Modeling the effect of intratumoral heterogeneity of radiosensitivity on tumor response over the course of fractionated radiation therapy

Biological dose optimization incorporating intra-tumoural cellular radiosensitivity heterogeneity in ion-beam therapy treatment planning

Objective.Treatment plans of ion-beam therapy have been made under an assumption that all cancer cells within a tumour equally respond to a given radiation dose. However, an intra-tumoural cellular radiosensitivity heterogeneity clearly exists, and it may lead to an overestimation of therapeutic effects of the radiation. The purpose of this study is to develop a biological model that can incorporate the radiosensitivity heterogeneity into biological optimization for ion-beam therapy treatment planning.Approach.The radiosensitivity heterogeneity was modeled as the variability of a cell-line specific parameter in the microdosimetric kinetic model following the gamma distribution. To validate the developed intra-tumoural-radiosensitivity-heterogeneity-incorporated microdosimetric kinetic (HMK) model, a treatment plan with H-ion beams was made for a chordoma case, assuming a radiosensitivity heterogeneous region within the tumour. To investigate the effects of the radiosensitivity heterogeneity on the biological effectiveness of H-, He-, C-, O-, and Ne-ion beams, the relative biological effectiveness (RBE)-weighted dose distributions were planned for a cuboid target with the stated ion beams without considering the heterogeneity. The planned dose distributions were then recalculated by taking the heterogeneity into account.Main results. The cell survival fraction and corresponding RBE-weighted dose were formulated based on the HMK model. The first derivative of the RBE-weighted dose distribution was also derived, which is needed for fast biological optimization. For the patient plan, the biological optimization increased the dose to the radiosensitivity heterogeneous region to compensate for the heterogeneity-induced reduction in biological effectiveness of the H-ion beams. The reduction in biological effectiveness due to the heterogeneity was pronounced for low linear energy transfer (LET) beams but moderate for high-LET beams. The RBE-weighted dose in the cuboid target decreased by 7.6% for the H-ion beam, while it decreased by just 1.4% for the Ne-ion beam.Significance.Optimal treatment plans that consider intra-tumoural cellular radiosensitivity heterogeneity can be devised using the HMK model.

Rethinking the potential role of dose painting in personalized ultra-fractionated stereotactic adaptive radiotherapy

Fractionated radiotherapy was established in the 1920s based upon two principles: (1) delivering daily treatments of equal quantity, unless the clinical situation requires adjustment, and (2) defining a specific treatment period to deliver a total dosage. Modern fractionated radiotherapy continues to adhere to these century-old principles, despite significant advancements in our understanding of radiobiology. At UT Southwestern, we are exploring a novel treatment approach called PULSAR (Personalized Ultra-Fractionated Stereotactic Adaptive Radiotherapy). This method involves administering tumoricidal doses in a pulse mode with extended intervals, typically spanning weeks or even a month. Extended intervals permit substantial recovery of normal tissues and afford the tumor and tumor microenvironment ample time to undergo significant changes, enabling more meaningful adaptation in response to the evolving characteristics of the tumor. The notion of dose painting in the realm of radiation therapy has long been a subject of contention. The debate primarily revolves around its clinical effectiveness and optimal methods of implementation. In this perspective, we discuss two facets concerning the potential integration of dose painting with PULSAR, along with several practical considerations. If successful, the combination of the two may not only provide another level of personal adaptation ("adaptive dose painting"), but also contribute to the establishment of a timely feedback loop throughout the treatment process. To substantiate our perspective, we conducted a fundamental modeling study focusing on PET-guided dose painting, incorporating tumor heterogeneity and tumor control probability (TCP).

Nicaraven attenuates the acquired radioresistance of established tumors in mouse models via PARP inhibition

Nicaraven has been reported to inhibit the activity of poly (ADP-ribose) polymerase (PARP). In this study, we investigated the probable ability of nicaraven to attenuate cancer radioresistance during fractionated radiotherapy. Tumor models were established in C57BL/6 mice and BALB/c nude mice by subcutaneous injection of Lewis mouse lung carcinoma cancer cells and A549 human lung cancer cells, respectively. When the tumors had grown to approximately 100 mm3, we initiated fractionated radiotherapy. Nicaraven or saline was administered immediately after each irradiation exposure. Compared to saline treatment, nicaraven administration significantly induced gamma-H2AX foci formation and cell apoptosis in tumors at 1 or 3 days after an additional challenge exposure to 10 Gy and inhibited tumor growth during the short-term follow-up period, suggesting increased radiosensitivity of cancer cells. Moreover, the expression of PARP in tumor tissue was decreased by nicaraven administration. Our data suggest that nicaraven likely attenuates the acquired radioresistance of cancers through PARP inhibition.

Simulating tumor volume dynamics in response to radiotherapy: Implications of model selection

From the beginning of the usage of radiotherapy (RT) for cancer treatment, mathematical modeling has been integral to understanding radiobiology and for designing treatment approaches and schedules. There has been extensive modeling of response to RT with the inclusion of various degrees of biological complexity. In this study, we compare three models of tumor volume dynamics: (1) exponential growth with RT directly reducing tumor volume, (2) logistic growth with direct tumor volume reduction, and (3) logistic growth with RT reducing the tumor carrying capacity with the objective of understanding the implications of model selection and informing the process of model calibration and parameterization. For all three models, we: examined the rates of change in tumor volume during and RT treatment course; performed parameter sensitivity and identifiability analyses; and investigated the impact of the parameter sensitivity on the tumor volume trajectories. In examining the tumor volume dynamics trends, we coined a new metric - the point of maximum reduction of tumor volume (MRV) - to quantify the magnitude and timing of the expected largest impact of RT during a treatment course. We found distinct timing differences in MRV, dependent on model selection. The parameter identifiability and sensitivity analyses revealed the interdependence of the different model parameters and that it is only possible to independently identify tumor growth and radiation response parameters if the underlying tumor growth rate is sufficiently large. Ultimately, the results of these analyses help us to better understand the implications of model selection while simultaneously generating falsifiable hypotheses about MRV timing that can be tested on longitudinal measurements of tumor volume from pre-clinical or clinical data with high acquisition frequency. Although, our study only compares three particular models, the results demonstrate that caution is necessary in selecting models of response to RT, given the artifacts imposed by each model.

