Radiogenomics predicting tumor responses to radiotherapy in lung cancer

Integrating Demographics and Imaging Features for Various Stages of Dementia Classification: Feed Forward Neural Network Multi-Class Approach

BACKGROUND

MRI magnetization-prepared rapid acquisition (MPRAGE) is an easily available imaging modality for dementia diagnosis. Previous studies suggested that volumetric analysis plays a crucial role in various stages of dementia classification. In this study, volumetry, radiomics and demographics were integrated as inputs to develop an artificial intelligence model for various stages, including Alzheimer's disease (AD), mild cognitive decline (MCI) and cognitive normal (CN) dementia classifications.

METHOD

The Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset was separated into training and testing groups, and the Open Access Series of Imaging Studies (OASIS) dataset was used as the second testing group. The MRI MPRAGE image was reoriented via statistical parametric mapping (SPM12). Freesurfer was employed for brain segmentation, and 45 regional brain volumes were retrieved. The 3D Slicer software was employed for 107 radiomics feature extractions from within the whole brain. Data on patient demographics were collected from the datasets. The feed-forward neural network (FFNN) and the other most common artificial intelligence algorithms, including support vector machine (SVM), ensemble classifier (EC) and decision tree (DT), were used to build the models using various features.

RESULTS

The integration of brain regional volumes, radiomics and patient demographics attained the highest overall accuracy at 76.57% and 73.14% in ADNI and OASIS testing, respectively. The subclass accuracies in MCI, AD and CN were 78.29%, 89.71% and 85.14%, respectively, in ADNI testing, as well as 74.86%, 88% and 83.43% in OASIS testing. Balanced sensitivity and specificity were obtained for all subclass classifications in MCI, AD and CN.

CONCLUSION

The FFNN yielded good overall accuracy for MCI, AD and CN categorization, with balanced subclass accuracy, sensitivity and specificity. The proposed FFNN model is simple, and it may support the triage of patients for further confirmation of the diagnosis.

Machine learning with multimodal data for COVID-19

In response to the unprecedented global healthcare crisis of the COVID-19 pandemic, the scientific community has joined forces to tackle the challenges and prepare for future pandemics. Multiple modalities of data have been investigated to understand the nature of COVID-19. In this paper, MIDRC investigators present an overview of the state-of-the-art development of multimodal machine learning for COVID-19 and model assessment considerations for future studies. We begin with a discussion of the lessons learned from radiogenomic studies for cancer diagnosis. We then summarize the multi-modality COVID-19 data investigated in the literature including symptoms and other clinical data, laboratory tests, imaging, pathology, physiology, and other omics data. Publicly available multimodal COVID-19 data provided by MIDRC and other sources are summarized. After an overview of machine learning developments using multimodal data for COVID-19, we present our perspectives on the future development of multimodal machine learning models for COVID-19.

Machine learning in non-small cell lung cancer radiotherapy: A bibliometric analysis

Background

Machine learning is now well-developed in non-small cell lung cancer (NSCLC) radiotherapy. But the research trend and hotspots are still unclear. To investigate the progress in machine learning in radiotherapy NSCLC, we performed a bibliometric analysis of associated research and discuss the current research hotspots and potential hot areas in the future.

Methods

The involved researches were obtained from the Web of Science Core Collection database (WoSCC). We used R-studio software, the Bibliometrix package and VOSviewer (Version 1.6.18) software to perform bibliometric analysis.

Results

We found 197 publications about machine learning in radiotherapy for NSCLC in the WoSCC, and the journal Medical Physics contributed the most articles. The University of Texas MD Anderson Cancer Center was the most frequent publishing institution, and the United States contributed most of the publications. In our bibliometric analysis, “radiomics” was the most frequent keyword, and we found that machine learning is mainly applied to analyze medical images in the radiotherapy of NSCLC.

Results

The research we identified about machine learning in NSCLC radiotherapy was mainly related to the radiotherapy planning of NSCLC and the prediction of treatment effects and adverse events in NSCLC patients who were under radiotherapy. Our research has added new insights into machine learning in NSCLC radiotherapy and could help researchers better identify hot research areas in the future.

Tumor response to radiotherapy and expression of epidermal growth factor receptor mutation and echinoderm microtubule-associated protein-like 4-anaplastic lymphoma kinase rearrangement in patients with non-small cell lung cancer

Background

One of the main limitations of radiation therapy is the resistance of tumor cells. This study aimed at evaluating the relationship between the expression of epidermal growth factor receptor (EGFR) and echinoderm microtubule-associated protein-like 4-anaplastic lymphoma kinase (EML4-ALK) and tumor radiosensitivity in patients with non-small cell lung cancer.

Methods

Medical case files, pathological results for EGFR and EML4-ALK, and computerized tomography scans of patients with NSCLC treated with thoracic radiation therapy were analyzed.

Results

The sample size was 101 patients with mean age 58.43 ± 9.89 years. Statistically significant differences were observed in the mean reduction of long tumor diameter during the early treatment phase in EGFR-positive versus EGFR-negative patients (p value = 0.04) and in short tumor diameter during the late treatment phase in EGFR-positive versus EGFR-negative patients (p value = 0.04).

Conclusion

Patients with overexpression of EGFR mutations are more radiosensitive during the early treatment phase, and EML4-ALK mutations were less radiosensitive regardless of phases.

