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African Journal of Urology
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Exploring racial disparities in bladder urothelial cancer: insights into survival and genetic variations

African Journal of Urology volume 30, Article number: 28 (2024) Cite this article

Abstract

Background

Bladder urothelial cancer is the most common malignancy of the urinary system and the 10th most common cancer worldwide with incidence appearing to show a geographical and exposure pattern. Advances in genomic technologies provide abundant data and insight into tumors at the single-cell resolution and are usually stored in repositories like The Cancer Genome Atlas (TCGA). However, data sources for the TCGA appear to be focused on European and American populations. The extent to which genomic and survival data can be applied to populations not included in the study remains somewhat uncertain.

Methods

We explored the genomic and survival characteristics of the TCGA pan-cancer atlas of bladder urothelial cancer. We decluttered these characteristics based on racial groups and compared between and among the races and the overall dataset.

Results

Significant variations were seen in age groups especially Asians (51–60) years and Blacks (61–70) years compared to Whites and the BLCA dataset with a statistically significant difference in mean diagnosis age (p = 0.0048) between Asians and the whole dataset. Overall survival characteristics were similar but genetic features were vastly different. Significant inter-racial alterations could be seen among genes involved in different pathways, oncogenes, tumor suppressors, cytoband amplification and/or deletion, mutation count, and aneuploidy scores.

Conclusion

The TCGA pan-cancer atlas for bladder urothelial cancer adequately represents White populations only. The genomic features do not apply to Blacks and Asians. We recommend better coverage for other populations to ensure adequate data for clinicians and researchers.

1 Background

Urothelial bladder cancer is the most common malignancy of the urinary system, the tenth most prevalent cancer worldwide, and is gradually increasing globally in industrialized nations [1]. There are about 573,000 new cases and 213,000 deaths worldwide with an approximately 4 times higher incidence in men [2, 3] with a 5-year survival rate of about 77.1%. Mortality is greatest in North and East Africa and the Middle East, where schistosomiasis infection leads to high incidence rates. Despite an increase in incidence, bladder cancer mortality has significantly decreased globally. However, death rates have risen in several nations, including the Philippines, Ecuador, and Iceland [4]. Geographically, incidence rates are highest in Europe and North America [2] with analysts predicting a steady rise in most countries except New Zealand which recorded a 10% decrease over a decade [1]. Incidence rates and 5-year survival among African Americans are lower than among Whites and are primarily attributable to an excess of localized tumors [5]. The lower incidence in other geographical areas like Central America and Africa was correlated to a lower human development index (HDI) [4]. The observed geographic and temporal patterns of bladder cancer incidence worldwide appear to reflect the prevalence of tobacco smoking, infection with Schistosoma Haematobium, and occupational exposures to aromatic amines and other chemicals affecting some populations [2].

Clinically, bladder cancer is classified into two distinct groups: non-muscle invasive bladder cancer (NMIBC) and muscle invasive bladder cancer (MIBC). The pathogenesis of NMIBC involves genetic alterations in receptor Tyrosine kinases HRAS, FGFR3, and PIK3CA genes. A synergistic oncogenic effect is said to occur between FGFR3 and PIK3CA, while RAS and FGFR3 are mutually exclusive. Mutation in FGFR3 was found to play a crucial role in initiation of low-grade tumors by triggering the RTK/RAS/RAF cell cycle regulation pathway [6,7,8]. However, pathogenesis of MIBC involves alterations in tumor suppressor genes such as p16, p53, PTEN, and Rb which are associated with cell cycle control [8]. TP53 and the amplification of mdm2 (which degrades p53) is implicated in bladder cancer, and carcinogenesis of other tumors may also be associated with development of MIBC [6].

The Cancer Genome Atlas (TCGA) is a rich computational resource for the genomic and mutational data for different cancer types [9, 10] and has been helpful in genomic studies [9, 11], validation of biomarkers [12], and the design of computational oncology models [13]. This means that accuracy and robustness of the data are essential for research, modeling, and clinical usage. However, data sources in genomic sequencing appear to reveal racial disparities [14] and clinical variance. These might be due to differences in tumor biology or even environmental, biological, behavioral, and socioeconomic factors, as well as healthcare systems [15,16,17]. The genetic ancestry informative markers that occur within different populations to an extent contribute to cancer risk and exposure, hence relevant to cancer research [18, 19].

In this work, we explored the genomic and clinical survival characteristics of bladder cancer using the TCGA pan-cancer atlas. We investigated whether these data adequately represent all races and which race is most representative of this landmark cancer genomics database. Also, we categorized the mutations and compared the differences among each racial group for conformity to the dataset.

