Alessandra Gennari (Novara, Italy)
Università Degli Studi Del Piemonte Orientale - Scuola di MedicinaAuthor Of 6 Presentations
In the current MBC landscape of HER2-/HR+ and TNBC, what’s the role of chemotherapy? (ID 465)
Welcome and Introduction (ID 464)
Closure (ID 468)
Invited Discussant 93MO (ID 9)
21P - Metabolomic profiling and response to neoadjuvant therapy in operable breast cancer (ID 39)
- Alessandra Gennari (Novara, Italy)
- Veronica Martini (Novara, Italy)
- Renzo Boldorini (Novara, Italy)
- Chiara Saggia (Novara, Italy)
- Valentina Rossi (Novara, Italy)
- Francesca D'Avanzo (Novara, Italy)
- Ivan Dodaro (Novara, Italy)
- Eleonora Gallarotti (Novara, Italy)
- Alessia Rua (Novara, Italy)
- Carmen Branni (Novara, Italy)
- Arianna Stella (Novara, Italy)
- Andrea Tassone (Novara, Italy)
- Erica Gioffi (Novara, Italy)
- Paola Maggiora (Novara, Italy)
- Anna Gambaro (Novara, Italy)
- Anna Rampi (Novara, Italy)
- Antonio Sica (Novara, Italy)
- Shahzaib Khoso (Novara, Italy)
- Elettra Barberis (Novara, Italy)
- Marcello Manfredi (Novara, Italy)
Abstract
Background
The use of neoadjuvant therapy (NAT) for operable breast cancer (BC) has progressively increased over time and it is today recommended by major guidelines. The achievement of a pathological complete response (pCR) is associated with an improved outcome. As a consequence new strategies are required to early identify patients who will not respond. In this persepctive, metabolomics may represent an innovative technology to identify host related factors correlated with outcome. In this research we evaluate the use metabolomics analysis coupled to artificial intelligence to predict treatment outcome for BC patients undergoing NAT.
Methods
Untargeted metabolomics analysis was performed on serum samples from 66 operable BC patients treated with NAT. Small molecules were extracted from serum, derivatized and then analyzed using bi-dimensional gas chromatography/mass spectrometer (GCxGC-MS). The metabolomics profiling was then evaluated according to response to therapy (pCR versus residual disease). A machine learning approach was implemented with Boruta features selection algorithm combined with genetical algorithm as classifier, on a training set including 28 plasma samples and externally validated on the remaing 12 samples.
Results
Among the 66 enrolled patients, 27 (41%) were HER2 +, 23 (35%) and 16 (24%) were luminal B. Overall, 52 patients have received surgery so far. pCR was achieved in 29 patients (56%) and residual disease in 23 (44%). A total of 670 small molecules were quantified by untargeted metabolomics analysis; 77 of these resulted differentially expressed (p <0.05 and fold change > 1.3) between patients achieving a pCR or residual disease. A prediction model, combining metabolomic signatures and machine learning, was implemented on 40 metabolomic profiles. With this approach we were able to correctly identify the type of response with an accuracy of 98%.
Conclusions
By using this omic approach, we were able to identify a metabolomic signature correlated with the type of response to NAT. Among differentially expressed molecules, we identified several fatty acids, amino acids and small molecules that could be targeted by selected dietary supplements. Updated analysis of the different biological BC subtypes will be presented.
Legal entity responsible for the study
The authors.
Funding
AIRC.
Disclosure
All authors have declared no conflicts of interest.
70P - Factors influencing patient treatment decisions in early breast cancer (eBC): discrete choice experiment (DCE) findings (ID 85)
- Alessandra Gennari (Novara, Italy)
- Christian Jackisch (Offenbach am Main, Germany)
- Susan McCutcheon (Cambridge, United Kingdom)
- Emuella Flood (Gaithersburg, United States of America)
- Bhavna Murali (New York, United States of America)
- Xavier Guillaume (Paris, France)
- Oliver Will (Malverne, United States of America)
- Chikako Shimizu (Shinjuku-ku, Japan)
- Stella Mokiou (Cambridge, United Kingdom)
Abstract
Background
The emerging treatment paradigm in eBC, with new systemic therapies varying in efficacy, safety and conditions of use, creates a complex patient journey involving many inflection points with respect to treatment sequence, duration and response to initial therapy. We quantified patient preferences for attributes of different treatment pathways in eBC in Germany, Italy and Japan.
