Breast Cancer Treatment Decisions Research Articles

Prospective observational study on the impact of the 21-gene assay on treatment decisions and resources optimization in breast cancer patients in Lombardy: The BONDX study

PurposeAdjuvant treatment decisions in early breast cancer (eBC) have traditionally been driven by risk stratification based on clinical and pathological risk factors. The 21-gene Oncotype DX® assay has been validated as a predictive test for benefit from adjuvant chemotherapy (CT), hence assessing its impact in clinical decisions is of high interest. The objective of this study was to estimate the rate of adjuvant treatment decision modification impacted by the Recurrence Score® result, and the consequent budget impact. MethodsThe study was a multicentre, prospective, real-life experience in Lombardy (Italy) including consecutive patients with T1–T3, N0–N1a, and ER+/HER2-eBC with clinical-pathologic “intermediate risk” of relapse. The change in treatment recommendations was assessed before and after availability of Recurrence Score result. A budget model evaluated the implications of 21-gene testing in the study population. ResultsThe overall proportion of CT recommendations was reduced from 24.6% to 15.2% after 21-gene testing, with a major impact in patients initially considered for CT plus hormone therapy (CHT). In these patients, the total budget was reduced, leading to a net saving of -€81,017. The greater the physician propensity to prescribe CHT, the higher the potential savings for the health system from sparing CT in most tested patients. ConclusionsOur real-life experience suggests that all intermediate-risk ER+/HER2-eBC patients who are initially deemed candidates for CHT should be tested with the 21-gene test. The potential to spare CT in at least half of them offers relevant advantages for patients and national health services.

Does Mode of Surgical Intervention Based on Oncotype DX Score Influence Disease Recurrence in Early Breast Cancer?

Introduction Routine utilization of multigene assays to inform operative decision-making in early breast cancer (EBC) treatment is yet to be established. In this pilot study, we sought to establish the potential benefits of surgical intervention in EBC based on recurrence risk quantification using the Oncotype DX (ODX) assay. Materials and Methods Consecutive ODX tests performed over a 9-year period from October 2007 to May 2016 were evaluated. Oncotype scores were classified into high (≥31), medium (18–30), or low-risk (0–17) groups. The primary outcome was breast cancer recurrence. Subgroup analysis offered assessment of the recurrence effect of mode of surgical intervention for patient groups as defined by the oncotype score. Results In total 361 patients underwent ODX testing. The mean age and follow-up were 55.25 (± 10.58) years and 38.59 (± 29.1) months, respectively. The majority of patients underwent wide local excision (86.7%) with 8.9 and 4.4% patients having a mastectomy or wide local excision with completion mastectomy, respectively. Fifty-one percent of patients fell into the low risk ODX category with a further 40.2 and 8.5% deemed to be of intermediate and high risk. Five patients (1.38%) had disease recurrence. Comparative analysis of operative groups in each oncotype group revealed no difference in recurrence scores in the low- ( p = 0.84) and high-risk groups ( p = 0.92) with a statistically significant difference identified in the intermediate risk group ( p = 0.002). Conclusion To date we have been unable to definitively identify a role for ODX in guiding surgical approach in EBC. There is, however, a need for larger studies to examine this hypothesis.

The Missing Lnc: The Potential of Targeting Triple-Negative Breast Cancer and Cancer Stem Cells by Inhibiting Long Non-Coding RNAs.