2 Methods

2.1 Patient data

Publicly accessible, deidentified data of patients with bladder urothelial carcinoma (TCGA Code—BLCA) were extracted from TCGA database consisting of previously compiled studies [20,21,22,23,24,25,26]. We reviewed tumor genetic data obtained using next-generation sequencing of 411 patients that had accompanying demographic and clinical attributes using the cBio Cancer Genomics Portal (http://cbioportal.org) [27, 28]. Patient’s data were segregated by racial groups and compared to the combined BLCA dataset. Racial terms used were Asian, Black, and White as per updated guidance on the reporting of race and ethnicity in medical and science journals [29].

2.2 Tumor staging and metastasis

The American Joint Committee on Cancer (AJCC) Tumor Stage Code and AJCC-Metastasis Stage Code was the criteria used to stage patient cancer [30]. Since data were from different studies, numerical tumor stage code was used to denote numero-alphabetical codes. For example, T2 consists of both T2A and T2B.

2.3 Survival time analysis

Survival time analysis of clinical data is calculated using the Kaplan–Meier method from disease-free survival (DFS), disease-specific survival (DSS), progression-free survival (PFS), and overall survival [OS] mentioned in Patient Data section.

2.4 Genomic data

From available data, we extracted only cancer-related genes using OncoKB [31], which is based on Food and Drug Administration (FDA) labeling and National Comprehensive Cancer Network (NCCN) guidelines. We analyzed frequency of mutated genes and copy number alterations among patients. BLCA-related genes [32] and super-enhancer genes [33] are based on previous studies on bladder carcinoma.

2.5 Microsatellite instability and aneuploidy score

Microsatelitte instability was assessed using MSISensor and the more refined MSI MANTIS score [34]. The aneuploidy score reflects the total burden of autosomal chromosome arm alteration and was ranged from values of 0 through 39 [21].

2.6 Statistical analysis

Normality of data was tested using Kolmogorov–Smirnov test. Normally distributed data were analyzed with one-way ANOVA and Dunnett’s multiple comparison test; otherwise, Kruskal–Wallis and Dunn’s multiple comparison tests were employed. Analyses were conducted on Prism (version 9.4.1) unless otherwise specified. Mantel-Cox (log-rank) test was used to compare difference between survival curves. A p value less than 0.05 was considered to be statistically significant.

3 Results

3.1 Significant demographic deviation in racial groups

Available data were skewed toward White populations with 79.6% of total patients while Asians and Blacks accounts for the remaining data. Only Asians have patients younger than 40 years old (4.65%) with Blacks and Whites having no representative. Of note also is that the most commonly diagnosed age group is 51–60 in Asians, 61–70 in Blacks and 71–80 in Whites indicate that the overall BLCA dataset (71–80) is skewed toward White population (Fig. 1A). There was a statistically significant difference (p = 0.0048) between mean diagnosis age between Asians and the BLCA dataset (Fig. 1B) revealing that Asians are not adequately captured in diagnosis age. Males are more commonly affected with bladder urothelial cancer than females (Fig. 1C), but the male:female ratio for Blacks (1.3:1) was lower than other groups (2.8:1 to 4.4:1).

Fig. 1
figure 1

Demographic deviation of racial groups from BLCA dataset. Stratification of patients by age groups (A), mean diagnosis age (B), and sex (C) of patients in the BLCA dataset. Data were compared between different racial groups and the BLCA dataset for conformity

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3.2 Tumor and survival characteristics

The BLCA dataset suggests that most patients are diagnosed with a T3 tumor stage (Fig. 2A). However, racial detachment indicates that most Asians and to a lesser degree Blacks present with T2 stage. Only Asians were found to have patients with T1 stage, and most patients with a T4 presentation were Blacks. As for metastasis, the BLCA dataset postulates an even distribution between M0 and MX. Analysis based on race groups shows that Asians have a significantly larger proportion of patients at M0, minimal MX presentation, and virtually no patient presenting with an M1 status. Overall, findings suggest that Asians presented with less advanced disease. We also analyzed for differences in patient survival (Fig. 2B) and found no statistically significant aberration in disease-free (p = 0.4391), disease-specific (p = 0.3020), progression-free (p = 0.3048), and overall survival (p = 0.4338) among all race groups and BLCA dataset. Perhaps, an indication of similar success in patient management among all groups.

Fig. 2
figure 2

Tumor and survival characteristics. Racial groups were analyzed for differences in their cancer stage (A) and metastasis status. Survival curves based on the Kaplan–Meier method were assessed for disease-free survival, disease-specific survival, progression-free survival, and overall survival for patients from all racial groups and BLCA dataset. Note—T2 comprises T2A and T2B, T3 comprises T3A and T3B, and T4 comprises T4A and T4B. we fococuses more on the relevant data among Asians with a significant number of patient M0, Mx and no patiets with M1 status as shown in fig. 2 (B)

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3.3 Wide variation in gene mutations among racial groups

