External Validation of the Colon Life Nomogram for Predicting 12-Week Mortality in Dutch Metastatic Colorectal Cancer Patients Treated with Trifluridine/Tipiracil in Daily Practice
Abstract
:Simple Summary
Abstract
1. Introduction
2. Methods
2.1. External Validation Cohort
2.2. External Validation of the Colon Life Nomogram
2.2.1. Sample Size Calculations
2.2.2. Missing Data and Imputation
2.2.3. Assessing Model Performance
2.2.4. Additional Prognostic Value of Baseline QoL
2.3. Prognostic Value of TK1 Expression
2.4. Statistical Analysis
3. Results
3.1. External Validation Cohort
3.2. Assessment of Colon Life Nomogram Performance in the QUALITAS Dataset
3.3. Additional Prognostic Value of Baseline QoL
3.4. Prognostic Value of TK1 Expression
4. Discussion
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
References
- Van Cutsem, E.; Cervantes, A.; Adam, R.; Sobrero, A.; Van Krieken, J.H.; Aderka, D.; Aranda Aguilar, E.; Bardelli, A.; Benson, A.; Bodoky, G.; et al. ESMO Consensus Guidelines for the Management of Patients with Metastatic Colorectal Cancer. Ann. Oncol. 2016, 27, 1386–1422. [Google Scholar] [CrossRef] [PubMed]
- Mayer, R.J.; Van Cutsem, E.; Falcone, A.; Yoshino, T.; Garcia-Carbonero, R.; Mizunuma, N.; Yamazaki, K.; Shimada, Y.; Tabernero, J.; Komatsu, Y.; et al. Randomized Trial of TAS-102 for Refractory Metastatic Colorectal Cancer. N. Engl. J. Med. 2015, 372, 1909–1919. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Hamers, P.A.H.; Vink, G.R.; Elferink, M.A.G.; Stellato, R.K.; Dijksterhuis, W.P.M.; Punt, C.J.A.; Koopman, M.; May, A.M.; Beerepoot, L.V.; Creemers, G.-J.; et al. Quality of Life and Survival of Metastatic Colorectal Cancer Patients Treated with Trifluridine-Tipiracil (QUALITAS). Clin. Colorectal Cancer 2022, 21, 154–166. [Google Scholar] [CrossRef]
- Bachet, J.B.; Wyrwicz, L.; Price, T.; Cremolini, C.; Phelip, J.M.; Portales, F.; Ozet, A.; Cicin, I.; Atlan, D.; Becquart, M.; et al. Safety, Efficacy and Patient-Reported Outcomes with Trifluridine/Tipiracil in Pretreated Metastatic Colorectal Cancer: Results of the PRECONNECT Study. ESMO Open 2020, 5, e000698. [Google Scholar] [CrossRef] [PubMed]
- Pietrantonio, F.; Miceli, R.; Rimassa, L.; Lonardi, S.; Aprile, G.; Mennitto, A.; Marmorino, F.; Bozzarelli, S.; Antonuzzo, L.; Tamburini, E.; et al. Estimating 12-Week Death Probability in Patients with Refractory Metastatic Colorectal Cancer: The Colon Life Nomogram. Ann. Oncol. 2017, 28, 555–561. [Google Scholar] [CrossRef]
- Cremolini, C.; Rossini, D.; Martinelli, E.; Pietrantonio, F.; Lonardi, S.; Noventa, S.; Tamburini, E.; Frassineti, G.L.; Mosconi, S.; Nichetti, F.; et al. Trifluridine/Tipiracil (TAS-102) in Refractory Metastatic Colorectal Cancer: A Multicenter Register in the Frame of the Italian Compassionate Use Program. Oncologist 2018, 23, 1178–1187. [Google Scholar] [CrossRef] [Green Version]
