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Multigene assays for molecular subtypes and biomarkers can aid management of early invasive breast cancer. Using RNA-sequencing we aimed to develop single-sample predictor (SSP) models for clinical markers, subtypes, and risk of recurrence (ROR). A cohort of 7743 patients was divided into training and test set. We trained SSPs for subtypes and ROR assigned by nearest-centroid (NC) methods and SSPs for biomarkers from histopathology. Classifications were compared with Prosigna in two external cohorts (ABiM, n = 100 and OSLO2-EMIT0, n = 103). Prognostic value was assessed using distant recurrence-free interval. Agreement between SSP and NC for PAM50 {five subtypes) was high (85%, Kappa = 0.78) for Subtype (four subtypes) very high {90%, Kappa = 0.84) and for ROR risk category high (84%, Kappa = 0.75, weighted Kappa = 0.90). Prognostic value was assessed as equivalent and clinically relevant. Agreement with histopathology was very high or high for receptor status, while moderate for Ki67 status and poor for Nottingham histological grade. SSP and Prosigna concordance was high for subtype (OSLO-EMIT0 83%, Kappa = 0.73 and ABiM 80%, Kappa = 0.72) and moderate and high for ROR risk category (68 and 84%, Kappa = 0.50 and 0.70, weighted Kappa = 0.70 and 0.78). Pooled concordance for emulated treatment recommendation dichotomized for chemotherapy was high (85%, Kappa = 0.66). Retrospective evaluation suggested that SSP application could change chemotherapy recommendations for up to 17% of postmenopausal ER+/HER2-/N0 patients with balanced escalation and de-escalation. Results suggest that NC and SSP models are interchangeable on a group-level and nearly so on a patient level and that SSP models can be derived to closely match clinical tests.

Multigene assays for molecular subtypes and biomarkers can aid management of early invasive breast cancer. Using RNA-sequencing we aimed to develop single-sample predictor (SSP) models for clinical markers, subtypes, and risk of recurrence (ROR). A cohort of 7743 patients was divided into training and test set. We trained SSPs for subtypes and ROR assigned by nearest-centroid (NC) methods and SSPs for biomarkers from histopathology. Classifications were compared with Prosigna in two external cohorts (ABiM, n = 100 and OSLO2-EMIT0, n = 103). Prognostic value was assessed using distant recurrence-free interval. Agreement between SSP and NC for PAM50 {five subtypes) was high (85%, Kappa = 0.78) for Subtype (four subtypes) very high {90%, Kappa = 0.84) and for ROR risk category high (84%, Kappa = 0.75, weighted Kappa = 0.90). Prognostic value was assessed as equivalent and clinically relevant. Agreement with histopathology was very high or high for receptor status, while moderate for Ki67 status and poor for Nottingham histological grade. SSP and Prosigna concordance was high for subtype (OSLO-EMIT0 83%, Kappa = 0.73 and ABiM 80%, Kappa = 0.72) and moderate and high for ROR risk category (68 and 84%, Kappa = 0.50 and 0.70, weighted Kappa = 0.70 and 0.78). Pooled concordance for emulated treatment recommendation dichotomized for chemotherapy was high (85%, Kappa = 0.66). Retrospective evaluation suggested that SSP application could change chemotherapy recommendations for up to 17% of postmenopausal ER+/HER2-/N0 patients with balanced escalation and de-escalation. Results suggest that NC and SSP models are interchangeable on a group-level and nearly so on a patient level and that SSP models can be derived to closely match clinical tests.

Multigene assays for molecular subtypes and biomarkers can aid management of early invasive breast cancer. Using RNA-sequencing we aimed to develop single-sample predictor (SSP) models for clinical markers, subtypes, and risk of recurrence (ROR). A cohort of 7743 patients was divided into training and test set. We trained SSPs for subtypes and ROR assigned by nearest-centroid (NC) methods and SSPs for biomarkers from histopathology. Classifications were compared with Prosigna in two external cohorts (ABiM, n = 100 and OSLO2-EMIT0, n = 103). Prognostic value was assessed using distant recurrence-free interval. Agreement between SSP and NC for PAM50 {five subtypes) was high (85%, Kappa = 0.78) for Subtype (four subtypes) very high {90%, Kappa = 0.84) and for ROR risk category high (84%, Kappa = 0.75, weighted Kappa = 0.90). Prognostic value was assessed as equivalent and clinically relevant. Agreement with histopathology was very high or high for receptor status, while moderate for Ki67 status and poor for Nottingham histological grade. SSP and Prosigna concordance was high for subtype (OSLO-EMIT0 83%, Kappa = 0.73 and ABiM 80%, Kappa = 0.72) and moderate and high for ROR risk category (68 and 84%, Kappa = 0.50 and 0.70, weighted Kappa = 0.70 and 0.78). Pooled concordance for emulated treatment recommendation dichotomized for chemotherapy was high (85%, Kappa = 0.66). Retrospective evaluation suggested that SSP application could change chemotherapy recommendations for up to 17% of postmenopausal ER+/HER2-/N0 patients with balanced escalation and de-escalation. Results suggest that NC and SSP models are interchangeable on a group-level and nearly so on a patient level and that SSP models can be derived to closely match clinical tests.

Cancer is associated with systemic pathologies that contribute to mortality, such as thrombosis and distant organ failure. The aim of this study was to investigate the potential role of neutrophil extracellular traps (NETs) in myocardial inflammation and tissue damage in treatment-naïve individuals with cancer. Mice with mammary carcinoma (MMTV-PyMT) had increased plasma levels of NETs measured as H3Cit-DNA complexes, paralleled with elevated coagulation, compared to healthy littermates. MMTV-PyMT mice displayed upregulation of pro-inflammatory markers in the heart, myocardial hypertrophy and elevated cardiac disease biomarkers in the blood, but not echocardiographic heart failure. Moreover, increased endothelial proliferation was observed in hearts from tumor-bearing mice. Removal of NETs by DNase I treatment suppressed the myocardial inflammation, expression of cardiac disease biomarkers and endothelial proliferation. Compared to a healthy control group, treatment-naïve cancer patients with different malignant disorders had increased NET formation, which correlated to plasma levels of the inflammatory marker CRP and the cardiac disease biomarkers NT-proBNP and sTNFR1, in agreement with the mouse data. Altogether, our data indicate that NETs contribute to inflammation and myocardial stress during malignancy. These findings suggest NETs as potential therapeutic targets to prevent cardiac inflammation and dysfunction in cancer patients.

Cancer is associated with systemic pathologies that contribute to mortality, such as thrombosis and distant organ failure. The aim of this study was to investigate the potential role of neutrophil extracellular traps (NETs) in myocardial inflammation and tissue damage in treatment-naïve individuals with cancer. Mice with mammary carcinoma (MMTV-PyMT) had increased plasma levels of NETs measured as H3Cit-DNA complexes, paralleled with elevated coagulation, compared to healthy littermates. MMTV-PyMT mice displayed upregulation of pro-inflammatory markers in the heart, myocardial hypertrophy and elevated cardiac disease biomarkers in the blood, but not echocardiographic heart failure. Moreover, increased endothelial proliferation was observed in hearts from tumor-bearing mice. Removal of NETs by DNase I treatment suppressed the myocardial inflammation, expression of cardiac disease biomarkers and endothelial proliferation. Compared to a healthy control group, treatment-naïve cancer patients with different malignant disorders had increased NET formation, which correlated to plasma levels of the inflammatory marker CRP and the cardiac disease biomarkers NT-proBNP and sTNFR1, in agreement with the mouse data. Altogether, our data indicate that NETs contribute to inflammation and myocardial stress during malignancy. These findings suggest NETs as potential therapeutic targets to prevent cardiac inflammation and dysfunction in cancer patients.

