Access and barriers to genomic classifiers for breast cancer and prostate cancer in India

The incidence of cancer in general, including breast and prostate cancer specifically, is increasing in India. Breast and prostate cancers have genomic classifiers developed to guide therapy decisions. However, these genomic classifiers are often inaccessible in India due to high cost. These classifiers may also be less suitable to the Indian population, as data primarily from patients in wealthy Western countries were used in developing these genomic classifiers. In addition to the limitations in using these existing genomic classifiers, developing and validating new genomic classifiers for breast and prostate cancer in India is challenging due to the heterogeneity in the Indian population. However, there are steps that can be taken to address the various barriers that currently exist for accurate, accessible genomic classifiers for cancer in India.

The impact of genomic biomarkers on a clinical risk prediction model for upgrading/upstaging among men with favorable-risk prostate cancer

BACKGROUND

The challenge of distinguishing indolent from aggressive prostate cancer (PCa) complicates decision-making for men considering active surveillance (AS). Genomic classifiers (GCs) may improve risk stratification by predicting end points such as upgrading or upstaging (UG/US). The aim of this study was to assess the impact of GCs on UG/US risk prediction in a clinicopathologic model.

METHODS

Participants had favorable-risk PCa (cT1-2, prostate-specific antigen [PSA] ≤15 ng/mL, and Gleason grade group 1 [GG1]/low-volume GG2). A prediction model was developed for 864 men at the University of California, San Francisco, with standard clinical variables (cohort 1), and the model was validated for 2267 participants from the Cancer of the Prostate Strategic Urologic Research Endeavor (CaPSURE) registry (cohort 2). Logistic regression was used to compute the area under the receiver operating characteristic curve (AUC) to develop a prediction model for UG/US at prostatectomy. A GC (Oncotype Dx Genomic Prostate Score [GPS] or Prolaris) was then assessed to improve risk prediction.

RESULTS

The prediction model included biopsy GG1 versus GG2 (odds ratio [OR], 5.83; 95% confidence interval [CI], 3.73-9.10); PSA (OR, 1.10; 95% CI, 1.01-1.20; per 1 ng/mL), percent positive cores (OR, 1.01; 95% CI, 1.01-1.02; per 1%), prostate volume (OR, 0.98; 95% CI, 0.97-0.99; per mL), and age (OR, 1.05; 95% CI, 1.02-1.07; per year), with AUC 0.70 (cohort 1) and AUC 0.69 (cohort 2). GPS was associated with UG/US (OR, 1.03; 95% CI, 1.01-1.06; p < .01) and AUC 0.72, which indicates a comparable performance to the prediction model.

CONCLUSIONS

GCs did not substantially improve a clinical prediction model for UG/US, a short-term and imperfect surrogate for clinically relevant disease outcomes.

Radiation Therapy After Radical Prostatectomy: What Has Changed Over Time?

The role and timing of radiotherapy (RT) in prostate cancer (PCa) patients treated with radical prostatectomy (RP) remains controversial. While recent trials support the oncological safety of early salvage RT (SRT) compared to adjuvant RT (ART) in selected patients, previous randomized studies demonstrated that ART might improve recurrence-free survival in patients at high risk for local recurrence based on adverse pathology. Although ART might improve survival, this approach is characterized by a risk of overtreatment in up to 40% of cases. SRT is defined as the administration of RT to the prostatic bed and to the surrounding tissues in the patient with PSA recurrence after surgery but no evidence of distant metastatic disease. The delivery of salvage therapies exclusively in men who experience biochemical recurrence (BCR) has the potential advantage of reducing the risk of side effects without theoretically compromising outcomes. However, how to select patients at risk of progression who are more likely to benefit from a more aggressive treatment after RP, the exact timing of RT after RP, and the use of hormone therapy and its duration at the time of RT are still open issues. Moreover, what the role of novel imaging techniques and genomic classifiers are in identifying the most optimal post-operative management of PCa patients treated with RP is yet to be clarified. This narrative review summarizes most relevant published data to guide a multidisciplinary team in selecting appropriate candidates for post-prostatectomy radiation therapy.