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Mitotic cell division increases tumour mutation burden and copy number load, predictive markers of the clinical benefit of immunotherapy. Cell division correlates also with genomic demethylation involving methylation loss in late-replicating partial methylation domains. Here we find that immunomodulatory pathway genes are concentrated in these domains and transcriptionally repressed in demethylated tumours with CpG island promoter hypermethylation. Global methylation loss correlated with immune evasion signatures independently of mutation burden and aneuploidy. Methylome data of our cohort ( n  = 60) and a published cohort ( n  = 81) in lung cancer and a melanoma cohort ( n  = 40) consistently demonstrated that genomic methylation alterations counteract the contribution of high mutation burden and increase immunotherapeutic resistance. Higher predictive power was observed for methylation loss than mutation burden. We also found that genomic hypomethylation correlates with the immune escape signatures of aneuploid tumours. Hence, DNA methylation alterations implicate epigenetic modulation in precision immunotherapy. Demethylation of the genome is found in cancer. Here, the authors show that genomic demethylation entails changes in promoter methylation and gene expression associated with immune escape and suggest that the epigenetic alterations may be an important determinant of responses to immunotherapy.

Advanced metastatic cancer poses utmost clinical challenges and may present molecular and cellular features distinct from an early-stage cancer. Herein, we present single-cell transcriptome profiling of metastatic lung adenocarcinoma, the most prevalent histological lung cancer type diagnosed at stage IV in over 40% of all cases. From 208,506 cells populating the normal tissues or early to metastatic stage cancer in 44 patients, we identify a cancer cell subtype deviating from the normal differentiation trajectory and dominating the metastatic stage. In all stages, the stromal and immune cell dynamics reveal ontological and functional changes that create a pro-tumoral and immunosuppressive microenvironment. Normal resident myeloid cell populations are gradually replaced with monocyte-derived macrophages and dendritic cells, along with T-cell exhaustion. This extensive single-cell analysis enhances our understanding of molecular and cellular dynamics in metastatic lung cancer and reveals potential diagnostic and therapeutic targets in cancer-microenvironment interactions. Understanding the mechanisms that lead to lung adenocarcinoma metastasis is important for identifying new therapeutics. Here, the authors document the changes in the transcriptome of human lung adenocarcinoma using single-cell sequencing and link cancer cell signatures to immune cell dynamics.

Patients with resectable lung cancer were assigned to neoadjuvant pembrolizumab or placebo plus chemotherapy and adjuvant pembrolizumab or placebo. Two-year event-free survival was 62.4% with pembrolizumab and 40.6% with placebo.

Accurate detection of genomic alterations using high-throughput sequencing is an essential component of precision cancer medicine. We characterize the variant allele fractions (VAFs) of somatic single nucleotide variants and indels across 5095 clinical samples profiled using a custom panel, CancerSCAN. Our results demonstrate that a significant fraction of clinically actionable variants have low VAFs, often due to low tumor purity and treatment-induced mutations. The percentages of mutations under 5% VAF across hotspots in EGFR , KRAS , PIK3CA , and BRAF are 16%, 11%, 12%, and 10%, respectively, with 24% for EGFR T790M and 17% for PIK3CA E545. For clinical relevance, we describe two patients for whom targeted therapy achieved remission despite low VAF mutations. We also characterize the read depths necessary to achieve sensitivity and specificity comparable to current laboratory assays. These results show that capturing low VAF mutations at hotspots by sufficient sequencing coverage and carefully tuned algorithms is imperative for a clinical assay. High-throughput sequencing is used to identify somatic variants in cancer patients. Here, the authors perform panel-based profiling of 5095 clinical samples and demonstrate that many clinically-actionable variants have low variant allele fractions, requiring assays with high detection sensitivity.

The complexity of the tumor microenvironment poses significant challenges in cancer therapy. Here, to comprehensively investigate the tumor-normal ecosystems, we perform an integrative analysis of 4.9 million single-cell transcriptomes from 1070 tumor and 493 normal samples in combination with pan-cancer 137 spatial transcriptomics, 8887 TCGA, and 1261 checkpoint inhibitor-treated bulk tumors. We define a myriad of cell states constituting the tumor-normal ecosystems and also identify hallmark gene signatures across different cell types and organs. Our atlas characterizes distinctions between inflammatory fibroblasts marked by AKR1C1 or WNT5A in terms of cellular interactions and spatial co-localization patterns. Co-occurrence analysis reveals interferon-enriched community states including tertiary lymphoid structure (TLS) components, which exhibit differential rewiring between tumor, adjacent normal, and healthy normal tissues. The favorable response of interferon-enriched community states to immunotherapy is validated using immunotherapy-treated cancers ( n  = 1261) including our lung cancer cohort ( n  = 497). Deconvolution of spatial transcriptomes discriminates TLS-enriched from non-enriched cell types among immunotherapy-favorable components. Our systematic dissection of tumor-normal ecosystems provides a deeper understanding of inter- and intra-tumoral heterogeneity. Single-cell sequencing has enabled detailed analyses of the tumour microenvironment (TME). Here, the authors perform an integrative analysis of the TME using single-cell and spatial transcriptomics data from over a thousand tumours across thirty cancer types, identifying interferon-enriched community states predictive of immunotherapeutic responses.

