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The current tumour-node-metastasis (TNM) staging system is insufficient for precise treatment decision-making and accurate survival prediction for patients with stage I lung adenocarcinoma (LUAD). Therefore, more reliable biomarkers are urgently needed to identify the high-risk subset in stage I patients to guide adjuvant therapy. This study retrospectively analysed the transcriptome profiles and clinical parameters of 1,400 stage I LUAD patients from 14 public datasets, including 13 microarray datasets from different platforms and 1 RNA-Seq dataset from The Cancer Genome Atlas (TCGA). A series of bioinformatic and machine learning approaches were combined to establish hypoxia-derived signatures to predict overall survival (OS) and immune checkpoint blockade (ICB) therapy response in stage I patients. In addition, enriched pathways, genomic and copy number alterations were analysed in different risk subgroups and compared to each other. Among various hallmarks of cancer, hypoxia was identified as a dominant risk factor for overall survival in stage I LUAD patients. The hypoxia-related prognostic risk score (HPRS) exhibited more powerful capacity of survival prediction compared to traditional clinicopathological features, and the hypoxia-related immunotherapeutic response score (HIRS) outperformed conventional biomarkers for ICB therapy. An integrated decision tree and nomogram were generated to optimize risk stratification and quantify risk assessment. In summary, the proposed hypoxia-derived signatures are promising biomarkers to predict clinical outcomes and therapeutic responses in stage I LUAD patients.

Comprehensive genomic profiling (CGP) testing by next‐generation sequencing has been introduced into clinical practice as part of precision cancer medicine to select effective targeted therapies. However, whether CGP testing at the time of first‐line chemotherapy could be clinically useful is not clear. We conducted this single‐center, prospective, observational study to investigate the feasibility of CGP testing for chemotherapy‐naïve patients with stage III/IV gastrointestinal cancer, rare cancer, and cancer of unknown primary, using the FoundationOne® companion diagnostic (F1CDx) assay. The primary outcome was the detection rate of at least one actionable/druggable cancer genomic alteration. Actionable/druggable cancer genomic alterations were determined by the F1CDx report. An institutional molecular tumor board determined the molecular‐based recommended therapies. A total of 197 patients were enrolled from October 2018 to June 2019. CGP success rate was 76.6% (151 of 197 patients), and median turnaround time was 19 days (range: 10‐329 days). Actionable and druggable cancer genomic alterations were reported in 145 (73.6%) and 124 (62.9%) patients, respectively. The highest detection rate of druggable genomic alterations in gastrointestinal cancers was 80% in colorectal cancer (48 of 60 patients). Molecular‐based recommended therapies were determined in 46 patients (23.4%). CGP testing would be a useful tool for the identification of a potentially effective first‐line chemotherapy. Comprehensive genomic profiling (CGP) testing by next‐generation sequencing has been introduced into clinical practice as part of precision cancer medicine to select effective targeted therapies; however, whether CGP testing at the time of first‐line chemotherapy could be clinically useful is not clear. We conducted this single‐center, prospective, observational study to investigate the feasibility of CGP testing for chemotherapy‐naïve patients with stage III/IV gastrointestinal cancer, rare cancer, and cancer of unknown primary, using the FoundationOne® companion diagnostic assay. We found that CGP testing could be feasible in Japanese clinical practice for chemotherapy‐naïve patients with these cancers, and that CGP testing might be a useful tool to identify a potentially effective first‐line treatment, which will lead to the establishment of precision cancer medicine.

