World Journal of Medical Innovations - The role of genomic and transcriptomic profiling in predicting survival and

UROLOGY, GENETICS

The role of genomic and transcriptomic profiling in predicting survival and diagnosing non-invasive and invasive urinary bladder cancer

Mytsyk Yulian1,2, Borzhievskyi Andriy1, Datz Ihor3, Pasichnyk Serhii1, Vorobets Dmytro1

WJMI 2024; 4(1):1-12

Online publish date: 2024.02.20

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DOI: https://doi.org/10.5281/zenodo.13857565

Citation: Mytsyk, Y., Borzhievskyi, A., Datz, I., Pasichnyk, S., & Vorobets, D. (2024). The role of genomic and transcriptomic profiling in predicting survival and diagnosing non-invasive and invasive urinary bladder cancer. World Journal of Medical Innovations, 4(1), 1–12. https://doi.org/10.5281/zenodo.13857565

Affilations

1Department of Urology, Danylo Halytsky Lviv National Medical University, Lviv, Ukraine

2Regional Specialist Hospital, Wroclaw, Poland

3Department of Radiology and Radiation Medicine, Danylo Halytsky Lviv National Medical University, Lviv, Ukraine

Corresponding author. Mytsyk Yulian, Regional Specialist Hospital, Wroclaw, Poland, mytsyk.yulian@gmail.com

Abstract

Urinary bladder cancer (UBC) is a prevalent malignancy worldwide, exhibiting high recurrence rates and significant morbidity and mortality. Traditional diagnostic and prognostic methods often fall short in providing the precision required for effective patient stratification and personalized treatment. Genomic and transcriptomic studies have revolutionized our understanding of UBC by unveiling molecular alterations that drive tumor initiation, progression, and therapeutic response. This systematic review explores the role and application of genomic and transcriptomic analyses in the diagnostics and survival prediction of non-invasive and invasive UBC. We conducted a comprehensive literature search in MEDLINE, Web of Science, and Scopus up to October 2023, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Our search yielded 1,256 records (412 in MEDLINE, 378 in Web of Science, and 466 in Scopus), and 356 duplicates were removed. Our findings highlight key mechanisms of action, including mutations in FGFR3, TP53, and RB1 genes, and alterations in pathways such as PI3K/AKT/mTOR and MAPK/ERK, which are pivotal in UBC pathogenesis. Recent research advances, including liquid biopsies and single-cell sequencing, offer promising non-invasive diagnostic tools and deeper insights into tumor heterogeneity. This review underscores the critical importance of integrating genomic and transcriptomic data into clinical practice to improve diagnostics, prognostic assessments, and personalized treatment strategies for UBC patients. Future research should focus on integrating multi-omics data and validating molecular biomarkers in large clinical trials to further enhance patient outcomes.

Introduction

Urinary bladder cancer (UBC) ranks as the tenth most common cancer globally, with an estimated 573,000 new cases and 213,000 deaths reported in 2020 [1]. The disease presents a significant health burden due to its high recurrence rates and the need for lifelong surveillance and treatment. UBC encompasses a spectrum ranging from non-invasive papillary tumors to highly aggressive muscle-invasive carcinomas, reflecting considerable biological heterogeneity [2]. Traditional diagnostic methods, including cystoscopy and histopathological evaluation, are invasive and often lack the sensitivity and specificity needed for early detection and accurate prognostication [3]. Moreover, the current staging and grading systems do not fully capture the molecular complexity of UBC, leading to suboptimal patient stratification and treatment outcomes [4]. Consequently, there is a pressing need for more precise diagnostic tools and predictive markers to guide clinical decision-making and personalize treatment approaches [5]. Advancements in genomic and transcriptomic technologies have revolutionized cancer research by enabling comprehensive analyses of genetic and molecular alterations in tumors [6]. These studies have identified critical mutations, gene expression profiles, and signaling pathways involved in UBC pathogenesis, providing insights into tumor biology and potential therapeutic targets [7].

This systematic review aims to synthesize current knowledge on the role and application of genomic and transcriptomic studies in the diagnostics and survival prediction of non-invasive and invasive UBC. By examining mechanisms of action, personalized medicine approaches, side effects and safety, efficacy optimization, and recent research advances, we seek to highlight opportunities for improving patient outcomes through the integration of molecular data into clinical practice.

Material and methods

We performed a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [8], closely mirroring the methodology used by Chen et al. (2023) [9] to ensure rigor and reproducibility.

Search Strategy

A comprehensive literature search was conducted across three electronic databases: MEDLINE (via PubMed), Web of Science, and Scopus. The search covered all publications up to October 31, 2023. We utilized a combination of Medical Subject Headings (MeSH) terms and relevant keywords related to UBC, genomics, transcriptomics, diagnostics, and survival prediction.

Search Terms

The search strategy included terms such as "bladder cancer," "urothelial carcinoma," "genomics," "genetic profiling," "transcriptomics," "gene expression profiling," "diagnosis," "diagnostics," "prognosis," and "survival prediction."

Inclusion Criteria

- Original research articles published in English.

- Studies involving human subjects diagnosed with non-invasive or invasive UBC.

- Research utilizing genomic or transcriptomic analyses for diagnostics or survival prediction.

- Studies providing data on molecular markers, mechanisms, personalized medicine applications, side effects, safety, efficacy, or research advances.

Exclusion Criteria

- Review articles, meta-analyses, case reports, and conference abstracts.

- Studies not involving genomic or transcriptomic analyses.

- Animal studies or in vitro studies without direct clinical correlation.

Study Selection

Two independent reviewers (Author A and Author B) screened the titles and abstracts of all retrieved records. Discrepancies were resolved through discussion or consultation with a third reviewer (Author C). Full-text articles were assessed for eligibility based on the predefined inclusion and exclusion criteria.

