Predictive value of tumor mutational burden for immunotherapy in non-small cell lung cancer: A systematic review and meta-analysis

Durvalumab with or without tremelimumab in combination with chemotherapy in first-line metastatic non-small-cell lung cancer: outcomes by tumor mutational burden in POSEIDON

BACKGROUND

In updated analyses from the phase III POSEIDON study, after a median follow-up of >5 years, tremelimumab plus durvalumab and chemotherapy (T + D + CT) showed durable long-term overall survival (OS) benefit versus CT alone in first-line metastatic non-small-cell lung cancer (mNSCLC). In this article, we report the associations of tumor mutational burden (TMB) with outcomes of D with or without T in combination with CT versus CT alone.

PATIENTS AND METHODS

A total of 1013 patients with EGFR/ALK wild-type mNSCLC were randomized (1 : 1 : 1) to T + D + CT, D + CT, or CT, stratified by programmed cell death-ligand 1 (PD-L1) tumor cell (TC) expression ≥50% versus <50%, disease stage (IVA versus IVB) and histology (squamous versus nonsquamous). Patient subgroups were defined by a range of blood TMB (bTMB) values, including at a prespecified cut-off of 20 mutations (mut)/megabase (Mb) and across further subdivisions by PD-L1 TC expression ≥1% or <1% and by tissue TMB (tTMB) values.

RESULTS

At the primary OS data cut-off (12 March 2021), at each bTMB or tTMB cut-off, the magnitude of OS benefit appeared greater among patients in the bTMB- or tTMB-high subgroups for the T + D + CT arm versus the CT arm but was similar between subgroups for the D + CT arm versus the CT arm. Updated OS analyses in the bTMB ≥20 and <20 mut/Mb subgroups, after median follow-up of >5 years (data cut-off 24 August 2023), were similar to those obtained at the primary OS data cut-off.

CONCLUSIONS

First-line treatment with T (limited course) plus D (until progression) and four cycles of CT consistently improved clinical outcomes versus CT alone in both bTMB-high and -low subgroups, and also in both high and low tTMB subgroups, in patients with mNSCLC. Benefit appeared greater in the TMB-high versus TMB-low subgroups; the addition of anti-cytotoxic T lymphocyte-associated antigen-4 to anti-PD-L1 and CT seemed to increase the magnitude of this difference.

Prognostic and predictive biomarkers with therapeutic targets in nonsmall-cell lung cancer: A 2023 update on current development, evidence, and recommendation

BackgroundSince the publication of the original work in 2014, significant progress has been made in the characterization of genomic alterations that drive oncogenic addiction of nonsmall cell lung cancer (NSCLC) and how the immune system can leverage non-oncogenic pathways to modulate therapeutic outcomes. This update evaluates and validates the recent and emerging data for prognostic and predictive biomarkers with therapeutic targets in NSCLC.Data sourcesWe performed a literature search from January 2015 to October 2023 using the keywords non-small cell lung cancer, clinical practice guidelines, gene mutations, genomic assay, immune cancer therapy, circulating tumor DNA, predictive and prognostic biomarkers, and targeted therapies.Study selection and data extractionWe identified, reviewed, and evaluated relevant clinical trials, meta-analyses, seminal articles, and published clinical practice guidelines in the English language.Data synthesisRegulatory-approved targeted therapies include those somatic gene alterations of EGFR ("classic" mutations, exon 20 insertion, and rare EGFR mutations), ALK, ROS1, BRAF V600, RET, MET, NTRK, HER2, and KRAS G12C. Data for immunotherapy and circulating tumor DNA in next-generation sequencing are considered emerging, whereas the predictive role for PIK3CA gene mutation is insufficient.ConclusionsAdvances in sequencing and other genomic technologies have led to identifying novel oncogenic drivers, novel resistance mechanisms, and co-occurring mutations that characterize NSCLC, creating further therapeutic opportunities. The benefits associated with immunotherapy in the perioperative setting hold initial promise, with their long-term results awaiting.

