Radiation Treatment Response Research Articles

Arterial spin labeled perfusion MRI for the assessment of radiation-treated meningiomas.

Conventional contrast-enhanced MRI is currently the primary imaging technique used to evaluate radiation treatment response in meningiomas. However, newer perfusion-weighted MRI techniques, such as 3D pseudocontinuous arterial spin labeling (3D pCASL) MRI, capture physiologic information beyond the structural information provided by conventional MRI and may provide additional complementary treatment response information. The purpose of this study is to assess 3D pCASL for the evaluation of radiation-treated meningiomas. Twenty patients with meningioma treated with surgical resection followed by radiation, or by radiation alone, were included in this retrospective single-institution study. Patients were evaluated with 3D pCASL and conventional contrast-enhanced MRI before and after radiation (median follow up 6.5 months). Maximum pre- and post-radiation ASL normalized cerebral blood flow (ASL-nCBF) was measured within each meningioma and radiation-treated meningioma (or residual resected and radiated meningioma), and the contrast-enhancing area was measured for each meningioma. Wilcoxon signed-rank tests were used to compare pre- and post-radiation ASL-nCBF and pre- and post-radiation area. All treated meningiomas demonstrated decreased ASL-nCBF following radiation (p < 0.001). Meningioma contrast-enhancing area also decreased after radiation (p = 0.008) but only for approximately half of the meningiomas (9), while half (10) remained stable. A larger effect size (Wilcoxon signed-rank effect size) was seen for ASL-nCBF measurements (r = 0.877) compared to contrast-enhanced area measurements (r = 0.597). ASL perfusion may provide complementary treatment response information in radiation-treated meningiomas. This complementary information could aid clinical decision-making and provide an additional endpoint for clinical trials.

Prediction of Radiation Treatment Response for Locally Advanced Rectal Cancer via a Longitudinal Trend Analysis Framework on Cone-Beam CT.

Locally advanced rectal cancer (LARC) presents a significant challenge in terms of treatment management, particularly with regards to identifying patients who are likely to respond to radiation therapy (RT) at an individualized level. Patients respond to the same radiation treatment course differently due to inter- and intra-patient variability in radiosensitivity. In-room volumetric cone-beam computed tomography (CBCT) is widely used to ensure proper alignment, but also allows us to assess tumor response during the treatment course. In this work, we proposed a longitudinal radiomic trend (LRT) framework for accurate and robust treatment response assessment using daily CBCT scans for early detection of patient response. The LRT framework consists of four modules: (1) Automated registration and evaluation of CBCT scans to planning CT; (2) Feature extraction and normalization; (3) Longitudinal trending analyses; and (4) Feature reduction and model creation. The effectiveness of the framework was validated via leave-one-out cross-validation (LOOCV), using a total of 840 CBCT scans for a retrospective cohort of LARC patients. The trending model demonstrates significant differences between the responder vs. non-responder groups with an Area Under the Curve (AUC) of 0.98, which allows for systematic monitoring and early prediction of patient response during the RT treatment course for potential adaptive management.

Early Prediction of Radiation Treatment Response via Longitudinal Analysis of CBCT Radiomic Features for Locally Advanced Rectal Cancer.

Patients respond to the same radiation treatment course differently due to inter- and intra- patient variability in radiosensitivity. Despite widespread use of AI/ML in radiation oncology, there is a lack of monitoring strategies used during treatment courses to evaluate early predictors of treatment response in a systematic fashion. This work advances a straightforward, yet effective, method for the early detection of treatment response through systematically analyzing daily CBCT radiomic features. The goal is to aid clinicians in assessing the treatment efficacy routinely with a view towards optimizing personalized treatment. We included a cohort of 30 patients diagnosed with locally advanced rectal cancer who underwent neo-adjuvant fractionated radiation treatment (RT) with a prescription dose of 50.4 Gy (28 fractions), followed by total mesorectal excision surgery after completion of ChemoRT. Daily IGRT imaging was acquired prior to each fraction resulting in a total of 840 CBCTs. Patients were divided into responder (14 patients) and non-responder (16 patients) groups based on post-RT pathological response. Mutual information algorithms were utilized to rigorously register daily CBCT images to the planning CT, and longitudinal radiomic features of the target were extracted from the daily CBCTs during the entire treatment course. All longitudinal features for a given patient were standardized with Z-Score normalization, followed by linear fitting using the least square method, resulting in radiomic feature trends (RFT) represented by slope values. Statistical significance was established via a two-sample U test and P-value with a threshold of 0.05. Logistic regression was performed to eliminate RFT with accuracy rates lower than 0.5. The final trending model was developed using random forest. For each patient at fraction N, our investigation involved independent 27 group experiments, where each experiment considered image group from fraction #1 to N, to confirm the effectiveness and stability of the model. The proposed RFT demonstrated a high level of precision and consistency for post-RT response based on longitudinal CBCT images for LARC patients. The trending model yielded an accuracy of 0.9556, 95% CI (0.94, 0.972) when each daily image was considered, the prediction consistency was 0.964. Given the first 14 experiments (considering group images of fraction #1-15), the prediction accuracy was 0.9357, 95% CI (0.915, 0.956) and the prediction consistency was 0.952. A strategy for monitoring and early prediction of LARC patients' radioresponse was evaluated via longitudinal CBCT assessment. Our trending models demonstrate a significant difference between the responder vs non-responder groups as early as the 15th fraction. Our strategy achieved superior accuracy and consistency to predict post-RT response of LARC patients.

