Managing RT Schedules of Early-Stage Breast Cancer Patients with a Genetic-Dosimetric Validated Model for Late Fibrosis

PURPOSE/OBJECTIVE(S)

Define a multifactorial risk prediction model for RT-induced fibrosis and investigate the benefit of a personalized approach for breast cancer (BC) patients (pts) treated with whole breast RT.

MATERIALS/METHODS

In a previous study, we confirmed the predictive role of 30 SNPs from the literature and built an interaction aware Polygenic Risk Score (PRS, following the methods from Franco RO 2021) for Late Fibrosis (FG2+) on a cohort of 1500 pts from the REQUITE EU/USA prospective observational study. The PRS weights the radiosensitive (RS) and radioresistant (RR) genetic components and can be included in NTCP models. In a subgroup from the same cohort (390 pts), we have also confirmed an NTCP model based on biologically Equivalent Uniform Dose (BEUD) from PTV DVHs for pts treated at 40-50 Gy and no RT boost. Here, we combine PRS and BEUD into a sigmoid model allowing PRS to modulate BEUD50 (BEUD leading to 50% FG2+), i.e., we permitted a personalized BEUD50. We can also consider this as translating the PRS into a personalized equivalent BEUD, which is added/subtracted to the treatment BEUD. We evaluated model performances through ROC-AUC, calibration plot and Precision-Recall AUC.

RESULTS

A total of 381 pts had complete dosimetric/genetic data, prescribed dose 40-50 Gy, and no fibrotic alteration at RT start. We scored FG2+ in 87 pts (23%). PRS ranged from -13 (more RR pts) to 7 (more RS), and a unit in PRS corresponds to 5.3 Gy BEUD or 3 Gy in EQ EUD2 Gy. Table 1 summarizes model performances, with details for subgroups below/above the quartiles I/III of the BEUD distribution. The PRS-only model correctly describes the toxicity rates in the whole population (calibration slope/offset = 0/1). Still, it overestimates/underestimates the absolute risks in the low/high dose ranges. The integrated model improves AUC-ROC and AUC-PRC by 5% and 10% and guarantees a better calibration in pts receiving low/high BEUD to the PTV.

CONCLUSION

We developed a multifactorial model for FG2+ based on two previously validated models and reported the improvement against single-factor models. The BEUD+PRS model is suitable for assisting clinicians in managing early-stage BC pts. The number of fractions or the daily dose could be reduced for RS pts. The integrated model resulted in a possible quantitative tool for driving the planning decision process. Also, it showed a better performance in the high BEUD region, suggesting the potential value of its extension toward RT including boost or ultra hypofractionation. We are testing this extension in the whole REQUITE cohort.

Approximation bounds for convolutional neural networks in operator learning

Recently, deep Convolutional Neural Networks (CNNs) have proven to be successful when employed in areas such as reduced order modeling of parametrized PDEs. Despite their accuracy and efficiency, the approaches available in the literature still lack a rigorous justification on their mathematical foundations. Motivated by this fact, in this paper we derive rigorous error bounds for the approximation of nonlinear operators by means of CNN models. More precisely, we address the case in which an operator maps a finite dimensional input μ∈R^p onto a functional output u_μ:[0,1]^d→R, and a neural network model is used to approximate a discretized version of the input-to-output map. The resulting error estimates provide a clear interpretation of the hyperparameters defining the neural network architecture. All the proofs are constructive, and they ultimately reveal a deep connection between CNNs and the Fourier transform. Finally, we complement the derived error bounds by numerical experiments that illustrate their application.

Development of a method for generating SNP interaction-aware polygenic risk scores for radiotherapy toxicity

AIM

To identify the effect of single nucleotide polymorphism (SNP) interactions on the risk of toxicity following radiotherapy (RT) for prostate cancer (PCa) and propose a new method for polygenic risk score incorporating SNP-SNP interactions (PRSi).

MATERIALS AND METHODS

Analysis included the REQUITE PCa cohort that received external beam RT and was followed for 2 years. Late toxicity endpoints were: rectal bleeding, urinary frequency, haematuria, nocturia, decreased urinary stream. Among 43 literature-identified SNPs, the 30% most strongly associated with each toxicity were tested. SNP-SNP combinations (named SNP-allele sets) seen in ≥10% of the cohort were condensed into risk (RS) and protection (PS) scores, respectively indicating increased or decreased toxicity risk. Performance of RS and PS was evaluated by logistic regression. RS and PS were then combined into a single PRSi evaluated by area under the receiver operating characteristic curve (AUC).

RESULTS

Among 1,387 analysed patients, toxicity rates were 11.7% (rectal bleeding), 4.0% (urinary frequency), 5.5% (haematuria), 7.8% (nocturia) and 17.1% (decreased urinary stream). RS and PS combined 8 to 15 different SNP-allele sets, depending on the toxicity endpoint. Distributions of PRSi differed significantly in patients with/without toxicity with AUCs ranging from 0.61 to 0.78. PRSi was better than the classical summed PRS, particularly for the urinary frequency, haematuria and decreased urinary stream endpoints.

CONCLUSIONS

Our method incorporates SNP-SNP interactions when calculating PRS for radiotherapy toxicity. Our approach is better than classical summation in discriminating patients with toxicity and should enable incorporating genetic information to improve normal tissue complication probability models.

A Deep Learning Approach Validates Genetic Risk Factors for Late Toxicity After Prostate Cancer Radiotherapy in a REQUITE Multi-National Cohort

Background: REQUITE (validating pREdictive models and biomarkers of radiotherapy toxicity to reduce side effects and improve QUalITy of lifE in cancer survivors) is an international prospective cohort study. The purpose of this project was to analyse a cohort of patients recruited into REQUITE using a deep learning algorithm to identify patient-specific features associated with the development of toxicity, and test the approach by attempting to validate previously published genetic risk factors. Methods: The study involved REQUITE prostate cancer patients treated with external beam radiotherapy who had complete 2-year follow-up. We used five separate late toxicity endpoints: ≥grade 1 late rectal bleeding, ≥grade 2 urinary frequency, ≥grade 1 haematuria, ≥ grade 2 nocturia, ≥ grade 1 decreased urinary stream. Forty-three single nucleotide polymorphisms (SNPs) already reported in the literature to be associated with the toxicity endpoints were included in the analysis. No SNP had been studied before in the REQUITE cohort. Deep Sparse AutoEncoders (DSAE) were trained to recognize features (SNPs) identifying patients with no toxicity and tested on a different independent mixed population including patients without and with toxicity. Results: One thousand, four hundred and one patients were included, and toxicity rates were: rectal bleeding 11.7%, urinary frequency 4%, haematuria 5.5%, nocturia 7.8%, decreased urinary stream 17.1%. Twenty-four of the 43 SNPs that were associated with the toxicity endpoints were validated as identifying patients with toxicity. Twenty of the 24 SNPs were associated with the same toxicity endpoint as reported in the literature: 9 SNPs for urinary symptoms and 11 SNPs for overall toxicity. The other 4 SNPs were associated with a different endpoint. Conclusion: Deep learning algorithms can validate SNPs associated with toxicity after radiotherapy for prostate cancer. The method should be studied further to identify polygenic SNP risk signatures for radiotherapy toxicity. The signatures could then be included in integrated normal tissue complication probability models and tested for their ability to personalize radiotherapy treatment planning.