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1. PredictCBC-2.0: a contralateral breast cancer risk prediction model developed and validated in ∼200,000 patients

Author

Giardiello, D, Hooning, MJ, Hauptmann, M, Keeman, R, Heemskerk-Gerritsen, BAM, Becher, H, Blomqvist, C, Bojesen, SE, Bolla, MK, Camp, NJ, Czene, K, Devilee, P, Eccles, DM, Fasching, PA, Figueroa, JD, Flyger, H, Garcia-Closas, M, Haiman, CA, Hamann, U, Hopper, JL, Jakubowska, A, Leeuwen, FE, Lindblom, A, Lubinski, J, Margolin, S, Martinez, ME, Nevanlinna, H, Nevelsteen, I, Pelders, S, Pharoah, PDP, Siesling, S, Southey, MC, van der Hout, AH, van Hest, LP, Chang-Claude, J, Hall, P, Easton, DF, Steyerberg, EW, Schmidt, MK, Giardiello, D, Hooning, MJ, Hauptmann, M, Keeman, R, Heemskerk-Gerritsen, BAM, Becher, H, Blomqvist, C, Bojesen, SE, Bolla, MK, Camp, NJ, Czene, K, Devilee, P, Eccles, DM, Fasching, PA, Figueroa, JD, Flyger, H, Garcia-Closas, M, Haiman, CA, Hamann, U, Hopper, JL, Jakubowska, A, Leeuwen, FE, Lindblom, A, Lubinski, J, Margolin, S, Martinez, ME, Nevanlinna, H, Nevelsteen, I, Pelders, S, Pharoah, PDP, Siesling, S, Southey, MC, van der Hout, AH, van Hest, LP, Chang-Claude, J, Hall, P, Easton, DF, Steyerberg, EW, and Schmidt, MK

Abstract

BACKGROUND: Prediction of contralateral breast cancer (CBC) risk is challenging due to moderate performances of the known risk factors. We aimed to improve our previous risk prediction model (PredictCBC) by updated follow-up and including additional risk factors. METHODS: We included data from 207,510 invasive breast cancer patients participating in 23 studies. In total, 8225 CBC events occurred over a median follow-up of 10.2 years. In addition to the previously included risk factors, PredictCBC-2.0 included CHEK2 c.1100delC, a 313 variant polygenic risk score (PRS-313), body mass index (BMI), and parity. Fine and Gray regression was used to fit the model. Calibration and a time-dependent area under the curve (AUC) at 5 and 10 years were assessed to determine the performance of the models. Decision curve analysis was performed to evaluate the net benefit of PredictCBC-2.0 and previous PredictCBC models. RESULTS: The discrimination of PredictCBC-2.0 at 10 years was higher than PredictCBC with an AUC of 0.65 (95% prediction intervals (PI) 0.56-0.74) versus 0.63 (95%PI 0.54-0.71). PredictCBC-2.0 was well calibrated with an observed/expected ratio at 10 years of 0.92 (95%PI 0.34-2.54). Decision curve analysis for contralateral preventive mastectomy (CPM) showed the potential clinical utility of PredictCBC-2.0 between thresholds of 4 and 12% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers. CONCLUSIONS: Additional genetic information beyond BRCA1/2 germline mutations improved CBC risk prediction and might help tailor clinical decision-making toward CPM or alternative preventive strategies. Identifying patients who benefit from CPM, especially in the general breast cancer population, remains challenging.

Published
2022

2. PredictCBC-2.0: a contralateral breast cancer risk prediction model developed and validated in ∼ 200,000 patients (vol 24, 69, 2022)

Author

Giardiello, D, Hooning, MJ, Hauptmann, M, Keeman, R, Heemskerk-Gerritsen, BAM, Becher, H, Blomqvist, C, Bojesen, SE, Bolla, MK, Camp, NJ, Czene, K, Devilee, P, Eccles, DM, Fasching, PA, Figueroa, JD, Flyger, H, Garcia-Closas, M, Haiman, CA, Hamann, U, Hopper, JL, Jakubowska, A, Leeuwen, FE, Lindblom, A, Lubinski, J, Margolin, S, Martinez, ME, Nevanlinna, H, Nevelsteen, I, Pelders, S, Pharoah, PDP, Siesling, S, Southey, MC, van der Hout, AH, van Hest, LP, Chang-Claude, J, Hall, P, Easton, DF, Steyerberg, EW, Schmidt, MK, Giardiello, D, Hooning, MJ, Hauptmann, M, Keeman, R, Heemskerk-Gerritsen, BAM, Becher, H, Blomqvist, C, Bojesen, SE, Bolla, MK, Camp, NJ, Czene, K, Devilee, P, Eccles, DM, Fasching, PA, Figueroa, JD, Flyger, H, Garcia-Closas, M, Haiman, CA, Hamann, U, Hopper, JL, Jakubowska, A, Leeuwen, FE, Lindblom, A, Lubinski, J, Margolin, S, Martinez, ME, Nevanlinna, H, Nevelsteen, I, Pelders, S, Pharoah, PDP, Siesling, S, Southey, MC, van der Hout, AH, van Hest, LP, Chang-Claude, J, Hall, P, Easton, DF, Steyerberg, EW, and Schmidt, MK

Published
2022

6. Breast Cancer Polygenic Risk Score and Contralateral Breast Cancer Risk

Author

Kramer, I., Hooning, M.J., Mavaddat, N., Hauptmann, M., Keeman, R., Steyerberg, Ewout W., Giardiello, D., Antoniou, A.C., Pharoah, P.D., Canisius, S., Abu-Ful, Z., Andrulis, I.L., Anton-Culver, H., Aronson, K.J., Augustinsson, A., Becher, H., Beckmann, M.W., Behrens, S., Benitez, J., Bermisheva, M., Bogdanova, N.V., Bojesen, S.E., Bolla, M.K., Bonanni, B., Brauch, H., Bremer, M., Brucker, S.Y., Burwinkel, B., Castelao, J.E., Chan, T.L., Chang-Claude, J., Chanock, S.J., Chenevix-Trench, G., Choi, J.Y., Clarke, C.L., Collée, J.M., Couch, F.J., Cox, A, Cross, S.S., Czene, K., Daly, M.B., Devilee, P., Dörk, T., Dos-Santos-Silva, I., Dunning, A.M., Dwek, M., Eccles, D.M., Evans, D.G., Fasching, P.A., Flyger, H., Gago-Dominguez, M., García-Closas, M., García-Sáenz, J.A., Giles, G.G., Goldgar, D.E., González-Neira, A., Haiman, C.A., Håkansson, N., Hamann, U., Hartman, M, Heemskerk-Gerritsen, B.A., Hollestelle, A., Hopper, J.L., Hou, M.F., Howell, A., Ito, H., Jakimovska, M., Jakubowska, A., Janni, W., John, E.M., Jung, A., Kang, D., Kets, C.M., Khusnutdinova, E., Ko, Y.D., Kristensen, V.N., Kurian, A.W., Kwong, A., Lambrechts, D., Marchand, L. Le, Li, J, Lindblom, A., Lubiński, J., Mannermaa, A., Manoochehri, M., Margolin, S., Matsuo, K., Mavroudis, D., Meindl, A., Milne, R.L., Mulligan, A.M., Muranen, T.A., Neuhausen, S.L., Nevanlinna, H., Newman, W.G., Olshan, A.F., Olson, J.E., Olsson, H., Park-Simon, T.W., Peto, J., et al., Kramer, I., Hooning, M.J., Mavaddat, N., Hauptmann, M., Keeman, R., Steyerberg, Ewout W., Giardiello, D., Antoniou, A.C., Pharoah, P.D., Canisius, S., Abu-Ful, Z., Andrulis, I.L., Anton-Culver, H., Aronson, K.J., Augustinsson, A., Becher, H., Beckmann, M.W., Behrens, S., Benitez, J., Bermisheva, M., Bogdanova, N.V., Bojesen, S.E., Bolla, M.K., Bonanni, B., Brauch, H., Bremer, M., Brucker, S.Y., Burwinkel, B., Castelao, J.E., Chan, T.L., Chang-Claude, J., Chanock, S.J., Chenevix-Trench, G., Choi, J.Y., Clarke, C.L., Collée, J.M., Couch, F.J., Cox, A, Cross, S.S., Czene, K., Daly, M.B., Devilee, P., Dörk, T., Dos-Santos-Silva, I., Dunning, A.M., Dwek, M., Eccles, D.M., Evans, D.G., Fasching, P.A., Flyger, H., Gago-Dominguez, M., García-Closas, M., García-Sáenz, J.A., Giles, G.G., Goldgar, D.E., González-Neira, A., Haiman, C.A., Håkansson, N., Hamann, U., Hartman, M, Heemskerk-Gerritsen, B.A., Hollestelle, A., Hopper, J.L., Hou, M.F., Howell, A., Ito, H., Jakimovska, M., Jakubowska, A., Janni, W., John, E.M., Jung, A., Kang, D., Kets, C.M., Khusnutdinova, E., Ko, Y.D., Kristensen, V.N., Kurian, A.W., Kwong, A., Lambrechts, D., Marchand, L. Le, Li, J, Lindblom, A., Lubiński, J., Mannermaa, A., Manoochehri, M., Margolin, S., Matsuo, K., Mavroudis, D., Meindl, A., Milne, R.L., Mulligan, A.M., Muranen, T.A., Neuhausen, S.L., Nevanlinna, H., Newman, W.G., Olshan, A.F., Olson, J.E., Olsson, H., Park-Simon, T.W., and Peto, J., et al.

Abstract

Contains fulltext : 229375.pdf (Publisher’s version ) (Open Access), Previous research has shown that polygenic risk scores (PRSs) can be used to stratify women according to their risk of developing primary invasive breast cancer. This study aimed to evaluate the association between a recently validated PRS of 313 germline variants (PRS(313)) and contralateral breast cancer (CBC) risk. We included 56,068 women of European ancestry diagnosed with first invasive breast cancer from 1990 onward with follow-up from the Breast Cancer Association Consortium. Metachronous CBC risk (N = 1,027) according to the distribution of PRS(313) was quantified using Cox regression analyses. We assessed PRS(313) interaction with age at first diagnosis, family history, morphology, ER status, PR status, and HER2 status, and (neo)adjuvant therapy. In studies of Asian women, with limited follow-up, CBC risk associated with PRS(313) was assessed using logistic regression for 340 women with CBC compared with 12,133 women with unilateral breast cancer. Higher PRS(313) was associated with increased CBC risk: hazard ratio per standard deviation (SD) = 1.25 (95%CI = 1.18-1.33) for Europeans, and an OR per SD = 1.15 (95%CI = 1.02-1.29) for Asians. The absolute lifetime risks of CBC, accounting for death as competing risk, were 12.4% for European women at the 10(th) percentile and 20.5% at the 90(th) percentile of PRS(313). We found no evidence of confounding by or interaction with individual characteristics, characteristics of the primary tumor, or treatment. The C-index for the PRS(313) alone was 0.563 (95%CI = 0.547-0.586). In conclusion, PRS(313) is an independent factor associated with CBC risk and can be incorporated into CBC risk prediction models to help improve stratification and optimize surveillance and treatment strategies.

Published
2020

7. Contralateral breast cancer risk in patients with ductal carcinoma in situ and invasive breast cancer

Author

Giardiello, D., Krämer, I. (Irene), Hooning, M.J. (Maartje), Hauptmann, M. (Michael), Lips, E.H. (Esther), Sawyer, E.J. (Elinor), Thompson, AM, Munck, L. (Linda) de, Siesling, S. (Sabine), Wesseling, J. (Jelle), Steyerberg, E.W. (Ewout), Schmidt, M.K. (Marjanka), Giardiello, D., Krämer, I. (Irene), Hooning, M.J. (Maartje), Hauptmann, M. (Michael), Lips, E.H. (Esther), Sawyer, E.J. (Elinor), Thompson, AM, Munck, L. (Linda) de, Siesling, S. (Sabine), Wesseling, J. (Jelle), Steyerberg, E.W. (Ewout), and Schmidt, M.K. (Marjanka)

Abstract

We aimed to assess contralateral breast cancer (CBC) risk in patients with ductal carcinoma in situ (DCIS) compared with invasive breast cancer (BC). Women diagnosed with DCIS (N = 28,003) or stage I–III BC (N = 275,836) between 1989 and 2017 were identified from the nationwide Netherlands Cancer Registry. Cumulative incidences were estimated, accounting for competing risks, and hazard ratios (HRs) for metachronous invasive CBC. To evaluate effects of adjuvant systemic therapy and screening, separate analyses were performed for stage I BC without adjuvant systemic therapy and by mode of first BC detection. Multivariable models including clinico-pathological and treatment data were created to assess CBC risk prediction performance in DCIS patients. The 10- year cumulative incidence of invasive CBC was 4.8% for DCIS patients (CBC = 1334). Invasive CBC risk was higher in DCIS patients compared with invasive BC overall (HR = 1.10, 95% confidence interval (CI) = 1.04–1.17), and lower compared with stage I BC without adjuvant systemic therapy (HR = 0.87; 95% CI = 0.82–0.92). In patients diagnosed ≥2011, the HR for invasive CBC was 1.38 (95% CI = 1.35–1.68) after screen-detected DCIS compared with screen-detected invasive BC, and was 2.14 (95% CI = 1.46–3.13) when not screen-detected. The C-index was 0.52 (95% CI = 0.50–0.54) for invasive CBC prediction in DCIS patients. In conclusion, CBC risks are low overall. DCIS patients had a slightly higher risk of invasive CBC compared with invasive BC, likely explained by the risk-reducing effect of (neo)adjuvant systemic therapy among BC patients. For support of clinical decision making more information is needed to differentiate CBC risks among DCIS patients

Published
2020
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8. Prediction of contralateral breast cancer: external validation of risk calculators in 20 international cohorts.

Author

Smit V.T.H.B.M., Pharoah P.D.P., Shah M., Siesling S., Southey M.C., Schmidt M.K., Hooning M.J., Westenend P.J., Wendt C., Wang Q., Van't Veer L.J., van Ongeval C., van Leeuwen F.E., van Deurzen C.H.M., van den Broek A.J., Tollenaar R.A.E.M., Tapper W.J., Giardiello D., Hauptmann M., Steyerberg E.W., Adank M.A., Akdeniz D., Blom J.C., Blomqvist C., Bojesen S.E., Bolla M.K., Brinkhuis M., Chang-Claude J., Czene K., Devilee P., Dunning A.M., Easton D.F., Eccles D.M., Fasching P.A., Figueroa J., Flyger H., Garcia-Closas M., Haeberle L., Haiman C.A., Hall P., Hamann U., Hopper J.L., Jager A., Jakubowska A., Jung A., Keeman R., Koppert L.B., Kramer I., Lambrechts D., Le Marchand L., Lindblom A., Lubinski J., Manoochehri M., Mariani L., Nevanlinna H., Oldenburg H.S.A., Pelders S., Smit V.T.H.B.M., Pharoah P.D.P., Shah M., Siesling S., Southey M.C., Schmidt M.K., Hooning M.J., Westenend P.J., Wendt C., Wang Q., Van't Veer L.J., van Ongeval C., van Leeuwen F.E., van Deurzen C.H.M., van den Broek A.J., Tollenaar R.A.E.M., Tapper W.J., Giardiello D., Hauptmann M., Steyerberg E.W., Adank M.A., Akdeniz D., Blom J.C., Blomqvist C., Bojesen S.E., Bolla M.K., Brinkhuis M., Chang-Claude J., Czene K., Devilee P., Dunning A.M., Easton D.F., Eccles D.M., Fasching P.A., Figueroa J., Flyger H., Garcia-Closas M., Haeberle L., Haiman C.A., Hall P., Hamann U., Hopper J.L., Jager A., Jakubowska A., Jung A., Keeman R., Koppert L.B., Kramer I., Lambrechts D., Le Marchand L., Lindblom A., Lubinski J., Manoochehri M., Mariani L., Nevanlinna H., Oldenburg H.S.A., and Pelders S.

