Beyond tissue biopsy: a diagnostic framework to address tumor heterogeneity in lung cancer

Unlocking the potential of Molecular Tumor Boards: from cutting-edge data interpretation to innovative clinical pathways

The emerging era of precision medicine is characterized by an increasing availability of targeted anticancer therapies and by the parallel development of techniques to obtain more refined molecular data, whose interpretation may not always be straightforward. Molecular tumor boards gather various professional figures, in order to leverage the analysis of molecular data and provide prognostic and predictive insights for clinicians. In addition to healthcare development, they could also become a tool to promote knowledge and research spreading. A growing body of evidence on the application of molecular tumor boards to clinical practice is forming and positive signals are emerging, although a certain degree of heterogeneity exists. This work analyzes molecular tumor boards' potential workflows, figures involved, data sources, sample matrices and eligible patients, as well as available evidence and learning examples. The emerging concept of multi-institutional, disease-specific molecular tumor boards is also considered by presenting two ongoing nationwide experiences.

Intra-Individual Variation in Disease-Specific IgG Fc Glycoform Ratios to Monitor the Disease Progression of Lung Cancer

Aberrant protein glycosylation is an active pathological alteration related to the progression of cancers. The speed of progression varies among individuals, increasing the difficulties of prognosis assessment. Hence, evaluating variation in glycosylation using patients themselves as their own controls is a potential way to reduce the impact of individual differences on progression monitoring. Here, following a longitudinal follow-up study involving 125 lung cancer (LC) patients with progressive disease, we isolated disease-specific IgG from serum using polyacrylamide gel electrophoresis, obtained IgG glycoform ratios using mass spectrometry, and then set a fold-change cutoff of 1.5 to utilize the intra-individual variation in IgG glycosylation to monitor PD. We found that the serial monitoring of 15 types of glycoform ratios provided an effective way for monitoring LC progression. Over 1.5-fold changes in glycoform ratios relative to the first observed value were detected in 117 of 125 LC patients (93.6%). Our established method predicted LC progression 55.8 (IQR 31.1-90.1) weeks earlier than imaging examination did. In summary, intra-individual variation in IgG glycoform ratios is useful to monitor LC progression, expanding our knowledge about the relationship between IgG glycosylation and cancer prognosis. The raw data files are available via the ProteomeXchange Consortium with the identifier PXD037541.

Cysteine-specific 89Zr-labeled anti-CD25 IgG allows immuno-PET imaging of interleukin-2 receptor-α on T cell lymphomas

Introduction

Positron emission tomography (PET) using radiolabeled Abs as imaging tracer is called immuno-PET. Immuno-PET can verify therapeutic Ab delivery and can noninvasively quantify global levels of target expression in tumors of living subjects. The interleukin-2 receptor α chain (IL-2Rα; CD25) is a promising target for immune therapy and radioimmunotherapy of lymphomas. Immuno-PET could facilitate this approach by visualizing CD25 expression in vivo.

Methods

We prepared ⁸⁹Zr-anti-CD25 IgG specifically labeled to sulfhydryl moieties by maleimide-deferoxamine conjugation.

Results and Discussion

CD25(+) SUDHL1 human T-cell lymphoma cells showed high anti-human ⁸⁹Zr-CD25 IgG binding that reached 32-fold of that of CD25(-) human lymphoma cells and was completely blocked by excess unlabeled Ab. In SUDHL1 tumor-bearing nude mice, pharmacokinetic studies demonstrated exponential reductions of whole blood and plasma activity following intravenous ⁸⁹Zr-anti-CD25 IgG injection, with half-lives of 26.0 and 23.3 h, respectively. SUDHL1 tumor uptake of ⁸⁹Zr-CD25 IgG was lower per weight in larger tumors, but blood activity did not correlate with tumor size or blood level of human CD25, indicating minimal influence by circulating soluble CD25 protein secreted from the lymphoma cells. ⁸⁹Zr-CD25 IgG PET allowed high-contrast SUDHL1 lymphoma visualization at five days. Biodistribution studies confirmed high tumor ⁸⁹Zr-CD25 IgG uptake (8.7 ± 0.9%ID/g) that was greater than blood (5.2 ± 1.6%ID/g) and organ uptakes (0.7 to 3.5%ID/g). Tumor CD25-specific targeting was confirmed by suppression of tumor uptake to 4.3 ± 0.2%ID by excess unlabeled CD25 IgG, as well as by low tumor uptake of ⁸⁹Zr-labeled IgG2a isotype control Ab (3.6 ± 0.9%ID). Unlike CD25(+) lymphocytes from mouse thymus that showed specific uptake of anti-mouse ⁸⁹Zr-CD25 IgG, EL4 mouse lymphoma cells had low CD25 expression and showed low uptake. In immunocompetent mice bearing EL4 tumors, anti-mouse ⁸⁹Zr-CD25 IgG displayed low uptakes in normal organs as well as in the tumor. Furthermore, the biodistribution was not influenced by Ab blocking, indicating that specific uptake in nontumor tissues was minimal. ⁸⁹Zr-CD25 IgG immuno-PET may thus be useful for imaging of T-cell lymphomas and noninvasive assessment of CD25 expression on target cells in vivo.

