Real-World Outcomes of Latinx Versus Non-Latinx Patients Treated With First-Line Immunotherapy for Metastatic Renal-Cell Carcinoma.

BACKGROUND: There are limited data regarding the impact of ethnicity among patients receiving immune checkpoint inhibitors. We evaluated real-world outcomes between Latinx and non-Latinx patients with metastatic renal-cell carcinoma (mRCC) treated with first-line nivolumab/ipilimumab within 2 different healthcare settings. METHODS: We performed a retrospective analysis of patients with mRCC who received nivolumab/ipilimumab within the Los Angeles County Department of Health Services (LAC-DHS), a safety-net healthcare system, and the City of Hope Comprehensive Cancer Center (COH), a tertiary oncology center, between January 1, 2015 and December 31, 2021. Progression-free survival (PFS) and overall survival (OS) were determined using the Kaplan-Meier method and covariates were adjusted using multivariate Cox proportional hazards regression. RESULTS: Of 94 patients, 40 patients (43%) were Latinx while the remainder were non-Latinx (44 pts [46%] White, 7 pts [7%] Asian, and 3 pts [3%] Other). Fifty (53%) and 44 (47%) patients received their care at COH and LAC-DHS, respectively. Most Latinx patients (95%) were treated at LAC-DHS, and most non-Latinx patients (89%) were treated at COH. Pooled analysis by ethnicity demonstrated significantly shorter PFS in Latinx versus non-Latinx patients (10.1 vs. 25.2 months, hazard ratios [HR] 3.61, 95% CI 1.96-6.66, P ≤ .01). Multivariate analysis revealed a HR of 3.41 (95% CI 1.31-8.84; P = .01). At a median follow-up of 11.0 months, the median OS was not reached in either arm at the time of data cutoff. CONCLUSION: Latinx patients with mRCC had a shorter PFS treated with frontline nivolumab/ipilimumab compared to their non-Latinx counterparts. No difference was observed in OS although these data were immature. Larger studies are needed to further interrogate the social and economic determinants of ethnicity on clinical outcomes in mRCC.

Analyzing TCGA Data to Identify Gene Mutations Linked to Hepatocellular Carcinoma in Asians.

Introduction: Liver cancer is the sixth most common and second most fatal type of cancer worldwide. Few treatment options are available as patients with liver cancer are often diagnosed in an advanced stage due to a lack of clinical symptoms. Effectively preventing and treating liver cancer relies heavily on early diagnosis; early diagnosis results from identifying and monitoring high-risk patients. Epigenetic risk factors, such as hepatitis B, hepatitis C, cirrhosis, nonalcoholic fatty liver disease, and alcohol/tobacco abuse, are highly prevalent in Asia and likely cause Asians to have a higher incidence and mortality rate of liver cancer. While these acquired risk factors are relatively well understood, the underlying genetic background of liver cancer in Asians has not been well established or correlated with clinical outcomes. Methods: In this study, we accessed The Cancer Genome Atlas (TCGA) hepatocellular carcinoma clinical and mutation data through TCGAbiolinksGUI. Results: We found that mutations in five genes (TP53, TTN, OBSCN, MUC5B, CSMD1) were statistically linked with increased mortality in Asians compared to non-Asians, four of which (TTN, OBSCN, MUC5B, CSMD1) were also more prevalent in the Asian population. Within the Asian cohort, two gene mutations (TTN, HMCN1) were statistically linked with worse outcomes. We also found that the TP53 mutation predicts worse outcomes within the non-Asian cohort but not within the Asian cohort. Discussion/Conclusion: Our findings can improve cancer care in the Asian population through better disease prognostication, evaluations for potential targeted therapy, and a deeper understanding of liver cancer pathogenesis.

DiscML: an R package for estimating evolutionary rates of discrete characters using maximum likelihood.

BACKGROUND: The study of discrete characters is crucial for the understanding of evolutionary processes. Even though great advances have been made in the analysis of nucleotide sequences, computer programs for non-DNA discrete characters are often dedicated to specific analyses and lack flexibility. Discrete characters often have different transition rate matrices, variable rates among sites and sometimes contain unobservable states. To obtain the ability to accurately estimate a variety of discrete characters, programs with sophisticated methodologies and flexible settings are desired. RESULTS: DiscML performs maximum likelihood estimation for evolutionary rates of discrete characters on a provided phylogeny with the options that correct for unobservable data, rate variations, and unknown prior root probabilities from the empirical data. It gives users options to customize the instantaneous transition rate matrices, or to choose pre-determined matrices from models such as birth-and-death (BD), birth-death-and-innovation (BDI), equal rates (ER), symmetric (SYM), general time-reversible (GTR) and all rates different (ARD). Moreover, we show application examples of DiscML on gene family data and on intron presence/absence data. CONCLUSION: DiscML was developed as a unified R program for estimating evolutionary rates of discrete characters with no restriction on the number of character states, and with flexibility to use different transition models. DiscML is ideal for the analyses of binary (1s/0s) patterns, multi-gene families, and multistate discrete morphological characteristics.