Detection of circulating tumor DNA for advanced bladder cancer: where are we going?

Bladder cancer (BC) is the ninth most common malignant disease and the thirteenth most common cause of cancer death worldwide. In 2012, 429,793 cases of BC were diagnosed and nearly 165,000 deaths were recorded globally (1). Urothelial carcinoma (UC) is the most common histologic subtype of BC, and represents nearly 90% of all cases (2). Around thirty percent of cases are diagnosed as muscle-invasive urothelial carcinoma (MIUC) and radical cystectomy (RC) remains the cornerstone of therapy (3). However, cancer-specific survival (CSS) after RC is relatively low, ranging from 72% at 5 years for patients with organ-confined disease, to 48–25% at 5 years in patients with extravesical extension or lymph node metastases. Around 50% of patients with clinical stages T2b–T4a will develop metastatic disease after two years of RC (4). This supports the use of neoadjuvant cisplatin-based chemotherapy (NAC) to achieve a better control disease and increase overall survival. NAC has demonstrated a 5% increase in 5-year cancer specific survival in MUIC compared to surgery alone (5). Interestingly, the 5-year CSS for patients who achieved pathological response (<ypT2) is 90%, in contrast to the 30–40% CSS for those who does not reach this degree of response (4,5).

To date, there are no biomarkers to predict these responses to NAC and most of the methods that we use to evaluate responses are based on clinical data and imaging procedures. Additionally, some patients will suffer toxicity, without any benefit. Therefore, the identification of reliable biomarkers to identify patients who will benefit from NAC is needed.

Several molecular biomarkers have been tested for prediction of response to NAC, including: regulators of apoptosis and cell survival (4-7), DNA repair genes (5-13), ERBB2 mutations (14) and gene expression signatures (15-17). Recently, immunological markers (18) and molecular subtypes of MIUC have been evaluated as potential biomarkers in patients treated with NAC (19).

DNA is continuously released into the bloodstream. In cancer patients, the majority of plasma DNA originates from healthy cells, whereas a minor fraction is related to the tumor (20). In 1989, Stroun et al. reported for the first time that the presence of circulating tumor DNA (ctDNA) originates from cancer cells (21). In 2008, Diehl et al. demonstrated that ctDNA reflect tumor response and progression after surgery and could be used as a marker for residual diseases and also as a prognostic factor (22).

In 2016, Birkenkamp-Demtröder et al. reported tumor DNA in blood and urine in patients diagnosed with MUIC and non-MUIC (23). In this retrospective pilot study, 12 patients initially diagnosed with non-MIUC were sequenced using next generation sequencing. Six patients were followed for 4–20 years developing later progression to MUIC or metastatic disease, and 6 patients had recurrence of non-MUIC during the following time of 7–20 years. ctDNA was detectable in plasma in 83% of the patients. Interestingly, higher levels of ctDNA in plasma were detectable from all patients with progressive disease. Recently, Christensen et al. evaluated droplet digital polymerase chain reaction (ddPCR) assays for hotspot FGFR3 and PI3KCA mutation detection in liquid biopsies (24). A total of 831 patients were included (363 with non-MUIC and 468 treated with RC). The authors reported, 36% of the non-MUIC and 11% of the patients treated with RC harboured at least one FGFR3 or PIK3KCA mutation. Furthermore, high levels of ctDNA were significantly associated with recurrence.

On the other hand, the same authors demonstrated early detection of metastatic relapse and indications of treatment response using ctDNA analysis (25). This exciting prospective study included 50 patients treated with NAC followed RC and 10 patients treated with first line chemotherapy for metastatic disease. All the patients were analyzed using a total of 84 specific tests, including PIK3Ca and FGFR3 mutation detection, detection of tumour and germinal DNA exons identifying a total of 61 genes. They found that ctDNA level after RC was detectable in 75% of the patients and survival analysis showed a significant association between clinical relapse and high ctDNA level. More important was that patients without relapse had no detectable ctDNA levels after RC. Interestingly, ctDNA levels decreased after two to five cycles of chemotherapy and were correlated with initial response by computed tomography.

This study provide an excellent clinical utility of ctDNA and demonstrate that ctDNA may reflect the presence of residual disease even in the absence of any other clinical evidence of disease and could be a useful tool for identify patients with higher risk of relapse when patients may benefit from changes in treatment schedules. In addition, ctDNA could facilitate to identify mechanisms of resistance related to emerging therapy such as PI3KCA alteration that leads resistance to FGFR inhibition in BC. Moreover, this finding provides a strong rationale to evaluate PI3K inhibitors in combination with FGFR inhibitors. This study is limited by the small cohort size, but the results are promising and could have a significant impact in our clinical practice.

A better understanding of the biology of ctDNA would aid the implementation of this approach. At the moment, most of the clinical trials in BC are testing ctDNA analysis for treatment monitoring. This will ensure, randomized trials comparing ctDNA guided decision-making against the analysis of the primary tumor.

Lastly, ctDNA could interrogate the heterogeneity of metastatic disease and the possibility to using it for screening is also fascinating and likely to be attractive in the future.

Acknowledgements

None.

Footnote

Conflicts of Interest: The authors have no conflicts of interest to declare.