Cancer is associated with systemic pathologies that contribute to mortality, such as thrombosis and distant organ failure. The aim of this study was to investigate the potential role of neutrophil extracellular traps (NETs) in myocardial inflammation and tissue damage in treatment-naïve individuals with cancer. Mice with mammary carcinoma (MMTV-PyMT) had increased plasma levels of NETs measured as H3Cit-DNA complexes, paralleled with elevated coagulation, compared to healthy littermates. MMTV-PyMT mice displayed upregulation of pro-inflammatory markers in the heart, myocardial hypertrophy and elevated cardiac disease biomarkers in the blood, but not echocardiographic heart failure. Moreover, increased endothelial proliferation was observed in hearts from tumor-bearing mice. Removal of NETs by DNase I treatment suppressed the myocardial inflammation, expression of cardiac disease biomarkers and endothelial proliferation. Compared to a healthy control group, treatment-naïve cancer patients with different malignant disorders had increased NET formation, which correlated to plasma levels of the inflammatory marker CRP and the cardiac disease biomarkers NT-proBNP and sTNFR1, in agreement with the mouse data. Altogether, our data indicate that NETs contribute to inflammation and myocardial stress during malignancy. These findings suggest NETs as potential therapeutic targets to prevent cardiac inflammation and dysfunction in cancer patients.

Multigene assays for molecular subtypes and biomarkers can aid management of early invasive breast cancer. Using RNA-sequencing we aimed to develop single-sample predictor (SSP) models for clinical markers, subtypes, and risk of recurrence (ROR). A cohort of 7743 patients was divided into training and test set. We trained SSPs for subtypes and ROR assigned by nearest-centroid (NC) methods and SSPs for biomarkers from histopathology. Classifications were compared with Prosigna in two external cohorts (ABiM, n = 100 and OSLO2-EMIT0, n = 103). Prognostic value was assessed using distant recurrence-free interval. Agreement between SSP and NC for PAM50 {five subtypes) was high (85%, Kappa = 0.78) for Subtype (four subtypes) very high {90%, Kappa = 0.84) and for ROR risk category high (84%, Kappa = 0.75, weighted Kappa = 0.90). Prognostic value was assessed as equivalent and clinically relevant. Agreement with histopathology was very high or high for receptor status, while moderate for Ki67 status and poor for Nottingham histological grade. SSP and Prosigna concordance was high for subtype (OSLO-EMIT0 83%, Kappa = 0.73 and ABiM 80%, Kappa = 0.72) and moderate and high for ROR risk category (68 and 84%, Kappa = 0.50 and 0.70, weighted Kappa = 0.70 and 0.78). Pooled concordance for emulated treatment recommendation dichotomized for chemotherapy was high (85%, Kappa = 0.66). Retrospective evaluation suggested that SSP application could change chemotherapy recommendations for up to 17% of postmenopausal ER+/HER2-/N0 patients with balanced escalation and de-escalation. Results suggest that NC and SSP models are interchangeable on a group-level and nearly so on a patient level and that SSP models can be derived to closely match clinical tests.

Cancer is associated with systemic pathologies that contribute to mortality, such as thrombosis and distant organ failure. The aim of this study was to investigate the potential role of neutrophil extracellular traps (NETs) in myocardial inflammation and tissue damage in treatment-naïve individuals with cancer. Mice with mammary carcinoma (MMTV-PyMT) had increased plasma levels of NETs measured as H3Cit-DNA complexes, paralleled with elevated coagulation, compared to healthy littermates. MMTV-PyMT mice displayed upregulation of pro-inflammatory markers in the heart, myocardial hypertrophy and elevated cardiac disease biomarkers in the blood, but not echocardiographic heart failure. Moreover, increased endothelial proliferation was observed in hearts from tumor-bearing mice. Removal of NETs by DNase I treatment suppressed the myocardial inflammation, expression of cardiac disease biomarkers and endothelial proliferation. Compared to a healthy control group, treatment-naïve cancer patients with different malignant disorders had increased NET formation, which correlated to plasma levels of the inflammatory marker CRP and the cardiac disease biomarkers NT-proBNP and sTNFR1, in agreement with the mouse data. Altogether, our data indicate that NETs contribute to inflammation and myocardial stress during malignancy. These findings suggest NETs as potential therapeutic targets to prevent cardiac inflammation and dysfunction in cancer patients.

Multigene assays for molecular subtypes and biomarkers can aid management of early invasive breast cancer. Using RNA-sequencing we aimed to develop single-sample predictor (SSP) models for clinical markers, subtypes, and risk of recurrence (ROR). A cohort of 7743 patients was divided into training and test set. We trained SSPs for subtypes and ROR assigned by nearest-centroid (NC) methods and SSPs for biomarkers from histopathology. Classifications were compared with Prosigna in two external cohorts (ABiM, n = 100 and OSLO2-EMIT0, n = 103). Prognostic value was assessed using distant recurrence-free interval. Agreement between SSP and NC for PAM50 {five subtypes) was high (85%, Kappa = 0.78) for Subtype (four subtypes) very high {90%, Kappa = 0.84) and for ROR risk category high (84%, Kappa = 0.75, weighted Kappa = 0.90). Prognostic value was assessed as equivalent and clinically relevant. Agreement with histopathology was very high or high for receptor status, while moderate for Ki67 status and poor for Nottingham histological grade. SSP and Prosigna concordance was high for subtype (OSLO-EMIT0 83%, Kappa = 0.73 and ABiM 80%, Kappa = 0.72) and moderate and high for ROR risk category (68 and 84%, Kappa = 0.50 and 0.70, weighted Kappa = 0.70 and 0.78). Pooled concordance for emulated treatment recommendation dichotomized for chemotherapy was high (85%, Kappa = 0.66). Retrospective evaluation suggested that SSP application could change chemotherapy recommendations for up to 17% of postmenopausal ER+/HER2-/N0 patients with balanced escalation and de-escalation. Results suggest that NC and SSP models are interchangeable on a group-level and nearly so on a patient level and that SSP models can be derived to closely match clinical tests.

Cancer is associated with systemic pathologies that contribute to mortality, such as thrombosis and distant organ failure. The aim of this study was to investigate the potential role of neutrophil extracellular traps (NETs) in myocardial inflammation and tissue damage in treatment-naïve individuals with cancer. Mice with mammary carcinoma (MMTV-PyMT) had increased plasma levels of NETs measured as H3Cit-DNA complexes, paralleled with elevated coagulation, compared to healthy littermates. MMTV-PyMT mice displayed upregulation of pro-inflammatory markers in the heart, myocardial hypertrophy and elevated cardiac disease biomarkers in the blood, but not echocardiographic heart failure. Moreover, increased endothelial proliferation was observed in hearts from tumor-bearing mice. Removal of NETs by DNase I treatment suppressed the myocardial inflammation, expression of cardiac disease biomarkers and endothelial proliferation. Compared to a healthy control group, treatment-naïve cancer patients with different malignant disorders had increased NET formation, which correlated to plasma levels of the inflammatory marker CRP and the cardiac disease biomarkers NT-proBNP and sTNFR1, in agreement with the mouse data. Altogether, our data indicate that NETs contribute to inflammation and myocardial stress during malignancy. These findings suggest NETs as potential therapeutic targets to prevent cardiac inflammation and dysfunction in cancer patients.

