Tumor mutational burden assessment as a predictive biomarker for immunotherapy in lung cancer patients: getting ready for prime-time or not?

Global research trends on biomarkers for cancer immunotherapy: Visualization and bibliometric analysis

The global burden of cancer continues to grow, posing a significant public health challenge. Although cancer immunotherapy has shown significant efficacy, the response rate is not high. Therefore, the objective of our research was to identify the latest research trends and hotspots on biomarkers from 1993 to 2023. Data were collected from the database Web of Science core collection. Bibliometric analysis and visualization were conducted with CiteSpace(6.3.1), VOSviewer (v1.6.20), R-bibliometrix(v4.3.3), and Microsoft Excel(2019). A total of 2686 literatures were retrieved. The sheer annual volume of publications has shown a rapid upward trend since 2015. The United States has generated the most publications and Harvard University ranked as a leading institution. The global biomarker research on immune checkpoint inhibitors (ICIs) revealed regional differences and in-depth explorations should be promoted in developing countries. Although China has become the second largest country in terms of publication, the average citation per paper and the total link strength were both lower than the other countries. The research on biomarkers mainly concentrated upon the following aspects: PD-1/PD-L1, CTLA-4, gene expression, adverse events, total mutational burden (TMB), body mass index (BMI), gut microbiota, cd8(+)/cd4(+) t-cells, and blood-related biomarkers such as lactate dehydrogenase (LDH), neutrophil-lymphocyte ratio (NLR), cytokines. Furthermore, "artificial intelligence" and "machine learning" have become the most important research hotspot over the last 2 y, which will help us to identify useful biomarkers from complex big data and provide a basis for precise medicine for malignant tumors.

Prognostic model based on disulfidptosis-related lncRNAs for predicting survival and therapeutic response in bladder cancer

Background

With poor treatment outcomes and prognosis, bladder cancer remains a focus for clinical research in the precision oncology era. However, the potential of disulfidptosis, a novel cell death mechanism, and its related long non-coding RNAs to support selective cancer cell killing in this disease is still unclear.

Methods

We identified key disulfidptosis-related lncRNAs in bladder cancer, constructed a prognostic risk model with potential therapeutic targets, and confirmed the findings through quantitative PCR analysis.

Results

We identified five crucial lncRNAs (AC005840.4, AC010331.1, AL021707.6, MIR4435-2HG and ARHGAP5-AS1) and integrated them into a predictive model centered on disulfidptosis-associated lncRNAs. Reliability and validity tests demonstrated that the lncRNA prediction index associated with disulfidptosis effectively discerns patients' prognosis outcomes. Additionally, high-risk patients exhibited elevated expression levels of genes involved in the PI3K-Akt signaling pathway, extracellular matrix organization, and immune escape mechanisms, which are associated with poor prognosis. Notably, high-risk patients demonstrated higher sensitivity to Sorafenib, Oxaliplatin and MK-2206, underscoring the promise of these lncRNAs as precise therapeutic targets in bladder cancer.

Conclusion

By revealing the predictive importance of disulfidptosis-associated lncRNAs in bladder cancer, our research offers new perspectives and pinpoints potential therapeutic targets in clinical environments.

Widespread Adoption of Precision Anticancer Therapies After Implementation of Pathologist-Directed Comprehensive Genomic Profiling Across a Large US Health System

PURPOSE

Precision therapies and immunotherapies have revolutionized cancer care, with novel genomic biomarker-associated therapies being introduced into clinical practice rapidly, resulting in notable gains in patient survival. Despite this, there is significant variability in the utilization of tumor molecular profiling that spans the timing of test ordering, comprehensiveness of gene panels, and clinical decision support through therapy and trial recommendations.

METHODS

To standardize testing, we designed a pathologist-directed test ordering system at the time of diagnosis using a 523-gene DNA/RNA hybrid comprehensive genomic profiling (CGP) panel and extensive clinical decision support tools. To comprehensively characterize the clinical impact of this protocol, we developed a novel natural language processing (NLP)-based approach to extract clinical features from physician chart notes. We assessed test actionability rates, therapy choice, and outcomes across a set of 3,216 patients with advanced cancer.

RESULTS

We observed 49% of patients had at least one actionable genomic biomarker-driven-approved and/or guideline-recommended targeted or immunotherapy (IO) and 53% of patients would have been eligible for a precision therapy clinical trial from three large basket trials. When assessing CGP versus an in silico 50-gene panel, 67% of tumors compared with 33% harbored actionable alterations including clinical trials. Among patients with 6 months or more of follow-up, over 52% received a targeted therapy (TT) or IO, versus 32% who received conventional chemotherapy alone. Furthermore, patients receiving TT had significantly improved overall survival compared with patients receiving chemotherapy alone (P < .001).

CONCLUSION

Overall, these data represent a major shift in standard clinical practice toward molecularly guided treatments (targeted and immunotherapies) over conventional systemic chemotherapy. As guidelines continue to evolve and more precision therapeutics gain approval, we expect this gap to continue to widen.

NRF2 Signaling Pathway in Chemo/Radio/Immuno-Therapy Resistance of Lung Cancer: Looking Beyond the Tip of the Iceberg

Lung cancer is one of the most common causes of cancer death in men and women worldwide. Various combinations of surgery, chemotherapy, radiation therapy and immunotherapy are currently used to treat lung cancer. However, the prognosis remains relatively poor due to the higher frequency of tumor mutational burden (TMB). Nuclear factor E2-related factor 2 (NFE2L2/NRF2) is often considered a primary regulator of the expression of antioxidant enzymes and detoxification proteins and is involved in cytoprotection. On the contrary, NRF2 is even known to induce metastasis and support tumor progression. Kelch-like ECH-associated protein 1 (KEAP1) plays an important role in negatively regulating NRF2 activity via CUL3-mediated ubiquitinylation and successive proteasomal degradation. Extensive research has shown that the genetic alterations of KEAP1/NFE2L2/CUL3 genes lead to increased expression of NRF2 and its target genes in lung cancer. Thus, these studies provide ample evidence for the dual role of NRF2 in lung cancer. In this review, we discussed the mechanistic insights into the role of NRF2 signaling in therapy resistance by focusing on cell lines, mouse models, and translational studies in lung cancer. Finally, we highlighted the potential therapeutic strategies targeting NRF2 inhibition, followed by the discussion of biomarkers related to NRF2 activity in lung cancer. Overall, our article exclusively discusses in detail the NRF2 signaling pathway in resistance to therapy, especially immunotherapy, and its therapeutic avenue in the treatment of lung cancer.

Regulatory T cells as crucial trigger and potential target for hyperprogressive disease subsequent to PD-1/PD-L1 blockade for cancer treatment

PD-1/PD-L1 blockade therapy has brought great success to cancer treatment. Nevertheless, limited beneficiary populations and even hyperprogressive disease (HPD) greatly constrain the application of PD-1/PD-L1 inhibitors in clinical treatment. HPD is a special pattern of disease progression with rapid tumor growth and even serious consequences of patient death, which requires urgent attention. Among the many predisposing causes of HPD, regulatory T cells (Tregs) are suspected because they are amplified in cases of HPD. Tregs express PD-1 thus PD-1/PD-L1 blockade therapy may have an impact on Tregs which leads to HPD. Tregs are a subset of CD4+ T cells expressing FoxP3 and play critical roles in suppressing immunity. Tregs migrate toward tumors in the presence of chemokines to suppress antitumor immune responses, causing cancer cells to grow and proliferate. Studies have shown that deleting Tregs could enhance the efficacy of PD-1/PD-L1 blockade therapy and reduce the occurrence of HPD. This suggests that immunotherapy combined with Treg depletion may be an effective means of avoiding HPD. In this review, we summarized the immunosuppressive-related functions of Tregs in antitumor therapy and focused on advances in therapy combining Tregs depletion with PD-1/PD-L1 blockade in clinical studies. Moreover, we provided an outlook on Treg-targeted HPD early warning for PD-1/PD-L1 blockade therapy.

Current challenges and practical aspects of molecular pathology for non-small cell lung cancers

The continuing evolution of treatment options in thoracic oncology requires the pathologist to regularly update diagnostic algorithms for management of tumor samples. It is essential to decide on the best way to use tissue biopsies, cytological samples, as well as liquid biopsies to identify the different mandatory predictive biomarkers of lung cancers in a short turnaround time. However, biological resources and laboratory member workforce are limited and may be not sufficient for the increased complexity of molecular pathological analyses and for complementary translational research development. In this context, the surgical pathologist is the only one who makes the decisions whether or not to send specimens to immunohistochemical and molecular pathology platforms. Moreover, the pathologist can rapidly contact the oncologist to obtain a new tissue biopsy and/or a liquid biopsy if he/she considers that the biological material is not sufficient in quantity or quality for assessment of predictive biomarkers. Inadequate control of algorithms and sampling workflow may lead to false negative, inconclusive, and incomplete findings, resulting in inappropriate choice of therapeutic strategy and potentially poor outcome for patients. International guidelines for lung cancer treatment are based on the results of the expression of different proteins and on genomic alterations. These guidelines have been established taking into consideration the best practices to be set up in clinical and molecular pathology laboratories. This review addresses the current predictive biomarkers and algorithms for use in thoracic oncology molecular pathology as well as the central role of the pathologist, notably in the molecular tumor board and her/his participation in the treatment decision-making. The perspectives in this setting will be discussed.