Methods
Patients diagnosed with HER2-negative stage I–IIIa breast cancer after 2014 who had undergone surgery and chemotherapy completed an online survey that included a DCE to assess attribute preferences of treatments for eBC. In 12 DCE tasks, patients indicated their preference between 2 hypothetical treatment profiles that varied in 8 attributes. Preference weights for each attribute level and relative attribute importance (RI) were estimated using hierarchical Bayesian modelling and calculated as a percentage based on the difference from the most to least favourable attribute level.
Results
Overall, 452 patients (Germany 151, Italy 151, Japan 150) participated; median (range) age was 48 (19–78) years; 80% were employed and most patients were satisfied (64%) or very satisfied (18%) with the prior therapy they received. Reducing risk of a serious side effect from 77% to 6% was most important (RI=27%), followed by increasing cancer-free survival at 3 years from 67% to 87% (RI=19%), decreasing treatment duration from 18 to 3 months (RI=16%) and reducing risk of nausea from 67% to 2% (RI=14%). Choice of a flexible vs fixed treatment plan was as important as reducing risk of neuropathy from 21% to 1% or fatigue from 41% to 3% (RI=7% for all). Choice of an oral vs intravenous regimen was least important (RI=5%). Some treatment attribute preferences differed by country; notably, efficacy was more important to patients in Japan.
Conclusions
Overall, this international cohort of patients regard the risk of serious side effects, treatment efficacy and treatment duration as highly important. Additionally, these patients would prefer a flexible treatment plan, with treatment escalation/de-escalation in line with response to initial therapy, over a fixed plan. This information may enhance physician–patient interactions in eBC.
Editorial acknowledgement
Editorial assistance was provided by Aaron Borg, PhD of PharmaGenesis Cambridge, Cambridge, UK, with funding from AstraZeneca and Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc.
Legal entity responsible for the study
AstraZeneca.
Funding
This study was funded by AstraZeneca and is part of an alliance between AstraZeneca and Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA.
Disclosure
A. Gennari: Financial Interests, Personal, Invited Speaker: Eisai; Financial Interests, Personal, Advisory Board: Eisai; Financial Interests, Personal, Advisory Board: Novartis; Financial Interests, Personal, Advisory Board: AstraZeneca; Financial Interests, Personal, Advisory Board: Roche; Financial Interests, Personal, Expert Testimony: Gentili; Financial Interests, Personal, Advisory Board: Daiichi Sankyo; Financial Interests, Personal, Advisory Board: Pfizer; Financial Interests, Personal, Advisory Board: Lilly; Financial Interests, Personal, Invited Speaker: Lilly. C. Jackisch: Financial Interests, Personal, Other, Honoraria, Consultancy, travel and accommodation expenses: AstraZeneca; Financial Interests, Personal, Other, Consultancy, travel and accommodation expenses: Lilly; Financial Interests, Personal, Leadership Role, Travel and accommodation expenses: Novartis; Financial Interests, Personal, Invited Speaker, Consultancy, travel and accommodation expenses: Roche. S. McCutcheon: Financial Interests, Personal, Full or part-time Employment: AstraZeneca; Financial Interests, Personal, Stocks/Shares: AstraZeneca. E. Flood: Financial Interests, Personal, Full or part-time Employment: AstraZeneca; Financial Interests, Personal, Stocks/Shares: AstraZeneca. B. Murali: Financial Interests, Personal, Full or part-time Employment: Cerner Enviza; Financial Interests, Personal, Other, Provides consultancy services to AstraZeneca as an employee of Cerner Enviza: AstraZeneca. X. Guillaume: Financial Interests, Personal, Full or part-time Employment: Cerner Enviza; Financial Interests, Personal, Other, Provides consultancy services to AstraZeneca as an employee of Cerner Enviza: AstraZeneca. O. Will: Financial Interests, Personal, Full or part-time Employment: Cerner Enviza; Financial Interests, Personal, Other, Provides consultancy services to AstraZeneca as an employee of Cerner Enviza: AstraZeneca. C. Shimizu: Financial Interests, Institutional, Research Grant: Chugai; Financial Interests, Personal, Other, Honoraria: Chugai; Financial Interests, Personal, Other, Honoraria: Eisai; Financial Interests, Personal, Other, Honoraria: Pfizer; Financial Interests, Institutional, Research Grant: AstraZeneca; Financial Interests, Institutional, Research Grant: Eli Lilly; Financial Interests, Institutional, Research Grant: Taiho. S. Mokiou: Financial Interests, Personal, Full or part-time Employment: AstraZeneca; Financial Interests, Personal, Stocks/Shares: AstraZeneca.