A Review of the Role of DWI in Radiation Therapy Planning and Treatment Response Assessment for NPC Management

Nasopharyngeal carcinoma (NPC) is a malignant tumor that occurs in the nasopharynx. Its high incidence rate in certain regions of the world poses a significant challenge for healthcare providers. Imaging plays a crucial role in the diagnosis, treatment, and prognosis of NPC. Among various imaging modalities, diffusion-weighted imaging (DWI) has emerged as a promising technique due to its ability to provide functional information on tissue activity. This paper reviews the principles of DWI, its role in the diagnosis, treatment, and prognosis of NPC, and its potential applications in radiation therapy planning and treatment monitoring. DWI has been shown to have higher sensitivity and specificity for the detection of NPC compared to other imaging modalities. It can aid in the selection of radiation dose and target volumes, and monitor the timing and extent of treatment response. Further research is needed to explore the full potential of DWI in the management of NPC.

Transcriptomic Analysis of ATM Expression in Localized Prostate Cancer – Implications for Radiotherapy Treatment

<h3>Purpose/Objective(s)</h3> The prognostic impact and therapeutic implication of ATM (ataxia telangiectasia mutated) somatic and germline alterations have been previously characterized in prostate cancer. However, ATM expression, particularly in the context of radiotherapy (RT) for localized disease, has not been well documented. Herein, genomic expression analyses were performed to evaluate associations between ATM expression, biological pathways, clinical outcomes and RT treatment response. <h3>Materials/Methods</h3> Whole-transcriptome assays were used to interrogate 5,780 radical prostatectomy (RP) samples for ATM expression (5,000 from a prospective cohort was used to evaluate biological signatures and 780 samples from a retrospective cohort with long term follow-up data to evaluate ATM prognostic impact). Additionally, we assessed 245 patients (subset of the 780) who were treated with adjuvant or salvage RT after RP. A 1:1 matched cohort [n=98 treated with RT, n=98 with no RT] was generated to assess ATM expression in relation to RT response. We next investigated the relationship using Pearson's correlation tests between ATM expression and more than 200 genomic signatures, biological pathways, and a Genomic Classifier (GC) (molecular subtypes). ATM high, medium, and low were defined as the top 25%, middle 50%, and bottom 25% of ATM expression, respectively. The prognostic and predictive ability of ATM expression was examined for the metastasis-free survival (MFS) endpoint with hazard ratios (HRs) and 95% confidence intervals (CIs) from Cox Proportional Hazard model. <h3>Results</h3> In the prospective cohort, ATM was significantly overexpressed in high GC patients (molecular surrogate for poor outcome) (p<0.0001). ATM high (top 25%) was associated with worse MFS (HR = 1.16, 95% CI: 1.51-1.96, p=0.002). ATM expression was highly correlated to signatures related to DNA repair, angiogenesis, immune cell infiltration and genomic instability and highly correlated to CDK2 and PARP1 genes (R>0.5, p<0.0001 for all). In patients treated with RT, ATM high was associated with worse MFS compared to lower ATM (10-year MFS 30% vs 61%; p=0.009), whereas in the patients treated without RT, ATM high was not significantly associated with worse MFS (10-year MFS 52% vs 66%; p=0.26) <h3>Conclusion</h3> Prostate cancer patients harboring high ATM expression by transcriptomic analysis had poorer outcomes with a significantly higher risk of metastasis. Given the worse outcomes, particularly in patients treated with RT, a high ATM expression may be a marker of resistance to DNA damaging agents. Additional studies are needed to elucidate further the predictive ability of ATM overexpression for RT personalization.