In spite of the development of new treatments for late stage prostate cancer, significant challenges persist to match individuals with effective targeted therapies. Genomic classification using high-throughput sequencing technologies has the potential to achieve this goal and make precision medicine a reality in the management of men with castrate-resistant prostate cancer. This chapter reviews some of the most recent studies that have resulted in significant progress in determining the landscape of somatic genomic alterations in this cohort and, more importantly, have provided clinically actionable information that could guide treatment decisions. This chapter reviews the current understanding of common alterations such as alterations of the androgen receptor and PTEN pathway, as well as ETS gene fusions and the growing importance of PARP inhibition. It also reviews recent studies that characterize the evolution to neuroendocrine tumors, which is becoming an increasingly important clinical problem. Finally, this chapter reviews recent innovative studies that characterize the compelling evolutionary history of lethal prostate cancer evidenced by polyclonal seeding and interclonal cooperation between metastasis and the importance of tumor clone dynamics measured serially in response to treatment. The genomic landscape of late stage prostate cancer is becoming better defined, and the prospect for assigning clinically actionable data to inform rationale treatment for individuals with this disease is becoming a reality.

Lung cancer is a public health problem and the first cause of cancer death worldwide. Radon is a radioactive gas that tends to accumulate inside homes, and it is the second lung cancer risk factor after smoking, and the first one in non-smokers. In Europe, there are several radon-prone areas, and although the 2013/59 EURATOM directive is aimed to regulate indoor radon exposition, regulating measures can vary between countries. Radon emits alpha-ionizing radiation that has been linked to a wide variety of cytotoxic and genotoxic effects; however, the link between lung cancer and radon from the genomic point of view remains poorly described. Driver molecular alterations have been recently identified in non-small lung cancer (NSCLC), such as somatic mutations (EGFR, BRAF, HER2, MET) or chromosomal rearrangements (ALK, ROS1, RET, NTRK), mainly in the non-smoking population, where no risk factor has been identified yet. An association between radon exposure and oncogenic NSCLC in non-smokers has been hypothesised. This paper provides a practical, concise and updated review on the implications of indoor radon in lung cancer carcinogenesis, and especially of its potential relation with NSCLC with driver genomic alterations.

The aim of this study was to determine the pan-cancer landscape of MUTYH alterations and the relationship between MUTYH mutations and potentially actionable biomarkers such as specific genomic alterations, tumor mutational burden, and mutational signatures. We used a large pan-cancer comprehensive genomic dataset from patients profiled (tissue next generation sequencing) during routine clinical care. Overall, 2.8% of 229 120 solid tumors had MUTYH alterations, of which 55% were predicted germline. Thirty tumor types had a 2% or greater MUTYH mutation rate. MUTYH-altered versus -WT cancers had significantly higher tumor mutational burden and more frequent alterations in KRAS G12C, but not in KRAS in general; these observations were statistically significant, especially in colorectal cancers. Across cancers, PD-L1 expression levels (immunohistochemistry) were not associated with MUTYH alteration status. In silico computation demonstrated that MUTYH mutational signatures are associated with higher levels of hydrophobicity (which may reflect higher immunogenicity of neoantigens) relative to several other signature types such as microsatellite instability. Survival of patients with MUTYH-altered versus -WT tumors was similar. In conclusion, comprehensive genomic profiling suggests that several features of MUTYH-altered cancers may be pharmacologically targetable. Drugs such as sotorasib (targeting KRAS G12C) and immune checkpoint inhibitors, targeting the increased mutational load and higher neo-antigen hydrophobicity/immunogenicity merit investigation in MUTYH-mutated malignancies. This study aimed to determine the pan-cancer landscape of MUTYH alterations and the relationship between MUTYH mutations and potentially actionable biomarkers, such as specific genomic alterations, tumor mutational burden, and mutational signatures.

