Meta-GWAS identifies the heritability of acute radiation-induced toxicities in head and neck cancer

Individual Sensitivity for Radiotherapy-related Adverse Tissue Reactions in Patients Treated Twice for Metachronous Cancers

The role of genetics in susceptibility to radiotherapy-induced toxicities is unclear. A strong impact of genetics should cause correlated toxicities in patients with metachronous double radiotherapy. We ascertained information about demographics, lifestyle, radiotherapy and early toxicities in irradiated tissues for a retrospective cohort of 98 patients from 2 hospitals who underwent two metachronous radiotherapeutic treatments (2000-2022) of different anatomical regions. European Organisation for Research and Treatment of Cancer/Radiation Therapy Oncology Group (EORTC/RTOG) toxicity scores per organ system were combined to a single mean score. We considered as genetic component the variation of toxicity not explained by radiation dose to the tumor, age at radiotherapy, sex, smoking status, and surgery. Variance components of toxicity were evaluated by ordinal logistic regression with random intercept. Common site combinations were breast/contralateral breast (N = 16), breast/endometrium (N = 6), and cervix/breast (N = 5). Mean toxicity over exposed tissues was 0.70 (range, 0-3). Prescribed radiation dose was significantly associated with mean toxicity, with a 5% (95% CI 3-8) increase of the odds for a higher toxicity level per Gy. Sex, surgery, age and smoking were not. There was no genetic contribution to risk of toxicities after adjustment. Toxicity levels were not more similar within patients than between patients, suggesting a negligible impact of genotype on radiotherapy-related toxicities.

Determining risk and predictors of head and neck cancer treatment-related lymphedema: A clinicopathologic and dosimetric data mining approach using interpretable machine learning and ensemble feature selection

Background and purpose

The ability to determine the risk and predictors of lymphedema is vital in improving the quality of life for head and neck (HN) cancer patients. However, selecting robust features is challenging due to the multicollinearity and high dimensionality of radiotherapy (RT) data. This study aims to overcome these challenges using an ensemble feature selection technique with machine learning (ML).

Materials and methods

Thirty organs-at-risk, including bilateral cervical lymph node levels, were contoured, and dose-volume data were extracted from 76 HN treatment plans. Clinicopathologic data was collected. Ensemble feature selection was used to reduce the number of features. Using the reduced features as input to ML and competing risk models, internal and external lymphedema prediction capability was evaluated with the ML models, and time to lymphedema event and risk stratification were estimated using the risk models.

Results

Two ML models, XGBoost and random forest, exhibited robust prediction performance. They achieved average F1-scores and AUCs of 84 ± 3.3 % and 79 ± 11.9 % (external lymphedema), and 64 ± 12 % and 78 ± 7.9 % (internal lymphedema). Predictive ML and risk models identified common predictors, including bulky node involvement, high dose to various lymph node levels, and lymph nodes removed during surgery. At 180 days, removing 0-25, 26-50, and > 50 lymph nodes increased external lymphedema risk to 72.1 %, 95.6 %, and 57.7 % respectively (p = 0.01).

Conclusion

Our approach, involving the reduction of HN RT data dimensionality, resulted in effective ML models for HN lymphedema prediction. Predictive dosimetric features emerged from both predictive and competing risk models. Consistency with clinicopathologic features from other studies supports our methodology.

Extreme acute radiation-induced toxicity in a patient with polymorphous low-grade adenocarcinoma of the nasopharynx and rare variants in DNA repair genes

BACKGROUND

Polymorphous low-grade adenocarcinoma (PLGA) is an extremely rare finding in the nasopharynx. There are no guidelines for the treatment of PLGA in this localization. Radiotherapy may be administered to treat this malignancy; however, in radiosensitive individuals, it is associated with a risk of severe radiotherapy-induced toxicity.

METHODS

We present a case of a 73-year-old woman with locally advanced polymorphous low-grade adenocarcinoma of the nasopharynx who developed a severe adverse acute reaction to radiotherapy leading to treatment discontinuation. Despite intensive treatment, the patient died 40 days after RT initiation. Whole genome sequencing was performed using DNA from peripheral blood mononuclear cells in the search for variants that could explain such extreme toxicity.