Identification and Validation of Proteasome Subunit Alpha Type-7 (PSMA7) as a Therapeutically Vulnerable Target in GBM

<h3>Purpose/Objective(s)</h3> A neomorphic gain-of-function mutation of isocitrate dehydrogenase (IDH1/2) is significantly associated with favorable clinical outcome of patients with glioblastoma (GBM; WHO grade IV) compared with ones with wildtype IDH(1/2). The current standard treatment for GBM consists of maximal safe surgical tumor resection followed by radiation treatment (RT) and concomitant and adjuvant temozolomide (TMZ) (Stupp protocol). Despite the standard treatment, the five-year survival rate remains dismal, only 5%. Therefore, there is an urgent need to identify druggable, therapeutically vulnerable targets in GBM. In this study we identified PSMA7, an alpha-type subunit of the 20S proteasome core particle that is involved in the regulated degradation of proteins in cells via the ubiquitin-proteasome system. PSMA7 has been shown to be frequently upregulated in multiple cancer types and blocking PSMA7 inhibits cell proliferation in cervical, colorectal, and gastric cancer. Although the connection between PSMA7 and tumor progression has been studied in several tumor models, little is known about the role of PSMA7 in GBM biology and treatment response where its expression is significantly upregulated. <h3>Materials/Methods</h3> PSMA7 was identified from publicly available mRNA profiling data of primary glioma patients in two patient cohorts from the Chinese Glioma Genome Atlas (CGGA) database. PSMA7 was functionally validated <i>in vitro</i> using RNAi-mediated knockdown approaches. Stable cell lines with shRNA-mediated knockdown of PSMA7 were generated by puromycin selection. Protein expression was quantified by Western blot analysis. mRNA expression from <i>in vitro</i> studies was determined by qRT-PCR. Cell viability and cell proliferation were assessed by MTS or ATP assays. A biological irradiator was used to treat cells with ionizing radiation for evaluating RT response. <h3>Results</h3> Primary glioma patients with high PSMA7 mRNA levels (median split) were found to have worse overall survival than patients with low mRNA levels in two primary glioma cohorts. Moreover, PSMA7 was significantly upregulated in IDH1/2 wildtype (IDHwt) primary gliomas compared to IDH1/2 mutant (IDHmut) primary gliomas in these two cohorts. We confirmed PSMA7 mRNA and protein expression to be significantly higher in U87MG-IDHwt GBM cells compared to U87MG-IDHmut cells <i>in vitro</i>. RNAi-mediated knockdown of PSMA7 in PDX GBM cell lines decreased cell viability and increased sensitivity to ionizing radiation <i>in vitro</i>. We observed that PSMA7 knockdown in a PDX GBM cell line prolonged mouse overall survival in an <i>in vivo</i> orthotopic xenograft mouse model. <h3>Conclusion</h3> Our data indicate that PSMA7 is a potentially vulnerable therapeutic target in GBM as PSMA7 knockdown slowed GBM cell proliferation, sensitized GBM cells to ionizing radiation, and prolonged overall survival in an orthotopic xenograft mouse model.

Abstract 1990: Exploring the immune-metabolic mechanisms of adipose tissue in oesophageal adenocarcinoma

Abstract Background: Oesophageal Adenocarcinoma (OAC) is the most strongly associated cancer with obesity. Approximately 75% of OAC patients are obese which results in chronic systemic low-grade inflammation, which is believed to drive carcinogenesis as well as influencing radiation treatment response. Adipose tissue is a regulatory organ with many downstream effects which are still not understood. This study aims to elucidate what influence adipose tissue metabolism and secretome have in treatment resistance and whether obesity alters this response. Methods: Following patient consent, ex-vivo Visceral Adipose Tissue (VAT) explants were exposed to increasing doses of radiation with/without Palmitate and Oleate. Agilent Seahorse Xfe24 was used to measure oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) in VAT explants and mitochondrial metabolic parameters in dendritic cells (DCs) following treatment with Adipose Conditioned Media (ACM). Levels of DC maturation following irradiated ACM exposure and Macrophage (Mɸ) polarisation following ACM or Tumour Conditioned Media (TCM) exposure were analyzed by flow cytometry. Irradiated ACM and TCM were analyzed via MSD 54plex ELISA to assess secreted factors on angiogenic, chemokine, cytokine, inflammatory, TH17 or vascular injury panels. Results: VAT explant energy metabolism showed a significant increase for OCR and ECAR with obesity and increasing radiation exposure however OCR was significantly decreased following Palmitate treatment compared with untreated and Oleate treated adipose explants (p&amp;lt;0.05). DCs treated with iACM showed decreased expression of CD11c, CD80, CD86, HLA-DR, CD40 and PD-L1 with increasing radiation in a dose-dependent manner and decreased HLA-DR and CD54 following ACM exposure from obese versus non-obese patients (p&amp;lt;0.05). Mɸ showed increased expression of pro-inflammatory and anti-inflammatory markers following ACM exposure but following TCM exposure only anti-inflammatory markers were increased (p&amp;lt;0.05). DCs showed altered Proton Leak, Basal and ATP linked respiration following exposure to ACM (p&amp;lt;0.05). Altered secretion of proinflammatory mediators was observed from the obese adipose secretome compared with non-obese patients and with increasing radiation doses (p&amp;lt;0.05). Conclusion: We have demonstrated that obesity and increasing radiation doses can significantly alter the immune-metabolic influences of adipose tissue and further studies are profiling the lipidomic and metabolomic landscape of the adipose secretome. Alterations in the secretome of adipose tissue could potentiate the tumor microenvironment and deleteriously affect immune cell function therefore further interrogation is required to fully elucidate the influence adipose tissue may have in treatment response. Citation Format: Fiona O'Connell, Eimear Mylod, Ailsing B. Heeran, Noel E. Donlon, Maria Davern, Christine Butler, Anshul Bhardwaj, Claire Donohoe, Narayanasamy Ravi, John V. Renyolds, Margaret R. Dunne, Helen M. Roche, Jacintha O'Sullivan. Exploring the immune-metabolic mechanisms of adipose tissue in oesophageal adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1990.