In the BLCA dataset, about 50% of patients had a mutation in the TP53 gene. The same pattern is seen in Blacks (47.8%) and Whites (53.1%) with about half of patients having this mutation. In Asians however, FGFG3 and KDM6A (27.9%) were the most common mutation with TP53 mutation occurring in about 20% of patients. A previous study [32] reviewed bladder urothelial cancer-related genes but in terms of racial conformity, only ERBB2, ELF3, and KDM6A shows similar findings among racial groups. The vast majority of mutated genes were similar between Whites and the BLCA dataset with genes like ARID1A and ERBB3; in some cases, there is no mutation in some genes like NFE2L2 and KLF5 in Asians or ERBB3 in Blacks. Well-known oncogenes like MYC and WWTR1 mutations are higher in Blacks and totally undetectable in Asians while tumor suppressors like TP53 and RB1 were also low in Asians. Asians had the highest alteration frequency in super-enhancer genes—PPARG and FGFR3 [33] as well as the Wnt signaling genes—CTNNB1 and LRP6. Patients with mismatch repair deficiency usually show remarkable response to PD-1 blockade [35]. Here, we observe that Blacks only have mutation in MLH1, and Asians only have mutation in MSH2; Whites on the other hand (as seen in BLCA dataset) have mutations in all the five identified mismatch repair genes. In addition, Blacks had higher mutations in checkpoint genes, Fanconi Anemia-related genes, and BRCA1 compared to other groups. Overall, genetic alteration frequency in BLCA dataset is more representative of White population than other racial groups (Fig. 3).

Fig. 3
figure 3

Genetic variation among racial groups. Genetic analysis from tumor samples of different racial groups shows varying differences in mutated genes. Only cancer-related genes were analyzed and were grouped based on function, pathway, or other publications. Central panels in red-dotted lines indicate the top 20 most commonly mutated genes and their gene alteration frequency among racial groups

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3.4 Copy number alteration pattern among racial groups

Cytobands with chromosomal abnormality show great variance in terms of genomic amplification (Fig. 4A). Of note is the dominance of 1q23.3 amplification in Asians, 11q13.3 amplification in Blacks, 6p23.3 amplification in Whites, and surprisingly 1q23.3 amplification in BLCA dataset. Even with marked difference in genetic mutation between Asians and the BLCA dataset, the most common amplified cytoband is strikingly the same. Cytobands with homologous deletion are very similar with a dominance of 9p21.3 deletion (genes—CDKN2A, CDKN2B and MTAP) in all racial groups. We only observed a relatively high alteration (4.7% of patients) in the X chromosome—Xq13.1 and Xq12 only in Asians. Tumor mutational burden (Fig. 4B) of Asians was significantly different with BLCA dataset (p =  < 0.0001) and among groups (p < 0.0001). Similarly, aneuploidy score (Fig. 4C) of Asians was also significantly different with BLCA group (p = 0.0010) and among groups (p = 0.0017). Bladder urothelial cancer is usually microsatellite stable [36]. The same pattern was seen (Fig. 4D) in all racial groups and BLCA dataset with no statistical deviation.

Fig. 4
figure 4

Copy number alteration pattern among racial groups. Commonly altered cytobands with gene amplification and homologous deletion shown for all racial groups and BLCA dataset (A). Genes located in the cytobands are shown to the right of each horizontal bar. Violin plot shows total mutation count per patient (B) and aneuploidy score (C) in all racial groups and BLCA dataset. Dot plots (D) show microsatellite instability score using MSI MANTIS and MSISensor for all racial groups and BLCA dataset

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4 Discussion

Bladder cancer is one of the top ten common tumors of the body [37]. In this study, we observed that the White populations were the most represented in this dataset comprising a total of 79.6% of the patients while Asians and Blacks have the lowest representation. The findings echo available data [1] which suggest a high incidence of bladder cancer in Europe and North America, but we suspect this to be as a result of sampling bias. The observed differences in the age groups where 4.65% of Asians were less than 40 years old seem to be at variance with studies, showing that Whites are more frequently diagnosed at a younger age [37]. The commonly diagnosed age group declines from 71–80 in Whites, 61–70 in Blacks, and 51–60 in Asians showing which is in stark contrast to documented findings that suggest all races are diagnosed at about age of 70–79 [38]. We found that males have the highest frequency of the urothelial bladder cancer which is in line with the findings in other studies [1, 3, 37, 38], possibly due to differences in exposure to risk factors [1, 37]. Our findings also pose a question—is the low male:female ratio in Blacks due to exposure to risk factors or other socioeconomic factors?

The present study indicates that most Asians presented with an earlier tumor stage of T2 compared to other races. As for late presentation—T4 stage, most of the patients were Blacks, while the BLCA datasets suggest that most patients were diagnosed with a T3 tumor stage. Only Asians were found to have patients with a T1 stage. The overall findings with respect to cancer staging indicate that majority of the patients across all racial groups and BLCA dataset were diagnosed with a T2 stage. This study presented some similarity as well as disparity to a study by Shu and colleagues who reported a higher presentation of T1, T3, and T4 stages in Blacks and to a lesser degree Asians, while Whites were found to have the highest presentation of T2 stage followed by Asians [19]. This indicates that multiple potential factors such as availability and access to cancer therapy, appropriate cancer surveillance, lymph node dissection, and radical cystectomy might contribute to disparities in bladder cancer stage presentation among different ethnic/racial groups.