Abstract

Background: Three tools are currently available to predict the risk of contralateral breast cancer (CBC). We aimed to compare the performance of the Manchester formula, CBCrisk, and PredictCBC in patients with invasive breast cancer (BC). Method(s): We analyzed data of 132,756 patients (4682 CBC) from 20 international studies with a median follow-up of 8.8 years. Prediction performance included discrimination, quantified as a time-dependent Area-Under-the-Curve (AUC) at 5 and 10 years after diagnosis of primary BC, and calibration, quantified as the expected-observed (E/O) ratio at 5 and 10 years and the calibration slope. Result(s): The AUC at 10 years was: 0.58 (95% confidence intervals [CI] 0.57-0.59) for CBCrisk; 0.60 (95% CI 0.59-0.61) for the Manchester formula; 0.63 (95% CI 0.59-0.66) and 0.59 (95% CI 0.56-0.62) for PredictCBC-1A (for settings where BRCA1/2 mutation status is available) and PredictCBC-1B (for the general population), respectively. The E/O at 10 years: 0.82 (95% CI 0.51-1.32) for CBCrisk; 1.53 (95% CI 0.63-3.73) for the Manchester formula; 1.28 (95% CI 0.63-2.58) for PredictCBC-1A and 1.35 (95% CI 0.65-2.77) for PredictCBC-1B. The calibration slope was 1.26 (95% CI 1.01-1.50) for CBCrisk; 0.90 (95% CI 0.79-1.02) for PredictCBC-1A; 0.81 (95% CI 0.63-0.99) for PredictCBC-1B, and 0.39 (95% CI 0.34-0.43) for the Manchester formula. Conclusion(s): Current CBC risk prediction tools provide only moderate discrimination and the Manchester formula was poorly calibrated. Better predictors and re-calibration are needed to improve CBC prediction and to identify low- and high-CBC risk patients for clinical decision-making.Copyright © 2020, Springer Science+Business Media, LLC, part of Springer Nature.

Published
2020

9. Prediction and clinical utility of a contralateral breast cancer risk model.

Author

Wendt C., Van Leeuwen F.E., Van Ongeval C., Van't Veer L.J., Wang Q., Westenend P.J., Schmidt M.K., Hooning M.J., Giardiello D., Steyerberg E.W., Hauptmann M., Adank M.A., Akdeniz D., Blomqvist C., Bojesen S.E., Bolla M.K., Brinkhuis M., Chang-Claude J., Czene K., Devilee P., Dunning A.M., Easton D.F., Eccles D.M., Fasching P.A., Figueroa J., Flyger H., Garcia-Closas M., Haeberle L., Haiman C.A., Hall P., Hamann U., Hopper J.L., Jager A., Jakubowska A., Jung A., Keeman R., Kramer I., Lambrechts D., Le Marchand L., Lindblom A., Lubinski J., Manoochehri M., Mariani L., Nevanlinna H., Oldenburg H.S.A., Pelders S., Pharoah P.D.P., Shah M., Siesling S., Smit V.T.H.B.M., Southey M.C., Tapper W.J., Tollenaar R.A.E.M., Van Den Broek A.J., Van Deurzen C.H.M., Wendt C., Van Leeuwen F.E., Van Ongeval C., Van't Veer L.J., Wang Q., Westenend P.J., Schmidt M.K., Hooning M.J., Giardiello D., Steyerberg E.W., Hauptmann M., Adank M.A., Akdeniz D., Blomqvist C., Bojesen S.E., Bolla M.K., Brinkhuis M., Chang-Claude J., Czene K., Devilee P., Dunning A.M., Easton D.F., Eccles D.M., Fasching P.A., Figueroa J., Flyger H., Garcia-Closas M., Haeberle L., Haiman C.A., Hall P., Hamann U., Hopper J.L., Jager A., Jakubowska A., Jung A., Keeman R., Kramer I., Lambrechts D., Le Marchand L., Lindblom A., Lubinski J., Manoochehri M., Mariani L., Nevanlinna H., Oldenburg H.S.A., Pelders S., Pharoah P.D.P., Shah M., Siesling S., Smit V.T.H.B.M., Southey M.C., Tapper W.J., Tollenaar R.A.E.M., Van Den Broek A.J., and Van Deurzen C.H.M.

Abstract

Background: Breast cancer survivors are at risk for contralateral breast cancer (CBC), with the consequent burden of further treatment and potentially less favorable prognosis. We aimed to develop and validate a CBC risk prediction model and evaluate its applicability for clinical decision-making. Method(s): We included data of 132,756 invasive non-metastatic breast cancer patients from 20 studies with 4682 CBC events and a median follow-up of 8.8 years. We developed a multivariable Fine and Gray prediction model (PredictCBC-1A) including patient, primary tumor, and treatment characteristics and BRCA1/2 germline mutation status, accounting for the competing risks of death and distant metastasis. We also developed a model without BRCA1/2 mutation status (PredictCBC-1B) since this information was available for only 6% of patients and is routinely unavailable in the general breast cancer population. Prediction performance was evaluated using calibration and discrimination, calculated by a time-dependent area under the curve (AUC) at 5 and 10 years after diagnosis of primary breast cancer, and an internal-external cross-validation procedure. Decision curve analysis was performed to evaluate the net benefit of the model to quantify clinical utility. Result(s): In the multivariable model, BRCA1/2 germline mutation status, family history, and systemic adjuvant treatment showed the strongest associations with CBC risk. The AUC of PredictCBC-1A was 0.63 (95% prediction interval (PI) at 5 years, 0.52-0.74; at 10 years, 0.53-0.72). Calibration-in-the-large was-0.13 (95% PI:-1.62-1.37), and the calibration slope was 0.90 (95% PI: 0.73-1.08). The AUC of Predict-1B at 10 years was 0.59 (95% PI: 0.52-0.66); calibration was slightly lower. Decision curve analysis for preventive contralateral mastectomy showed potential clinical utility of PredictCBC-1A between thresholds of 4-10% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers. Conclusion(s): We developed a reasonab

Published
2020

10. Prediction of contralateral breast cancer: external validation of risk calculators in 20 international cohorts

Author

Giardiello, D., Hauptmann, M. (Michael), Steyerberg, E.W. (Ewout), Adank, M.A. (Muriel), Akdeniz, D., Blom, JC, Schmidt, Marjanka K., Giardiello, D., Hauptmann, M. (Michael), Steyerberg, E.W. (Ewout), Adank, M.A. (Muriel), Akdeniz, D., Blom, JC, and Schmidt, Marjanka K.

Abstract

Background Three tools are currently available to predict the risk of contralateral breast cancer (CBC). We aimed to compare the performance of the Manchester formula, CBCrisk, and PredictCBC in patients with invasive breast cancer (BC). Methods We analyzed data of 132,756 patients (4682 CBC) from 20 international studies with a median follow-up of 8.8 years. Prediction performance included discrimination, quantifed as a time-dependent Area-Under-the-Curve (AUC) at 5 and 10 years after diagnosis of primary BC, and

Published
2020
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11. Prediction of contralateral breast cancer: external validation of risk calculators in 20 international cohorts.

Author

Smit V.T.H.B.M., Pharoah P.D.P., Shah M., Siesling S., Southey M.C., Schmidt M.K., Hooning M.J., Westenend P.J., Wendt C., Wang Q., Van't Veer L.J., van Ongeval C., van Leeuwen F.E., van Deurzen C.H.M., van den Broek A.J., Tollenaar R.A.E.M., Tapper W.J., Giardiello D., Hauptmann M., Steyerberg E.W., Adank M.A., Akdeniz D., Blom J.C., Blomqvist C., Bojesen S.E., Bolla M.K., Brinkhuis M., Chang-Claude J., Czene K., Devilee P., Dunning A.M., Easton D.F., Eccles D.M., Fasching P.A., Figueroa J., Flyger H., Garcia-Closas M., Haeberle L., Haiman C.A., Hall P., Hamann U., Hopper J.L., Jager A., Jakubowska A., Jung A., Keeman R., Koppert L.B., Kramer I., Lambrechts D., Le Marchand L., Lindblom A., Lubinski J., Manoochehri M., Mariani L., Nevanlinna H., Oldenburg H.S.A., Pelders S., Smit V.T.H.B.M., Pharoah P.D.P., Shah M., Siesling S., Southey M.C., Schmidt M.K., Hooning M.J., Westenend P.J., Wendt C., Wang Q., Van't Veer L.J., van Ongeval C., van Leeuwen F.E., van Deurzen C.H.M., van den Broek A.J., Tollenaar R.A.E.M., Tapper W.J., Giardiello D., Hauptmann M., Steyerberg E.W., Adank M.A., Akdeniz D., Blom J.C., Blomqvist C., Bojesen S.E., Bolla M.K., Brinkhuis M., Chang-Claude J., Czene K., Devilee P., Dunning A.M., Easton D.F., Eccles D.M., Fasching P.A., Figueroa J., Flyger H., Garcia-Closas M., Haeberle L., Haiman C.A., Hall P., Hamann U., Hopper J.L., Jager A., Jakubowska A., Jung A., Keeman R., Koppert L.B., Kramer I., Lambrechts D., Le Marchand L., Lindblom A., Lubinski J., Manoochehri M., Mariani L., Nevanlinna H., Oldenburg H.S.A., and Pelders S.

Abstract

Background: Three tools are currently available to predict the risk of contralateral breast cancer (CBC). We aimed to compare the performance of the Manchester formula, CBCrisk, and PredictCBC in patients with invasive breast cancer (BC). Method(s): We analyzed data of 132,756 patients (4682 CBC) from 20 international studies with a median follow-up of 8.8 years. Prediction performance included discrimination, quantified as a time-dependent Area-Under-the-Curve (AUC) at 5 and 10 years after diagnosis of primary BC, and calibration, quantified as the expected-observed (E/O) ratio at 5 and 10 years and the calibration slope. Result(s): The AUC at 10 years was: 0.58 (95% confidence intervals [CI] 0.57-0.59) for CBCrisk; 0.60 (95% CI 0.59-0.61) for the Manchester formula; 0.63 (95% CI 0.59-0.66) and 0.59 (95% CI 0.56-0.62) for PredictCBC-1A (for settings where BRCA1/2 mutation status is available) and PredictCBC-1B (for the general population), respectively. The E/O at 10 years: 0.82 (95% CI 0.51-1.32) for CBCrisk; 1.53 (95% CI 0.63-3.73) for the Manchester formula; 1.28 (95% CI 0.63-2.58) for PredictCBC-1A and 1.35 (95% CI 0.65-2.77) for PredictCBC-1B. The calibration slope was 1.26 (95% CI 1.01-1.50) for CBCrisk; 0.90 (95% CI 0.79-1.02) for PredictCBC-1A; 0.81 (95% CI 0.63-0.99) for PredictCBC-1B, and 0.39 (95% CI 0.34-0.43) for the Manchester formula. Conclusion(s): Current CBC risk prediction tools provide only moderate discrimination and the Manchester formula was poorly calibrated. Better predictors and re-calibration are needed to improve CBC prediction and to identify low- and high-CBC risk patients for clinical decision-making.Copyright © 2020, Springer Science+Business Media, LLC, part of Springer Nature.

Published
2020

12. Prediction and clinical utility of a contralateral breast cancer risk model.

Author

Wendt C., Van Leeuwen F.E., Van Ongeval C., Van't Veer L.J., Wang Q., Westenend P.J., Schmidt M.K., Hooning M.J., Giardiello D., Steyerberg E.W., Hauptmann M., Adank M.A., Akdeniz D., Blomqvist C., Bojesen S.E., Bolla M.K., Brinkhuis M., Chang-Claude J., Czene K., Devilee P., Dunning A.M., Easton D.F., Eccles D.M., Fasching P.A., Figueroa J., Flyger H., Garcia-Closas M., Haeberle L., Haiman C.A., Hall P., Hamann U., Hopper J.L., Jager A., Jakubowska A., Jung A., Keeman R., Kramer I., Lambrechts D., Le Marchand L., Lindblom A., Lubinski J., Manoochehri M., Mariani L., Nevanlinna H., Oldenburg H.S.A., Pelders S., Pharoah P.D.P., Shah M., Siesling S., Smit V.T.H.B.M., Southey M.C., Tapper W.J., Tollenaar R.A.E.M., Van Den Broek A.J., Van Deurzen C.H.M., Wendt C., Van Leeuwen F.E., Van Ongeval C., Van't Veer L.J., Wang Q., Westenend P.J., Schmidt M.K., Hooning M.J., Giardiello D., Steyerberg E.W., Hauptmann M., Adank M.A., Akdeniz D., Blomqvist C., Bojesen S.E., Bolla M.K., Brinkhuis M., Chang-Claude J., Czene K., Devilee P., Dunning A.M., Easton D.F., Eccles D.M., Fasching P.A., Figueroa J., Flyger H., Garcia-Closas M., Haeberle L., Haiman C.A., Hall P., Hamann U., Hopper J.L., Jager A., Jakubowska A., Jung A., Keeman R., Kramer I., Lambrechts D., Le Marchand L., Lindblom A., Lubinski J., Manoochehri M., Mariani L., Nevanlinna H., Oldenburg H.S.A., Pelders S., Pharoah P.D.P., Shah M., Siesling S., Smit V.T.H.B.M., Southey M.C., Tapper W.J., Tollenaar R.A.E.M., Van Den Broek A.J., and Van Deurzen C.H.M.

Abstract

Background: Breast cancer survivors are at risk for contralateral breast cancer (CBC), with the consequent burden of further treatment and potentially less favorable prognosis. We aimed to develop and validate a CBC risk prediction model and evaluate its applicability for clinical decision-making. Method(s): We included data of 132,756 invasive non-metastatic breast cancer patients from 20 studies with 4682 CBC events and a median follow-up of 8.8 years. We developed a multivariable Fine and Gray prediction model (PredictCBC-1A) including patient, primary tumor, and treatment characteristics and BRCA1/2 germline mutation status, accounting for the competing risks of death and distant metastasis. We also developed a model without BRCA1/2 mutation status (PredictCBC-1B) since this information was available for only 6% of patients and is routinely unavailable in the general breast cancer population. Prediction performance was evaluated using calibration and discrimination, calculated by a time-dependent area under the curve (AUC) at 5 and 10 years after diagnosis of primary breast cancer, and an internal-external cross-validation procedure. Decision curve analysis was performed to evaluate the net benefit of the model to quantify clinical utility. Result(s): In the multivariable model, BRCA1/2 germline mutation status, family history, and systemic adjuvant treatment showed the strongest associations with CBC risk. The AUC of PredictCBC-1A was 0.63 (95% prediction interval (PI) at 5 years, 0.52-0.74; at 10 years, 0.53-0.72). Calibration-in-the-large was-0.13 (95% PI:-1.62-1.37), and the calibration slope was 0.90 (95% PI: 0.73-1.08). The AUC of Predict-1B at 10 years was 0.59 (95% PI: 0.52-0.66); calibration was slightly lower. Decision curve analysis for preventive contralateral mastectomy showed potential clinical utility of PredictCBC-1A between thresholds of 4-10% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers. Conclusion(s): We developed a reasonab

