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Refined histopathological predictors of BRCA1 and BRCA2 mutation status: a large-scale analysis of breast cancer characteristics from the BCAC, CIMBA, and ENIGMA consortia.

Spurdle AB, Couch FJ, Parsons MT, McGuffog L, Barrowdale D, Bolla MK, Wang Q, Healey S, Schmutzler R, Wappenschmidt B, Rhiem K, Hahnen E, Engel C, Meindl A, Ditsch N, Arnold N, Plendl H, Niederacher D, Sutter C, Wang-Gohrke S, Steinemann D, Preisler-Adams S, Kast K, Varon-Mateeva R, Ellis S, Frost D, Platte R, Perkins J, Evans DG, Izatt L, Eeles R, Adlard J, Davidson R, Cole T, Scuvera G, Manoukian S, Bonanni B, Mariette F, Fortuzzi S, Viel A, Pasini B, Papi L, Varesco L, Balleine R, Nathanson KL, Domchek SM, Offitt K, Jakubowska A, Lindor N, Thomassen M, Jensen UB, Rantala J, Borg Å, Andrulis IL, Miron A, Hansen TV, Caldes T, Neuhausen SL, Toland AE, Nevanlinna H, Montagna M, Garber J, Godwin AK, Osorio A, Factor RE, Terry MB, Rebbeck TR, Karlan BY, Southey M, Rashid MU, Tung N, Pharoah PD, Blows FM, Dunning AM, Provenzano E, Hall P, Czene K, Schmidt MK, Broeks A, Cornelissen S, Verhoef S, Fasching PA, Beckmann MW, Ekici AB, Slamon DJ, Bojesen SE, Nordestgaard BG, Nielsen SF, Flyger H, Chang-Claude J, Flesch-Janys D, Rudolph A, Seibold P, Aittomäki K, Muranen TA, Heikkilä P, Blomqvist C, Figueroa J, Chanock SJ, Brinton L, Lissowska J, Olson JE, Pankratz VS, John EM, Whittemore AS, West DW, Hamann U, Torres D, Ulmer HU, Rüdiger T, Devilee P, Tollenaar RA, Seynaeve C, Van Asperen CJ, Eccles DM, Tapper WJ, Durcan L, Jones L, Peto J, dos-Santos-Silva I, Fletcher O, Johnson N, Dwek M, Swann R, Bane AL, Glendon G, Mulligan AM, Giles GG, Milne RL, Baglietto L, McLean C, Carpenter J, Clarke C, Scott R, Brauch H, Brüning T, Ko YD, Cox A, Cross SS, Reed MW, Lubinski J, Jaworska-Bieniek K, Durda K, Gronwald J, Dörk T, Bogdanova N, Park-Simon TW, Hillemanns P, Haiman CA, Henderson BE, Schumacher F, Le Marchand L, Burwinkel B, Marme F, Surovy H, Yang R, Anton-Culver H, Ziogas A, Hooning MJ, Collée JM, Martens JW, Tilanus-Linthorst MM, Brenner H, Dieffenbach AK, Arndt V, Stegmaier C, Winqvist R, Pylkäs K, Jukkola-Vuorinen A, Grip M, Lindblom A, Margolin S, Joseph V, Robson M, Rau-Murthy R, González-Neira A, Arias JI, Zamora P, Benítez J, Mannermaa A, Kataja V, Kosma VM, Hartikainen JM, Peterlongo P, Zaffaroni D, Barile M, Capra F, Radice P, Teo SH, Easton DF, Antoniou AC, Chenevix-Trench G, Goldgar DE, ABCTB InvestigatorsEMBRACE GroupGENICA NetworkHEBON GroupkConFab Investigato - Breast Cancer Res. (2014)

Bottom Line: These results refine likelihood-ratio estimates for predicting BRCA1 and BRCA2 mutation status by using commonly measured histopathological features.Age at diagnosis is an important variable for most analyses, and grade is more informative than ER status for BRCA2 mutation carrier prediction.The estimates will improve BRCA1 and BRCA2 variant classification and inform patient mutation testing and clinical management.

View Article: PubMed Central - PubMed

ABSTRACT

Introduction: The distribution of histopathological features of invasive breast tumors in BRCA1 or BRCA2 germline mutation carriers differs from that of individuals with no known mutation. Histopathological features thus have utility for mutation prediction, including statistical modeling to assess pathogenicity of BRCA1 or BRCA2 variants of uncertain clinical significance. We analyzed large pathology datasets accrued by the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA) and the Breast Cancer Association Consortium (BCAC) to reassess histopathological predictors of BRCA1 and BRCA2 mutation status, and provide robust likelihood ratio (LR) estimates for statistical modeling.

