Performance of Lynch syndrome predictive models in quantifying the likelihood of germline mutations in patients with abnormal MLH1 immunoexpression
Abstract Lynch syndrome (LS) accounts for up to 4 % of all colorectal cancers (CRC). Detection of a pathogenic germline mutation in one of the mismatch repair genes is the definitive criterion for LS diagnosis, but it is time-consuming and expensive. Immunohistochemistry is the most sensitive prescr...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Cabreira, Verónica [verfasserIn] Pinto, Carla [verfasserIn] Pinheiro, Manuela [verfasserIn] Lopes, Paula [verfasserIn] Peixoto, Ana [verfasserIn] Santos, Catarina [verfasserIn] Veiga, Isabel [verfasserIn] Rocha, Patrícia [verfasserIn] Pinto, Pedro [verfasserIn] Henrique, Rui [verfasserIn] Teixeira, Manuel R. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Familial cancer - Dordrecht [u.a.] : Springer Science + Business Media B.V, 2000, 16(2016), 1 vom: 31. Aug., Seite 73-81 |
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| Übergeordnetes Werk: |
volume:16 ; year:2016 ; number:1 ; day:31 ; month:08 ; pages:73-81 |
| Links: |
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| DOI / URN: |
10.1007/s10689-016-9926-0 |
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| Katalog-ID: |
SPR012478423 |
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| 245 | 1 | 0 | |a Performance of Lynch syndrome predictive models in quantifying the likelihood of germline mutations in patients with abnormal MLH1 immunoexpression |
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| 520 | |a Abstract Lynch syndrome (LS) accounts for up to 4 % of all colorectal cancers (CRC). Detection of a pathogenic germline mutation in one of the mismatch repair genes is the definitive criterion for LS diagnosis, but it is time-consuming and expensive. Immunohistochemistry is the most sensitive prescreening test and its predictive value is very high for loss of expression of MSH2, MSH6, and (isolated) PMS2, but not for MLH1. We evaluated if LS predictive models have a role to improve the molecular testing algorithm in this specific setting by studying 38 individuals referred for molecular testing and who were subsequently shown to have loss of MLH1 immunoexpression in their tumors. For each proband we calculated a risk score, which represents the probability that the patient with CRC carries a pathogenic MLH1 germline mutation, using the $ PREMM_{1,2,6} $ and MMRpro predictive models. Of the 38 individuals, 18.4 % had a pathogenic MLH1 germline mutation. MMRpro performed better for the purpose of this study, presenting a AUC of 0.83 (95 % CI 0.67–0.9; P < 0.001) compared with a AUC of 0.68 (95 % CI 0.51–0.82, P = 0.09) for $ PREMM_{1,2,6} $. Considering a threshold of 5 %, MMRpro would eliminate unnecessary germline mutation analysis in a significant proportion of cases while keeping very high sensitivity. We conclude that MMRpro is useful to correctly predict who should be screened for a germline MLH1 gene mutation and propose an algorithm to improve the cost-effectiveness of LS diagnosis. | ||
| 650 | 4 | |a Lynch syndrome |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a MLH1 |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Lynch syndrome predictive models |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Immunohistochemistry |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Pinto, Carla |e verfasserin |4 aut | |
| 700 | 1 | |a Pinheiro, Manuela |e verfasserin |4 aut | |
| 700 | 1 | |a Lopes, Paula |e verfasserin |4 aut | |
| 700 | 1 | |a Peixoto, Ana |e verfasserin |4 aut | |
| 700 | 1 | |a Santos, Catarina |e verfasserin |4 aut | |
| 700 | 1 | |a Veiga, Isabel |e verfasserin |4 aut | |
| 700 | 1 | |a Rocha, Patrícia |e verfasserin |4 aut | |
