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Exploring breast and prostate cancer RNA-seq derived radiosensitivity with the Genomic Adjusted Radiation Dose (GARD) model.

ABSTRACT: The use of a 10 gene transcriptional signature as part of the GARD model has been shown to be predictive of radiotherapy benefit for a range of cancers, with the potential to determine an optimal overall dose per patient. We used publicly available RNA-seq transcriptomics data from a luminal B breast cancer patient and from 14 prostate cancer patients to explore the radiosensitivity indices (RSI) and so GARD estimates of both tumour and proximal normal biopsies from each individual. Clear differences of clinical relevance in derived radiobiological properties between tumour and proximal normal tissues were evident for the breast cancer patient, whilst such differences across the prostate cancer cohort were more equivocal. Using the prostate cancer cohort's median tumour predicted GARD value as a threshold for high therapeutic effect for radiotherapy, we found evidence that a higher overall prescribed dose than the widely used 72 Gy/36fx could benefit half of these patients. This exploratory study demonstrates the potential combining the GARD model with sequencing based transcriptomics could have in informing personalised radiotherapeutic practise for both breast and prostate cancer patients.

SUBMITTER: Nolan B

PROVIDER: S-EPMC9396042 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

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Exploring breast and prostate cancer RNA-seq derived radiosensitivity with the Genomic Adjusted Radiation Dose (GARD) model.

Nolan Ben B O'Sullivan Brian B Golden Aaron A

Clinical and translational radiation oncology 20220809

The use of a 10 gene transcriptional signature as part of the GARD model has been shown to be predictive of radiotherapy benefit for a range of cancers, with the potential to determine an optimal overall dose per patient. We used publicly available RNA-seq transcriptomics data from a luminal B breast cancer patient and from 14 prostate cancer patients to explore the radiosensitivity indices (RSI) and so GARD estimates of both tumour and proximal normal biopsies from each individual. Clear differ ...[more]

PMID: 36017133

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Project description:PurposeThis study aimed to establish a nomogram combining 31-gene signature (31-GS), radiosensitivity index (RSI), and radiation-resistance-related gene index (RRRI) to predict recurrence in prostate cancer (PCa) patients.Methods and materialsTranscriptome data of PCa were obtained from gene expression omnibus and the cancer genome atlas to validate the predictive potential of 3 sets of published biomarkers, namely, 31-GS, RSI, and RRRI. To adjust these markers for the characteristics of PCa, we analyzed 4 PCa-associated radiosensitivity predictive indices based on 31-GS, RSI, and RRRI by the Cox analysis and least absolute shrinkage and selection operator regression analysis. Time-dependent receiver operating characteristic curves, decision curve analyses, integrated discrimination improvement, net reclassification improvement and decision tree model construction were used to compare the radiosensitivity predictive ability of these 4 gene signatures. Key modules and associated functions were identified through a weighted gene co-expression network analysis and gene function enrichment analysis. A nomogram was built to improve the recurrence-prediction capability.ResultsWe validated and compared the predictive potential of 2 published predictive indices. Based on the 31-GS, RSI, and RRRI, we analyzed 4 PCa-associated radiosensitivity predictive indices: 14Genes, RSI, RRRI, and 20Genes. Among them, 14Genes showed the most promising predictive performance and discriminative capacity. Genes in the key module defined by the 14Genes model were significantly enriched in radiation therapy-related cell death pathways. The area under receiver operating characteristic curve and decision tree variable importance for 14Genes was the highest in the cancer genome atlas and Gene Expression Omnibus Series (GSE) cohorts.ConclusionsThis study successfully established a radiosensitivity-related nomogram, which had excellent performance in predicting recurrence in patients with PCa. For patients who received radiation therapy, the 20Genes and RRRI models can be used to predict recurrence-free survival, whereas 20Genes is more radiation therapy-specific but needs further external validation.

Project description:Follistatin is a potent regulator of the inflammatory response and binds to and inhibits activin A action. Activin A is a member of the TGF? protein superfamily which has regulatory roles in the inflammatory response and in the fibrotic process. Fibrosis can occur following cell injury and cell death induced by agents such as ionizing radiation (IR). IR is used to treat cancer and marked fibrotic response is a normal tissue (non-tumour) consequence in a fraction of patients under the current dose regimes. The discovery and development of a therapeutic to abate fibrosis in these radiosensitive patients would be a major advance for cancer radiotherapy. Likewise, prediction of which patients are susceptible to fibrosis would enable individualization of treatment and provide an opportunity for pre-emptive fibrosis control and better tumour treatment outcomes. The levels of activin A and follistatin were measured in fibroblasts derived from patients who developed severe radiation-induced fibrosis following radiotherapy and compared to fibroblasts from patients who did not. Both follistatin and activin A gene expression levels were increased following IR and the follistatin gene expression level was lower in the fibroblasts from fibrosis patients compared to controls at both basal levels and after IR. The major follistatin transcript variants were found to have a similar response to IR and both were reduced in fibrosis patients. Levels of follistatin and activin A secreted in the fibroblast culture medium also increased in response to IR and the relative follistatin protein levels were significantly lower in the samples derived from fibrosis patients. The decrease in the follistatin levels can lead to an increased bioactivity of activin A and hence may provide a useful measurement to identify patients at risk of a severe fibrotic response to IR. Additionally, follistatin, by its ability to neutralise the actions of activin A may be of value as an anti-fibrotic for radiation induced fibrosis.