Quality of life improvement in patients with bone metastases undergoing palliative radiotherapy.

BackgroundThe aim of the study was to analyze the impact of palliative radiotherapy on quality of life (QoL) in patients with symptomatic bone metastases.Materials and methodsWe present the results from a prospective multicentric study including 128 patients who provided pre- and post-radiotherapy (one month after treatment) brief pain inventory (BPI) assessments. Worst pain was recorded using the BPI (range: 0–10). Pain response was described according to the International Bone Metastases Consensus on palliative radiation. Regarding QoL, for each pre- and post-radiation BPI-questionnaire, scores from the interference domains were summed and averaged to obtain an overall interference score.ResultsThere was a significant correlation between radiation treatment response and improvement in all functional interference domains except sleeping. Patients > 75 years old presented a significantly higher improvement in general activity, mood and relationships with others compared to patients ≤ 75 years old. Patients presenting a baseline pain score ≥ 8 showed a higher improvement in the general activity item (p = 0.049). There was no statistically significant association between pretreatment ECOG, chemotherapy, primary tumor location and radiation schedule with any of the functional interference items.ConclusionsPatients who report pain relief after palliative radiotherapy also present a better quality of life including physical and psychosocial aspects.

T1ρ for Radiotherapy Treatment Response Monitoring in Rectal Cancer Patients: A Pilot Study.

Quantitative MRI has the potential to produce imaging biomarkers for the prediction of early response to radiotherapy treatment. In this pilot study, a potential imaging biomarker, the T1ρ relaxation time, is assessed for this purpose. A T1ρ sequence was implemented on a 1.5 T MR-linac system, a system that combines an MRI with a linear accelerator for radiation treatment. An agar phantom with concentrations of 1–4% w/w was constructed for technical validation of the sequence. Phantom images were assessed in terms of short-term repeatability and signal-to-noise ratio. Twelve rectal cancer patients, who were treated with 5 × 5 Gy, were imaged on each treatment fraction. Individual changes in the T1ρ values of the gross tumor volume (GTV) showed an increase for most patients, although a paired t-test comparing values in the GTV from the first to the last treatment fraction showed no statistically significant difference. The phantom measurements showed excellent short-term repeatability (0.5–1.5 ms), and phantom T1ρ values corresponded to the literature values. T1ρ imaging was implemented successfully on the MR-linac, with a repeatability comparable to diagnostic systems, although clinical benefit in terms of treatment response monitoring remains to be demonstrated.

VERDICT MRI for radiation treatment response assessment in neuroendocrine tumors.

Noninvasive methods to study changes in tumor microstructure enable early assessment of treatment response and thus facilitate personalized treatment. The aim of this study was to evaluate the diffusion MRI model, Vascular, Extracellular and Restricted Diffusion for Cytometry in Tumors (VERDICT), for early response assessment to external radiation treatment and to compare the results with those of more studied sets of parameters derived from diffusion-weighted MRI data. Mice xenografted with human small intestine tumors were treated with external radiation treatment, and diffusion MRI experiments were performed on the day before and up to 2 weeks after treatment. The diffusion models VERDICT, ADC, IVIM, and DKI were fitted to MRI data, and the treatment response of each tumor was calculated based on pretreatment tumor growth and post-treatment tumor volume regression. Linear regression and correlation analysis were used to evaluate each model and their respective parameters for explaining the treatment response. VERDICT analysis showed significant changes from day -1 to day 3 for the intracellular and extracellular volume fraction, as well as the cell radius index (p<0.05; Wilcoxon signed-rank test). The strongest correlation between the diffusion model parameters and the tumor treatment response was seen for the ADC, kurtosis-corrected diffusion coefficient, and intracellular volume fraction on day 3 (τ =0.47, 0.52, and -0.49, respectively, p<0.05; Kendall rank correlation coefficient). Of all the tested models, VERDICT held the strongest explanatory value for the tumor treatment response on day 3 (R2 =0.75, p<0.01; linear regression). In conclusion, VERDICT has potential for early assessment of external radiation treatment and may provide further insights into the underlying biological effects of radiation on tumor tissue. In addition, the results suggest that the time window for assessment of treatment response using dMRI may be narrow.