Presenter Of 6 Presentations
Welcome and Introduction (ID 464)
Closure (ID 468)
Invited Discussant 93MO (ID 9)
In the current MBC landscape of HER2-/HR+ and TNBC, what’s the role of chemotherapy? (ID 465)
21P - Metabolomic profiling and response to neoadjuvant therapy in operable breast cancer (ID 39)
- Alessandra Gennari (Novara, Italy)
- Veronica Martini (Novara, Italy)
- Renzo Boldorini (Novara, Italy)
- Chiara Saggia (Novara, Italy)
- Valentina Rossi (Novara, Italy)
- Francesca D'Avanzo (Novara, Italy)
- Ivan Dodaro (Novara, Italy)
- Eleonora Gallarotti (Novara, Italy)
- Alessia Rua (Novara, Italy)
- Carmen Branni (Novara, Italy)
- Arianna Stella (Novara, Italy)
- Andrea Tassone (Novara, Italy)
- Erica Gioffi (Novara, Italy)
- Paola Maggiora (Novara, Italy)
- Anna Gambaro (Novara, Italy)
- Anna Rampi (Novara, Italy)
- Antonio Sica (Novara, Italy)
- Shahzaib Khoso (Novara, Italy)
- Elettra Barberis (Novara, Italy)
- Marcello Manfredi (Novara, Italy)
Abstract
Background
The use of neoadjuvant therapy (NAT) for operable breast cancer (BC) has progressively increased over time and it is today recommended by major guidelines. The achievement of a pathological complete response (pCR) is associated with an improved outcome. As a consequence new strategies are required to early identify patients who will not respond. In this persepctive, metabolomics may represent an innovative technology to identify host related factors correlated with outcome. In this research we evaluate the use metabolomics analysis coupled to artificial intelligence to predict treatment outcome for BC patients undergoing NAT.
Methods
Untargeted metabolomics analysis was performed on serum samples from 66 operable BC patients treated with NAT. Small molecules were extracted from serum, derivatized and then analyzed using bi-dimensional gas chromatography/mass spectrometer (GCxGC-MS). The metabolomics profiling was then evaluated according to response to therapy (pCR versus residual disease). A machine learning approach was implemented with Boruta features selection algorithm combined with genetical algorithm as classifier, on a training set including 28 plasma samples and externally validated on the remaing 12 samples.
Results
Among the 66 enrolled patients, 27 (41%) were HER2 +, 23 (35%) and 16 (24%) were luminal B. Overall, 52 patients have received surgery so far. pCR was achieved in 29 patients (56%) and residual disease in 23 (44%). A total of 670 small molecules were quantified by untargeted metabolomics analysis; 77 of these resulted differentially expressed (p <0.05 and fold change > 1.3) between patients achieving a pCR or residual disease. A prediction model, combining metabolomic signatures and machine learning, was implemented on 40 metabolomic profiles. With this approach we were able to correctly identify the type of response with an accuracy of 98%.
Conclusions
By using this omic approach, we were able to identify a metabolomic signature correlated with the type of response to NAT. Among differentially expressed molecules, we identified several fatty acids, amino acids and small molecules that could be targeted by selected dietary supplements. Updated analysis of the different biological BC subtypes will be presented.
Legal entity responsible for the study
The authors.
Funding
AIRC.
Disclosure
All authors have declared no conflicts of interest.
70P - Factors influencing patient treatment decisions in early breast cancer (eBC): discrete choice experiment (DCE) findings (ID 85)
- Alessandra Gennari (Novara, Italy)
- Christian Jackisch (Offenbach am Main, Germany)
- Susan McCutcheon (Cambridge, United Kingdom)
- Emuella Flood (Gaithersburg, United States of America)
- Bhavna Murali (New York, United States of America)
- Xavier Guillaume (Paris, France)
- Oliver Will (Malverne, United States of America)
- Chikako Shimizu (Shinjuku-ku, Japan)
- Stella Mokiou (Cambridge, United Kingdom)
Abstract
Background
The emerging treatment paradigm in eBC, with new systemic therapies varying in efficacy, safety and conditions of use, creates a complex patient journey involving many inflection points with respect to treatment sequence, duration and response to initial therapy. We quantified patient preferences for attributes of different treatment pathways in eBC in Germany, Italy and Japan.