Obtaining organ-specific radiobiological parameters from clinical data for radiation therapy planning of head and neck cancers

Objective.Different radiation therapy (RT) strategies, e.g. conventional fractionation RT (CFRT), hypofractionation RT (HFRT), stereotactic body RT (SBRT), adaptive RT, and re-irradiation are often used to treat head and neck (HN) cancers. Combining and/or comparing these strategies requires calculating biological effective dose (BED). The purpose of this study is to develop a practical process to estimate organ-specific radiobiologic model parameters that may be used for BED calculations in individualized RT planning for HN cancers.Approach.Clinical dose constraint data for CFRT, HFRT and SBRT for 5 organs at risk (OARs) namely spinal cord, brainstem, brachial plexus, optic pathway, and esophagus obtained from literature were analyzed. These clinical data correspond to a particular endpoint. The linear-quadratic (LQ) and linear-quadratic-linear (LQ-L) models were used to fit these clinical data and extract relevant model parameters (alpha/beta ratio, gamma/alpha,dTand BED) from the iso-effective curve. The dose constraints in terms of equivalent physical dose in 2 Gy-fraction (EQD2) were calculated using the obtained parameters.Main results.The LQ-L and LQ models fitted clinical data well from the CFRT to SBRT with the LQ-L representing a better fit for most of the OARs. The alpha/beta values for LQ-L (LQ) were found to be 2.72 (2.11) Gy, 0.55 (0.30) Gy, 2.82 (2.90) Gy, 6.57 (3.86) Gy, 5.38 (4.71) Gy, and the dose constraint EQD2 were 55.91 (54.90) Gy, 57.35 (56.79) Gy, 57.54 (56.35) Gy, 60.13 (59.72) Gy and 65.66 (64.50) Gy for spinal cord, optic pathway, brainstem, brachial plexus, and esophagus, respectively. Additional two LQ-L parametersdTwere 5.24 Gy, 5.09 Gy, 7.00 Gy, 5.23 Gy, and 6.16 Gy, and gamma/alpha were 7.91, 34.02, 8.67, 5.62 and 4.95.Significance.A practical process was developed to extract organ-specific radiobiological model parameters from clinical data. The obtained parameters can be used for biologically based radiation planning such as calculating dose constraints of different fractionation regimens.

The Mechanism of Ubiquitination or Deubiquitination Modifications in Regulating Solid Tumor Radiosensitivity

Radiotherapy, a treatment method employing radiation to eradicate tumor cells and subsequently reduce or eliminate tumor masses, is widely applied in the management of numerous patients with tumors. However, its therapeutic effectiveness is somewhat constrained by various drug-resistant factors. Recent studies have highlighted the ubiquitination/deubiquitination system, a reversible molecular modification pathway, for its dual role in influencing tumor behaviors. It can either promote or inhibit tumor progression, impacting tumor proliferation, migration, invasion, and associated therapeutic resistance. Consequently, delving into the potential mechanisms through which ubiquitination and deubiquitination systems modulate the response to radiotherapy in malignant tumors holds paramount significance in augmenting its efficacy. In this paper, we comprehensively examine the strides made in research and the pertinent mechanisms of ubiquitination and deubiquitination systems in governing radiotherapy resistance in tumors. This underscores the potential for developing diverse radiosensitizers targeting distinct mechanisms, with the aim of enhancing the effectiveness of radiotherapy.

Simulating Interclonal Interactions in Diffuse Large B-Cell Lymphoma

Diffuse large B-cell lymphoma (DLBCL) is one of the most common types of cancers, accounting for 37% of B-cell tumor cases globally. DLBCL is known to be a heterogeneous disease, resulting in variable clinical presentations and the development of drug resistance. One underexplored aspect of drug resistance is the evolving dynamics between parental and drug-resistant clones within the same microenvironment. In this work, the effects of interclonal interactions between two cell populations-one sensitive to treatment and the other resistant to treatment-on tumor growth behaviors were explored through a mathematical model. In vitro cultures of mixed DLBCL populations demonstrated cooperative interactions and revealed the need for modifying the model to account for complex interactions. Multiple best-fit models derived from in vitro data indicated a difference in steady-state behaviors based on therapy administrations in simulations. The model and methods may serve as a tool for understanding the behaviors of heterogeneous tumors and identifying the optimal therapeutic regimen to eliminate cancer cell populations using computer-guided simulations.