Knockdown of Peroxiredoxin V increased the cytotoxicity of non-thermal plasma-treated culture medium to A549 cells

Administration of non-thermal plasma therapy via the use of plasma-activated medium (PAM) might be a novel strategy for cancer treatment, as it induces apoptosis by increasing reactive oxygen species (ROS) levels. Peroxiredoxin V (PRDX5) scavenges ROS and reactive nitrogen species and is known to regulate several physiological and pathological reactions. However, its role in lung cancer cells exposed to PAM is unknown. Here, we investigated the effect of PRDX5 in PAM-treated A549 lung cancer cells and determined the mechanism underlying its cytotoxicity. Cell culture medium was treated with low temperature plasma at 16.4 kV for 0, 60, 120, or 180 s to develop PAM. PRDX5 was knocked down in A549 cells via transfection with short hairpin RNA targeting PRDX5. Colony formation and wound healing assays, flow cytometry, fluorescence microscopy, and western blotting were performed to detect the effect of PRDX5 knockdown on PAM-treated A549 cells. PAM showed higher cytotoxicity in lung cancer cells than in control cells, downregulated the mitogen-activated protein kinase signaling pathway, and induced apoptosis. PRDX5 knockdown significantly inhibited cell colony formation and migration, increased ROS accumulation, and reduced mitochondrial membrane potential in lung cancer cells. Hence, PRDX5 knockdown combined with PAM treatment represents an effective option for lung cancer treatment.

The Diverse Analysis Identifies Mutated KRAS Associated With Radioresistance in Non-Small Cell Lung Cancer

Background

To analyze the relationship between V-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) status and radioresistance in non-small cell lung cancer (NSCLC), we identified potential genotypic differences and pathways involved.

Methods

We retrospectively analyzed epidermal growth factor receptor (EGFR) and KRAS status in patients undergoing definitive radiotherapy for NSCLC between 2004 and 2018. Cox proportional hazard models were used to evaluate local progression-free survival (LPFS). Using clonogenic survival and measurement of γH2AX foci, we analyzed the difference in radiosensitivity between NSCLC cell lines with different KRAS status. The Cancer Genome Atlas (TCGA) analysis was used to explore the potential pathways involved.

Results

The results showed that of the 286 patients identified, 68 (24%) had local tumor progression (mean ± standard deviation (SD), 27 ± 17.4 months); of these patients, KRAS mutations were found in 14 (23%), and KRAS status was associated with LPFS. After adjusting for concurrent chemotherapy, gross tumor volume, and mutation status in multivariate analysis, KRAS mutation was associated with shorter LPFS (hazard ratio: 1.961; 95% confidence interval: 1.03 - 2.17; P = 0.032). KRAS mutation showed higher radioresistance in vitro. TCGA data showed that the ERK1/2 pathway, phosphatidylinositol I3 kinase (PI3K)/mTOR, p38 MAPK pathway, cell cycle checkpoint signaling, DNA damage, repair pathways, and EGFR/PKC/AKT pathway were differentially expressed in patients with KRAS mutations or cell lines compared with their expression in the wild-type group.

Conclusions

Diverse analyses identified that KRAS mutation was associated with radioresistance in NSCLC. KRAS mutation status may be helpful as a biomarker of radioresistance and a potential target to increase radiosensitivity.

Early prediction of tumour-response to radiotherapy in NSCLC patients

Objective. In this study we developed an automatic method to predict tumour volume and shape in weeks 3 and 4 of radiotherapy (RT), using cone-beam computed tomography (CBCT) scans acquired up to week 2, allowing identification of large tumour changes.Approach. 240 non-small cell lung cancer (NSCLC) patients, treated with 55 Gy in 20 fractions, were collected. CBCTs were rigidly registered to the planning CT. Intensity values were extracted in each voxel of the planning target volume across all CBCT images from days 1, 2, 3, 7 and 14. For each patient and in each voxel, four regression models were fitted to voxel intensity; applying linear, Gaussian, quadratic and cubic methods. These models predicted the intensity value for each voxel in weeks 3 and 4, and the tumour volume found by thresholding. Each model was evaluated by computing the root mean square error in pixel value and structural similarity index metric (SSIM) for all patients. Finally, the sensitivity and specificity to predict a 30% change in volume were calculated for each model.Main results. The linear, Gaussian, quadratic and cubic models achieved a comparable similarity score, the average SSIM for all patients was 0.94, 0.94, 0.90, 0.83 in week 3, respectively. At week 3, a sensitivity of 84%, 53%, 90% and 88%, and specificity of 99%, 100%, 91% and 42% were observed for the linear, Gaussian, quadratic and cubic models respectively. Overall, the linear model performed best at predicting those patients that will benefit from RT adaptation. The linear model identified 21% and 23% of patients in our cohort with more than 30% tumour volume reduction to benefit from treatment adaptation in weeks 3 and 4 respectively.Significance. We have shown that it is feasible to predict the shape and volume of NSCLC tumours from routine CBCTs and effectively identify patients who will respond to treatment early.

Radiomics AI prediction for head and neck squamous cell carcinoma (HNSCC) prognosis and recurrence with target volume approach

OBJECTIVES

To evaluate the performance of radiomics features extracted from planning target volume (PTV) and gross tumor volume (GTV) in the prediction of the death prognosis and cancer recurrence rate for head and neck squamous cell carcinoma (HNSCC).

METHODS

188 HNSCC patients' planning CT images with radiotherapy structures sets were acquired from Cancer Imaging Archive (TCIA). The 3D slicer (v. 4.10.2) with the PyRadiomics extension (Computational Imaging and Bioinformatics Lab, Harvard medical School) was used to extract radiomics features from the radiotherapy planning images. An in-house developed deep learning artificial neural networks (DL-ANN) model was used to predict death prognosis and cancer recurrence rate based on the features extracted from GTV and PTV of the CT images.

RESULTS

The PTV radiomics features with DL-ANN model could achieve 77.7% accuracy with overall AUC equal to 0.934 and 0.932 when predicting HNSCC-related death prognosis and cancer recurrence respectively. Furthermore, the DL-ANN model can achieve an accuracy of 74.3% with AUC equal to 0.947 and 0.956 for the HNSCC-related death prognosis and cancer recurrence respectively using GTV features.

CONCLUSION

Using both GTV and PTV radiomics features in the DL-ANN model, can aid in predicting HNSCC-related death prognosis and cancer recurrence. Clinicians may find it helpful in formulating different treatment regimens and facilitate personized medicine based on the predicted outcome when performing GTV and PTV delineation.