| 700 | 1 | |a Pinto, Pedro |e verfasserin |4 aut | |
| 700 | 1 | |a Henrique, Rui |e verfasserin |4 aut | |
| 700 | 1 | |a Teixeira, Manuel R. |e verfasserin |4 aut | |
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10.1007/s10689-016-9926-0 doi (DE-627)SPR012478423 (SPR)s10689-016-9926-0-e DE-627 ger DE-627 rakwb eng 610 ASE 44.81 bkl Cabreira, Verónica verfasserin aut Performance of Lynch syndrome predictive models in quantifying the likelihood of germline mutations in patients with abnormal MLH1 immunoexpression 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Lynch syndrome (LS) accounts for up to 4 % of all colorectal cancers (CRC). Detection of a pathogenic germline mutation in one of the mismatch repair genes is the definitive criterion for LS diagnosis, but it is time-consuming and expensive. Immunohistochemistry is the most sensitive prescreening test and its predictive value is very high for loss of expression of MSH2, MSH6, and (isolated) PMS2, but not for MLH1. We evaluated if LS predictive models have a role to improve the molecular testing algorithm in this specific setting by studying 38 individuals referred for molecular testing and who were subsequently shown to have loss of MLH1 immunoexpression in their tumors. For each proband we calculated a risk score, which represents the probability that the patient with CRC carries a pathogenic MLH1 germline mutation, using the $ PREMM_{1,2,6} $ and MMRpro predictive models. Of the 38 individuals, 18.4 % had a pathogenic MLH1 germline mutation. MMRpro performed better for the purpose of this study, presenting a AUC of 0.83 (95 % CI 0.67–0.9; P < 0.001) compared with a AUC of 0.68 (95 % CI 0.51–0.82, P = 0.09) for $ PREMM_{1,2,6} $. Considering a threshold of 5 %, MMRpro would eliminate unnecessary germline mutation analysis in a significant proportion of cases while keeping very high sensitivity. We conclude that MMRpro is useful to correctly predict who should be screened for a germline MLH1 gene mutation and propose an algorithm to improve the cost-effectiveness of LS diagnosis. Lynch syndrome (dpeaa)DE-He213 MLH1 (dpeaa)DE-He213 Lynch syndrome predictive models (dpeaa)DE-He213 Immunohistochemistry (dpeaa)DE-He213 Pinto, Carla verfasserin aut Pinheiro, Manuela verfasserin aut Lopes, Paula verfasserin aut Peixoto, Ana verfasserin aut Santos, Catarina verfasserin aut Veiga, Isabel verfasserin aut Rocha, Patrícia verfasserin aut Pinto, Pedro verfasserin aut Henrique, Rui verfasserin aut Teixeira, Manuel R. verfasserin aut Enthalten in Familial cancer Dordrecht [u.a.] : Springer Science + Business Media B.V, 2000 16(2016), 1 vom: 31. Aug., Seite 73-81 (DE-627)320528324 (DE-600)2015448-3 1573-7292 nnns volume:16 year:2016 number:1 day:31 month:08 pages:73-81 https://dx.doi.org/10.1007/s10689-016-9926-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.81 ASE AR 16 2016 1 31 08 73-81 |
| spelling |
10.1007/s10689-016-9926-0 doi (DE-627)SPR012478423 (SPR)s10689-016-9926-0-e DE-627 ger DE-627 rakwb eng 610 ASE 44.81 bkl Cabreira, Verónica verfasserin aut Performance of Lynch syndrome predictive models in quantifying the likelihood of germline mutations in patients with abnormal MLH1 immunoexpression 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Lynch syndrome (LS) accounts for up to 4 % of all colorectal cancers (CRC). Detection of a pathogenic germline mutation in one of the mismatch repair genes is the definitive criterion for LS diagnosis, but it is time-consuming and expensive. Immunohistochemistry is the most sensitive prescreening test and its predictive value is very high for loss of expression of MSH2, MSH6, and (isolated) PMS2, but not for MLH1. We evaluated if LS predictive models have a role to improve the molecular testing algorithm in this