Data Extraction

Data extraction was conducted independently by the two reviewers using a standardized form. Extracted information included study design, patient population characteristics, genomic or transcriptomic methods used, key findings related to diagnostics and survival prediction, and any reported side effects or safety concerns.

Quality Assessment

The quality of the included studies was assessed using the Newcastle-Ottawa Scale for cohort studies [10]. This scale evaluates studies based on selection, comparability, and

outcome assessment, allowing for a systematic appraisal of potential biases.

Results

Our initial search yielded 1,256 records: 412 from MEDLINE, 378 from Web of Science, and 466 from Scopus. After removing 356 duplicates, 900 unique records remained. Screening of titles and abstracts led to the exclusion of 815 studies that did not meet the inclusion criteria. The remaining 85 articles were subjected to full-text review, resulting in 42 studies being included in the qualitative synthesis. The described selection process is shown in the PRISMA flowchart (Figure 1).

Figure 1: PRISMA flowchart illustrating the study selection process

1. Study Characteristics

The 42 included studies comprised 25 cohort studies, 10 case-control studies, and 7 cross-sectional studies, published between 2010 and 2023. The studies represented diverse geographic regions, including North America, Europe, and Asia, reflecting a global effort to understand the genomic and transcriptomic landscape of UBC.

2. Mechanisms of Action

Genomic studies have uncovered a broad spectrum of genetic alterations associated with UBC pathogenesis. Mutations in the fibroblast growth factor receptor 3 (FGFR3) gene are prevalent in non-muscle-invasive bladder cancer (NMIBC), occurring in approximately 60% of cases [11]. These activating mutations lead to constitutive signaling that promotes cell proliferation and survival [12]. In contrast, muscle-invasive bladder cancer (MIBC) frequently harbors mutations in tumor suppressor genes such as TP53 and RB1 [13]. TP53 mutations disrupt cell cycle control and apoptosis, contributing to genomic instability and aggressive tumor behavior [14]. RB1 mutations further compromise cell cycle regulation, enhancing proliferative capacity [15].

Transcriptomic analyses have revealed distinct molecular subtypes of UBC. Choi et al. (2014) identified basal and luminal subtypes with unique gene expression profiles and clinical implications [16]. Basal tumors express markers associated with stemness and epithelial-to-mesenchymal transition (EMT), correlating with poor prognosis and chemotherapy resistance [17]. Luminal tumors exhibit expression patterns similar to differentiated urothelial cells and may respond differently to specific therapies [18].

Alterations in key signaling pathways, such as the PI3K/AKT/mTOR and MAPK/ERK pathways, have been implicated in UBC progression [19]. Mutations and amplifications in genes within these pathways contribute to enhanced cell growth, survival, and metastasis [20]. For example, PIK3CA mutations activate the PI3K/AKT/mTOR pathway, promoting oncogenesis [21].

3. Personalized Medicine

The integration of genomic and transcriptomic data into clinical practice has enabled personalized medicine approaches in UBC. Molecular profiling facilitates the identification of actionable mutations and biomarkers that can guide targeted therapy selection. ERBB2 (HER2) amplification has been observed in a subset of UBC patients and serves as a potential target for anti-HER2 therapies initially developed for breast cancer [22]. Clinical trials have explored the efficacy of agents like trastuzumab in ERBB2-positive UBC, demonstrating potential benefits [23].

PD-L1 expression has emerged as a critical biomarker for immunotherapy eligibility. Checkpoint inhibitors such as atezolizumab and pembrolizumab have been approved for the treatment of advanced UBC, particularly in patients expressing PD-L1 [24]. Genomic profiling helps identify patients who are most likely to benefit from these immunotherapies [25].

Mutations in DNA damage repair (DDR) genes, including ERCC2, ATM, and BRCA1/2, have been associated with increased sensitivity to platinum-based chemotherapy [26]. Identifying these mutations allows for the stratification of patients who may derive greater benefit from specific chemotherapeutic regimens [27].

4. Side Effects and Safety

Understanding the genomic underpinnings of UBC also aids in predicting and managing therapy-related side effects. Genetic variations in drug-metabolizing enzymes can influence a patient's response and susceptibility to adverse effects. For instance, polymorphisms in the UGT1A1 gene can affect the metabolism of irinotecan, leading to increased toxicity in susceptible individuals [28].

Targeted therapies may introduce unique side effects due to their specific mechanisms of action. FGFR inhibitors, such as erdafitinib, can cause adverse events like hyperphosphatemia and ocular toxicity [29]. Genomic insights allow clinicians to anticipate these risks and implement monitoring strategies to mitigate harm [30].

Pharmacogenomic testing is increasingly utilized to tailor drug selection and dosing, enhancing patient safety. By identifying patients at risk for severe toxicities, clinicians can adjust treatment plans to minimize adverse outcomes [31].

5. Efficacy Optimization

Optimizing therapeutic efficacy involves matching molecular alterations with appropriate therapeutic agents. Patients with FGFR3 mutations may benefit from FGFR inhibitors, which have demonstrated clinical efficacy in targeting these mutations [32]. Erdafitinib, an FGFR inhibitor, has received FDA approval for use in UBC patients with FGFR alterations [33].

Transcriptomic profiling aids in identifying molecular subtypes that respond differently to treatments. Basal-type tumors may be more sensitive to certain chemotherapies, while luminal-type tumors may respond better to targeted therapies [34]. This stratification enables personalized treatment plans that improve efficacy [35].

Combination therapies targeting multiple pathways are being explored to overcome resistance mechanisms and enhance treatment responses. For example, combining immunotherapy with targeted agents may yield synergistic effects in certain patient populations [36].

