Efficacy of dose escalation on TCP, recurrence and second cancer risks: a mathematical study

Gene expression signature predicts radiation sensitivity in cell lines using the integral of dose-response curve

BACKGROUND

Although substantial efforts have been made to build molecular biomarkers to predict radiation sensitivity, the ability to accurately stratify the patients is still limited. In this study, we aim to leverage large-scale radiogenomics datasets to build genomic predictors of radiation response using the integral of the radiation dose-response curve.

METHODS

Two radiogenomics datasets consisting of 511 and 60 cancer cell lines were utilized to develop genomic predictors of radiation sensitivity. The intrinsic radiation sensitivity, defined as the integral of the dose-response curve (AUC) was used as the radioresponse variable. The biological determinants driving AUC and SF2 were compared using pathway analysis. To build the predictive model, the largest and smallest datasets consisting of 511 and 60 cancer cell lines were used as the discovery and validation cohorts, respectively, with AUC as the response variable.

RESULTS

Utilizing a compendium of three pathway databases, we illustrated that integral of the radiobiological model provides a more comprehensive characterization of molecular processes underpinning radioresponse compared to SF2. Furthermore, more pathways were found to be unique to AUC than SF2-30, 288 and 38 in KEGG, REACTOME and WIKIPATHWAYS, respectively. Also, the leading-edge genes driving the biological pathways using AUC were unique and different compared to SF2. With regards to radiation sensitivity gene signature, we obtained a concordance index of 0.65 and 0.61 on the discovery and validation cohorts, respectively.

CONCLUSION

We developed an integrated framework that quantifies the impact of physical radiation dose and the biological effect of radiation therapy in interventional pre-clinical model systems. With the availability of more data in the future, the clinical potential of this signature can be assessed, which will eventually provide a framework to integrate genomics into biologically-driven precision radiation oncology.

Development and validation of genomic predictors of radiation sensitivity using preclinical data

Background

Radiation therapy is among the most effective and commonly used therapeutic modalities of cancer treatments in current clinical practice. The fundamental paradigm that has guided radiotherapeutic regimens are ‘one-size-fits-all’, which are not in line with the dogma of precision medicine. While there were efforts to build radioresponse signatures using OMICS data, their ability to accurately predict in patients is still limited.

Methods

We proposed to integrate two large-scale radiogenomics datasets consisting of 511 with 23 tissues and 60 cancer cell lines with 9 tissues to build and validate radiation response biomarkers. We used intrinsic radiation sensitivity, i.e., surviving fraction of cells (SF2) as the radiation response indicator. Gene set enrichment analysis was used to examine the biological determinants driving SF2. Using SF2 as a continuous variable, we used five different approaches, univariate, rank gene ensemble, rank gene multivariate, mRMR and elasticNet to build genomic predictors of radiation response through a cross-validation framework.

Results

Through the pathway analysis, we found 159 pathways to be statistically significant, out of which 54 and 105 were positively and negatively enriched with SF2. More importantly, we found cell cycle and repair pathways to be enriched with SF2, which are inline with the fundamental aspects of radiation biology. With regards to the radiation response gene signature, we found that all multivariate models outperformed the univariate model with a ranking based approach performing well compared to other models, indicating complex biological processes underpinning radiation response.

Conclusion

To summarize, we found biological processes underpinning SF2 and systematically compared different machine learning approaches to develop and validate predictors of radiation response. With more patient data available in the future, the clinical value of these biomarkers can be assessed that would allow for personalization of radiotherapy.

Mathematical oncology and it's application in non melanoma skin cancer - A primer for radiation oncology professionals

Cancers of the skin (the majority of which are basal and squamous cell skin carcinomas, but also include the rarer Merkel cell carcinoma) are overwhelmingly the most common of all types of cancer. Most of these are treated surgically, with radiation reserved for those patients with high risk features or anatomical locations less suitable for surgery. Given the high incidence of both basal and squamous cell carcinomas, as well as the relatively poor outcome for Merkel cell carcinoma, it is useful to investigate the role of other disciplines regarding their diagnosis, staging and treatment. Mathematical modelling is one such area of investigation. The use of mathematical modelling is a relatively recent addition to the armamentarium of cancer treatment. It has long been recognised that tumour growth and treatment response is a complex, non-linear biological phenomenon with many mechanisms yet to be understood. Despite decades of research, including clinical, population and basic science approaches, we continue to be challenged by the complexity, heterogeneity and adaptability of tumours, both in individual patients in the oncology clinic and across wider patient populations. Prospective clinical trials predominantly focus on average outcome, with little understanding as to why individual patients may or may not respond. The use of mathematical models may lead to a greater understanding of tumour initiation, growth dynamics and treatment response.