Towards the Prediction of Responses to Cancer Immunotherapy: A Multi-Omics Review

Tumor treatment has undergone revolutionary changes with the development of immunotherapy, especially immune checkpoint inhibitors. Because not all patients respond positively to immune therapeutic agents, and severe immune-related adverse events (irAEs) are frequently observed, the development of the biomarkers evaluating the response of a patient is key for the application of immunotherapy in a wider range. Recently, various multi-omics features measured by high-throughput technologies, such as tumor mutation burden (TMB), gene expression profiles, and DNA methylation profiles, have been proved to be sensitive and accurate predictors of the response to immunotherapy. A large number of predictive models based on these features, utilizing traditional machine learning or deep learning frameworks, have also been proposed. In this review, we aim to cover recent advances in predicting tumor immunotherapy response using multi-omics features. These include new measurements, research cohorts, data sources, and predictive models. Key findings emphasize the importance of TMB, neoantigens, MSI, and mutational signatures in predicting ICI responses. The integration of bulk and single-cell RNA sequencing has enhanced our understanding of the tumor immune microenvironment and enabled the identification of predictive biomarkers like PD-L1 and IFN-γ signatures. Public datasets and machine learning models have also improved predictive tools. However, challenges remain, such as the need for large and diverse clinical datasets, standardization of multi-omics data, and model interpretability. Future research will require collaboration among researchers, clinicians, and data scientists to address these issues and enhance cancer immunotherapy precision.

Potential predictive biomarkers in antitumor immunotherapy: navigating the future of antitumor treatment and immune checkpoint inhibitor efficacy

Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment modality, offering promising outcomes for various malignancies. However, the efficacy of ICIs varies among patients, highlighting the essential need of accurate predictive biomarkers. This review synthesizes the current understanding of biomarkers for ICI therapy, and discusses the clinical utility and limitations of these biomarkers in predicting treatment outcomes. It discusses three US Food and Drug Administration (FDA)-approved biomarkers, programmed cell death ligand 1 (PD-L1) expression, tumor mutational burden (TMB), and microsatellite instability (MSI), and explores other potential biomarkers, including tumor immune microenvironment (TIME)-related signatures, human leukocyte antigen (HLA) diversity, non-invasive biomarkers such as circulating tumor DNA (ctDNA), and combination biomarker strategies. The review also addresses multivariable predictive models integrating multiple features of patients, tumors, and TIME, which could be a promising approach to enhance predictive accuracy. The existing challenges are also pointed out, such as the tumor heterogeneity, the inconstant nature of TIME, nonuniformed thresholds and standardization approaches. The review concludes by emphasizing the importance of biomarker research in realizing the potential of personalized immunotherapy, with the goal of improving patient selection, treatment strategies, and overall outcomes in cancer treatment.

The oncogenic axis YAP/MYC/EZH2 impairs PTEN tumor suppression activity enhancing lung tumorigenicity

The tumor suppressor PTEN (phosphatase and tensin homolog deleted in chromosome 10) is genetically deleted or downregulated in many cancer types. Loss of PTEN protein expression is frequently found in lung cancer while genetic alterations are less abundant. PTEN expression is regulated at multiple genetic and epigenetic levels and even partial reduction of its expression increases cancer occurrence. We show that YAP and TAZ cooperate with EZH2, and MYC to transcriptionally repress onco-suppressor genes, including PTEN, in non-small cell lung cancer (NSCLC) cells. YAP/TAZ-EZH2-MYC transcriptional regulators form a nuclear complex that represses PTEN transcription, while their combinatorial targeting restores PTEN expression, attenuates NSCLC cell growth, and prevents compensatory responses induced by single treatments. Datasets analysis of NSCLC patients revealed that PTEN expression is negatively correlated to YAP/TAZ, EZH2 and MYC and that low expression of PTEN is predictive of poor prognosis, especially at earlier stages of the disease. These findings highlight the repressive role of the YAP/TAZ-EZH2-MYC axis on tumor-suppressor genes and offer a potential therapeutic strategy for lung cancer patients with low PTEN levels.

[Perioperative Immunotherapy for Resectable Non-Small Cell Lung Cancer: Current Evidence and New Standard of Care]

Immunotherapy has drastically changed the treatment of lung cancer not only in systemic disease but also in the perioperative setting in locally advanced non-small cell lung cancer. In particular, the neoadjuvant and perioperative therapy regimes of the CheckMate 816 and KEYNOTE-671 studies as well as the adjuvant therapy according to the IMPower010 and the PEARLS/KEYNOTE-091 protocols have already been approved by the European Medicines Agency (EMA) for the treatment of selected cases. Other therapy protocols and combination therapies with varying drug classes and therapy modalities are currently being examined for their effectiveness and tolerance. The new treatment landscape creates new opportunities but also challenges for the treating disciplines. This article will focus on the current evidence for perioperative immunotherapy for resectable lung cancer and the resulting therapy standards, especially with regard to patient selection for both neoadjuvant and adjuvant immunotherapy, as well as current research efforts.

