Blood-based biomarkers for precision medicine in lung cancer: precision radiation therapy

Metabolic profiling of murine radiation-induced lung injury with Raman spectroscopy and comparative machine learning

Radiation-induced lung injury (RILI) is a dose-limiting toxicity for cancer patients receiving thoracic radiotherapy. As such, it is important to characterize metabolic associations with the early and late stages of RILI, namely pneumonitis and pulmonary fibrosis. Recently, Raman spectroscopy has shown utility for the differentiation of pneumonitic and fibrotic tissue states in a mouse model; however, the specific metabolite-disease associations remain relatively unexplored from a Raman perspective. This work harnesses Raman spectroscopy and supervised machine learning to investigate metabolic associations with radiation pneumonitis and pulmonary fibrosis in a mouse model. To this end, Raman spectra were collected from lung tissues of irradiated/non-irradiated C3H/HeJ and C57BL/6J mice and labelled as normal, pneumonitis, or fibrosis, based on histological assessment. Spectra were decomposed into metabolic scores via group and basis restricted non-negative matrix factorization, classified with random forest (GBR-NMF-RF), and metabolites predictive of RILI were identified. To provide comparative context, spectra were decomposed and classified via principal component analysis with random forest (PCA-RF), and full spectra were classified with a convolutional neural network (CNN), as well as logistic regression (LR). Through leave-one-mouse-out cross-validation, we observed that GBR-NMF-RF was comparable to other methods by measure of accuracy and log-loss (p > 0.10 by Mann-Whitney U test), and no methodology was dominant across all classification tasks by measure of area under the receiver operating characteristic curve. Moreover, GBR-NMF-RF results were directly interpretable and identified collagen and specific collagen precursors as top fibrosis predictors, while metabolites with immune and inflammatory functions, such as serine and histidine, were top pneumonitis predictors. Further support for GBR-NMF-RF and the identified metabolite associations with RILI was found as CNN interpretation heatmaps revealed spectral regions consistent with these metabolites.

Plasma-Free Blood as a Potential Alternative to Whole Blood for Transcriptomic Analysis

RNA sequencing (RNAseq) technology has become increasingly important in precision medicine and clinical diagnostics, and emerged as a powerful tool for identifying protein-coding genes, performing differential gene analysis, and inferring immune cell composition. Human peripheral blood samples are widely used for RNAseq, providing valuable insights into individual biomolecular information. Blood samples can be classified as whole blood (WB), plasma, serum, and remaining sediment samples, including plasma-free blood (PFB) and serum-free blood (SFB) samples that are generally considered less useful byproducts during the processes of plasma and serum separation, respectively. However, the feasibility of using PFB and SFB samples for transcriptome analysis remains unclear. In this study, we aimed to assess the suitability of employing PFB or SFB samples as an alternative RNA source in transcriptomic analysis. We performed a comparative analysis of WB, PFB, and SFB samples for different applications. Our results revealed that PFB samples exhibit greater similarity to WB samples than SFB samples in terms of protein-coding gene expression patterns, detection of differentially expressed genes, and immunological characterizations, suggesting that PFB can serve as a viable alternative to WB for transcriptomic analysis. Our study contributes to the optimization of blood sample utilization and the advancement of precision medicine research.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-023-00121-1.

A Review of Radiation-Induced Alterations of Multi-Omic Profiles, Radiation Injury Biomarkers, and Countermeasures

Increasing utilization of nuclear power enhances the risks associated with industrial accidents, occupational hazards, and the threat of nuclear terrorism. Exposure to ionizing radiation interferes with genomic stability and gene expression resulting in the disruption of normal metabolic processes in cells and organs by inducing complex biological responses. Exposure to high-dose radiation causes acute radiation syndrome, which leads to hematopoietic, gastrointestinal, cerebrovascular, and many other organ-specific injuries. Altered genomic variations, gene expression, metabolite concentrations, and microbiota profiles in blood plasma or tissue samples reflect the whole-body radiation injuries. Hence, multi-omic profiles obtained from high-resolution omics platforms offer a holistic approach for identifying reliable biomarkers to predict the radiation injury of organs and tissues resulting from radiation exposures. In this review, we performed a literature search to systematically catalog the radiation-induced alterations from multi-omic studies and radiation countermeasures. We covered radiation-induced changes in the genomic, transcriptomic, proteomic, metabolomic, lipidomic, and microbiome profiles. Furthermore, we have covered promising multi-omic biomarkers, FDA-approved countermeasure drugs, and other radiation countermeasures that include radioprotectors and radiomitigators. This review presents an overview of radiation-induced alterations of multi-omics profiles and biomarkers, and associated radiation countermeasures.