Immune infiltration of tumors is closely associated with clinical outcome in renal cell carcinoma (RCC). Tumor‐infiltrating immune cells (TIICs) regulate cancer progression and are appealing therapeutic targets. The purpose of this study was to determine the composition of TIICs in RCC and further reveal the independent prognostic values of TIICs. CIBERSORT, an established algorithm, was applied to estimate the proportions of 22 immune cell types based on gene expression profiles of 891 tumors. Cox regression was used to evaluate the association of TIICs and immune checkpoint modulators with overall survival (OS). We found that CD8+ T cells were associated with prolonged OS (hazard ratio [HR] = 0.09, 95% confidence interval [CI].01‐.53; P = 0.03) in chromophobe carcinoma (KICH). A higher proportion of regulatory T cells was associated with a worse outcome (HR = 1.59, 95% CI 1.23‐.06; P < 0.01) in renal clear cell carcinoma (KIRC). In renal papillary cell carcinoma (KIRP), M1 macrophages were associated with a favorable outcome (HR = .43, 95% CI .25‐.72; P < 0.01), while M2 macrophages indicated a worse outcome (HR = 2.55, 95% CI 1.45‐4.47; P < 0.01). Moreover, the immunomodulator molecules CTLA4 and LAG3 were associated with a poor prognosis in KIRC, and IDO1 and PD‐L2 were associated with a poor prognosis in KIRP. This study indicates TIICs are important determinants of prognosis in RCC meanwhile reveals potential targets and biomarkers for immunotherapy development. We described the immune landscape in detail, revealing the distinct immune infiltration patterns of different subtypes and stages of RCC. We further revealed relationships between TIIC and molecular subtypes, tumor stages, recurrent genomic alterations and survival in RCC. Our work advances the understanding of immune response meanwhile reveals potential targets and biomarkers for immunotherapy development.

Lung cancer and chronic obstructive pulmonary disease (COPD) often co-occur, and individuals with COPD are at a higher risk of developing lung cancer. While the underlying mechanism for this risk is not well understood, its major contributing factors have been proposed to include genomic, immune, and microenvironment dysregulation. Here, we review the evidence and significant studies that explore the mechanisms underlying the heightened lung cancer risk in people with COPD. Genetic and epigenetic changes, as well as the aberrant expression of non-coding RNAs, predispose the lung epithelium to carcinogenesis by altering the expression of cancer- and immune-related genes. Oxidative stress generated by tobacco smoking plays a role in reducing genomic integrity, promoting epithelial-mesenchymal-transition, and generating a chronic inflammatory environment. This leads to abnormal immune responses that promote cancer development, though not all smokers develop lung cancer. Sex differences in the metabolism of tobacco smoke predispose females to developing COPD and accumulating damage from oxidative stress that poses a risk for the development of lung cancer. Dysregulation of the lung microenvironment and microbiome contributes to chronic inflammation, which is observed in COPD and known to facilitate cancer initiation in various tumor types. Further, there is a need to better characterize and identify the proportion of individuals with COPD who are at a high risk for developing lung cancer. We evaluate possible novel and individualized screening strategies, including biomarkers identified in genetic studies and exhaled breath condensate analysis. We also discuss the use of corticosteroids and statins as chemopreventive agents to prevent lung cancer. It is crucial that we optimize the current methods for the early detection and management of lung cancer and COPD in order to improve the health outcomes for a large affected population.

Purpose Plasma cell-free DNA (cfDNA) variant analysis is commonly used in many cancer subtypes. Cell-free methylated DNA immunoprecipitation sequencing (cfMeDIP-seq) has shown high sensitivity for cancer detection. To date, studies have not compared the sensitivity of both methods in a single cancer subtype. Methods cfDNA from 40 metastatic RCC (mRCC) patients was subjected to targeted panel variant analysis. For 34 of 40, cfMeDIP-seq was also performed. A separate cohort of 38 mRCC patients were used in cfMeDIP-seq analysis to train an RCC classifier. Results cfDNA variant analysis detected 21 candidate variants in 11 of 40 mRCC patients (28%), after exclusion of 2 germline variants and 6 variants reflecting clonal hematopoiesis. Among 23 patients with parallel tumor sequencing, cfDNA analysis alone identified variants in 9 patients (39%), while cfDNA analysis focused on tumor sequencing variant findings improved the sensitivity to 52%. In 34 mRCC patients undergoing cfMeDIP-seq, cfDNA variant analysis identified variants in 7 (21%), while cfMeDIP-seq detected all mRCC cases (100% sensitivity) with 88% specificity in 34 control subjects. In 5 patients with cfDNA variants and serial samples, variant frequency correlated with response to therapy. Conclusion cfMeDIP-seq is significantly more sensitive for mRCC detection than cfDNA variant analysis. However, cfDNA variant analysis may be useful for monitoring response to therapy.