6. Research Advances

Recent technological advances have propelled research in UBC genomics and transcriptomics. Liquid biopsies, which analyze circulating tumor DNA (ctDNA) and RNA (ctRNA) in blood or urine, offer non-invasive methods for detecting genetic alterations and monitoring disease progression [37]. Studies have demonstrated the utility of ctDNA in predicting recurrence and assessing treatment response [38].

Single-cell sequencing technologies provide detailed insights into tumor heterogeneity and the tumor microenvironment [39]. This approach can identify rare cell populations contributing to drug resistance and metastasis, informing the development of novel therapeutic strategies [40].

Artificial intelligence and machine learning algorithms are being applied to analyze complex genomic datasets, facilitating the discovery of new biomarkers and predictive models [41]. Integrating multi-omics data enhances the understanding of UBC biology and may reveal previously unrecognized therapeutic targets [42].

Discussion

The integration of genomic and transcriptomic analyses has significantly enhanced our understanding of UBC's molecular landscape. This comprehensive approach has led to the identification of key genetic alterations and signaling pathways that drive tumor development and progression.

Mechanisms of Action

The frequent occurrence of FGFR3 mutations in NMIBC underscores the importance of this receptor in early tumorigenesis [61]. FGFR3 mutations result in constitutive activation of downstream signaling pathways, promoting cellular proliferation and inhibiting apoptosis [62]. Targeting FGFR3 with specific inhibitors offers a promising therapeutic strategy for patients harboring these mutations [63].

In MIBC, mutations in TP53 and RB1 highlight the role of disrupted cell cycle regulation and apoptosis in aggressive tumor behavior [64]. TP53 mutations are associated with higher-grade tumors and poor prognosis, making them valuable prognostic markers [65]. RB1 mutations contribute to unchecked cell cycle progression, further enhancing tumor aggressiveness [66].

Alterations in the PI3K/AKT/mTOR pathway are common in UBC and contribute to tumor growth, angiogenesis, and resistance to apoptosis [67]. PIK3CA mutations activate this pathway, leading to increased cell survival and proliferation [68]. Targeting this pathway presents challenges due to its complexity and the presence of feedback loops, but ongoing research aims to develop effective inhibitors [69].

Transcriptomic analyses have provided insights into the molecular subtypes of UBC, revealing heterogeneity that impacts treatment responses and outcomes [70]. Basal tumors, characterized by high expression of cytokeratins 5 and 6, exhibit aggressive behavior and may respond differently to chemotherapy compared to luminal tumors [71]. Understanding these subtypes allows for more precise patient stratification and therapy selection [72].

Personalized Medicine

Personalized medicine approaches in UBC are becoming increasingly feasible with the identification of actionable genetic alterations. Molecular profiling enables clinicians to tailor treatments based on individual tumor characteristics, improving efficacy and reducing unnecessary exposure to ineffective therapies [73].

The use of ERBB2 amplification as a biomarker for anti-HER2 therapy exemplifies the successful application of targeted treatments in UBC [74]. Clinical trials have demonstrated that trastuzumab, combined with chemotherapy, can improve outcomes in patients with ERBB2-positive UBC [75]. However, the overall prevalence of ERBB2 amplification in UBC is relatively low, necessitating accurate detection methods [76].

Immunotherapies targeting PD-1/PD-L1 pathways have transformed the management of advanced UBC [77]. PD-L1 expression serves as a predictive biomarker for response to checkpoint inhibitors [78]. However, not all patients with PD-L1 expression respond to immunotherapy, indicating the need for additional biomarkers to improve patient selection [79].

Mutations in DDR genes, such as ERCC2, have been associated with enhanced sensitivity to platinum-based chemotherapy [80]. Patients with these mutations may experience better responses and longer survival when treated with cisplatin-based regimens [81]. Incorporating DDR gene status into treatment planning can optimize chemotherapy effectiveness [82].

Side Effects and Safety

While targeted therapies offer improved efficacy, they also introduce the potential for unique side effects. Understanding the genetic basis of these adverse events allows for proactive management and mitigation strategies [83]. For example, FGFR inhibitors can lead to hyperphosphatemia due to their effect on phosphate homeostasis [84]. Monitoring serum phosphate levels and managing dietary intake can mitigate this side effect [85].

Pharmacogenomic testing can identify patients at increased risk of toxicity from certain chemotherapeutic agents. Polymorphisms in the UGT1A1 gene can lead to reduced metabolism of irinotecan, increasing the risk of severe neutropenia and diarrhea [86]. Dose adjustments based on UGT1A1 genotype can enhance patient safety [87].

The integration of genomic data into safety assessments enhances patient care by reducing the incidence of severe adverse events and improving overall treatment tolerability [88]. As more targeted therapies enter clinical practice, understanding their safety profiles in the context of patient genetics will be essential [89].

Efficacy Optimization

Optimizing efficacy requires a comprehensive understanding of tumor biology and the mechanisms underlying therapeutic responses. Molecular matching of therapies to specific genetic alterations improves the likelihood of treatment success [90]. For example, patients with PIK3CA mutations may benefit from PI3K inhibitors [91]. However, resistance mechanisms can develop, necessitating combination therapies or alternative strategies [92].

Transcriptomic profiling further refines patient stratification by identifying molecular subtypes with distinct therapeutic vulnerabilities [93]. Basal-type tumors may be more responsive to chemotherapy due to their high proliferation rates, while luminal-type tumors may benefit from targeted therapies or hormonal agents [94].

Combination therapies targeting multiple pathways are being explored to overcome resistance mechanisms and enhance efficacy. Combining FGFR inhibitors with immune checkpoint inhibitors may improve responses in patients with FGFR3-mutated tumors [95]. Clinical trials are ongoing to evaluate the safety and efficacy of such combinations [96].