Purpose: Oestrogen receptor-positive (ER+) and human epidermal receptor 2-negative (HER2-) breast cancers are classified as Luminal A or B based on gene expression, but immunohistochemical markers are used for surrogate subtyping. The aims of this study were to examine the agreement between molecular subtyping (MS) and surrogate subtyping and to identify subgroups consisting mainly of Luminal A or B tumours. Methods: The cohort consisted of 2063 patients diagnosed between 2013-2017, with primary ER+/HER2- breast cancer, analysed by RNA sequencing. Surrogate subtyping was performed according to three algorithms (St. Gallen 2013, Maisonneuve and our proposed Grade-based classification). Agreement (%) and kappa statistics (kappa) were used as concordance measures and ROC analysis for luminal distinction. Ki67, progesterone receptor (PR) and histological grade (HG) were further investigated as surrogate markers. Results: The agreement rates between the MS and St. Gallen 2013, Maisonneuve and Grade-based classifications were 62% (kappa = 0.30), 66% (kappa = 0.35) and 70% (kappa = 0.41), respectively. PR did not contribute to distinguishing Luminal A from B tumours (auROC = 0.56). By classifying HG1-2 tumours as Luminal A-like and HG3 as Luminal B-like, agreement with MS was 80% (kappa = 0.46). Moreover, by combining HG and Ki67 status, a large subgroup of patients (51% of the cohort) having > 90% Luminal A tumours could be identified. Conclusions: Agreement between MS and surrogate classifications was generally poor. However, a post hoc analysis showed that a combination of HG and Ki67 could identify patients very likely to have Luminal A tumours according to MS.

Multigene expression signatures provide a molecular subdivision of early breast cancer associated with patient outcome. A gap remains in the validation of such signatures in clinical treatment groups of patients within population-based cohorts of unselected primary breast cancer representing contemporary disease stages and current treatments. A cohort of 3520 resectable breast cancers with RNA sequencing data included in the population-based SCAN-B initiative (ClinicalTrials.gov ID NCT02306096) were selected from a healthcare background population of 8587 patients diagnosed within the years 2010-2015. RNA profiles were classified according to 19 reported gene signatures including both gene expression subtypes (e.g. PAM50, IC10, CIT) and risk predictors (e.g. Oncotype DX, 70-gene, ROR). Classifications were analyzed in nine adjuvant clinical assessment groups: TNBC-ACT (adjuvant chemotherapy, n = 239), TNBC-untreated (n = 82), HER2+/ER- with anti-HER2+ACT treatment (n = 110), HER2+/ER+ with anti-HER2 + ACT + endocrine treatment (n = 239), ER+/HER2-/LN- with endocrine treatment (n = 1113), ER+/HER2-/LN- with endocrine +ACT treatment (n = 243), ER+/HER2-/LN+ with endocrine treatment (n = 423), ER+/HER2-/LN+ with endocrine +ACT treatment (n = 433), and ER+/HER2-/LN- untreated (n = 200). Gene signature classification (e.g., proportion low-, high-risk) was generally well aligned with stratification based on current immunohistochemistry-based clinical practice. Most signatures did not provide any further risk stratification in TNBC and HER2+/ER- disease. Risk classifier agreement (low-, medium/intermediate-, high-risk groups) in ER+ assessment groups was on average 50-60% with occasional pair-wise comparisons having <30% agreement. Disregarding the intermediate-risk groups, the exact agreement between low- and high-risk groups was on average similar to 80-95%, for risk prediction signatures across all assessment groups. Outcome analyses were restricted to assessment gro

Purpose: Oestrogen receptor-positive (ER+) and human epidermal receptor 2-negative (HER2-) breast cancers are classified as Luminal A or B based on gene expression, but immunohistochemical markers are used for surrogate subtyping. The aims of this study were to examine the agreement between molecular subtyping (MS) and surrogate subtyping and to identify subgroups consisting mainly of Luminal A or B tumours. Methods: The cohort consisted of 2063 patients diagnosed between 2013-2017, with primary ER+/HER2- breast cancer, analysed by RNA sequencing. Surrogate subtyping was performed according to three algorithms (St. Gallen 2013, Maisonneuve and our proposed Grade-based classification). Agreement (%) and kappa statistics (kappa) were used as concordance measures and ROC analysis for luminal distinction. Ki67, progesterone receptor (PR) and histological grade (HG) were further investigated as surrogate markers. Results: The agreement rates between the MS and St. Gallen 2013, Maisonneuve and Grade-based classifications were 62% (kappa = 0.30), 66% (kappa = 0.35) and 70% (kappa = 0.41), respectively. PR did not contribute to distinguishing Luminal A from B tumours (auROC = 0.56). By classifying HG1-2 tumours as Luminal A-like and HG3 as Luminal B-like, agreement with MS was 80% (kappa = 0.46). Moreover, by combining HG and Ki67 status, a large subgroup of patients (51% of the cohort) having > 90% Luminal A tumours could be identified. Conclusions: Agreement between MS and surrogate classifications was generally poor. However, a post hoc analysis showed that a combination of HG and Ki67 could identify patients very likely to have Luminal A tumours according to MS.

Purpose: Oestrogen receptor-positive (ER+) and human epidermal receptor 2-negative (HER2-) breast cancers are classified as Luminal A or B based on gene expression, but immunohistochemical markers are used for surrogate subtyping. The aims of this study were to examine the agreement between molecular subtyping (MS) and surrogate subtyping and to identify subgroups consisting mainly of Luminal A or B tumours. Methods: The cohort consisted of 2063 patients diagnosed between 2013-2017, with primary ER+/HER2- breast cancer, analysed by RNA sequencing. Surrogate subtyping was performed according to three algorithms (St. Gallen 2013, Maisonneuve and our proposed Grade-based classification). Agreement (%) and kappa statistics (kappa) were used as concordance measures and ROC analysis for luminal distinction. Ki67, progesterone receptor (PR) and histological grade (HG) were further investigated as surrogate markers. Results: The agreement rates between the MS and St. Gallen 2013, Maisonneuve and Grade-based classifications were 62% (kappa = 0.30), 66% (kappa = 0.35) and 70% (kappa = 0.41), respectively. PR did not contribute to distinguishing Luminal A from B tumours (auROC = 0.56). By classifying HG1-2 tumours as Luminal A-like and HG3 as Luminal B-like, agreement with MS was 80% (kappa = 0.46). Moreover, by combining HG and Ki67 status, a large subgroup of patients (51% of the cohort) having > 90% Luminal A tumours could be identified. Conclusions: Agreement between MS and surrogate classifications was generally poor. However, a post hoc analysis showed that a combination of HG and Ki67 could identify patients very likely to have Luminal A tumours according to MS.

Multigene expression signatures provide a molecular subdivision of early breast cancer associated with patient outcome. A gap remains in the validation of such signatures in clinical treatment groups of patients within population-based cohorts of unselected primary breast cancer representing contemporary disease stages and current treatments. A cohort of 3520 resectable breast cancers with RNA sequencing data included in the population-based SCAN-B initiative (ClinicalTrials.gov ID NCT02306096) were selected from a healthcare background population of 8587 patients diagnosed within the years 2010-2015. RNA profiles were classified according to 19 reported gene signatures including both gene expression subtypes (e.g. PAM50, IC10, CIT) and risk predictors (e.g. Oncotype DX, 70-gene, ROR). Classifications were analyzed in nine adjuvant clinical assessment groups: TNBC-ACT (adjuvant chemotherapy, n = 239), TNBC-untreated (n = 82), HER2+/ER- with anti-HER2+ACT treatment (n = 110), HER2+/ER+ with anti-HER2 + ACT + endocrine treatment (n = 239), ER+/HER2-/LN- with endocrine treatment (n = 1113), ER+/HER2-/LN- with endocrine +ACT treatment (n = 243), ER+/HER2-/LN+ with endocrine treatment (n = 423), ER+/HER2-/LN+ with endocrine +ACT treatment (n = 433), and ER+/HER2-/LN- untreated (n = 200). Gene signature classification (e.g., proportion low-, high-risk) was generally well aligned with stratification based on current immunohistochemistry-based clinical practice. Most signatures did not provide any further risk stratification in TNBC and HER2+/ER- disease. Risk classifier agreement (low-, medium/intermediate-, high-risk groups) in ER+ assessment groups was on average 50-60% with occasional pair-wise comparisons having <30% agreement. Disregarding the intermediate-risk groups, the exact agreement between low- and high-risk groups was on average similar to 80-95%, for risk prediction signatures across all assessment groups. Outcome analyses were restricted to assessment gro