ADVANCES IN KNOWLEDGE

Radiomics features of GTV and PTV are reliable prognosis and recurrence predicting tools, which may help clinicians in GTV and PTV delineation to facilitate delivery of personalized treatment.

Radiomics at a Glance: A Few Lessons Learned from Learning Approaches

Simple Summary

Radiomics has become a prominent component of medical imaging research and many studies show its specific value as a support tool for clinical decision-making processes. Radiomic data are typically analyzed with statistical and machine learning methods, which change depending on the disease context and the imaging modality. We found a certain bias in the literature towards the use of such methods and believe that this limitation may influence the capacity of producing accurate and reliable decisions. Therefore, in view of the relevance of various types of learning methods, we report their significance and discuss their unrevealed potential.

Abstract

Processing and modeling medical images have traditionally represented complex tasks requiring multidisciplinary collaboration. The advent of radiomics has assigned a central role to quantitative data analytics targeting medical image features algorithmically extracted from large volumes of images. Apart from the ultimate goal of supporting diagnostic, prognostic, and therapeutic decisions, radiomics is computationally attractive due to specific strengths: scalability, efficiency, and precision. Optimization is achieved by highly sophisticated statistical and machine learning algorithms, but it is especially deep learning that stands out as the leading inference approach. Various types of hybrid learning can be considered when building complex integrative approaches aimed to deliver gains in accuracy for both classification and prediction tasks. This perspective reviews some selected learning methods by focusing on both their significance for radiomics and their unveiled potential.

Radiotranscriptomics signature-based predictive nomograms for radiotherapy response in patients with nonsmall cell lung cancer: Combination and association of CT features and serum miRNAs levels

Purpose

We aimed to establish radiotranscriptomics signatures based on serum miRNA levels and computed tomography (CT) texture features and develop nomogram models for predicting radiotherapy response in patients with nonsmall cell lung cancer (NSCLC).

Methods

We first used established radioresistant NSCLC cell lines for miRNA selection. At the same time, patients (103 for training set and 71 for validation set) with NSCLC were enrolled. Their pretreatment contrast‐enhanced CT texture features were extracted and their serum miRNA levels were obtained. Then, radiotranscriptomics feature selection was implemented with the least absolute shrinkage and selection operator (LASSO), and signatures were generated by logistic or Cox regression for objective response rate (ORR), overall survival (OS), and progression‐free survival (PFS). Afterward, radiotranscriptomics signature‐based nomograms were constructed and assessed for clinical use.

Results

Four miRNAs and 22 reproducible contrast‐enhanced CT features were used for radiotranscriptomics feature selection and we generated ORR‐, OS‐, and PFS‐ related radiotranscriptomics signatures. In patients with NSCLC who received radiotherapy, the radiotranscriptomics signatures were independently associated with ORR, OS, and PFS in both the training (OR: 2.94, P < .001; HR: 2.90, P < .001; HR: 3.58, P = .001) and validation set (OR: 2.94, P = .026; HR: 2.14, P = .004; HR: 2.64, P = .016). We also obtained a satisfactory nomogram for ORR. The C‐index values for the ORR nomogram were 0.86 [95% confidence interval (CI), 0.75 to 0.92] in the training set and 0.81 (95% CI, 0.69 to 0.89) in the validation set. The calibration‐in‐the‐large and calibration slope performed well. Decision curve analysis indicated a satisfactory net benefit.

Conclusions

The radiotranscriptomics signature could be an independent biomarker for evaluating radiotherapeutic responses in patients with NSCLC. The radiotranscriptomics signature‐based nomogram could be used to predict patients’ ORR, which would represent progress in individualized medicine.

Mustard Seed (Brassica nigra) Extract Exhibits Antiproliferative Effect against Human Lung Cancer Cells through Differential Regulation of Apoptosis, Cell Cycle, Migration, and Invasion

Lung cancer is the primary cause of cancer-related death worldwide, and development of novel lung cancer preventive and therapeutic agents are urgently needed. Brassica nigra (black mustard) seeds are commonly consumed in several Asian and African countries. Mustard seeds previously exhibited significant anticancer activities against several cancer types. In the present study, we have investigated various cellular and molecular mechanisms of anticancer effects of an ethanolic extract of B. nigra seeds against A549 and H1299 human non-small cell lung cancer cell lines. B. nigra extract showed a substantial growth-inhibitory effect as it reduced the viability and clonogenic survival of A549 and H1299 cells in a concentration-dependent manner. B. nigra extract induced cellular apoptosis in a time- and concentration-dependent fashion as evidenced from increased caspase-3 activity. Furthermore, treatment of both A549 and H1299 cells with B. nigra extract alone or in combination with camptothecin induced DNA double-strand breaks as evidenced by upregulation of γH2A histone family member X, Fanconi anemia group D2 protein, Fanconi anemia group J protein, ataxia-telangiectesia mutated and Rad3-related protein. Based on cell cycle analysis, B. nigra extract significantly arrested A549 and H1299 cells at S and G2/M phases. Additionally, B. nigra extract suppressed the migratory and invasive properties of both cell lines, downregulated the expression of matrix metalloproteinase-2 (MMP2), MMP9, and Snail and upregulated the expression of E-cadherin at mRNA and protein levels. Taken together, these findings indicate that B. nigra seed extract may have an important anticancer potential against human lung cancer which could be mediated through simultaneous and differential regulation of proliferation, apoptosis, DNA damage, cell cycle, migration, and invasion.