Transfer Learning in Cancer Genetics, Mutation Detection, Gene Expression Analysis, and Syndrome Recognition

Artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), has revolutionized medical research, facilitating advancements in drug discovery and cancer diagnosis. ML identifies patterns in data, while DL employs neural networks for intricate processing. Predictive modeling challenges, such as data labeling, are addressed by transfer learning (TL), leveraging pre-existing models for faster training. TL shows potential in genetic research, improving tasks like gene expression analysis, mutation detection, genetic syndrome recognition, and genotype-phenotype association. This review explores the role of TL in overcoming challenges in mutation detection, genetic syndrome detection, gene expression, or phenotype-genotype association. TL has shown effectiveness in various aspects of genetic research. TL enhances the accuracy and efficiency of mutation detection, aiding in the identification of genetic abnormalities. TL can improve the diagnostic accuracy of syndrome-related genetic patterns. Moreover, TL plays a crucial role in gene expression analysis in order to accurately predict gene expression levels and their interactions. Additionally, TL enhances phenotype-genotype association studies by leveraging pre-trained models. In conclusion, TL enhances AI efficiency by improving mutation prediction, gene expression analysis, and genetic syndrome detection. Future studies should focus on increasing domain similarities, expanding databases, and incorporating clinical data for better predictions.

Exploring the Cost Effectiveness of a Whole-Genome Sequencing-Based Biomarker for Treatment Selection in Patients with Advanced Lung Cancer Ineligible for Targeted Therapy

OBJECTIVE

We aimed to perform an early cost-effectiveness analysis of using a whole-genome sequencing-based tumor mutation burden (WGS-TMB), instead of programmed death-ligand 1 (PD-L1), for immunotherapy treatment selection in patients with non-squamous advanced/metastatic non-small cell lung cancer ineligible for targeted therapy, from a Dutch healthcare perspective.

METHODS

A decision-model simulating individual patients with metastatic non-small cell lung cancer was used to evaluate diagnostic strategies to select first-line immunotherapy only or the immunotherapy plus chemotherapy combination. Treatment was selected using PD-L1 [A, current practice], WGS-TMB [B], and both PD-L1 and WGS-TMB [C]. Strategies D, E, and F take into account a patient's disease burden, in addition to PD-L1, WGS-TMB, and both PD-L1 and WGS-TMB, respectively. Disease burden was defined as a fast-growing tumor, a high number of metastases, and/or weight loss. A threshold of 10 mutations per mega-base was used to classify patients into TMB-high and TMB-low groups. Outcomes were discounted quality-adjusted life-years (QALYs) and healthcare costs measured from the start of first-line treatment to death. Healthcare costs includes drug acquisition, follow-up costs, and molecular diagnostic tests (i.e., standard diagnostic techniques and/or WGS for strategies involving TMB). Results were reported using the net monetary benefit at a willingness-to-pay threshold of €80,000/QALY. Additional scenario and threshold analyses were performed.

RESULTS

Strategy B had the lowest QALYs (1.84) and lowest healthcare costs (€120,800). The highest QALYs and healthcare costs were 2.00 and €140,400 in strategy F. In the base-case analysis, strategy A was cost effective with the highest net monetary benefit (€27,300), followed by strategy B (€26,700). Strategy B was cost effective when the cost of WGS testing was decreased by at least 24% or when immunotherapy results in an additional 0.5 year of life gained or more for TMB high compared with TMB low. Strategies C and F, which combined TMB and PD-L1 had the highest net monetary benefit (≥ €76,900) when the cost of WGS testing, immunotherapy, and chemotherapy acquisition were simultaneously reduced by at least 47%, 39%, and 43%, respectively. Furthermore, strategy C resulted in the highest net monetary benefit (≥ €39,900) in a scenario where patients with both PD-L1 low and TMB low were treated with chemotherapy instead of immunotherapy plus chemotherapy.

CONCLUSIONS

The use of WGS-TMB is not cost effective compared to PD-L1 for immunotherapy treatment selection in non-squamous metastatic non-small cell lung cancer in the Netherlands. WGS-TMB could become cost effective provided there is a reduction in the cost of WGS testing or there is an increase in the predictive value of WGS-TMB for immunotherapy effectiveness. Alternatively, a combination strategy of PD-L1 testing with WGS-TMB would be cost effective if used to support the choice to withhold immunotherapy in patients with a low expected benefit of immunotherapy.