Changes in Blood Biomarkers of Angiogenesis and Immune Modulation after Radiation Therapy and Their Association with Outcomes in Thoracic Malignancies

The effects of radiotherapy on systemic immunity remain to be fully characterized in a disease-specific manner. The aim of the study was to examine potential biomarkers of systemic immunomodulation when using radiotherapy for thoracic malignancies. Serial blood samples were collected from 56 patients with thoracic malignancies prior (RTbaseline), during (RTduring) and at the end of radiotherapy (RTend), as well as at the first (FU1) and second follow-up (FU2). The changes in serum levels of IL-10, IFN-γ, IL-12p70, IL-13, IL-1β, IL-4, IL-6, IL-8, TNF-α, bFGF, sFlt-1, PlGF, VEGF, VEGF-C, VEGF-D and HGF were measured by multiplexed array and tested for associations with clinical outcomes. We observed an increase in the levels of IL-10, IFN-γ, PlGF and VEGF-D and a decrease in those of IL-8, VEGF, VEGF-C and sFlt-1 during and at the end of radiotherapy. Furthermore, baseline concentration of TNF-α significantly correlated with OS. IL-6 level at RTend and FU1,2 correlated with OS (RTend: p = 0.039, HR: 1.041, 95% CI: 1.002-1.082, FU1: p = 0.001, HR: 1.139, 95% CI: 1.056-1.228, FU2: p = 0.017, HR: 1.101 95% CI: 1.018-1.192), while IL-8 level correlated with OS at RTduring and RTend (RTduring: p = 0.017, HR: 1.014, 95% CI: 1.002-1.026, RTend: p = 0.004, HR: 1.007, 95% CI: 1.061-1.686). In conclusion, serum levels of TNF-α, IL-6 and IL-8 are potential biomarkers of response to radiotherapy. Given the recent implementation of immunotherapy in lung and esophageal cancer, these putative blood biomarkers should be further validated and evaluated in the combination or sequential therapy setting.

Overexpression of excision repair cross-complementing 1 gene associates with higher risk of therapeutic failure after definitive chemoradiation for unresectable non-small cell lung cancer

BACKGROUND

Locally advanced non-small cell lung cancer (NSCLC) is typically treated with concurrent chemoradiation (CRT). Excision Repair Cross-Complementing 1 (ERCC1) is a protein involved in DNA damage repair. The objective of this study was to assess whether higher tumoral ERCC1 expression would associate with worse clinical outcomes in NSCLC treated with CRT.

METHODS

Twenty-five patients were included. Relative expression levels of messenger RNA (mRNA) for ERCC1 were measured with a quantitative reverse transcription polymerase chain reaction (qRT-PCR) and expressed as scaled ERCC1 mRNA gene expression value. Patients were followed every 3 months with history, physical exam, and imaging to assess clinical outcomes. We evaluated the associations between ERCC1, as well as other prognostic variables including stage, age, gender, race, histology, RT dose, performance status, and progression free survival (PFS) and overall survival (OS) with Kaplan-Meier method and Cox regression.

RESULTS

Recursive partitioning analysis identified a GeneExp cutoff of 1.54. Higher ERCC1 expression was associated with worse PFS [hazard ratio (HR) =1.70, P=0.04] and trended towards worse OS (HR =1.53, P=0.11). Increasing tumor volume (HR =1.001, P=0.055), squamous cell (HR =7.86, P=0.008) and poorly differentiated histology (HR =5.25, P=0.06) also associated with worse OS. The cumulative incidence of local recurrence at 1 year trended higher with ERCC1 GeneExp ≥1.54 (78.1%) compared to ERCC1 GeneExp <1.54 (14.9%, P=0.08). Distant relapse at 1 year was 72% with tumor ERCC1 expression ≥1.54 and 52% with ERCC1 expression <1.54 (P=0.28).