specific setting by studying 38 individuals referred for molecular testing and who were subsequently shown to have loss of MLH1 immunoexpression in their tumors. For each proband we calculated a risk score, which represents the probability that the patient with CRC carries a pathogenic MLH1 germline mutation, using the $ PREMM_{1,2,6} $ and MMRpro predictive models. Of the 38 individuals, 18.4 % had a pathogenic MLH1 germline mutation. MMRpro performed better for the purpose of this study, presenting a AUC of 0.83 (95 % CI 0.67–0.9; P < 0.001) compared with a AUC of 0.68 (95 % CI 0.51–0.82, P = 0.09) for $ PREMM_{1,2,6} $. Considering a threshold of 5 %, MMRpro would eliminate unnecessary germline mutation analysis in a significant proportion of cases while keeping very high sensitivity. We conclude that MMRpro is useful to correctly predict who should be screened for a germline MLH1 gene mutation and propose an algorithm to improve the cost-effectiveness of LS diagnosis. Lynch syndrome (dpeaa)DE-He213 MLH1 (dpeaa)DE-He213 Lynch syndrome predictive models (dpeaa)DE-He213 Immunohistochemistry (dpeaa)DE-He213 Pinto, Carla verfasserin aut Pinheiro, Manuela verfasserin aut Lopes, Paula verfasserin aut Peixoto, Ana verfasserin aut Santos, Catarina verfasserin aut Veiga, Isabel verfasserin aut Rocha, Patrícia verfasserin aut Pinto, Pedro verfasserin aut Henrique, Rui verfasserin aut Teixeira, Manuel R. verfasserin aut Enthalten in Familial cancer Dordrecht [u.a.] : Springer Science + Business Media B.V, 2000 16(2016), 1 vom: 31. Aug., Seite 73-81 (DE-627)320528324 (DE-600)2015448-3 1573-7292 nnns volume:16 year:2016 number:1 day:31 month:08 pages:73-81 https://dx.doi.org/10.1007/s10689-016-9926-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.81 ASE AR 16 2016 1 31 08 73-81 |
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10.1007/s10689-016-9926-0 doi (DE-627)SPR012478423 (SPR)s10689-016-9926-0-e DE-627 ger DE-627 rakwb eng 610 ASE 44.81 bkl Cabreira, Verónica verfasserin aut Performance of Lynch syndrome predictive models in quantifying the likelihood of germline mutations in patients with abnormal MLH1 immunoexpression 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Lynch syndrome (LS) accounts for up to 4 % of all colorectal cancers (CRC). Detection of a pathogenic germline mutation in one of the mismatch repair genes is the definitive criterion for LS diagnosis, but it is time-consuming and expensive. Immunohistochemistry is the most sensitive prescreening test and its predictive value is very high for loss of expression of MSH2, MSH6, and (isolated) PMS2, but not for MLH1. We evaluated if LS predictive models have a role to improve the molecular testing algorithm in this specific setting by studying 38 individuals referred for molecular testing and who were subsequently shown to have loss of MLH1 immunoexpression in their tumors. For each proband we calculated a risk score, which represents the probability that the patient with CRC carries a pathogenic MLH1 germline mutation, using the $ PREMM_{1,2,6} $ and MMRpro predictive models. Of the 38 individuals, 18.4 % had a pathogenic MLH1 germline mutation. MMRpro performed better for the purpose of this study, presenting a AUC of 0.83 (95 % CI 0.67–0.9; P < 0.001) compared with a AUC of 0.68 (95 % CI 0.51–0.82, P = 0.09) for $ PREMM_{1,2,6} $. Considering a threshold of 5 %, MMRpro would eliminate unnecessary germline mutation analysis in a significant proportion of cases while keeping very high sensitivity. We conclude that MMRpro is useful to correctly predict who should be screened for a germline MLH1 gene mutation and propose an algorithm to improve the cost-effectiveness of LS diagnosis. Lynch syndrome (dpeaa)DE-He213 MLH1 (dpeaa)DE-He213 Lynch syndrome predictive