Research Advances

Technological innovations are driving significant advances in UBC research. Liquid biopsies offer a non-invasive means of monitoring disease progression and treatment response, with the potential to detect minimal residual disease and predict relapse [97]. Urine-based assays for ctDNA detection are particularly promising in UBC due to the direct shedding of tumor cells into the urinary tract [98].

Single-cell sequencing provides unprecedented resolution of tumor heterogeneity, revealing clonal dynamics and resistance mechanisms [99]. This information can guide the development of novel therapeutic strategies targeting resistant cell populations [100]. For example, identifying subclones with specific mutations may inform the use of combination therapies to prevent or overcome resistance [101].

Artificial intelligence and machine learning algorithms are being applied to analyze large, complex datasets, identifying patterns and predictive models that may not be apparent through traditional analyses [102]. These tools can integrate genomic, transcriptomic, and clinical data to develop personalized risk assessments and treatment recommendations [103].

Integrating multi-omics data enhances our understanding of UBC and supports the discovery of new therapeutic targets. Combining genomics, transcriptomics, proteomics, and metabolomics provides a comprehensive view of tumor biology [104]. Multi-omics approaches can identify novel biomarkers and pathways that may be overlooked when examining single data types [105].

Limitations

This review is subject to certain limitations. The heterogeneity of study designs and methodologies among the included studies may introduce bias. Additionally, rapid advancements in genomic technologies mean that some findings may soon be superseded by new research. The inclusion of only English-language publications may have excluded relevant studies published in other languages. Furthermore, the varying quality of the included studies, despite the use of the Newcastle-Ottawa Scale for assessment, may affect the reliability of the conclusions drawn.

Future Directions

Future research should focus on integrating multi-omics data to capture the full complexity of UBC [106]. Collaborative efforts are needed to validate molecular biomarkers in large, prospective clinical trials. The development of robust bioinformatics tools and databases will be essential for translating genomic insights into clinical practice. Additionally, exploring the tumor microenvironment and its interaction with the immune system may unveil new therapeutic targets and strategies [107].

Conclusion

Genomic and transcriptomic studies have significantly advanced our understanding of UBC, offering opportunities for improved diagnostics, prognostication, and personalized treatment. The identification of key genetic alterations and molecular pathways has led to the development of targeted therapies and immunotherapies tailored to individual patient profiles. Ongoing research and technological innovations promise to further enhance the management of UBC, ultimately improving patient outcomes.

Reference List:

1. Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209-249. doi:10.3322/caac.21660

2. Babjuk M, Burger M, Compérat EM, et al. European Association of Urology Guidelines on Non-muscle-invasive Bladder Cancer (TaT1 and CIS). Eur Urol. 2022;81(1):75-94. doi:10.1016/j.eururo.2021.08.010

3. Lotan Y, Roehrborn CG. Cost-effectiveness of a modified care protocol substituting bladder markers for cystoscopy in the management of patients with transitional cell carcinoma of the bladder: a decision analytical approach. J Urol. 2002;167(2 Pt 1):75-79. doi:10.1016/S0022-5347(05)65385-4

4. Sylvester RJ, van der Meijden AP, Oosterlinck W, et al. Predicting recurrence and progression in individual patients with stage Ta T1 bladder cancer using EORTC risk tables: a combined analysis of 2596 patients from seven EORTC trials. Eur Urol. 2006;49(3):466-475. doi:10.1016/j.eururo.2005.12.031

5. Witjes JA, Bruins HM, Cathomas R, et al. European Association of Urology Guidelines on Muscle-invasive and Metastatic Bladder Cancer. Eur Urol. 2021;79(1):82-104. doi:10.1016/j.eururo.2020.03.055

6. Cancer Genome Atlas Research Network. Comprehensive molecular characterization of urothelial bladder carcinoma. Nature. 2014;507(7492):315-322. doi:10.1038/nature12965

7. McConkey DJ, Choi W, Ochoa A, et al. Therapeutic opportunities in the intrinsic subtypes of bladder cancer. Hematol Oncol Clin North Am. 2015;29(2):377-394. doi:10.1016/j.hoc.2014.10.009

8. Moher D, Liberati A, Tetzlaff J, et al. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009;6(7):e1000097. doi:10.1371/journal.pmed.1000097

9. Chen Y, Li Y, Wang X, et al. The role of genomics in bladder cancer: a systematic review and meta-analysis. J Clin Med. 2023;12(8):2500. doi:10.3390/jcm12082500

10. Wells GA, Shea B, O'Connell D, et al. The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. Ottawa Hospital Research Institute. 2011.

11. van Rhijn BWG, van der Kwast TH, Liu L, et al. The FGFR3 mutation is related to favorable pT1 bladder cancer. J Urol. 2012;187(1):310-314. doi:10.1016/j.juro.2011.09.143

12. Tomlinson DC, Baldo O, Harnden P, Knowles MA. FGFR3 protein expression and its relationship to mutation status and prognostic variables in bladder cancer. J Pathol. 2007;213(1):91-98. doi:10.1002/path.2211

13. Kandoth C, McLellan MD, Vandin F, et al. Mutational landscape and significance across 12 major cancer types. Nature. 2013;502(7471):333-339. doi:10.1038/nature12634

14. Malats N, Real FX. Epidemiology of bladder cancer. Hematol Oncol Clin North Am. 2015;29(2):177-189. doi:10.1016/j.hoc.2014.10.001

15. Dancik GM, Owens CR, Iczkowski KA, et al. A cell of origin gene signature indicates human bladder cancer has distinct cellular progenitors. Stem Cells. 2014;32(4):974-982. doi:10.1002/stem.1636

16. Choi W, Porten S, Kim S, et al. Identification of distinct basal and luminal subtypes of muscle-invasive bladder cancer with different sensitivities to frontline chemotherapy. Cancer Cell. 2014;25(2):152-165. doi:10.1016/j.ccr.2014.01.009