- Pietrantonio, F.; Fucà, G.; Manca, P.; Pagani, F.; Raimondi, A.; Prisciandaro, M.; Randon, G.; Corti, F.; de Braud, F.; Cremolini, C.; et al. Validation of the Colon Life Nomogram in Patients with Refractory Metastatic Colorectal Cancer Enrolled in the RECOURSE Trial. Tumori J. 2021, 107, 353–359. [Google Scholar] [CrossRef]
- Moons, K.G.M.; Altman, D.G.; Reitsma, J.B.; Ioannidis, J.P.A.; Macaskill, P.; Steyerberg, E.W.; Vickers, A.J.; Ransohoff, D.F.; Collins, G.S. Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): Explanation and Elaboration. Ann. Intern. Med. 2015, 162, W1–W73. [Google Scholar] [CrossRef] [Green Version]
- Collins, G.S.; De Groot, J.A.; Dutton, S.; Omar, O.; Shanyinde, M.; Tajar, A.; Voysey, M.; Wharton, R.; Yu, L.M.; Moons, K.G.; et al. External Validation of Multivariable Prediction Models: A Systematic Review of Methodological Conduct and Reporting. BMC Med. Res. Methodol. 2014, 14, 40. [Google Scholar] [CrossRef] [Green Version]
- Kuboki, Y.; Nishina, T.; Shinozaki, E.; Yamazaki, K.; Shitara, K.; Okamoto, W.; Kajiwara, T.; Matsumoto, T.; Tsushima, T.; Mochizuki, N.; et al. TAS-102 plus Bevacizumab for Patients with Metastatic Colorectal Cancer Refractory to Standard Therapies (C-TASK FORCE): An Investigator-Initiated, Open-Label, Single-Arm, Multicentre, Phase 1/2 Study. Lancet Oncol. 2017, 18, 1172–1181. [Google Scholar] [CrossRef]
- Yoshino, T.; Yamazaki, K.; Shinozaki, E.; Komatsu, Y.; Nishina, T.; Baba, H.; Tsuji, A.; Tsuji, Y.; Yamaguchi, K.; Sugimoto, N.; et al. Relationship Between Thymidine Kinase 1 Expression and Trifluridine/Tipiracil Therapy in Refractory Metastatic Colorectal Cancer: A Pooled Analysis of 2 Randomized Clinical Trials. Clin. Colorectal Cancer 2018, 17, e719–e732. [Google Scholar] [CrossRef] [Green Version]
- Sakamoto, K.; Yokogawa, T.; Ueno, H.; Oguchi, K.; Kazuno, H.; Ishida, K.; Tanaka, N.; Osada, A.; Yamada, Y.; Okabe, H.; et al. Crucial Roles of Thymidine Kinase 1 and DeoxyUTPase in Incorporating the Antineoplastic Nucleosides Trifluridine and 2′-Deoxy-5-Fluorouridine into DNA. Int. J. Oncol. 2015, 46, 2327–2334. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Burbach, J.P.M.; Kurk, S.A.; Coebergh van den Braak, R.R.J.; Dik, V.K.; May, A.M.; Meijer, G.A.; Punt, C.J.A.; Vink, G.R.; Los, M.; Hoogerbrugge, N.; et al. Prospective Dutch Colorectal Cancer Cohort: An Infrastructure for Long-Term Observational, Prognostic, Predictive and (Randomized) Intervention Research. Acta Oncol. 2016, 55, 1273–1280. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Riley, R.D.; Debray, T.P.A.; Collins, G.S.; Archer, L.; Ensor, J.; van Smeden, M.; Snell, K.I.E. Minimum Sample Size for External Validation of a Clinical Prediction Model with a Binary Outcome. Stat. Med. 2021, 40, 4230–4251. [Google Scholar] [CrossRef]
- Van Buuren, S.; Oudshoorn, C.G.M. MICE: Multivariate Imputation by Chained Equations. J. Stat. Softw. 2011, 45, 1–67. [Google Scholar] [CrossRef] [Green Version]