The BLCA dataset suggests an even distribution between M0 and MX with regards to metastasis, with minimal presentation in M1. Analysis of metastasis based on race shows that Asians have a significantly larger proportion of patients at M0, minimal MX presentation, and virtually no patient presenting with an M1 status. Overall, findings suggest that Asians presented with less advanced disease, while Blacks presented with more advanced disease. These findings suggest that racial disparity exists between ethnic and racial groups with respect to bladder cancer disease progression, which might be due to increased miR-34b and miR-886-5p expression in Blacks as compared to other ethnic groups. A study conducted on Iranian bladder cancer patients reported that patients with advanced metastasis presented with overexpression of microRNAs miR-34b, miR-886-5p, miR200c, miR-30b, miR-141, and miR-21-5p [39]. These microRNAs induce tumor cell proliferation, migration through PTEN regulation, and negative regulation of apoptosis [40].

Patient survival was analyzed in this study, and no statistically significant aberration was found in disease-free, disease-specific, progression-free, and overall survival in the BLCA dataset and all race groups. However, other studies reported that Blacks have the least chances of surviving. A study reported that among patients diagnosed with bladder cancer in Ohio cancer registry, Black women had a 68% risk of dying than White men [41]. Another finding reported a sharp increase in cancer-specific mortality after 24-month follow-up despite differences in bladder cancer severity at the time of presentation and found worst survival rate among female and African American patients [19]. This disparity might be attributed to differences in disease stage at diagnosis, utilization of cancer therapy, biologic factors, social determinants of health, diet, physical activity, and socioeconomic factors as females and African American bladder cancer patients are known to present with more advanced stage of cancer and are more at risk of occupational exposure associated with bladder cancer than Whites and Asians [19, 37, 41,42,43,44,45,46].

Earlier findings [47] which identified the prevalence of clonal mutations in several significantly mutated genes (SMGs) show variation among different race groups with Black and White patients showing higher fractions of clonal TP53 mutations than Asian patients. The frequency of FGFR3, NRAS, TSC1, and ELF3 mutations was higher in Blacks than Whites and showed higher frequencies of clonal mutations in Whites than in Asian patients. It is interesting to note that, FGFR3 clonal mutations were observed to occur more frequently not only in Asian patients, but FGFR3 mutations are the early driver events during non-muscle invasive bladder cancer (NMIBC) evolution, and we also observed that FGFR3 clonal mutations occurred more frequently in NMIBC than in muscle invasive bladder cancer (MIBC) [47]. Activating mutations in FGFR3 are the most common and most specific genetic variation in noninvasive bladder cancer. The T allele accounts for 70% of non-muscle-invasive bladder cancer and is associated with an increased expression of FGFR3 in adipose tissue, which was used as a surrogate tissue due to lack of expression data from normal urothelial tissue [48]. The result obtained from the previous study revealed that the most frequently mutated genes were tumor suppressor genes TP53, APC, FAT1, RB1, BRCA2, and NF1 with TP53 and showed a high frequency of mutation across all cancer types [49]. Numerous studies have shown that mutations of TP53 are associated with poor prognosis in cancer [49]. Therefore, genomic stability regulated by TP53 may be a reason attributed to cancer disparities among different racial groups. From our findings, we also observed that Blacks may potentially not respond well to PD1 blockade due to having an intact mismatch repair gene [35].

Our analysis found a higher amplification of 12q cytoband specifically in Asians compared to the other racial groups and BLCA dataset. A study by Basu and colleagues [50] reported a complete amplification of 12q and frequent losses at 3p24.2–24.1 (20%), 5q11.1–35.3 (40%), 6q25.3–27 (40%), 9p24.3–21.5 (60%), and 9q21.11–33.3 (40%) in patients exposed to high Arsenic content. They also found that 2p25.3, 4p16.3, 7p22.3, 10q26.13, 16q24.3, 17p11.2, 19q13.33, and 20q13.33 cytoband regions showed ≥ 40% amplification while in the present comparative study, none of the above regions showed up to 20% amplification in BLCA, with only 11q13.3 appearing in Blacks. We also showed that 9p21.3 (genes—CDKN2A, CDKN2B, and MTAP) cytoband to be the most frequently deleted region with Blacks accounting for 43%, Whites and BLCA dataset accounting for 34% each, and Asians accounting for 28%. These findings are at variance with the study by Basu and colleagues [50] further suggesting a pan-cancer atlas study involving various cohorts might yield entirely different findings due to heterogeneity of the population. Blacks had the highest amplification (38%) of the 11q13.3 cytoband (genes—FGF4, FGF3, FGF19, and CCND1) which is prevalent in many other tumors [51] and the highest frequency of deletion (43%) of the 9p21.3 cytoband (genes—CDKN2A, CDKN2B, MTAP, and MLLT3) which favors immune evasion and metastasis [52]. We found that Whites and BLCA dataset accounted for the highest Tumor Mutational Burden, followed by Blacks, while Asians accounted for the least TMB in our comparison. A higher tumor mutational burden has been correlated with longer survival after immune checkpoint inhibitors and a poorer survival without immunotherapy [53]. Similarly, aneuploidy score differed significantly in Asians compared to the other groups indicating a potentially poorer prognosis in such populations [54]. All groups revealed a microsatellite stable tumor as confirmed by numerous [36].

