Reconsidering adjuvant versus salvage radiation therapy for prostate cancer in the genomics era

Markov models for clinical decision-making in radiation oncology: A systematic review

The intrinsic stochasticity of patients' response to treatment is a major consideration for clinical decision-making in radiation therapy. Markov models are powerful tools to capture this stochasticity and render effective treatment decisions. This paper provides an overview of the Markov models for clinical decision analysis in radiation oncology. A comprehensive literature search was conducted within MEDLINE using PubMed, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Only studies published from 2000 to 2023 were considered. Selected publications were summarized in two categories: (i) studies that compare two (or more) fixed treatment policies using Monte Carlo simulation and (ii) studies that seek an optimal treatment policy through Markov Decision Processes (MDPs). Relevant to the scope of this study, 61 publications were selected for detailed review. The majority of these publications (n = 56) focused on comparative analysis of two or more fixed treatment policies using Monte Carlo simulation. Classifications based on cancer site, utility measures and the type of sensitivity analysis are presented. Five publications considered MDPs with the aim of computing an optimal treatment policy; a detailed statement of the analysis and results is provided for each work. As an extension of Markov model-based simulation analysis, MDP offers a flexible framework to identify an optimal treatment policy among a possibly large set of treatment policies. However, the applications of MDPs to oncological decision-making have been understudied, and the full capacity of this framework to render complex optimal treatment decisions warrants further consideration.

Genomic Classifiers in Personalized Prostate Cancer Radiation Therapy Approaches: A Systematic Review and Future Perspectives Based on International Consensus

Current risk-stratification systems for prostate cancer (PCa) do not sufficiently reflect the disease heterogeneity. Genomic classifiers (GC) enable improved risk stratification after surgery, but less data exist for patients treated with definitive radiation therapy (RT) or RT in oligo-/metastatic disease stages. To guide future perspectives of GCs for RT, we conducted (1) a systematic review on the evidence of GCs for patients treated with RT and (2) a survey of experts using the Delphi method, addressing the role of GCs in personalized treatments to identify relevant fields of future clinical and translational research. We performed a systematic review and screened ongoing clinical trials on ClinicalTrials.gov. Based on these results, a multidisciplinary international team of experts received an adapted Delphi method survey. Thirty-one and 30 experts answered round 1 and round 2, respectively. Questions with ≥75% agreement were considered relevant and included in the qualitative synthesis. Evidence for GCs as predictive biomarkers is mainly available to the postoperative RT setting. Validation of GCs as prognostic markers in the definitive RT setting is emerging. Experts used GCs in patients with PCa with extensive metastases (30%), in postoperative settings (27%), and in newly diagnosed PCa (23%). Forty-seven percent of experts do not currently use GCs in clinical practice. Expert consensus demonstrates that GCs are promising tools to improve risk-stratification in primary and oligo-/metastatic patients in addition to existing classifications. Experts were convinced that GCs might guide treatment decisions in terms of RT-field definition and intensification/deintensification in various disease stages. This work confirms the value of GCs and the promising evidence of GC utility in the setting of RT. Additional studies of GCs as prognostic biomarkers are anticipated and form the basis for future studies addressing predictive capabilities of GCs to optimize RT and systemic therapy. The expert consensus points out future directions for GC research in the management of PCa.

Identification of a five gene signature to predict time to biochemical recurrence after radical prostatectomy

BACKGROUND

Identification of novel biomarkers associated with high-risk prostate cancer or biochemical recurrence can drive improvement in detection, prognosis, and treatment. However, studies can be limited by small sample sizes and sparse clinical follow-up data. We utilized a large sample of prostate specimens to identify a predictive model of biochemical recurrence following radical prostatectomy and we validated this model in two external data sets.

METHODS

We analyzed prostate specimens from patients undergoing radical prostatectomy at Hartford Hospital between 2008 and 2011. RNA isolated from formalin-fixed paraffin-embedded prostates was hybridized to a custom Affymetrix microarray. Regularized (least absolute shrinkage and selection operator [Lasso]) Cox regression was performed with cross-validation to identify a model that incorporated gene expression and clinical factors to predict biochemical recurrence, defined as postoperative prostate-specific antigen (PSA) > 0.2 ng/ml or receipt of triggered salvage treatment. Model performance was assessed using time-dependent receiver operating curve (ROC) curves and survival plots.

RESULTS

A total of 606 prostate specimens with gene expression and both pre- and postoperative PSA data were available for analysis. We identified a model that included Gleason grade and stage as well as five genes (CNRIP1, endoplasmic reticulum protein 44 [ERP44], metaxin-2 [MTX2], Ras homolog family member U [RHOU], and OXR1). Using the Lasso method, we determined that the five gene model independently predicted biochemical recurrence better than a model that included Gleason grade and tumor stage alone. The time-dependent ROCAUC for the five gene signature including Gleason grade and tumor stage was 0.868 compared to an AUC of 0.767 when Gleason grade and tumor stage were included alone. Low and high-risk groups displayed significant differences in their recurrence-free survival curves. The predictive model was subsequently validated on two independent data sets identified through the Gene Expression Omnibus. The model included genes (RHOU, MTX2, and ERP44) that have previously been implicated in prostate cancer biology.

CONCLUSIONS

Expression of a small number of genes is associated with an increased risk of biochemical recurrence independent of classical pathological hallmarks.