models (dpeaa)DE-He213 Immunohistochemistry (dpeaa)DE-He213 Pinto, Carla verfasserin aut Pinheiro, Manuela verfasserin aut Lopes, Paula verfasserin aut Peixoto, Ana verfasserin aut Santos, Catarina verfasserin aut Veiga, Isabel verfasserin aut Rocha, Patrícia verfasserin aut Pinto, Pedro verfasserin aut Henrique, Rui verfasserin aut Teixeira, Manuel R. verfasserin aut Enthalten in Familial cancer Dordrecht [u.a.] : Springer Science + Business Media B.V, 2000 16(2016), 1 vom: 31. Aug., Seite 73-81 (DE-627)320528324 (DE-600)2015448-3 1573-7292 nnns volume:16 year:2016 number:1 day:31 month:08 pages:73-81 https://dx.doi.org/10.1007/s10689-016-9926-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.81 ASE AR 16 2016 1 31 08 73-81 |
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10.1007/s10689-016-9926-0 doi (DE-627)SPR012478423 (SPR)s10689-016-9926-0-e DE-627 ger DE-627 rakwb eng 610 ASE 44.81 bkl Cabreira, Verónica verfasserin aut Performance of Lynch syndrome predictive models in quantifying the likelihood of germline mutations in patients with abnormal MLH1 immunoexpression 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Lynch syndrome (LS) accounts for up to 4 % of all colorectal cancers (CRC). Detection of a pathogenic germline mutation in one of the mismatch repair genes is the definitive criterion for LS diagnosis, but it is time-consuming and expensive. Immunohistochemistry is the most sensitive prescreening test and its predictive value is very high for loss of expression of MSH2, MSH6, and (isolated) PMS2, but not for MLH1. We evaluated if LS predictive models have a role to improve the molecular testing algorithm in this specific setting by studying 38 individuals referred for molecular testing and who were subsequently shown to have loss of MLH1 immunoexpression in their tumors. For each proband we calculated a risk score, which represents the probability that the patient with CRC carries a pathogenic MLH1 germline mutation, using the $ PREMM_{1,2,6} $ and MMRpro predictive models. Of the 38 individuals, 18.4 % had a pathogenic MLH1 germline mutation. MMRpro performed better for the purpose of this study, presenting a AUC of 0.83 (95 % CI 0.67–0.9; P < 0.001) compared with a AUC of 0.68 (95 % CI 0.51–0.82, P = 0.09) for $ PREMM_{1,2,6} $. Considering a threshold of 5 %, MMRpro would eliminate unnecessary germline mutation analysis in a significant proportion of cases while keeping very high sensitivity. We conclude that MMRpro is useful to correctly predict who should be screened for a germline MLH1 gene mutation and propose an algorithm to improve the cost-effectiveness of LS diagnosis. Lynch syndrome (dpeaa)DE-He213 MLH1 (dpeaa)DE-He213 Lynch syndrome predictive models (dpeaa)DE-He213 Immunohistochemistry (dpeaa)DE-He213 Pinto, Carla verfasserin aut Pinheiro, Manuela verfasserin aut Lopes, Paula verfasserin aut Peixoto, Ana verfasserin aut Santos, Catarina verfasserin aut Veiga, Isabel verfasserin aut Rocha, Patrícia verfasserin aut Pinto, Pedro verfasserin aut Henrique, Rui verfasserin aut Teixeira, Manuel R. verfasserin aut Enthalten in Familial cancer Dordrecht [u.a.] : Springer Science + Business Media B.V, 2000 16(2016), 1 vom: 31. Aug., Seite 73-81 (DE-627)320528324 (DE-600)2015448-3 1573-7292 nnns volume:16 year:2016 number:1 day:31 month:08 pages:73-81 https://dx.doi.org/10.1007/s10689-016-9926-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.81 ASE AR 16 2016 1 31 08 73-81 |