Knockdown of Simulated-Solar-Radiation-Sensitive miR-205-5p Does Not Induce Progression of Cutaneous Squamous Cell Carcinoma In Vitro

Solar radiation is the main risk factor for cSCC development, yet it is unclear whether the progression of cSCC is promoted by solar radiation in the same way as initial tumorigenesis. Additionally, the role of miRNAs, which exert crucial functions in various tumors, needs to be further elucidated in the context of cSCC progression and connection to solar radiation. Thus, we chronically irradiated five cSCC cell lines (Met-1, Met-4, SCC-12, SCC-13, SCL-II) with a custom-built irradiation device mimicking the solar spectrum (UVB, UVA, visible light (VIS), and near-infrared (IRA)). Subsequently, miRNA expression of 51 cancer-associated miRNAs was scrutinized using a flow cytometric multiplex quantification assay (FirePlex®, Abcam). In total, nine miRNAs were differentially expressed in cell-type-specific as well as universal manners. miR-205-5p was the only miRNA downregulated after SSR-irradiation in agreement with previously gathered data in tissue samples. However, inhibition of miR-205-5p with an antagomir did not affect cell cycle, cell growth, apoptosis, or migration in vitro despite transient upregulation of oncogenic target genes after miR-205-5p knockdown. These results render miR-205-5p an unlikely intracellular effector in cSCC progression. Thus, effects on intercellular communication in cSCC or the simultaneous examination of complementary miRNA sets should be investigated.

Generating missing patient anatomy from partially acquired cone-beam computed tomography images using deep learning: a proof of concept

The patient setup technique currently in practice in most radiotherapy departments utilises on-couch cone-beam computed tomography (CBCT) imaging. Patients are positioned on the treatment couch using visual markers, followed by fine adjustments to the treatment couch position depending on the shift observed between the computed tomography (CT) image acquired for treatment planning and the CBCT image acquired immediately before commencing treatment. The field of view of CBCT images is limited to the size of the kV imager which leads to the acquisition of partial CBCT scans for lateralised tumors. The cone-beam geometry results in high amounts of streaking artifacts and in conjunction with limited anatomical information reduces the registration accuracy between planning CT and the CBCT image. This study proposes a methodology that can improve radiotherapy patient setup CBCT images by removing streaking artifacts and generating the missing patient anatomy with patient-specific precision. This research was split into two separate studies. In Study A, synthetic CBCT (sCBCT) data was created and used to train two machine learning models, one for removing streaking artifacts and the other for generating the missing patient anatomy. In Study B, planning CT and on-couch CBCT data from several patients was used to train a base model, from which a transfer of learning was performed using imagery from a single patient, producing a patient-specific model. The models developed for Study A performed well at removing streaking artifacts and generating the missing anatomy. The outputs yielded in Study B show that the model understands the individual patient and can generate the missing anatomy from partial CBCT datasets. The outputs generated demonstrate that there is utility in the proposed methodology which could improve the patient setup and ultimately lead to improving overall treatment quality.

Investigating the Influence of Growth Arrest Mechanisms on Tumour Responses to Radiotherapy

Cancer is a heterogeneous disease and tumours of the same type can differ greatly at the genetic and phenotypic levels. Understanding how these differences impact sensitivity to treatment is an essential step towards patient-specific treatment design. In this paper, we investigate how two different mechanisms for growth control may affect tumour cell responses to fractionated radiotherapy (RT) by extending an existing ordinary differential equation model of tumour growth. In the absence of treatment, this model distinguishes between growth arrest due to nutrient insufficiency and competition for space and exhibits three growth regimes: nutrient limited, space limited (SL) and bistable (BS), where both mechanisms for growth arrest coexist. We study the effect of RT for tumours in each regime, finding that tumours in the SL regime typically respond best to RT, while tumours in the BS regime typically respond worst to RT. For tumours in each regime, we also identify the biological processes that may explain positive and negative treatment outcomes and the dosing regimen which maximises the reduction in tumour burden.

Predicting the Tumour Response to Radiation by Modelling the Five Rs of Radiotherapy Using PET Images

Despite the intensive use of radiotherapy in clinical practice, its effectiveness depends on several factors. Several studies showed that the tumour response to radiation differs from one patient to another. The non-uniform response of the tumour is mainly caused by multiple interactions between the tumour microenvironment and healthy cells. To understand these interactions, five major biologic concepts called the "5 Rs" have emerged. These concepts include reoxygenation, DNA damage repair, cell cycle redistribution, cellular radiosensitivity and cellular repopulation. In this study, we used a multi-scale model, which included the five Rs of radiotherapy, to predict the effects of radiation on tumour growth. In this model, the oxygen level was varied in both time and space. When radiotherapy was given, the sensitivity of cells depending on their location in the cell cycle was taken in account. This model also considered the repair of cells by giving a different probability of survival after radiation for tumour and normal cells. Here, we developed four fractionation protocol schemes. We used simulated and positron emission tomography (PET) imaging with the hypoxia tracer 18F-flortanidazole (18F-HX4) images as input data of our model. In addition, tumour control probability curves were simulated. The result showed the evolution of tumours and normal cells. The increase in the cell number after radiation was seen in both normal and malignant cells, which proves that repopulation was included in this model. The proposed model predicts the tumour response to radiation and forms the basis for a more patient-specific clinical tool where related biological data will be included.

Exploiting radiation immunostimulatory effects to improve glioblastoma outcome

Cancer treatment protocols depend on tumor type, localization, grade, and patient. Despite aggressive treatments, median survival of patients with Glioblastoma (GBM), the most common primary brain tumor in adults, does not exceed 18 months, and all patients eventually relapse. Thus, novel therapeutic approaches are urgently needed. Radiotherapy (RT) induces a multitude of alterations within the tumor ecosystem, ultimately modifying the degree of tumor immunogenicity at GBM relapse. The present manuscript reviews the diverse effects of RT radiotherapy on tumors, with a special focus on its immunomodulatory impact to finally discuss how RT could be exploited in GBM treatment through immunotherapy targeting. Indeed, while further experimental and clinical studies are definitively required to successfully translate preclinical results in clinical trials, current studies highlight the therapeutic potential of immunotherapy to uncover novel avenues to fight GBM.