Liquid biopsy for children with central nervous system tumours: Clinical integration and technical considerations

Circulating cell-free DNA (cfDNA) analysis has the potential to revolutionise the care of patients with cancer and is already moving towards standard of care in some adult malignancies. Evidence for the utility of cfDNA analysis in paediatric cancer patients is also accumulating. In this review we discuss the limitations of blood-based assays in patients with brain tumours and describe the evidence supporting cerebrospinal fluid (CSF) cfDNA analysis. We make recommendations for CSF cfDNA processing to aid the standardisation and technical validation of future assays. We discuss the considerations for interpretation of cfDNA analysis and highlight promising future directions. Overall, cfDNA profiling shows great potential as an adjunct to the analysis of biopsy tissue in paediatric cancer patients, with the potential to provide a genetic molecular profile of the tumour when tissue biopsy is not feasible. However, to fully realise the potential of cfDNA analysis for children with brain tumours larger prospective studies incorporating serial CSF sampling are required.

Immune cell-lipoprotein imbalance as a marker for early diagnosis of non-small cell lung cancer metastasis

The underlying molecular mechanisms and evolutionary patterns of lung cancer metastasis remain unclear, resulting in a lack of effective indicators for early diagnosis of metastasis. We retrospectively analyzed 117 patients with primary non-small cell lung cancer (NSCLC) admitted to Tongji Hospital of Tongji University in 2021, of which 93 patients with tumor metastasis were set as the metastasis group. 24 patients without metastasis were set as the non-metastasis group. The differences of each index in the two groups of patients and the expression levels in different TNM stages were compared. This study intends to evaluate the diagnostic value and net clinical benefit of common blood-related indicators Neutrophil/lymphocyte (NLR), lymphocyte/monocyte (LMR), High density lipoprotein/neutrophil (HNR), High density lipoprotein/monocyte (HMR) and combined assays in NSCLC metastasis for the early diagnosis of patients with NSCLC metastasis. It was found that the level of NLR was higher in metastatic NSCLC than non-metastatic, but the level of LMR, HNR and HMR was lower. The levels of NLR, LMR, HNR and HMR in patients with different TNM stages showed that NLR levels increased with TNM stage, while LMR, HNR and HMR levels decreased. The threshold probability range of the 4 combined tests was greater and the overall clinical benefit rate was higher compared to the individual tests. Our findings suggest that NLR, LMR, HNR and HMR have better diagnostic value for NSCLC metastasis. This study provides a clinical basis for investigating the mechanisms by which immune cells and lipid metabolism-related proteins remodel the microenvironment prior to NSCLC metastasis.

Accuracy of next-generation sequencing for molecular profiling of small specimen of lung cancer: a prospective pilot study of side-by-side comparison

Background

Successful practice of precision medicine in advanced lung cancers relies on therapeutic regimens tailored to individual molecular characteristics. The aim of this study was to investigate the accuracy of small specimens for molecular profiling using next-generation sequencing (NGS).

Methods

Genetic alternations, tumor mutational burden (TMB), status of microsatellite instability (MSI), and expression of programmed death ligand 1 (PD-L1) were compared side-by-side between the concurrently obtained core needle biopsy (CNB) and resection specimens in 17 patients with resectable non-small cell lung cancers.