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10.1007/s10689-016-9926-0 doi (DE-627)SPR012478423 (SPR)s10689-016-9926-0-e DE-627 ger DE-627 rakwb eng 610 ASE 44.81 bkl Cabreira, Verónica verfasserin aut Performance of Lynch syndrome predictive models in quantifying the likelihood of germline mutations in patients with abnormal MLH1 immunoexpression 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Lynch syndrome (LS) accounts for up to 4 % of all colorectal cancers (CRC). Detection of a pathogenic germline mutation in one of the mismatch repair genes is the definitive criterion for LS diagnosis, but it is time-consuming and expensive. Immunohistochemistry is the most sensitive prescreening test and its predictive value is very high for loss of expression of MSH2, MSH6, and (isolated) PMS2, but not for MLH1. We evaluated if LS predictive models have a role to improve the molecular testing algorithm in this specific setting by studying 38 individuals referred for molecular testing and who were subsequently shown to have loss of MLH1 immunoexpression in their tumors. For each proband we calculated a risk score, which represents the probability that the patient with CRC carries a pathogenic MLH1 germline mutation, using the $ PREMM_{1,2,6} $ and MMRpro predictive models. Of the 38 individuals, 18.4 % had a pathogenic MLH1 germline mutation. MMRpro performed better for the purpose of this study, presenting a AUC of 0.83 (95 % CI 0.67–0.9; P < 0.001) compared with a AUC of 0.68 (95 % CI 0.51–0.82, P = 0.09) for $ PREMM_{1,2,6} $. Considering a threshold of 5 %, MMRpro would eliminate unnecessary germline mutation analysis in a significant proportion of cases while keeping very high sensitivity. We conclude that MMRpro is useful to correctly predict who should be screened for a germline MLH1 gene mutation and propose an algorithm to improve the cost-effectiveness of LS diagnosis. Lynch syndrome (dpeaa)DE-He213 MLH1 (dpeaa)DE-He213 Lynch syndrome predictive models (dpeaa)DE-He213 Immunohistochemistry (dpeaa)DE-He213 Pinto, Carla verfasserin aut Pinheiro, Manuela verfasserin aut Lopes, Paula verfasserin aut Peixoto, Ana verfasserin aut Santos, Catarina verfasserin aut Veiga, Isabel verfasserin aut Rocha, Patrícia verfasserin aut Pinto, Pedro verfasserin aut Henrique, Rui verfasserin aut Teixeira, Manuel R. verfasserin aut Enthalten in Familial cancer Dordrecht [u.a.] : Springer Science + Business Media B.V, 2000 16(2016), 1 vom: 31. Aug., Seite 73-81 (DE-627)320528324 (DE-600)2015448-3 1573-7292 nnns volume:16 year:2016 number:1 day:31 month:08 pages:73-81 https://dx.doi.org/10.1007/s10689-016-9926-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.81 ASE AR 16 2016 1 31 08 73-81 |
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Enthalten in Familial cancer 16(2016), 1 vom: 31. Aug., Seite 73-81 volume:16 year:2016 number:1 day:31 month:08 pages:73-81 |
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Enthalten in Familial cancer 16(2016), 1 vom: 31. Aug., Seite 73-81 volume:16 year:2016 number:1 day:31 month:08 pages:73-81 |
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Lynch syndrome MLH1 Lynch syndrome predictive models Immunohistochemistry |
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Familial cancer |
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Cabreira, Verónica @@aut@@ Pinto, Carla @@aut@@ Pinheiro, Manuela @@aut@@ Lopes, Paula @@aut@@ Peixoto, Ana @@aut@@ Santos, Catarina @@aut@@ Veiga, Isabel @@aut@@ Rocha, Patrícia @@aut@@ Pinto, Pedro @@aut@@ Henrique, Rui @@aut@@ Teixeira, Manuel R. @@aut@@ |