References

1. Ferlay J, Soerjomataram I, Diskshit R, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer 2015;136:E359-86. [Crossref] [PubMed]
2. Warrick JI. Clinical Significance of Histologic Variants of Bladder Cancer. J Natl Compr Canc Netw 2017;15:1268-74. [Crossref] [PubMed]
3. Sanli O, Dobruch J, Knowles MA, et al. Bladder Cancer. Nat Rev Dis Primers 2017;3:17022. [Crossref] [PubMed]
4. Stein JP, Lieskovsky G, Cote R, et al. Radical cystectomy in the treatment of invasive bladder cancer: long-term results in 1,054 patients. J Clin Oncol 2001;19:666-75. [Crossref] [PubMed]
5. I International Collaboration of Trialists. Medical Research Council Advanced Bladder Cancer Working Party. International phase III trial assessing neoadjuvant cisplatin, methotrexate, and vinblastine chemotherapy for muscle-invasive bladder cancer: long-term results of the BA0630894 trial. J Clin Oncol 2011;29:2171-7. [Crossref] [PubMed]
6. Plimack ER, Hoffman-Censits JH, Viterbo R, et al. Accelerated methotrexate, vinblastine, doxorubicin, and cisplatin is safe, effective, and efficient neoadjuvant treatment for muscle-invasive bladder cancer: results of a multicenter phase II study with molecular correlates of response and toxicity. J Clin Oncol 2014;32:1895-901. [Crossref] [PubMed]
7. Qureshi KN, Griffiths TR, Robinson MC, et al. TP53 accumulation predicts improved survival in patients resistant to systemic cisplatin-based chemotherapy for muscle-invasive bladder cancer. Clin Cancer Res 1999;5:3500-7. [PubMed]
8. Cooke PW, James ND, Ganesan R, et al. Bcl-2 expression identifies patients with advanced bladder cancer treated by radiotherapy who benefit from neoadjuvant chemotherapy. BJU Int 2000;85:829-35. [Crossref] [PubMed]
9. Choueiri TK, Jacobus S, Bellmunt J, et al. Neoadjuvant dose-dense methotrexate, vinblastine, doxorubicin, and cisplatin with pegfilgrastim support in muscle-invasive urothelial cancer: pathologic, radiologic, and biomarker correlates. J Clin Oncol 2014;32:1889-94. [Crossref] [PubMed]
10. Van Allen EM, Mouw KW, Kim P, et al. Somatic ERCC2 mutations correlate with cisplatin sensitivity in muscle-invasive urothelial carcinoma. Cancer Discov 2014;4:1140-53. [Crossref] [PubMed]
11. Liu D, Plimack ER, Hoffman-Censits J, et al. Clinical Validation of Chemotherapy Response Biomarker ERCC2 in Muscle-Invasive Urothelial Bladder Carcinoma. JAMA Oncol 2016;2:1094-6. [Crossref] [PubMed]
12. Iyer G, Balar AV, Milowsky MI, et al. Correlation of DNA damage response (DDR) gene alterations with response to neoadjuvant (neo) dose-dense gemcitabine and cisplatin (ddGC) in urothelial carcinoma (UC). J Clin Oncol 2016;34:abstr 5011.
13. 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;68:959-67. [Crossref] [PubMed]
14. Groenendijk FH, de Jong J, Fransen van de Putte EE, et al. ERBB2 Mutations Characterize a Subgroup of Muscle-invasive Bladder Cancers with Excellent Response to Neoadjuvant Chemotherapy. Eur Urol 2016;69:384-8. [Crossref] [PubMed]
15. Takata R, Katagiri T, Kanehira M, et al. Predicting response to methotrexate, vinblastine, doxorubicin, and cisplatin neoadjuvant chemotherapy for bladder cancers through genome-wide gene expression profiling. Clin Cancer Res 2005;11:2625-36. [Crossref] [PubMed]
16. Takata R, Katagiri T, Kanehira M, et al. Validation study of the prediction system for clinical response of M-VAC neoadjuvant chemotherapy. Cancer Sci 2007;98:113-7. [Crossref] [PubMed]
17. Kato Y, Zembutsu H, Takata R, et al. Predicting response of bladder cancers to gemcitabine and carboplatin neoadjuvant chemotherapy through genome-wide gene expression profiling. Exp Ther Med 2011;2:47-56. [Crossref] [PubMed]
18. Tervahartiala M, Taimen P, Mirtti T. el al. Immunological tumor status may predict response to neoadjuvant chemotherapy and outcome after radical cystectomy in bladder cancer. Sci Rep 2017;7:12682. [Crossref] [PubMed]
19. Longo T, McGinley KF, Freedman JA, et al. Targeted Exome Sequencing of the Cancer Genome in Patients with Very High-risk Bladder Cancer. Eur Urol 2016;70:714-7. [Crossref] [PubMed]
20. Wan JCM, Massie C, Garcia-Corbacho J, et al. Liquid biopsies come of age: towards implementation of circulating tumour DNA. Nat Rev Cancer 2017;17:223-38. [Crossref] [PubMed]
21. Stroun M, Anker P, Maurice P, et al. Neoplastic characteristics of the DNA found in the plasma of cancer patients. Oncology 1989;46:318-22. [Crossref] [PubMed]
22. Diehl F, Schmidt K, Choti MA, et al. Circulating mutant DNA to assess tumor dynamics. Nat Med 2008;14:985-90. [Crossref] [PubMed]
23. Birkenkamp-Demtröder K, Nordentoft I, Christensen E, et al. Genomic Alterations in Liquid Biopsies from Patients with Bladder Cancer. Eur Urol 2016;70:75-82. [Crossref] [PubMed]
24. Christensen E, Birkenkamp-Demtröder K, Nordentoft I, et al. Liquid Biopsy Analysis of FGFR3 and PIK3CA Hotspot Mutations for Disease Surveillance in Bladder Cancer. Eur Urol 2017;71:961-9. [Crossref] [PubMed]
25. 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 2017. [Epub ahead of print]. [Crossref] [PubMed]