Cancer stem cells (CSCs): key player of radiotherapy resistance and its clinical significance

Cancer stem cells (CSCs) are self-renewing and slow-multiplying micro subpopulations in tumour microenvironments. CSCs contribute to cancer's resistance to radiation (including radiation) and other treatments. CSCs control the heterogeneity of the tumour. It alters the tumour's microenvironment cellular singling and promotes epithelial-to-mesenchymal transition (EMT). Current research decodes the role of extracellular vesicles (EVs) and CSCs interlink in radiation resistance. Exosome is a subpopulation of EVs and originated from plasma membrane. It is secreted by several active cells. It involed in cellular communication and messenger of healthly and multiple pathological complications. Exosomal biological active cargos (DNA, RNA, protein, lipid and glycan), are capable to transform recipient cells' nature. The molecular signatures of CSCs and CSC-derived exosomes are potential source of cancer theranostics development. This review discusse cancer stem cells, radiation-mediated CSCs development, EMT associated with CSCs, the role of exosomes in radioresistance development, the current state of radiation therapy and the use of CSCs and CSCs-derived exosomes biomolecules as a clinical screening biomarker for cancer. This review gives new researchers a reason to keep an eye on the next phase of scientific research into cancer theranostics that will help mankind.

Translational study for stereotactic body radiotherapy against non-small cell lung cancer, including oligometastases, considering cancer stem-like cells enable predicting clinical outcome from in vitro data

BACKGROUND

Curative effects of stereotactic body radiotherapy (SBRT) for non-small cell lung cancer (NSCLC) have been evaluated using various biophysical models. Because such model parameters are empirically determined based on clinical experience, there is a large gap between in vitro and clinical studies. In this study, considering the heterogeneous cell population, we performed a translational study to realize the possible linkage based on a modeling approach.

METHODS

We modeled cell-killing and tumor control probability (TCP) considering two populations: progeny and cancer stem-like cells. The model parameters were determined from in vitro survival data of A549 and EBC-1 cells. Based on the cellular parameters, we predicted TCP and compared it with the corresponding clinical data from 553 patients collected at Hirosaki University Hospital.

RESULTS

Using an all-in-one developed model, the so-called integrated microdosimetric-kinetic (IMK) model, we successfully reproduced both in vitro survival after acute irradiation and the 3-year TCP with various fractionation schemes (6-10 Gy per fraction). From the conventional prediction without considering cancer stem cells (CSCs), this study revealed that radioresistant CSCs play a key role in the linkage between in vitro and clinical outcomes.

CONCLUSIONS

This modeling study provides a possible generalized biophysical model that enables precise estimation of SBRT worldwide.

Tumour Genetic Heterogeneity in Relation to Oral Squamous Cell Carcinoma and Anti-Cancer Treatment

Oral squamous cell carcinoma (SCC) represents more than 90% of all oral cancers and is the most frequent SCC of the head and neck region. It may affect any oral mucosal subsite but most frequently the tongue, followed by the floor of the mouth. The use of tobacco and betel nut, either smoked or chewed, and abuse of alcohol are the main risk factors for oral SCC. Oral SCC is characterized by considerable genetic heterogeneity and diversity, which together have a significant impact on the biological behaviour, clinical course, and response to treatment and on the generally poor prognosis of this carcinoma. Characterization of spatial and temporal tumour-specific molecular profiles and of person-specific resource availability and environmental and biological selective pressures could assist in personalizing anti-cancer treatment for individual patients, with the aim of improving treatment outcomes. In this narrative review, we discuss some of the events in cancer evolution and the functional significance of driver-mutations in carcinoma-related genes in general and elaborate on mechanisms mediating resistance to anti-cancer treatment.

Opportunities for improving brain cancer treatment outcomes through imaging-based mathematical modeling of the delivery of radiotherapy and immunotherapy

Immunotherapy has become a fourth pillar in the treatment of brain tumors and, when combined with radiation therapy, may improve patient outcomes and reduce the neurotoxicity. As with other combination therapies, the identification of a treatment schedule that maximizes the synergistic effect of radiation- and immune-therapy is a fundamental challenge. Mechanism-based mathematical modeling is one promising approach to systematically investigate therapeutic combinations to maximize positive outcomes within a rigorous framework. However, successful clinical translation of model-generated combinations of treatment requires patient-specific data to allow the models to be meaningfully initialized and parameterized. Quantitative imaging techniques have emerged as a promising source of high quality, spatially and temporally resolved data for the development and validation of mathematical models. In this review, we will present approaches to personalize mechanism-based modeling frameworks with patient data, and then discuss how these techniques could be leveraged to improve brain cancer outcomes through patient-specific modeling and optimization of treatment strategies.

A Multi-Compartment Model of Glioma Response to Fractionated Radiation Therapy Parameterized via Time-Resolved Microscopy Data

Purpose

Conventional radiobiology models, including the linear-quadratic model, do not explicitly account for the temporal effects of radiation, thereby making it difficult to make time-resolved predictions of tumor response to fractionated radiation. To overcome this limitation, we propose and validate an experimental-computational approach that predicts the changes in cell number over time in response to fractionated radiation.