- Shanahan, T.A.G.; Fuller, G.W.; Sheldon, T.; Turton, E.; Quilty, F.M.A.; Marincowitz, C. External Validation of the Dutch Prediction Model for Prehospital Triage of Trauma Patients in South West Region of England, United Kingdom. Injury 2021, 52, 1108–1116. [Google Scholar] [CrossRef]
- Grant, S.W.; Collins, G.S.; Nashef, S.A.M. Statistical Primer: Developing and Validating a Risk Prediction Model. Eur. J. Cardio-Thorac. Surg. 2018, 54, 203–208. [Google Scholar] [CrossRef]
- Vickers, A.J.; Elkin, E.B. Decision Curve Analysis: A Novel Method for Evaluating Prediction Models. Med. Decis. Mak. 2006, 26, 565–574. [Google Scholar] [CrossRef] [Green Version]
- Vickers, A.J.; van Calster, B.; Steyerberg, E.W. A Simple, Step-by-Step Guide to Interpreting Decision Curve Analysis. Diagnostic Progn. Res. 2019, 3, 18. [Google Scholar] [CrossRef] [Green Version]
- Rousson, V.; Zumbrunn, T. Decision Curve Analysis Revisited: Overall Net Benefit, Relationships to ROC Curve Analysis, and Application to Case-Control Studies. BMC Med. Inform. Decis. Mak. 2011, 11, 45. [Google Scholar] [CrossRef]
- Giesinger, J.M.; Kieffer, J.M.; Fayers, P.M.; Groenvold, M.; Petersen, M.A.; Scott, N.W.; Sprangers, M.A.G.; Velikova, G.; Aaronson, N.K. Replication and Validation of Higher Order Models Demonstrated That a Summary Score for the EORTC QLQ-C30 Is Robust. J. Clin. Epidemiol. 2016, 69, 79–88. [Google Scholar] [CrossRef] [Green Version]
- Casparie, M.; Tiebosch, A.T.M.G.; Burger, G.; Blauwgeers, H.; Van De Pol, A.; Van Krieken, J.H.J.M.; Meijer, G.A. Pathology Databanking and Biobanking in The Netherlands, a Central Role for PALGA, the Nationwide Histopathology and Cytopathology Data Network and Archive. Cell. Oncol. 2007, 29, 19–24. [Google Scholar] [CrossRef] [PubMed]
- Bankhead, P.; Loughrey, M.B.; Fernández, J.A.; Dombrowski, Y.; McArt, D.G.; Dunne, P.D.; McQuaid, S.; Gray, R.T.; Murray, L.J.; Coleman, H.G.; et al. QuPath: Open Source Software for Digital Pathology Image Analysis. Sci. Rep. 2017, 7, 16878. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Fedchenko, N.; Reifenrath, J. Different Approaches for Interpretation and Reporting of Immunohistochemistry Analysis Results in the Bone Tissue—A Review. Diagn. Pathol. 2014, 9, 221. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- R: A Language and Environment for Statistical Computing. Available online: https://www.r-project.org/ (accessed on 1 March 2022).
- Mahar, A.L.; Compton, C.; Halabi, S.; Hess, K.R.; Weiser, M.R.; Groome, P.A. Personalizing Prognosis in Colorectal Cancer: A Systematic Review of the Quality and Nature of Clinical Prognostic Tools for Survival Outcomes. J. Surg. Oncol. 2017, 116, 969–982. [Google Scholar] [CrossRef]
- Van Calster, B.; McLernon, D.J.; Van Smeden, M.; Wynants, L.; Steyerberg, E.W.; Bossuyt, P.; Collins, G.S.; MacAskill, P.; Moons, K.G.M.; Vickers, A.J. Calibration: The Achilles Heel of Predictive Analytics. BMC Med. 2019, 17, 230. [Google Scholar] [CrossRef] [Green Version]