Treatment decisions for breast cancer are based on staging and hormone receptor expression and include chemotherapies and endocrine therapy. While effective in many cases, some breast cancers are resistant to therapy, metastasize and recur, leading to eventual death. Higher percentages of tumor-initiating cancer stem cells (CSCs) may contribute to the increased aggressiveness, chemoresistance, and worse outcomes among breast cancer. This may be particularly true in triple-negative breast cancers (TNBCs) which have higher percentages of CSCs and are associated with worse outcomes. In recent years, increasing numbers of long non-coding RNAs (lncRNAs) have been identified as playing an important role in breast cancer progression and some of these have been specifically associated within the CSC populations of breast cancers. LncRNAs are non-protein-coding transcripts greater than 200 nucleotides which can have critical functions in gene expression regulation. The preclinical evidence regarding lncRNA antagonists for the treatment of cancer is promising and therefore, presents a potential novel approach for treating breast cancer and targeting therapy-resistant CSCs within these tumors. Herein, we summarize the lncRNAs that have been identified as functionally relevant in breast CSCs. Furthermore, our review of the literature and analysis of patient datasets has revealed that many of these breast CSC-associated lncRNAs are also enriched in TNBC. Together, this suggests that these lncRNAs may be playing a particularly important role in TNBC. Thus, certain breast cancer-promoting/CSC-associated lncRNAs could be targeted in the treatment of TNBCs and the CSCs within these tumors should be susceptible to anti-lncRNA therapy.

Abstract P2-14-20: The impact of recurrence score versus sentinel lymph node biopsy on treatment decisions in early breast cancer

Abstract Rationale: Choosing Wisely: In an era of genomic medicine, what role does nodal information play in decisions regarding adjuvant chemotherapy for early-stage, hormone receptor-positive breast cancer ? Authors: Fowler RL, Lew KP, Dizon DS, Ciambella CC, Wakeley M, Taneja C, Wiggins DL, Lopresti ML, Constantinou M, Strenger R, Wang Y, Fenton MA, Graves TA. BACKGROUND/OBJECTIVES: For women with hormone receptor-positive (HR+) breast cancer, the Recurrence Score (RS) informs prognosis and impacts treatment decisions; whether and to what degree nodal information impacts these decisions is not clear. We conducted a retrospective review of patients with newly diagnosed HR+ early stage breast cancer who underwent a sentinel lymph node biopsy (SLNB) and had the RS obtained to discriminate the impact both evaluations had on the use of adjuvant chemotherapy. We hypothesized that for these patients, the RS supersedes nodal information in determining adjuvant treatment. METHODS: 416 patients with T1 to T2 breast cancers seen at our multidisciplinary clinic between 2013 and 2017 were identified. Data collected included demographics, node stage, RS, and adjuvant therapies. RS was stratified in to High, Intermediate, and Low Risk according to criteria used in the TAILORx trial. Continuous variables were analyzed using descriptive statistics and categorical variables were analyzed by Chi-Square or Fisher’s Exact Test. All statistics were performed using STATA 15.0. RESULTS: For this cohort, the median age was 59 years (range 27-79). The vast majority (94%) underwent initial surgical excision or mastectomy with SLNB. Of these, 338 patients (81%) had node-negative disease. Of the remaining, 37 patients (8.9%) had micro-metastasis to a single node, 39 (9.3%) had N1a disease, including one patient with 3 positive lymph nodes. RS risk was characterized as high, intermediate, and low in 12, 65, and 23%, respectively. On multivariate analysis, only age at diagnosis (p<0.05) and RS (p<0.0005) were significantly associated with the use of adjuvant chemotherapy; nodal status was not significant (p>0.05). CONCLUSIONS: For women with early-stage, clinically node-negative, HR+ breast cancer, the RS and age of the patient was associated with the administration of adjuvant chemotherapy. Although these data are limited to recommendations surrounding adjuvant chemotherapy and not decisions surrounding adjuvant radiation therapy, these data support the Choosing Wisely™ campaign against routine use of SLNB in low-risk patients. Citation Format: Rachel L Fowler, Kaitlyn P Lew, Don S Dizon, Chelsey C Ciambella, Michelle Wakeley, Charu Taneja, Doreen L Wiggins, Mary L Lopresti, Maria Constantinou, Rochelle Strenger, Yihong Wang, Mary A Fenton, Theresa Graves. The impact of recurrence score versus sentinel lymph node biopsy on treatment decisions in early breast cancer [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P2-14-20.