Predicting colorectal cancer prognosis based on long noncoding RNAs of disulfidptosis genes

BACKGROUND

A recently hypothesized cause of cell death called disulfidptosis has been linked to the expansion, emigration, and vascular rebuilding of cancer cells. Cancer can be treated by targeting the pathways that trigger cell death.

AIM

To discover the long non-coding RNA of the disulfidaptosis-related lncRNAs (DRLs), prognosis clinical survival, and treat patients with colorectal cancer with medications.

METHODS

Initially, we queried the Cancer Genome Atlas database to collect transcriptome, clinical, and genetic mutation data for colorectal cancer (CRC). Training and testing sets for CRC patient transcriptome data were generated randomly. Key long non-coding RNAs (lncRNAs) related to DRLs were then identified and evaluated using a least absolute shrinkage and selection operator procedure, as well as univariate and multivariate Cox regression models. A prognostic model was then created after risk scoring. Also, Immune infiltration analysis, immune checkpoint analysis, and medication susceptibility analysis were used to investigate the causes of the different prognoses between high and low risk groups. Finally, we validated the differential expression and biomarker potential of risk-predictive lncRNAs through induction using both NCM460 and HT-29 cell lines, as well as a disulfidptosis model.

RESULTS

In this work, eight significant lncRNAs linked to disulfidptosis were found. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of differentially expressed genes between high- and low-risk groups from the prognostic model showed a close relationship with the immune response as well as significant enrichment in neutrophil extracellular trap formation and the IL-17 signaling pathway. Furthermore, significant immune cell variations between the high-risk and low-risk groups were seen, as well as a higher incidence of immunological escape risk in the high-risk group. Finally, Epirubicin, bortezomib, teniposide, and BMS-754807 were shown to have the lowest sensitivity among the four immunotherapy drugs.

CONCLUSION

Our findings emphasizes the role of disulfidptosis in regulating tumor development, therapeutic response, and patient survival in CRC patients. For the clinical treatment of CRC, these important LncRNAs could serve as viable therapeutic targets.

Anoikis-related CTNND1 is associated with immuno-suppressive tumor microenvironment and predicts unfavorable immunotherapeutic outcome in non-small cell lung cancer

Background: Immunotherapy has greatly changed the treatment of advanced non-small cell lung cancer (NSCLC). Anoikis is a programmed cell death process associated with cancer. However, the correlation between anoikis-related genes and the tumor microenvironment (TME) features and immunotherapeutic outcome in NSCLC has not been fully explored. Methods: The bulk and single-cell transcriptome data of NSCLC were downloaded from TCGA and GEO databases. The distribution of anoikis-related genes on different cell types at the single-cell level was analyzed, and these genes specifically expressed by tumor cells and immunotherapy-related were further extracted. Next, the candidate gene CTNND1 was identified and its correlations with the TME features and immunotherapeutic outcome in NSCLC were explored in multiple public cohorts. Finally, an in-house cohort was used to determine the CTNND1 expression and immuno-correlation in NSCLC. Results: At single-cell atlas, we found that anoikis-related genes expressed specifically in tumor cells of NSCLC. By intersecting anoikis-related genes, immunotherapy-associated genes, and the genes expressed in tumor cells, we obtained a special biomarker CTNND1. In addition, cell-cell communication analysis revealed that CTNND1+ tumor cells communicated with immune subpopulations frequently. Moreover, we found that high expression of CTNND1 was related to immuno-suppressive status of NSCLC. The expression of CTNND1 and its immuno-correlation were also validated, and the results showed that CTNND1 was highly expressed in NSCLC tissues and tumors with high CTNND1 expression accompanied with low CD8+ T cells infiltration. Conclusions: Overall, our study reported that CTNND1 can be considered as a novel biomarker for the predication of immunotherapeutic responses and a potential target for NSCLC therapy.

Contrast-enhanced CT-based radiomic analysis for determining the response to anti-programmed death-1 therapy in esophageal squamous cell carcinoma patients: A pilot study

BACKGROUND

In view of the fact that radiomics features have been reported as predictors of immunotherapy to various cancers, this study aimed to develop a prediction model to determine the response to anti-programmed death-1 (anti-PD-1) therapy in esophageal squamous cell carcinoma (ESCC) patients from contrast-enhanced CT (CECT) radiomics features.

METHODS

Radiomic analysis of images was performed retrospectively for image samples before and after anti-PD-1 treatment, and efficacy analysis was performed for the results of two different time node evaluations. A total of 68 image samples were included in this study. Quantitative radiomic features were extracted from the images, and the least absolute shrinkage and selection operator method was applied to select radiomic features. After obtaining selected features, three classification models were used to establish a radiomics model to predict the ESCC status and efficacy of therapy. A cross-validation strategy utilizing three folds was employed to train and test the model. Performance evaluation of the model was done using the area under the curve (AUC) of receiver operating characteristic, sensitivity, specificity, and precision metric.

RESULTS

Wavelet and area of gray level change (log-sigma) were the most significant radiomic features for predicting therapy efficacy. Fifteen radiomic features from the whole tumor and peritumoral regions were selected and comprised of the fusion radiomics score. A radiomics classification was developed with AUC of 0.82 and 0.884 in the before and after-therapy cohorts, respectively.

CONCLUSIONS

The combined model incorporating radiomic features and clinical CECT predictors helps to predict the response to anti-PD-1therapy in patients with ESCC.

Research progress of biomarkers in the prediction of anti-PD-1/PD-L1 immunotherapeutic efficiency in lung cancer

Currently, anti-PD-1/PD-L1 immunotherapy using immune checkpoint inhibitors is widely used in the treatment of multiple cancer types including lung cancer, which is a leading cause of cancer death in the world. However, only a limited proportion of lung cancer patients will benefit from anti-PD-1/PD-L1 therapy. Therefore, it is of importance to predict the response to immunotherapy for the precision treatment of patients. Although the expression of PD-L1 and tumor mutation burden (TMB) are commonly used to predict the clinical response of anti-PD-1/PD-L1 therapy, other factors such as tumor-specific genes, dMMR/MSI, and gut microbiome are also promising predictors for immunotherapy in lung cancer. Furthermore, invasive peripheral blood biomarkers including blood DNA-related biomarkers (e.g., ctDNA and bTMB), blood cell-related biomarkers (e.g., immune cells and TCR), and other blood-related biomarkers (e.g., soluble PD-L1 and cytokines) were utilized to predict the immunotherapeutic response. In this review, the current achievements of anti-PD-1/PD-L1 therapy and the potential biomarkers for the prediction of anti-PD-1/PD-L1 immunotherapy in lung cancer treatment were summarized and discussed.

Systematic pan-cancer analysis identifies cuproptosis-related gene DLAT as an immunological and prognostic biomarker

Lipoylated dihydrolipoamide S-acetyltransferase (DLAT), the component E2 of the multi-enzyme pyruvate dehydrogenase complex, is one of the key molecules of cuproptosis. However, the prognostic value and immunological role of DLAT in pan-cancer are still unclear. Using a series of bioinformatics approaches, we studied combined data from different databases, including the Cancer Genome Atlas, Genotype Tissue-Expression, the Cancer Cell Line Encyclopedia, Human Protein Atlas, and cBioPortal to investigate the role of DLAT expression in prognosis and tumor immunity response. We also reveal the potential correlations between DLAT expression and gene alterations, DNA methylation, copy number variation (CNV), tumor mutational burden (TMB), microsatellite instability (MSI), tumor microenvironment (TME), immune infiltration levels, and various immune-related genes across different cancers. The results show that DLAT displays abnormal expression within most malignant tumors. Through gene set enrichment analysis (GSEA), we found that DLAT was significantly associated with immune-related pathways. Further, the expression of DLAT was also confirmed to be correlated with the tumor microenvironment and diverse infiltration of immune cells, especially tumor-associated macrophages (TAMs). In addition, we found that DLAT is co-expressed with genes encoding major histocompatibility complex (MHC), immunostimulators, immune inhibitors, chemokines, and chemokine receptors. Meanwhile, we demonstrate that DLAT expression is correlated with TMB in 10 cancers and MSI in 11 cancers. Our study reveals that DLAT plays an essential role in tumorigenesis and cancer immunity, which may be used to function as a prognostic biomarker and potential target for cancer immunotherapy.

Liquid biopsy on the horizon in immunotherapy of non-small cell lung cancer: current status, challenges, and perspectives

Non-small cell lung cancer (NSCLC) is one of the most threatening malignancies to human health and life. In most cases, patients with NSCLC are already at an advanced stage when they are diagnosed. In recent years, lung cancer has made great progress in precision therapy, but the efficacy of immunotherapy is unstable, and its response rate varies from patient to patient. Several biomarkers have been proposed to predict the outcomes of immunotherapy, such as programmed cell death-ligand 1 (PD-L1) expression and tumor mutational burden (TMB). Nevertheless, the detection assays are invasive and demanding on tumor tissue. To effectively predict the outcomes of immunotherapy, novel biomarkers are needed to improve the performance of conventional biomarkers. Liquid biopsy is to capture and detect circulating tumor cells (CTCs), circulating tumor DNA (ctDNA) and exosomes in body fluids, such as blood, saliva, urine, pleural fluid and cerebrospinal fluid as samples from patients, so as to make analysis and diagnosis of cancer and other diseases. The application of liquid biopsy provides a new possible solution, as it has several advantages such as non-invasive, real-time dynamic monitoring, and overcoming tumor heterogeneity. Liquid biopsy has shown predictive value in immunotherapy, significantly improving the precision treatment of lung cancer patients. Herein, we review the application of liquid biopsy in predicting the outcomes of immunotherapy in NSCLC patients, and discuss the challenges and future directions in this field.