Methods: Selection criteria for study/center inclusion were estrogen receptor (ER) status or grade data available for invasive breast cancer diagnosed younger than 70 years. The dataset included 4,477 BRCA1 mutation carriers, 2,565 BRCA2 mutation carriers, and 47,565 BCAC breast cancer cases. Country-stratified estimates of the likelihood of mutation status by histopathological markers were derived using a Mantel-Haenszel approach.

Results: ER-positive phenotype negatively predicted BRCA1 mutation status, irrespective of grade (LRs from 0.08 to 0.90). ER-negative grade 3 histopathology was more predictive of positive BRCA1 mutation status in women 50 years or older (LR = 4.13 (3.70 to 4.62)) versus younger than 50 years (LR = 3.16 (2.96 to 3.37)). For BRCA2, ER-positive grade 3 phenotype modestly predicted positive mutation status irrespective of age (LR = 1.7-fold), whereas ER-negative grade 3 features modestly predicted positive mutation status at 50 years or older (LR = 1.54 (1.27 to 1.88)). Triple-negative tumor status was highly predictive of BRCA1 mutation status for women younger than 50 years (LR = 3.73 (3.43 to 4.05)) and 50 years or older (LR = 4.41 (3.86 to 5.04)), and modestly predictive of positive BRCA2 mutation status in women 50 years or older (LR = 1.79 (1.42 to 2.24)).

Conclusions: These results refine likelihood-ratio estimates for predicting BRCA1 and BRCA2 mutation status by using commonly measured histopathological features. Age at diagnosis is an important variable for most analyses, and grade is more informative than ER status for BRCA2 mutation carrier prediction. The estimates will improve BRCA1 and BRCA2 variant classification and inform patient mutation testing and clinical management.

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Related in: MedlinePlus

Proposed strategy for application of pathology likelihood ratios in multifactorial likelihood analysis ofBRCA1orBRCA2rare sequence variants. Cases carrying a variant of uncertain clinical significance, and with information on relevant pathology variables, are first assessed to determine that breast tumor pathology information was not a criterion used to trigger gene testing. ER, estrogen-receptor breast tumor status; PR, progesterone-receptor breast tumor status; HER2, HER2 breast tumor status; TN, triple-negative breast tumor status; Not TN, breast tumor status not triple-negative, after measurement of ER, PR, and HER2 status; ER-neg, ER-negative status; ER-pos, ER-positive status; G, grade; <50, breast cancer diagnosis at younger than 50 years for tumor with relevant pathology data; ≥50, breast cancer diagnosis at 50 to 70 years for tumor with relevant pathology data.
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Fig1: Proposed strategy for application of pathology likelihood ratios in multifactorial likelihood analysis ofBRCA1orBRCA2rare sequence variants. Cases carrying a variant of uncertain clinical significance, and with information on relevant pathology variables, are first assessed to determine that breast tumor pathology information was not a criterion used to trigger gene testing. ER, estrogen-receptor breast tumor status; PR, progesterone-receptor breast tumor status; HER2, HER2 breast tumor status; TN, triple-negative breast tumor status; Not TN, breast tumor status not triple-negative, after measurement of ER, PR, and HER2 status; ER-neg, ER-negative status; ER-pos, ER-positive status; G, grade; <50, breast cancer diagnosis at younger than 50 years for tumor with relevant pathology data; ≥50, breast cancer diagnosis at 50 to 70 years for tumor with relevant pathology data.

Mentions: This study has re-estimated the likelihood of BRCA1 or BRCA2 mutation status associated with breast tumor features commonly measured in the clinical setting, by analyzing much larger datasets than previously used for this purpose. Our findings provide measures of confidence in the individual LR estimates, and in particular, allow age at diagnosis to be incorporated into the pathology component of the multifactorial likelihood model. Figure 1 provides a flowchart indicating the proposed application of pathology-based LRs, dependent on what breast tumor pathology information is available for a variant carrier. As indicated, ER-grade LRs should be applied in preference to other pathology LR estimates, where both ER and grade information is available. The ER-grade LRs were derived from analysis of the largest sample sizes and thus have the greatest precision, and application of 12 strata provided by three grade categories refines both positive and negative prediction of mutation status. For example, a patient with a high-grade ER-negative tumor is three- to fourfold more likely to carry a BRCA1 mutation than not, whereas a patient with a low-grade ER-positive tumor is about 10 times more likely to be mutation-negative than mutation-positive. Given that grade and ER are almost universally used to assess prognosis and predict response to antiestrogen therapies, these features are generally readily available on standard pathology reports.Figure 1


Refined histopathological predictors of BRCA1 and BRCA2 mutation status: a large-scale analysis of breast cancer characteristics from the BCAC, CIMBA, and ENIGMA consortia.