Abstract P3-08-09: Validation of the online prediction model CancerMath in the Dutch breast cancer population

Abstract Background: In breast cancer treatment decision-making, PREDICT is one of the most frequently used tools in clinical practice. The tool estimates the expected benefit of adjuvant systemic therapy on 5- and 10-year overall survival (OS). The model is based on an UK population and was previously validated on several other populations. OS is accurately predicted in these populations. However, PREDICT under- or overestimated OS in several predefined subgroups, which may lead to inadequate treatment decisions. A possible solution for this may be to use CancerMath. This model is based on a UK population and estimates not only OS, but also long-term breast cancer specific survival (BCSS). In this study, we aimed to validate the CancerMath model to determine its accuracy in several predefined subgroups based on patient-, tumor- and treatment-related characteristics. Methods: All women diagnosed with stage I-III primary invasive breast cancer in 2005 who received surgery were identified from the Netherlands Cancer Registry. The accuracy of CancerMath was evaluated by its calibration and discrimination overall, and in several predefined subgroups based on patient-, tumor- and treatment-related characteristics. For BCSS, we used distant metastasis followed by death as a proxy for breast cancer mortality. Calibration was assessed by comparing 5- and 10-year predicted and observed OS and BCSS using chi-squared tests. A difference >3% was considered to be clinically relevant, as the Dutch guidelines advise adjuvant systemic therapy in case of an at least 3-5% expected benefit on survival. Discrimination was assessed by area under the receiver operating characteristic (AUC) curves. Results: In total, 8,032 women were included. CancerMath underestimated 5- and 10-year OS by 2.2% and 1.9%, respectively. AUCs of 5- and 10-year OS were both 0.77. Divergence between predicted and observed OS was most pronounced in grade II tumors, patients without positive nodes, tumors 1.01-2.00 cm, hormonal receptor positive disease, and patients 60-69 years, in which OS was significantly underestimated. CancerMath underestimated 5- and 10-year BCSS by 0.5% and 0.6%, respectively. AUCs were 0.78 and 0.73, respectively. No significant difference was found in any subgroup. Conclusion: CancerMath predicts OS accurately for most patients with early breast cancer although outcomes should be interpreted with care in some subgroups. BCSS is predicted accurately in all subgroups. Importantly, a 3% difference may not be clinically relevant for someone with a predicted benefit of 12% and an observed benefit of 15%, as adjuvant systemic therapy will be indicated in both cases. However, in case these percentages are 1% and 4%, the indication for adjuvant systemic therapy will change. It is therefore crucial to interpret the over- or underestimations for individual patients with consideration of the initial predicted survival without adjuvant systemic therapy. In conclusion, CancerMath can reliably be used in (Dutch) clinical practice to estimate 5- and 10-year OS and BCSS, with a careful interpretation of OS in some subgroups. We encourage health care professionals and researchers to validate clinical prediction models in the specific target population before using them in that population. Citation Format: Marissa Corine van Maaren, Liza Hoveling, Tom Hueting, Luc JA Strobbe, Mathijs P Hendriks, Gabe S Sonke, Sabine Siesling. Validation of the online prediction model CancerMath in the Dutch breast cancer population [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P3-08-09.

Abstract P2-08-03: Machine learning to identify and characterize metastatic breast cancer patients in Sweden: A population-based study. CLEE011ASE01