Results from clinical trials have established sunitinib as a standard of care for first-line treatment of advanced or metastatic renal-cell carcinoma (RCC); however, many patients, particularly those with a poorer prognosis, do not meet inclusion criteria and little is known about the activity of sunitinib in these subgroups. The primary objective of this trial was to provide sunitinib on a compassionate-use basis to trial-ineligible patients with RCC from countries where regulatory approval had not been granted. Previously treated and treatment-naive patients at least 18 years of age with metastatic RCC were eligible. All patients received open-label sunitinib 50 mg orally once daily on schedule 4-2 (4 weeks on treatment, 2 weeks off). Safety was assessed regularly, tumour measurements done per local practice, and survival data collected where possible. Analyses were done in the modified intention-to-treat (ITT) population, which consisted of all patients who received at least one dose of sunitinib. This study is registered with ClinicalTrials.gov, NCT00130897. As of December, 2007, 4564 patients were enrolled in 52 countries. 4371 patients were included in the modified ITT population. This population included 321 (7%) patients with brain metastases, 582 (13%) with Eastern Cooperative Oncology Group (ECOG) performance status of 2 or higher, 588 (13%) non-clear-cell RCC, and 1418 (32%) aged 65 years or more. Patients received a median of five treatment cycles (range 1–25). Reasons for discontinuation included lack of efficacy (n=1168 [27%]) and adverse events (n=362 [8%]). The most common treatment-related adverse events were diarrhoea (n=1936 [44%]) and fatigue (n=1606 [37%]). The most common grade 3–4 adverse events were fatigue (n=344 [8%]) and thrombocytopenia (n=338 [8%]) with incidences of grade 3–4 adverse events similar across subgroups. In 3464 evaluable patients, the objective response rate (ORR) was 17% (n=603), with subgroup ORR as follows: brain metastases (26 of 213 [12%]), ECOG performance status 2 or higher (29 of 319 [9%]), non-clear-cell RCC (48 of 437 [11%]) and age 65 years or more (176 of 1056 [17%]). Median progression-free survival was 10·9 months (95% CI 10·3–11·2) and overall survival was 18·4 months (17·4–19·2). In a broad population of patients with metastatic RCC, the safety profile of sunitinib 50 mg once-daily (initial dose) on schedule 4-2 was manageable and efficacy results were encouraging, particularly in subgroups associated with poor prognosis who are not usually entered into clinical trials. Pfizer Inc.

Neoantigen burden is regarded as a fundamental determinant of response to immunotherapy. However, its predictive value remains in question because some tumours with high neoantigen load show resistance. Here, we investigate our patient cohort together with a public cohort by our algorithms for the modelling of peptide-MHC binding and inter-cohort genomic prediction of therapeutic resistance. We first attempt to predict MHC-binding peptides at high accuracy with convolutional neural networks. Our prediction outperforms previous methods in > 70% of test cases. We then develop a classifier that can predict resistance from functional mutations. The predictive genes are involved in immune response and EGFR signalling, whereas their mutation patterns reflect positive selection. When integrated with our neoantigen profiling, these anti-immunogenic mutations reveal higher predictive power than known resistance factors. Our results suggest that the clinical benefit of immunotherapy can be determined by neoantigens that induce immunity and functional mutations that facilitate immune evasion. Predicting response to cancer immunotherapy is still a challenge. Here, the authors show that their method of predicting MHC-binding peptides, combined with profiling anti-immunogenic mutations, can better predict the clinical benefit of immunotherapy.