Radiomics and Delta-Radiomics Signatures to Predict Response and Survival in Patients with Non-Small-Cell Lung Cancer Treated with Immune Checkpoint Inhibitors

The aim of our study was to determine the potential role of CT-based radiomics in predicting treatment response and survival in patients with advanced NSCLC treated with immune checkpoint inhibitors. We retrospectively included 188 patients with NSCLC treated with PD-1/PD-L1 inhibitors from two independent centers. Radiomics analysis was performed on pre-treatment contrast-enhanced CT. A delta-radiomics analysis was also conducted on a subset of 160 patients who underwent a follow-up contrast-enhanced CT after 2 to 4 treatment cycles. Linear and random forest (RF) models were tested to predict response at 6 months and overall survival. Models based on clinical parameters only and combined clinical and radiomics models were also tested and compared to the radiomics and delta-radiomics models. The RF delta-radiomics model showed the best performance for response prediction with an AUC of 0.8 (95% CI: 0.65−0.95) on the external test dataset. The Cox regression delta-radiomics model was the most accurate at predicting survival with a concordance index of 0.68 (95% CI: 0.56−0.80) (p = 0.02). The baseline CT radiomics signatures did not show any significant results for treatment response prediction or survival. In conclusion, our results demonstrated the ability of a CT-based delta-radiomics signature to identify early on patients with NSCLC who were more likely to benefit from immunotherapy.

Challenges associated with the integration of immuno-oncology agents in clinical practice

BACKGROUND

The availability of new immuno-oncology therapeutics markedly impacts oncology clinicians' treatment decision-making. To effectively support healthcare professionals (HCPs) in their practice, it is important to better understand the challenges and barriers that can accompany the introduction of these agents. This study aimed to establish the types and causes of clinical challenges posed by the introduction of new immuno-oncology agents.

METHODS

The mixed-methods design included qualitative in-depth interviews and group discussions with HCPs, in which participants discussed clinical challenges and potential underlying reasons for these challenges. Qualitative findings informed a quantitative survey. This survey investigated the extent and distribution of challenges using HCPs' self-rating of knowledge, skill, confidence, and exposure to system-level effects. These two phases were conducted sequentially with distinctly stratified samples of oncologists, nurse practitioners (NPs), physician assistants (PAs), pathologists, clinical pharmacists, interventional radiologists, rheumatologists, pulmonologists, and emergency department physicians. Participants were from the United States and had various levels of clinical experience and represented both academic and community-based settings.

RESULTS

The final sample included 107 HCPs in the qualitative phase and 554 in the quantitative phase. Analyses revealed clinical challenges related to the use of pharmacodiagnostics. For example, 47% of pathologists and 42% of oncologists reported skill gaps in identifying the appropriate marker and 46% of oncologists, 61% of PAs, 66% of NPs, 74% of pulmonologists and 81% of clinical pharmacists reported skill gaps in selecting treatment based on test results. Challenges also emerged regarding the integration of immuno-oncology agents, as oncologists, rheumatologists, pulmonologists, clinical pharmacists, PAs, and NPs reported knowledge gaps (74-81%) of the safety profiles of recently approved agents. In addition, 90% of clinical pharmacists reported skill gaps weighing the risks and benefits of treating patients with immuno-oncology agents while affected by lupus. Finally, patient communication challenges were identified: HCPs reported difficulties discussing essential aspects of immunotherapy to patients as well as how they might compare to other types of therapies.

CONCLUSION

The challenges highlighted in this study reveal substantial educational gaps related to the integration of immuno-oncology agents into practice for various groups of HCPs. These findings provide a strong base of evidence for future educational initiatives.

SMO mutation predicts the effect of immune checkpoint inhibitor: From NSCLC to multiple cancers

Background

The emergence of immune checkpoint inhibitors (ICIs) is one of the most promising breakthroughs for the treatment of multiple cancer types, but responses vary. Growing evidence points to a link between developmental signaling pathway-related genes and antitumor immunity, but the association between the genomic alterations in these genes and the response to ICIs still needs to be elucidated.

Methods

Clinical data and sequencing data from published studies and our cohort were collected to analyze the association of the mutation status of SMO with the efficacy of ICI therapy in the non-small cell lung cancer (NSCLC) cohort and the pan-cancer cohort. Furthermore, the correlation between SMO mutation and immunotherapeutic biomarkers such as immune cell infiltration, immune-related genes, and underlying signaling pathways was analyzed. Three SMO mutant plasmids were transfected into cells to explore the SMO mutation status in the context of its expression and cell growth.

Result

In the NSCLC discovery cohort, the median progression-free survival in the SMO mutant (SMO_MUT) was longer than that in the wild type (SMO_WT) (23.0 vs. 3.8 months, adjusted p = 0.041). This finding was further confirmed in the NSCLC validation cohort (8.7 vs. 5.1 months, adjusted p = 0.013). In the pan-cancer cohort (n = 1,347), a significant overall survival advantage was observed in patients with SMO mutations [not reached (NR) vs. 18 months, adjusted p = 0.024]. In the subgroup analysis, the survival advantage of SMO_MUT against SMO_WT was prominent and consistent across genders, ages, treatment types, cancer types, and the tumor mutation burden (TMB) status (all pinteraction &gt; 0.05). In an in vitro experiment, we found that both the mutant and wild-type plasmids can promote the expression of SMO, but the mutant plasmid had lower SMO mRNA and protein levels than the wild type. In CCK-8 experiments, we found that SMO_MUT plasmids can improve the growth of Calu-1 and PC-9 cells, but this capability varied between different mutations and cells. Upon further exploration, the SMO mutation status was found to be related to a higher TMB, more neoantigen load, more DNA damage repair (DDR) mutations, higher microsatellite instability (MSI) score, and higher CD8+ T-cell infiltration.

Conclusions

The SMO mutation status is an independent prognostic factor that can be used to predict better clinical outcomes of ICI treatment across multiple cancer types.

Automated next-generation profiling of genomic alterations in human cancers

The lack of validated, distributed comprehensive genomic profiling assays for patients with cancer inhibits access to precision oncology treatment. To address this, we describe elio tissue complete, which has been FDA-cleared for examination of 505 cancer-related genes. Independent analyses of clinically and biologically relevant sequence changes across 170 clinical tumor samples using MSK-IMPACT, FoundationOne, and PCR-based methods reveals a positive percent agreement of >97%. We observe high concordance with whole-exome sequencing for evaluation of tumor mutational burden for 307 solid tumors (Pearson r = 0.95) and comparison of the elio tissue complete microsatellite instability detection approach with an independent PCR assay for 223 samples displays a positive percent agreement of 99%. Finally, evaluation of amplifications and translocations against DNA- and RNA-based approaches exhibits >98% negative percent agreement and positive percent agreement of 86% and 82%, respectively. These methods provide an approach for pan-solid tumor comprehensive genomic profiling with high analytical performance.

Identification of mutation patterns and circulating tumour DNA-derived prognostic markers in advanced breast cancer patients

BACKGROUND

The correlations between circulating tumour DNA (ctDNA)-derived genomic markers and treatment response and survival outcome in Chinese patients with advanced breast cancer (ABC) have not been extensively characterized.

METHODS

Blood samples from 141 ABC patients who underwent first-line standard treatment in Peking University Cancer Hospital were collected. A next-generation sequencing based liquid biopsy assay (PredicineCARE) was used to detect somatic mutations and copy number variations (CNVs) in ctDNA. A subset of matched blood samples and tumour tissue biopsies were compared to evaluate the concordance.

RESULTS

Overall, TP53 (44.0%) and PIK3CA (28.4%) were the top two altered genes. Frequent CNVs included amplifications of ERBB2 (24.8%) and FGFR1 (8.5%) and deletions of CDKN2A (3.5%). PIK3CA/TP53 and FGFR1/2/3 variants were associated with drug resistance in hormone receptor-positive (HR +) and human epidermal growth factor receptor 2-positive (HER2 +) patients. The comparison of genomic variants across matched tumour tissue and ctDNA samples revealed a moderate to high concordance that was gene dependent. Triple-negative breast cancer (TNBC) patients harbouring TP53 or PIK3CA alterations had a shorter overall survival than those without corresponding mutations (P = 0.03 and 0.008). A high ctDNA fraction was correlated with a shorter progression-free survival (PFS) (P = 0.005) in TNBC patients. High blood-based tumor mutation burden (bTMB) was associated with a shorter PFS for HER2 + and TNBC patients (P = 0.009 and 0.05). Moreover, disease monitoring revealed several acquired genomic variants such as ESR1 mutations, CDKN2A deletions, and FGFR1 amplifications.

CONCLUSIONS

This study revealed the molecular profiles of Chinese patients with ABC and the clinical validity of ctDNA-derived markers, including the ctDNA fraction and bTMB, for predicting treatment response, prognosis, and disease progression.