Adjuvant and Salvage Radiation Therapy After Prostatectomy: ASTRO/AUA Guideline Amendment, Executive Summary 2018

PURPOSE

The purpose of this amendment is to incorporate newly published literature into the original American Society for Radiation Oncology/American Urological Association Adjuvant and Salvage Radiotherapy After Prostatectomy Guideline and provide an updated clinical framework for clinicians.

METHODS AND MATERIALS

The original systematic review yielded 294 studies published between January 1990 and December 2012. In April 2018, the guideline underwent an amendment and incorporated 155 references that were published between January 1990 and December 2017. Two new key questions were added: one on the use of genomic classifiers and the other on the treatment of oligo-metastases with radiation after radical prostatectomy.

RESULTS

A new statement on the use of hormone therapy with salvage radiation therapy (RT) after radical prostatectomy was added, and long-term data were used to update an existing statement on adjuvant RT. The balance of the guideline statements were reaffirmed, and references added to the existing literature base. A discussion on the use of genomic classifiers as a risk stratification tool was added to the future research discussion. No relevant data on oligo-metastases were found.

CONCLUSIONS

Hormone therapy should be offered to patients who have had radical prostatectomy and who are candidates for salvage RT. Clinicians should discuss possible short- and long-term side effects with patients in addition to the potential benefits of preventing recurrence. The decision to use hormone therapy should be made by the patient and a multidisciplinary team of providers with full consideration of the patient's history, values, preferences, quality of life, and functional status.

Providing guidance for genomics-based cancer treatment decisions: insights from stakeholder engagement for post-prostatectomy radiation therapy

BACKGROUND

Despite the emergence of genomics-based risk prediction tools in oncology, there is not yet an established framework for communication of test results to cancer patients to support shared decision-making. We report findings from a stakeholder engagement program that aimed to develop a framework for using Markov models with individualized model inputs, including genomics-based estimates of cancer recurrence probability, to generate personalized decision aids for prostate cancer patients faced with radiation therapy treatment decisions after prostatectomy.

METHODS

We engaged a total of 22 stakeholders, including: prostate cancer patients, urological surgeons, radiation oncologists, genomic testing industry representatives, and biomedical informatics faculty. Slides were at each meeting to provide background information regarding the analytical framework. Participants were invited to provide feedback during the meeting, including revising the overall project aims. Stakeholder meeting content was reviewed and summarized by stakeholder group and by theme.

RESULTS

The majority of stakeholder suggestions focused on aspects of decision aid design and formatting. Stakeholders were enthusiastic about the potential value of using decision analysis modeling with personalized model inputs for cancer recurrence risk, as well as competing risks from age and comorbidities, to generate a patient-centered tool to assist decision-making. Stakeholders did not view privacy considerations as a major barrier to the proposed decision aid program. A common theme was that decision aids should be portable across multiple platforms (electronic and paper), should allow for interaction by the user to adjust model inputs iteratively, and available to patients both before and during consult appointments. Emphasis was placed on the challenge of explaining the model's composite result of quality-adjusted life years.

CONCLUSIONS

A range of stakeholders provided valuable insights regarding the design of a personalized decision aid program, based upon Markov modeling with individualized model inputs, to provide a patient-centered framework to support for genomic-based treatment decisions for cancer patients. The guidance provided by our stakeholders may be broadly applicable to the communication of genomic test results to patients in a patient-centered fashion that supports effective shared decision-making that represents a spectrum of personal factors such as age, medical comorbidities, and individual priorities and values.

Adjuvant Versus Salvage Radiotherapy for Patients With Adverse Pathological Findings Following Radical Prostatectomy: A Decision Analysis

Background: Patients undergoing surgery for prostate cancer who have adverse pathological findings experience high rates of recurrence. While there are data supporting adjuvant radiotherapy compared to a wait-and-watch strategy to reduce recurrence rates, there are no randomized controlled trials comparing adjuvant radiotherapy with the other standard of care, salvage radiotherapy (radiotherapy administered at the time of recurrence). Methods: We constructed a health state transition (Markov) model employing two-dimensional Monte Carlo simulation using a lifetime horizon to compare the quality-adjusted survival associated with postoperative strategies using adjuvant or salvage radiotherapy. Prior to analysis, we calibrated and validated our model using the results of previous randomized controlled trials. We considered clinically important oncological health states from immediately postoperative to prostate cancer–specific death, commonly described complications from prostate cancer treatment, and other causes of mortality. Transition probabilities and utilities for disease states were derived from a literature search of MEDLINE and expert consensus. Results: Salvage radiotherapy was associated with an increased quality-adjusted life expectancy (QALE) (58.3 months) as compared with adjuvant radiotherapy (53.7 months), a difference of 4.6 months (standard deviation 8.8). Salvage radiotherapy had higher QALE in 53% of hypothetical cohorts. There was a minimal difference in overall life expectancy (-0.1 months). Examining recurrence rates, our model showed validity when compared with available randomized controlled data. Conclusions: A salvage radiotherapy strategy appears to provide improved QALE for patients with adverse pathological findings following radical prostatectomy, compared with adjuvant radiotherapy. As these findings reflect, population averages, specific patient and tumor factors, and patient preferences remain central for individualized management.