Refining PD-1/PD-L1 assessment for biomarker-guided immunotherapy: A review

Anti-programmed cell death ligand 1 (anti-PD-L1)  immunotherapy is an increasingly crucial in cancer treatment. To date, the Federal Drug Administration (FDA) has approved four PD-L1 immunohistochemistry (IHC) staining protocols, commercially available in the form of "kits", facilitating testing for PD-L1 expression. These kits comprise four PD-L1 antibodies on two separate IHC platforms, each utilizing distinct, non-interchangeable scoring systems. Several factors, including tumor heterogeneity and the size of the tissue specimens assessed, can lead to PD-L1 status misclassification, potentially hindering the initiation of therapy. Therefore, the development of more accurate predictive biomarkers to distinguish between responders and non-responders prior to anti-PD-1/PD-L1 therapy warrants further research. Achieving this goal necessitates refining sampling criteria, enhancing current methods of PD-L1 detection, and deepening our understanding of the impact of additional biomarkers. In this article, we review potential solutions to improve the predictive accuracy of PD-L1 assessment in order to more precisely anticipate patients' responses to anti-PD-1/PD-L1 therapy, monitor disease progression and predict clinical outcomes.

The predictive value of tumor mutation burden on survival of gastric cancer patients treated with immune checkpoint inhibitors: A systematic review and meta-analysis

BACKGROUND

Tumor mutation burden (TMB) is a complement to traditional biomarkers related to the efficacy of immune checkpoint inhibitors (ICIs). The relationship between TMB and the efficacy of ICIs in gastric cancer was controversial. The systematic review and meta-analysis were conducted to investigate the predictive value of TMB on survival of gastric cancer patients treated with ICIs.

METHODS

We searched the databases PubMed, Embase, and Web of Science for articles, then screened eligible articles according to inclusion criteria. The effective data were extracted to calculate the pooled effects of hazard ratio (HR) for overall survival (OS) and progression-free survival (PFS), then perform publication bias, sensitivity analysis, and subgroup analysis by STATA 16.0.

RESULTS

The high TMB patients showed significantly longer survival than the low TMB patients (OS: HR 0.65,95% CI 0.55, 0.77, p < 0.001; PFS: HR 0.51, 95% CI 0.33, 0.77, p = 0.001). In the Asian subgroup, patients with high TMB exhibited better prognosis compared to low TMB (OS: HR 0.56, 95% CI 0.43, 0.72, p < 0.001; PFS: HR 0.45, 95% CI 0.28, 0.72, p = 0.001). In the non-Asian subgroup, the survival benefit was observed to be skewed toward patients with high TMB, but it was not statistically significant (OS:HR 0.61, 95% CI 0.32, 1.16, p = 0.133; PFS:HR 0.68, 95% CI 0.31, 1.48, p = 0.322).

CONCLUSIONS

This meta-analysis demonstrated that gastric cancer patients with high TMB showed significant benefits from ICIs compared to those with low TMB patients, particularly in Asian populations.

Progress of immune checkpoint inhibitors therapy for non-small cell lung cancer with brain metastases

About 40% of patients with non-small cell lung cancer (NSCLC) develop brain metastases (BMs) throughout the disease, and the occurrence of BMs is considered to have a fairly high mortality rate. Therefore, the management of brain metastases in NSCLC patients is a clinical challenge. Currently, multidisciplinary diagnosis and treatment methods are often used to achieve effective control of intracranial disease and prolong survival. Immunotherapy (IT) is one of the core therapies for NSCLC. Single or combined IT represented by immune checkpoint inhibitors(ICIs) of programmed death-1(PD-1)/ programmed cell death-ligand 1 (PD-L1) can significantly improve the prognosis of patients with advanced NSCLC.ICIs has been shown to be safe and effective in patients with BMs, although patients with BMs are mostly underrepresented in randomized clinical trials. In this review, we summarized the mechanism of ICIs in the treatment of BMs, and the clinical research and treatment progress of ICIs and their combination with other therapies in patients with BMs s from NSCLC.