Multigene expression signatures provide a molecular subdivision of early breast cancer associated with patient outcome. A gap remains in the validation of such signatures in clinical treatment groups of patients within population-based cohorts of unselected primary breast cancer representing contemporary disease stages and current treatments. A cohort of 3520 resectable breast cancers with RNA sequencing data included in the population-based SCAN-B initiative (ClinicalTrials.gov ID NCT02306096) were selected from a healthcare background population of 8587 patients diagnosed within the years 2010-2015. RNA profiles were classified according to 19 reported gene signatures including both gene expression subtypes (e.g. PAM50, IC10, CIT) and risk predictors (e.g. Oncotype DX, 70-gene, ROR). Classifications were analyzed in nine adjuvant clinical assessment groups: TNBC-ACT (adjuvant chemotherapy, n = 239), TNBC-untreated (n = 82), HER2+/ER- with anti-HER2+ACT treatment (n = 110), HER2+/ER+ with anti-HER2 + ACT + endocrine treatment (n = 239), ER+/HER2-/LN- with endocrine treatment (n = 1113), ER+/HER2-/LN- with endocrine +ACT treatment (n = 243), ER+/HER2-/LN+ with endocrine treatment (n = 423), ER+/HER2-/LN+ with endocrine +ACT treatment (n = 433), and ER+/HER2-/LN- untreated (n = 200). Gene signature classification (e.g., proportion low-, high-risk) was generally well aligned with stratification based on current immunohistochemistry-based clinical practice. Most signatures did not provide any further risk stratification in TNBC and HER2+/ER- disease. Risk classifier agreement (low-, medium/intermediate-, high-risk groups) in ER+ assessment groups was on average 50-60% with occasional pair-wise comparisons having <30% agreement. Disregarding the intermediate-risk groups, the exact agreement between low- and high-risk groups was on average similar to 80-95%, for risk prediction signatures across all assessment groups. Outcome analyses were restricted to assessment gro

Purpose: Oestrogen receptor-positive (ER+) and human epidermal receptor 2-negative (HER2-) breast cancers are classified as Luminal A or B based on gene expression, but immunohistochemical markers are used for surrogate subtyping. The aims of this study were to examine the agreement between molecular subtyping (MS) and surrogate subtyping and to identify subgroups consisting mainly of Luminal A or B tumours. Methods: The cohort consisted of 2063 patients diagnosed between 2013-2017, with primary ER+/HER2- breast cancer, analysed by RNA sequencing. Surrogate subtyping was performed according to three algorithms (St. Gallen 2013, Maisonneuve and our proposed Grade-based classification). Agreement (%) and kappa statistics (kappa) were used as concordance measures and ROC analysis for luminal distinction. Ki67, progesterone receptor (PR) and histological grade (HG) were further investigated as surrogate markers. Results: The agreement rates between the MS and St. Gallen 2013, Maisonneuve and Grade-based classifications were 62% (kappa = 0.30), 66% (kappa = 0.35) and 70% (kappa = 0.41), respectively. PR did not contribute to distinguishing Luminal A from B tumours (auROC = 0.56). By classifying HG1-2 tumours as Luminal A-like and HG3 as Luminal B-like, agreement with MS was 80% (kappa = 0.46). Moreover, by combining HG and Ki67 status, a large subgroup of patients (51% of the cohort) having > 90% Luminal A tumours could be identified. Conclusions: Agreement between MS and surrogate classifications was generally poor. However, a post hoc analysis showed that a combination of HG and Ki67 could identify patients very likely to have Luminal A tumours according to MS.

Purpose: Oestrogen receptor-positive (ER+) and human epidermal receptor 2-negative (HER2-) breast cancers are classified as Luminal A or B based on gene expression, but immunohistochemical markers are used for surrogate subtyping. The aims of this study were to examine the agreement between molecular subtyping (MS) and surrogate subtyping and to identify subgroups consisting mainly of Luminal A or B tumours. Methods: The cohort consisted of 2063 patients diagnosed between 2013-2017, with primary ER+/HER2- breast cancer, analysed by RNA sequencing. Surrogate subtyping was performed according to three algorithms (St. Gallen 2013, Maisonneuve and our proposed Grade-based classification). Agreement (%) and kappa statistics (kappa) were used as concordance measures and ROC analysis for luminal distinction. Ki67, progesterone receptor (PR) and histological grade (HG) were further investigated as surrogate markers. Results: The agreement rates between the MS and St. Gallen 2013, Maisonneuve and Grade-based classifications were 62% (kappa = 0.30), 66% (kappa = 0.35) and 70% (kappa = 0.41), respectively. PR did not contribute to distinguishing Luminal A from B tumours (auROC = 0.56). By classifying HG1-2 tumours as Luminal A-like and HG3 as Luminal B-like, agreement with MS was 80% (kappa = 0.46). Moreover, by combining HG and Ki67 status, a large subgroup of patients (51% of the cohort) having > 90% Luminal A tumours could be identified. Conclusions: Agreement between MS and surrogate classifications was generally poor. However, a post hoc analysis showed that a combination of HG and Ki67 could identify patients very likely to have Luminal A tumours according to MS.

Multigene expression signatures provide a molecular subdivision of early breast cancer associated with patient outcome. A gap remains in the validation of such signatures in clinical treatment groups of patients within population-based cohorts of unselected primary breast cancer representing contemporary disease stages and current treatments. A cohort of 3520 resectable breast cancers with RNA sequencing data included in the population-based SCAN-B initiative (ClinicalTrials.gov ID NCT02306096) were selected from a healthcare background population of 8587 patients diagnosed within the years 2010-2015. RNA profiles were classified according to 19 reported gene signatures including both gene expression subtypes (e.g. PAM50, IC10, CIT) and risk predictors (e.g. Oncotype DX, 70-gene, ROR). Classifications were analyzed in nine adjuvant clinical assessment groups: TNBC-ACT (adjuvant chemotherapy, n = 239), TNBC-untreated (n = 82), HER2+/ER- with anti-HER2+ACT treatment (n = 110), HER2+/ER+ with anti-HER2 + ACT + endocrine treatment (n = 239), ER+/HER2-/LN- with endocrine treatment (n = 1113), ER+/HER2-/LN- with endocrine +ACT treatment (n = 243), ER+/HER2-/LN+ with endocrine treatment (n = 423), ER+/HER2-/LN+ with endocrine +ACT treatment (n = 433), and ER+/HER2-/LN- untreated (n = 200). Gene signature classification (e.g., proportion low-, high-risk) was generally well aligned with stratification based on current immunohistochemistry-based clinical practice. Most signatures did not provide any further risk stratification in TNBC and HER2+/ER- disease. Risk classifier agreement (low-, medium/intermediate-, high-risk groups) in ER+ assessment groups was on average 50-60% with occasional pair-wise comparisons having <30% agreement. Disregarding the intermediate-risk groups, the exact agreement between low- and high-risk groups was on average similar to 80-95%, for risk prediction signatures across all assessment groups. Outcome analyses were restricted to assessment gro