Joint EANM/SNMMI/ESTRO practice recommendations for the use of 2-[18F]FDG PET/CT external beam radiation treatment planning in lung cancer V1.0

Purpose2-[18F]FDGPET/CT is of utmost importance for radiation treatment (RT) planning and response monitoring in lung cancer patients, in both non-small and small cell lung cancer (NSCLC and SCLC). This topic has been addressed in guidelines composed by experts within the field of radiation oncology. However, up to present, there is no procedural guideline on this subject, with involvement of the nuclear medicine societies.MethodsA literature review was performed, followed by a discussion between a multidisciplinary team of experts in the different fields involved in the RT planning of lung cancer, in order to guide clinical management. The project was led by experts of the two nuclear medicine societies (EANM and SNMMI) and radiation oncology (ESTRO).Results and conclusionThis guideline results from a joint and dynamic collaboration between the relevant disciplines for this topic. It provides a worldwide, state of the art, and multidisciplinary guide to 2-[18F]FDG PET/CT RT planning in NSCLC and SCLC. These practical recommendations describe applicable updates for existing clinical practices, highlight potential flaws, and provide solutions to overcome these as well. Finally, the recent developments considered for future application are also reviewed.

Radiation treatment response and hypoxia biomarkers revealed by machine learning assisted Raman spectroscopy in tumour cells and xenograft tissues.

Recent advancements in anatomical imaging of tumours as treatment targets have led to improvements in RT. However, it is unlikely that improved anatomical imaging alone will be the sole driver for new advances in personalised RT. Biochemically based radiobiological information is likely to be required for next-generation improvements in the personalisation of radiotherapy dose prescriptions to individual patients. In this paper, we use Raman spectroscopy (RS), an optical technique, to monitor individual biochemical response to radiation within a tumour microenvironment. We spatially correlate individual biochemical responses to augmentatively derived hypoxic maps within the tumour microenvironment. Furthermore, we pair RS with a data analytical framework combining (i) group and basis restricted non-negative matrix factorization (GBR-NMF), (ii) a random forest (RF) classifier, (iii) and a feature metric importance calculation method, Shapley Additive exPlanations (SHAP), in order to ascertain the relative importance of individual biochemicals in describing the overall biological response as observed with RS. The current study found that the GBR-NMF-RF-SHAP model helped identify a wide range of radiation response biomarkers and hypoxia indicators (e.g., glycogen, lipids, DNA, amino acids) in H460 human lung cancer cells and H460 xenografts. Correlations between the hypoxic regions and Raman chemical biomarkers (e.g., glycogen, alanine, and arginine) were also identified in H460 xenografts. To summarize, GBR-NMF-RF-SHAP combined with RS can be applied to monitor the RT-induced biochemical response within cellular and tissue environments. Individual biochemicals were identified that (i) contributed to overall biological response to radiation, and (ii) spatially correlated with hypoxic regions of the tumour. RS combined with our analytical pipeline shows promise for further understanding of individual biochemical dynamics in radiation response for use in cancer therapy.

On the Evaluation of a Novel Hypoxic 3D Pancreatic Cancer Model as a Tool for Radiotherapy Treatment Screening.