Published
2020

13. Breast Cancer Polygenic Risk Score and Contralateral Breast Cancer Risk.

Author

Heemskerk-Gerritsen B.A.M., Ito H., Jakimovska M., Jakubowska A., Janni W., John E.M., Jung A., Kang D., Kets C.M., Khusnutdinova E., Ko Y.-D., Kristensen V.N., Kurian A.W., Kwong A., Lambrechts D., Le Marchand L., Li J., Lindblom A., Lubinski J., Mannermaa A., Manoochehri M., Margolin S., Matsuo K., Mavroudis D., Meindl A., Milne R.L., Mulligan A.M., Muranen T.A., Neuhausen S.L., Nevanlinna H., Newman W.G., Olshan A.F., Olson J.E., Olsson H., Park-Simon T.-W., Peto J., Petridis C., Plaseska-Karanfilska D., Presneau N., Pylkas K., Radice P., Rennert G., Romero A., Roylance R., Saloustros E., Sawyer E.J., Schmutzler R.K., Schwentner L., Scott C., See M.-H., Shah M., Shen C.-Y., Shu X.-O., Siesling S., Slager S., Sohn C., Spinelli J.J., Stone J., Tapper W.J., Tengstrom M., Teo S.H., Terry M.B., Tollenaar R.A.E.M., Tomlinson I., Troester M.A., Vachon C.M., van Ongeval C., van Veen E.M., Winqvist R., Wolk A., Zheng W., Ziogas A., Easton D.F., Hall P., Schmidt M.K., Kramer I., Hooning M.J., Mavaddat N., Hauptmann M., Keeman R., Steyerberg E.W., Giardiello D., Antoniou A.C., Pharoah P.D.P., Canisius S., Abu-Ful Z., Andrulis I.L., Anton-Culver H., Aronson K.J., Augustinsson A., Becher H., Beckmann M.W., Behrens S., Benitez J., Bermisheva M., Bogdanova N.V., Bojesen S.E., Bolla M.K., Bonanni B., Brauch H., Bremer M., Brucker S.Y., Burwinkel B., Castelao J.E., Chan T.L., Chang-Claude J., Chanock S.J., Chenevix-Trench G., Choi J.-Y., Clarke C.L., Borresen-Dale A.-L., Sahlberg K., Ottestad L., Karesen R., Schlichting E., Holmen M.M., Sauer T., Haakensen V., Engebraten O., Naume B., Fossa A., Kiserud C., Reinertsen K., Helland A., Riis M., Geisler J., Alnaes G.G., Collee J.M., Couch F.J., Cox A., Cross S.S., Czene K., Daly M.B., Devilee P., Dork T., dos-Santos-Silva I., Dunning A.M., Dwek M., Eccles D.M., Evans D.G., Fasching P.A., Flyger H., Gago-Dominguez M., Garcia-Closas M., Garcia-Saenz J.A., Giles G.G., Goldgar D.E., Gonzalez-Neira A., Haiman C.A., Hakansson N., Hamann U., Hartman M., Hollestelle A., Hopper J.L., Hou M.-F., Howell A., Clarke C., Marsh D., Scott R., Baxter R., Yip D., Carpenter J., Davis A., Pathmanathan N., Simpson P., Graham J.D., Sachchithananthan M., Amor D., Andrews L., Antill Y., Balleine R., Beesley J., Bennett I., Bogwitz M., Botes L., Brennan M., Brown M., Buckley M., Burke J., Butow P., Caldon L., Campbell I., Chauhan D., Chauhan M., Christian A., Cohen P., Colley A., Crook A., Cui J., Cummings M., Dawson S.-J., deFazio A., Delatycki M., Dickson R., Dixon J., Edkins T., Edwards S., Farshid G., Fellows A., Fenton G., Field M., Flanagan J., Fong P., Forrest L., Fox S., French J., Friedlander M., Gaff C., Gattas M., George P., Greening S., Harris M., Hart S., Hayward N., Hopper J., Hoskins C., Hunt C., James P., Jenkins M., Kidd A., Kirk J., Koehler J., Kollias J., Lakhani S., Lawrence M., Lindeman G., Lipton L., Lobb L., Mann G., McLachlan S.A., Meiser B., Nightingale S., O'Connell S., O'Sullivan S., Ortega D.G., Pachter N., Patterson B., Pearn A., Phillips K., Pieper E., Rickard E., Robinson B., Saleh M., Salisbury E., Saunders C., Saunus J., Sexton A., Shelling A., Southey M.C., Spurdle A., Taylor J., Taylor R., Thorne H., Trainer A., Tucker K., Visvader J., Walker L., Williams R., Winship I., Young M.A., Heemskerk-Gerritsen B.A.M., Ito H., Jakimovska M., Jakubowska A., Janni W., John E.M., Jung A., Kang D., Kets C.M., Khusnutdinova E., Ko Y.-D., Kristensen V.N., Kurian A.W., Kwong A., Lambrechts D., Le Marchand L., Li J., Lindblom A., Lubinski J., Mannermaa A., Manoochehri M., Margolin S., Matsuo K., Mavroudis D., Meindl A., Milne R.L., Mulligan A.M., Muranen T.A., Neuhausen S.L., Nevanlinna H., Newman W.G., Olshan A.F., Olson J.E., Olsson H., Park-Simon T.-W., Peto J., Petridis C., Plaseska-Karanfilska D., Presneau N., Pylkas K., Radice P., Rennert G., Romero A., Roylance R., Saloustros E., Sawyer E.J., Schmutzler R.K., Schwentner L., Scott C., See M.-H., Shah M., Shen C.-Y., Shu X.-O., Siesling S., Slager S., Sohn C., Spinelli J.J., Stone J., Tapper W.J., Tengstrom M., Teo S.H., Terry M.B., Tollenaar R.A.E.M., Tomlinson I., Troester M.A., Vachon C.M., van Ongeval C., van Veen E.M., Winqvist R., Wolk A., Zheng W., Ziogas A., Easton D.F., Hall P., Schmidt M.K., Kramer I., Hooning M.J., Mavaddat N., Hauptmann M., Keeman R., Steyerberg E.W., Giardiello D., Antoniou A.C., Pharoah P.D.P., Canisius S., Abu-Ful Z., Andrulis I.L., Anton-Culver H., Aronson K.J., Augustinsson A., Becher H., Beckmann M.W., Behrens S., Benitez J., Bermisheva M., Bogdanova N.V., Bojesen S.E., Bolla M.K., Bonanni B., Brauch H., Bremer M., Brucker S.Y., Burwinkel B., Castelao J.E., Chan T.L., Chang-Claude J., Chanock S.J., Chenevix-Trench G., Choi J.-Y., Clarke C.L., Borresen-Dale A.-L., Sahlberg K., Ottestad L., Karesen R., Schlichting E., Holmen M.M., Sauer T., Haakensen V., Engebraten O., Naume B., Fossa A., Kiserud C., Reinertsen K., Helland A., Riis M., Geisler J., Alnaes G.G., Collee J.M., Couch F.J., Cox A., Cross S.S., Czene K., Daly M.B., Devilee P., Dork T., dos-Santos-Silva I., Dunning A.M., Dwek M., Eccles D.M., Evans D.G., Fasching P.A., Flyger H., Gago-Dominguez M., Garcia-Closas M., Garcia-Saenz J.A., Giles G.G., Goldgar D.E., Gonzalez-Neira A., Haiman C.A., Hakansson N., Hamann U., Hartman M., Hollestelle A., Hopper J.L., Hou M.-F., Howell A., Clarke C., Marsh D., Scott R., Baxter R., Yip D., Carpenter J., Davis A., Pathmanathan N., Simpson P., Graham J.D., Sachchithananthan M., Amor D., Andrews L., Antill Y., Balleine R., Beesley J., Bennett I., Bogwitz M., Botes L., Brennan M., Brown M., Buckley M., Burke J., Butow P., Caldon L., Campbell I., Chauhan D., Chauhan M., Christian A., Cohen P., Colley A., Crook A., Cui J., Cummings M., Dawson S.-J., deFazio A., Delatycki M., Dickson R., Dixon J., Edkins T., Edwards S., Farshid G., Fellows A., Fenton G., Field M., Flanagan J., Fong P., Forrest L., Fox S., French J., Friedlander M., Gaff C., Gattas M., George P., Greening S., Harris M., Hart S., Hayward N., Hopper J., Hoskins C., Hunt C., James P., Jenkins M., Kidd A., Kirk J., Koehler J., Kollias J., Lakhani S., Lawrence M., Lindeman G., Lipton L., Lobb L., Mann G., McLachlan S.A., Meiser B., Nightingale S., O'Connell S., O'Sullivan S., Ortega D.G., Pachter N., Patterson B., Pearn A., Phillips K., Pieper E., Rickard E., Robinson B., Saleh M., Salisbury E., Saunders C., Saunus J., Sexton A., Shelling A., Southey M.C., Spurdle A., Taylor J., Taylor R., Thorne H., Trainer A., Tucker K., Visvader J., Walker L., Williams R., Winship I., and Young M.A.

Abstract

Previous research has shown that polygenic risk scores (PRSs) can be used to stratify women according to their risk of developing primary invasive breast cancer. This study aimed to evaluate the association between a recently validated PRS of 313 germline variants (PRS313) and contralateral breast cancer (CBC) risk. We included 56,068 women of European ancestry diagnosed with first invasive breast cancer from 1990 onward with follow-up from the Breast Cancer Association Consortium. Metachronous CBC risk (N = 1,027) according to the distribution of PRS313 was quantified using Cox regression analyses. We assessed PRS313 interaction with age at first diagnosis, family history, morphology, ER status, PR status, and HER2 status, and (neo)adjuvant therapy. In studies of Asian women, with limited follow-up, CBC risk associated with PRS313 was assessed using logistic regression for 340 women with CBC compared with 12,133 women with unilateral breast cancer. Higher PRS313 was associated with increased CBC risk: hazard ratio per standard deviation (SD) = 1.25 (95%CI = 1.18-1.33) for Europeans, and an OR per SD = 1.15 (95%CI = 1.02-1.29) for Asians. The absolute lifetime risks of CBC, accounting for death as competing risk, were 12.4% for European women at the 10th percentile and 20.5% at the 90th percentile of PRS313. We found no evidence of confounding by or interaction with individual characteristics, characteristics of the primary tumor, or treatment. The C-index for the PRS313 alone was 0.563 (95%CI = 0.547-0.586). In conclusion, PRS313 is an independent factor associated with CBC risk and can be incorporated into CBC risk prediction models to help improve stratification and optimize surveillance and treatment strategies.Copyright © 2020 American Society of Human Genetics

Published
2020

14. Prediction of contralateral breast cancer: external validation of risk calculators in 20 international cohorts

Author

Giardiello, D, Hauptmann, M, Steyerberg, EW, Adank, MA, Akdeniz, D, Blom, JC, Blomqvist, C, Bojesen, SE, Bolla, MK, Brinkhuis, M, Chang-Claude, J, Czene, K, Devilee, P, Dunning, AM, Easton, DF, Eccles, DM, Fasching, PA, Figueroa, J, Flyger, H, Garcia-Closas, M, Haeberle, L, Haiman, CA, Hall, P, Hamann, U, Hopper, JL, Jager, A, Jakubowska, A, Jung, A, Keeman, R, Koppert, LB, Kramer, I, Lambrechts, D, Le Marchand, L, Lindblom, A, Lubinski, J, Manoochehri, M, Mariani, L, Nevanlinna, H, Oldenburg, HSA, Pelders, S, Pharoah, PDP, Shah, M, Siesling, S, Smit, VTHBM, Southey, MC, Tapper, WJ, Tollenaar, RAEM, van den Broek, AJ, van Deurzen, CHM, van Leeuwen, FE, van Ongeval, C, Van't Veer, LJ, Wang, Q, Wendt, C, Westenend, PJ, Hooning, MJ, Schmidt, MK, Giardiello, D, Hauptmann, M, Steyerberg, EW, Adank, MA, Akdeniz, D, Blom, JC, Blomqvist, C, Bojesen, SE, Bolla, MK, Brinkhuis, M, Chang-Claude, J, Czene, K, Devilee, P, Dunning, AM, Easton, DF, Eccles, DM, Fasching, PA, Figueroa, J, Flyger, H, Garcia-Closas, M, Haeberle, L, Haiman, CA, Hall, P, Hamann, U, Hopper, JL, Jager, A, Jakubowska, A, Jung, A, Keeman, R, Koppert, LB, Kramer, I, Lambrechts, D, Le Marchand, L, Lindblom, A, Lubinski, J, Manoochehri, M, Mariani, L, Nevanlinna, H, Oldenburg, HSA, Pelders, S, Pharoah, PDP, Shah, M, Siesling, S, Smit, VTHBM, Southey, MC, Tapper, WJ, Tollenaar, RAEM, van den Broek, AJ, van Deurzen, CHM, van Leeuwen, FE, van Ongeval, C, Van't Veer, LJ, Wang, Q, Wendt, C, Westenend, PJ, Hooning, MJ, and Schmidt, MK

Abstract

BACKGROUND: Three tools are currently available to predict the risk of contralateral breast cancer (CBC). We aimed to compare the performance of the Manchester formula, CBCrisk, and PredictCBC in patients with invasive breast cancer (BC). METHODS: We analyzed data of 132,756 patients (4682 CBC) from 20 international studies with a median follow-up of 8.8 years. Prediction performance included discrimination, quantified as a time-dependent Area-Under-the-Curve (AUC) at 5 and 10 years after diagnosis of primary BC, and calibration, quantified as the expected-observed (E/O) ratio at 5 and 10 years and the calibration slope. RESULTS: The AUC at 10 years was: 0.58 (95% confidence intervals [CI] 0.57-0.59) for CBCrisk; 0.60 (95% CI 0.59-0.61) for the Manchester formula; 0.63 (95% CI 0.59-0.66) and 0.59 (95% CI 0.56-0.62) for PredictCBC-1A (for settings where BRCA1/2 mutation status is available) and PredictCBC-1B (for the general population), respectively. The E/O at 10 years: 0.82 (95% CI 0.51-1.32) for CBCrisk; 1.53 (95% CI 0.63-3.73) for the Manchester formula; 1.28 (95% CI 0.63-2.58) for PredictCBC-1A and 1.35 (95% CI 0.65-2.77) for PredictCBC-1B. The calibration slope was 1.26 (95% CI 1.01-1.50) for CBCrisk; 0.90 (95% CI 0.79-1.02) for PredictCBC-1A; 0.81 (95% CI 0.63-0.99) for PredictCBC-1B, and 0.39 (95% CI 0.34-0.43) for the Manchester formula. CONCLUSIONS: Current CBC risk prediction tools provide only moderate discrimination and the Manchester formula was poorly calibrated. Better predictors and re-calibration are needed to improve CBC prediction and to identify low- and high-CBC risk patients for clinical decision-making.

Published
2020

15. Breast Cancer Polygenic Risk Score and Contralateral Breast Cancer Risk

Author

Kramer, I., Hooning, M.J., Mavaddat, N., Hauptmann, M., Keeman, R., Steyerberg, Ewout W., Giardiello, D., Antoniou, A.C., Pharoah, P.D., Canisius, S., Abu-Ful, Z., Andrulis, I.L., Anton-Culver, H., Aronson, K.J., Augustinsson, A., Becher, H., Beckmann, M.W., Behrens, S., Benitez, J., Bermisheva, M., Bogdanova, N.V., Bojesen, S.E., Bolla, M.K., Bonanni, B., Brauch, H., Bremer, M., Brucker, S.Y., Burwinkel, B., Castelao, J.E., Chan, T.L., Chang-Claude, J., Chanock, S.J., Chenevix-Trench, G., Choi, J.Y., Clarke, C.L., Collée, J.M., Couch, F.J., Cox, A, Cross, S.S., Czene, K., Daly, M.B., Devilee, P., Dörk, T., Dos-Santos-Silva, I., Dunning, A.M., Dwek, M., Eccles, D.M., Evans, D.G., Fasching, P.A., Flyger, H., Gago-Dominguez, M., García-Closas, M., García-Sáenz, J.A., Giles, G.G., Goldgar, D.E., González-Neira, A., Haiman, C.A., Håkansson, N., Hamann, U., Hartman, M, Heemskerk-Gerritsen, B.A., Hollestelle, A., Hopper, J.L., Hou, M.F., Howell, A., Ito, H., Jakimovska, M., Jakubowska, A., Janni, W., John, E.M., Jung, A., Kang, D., Kets, C.M., Khusnutdinova, E., Ko, Y.D., Kristensen, V.N., Kurian, A.W., Kwong, A., Lambrechts, D., Marchand, L. Le, Li, J, Lindblom, A., Lubiński, J., Mannermaa, A., Manoochehri, M., Margolin, S., Matsuo, K., Mavroudis, D., Meindl, A., Milne, R.L., Mulligan, A.M., Muranen, T.A., Neuhausen, S.L., Nevanlinna, H., Newman, W.G., Olshan, A.F., Olson, J.E., Olsson, H., Park-Simon, T.W., Peto, J., et al., Kramer, I., Hooning, M.J., Mavaddat, N., Hauptmann, M., Keeman, R., Steyerberg, Ewout W., Giardiello, D., Antoniou, A.C., Pharoah, P.D., Canisius, S., Abu-Ful, Z., Andrulis, I.L., Anton-Culver, H., Aronson, K.J., Augustinsson, A., Becher, H., Beckmann, M.W., Behrens, S., Benitez, J., Bermisheva, M., Bogdanova, N.V., Bojesen, S.E., Bolla, M.K., Bonanni, B., Brauch, H., Bremer, M., Brucker, S.Y., Burwinkel, B., Castelao, J.E., Chan, T.L., Chang-Claude, J., Chanock, S.J., Chenevix-Trench, G., Choi, J.Y., Clarke, C.L., Collée, J.M., Couch, F.J., Cox, A, Cross, S.S., Czene, K., Daly, M.B., Devilee, P., Dörk, T., Dos-Santos-Silva, I., Dunning, A.M., Dwek, M., Eccles, D.M., Evans, D.G., Fasching, P.A., Flyger, H., Gago-Dominguez, M., García-Closas, M., García-Sáenz, J.A., Giles, G.G., Goldgar, D.E., González-Neira, A., Haiman, C.A., Håkansson, N., Hamann, U., Hartman, M, Heemskerk-Gerritsen, B.A., Hollestelle, A., Hopper, J.L., Hou, M.F., Howell, A., Ito, H., Jakimovska, M., Jakubowska, A., Janni, W., John, E.M., Jung, A., Kang, D., Kets, C.M., Khusnutdinova, E., Ko, Y.D., Kristensen, V.N., Kurian, A.W., Kwong, A., Lambrechts, D., Marchand, L. Le, Li, J, Lindblom, A., Lubiński, J., Mannermaa, A., Manoochehri, M., Margolin, S., Matsuo, K., Mavroudis, D., Meindl, A., Milne, R.L., Mulligan, A.M., Muranen, T.A., Neuhausen, S.L., Nevanlinna, H., Newman, W.G., Olshan, A.F., Olson, J.E., Olsson, H., Park-Simon, T.W., and Peto, J., et al.