DNA copy number variations (CNVs) play an important role in the pathogenesis and progression of cancer and confer susceptibility to a variety of human disorders. Array comparative genomic hybridization has been used widely to identify CNVs genome wide, but the next-generation sequencing technology provides an opportunity to characterize CNVs genome wide with unprecedented resolution. In this study, we developed an algorithm to detect CNVs from whole-genome sequencing data and applied it to a newly sequenced glioblastoma genome with a matched control. This read-depth algorithm, called BIC-seq, can accurately and efficiently identify CNVs via minimizing the Bayesian information criterion. Using BIC-seq, we identified hundreds of CNVs as small as 40 bp in the cancer genome sequenced at 10x coverage, whereas we could only detect large CNVs (> 15 kb) in the array comparative genomic hybridization profiles for the same genome. Eighty percent (14/16) of the small variants tested (110 bp to 14 kb) were experimentally validated by quantitative PCR, demonstrating high sensitivity and true positive rate of the algorithm. We also extended the algorithm to detect recurrent CNVs in multiple samples as well as deriving error bars for breakpoints using a Gibbs sampling approach. We propose this statistical approach as a principled yet practical and efficient method to estimate CNVs in whole-genome sequencing data.

Background/Objectives: This study investigates genomic alterations (GA) between NPM1-mutated (NPM1mut) and wild-type (NPM1wt) acute myeloid leukemia (AML), aiming to better understand the AML genomic profile. NPM1mut AML represents a distinct clinical AML subtype with high relapse rates despite initial responsiveness to chemotherapy. Methods: A total of 4206 AML cases from 2019 to 2024 were analyzed using the FoundationOne Heme assay, incorporating comprehensive DNA and RNA sequencing. Patients were stratified into NPM1mut and NPM1wt cohorts, and genomic differences were systematically compared between the two groups. Results: Among 4206 cases, 633 (15.1%) featured NPM1 GA, with over 99% exhibiting short variant mutations. NPM1mut AML was more common in females (53.4% vs. 41.5%) and associated with a slightly higher median age (62 vs. 60 years). GA was more frequent in NPM1mut AML compared to the NPM1wt and included DNMT3A (39.2% vs. 12.6%; p < 0.0001), PTPN11 (18.3% vs. 7.5%; p < 0.0001), FLT3 (54.5% vs. 14.7%; p < 0.0001), IDH1 (16.1% vs. 5.6%; p < 0.0001), IDH2 (19.0% vs. 9.0%; p < 0.0001), TET2 (23.4% vs. 13.5%; p < 0.0001), and WT1 (12.5% vs. 9.4%; p = 0.02). GA was more frequent in NPM1wt AML and included ASXL1 (17.1% vs. 3.6%; p 0.0001), BCOR (7.5% vs. 1.6%; p < 0.0001), KMT2A (14.7% vs. 0.2%; p < 0.0001), RUNX1 (22.5% vs. 1.9%; p 0.0001), STAG2 (6.9% vs. 1.6%; p < 0.0001) and TP53 (19.1% vs. 4.1%; p < 0.0001). Conclusions: Mutations linked to therapy targets in AML, such as (FLT3 and IDH1/2), PTPN11, and DNMT3A (both associated with inferior outcomes), are more commonly observed in NPM1mut AML, whereas KMT2A, TP53, and myelodysplastic-related mutations are more commonly observed in NPM1wt AML.