Methods

We irradiated 9L and C6 glioma cells with six different fractionation schemes yielding a total dose of either 16 Gy or 20 Gy, and then observed their response via time-resolved microscopy. Phase-contrast images and Cytotox Red images (to label dead cells) were collected every 4 to 6 hours up to 330 hours post-radiation. Using 75% of the total data (i.e., 262 9L curves and 211 C6 curves), we calibrated a two-species model describing proliferative and senescent cells. We then applied the calibrated parameters to a validation dataset (the remaining 25% of the data, i.e., 91 9L curves and 74 C6 curves) to predict radiation response. Model predictions were compared to the microscopy measurements using the Pearson correlation coefficient (PCC) and the concordance correlation coefficient (CCC).

Results

For the 9L cells, we observed PCCs and CCCs between the model predictions and validation data of (mean ± standard error) 0.96 ± 0.007 and 0.88 ± 0.013, respectively, across all fractionation schemes. For the C6 cells, we observed PCCs and CCCs between model predictions and the validation data were 0.89 ± 0.008 and 0.75 ± 0.017, respectively, across all fractionation schemes.

Conclusion

By proposing a time-resolved mathematical model of fractionated radiation response that can be experimentally verified in vitro, this study is the first to establish a framework for quantitative characterization and prediction of the dynamic radiobiological response of 9L and C6 gliomas to fractionated radiotherapy.

A Century of Fractionated Radiotherapy: How Mathematical Oncology Can Break the Rules

Radiotherapy is involved in 50% of all cancer treatments and 40% of cancer cures. Most of these treatments are delivered in fractions of equal doses of radiation (Fractional Equivalent Dosing (FED)) in days to weeks. This treatment paradigm has remained unchanged in the past century and does not account for the development of radioresistance during treatment. Even if under-optimized, deviating from a century of successful therapy delivered in FED can be difficult. One way of exploring the infinite space of fraction size and scheduling to identify optimal fractionation schedules is through mathematical oncology simulations that allow for in silico evaluation. This review article explores the evidence that current fractionation promotes the development of radioresistance, summarizes mathematical solutions to account for radioresistance, both in the curative and non-curative setting, and reviews current clinical data investigating non-FED fractionated radiotherapy.

Biological Adaptations of Tumor Cells to Radiation Therapy

Radiation therapy has been used worldwide for many decades as a therapeutic regimen for the treatment of different types of cancer. Just over 50% of cancer patients are treated with radiotherapy alone or with other types of antitumor therapy. Radiation can induce different types of cell damage: directly, it can induce DNA single- and double-strand breaks; indirectly, it can induce the formation of free radicals, which can interact with different components of cells, including the genome, promoting structural alterations. During treatment, radiosensitive tumor cells decrease their rate of cell proliferation through cell cycle arrest stimulated by DNA damage. Then, DNA repair mechanisms are turned on to alleviate the damage, but cell death mechanisms are activated if damage persists and cannot be repaired. Interestingly, some cells can evade apoptosis because genome damage triggers the cellular overactivation of some DNA repair pathways. Additionally, some surviving cells exposed to radiation may have alterations in the expression of tumor suppressor genes and oncogenes, enhancing different hallmarks of cancer, such as migration, invasion, and metastasis. The activation of these genetic pathways and other epigenetic and structural cellular changes in the irradiated cells and extracellular factors, such as the tumor microenvironment, is crucial in developing tumor radioresistance. The tumor microenvironment is largely responsible for the poor efficacy of antitumor therapy, tumor relapse, and poor prognosis observed in some patients. In this review, we describe strategies that tumor cells use to respond to radiation stress, adapt, and proliferate after radiotherapy, promoting the appearance of tumor radioresistance. Also, we discuss the clinical impact of radioresistance in patient outcomes. Knowledge of such cellular strategies could help the development of new clinical interventions, increasing the radiosensitization of tumor cells, improving the effectiveness of these therapies, and increasing the survival of patients.

Clinically validated model predicts the effect of intratumoral heterogeneity on overall survival for non-small cell lung cancer (NSCLC) patients

BACKGROUND AND OBJECTIVE

Radiation therapy is used in nearly 50% of cancer treatments in the developed world. Currently, radiation treatments are homogenous and fail to take into consideration intratumoral heterogeneity. We demonstrate the importance of considering intratumoral heterogeneity and the development of resistance during fractionated radiotherapy when the same dose of radiation is delivered for all fractions (Fractional Equivalent Dosing FED).

METHODS

A mathematical model was developed with the following parameters: a starting population of 10¹¹ non-small cell lung cancer (NSCLC) tumor cells, 48 h doubling time, and cell death per the linear-quadratic (LQ) model with α and β values derived from RSIα/β, in a previously described gene expression based model that estimates α and β. To incorporate both inter- and intratumor radiation sensitivity, RSIα/β output for each patient sample is assumed to represent an average value in a gamma distribution with the bounds set to -50% and +50% of RSIα/b. Therefore, we assume that within a given tumor there are subpopulations that have varying radiation sensitivity parameters that are distinct from other tumor samples with a different mean RSIα/β. A simulation cohort (SC) comprised of 100 lung cancer patients with available RSIα/β (patient specific α and β values) was used to investigate 60 Gy in 30 fractions with fractionally equivalent dosing (FED). A separate validation cohort (VC) of 57 lung cancer patients treated with radiation with available local control (LC), overall survival (OS), and tumor gene expression was used to clinically validate the model. Cox regression was used to test for significance to predict clinical outcomes as a continuous variable in multivariate analysis (MVA). Finally, the VC was used to compare FED schedules with various altered fractionation schema utilizing a Kruskal-Wallis test. This was examined using the end points of end of treatment log cell count (LCC) and by a parameter described as mean log kill efficiency (LKE) defined as: LCC  =  log10(tumorcellcount) [Formula: see text] RESULTS: Cox regression analysis on LCC for the VC demonstrates that, after incorporation of intratumoral heterogeneity, LCC has a linear correlation with local control (p = 0.002) and overall survival (p = < 0.001). Other suggested treatment schedules labeled as High Intensity Treatment (HIT) with a total 60 Gy delivered over 6 weeks have a lower mean LCC and an increased LKE compared to standard of care 60 Gy delivered in FED in the VC.