Jumonji Inhibitors Overcome Radioresistance in Cancer through Changes in H3K4 Methylation at Double-Strand Breaks

We have uncovered a role for Jumonji inhibitors in overcoming radioresistance through KDM5B inhibition. Pharmacological blockade of Jumonji demethy-lases with JIB-04 leads to specific accumulation of H3K4me3 at sites marked by γH2AX and impaired recruitment of DNA repair factors, preventing resolution of damage and resulting in robust sensitization to radiation therapy. In DNA-repair-proficient cancer cells, knockdown of the H3K4me3 demethylase KDM5B, but not other Jumonji enzymes, mimics pharmacological inhibition, and KDM5B overexpression rescues this phenotype and increases radioresistance. The H3K4me3 demethylase inhibitor PBIT also sensitizes cancer cells to radiation, while an H3K27me3 demethylase inhibitor does not. In vivo co-administration of radiation with JIB-04 significantly prolongs the survival of mice with tumors even long after cessation of treatment. In human patients, lung squamous cell carcinomas highly ex-pressing KDM5B respond poorly to radiation. Thus, we propose the use of Jumonji KDM inhibitors as potent radiosensitizers.

Radioresistance is an obstacle to lung cancer cures. Bayo et al. reveal that JARID1B removes H3K4me3 marks at sites of DNA damage. Genetic or pharmacological inhibition of JARID1B robustly radiosensitizes cancers in vitro and in vivo through defects in DNA repair, providing a therapeutic option for radioresistant tumors.

Radiotherapy dose painting by circadian rhythm based radiomics

Radiotherapy dose painting is a new dose delivery technique to achieve higher treatment outcome. In this approach, does is escalated to high progressive regions which are heterogeneous and determined by advanced medical imaging. Radiomics is issued as a feasible image quantification method to reveal tumor heterogeneity by extraction of high throughput mineable texture features. On the other hand, circadian rhythm is a given biological process that studied as a critical factor to obtain more effective treatment outcome. In this study, we hypothesized that radiotherapy dose painting could be enhanced by using circadian rhythm that is determined on the radiomics maps obtained from medical images. This hypothesis is based on the idea which circadian rhythm could change the tumor heterogeneity and therefore image features.

The single nucleotide variant rs2868371 associates with the risk of mortality in non-small cell lung cancer patients: A multicenter prospective validation

BACKGROUND AND PURPOSE

Definitive radiation therapy (RT) with or without chemotherapy has become the standard treatment for non-metastatic unresectable non-small cell lung cancer (NSCLC). However, treatment outcomes can differ substantially and patients' genetic background could play a crucial role. Potential associations between single-nucleotide polymorphisms (SNP) in Heat shock protein beta-1 (HSPB1) and survival have been reported in prior single-institution retrospective reports.

MATERIALS AND METHODS

The current assay aims to validate such connection in a prospective multicenter study in a European cohort including 181 NSCLC patients. Median follow-up time for all patients was 13 months (range, 3-57 months).

RESULTS

The results obtained show an association between the rs2868371 GG genotype and better overall survival (HR: 0.35; 95%CI: 0.13-0.96; p = 0.042) in multivariate analysis. Two-year overall survival rate was 72% for patients carrying the rs2868371 GG genotype versus 36% for those patients harboring the rs2868371 CC/CG genotypes (p = 0.013). Additionally, the rs2868371 GG genotype was found to be associated with better disease-free survival in the multivariate analysis (HR: 0.36; 95%CI: 0.13-0.99; p = 0.048). In silico analysis of the potential functional SNP suggested significant difference in the affinity of the Glucocorticoid Receptor binding site between alternative allelic variants, confirmed by chromatin immunoprecipitation analysis displaying stronger affinity for the risk allele (C). Furthermore, our findings indicate that the rs2868371 influences (mRNA) HSPB1 expression, offering insight into the regulation of HSPB1 transcription.

CONCLUSION

The functional HSPB1 rs2868371 promoter variant may affect lung cancer survival by regulation of HSPB1 expression levels through glucocorticoid receptor interaction.

Multicenter CT phantoms public dataset for radiomics reproducibility tests

PURPOSE

The aim of this paper is to describe a public, open-access, computed tomography (CT) phantom image set acquired at three centers and collected especially for radiomics reproducibility research. The dataset is useful to test radiomic features reproducibility with respect to various parameters, such as acquisition settings, scanners, and reconstruction algorithms.

ACQUISITION AND VALIDATION METHODS

Three phantoms were scanned in three independent institutions. Images of the following phantoms were acquired: Catphan 700 and COPDGene Phantom II (Phantom Laboratory, Greenwich, NY, USA), and the Triple modality 3D Abdominal Phantom (CIRS, Norfolk, VA, USA). Data were collected at three Dutch medical centers: MAASTRO Clinic (Maastricht, NL), Radboud University Medical Center (Nijmegen, NL), and University Medical Center Groningen (Groningen, NL) with scanners from two different manufacturers Siemens Healthcare and Philips Healthcare. The following acquisition parameter were varied in the phantom scans: slice thickness, reconstruction kernels, and tube current.

DATA FORMAT AND USAGE NOTES

We made the dataset publically available on the Dutch instance of "Extensible Neuroimaging Archive Toolkit-XNAT" (https://xnat.bmia.nl). The dataset is freely available and reusable with attribution (Creative Commons 3.0 license).

POTENTIAL APPLICATIONS

Our goal was to provide a findable, open-access, annotated, and reusable CT phantom dataset for radiomics reproducibility studies. Reproducibility testing and harmonization are fundamental requirements for wide generalizability of radiomics-based clinical prediction models. It is highly desirable to include only reproducible features into models, to be more assured of external validity across hitherto unseen contexts. In this view, phantom data from different centers represent a valuable source of information to exclude CT radiomic features that may already be unstable with respect to simplified structures and tightly controlled scan settings. The intended extension of our shared dataset is to include other modalities and phantoms with more realistic lesion simulations.