Spurdle AB, Couch FJ, Parsons MT, McGuffog L, Barrowdale D, Bolla MK, Wang Q, Healey S, Schmutzler R, Wappenschmidt B, Rhiem K, Hahnen E, Engel C, Meindl A, Ditsch N, Arnold N, Plendl H, Niederacher D, Sutter C, Wang-Gohrke S, Steinemann D, Preisler-Adams S, Kast K, Varon-Mateeva R, Ellis S, Frost D, Platte R, Perkins J, Evans DG, Izatt L, Eeles R, Adlard J, Davidson R, Cole T, Scuvera G, Manoukian S, Bonanni B, Mariette F, Fortuzzi S, Viel A, Pasini B, Papi L, Varesco L, Balleine R, Nathanson KL, Domchek SM, Offitt K, Jakubowska A, Lindor N, Thomassen M, Jensen UB, Rantala J, Borg Å, Andrulis IL, Miron A, Hansen TV, Caldes T, Neuhausen SL, Toland AE, Nevanlinna H, Montagna M, Garber J, Godwin AK, Osorio A, Factor RE, Terry MB, Rebbeck TR, Karlan BY, Southey M, Rashid MU, Tung N, Pharoah PD, Blows FM, Dunning AM, Provenzano E, Hall P, Czene K, Schmidt MK, Broeks A, Cornelissen S, Verhoef S, Fasching PA, Beckmann MW, Ekici AB, Slamon DJ, Bojesen SE, Nordestgaard BG, Nielsen SF, Flyger H, Chang-Claude J, Flesch-Janys D, Rudolph A, Seibold P, Aittomäki K, Muranen TA, Heikkilä P, Blomqvist C, Figueroa J, Chanock SJ, Brinton L, Lissowska J, Olson JE, Pankratz VS, John EM, Whittemore AS, West DW, Hamann U, Torres D, Ulmer HU, Rüdiger T, Devilee P, Tollenaar RA, Seynaeve C, Van Asperen CJ, Eccles DM, Tapper WJ, Durcan L, Jones L, Peto J, dos-Santos-Silva I, Fletcher O, Johnson N, Dwek M, Swann R, Bane AL, Glendon G, Mulligan AM, Giles GG, Milne RL, Baglietto L, McLean C, Carpenter J, Clarke C, Scott R, Brauch H, Brüning T, Ko YD, Cox A, Cross SS, Reed MW, Lubinski J, Jaworska-Bieniek K, Durda K, Gronwald J, Dörk T, Bogdanova N, Park-Simon TW, Hillemanns P, Haiman CA, Henderson BE, Schumacher F, Le Marchand L, Burwinkel B, Marme F, Surovy H, Yang R, Anton-Culver H, Ziogas A, Hooning MJ, Collée JM, Martens JW, Tilanus-Linthorst MM, Brenner H, Dieffenbach AK, Arndt V, Stegmaier C, Winqvist R, Pylkäs K, Jukkola-Vuorinen A, Grip M, Lindblom A, Margolin S, Joseph V, Robson M, Rau-Murthy R, González-Neira A, Arias JI, Zamora P, Benítez J, Mannermaa A, Kataja V, Kosma VM, Hartikainen JM, Peterlongo P, Zaffaroni D, Barile M, Capra F, Radice P, Teo SH, Easton DF, Antoniou AC, Chenevix-Trench G, Goldgar DE, ABCTB InvestigatorsEMBRACE GroupGENICA NetworkHEBON GroupkConFab Investigato - Breast Cancer Res. (2014)

Proposed strategy for application of pathology likelihood ratios in multifactorial likelihood analysis ofBRCA1orBRCA2rare sequence variants. Cases carrying a variant of uncertain clinical significance, and with information on relevant pathology variables, are first assessed to determine that breast tumor pathology information was not a criterion used to trigger gene testing. ER, estrogen-receptor breast tumor status; PR, progesterone-receptor breast tumor status; HER2, HER2 breast tumor status; TN, triple-negative breast tumor status; Not TN, breast tumor status not triple-negative, after measurement of ER, PR, and HER2 status; ER-neg, ER-negative status; ER-pos, ER-positive status; G, grade; <50, breast cancer diagnosis at younger than 50 years for tumor with relevant pathology data; ≥50, breast cancer diagnosis at 50 to 70 years for tumor with relevant pathology data.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4352262&req=5