- Prigerson, H.G.; Bao, Y.; Shah, M.A.; Elizabeth Paulk, M.; LeBlanc, T.W.; Schneider, B.J.; Garrido, M.M.; Carrington Reid, M.; Berlin, D.A.; Adelson, K.B.; et al. Chemotherapy Use, Performance Status, and Quality of Life at the End of Life. JAMA Oncol. 2015, 1, 778–784. [Google Scholar] [CrossRef]
- Akhlaghi, E.; Lehto, R.H.; Torabikhah, M.; Sharif Nia, H.; Taheri, A.; Zaboli, E.; Yaghoobzadeh, A. Chemotherapy Use and Quality of Life in Cancer Patients at the End of Life: An Integrative Review. Health Qual. Life Outcomes 2020, 18, 332. [Google Scholar] [CrossRef]
- Vergouwe, Y.; Steyerberg, E.W.; Eijkemans, M.J.C.; Habbema, J.D.F. Substantial Effective Sample Sizes Were Required for External Validation Studies of Predictive Logistic Regression Models. J. Clin. Epidemiol. 2005, 58, 475–483. [Google Scholar] [CrossRef] [PubMed]
- Collins, G.S.; Ogundimu, E.O.; Altman, D.G. Sample Size Considerations for the External Validation of a Multivariable Prognostic Model: A Resampling Study. Stat. Med. 2016, 35, 214–226. [Google Scholar] [CrossRef]
- Gotay, C.C.; Kawamoto, C.T.; Bottomley, A.; Efficace, F. The Prognostic Significance of Patient-Reported Outcomes in Cancer Clinical Trials. J. Clin. Oncol. 2008, 26, 1355–1363. [Google Scholar] [CrossRef] [PubMed]
- Bonnetain, F.; Borg, C.; Adams, R.R.; Ajani, J.A.; Benson, A.; Bleiberg, H.; Chibaudel, B.; Diaz-Rubio, E.; Douillard, J.Y.; Fuchs, C.S.; et al. How Health-Related Quality of Life Assessment Should Be Used in Advanced Colorectal Cancer Clinical Trials. Ann. Oncol. 2017, 28, 2077–2085. [Google Scholar] [CrossRef]
- Mol, L.; Ottevanger, P.B.; Koopman, M.; Punt, C.J.A. The Prognostic Value of WHO Performance Status in Relation to Quality of Life in Advanced Colorectal Cancer Patients. Eur. J. Cancer 2016, 66, 138–143. [Google Scholar] [CrossRef] [PubMed]
- Jagarlamudi, K.K.; Shaw, M. Thymidine Kinase 1 as a Tumor Biomarker: Technical Advances Offer New Potential to an Old Biomarker. Biomark. Med. 2018, 12, 1035–1048. [Google Scholar] [CrossRef] [PubMed]
QUALITAS [3] | Development Dataset [5] | External Validation Dataset [5] | Italian CUP [6] | RECOURSE [7] | |
---|---|---|---|---|---|
Total study population | 150 (100%) | 411 | 410 | 341 | 161 FTD/TPI |
Number of deaths within 12 weeks | 25 (17%) | 124 (30%) | 89 (22%) | 60 (18%) | 22 (14%) |
Inclusion period | 2017–2019 | 2006–2015 | 2010–2016 | 2015–2016 | 2012–2013 |
Country | the Netherlands | Italy | Italy | Italy | Japan, USA, EU, Australia |
Male sex | 102 (68%) | 242 (59%) | 251 (61%) | 212 (62%) | 109 (68%) |
Age | |||||
Mean (±SD) | 65.0 (±9.1) | NR | NR | NR | NR |
Median (IQR) | 65 (59–72) | 66 (58–72) | 65 (55–71) | 61 (NR) | NR |
Range | 33–86 | NR | NR | 33–81 | NR |
<65 | 68 (45%) | NR | NR | NR | 96 (60%) |
65–74 | 60 (40%) | NR | NR | NR | 7 (4%) |