Epidermal growth factor receptor gene mutations in non-small-cell lung cancer cells are associated with increased radiosensitivity in vitro

Introduction

There is still lack of specific biomarkers in predicting the radiosensitivity of non-small cell lung cancer (NSCLC) patients in clinic. Previous studies have shown that the EGFR gene status may correlate with radiosensitivity of NSCLC. However, the underlying mechanisms remain unknown. The aim of this study was to further investigate the correlation between EGFR mutation status and the NSCLC cell radiosensitivity and to explore the possible cellular mechanism.

Methods

Eight NSCLC cell lines with different EGFR gene status were irradiated by linear accelerator, and the radiosensitivity between the cell lines was compared by cell colony formation assay and cell proliferation assay. Cell cycle and apoptosis were analysed by flow cytometry. Radiosensitivity-related protein expression was detected by Western blotting.

Results

In the present study, we found that NSCLC cell lines with the epidermal growth factor receptor (EGFR) gene mutations were more sensitive to X-ray irradiation than those with wild-type EGFR (P<0.05). No difference in radiosensitivity was observed between NSCLC cells with EGFR exon19 deletion (Del 19) mutation and exon 21 point mutation at position 858 (L858R) with or without T790M mutation (P<0.05), as well as between NSCLC cells with EGFR mutation and those with acquired EGFR-tyrosine kinase inhibitors (TKIs) resistance. Mechanistically, EGFR mutations promoted NSCLC cell apoptosis in response to X-ray irradiation through the upregulation of proapoptotic protein Bax and downregulation of anti-apoptotic proteins such as Bcl-2 and DNA-dependent protein kinase catalytic subunit. In addition, phosphorylated histone (γ-H2AX) foci assay showed that EGFR mutations sustained irradiation-induced DNA damage.

Conclusion

Taken together, our study demonstrates that EGFR mutations are closely associated with the increased sensitivity of NSCLC cell lines to X-ray irradiation and that EGFR mutation status is a potentially useful indicator to evaluate the effectiveness of radiotherapy in the treatment of NSCLC.

Precision medicine and molecular imaging: new targeted approaches toward cancer therapeutic and diagnosis

This paper presents a review of the importance and role of precision medicine and molecular imaging technologies in cancer diagnosis with therapeutics and diagnostics purposes. Precision medicine is progressively becoming a hot topic in all disciplines related to biomedical investigation and has the capacity to become the paradigm for clinical practice. The future of medicine lies in early diagnosis and individually appropriate treatments, a concept that has been named precision medicine, i.e. delivering the right treatment to the right patient at the right time. Molecular imaging is quickly being recognized as a tool with the potential to ameliorate every aspect of cancer treatment. On the other hand, emerging high-throughput technologies such as omics techniques and systems approaches have generated a paradigm shift for biological systems in advanced life science research. In this review, we describe the precision medicine, difference between precision medicine and personalized medicine, precision medicine initiative, systems biology/medicine approaches (such as genomics, radiogenomics, transcriptomics, proteomics, and metabolomics), P4 medicine, relationship between systems biology/medicine approaches and precision medicine, and molecular imaging modalities and their utility in cancer treatment and diagnosis. Accordingly, the precision medicine and molecular imaging will enable us to accelerate and improve cancer management in future medicine.

Torin2 Suppresses Ionizing Radiation-Induced DNA Damage Repair

Several classes of inhibitors of the mammalian target of rapamycin (mTOR) have been developed based on its central role in sensing growth factor and nutrient levels to regulate cellular metabolism. However, its ATP-binding site closely resembles other phosphatidylinositol 3-kinase-related kinase (PIKK) family members, resulting in reactivity with these targets that may also be therapeutically useful. The ATP-competitive mTOR inhibitor, Torin2, shows biochemical activity against the DNA repair-associated proteins ATM, ATR and DNA-PK, which raises the possibility that Torin2 and related compounds might radiosensitize cancerous tumors. In this study Torin2 was also found to enhance ionizing radiation-induced cell killing in conditions where ATM was dispensable, confirming the requirement for multiple PIKK targets. Moreover, Torin2 did not influence the initial appearance of γ-H2AX foci after irradiation but significantly delayed the disappearance of radiation-induced γ-H2AX foci, indicating a DNA repair defect. Torin2 increased the number of radiation-induced S-phase specific chromosome aberrations and reduced the frequency of radiation-induced CtIP and Rad51 foci formation, suggesting that Torin2 works by blocking homologous recombination (HR)-mediated DNA repair resulting in an S-phase specific DNA repair defect. Accordingly, Torin2 reduced HR-mediated repair of I-Sce1-induced DNA damage and contributed to replication fork stalling. We conclude that radiosensitization of tumor cells by Torin2 is associated with disrupting ATR- and ATM-dependent DNA damage responses. Our findings support the concept of developing combination cancer therapies that incorporate ionizing radiation therapy and Torin2 or compounds with similar properties.

New Ions for Therapy

Purpose

Charged particle therapy (CPT) is currently based on the use of protons or carbon ions for the treatment of deep-seated and/or radioresistant tumors, which are known to return poor prognosis in photon treatments. A renovated interest has recently been observed in the possibility of extending the spectrum of ions used in CPT. The potential and limitations of different particle species will be discussed in this work, with special regard to ¹H, ⁴He, ¹²C, and ¹⁶O, that is, those presently available in the most advanced particle therapy clinical centers.

Materials and Methods

Literature information has been screened, as well as additional analysis has been performed, aimed at the comparison of basic physical and biological properties of several ions. The research treatment planning system TRiP98 is also employed to compare the dose distribution resulting from exposure to the different ions in different configurations, including the irradiation of hypoxic targets.

Results

Particles of intermediate charge, such as helium and lithium, offer an increased physical selectivity compared with protons, while having reduced biological effectiveness compared with carbon. The latter aspect translates into a less sensitive biological optimization of CPT treatments, though still more effective than protons in killing cancer cells. At the same time, in view of their increased linear energy transfer, heavier ions, like oxygen, are considered attractive, especially for the treatment of hypoxic tumors. While the higher biological dose released in the entrance dose represents in general a drawback for ions heavier than carbon, for oxygen beam this effect may be balanced by the lower dose increase requested to overcome hypoxia.