Fig1: Proposed strategy for application of pathology likelihood ratios in multifactorial likelihood analysis ofBRCA1orBRCA2rare sequence variants. Cases carrying a variant of uncertain clinical significance, and with information on relevant pathology variables, are first assessed to determine that breast tumor pathology information was not a criterion used to trigger gene testing. ER, estrogen-receptor breast tumor status; PR, progesterone-receptor breast tumor status; HER2, HER2 breast tumor status; TN, triple-negative breast tumor status; Not TN, breast tumor status not triple-negative, after measurement of ER, PR, and HER2 status; ER-neg, ER-negative status; ER-pos, ER-positive status; G, grade; <50, breast cancer diagnosis at younger than 50 years for tumor with relevant pathology data; ≥50, breast cancer diagnosis at 50 to 70 years for tumor with relevant pathology data.
Mentions: This study has re-estimated the likelihood of BRCA1 or BRCA2 mutation status associated with breast tumor features commonly measured in the clinical setting, by analyzing much larger datasets than previously used for this purpose. Our findings provide measures of confidence in the individual LR estimates, and in particular, allow age at diagnosis to be incorporated into the pathology component of the multifactorial likelihood model. Figure 1 provides a flowchart indicating the proposed application of pathology-based LRs, dependent on what breast tumor pathology information is available for a variant carrier. As indicated, ER-grade LRs should be applied in preference to other pathology LR estimates, where both ER and grade information is available. The ER-grade LRs were derived from analysis of the largest sample sizes and thus have the greatest precision, and application of 12 strata provided by three grade categories refines both positive and negative prediction of mutation status. For example, a patient with a high-grade ER-negative tumor is three- to fourfold more likely to carry a BRCA1 mutation than not, whereas a patient with a low-grade ER-positive tumor is about 10 times more likely to be mutation-negative than mutation-positive. Given that grade and ER are almost universally used to assess prognosis and predict response to antiestrogen therapies, these features are generally readily available on standard pathology reports.Figure 1

Bottom Line: These results refine likelihood-ratio estimates for predicting BRCA1 and BRCA2 mutation status by using commonly measured histopathological features.Age at diagnosis is an important variable for most analyses, and grade is more informative than ER status for BRCA2 mutation carrier prediction.The estimates will improve BRCA1 and BRCA2 variant classification and inform patient mutation testing and clinical management.

View Article: PubMed Central - PubMed

ABSTRACT

Introduction: The distribution of histopathological features of invasive breast tumors in BRCA1 or BRCA2 germline mutation carriers differs from that of individuals with no known mutation. Histopathological features thus have utility for mutation prediction, including statistical modeling to assess pathogenicity of BRCA1 or BRCA2 variants of uncertain clinical significance. We analyzed large pathology datasets accrued by the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA) and the Breast Cancer Association Consortium (BCAC) to reassess histopathological predictors of BRCA1 and BRCA2 mutation status, and provide robust likelihood ratio (LR) estimates for statistical modeling.

Methods: Selection criteria for study/center inclusion were estrogen receptor (ER) status or grade data available for invasive breast cancer diagnosed younger than 70 years. The dataset included 4,477 BRCA1 mutation carriers, 2,565 BRCA2 mutation carriers, and 47,565 BCAC breast cancer cases. Country-stratified estimates of the likelihood of mutation status by histopathological markers were derived using a Mantel-Haenszel approach.

Results: ER-positive phenotype negatively predicted BRCA1 mutation status, irrespective of grade (LRs from 0.08 to 0.90). ER-negative grade 3 histopathology was more predictive of positive BRCA1 mutation status in women 50 years or older (LR = 4.13 (3.70 to 4.62)) versus younger than 50 years (LR = 3.16 (2.96 to 3.37)). For BRCA2, ER-positive grade 3 phenotype modestly predicted positive mutation status irrespective of age (LR = 1.7-fold), whereas ER-negative grade 3 features modestly predicted positive mutation status at 50 years or older (LR = 1.54 (1.27 to 1.88)). Triple-negative tumor status was highly predictive of BRCA1 mutation status for women younger than 50 years (LR = 3.73 (3.43 to 4.05)) and 50 years or older (LR = 4.41 (3.86 to 5.04)), and modestly predictive of positive BRCA2 mutation status in women 50 years or older (LR = 1.79 (1.42 to 2.24)).

Conclusions: These results refine likelihood-ratio estimates for predicting BRCA1 and BRCA2 mutation status by using commonly measured histopathological features. Age at diagnosis is an important variable for most analyses, and grade is more informative than ER status for BRCA2 mutation carrier prediction. The estimates will improve BRCA1 and BRCA2 variant classification and inform patient mutation testing and clinical management.

Show MeSH
Related in: MedlinePlus