Simple SummaryPancreatic cancer challenges global health with non-specific symptoms, devastatingly low survival rates, and high treatment resistance profiles. Tissue engineering is advancing to facilitate animal free tissue biomimicry, allowing the replication of tumour tissue specific hallmarks of pancreatic cancer that challenge modern treatments. Here, we report the development and characterisation of a low oxygen (hypoxic) 3D polyurethane scaffold system for long-term analysis of radiation responses. This finely tuned platform more accurately recapitulates bio-physical, bio-chemical, and structural-bio-mechanical in vivo tissue niches as well as tumour hypoxia. The latter is a treatment-limiting feature for radiotherapy, allowing the system to streamline the transition of clinical testing from bench to bedside.Tissue engineering is evolving to mimic intricate ecosystems of tumour microenvironments (TME) to more readily map realistic in vivo niches of cancerous tissues. Such advanced cancer tissue models enable more accurate preclinical assessment of treatment strategies. Pancreatic cancer is a dangerous disease with high treatment resistance that is directly associated with a highly complex TME. More specifically, the pancreatic cancer TME includes (i) complex structure and complex extracellular matrix (ECM) protein composition; (ii) diverse cell populations (e.g., stellate cells), cancer associated fibroblasts, endothelial cells, which interact with the cancer cells and promote resistance to treatment and metastasis; (iii) accumulation of high amounts of (ECM), which leads to the creation of a fibrotic/desmoplastic reaction around the tumour; and (iv) heterogeneous environmental gradients such as hypoxia, which result from vessel collapse and stiffness increase in the fibrotic/desmoplastic area of the TME. These unique hallmarks are not effectively recapitulated in traditional preclinical research despite radiotherapeutic resistance being largely connected to them. Herein, we investigate, for the first time, the impact of in vitro hypoxia (5% O2) on the radiotherapy treatment response of pancreatic cancer cells (PANC-1) in a novel polymer (polyurethane) based highly macroporous scaffold that was surface modified with proteins (fibronectin) for ECM mimicry. More specifically, PANC-1 cells were seeded in fibronectin coated macroporous scaffolds and were cultured for four weeks in in vitro normoxia (21% O2), followed by a two day exposure to either in vitro hypoxia (5% O2) or maintenance in in vitro normoxia. Thereafter, in situ post-radiation monitoring (one day, three days, seven days post-irradiation) of the 3D cell cultures took place via quantification of (i) live/dead and apoptotic profiles and (ii) ECM (collagen-I) and HIF-1a secretion by the cancer cells. Our results showed increased post-radiation viability, reduced apoptosis, and increased collagen-I and HIF-1a secretion in in vitro hypoxia compared to normoxic cultures, revealing hypoxia-induced radioprotection. Overall, this study employed a low cost, animal free model enabling (i) the possibility of long-term in vitro hypoxic 3D cell culture for pancreatic cancer, and (ii) in vitro hypoxia associated PDAC radio-protection development. Our novel platform for radiation treatment screening can be used for long-term in vitro post-treatment observations as well as for fractionated radiotherapy treatment.

Role of caveolin-1 as a biomarker for radiation resistance and tumor aggression in lung cancer.

Radiation therapy plays a major role in the treatment of lung cancer patients. However, cancer cells develop resistance to radiation. Tumor radioresistance is a complex multifactorial mechanism which may be dependent on DNA damage and repair, hypoxic conditions inside tumor microenvironment, and the clonal selection of radioresistant cells from the heterogeneous tumor site, and it is a major cause of treatment failure in non–small cell lung cancer (NSCLC). In the present investigation caveolin-1 (CAV-1) has been observed to be highly expressed in radiation resistant A549 lung cancer cells. CRISPR-Cas9 knockout of CAV-1 reverted the cells to a radio sensitive phenotype. In addition, CAV-1 overexpression in parental A549 cells, led to radiation resistance. Further, gene expression analysis of A549 parental, radiation resistant, and caveolin-1 overexpressed cells, exhibited overexpression of DNA repair genes RAD51B, RAD18, SOX2 cancer stem cell marker, MMPs, mucins and cytoskeleton proteins in resistant and caveolin-1 over expressed A549 cells, as compared to parental A549 cells. Bioinformatic analysis shows upregulation of BRCA1, Nuclear Excision DNA repair, TGFB and JAK/STAT signaling pathways in radioresistant and caveolin-1 overexpressed cells, which may functionally mediate radiation resistance. Immunohistochemistry data demonstrated heterogeneous expression of CAV-1 gene in human lung cancer tissues, which was analogous to its enhanced expression in human lung cancer cell line model and mouse orthotopic xenograft lung cancer model. Also, TCGA PanCancer clinical studies have demonstrated amplification, deletions and missense mutation in CAV-1 gene in lung cancer patients, and that CAV-1 alteration has been linked to poor prognosis, and poor survival in lung cancer patients. Interestingly, we have also optimized ELISA assay to measure caveolin-1 protein in the blood of A549 radiation resistant human xenograft preclinical mouse model and discovered higher level of caveolin-1 (950 pg/ml) in tumor bearing animals treated with radiation, as compared to xenograft with radiosensitive lung cancer cells (450 pg/ml). Thus, we conclude that caveolin-1 is involved in radio-resistance and contributes to tumor aggression, and it has potential to be used as prognostic biomarker for radiation treatment response, and tumor progression for precision medicine in lung cancer patients.

Ex vivo functional assay for predicting radiation treatment response in squamous cell carcinoma of the head and neck

Ex vivo functional assay for predicting radiation treatment response in squamous cell carcinoma of the head and neck

Gene expression signature to predict radiation response in lower-grade gliomas.