Conclusions

The possibility of delivering radiation quality-optimized CPT treatments seems to be the new challenge in heavy ion therapy. The potential and limitations of different particle species, according to different sensitivity and morphological scenarios, makes combined treatments of different ions an intriguing option. New ions could open new scenarios in cancer therapy, but would represent as well an opportunity for the treatment of specific non-cancer disease such as atrial fibrillation.

Allyl isothiocyanate induces replication-associated DNA damage response in NSCLC cells and sensitizes to ionizing radiation

Allyl isothiocyanate (AITC), a constituent of many cruciferous vegetables exhibits significant anticancer activities in many cancer models. Our studies provide novel insights into AITC-induced anticancer mechanisms in human A549 and H1299 non-small cell lung cancer (NSCLC) cells. AITC exposure induced replication stress in NSCLC cells as evidenced by γH2AX and FANCD2 foci, ATM/ATR-mediated checkpoint responses and S and G2/M cell cycle arrest. Furthermore, AITC-induced FANCD2 foci displayed co-localization with BrdU foci, indicating stalled or collapsed replication forks in these cells. Although PITC (phenyl isothiocyanate) exhibited concentration-dependent cytotoxic effects, treatment was less effective compared to AITC. Previously, agents that induce cell cycle arrest in S and G2/M phases were shown to sensitize tumor cells to radiation. Similar to these observations, combination therapy involving AITC followed by radiation treatment exhibited increased DDR and cell killing in NSCLC cells compared to single agent treatment. Combination index (CI) analysis revealed synergistic effects at multiple doses of AITC and radiation, resulting in CI values of less than 0.7 at Fa of 0.5 (50% reduction in survival). Collectively, these studies identify an important anticancer mechanism displayed by AITC, and suggest that the combination of AITC and radiation could be an effective therapy for NSCLC.

Predictive SNPs for radiation-induced damage in lung cancer patients with radiotherapy: a potential strategy to individualize treatment

In the treatment of lung cancer, radiotherapy has become one of the most important therapies, despite its sometimes unpredictable side effects. As such, identifying lung cancer patients who are at high risk of developing severe radiation-induced damage (mainly radiation pneumonitis and radiation-induced esophageal toxicity) and applying effect intervention or monitoring techniques are important. Although human diversity to a certain amount is explained by clinical and dosimetric factors, the presence of specific genetic determinants also influences the occurrence of radiation-induced damage. Here we summarize the data on mechanisms of radiation pneumonitis and radiation-induced esophageal toxicity supporting the involvement of variances of genes in the evolution of radiation-induced damage. Furthermore, the available evidence from current clinical studies of genetic polymorphisms for the prediction of radiation pneumonitis and radiation-induced esophageal toxicity is discussed. Eventually, this may help to truly individualize radiotherapy, using a personal genetic profile of the most relevant genes for each lung cancer patient.

Proton radiobiology

In addition to the physical advantages (Bragg peak), the use of charged particles in cancer therapy can be associated with distinct biological effects compared to X-rays. While heavy ions (densely ionizing radiation) are known to have an energy- and charge-dependent increased Relative Biological Effectiveness (RBE), protons should not be very different from sparsely ionizing photons. A slightly increased biological effectiveness is taken into account in proton treatment planning by assuming a fixed RBE of 1.1 for the whole radiation field. However, data emerging from recent studies suggest that, for several end points of clinical relevance, the biological response is differentially modulated by protons compared to photons. In parallel, research in the field of medical physics highlighted how variations in RBE that are currently neglected might actually result in deposition of significant doses in healthy organs. This seems to be relevant in particular for normal tissues in the entrance region and for organs at risk close behind the tumor. All these aspects will be considered and discussed in this review, highlighting how a re-discussion of the role of a variable RBE in proton therapy might be well-timed.

Adapting a drug screening platform to discover associations of molecular targeted radiosensitizers with genomic biomarkers

Large collections of annotated cancer cell lines are powerful tools for precisely matching targeted drugs with genomic alterations that can be tested as biomarkers in the clinic. Whether these screening platforms, which utilize short-term cell survival to assess drug responses, can be applied to precision radiation medicine is not established. To this end, 32 cancer cell lines were screened using 18 targeted therapeutic agents with known or putative radiosensitizing properties (227 combinations). The cell number remaining after drug exposure with or without radiation was assessed by nonclonogenic assays. We derived short-term radiosensitization factors (SRF2Gy) and calculated clonogenic survival assay-based dose enhancement factors (DEFSF0.1). Radiosensitization was characterized by SRF2Gy values of mostly ∼1.05 to 1.2 and significantly correlated with drug-induced changes in apoptosis and senescence frequencies. SRF2Gy was significantly correlated with DEFSF0.1, with a respective sensitivity and specificity of 91.7% and 81.5% for a 3-day endpoint, and 82.8% and 84.2% for a robotic 5-day assay. KRAS mutations (codons 12/13) were found to be a biomarker of radiosensitization by midostaurin in lung cancer, which was pronounced under conditions that enriched for stem cell-like cells. In conclusion, although short-term proliferation/survival assays cannot replace the gold-standard clonogenic survival assay for measuring cellular radiosensitivity, they capture with high accuracy the relative change in radiosensitivity that is caused by a radiosensitzing targeted agent.

IMPLICATIONS

This study supports a paradigm shift regarding the utility of short-term assays for precision radiation medicine, which should facilitate the identification of genomic biomarkers to guide the testing of novel drug/radiation combinations.