An Analysis of JADE2 in Non-Small Cell Lung Cancer (NSCLC)

The JADE family comprises three members encoded by individual genes and roles for these proteins have been identified in chromatin remodeling, cell cycle progression, cell regeneration and the DNA damage response. JADE family members, and in particular JADE2 have not been studied in any great detail in cancer. Using a series of standard biological and bioinformatics approaches we investigated JADE2 expression in surgically resected non-small cell lung cancer (NSCLC) for both mRNA and protein to examine for correlations between JADE2 expression and overall survival. Additional correlations were identified using bioinformatic analyses on multiple online datasets. Our analysis demonstrates that JADE2 expression is significantly altered in NSCLC. High expression of JADE2 is associated with a better 5-year overall survival. Links between JADE2 mRNA expression and a number of mutated genes were identified, and associations between JADE2 expression and tumor mutational burden and immune cell infiltration were explored. Potential new drugs that can target JADE2 were identified. The results of this biomarker-driven study suggest that JADE2 may have potential clinical utility in the diagnosis, prognosis and stratification of patients into various therapeutically targetable options.

Predictive biomarkers for immune checkpoint inhibitor response in urothelial cancer

Immune checkpoint inhibitors (ICIs) are commonly used to treat patients with advanced urothelial cancer. However, a significant number of patients do not respond to ICI, and the lack of validated predictive biomarkers impedes the success of the ICI strategy alone or in combination with chemotherapy or targeted therapies. In addition, some patients experience potentially severe adverse events with limited clinical benefit. Therefore, identifying biomarkers of response to ICI is crucial to guide treatment decisions. The most evaluated biomarkers to date are programmed death ligand 1 expression, microsatellite instability/defective mismatch repair phenotype, and tumor mutational burden. Other emerging biomarkers, such as circulating tumor DNA and microbiota, require evaluation in clinical trials. This review aims to examine these biomarkers for ICI response in urothelial cancer and assess their analytical and clinical validation.

Role of Surgical Pathologist for the Detection of Immuno-oncologic Predictive Factors in Non-small Cell Lung Cancers

Until very recently, surgery, chemotherapy, and radiation therapy have been the mainstay of treatment in non-small cell carcinomas (NSCLCs). However, recent advances in molecular immunology have unveiled some of the complexity of the mechanisms regulating cellular immune responses and led to the successful targeting of immune checkpoints in attempts to enhance antitumor T-cell responses. Immune checkpoint molecules such as cytotoxic T-lymphocyte associated protein-4, programmed cell death protein-1, and programmed death ligand (PD-L) 1 have been shown to play central roles in evading cancer immunity. Thus, these molecules have been targeted by inhibitors for the management of cancers forming the basis of immunotherapy. Advanced NSCLC has been the paradigm for the benefits of immunotherapy in any cancer. Treatment decisions are made based on the expression of PD-L1 on the tumor cells and the presence or absence of driver mutations. Patients with high PD-L1 expression (≥50%) and no driver mutations are treated with single-agent immunotherapy whereas, for all other patients with a lower level of PD-L1 expression, a combination of chemotherapy and immunotherapy is preferred. Thus, PD-L1 blockers are the only immunotherapeutic agents approved in advanced NSCLC without any oncogenic driver mutations. PD-L1 immunohistochemistry, however, may not be the best biomarker in view of its dynamic nature in time and space, and the benefits may be seen regardless of PD -L1 expression. Each immunotherapy molecule is prescribed based on the levels of PD-L1 expression as assessed by a Food and Drug Administration-approved companion diagnostic assay. Other biomarkers that have been studied include tumor mutational burden, the T-effector signature, tumor-infiltrating lymphocytes, radiomic assays, inflammation index, presence or absence of immune-related adverse events and specific driver mutations, and gut as well as local microbiome. At the current time, none of these biomarkers are routinely used in the clinical decision-making process for immunotherapy in NSCLC. However, in individual cases, they can be useful adjuncts to conventional therapy. This review describes our current understanding of the role of biomarkers as predictors of response to immune checkpoint molecules. To begin with a brief on cancer immunology in general and in NSCLC, in particular, is discussed. In the end, recent advancements in laboratory techniques for refining biomarker assays are described.

Durable responders in advanced NSCLC with elevated TMB and treated with 1L immune checkpoint inhibitor: a real-world outcomes analysis

BACKGROUND

For patients with advanced non-small cell lung carcinoma (NSCLC), immune checkpoint inhibitor (ICPI) and chemotherapy (chemo) ICPI represent two distinct first-line standard-of-care regimens without clear and established biomarkers to inform the optimal choice for individual patients. Here, we examined the complementary roles of tumor mutational burden (TMB) and programmed death ligand-1 (PD-L1) immunohistochemistry (IHC) to inform first-line therapy using a large real-world (rw) data set.

