Background: Metastatic triple-negative breast cancer (TNBC) is aggressive with poor median overall survival (OS) ranging from 8 to 13 months. There exists considerable heterogeneity in survival at the individual patient level. To better understand the survival heterogeneity and improve risk stratification, our study aims to identify the factors influencing survival, utilizing a large patient sample from the National Cancer Database (NCDB). Methods: Women diagnosed with metastatic TNBC from 2010 to 2020 in the NCDB were included. Demographic, clinicopathological, and treatment data and overall survival (OS) outcomes were collected. Kaplan–Meier curves were used to estimate OS. The log-rank test was used to identify OS differences between groups for each variable in the univariate analysis. For the multivariate analysis, the Cox proportional hazard model with backward elimination was used to identify factors affecting OS. Adjusted hazard ratios and 95% confidence intervals are presented. Results: In this sample, 2273 women had a median overall survival of 13.6 months. Factors associated with statistically significantly worse OS included older age, higher comorbidity scores, specific histologies, higher number of metastatic sites, presence of liver or other site metastases in those with only one metastatic site (excluding brain metastases), presence of cranial and extra-cranial metastases, lack of chemotherapy, lack of immunotherapy, lack of surgery to distant sites, lack of radiation to distant sites, and receipt of palliative treatment to alleviate symptoms. In the multivariate analysis, comorbidity score, histology, number of metastatic sites, immunotherapy, and chemotherapy had a statistically significant effect on OS. Conclusions: Through NCDB analysis, we have identified prognostic factors for metastatic TNBC. These findings will help individualize prognostication at diagnosis, optimize treatment strategies, and facilitate patient stratification in future clinical trials.

Multiple treatment options with different mechanisms of action are currently available for the management of metastatic prostate cancer. However, the optimal use of these therapies—specifically, the sequencing of therapies—is not well defined. In order to obtain the best clinical outcomes, patients need to be treated with the therapies that are most likely to provide benefit and avoid toxic therapies that are unlikely to be effective. Ideally, predictive biomarkers that allow for the selection of the therapies most likely to be of benefit would be employed for each treatment decision. In practice, biomarkers including tumor molecular sequencing, circulating tumor DNA, circulating tumor cell enumeration and androgen receptor characteristics, and tumor cell surface expression (PSMA), all may have a role in therapy selection. In this review, we define the established prognostic and predictive biomarkers for therapy in advanced prostate cancer and explore emerging biomarkers.

Background: Metastatic triple-negative breast cancer (TNBC) is aggressive with a poor median overall survival (OS), ranging from 8 to 13 months. There exists considerable heterogeneity in the survival at the individual patient level. To better understand the survival heterogeneity and improve risk stratification, our study aims to identify the factors influencing survival utilizing a large patient sample in the National Cancer Database (NCDB). Methods: Women diagnosed with metastatic TNBC from 2010 to 2020 in the NCDB were included. Demographic, clinic-pathological, treatment data, and overall survival (OS) outcomes were collected. Kaplan-Meier curves were used to estimate OS. The log-rank test was used to identify OS differences between groups for each variable in the univariate analysis. For the multivariate analysis, the Cox proportional hazard model with backward elimination was used to identify factors affecting OS. Adjusted hazard ratios and 95% confidence intervals are presented. Results: 2,273 women had a median overall survival of 13.6 months. Factors associated with statistically significantly worse OS included older age, higher comorbidity scores, specific histologies, higher number of metastatic sites, presence of liver or other site metastases in those with only one metastatic site (excluding brain metastases), presence of cranial and extra-cranial metastases, lack of chemotherapy, lack of immunotherapy, lack of surgery to distant sites, lack of radiation to distant sites, and receipt of palliative treatment to alleviate symptoms. In the multivariate analysis, comorbidity score, histology, number of metastatic sites, immunotherapy, and chemotherapy had a statistically significant effect on the OS. Conclusions: Through NCDB analysis, we have identified prognostic factors for metastatic TNBC. These findings will help improve prognostication at diagnosis, optimize treatment strategies, and facilitate patient stratification in future clinical trials.