Methods
Patients diagnosed with HER2-negative stage I–IIIa breast cancer after 2014 who had undergone surgery and chemotherapy completed an online survey that included a DCE to assess attribute preferences of treatments for eBC. In 12 DCE tasks, patients indicated their preference between 2 hypothetical treatment profiles that varied in 8 attributes. Preference weights for each attribute level and relative attribute importance (RI) were estimated using hierarchical Bayesian modelling and calculated as a percentage based on the difference from the most to least favourable attribute level.
Results
Overall, 452 patients (Germany 151, Italy 151, Japan 150) participated; median (range) age was 48 (19–78) years; 80% were employed and most patients were satisfied (64%) or very satisfied (18%) with the prior therapy they received. Reducing risk of a serious side effect from 77% to 6% was most important (RI=27%), followed by increasing cancer-free survival at 3 years from 67% to 87% (RI=19%), decreasing treatment duration from 18 to 3 months (RI=16%) and reducing risk of nausea from 67% to 2% (RI=14%). Choice of a flexible vs fixed treatment plan was as important as reducing risk of neuropathy from 21% to 1% or fatigue from 41% to 3% (RI=7% for all). Choice of an oral vs intravenous regimen was least important (RI=5%). Some treatment attribute preferences differed by country; notably, efficacy was more important to patients in Japan.
Conclusions
Overall, this international cohort of patients regard the risk of serious side effects, treatment efficacy and treatment duration as highly important. Additionally, these patients would prefer a flexible treatment plan, with treatment escalation/de-escalation in line with response to initial therapy, over a fixed plan. This information may enhance physician–patient interactions in eBC.
Editorial acknowledgement
Editorial assistance was provided by Aaron Borg, PhD of PharmaGenesis Cambridge, Cambridge, UK, with funding from AstraZeneca and Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc.
Legal entity responsible for the study
AstraZeneca.
Funding
This study was funded by AstraZeneca and is part of an alliance between AstraZeneca and Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA.
Disclosure
A. Gennari: Financial Interests, Personal, Invited Speaker: Eisai; Financial Interests, Personal, Advisory Board: Eisai; Financial Interests, Personal, Advisory Board: Novartis; Financial Interests, Personal, Advisory Board: AstraZeneca; Financial Interests, Personal, Advisory Board: Roche; Financial Interests, Personal, Expert Testimony: Gentili; Financial Interests, Personal, Advisory Board: Daiichi Sankyo; Financial Interests, Personal, Advisory Board: Pfizer; Financial Interests, Personal, Advisory Board: Lilly; Financial Interests, Personal, Invited Speaker: Lilly. C. Jackisch: Financial Interests, Personal, Other, Honoraria, Consultancy, travel and accommodation expenses: AstraZeneca; Financial Interests, Personal, Other, Consultancy, travel and accommodation expenses: Lilly; Financial Interests, Personal, Leadership Role, Travel and accommodation expenses: Novartis; Financial Interests, Personal, Invited Speaker, Consultancy, travel and accommodation expenses: Roche. S. McCutcheon: Financial Interests, Personal, Full or part-time Employment: AstraZeneca; Financial Interests, Personal, Stocks/Shares: AstraZeneca. E. Flood: Financial Interests, Personal, Full or part-time Employment: AstraZeneca; Financial Interests, Personal, Stocks/Shares: AstraZeneca. B. Murali: Financial Interests, Personal, Full or part-time Employment: Cerner Enviza; Financial Interests, Personal, Other, Provides consultancy services to AstraZeneca as an employee of Cerner Enviza: AstraZeneca. X. Guillaume: Financial Interests, Personal, Full or part-time Employment: Cerner Enviza; Financial Interests, Personal, Other, Provides consultancy services to AstraZeneca as an employee of Cerner Enviza: AstraZeneca. O. Will: Financial Interests, Personal, Full or part-time Employment: Cerner Enviza; Financial Interests, Personal, Other, Provides consultancy services to AstraZeneca as an employee of Cerner Enviza: AstraZeneca. C. Shimizu: Financial Interests, Institutional, Research Grant: Chugai; Financial Interests, Personal, Other, Honoraria: Chugai; Financial Interests, Personal, Other, Honoraria: Eisai; Financial Interests, Personal, Other, Honoraria: Pfizer; Financial Interests, Institutional, Research Grant: AstraZeneca; Financial Interests, Institutional, Research Grant: Eli Lilly; Financial Interests, Institutional, Research Grant: Taiho. S. Mokiou: Financial Interests, Personal, Full or part-time Employment: AstraZeneca; Financial Interests, Personal, Stocks/Shares: AstraZeneca.