Multigene expression signatures provide a molecular subdivision of early breast cancer associated with patient outcome. A gap remains in the validation of such signatures in clinical treatment groups of patients within population-based cohorts of unselected primary breast cancer representing contemporary disease stages and current treatments. A cohort of 3520 resectable breast cancers with RNA sequencing data included in the population-based SCAN-B initiative (ClinicalTrials.gov ID NCT02306096) were selected from a healthcare background population of 8587 patients diagnosed within the years 2010-2015. RNA profiles were classified according to 19 reported gene signatures including both gene expression subtypes (e.g. PAM50, IC10, CIT) and risk predictors (e.g. Oncotype DX, 70-gene, ROR). Classifications were analyzed in nine adjuvant clinical assessment groups: TNBC-ACT (adjuvant chemotherapy, n = 239), TNBC-untreated (n = 82), HER2+/ER- with anti-HER2+ACT treatment (n = 110), HER2+/ER+ with anti-HER2 + ACT + endocrine treatment (n = 239), ER+/HER2-/LN- with endocrine treatment (n = 1113), ER+/HER2-/LN- with endocrine +ACT treatment (n = 243), ER+/HER2-/LN+ with endocrine treatment (n = 423), ER+/HER2-/LN+ with endocrine +ACT treatment (n = 433), and ER+/HER2-/LN- untreated (n = 200). Gene signature classification (e.g., proportion low-, high-risk) was generally well aligned with stratification based on current immunohistochemistry-based clinical practice. Most signatures did not provide any further risk stratification in TNBC and HER2+/ER- disease. Risk classifier agreement (low-, medium/intermediate-, high-risk groups) in ER+ assessment groups was on average 50-60% with occasional pair-wise comparisons having <30% agreement. Disregarding the intermediate-risk groups, the exact agreement between low- and high-risk groups was on average similar to 80-95%, for risk prediction signatures across all assessment groups. Outcome analyses were restricted to assessment gro

Purpose: Oestrogen receptor-positive (ER+) and human epidermal receptor 2-negative (HER2-) breast cancers are classified as Luminal A or B based on gene expression, but immunohistochemical markers are used for surrogate subtyping. The aims of this study were to examine the agreement between molecular subtyping (MS) and surrogate subtyping and to identify subgroups consisting mainly of Luminal A or B tumours. Methods: The cohort consisted of 2063 patients diagnosed between 2013-2017, with primary ER+/HER2- breast cancer, analysed by RNA sequencing. Surrogate subtyping was performed according to three algorithms (St. Gallen 2013, Maisonneuve and our proposed Grade-based classification). Agreement (%) and kappa statistics (kappa) were used as concordance measures and ROC analysis for luminal distinction. Ki67, progesterone receptor (PR) and histological grade (HG) were further investigated as surrogate markers. Results: The agreement rates between the MS and St. Gallen 2013, Maisonneuve and Grade-based classifications were 62% (kappa = 0.30), 66% (kappa = 0.35) and 70% (kappa = 0.41), respectively. PR did not contribute to distinguishing Luminal A from B tumours (auROC = 0.56). By classifying HG1-2 tumours as Luminal A-like and HG3 as Luminal B-like, agreement with MS was 80% (kappa = 0.46). Moreover, by combining HG and Ki67 status, a large subgroup of patients (51% of the cohort) having > 90% Luminal A tumours could be identified. Conclusions: Agreement between MS and surrogate classifications was generally poor. However, a post hoc analysis showed that a combination of HG and Ki67 could identify patients very likely to have Luminal A tumours according to MS.

Background: Progress in cancer biomarker discovery is dependent on access to high-quality biological materials and high-resolution clinical data from the same cases. To overcome current limitations, a systematic prospective longitudinal sampling of multidisciplinary clinical data, blood and tissue from cancer patients was therefore initiated in 2010 by Uppsala and Umeå Universities and involving their corresponding University Hospitals, which are referral centers for one third of the Swedish population. Material and Methods: Patients with cancer of selected types who are treated at one of the participating hospitals are eligible for inclusion. The healthcare-integrated sampling scheme encompasses clinical data, questionnaires, blood, fresh frozen and formalin-fixed paraffin-embedded tissue specimens, diagnostic slides and radiology bioimaging data. Results: In this ongoing effort, 12,265 patients with brain tumors, breast cancers, colorectal cancers, gynecological cancers, hematological malignancies, lung cancers, neuroendocrine tumors or prostate cancers have been included until the end of 2016. From the 6914 patients included during the first five years, 98% were sampled for blood at diagnosis, 83% had paraffin-embedded and 58% had fresh frozen tissues collected. For Uppsala County, 55% of all cancer patients were included in the cohort. Conclusions: Close collaboration between participating hospitals and universities enabled prospective, longitudinal biobanking of blood and tissues and collection of multidisciplinary clinical data from cancer patients in the U-CAN cohort. Here, we summarize the first five years of operations, present U-CAN as a highly valuable cohort that will contribute to enhanced cancer research and describe the procedures to access samples and data.

Background: Progress in cancer biomarker discovery is dependent on access to high-quality biological materials and high-resolution clinical data from the same cases. To overcome current limitations, a systematic prospective longitudinal sampling of multidisciplinary clinical data, blood and tissue from cancer patients was therefore initiated in 2010 by Uppsala and Umeå Universities and involving their corresponding University Hospitals, which are referral centers for one third of the Swedish population. Material and Methods: Patients with cancer of selected types who are treated at one of the participating hospitals are eligible for inclusion. The healthcare-integrated sampling scheme encompasses clinical data, questionnaires, blood, fresh frozen and formalin-fixed paraffin-embedded tissue specimens, diagnostic slides and radiology bioimaging data. Results: In this ongoing effort, 12,265 patients with brain tumors, breast cancers, colorectal cancers, gynecological cancers, hematological malignancies, lung cancers, neuroendocrine tumors or prostate cancers have been included until the end of 2016. From the 6914 patients included during the first five years, 98% were sampled for blood at diagnosis, 83% had paraffin-embedded and 58% had fresh frozen tissues collected. For Uppsala County, 55% of all cancer patients were included in the cohort. Conclusions: Close collaboration between participating hospitals and universities enabled prospective, longitudinal biobanking of blood and tissues and collection of multidisciplinary clinical data from cancer patients in the U-CAN cohort. Here, we summarize the first five years of operations, present U-CAN as a highly valuable cohort that will contribute to enhanced cancer research and describe the procedures to access samples and data.

Background: Progress in cancer biomarker discovery is dependent on access to high-quality biological materials and high-resolution clinical data from the same cases. To overcome current limitations, a systematic prospective longitudinal sampling of multidisciplinary clinical data, blood and tissue from cancer patients was therefore initiated in 2010 by Uppsala and Umeå Universities and involving their corresponding University Hospitals, which are referral centers for one third of the Swedish population. Material and Methods: Patients with cancer of selected types who are treated at one of the participating hospitals are eligible for inclusion. The healthcare-integrated sampling scheme encompasses clinical data, questionnaires, blood, fresh frozen and formalin-fixed paraffin-embedded tissue specimens, diagnostic slides and radiology bioimaging data. Results: In this ongoing effort, 12,265 patients with brain tumors, breast cancers, colorectal cancers, gynecological cancers, hematological malignancies, lung cancers, neuroendocrine tumors or prostate cancers have been included until the end of 2016. From the 6914 patients included during the first five years, 98% were sampled for blood at diagnosis, 83% had paraffin-embedded and 58% had fresh frozen tissues collected. For Uppsala County, 55% of all cancer patients were included in the cohort. Conclusions: Close collaboration between participating hospitals and universities enabled prospective, longitudinal biobanking of blood and tissues and collection of multidisciplinary clinical data from cancer patients in the U-CAN cohort. Here, we summarize the first five years of operations, present U-CAN as a highly valuable cohort that will contribute to enhanced cancer research and describe the procedures to access samples and data.