2019 Background: Standard of care for lower-grade glioma (LGG) is maximal safe resection and risk-adaptive adjuvant therapy. While patients who benefit the most from adjuvant chemotherapy have been elucidated in prospective randomized studies, comparable insights for adjuvant radiotherapy (RT) are lacking. We sought to identify and validate patterns of gene expression that are associated with differential outcomes among LGG patients treated by RT from two large genomics databases. Methods: Patients from The Cancer Genome Atlas (TCGA) with LGG (WHO grade II–III gliomas) treated by surgery and adjuvant RT were randomized 1:1 to a training set or an internal validation set. Using patients in the training set, association between gene expression from resected tumor and progression-free survival (PFS) as well as overall survival (OS) was evaluated with adjustment for clinicopathologic covariates. A genomic risk score (GRS) was then constructed from the expression levels of top genes also screened for involvement in glioma carcinogenesis. The prognostic value of GRS was subsequently validated in the internal validation set of TCGA and a second distinct database, compiled by the Chinese Glioma Genome Association (CGGA). Results: From TCGA, 289 patients with LGG received adjuvant RT alone (38 grade II, 30 grade III) or chemoradiotherapy (CRT) (51 grade II, 170 grade III) between 2009 and 2015. From CGGA, 178 patients with LGG received adjuvant RT alone (40 grade II, 13 grade III) or CRT (41 grade II, 84 grade III) between 2004 and 2016. The genes comprising GRS are MAP3K15, MAPK10, CCL3, CCL4, and ADAMTS1, involved in MAP kinase activity, T cell chemotaxis, and cell cycle transition. High GRS, defined as having a GRS in the top third, was significantly associated with worse outcomes independent of age, sex, glioma histology, WHO grade, IDH mutation, 1p/19q co-deletion, and chemotherapy status in the training set (OS HR 2.74, P &lt; 0.001; PFS HR 1.61, P = 0.014). These findings were further validated in the internal validation set (OS HR 1.84, P = 0.015; PFS HR 1.58, P = 0.027) and again in the CGGA external validation set (OS HR 1.72, P = 0.001). Association between GRS and outcomes was observed only among patients who received RT (RT alone or CRT), in both TCGA and CGGA. Conclusions: This study successfully identified an expression signature of five genes that stratified outcomes among LGG patients who received adjuvant RT, with two rounds of validation leveraging independent genomics databases. Expression levels of the highlighted genes were associated with survival only among patients whose treatments included RT, but not among those with omission of RT, suggesting that expression of these genes may be predictive of radiation treatment response. While additional prospective studies are warranted, interrogation of these genes to determine high/low GRS may be considered in the multidisciplinary management of LGGs.

Optimal treatment plan adaptation using mid-treatment imaging biomarkers

Previous studies on personalized radiotherapy (RT) have mostly focused on baseline patient stratification, adapting the treatment plan according to mid-treatment anatomical changes, or dose boosting to selected tumor subregions using mid-treatment radiological findings. However, the question of how to find the optimal adapted plan has not been properly tackled. Moreover, the effect of information uncertainty on the resulting adaptation has not been explored.In this paper, we present a framework to optimally adapt radiation therapy treatments to early radiation treatment response estimates derived from pre- and mid-treatment imaging data while considering the information uncertainty. The framework is based on the optimal stopping in radiation therapy (OSRT) framework. Biological response is quantified using tumor control probability (TCP) and normal tissue complication probability (NTCP) models, and these are directly optimized for in the adaptation step. Two adaptation strategies are discussed: (1) uniform dose adaptation and (2) continuous dose adaptation. In the first strategy, the original fluence-map is simply scaled upwards or downwards, depending on whether dose escalation or de-escalation is deemed appropriate based on the mid-treatment response observed from the radiological images. In the second strategy, a full NTCP-TCP-based fluence map re-optimization is performed to achieve the optimal adapted plans.We retrospectively tested the performance of these strategies on 14 canine head and neck cases treated with tomotherapy, using as response biomarker the change in the 3’-deoxy-3’[(18)F]-fluorothymidine (FLT)-PET signals between the pre- and mid-treatment images, and accounting for information uncertainty. Using a 10% uncertainty level, the two adaptation strategies both yield a noteworthy average improvement in guaranteed (worst-case) TCP.

A convolutional neural network for contouring metastatic lymph nodes on diffusion-weighted magnetic resonance images for assessment of radiotherapy response.