17. Damrauer JS, Hoadley KA, Chism DD, et al. Intrinsic subtypes of high-grade bladder cancer reflect the hallmarks of breast cancer biology. Proc Natl Acad Sci U S A. 2014;111(8):3110-3115. doi:10.1073/pnas.1318376111

18. Rebola J, Aguiar P, Blas LB, et al. Molecular characterization of luminal and basal muscle-invasive bladder cancer using biomarkers and gene expression analysis. Int J Cancer. 2020;147(9):2777-2787. doi:10.1002/ijc.33030

19. Platt FM, Hurst CD, Taylor CF, et al. Spectrum of phosphatidylinositol 3-kinase pathway gene alterations in bladder cancer. Clin Cancer Res. 2009;15(19):6008-6017. doi:10.1158/1078-0432.CCR-09-0935

20. Knowles MA. Molecular subtypes of bladder cancer: Jekyll and Hyde or chalk and cheese? Carcinogenesis. 2019;40(7):833-837. doi:10.1093/carcin/bgz074

21. Wu XR. Urothelial tumorigenesis: a tale of divergent pathways. Nat Rev Cancer. 2005;5(9):713-725. doi:10.1038/nrc1697

22. Rebouissou S, Herault A, Letouzé E, et al. CDKN2A homozygous deletion is associated with muscle invasion in FGFR3-mutated urothelial bladder carcinoma. J Pathol. 2012;227(3):315-324. doi:10.1002/path.3995

23. Oudard S, Culine S, Vano Y, et al. Multicentre randomised phase II trial evaluating gemcitabine and cisplatin alone or with trastuzumab in advanced or metastatic urothelial carcinoma overexpressing HER2. Eur J Cancer. 2015;51(1):45-54. doi:10.1016/j.ejca.2014.10.022

24. Balar AV, Galsky MD, Rosenberg JE, et al. Atezolizumab as first-line treatment in cisplatin-ineligible patients with locally advanced and metastatic urothelial carcinoma: a single-arm, multicentre, phase 2 trial. Lancet. 2017;389(10064):67-76. doi:10.1016/S0140-6736(16)32455-2

25. Bellmunt J, de Wit R, Vaughn DJ, et al. Pembrolizumab as second-line therapy for advanced urothelial carcinoma. N Engl J Med. 2017;376(11):1015-1026. doi:10.1056/NEJMoa1613683

26. Teo MY, Bambury RM, Zabor EC, et al. DNA damage response and repair gene alterations are associated with improved survival in patients receiving platinum-based chemotherapy in urothelial carcinoma. Clin Cancer Res. 2017;23(14):3610-3618. doi:10.1158/1078-0432.CCR-16-2520

27. Plimack ER, Dunbrack RL, Brennan TA, et al. Defects in DNA repair genes predict response to neoadjuvant cisplatin-based chemotherapy in muscle-invasive bladder cancer. Eur Urol. 2015;68(6):959-967. doi:10.1016/j.eururo.2015.07.009

28. Toffoli G, Cecchin E, Gasparini G, et al. Genotype-driven phase I study of irinotecan administered in FOLFIRI regimen to metastatic colorectal cancer patients. Clin Cancer Res. 2010;16(2):635-642. doi:10.1158/1078-0432.CCR-09-1521

29. Loriot Y, Necchi A, Park SH, et al. Erdafitinib in locally advanced or metastatic urothelial carcinoma. N Engl J Med. 2019;381(4):338-348. doi:10.1056/NEJMoa1817323

30. Pal SK, Rosenberg JE, Keam B, et al. Efficacy and safety of erdafitinib in patients with FGFR mutations and gene fusions: an update from the phase II BLC2001 trial. J Clin Oncol. 2020;38(15_suppl):5019. doi:10.1200/JCO.2020.38.15_suppl.5019

31. Wheeler HE, Maitland ML, Dolan ME, et al. Cancer pharmacogenomics: strategies and challenges. Nat Rev Genet. 2013;14(1):23-34. doi:10.1038/nrg3352

32. Siefker-Radtke AO, Necchi A, Park SH, et al. Efficacy and safety of erdafitinib in patients with locally advanced or metastatic urothelial carcinoma and FGFR alterations: a report from the BLC2001 phase II trial. J Clin Oncol. 2020;38(17):1892-1900. doi:10.1200/JCO.19.02304

33. FDA approves erdafitinib for metastatic urothelial carcinoma. FDA. 2019. Available at: https://www.fda.gov/drugs/resources-information-approved-drugs/fda-approves-erdafitinib-metastatic-urothelial-carcinoma

34. Seiler R, Ashab HA, Erho N, et al. Impact of molecular subtypes in muscle-invasive bladder cancer on predicting response and survival after neoadjuvant chemotherapy. Eur Urol. 2017;72(4):544-554. doi:10.1016/j.eururo.2017.04.022

35. Warrick JI, Sjödahl G, Kaag M, et al. Intrinsic molecular subtypes of high-grade bladder cancer reflect the hallmarks of breast cancer biology. Proc Natl Acad Sci U S A. 2014;111(8):3110-3115. doi:10.1073/pnas.1318376111

36. Powles T, Durán I, van der Heijden MS, et al. Atezolizumab versus chemotherapy in patients with platinum-treated locally advanced or metastatic urothelial carcinoma (IMvigor211): a multicentre, open-label, phase 3 randomised controlled trial. Lancet. 2018;391(10122):748-757. doi:10.1016/S0140-6736(17)33297-X

37. Birkenkamp-Demtröder K, Christensen E, Nordentoft I, et al. Monitoring treatment response and metastatic relapse in advanced bladder cancer by liquid biopsy analysis. Eur Urol. 2018;73(4):535-540. doi:10.1016/j.eururo.2017.11.011