TRIAL REGISTRATION

ClinicalTrials.gov ID: NCT03792529. Registered January 3rd 2019, https://clinicaltrials.gov/ct2/show/NCT03792529 .

Daily Practice Assessment of KRAS Status in NSCLC Patients: A New Challenge for the Thoracic Pathologist Is Right around the Corner

Simple Summary

RAS mutation is the most frequent oncogenic alteration in human cancers and KRAS is the most frequently mutated, notably in non-small cell lung carcinomas (NSCLC). Various attempts to inhibit KRAS in the past were unsuccessful in these latter tumors. However, recently, several small molecules (AMG510, MRTX849, JNJ-74699157, and LY3499446) have been developed to specifically target KRAS G12C-mutated tumors, which seems promising for patient treatment and should soon be administered in daily practice for non-squamous (NS)-NSCLC. In this context, it will be mandatory to systematically assess the KRAS status in routine clinical practice, at least in advanced NS-NSCLC, leading to new challenges for thoracic oncologists.

Abstract

KRAS mutations are among the most frequent genomic alterations identified in non-squamous non-small cell lung carcinomas (NS-NSCLC), notably in lung adenocarcinomas. In most cases, these mutations are mutually exclusive, with different genomic alterations currently known to be sensitive to therapies targeting EGFR, ALK, BRAF, ROS1, and NTRK. Recently, several promising clinical trials targeting KRAS mutations, particularly for KRAS G12C-mutated NSCLC, have established new hope for better treatment of patients. In parallel, other studies have shown that NSCLC harboring co-mutations in KRAS and STK11 or KEAP1 have demonstrated primary resistance to immune checkpoint inhibitors. Thus, the assessment of the KRAS status in advanced-stage NS-NSCLC has become essential to setting up an optimal therapeutic strategy in these patients. This stimulated the development of new algorithms for the management of NSCLC samples in pathology laboratories and conditioned reorganization of optimal health care of lung cancer patients by the thoracic pathologists. This review addresses the recent data concerning the detection of KRAS mutations in NSCLC and focuses on the new challenges facing pathologists in daily practice for KRAS status assessment.

The key to immunotherapy: how to choose better therapeutic biomarkers for patients with non-small cell lung cancer

Immunotherapy has become the standard of care for non-small cell lung cancer (NSCLC), either in combination or monotherapy. However, there are still some patients who cannot benefit from it. Immunization strategies for NSCLC are based on the expression of PD-L1 on tumor cells and TMB, and although these indicators have a certain predictive effect, their predictive performance is not good. Therefore, clinicians must make adjustments to recognize markers. This is a review article that summarized immunotherapeutic biomarkers according to the "seed-soil-environment", generalizes primary resistance to immunotherapy, and summarizes the integration of markers.

A Combined-Radiomics Approach of CT Images to Predict Response to Anti-PD-1 Immunotherapy in NSCLC: A Retrospective Multicenter Study

OBJECTIVE

Based on non-contrast-enhanced (NCE)/contrast-enhanced (CE) computed tomography (CT) images, we try to identify a combined-radiomics model and evaluate its predictive capacity regarding response to anti-PD1 immunotherapy of patients with non-small-cell lung cancer (NSCLC).

METHODS

131 patients with NSCLC undergoing anti-PD1 immunotherapy were retrospectively enrolled from 7 institutions. Using largest lesion (LL) and target lesions (TL) approaches, we performed a radiomics analysis based on pretreatment NCE-CT (NCE-radiomics) and CE-CT images (CE-radiomics), respectively. Meanwhile, a combined-radiomics model based on NCE-CT and CE-CT images was constructed. Finally, we developed their corresponding nomograms incorporating clinical factors. ROC was used to evaluate models' predictive performance in the training and testing set, and a DeLong test was employed to compare the differences between different models.

RESULTS

For TL approach, both NCE-radiomics and CE-radiomics performed poorly in predicting response to immunotherapy. For LL approach, NCE-radiomics nomograms and CE-radiomics nomograms incorporating with clinical factor of distant metastasis all showed satisfactory results, reflected by the AUCs in the training (AUC=0.84, 95% CI: 0.75-0.92; AUC=0.77, 95% CI: 0.67-0.87) and test sets (AUC=0.78, 95% CI: 0.64-0.92, AUC=0.73, 95% CI: 0.57-0.88), respectively. Compared with the NCE-radiomics nomograms, the combined-radiomics nomogram showed incremental predictive capacity in the training set (AUC=0.85, 95% CI: 0.77-0.92) and test set (AUC=0.81, 95% CI: 0.67-0.94), respectively, but no statistical difference (P=0.86, P=0.79).

CONCLUSION

Compared with radiomics based on single NCE or CE-CT images, the combined-radiomics model has potential advantages to identify patients with NSCLC most likely to benefit from immunotherapy, and may effectively improve more precise and individualized decision support.

EMSY inhibits homologous recombination repair and the interferon response, promoting lung cancer immune evasion

Non-small cell lung cancers (NSCLCs) harboring KEAP1 mutations are often resistant to immunotherapy. Here, we show that KEAP1 targets EMSY for ubiquitin-mediated degradation to regulate homologous recombination repair (HRR) and anti-tumor immunity. Loss of KEAP1 in NSCLC induces stabilization of EMSY, producing a BRCAness phenotype, i.e., HRR defects and sensitivity to PARP inhibitors. Defective HRR contributes to a high tumor mutational burden that, in turn, is expected to prompt an innate immune response. Notably, EMSY accumulation suppresses the type I interferon response and impairs innate immune signaling, fostering cancer immune evasion. Activation of the type I interferon response in the tumor microenvironment using a STING agonist results in the engagement of innate and adaptive immune signaling and impairs the growth of KEAP1-mutant tumors. Our results suggest that targeting PARP and STING pathways, individually or in combination, represents a therapeutic strategy in NSCLC patients harboring alterations in KEAP1.

Prognostic Biomarkers in Uveal Melanoma: The Status Quo, Recent Advances and Future Directions

Simple Summary

Although rare, uveal melanoma (UM) is the most common cancer that develops inside adult eyes. The prognosis is poor, since 50% of patients will develop lethal metastases in the first decade, especially to the liver. Once metastases are detected, life expectancy is limited, given that the available treatments are mostly unsuccessful. Thus, there is a need to find methods that can accurately predict UM prognosis and also effective therapeutic strategies to treat this cancer. In this manuscript, we initially compile the current knowledge on epidemiological, clinical, pathological and molecular features of UM. Then, we cover the most relevant prognostic factors currently used for the evaluation and follow-up of UM patients. Afterwards, we highlight emerging molecular markers in UM published over the last three years. Finally, we discuss the problems preventing meaningful advances in the treatment and prognostication of UM patients, as well as forecast new roadblocks and paths of UM-related research.

Abstract

Uveal melanoma (UM) is the most common malignant intraocular tumour in the adult population. It is a rare cancer with an incidence of nearly five cases per million inhabitants per year, which develops from the uncontrolled proliferation of melanocytes in the choroid (≈90%), ciliary body (≈6%) or iris (≈4%). Patients initially present either with symptoms like blurred vision or photopsia, or without symptoms, with the tumour being detected in routine eye exams. Over the course of the disease, metastases, which are initially dormant, develop in nearly 50% of patients, preferentially in the liver. Despite decades of intensive research, the only approach proven to mildly control disease spread are early treatments directed to ablate liver metastases, such as surgical excision or chemoembolization. However, most patients have a limited life expectancy once metastases are detected, since there are limited therapeutic approaches for the metastatic disease, including immunotherapy, which unlike in cutaneous melanoma, has been mostly ineffective for UM patients. Therefore, in order to offer the best care possible to these patients, there is an urgent need to find robust models that can accurately predict the prognosis of UM, as well as therapeutic strategies that effectively block and/or limit the spread of the metastatic disease. Here, we initially summarized the current knowledge about UM by compiling the most relevant epidemiological, clinical, pathological and molecular data. Then, we revisited the most important prognostic factors currently used for the evaluation and follow-up of primary UM cases. Afterwards, we addressed emerging prognostic biomarkers in UM, by comprehensively reviewing gene signatures, immunohistochemistry-based markers and proteomic markers resulting from research studies conducted over the past three years. Finally, we discussed the current hurdles in the field and anticipated the future challenges and novel avenues of research in UM.

The relationship between blood-based tumor mutation burden level and efficacy of PD-1/PD-L1 inhibitors in advanced non-small cell lung cancer: a systematic review and meta-analysis

Background

The predictive role of blood-based tumor mutation burden (bTMB) for selecting advanced nonsmall cell lung cancer (NSCLC) patients who might benefit from immune checkpoint inhibitors (ICIs) is still under debate. Therefore, the purpose of this meta-analysis was to evaluate the efficacy of programmed cell death 1 (PD-1) /programmed cell death ligand 1 (PD-L1) inhibitors versus that of standard-of-care therapy in patients with NSCLC who were bTMB high and bTMB low.

Methods

PubMed, Embase, Cochrane, the Web of Science, and ClinicalTrials.gov were searched systematically from inception to February 2021 for studies of PD-1/PD-L1 inhibitors (durvalumab OR atezolizumab OR avelumab OR pembrolizumab OR Nivolumab) that provided hazard ratios (HRs) for overall survival (OS) or progression-free survival (PFS), or odds ratios (ORs) for objective response rate (ORR) in both bTMB high and bTMB low groups.

