastases and primary tumors suggest that approved therapies may result in selection of BRAFV600E‐ and KRAS‐resistant clones and an increase in genomic heterogeneity with acquired TP53 alterations. Despite significant differences in prognosis according to mutations in driver oncogenes, adjMAFs levels did not impact on survival and did not help predict benefit with matched targeted agents in the metastatic setting. A comprehensive analysis of mutant allele fractions of driver oncogene mutations in colorectal cancer suggests that (i) some events, including BRAFV600E, may be subclonal; (ii) standard chemotherapies and EGFR antibodies may change the genomic structure of metastatic lesions, with acquired gene alterations and selection of resistant clones; and (iii) the clonality of events does not affect patient outcome or response to matched targeted agents.

In the immunoncology era, an acceleration of tumor growth upon immune checkpoint inhibitors (ICI), defined as hyperprogressive disease (HPD) has been observed across different cancers. Although in non-small-cell lung cancer, most of the available evidence regarding HPD has been reported for patients treated with single agent PD-1 and PD-L1 inhibitors, in retrospective series a variable proportion of patients receiving ICI combinations also experienced HPD. Similarly, the shape of survival curves and the progression rates in clinical trials testing combinations of PD-1/PD-L1 inhibitors and anti-CTLA-4 agents suggest the occurrence of HPD. Few data are available regarding pseudoprogression upon ICI combinations. However, considering that pseudoprogression has been reported for anti-PD-1/PD-L1 agents and for CTLA-4 inhibitors separately, it is likely that it may occur also upon combinations of these two classes of drugs.

The introduction of the International Association for the Study of Lung Cancer grading system has furthered interest in histopathological grading for risk stratification in lung adenocarcinoma. Complex morphology and high intratumoral heterogeneity present challenges to pathologists, prompting the development of artificial intelligence (AI) methods. Here we developed ANORAK (pyrAmid pooliNg crOss stReam Attention networK), encoding multiresolution inputs with an attention mechanism, to delineate growth patterns from hematoxylin and eosin-stained slides. In 1,372 lung adenocarcinomas across four independent cohorts, AI-based grading was prognostic of disease-free survival, and further assisted pathologists by consistently improving prognostication in stage I tumors. Tumors with discrepant patterns between AI and pathologists had notably higher intratumoral heterogeneity. Furthermore, ANORAK facilitates the morphological and spatial assessment of the acinar pattern, capturing acinus variations with pattern transition. Collectively, our AI method enabled the precision quantification and morphology investigation of growth patterns, reflecting intratumoral histological transitions in lung adenocarcinoma.

BackgroundDespite recent advances in exome and RNA sequencing to identify tumor-rejection antigens including neoantigens, the existing techniques fail to identify the vast majority of antigens targeted by tumor-reactive cells. A growing number of studies suggest that HLA-I peptides derived from non-canonical (nonC) open reading frames or derived from allegedly non-coding regions can contribute to tumor immunogenicity. Here we use proteogenomics to identify personalized candidate canonical and non-canonical tumor-rejection antigens and to evaluate their contribution to cancer immune surveillance in patients.MethodsWhole exome sequencing was performed to identify the non-synonymous somatic mutations (NSM) and immunopeptidomics to identify the HLA-I presented peptides (pHLA) in 9 patient-derived tumor cell lines (TCL). Peptid-PRISM proteogenomics pipeline was used to identify both canonical and non-canonical pHLA, including those derived from NSM in coding regions. All peptides containing mutations and derived from either cancer-testis (CTA) or tumor-associated antigens (TAA) were selected as candidate tumor antigens. For nonC peptides, an immunopeptidomics healthy dataset containing several tissues and HLA-allotypes was used to eliminate those derived from normal ORFs and select nonC peptides preferentially expressed in tumor cells (nonC-TE). The selected candidate peptides were synthesized, pulsed onto autologous APCs and co-cultured with tumor-reactive ex vivo expanded lymphocytes to assess immune recognition (figure 1).ResultsNonC-TE peptides were identified in all TCL studied, ranging from 0.5% to 5.4% of the total HLA-I presented peptides (n= 506). As described previoulsy, 5’UTR were the main source. Of note, the tumor type did not have an impact on the frequency of presented nonC peptides, but rather the presence of HLA-A*11:01 and HLA-A*03:01 was a major determinant. T cell responses were detected against at least 13/33 putative neoantigens, 2/24 CTA and 2/61 TAA. On the contrary, none of the 471 nonC-TE candidate peptides tested thus far, including one containing a NSM were able to elicit a recall immune response. Nevertheless, T cells recognizing at least 3 of them were detected through in vitro sensitization of non-autologous PBMCs.Abstract 912 Figure 1Workflow diagramTumor biopsies and blood samples are obtained from cancer patients (left panel). Patient-derived tumor cell lines are generated in vitro, the peptides presented on HLA molecules are further isolated and analyzed in a mass-spectrometer (top panel). Whole exome sequencing (WES) from matched tumor and healthy tissue is performed to identify the non-synonymous somatic mutations (NSM) (middle panel). Peptide-PRISM proteogenomics pipeline combines the information from the immunopeptidomics data and WES to identify pHLA sequences from both canonical and non-canonical candidate tumor antigens (top right panel). Lymphocyte populations either TILs or sorted PBMCs are expanded and further screened for pre-existing T cell responses (bottom panel) against the candidate epitopes by co-culturing the T cells with peptide-pulsed autologous APC. The recognition is assessed by measuring IFNg release by elispot and the upregulation of activation surface markers by FACS (bottom right panel).ConclusionsOur results show that although HLA-I nonC peptides were frequently presented in all TCLs studied and they can be immunogenic, neoantigens derived from mutations in canonical coding regions were preferentially recognized by tumor-reactive lymphocytes, suggesting T cells targeting the latter are primed more efficiently. The identification of mutated nonC antigens using whole genome sequencing to identify mutations in non-coding regions warrants further examination. Still, the specificity of many tumor-reactive TILs remains unknown.Ethics Approval”This study was approved by the ”Comité de Ética de Investigación con Medicamentos del Hospital Universitario Vall d’Hebron” institution’s Ethics Board; approval number PR(AG)537/2019.”