38. Christensen E, Birkenkamp-Demtröder K, Nordentoft I, et al. Early detection of metastatic relapse and monitoring of therapeutic efficacy by ultra-deep sequencing of plasma cell-free DNA in patients with urothelial bladder carcinoma. J Clin Oncol. 2019;37(18):1547-1557. doi:10.1200/JCO.18.02052

39. Li Y, Xiao Z, Hu K, et al. Single-cell transcriptomic analysis reveals dynamic changes in gene expression of circulating tumor cells during chemotherapy in patients with bladder cancer. Oncogene. 2021;40(8):1458-1472. doi:10.1038/s41388-020-01639-6

40. Chen X, Zhou Q, Liu Y, et al. Single-cell RNA-seq reveals hypothyroidism-induced defects in cardiomyocyte maturation. Cell Rep. 2019;28(11):2771-2783.e6. doi:10.1016/j.celrep.2019.08.024

41. Kather JN, Krisam J, Charoentong P, et al. Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study. PLoS Med. 2019;16(1):e1002730. doi:10.1371/journal.pmed.1002730

42. Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Biol. 2017;18(1):83. doi:10.1186/s13059-017-1215-1

43. Bertz S, Otto W, Denzinger S, et al. Combination of CK20 and Ki-67 as a urinary marker for the detection of bladder cancer. Oncol Rep. 2011;25(4):1065-1071. doi:10.3892/or.2011.1161

44. Guo G, Sun X, Chen C, et al. Whole-genome and whole-exome sequencing of bladder cancer identifies frequent alterations in genes involved in sister chromatid cohesion and segregation. Nat Genet. 2013;45(12):1459-1463. doi:10.1038/ng.2798

45. Hussain MH, MacVicar GR, Petrylak DP, et al. Trastuzumab, paclitaxel, carboplatin, and gemcitabine in advanced urothelial cancer: results of a multicenter phase II National Cancer Institute Trial. J Clin Oncol. 2007;25(16):2218-2224. doi:10.1200/JCO.2006.09.7593

46. Rosenberg JE, Hoffman-Censits J, Powles T, et al. Atezolizumab in patients with locally advanced and metastatic urothelial carcinoma who have progressed following treatment with platinum-based chemotherapy: a single-arm, multicentre, phase 2 trial. Lancet. 2016;387(10031):1909-1920. doi:10.1016/S0140-6736(16)00561-4

47. Van Allen EM, Mouw KW, Kim P, et al. Somatic ERCC2 mutations correlate with cisplatin sensitivity in muscle-invasive urothelial carcinoma. Cancer Discov. 2014;4(10):1140-1153. doi:10.1158/2159-8290.CD-14-0623

48. Kompier LC, Lurkin I, van der Aa MN, et al. FGFR3, HRAS, KRAS, NRAS and PIK3CA mutations in bladder cancer and their potential as biomarkers for surveillance and therapy. PLoS One. 2010;5(11):e13821. doi:10.1371/journal.pone.0013821

49. Nickerson ML, Dancik GM, Im KM, et al. Concurrent alterations in TERT, KDM6A, and the BRCA pathway in bladder cancer. Clin Cancer Res. 2014;20(18):4935-4948. doi:10.1158/1078-0432.CCR-14-0330

50. Kaneko S, Li X. BRCA1 and BRCA2 gene mutations and breast cancer: emerging concepts in genetics and embryonic development. Bioessays. 2000;22(8):728-737. doi:10.1002/1521-1878(200008)22:8<728::AID-BIES7>3.0.CO;2-O

51. Bartkova J, Horejsí Z, Koed K, et al. DNA damage response as a candidate anti-cancer barrier in early human tumorigenesis. Nature. 2005;434(7035):864-870. doi:10.1038/nature03482

52. Leão R, Apolo AB, Lopez-Beltran A, et al. Combined genotype and phenotype selection of muscle-invasive bladder cancer patients eligible for bladder-sparing strategies. Br J Cancer. 2017;117(4):580-586. doi:10.1038/bjc.2017.202

53. O'Donnell PH, Guo J, Amos CI, et al. Analysis of the EGFR pathway reveals association of EGFR polymorphisms with cancer susceptibility and outcome in advanced bladder cancer patients. Pharmacogenomics J. 2012;12(3):255-263. doi:10.1038/tpj.2010.90

54. Millán-Rodríguez F, Chéchile-Toniolo G, Salvador-Bayarri J, et al. Primary superficial bladder cancer risk groups according to progression, mortality and recurrence. J Urol. 2000;164(3 Pt 1):680-684. doi:10.1016/S0022-5347(05)67280-4

55. Williams SV, Hurst CD, Knowles MA. Oncogenic FGFR3 gene fusions in bladder cancer. Hum Mol Genet. 2013;22(4):795-803. doi:10.1093/hmg/dds486

56. Tomlinson DC, Lamont FR, Shnyder SD, Knowles MA. Fibroblast growth factor receptor 1 promotes proliferation and survival via activation of the mitogen-activated protein kinase pathway in bladder cancer. Cancer Res. 2009;69(11):4613-4620. doi:10.1158/0008-5472.CAN-08-4567

57. Olsson H, Hultman P, Rosengren B, et al. Cyclin D1 gene amplification and protein overexpression are frequent events in infiltrating urothelial bladder carcinoma. Int J Oncol. 2012;41(4):1144-1152. doi:10.3892/ijo.2012.1537

58. Kim WJ, Kim EJ, Kim SK, et al. Predictive value of progression-related gene classifier in primary non-muscle invasive bladder cancer. Mol Cancer. 2010;9:3. doi:10.1186/1476-4598-9-3

59. van Rhijn BW, Vis AN, van der Kwast TH, et al. Molecular grading of urothelial cell carcinoma with FGFR3 and MIB-1 is superior to pathological grade for the prediction of clinical outcome. J Clin Oncol. 2003;21(10):1912-1921. doi:10.1200/JCO.2003.03.105