Results

DNA yield and library complexity were significantly lower in CNB specimens (both p < 0.01), whereas the insert size, sequencing depth, and Q30 ratio were similar between the matched specimens (all p > 0.05). The total numbers of genetic alternations detected in resection and CNB specimens were 186 and 211, respectively, with 156 alternations in common, yielding a specific concordance rate of 83.9%. The prevalence of mutations in 8 major driver genes was 100% identical between surgical and CNB specimens, though the allele frequency was lower in CNB specimens, with a median underestimation of 57%. Results of TMB were similar (p = 0.547) and MSI status was 100% matched in all paired specimens.

Conclusions

Pulmonary CNB specimens were suitable for NGS given the satisfactory accuracy when compared to corresponding surgical specimens. NGS results yielding from CNB specimens should be deemed reliable to provide instructive information for the treatment of advanced lung cancers.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13000-022-01255-y.

Assessing dynamic metabolic heterogeneity in non-small cell lung cancer patients via ultra-high sensitivity total-body [18F]FDG PET/CT imaging: quantitative analysis of [18F]FDG uptake in primary tumors and metastatic lymph nodes

PURPOSE

This study aimed to quantitatively assess [¹⁸F]FDG uptake in primary tumor (PT) and metastatic lymph node (mLN) in newly diagnosed non-small cell lung cancer (NSCLC) using the total-body [¹⁸F]FDG PET/CT and to characterize the dynamic metabolic heterogeneity of NSCLC.

METHODS

The 60-min dynamic total-body [¹⁸F]FDG PET/CT was performed before treatment. The PTs and mLNs were manually delineated. An unsupervised K-means classification method was used to cluster patients based on the imaging features of PTs. The metabolic features, including Patlak-Ki, Patlak-Intercept, SUV_mean, metabolic tumor volume (MTV), total lesion glycolysis (TLG), and textural features, were extracted from PTs and mLNs. The targeted next-generation sequencing of tumor-associated genes was performed. The expression of Ki67, CD3, CD8, CD34, CD68, and CD163 in PTs was determined by immunohistochemistry.

RESULTS

A total of 30 patients with stage IIIA-IV NSCLC were enrolled. Patients were divided into fast dynamic FDG metabolic group (F-DFM) and slow dynamic FDG metabolic group (S-DFM) by the unsupervised K-means classification of PTs. The F-DFM group showed significantly higher Patlak-Ki (P < 0.001) and SUV_mean (P < 0.001) of PTs compared with the S-DFM group, while no significant difference was observed in Patlak-Ki and SUV_mean of mLNs between the two groups. The texture analysis indicated that PTs in the S-DFM group were more heterogeneous in FDG uptake than those in the F-DFM group. Higher T cells (CD3⁺/CD8⁺) and macrophages (CD68⁺/CD163⁺) infiltration in the PTs were observed in the F-DFM group. No significant difference was observed in tumor mutational burden between the two groups.

CONCLUSION

The dynamic total-body [¹⁸F]FDG PET/CT stratified NSCLC patients into the F-DFM and S-DFM groups, based on Patlak-Ki and SUV_mean of PTs. PTs in the F-DFM group seemed to be more homogenous in terms of [¹⁸F]FDG uptake than those in the S-DFM group. The higher infiltrations of T cells and macrophages were observed in the F-DFM group, which suggested a potential benefit from immunotherapy.

Imaging Biomarkers in Thoracic Oncology: Current Advances in the Use of Radiomics in Lung Cancer Patients and its Potential Use for Therapy Response Prediction and Monitoring

BACKGROUND

 Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related deaths. The development of therapies targeting molecular alterations has significantly improved the treatment of NSCLC patients. To identify these targets, tumor phenotyping is required, with tissue biopsies and molecular pathology being the gold standard. Some patients do not respond to targeted therapies and many patients suffer from tumor recurrence, which can in part be explained by tumor heterogeneity. This points out the need for new biomarkers allowing for better tumor phenotyping and monitoring during treatment to assess patient outcome.

METHOD

 The contents of this review are based on a literature search conducted using the PubMed database in March 2021 and the authors' experience.