Background: About 15–20% of breast cancers are HER2 positive. Approximately 15–24% of individuals with localized HER2-positive cancer develop metastatic disease after curative treatment, while 3–10% present with de novo metastasis. Survival has significantly improved with various anti-HER2 agents, but there is considerable heterogeneity at the individual level. Our study aims to identify factors influencing survival in de novo metastatic HER2-positive breast cancer using a large sample from the National Cancer Database (NCDB). Methods: Women with metastatic HER2-positive breast cancer diagnosed from 2010 to 2020 in the NCDB were included. Demographic, clinicopathological, treatment data, and overall survival (OS) were collected. Kaplan–Meier curves estimated OS. The log-rank test identified OS differences between groups in univariate analysis. The Cox proportional hazard model with backward elimination identified factors affecting OS in multivariate analysis. The 12-month, 36-month, and 60-month survival estimates, 95% confidence intervals (CIs), and adjusted hazard ratios were reported. Results: Among 5376 women with metastatic HER2-positive breast cancer from 2010 to 2020, the median OS was 55.95 months (95% CI 53.55-NE). Multivariate analysis identified age, Charlson–Deyo comorbidity score, histology, HER2 IHC expression, hormone receptor status, the number of metastatic sites, metastasis location, first-line chemotherapy, anti-HER2 therapy, hormone-blocking therapy, surgery at primary/non-primary sites, and palliative treatment as significant factors affecting OS. Race and radiation receipt were not significant. Conclusions: This is the largest analysis of overall survival estimates in de novo metastatic HER2-positive breast cancer to date in the real-world setting. We identified several independent prognostic factors influencing OS in this population. These findings will help individualize prognostication at diagnosis, optimize treatment strategies, and facilitate patient stratification in future trials.

(1) Background: Advanced-stage lung cancer poses significant management challenges. The goal of this study was to identify crucial clinical and PET radiomics features that enable prognostic stratification for predicting outcomes. (2) Methods: PET radiomics features of the primary lung lesions were extracted from 99 patients with stage IVB NSCLC, and the robustness of these PET radiomics features was evaluated against uncertainties stemming from extraction parameters and contour variation. We trained three survival risk models (clinical, radiomics, and a composite) through a penalized Cox model framework. We also created a Balanced Random Forest classification predictive model, using the selected features, to predict 1-year survival. (3) Results: We identified 367 common PET radiomics features that exhibited robustness to perturbations introduced by contour variation and extraction parameters. Our findings indicated that both the radiomics and the composite model outperformed the clinical model in stratifying the risk for survival with statistical significance. In predicting 1-year survival, the radiomics model and the composite model also achieved better predicting accuracies compared to the clinical model. (4) Conclusions: Robust PET radiomics analysis successfully facilitated the stratification of patient risk for survival outcomes and predicted 1-year survival in stage IVB NSCLC.

BACKGROUND Clear cell sarcoma (CCS) is a rare but aggressive malignancy that typically occurs in young adults and is characterized by soft tissue tumors of the extremities. CCS can be difficult to distinguish from metastatic melanoma based solely on histology and immunohistochemistry (IHC) because of the significant overlap between them. However, it is imperative to get an accurate clinical diagnosis, as it informs disease staging and treatment options for patient care. Present in approximately 75% of CCS cases, the EWSR1 gene rearrangement detected by fluorescence in situ hybridization (FISH) can help with establishing a diagnosis; the underlying reciprocal translocation has never been reported in cutaneous melanoma. CASE DESCRIPTION We reviewed a case of a young woman who presented with a confusing picture of widespread lymphadenopathy, cutaneous metastases, and electrolyte derangements and was subsequently diagnosed with metastatic CCS. CONCLUSIONS This case suggests possible value in performing molecular testing when a clinical picture does not correspond with what is expected for melanoma. It also raises the question of whether CCS cases may be underreported. This case highlights an uncommon presentation that may not be recognized as a manifestation of CCS by an oncologist who is not a sarcoma specialist. It is unclear how COVID-19 vaccination contributed to her clinical presentation, and it is also unclear whether an early diagnosis would have changed her clinical outcome.