Abstract Background: Treatment decisions for metastatic breast cancer (mBC) patients are increasingly complex and this patient subset is poorly characterized. In Sweden, despite the availability of national health registers, it is currently difficult to identify mBC patients’ true prevalence and characteristics at the national level due to lack of variables or missing information on recurrence from early to late disease. Aim: Develop an algorithm trained to identify mBC patients in Swedish national health registers to estimate the number of mBC patients and to describe their characteristics and survival outcomes. Methods: This study was a retrospective database study performed on Swedish national data (National Patient Register, Prescribed Drug Register, Cancer Register and the Cause of Death Register) linked with metastatic status, outcome and biomarker data from a regional BC register (Uppsala University Hospital) via unique personal identification number. The regional BC register data, containing medical records of known mBC and non-mBC patients between 2009-2016, were divided into a training set (n=2,680) and a test set (n=670). Based on known mBC patients’ unique features, derived from linked national data, we developed a support-vector machine (SVM) trained to identify mBC patients (further detailed in the poster). The model’s performance to classify mBC patients was measured in the test set; accuracy: 97.3%, sensitivity: 90.0%, specificity: 98.2%, balanced accuracy: 94.1%. The SVM algorithm was utilized to predict prevalent mBC cases 2009-2016 nationally. Kaplan-Meier estimates were used to model overall survival (OS) as defined from date of first metastatic diagnosis (ICD10 C78 and/or C79). Cox proportional hazard models were used to test the association between hormone receptor status (HR+/HR-) and de novo/recurrent patients. Patients were assumed to be HR+ if they had ≥2 endocrine prescriptions and de novo in case of DFS ≤3 months. Results: Between 2009 and 2016 we found a total of 150,235 patients alive with a BC diagnosis. Within this population the SVM algorithm identified a subset of 13,826 (9.2%) mBC patients corresponding to an incidence of 1,318 per year (13.7 per 100,000) and a prevalence of 5,171 per year (53.8 per 100,000). Median age at mBC diagnosis was 67.5 years and median survival was estimated at 29.8 months. 18.3% of the total mBC population were de novo patients. Median age at diagnosis was 68.7 years for de novo mBC and 67.2 years for recurrent mBC, and survival was estimated at 30.1 months and 29.7 months, respectively, with a slightly better prognosis for de novo patients (HR: 0.92; p-value<0.01) after adjusting for age and HR status. HR+ expression showed statistically significant association with OS (HR: 0.50; p-value<0.001). Median survival was calculated for the mBC population by age group. Patients <50 years, 50-70 years and >70 years at mBC diagnosis had a median survival of 43.3, 37.2, 20.1 months, respectively. Conclusion: Previous studies have used machine learning for cancer detection or prognostication. Here we show that machine learning algorithms can be applied to identify patient subsets in national population health registers. With this study design we have been able to describe the epidemiology and survival of the full national Swedish mBC population; to our knowledge this is the first study to do so. Citation Format: Henrik Lindman, Mate Szilcz, Jonatan Freilich, Peter Carlqvist, Simona Vertuani, Barbro Holm. Machine learning to identify and characterize metastatic breast cancer patients in Sweden: A population-based study. CLEE011ASE01 [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P2-08-03.

Abstract P4-14-05: A systematic review of concordance studies using Watson for Oncology (WfO) to support breast cancer treatment decisions: A four-year global experience