5 Conclusion

In summary, we found an overrepresentation of Whites in the TCGA dataset which is a reference database and reliable repository for cancer genomics. The overall genomic and clinical data are by and large representative of the White population. The survival data could be applicable for Asians and Blacks, but the genomic information is quite different. We recommend the acceleration of data uptake from diverse geographical areas to augment the available data in the database. It is still not completely clear if the racial differences seen are due to limited sample size in racial groups or a true representation of such populations. Poor funding and limited collaborations might be implicated in these circumstances but nevertheless, more data are needed from Africa and Asia to overcome this possible sampling bias.

Availability of data and materials

The dataset analyzed during the current study is available in the cBio Portal for Cancer Genomics at https://www.cbioportal.org/study/clinicalData?id=blca_tcga_pan_can_atlas_2018

Abbreviations

TCGA:

The Cancer Genome Atlas

HDI:

Human development index

NMIBC :

Non-muscle invasive bladder cancer

MIBC :

Muscle invasive bladder cancer

BLCA :

Bladder cancer

T :

Tumor stage

DFS :

Disease-free survival

DSS :

Disease-specific survival

PFS :

Progression-free survival

OS :

Overall survival

FDA :

Food and drug administration

MSI :

Microsatelitte instability

ANOVA :

Analysis of variance

M :

Metastasis

SMGs :

Significantly mutated genes

References

  1. Halaseh SA, Halaseh S, Alali Y, Ashour ME, Alharayzah MJ (2022) A review of the etiology and epidemiology of bladder cancer: all you need to know. Cureus 14(7):e27330. https://doi.org/10.7759/cureus.27330. (Epub 20220727)

    Article  PubMed  PubMed Central  Google Scholar 

  2. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A et al (2021) Global Cancer Statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 71(3):209–249. https://doi.org/10.3322/caac.21660. (Epub 20210204)

    Article  CAS  PubMed  Google Scholar 

  3. Siegel RL, Miller KD, Fuchs HE, Jemal A (2022) Cancer statistics, 2022. CA Cancer J Clin 72(1):7–33. https://doi.org/10.3322/caac.21708. (Epub 20220112)

    Article  PubMed  Google Scholar 

  4. Saginala K, Barsouk A, Aluru JS, Rawla P, Padala SA, Barsouk A (2020) Epidemiology of bladder cancer. Med Sci Basel. https://doi.org/10.3390/medsci8010015. (Epub 20200313)

    Article  PubMed  PubMed Central  Google Scholar 

  5. Walker B, Figgs LW, Zahm SH (1995) Differences in cancer incidence, mortality, and survival between African Americans and Whites. Environ Health Perspect 103(8):275–281. https://doi.org/10.1289/ehp.95103s8275

    Article  PubMed  PubMed Central  Google Scholar 

  6. Audenet F, Attalla K, Sfakianos JP (2018) The evolution of bladder cancer genomics: What have we learned and how can we use it? Urol Oncol 36(7):313–320. https://doi.org/10.1016/j.urolonc.2018.02.017. (Epub 20180321)

    Article  CAS  PubMed  Google Scholar 

  7. Piao XM, Byun YJ, Kim WJ, Kim J (2018) Unmasking molecular profiles of bladder cancer. Investig Clin Urol 59(2):72–82. https://doi.org/10.4111/icu.2018.59.2.72. (Epub 20180201)

    Article  PubMed  PubMed Central  Google Scholar 

  8. Netto GJ (2013) Molecular genetics and genomics progress in urothelial bladder cancer. Semin Diagn Pathol 30(4):313–320. https://doi.org/10.1053/j.semdp.2013.11.005. (Epub 20131111)

    Article  PubMed  Google Scholar 

  9. Tomczak K, Czerwinska P, Wiznerowicz M (2015) The cancer genome atlas (Tcga): an immeasurable source of knowledge. Contemp Oncol (Pozn) 19(1A):A68-77. https://doi.org/10.5114/wo.2014.47136

    Article  PubMed  Google Scholar 

  10. Wang Z, Jensen MA, Zenklusen JC (2016) A practical guide to the cancer genome atlas (Tcga). Methods Mol Biol 1418:111–141. https://doi.org/10.1007/978-1-4939-3578-9_6