CONCLUSION

We find that LCC is a clinically relevant metric that is correlated with local control and overall survival in NSCLC. We conclude that 60 Gy delivered over 6 weeks with altered HIT fractionation leads to an enhancement in tumor control compared to FED when intratumoral heterogeneity is considered.

Multi-scale Monte Carlo simulations of gold nanoparticle-induced DNA damages for kilovoltage X-ray irradiation in a xenograft mouse model using TOPAS-nBio

Background

Gold nanoparticles (AuNPs) are considered as promising agents to increase the radiosensitivity of tumor cells. However, the biological mechanisms of radiation enhancement effects of AuNPs are still not well understood. We present a multi-scale Monte Carlo simulation framework within TOPAS-nBio to investigate the increase of DNA damage due to the presence of AuNPs in mouse tumor models.

Methods

A tumor was placed inside a voxel mouse model and irradiated with either 100 kVp or 200 kVp x-ray beams. Phase spaces were employed to transfer particles from the macroscopic (voxel) scale to the microscopic scale, which consists of a cell geometry including a detailed mouse DNA model. Radiosensitizing effects were calculated in the presence and absence of hybrid nanoparticles with a Fe2O3 core surrounded by a gold layer (AuFeNPs). To simulate DNA damage even for very small energy tracks, Geant4-DNA physics and chemistry models were used on microscopic scale.

Results

An AuFeNP induced enhancement of both dose and DNA strand breaks has been established for different scenarios. Produced chemical radicals including hydroxyl molecules, which were assumed to be responsible for DNA damage through chemical reactions, were found to be significantly increased. We further observed a dependency of the results on the location of the cells within the tumor for 200 kVp x-ray beams.

Conclusions

Our multi-scale approach allows to study irradiation induced physical and chemical effects on cells. We showed a potential increase in cell radiosensitization caused by relatively small concentrations of AuFeNPs. Our new methodology allows the individual adjustment of parameters in each simulation step and therefore can be used for other studies investigating the radiosensitizing effects of AuFeNPs or AuNPs in living cells.

MiRNA-4537 functions as a tumor suppressor in gastric cancer and increases the radiosensitivity of gastric cancer cells

Radiotherapy is a common method to treat gastric cancer (GC). However, the clinical outcomes of GC radiotherapy face challenges, and the mechanisms of GC radioresistance remain unclear. Our study aimed to investigate the role and mechanism of miR-4537 in the radiation sensitivity of GC cells. Cell viability was determined by Cell Counting Kit-8. The proliferation of HGC27 and KATO III cells was measured using a colony formation assay. Flow cytometry was performed to examine the changes in cell apoptosis. Western blotting was conducted to detect the expression of zinc finger protein 587 (ZNF587) protein in HGC27 and KATO III cells. To confirm the relationship between miR-4537 and ZNF587, a luciferase reporter assay was performed. MiR-4537 was downregulated in GC tumors and cells and suppressed cell proliferation, while promoting cell apoptosis in GC. Importantly, we found that miR-4537 reduced the radioresistance of GC cells. In addition, we also confirmed that miR-4537 expression is negatively correlated with ZNF587 expression in GC tissues. MiR-4537 bound to ZNF587 and suppressed the expression level of ZNF587. Overexpression of ZNF587 partially counteracted the effects of miR-4537 on cell proliferation and apoptosis. In conclusion, in GC cells, miR-4537 inhibited the ability of cell proliferation, but on the contrary, it promoted the ability of cell apoptosis and improved radiosensitivity of the cells.

Liquid Biopsies for Molecular Biology-Based Radiotherapy

Molecular alterations drive cancer initiation and evolution during development and in response to therapy. Radiotherapy is one of the most commonly employed cancer treatment modalities, but radiobiologic approaches for personalizing therapy based on tumor biology and individual risks remain to be defined. In recent years, analysis of circulating nucleic acids has emerged as a non-invasive approach to leverage tumor molecular abnormalities as biomarkers of prognosis and treatment response. Here, we evaluate the roles of circulating tumor DNA and related analyses as powerful tools for precision radiotherapy. We highlight emerging work advancing liquid biopsies beyond biomarker studies into translational research investigating tumor clonal evolution and acquired resistance.

Tumor-immune ecosystem dynamics define an individual Radiation Immune Score to predict pan-cancer radiocurability

Radiotherapy efficacy is the result of radiation-mediated cytotoxicity coupled with stimulation of antitumor immune responses. We develop an in silico 3-dimensional agent-based model of diverse tumor-immune ecosystems (TIES) represented as anti- or pro-tumor immune phenotypes. We validate the model in 10,469 patients across 31 tumor types by demonstrating that clinically detected tumors have pro-tumor TIES. We then quantify the likelihood radiation induces antitumor TIES shifts toward immune-mediated tumor elimination by developing the individual Radiation Immune Score (iRIS). We show iRIS distribution across 31 tumor types is consistent with the clinical effectiveness of radiotherapy, and in combination with a molecular radiosensitivity index (RSI) combines to predict pan-cancer radiocurability. We show that iRIS correlates with local control and survival in a separate cohort of 59 lung cancer patients treated with radiation. In combination, iRIS and RSI predict radiation-induced TIES shifts in individual patients and identify candidates for radiation de-escalation and treatment escalation. This is the first clinically and biologically validated computational model to simulate and predict pan-cancer response and outcomes via the perturbation of the TIES by radiotherapy.