Results

A total of 2338 patients with advanced or metastatic NSCLC from six randomized controlled trials, which all used chemotherapy (CT) as a control, were included in this study. Compared with CT, PD-1/PD-L1 inhibitor therapy improved OS (HR 0.62, 95% CI 0.52–0.75, P < 0.01), PFS (HR 0.57, 95% CI 0.48–0.67, P < 0.01), and ORR (OR 2.69, 95% CI 1.84–3.93, P < 0.01) in bTMB-high NSCLC patients but not in bTMB-low patients (OS HR 0.86, 95% CI 0.69–1.07, P = 0.17; PFS HR 1.00, 95% CI 0.78–1.27, P = 0.98; ORR OR 0.63, 95% CI 0.49–0.80, P = 0.03). Subgroup analyses showed that these results were consistent across all subgroups (line of therapy, therapy regimen, type of NGS panel, PD-L1 expression, and cutoff value). Meta-regression analysis showed that the proportion of patients with squamous cell histology had no statistical effect on clinical outcomes. Sensitivity analyses illustrated that all results were stable.

Conclusions

The efficacy of PD-1/PD-L1 inhibitor therapy in advanced NSCLC patients may be dependent on bTMB level. Patients with high bTMB tend to obtain significantly better OS, PFS, and ORR from PD-1/PD-L1 inhibitor therapy than from CT. However, because of multiple limitations, including those related to reproducibility, the results are exploratory and should be interpreted with caution.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08924-z.

Intra-patient stability of tumor mutational burden from tissue biopsies at different time points in advanced cancers

Background

Tumor mutational burden (TMB) may be a predictive biomarker of immune checkpoint inhibitor (ICI) responsiveness. Genomic landscape heterogeneity is a well-established cancer feature. Molecular characteristics may differ even within the same tumor specimen and undoubtedly evolve with time. However, the degree to which TMB differs between tumor biopsies within the same patient has not been established.

Methods

We curated data on 202 patients enrolled in the PREDICT study (NCT02478931), seen at the University of California San Diego (UCSD), who had 404 tissue biopsies for TMB (two per patient, mean of 722 days between biopsies) to assess difference in TMB before and after treatment in a pan-cancer cohort. We also performed an orthogonal analysis of 2872 paired pan-solid tumor biopsies in the Foundation Medicine database to examine difference in TMB between first and last biopsies.

Results

The mean (95% CI) TMB difference between samples was 0.583 [− 0.900–2.064] (p = 0.15). Pearson correlation showed a flat line for time elapsed between biopsies versus TMB change indicating no correlation (R² = 0.0001; p = 0.8778). However, in 55 patients who received ICIs, there was an increase in TMB (before versus after mean mutations/megabase [range] 12.50 [range, 0.00–98.31] versus 14.14 [range, 0.00–100.0], p = 0.025). Analysis of 2872 paired pan-solid tumor biopsies in the Foundation Medicine database also indicated largely stable TMB patterns; TMB increases were only observed in specific tumors (e.g., breast, colorectal, glioma) within certain time intervals.

Conclusions

Overall, our results suggest that tissue TMB remains stable with time, though specific therapies such as immunotherapy may correlate with an increase in TMB.

Trial registration

NCT02478931, registered June 23, 2015.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-021-00979-8.

Clinical Efficacy and Future Prospects of Immunotherapy in Lung Cancer

The three major conventional treatments: surgery, chemotherapy, and radiation therapy, have been commonly performed for lung cancer. However, lung cancer is still the leading cause of cancer-related mortality. Immunotherapy has recently emerged as a very effective new treatment modality, and there is now growing enthusiasm for cancer immunotherapy worldwide. However, the results of clinical studies using immunotherapy are not always favorable. Understanding the steps involved in the recognition and eradication of cancer cells by the immune system seems essential to understanding why past immunotherapies have failed and how current therapies can be optimally utilized. In addition, the combination of immunotherapies, such as cancer vaccines and immune checkpoint inhibitors, as well as the combination of these therapies with three conventional therapies, may pave the way for personalized immunotherapy. In this review, we summarize the results of immunotherapies used in phase III clinical trials, including immune checkpoint inhibitors, and discuss the future prospects of immunotherapies in lung cancer treatment.

Soluble Biomarkers with Prognostic and Predictive Value in Advanced Non-Small Cell Lung Cancer Treated with Immunotherapy

Numerous targeted therapies have been evaluated for the treatment of non-small cell lung cancer (NSCLC). To date, however, only a few agents have shown promising results. Recent advances in cancer immunotherapy, most notably immune checkpoint inhibitors (ICI), have transformed the treatment scenario for these patients. Although some patients respond well to ICIs, many patients do not benefit from ICIs, leading to disease progression and/or immune-related adverse events. New biomarkers capable of reliably predicting response to ICIs are urgently needed to improve patient selection. Currently available biomarkers-including programmed death protein 1 (PD-1) and its ligand (PD-L1), and tumor mutational burden (TMB)-have major limitations. At present, no well-validated, reliable biomarkers are available. Ideally, these biomarkers would be obtained through less invasive methods such as plasma determination or liquid biopsy. In the present review, we describe recent advances in the development of novel soluble biomarkers (e.g., circulating immune cells, TMB, circulating tumor cells, circulating tumor DNA, soluble factor PD-L1, tumor necrosis factor, etc.) for patients with NSCLC treated with ICIs. We also describe the potential use of these biomarkers as prognostic indicators of treatment response and toxicity.

What Is New in Biomarker Testing at Diagnosis of Advanced Non-Squamous Non-Small Cell Lung Carcinoma? Implications for Cytology and Liquid Biopsy

The discovery and clinical validation of biomarkers predictive of the response of non-squamous non-small-cell lung carcinomas (NS-NSCLC) to therapeutic strategies continue to provide new data. The evaluation of novel treatments is based on molecular analyses aimed at determining their efficacy. These tests are increasing in number, but the tissue specimens are smaller and smaller and/or can have few tumor cells. Indeed, in addition to tissue samples, complementary cytological and/or blood samples can also give access to these biomarkers. To date, it is recommended and necessary to look for the status of five genomic molecular biomarkers (EGFR, ALK, ROS1, BRAFV600, NTRK) and of a protein biomarker (PD-L1). However, the short- and more or less long-term emergence of new targeted treatments of genomic alterations on RET and MET, but also on others’ genomic alteration, notably on KRAS, HER2, NRG1, SMARCA4, and NUT, have made cellular and blood samples essential for molecular testing. The aim of this review is to present the interest in using cytological and/or liquid biopsies as complementary biological material, or as an alternative to tissue specimens, for detection at diagnosis of new predictive biomarkers of NS-NSCLC.

The prognostic impact of tumor mutational burden (TMB) in the first-line management of advanced non-oncogene addicted non-small-cell lung cancer (NSCLC): a systematic review and meta-analysis of randomized controlled trials

BACKGROUND

The role of tumor mutational burden (TMB) is still debated for selecting advanced non-oncogene addicted non-small-cell lung cancer (NSCLC) patients who might benefit from immune checkpoint inhibitors (ICIs). Of note, TMB failed to predict a benefit in overall survival (OS) among such patients.

MATERIALS AND METHODS

The purpose of this meta-analysis was to compare efficacy outcomes among first-line immune-oncology (IO) agents versus standard platinum-based chemotherapy (CT) within two subgroups (TMB-low and TMB-high on either tissue or blood). We collected hazard ratios (HRs) to evaluate the association for progression-free survival (PFS) and OS, with the relative 95% confidence intervals (CIs). Risk ratios (RRs) were used as an association measure for objective response rate (ORR).

RESULTS

Eight different cohorts of five randomized controlled phase III studies (3848 patients) were analyzed. In TMB-high patients, IO agents were associated with improved ORR (RRs 1.37, 95% CI 1.13-1.66), PFS (HR 0.69, 95% CI 0.61-0.79) and OS (HR 0.67, 95% CI 0.59-0.77) when compared with CT, thus suggesting a possible predictive role of high TMB for IO regimens. In TMB-low patients, the IO strategy did not lead to any significant benefit in survival and activity, whereas the pooled results of both ORR and PFS were intriguingly associated with a statistical significance in favor of CT.

CONCLUSIONS

This meta-analysis resulted in a proven benefit in OS in favor of IO agents in the TMB-high population. Although more prospective data are warranted, we postulated the hypothesis that monitoring TMB, in addition to the existing programmed death-ligand 1 (PD-L1) expression level, could represent the preferable option for future clinical research in the first-line management of advanced non-oncogene addicted NSCLC patients.