Abstract

Contains fulltext : 229375.pdf (Publisher’s version ) (Open Access), Previous research has shown that polygenic risk scores (PRSs) can be used to stratify women according to their risk of developing primary invasive breast cancer. This study aimed to evaluate the association between a recently validated PRS of 313 germline variants (PRS(313)) and contralateral breast cancer (CBC) risk. We included 56,068 women of European ancestry diagnosed with first invasive breast cancer from 1990 onward with follow-up from the Breast Cancer Association Consortium. Metachronous CBC risk (N = 1,027) according to the distribution of PRS(313) was quantified using Cox regression analyses. We assessed PRS(313) interaction with age at first diagnosis, family history, morphology, ER status, PR status, and HER2 status, and (neo)adjuvant therapy. In studies of Asian women, with limited follow-up, CBC risk associated with PRS(313) was assessed using logistic regression for 340 women with CBC compared with 12,133 women with unilateral breast cancer. Higher PRS(313) was associated with increased CBC risk: hazard ratio per standard deviation (SD) = 1.25 (95%CI = 1.18-1.33) for Europeans, and an OR per SD = 1.15 (95%CI = 1.02-1.29) for Asians. The absolute lifetime risks of CBC, accounting for death as competing risk, were 12.4% for European women at the 10(th) percentile and 20.5% at the 90(th) percentile of PRS(313). We found no evidence of confounding by or interaction with individual characteristics, characteristics of the primary tumor, or treatment. The C-index for the PRS(313) alone was 0.563 (95%CI = 0.547-0.586). In conclusion, PRS(313) is an independent factor associated with CBC risk and can be incorporated into CBC risk prediction models to help improve stratification and optimize surveillance and treatment strategies.

Published
2020

16. Breast Cancer Polygenic Risk Score and Contralateral Breast Cancer Risk

Author

Kramer, I., Hooning, M.J., Mavaddat, N., Hauptmann, M., Keeman, R., Steyerberg, Ewout W., Giardiello, D., Antoniou, A.C., Pharoah, P.D., Canisius, S., Abu-Ful, Z., Andrulis, I.L., Anton-Culver, H., Aronson, K.J., Augustinsson, A., Becher, H., Beckmann, M.W., Behrens, S., Benitez, J., Bermisheva, M., Bogdanova, N.V., Bojesen, S.E., Bolla, M.K., Bonanni, B., Brauch, H., Bremer, M., Brucker, S.Y., Burwinkel, B., Castelao, J.E., Chan, T.L., Chang-Claude, J., Chanock, S.J., Chenevix-Trench, G., Choi, J.Y., Clarke, C.L., Collée, J.M., Couch, F.J., Cox, A, Cross, S.S., Czene, K., Daly, M.B., Devilee, P., Dörk, T., Dos-Santos-Silva, I., Dunning, A.M., Dwek, M., Eccles, D.M., Evans, D.G., Fasching, P.A., Flyger, H., Gago-Dominguez, M., García-Closas, M., García-Sáenz, J.A., Giles, G.G., Goldgar, D.E., González-Neira, A., Haiman, C.A., Håkansson, N., Hamann, U., Hartman, M, Heemskerk-Gerritsen, B.A., Hollestelle, A., Hopper, J.L., Hou, M.F., Howell, A., Ito, H., Jakimovska, M., Jakubowska, A., Janni, W., John, E.M., Jung, A., Kang, D., Kets, C.M., Khusnutdinova, E., Ko, Y.D., Kristensen, V.N., Kurian, A.W., Kwong, A., Lambrechts, D., Marchand, L. Le, Li, J, Lindblom, A., Lubiński, J., Mannermaa, A., Manoochehri, M., Margolin, S., Matsuo, K., Mavroudis, D., Meindl, A., Milne, R.L., Mulligan, A.M., Muranen, T.A., Neuhausen, S.L., Nevanlinna, H., Newman, W.G., Olshan, A.F., Olson, J.E., Olsson, H., Park-Simon, T.W., Peto, J., et al., Kramer, I., Hooning, M.J., Mavaddat, N., Hauptmann, M., Keeman, R., Steyerberg, Ewout W., Giardiello, D., Antoniou, A.C., Pharoah, P.D., Canisius, S., Abu-Ful, Z., Andrulis, I.L., Anton-Culver, H., Aronson, K.J., Augustinsson, A., Becher, H., Beckmann, M.W., Behrens, S., Benitez, J., Bermisheva, M., Bogdanova, N.V., Bojesen, S.E., Bolla, M.K., Bonanni, B., Brauch, H., Bremer, M., Brucker, S.Y., Burwinkel, B., Castelao, J.E., Chan, T.L., Chang-Claude, J., Chanock, S.J., Chenevix-Trench, G., Choi, J.Y., Clarke, C.L., Collée, J.M., Couch, F.J., Cox, A, Cross, S.S., Czene, K., Daly, M.B., Devilee, P., Dörk, T., Dos-Santos-Silva, I., Dunning, A.M., Dwek, M., Eccles, D.M., Evans, D.G., Fasching, P.A., Flyger, H., Gago-Dominguez, M., García-Closas, M., García-Sáenz, J.A., Giles, G.G., Goldgar, D.E., González-Neira, A., Haiman, C.A., Håkansson, N., Hamann, U., Hartman, M, Heemskerk-Gerritsen, B.A., Hollestelle, A., Hopper, J.L., Hou, M.F., Howell, A., Ito, H., Jakimovska, M., Jakubowska, A., Janni, W., John, E.M., Jung, A., Kang, D., Kets, C.M., Khusnutdinova, E., Ko, Y.D., Kristensen, V.N., Kurian, A.W., Kwong, A., Lambrechts, D., Marchand, L. Le, Li, J, Lindblom, A., Lubiński, J., Mannermaa, A., Manoochehri, M., Margolin, S., Matsuo, K., Mavroudis, D., Meindl, A., Milne, R.L., Mulligan, A.M., Muranen, T.A., Neuhausen, S.L., Nevanlinna, H., Newman, W.G., Olshan, A.F., Olson, J.E., Olsson, H., Park-Simon, T.W., and Peto, J., et al.

Abstract

Contains fulltext : 229375.pdf (Publisher’s version ) (Open Access), Previous research has shown that polygenic risk scores (PRSs) can be used to stratify women according to their risk of developing primary invasive breast cancer. This study aimed to evaluate the association between a recently validated PRS of 313 germline variants (PRS(313)) and contralateral breast cancer (CBC) risk. We included 56,068 women of European ancestry diagnosed with first invasive breast cancer from 1990 onward with follow-up from the Breast Cancer Association Consortium. Metachronous CBC risk (N = 1,027) according to the distribution of PRS(313) was quantified using Cox regression analyses. We assessed PRS(313) interaction with age at first diagnosis, family history, morphology, ER status, PR status, and HER2 status, and (neo)adjuvant therapy. In studies of Asian women, with limited follow-up, CBC risk associated with PRS(313) was assessed using logistic regression for 340 women with CBC compared with 12,133 women with unilateral breast cancer. Higher PRS(313) was associated with increased CBC risk: hazard ratio per standard deviation (SD) = 1.25 (95%CI = 1.18-1.33) for Europeans, and an OR per SD = 1.15 (95%CI = 1.02-1.29) for Asians. The absolute lifetime risks of CBC, accounting for death as competing risk, were 12.4% for European women at the 10(th) percentile and 20.5% at the 90(th) percentile of PRS(313). We found no evidence of confounding by or interaction with individual characteristics, characteristics of the primary tumor, or treatment. The C-index for the PRS(313) alone was 0.563 (95%CI = 0.547-0.586). In conclusion, PRS(313) is an independent factor associated with CBC risk and can be incorporated into CBC risk prediction models to help improve stratification and optimize surveillance and treatment strategies.

Published
2020

17. Breast Cancer Polygenic Risk Score and Contralateral Breast Cancer Risk.

Author

Kramer I, Hooning MJ, Mavaddat N, Hauptmann M, Keeman R, Steyerberg EW, Giardiello D, Antoniou AC, Pharoah PDP, Canisius S, Abu-Ful Z, Andrulis IL, Anton-Culver H, Aronson KJ, Augustinsson A, Becher H, Beckmann MW, Behrens S, Benitez J, Bermisheva M, Bogdanova NV, Bojesen SE, Bolla MK, Bonanni B, Brauch H, Bremer M, Brucker SY, Burwinkel B, Castelao JE, Chan TL, Chang-Claude J, Chanock SJ, Chenevix-Trench G, Choi J-Y, Clarke CL, NBCS Collaborators, Collée JM, Couch FJ, Cox A, Cross SS, Czene K, Daly MB, Devilee P, Dörk T, Dos-Santos-Silva I, Dunning AM, Dwek M, Eccles DM, Evans DG, Fasching PA, Flyger H, Gago-Dominguez M, García-Closas M, García-Sáenz JA, Giles GG, Goldgar DE, González-Neira A, Haiman CA, Håkansson N, Hamann U, Hartman M, Heemskerk-Gerritsen BAM, Hollestelle A, Hopper JL, Hou M-F, Howell A, ABCTB Investigators, kConFab Investigators, Ito H, Jakimovska M, Jakubowska A, Janni W, John EM, Jung A, Kang D, Kets CM, Khusnutdinova E, Ko Y-D, Kristensen VN, Kurian AW, Kwong A, Lambrechts D, Le Marchand L, Li J, Lindblom A, Lubiński J, Mannermaa A, Manoochehri M, Margolin S, Matsuo K, Mavroudis D, Meindl A, Milne RL, Mulligan AM, Muranen TA, Neuhausen SL, Nevanlinna H, Newman WG, Olshan AF, Olson JE, Olsson H, Park-Simon T-W, Peto J, Petridis C, Plaseska-Karanfilska D, Presneau N, Pylkäs K, Radice P, Rennert G, Romero A, Roylance R, Saloustros E, Sawyer EJ, Schmutzler RK, Schwentner L, Scott C, See M-H, Shah M, Shen C-Y, Shu X-O, Siesling S, Slager S, Sohn C, Southey MC, Spinelli JJ, Stone J, Tapper WJ, Tengström M, Teo SH, Terry MB, Tollenaar RAEM, Tomlinson I, Troester MA, Vachon CM, van Ongeval C, van Veen EM, Winqvist R, Wolk A, Zheng W, Ziogas A, Easton DF, Hall P, Schmidt MK, Kramer I, Hooning MJ, Mavaddat N, Hauptmann M, Keeman R, Steyerberg EW, Giardiello D, Antoniou AC, Pharoah PDP, Canisius S, Abu-Ful Z, Andrulis IL, Anton-Culver H, Aronson KJ, Augustinsson A, Becher H, Beckmann MW, Behrens S, Benitez J, Bermisheva M, Bogdanova NV, Bojesen SE, Bolla MK, Bonanni B, Brauch H, Bremer M, Brucker SY, Burwinkel B, Castelao JE, Chan TL, Chang-Claude J, Chanock SJ, Chenevix-Trench G, Choi J-Y, Clarke CL, NBCS Collaborators, Collée JM, Couch FJ, Cox A, Cross SS, Czene K, Daly MB, Devilee P, Dörk T, Dos-Santos-Silva I, Dunning AM, Dwek M, Eccles DM, Evans DG, Fasching PA, Flyger H, Gago-Dominguez M, García-Closas M, García-Sáenz JA, Giles GG, Goldgar DE, González-Neira A, Haiman CA, Håkansson N, Hamann U, Hartman M, Heemskerk-Gerritsen BAM, Hollestelle A, Hopper JL, Hou M-F, Howell A, ABCTB Investigators, kConFab Investigators, Ito H, Jakimovska M, Jakubowska A, Janni W, John EM, Jung A, Kang D, Kets CM, Khusnutdinova E, Ko Y-D, Kristensen VN, Kurian AW, Kwong A, Lambrechts D, Le Marchand L, Li J, Lindblom A, Lubiński J, Mannermaa A, Manoochehri M, Margolin S, Matsuo K, Mavroudis D, Meindl A, Milne RL, Mulligan AM, Muranen TA, Neuhausen SL, Nevanlinna H, Newman WG, Olshan AF, Olson JE, Olsson H, Park-Simon T-W, Peto J, Petridis C, Plaseska-Karanfilska D, Presneau N, Pylkäs K, Radice P, Rennert G, Romero A, Roylance R, Saloustros E, Sawyer EJ, Schmutzler RK, Schwentner L, Scott C, See M-H, Shah M, Shen C-Y, Shu X-O, Siesling S, Slager S, Sohn C, Southey MC, Spinelli JJ, Stone J, Tapper WJ, Tengström M, Teo SH, Terry MB, Tollenaar RAEM, Tomlinson I, Troester MA, Vachon CM, van Ongeval C, van Veen EM, Winqvist R, Wolk A, Zheng W, Ziogas A, Easton DF, Hall P, and Schmidt MK

Abstract

Previous research has shown that polygenic risk scores (PRSs) can be used to stratify women according to their risk of developing primary invasive breast cancer. This study aimed to evaluate the association between a recently validated PRS of 313 germline variants (PRS313) and contralateral breast cancer (CBC) risk. We included 56,068 women of European ancestry diagnosed with first invasive breast cancer from 1990 onward with follow-up from the Breast Cancer Association Consortium. Metachronous CBC risk (N = 1,027) according to the distribution of PRS313 was quantified using Cox regression analyses. We assessed PRS313 interaction with age at first diagnosis, family history, morphology, ER status, PR status, and HER2 status, and (neo)adjuvant therapy. In studies of Asian women, with limited follow-up, CBC risk associated with PRS313 was assessed using logistic regression for 340 women with CBC compared with 12,133 women with unilateral breast cancer. Higher PRS313 was associated with increased CBC risk: hazard ratio per standard deviation (SD) = 1.25 (95%CI = 1.18-1.33) for Europeans, and an OR per SD = 1.15 (95%CI = 1.02-1.29) for Asians. The absolute lifetime risks of CBC, accounting for death as competing risk, were 12.4% for European women at the 10th percentile and 20.5% at the 90th percentile of PRS313. We found no evidence of confounding by or interaction with individual characteristics, characteristics of the primary tumor, or treatment. The C-index for the PRS313 alone was 0.563 (95%CI = 0.547-0.586). In conclusion, PRS313 is an independent factor associated with CBC risk and can be incorporated into CBC risk prediction models to help improve stratification and optimize surveillance and treatment strategies.