    Article  PubMed  Google Scholar 

  11. Cline MS, Craft B, Swatloski T, Goldman M, Ma S, Haussler D et al (2013) Exploring Tcga pan-cancer data at the Ucsc cancer genomics browser. Sci Rep 3:2652. https://doi.org/10.1038/srep02652. (Epub 20131002)

    Article  PubMed  PubMed Central  Google Scholar 

  12. Seiler R, Black PC, Thalmann G, Stenzl A, Todenhofer T (2017) Is the cancer genome atlas (Tcga) bladder cancer cohort representative of invasive bladder cancer? Urol Oncol 35(7):458e1-458e7. https://doi.org/10.1016/j.urolonc.2017.01.024. (Epub 20170311)

    Article  CAS  Google Scholar 

  13. Abubakar SD, Takaki M, Haeno H (2023) Computational modeling of locoregional recurrence with spatial structure identifies tissue-specific carcinogenic profiles. Front Oncol 13:1116210. https://doi.org/10.3389/fonc.2023.1116210. (Epub 20230406)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Spratt DE, Chan T, Waldron L, Speers C, Feng FY, Ogunwobi OO et al (2016) Racial/ethnic disparities in genomic sequencing. JAMA Oncol 2(8):1070–1074. https://doi.org/10.1001/jamaoncol.2016.1854

    Article  PubMed  PubMed Central  Google Scholar 

  15. Vantaku V, Amara CS, Piyarathna DWB, Donepudi SR, Ambati CR, Putluri V et al (2019) DNA methylation patterns in bladder tumors of african american patients point to distinct alterations in xenobiotic metabolism. Carcinogenesis 40(11):1332–1340. https://doi.org/10.1093/carcin/bgz128

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Heath EI, Lynce F, Xiu J, Ellerbrock A, Reddy SK, Obeid E et al (2018) Racial disparities in the molecular landscape of cancer. Anticancer Res 38(4):2235–2240. https://doi.org/10.21873/anticanres.12466

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Duello TM, Rivedal S, Wickland C, Weller A (2021) Race and genetics versus “race” in genetics: a systematic review of the use of african ancestry in genetic studies. Evol Med Public Health 9(1):232–245. https://doi.org/10.1093/emph/eoab018. (Epub 20210615)

    Article  PubMed  PubMed Central  Google Scholar 

  18. Newman LA, Carpten J (2018) Integrating the genetics of race and ethnicity into cancer research: trailing Jane and John Q. Public JAMA Surg 153(4):299–300. https://doi.org/10.1001/jamasurg.2017.5080

    Article  PubMed  Google Scholar 

  19. Shu TD, Schumacher FR, Conroy B, Ponsky L, Mahran A, Bukavina L et al (2023) Disparities in cause-specific mortality by race and sex among bladder cancer patients from the seer database. Cancer Causes Control 34(6):521–531. https://doi.org/10.1007/s10552-023-01679-x. (Epub 20230308)

    Article  PubMed  Google Scholar 

  20. Bonneville R, Krook MA, Kautto EA, Miya J, Wing MR, Chen HZ et al (2017) Landscape of microsatellit e instability across 39 cancer types. JCO Precis Oncol. https://doi.org/10.1200/PO.17.00073. (Epub 20171003)

    Article  PubMed  PubMed Central  Google Scholar 

  21. Taylor AM, Shih J, Ha G, Gao GF, Zhang X, Berger AC et al (2018) Genomic and functional approaches to understanding cancer aneuploidy. Cancer Cell 33(4):676–689. https://doi.org/10.1016/j.ccell.2018.03.007. (Epub 20180402)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Hoadley KA, Yau C, Hinoue T, Wolf DM, Lazar AJ, Drill E et al (2018) Cell-of-origin patterns dominate the molecular classification of 10,000 tumors from 33 types of cancer. Cell 173(2):291–304. https://doi.org/10.1016/j.cell.2018.03.022

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Ding L, Bailey MH, Porta-Pardo E, Thorsson V, Colaprico A, Bertrand D et al (2018) Perspective on oncogenic processes at the end of the beginning of cancer genomics. Cell 173(2):305–320. https://doi.org/10.1016/j.cell.2018.03.033

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Sanchez-Vega F, Mina M, Armenia J, Chatila WK, Luna A, La KC et al (2018) Oncogenic signaling pathways in the cancer genome atlas. Cell 173(2):321–337. https://doi.org/10.1016/j.cell.2018.03.035

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Huang KL, Mashl RJ, Wu Y, Ritter DI, Wang J, Oh C et al (2018) Pathogenic germline variants in 10,389 adult cancers. Cell 173(2):355–370. https://doi.org/10.1016/j.cell.2018.03.039

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Bailey MH, Tokheim C, Porta-Pardo E, Sengupta S, Bertrand D, Weerasinghe A et al (2018) Comprehensive characterization of cancer driver genes and mutations. Cell 173(2):371–385. https://doi.org/10.1016/j.cell.2018.02.060