Liquid biopsy in non-small cell lung cancer-current status and future outlook-a narrative review

Lung cancer ranks first as the cause of cancer-associated deaths gobally. The American Cancer Society estimates for 228,820 new cases and 135,720 deaths from lung cancer in the United States for the year 2020. Targeted treatment options have rapidly emerged for non-small cell lung cancer (NSCLC) within the past decade. Screening for molecular aberrations is mainly done by tissue biopsy. However, in some cases a biopsy is not possible, or patients do not consent to it. Hence, liquid biopsy remains the only option. Relevant data about the topic of liquid biopsy, with a special focus on NSCLC, was obtained via a PubMed search. We included mainly literature published from 2010 onwards, omitting older studies whenever possible. With this review of the literature, we give an overview of different liquid biopsy approaches, as well as their respective advantages and disadvantages. We have reviewed the assessment of epidermal growth factor receptor (EGFR) mutation status in particular, and go into detail with current use of liquid biopsy in everyday clinical practice. Today, liquid biopsy is still infrequently used, depending on the treatment center, but popularity is steadily increasing. Various different approaches are already available, but costs and level of sensitivity significantly differ between techniques. By using liquid biopsy more widely in selected patients, complication rates can be reduced, and constant disease monitoring is made considerably easier.

Association between Smoking History and Tumor Mutation Burden in Advanced Non-Small Cell Lung Cancer

Lung carcinogenesis is a complex and stepwise process involving accumulation of genetic mutations in signaling and oncogenic pathways via interactions with environmental factors and host susceptibility. Tobacco exposure is the leading cause of lung cancer, but its relationship to clinically relevant mutations and the composite tumor mutation burden (TMB) has not been fully elucidated. In this study, we investigated the dose-response relationship in a retrospective observational study of 931 patients treated for advanced-stage non-small cell lung cancer (NSCLC) between April 2013 and February 2020 at the Dana Farber Cancer Institute and Brigham and Women's Hospital. Doubling smoking pack-years was associated with increased KRASG12C and less frequent EGFRdel19 and EGFRL858R mutations, whereas doubling smoking-free months was associated with more frequent EGFRL858R . In advanced lung adenocarcinoma, doubling smoking pack-years was associated with an increase in TMB, whereas doubling smoking-free months was associated with a decrease in TMB, after controlling for age, gender, and stage. There is a significant dose-response association of smoking history with genetic alterations in cancer-related pathways and TMB in advanced lung adenocarcinoma. SIGNIFICANCE: This study clarifies the relationship between smoking history and clinically relevant mutations in non-small cell lung cancer, revealing the potential of smoking history as a surrogate for tumor mutation burden.

Exploration of the Associations of lncRNA Expression Patterns with Tumor Mutation Burden and Prognosis in Colon Cancer

Background

Tumor mutation burden (TMB) is emerging as a new biomarker to monitor the response of cancer patients to immunotherapy. Long non-coding RNAs (lncRNAs) are critical in regulating gene expression and play a significant role in cancer-associated immune responses. However, the association between lncRNA expression patterns and TMB levels and survival outcomes remains unknown in colon cancer.

Methods

In colon cancer patients from The Cancer Genome Atlas Program (TCGA), a multi-lncRNAs based classifier for predicting TMB levels was established using the least absolute shrinkage and selection operator (LASSO) method. The association between classifier index and immune-related characteristics of patients was also investigated. Quantitative polymerase chain reaction (qPCR) was used to verify the expression levels of these lncRNAs in normal and CRC cell lines.

Results

The multi-lncRNAs based classifier had ability to predict TMB level of patients with accuracy (AUC= 0.70), and the general applicability of this classifier was proved in the validation set (AUC= 0.71) and the pooled set (AUC= 0.70). The classifier index was related to three immune checkpoints (PD1, PD-L1, and CTLA-4), the infiltration level of immune cells, and immune response-related score (IFN-γ score, gene expression profiles (GEP) score, cytolytic activity (CYT) score and MHC score). A nomogram, which integrates classifier and some common clinical information, was able to predict the overall survival of colon cancer patients accurately.

Conclusion

LncRNA expression patterns are associated with TMB, which may serve as a classifier to predict the TMB in colon cancer patients. The nomogram could potentially evaluate survival outcomes and provide a reference to better manage colon cancer patients.

Next-Generation Sequencing with Liquid Biopsies from Treatment-Naïve Non-Small Cell Lung Carcinoma Patients

Recently, the liquid biopsy (LB), a non-invasive and easy to repeat approach, has started to compete with the tissue biopsy (TB) for detection of targets for administration of therapeutic strategies for patients with advanced stages of lung cancer at tumor progression. A LB at diagnosis of late stage non-small cell lung carcinoma (NSCLC) is also being performed. It may be asked if a LB can be complementary (according to the clinical presentation or systematics) or even an alternative to a TB for treatment-naïve advanced NSCLC patients. Nucleic acid analysis with a TB by next-generation sequencing (NGS) is gradually replacing targeted sequencing methods for assessment of genomic alterations in lung cancer patients with tumor progression, but also at baseline. However, LB is still not often used in daily practice for NGS. This review addresses different aspects relating to the use of LB for NGS at diagnosis in advanced NSCLC, including its advantages and limitations.

Prognostic and Predictive Biomarkers in Non-Small Cell Lung Cancer Patients on Immunotherapy-The Role of Liquid Biopsy in Unraveling the Puzzle

In the last decade, immunotherapy has been one of the most important advances in the non-small cell lung cancer (NSCLC) treatment landscape. Nevertheless, only a subset of NSCLC patients benefits from it. Currently, the only Food and Drug Administration (FDA) approved diagnostic test for first-line immunotherapy in metastatic NSCLC patients uses tissue biopsies to determine the programmed death ligand 1 (PD-L1) status. However, obtaining tumor tissue is not always feasible and puts the patient at risk. Liquid biopsy, which refers to the tumor-derived material present in body fluids, offers an alternative approach. This less invasive technique gives real-time information on the tumor characteristics. This review addresses different promising liquid biopsy based biomarkers in NSCLC patients that enable the selection of patients who benefit from immunotherapy and the monitoring of patients during this therapy. The challenges and the opportunities of blood-based biomarkers such as cell-free DNA (cfDNA), circulating tumor cells (CTCs), exosomes, epigenetic signatures, microRNAs (miRNAs) and the T cell repertoire will be addressed. This review also focuses on the less-studied feces-based and breath-based biomarkers.

PEG3 mutation is associated with elevated tumor mutation burden and poor prognosis in breast cancer

Background: Breast cancer is the second most common malignancy in women and considered as a severe health burden. PEG3 mutations have been observed in several cancers. However, the associations of PEG3 mutation with tumor mutation burden (TMB) and prognosis in breast cancer have not been investigated. Methods: In our study, the somatic mutation data of 986 breast cancer patients from The Cancer Genome Atlas (TCGA) were analyzed. Results: It showed that PEG3 had a relatively high mutation rate (2%). After calculated the TMB in PEG3 mutant and PEG3 wild-type groups, we found the TMB value was significantly higher in PEG3 mutant samples than that in PEG3 wild-type samples (P = 5.6e-07), which was independent of the confounding factors including age, stage, mutations of BRCA1, BRCA2 and POLE (odd ratio, 0.45; 95% CI, 0.20–0.98; P=0.044). Survival analysis revealed that PEG3 mutant samples had inferior survival outcome compared with the PEG3 wild-type samples after adjusted for the confounding factors above (hazard ratio, 0.27; 95% CI: 0.12–0.57; P<0.001). Conclusion: These results illustrated that PEG3 mutation was associated with high TMB and inferior prognosis, suggesting PEG3 mutation might play a guiding role in prognosis prediction and immunotherapy selection in breast cancer.

Imaging Biomarkers to Predict and Evaluate the Effectiveness of Immunotherapy in Advanced Non-Small-Cell Lung Cancer

Objective

We aimed to identify imaging biomarkers to assess predictive capacity of radiomics nomogram regarding treatment response status (responder/non-responder) in patients with advanced NSCLC undergoing anti-PD1 immunotherapy.

Methods

197 eligible patients with histologically confirmed NSCLC were retrospectively enrolled from nine hospitals. We carried out a radiomics characterization from target lesions (TL) approach and largest target lesion (LL) approach on baseline and first follow-up (TP1) CT imaging data. Delta-radiomics feature was calculated as the relative net change in radiomics feature between baseline and TP1. Minimum Redundancy Maximum Relevance (mRMR) and Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression were applied for feature selection and radiomics signature construction.

Results

Radiomics signature at baseline did not show significant predictive value regarding response status for LL approach (P = 0.10), nor in terms of TL approach (P = 0.27). A combined Delta-radiomics nomogram incorporating Delta-radiomics signature with clinical factor of distant metastasis for target lesions had satisfactory performance in distinguishing responders from non-responders with AUCs of 0.83 (95% CI: 0.75–0.91) and 0.81 (95% CI: 0.68–0.95) in the training and test sets respectively, which was comparable with that from LL approach (P = 0.92, P = 0.97). Among a subset of those patients with available pretreatment PD-L1 expression status (n = 66), models that incorporating Delta-radiomics features showed superior predictive accuracy than that of PD-L1 expression status alone (P <0.001).

Conclusion

Early response assessment using combined Delta-radiomics nomograms have potential advantages to identify patients that were more likely to benefit from immunotherapy, and help oncologists modify treatments tailored individually to each patient under therapy.