Metabolic reprograming of antioxidant defense: a precision medicine perspective for radiotherapy of lung cancer?

Radiotherapy plays a key role in the management of lung cancer patients in curative and palliative settings. Traditionally, radiotherapy was either given alone or in combination with surgery, classical cytotoxic chemotherapy, or both. Technical and physical innovations achieved during the last two decades have helped to enhance the accuracy of radiotherapy dose delivery and have facilitated geometric radiotherapy individualization. Furthermore, multimodal combinations with molecularly tailored drugs or immunotherapy yielded promising survival benefits in selected patients. Yet high locoregional failure rates and frequent development of metastases still limit the patient outcome. One major obstacle to successful treatment is the high molecular heterogeneity observed in lung cancer. So far, clinical radiotherapy does not routinely use the knowledge on molecular subtypes with regard to therapy individualization and predictive biomarkers are missing. Herein, altered cancer metabolism has attracted novel attention during recent years as it promotes tumor growth and progression as well as resistance to anticancer therapies. The present perspective will exemplarily highlight how clinically relevant molecular subtypes defined by co-occurring somatic mutations in KRAS-driven lung cancer impact the metabolic phenotype of cancer cells, how the metabolic phenotype supports intrinsic radioresistance by the improved antioxidant defense, and also discuss potential subtype-specific actionable metabolic vulnerabilities. Understanding metabolic phenotypes of radioresistance and metabolic bottlenecks of cancer cells undergoing radiotherapy in a cancer-specific context will offer largely unexploited future avenues for biological individualization and optimization of radiotherapy. Transcriptional profiles will provide additional benefit in defining metabolic phenotypes associated with radioresistance, particularly in cases, where such dependencies cannot be identified by specific somatic mutations.

Re-irradiation with Moderate Hypo-Fractionation Using Intensity Modulated Photon or Proton Radiation Therapy in Locally Recurrent Squamous Cell Carcinoma of Nasopharynx

Purpose

This study aimed to analyze the treatment outcomes of locally recurrent nasopharyngeal cancer (NPC) patients following moderate hypo-fractionation re-irradiation (re-RT).

Materials and Methods

Sixty locally recurrent NPC patients underwent hypo-fractionation re-RT. 48.3% had rT3-4, and 30.0% did keratinizing squamous cell carcinoma. Intensity-modulated radiation therapy (IMRT), with or without intensity-modulated proton therapy (IMPT), was used in 66.7% of patients.

Results

With the median follow up of 22 (2~254) months, 31 patients (51.7%) died, 38 (63.3%) developed further treatment failure, and 30 (50.0%) developed ≥Grade 3 toxicity (including seven Grade 5) at time of analysis. The 2- and 5-year rates of overall survival, local failure-free survival, and ≥Grade 3 toxicity-free survival were 57.9% and 45.8%, 64.1% and 52.5%, and 54.8% and 44.9%, respectively. In multivariate analyses, worse factors for OS were iT3-4 (p=0.010) and age at re-RT ≥53 years (p=0.003), those for LFFS were rT3-4 (p=0.022) and rN0-1 (p=0.035), and those for TFS were iT3-4 (p=0.020) and rIMRT/IMPT (p=0.030), respectively. Cumulative dose or fraction size ≥3 Gy at re-RT, however, showed no significance for OS, LFFS and TFS.

Conclusion

Current re-RT with modern RT techniques by moderate hypo-fractionation scheme seemed feasible in treating locally recurrent NPC patients.

Hypoxia in Solid Tumors: How Low Oxygenation Impacts the "Six Rs" of Radiotherapy

Radiotherapy is an important component of cancer treatment, with approximately 50% of all cancer patients receiving radiation therapy during their course of illness. Nevertheless, solid tumors frequently exhibit hypoxic areas, which can hinder therapies efficacy, especially radiotherapy one. Indeed, hypoxia impacts the six parameters governing the radiotherapy response, called the « six Rs of radiation biology » (for Radiosensitivity, Repair, Repopulation, Redistribution, Reoxygenation, and Reactivation of anti-tumor immune response), by inducing pleiotropic cellular adaptions, such as cell metabolism rewiring, epigenetic landscape remodeling, and cell death weakening, with significant clinical repercussions. In this review, according to the six Rs, we detail how hypoxia, and associated mechanisms and pathways, impact the radiotherapy response of solid tumors and the resulting clinical implications. We finally illustrate it in hypoxic endocrine cancers through a focus on anaplastic thyroid carcinomas.

