|Year : 2018 | Volume
| Issue : 4 | Page : 316-317
Risk of second malignancy after modern conformal and proton beam radiotherapy-Minimizing risks, improving survivorshipInstructions to authors and other literary works of fiction
Srinivas Chilukuri, Rakesh Jalali
Department of Radiation Oncology, Apollo Proton Cancer Centre, 100 Feet Road Tharamani, Chennai, Tamil Nadu, India
|Date of Web Publication||28-Feb-2019|
Department of Radiation Oncology, Apollo Proton Cancer Centre, 100 Feet Road Tharamani, Chennai, Tamil Nadu
Source of Support: None, Conflict of Interest: None
|How to cite this article:|
Chilukuri S, Jalali R. Risk of second malignancy after modern conformal and proton beam radiotherapy-Minimizing risks, improving survivorshipInstructions to authors and other literary works of fiction. Indian J Cancer 2018;55:316-7
|How to cite this URL:|
Chilukuri S, Jalali R. Risk of second malignancy after modern conformal and proton beam radiotherapy-Minimizing risks, improving survivorshipInstructions to authors and other literary works of fiction. Indian J Cancer [serial online] 2018 [cited 2020 May 26];55:316-7. Available from: http://www.indianjcancer.com/text.asp?2018/55/4/316/253296
Secondary malignant neoplasms (SMNs) after initial therapy in cancer patients, is of increasingly important concern globally, as we continue to improve long-term survivorship based clinical outcomes especially in childhood and young adult cancer survivor cohorts. Apart from other possible causative agents, radiation is often associated with significant increase in SMN risk. A vast majority of data on radiation associated SMN is largely based on epidemiological studies and a few predictive models. Both these sources are, however, influenced by several and often large uncertainties. Within the limitations of available evidence, however, there is a consensus that this risk needs to be mitigated as much as possible.
One of the key challenges in this field is to generate high-quality evidence for accurate estimation risk of SMN. Until last couple of decades, there was a relative lack of sensitization regarding SMN as the main focus was to improve local control and survival (virtually at all costs) and acute toxicity. Determining the exact risk of SMN does mandate long meticulous evaluations (10–30 years and beyond) in a preferably consecutively followed up large patient cohorts. Also, in the modern era with the use of multimodality treatment, it is hard to identify the exact contribution of each of the confounding factors, such as inherent genetic tendencies, systemic therapies, and others. The other challenge is the limited understanding of biological complexities involved in radiation-induced carcinogenesis. Most of the physical dose models predicting SMN risks do not take in to account these biological complexities that may play a pivotal role.
A study published  in the current issue of Indian Journal of Cancer adds to the growing body of literature aiming to sensitize physicians across specialities regarding this important and relevant issue. Sherif et al., estimated lifetime risk of SMN among patients of medulloblastoma receiving craniospinal irradiation (CSI) using nominal risk coefficients from ICRP data, and the risk was compared between 3D conformal RT (3DCRT) and Intensity-Modulated Radiation Therapy (IMRT) plans. This study is simple, easy to understand, and found that lifetime risk of SMN for thyroid, liver, and stomach was significantly less in IMRT plans, whereas risk for lung and bladder was higher in IMRT plans when compared to the 3DCRT plans. The study is potentially relevant in clinical practice, as IMRT is being increasingly used during CSI and the SMN risk may be an important parameter to assess during plan evaluation. The study does have a few limitations. The model used in this study seems to be oversimplified and likely to be applicable over a very narrow dose range. Also, apart from physical dose this model does not take into account basic known factors, such as age at treatment, inherent genetic susceptibilities, and effect of systemic treatments. Nevertheless, the authors need to be complimented for focusing on an important subject, which is likely to get more attention in the coming years especially with more conformal techniques including particle beam therapies being increasingly employed in CSI and indeed in other childhood malignancies.