RESULTS AND CONCLUSION

 The use of radiomics and artificial intelligence-based approaches allows for the identification of imaging biomarkers in NSCLC patients for tumor phenotyping. Several studies show promising results for models predicting molecular alterations, with the best results being achieved by combining structural and functional imaging. Radiomics could help solve the pressing clinical need for assessing and predicting therapy response. To reach this goal, advanced tumor phenotyping, considering tumor heterogeneity, is required. This could be achieved by integrating structural and functional imaging biomarkers with clinical data sources, such as liquid biopsy results. However, to allow for radiomics-based approaches to be introduced into clinical practice, further standardization using large, multi-center datasets is required.

KEY POINTS

  · Some NSCLC patients do not benefit from targeted therapies, and many patients suffer from tumor recurrence, pointing out the need for new biomarkers allowing for better tumor phenotyping and monitoring during treatment.. · The use of radiomics-based approaches allows for the identification of imaging biomarkers in NSCLC patients for tumor phenotyping.. · A multi-omics approach integrating not only structural and functional imaging biomarkers but also clinical data sources, such as liquid biopsy results, could further enhance the prediction and assessment of therapy response..

CITATION FORMAT

· Kroschke J, von Stackelberg O, Heußel CP et al. Imaging Biomarkers in Thoracic Oncology: Current Advances in the Use of Radiomics in Lung Cancer Patients and its Potential Use for Therapy Response Prediction and Monitoring. Fortschr Röntgenstr 2022; DOI: 10.1055/a-1729-1516.

Unveiling mutational dynamics in non-small cell lung cancer patients by quantitative EGFR profiling in vesicular RNA

The mutational status of the epidermal growth factor receptor (EGFR) guides the stratification of non‐small cell lung cancer (NSCLC) patients for treatment with tyrosine kinase inhibitors (TKIs). A liquid biopsy test on cell‐free DNA is recommended as a clinical decision‐supporting tool, although it has limited sensitivity. Here, we comparatively investigated the extracellular vesicle (EV)‐RNA as an independent source for multidimensional and longitudinal EGFR profiling in a cohort of 27 NSCLC patients. We introduced and validated a new rapid, highly specific EV‐RNA test with wild‐type (WT) and mutant‐sensitive probes (E746‐A750del, L858R, and T790M). We included a cohort of 20 NSCLC patients with EGFR WT tumor tissues and systematically performed molecular EV‐RNA and circulating tumor DNA analyses with clinical data statistics and biophysical profiles of EVs. At the single‐patient level, we detected variegated tumor heterogeneity dynamics supported by combinations of driver EGFR mutations. EV‐RNA‐based mutation analysis showed an unprecedented sensitivity of over 90%. The resistance‐associated mutation T790M frequently pre‐existed at baseline with a gained EV‐transcript copy number at progression, while the general mutational burden was mostly decreasing during the intermediate follow‐up. The biophysical profile of EVs and the quantitative assessment of T790M revealed an association with tumor size determined by the sum of the longest diameters in target lesions. Vesicular RNA provides a validated tool suitable for use in clinical practice to investigate the dynamics of common driver EGFR mutations in NSCLC patients receiving TKIs.

Non-Coding RNAs in Cancer Diagnosis and Therapy: Focus on Lung Cancer

Over the last several decades, clinical evaluation and treatment of lung cancers have largely improved with the classification of genetic drivers of the disease, such as EGFR, ALK, and ROS1. There are numerous regulatory factors that exert cellular control over key oncogenic pathways involved in lung cancers. In particular, non-coding RNAs (ncRNAs) have a diversity of regulatory roles in lung cancers such that they have been shown to be involved in inducing proliferation, suppressing apoptotic pathways, increasing metastatic potential of cancer cells, and acquiring drug resistance. The dysregulation of various ncRNAs in human cancers has prompted preclinical studies examining the therapeutic potential of restoring and/or inhibiting these ncRNAs. Furthermore, ncRNAs demonstrate tissue-specific expression in addition to high stability within biological fluids. This makes them excellent candidates as cancer biomarkers. This review aims to discuss the relevance of ncRNAs in cancer pathology, diagnosis, and therapy, with a focus on lung cancer.