≥75 | 22 (15%) | NR | NR | NR | 58 (36%) |
ECOG Performance Status | |||||
0 | 34 (23%) | 194 (47%) | 210 (51%) | 200 (59%) | 87 (54%) |
1 | 63 (42%) | 154 (38%) | 200 (49%) | 132 (39%) | 74 (46%) |
2 | 14 (9%) | 63 (15%) | 0 (0%) | 9 (2%) | 0 (0%) |
Missing | 39 (26%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) |
Primary tumour site | |||||
Right-sided colon | 44 (29%) | 128 (31%) | 135 (33%) | 99 (29%) | NR |
Left-sided colon | 54 (36%) | 156 (38%) | 179 (44%) | NR | NR |
Rectal | 52 (35%) | 127 (31%) | 96 (23%) | NR | 68 (42%) |
Primary tumour resection | 113 (75%) | 348 (85%) | 358 (87%) | 312 (91%) | 124 (77%) |
Synchronous mCRC a | 98 (65%) | 292 (71%) | 272 (66%) | 209 (61%) | NR |
Metachronous mCRC | 52 (35%) | 119 (29%) | 138 (34%) | 132 (39%) | NR |
Molecular pathologyb | |||||
BRAF mutation | 4 (3%) | 13 (3%) | 17 (4%) | 17 (5%) | NR |
BRAF wildtype | 108 (72%) | 219 (53%) | 224 (55%) | NR | NR |
BRAF status unavailable | 38 (25%) | 179 (44%) | 169 (41%) | NR | NR |
RAS mutation | 67 (45%) | 167 (41%) | 198 (49%) | 200 (59%) | NR |
RAS wildtype | 49 (33%) | 173 (42%) | 133 (32%) | NR | NR |
RAS status unavailable | 34 (23%) | 71 (17%) | 79 (19%) | NR | NR |
MSI | 2 (1%) | NR | NR | 6 (2%) | NR |
MSS | 87 (58%) | NR | NR | 172 (50% | NR |
MS status unavailable | 61 (41%) | NR | NR | 163 (48%) | NR |
Number of metastatic sites | |||||
No distant metastasis | 1 (0.7%) | 0 (0%) | 0 (0%) | 0 (0%) | NR |
1 organ | 20 (13%) | 87 (21%) | 81 (20%) | 14 (4%) | NR |
2 organs | 57 (38%) | 172 (42%) | 147 (36%) | 64 (19%) | NR |
3 organs | 48 (32%) | NR | NR | 122 (36%) | NR |
≥3 organs | 72 (48%) | 152 (37%) | 182 (44%) | 263 (77%) | NR |
≥4 organs | 24 (16%) | NR | NR | 141 (41%) | NR |
Localization of metastases | |||||
Liver | 115 (77%) | 313 (76%) | 308 (75%) | 267 (78%) | NR |
Liver only | 10 (7%) | NR | NR | 12 (4%) | NR |
Lung | 102 (68%) | 258 (63%) | 254 (62%) | 255 (75%) | NR |
Lung only | 6 (4%) | NR | NR | NR | NR |
Peritoneal | 31 (21%) | 95 (23%) | 102 (25%) | 82 (24%) | 30 (19%) |
Peritoneal only | 4 (3%) | NR | NR | NR | NR |
Bone | 28 (19%) | 36 (9%) | 29 (7%) | NR | NR |
Brain | 3 (2%) | 10 (2%) | 11 (3%) | 10 (3%) | NR |
No. of prior systemic treatment regimens | |||||
Median (range) | NR | 3 (1–7) | 3 (1–9) | NR | NR |
0 | 1 (1%) | 0 (0%) | 0 (0%) | 0 (0%) | NR |
1 | 18 (12%) | NR | NR | 21 (6%) | NR |
2 | 76 (51%) | NR | NR | 93 (27%) | NR |
3 | 41 (27%) | NR | NR | 96 (28%) | NR |
4 | 14 (9%) | NR | NR | 78 (23%) | NR |
5 | 0 (0%) | NR | NR | 31 (9%) | NR |
6 | 0 (0%) | NR | NR | 18 (5%) | NR |
≥7 | 0 (0%) | NR | NR | 4 (1%) | NR |
FTD/TPI-treated patients | 150 (100%) | 27 (6.6%) | 100 (24%) | 341 (100%) | 161 (100%) |
Regorafenib-treated patients | 0 (0%) | 113 (27%) | 91 (22%) | NR | NR |
Exposure to prior systemic anticancer agents | |||||
fluoropyrimidine | 150 (100%) | NR | NR | 337 (99%) | 161 (100%) |