Comparative Efficacy and Safety of Immunotherapy Alone and in Combination With Chemotherapy for Advanced Non-small Cell Lung Cancer

There is a lack of direct cross-comparison studies in clinical trials between immunotherapy alone and combination treatment, especially in Non-Small Cell Lung Cancer (NSCLC) patients with high PD-L1 expression. To determine if anti-PD-(L)1 antibody combined with chemotherapy is more efficient than immune checkpoint inhibitor (ICI) monotherapy for advanced NSCLC patients in the real-world data. We retrospectively collected 325 patients with advanced NSCLC treated with ICI alone with or without chemotherapy from 11th July 2016 to 26th May 2020 to investigate which treatment scenario is the most efficient, and how clinical factors impact response. Patients with advanced NSCLC were treated with ICI monotherapy (178/325, 54.8%) or in combination with chemotherapy (147/325, 45.2%). The objective response rate and disease control rate were higher in the combination group than the monotherapy group. Patients (including those with distant metastasis) treated with chemo-immunotherapy were associated with a significantly longer median PFS and OS compared with the monotherapy group, irrespective of the PD-L1 expression level and previous treatment lines. No significant increase in the risk of immune-related adverse events (irAEs) was found after combination with chemotherapy (50.6 vs. 57.8%). IrAEs predicted better PFS of immunotherapy in the monotherapy group, especially for patients with late irAEs (after ≥4 cycles). Collectively, we demonstrated that ICI monotherapy plus chemotherapy might have better anti-tumor activity and an acceptable side-effect profile regardless of PD-L1 level or previous treatment lines. Both regimens were well-tolerated and cost-effective, the more efficient is usually recommended.

Patient-Reported Outcomes with Durvalumab With or Without Tremelimumab Versus Standard Chemotherapy as First-Line Treatment of Metastatic Non-Small-Cell Lung Cancer (MYSTIC)

BACKGROUND

The phase 3 MYSTIC study of durvalumab ± tremelimumab versus chemotherapy in metastatic non-small-cell lung cancer (NSCLC) patients with tumor cell (TC) programmed cell death ligand 1 (PD-L1) expression ≥ 25% did not meet its primary endpoints. We report patient-reported outcomes (PROs).

PATIENTS AND METHODS

Treatment-naïve patients were randomized (1:1:1) to durvalumab, durvalumab + tremelimumab, or chemotherapy. PROs were assessed in patients with PD-L1 TC ≥ 25% using EORTC Quality of Life Questionnaire (QLQ)-C30/LC13. Changes from baseline (12 months) for prespecified PRO endpoints of interest were analyzed by mixed model for repeated measures (MMRM) and time to deterioration (TTD) by stratified log-rank tests.

RESULTS

There were no between-arm differences in baseline PROs (N = 488). Between-arm differences in MMRM-adjusted mean changes from baseline favored at least one of the durvalumab-containing arms versus chemotherapy (nominal P < .01) for C30 fatigue: durvalumab (-9.5; 99% confidence interval [CI], -17.0 to -2.0), durvalumab + tremelimumab (-11.7; 99% CI, -19.4 to -4.1); and for C30 appetite loss: durvalumab (-11.9; 99% CI, -21.1 to -2.7). TTD was longer with at least one of the durvalumab-containing arms versus chemotherapy (nominal P < .01) for global health status/quality of life: durvalumab (hazard ratio [HR] = 0.7; 95% CI, 0.5-1.0), durvalumab + tremelimumab (HR = 0.7; 95% CI, 0.5-1.0); and for physical functioning: durvalumab (HR = 0.6; 95% CI, 0.4-0.8), durvalumab + tremelimumab (HR = 0.6; 95% CI, 0.5-0.9) (both C30); as well as for the key symptoms of dyspnea: durvalumab (HR = 0.6; 95% CI, 0.5-0.9), durvalumab + tremelimumab (HR = 0.7; 95% CI, 0.5-1.0) (both LC13); fatigue: durvalumab + tremelimumab (HR = 0.6; 95% CI, 0.4-0.8); and appetite loss: durvalumab (HR = 0.5; 95% CI, 0.4-0.7), durvalumab + tremelimumab (HR = 0.7; 95% CI, 0.5-0.9) (both C30).

CONCLUSION

Durvalumab ± tremelimumab versus chemotherapy reduced symptom burden and improved TTD of PROs, suggesting it had no detrimental effects on quality of life in metastatic NSCLC patients.

The Importance of STK11/LKB1 Assessment in Non-Small Cell Lung Carcinomas

Despite the recent implementation of immunotherapy as a single treatment or in combination with chemotherapy for first-line treatment of advanced non-small cell lung cancer (NSCLC), many patients do not benefit from this regimen due to primary treatment resistance or toxicity. Consequently, there is an urgent need to develop efficient biomarkers that can select patients who will benefit from immunotherapy thereby providing the appropriate treatment and avoiding toxicity. One of the biomarkers recently described for the stratification of NSCLC patients undergoing immunotherapy are mutations in STK11/LKB1, which are often associated with a lack of response to immunotherapy in some patients. Therefore, the purpose of this review is to describe the different cellular mechanisms associated with STK11/LKB1 mutations, which may explain the lack of response to immunotherapy. Moreover the review addresses the co-occurrence of additional mutations that may influence the response to immunotherapy and the current clinical studies that have further explored STK11/LKB1 as a predictive biomarker. Additionally this work includes the opportunities and limitations to look for the STK11/LKB1 status in the therapeutic strategy for NSCLC patients.

Mining The Cancer Genome Atlas database for tumor mutation burden and its clinical implications in gastric cancer

BACKGROUND

Tumor mutational burden (TMB) is an important independent biomarker for the response to immunotherapy in multiple cancers. However, the clinical implications of TMB in gastric cancer (GC) have not been fully elucidated.

AIM

To explore the landscape of mutation profiles and determine the correlation between TMB and microRNA (miRNA) expression in GC.

METHODS

Genomic, transcriptomic, and clinical data from The Cancer Genome Atlas were used to obtain mutational profiles and investigate the statistical correlation between mutational burden and the overall survival of GC patients. The difference in immune infiltration between high- and low-TMB subgroups was evaluated by Wilcoxon rank-sum test. Furthermore, miRNAs differentially expressed between the high- and low-TMB subgroups were identified and the least absolute shrinkage and selection operator method was employed to construct a miRNA-based signature for TMB prediction. The biological functions of the predictive miRNAs were identified with DIANA-miRPath v3.0.

RESULTS

C>T single nucleotide mutations exhibited the highest mutation incidence, and the top three mutated genes were TTN, TP53, and MUC16 in GC. High TMB values (top 20%) were markedly correlated with better survival outcome, and multivariable regression analysis indicated that TMB remained prognostic independent of TNM stage, histological grade, age, and gender. Different TMB levels exhibited different immune infiltration patterns. Significant differences between the high- and low-TMB subgroups were observed in the infiltration of CD8+ T cells, M1 macrophages, regulatory T cells, and CD4+ T cells. In addition, we developed a miRNA-based signature using 23 differentially expressed miRNAs to predict TMB values of GC patients. The predictive performance of the signature was confirmed in the testing and the whole set. Receiver operating characteristic curve analysis demonstrated the optimal performance of the signature. Finally, enrichment analysis demonstrated that the set of miRNAs was significantly enriched in many key cancer and immune-related pathways.

CONCLUSION

TMB

A multiparametric approach to improve the prediction of response to immunotherapy in patients with metastatic NSCLC

BACKGROUND

It is still unclear how to combine biomarkers to identify patients who will truly benefit from anti-PD-1 agents in NSCLC. This study investigates exosomal mRNA expression of PD-L1 and IFN-γ, PD-L1 polymorphisms, tumor mutational load (TML) in circulating cell-free DNA (cfDNA) and radiomic features as possible predictive markers of response to nivolumab and pembrolizumab in metastatic NSCLC patients.

METHODS

Patients were enrolled and blood (12 ml) was collected at baseline before receiving anti-PD-1 therapy. Exosome-derived mRNA and cfDNA were extracted to analyse PD-L1 and IFN-γ expression and tumor mutational load (TML) by digital droplet PCR (ddPCR) and next-generation sequencing (NGS), respectively. The PD-L1 single nucleotide polymorphisms (SNPs) c.-14-368 T > C and c.*395G > C, were analysed on genomic DNA by Real-Time PCR. A radiomic analysis was performed on the QUIBIM Precision^® V3.0 platform.

RESULTS

Thirty-eight patients were enrolled. High baseline IFN-γ was independently associated with shorter median PFS (5.6 months vs. not reached p = 0.0057), and levels of PD-L1 showed an increase at 3 months vs. baseline in patients who progressed (p = 0.01). PD-L1 baseline levels showed significant direct and inverse relationships with radiomic features. Radiomic features also inversely correlated with PD-L1 expression in tumor tissue. In subjects receiving nivolumab, median PFS was shorter in carriers of c.*395GG vs. c.*395GC/CC genotype (2.3 months vs. not reached, p = 0.041). Lastly, responders had higher non-synonymous mutations and more links between co-occurring genetic somatic mutations and ARID1A alterations as well.

CONCLUSIONS

A combined multiparametric approach may provide a better understanding of the molecular determinants of response to immunotherapy.

Computational identification of biomarker genes for lung cancer considering treatment and non-treatment studies

BACKGROUND

Lung cancer is the number one cancer killer in the world with more than 142,670 deaths estimated in the United States alone in the year 2019. Consequently, there is an overreaching need to identify the key biomarkers for lung cancer. The aim of this study is to computationally identify biomarker genes for lung cancer that can aid in its diagnosis and treatment. The gene expression profiles of two different types of studies, namely non-treatment and treatment, are considered for discovering biomarker genes. In non-treatment studies healthy samples are control and cancer samples are cases. Whereas, in treatment studies, controls are cancer cell lines without treatment and cases are cancer cell lines with treatment.