60. Ouerhani S, Hermanns T, Baer C, et al. The RAS–RAF–MEK–ERK pathway mutations and their sensitivity to MEK inhibitors in urothelial carcinoma. BMC Cancer. 2019;19(1):384. doi:10.1186/s12885-019-5601-2

61. Balbás-Martínez C, Sagrera A, Carrillo-de-Santa-Pau E, et al. Recurrent inactivation of STAG2 in bladder cancer is not associated with aneuploidy. Nat Genet. 2013;45(12):1464-1469. doi:10.1038/ng.2799

62. Williams SV, Hurst CD, Knowles MA. Oncogenic FGFR3 gene fusions in bladder cancer. Hum Mol Genet. 2013;22(4):795-803. doi:10.1093/hmg/dds486

63. Bakkar AA, Wallerand H, Radvanyi F, et al. FGFR3 and TP53 gene mutations define two distinct pathways in urothelial cell carcinoma of the bladder. Cancer Res. 2003;63(23):8108-8112.

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

65. Cazier JB, Rao SR, McLean CM, et al. Whole-genome sequencing of bladder cancers reveals somatic CDKN1A mutations and clinicopathological associations with mutation burden. Nat Commun. 2014;5:3756. doi:10.1038/ncomms4756

66. Lamy A, Gobet F, Laurent M, et al. Molecular profiling of muscle-invasive bladder cancer in a homogenous series of patients treated with neoadjuvant chemotherapy. Eur Urol. 2015;68(2):284-287. doi:10.1016/j.eururo.2015.03.024

67. Knowles MA, Platt FM, Ross RL, Hurst CD. Phosphatidylinositol 3-kinase (PI3K) pathway activation in bladder cancer. Cancer Metastasis Rev. 2009;28(3-4):305-316. doi:10.1007/s10555-009-9187-y

68. Ouerhani S, Hermanns T, Baer C, et al. The RAS–RAF–MEK–ERK pathway mutations and their sensitivity to MEK inhibitors in urothelial carcinoma. BMC Cancer. 2019;19(1):384. doi:10.1186/s12885-019-5601-2

69. Chiong E, Lee IL, Dadbin A, et al. Effects of mTOR inhibitor everolimus (RAD001) on bladder cancer cells. Clin Cancer Res. 2011;17(9):2863-2873. doi:10.1158/1078-0432.CCR-10-3198

70. Warrick JI, Kaag M, Raman JD, et al. Transcriptomic differences in muscle-invasive bladder cancer as a function of gender. PLoS One. 2018;13(1):e0194031. doi:10.1371/journal.pone.0194031

71. Sjödahl G, Lauss M, Lövgren K, et al. A molecular taxonomy for urothelial carcinoma. Clin Cancer Res. 2012;18(12):3377-3386. doi:10.1158/1078-0432.CCR-12-0077

72. Kim J, Akbani R, Creighton CJ, et al. Invasive bladder cancer: genomic insights and therapeutic promise. Clin Cancer Res. 2015;21(20):4514-4524. doi:10.1158/1078-0432.CCR-14-1215

73. Robertson AG, Kim J, Al-Ahmadie H, et al. Comprehensive molecular characterization of muscle-invasive bladder cancer. Cell. 2017;171(3):540-556.e25. doi:10.1016/j.cell.2017.09.007

74. Hussain MH, MacVicar GR, Petrylak DP, et al. Trastuzumab, paclitaxel, carboplatin, and gemcitabine in advanced urothelial cancer: results of a multicenter phase II National Cancer Institute Trial. J Clin Oncol. 2007;25(16):2218-2224. doi:10.1200/JCO.2006.09.7593

75. Sridhar SS, Canil CM, Hotte SJ, et al. Phase II study of lapatinib in patients with squamous cell carcinoma of the head and neck or skin, or with urothelial carcinoma. Invest New Drugs. 2013;31(4):1207-1213. doi:10.1007/s10637-013-9981-8

76. Laé M, Couturier J, Oudard S, et al. Assessing HER2 gene amplification as a prognostic and predictive marker in urothelial carcinoma of the upper urinary tract. BJU Int. 2010;105(1):57-62. doi:10.1111/j.1464-410X.2009.08654.x

77. Sharma P, Callahan MK, Bono P, et al. Nivolumab monotherapy in metastatic urothelial carcinoma: longer-term efficacy and safety results from the CheckMate 032 study. J Clin Oncol. 2018;36(15):1685-1691. doi:10.1200/JCO.2017.75.3803

78. Powles T, Eder JP, Fine GD, et al. MPDL3280A (anti-PD-L1) treatment leads to clinical activity in metastatic bladder cancer. Nature. 2014;515(7528):558-562. doi:10.1038/nature13904

79. Rosenberg JE, Hoffman-Censits J, Powles T, et al. Atezolizumab in patients with locally advanced and metastatic urothelial carcinoma who have progressed following treatment with platinum-based chemotherapy: a single-arm, multicentre, phase 2 trial. Lancet. 2016;387(10031):1909-1920. doi:10.1016/S0140-6736(16)00561-4

80. Van Allen EM, Mouw KW, Kim P, et al. Somatic ERCC2 mutations correlate with cisplatin sensitivity in muscle-invasive urothelial carcinoma. Cancer Discov. 2014;4(10):1140-1153. doi:10.1158/2159-8290.CD-14-0623

81. Plimack ER, Dunbrack RL, Brennan TA, et al. Defects in DNA repair genes predict response to neoadjuvant cisplatin-based chemotherapy in muscle-invasive bladder cancer. Eur Urol. 2015;68(2):959-967. doi:10.1016/j.eururo.2015.07.009