RESULTS

We identified a combination of pathogenic variants that may have contributed to the patient's reaction to radiation therapy, including predisposing variants in XRCC1, XRCC3, and LIG4. We also identified candidate variants, not previously described in this context, which could be associated with radiation toxicity based on plausible mechanisms. We discuss previous reports of this rare tumor from the literature and known contributors to radiation-induced toxicity.

CONCLUSIONS

Genetic causes should be considered in cases of extreme radiosensitivity, especially when is not explained by clinical factors.

Large-scale meta-genome-wide association study reveals common genetic factors linked to radiation-induced acute toxicities across cancer types

BACKGROUND

This study was designed to identify common genetic susceptibility and shared genetic variants associated with acute radiation-induced toxicity across 4 cancer types (prostate, head and neck, breast, and lung).

METHODS

A genome-wide association study meta-analysis was performed using 19 cohorts totaling 12 042 patients. Acute standardized total average toxicity (STATacute) was modelled using a generalized linear regression model for additive effect of genetic variants, adjusted for demographic and clinical covariates (rSTATacute). Linkage disequilibrium score regression estimated shared single-nucleotide variation (SNV-formerly SNP)-based heritability of rSTATacute in all patients and for each cancer type.

RESULTS

Shared SNV-based heritability of STATacute among all cancer types was estimated at 10% (SE = 0.02) and was higher for prostate (17%, SE = 0.07), head and neck (27%, SE = 0.09), and breast (16%, SE = 0.09) cancers. We identified 130 suggestive associated SNVs with rSTATacute (5.0 × 10‒8 < P < 1.0 × 10‒5) across 25 genomic regions. rs142667902 showed the strongest association (effect allele A; effect size ‒0.17; P = 1.7 × 10‒7), which is located near DPPA4, encoding a protein involved in pluripotency in stem cells, which are essential for repair of radiation-induced tissue injury. Gene-set enrichment analysis identified 'RNA splicing via endonucleolytic cleavage and ligation' (P = 5.1 × 10‒6, P = .079 corrected) as the top gene set associated with rSTATacute among all patients. In silico gene expression analysis showed that the genes associated with rSTATacute were statistically significantly up-regulated in skin (not sun exposed P = .004 corrected; sun exposed P = .026 corrected).

CONCLUSIONS

There is shared SNV-based heritability for acute radiation-induced toxicity across and within individual cancer sites. Future meta-genome-wide association studies among large radiation therapy patient cohorts are worthwhile to identify the common causal variants for acute radiotoxicity across cancer types.

Normal Tissue Toxicity Prediction: Clinical Translation on the Horizon

Improvements in radiotherapy delivery have enabled higher therapeutic doses and improved efficacy, contributing to the growing number of long-term cancer survivors. These survivors are at risk of developing late toxicity from radiotherapy, and the inability to predict who is most susceptible results in substantial impact on quality of life and limits further curative dose escalation. A predictive assay or algorithm for normal tissue radiosensitivity would allow more personalized treatment planning, reducing the burden of late toxicity, and improving the therapeutic index. Progress over the last 10 years has shown that the etiology of late clinical radiotoxicity is multifactorial and informs development of predictive models that combine information on treatment (eg, dose, adjuvant treatment), demographic and health behaviors (eg, smoking, age), co-morbidities (eg, diabetes, collagen vascular disease), and biology (eg, genetics, ex vivo functional assays). AI has emerged as a useful tool and is facilitating extraction of signal from large datasets and development of high-level multivariable models. Some models are progressing to evaluation in clinical trials, and we anticipate adoption of these into the clinical workflow in the coming years. Information on predicted risk of toxicity could prompt modification of radiotherapy delivery (eg, use of protons, altered dose and/or fractionation, reduced volume) or, in rare instances of very high predicted risk, avoidance of radiotherapy. Risk information can also be used to assist treatment decision-making for cancers where efficacy of radiotherapy is equivalent to other treatments (eg, low-risk prostate cancer) and can be used to guide follow-up screening in instances where radiotherapy is still the best choice to maximize tumor control probability. Here, we review promising predictive assays for clinical radiotoxicity and highlight studies that are progressing to develop an evidence base for clinical utility.