Published
2020

18. Breast Cancer Polygenic Risk Score and Contralateral Breast Cancer Risk.

Author

Kramer I, Hooning MJ, Mavaddat N, Hauptmann M, Keeman R, Steyerberg EW, Giardiello D, Antoniou AC, Pharoah PDP, Canisius S, Abu-Ful Z, Andrulis IL, Anton-Culver H, Aronson KJ, Augustinsson A, Becher H, Beckmann MW, Behrens S, Benitez J, Bermisheva M, Bogdanova NV, Bojesen SE, Bolla MK, Bonanni B, Brauch H, Bremer M, Brucker SY, Burwinkel B, Castelao JE, Chan TL, Chang-Claude J, Chanock SJ, Chenevix-Trench G, Choi J-Y, Clarke CL, NBCS Collaborators, Collée JM, Couch FJ, Cox A, Cross SS, Czene K, Daly MB, Devilee P, Dörk T, Dos-Santos-Silva I, Dunning AM, Dwek M, Eccles DM, Evans DG, Fasching PA, Flyger H, Gago-Dominguez M, García-Closas M, García-Sáenz JA, Giles GG, Goldgar DE, González-Neira A, Haiman CA, Håkansson N, Hamann U, Hartman M, Heemskerk-Gerritsen BAM, Hollestelle A, Hopper JL, Hou M-F, Howell A, ABCTB Investigators, kConFab Investigators, Ito H, Jakimovska M, Jakubowska A, Janni W, John EM, Jung A, Kang D, Kets CM, Khusnutdinova E, Ko Y-D, Kristensen VN, Kurian AW, Kwong A, Lambrechts D, Le Marchand L, Li J, Lindblom A, Lubiński J, Mannermaa A, Manoochehri M, Margolin S, Matsuo K, Mavroudis D, Meindl A, Milne RL, Mulligan AM, Muranen TA, Neuhausen SL, Nevanlinna H, Newman WG, Olshan AF, Olson JE, Olsson H, Park-Simon T-W, Peto J, Petridis C, Plaseska-Karanfilska D, Presneau N, Pylkäs K, Radice P, Rennert G, Romero A, Roylance R, Saloustros E, Sawyer EJ, Schmutzler RK, Schwentner L, Scott C, See M-H, Shah M, Shen C-Y, Shu X-O, Siesling S, Slager S, Sohn C, Southey MC, Spinelli JJ, Stone J, Tapper WJ, Tengström M, Teo SH, Terry MB, Tollenaar RAEM, Tomlinson I, Troester MA, Vachon CM, van Ongeval C, van Veen EM, Winqvist R, Wolk A, Zheng W, Ziogas A, Easton DF, Hall P, Schmidt MK, Kramer I, Hooning MJ, Mavaddat N, Hauptmann M, Keeman R, Steyerberg EW, Giardiello D, Antoniou AC, Pharoah PDP, Canisius S, Abu-Ful Z, Andrulis IL, Anton-Culver H, Aronson KJ, Augustinsson A, Becher H, Beckmann MW, Behrens S, Benitez J, Bermisheva M, Bogdanova NV, Bojesen SE, Bolla MK, Bonanni B, Brauch H, Bremer M, Brucker SY, Burwinkel B, Castelao JE, Chan TL, Chang-Claude J, Chanock SJ, Chenevix-Trench G, Choi J-Y, Clarke CL, NBCS Collaborators, Collée JM, Couch FJ, Cox A, Cross SS, Czene K, Daly MB, Devilee P, Dörk T, Dos-Santos-Silva I, Dunning AM, Dwek M, Eccles DM, Evans DG, Fasching PA, Flyger H, Gago-Dominguez M, García-Closas M, García-Sáenz JA, Giles GG, Goldgar DE, González-Neira A, Haiman CA, Håkansson N, Hamann U, Hartman M, Heemskerk-Gerritsen BAM, Hollestelle A, Hopper JL, Hou M-F, Howell A, ABCTB Investigators, kConFab Investigators, Ito H, Jakimovska M, Jakubowska A, Janni W, John EM, Jung A, Kang D, Kets CM, Khusnutdinova E, Ko Y-D, Kristensen VN, Kurian AW, Kwong A, Lambrechts D, Le Marchand L, Li J, Lindblom A, Lubiński J, Mannermaa A, Manoochehri M, Margolin S, Matsuo K, Mavroudis D, Meindl A, Milne RL, Mulligan AM, Muranen TA, Neuhausen SL, Nevanlinna H, Newman WG, Olshan AF, Olson JE, Olsson H, Park-Simon T-W, Peto J, Petridis C, Plaseska-Karanfilska D, Presneau N, Pylkäs K, Radice P, Rennert G, Romero A, Roylance R, Saloustros E, Sawyer EJ, Schmutzler RK, Schwentner L, Scott C, See M-H, Shah M, Shen C-Y, Shu X-O, Siesling S, Slager S, Sohn C, Southey MC, Spinelli JJ, Stone J, Tapper WJ, Tengström M, Teo SH, Terry MB, Tollenaar RAEM, Tomlinson I, Troester MA, Vachon CM, van Ongeval C, van Veen EM, Winqvist R, Wolk A, Zheng W, Ziogas A, Easton DF, Hall P, and Schmidt MK

Abstract

Previous research has shown that polygenic risk scores (PRSs) can be used to stratify women according to their risk of developing primary invasive breast cancer. This study aimed to evaluate the association between a recently validated PRS of 313 germline variants (PRS313) and contralateral breast cancer (CBC) risk. We included 56,068 women of European ancestry diagnosed with first invasive breast cancer from 1990 onward with follow-up from the Breast Cancer Association Consortium. Metachronous CBC risk (N = 1,027) according to the distribution of PRS313 was quantified using Cox regression analyses. We assessed PRS313 interaction with age at first diagnosis, family history, morphology, ER status, PR status, and HER2 status, and (neo)adjuvant therapy. In studies of Asian women, with limited follow-up, CBC risk associated with PRS313 was assessed using logistic regression for 340 women with CBC compared with 12,133 women with unilateral breast cancer. Higher PRS313 was associated with increased CBC risk: hazard ratio per standard deviation (SD) = 1.25 (95%CI = 1.18-1.33) for Europeans, and an OR per SD = 1.15 (95%CI = 1.02-1.29) for Asians. The absolute lifetime risks of CBC, accounting for death as competing risk, were 12.4% for European women at the 10th percentile and 20.5% at the 90th percentile of PRS313. We found no evidence of confounding by or interaction with individual characteristics, characteristics of the primary tumor, or treatment. The C-index for the PRS313 alone was 0.563 (95%CI = 0.547-0.586). In conclusion, PRS313 is an independent factor associated with CBC risk and can be incorporated into CBC risk prediction models to help improve stratification and optimize surveillance and treatment strategies.

Published
2020

19. Prediction and clinical utility of a contralateral breast cancer risk model

Author

Giardiello, D, Steyerberg, EW, Hauptmann, M, Adank, MA, Akdeniz, D, Blomqvist, C, Bojesen, SE, Bolla, MK, Brinkhuis, M, Chang-Claude, J, Czene, K, Devilee, P, Dunning, AM, Easton, DF, Eccles, DM, Fasching, PA, Figueroa, J, Flyger, H, Garcia-Closas, M, Haeberle, L, Haiman, CA, Hall, P, Hamann, U, Hopper, JL, Jager, A, Jakubowska, A, Jung, A, Keeman, R, Kramer, I, Lambrechts, D, Le Marchand, L, Lindblom, A, Lubinski, J, Manoochehri, M, Mariani, L, Nevanlinna, H, Oldenburg, HSA, Pelders, S, Pharoah, PDP, Shah, M, Siesling, S, Smit, VTHBM, Southey, MC, Tapper, WJ, Tollenaar, RAEM, Van den Broek, AJ, Van Deurzen, CHM, Van Leeuwen, FE, Van Ongeval, C, Van't Veer, LJ, Wang, Q, Wendt, C, Westenend, PJ, Hooning, MJ, Schmidt, MK, Giardiello, D, Steyerberg, EW, Hauptmann, M, Adank, MA, Akdeniz, D, Blomqvist, C, Bojesen, SE, Bolla, MK, Brinkhuis, M, Chang-Claude, J, Czene, K, Devilee, P, Dunning, AM, Easton, DF, Eccles, DM, Fasching, PA, Figueroa, J, Flyger, H, Garcia-Closas, M, Haeberle, L, Haiman, CA, Hall, P, Hamann, U, Hopper, JL, Jager, A, Jakubowska, A, Jung, A, Keeman, R, Kramer, I, Lambrechts, D, Le Marchand, L, Lindblom, A, Lubinski, J, Manoochehri, M, Mariani, L, Nevanlinna, H, Oldenburg, HSA, Pelders, S, Pharoah, PDP, Shah, M, Siesling, S, Smit, VTHBM, Southey, MC, Tapper, WJ, Tollenaar, RAEM, Van den Broek, AJ, Van Deurzen, CHM, Van Leeuwen, FE, Van Ongeval, C, Van't Veer, LJ, Wang, Q, Wendt, C, Westenend, PJ, Hooning, MJ, and Schmidt, MK

Abstract

BACKGROUND: Breast cancer survivors are at risk for contralateral breast cancer (CBC), with the consequent burden of further treatment and potentially less favorable prognosis. We aimed to develop and validate a CBC risk prediction model and evaluate its applicability for clinical decision-making. METHODS: We included data of 132,756 invasive non-metastatic breast cancer patients from 20 studies with 4682 CBC events and a median follow-up of 8.8 years. We developed a multivariable Fine and Gray prediction model (PredictCBC-1A) including patient, primary tumor, and treatment characteristics and BRCA1/2 germline mutation status, accounting for the competing risks of death and distant metastasis. We also developed a model without BRCA1/2 mutation status (PredictCBC-1B) since this information was available for only 6% of patients and is routinely unavailable in the general breast cancer population. Prediction performance was evaluated using calibration and discrimination, calculated by a time-dependent area under the curve (AUC) at 5 and 10 years after diagnosis of primary breast cancer, and an internal-external cross-validation procedure. Decision curve analysis was performed to evaluate the net benefit of the model to quantify clinical utility. RESULTS: In the multivariable model, BRCA1/2 germline mutation status, family history, and systemic adjuvant treatment showed the strongest associations with CBC risk. The AUC of PredictCBC-1A was 0.63 (95% prediction interval (PI) at 5 years, 0.52-0.74; at 10 years, 0.53-0.72). Calibration-in-the-large was -0.13 (95% PI: -1.62-1.37), and the calibration slope was 0.90 (95% PI: 0.73-1.08). The AUC of Predict-1B at 10 years was 0.59 (95% PI: 0.52-0.66); calibration was slightly lower. Decision curve analysis for preventive contralateral mastectomy showed potential clinical utility of PredictCBC-1A between thresholds of 4-10% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers. CONCLUSIONS: We developed a reasonably c

Published
2019

20. Immunomodulatory Factors Control the Fate of Melanoma Tumor Initiating Cells

Author

Tuccitto, A., Tazzari, M., Beretta, V., Rini, F., Miranda, C., Greco, A, Santinami, M., Patuzzo, R., Vergani, B., Villa, A., Manenti, G., Cleris, L., Giardiello, D., Alison, M., Rivoltini, L., Castelli, C., Perego, M., Tuccitto, A., Tazzari, M., Beretta, V., Rini, F., Miranda, C., Greco, A, Santinami, M., Patuzzo, R., Vergani, B., Villa, A., Manenti, G., Cleris, L., Giardiello, D., Alison, M., Rivoltini, L., Castelli, C., and Perego, M.

Abstract

Item does not contain fulltext, Melanoma is a highly heterogeneous tumor for which recent evidence supports a model of dynamic stemness. Melanoma cells might temporally acquire tumor-initiating properties or switch from a status of tumor-initiating cells (TICs) to a more differentiated one depending on the tumor context. However, factors driving these functional changes are still unknown. We focused on the role of cyto/chemokines in shaping TICs isolated directly from tumor specimens of two melanoma patients, namely Me14346S and Me15888S. We analyzed the secretion profile of TICs and of their corresponding melanoma differentiated cells and we tested the ability of cyto/chemokines to influence TIC self-renewal and differentiation. We found that TICs, grown in vitro as melanospheres, had a complex secretory profile as compared to their differentiated counterparts. Some factors, such as CCL-2 and IL-8, also produced by adherent melanoma cells and melanocytes did not influence TIC properties. Conversely, IL-6, released by differentiated cells, reduced TIC self-renewal and induced TIC differentiation while IL-10, produced by Me15888S, strongly promoted TIC self-renewal through paracrine/autocrine actions. Complete neutralization of IL-10 activity by gene silencing and antibody-mediated blocking of the IL-10Ralpha was required to sensitize Me15888S to IL-6-induced differentiation. For the first time these results show that functional heterogeneity of melanoma could be directly influenced by inflammatory and suppressive soluble factors, with IL-6 favoring TIC differentiation, and IL-10 supporting TIC self-renewal. Thus, understanding the tumor microenvironment (TME) role in modulating melanoma TIC phenotype is fundamental to identifying novel therapeutic targets to achieve long-lasting regression of metastatic melanoma. Stem Cells 2016;34:2449-2460.

Published
2016

21. Immunomodulatory Factors Control the Fate of Melanoma Tumor Initiating Cells

Author

Tuccitto, A., Tazzari, M., Beretta, V., Rini, F., Miranda, C., Greco, A, Santinami, M., Patuzzo, R., Vergani, B., Villa, A., Manenti, G., Cleris, L., Giardiello, D., Alison, M., Rivoltini, L., Castelli, C., Perego, M., Tuccitto, A., Tazzari, M., Beretta, V., Rini, F., Miranda, C., Greco, A, Santinami, M., Patuzzo, R., Vergani, B., Villa, A., Manenti, G., Cleris, L., Giardiello, D., Alison, M., Rivoltini, L., Castelli, C., and Perego, M.

Abstract

Item does not contain fulltext, Melanoma is a highly heterogeneous tumor for which recent evidence supports a model of dynamic stemness. Melanoma cells might temporally acquire tumor-initiating properties or switch from a status of tumor-initiating cells (TICs) to a more differentiated one depending on the tumor context. However, factors driving these functional changes are still unknown. We focused on the role of cyto/chemokines in shaping TICs isolated directly from tumor specimens of two melanoma patients, namely Me14346S and Me15888S. We analyzed the secretion profile of TICs and of their corresponding melanoma differentiated cells and we tested the ability of cyto/chemokines to influence TIC self-renewal and differentiation. We found that TICs, grown in vitro as melanospheres, had a complex secretory profile as compared to their differentiated counterparts. Some factors, such as CCL-2 and IL-8, also produced by adherent melanoma cells and melanocytes did not influence TIC properties. Conversely, IL-6, released by differentiated cells, reduced TIC self-renewal and induced TIC differentiation while IL-10, produced by Me15888S, strongly promoted TIC self-renewal through paracrine/autocrine actions. Complete neutralization of IL-10 activity by gene silencing and antibody-mediated blocking of the IL-10Ralpha was required to sensitize Me15888S to IL-6-induced differentiation. For the first time these results show that functional heterogeneity of melanoma could be directly influenced by inflammatory and suppressive soluble factors, with IL-6 favoring TIC differentiation, and IL-10 supporting TIC self-renewal. Thus, understanding the tumor microenvironment (TME) role in modulating melanoma TIC phenotype is fundamental to identifying novel therapeutic targets to achieve long-lasting regression of metastatic melanoma. Stem Cells 2016;34:2449-2460.

Published
2016

22. Immunomodulatory Factors Control the Fate of Melanoma Tumor Initiating Cells

Author

Tuccitto, A., Tazzari, M., Beretta, V., Rini, F., Miranda, C., Greco, A, Santinami, M., Patuzzo, R., Vergani, B., Villa, A., Manenti, G., Cleris, L., Giardiello, D., Alison, M., Rivoltini, L., Castelli, C., Perego, M., Tuccitto, A., Tazzari, M., Beretta, V., Rini, F., Miranda, C., Greco, A, Santinami, M., Patuzzo, R., Vergani, B., Villa, A., Manenti, G., Cleris, L., Giardiello, D., Alison, M., Rivoltini, L., Castelli, C., and Perego, M.