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Detection of a pathogenic germline mutation in one of the mismatch repair genes is the definitive criterion for LS diagnosis, but it is time-consuming and expensive. Immunohistochemistry is the most sensitive prescreening test and its predictive value is very high for loss of expression of MSH2, MSH6, and (isolated) PMS2, but not for MLH1. We evaluated if LS predictive models have a role to improve the molecular testing algorithm in this specific setting by studying 38 individuals referred for molecular testing and who were subsequently shown to have loss of MLH1 immunoexpression in their tumors. For each proband we calculated a risk score, which represents the probability that the patient with CRC carries a pathogenic MLH1 germline mutation, using the $ PREMM_{1,2,6} $ and MMRpro predictive models. Of the 38 individuals, 18.4 % had a pathogenic MLH1 germline mutation. MMRpro performed better for the purpose of this study, presenting a AUC of 0.83 (95 % CI 0.67–0.9; P < 0.001) compared with a AUC of 0.68 (95 % CI 0.51–0.82, P = 0.09) for $ PREMM_{1,2,6} $. Considering a threshold of 5 %, MMRpro would eliminate unnecessary germline mutation analysis in a significant proportion of cases while keeping very high sensitivity. 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Cabreira, Verónica |
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Cabreira, Verónica ddc 610 bkl 44.81 misc Lynch syndrome misc MLH1 misc Lynch syndrome predictive models misc Immunohistochemistry Performance of Lynch syndrome predictive models in quantifying the likelihood of germline mutations in patients with abnormal MLH1 immunoexpression |
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610 ASE 44.81 bkl Performance of Lynch syndrome predictive models in quantifying the likelihood of germline mutations in patients with abnormal MLH1 immunoexpression Lynch syndrome (dpeaa)DE-He213 MLH1 (dpeaa)DE-He213 Lynch syndrome predictive models (dpeaa)DE-He213 Immunohistochemistry (dpeaa)DE-He213 |
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Cabreira, Verónica Pinto, Carla Pinheiro, Manuela Lopes, Paula Peixoto, Ana Santos, Catarina Veiga, Isabel Rocha, Patrícia Pinto, Pedro Henrique, Rui Teixeira, Manuel R. |
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Cabreira, Verónica |
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10.1007/s10689-016-9926-0 |
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performance of lynch syndrome predictive models in quantifying the likelihood of germline mutations in patients with abnormal mlh1 immunoexpression |
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Performance of Lynch syndrome predictive models in quantifying the likelihood of germline mutations in patients with abnormal MLH1 immunoexpression |
| abstract |
Abstract Lynch syndrome (LS) accounts for up to 4 % of all colorectal cancers (CRC). Detection of a pathogenic germline mutation in one of the mismatch repair genes is the definitive criterion for LS diagnosis, but it is time-consuming and expensive. Immunohistochemistry is the most sensitive prescreening test and its predictive value is very high for loss of expression of MSH2, MSH6, and (isolated) PMS2, but not for MLH1. We evaluated if LS predictive models have a role to improve the molecular testing algorithm in this specific setting by studying 38 individuals referred for molecular testing and who were subsequently shown to have loss of MLH1 immunoexpression in their tumors. For each proband we calculated a risk score, which represents the probability that the patient with CRC carries a pathogenic MLH1 germline mutation, using the $ PREMM_{1,2,6} $ and MMRpro predictive models. Of the 38 individuals, 18.4 % had a pathogenic MLH1 germline mutation. MMRpro performed better for the purpose of this study, presenting a AUC of 0.83 (95 % CI 0.67–0.9; P < 0.001) compared with a AUC of 0.68 (95 % CI 0.51–0.82, P = 0.09) for $ PREMM_{1,2,6} $. Considering a threshold of 5 %, MMRpro would eliminate unnecessary germline mutation analysis in a significant proportion of cases while keeping very high sensitivity. We conclude that MMRpro is useful to correctly predict who should be screened for a germline MLH1 gene mutation and propose an algorithm to improve the cost-effectiveness of LS diagnosis. |