Background: Progress in cancer biomarker discovery is dependent on access to high-quality biological materials and high-resolution clinical data from the same cases. To overcome current limitations, a systematic prospective longitudinal sampling of multidisciplinary clinical data, blood and tissue from cancer patients was therefore initiated in 2010 by Uppsala and Umeå Universities and involving their corresponding University Hospitals, which are referral centers for one third of the Swedish population. Material and Methods: Patients with cancer of selected types who are treated at one of the participating hospitals are eligible for inclusion. The healthcare-integrated sampling scheme encompasses clinical data, questionnaires, blood, fresh frozen and formalin-fixed paraffin-embedded tissue specimens, diagnostic slides and radiology bioimaging data. Results: In this ongoing effort, 12,265 patients with brain tumors, breast cancers, colorectal cancers, gynecological cancers, hematological malignancies, lung cancers, neuroendocrine tumors or prostate cancers have been included until the end of 2016. From the 6914 patients included during the first five years, 98% were sampled for blood at diagnosis, 83% had paraffin-embedded and 58% had fresh frozen tissues collected. For Uppsala County, 55% of all cancer patients were included in the cohort. Conclusions: Close collaboration between participating hospitals and universities enabled prospective, longitudinal biobanking of blood and tissues and collection of multidisciplinary clinical data from cancer patients in the U-CAN cohort. Here, we summarize the first five years of operations, present U-CAN as a highly valuable cohort that will contribute to enhanced cancer research and describe the procedures to access samples and data.

Background: Progress in cancer biomarker discovery is dependent on access to high-quality biological materials and high-resolution clinical data from the same cases. To overcome current limitations, a systematic prospective longitudinal sampling of multidisciplinary clinical data, blood and tissue from cancer patients was therefore initiated in 2010 by Uppsala and Umeå Universities and involving their corresponding University Hospitals, which are referral centers for one third of the Swedish population. Material and Methods: Patients with cancer of selected types who are treated at one of the participating hospitals are eligible for inclusion. The healthcare-integrated sampling scheme encompasses clinical data, questionnaires, blood, fresh frozen and formalin-fixed paraffin-embedded tissue specimens, diagnostic slides and radiology bioimaging data. Results: In this ongoing effort, 12,265 patients with brain tumors, breast cancers, colorectal cancers, gynecological cancers, hematological malignancies, lung cancers, neuroendocrine tumors or prostate cancers have been included until the end of 2016. From the 6914 patients included during the first five years, 98% were sampled for blood at diagnosis, 83% had paraffin-embedded and 58% had fresh frozen tissues collected. For Uppsala County, 55% of all cancer patients were included in the cohort. Conclusions: Close collaboration between participating hospitals and universities enabled prospective, longitudinal biobanking of blood and tissues and collection of multidisciplinary clinical data from cancer patients in the U-CAN cohort. Here, we summarize the first five years of operations, present U-CAN as a highly valuable cohort that will contribute to enhanced cancer research and describe the procedures to access samples and data.

PURPOSE: In phase I/II-studies radiolabelled ABY-025 Affibody molecules identified human epidermal growth factor receptor 2 (HER2) expression in breast cancer metastases using PET and SPECT imaging. Here, we wanted to investigate the utility of a simple intra-image normalization using tumour-to-reference tissue-ratio (T/R) as a HER2 status discrimination strategy to overcome potential issues related to cross-calibration of scanning devices. METHODS: Twenty-three women with pre-diagnosed HER2-positive/negative metastasized breast cancer were scanned with [(111)In]-ABY-025 SPECT/CT (n = 7) or [(68)Ga]-ABY-025 PET/CT (n = 16). Uptake was measured in all metastases and in normal spleen, lung, liver, muscle, and blood pool. Normal tissue uptake variation and T/R-ratios were established for various time points and for two different doses of injected peptide from a total of 94 whole-body image acquisitions. Immunohistochemistry (IHC) was used to verify HER2 expression in 28 biopsied metastases. T/R-ratios were compared to IHC findings to establish the best reference tissue for each modality and each imaging time-point. The impact of shed HER2 in serum was investigated. RESULTS: Spleen was the best reference tissue across modalities, followed by blood pool and lung. Spleen-T/R was highly correlated to PET SUV in metastases after 2 h (r = 0.96, P < 0.001) and reached an accuracy of 100% for discriminating IHC HER2-positive and negative metastases at 4 h (PET) and 24 h (SPECT) after injection. In a single case, shed HER2 resulted in intense tracer retention in blood. In the remaining patients shed HER2 was elevated, but without significant impact on ABY-025 biodistribution. CONCLUSION: T/R-ratios using spleen as reference tissue accurately quantify HER2 expression with radiolabelled ABY-025 imaging in breast cancer metastases with SPECT and PET. Tracer binding to shed HER2 in serum might affect quantification in the extreme case.

Aim: Compared to middle-aged women, young women with breast cancer have a higher risk of systemic disease. We studied expression of proliferation markers in relation to age and subtype and their association with long-term prognosis. Methods: Distant disease-free survival (DDFS) was studied in 504 women aged <40 years and 383 women aged >= 40 years from a population-based cohort. Information on patient characteristics, treatment and follow-up was collected from medical records. Tissue microarrays were produced for analysis of oestrogen receptor, progesterone receptor (PR), Her2, Ki-67 and cyclins. Results: Young women with luminal tumours had significantly higher expression of Ki-67 and cyclins. Proliferation markers were prognostic only within this subtype. Ki-67 was a prognostic indicator only in young women with luminal PR+ tumours. The optimal cut-off for Ki-67 varied by age. High expression of cyclin E1 conferred a better DDFS in women aged <40 years with luminal PR- tumours (hazard ratio [HR] 0.47 [0.24-0.92]). Age < 40 years was an independent risk factor of DDFS exclusively in women with luminal B PR+ tumours (HR 2.35 [1.22-4.50]). Young women with luminal B PR- tumours expressing low cyclin E1 had a six-fold risk of distant disease compared with luminal A ( HR 6.21 [2.17-17.6]). Conclusions: The higher expression of proliferation markers in young women does not have a strong impact on prognosis. Ki-67 is only prognostic in the subgroup of young women with luminal PR tumours. The only cyclin adding prognostic value beyond subtype is cyclin E1. Age is an independent prognostic factor only in women with luminal B PR+ tumours.

PURPOSE: In phase I/II-studies radiolabelled ABY-025 Affibody molecules identified human epidermal growth factor receptor 2 (HER2) expression in breast cancer metastases using PET and SPECT imaging. Here, we wanted to investigate the utility of a simple intra-image normalization using tumour-to-reference tissue-ratio (T/R) as a HER2 status discrimination strategy to overcome potential issues related to cross-calibration of scanning devices. METHODS: Twenty-three women with pre-diagnosed HER2-positive/negative metastasized breast cancer were scanned with [(111)In]-ABY-025 SPECT/CT (n = 7) or [(68)Ga]-ABY-025 PET/CT (n = 16). Uptake was measured in all metastases and in normal spleen, lung, liver, muscle, and blood pool. Normal tissue uptake variation and T/R-ratios were established for various time points and for two different doses of injected peptide from a total of 94 whole-body image acquisitions. Immunohistochemistry (IHC) was used to verify HER2 expression in 28 biopsied metastases. T/R-ratios were compared to IHC findings to establish the best reference tissue for each modality and each imaging time-point. The impact of shed HER2 in serum was investigated. RESULTS: Spleen was the best reference tissue across modalities, followed by blood pool and lung. Spleen-T/R was highly correlated to PET SUV in metastases after 2 h (r = 0.96, P < 0.001) and reached an accuracy of 100% for discriminating IHC HER2-positive and negative metastases at 4 h (PET) and 24 h (SPECT) after injection. In a single case, shed HER2 resulted in intense tracer retention in blood. In the remaining patients shed HER2 was elevated, but without significant impact on ABY-025 biodistribution. CONCLUSION: T/R-ratios using spleen as reference tissue accurately quantify HER2 expression with radiolabelled ABY-025 imaging in breast cancer metastases with SPECT and PET. Tracer binding to shed HER2 in serum might affect quantification in the extreme case.