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA et al (2012) The Cbio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov 2(5):401–404. https://doi.org/10.1158/2159-8290.CD-12-0095

    Article  PubMed  Google Scholar 

  28. Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO et al (2013) Integrative analysis of complex cancer genomics and clinical profiles using the cbioportal. Sci Signal 6(269):p11. https://doi.org/10.1126/scisignal.2004088. (Epub 20130402)

    Article  CAS  Google Scholar 

  29. Flanagin A, Frey T, Christiansen SL, Committee AMAMoS (2021) Updated guidance on the reporting of race and ethnicity in medical and science journals. JAMA 326(7):621–627. https://doi.org/10.1001/jama.2021.13304

    Article  PubMed  Google Scholar 

  30. Wang G, McKenney JK (2019) Urinary bladder pathology: world health organization classification and american joint committee on cancer staging update. Arch Pathol Lab Med 143(5):571–577. https://doi.org/10.5858/arpa.2017-0539-RA. (Epub 20180725)

    Article  CAS  PubMed  Google Scholar 

  31. Chakravarty D, Gao J, Phillips SM, Kundra R, Zhang H, Wang J et al (2017) Oncokb: a precision oncology knowledge base. JCO Precis Oncol. https://doi.org/10.1200/PO.17.00011. (Epub 20170516)

    Article  PubMed  PubMed Central  Google Scholar 

  32. Knowles MA, Hurst CD (2015) Molecular biology of bladder cancer: new insights into pathogenesis and clinical diversity. Nat Rev Cancer 15(1):25–41. https://doi.org/10.1038/nrc3817

    Article  CAS  PubMed  Google Scholar 

  33. Neyret-Kahn H, Fontugne J, Meng XY, Groeneveld CS, Cabel L, Ye T et al (2023) Epigenomic mapping identifies an enhancer repertoire that regulates cell identity in bladder cancer through distinct transcription factor networks. Oncogene 42(19):1524–1542. https://doi.org/10.1038/s41388-023-02662-1. (Epub 20230322)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Kautto EA, Bonneville R, Miya J, Yu L, Krook MA, Reeser JW et al (2017) Performance evaluation for rapid detection of pan-cancer microsatellite instability with mantis. Oncotarget 8(5):7452–7463. https://doi.org/10.18632/oncotarget.13918

    Article  PubMed  Google Scholar 

  35. Cercek A, Lumish M, Sinopoli J, Weiss J, Shia J, Lamendola-Essel M et al (2022) Pd-1 blockade in mismatch repair-deficient, locally advanced rectal cancer. N Engl J Med 386(25):2363–2376. https://doi.org/10.1056/NEJMoa2201445. (Epub 20220605)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Kullmann F, Strissel PL, Strick R, Stoehr R, Eckstein M, Bertz S et al (2023) Frequency of microsatellite instability (Msi) in upper tract urothelial carcinoma: comparison of the bethesda panel and the Idylla Msi assay in a consecutively collected Multi-Institutional Cohort. J Clin Pathol 76(2):126–132. https://doi.org/10.1136/jclinpath-2021-207855. (Epub 20210928)

    Article  CAS  PubMed  Google Scholar 

  37. Wang Y, Chang Q, Li Y (2018) Racial differences in urinary bladder cancer in the United States. Sci Rep 8(1):12521. https://doi.org/10.1038/s41598-018-29987-2. (Epub 20180821)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Yee DS, Ishill NM, Lowrance WT, Herr HW, Elkin EB (2011) Ethnic differences in bladder cancer survival. Urology 78(3):544–549. https://doi.org/10.1016/j.urology.2011.02.042. (Epub 20110722)

    Article  PubMed  Google Scholar 

  39. Mojarrad M, Moghbeli M (2020) Genetic and molecular biology of bladder cancer among Iranian patients. Mol Genet Genomic Med 8(6):e1233. https://doi.org/10.1002/mgg3.1233. (Epub 20200406)

    Article  PubMed  PubMed Central  Google Scholar 

  40. Folini M, Gandellini P, Longoni N, Profumo V, Callari M, Pennati M et al (2010) Mir-21: An Oncomir on strike in prostate cancer. Mol Cancer 9:12. https://doi.org/10.1186/1476-4598-9-12. (Epub 20100121)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Bukavina L, Prunty M, Mishra K, Sun H, Sheyn D, Conroy B et al (2021) Gender disparities in bladder cancer-specific survival in high poverty areas utilizing Ohio cancer incidence surveillance system (Ociss). Urology 151:163–168. https://doi.org/10.1016/j.urology.2020.07.013. (Epub 20200722)

    Article  PubMed  Google Scholar 

  42. Scosyrev E, Noyes K, Feng C, Messing E (2009) Sex and racial differences in bladder cancer presentation and mortality in the us. Cancer 115(1):68–74. https://doi.org/10.1002/cncr.23986