MATERIALS AND METHODS

The study included patients with NSCLC from an rw de-identified clinico-genomic database. All patients underwent genomic testing using Foundation Medicine's tissue comprehensive genomic profiling assay and PD-L1 IHC assay scored for tumor cell staining (TS).

RESULTS

Of 2165 patients included in the analysis, 150 exhibited durable benefit from first-line ICPI regimens (these patients were enriched for PD-L1 TS ≥50, non-squamous histology, and TMB ≥20 mutations/megabase (muts/Mb)). Comparing low TMB (<10 muts/Mb), high TMB (10-19 muts/Mb), and very high TMB (≥20 muts/Mb) receiving ICPI alone, we observed a stepwise increase in median rwPFS (real world-progression free survival) (6.5, 7.5, 17.2 months) and rwOS (real world-overall survival) (10.1, 11.8, 26.9 months) as TMB increased. In the low PD-L1 (TS <50%) cohort, TMB <20 muts/Mb showed a more favorable rwPFS (HR: 0.56 (95% CI: 0.40 to 0.79)) and rwOS (HR 0.74 (95% CI: 0.58 to 0.96)) on chemoICPI when compared with ICPI alone while the point estimate in rwPFS favored monoICPI in the TMB ≥20 muts/Mb cohort, the CI is wide and does not reach statistical significance (HR: 1.68 (95% CI: 0.52 to 5.48)).

CONCLUSION

This study provides evidence that higher TMB cut-offs, such as 20 muts/Mb, can identify patients with prolonged benefit from ICPI. TMB ≥20 muts/Mb is a potential biomarker that may identify patients in whom an ICPI without chemo could be considered, even in the setting of lower PD-L1 levels. Prospective validation of these findings could increase access to chemo-sparing regimens for the first-line treatment of advanced NSCLC.

The Role of Genomics and Proteomics in Lung Cancer Early Detection and Treatment

Lung cancer is the leading cause of cancer-related death worldwide, with non-small-cell lung cancer (NSCLC) being the primary type. Unfortunately, it is often diagnosed at advanced stages, when therapy leaves patients with a dismal prognosis. Despite the advances in genomics and proteomics in the past decade, leading to progress in developing tools for early diagnosis, targeted therapies have shown promising results; however, the 5-year survival of NSCLC patients is only about 15%. Low-dose computed tomography or chest X-ray are the main types of screening tools. Lung cancer patients without specific, actionable mutations are currently treated with conventional therapies, such as platinum-based chemotherapy; however, resistances and relapses often occur in these patients. More noninvasive, inexpensive, and safer diagnostic methods based on novel biomarkers for NSCLC are of paramount importance. In the current review, we summarize genomic and proteomic biomarkers utilized for the early detection and treatment of NSCLC. We further discuss future opportunities to improve biomarkers for early detection and the effective treatment of NSCLC.

Altered expression of ACOX2 in non-small cell lung cancer

Peroxisomes are organelles that play essential roles in many metabolic processes, but also play roles in innate immunity, signal transduction, aging and cancer. One of the main functions of peroxisomes is the processing of very-long chain fatty acids into metabolites that can be directed to the mitochondria. One key family of enzymes in this process are the peroxisomal acyl-CoA oxidases (ACOX1, ACOX2 and ACOX3), the expression of which has been shown to be dysregulated in some cancers. Very little is however known about the expression of this family of oxidases in non-small cell lung cancer (NSCLC). ACOX2 has however been suggested to be elevated at the mRNA level in over 10% of NSCLC, and in the present study using both standard and bioinformatics approaches we show that expression of ACOX2 is significantly altered in NSCLC. ACOX2 mRNA expression is linked to a number of mutated genes, and associations between ACOX2 expression and tumour mutational burden and immune cell infiltration were explored. Links between ACOX2 expression and candidate therapies for oncogenic driver mutations such as KRAS were also identified. Furthermore, levels of acyl-CoA oxidases and other associated peroxisomal genes were explored to identify further links between the peroxisomal pathway and NSCLC. The results of this biomarker driven study suggest that ACOX2 may have potential clinical utility in the diagnosis, prognosis and stratification of patients into various therapeutically targetable options.