| abstractGer |
Abstract Lynch syndrome (LS) accounts for up to 4 % of all colorectal cancers (CRC). Detection of a pathogenic germline mutation in one of the mismatch repair genes is the definitive criterion for LS diagnosis, but it is time-consuming and expensive. Immunohistochemistry is the most sensitive prescreening test and its predictive value is very high for loss of expression of MSH2, MSH6, and (isolated) PMS2, but not for MLH1. We evaluated if LS predictive models have a role to improve the molecular testing algorithm in this specific setting by studying 38 individuals referred for molecular testing and who were subsequently shown to have loss of MLH1 immunoexpression in their tumors. For each proband we calculated a risk score, which represents the probability that the patient with CRC carries a pathogenic MLH1 germline mutation, using the $ PREMM_{1,2,6} $ and MMRpro predictive models. Of the 38 individuals, 18.4 % had a pathogenic MLH1 germline mutation. MMRpro performed better for the purpose of this study, presenting a AUC of 0.83 (95 % CI 0.67–0.9; P < 0.001) compared with a AUC of 0.68 (95 % CI 0.51–0.82, P = 0.09) for $ PREMM_{1,2,6} $. Considering a threshold of 5 %, MMRpro would eliminate unnecessary germline mutation analysis in a significant proportion of cases while keeping very high sensitivity. We conclude that MMRpro is useful to correctly predict who should be screened for a germline MLH1 gene mutation and propose an algorithm to improve the cost-effectiveness of LS diagnosis. |
| abstract_unstemmed |
Abstract Lynch syndrome (LS) accounts for up to 4 % of all colorectal cancers (CRC). Detection of a pathogenic germline mutation in one of the mismatch repair genes is the definitive criterion for LS diagnosis, but it is time-consuming and expensive. Immunohistochemistry is the most sensitive prescreening test and its predictive value is very high for loss of expression of MSH2, MSH6, and (isolated) PMS2, but not for MLH1. We evaluated if LS predictive models have a role to improve the molecular testing algorithm in this specific setting by studying 38 individuals referred for molecular testing and who were subsequently shown to have loss of MLH1 immunoexpression in their tumors. For each proband we calculated a risk score, which represents the probability that the patient with CRC carries a pathogenic MLH1 germline mutation, using the $ PREMM_{1,2,6} $ and MMRpro predictive models. Of the 38 individuals, 18.4 % had a pathogenic MLH1 germline mutation. MMRpro performed better for the purpose of this study, presenting a AUC of 0.83 (95 % CI 0.67–0.9; P < 0.001) compared with a AUC of 0.68 (95 % CI 0.51–0.82, P = 0.09) for $ PREMM_{1,2,6} $. Considering a threshold of 5 %, MMRpro would eliminate unnecessary germline mutation analysis in a significant proportion of cases while keeping very high sensitivity. We conclude that MMRpro is useful to correctly predict who should be screened for a germline MLH1 gene mutation and propose an algorithm to improve the cost-effectiveness of LS diagnosis. |
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Performance of Lynch syndrome predictive models in quantifying the likelihood of germline mutations in patients with abnormal MLH1 immunoexpression |
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Pinto, Carla Pinheiro, Manuela Lopes, Paula Peixoto, Ana Santos, Catarina Veiga, Isabel Rocha, Patrícia Pinto, Pedro Henrique, Rui Teixeira, Manuel R. |
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Pinto, Carla Pinheiro, Manuela Lopes, Paula Peixoto, Ana Santos, Catarina Veiga, Isabel Rocha, Patrícia Pinto, Pedro Henrique, Rui Teixeira, Manuel R. |
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| score |
7.399522 |