    Article  PubMed  Google Scholar 

  43. Lee CT, Dunn RL, Williams C, Underwood W 3rd (2006) Racial disparity in bladder cancer: trends in tumor presentation at diagnosis. J Urol 176(3):927–933. https://doi.org/10.1016/j.juro.2006.04.074

    Article  PubMed  Google Scholar 

  44. Danforth KN, Luong TQ, Yi DK, Yamamoto A, Kawatkar AA, Kim PH et al (2020) Disparities in stage at diagnosis in an equal-access integrated delivery system: a retrospective cohort study of 7244 patients with bladder cancer. Clin Genitourin Cancer 18(2):e91–e102. https://doi.org/10.1016/j.clgc.2019.09.002. (Epub 20190912)

    Article  PubMed  Google Scholar 

  45. Washington SL 3rd, Neuhaus J, Meng MV, Porten SP (2019) Social determinants of appropriate treatment for muscle-invasive bladder cancer. Cancer Epidemiol Biomarkers Prev 28(8):1339–1344. https://doi.org/10.1158/1055-9965.EPI-18-1280. (Epub 20190515)

    Article  PubMed  PubMed Central  Google Scholar 

  46. Robinson JC (1989) Exposure to occupational hazards among hispanics, blacks and non-hispanic whites in California. Am J Public Health 79(5):629–630. https://doi.org/10.2105/ajph.79.5.629

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Zhang B, Jia P, Wang J, Pei G, Wang C, Pei S et al (2023) Integrated analysis of racial disparities in genomic architecture identifies a trans-ancestry prognostic subtype in bladder cancer. Mol Oncol 17(4):564–581. https://doi.org/10.1002/1878-0261.13360. (Epub 20221229)

    Article  PubMed  Google Scholar 

  48. Gu J, Wu X (2011) Genetic susceptibility to bladder cancer risk and outcome. Per Med 8(3):365–374. https://doi.org/10.2217/pme.11.15

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Kytola V, Topaloglu U, Miller LD, Bitting RL, Goodman MM, Agostino DRB J, et al (2017) Mutational landscapes of smoking-related cancers in caucasians and african americans: precision oncology perspectives at wake forest baptist comprehensive cancer center. Theranostics 7(11):2914–2923. https://doi.org/10.7150/thno.20355. (Epub 20170712)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Basu M, Ghosh S, Roychowdhury A, Samadder S, Das P, Addya S et al (2020) Integrative genomics and pathway analysis identified prevalent Fa-Brca pathway alterations in arsenic-associated urinary bladder carcinoma: chronic arsenic accumulation in cancer tissues hampers the fa-brca pathway. Genomics 112(6):5055–5065. https://doi.org/10.1016/j.ygeno.2020.09.012. (Epub 20200910)

    Article  CAS  PubMed  Google Scholar 

  51. Zhou R, Zhu X, Peng Y, Zhong L, Peng L, Yang B et al (2022) Clinical Impact of 11q13.3 amplification on immune cell infiltration and prognosis in breast cancer. Int J Gen Med 15:4037–4052. https://doi.org/10.2147/IJGM.S360177. (Epub 20220414)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Barriga FM, Tsanov KM, Ho YJ, Sohail N, Zhang A, Baslan T et al (2022) Machete identifies interferon-encompassing chromosome 9p21.3 deletions as mediators of immune evasion and metastasis. Nat Cancer 3(11):1367–1385. https://doi.org/10.1038/s43018-022-00443-5. (Epub 20221107)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Valero C, Lee M, Hoen D, Wang J, Nadeem Z, Patel N et al (2021) The association between tumor mutational burden and prognosis is dependent on treatment context. Nat Genet 53(1):11–15. https://doi.org/10.1038/s41588-020-00752-4. (Epub 20210104)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Spurr LF, Weichselbaum RR, Pitroda SP (2022) Tumor aneuploidy predicts survival following immunotherapy across multiple cancers. Nat Genet 54(12):1782–1785. https://doi.org/10.1038/s41588-022-01235-4. (Epub 20221128)

    Article  CAS  PubMed  Google Scholar 

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Authors and Affiliations

  1. Department of Medical Laboratory Science, College of Medical Sciences, Ahmadu Bello University, Zaria, 810710, Kaduna State, Nigeria

    Sharafudeen Dahiru Abubakar, Zakariyya Muhammad Bello & Sufiyanu Sani Gusau

  2. Department of Medical Laboratory Science, Bayero University, Kano, Nigeria

    Imam Malik Kabir

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  1. Sharafudeen Dahiru Abubakar
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  2. Zakariyya Muhammad Bello
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  3. Sufiyanu Sani Gusau
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  4. Imam Malik Kabir
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SDA, ZMB, SSG, and IMK conceptualized and jointly wrote the manuscript. SDA did the analyses.

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Abubakar, S.D., Bello, Z.M., Gusau, S.S. et al. Exploring racial disparities in bladder urothelial cancer: insights into survival and genetic variations. Afr J Urol 30, 28 (2024). https://doi.org/10.1186/s12301-024-00430-5

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