Cancer evolution: A means by which tumors evade treatment

Although various methods have been tried to study and treat cancer, the cancer remains a major challenge for human medicine today. One important reason for this is the presence of cancer evolution. Cancer evolution is a process in which tumor cells adapt to the external environment, which can suppress the human immune system's ability to recognize and attack tumors, and also reduce the reproducibility of cancer research. Among them, heterogeneity of the tumor provides intrinsic motivation for this process. Recently, with the development of related technologies such as liquid biopsy, more and more knowledge about cancer evolution has been gained and interest in this topic has also increased. Therefore, starting from the causes of tumorigenesis, this paper introduces several tumorigenesis processes and pathways, as well as treatment options for different targets.

Development of a Radiomics Prediction Model for Histological Type Diagnosis in Solitary Pulmonary Nodules: The Combination of CT and FDG PET

PURPOSE

To develop a diagnostic model for histological subtypes in lung cancer combined CT and FDG PET.

METHODS

Machine learning binary and four class classification of a cohort of 445 lung cancer patients who have CT and PET simultaneously. The outcomes to be predicted were primary, metastases (Mts), adenocarcinoma (Adc), and squamous cell carcinoma (Sqc). The classification method is a combination of machine learning and feature selection that is a Partition-Membership. The performance metrics include accuracy (Acc), precision (Pre), area under curve (AUC) and kappa statistics.

RESULTS

The combination of CT and PET radiomics (CPR) binary model showed more than 98% Acc and AUC on predicting Adc, Sqc, primary, and metastases, CPR four-class classification model showed 91% Acc and 0.89 Kappa.

CONCLUSION

The proposed CPR models can be used to obtain valid predictions of histological subtypes in lung cancer patients, assisting in diagnosis and shortening the time to diagnostic.

Resolving Metabolic Heterogeneity in Experimental Models of the Tumor Microenvironment from a Stable Isotope Resolved Metabolomics Perspective

The tumor microenvironment (TME) comprises complex interactions of multiple cell types that determines cell behavior and metabolism such as nutrient competition and immune suppression. We discuss the various types of heterogeneity that exist in solid tumors, and the complications this invokes for studies of TME. As human subjects and in vivo model systems are complex and difficult to manipulate, simpler 3D model systems that are compatible with flexible experimental control are necessary for studying metabolic regulation in TME. Stable Isotope Resolved Metabolomics (SIRM) is a valuable tool for tracing metabolic networks in complex systems, but at present does not directly address heterogeneous metabolism at the individual cell level. We compare the advantages and disadvantages of different model systems for SIRM experiments, with a focus on lung cancer cells, their interactions with macrophages and T cells, and their response to modulators in the immune microenvironment. We describe the experimental set up, illustrate results from 3D cultures and co-cultures of lung cancer cells with human macrophages, and outline strategies to address the heterogeneous TME.

A Non-invasive Method to Diagnose Lung Adenocarcinoma

Purpose: To find out the CT radiomics features of differentiating lung adenocarcinoma from another lung cancer histological type. Methods: This was a historical cohort study, three independent lung cancer cohorts included. One cohort was used to evaluate the stability of radiomics features, one cohort was used to feature selection, and the last was used to construct and evaluate classification models. The research is divided into four steps: region of interest segmentation, feature extraction, feature selection, and model building and validation. The feature selection methods included the intraclass correlation coefficient, ReliefF coefficient, and Partition-Membership filter. The performance metrics of the classification model included accuracy (Acc), precision (Pre), area under curve (AUC), and kappa statistics. Results: The 10 features (First order shape features: Sphericity and Compacity, Gray-Level Run Length Matrix: Short-Run Emphasis, Low Gray-level Run Emphasis, and High Gray-level Run Emphasis, Gray Level Co-occurrence Matrix: Homogeneity, Energy, Contrast, Correlation, and Dissimilarity) showed the most stable and classification capability. The 6 classifiers, Logistic regression classifier (LR), Sequence Minimum Optimization algorithm, Random Forest, KStar, Naive Bayes and Random Committee, have great performance both on the train and the test sets, and especially LR has the best performance on the test set (Acc = 98.72, Pre = 0.988, AUC = 1, and kappa = 0.974). Conclusion: Lung adenocarcinoma can be identified based on CT radiomics features. We can diagnose lung adenocarcinoma with CT non-invasively.