Abstract Background: Watson for Oncology (WfO) is an artificial intelligence (AI)-based clinical decision-support system (CDSS) that presents personalized therapeutic options to support cancer-treating physicians in making treatment decisions for cancer patients. WfO was released in 2015, and it has been deployed in over 200 institutions across the world. Many academic centers have examined WfO’s performance by measuring concordance between WfO therapeutic options and treatment recommendations by multidisciplinary tumor boards (MTBs) or individual clinicians (ICs). This study systematically reviewed the results of such concordance studies for breast cancer. Methods: We conducted a review of the WfO publication database and a PubMed search to identify WfO concordance studies in breast cancer patients, published from 01/01/2015 to 06/30/2019. Studies were excluded if they measured concordance for multiple cancer types but did not include individual concordance for breast cancer. Concordance was defined as agreement between WfO “Recommended” and “For Consideration” treatment options and treatments prescribed by MTBs or ICs. Mean concordance rates were calculated as an average, weighted by the number of patients in each study. Concordance rates between MTBs and ICs were compared with z-test of two proportions. Subgroup analyses for larger studies were summarized. Results: Table 1 presents the results of nine identified breast cancer concordance studies (4,427 patients) from China, India, and Thailand. Five studies (1,528 patients) determined concordance with MTB and 4 (2,899 patients) with ICs. WfO treatment options were compared to historical treatment recommendations by MTBs and ICs. Mean concordance for all studies was 70.8% (range 55 - 98%). Mean concordance with MTBs of 90.7% (range 79 - 98%) was significantly higher than the concordance between WfO and ICs of 59.9% (range 55-76%) p<0.0001. Table 1Study/LocationNumber of patientsConcordance Multidisciplinary Tumor Board Studies (MTBs)Zhang XC, et al. Ann Oncol 2017;28:x170 / China11979%Yue L, Yang L. Ann Oncol 2017;28:x162 / China3198%Somashekhar SP, et al. Ann Oncol 2018 1; 29(2):418-423 / India63893%Zhou N, et al. The Oncologist 2018;23:1-8 / China12082%Somashekhar SP, et al. J Clin Oncol 2019; 37 (suppl; abstr 6533) / India62092%MTB subtotals152890.7%Individual Clinicians Studies (ICs)Suwanvecho S, et al. J Clin Oncol 2017;35 (suppl; abstr 6589) / Thailand21176%Jiang Z, et al. J Clin Oncol 2018;36 (suppl; abstr 18566) / China1,99755%Suwanrusme H, et al. J Clin Oncol 2018;36 (suppl; abstr 18584) / Thailand26470%32672%Suwanvecho S et al. J Clin Oncol 2019; 37 (suppl; abstr 6553) / Thailand10160%ICs subtotals289959.9% Mean concordance in China, Thailand and India were 78.5%, 69.5%, and 92.5%, respectively. A large IC study from China including 1,997 patients found concordance in triple negative, high risk non-metastatic, and metastatic breast cancers of 69%, 66%, and 50%, respectively. A MTB study from China in 120 patients found concordance for luminal A, luminal B and triple negative breast cancers of 63%, 87% and 79%, respectively. Reported reasons for discordance varied among studies and included locally unavailable treatments, individual physician or MTB management preferences, and age older than 75 years. Conclusions Overall concordance between the WfO therapeutic options and decisions of both MTBs and ICs was high, demonstrating performance comparable with experts across the world. Higher concordance was observed between WfO and MTBs versus WfO and ICs, likely reflecting the multidisciplinary expertise having greater agreement with evidence and guideline-based recommendations of WfO than decisions of individual clinicians. This finding illustrates a role for clinical decision support in practice. Concordance varied across countries, reflecting the need for localization to address regional differences in practice. Citation Format: Yull Arriaga, Rezzan Hekmat, Karlis Draulis, Suwei Wang, Winnie Felix, Irene Dankwa-mullan, Kyu Rhee, Gretchen Jackson. A systematic review of concordance studies using Watson for Oncology (WfO) to support breast cancer treatment decisions: A four-year global experience [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P4-14-05.

Abstract P4-09-02: Somatic tumor profiles of breast cancer in Latinas

Abstract Background: Latinos represent the largest and fastest-growing minority population in the US; breast cancer is the most common cancer and causes the most cancer deaths in this population. Precision medicine is being applied increasingly to guide breast cancer treatment decisions. However, the ability to apply precision medicine for prognosis and targeted treatments for breast cancer in Latinas is limited by insufficient data on somatic mutation and gene expression profiles in available tumor mutation datasets. In The Cancer Genome Atlas (TCGA), one of the largest publicly available tumor mutation datasets, only 31 of ~1100 total breast cancers are Latina. Therefore, we performed whole exome sequencing on matched tumor/normal pairs in 146 tumors from 142 women reporting here on their somatic mutational profile compared to non-Latino women of European ancestry from TCGA. Methods: We enrolled 142 Latinas with invasive breast cancer and obtained blood and tumor tissue (formalin-fixed paraffin embedded). After whole exome sequencing of 146 pairs of tumor (somatic) and normal (germline) samples, high quality data were available for 142 tumor-normal pairs for analysis. Target coverage for normal was ~30X and for tumor was ~100X. Sequence data were aligned using Burrows-Wheeler Aligner, genotype calls were made using GATK and Mutect2 was used to call somatic mutations including base substitutions and small insertions and deletions. We estimated genetic ancestry from the germline genotype data using ADMIXTURE. Results: Age at diagnosis ranged from 31 to 75 years with a median age of 48 years. Histologically, 83% were estrogen-receptor positive, 71 % were progesterone-receptor positive, and 17% were human epidermal growth factor receptor positive; 87% were Stage 1 or 2 at diagnosis. The mean genetic ancestry in our sample was 52.3% European, 41.6% Indigenous American, and 6.2% African. We compared the 30 genes most commonly mutated in breast cancers (e.g., PIK3CA, TP53, MAP3K1, MLL3) in non-Latina Whites in TCGA to our data both overall and by subtype and searched for additional recurrently mutated genes not commonly observed in other populations. Similar to non-Latinos, PIK3CA and TP53 were the most commonly mutated genes; mutations in PIK3CA, GATA3, MAP3K1, and RYR2 were significantly more frequent in Latinas (p-value range from 0.001-0.002). Summary: In general, breast tumors from Latinas have similar rates of somatic mutations in most of the top commonly observed genes in European-ancestry women. Citation Format: Susan Neuhausen, Daniel Schmolze, Linda Steele, Shu Tao, Aaron Adamson, Charleen Adams, Donglei Hu, Scott Huntsman, Jung Byun, Kevin Gardner, Eliseo Perez-Stable, Anna Napoles, Jeffrey Weitzel, Elad Ziv. Somatic tumor profiles of breast cancer in Latinas [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P4-09-02.