PURPOSE: In phase I/II-studies radiolabelled ABY-025 Affibody molecules identified human epidermal growth factor receptor 2 (HER2) expression in breast cancer metastases using PET and SPECT imaging. Here, we wanted to investigate the utility of a simple intra-image normalization using tumour-to-reference tissue-ratio (T/R) as a HER2 status discrimination strategy to overcome potential issues related to cross-calibration of scanning devices. METHODS: Twenty-three women with pre-diagnosed HER2-positive/negative metastasized breast cancer were scanned with [(111)In]-ABY-025 SPECT/CT (n = 7) or [(68)Ga]-ABY-025 PET/CT (n = 16). Uptake was measured in all metastases and in normal spleen, lung, liver, muscle, and blood pool. Normal tissue uptake variation and T/R-ratios were established for various time points and for two different doses of injected peptide from a total of 94 whole-body image acquisitions. Immunohistochemistry (IHC) was used to verify HER2 expression in 28 biopsied metastases. T/R-ratios were compared to IHC findings to establish the best reference tissue for each modality and each imaging time-point. The impact of shed HER2 in serum was investigated. RESULTS: Spleen was the best reference tissue across modalities, followed by blood pool and lung. Spleen-T/R was highly correlated to PET SUV in metastases after 2 h (r = 0.96, P < 0.001) and reached an accuracy of 100% for discriminating IHC HER2-positive and negative metastases at 4 h (PET) and 24 h (SPECT) after injection. In a single case, shed HER2 resulted in intense tracer retention in blood. In the remaining patients shed HER2 was elevated, but without significant impact on ABY-025 biodistribution. CONCLUSION: T/R-ratios using spleen as reference tissue accurately quantify HER2 expression with radiolabelled ABY-025 imaging in breast cancer metastases with SPECT and PET. Tracer binding to shed HER2 in serum might affect quantification in the extreme case.

PURPOSE: In phase I/II-studies radiolabelled ABY-025 Affibody molecules identified human epidermal growth factor receptor 2 (HER2) expression in breast cancer metastases using PET and SPECT imaging. Here, we wanted to investigate the utility of a simple intra-image normalization using tumour-to-reference tissue-ratio (T/R) as a HER2 status discrimination strategy to overcome potential issues related to cross-calibration of scanning devices. METHODS: Twenty-three women with pre-diagnosed HER2-positive/negative metastasized breast cancer were scanned with [(111)In]-ABY-025 SPECT/CT (n = 7) or [(68)Ga]-ABY-025 PET/CT (n = 16). Uptake was measured in all metastases and in normal spleen, lung, liver, muscle, and blood pool. Normal tissue uptake variation and T/R-ratios were established for various time points and for two different doses of injected peptide from a total of 94 whole-body image acquisitions. Immunohistochemistry (IHC) was used to verify HER2 expression in 28 biopsied metastases. T/R-ratios were compared to IHC findings to establish the best reference tissue for each modality and each imaging time-point. The impact of shed HER2 in serum was investigated. RESULTS: Spleen was the best reference tissue across modalities, followed by blood pool and lung. Spleen-T/R was highly correlated to PET SUV in metastases after 2 h (r = 0.96, P < 0.001) and reached an accuracy of 100% for discriminating IHC HER2-positive and negative metastases at 4 h (PET) and 24 h (SPECT) after injection. In a single case, shed HER2 resulted in intense tracer retention in blood. In the remaining patients shed HER2 was elevated, but without significant impact on ABY-025 biodistribution. CONCLUSION: T/R-ratios using spleen as reference tissue accurately quantify HER2 expression with radiolabelled ABY-025 imaging in breast cancer metastases with SPECT and PET. Tracer binding to shed HER2 in serum might affect quantification in the extreme case.

PURPOSE: In phase I/II-studies radiolabelled ABY-025 Affibody molecules identified human epidermal growth factor receptor 2 (HER2) expression in breast cancer metastases using PET and SPECT imaging. Here, we wanted to investigate the utility of a simple intra-image normalization using tumour-to-reference tissue-ratio (T/R) as a HER2 status discrimination strategy to overcome potential issues related to cross-calibration of scanning devices. METHODS: Twenty-three women with pre-diagnosed HER2-positive/negative metastasized breast cancer were scanned with [(111)In]-ABY-025 SPECT/CT (n = 7) or [(68)Ga]-ABY-025 PET/CT (n = 16). Uptake was measured in all metastases and in normal spleen, lung, liver, muscle, and blood pool. Normal tissue uptake variation and T/R-ratios were established for various time points and for two different doses of injected peptide from a total of 94 whole-body image acquisitions. Immunohistochemistry (IHC) was used to verify HER2 expression in 28 biopsied metastases. T/R-ratios were compared to IHC findings to establish the best reference tissue for each modality and each imaging time-point. The impact of shed HER2 in serum was investigated. RESULTS: Spleen was the best reference tissue across modalities, followed by blood pool and lung. Spleen-T/R was highly correlated to PET SUV in metastases after 2 h (r = 0.96, P < 0.001) and reached an accuracy of 100% for discriminating IHC HER2-positive and negative metastases at 4 h (PET) and 24 h (SPECT) after injection. In a single case, shed HER2 resulted in intense tracer retention in blood. In the remaining patients shed HER2 was elevated, but without significant impact on ABY-025 biodistribution. CONCLUSION: T/R-ratios using spleen as reference tissue accurately quantify HER2 expression with radiolabelled ABY-025 imaging in breast cancer metastases with SPECT and PET. Tracer binding to shed HER2 in serum might affect quantification in the extreme case.

PURPOSE: Positron Emission Tomography (PET) imaging of HER2 expression could potentially be used to select patients for HER2-targed therapy, predict response based on uptake and be used for monitoring. In this phase I/II study the HER2-binding Affibody molecule ABY-025 was labeled with (68)Ga-gallium ([(68)Ga]ABY-025) for PET to study effect of peptide mass, test-retest variability and correlation of quantified uptake in tumors to histopathology. EXPERIMENTAL DESIGN: Sixteen women with known metastatic breast cancer and on-going treatment were included and underwent FDG PET/CT to identify viable metastases. After iv injection of 212±46 MBq [(68)Ga]ABY-025 whole-body PET was performed at 1, 2 and 4 h. In the first 10 patients (6 with HER2-positive and 4 with HER2-negative primary tumors), [(68)Ga]ABY-025 PET/CT with two different doses of injected peptide was performed one week apart. In the last six patients (5 HER2-positive and 1 HER2-negative primary tumors), repeated [(68)Ga]ABY-025 PET were performed one week apart as a test-retest of uptake in individual lesions. Biopsies from 16 metastases in 12 patients were collected for verification of HER2 expression by immunohistochemistry and in-situ hybridization. RESULTS: Imaging 4h after injection with high peptide content discriminated HER2-positive metastases best (p<0.01). PET SUV correlated with biopsy HER2-scores (r=0.91, p<0.001). Uptake was five times higher in HER2-positive than in HER2-negative lesions with no overlap (p=0.005). The test-retest intra-class correlation was r=0.996. [(68)Ga]ABY-025 PET correctly identified conversion and mixed expression of HER2 and targeted treatment was changed in 3 of the 16 patients. CONCLUSION: [(68)Ga]ABY-025 PET accurately quantifies whole-body HER2-receptor status in metastatic breast cancer.