Modelling recurrence and second cancer risks induced by proton therapy

In the past few years, proton therapy has taken the centre stage in treating various tumour types. The primary contribution of this study is to investigate the tumour control probability (TCP), relapse time and the corresponding secondary cancer risks induced by proton beam radiation therapy. We incorporate tumour relapse kinetics into the TCP framework and calculate the associated second cancer risks. To calculate proton therapy-induced secondary cancer induction, we used the well-known biologically motivated mathematical model, initiation-inactivation-proliferation formalism. We used the available in vitro data for the linear energy transfer (LET) dependence of cell killing and mutation induction parameters. We evaluated the TCP and radiation-induced second cancer risks for protons in the clinical range of LETs, i.e. approximately 8 $\mathrm{keV/\mu m}$ for the tumour volume and 1-3 $\mathrm{keV/\mu m}$ for the organs at risk. This study may serve as a framework for further work in this field and elucidates proton-induced TCP and the associated secondary cancer risks, not previously reported in the literature. Although studies with a greater number of cell lines would reduce uncertainties within the model parameters, we argue that the theoretical framework presented within is a sufficient rationale to assess proton radiation TCP, relapse and carcinogenic effects in various treatment plans. We show that compared with photon therapy, proton therapy markedly reduces the risk of secondary malignancies and for equivalent dosing regimens achieves better tumour control as well as a reduced primary recurrence outcome, especially within a hypo-fractionated regimen.

Estimation of second cancer risk after radiotherapy for rectal cancer: comparison of 3D conformal radiotherapy and volumetric modulated arc therapy using different high dose fractionation schemes

Purpose

To investigate second cancer risk (SCR) comparing volumetric modulated arc therapy (VMAT) and 3D conformal radiotherapy (3DCRT) with different high dose fractionation schemes.

Methods

VMAT and 3DCRT virtual treatment plans for 25 patients previously treated with radiotherapy for rectal cancer were evaluated retrospectively. Doses prescribed were 25 × 1.8 Gy and 5 × 5 Gy, respectively. SCR was estimated using a carcinogenesis model and epidemiological data for carcinoma and sarcoma induction. SCR was determined by lifetime attributable risk (LAR).

Results

Mean excess LAR was highest for organs adjacent to the PTV. Total LAR for VMAT and 3DCRT was 2.3–3.0 and 2.0–2.7 %, respectively. For 5 × 5 Gy, LAR was 1.4–1.9 % for VMAT and 1.2–1.6 % for 3DCRT. Organ-specific excess LAR was significantly higher for VMAT, and highest for bladder and colon. Size and shape of the PTV influenced SCR and was highest for age ≤ 40 years. For a patient with an additional lifetime risk of 60 years, LAR was 10 % for 25 × 1.8 Gy and 6 % for 5 × 5 Gy.

Conclusions

No statistically significant difference was detected in SCR using VMAT or 3DCRT. For bladder and colon, organ-specific excess LAR was statistically lower using 3DCRT, however the difference was small. Compared to epidemiological data, SCR was smaller when using a hypofractionated schedule. SCR was 2 % higher at normal life expectancy.

Trial registration

ClinicalTrials.gov Identifier NCT02572362. Registered 4 October 2015. Retrospectively registered.

Modeling age-dependent radiation-induced second cancer risks and estimation of mutation rate: an evolutionary approach

Although the survival rate of cancer patients has significantly increased due to advances in anti-cancer therapeutics, one of the major side effects of these therapies, particularly radiotherapy, is the potential manifestation of radiation-induced secondary malignancies. In this work, a novel evolutionary stochastic model is introduced that couples short-term formalism (during radiotherapy) and long-term formalism (post-treatment). This framework is used to estimate the risks of second cancer as a function of spontaneous background and radiation-induced mutation rates of normal and pre-malignant cells. By fitting the model to available clinical data for spontaneous background risk together with data of Hodgkin's lymphoma survivors (for various organs), the second cancer mutation rate is estimated. The model predicts a significant increase in mutation rate for some cancer types, which may be a sign of genomic instability. Finally, it is shown that the model results are in agreement with the measured results for excess relative risk (ERR) as a function of exposure age and that the model predicts a negative correlation of ERR with increase in attained age. This novel approach can be used to analyze several radiotherapy protocols in current clinical practice and to forecast the second cancer risks over time for individual patients.