Project description:IntroductionCancer sequencing efforts have revealed that cancer is the most complex and heterogeneous disease that affects humans. However, radiation therapy (RT), one of the most common cancer treatments, is prescribed on the basis of an empirical one-size-fits-all approach. We propose that the field of radiation oncology is operating under an outdated null hypothesis: that all patients are biologically similar and should uniformly respond to the same dose of radiation.MethodsWe have previously developed the genomic-adjusted radiation dose, a method that accounts for biological heterogeneity and can be used to predict optimal RT dose for an individual patient. In this article, we use genomic-adjusted radiation dose to characterize the biological imprecision of one-size-fits-all RT dosing schemes that result in both over- and under-dosing for most patients treated with RT. To elucidate this inefficiency, and therefore the opportunity for improvement using a personalized dosing scheme, we develop a patient-specific competing hazards style mathematical model combining the canonical equations for tumor control probability and normal tissue complication probability. This model simultaneously optimizes tumor control and toxicity by personalizing RT dose using patient-specific genomics.ResultsUsing data from two prospectively collected cohorts of patients with NSCLC, we validate the competing hazards model by revealing that it predicts the results of RTOG 0617. We report how the failure of RTOG 0617 can be explained by the biological imprecision of empirical uniform dose escalation which results in 80% of patients being overexposed to normal tissue toxicity without potential tumor control benefit.ConclusionsOur data reveal a tapestry of radiosensitivity heterogeneity, provide a biological framework that explains the failure of empirical RT dose escalation, and quantify the opportunity to improve clinical outcomes in lung cancer by incorporating genomics into RT.

Project description:BackgroundTo date, there have been no published trials examining the impact of salvage radiation therapy (SRT) in the post-operative setting for prostate cancer (PCa). We conducted a retrospective, comparative study of post-operative radiation following radical prostatectomy (RP) for men with pT3 disease or positive margins (adverse pathological features, APF).Methods422 PCa men treated at four institutions with RP and having APF were analyzed with a primary end point of metastasis. Adjuvant radiation treatment (ART, n=111), minimal residual disease (MRD) SRT (n=70) and SRT (n=83) were defined by PSA levels of <0.2, 0.2-0.49 and ⩾0.5 ng ml(-1), respectively, before radiation therapy (RT) initiation. Remaining 157 men who did not receive additional therapy before metastasis formed the no RT arm. Clinical-genomic risk was assessed by Cancer of the Prostate Risk Assessment Post-Surgical (CAPRA-S) and Decipher. Cox regression was used to evaluate the impact of treatment on outcome.ResultsDuring the study follow-up, 37 men developed metastasis with a median follow-up of 8 years. Both CAPRA-S and Decipher had independent predictive value on multivariable analysis for metastasis (P<0.05). Adjusting for clinical-genomic risk, SRT and no RT had hazard ratios of 4.31 (95% confidence interval, 1.20-15.47) and 5.42 (95% confidence interval, 1.59-18.44) for metastasis compared with ART, respectively. No significant difference was observed between MRD-SRT and ART (P=0.28). Men with low-to-intermediate CAPRA-S and low Decipher value have a low rate of metastatic events regardless of treatment selection. In contrast, men with high CAPRA-S and Decipher benefit from ART, however the cumulative incidence of metastasis remains high.ConclusionsThe decision as to the timing and need for additional local therapy following RP is nuanced and requires providers and patients to balance risks of morbidity with improved oncological outcomes. Post-RP treatment can be safely avoided for men who are low risk by clinical-genomic risk, whereas those at high risk should favor enrollment in clinical trials.

Project description:BackgroundDespite its common use in cancer treatment, radiotherapy has not yet entered the era of precision medicine, and there have been no approaches to adjust dose based on biological differences between or within tumours. We aimed to assess whether a patient-specific molecular signature of radiation sensitivity could be used to identify the optimum radiotherapy dose.MethodsWe used the gene-expression-based radiation-sensitivity index and the linear quadratic model to derive the genomic-adjusted radiation dose (GARD). A high GARD value predicts for high therapeutic effect for radiotherapy; which we postulate would relate to clinical outcome. Using data from the prospective, observational Total Cancer Care (TCC) protocol, we calculated GARD for primary tumours from 20 disease sites treated using standard radiotherapy doses for each disease type. We also used multivariable Cox modelling to assess whether GARD was independently associated with clinical outcome in five clinical cohorts: Erasmus Breast Cancer Cohort (n=263); Karolinska Breast Cancer Cohort (n=77); Moffitt Lung Cancer Cohort (n=60); Moffitt Pancreas Cancer Cohort (n=40); and The Cancer Genome Atlas Glioblastoma Patient Cohort (n=98).FindingsWe calculated GARD for 8271 tissue samples from the TCC cohort. There was a wide range of GARD values (range 1·66-172·4) across the TCC cohort despite assignment of uniform radiotherapy doses within disease types. Median GARD values were lowest for gliomas and sarcomas and highest for cervical cancer and oropharyngeal head and neck cancer. There was a wide range of GARD values within tumour type groups. GARD independently predicted clinical outcome in breast cancer, lung cancer, glioblastoma, and pancreatic cancer. In the Erasmus Breast Cancer Cohort, 5-year distant-metastasis-free survival was longer in patients with high GARD values than in those with low GARD values (hazard ratio 2·11, 95% 1·13-3·94, p=0·018).InterpretationA GARD-based clinical model could allow the individualisation of radiotherapy dose to tumour radiosensitivity and could provide a framework to design genomically-guided clinical trials in radiation oncology.FundingNone.

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Exploring breast and prostate cancer RNA-seq derived radiosensitivity with the Genomic Adjusted Radiation Dose (GARD) model.

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Exploring breast and prostate cancer RNA-seq derived radiosensitivity with the Genomic Adjusted Radiation Dose (GARD) model.

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