RESULTS

The Differentially Expressed Genes (DEGs) for lung cancer were isolated from Gene Expression Omnibus (GEO) database using R software tool GEO2R. A total of 407 DEGs (254 upregulated and 153 downregulated) from non-treatment studies and 547 DEGs (133 upregulated and 414 downregulated) from treatment studies were isolated. Two Cytoscape apps, namely, CytoHubba and MCODE, were used for identifying biomarker genes from functional networks developed using DEG genes. This study discovered two distinct sets of biomarker genes - one from non-treatment studies and the other from treatment studies, each set containing 16 genes. Survival analysis results show that most non-treatment biomarker genes have prognostic capability by indicating low-expression groups have higher chance of survival compare to high-expression groups. Whereas, most treatment biomarkers have prognostic capability by indicating high-expression groups have higher chance of survival compare to low-expression groups.

CONCLUSION

A computational framework is developed to identify biomarker genes for lung cancer using gene expression profiles. Two different types of studies - non-treatment and treatment - are considered for experiment. Most of the biomarker genes from non-treatment studies are part of mitosis and play vital role in DNA repair and cell-cycle regulation. Whereas, most of the biomarker genes from treatment studies are associated to ubiquitination and cellular response to stress. This study discovered a list of biomarkers, which would help experimental scientists to design a lab experiment for further exploration of detail dynamics of lung cancer development.

From Tumor Mutational Burden to Blood T Cell Receptor: Looking for the Best Predictive Biomarker in Lung Cancer Treated with Immunotherapy

Despite therapeutic advances, lung cancer (LC) is one of the leading causes of cancer morbidity and mortality worldwide. Recently, the treatment of advanced LC has experienced important changes in survival benefit due to immune checkpoint inhibitors (ICIs). However, overall response rates (ORR) remain low in unselected patients and a large proportion of patients undergo disease progression in the first weeks of treatment. Therefore, there is a need of biomarkers to identify patients who will benefit from ICIs. The programmed cell death ligand 1 (PD-L1) expression has been the first biomarker developed. However, its use as a robust predictive biomarker has been limited due to the variability of techniques used, with different antibodies and thresholds. In this context, tumor mutational burden (TMB) has emerged as an additional powerful biomarker based on the observation of successful response to ICIs in solid tumors with high TMB. TMB can be defined as the total number of nonsynonymous mutations per DNA megabases being a mechanism generating neoantigens conditioning the tumor immunogenicity and response to ICIs. However, the latest data provide conflicting results regarding its role as a biomarker. Moreover, considering the results of the recent data, the use of peripheral blood T cell receptor (TCR) repertoire could be a new predictive biomarker. This review summarises recent findings describing the clinical utility of TMB and TCRβ (TCRB) and concludes that immune, neontigen, and checkpoint targeted variables are required in combination for accurately identifying patients who most likely will benefit of ICIs.

The oncogenic potential of a mutant TP53 gene explored in two spontaneous lung cancer mice models

Background

Lung cancer is the number one cancer killer worldwide. A major drawback in the lung cancer treatment field is the lack of realistic mouse models that replicate the complexity of human malignancy and immune contexture within the tumor microenvironment. Such models are urgently needed. Mutations of the tumor protein p53 are among the most common alterations in human lung cancers.

Methods

Previously, we developed a line of lung cancer mouse model where mutant human TP53-273H is expressed in a lung specific manner in FVB/N background. To investigate whether the human TP53 mutant has a similar oncogenic potential when it is expressed in another strain of mouse, we crossed the FVB/N-SPC-TP53-273H mice to A/J strain and created A/J-SPC-TP53-273H transgenic mice. We then compared lung tumor formation between A/J-SPC-TP53-273H and FVB/N-SPC-TP53-273H.

Results

We found the TP53-273H mutant gene has a similar oncogenic potential in lung tumor formation in both mice strains, although A/J strain mice have been found to be a highly susceptible strain in terms of carcinogen-induced lung cancer. Both transgenic lines survived more than 18 months and developed age related lung adenocarcinomas. With micro CT imaging, we found the FVB-SPC-TP53-273H mice survived more than 8 weeks after initial detection of lung cancer, providing a sufficient window for evaluating new anti-cancer agents.

Conclusions

Oncogenic potential of the most common genetic mutation, TP53-273H, in human lung cancer is unique when it is expressed in different strains of mice. Our mouse models are useful tools for testing novel immune checkpoint inhibitors or other therapeutic strategies in the treatment of lung cancer.

The biomarkers of hyperprogressive disease in PD-1/PD-L1 blockage therapy

Immune checkpoint inhibitors (ICIs), such as PD-1/PD-L1 antibodies (Abs) and anti-cytotoxic T lymphocyte antigen 4 (CTLA-4) Abs, are effective for patients with various cancers. However, low response rates to ICI monotherapies and even hyperprogressive disease (HPD) have limited the clinical application of ICIs. HPD is a novel pattern of progression, with an unexpected and fast progression in tumor volume and rate, poor survival of patients and early fatality. Considering the limitations of ICI due to HPD incidence, valid biomarkers are urgently needed to predict the occurrence of HPD and the efficacy of ICI. Here, we reviewed and summarized the known biomarkers of HPD, including tumor cell biomarkers, tumor microenvironment biomarkers, laboratory biomarkers and clinical indicators, which provide a potential effective approach for selecting patients sensitive to ICI cancer treatments.

The biomarkers of hyperprogressive disease in PD-1/PD-L1 blockage therapy

Immune checkpoint inhibitors (ICIs), such as PD-1/PD-L1 antibodies (Abs) and anti-cytotoxic T lymphocyte antigen 4 (CTLA-4) Abs, are effective for patients with various cancers. However, low response rates to ICI monotherapies and even hyperprogressive disease (HPD) have limited the clinical application of ICIs. HPD is a novel pattern of progression, with an unexpected and fast progression in tumor volume and rate, poor survival of patients and early fatality. Considering the limitations of ICI due to HPD incidence, valid biomarkers are urgently needed to predict the occurrence of HPD and the efficacy of ICI. Here, we reviewed and summarized the known biomarkers of HPD, including tumor cell biomarkers, tumor microenvironment biomarkers, laboratory biomarkers and clinical indicators, which provide a potential effective approach for selecting patients sensitive to ICI cancer treatments.

Novel prognostic molecular markers in lung cancer

Lung carcinoma, especially in its most commonly diagnosed non-small cell histological form, is a challenge to diagnose and treat worldwide, due to the prognosis in patients with this type of cancer being poor and mortality rates being high. However, a number of patients with this type of lung carcinoma exhibit a longer than average overall survival. The specific molecular background of non-small-cell lung cancer that favors longer survival has not yet been determined. The aim of the current study was to review articles published in the years 2017-2018 and create a list of the most important and strongest non-conventional factors that could be used in the future assessment of the prognosis of patients with adenocarcinoma and squamous cell carcinoma of the lung who cannot undergo current targeted therapy. Analysis identified multiple prognostic factors in non-small cell lung carcinoma, including tumor mutational burden, which was revealed to be independent of the tumor stage or grade as well as other factors, including age, sex or targeted therapy effects. The selected molecular factors exhibit the potential to be used in the treatment of patients with specific problematic lung cancer, and may contribute to setting recommendations for the diagnosis, prognosis and treatment of individual patients with lung cancer.

Harmonization of Tumor Mutational Burden Quantification and Association With Response to Immune Checkpoint Blockade in Non-Small-Cell Lung Cancer

Purpose

Heterogeneity in tumor mutational burden (TMB) quantification across sequencing platforms limits the application and further study of this potential biomarker of response to immune checkpoint inhibitors (ICI). We hypothesized that harmonization of TMB across platforms would enable integration of distinct clinical datasets to better characterize the association between TMB and ICI response.

Methods

Cohorts of NSCLC patients sequenced by one of three targeted panels or by whole exome sequencing (WES) were compared (total n=7297). TMB was calculated uniformly and compared across cohorts. TMB distributions were harmonized by applying a normal transformation followed by standardization to z-scores. In sub-cohorts of patients treated with ICIs (DFCI n=272; MSKCC n=227), the association between TMB and outcome was assessed. Durable clinical benefit (DCB) was defined as responsive/stable disease lasting ≥6 months.

Results

TMB values were higher in the panel cohorts than the WES cohort. Average mutation rates per gene were highly concordant across cohorts (Pearson coefficient 0.842-0.866). Subsetting the WES cohort by gene panels only partially reproduced the observed differences in TMB. Standardization of TMB into z-scores harmonized TMB distributions and enabled integration of the ICI-treated sub-cohorts. Simulations indicated that cohorts >900 are necessary for this approach. TMB did not associate with response in never smokers or patients harboring targetable driver alterations, although these analyses were under-powered. Increasing TMB thresholds increased DCB rate, but DCB rates within deciles varied. Receiver operator curves yielded an area under the curve of 0.614 with no natural inflection point.

Conclusion

Z-score conversion harmonizes TMB values and enables integration of datasets derived from different sequencing panels. Clinical and biologic features may provide context to the clinical application of TMB, and warrant further study.