Background Although atezolizumab has demonstrated efficacy in advanced non-small-cell lung cancer (NSCLC), antidrug antibody (ADA) may reduce its effectiveness by lowering drug exposure. We explored the association between ADA levels and clinical outcomes. Methods We retrospectively analyzed 86 patients with advanced NSCLC who received atezolizumab monotherapy (1200 mg every 3 weeks) between August 2018 and September 2022 at Samsung Medical Center, Seoul, Korea. Blood samples were collected prior to the first and second doses (baseline and week 3). ADA levels were measured by ELISA and correlated with plasma atezolizumab concentrations and clinical outcomes using Kaplan–Meier estimates and Cox proportional hazards models. Results All 86 patients received atezolizumab as a second-line or later treatment (second-line, n = 65; third-line, n = 12; ≥ fourth-line, n = 9). Patients (median age [IQR], 67 [61–73] years; 73 [84.9%] male) showed significantly elevated ADA levels three weeks after treatment (median [IQR] 0 [0–0] vs. 530.3 [146.9–3050.5] ng/mL; p  < 0.001). Strong ADA levels (≥ 1000 ng/mL) were observed in 32 (37.2%) patients and were associated with shorter PFS (HR = 1.86, 95% CI: 1.15–3.02, p  = 0.010) and OS (HR = 1.92, 95% CI: 1.14–3.23, p  = 0.013). Furthermore, patients with high ADA levels exhibited lower atezolizumab concentrations and reduced response rates compared to those with low ADA levels. Importantly, high ADA levels independently predicted poor prognosis in a multivariable analysis adjusted for clinical variables. Conclusions High ADA levels were linked to lower atezolizumab exposure and worse outcomes. ADA monitoring may help predict prognosis and guide immunotherapy strategies.

Intratumoral heterogeneity has been suggested to be an important resistance mechanism leading to treatment failure. We hypothesized that radiologic images could be an alternative method for identification of tumor heterogeneity. We tested heterogeneity textural parameters on pretreatment FDG-PET/CT in order to assess the predictive value of target therapy. Recurred or metastatic non-small cell lung cancer (NSCLC) subjects with an activating EGFR mutation treated with either gefitinib or erlotinib were reviewed. An exploratory data set (n = 161) and a validation data set (n = 21) were evaluated, and eight parameters were selected for survival analysis. The optimal cutoff value was determined by the recursive partitioning method, and the predictive value was calculated using Harrell's C-index. Univariate analysis revealed that all eight parameters showed an increased hazard ratio (HR) for progression-free survival (PFS). The highest HR was 6.41 (P<0.01) with co-occurrence (Co) entropy. Increased risk remained present after adjusting for initial stage, performance status (PS), and metabolic volume (MV) (aHR: 4.86, P<0.01). Textural parameters were found to have an incremental predictive value of early EGFR tyrosine kinase inhibitor (TKI) failure compared to that of the base model of the stage and PS (C-index 0.596 vs. 0.662, P = 0.02, by Co entropy). Heterogeneity textural parameters acquired from pretreatment FDG-PET/CT are highly predictive factors for PFS of EGFR TKI in EGFR-mutated NSCLC patients. These parameters are easily applicable to the identification of a subpopulation at increased risk of early EGFR TKI failure. Correlation to genomic alteration should be determined in future studies.

Background Intratumoral heterogeneity (ITH) and tumor microenvironment (TME) of pancreatic ductal adenocarcinoma (PDAC) play important roles in tumor evolution and patient outcomes. However, the precise characterization of diverse cell populations and their crosstalk associated with PDAC progression and metastasis is still challenging. Methods We performed single-cell RNA sequencing (scRNA-seq) of treatment-naïve primary PDAC samples with and without paired liver metastasis samples to understand the interplay between ITH and TME in the PDAC evolution and its clinical associations. Results scRNA-seq analysis revealed that even a small proportion (22%) of basal-like malignant ductal cells could lead to poor chemotherapy response and patient survival and that epithelial-mesenchymal transition programs were largely subtype-specific. The clonal homogeneity significantly increased with more prevalent and pronounced copy number gains of oncogenes, such as KRAS and ETV1 , and losses of tumor suppressor genes, such as SMAD2 and MAP2K4 , along PDAC progression and metastasis. Moreover, diverse immune cell populations, including naïve SELL hi regulatory T cells (Tregs) and activated TIGIT hi Tregs, contributed to shaping immunosuppressive TMEs of PDAC through cellular interactions with malignant ductal cells in PDAC evolution. Importantly, the proportion of basal-like ductal cells negatively correlated with that of immunoreactive cell populations, such as cytotoxic T cells, but positively correlated with that of immunosuppressive cell populations, such as Tregs. Conclusion We uncover that the proportion of basal-like subtype is a key determinant for chemotherapy response and patient outcome, and that PDAC clonally evolves with subtype-specific dosage changes of cancer-associated genes by forming immunosuppressive microenvironments in its progression and metastasis.