Fluorescent imaging of cancerous tissues for targeted surgery

To maximize tumor excision and minimize collateral damage are the primary goals of cancer surgery. Emerging molecular imaging techniques have made "image-guided surgery" developed into "molecular imaging-guided surgery", which is termed as "targeted surgery" in this review. Consequently, the precision of surgery can be advanced from tissue-scale to molecule-scale, enabling "targeted surgery" to be a component of "targeted therapy". Evidence from numerous experimental and clinical studies has demonstrated significant benefits of fluorescent imaging in targeted surgery with preoperative molecular diagnostic screening. Fluorescent imaging can help to improve intraoperative staging and enable more radical cytoreduction, detect obscure tumor lesions in special organs, highlight tumor margins, better map lymph node metastases, and identify important normal structures intraoperatively. Though limited tissue penetration of fluorescent imaging and tumor heterogeneity are two major hurdles for current targeted surgery, multimodality imaging and multiplex imaging may provide potential solutions to overcome these issues, respectively. Moreover, though many fluorescent imaging techniques and probes have been investigated, targeted surgery remains at a proof-of-principle stage. The impact of fluorescent imaging on cancer surgery will likely be realized through persistent interdisciplinary amalgamation of research in diverse fields.

Recent advances in radiotherapy for thoracic tumours

Radiation Oncology technology has continued to advance at a rapid rate and is bringing significant benefits to patients. This review outlines some of the advances in technology and radiotherapy treatment of thoracic cancers including brachytherapy, stereotactic radiotherapy, tomotherapy and intensity modulated radiotherapy. The importance of functional imaging with PET and management of movement are highlighted. Most of the discussion relates to non-small cell lung cancer but management of mesothelioma and small cell lung cancer are also covered. This technology has substantial benefits to patients in terms of decreasing toxicity both in the short and longer term.

New challenges in high-energy particle radiobiology

Densely ionizing radiation has always been a main topic in radiobiology. In fact, α-particles and neutrons are sources of radiation exposure for the general population and workers in nuclear power plants. More recently, high-energy protons and heavy ions attracted a large interest for two applications: hadrontherapy in oncology and space radiation protection in manned space missions. For many years, studies concentrated on measurements of the relative biological effectiveness (RBE) of the energetic particles for different end points, especially cell killing (for radiotherapy) and carcinogenesis (for late effects). Although more recently, it has been shown that densely ionizing radiation elicits signalling pathways quite distinct from those involved in the cell and tissue response to photons. The response of the microenvironment to charged particles is therefore under scrutiny, and both the damage in the target and non-target tissues are relevant. The role of individual susceptibility in therapy and risk is obviously a major topic in radiation research in general, and for ion radiobiology as well. Particle radiobiology is therefore now entering into a new phase, where beyond RBE, the tissue response is considered. These results may open new applications for both cancer therapy and protection in deep space.

Relationship between uptake of a radioiodinated quinazoline derivative and radiosensitivity in non-small cell lung cancer

The aim of this study was to determine whether the uptake of radioiodinated 6-(3-morpholinopropoxy)-7-ethoxy-4-(3'-iodophenoxy) quinazoline ([(125)I]PYK) could predict the response of non-small cell lung cancer (NSCLC) cells to radiotherapy in vitro. Four NSCLC cell lines, PC9, HCC827, A549, and H1975 were used. Cells were irradiated with doses ranging from 2 Gy to 8 Gy and/or exposed to 1 μM gefitinib. The effects of radiation and gefitinib were assessed by the CCK-8 assay and confirmed by reverse transcription-polymerase chain reaction and immunoflorescence microscopy. The uptake of [(125)I]PYK was determined by incubating cells with a tracer. The cell cycle was assessed by flow cytometry. The expression of EGFR was measured by western blotting. The results obtained revealed that the uptake of [(125)I]PYK was higher in PC9 and HCC827 cells than in A549 and H1975 cells. PC9 cells and HCC827 cells were also more radiosensitive than A549 and H1975 cells. The gefitinib pretreatment reduced the S phase fraction and enhanced radiation effects in PC9 and HCC827 cells. These results indicate that the uptake of [(125)I]PYK is related to the effects of radiation in NSCLC cells. Radioiodinated PYK may be useful in predicting the response of NSCLC in patients to radiotherapy.

Quantification of proton dose calculation accuracy in the lung

PURPOSE

To quantify the accuracy of a clinical proton treatment planning system (TPS) as well as Monte Carlo (MC)-based dose calculation through measurements and to assess the clinical impact in a cohort of patients with tumors located in the lung.

METHODS AND MATERIALS

A lung phantom and ion chamber array were used to measure the dose to a plane through a tumor embedded in the lung, and to determine the distal fall-off of the proton beam. Results were compared with TPS and MC calculations. Dose distributions in 19 patients (54 fields total) were simulated using MC and compared to the TPS algorithm.

RESULTS

MC increased dose calculation accuracy in lung tissue compared with the TPS and reproduced dose measurements in the target to within ±2%. The average difference between measured and predicted dose in a plane through the center of the target was 5.6% for the TPS and 1.6% for MC. MC recalculations in patients showed a mean dose to the clinical target volume on average 3.4% lower than the TPS, exceeding 5% for small fields. For large tumors, MC also predicted consistently higher V5 and V10 to the normal lung, because of a wider lateral penumbra, which was also observed experimentally. Critical structures located distal to the target could show large deviations, although this effect was highly patient specific. Range measurements showed that MC can reduce range uncertainty by a factor of ~2: the average (maximum) difference to the measured range was 3.9 mm (7.5 mm) for MC and 7 mm (17 mm) for the TPS in lung tissue.

CONCLUSION

Integration of Monte Carlo dose calculation techniques into the clinic would improve treatment quality in proton therapy for lung cancer by avoiding systematic overestimation of target dose and underestimation of dose to normal lung. In addition, the ability to confidently reduce range margins would benefit all patients by potentially lowering toxicity.