Retrieving quantitative parameters from magnetic resonance imaging (MRI), e.g. for early assessment of radiotherapy treatment response, necessitates contouring regions of interest, which is time-consuming and prone to errors. This becomes more pressing for daily imaging on MRI-guided radiotherapy systems. Therefore, we trained a deep convolutional neural network to automatically contour involved lymph nodes on diffusion-weighted (DW) MRI of head and neck cancer (HNC) patients receiving radiotherapy. DW-images from 48 HNC patients (18 induction-chemotherapy+chemoradiotherapy; 30 definitive chemoradiotherapy) with 68 involved lymph nodes were obtained on a diagnostic 1.5T MR-scanner prior to and 2-3 timepoints throughout treatment. A radiation oncologist delineated the lymph nodes on the b=50s/mm2 images. A 3D U-net was trained to contour involved lymph nodes. Its performance was evaluated in all 48 patients using 8-fold cross-validation and calculating the Dice similarity coefficient (DSC) and the absolute difference in median apparent diffusion coefficient (ΔADC) between the manual and generated contours. Additionally, the performance was evaluated in an independent dataset of three patients obtained on a 1.5T MR-Linac. In the definitive chemoradiotherapy patients (n=96 patients/lymphnodes/timepoints) the DSC was 0.87 (0.81-0.91) [median (1st-3rd quantiles)] and ΔADC was 1.9% (0.8-3.4%) and both remained stable throughout treatment. The network performed worse in the patients receiving induction-chemotherapy (n=65), with DSC=0.80 (0.71-0.87) and ΔADC=3.3% (1.6-8.0%). The network performed well on the MR-Linac data (n=8) with DSC=0.80 (0.75-0.82) and ΔADC=4.0% (0.6-9.1%). We established accurate automatic contouring of involved lymph nodes for HNC patients on diagnostic and MR-Linac DW-images.

Targeting CXCL12/CXCR4 and myeloid cells to improve the therapeutic ratio in patient-derived cervical cancer models treated with radio-chemotherapy

BackgroundThe CXCL12/CXCR4 chemokine pathway is involved in cervical cancer pathogenesis and radiation treatment (RT) response. We previously reported that radiochemotherapy (RTCT) and concurrent administration of the CXCR4 inhibitor plerixafor improved primary tumour response. The aims of this study were to determine optimal sequencing of RTCT and plerixafor, the mechanisms responsible for improved response and the effect of plerixafor on late intestinal toxicity.MethodsOrthotopic cervical cancer xenografts were treated with RTCT (30 Gy in 2 Gy fractions and cisplatin) with or without concurrent, adjuvant or continuous plerixafor. The endpoints were growth delay and molecular and immune cell changes at the end of treatment. Late intestinal toxicity was assessed by histologic examination of the rectum 90 days after a single 20 Gy fraction.ResultsRTCT increased CXCL12/CXCR4 signalling and the intratumoral accumulation of myeloid cells; the addition of plerixafor mitigated these effects. All of the RTCT and plerixafor arms showed prolonged tumour growth delay compared to RTCT alone, with the adjuvant arm showing the greatest improvement. Plerixafor also reduced late intestinal toxicity.ConclusionAdding Plerixafor to RTCT blunts treatment-induced increases in CXCL12/CXCR4 signalling, improves primary tumour response and reduces intestinal side effects. This combination warrants testing in future clinical trials.

Dynamic contrast-enhanced magnetic resonance imaging perfusion characteristics in meningiomas treated with resection and adjuvant radiosurgery.

OBJECTIVEThere is a need for advanced imaging biomarkers to improve radiation treatment planning and response assessment. T1-weighted dynamic contrast-enhanced perfusion MRI (DCE MRI) allows quantitative assessment of tissue perfusion and blood-brain barrier dysfunction and has entered clinical practice in the management of primary and secondary brain neoplasms. The authors sought to retrospectively investigate DCE MRI parameters in meningiomas treated with resection and adjuvant radiation therapy using volumetric segmentation.METHODSA retrospective review of more than 300 patients with meningiomas resected between January 2015 and December 2018 identified 14 eligible patients with 18 meningiomas who underwent resection and adjuvant radiotherapy. Patients were excluded if they did not undergo adjuvant radiation therapy or DCE MRI. Demographic and clinical characteristics were obtained and compared to DCE perfusion metrics, including mean plasma volume (vp), extracellular volume (ve), volume transfer constant (Ktrans), rate constant (kep), and wash-in rate of contrast into the tissue, which were derived from volumetric analysis of the enhancing volumes of interest.RESULTSThe mean patient age was 64 years (range 49-86 years), and 50% of patients (7/14) were female. The average tumor volume was 8.07 cm3 (range 0.21-27.89 cm3). The median Ki-67 in the cohort was 15%. When stratified by median Ki-67, patients with Ki-67 greater than 15% had lower median vp (0.02 vs 0.10, p = 0.002), and lower median wash-in rate (1.27 vs 4.08 sec-1, p = 0.04) than patients with Ki-67 of 15% or below. Logistic regression analysis demonstrated a statistically significant, moderate positive correlation between ve and time to progression (r = 0.49, p < 0.05). Furthermore, there was a moderate positive correlation between Ktrans and time to progression, which approached, but did not reach, statistical significance (r = 0.48, p = 0.05).CONCLUSIONSThis study demonstrates a potential role for DCE MRI in the preoperative characterization and stratification of meningiomas, laying the foundation for future prospective studies incorporating DCE as a biomarker in meningioma diagnosis and treatment planning.