What have we learnt so far from decades of research on SMN risk after radiation is summarised as follows:
- Age dependence of SMN risk: There has been a consistent correlation between age at exposure to radiation and risk of SMN. The best available evidence for age dependence comes from atomic bomb survivor life span study, which estimated that relative risk decreased by about 17% per decade increase in the age at exposure
- Genetic biomarkers: Genome wide association studies have been used to identify potential genetic markers that may be associated with increased risk of SMN particularly single nucleotide polymorphism or genes associated with radiation response pathway. A large study from the prominent group, Women's Environment, Cancer and Radiation Epidemiology Study  group looked at 52,000 breast cancer survivors found an increased risk of secondary contralateral breast cancer among those with ATM mutations. In addition, other markers such as p53, CHEK2, PALB2, and PTEN are being studied for a possible association. PRDM-1 gene is being proposed as marker of interest for radiation induced SMN in HL survivors possibly by acting as a radiation-responsive tumor suppressor. Future studies will provide more insight to these complex biological processes involved in SMN
- Tissue-type dependence: The strongest evidence for this comes from atomic bomb survivor's life span study, which showed that certain types of cancers were more common than others after uniform radiation exposure and were dependent on age and dose of exposure. Biological Effects of Ionizing Radiation (BEIR) reports describe models to guide us to estimate the risk for each site based on age at exposure and dose absorbed., Although these are extremely simplified models, they are unreliable and not robust for all dose ranges. It is extremely crucial to have more data to make these models more robust and this can only arise from studies with reliable and long-term follow-up
- Influence of perpetually changing modern techniques and technologies: The marked shift in radiotherapy from 3DCRT to IMRT and its implications for future second cancer risks has been a subject of debate. While IMRT often delivers more conformal radiation doses to the treated target volume, it exposes a larger volume of normal tissue to lower doses. In addition, there is often higher background leakage radiation. The fact that large areas receive low doses of radiation has led some to hypothesize that the risk of SMN could be even double compared to the risk after 3DCRT. Despite this prediction, fortunately there has not been a single clinical study, which has shown that this can happen in reality. It is also possible that this increase in risk is nullified by the fact that clinicians around the world are treating smaller volumes to lesser peripheral doses for most sites
- Carcinogenesis models: Biologically motivated mathematical models differ significantly in sophistication and broadly classified into short-term models (like LQ exponential model which focus on the processes during and shortly after radiation) and long-term models (models which track carcinogenesis mechanisms during the entire human life span). Long-term models (Armitage–Doll Model and two stage clonal expansion model) include the modulation of the radiation dose response during the long latency period between radiation exposure and diagnosis of cancer. The other advantage of these models is the fact that radiation carcinogenesis is usually treated as just a perturbation of background carcinogenesis, so that extensive data on spontaneous cancers can be used to help determine the adjustable parameters needed to estimate cancer risks. All the biomathematical models have several limitations and cannot accurately predict SMN risk in genetically diverse population with several treatment variations. Theoretical modelling should be regarded as complementary to epidemiological studies as it could also be used to explore aspects that may influence the risk and could thus help with the design of epidemiological studies. Further layers of complexity could be added to risk models depending on future epidemiological findings to account for other aspects that might influence the risk for cancer, such as genetic susceptibility and effect of adjuvant treatments
There is now a strong emphasis on secondary cancer risk mitigation across all Children's Oncology Group protocols as they are now evaluating dose de-escalation of radiation therapy as well as alkylating chemotherapy. Most of the newer trials in pediatric cancers are evaluating clinical scenarios where radiation can be safely omitted. The volumes of treatment have undergone a massive revamp in most cancers to restrict late effects including SMN. These put together with advancing radiation delivery technologies with reduced head scatter and inter/intra leaf scatter will hopefully play a major role in achieving this goal. In this context, proton therapy will play a major role in risk mitigation especially in pediatric patients
Impact of Protons: Several mathematical models have uniformly predicted a significantly reduced risk of SMN with proton beam therapy (PBT) especially with pencil beam scanning (PBS) technique among multiple pediatric and adult malignancies by a factor of 2–15. Though neutron scatter may be higher in tissues outside of the target volume with proton treatment, the secondary dose contribution is small and thus the total integral dose remains less with protons than with photon therapy. PBS systems provide the greatest opportunity to reduced secondary dose from neutron scatter and further reduce the secondary malignancy risk compared to passive scattering techniques. Chung et al. performed a large case matched comparison of passive scattering PBT with photons from the surveillance, epidemiology, and end results database. The crude rate of SMN was 5.2% among the proton cohort versus 7.5% in photon cohort. A clinical study from Harvard showed higher incidence of in field SMN in patients of retinoblastoma treated with contemporary IMRT technique (14%) compared to proton therapy (0%) after 10 years of follow-up. Unfortunately, there are limited clinical data on SMN risk in patients treated with PBS systems
As India is on the threshold of adopting proton therapy in clinical practice, it is an excellent opportunity to generate quality data and evidence, and add to the growing body of work globally to judiciously use this resource intensive technology optimally for the mitigation of SMN risk, as indeed other long-term treatment-related sequalae particularly in children and young adult cancer survivors.
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