Abstract

Item does not contain fulltext, Melanoma is a highly heterogeneous tumor for which recent evidence supports a model of dynamic stemness. Melanoma cells might temporally acquire tumor-initiating properties or switch from a status of tumor-initiating cells (TICs) to a more differentiated one depending on the tumor context. However, factors driving these functional changes are still unknown. We focused on the role of cyto/chemokines in shaping TICs isolated directly from tumor specimens of two melanoma patients, namely Me14346S and Me15888S. We analyzed the secretion profile of TICs and of their corresponding melanoma differentiated cells and we tested the ability of cyto/chemokines to influence TIC self-renewal and differentiation. We found that TICs, grown in vitro as melanospheres, had a complex secretory profile as compared to their differentiated counterparts. Some factors, such as CCL-2 and IL-8, also produced by adherent melanoma cells and melanocytes did not influence TIC properties. Conversely, IL-6, released by differentiated cells, reduced TIC self-renewal and induced TIC differentiation while IL-10, produced by Me15888S, strongly promoted TIC self-renewal through paracrine/autocrine actions. Complete neutralization of IL-10 activity by gene silencing and antibody-mediated blocking of the IL-10Ralpha was required to sensitize Me15888S to IL-6-induced differentiation. For the first time these results show that functional heterogeneity of melanoma could be directly influenced by inflammatory and suppressive soluble factors, with IL-6 favoring TIC differentiation, and IL-10 supporting TIC self-renewal. Thus, understanding the tumor microenvironment (TME) role in modulating melanoma TIC phenotype is fundamental to identifying novel therapeutic targets to achieve long-lasting regression of metastatic melanoma. Stem Cells 2016;34:2449-2460.

Published
2016

23. External validation and clinical utility assessment of PREDICT breast cancer prognostic model in young, systemic treatment-naïve women with node-negative breast cancer

Author

Wang, Yuwei, Broeks, Annegien, Giardiello, Daniele, Hauptmann, Michael, Jóźwiak, Katarzyna, Koop, Esther A., Opdam, Mark, Siesling, Sabine, Sonke, Gabe S., Stathonikos, Nikolas, ter Hoeve, Natalie D., van der Wall, Elsken, van Deurzen, Carolien H.M., van Diest, Paul J., Voogd, Adri C., Vreuls, Willem, Linn, Sabine C., Dackus, Gwen M.H.E., Schmidt, Marjanka K., Wang, Yuwei, Broeks, Annegien, Giardiello, Daniele, Hauptmann, Michael, Jóźwiak, Katarzyna, Koop, Esther A., Opdam, Mark, Siesling, Sabine, Sonke, Gabe S., Stathonikos, Nikolas, ter Hoeve, Natalie D., van der Wall, Elsken, van Deurzen, Carolien H.M., van Diest, Paul J., Voogd, Adri C., Vreuls, Willem, Linn, Sabine C., Dackus, Gwen M.H.E., and Schmidt, Marjanka K.

Abstract

Background: The validity of the PREDICT breast cancer prognostic model is unclear for young patients without adjuvant systemic treatment. This study aimed to validate PREDICT and assess its clinical utility in young women with node-negative breast cancer who did not receive systemic treatment. Methods: We selected all women from the Netherlands Cancer Registry who were diagnosed with node-negative breast cancer under age 40 between 1989 and 2000, a period when adjuvant systemic treatment was not standard practice for women with node-negative disease. We evaluated the calibration and discrimination of PREDICT using the observed/expected (O/E) mortality ratio, and the area under the receiver operating characteristic curve (AUC), respectively. Additionally, we compared the potential clinical utility of PREDICT for selectively administering chemotherapy to the chemotherapy-to-all strategy using decision curve analysis at predefined thresholds. Results: A total of 2264 women with a median age at diagnosis of 36 years were included. Of them, 71.2% had estrogen receptor (ER)-positive tumors and 44.0% had grade 3 tumors. Median tumor size was 16 mm. PREDICT v2.2 underestimated 10-year all-cause mortality by 33% in all women (O/E ratio:1.33, 95%CI:1.22–1.43). Model discrimination was moderate overall (AUC10-year:0.65, 95%CI:0.62–0.68), and poor for women with ER-negative tumors (AUC10-year:0.56, 95%CI:0.51–0.62). Compared to the chemotherapy-to-all strategy, PREDICT only showed a slightly higher net benefit in women with ER-positive tumors, but not in women with ER-negative tumors. Conclusions: PREDICT yields unreliable predictions for young women with node-negative breast cancer. Further model updates are needed before PREDICT can be routinely used in this patient subset.

Published
2023

24. Graphical calibration curves and the integrated calibration index (ICI) for competing risk models

Author

Austin, Peter C, Putter, Hein, Giardiello, Daniele, van Klaveren, David, Austin, Peter C, Putter, Hein, Giardiello, Daniele, and van Klaveren, David

Abstract

BACKGROUND: Assessing calibration-the agreement between estimated risk and observed proportions-is an important component of deriving and validating clinical prediction models. Methods for assessing the calibration of prognostic models for use with competing risk data have received little attention.METHODS: We propose a method for graphically assessing the calibration of competing risk regression models. Our proposed method can be used to assess the calibration of any model for estimating incidence in the presence of competing risk (e.g., a Fine-Gray subdistribution hazard model; a combination of cause-specific hazard functions; or a random survival forest). Our method is based on using the Fine-Gray subdistribution hazard model to regress the cumulative incidence function of the cause-specific outcome of interest on the predicted outcome risk of the model whose calibration we want to assess. We provide modifications of the integrated calibration index (ICI), of E50 and of E90, which are numerical calibration metrics, for use with competing risk data. We conducted a series of Monte Carlo simulations to evaluate the performance of these calibration measures when the underlying model has been correctly specified and when the model was mis-specified and when the incidence of the cause-specific outcome differed between the derivation and validation samples. We illustrated the usefulness of calibration curves and the numerical calibration metrics by comparing the calibration of a Fine-Gray subdistribution hazards regression model with that of random survival forests for predicting cardiovascular mortality in patients hospitalized with heart failure.RESULTS: The simulations indicated that the method for constructing graphical calibration curves and the associated calibration metrics performed as desired. We also demonstrated that the numerical calibration metrics can be used as optimization criteria when tuning machine learning methods for competing risk

Published
2022

25. PredictCBC-2.0:a contralateral breast cancer risk prediction model developed and validated in ~ 200,000 patients

Author

Giardiello, Daniele, Hooning, Maartje J., Hauptmann, Michael, Keeman, Renske, Heemskerk-Gerritsen, B. A.M., Becher, Heiko, Blomqvist, Carl, Bojesen, Stig E., Bolla, Manjeet K., Camp, Nicola J., Czene, Kamila, Devilee, Peter, Eccles, Diana M., Fasching, Peter A., Figueroa, Jonine D., Flyger, Henrik, García-Closas, Montserrat, Haiman, Christopher A., Hamann, Ute, Hopper, John L., Jakubowska, Anna, Leeuwen, Floor E., Lindblom, Annika, Lubiński, Jan, Margolin, Sara, Martinez, Maria Elena, Nevanlinna, Heli, Nevelsteen, Ines, Pelders, Saskia, Pharoah, Paul D.P., Siesling, Sabine, Southey, Melissa C., van der Hout, Annemieke H., van Hest, Liselotte P., Chang-Claude, Jenny, Hall, Per, Easton, Douglas F., Steyerberg, Ewout W., Schmidt, Marjanka K., Giardiello, Daniele, Hooning, Maartje J., Hauptmann, Michael, Keeman, Renske, Heemskerk-Gerritsen, B. A.M., Becher, Heiko, Blomqvist, Carl, Bojesen, Stig E., Bolla, Manjeet K., Camp, Nicola J., Czene, Kamila, Devilee, Peter, Eccles, Diana M., Fasching, Peter A., Figueroa, Jonine D., Flyger, Henrik, García-Closas, Montserrat, Haiman, Christopher A., Hamann, Ute, Hopper, John L., Jakubowska, Anna, Leeuwen, Floor E., Lindblom, Annika, Lubiński, Jan, Margolin, Sara, Martinez, Maria Elena, Nevanlinna, Heli, Nevelsteen, Ines, Pelders, Saskia, Pharoah, Paul D.P., Siesling, Sabine, Southey, Melissa C., van der Hout, Annemieke H., van Hest, Liselotte P., Chang-Claude, Jenny, Hall, Per, Easton, Douglas F., Steyerberg, Ewout W., and Schmidt, Marjanka K.

Abstract

Background: Prediction of contralateral breast cancer (CBC) risk is challenging due to moderate performances of the known risk factors. We aimed to improve our previous risk prediction model (PredictCBC) by updated follow-up and including additional risk factors. Methods: We included data from 207,510 invasive breast cancer patients participating in 23 studies. In total, 8225 CBC events occurred over a median follow-up of 10.2 years. In addition to the previously included risk factors, PredictCBC-2.0 included CHEK2 c.1100delC, a 313 variant polygenic risk score (PRS-313), body mass index (BMI), and parity. Fine and Gray regression was used to fit the model. Calibration and a time-dependent area under the curve (AUC) at 5 and 10 years were assessed to determine the performance of the models. Decision curve analysis was performed to evaluate the net benefit of PredictCBC-2.0 and previous PredictCBC models. Results: The discrimination of PredictCBC-2.0 at 10 years was higher than PredictCBC with an AUC of 0.65 (95% prediction intervals (PI) 0.56–0.74) versus 0.63 (95%PI 0.54–0.71). PredictCBC-2.0 was well calibrated with an observed/expected ratio at 10 years of 0.92 (95%PI 0.34–2.54). Decision curve analysis for contralateral preventive mastectomy (CPM) showed the potential clinical utility of PredictCBC-2.0 between thresholds of 4 and 12% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers. Conclusions: Additional genetic information beyond BRCA1/2 germline mutations improved CBC risk prediction and might help tailor clinical decision-making toward CPM or alternative preventive strategies. Identifying patients who benefit from CPM, especially in the general breast cancer population, remains challenging.

Published
2022

26. PredictCBC-2.0:a contralateral breast cancer risk prediction model developed and validated in ~ 200,000 patients

Author

Giardiello, Daniele, Hooning, Maartje J., Hauptmann, Michael, Keeman, Renske, Heemskerk-Gerritsen, B. A.M., Becher, Heiko, Blomqvist, Carl, Bojesen, Stig E., Bolla, Manjeet K., Camp, Nicola J., Czene, Kamila, Devilee, Peter, Eccles, Diana M., Fasching, Peter A., Figueroa, Jonine D., Flyger, Henrik, García-Closas, Montserrat, Haiman, Christopher A., Hamann, Ute, Hopper, John L., Jakubowska, Anna, Leeuwen, Floor E., Lindblom, Annika, Lubiński, Jan, Margolin, Sara, Martinez, Maria Elena, Nevanlinna, Heli, Nevelsteen, Ines, Pelders, Saskia, Pharoah, Paul D.P., Siesling, Sabine, Southey, Melissa C., van der Hout, Annemieke H., van Hest, Liselotte P., Chang-Claude, Jenny, Hall, Per, Easton, Douglas F., Steyerberg, Ewout W., Schmidt, Marjanka K., Giardiello, Daniele, Hooning, Maartje J., Hauptmann, Michael, Keeman, Renske, Heemskerk-Gerritsen, B. A.M., Becher, Heiko, Blomqvist, Carl, Bojesen, Stig E., Bolla, Manjeet K., Camp, Nicola J., Czene, Kamila, Devilee, Peter, Eccles, Diana M., Fasching, Peter A., Figueroa, Jonine D., Flyger, Henrik, García-Closas, Montserrat, Haiman, Christopher A., Hamann, Ute, Hopper, John L., Jakubowska, Anna, Leeuwen, Floor E., Lindblom, Annika, Lubiński, Jan, Margolin, Sara, Martinez, Maria Elena, Nevanlinna, Heli, Nevelsteen, Ines, Pelders, Saskia, Pharoah, Paul D.P., Siesling, Sabine, Southey, Melissa C., van der Hout, Annemieke H., van Hest, Liselotte P., Chang-Claude, Jenny, Hall, Per, Easton, Douglas F., Steyerberg, Ewout W., and Schmidt, Marjanka K.

Abstract

Background: Prediction of contralateral breast cancer (CBC) risk is challenging due to moderate performances of the known risk factors. We aimed to improve our previous risk prediction model (PredictCBC) by updated follow-up and including additional risk factors. Methods: We included data from 207,510 invasive breast cancer patients participating in 23 studies. In total, 8225 CBC events occurred over a median follow-up of 10.2 years. In addition to the previously included risk factors, PredictCBC-2.0 included CHEK2 c.1100delC, a 313 variant polygenic risk score (PRS-313), body mass index (BMI), and parity. Fine and Gray regression was used to fit the model. Calibration and a time-dependent area under the curve (AUC) at 5 and 10 years were assessed to determine the performance of the models. Decision curve analysis was performed to evaluate the net benefit of PredictCBC-2.0 and previous PredictCBC models. Results: The discrimination of PredictCBC-2.0 at 10 years was higher than PredictCBC with an AUC of 0.65 (95% prediction intervals (PI) 0.56–0.74) versus 0.63 (95%PI 0.54–0.71). PredictCBC-2.0 was well calibrated with an observed/expected ratio at 10 years of 0.92 (95%PI 0.34–2.54). Decision curve analysis for contralateral preventive mastectomy (CPM) showed the potential clinical utility of PredictCBC-2.0 between thresholds of 4 and 12% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers. Conclusions: Additional genetic information beyond BRCA1/2 germline mutations improved CBC risk prediction and might help tailor clinical decision-making toward CPM or alternative preventive strategies. Identifying patients who benefit from CPM, especially in the general breast cancer population, remains challenging.

Published
2022

27. Predicting 10-year survival after resection of colorectal liver metastases:an international study including biomarkers and perioperative treatment

Author

Buisman, Florian E., Giardiello, Daniele, Kemeny, Nancy E., Steyerberg, Ewout W., Höppener, Diederik J., Galjart, Boris, Nierop, Pieter M.H., Balachandran, Vinod P., Cercek, Andrea, Drebin, Jeffrey A., Gönen, Mithat, Jarnagin, William R., Kingham, T. P., Vermeulen, Peter B., Wei, Alice C., Grünhagen, Dirk J., Verhoef, Cornelis, D'Angelica, Micheal I., Koerkamp, Bas Groot, Buisman, Florian E., Giardiello, Daniele, Kemeny, Nancy E., Steyerberg, Ewout W., Höppener, Diederik J., Galjart, Boris, Nierop, Pieter M.H., Balachandran, Vinod P., Cercek, Andrea, Drebin, Jeffrey A., Gönen, Mithat, Jarnagin, William R., Kingham, T. P., Vermeulen, Peter B., Wei, Alice C., Grünhagen, Dirk J., Verhoef, Cornelis, D'Angelica, Micheal I., and Koerkamp, Bas Groot

Abstract

BACKGROUND: The aim of this study was to develop a prediction model for 10-year overall survival (OS) after resection of colorectal liver metastasis (CRLM) based on patient, tumour and treatment characteristics.METHODS: Consecutive patients after complete resection of CRLM were included from two centres (1992-2019). A prediction model providing 10-year OS probabilities was developed using Cox regression analysis, including KRAS, BRAF and histopathological growth patterns. Discrimination and calibration were assessed using cross-validation. A web-based calculator was built to predict individual 10-year OS probabilities.RESULTS: A total of 4112 patients were included. The estimated 10-year OS was 30% (95% CI 29-32). Fifteen patient, tumour and treatment characteristics were independent prognostic factors for 10-year OS; age, gender, location and nodal status of the primary tumour, disease-free interval, number and diameter of CRLM, preoperative CEA, resection margin, extrahepatic disease, KRAS and BRAF mutation status, histopathological growth patterns, perioperative systemic chemotherapy and hepatic arterial infusion pump chemotherapy. The discrimination at 10-years was 0.73 for both centres. A simplified risk score identified four risk groups with a 10-year OS of 57%, 38%, 24%, and 12%.CONCLUSIONS: Ten-year OS after resection of CRLM is best predicted with a model including 15 patient, tumour, and treatment characteristics. The web-based calculator can be used to inform patients. This model serves as a benchmark to determine the prognostic value of novel biomarkers.