Identifying the mesenchymal molecular subtype of glioblastoma using quantitative volumetric analysis of anatomic magnetic resonance images

BACKGROUND

Subtypes of glioblastoma multiforme (GBM) based on genetic and molecular alterations are thought to cause alterations in anatomic MRI owing to downstream biological changes, such as edema production, blood-brain barrier breakdown, and necrosis. The purpose of the current study was to identify a potential relationship between imaging features and the mesenchymal (MES) GBM subtype, which has the worst patient prognosis.

METHODS

MRIs from 46 patients with histologically confirmed GBM were retrospectively analyzed. The volume of contrast enhancement, regions of central necrosis, and hyperintensity of T2/fluid attenuated inversion recovery (FLAIR) were measured. Additionally, the ratio of T2/FLAIR hyperintense volume to the volume of contrast enhancement and necrosis was calculated.

RESULTS

The volume of contrast enhancement, volume of central necrosis, combined volume of contrast enhancement and central necrosis, and the ratio of T2/FLAIR to contrast enhancement and necrosis were significantly different in MES compared with non-MES GBM (Mann-Whitney, P < .05). Receiver-operator characteristics indicated that these 4 metrics were all significant predictors of the MES phenotype. The volume ratio of T2 hyperintensity to contrast enhancement and central necrosis was significantly lower in MES vs non-MES GBM (P < .0001), was a significant predictor of the MES phenotype (area under the curve = 0.93, P < .001), and could be used to stratify short- and long-term overall survival (log-rank, P = .0064 using cutoff of 3.0). These trends were also present when excluding isocitrate dehydrogenase 1 mutant tumors and incorporating covariates such as age and KPS score.

CONCLUSIONS

Results suggest that volume ratio may be a simple, cost-effective, and noninvasive biomarker for quickly identifying MES GBM.

Molecular profiling to optimize treatment in non-small cell lung cancer: a review of potential molecular targets for radiation therapy by the translational research program of the radiation therapy oncology group

Therapeutic decisions in non-small cell lung cancer (NSCLC) have been mainly based on disease stage, performance status, and co-morbidities, and rarely on histological or molecular classification. Rather than applying broad treatments to unselected patients that may result in survival increase of only weeks to months, research efforts should be, and are being, focused on identifying predictive markers for molecularly targeted therapy and determining genomic signatures that predict survival and response to specific therapies. The availability of such targeted biologics requires their use to be matched to tumors of corresponding molecular vulnerability for maximum efficacy. Molecular markers such as epidermal growth factor receptor (EGFR), K-ras, vascular endothelial growth factor (VEGF), mammalian target of rapamycin (mTOR), and anaplastic lymphoma kinase (ALK) represent potential parameters guide treatment decisions. Ultimately, identifying patients who will respond to specific therapies will allow optimal efficacy with minimal toxicity, which will result in more judicious and effective application of expensive targeted therapy as the new paradigm of personalized medicine develops.

Radiation Dose Escalation in Stage III Non-Small-Cell Lung Cancer

For patients with stage III non-small-cell lung cancer with unresectable or inoperable tumors, definitive chemoradiotherapy is often utilized. Historically, local control and overall survival rates have been poor. In an effort to improve local control, new chemotherapeutic agents in combination with higher doses of radiotherapy have been investigated. Early dose escalation trials date back to the 1980s, and the feasibility and efficacy of dose escalation for patients with inoperable stage III lung cancer continue to be topics of investigation. Herein, we review the evolution of chemotherapy as it relates to treatment of unresectable stage III lung cancer, and we outline the early and the more recent dose escalation studies. While dose escalation appears to provide a modest benefit in terms of preventing local failure and improving overall survival, advances in diagnostic imaging and radiotherapy treatment have possibly resulted in selection of a more favorable patient population. These variables make statements regarding the benefit of dose escalation challenging.

Molecular markers for cancer prognosis and treatment: have we struck gold?

The last decade has witnessed an emerging role for molecular or biochemical markers indicating a specific cellular mechanism or tissue function, often called 'biomarkers'. Biomarkers such as altered DNA, proteins and inflammatory cytokines are critical in cancer research and strategizing treatment in the clinic. In this review we look at the application of biological indicators to cancer research and highlight their roles in cancer detection and treatment. With technological advances in gene expression, genomic and proteomic analysis, biomarker discovery is expanding fast. We focus on some of the predominantly used markers in different types of malignancies, their advantages, and their limitations. Finally we conclude by looking at the future of biomarkers, their utility in the tumorigenic studies, and the progress towards personalized treatment strategies.

EGF receptor inhibition radiosensitizes NSCLC cells by inducing senescence in cells sustaining DNA double-strand breaks

The mechanisms by which inhibition of the epidermal growth factor receptor (EGFR) sensitizes non-small cell lung cancer (NSCLC) cells to ionizing radiation remain poorly understood. We set out to characterize the radiosensitizing effects of the tyrosine kinase inhibitor erlotinib and the monoclonal antibody cetuximab in NSCLC cells that contain wild-type p53. Unexpectedly, EGFR inhibition led to pronounced cellular senescence but not apoptosis of irradiated cells, both in vitro and in vivo. Senescence was completely dependent on wild-type p53 and associated with a reduction in cell number as well as impaired clonogenic radiation survival. Study of ten additional NSCLC cell lines revealed that senescence is a prominent mechanism of radiosensitization in 45% of cell lines and occurs not only in cells with wild-type p53 but also in cells with mutant p53, where it is associated with an induction of p16. Interestingly, senescence and radiosensitization were linked to an increase in residual radiation-induced DNA double-strand breaks irrespective of p53/p16 status. This effect of EGFR inhibition was at least partially mediated by disruption of the MEK-ERK pathway. Thus, our data indicate a common mechanism of radiosensitization by erlotinib or cetuximab across diverse genetic backgrounds. Our findings also suggest that assays that are able to capture the initial proliferative delay that is associated with senescence should be useful for screening large cell line panels to identify genomic biomarkers of EGFR inhibitor-mediated radiosensitization.