PURPOSE: Positron Emission Tomography (PET) imaging of HER2 expression could potentially be used to select patients for HER2-targed therapy, predict response based on uptake and be used for monitoring. In this phase I/II study the HER2-binding Affibody molecule ABY-025 was labeled with (68)Ga-gallium ([(68)Ga]ABY-025) for PET to study effect of peptide mass, test-retest variability and correlation of quantified uptake in tumors to histopathology. EXPERIMENTAL DESIGN: Sixteen women with known metastatic breast cancer and on-going treatment were included and underwent FDG PET/CT to identify viable metastases. After iv injection of 212±46 MBq [(68)Ga]ABY-025 whole-body PET was performed at 1, 2 and 4 h. In the first 10 patients (6 with HER2-positive and 4 with HER2-negative primary tumors), [(68)Ga]ABY-025 PET/CT with two different doses of injected peptide was performed one week apart. In the last six patients (5 HER2-positive and 1 HER2-negative primary tumors), repeated [(68)Ga]ABY-025 PET were performed one week apart as a test-retest of uptake in individual lesions. Biopsies from 16 metastases in 12 patients were collected for verification of HER2 expression by immunohistochemistry and in-situ hybridization. RESULTS: Imaging 4h after injection with high peptide content discriminated HER2-positive metastases best (p<0.01). PET SUV correlated with biopsy HER2-scores (r=0.91, p<0.001). Uptake was five times higher in HER2-positive than in HER2-negative lesions with no overlap (p=0.005). The test-retest intra-class correlation was r=0.996. [(68)Ga]ABY-025 PET correctly identified conversion and mixed expression of HER2 and targeted treatment was changed in 3 of the 16 patients. CONCLUSION: [(68)Ga]ABY-025 PET accurately quantifies whole-body HER2-receptor status in metastatic breast cancer.

PURPOSE: Positron Emission Tomography (PET) imaging of HER2 expression could potentially be used to select patients for HER2-targed therapy, predict response based on uptake and be used for monitoring. In this phase I/II study the HER2-binding Affibody molecule ABY-025 was labeled with (68)Ga-gallium ([(68)Ga]ABY-025) for PET to study effect of peptide mass, test-retest variability and correlation of quantified uptake in tumors to histopathology. EXPERIMENTAL DESIGN: Sixteen women with known metastatic breast cancer and on-going treatment were included and underwent FDG PET/CT to identify viable metastases. After iv injection of 212±46 MBq [(68)Ga]ABY-025 whole-body PET was performed at 1, 2 and 4 h. In the first 10 patients (6 with HER2-positive and 4 with HER2-negative primary tumors), [(68)Ga]ABY-025 PET/CT with two different doses of injected peptide was performed one week apart. In the last six patients (5 HER2-positive and 1 HER2-negative primary tumors), repeated [(68)Ga]ABY-025 PET were performed one week apart as a test-retest of uptake in individual lesions. Biopsies from 16 metastases in 12 patients were collected for verification of HER2 expression by immunohistochemistry and in-situ hybridization. RESULTS: Imaging 4h after injection with high peptide content discriminated HER2-positive metastases best (p<0.01). PET SUV correlated with biopsy HER2-scores (r=0.91, p<0.001). Uptake was five times higher in HER2-positive than in HER2-negative lesions with no overlap (p=0.005). The test-retest intra-class correlation was r=0.996. [(68)Ga]ABY-025 PET correctly identified conversion and mixed expression of HER2 and targeted treatment was changed in 3 of the 16 patients. CONCLUSION: [(68)Ga]ABY-025 PET accurately quantifies whole-body HER2-receptor status in metastatic breast cancer.

PURPOSE: Positron Emission Tomography (PET) imaging of HER2 expression could potentially be used to select patients for HER2-targed therapy, predict response based on uptake and be used for monitoring. In this phase I/II study the HER2-binding Affibody molecule ABY-025 was labeled with (68)Ga-gallium ([(68)Ga]ABY-025) for PET to study effect of peptide mass, test-retest variability and correlation of quantified uptake in tumors to histopathology. EXPERIMENTAL DESIGN: Sixteen women with known metastatic breast cancer and on-going treatment were included and underwent FDG PET/CT to identify viable metastases. After iv injection of 212±46 MBq [(68)Ga]ABY-025 whole-body PET was performed at 1, 2 and 4 h. In the first 10 patients (6 with HER2-positive and 4 with HER2-negative primary tumors), [(68)Ga]ABY-025 PET/CT with two different doses of injected peptide was performed one week apart. In the last six patients (5 HER2-positive and 1 HER2-negative primary tumors), repeated [(68)Ga]ABY-025 PET were performed one week apart as a test-retest of uptake in individual lesions. Biopsies from 16 metastases in 12 patients were collected for verification of HER2 expression by immunohistochemistry and in-situ hybridization. RESULTS: Imaging 4h after injection with high peptide content discriminated HER2-positive metastases best (p<0.01). PET SUV correlated with biopsy HER2-scores (r=0.91, p<0.001). Uptake was five times higher in HER2-positive than in HER2-negative lesions with no overlap (p=0.005). The test-retest intra-class correlation was r=0.996. [(68)Ga]ABY-025 PET correctly identified conversion and mixed expression of HER2 and targeted treatment was changed in 3 of the 16 patients. CONCLUSION: [(68)Ga]ABY-025 PET accurately quantifies whole-body HER2-receptor status in metastatic breast cancer.

PURPOSE: Positron Emission Tomography (PET) imaging of HER2 expression could potentially be used to select patients for HER2-targed therapy, predict response based on uptake and be used for monitoring. In this phase I/II study the HER2-binding Affibody molecule ABY-025 was labeled with (68)Ga-gallium ([(68)Ga]ABY-025) for PET to study effect of peptide mass, test-retest variability and correlation of quantified uptake in tumors to histopathology. EXPERIMENTAL DESIGN: Sixteen women with known metastatic breast cancer and on-going treatment were included and underwent FDG PET/CT to identify viable metastases. After iv injection of 212±46 MBq [(68)Ga]ABY-025 whole-body PET was performed at 1, 2 and 4 h. In the first 10 patients (6 with HER2-positive and 4 with HER2-negative primary tumors), [(68)Ga]ABY-025 PET/CT with two different doses of injected peptide was performed one week apart. In the last six patients (5 HER2-positive and 1 HER2-negative primary tumors), repeated [(68)Ga]ABY-025 PET were performed one week apart as a test-retest of uptake in individual lesions. Biopsies from 16 metastases in 12 patients were collected for verification of HER2 expression by immunohistochemistry and in-situ hybridization. RESULTS: Imaging 4h after injection with high peptide content discriminated HER2-positive metastases best (p<0.01). PET SUV correlated with biopsy HER2-scores (r=0.91, p<0.001). Uptake was five times higher in HER2-positive than in HER2-negative lesions with no overlap (p=0.005). The test-retest intra-class correlation was r=0.996. [(68)Ga]ABY-025 PET correctly identified conversion and mixed expression of HER2 and targeted treatment was changed in 3 of the 16 patients. CONCLUSION: [(68)Ga]ABY-025 PET accurately quantifies whole-body HER2-receptor status in metastatic breast cancer.