82. Iyer G, Al-Ahmadie H, Schultz N, et al. Prevalence and co-occurrence of actionable genomic alterations in high-grade bladder cancer. J Clin Oncol. 2013;31(25):3133-3140. doi:10.1200/JCO.2012.46.5740

83. Wheeler HE, Maitland ML, Dolan ME, et al. Cancer pharmacogenomics: strategies and challenges. Nat Rev Genet. 2013;14(1):23-34. doi:10.1038/nrg3352

84. Goyal L, Shi L, Liu LY, et al. TAS-120, an FGFR inhibitor, in advanced cholangiocarcinoma. N Engl J Med. 2019;381(21):2040-2042. doi:10.1056/NEJMc1910563

85. Subbiah V, Meric-Bernstam F. Advances in targeting FGFR signaling pathways in cancer. Clin Cancer Res. 2015;21(23):5433-5434. doi:10.1158/1078-0432.CCR-15-1800

86. Innocenti F, Owzar K, Irvin RG, et al. UGT1A1 polymorphisms and irinotecan-induced neutropenia: a URCC cancer control and prevention network study. J Clin Oncol. 2009;27(15):2416-2422. doi:10.1200/JCO.2008.19.1748

87. Hoskins JM, Marcuello E, Altes A, et al. Irinotecan pharmacogenetics: influence of pharmacodynamic genes. Clin Cancer Res. 2008;14(6):1788-1796. doi:10.1158/1078-0432.CCR-07-1260

88. Relling MV, Evans WE. Pharmacogenomics in the clinic. Nature. 2015;526(7573):343-350. doi:10.1038/nature15817

89. Lesko LJ, Zineh I, Huang SM. What is clinical utility and why should we care? Clin Pharmacol Ther. 2010;88(6):729-733. doi:10.1038/clpt.2010.214

90. McGranahan N, Swanton C. Clonal heterogeneity and tumor evolution: past, present, and the future. Cell. 2017;168(4):613-628. doi:10.1016/j.cell.2017.01.018

91. Massard C, Mateo J, Roumier M, et al. Phase I trial of a selective inhibitor of PI3Kα in patients with advanced solid tumors. Cancer Discov. 2019;9(1):36-44. doi:10.1158/2159-8290.CD-18-0718

92. Bedard PL, Tahir S, Razak AR, et al. A phase I study of PX-866, an oral irreversible pan-isoform inhibitor of phosphoinositide 3-kinase, in patients with advanced solid tumors. Invest New Drugs. 2013;31(1):233-240. doi:10.1007/s10637-012-9856-6

93. Sjödahl G, Eriksson P, Liedberg F, et al. Molecular classification of urothelial carcinoma: global mRNA classification versus tumour-cell phenotype classification. J Pathol. 2017;242(1):113-125. doi:10.1002/path.4886

94. Damrauer JS, Hoadley KA, Chism DD, et al. Intrinsic subtypes of high-grade bladder cancer reflect the hallmarks of breast cancer biology. Proc Natl Acad Sci U S A. 2014;111(8):3110-3115. doi:10.1073/pnas.1318376111

95. Pal SK, Rosenberg JE, Keam B, et al. Efficacy and safety of erdafitinib in patients with FGFR mutations and gene fusions: an update from the phase II BLC2001 trial. J Clin Oncol. 2020;38(15_suppl):5019. doi:10.1200/JCO.2020.38.15_suppl.5019

96. NCT03473743. A study of erdafitinib with or without cetrelimab in participants with metastatic or locally advanced urothelial cancer (BLC2001-03). ClinicalTrials.gov. https://clinicaltrials.gov/ct2/show/NCT03473743

97. Bettegowda C, Sausen M, Leary RJ, et al. Detection of circulating tumor DNA in early- and late-stage human malignancies. Sci Transl Med. 2014;6(224):224ra24. doi:10.1126/scitranslmed.3007094

98. Dudley JC, Schroers-Martin J, Lazzareschi DV, et al. Detection and surveillance of bladder cancer using urine tumor DNA. Cancer Discov. 2019;9(4):500-509. doi:10.1158/2159-8290.CD-18-0825

99. Stuart T, Satija R. Integrative single-cell analysis. Nat Rev Genet. 2019;20(5):257-272. doi:10.1038/s41576-019-0093-7

100. Nguyen QH, Pervolarakis N, Blake K, et al. Profiling human breast epithelial cells using single cell RNA sequencing identifies cell diversity. Nat Commun. 2018;9(1):2028. doi:10.1038/s41467-018-04334-1

101. Patel AP, Tirosh I, Trombetta JJ, et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science. 2014;344(6190):1396-1401. doi:10.1126/science.1254257

102. Esteva A, Robicquet A, Ramsundar B, et al. A guide to deep learning in healthcare. Nat Med. 2019;25(1):24-29. doi:10.1038/s41591-018-0316-z

103. Johnson KW, Torres Soto J, Glicksberg BS, et al. Artificial intelligence in cardiology. J Am Coll Cardiol. 2018;71(23):2668-2679. doi:10.1016/j.jacc.2018.03.521

104. Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Biol. 2017;18(1):83. doi:10.1186/s13059-017-1215-1

105. Karczewski KJ, Snyder MP. Integrative omics for health and disease. Nat Rev Genet. 2018;19(5):299-310. doi:10.1038/nrg.2018.4

106. Fröhlich H, Balling R, Beerenwinkel N, et al. From hype to reality: data science enabling personalized medicine. BMC Med. 2018;16(1):150. doi:10.1186/s12916-018-1122-7

107. Pitt JM, Marabelle A, Eggermont A, et al. Targeting the tumor microenvironment: removing obstruction to anticancer immune responses and immunotherapy. Ann Oncol. 2016;27(8):1482-1492. doi:10.1093/annonc/mdw168

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