Published
2022

28. PredictCBC-2.0:a contralateral breast cancer risk prediction model developed and validated in ~ 200,000 patients

Author

Giardiello, Daniele, Hooning, Maartje J., Hauptmann, Michael, Keeman, Renske, Heemskerk-Gerritsen, B. A.M., Becher, Heiko, Blomqvist, Carl, Bojesen, Stig E., Bolla, Manjeet K., Camp, Nicola J., Czene, Kamila, Devilee, Peter, Eccles, Diana M., Fasching, Peter A., Figueroa, Jonine D., Flyger, Henrik, García-Closas, Montserrat, Haiman, Christopher A., Hamann, Ute, Hopper, John L., Jakubowska, Anna, Leeuwen, Floor E., Lindblom, Annika, Lubiński, Jan, Margolin, Sara, Martinez, Maria Elena, Nevanlinna, Heli, Nevelsteen, Ines, Pelders, Saskia, Pharoah, Paul D.P., Siesling, Sabine, Southey, Melissa C., van der Hout, Annemieke H., van Hest, Liselotte P., Chang-Claude, Jenny, Hall, Per, Easton, Douglas F., Steyerberg, Ewout W., Schmidt, Marjanka K., Giardiello, Daniele, Hooning, Maartje J., Hauptmann, Michael, Keeman, Renske, Heemskerk-Gerritsen, B. A.M., Becher, Heiko, Blomqvist, Carl, Bojesen, Stig E., Bolla, Manjeet K., Camp, Nicola J., Czene, Kamila, Devilee, Peter, Eccles, Diana M., Fasching, Peter A., Figueroa, Jonine D., Flyger, Henrik, García-Closas, Montserrat, Haiman, Christopher A., Hamann, Ute, Hopper, John L., Jakubowska, Anna, Leeuwen, Floor E., Lindblom, Annika, Lubiński, Jan, Margolin, Sara, Martinez, Maria Elena, Nevanlinna, Heli, Nevelsteen, Ines, Pelders, Saskia, Pharoah, Paul D.P., Siesling, Sabine, Southey, Melissa C., van der Hout, Annemieke H., van Hest, Liselotte P., Chang-Claude, Jenny, Hall, Per, Easton, Douglas F., Steyerberg, Ewout W., and Schmidt, Marjanka K.

Abstract

Background: Prediction of contralateral breast cancer (CBC) risk is challenging due to moderate performances of the known risk factors. We aimed to improve our previous risk prediction model (PredictCBC) by updated follow-up and including additional risk factors. Methods: We included data from 207,510 invasive breast cancer patients participating in 23 studies. In total, 8225 CBC events occurred over a median follow-up of 10.2 years. In addition to the previously included risk factors, PredictCBC-2.0 included CHEK2 c.1100delC, a 313 variant polygenic risk score (PRS-313), body mass index (BMI), and parity. Fine and Gray regression was used to fit the model. Calibration and a time-dependent area under the curve (AUC) at 5 and 10 years were assessed to determine the performance of the models. Decision curve analysis was performed to evaluate the net benefit of PredictCBC-2.0 and previous PredictCBC models. Results: The discrimination of PredictCBC-2.0 at 10 years was higher than PredictCBC with an AUC of 0.65 (95% prediction intervals (PI) 0.56–0.74) versus 0.63 (95%PI 0.54–0.71). PredictCBC-2.0 was well calibrated with an observed/expected ratio at 10 years of 0.92 (95%PI 0.34–2.54). Decision curve analysis for contralateral preventive mastectomy (CPM) showed the potential clinical utility of PredictCBC-2.0 between thresholds of 4 and 12% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers. Conclusions: Additional genetic information beyond BRCA1/2 germline mutations improved CBC risk prediction and might help tailor clinical decision-making toward CPM or alternative preventive strategies. Identifying patients who benefit from CPM, especially in the general breast cancer population, remains challenging.

Published
2022

29. Development and validation of the PORTRET tool to predict recurrence, overall survival, and other-cause mortality in older patients with breast cancer in the Netherlands:a population-based study

Author

van der Plas-Krijgsman, Willeke G., Giardiello, Daniele, Putter, Hein, Steyerberg, Ewout W., Bastiaannet, Esther, Stiggelbout, Anne M., Mooijaart, Simon P., Kroep, Judith R., Portielje, Johanneke E.A., Liefers, Gerrit Jan, de Glas, Nienke A., van der Plas-Krijgsman, Willeke G., Giardiello, Daniele, Putter, Hein, Steyerberg, Ewout W., Bastiaannet, Esther, Stiggelbout, Anne M., Mooijaart, Simon P., Kroep, Judith R., Portielje, Johanneke E.A., Liefers, Gerrit Jan, and de Glas, Nienke A.

Abstract

Background: Current prediction tools for breast cancer outcomes are not tailored to the older patient, in whom competing risk strongly influences treatment effects. We aimed to develop and validate a prediction tool for 5-year recurrence, overall mortality, and other-cause mortality for older patients (aged ≥65 years) with early invasive breast cancer and to estimate individualised expected benefits of adjuvant systemic treatment. Methods: We selected surgically treated patients with early invasive breast cancer (stage I–III) aged 65 years or older from the population-based FOCUS cohort in the Netherlands. We developed prediction models for 5-year recurrence, overall mortality, and other-cause mortality using cause-specific Cox proportional hazard models. External validation was performed in a Dutch Cancer registry cohort. Performance was evaluated with discrimination accuracy and calibration plots. Findings: We included 2744 female patients in the development cohort and 13631 female patients in the validation cohort. Median age was 74·8 years (range 65–98) in the development cohort and 76·0 years (70–101) in the validation cohort. 5-year follow-up was complete for more than 99% of all patients. We observed 343 and 1462 recurrences, and 831 and 3594 deaths, of which 586 and 2565 were without recurrence, in the development and validation cohort, respectively. The area under the receiver-operating-characteristic curve at 5 years in the external dataset was 0·76 (95% CI 0·75–0·76) for overall mortality, 0·76 (0·76–0·77) for recurrence, and 0·75 (0·74–0·75) for other-cause mortality. Interpretation: The PORTRET tool can accurately predict 5-year recurrence, overall mortality, and other-cause mortality in older patients with breast cancer. The tool can support shared decision making, especially since it provides individualised estimated benefits of adjuvant treatment. Funding: Dutch Cancer Foundation and ZonMw.

Published
2021

30. Prediction of contralateral breast cancer:external validation of risk calculators in 20 international cohorts

Author

Giardiello, Daniele, Hauptmann, Michael, Steyerberg, Ewout W., Adank, Muriel A., Akdeniz, Delal, Blom, Jannet C., Blomqvist, Carl, Bojesen, Stig E., Bolla, Manjeet K., Brinkhuis, Mariël, Chang-Claude, Jenny, Czene, Kamila, Devilee, Peter, Dunning, Alison M., Easton, Douglas F., Eccles, Diana M., Fasching, Peter A., Figueroa, Jonine, Flyger, Henrik, García-Closas, Montserrat, Haeberle, Lothar, Haiman, Christopher A., Hall, Per, Hamann, Ute, Hopper, John L., Jager, Agnes, Jakubowska, Anna, Jung, Audrey, Keeman, Renske, Koppert, Linetta B., Kramer, Iris, Lambrechts, Diether, Le Marchand, Loic, Lindblom, Annika, Lubiński, Jan, Manoochehri, Mehdi, Mariani, Luigi, Nevanlinna, Heli, Oldenburg, Hester S.A., Pelders, Saskia, Pharoah, Paul D.P., Shah, Mitul, Siesling, Sabine, Smit, Vincent T.H.B.M., Southey, Melissa C., Tapper, William J., Tollenaar, Rob A.E.M., van den Broek, Alexandra J., van Deurzen, Carolien H.M., van Leeuwen, Flora E., van Ongeval, Chantal, Van’t Veer, Laura J., Wang, Qin, Wendt, Camilla, Westenend, Pieter J., Hooning, Maartje J., Schmidt, Marjanka K., Giardiello, Daniele, Hauptmann, Michael, Steyerberg, Ewout W., Adank, Muriel A., Akdeniz, Delal, Blom, Jannet C., Blomqvist, Carl, Bojesen, Stig E., Bolla, Manjeet K., Brinkhuis, Mariël, Chang-Claude, Jenny, Czene, Kamila, Devilee, Peter, Dunning, Alison M., Easton, Douglas F., Eccles, Diana M., Fasching, Peter A., Figueroa, Jonine, Flyger, Henrik, García-Closas, Montserrat, Haeberle, Lothar, Haiman, Christopher A., Hall, Per, Hamann, Ute, Hopper, John L., Jager, Agnes, Jakubowska, Anna, Jung, Audrey, Keeman, Renske, Koppert, Linetta B., Kramer, Iris, Lambrechts, Diether, Le Marchand, Loic, Lindblom, Annika, Lubiński, Jan, Manoochehri, Mehdi, Mariani, Luigi, Nevanlinna, Heli, Oldenburg, Hester S.A., Pelders, Saskia, Pharoah, Paul D.P., Shah, Mitul, Siesling, Sabine, Smit, Vincent T.H.B.M., Southey, Melissa C., Tapper, William J., Tollenaar, Rob A.E.M., van den Broek, Alexandra J., van Deurzen, Carolien H.M., van Leeuwen, Flora E., van Ongeval, Chantal, Van’t Veer, Laura J., Wang, Qin, Wendt, Camilla, Westenend, Pieter J., Hooning, Maartje J., and Schmidt, Marjanka K.

Abstract

Background: Three tools are currently available to predict the risk of contralateral breast cancer (CBC). We aimed to compare the performance of the Manchester formula, CBCrisk, and PredictCBC in patients with invasive breast cancer (BC). Methods: We analyzed data of 132,756 patients (4682 CBC) from 20 international studies with a median follow-up of 8.8 years. Prediction performance included discrimination, quantified as a time-dependent Area-Under-the-Curve (AUC) at 5 and 10 years after diagnosis of primary BC, and calibration, quantified as the expected-observed (E/O) ratio at 5 and 10 years and the calibration slope. Results: The AUC at 10 years was: 0.58 (95% confidence intervals [CI] 0.57–0.59) for CBCrisk; 0.60 (95% CI 0.59–0.61) for the Manchester formula; 0.63 (95% CI 0.59–0.66) and 0.59 (95% CI 0.56–0.62) for PredictCBC-1A (for settings where BRCA1/2 mutation status is available) and PredictCBC-1B (for the general population), respectively. The E/O at 10 years: 0.82 (95% CI 0.51–1.32) for CBCrisk; 1.53 (95% CI 0.63–3.73) for the Manchester formula; 1.28 (95% CI 0.63–2.58) for PredictCBC-1A and 1.35 (95% CI 0.65–2.77) for PredictCBC-1B. The calibration slope was 1.26 (95% CI 1.01–1.50) for CBCrisk; 0.90 (95% CI 0.79–1.02) for PredictCBC-1A; 0.81 (95% CI 0.63–0.99) for PredictCBC-1B, and 0.39 (95% CI 0.34–0.43) for the Manchester formula. Conclusions: Current CBC risk prediction tools provide only moderate discrimination and the Manchester formula was poorly calibrated. Better predictors and re-calibration are needed to improve CBC prediction and to identify low- and high-CBC risk patients for clinical decision-making.

Published
2020

31. Breast Cancer Polygenic Risk Score and Contralateral Breast Cancer Risk

Author

Kramer, Iris, Hooning, Maartje J, Mavaddat, Nasim, Hauptmann, Michael, Keeman, Renske, Steyerberg, Ewout W, Giardiello, Daniele, Antoniou, Antonis C, Pharoah, Paul D P, Canisius, Sander, Abu-Ful, Zumuruda, Andrulis, Irene L, Anton-Culver, Hoda, Aronson, Kristan J, Augustinsson, Annelie, Becher, Heiko, Beckmann, Matthias W, Behrens, Sabine, Benitez, Javier, Bermisheva, Marina, Bogdanova, Natalia V, Bojesen, Stig E, Bolla, Manjeet K, Bonanni, Bernardo, Brauch, Hiltrud, Bremer, Michael, Brucker, Sara Y, Burwinkel, Barbara, Castelao, Jose E, Chan, Tsun L, Chang-Claude, Jenny, Chanock, Stephen J, Chenevix-Trench, Georgia, Choi, Ji-Yeob, Clarke, Christine L, Collée, J Margriet, Couch, Fergus J, Cox, Angela, Cross, Simon S, Czene, Kamila, Daly, Mary B, Devilee, Peter, Dörk, Thilo, Dos-Santos-Silva, Isabel, Dunning, Alison M, Dwek, Miriam, Eccles, Diana M, Evans, D Gareth, Flyger, Henrik, Scott, Christopher, Kramer, Iris, Hooning, Maartje J, Mavaddat, Nasim, Hauptmann, Michael, Keeman, Renske, Steyerberg, Ewout W, Giardiello, Daniele, Antoniou, Antonis C, Pharoah, Paul D P, Canisius, Sander, Abu-Ful, Zumuruda, Andrulis, Irene L, Anton-Culver, Hoda, Aronson, Kristan J, Augustinsson, Annelie, Becher, Heiko, Beckmann, Matthias W, Behrens, Sabine, Benitez, Javier, Bermisheva, Marina, Bogdanova, Natalia V, Bojesen, Stig E, Bolla, Manjeet K, Bonanni, Bernardo, Brauch, Hiltrud, Bremer, Michael, Brucker, Sara Y, Burwinkel, Barbara, Castelao, Jose E, Chan, Tsun L, Chang-Claude, Jenny, Chanock, Stephen J, Chenevix-Trench, Georgia, Choi, Ji-Yeob, Clarke, Christine L, Collée, J Margriet, Couch, Fergus J, Cox, Angela, Cross, Simon S, Czene, Kamila, Daly, Mary B, Devilee, Peter, Dörk, Thilo, Dos-Santos-Silva, Isabel, Dunning, Alison M, Dwek, Miriam, Eccles, Diana M, Evans, D Gareth, Flyger, Henrik, and Scott, Christopher

Abstract

Previous research has shown that polygenic risk scores (PRSs) can be used to stratify women according to their risk of developing primary invasive breast cancer. This study aimed to evaluate the association between a recently validated PRS of 313 germline variants (PRS313) and contralateral breast cancer (CBC) risk. We included 56,068 women of European ancestry diagnosed with first invasive breast cancer from 1990 onward with follow-up from the Breast Cancer Association Consortium. Metachronous CBC risk (N = 1,027) according to the distribution of PRS313 was quantified using Cox regression analyses. We assessed PRS313 interaction with age at first diagnosis, family history, morphology, ER status, PR status, and HER2 status, and (neo)adjuvant therapy. In studies of Asian women, with limited follow-up, CBC risk associated with PRS313 was assessed using logistic regression for 340 women with CBC compared with 12,133 women with unilateral breast cancer. Higher PRS313 was associated with increased CBC risk: hazard ratio per standard deviation (SD) = 1.25 (95%CI = 1.18-1.33) for Europeans, and an OR per SD = 1.15 (95%CI = 1.02-1.29) for Asians. The absolute lifetime risks of CBC, accounting for death as competing risk, were 12.4% for European women at the 10th percentile and 20.5% at the 90th percentile of PRS313. We found no evidence of confounding by or interaction with individual characteristics, characteristics of the primary tumor, or treatment. The C-index for the PRS313 alone was 0.563 (95%CI = 0.547-0.586). In conclusion, PRS313 is an independent factor associated with CBC risk and can be incorporated into CBC risk prediction models to help improve stratification and optimize surveillance and treatment strategies.