irinotecan | 82 (55%) | NR | NR | 334 (98%) | 161 (100%) |
oxaliplatin | 132 (88%) | NR | NR | 312 (91%) | 161 (100%) |
bevacizumab | 95 (63%) | NR | NR | 294 (86%) | NR |
aflibercept | 0 (0%) | NR | NR | 31 (9%) | NR |
anti-EGFR | 47 (31%) | NR | NR | 143 (42%) | NR |
regorafenib | 0 (0%) | NR | NR | 121 (35%) | NR |
Exposure to treatments: | |||||
Standard chemotherapy agents c | 75 (50%) | NR | NR | NR | 161 (100%) |
Standard chemotherapy agents c + bevacizumab | 55 (37%) | NR | NR | NR | NR |
Time from diagnosis mCRC to start FTD/TPI (months) | |||||
Median (IQR) | 26.2 (16.8–40.8) | 19 (13–29) d | 26 (17–40) d | NR | NR |
<18 months | 43 (29%) | NR | NR | 90 (26%) | NR |
≥18 months | 107 (71%) | NR | NR | 248 (73%) | NR |
CEA (ng/mL) | |||||
Median (IQR) | 46 (16–259) | 42 (7–190) | 58 (15–252) | NR | NR |
Mean (SD) | 427 | NR | NR | NR | NR |
Missing | 10 (7%) | NR | NR | NR | NR |
Leucocytes (/μL) | |||||
<10.000 | 122 (81%) | 345 (84%) | 336 (82%) | NR | NR |
≥10.000 | 21 (14%) | 66 (16%) | 74 (18%) | NR | NR |
Missing | 7 (5%) | 0 (0%) | 0 (0%) | NR | NR |
Lactate dehydrogenase (U/L) | |||||
Median (IQR) | 272 (210–391) | 271 (191–480) | 353 (215–529) | NR | 351 (245–561) |
Missing | 7 (5%) | NR | NR | NR | NR |
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Hamers, P.A.H.; Wensink, G.E.; van Smeden, M.; Vink, G.R.; Smabers, L.P.; Lunenberg, R.A.; Laclé, M.M.; Koopman, M.; May, A.M.; Roodhart, J.M.L. External Validation of the Colon Life Nomogram for Predicting 12-Week Mortality in Dutch Metastatic Colorectal Cancer Patients Treated with Trifluridine/Tipiracil in Daily Practice. Cancers 2022, 14, 5094. https://doi.org/10.3390/cancers14205094
Hamers PAH, Wensink GE, van Smeden M, Vink GR, Smabers LP, Lunenberg RA, Laclé MM, Koopman M, May AM, Roodhart JML. External Validation of the Colon Life Nomogram for Predicting 12-Week Mortality in Dutch Metastatic Colorectal Cancer Patients Treated with Trifluridine/Tipiracil in Daily Practice. Cancers. 2022; 14(20):5094. https://doi.org/10.3390/cancers14205094
Chicago/Turabian StyleHamers, Patricia A. H., G. Emerens Wensink, Maarten van Smeden, Geraldine R. Vink, Lidwien P. Smabers, Renee A. Lunenberg, Miangela M. Laclé, Miriam Koopman, Anne M. May, and Jeanine M. L. Roodhart. 2022. "External Validation of the Colon Life Nomogram for Predicting 12-Week Mortality in Dutch Metastatic Colorectal Cancer Patients Treated with Trifluridine/Tipiracil in Daily Practice" Cancers 14, no. 20: 5094. https://doi.org/10.3390/cancers14205094
APA StyleHamers, P. A. H., Wensink, G. E., van Smeden, M., Vink, G. R., Smabers, L. P., Lunenberg, R. A., Laclé, M. M., Koopman, M., May, A. M., & Roodhart, J. M. L. (2022). External Validation of the Colon Life Nomogram for Predicting 12-Week Mortality in Dutch Metastatic Colorectal Cancer Patients Treated with Trifluridine/Tipiracil in Daily Practice. Cancers, 14(20), 5094. https://doi.org/10.3390/cancers14205094