CSC Radioresistance: A Therapeutic Challenge to Improve Radiotherapy Effectiveness in Cancer

Radiotherapy (RT) is a modality of oncologic treatment that can be used to treat approximately 50% of all cancer patients either alone or in combination with other treatment modalities such as surgery, chemotherapy, immunotherapy, and therapeutic targeting. Despite the technological advances in RT, which allow a more precise delivery of radiation while progressively minimizing the impact on normal tissues, issues like radioresistance and tumor recurrence remain important challenges. Tumor heterogeneity is responsible for the variation in the radiation response of the different tumor subpopulations. A main factor related to radioresistance is the presence of cancer stem cells (CSC) inside tumors, which are responsible for metastases, relapses, RT failure, and a poor prognosis in cancer patients. The plasticity of CSCs, a process highly dependent on the epithelial–mesenchymal transition (EMT) and associated to cell dedifferentiation, complicates the identification and eradication of CSCs and it might be involved in disease relapse and progression after irradiation. The tumor microenvironment and the interactions of CSCs with their niches also play an important role in the response to RT. This review provides a deep insight into the characteristics and radioresistance mechanisms of CSCs and into the role of CSCs and tumor microenvironment in both the primary tumor and metastasis in response to radiation, and the radiobiological principles related to the CSC response to RT. Finally, we summarize the major advances and clinical trials on the development of CSC-based therapies combined with RT to overcome radioresistance. A better understanding of the potential therapeutic targets for CSC radiosensitization will provide safer and more efficient combination strategies, which in turn will improve the live expectancy and curability of cancer patients.

miR-144-3p increases radiosensibility of gastric cancer cells by targeting inhibition of ZEB1

PURPOSE

This study set out to probe into the effect and mechanism of miR-144-3p on radiosensitivity of gastric cancer (GC) cells.

METHODS

Cancer tissue and paracancerous tissue of GC patients admitted to our hospital were collected, their miR-144-3p expression was tested, GC cells were transfected, and survival and biological behavior of those cells under radiation were detected.

RESULTS

After detection, miR-144-3p expression was down-regulated in GC tissue, while ZEB1 was up-regulated. There was no remarkable difference in the survival fraction of cells in each group before receiving radiation, but that of tumor cells decreased obviously (p < 0.05) after radiation exposure. Survival fraction of cells overexpressing miR-144-3p or silencing ZEB1 decreased more obviously, while the inhibition of miR-144-3p or overexpressing ZEB1 was weaker. Biological behavior of cells under 6 Gy radiation was detected. It was found that miR-144-3p overexpression or silencing ZEB1 dramatically inhibited the proliferation activity of GC cells under 6 Gy radiation, increased the levels of pro-apoptotic Bax and caspase-3 proteins (p < 0.05) and decreased the anti-apoptotic protein Bcl-2 level (p < 0.05), resulting in an increase in the apoptosis rate of cells. miR-144-3p was confirmed to be ZEB1 targeting site by dual luciferase report. Moreover, rescue experiments prove that it can increase the radiosensitivity of GC cells by regulating ZEB1 expression.

CONCLUSION

miR-144-3p expression was down-regulated in GC, and it can increase the radiosensitivity of those cells by inhibiting ZEB1 expression.

Applications of Nonlinear Programming to the Optimization of Fractionated Protocols in Cancer Radiotherapy

The present work of review collects and evidences the main results of our previous papers on the optimization of fractionated radiotherapy protocols. The problem under investigation is presented here in a unitary framework as a nonlinear programming application that aims to determine the optimal schemes of dose fractionation commonly used in external beam radiotherapy. The radiation responses of tumor and normal tissues are described by means of the linear quadratic model. We formulate a nonlinear, non-convex optimization problem including two quadratic constraints to limit the collateral normal tissue damages and linear box constraints on the fractional dose sizes. The general problem is decomposed into two subproblems: (1) analytical determination of the optimal fraction dose sizes as a function of the model parameters for arbitrarily fixed treatment lengths; and (2) numerical determination of the optimal fraction number, and of the optimal treatment time, in different parameter settings. After establishing the boundedness of the optimal number of fractions, we investigate by numerical simulation the optimal solution behavior for experimentally meaningful parameter ranges, recognizing the crucial role of some parameters, such as the radiosensitivity ratio, in determining the optimality of hypo- or equi-fractionated treatments. Our results agree with findings of the theoretical and clinical literature.

Forecasting tumor and vasculature response dynamics to radiation therapy via image based mathematical modeling

Background

Intra-and inter-tumoral heterogeneity in growth dynamics and vascularity influence tumor response to radiation therapy. Quantitative imaging techniques capture these dynamics non-invasively, and these data can initialize and constrain predictive models of response on an individual basis.

Methods

We have developed a family of 10 biologically-based mathematical models describing the spatiotemporal dynamics of tumor volume fraction, blood volume fraction, and response to radiation therapy. To evaluate this family of models, rats (n = 13) with C6 gliomas were imaged with magnetic resonance imaging (MRI) three times before, and four times following a single fraction of 20 Gy or 40 Gy whole brain irradiation. The first five 3D time series data of tumor volume fraction, estimated from diffusion-weighted (DW-) MRI, and blood volume fraction, estimated from dynamic contrast-enhanced (DCE-) MRI, were used to calibrate tumor-specific model parameters. The most parsimonious and well calibrated of the 10 models, selected using the Akaike information criterion, was then utilized to predict future growth and response at the final two imaging time points. Model predictions were compared at the global level (percent error in tumor volume, and Dice coefficient) as well as at the local or voxel level (concordance correlation coefficient).

Result

The selected model resulted in < 12% error in tumor volume predictions, strong spatial agreement between predicted and observed tumor volumes (Dice coefficient > 0.74), and high level of agreement at the voxel level between the predicted and observed tumor volume fraction and blood volume fraction (concordance correlation coefficient > 0.77 and > 0.65, respectively).

Conclusions

This study demonstrates that serial quantitative MRI data collected before and following radiation therapy can be used to accurately predict tumor and vasculature response with a biologically-based mathematical model that is calibrated on an individual basis. To the best of our knowledge, this is the first effort to characterize the tumor and vasculature response to radiation therapy temporally and spatially using imaging-driven mathematical models.