Published
2020

32. Prediction and clinical utility of a contralateral breast cancer risk model

Author

Giardiello, Daniele, Steyerberg, Ewout W., Hauptmann, Michael, Adank, Muriel A., Akdeniz, Delal, Blomqvist, Carl, Bojesen, Stig E., Bolla, Manjeet K., Brinkhuis, Mariël, Chang-Claude, Jenny, Czene, Kamila, Devilee, Peter, Dunning, Alison M., Easton, Douglas F., Eccles, Diana M., Fasching, Peter A., Figueroa, Jonine, Flyger, Henrik, Garciá-Closas, Montserrat, Haeberle, Lothar, Haiman, Christopher A., Hall, Per, Hamann, Ute, Hopper, John L., Jager, Agnes, Jakubowska, Anna, Jung, Audrey, Keeman, Renske, Kramer, Iris, Lambrechts, Diether, Le Marchand, Loic, Lindblom, Annika, Lubiński, Jan, Manoochehri, Mehdi, Mariani, Luigi, Nevanlinna, Heli, Oldenburg, Hester S.A., Pelders, Saskia, Pharoah, Paul D.P., Shah, Mitul, Siesling, Sabine, Smit, Vincent T.H.B.M., Southey, Melissa C., Tapper, William J., Tollenaar, Rob A.E.M., Van Den Broek, Alexandra J., Van Deurzen, Carolien H.M., Van Leeuwen, Flora E., Van Ongeval, Chantal, Van't Veer, Laura J., Wang, Qin, Wendt, Camilla, Westenend, Pieter J., Hooning, Maartje J., Schmidt, Marjanka K., Giardiello, Daniele, Steyerberg, Ewout W., Hauptmann, Michael, Adank, Muriel A., Akdeniz, Delal, Blomqvist, Carl, Bojesen, Stig E., Bolla, Manjeet K., Brinkhuis, Mariël, Chang-Claude, Jenny, Czene, Kamila, Devilee, Peter, Dunning, Alison M., Easton, Douglas F., Eccles, Diana M., Fasching, Peter A., Figueroa, Jonine, Flyger, Henrik, Garciá-Closas, Montserrat, Haeberle, Lothar, Haiman, Christopher A., Hall, Per, Hamann, Ute, Hopper, John L., Jager, Agnes, Jakubowska, Anna, Jung, Audrey, Keeman, Renske, Kramer, Iris, Lambrechts, Diether, Le Marchand, Loic, Lindblom, Annika, Lubiński, Jan, Manoochehri, Mehdi, Mariani, Luigi, Nevanlinna, Heli, Oldenburg, Hester S.A., Pelders, Saskia, Pharoah, Paul D.P., Shah, Mitul, Siesling, Sabine, Smit, Vincent T.H.B.M., Southey, Melissa C., Tapper, William J., Tollenaar, Rob A.E.M., Van Den Broek, Alexandra J., Van Deurzen, Carolien H.M., Van Leeuwen, Flora E., Van Ongeval, Chantal, Van't Veer, Laura J., Wang, Qin, Wendt, Camilla, Westenend, Pieter J., Hooning, Maartje J., and Schmidt, Marjanka K.

Abstract

Background: Breast cancer survivors are at risk for contralateral breast cancer (CBC), with the consequent burden of further treatment and potentially less favorable prognosis. We aimed to develop and validate a CBC risk prediction model and evaluate its applicability for clinical decision-making. Methods: We included data of 132,756 invasive non-metastatic breast cancer patients from 20 studies with 4682 CBC events and a median follow-up of 8.8 years. We developed a multivariable Fine and Gray prediction model (PredictCBC-1A) including patient, primary tumor, and treatment characteristics and BRCA1/2 germline mutation status, accounting for the competing risks of death and distant metastasis. We also developed a model without BRCA1/2 mutation status (PredictCBC-1B) since this information was available for only 6% of patients and is routinely unavailable in the general breast cancer population. Prediction performance was evaluated using calibration and discrimination, calculated by a time-dependent area under the curve (AUC) at 5 and 10 years after diagnosis of primary breast cancer, and an internal-external cross-validation procedure. Decision curve analysis was performed to evaluate the net benefit of the model to quantify clinical utility. Results: In the multivariable model, BRCA1/2 germline mutation status, family history, and systemic adjuvant treatment showed the strongest associations with CBC risk. The AUC of PredictCBC-1A was 0.63 (95% prediction interval (PI) at 5 years, 0.52-0.74; at 10 years, 0.53-0.72). Calibration-in-the-large was-0.13 (95% PI:-1.62-1.37), and the calibration slope was 0.90 (95% PI: 0.73-1.08). The AUC of Predict-1B at 10 years was 0.59 (95% PI: 0.52-0.66); calibration was slightly lower. Decision curve analysis for preventive contralateral mastectomy showed potential clinical utility of PredictCBC-1A between thresholds of 4-10% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers. Conclusions: We developed a reasonably

Published
2019

33. Prediction and clinical utility of a contralateral breast cancer risk model

Author

Giardiello, Daniele, Steyerberg, Ewout W., Hauptmann, Michael, Adank, Muriel A., Akdeniz, Delal, Blomqvist, Carl, Bojesen, Stig E., Bolla, Manjeet K., Brinkhuis, Mariël, Chang-Claude, Jenny, Czene, Kamila, Devilee, Peter, Dunning, Alison M., Easton, Douglas F., Eccles, Diana M., Fasching, Peter A., Figueroa, Jonine, Flyger, Henrik, Garciá-Closas, Montserrat, Haeberle, Lothar, Haiman, Christopher A., Hall, Per, Hamann, Ute, Hopper, John L., Jager, Agnes, Jakubowska, Anna, Jung, Audrey, Keeman, Renske, Kramer, Iris, Lambrechts, Diether, Le Marchand, Loic, Lindblom, Annika, Lubiński, Jan, Manoochehri, Mehdi, Mariani, Luigi, Nevanlinna, Heli, Oldenburg, Hester S.A., Pelders, Saskia, Pharoah, Paul D.P., Shah, Mitul, Siesling, Sabine, Smit, Vincent T.H.B.M., Southey, Melissa C., Tapper, William J., Tollenaar, Rob A.E.M., Van Den Broek, Alexandra J., Van Deurzen, Carolien H.M., Van Leeuwen, Flora E., Van Ongeval, Chantal, Van't Veer, Laura J., Wang, Qin, Wendt, Camilla, Westenend, Pieter J., Hooning, Maartje J., Schmidt, Marjanka K., Giardiello, Daniele, Steyerberg, Ewout W., Hauptmann, Michael, Adank, Muriel A., Akdeniz, Delal, Blomqvist, Carl, Bojesen, Stig E., Bolla, Manjeet K., Brinkhuis, Mariël, Chang-Claude, Jenny, Czene, Kamila, Devilee, Peter, Dunning, Alison M., Easton, Douglas F., Eccles, Diana M., Fasching, Peter A., Figueroa, Jonine, Flyger, Henrik, Garciá-Closas, Montserrat, Haeberle, Lothar, Haiman, Christopher A., Hall, Per, Hamann, Ute, Hopper, John L., Jager, Agnes, Jakubowska, Anna, Jung, Audrey, Keeman, Renske, Kramer, Iris, Lambrechts, Diether, Le Marchand, Loic, Lindblom, Annika, Lubiński, Jan, Manoochehri, Mehdi, Mariani, Luigi, Nevanlinna, Heli, Oldenburg, Hester S.A., Pelders, Saskia, Pharoah, Paul D.P., Shah, Mitul, Siesling, Sabine, Smit, Vincent T.H.B.M., Southey, Melissa C., Tapper, William J., Tollenaar, Rob A.E.M., Van Den Broek, Alexandra J., Van Deurzen, Carolien H.M., Van Leeuwen, Flora E., Van Ongeval, Chantal, Van't Veer, Laura J., Wang, Qin, Wendt, Camilla, Westenend, Pieter J., Hooning, Maartje J., and Schmidt, Marjanka K.

Abstract

Background: Breast cancer survivors are at risk for contralateral breast cancer (CBC), with the consequent burden of further treatment and potentially less favorable prognosis. We aimed to develop and validate a CBC risk prediction model and evaluate its applicability for clinical decision-making. Methods: We included data of 132,756 invasive non-metastatic breast cancer patients from 20 studies with 4682 CBC events and a median follow-up of 8.8 years. We developed a multivariable Fine and Gray prediction model (PredictCBC-1A) including patient, primary tumor, and treatment characteristics and BRCA1/2 germline mutation status, accounting for the competing risks of death and distant metastasis. We also developed a model without BRCA1/2 mutation status (PredictCBC-1B) since this information was available for only 6% of patients and is routinely unavailable in the general breast cancer population. Prediction performance was evaluated using calibration and discrimination, calculated by a time-dependent area under the curve (AUC) at 5 and 10 years after diagnosis of primary breast cancer, and an internal-external cross-validation procedure. Decision curve analysis was performed to evaluate the net benefit of the model to quantify clinical utility. Results: In the multivariable model, BRCA1/2 germline mutation status, family history, and systemic adjuvant treatment showed the strongest associations with CBC risk. The AUC of PredictCBC-1A was 0.63 (95% prediction interval (PI) at 5 years, 0.52-0.74; at 10 years, 0.53-0.72). Calibration-in-the-large was-0.13 (95% PI:-1.62-1.37), and the calibration slope was 0.90 (95% PI: 0.73-1.08). The AUC of Predict-1B at 10 years was 0.59 (95% PI: 0.52-0.66); calibration was slightly lower. Decision curve analysis for preventive contralateral mastectomy showed potential clinical utility of PredictCBC-1A between thresholds of 4-10% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers. Conclusions: We developed a reasonably

Published
2019

34. Nomogram-based Prediction of Overall Survival in Patients with Metastatic Urothelial Carcinoma Receiving First-line Platinum-based Chemotherapy:Retrospective International Study of Invasive/Advanced Cancer of the Urothelium (RISC)

Author

Necchi, Andrea, Sonpavde, Guru, Lo Vullo, Salvatore, Giardiello, Daniele, Bamias, Aristotelis, Crabb, Simon J, Harshman, Lauren C, Bellmunt, Joaquim, De Giorgi, Ugo, Sternberg, Cora N, Cerbone, Linda, Ladoire, Sylvain, Wong, Yu-Ning, Yu, Evan Y, Chowdhury, Simon, Niegisch, Gunter, Srinivas, Sandy, Vaishampayan, Ulka N, Pal, Sumanta K, Agarwal, Neeraj, Alva, Ajjai, Baniel, Jack, Golshayan, Ali-Reza, Morales-Barrera, Rafael, Bowles, Daniel W, Milowsky, Matthew I, Theodore, Christine, Berthold, Dominik R, Daugaard, Gedske, Sridhar, Srikala S, Powles, Thomas, Rosenberg, Jonathan E, Galsky, Matthew D, Mariani, Luigi, Necchi, Andrea, Sonpavde, Guru, Lo Vullo, Salvatore, Giardiello, Daniele, Bamias, Aristotelis, Crabb, Simon J, Harshman, Lauren C, Bellmunt, Joaquim, De Giorgi, Ugo, Sternberg, Cora N, Cerbone, Linda, Ladoire, Sylvain, Wong, Yu-Ning, Yu, Evan Y, Chowdhury, Simon, Niegisch, Gunter, Srinivas, Sandy, Vaishampayan, Ulka N, Pal, Sumanta K, Agarwal, Neeraj, Alva, Ajjai, Baniel, Jack, Golshayan, Ali-Reza, Morales-Barrera, Rafael, Bowles, Daniel W, Milowsky, Matthew I, Theodore, Christine, Berthold, Dominik R, Daugaard, Gedske, Sridhar, Srikala S, Powles, Thomas, Rosenberg, Jonathan E, Galsky, Matthew D, and Mariani, Luigi

Abstract

BACKGROUND: The available prognostic models for overall survival (OS) in patients with metastatic urothelial carcinoma (UC) have been derived from clinical trial populations of cisplatin-treated patients.OBJECTIVE: To develop a new model based on real-world patients.DESIGN, SETTING, AND PARTICIPANTS: Individual patient-level data from 29 centers were collected, including metastatic UC and first-line cisplatin- or carboplatin-based chemotherapy administered between January 2006 and January 2011.INTERVENTION: First-line, platinum-based, combination chemotherapy.OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The population was randomly split into a development and a validation cohort. Generalized boosted regression modelling was used to screen out irrelevant variables and address multivariable analyses. Two nomograms were built to estimate OS probability, the first based on baseline factors and platinum agent, the second incorporating objective response (OR). The performance of the above nomograms and that of other available models was assessed. We plotted decision curves to evaluate the clinical usefulness of the two nomograms.RESULTS AND LIMITATIONS: A total of 1020 patients were analyzed (development: 687, validation: 333). In a platinum-stratified Cox model, significant variables for OS were performance status (p<0.001), white blood cell count (p=0.013), body mass index (p=0.003), ethnicity (p=0.012), lung, liver, or bone metastases (p<0.001), and prior perioperative chemotherapy (p=0.012). The c-index was 0.660. The distribution of the nomogram scores was associated with OR (p<0.001), and incorporating OR into the model further improved the c-index in the validation cohort (0.670).CONCLUSIONS: We developed and validated two nomograms for OS to be used before and after completion of first-line chemotherapy for metastatic UC.PATIENT SUMMARY: We proposed two models for estimating overall survival of patient

Published
2017

35. Assessing Performance and Clinical Usefulness in Prediction Models with Survival Outcomes: Practical Guidance for Cox Proportional Hazards Models

Author

Infection & Immunity, Epi Methoden Team 3, McLernon, David J., Giardiello, Daniele, Van Calster, Ben, Wynants, Laure, van Geloven, Nan, van Smeden, Maarten, Therneau, Terry, Steyerberg, Ewout W., Topic Groups 6 and 8 of The STRATOS Initiative, Infection & Immunity, Epi Methoden Team 3, McLernon, David J., Giardiello, Daniele, Van Calster, Ben, Wynants, Laure, van Geloven, Nan, van Smeden, Maarten, Therneau, Terry, Steyerberg, Ewout W., and Topic Groups 6 and 8 of The STRATOS Initiative

Published
2023

37. External validation and clinical utility assessment of PREDICT breast cancer prognostic model in young, systemic treatment-naïve women with node-negative breast cancer

Author

Pathologie Pathologen staf, Pathologie Support, Speerpunt Cancer, Cancer, Pathologie, MS MOD, Pathologie Research, Pathologie Opleiding, Wang, Yuwei, Broeks, Annegien, Giardiello, Daniele, Hauptmann, Michael, Jóźwiak, Katarzyna, Koop, Esther A., Opdam, Mark, Siesling, Sabine, Sonke, Gabe S., Stathonikos, Nikolas, ter Hoeve, Natalie D., van der Wall, Elsken, van Deurzen, Carolien H.M., van Diest, Paul J., Voogd, Adri C., Vreuls, Willem, Linn, Sabine C., Dackus, Gwen M.H.E., Schmidt, Marjanka K., Pathologie Pathologen staf, Pathologie Support, Speerpunt Cancer, Cancer, Pathologie, MS MOD, Pathologie Research, Pathologie Opleiding, Wang, Yuwei, Broeks, Annegien, Giardiello, Daniele, Hauptmann, Michael, Jóźwiak, Katarzyna, Koop, Esther A., Opdam, Mark, Siesling, Sabine, Sonke, Gabe S., Stathonikos, Nikolas, ter Hoeve, Natalie D., van der Wall, Elsken, van Deurzen, Carolien H.M., van Diest, Paul J., Voogd, Adri C., Vreuls, Willem, Linn, Sabine C., Dackus, Gwen M.H.E., and Schmidt, Marjanka K.

Published
2023

38. Validation of prediction models in the presence of competing risks: a guide through modern methods

Author

Epidemiology & Health Economics, Epi Methoden Team 3, Infection & Immunity, Van Geloven, Nan, Giardiello, Daniele, Bonneville, Edouard F., Teece, Lucy, Ramspek, Chava L., Van Smeden, Maarten, Snell, Kym I.E., Van Calster, Ben, Pohar-Perme, Maja, Riley, Richard D., Putter, Hein, Steyerberg, Ewout, Epidemiology & Health Economics, Epi Methoden Team 3, Infection & Immunity, Van Geloven, Nan, Giardiello, Daniele, Bonneville, Edouard F., Teece, Lucy, Ramspek, Chava L., Van Smeden, Maarten, Snell, Kym I.E., Van Calster, Ben, Pohar-Perme, Maja, Riley, Richard D., Putter, Hein, and Steyerberg, Ewout

Published
2022

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