CONCLUSIONS

Higher ERCC1 expression by qRT-PCR appears to correlate with worse PFS in locally advanced NSCLC treated with CRT. However, the overall sample size of the population was small; thus, larger studies are warranted to integrate molecular biomarkers to identify patients who might benefit from treatment intensification.

Prediction of Clinical Outcome in Locally Advanced Non-Small Cell Lung Cancer Patients Treated With Chemoradiotherapy by Plasma Markers

Purpose

To identify cytokines in plasma that may predict objective response and progression-free survival (PFS) in patients with locally advanced non-small cell lung cancer (NSCLC) treated with chemoradiotherapy.

Materials and Methods

From April 2016 to May 2017, thirty-one patients with locally advanced inoperable/unresectable NSCLC were included, and treated with concurrent chemoradiotherapy (CCRT). No immune checkpoint inhibitors were administered after CCRT. Plasma from each patient was collected before radiotherapy, and 25 cytokines in the plasma were measured by Luminex or U-PLEX assays. Logistic regression and COX regression were performed to identify the predictive factors for objective response and PFS, respectively. Kaplan-Meier survival analysis was used to compare the PFS between the groups.

Results

High levels of IL-13 and TNF-α, and low levels of ICAM-1, IFN-γ, and soluble PD-L1 (sPD-L1) were significantly associated with objective response (P <0.05). High levels of IL-8, CCL5, and CXCL3 also showed a trend toward association with objective response (P <0.1). The combination of cytokines (IL-8 and ICAM-1, or TNF-α and sPD-L1) improved predictive accuracy. Univariate analysis identified IL-8 and ICAM-1 as potential markers to predict PFS. Multivariate analysis suggested that high level of IL-8 (P =0.010) and low level of ICAM-1 (P =0.011) correlated significantly with a longer PFS.

Conclusion

IL-8 and ICAM-1 in plasma have the potential to predict objective response and PFS in patients with locally advanced NSCLC underwent chemoradiotherapy.

Application of Radiomics for the Prediction of Radiation-Induced Toxicity in the IMRT Era: Current State-of-the-Art

Normal tissue complication probability (NTCP) models that were formulated in the Quantitative Analyses of Normal Tissue Effects in the Clinic (QUANTEC) are one of the pillars in support of everyday’s clinical radiation oncology. Because of steady therapeutic refinements and the availability of cutting-edge technical solutions, the ceiling of organs-at-risk-sparing has been reached for photon-based intensity modulated radiotherapy (IMRT). The possibility to capture heterogeneity of patients and tissues in the prediction of toxicity is still an unmet need in modern radiation therapy. Potentially, a major step towards a wider therapeutic index could be obtained from refined assessment of radiation-induced morbidity at an individual level. The rising integration of quantitative imaging and machine learning applications into radiation oncology workflow offers an unprecedented opportunity to further explore the biologic interplay underlying the normal tissue response to radiation. Based on these premises, in this review we focused on the current-state-of-the-art on the use of radiomics for the prediction of toxicity in the field of head and neck, lung, breast and prostate radiotherapy.

Methodological Development of Combination Drug and Radiotherapy in Basic and Clinical Research

Newer technical improvements in radiation oncology have been rapidly implemented in recent decades, allowing an improved therapeutic ratio. The development of strategies using local and systemic treatments concurrently, mainly targeted therapies, has however plateaued. Targeted molecular compounds and immunotherapy are increasingly being incorporated as the new standard of care for a wide array of cancers. A better understanding of possible prior methodology issues is therefore required and should be integrated into upcoming early clinical trials including individualized radiotherapy-drug combinations. The outcome of clinical trials is influenced by the validity of the preclinical proofs of concept, the impact on normal tissue, the robustness of biomarkers and the quality of the delivery of radiation. Herein, key methodological aspects are discussed with the aim of optimizing the design and implementation of future precision drug-radiotherapy trials.