Clinical Decision Support Systems in Breast Cancer: A Systematic Review.

Breast cancer is the most frequently diagnosed cancer in women, with more than 2.1 million new diagnoses worldwide every year. Personalised treatment is critical to optimising outcomes for patients with breast cancer. A major advance in medical practice is the incorporation of Clinical Decision Support Systems (CDSSs) to assist and support healthcare staff in clinical decision-making, thus improving the quality of decisions and overall patient care whilst minimising costs. The usage and availability of CDSSs in breast cancer care in healthcare settings is increasing. However, there may be differences in how particular CDSSs are developed, the information they include, the decisions they recommend, and how they are used in practice. This systematic review examines various CDSSs to determine their availability, intended use, medical characteristics, and expected outputs concerning breast cancer therapeutic decisions, an area that is known to have varying degrees of subjectivity in clinical practice. Utilising the methodology of Kitchenham and Charter, a systematic search of the literature was performed in Springer, Science Direct, Google Scholar, PubMed, ACM, IEEE, and Scopus. An overview of CDSS which supports decision-making in breast cancer treatment is provided along with a critical appraisal of their benefits, limitations, and opportunities for improvement.

MammaPrint guides treatment decisions in breast Cancer: results of the IMPACt trial

BackgroundIncreased usage of genomic risk assessment assays suggests increased reliance on data provided by these assays to guide therapy decisions. The current study aimed to assess the change in treatment decision and physician confidence based on the 70-gene risk of recurrence signature (70-GS, MammaPrint) and the 80-gene molecular subtype signature (80-GS, BluePrint) in early stage breast cancer patients.MethodsIMPACt, a prospective, case-only study, enrolled 452 patients between November 2015 and August 2017. The primary objective population included 358 patients with stage I-II, hormone receptor-positive, HER2-negative breast cancer. The recommended treatment plan and physician confidence were captured before and after receiving results for 70-GS and 80-GS. Treatment was started after obtaining results. The distribution of 70-GS High Risk (HR) and Low Risk (LR) patients was evaluated, in addition to the distribution of 80-GS compared to IHC status.ResultsThe 70-GS classified 62.5% (n = 224/358) of patients as LR and 37.5% (n = 134/358) as HR. Treatment decisions were changed for 24.0% (n = 86/358) of patients after receiving 70-GS and 80-GS results. Of the LR patients initially prescribed CT, 71.0% (44/62) had CT removed from their treatment recommendation. Of the HR patients not initially prescribed CT, 65.1% (41/63) had CT added. After receiving 70-GS results, CT was included in 83.6% (n = 112/134) of 70-GS HR patient treatment plans, and 91.5% (n = 205/224) of 70-GS LR patient treatment plans did not include CT. For patients who disagreed with the treatment recommended by their physicians, most (94.1%, n = 16/17) elected not to receive CT when it was recommended. For patients whose physician-recommended treatment plan was discordant with 70-GS results, discordance was significantly associated with age and lymph node status.ConclusionsThe IMPACt trial showed that treatment plans were 88.5% (n = 317/358) in agreement with 70-GS results, indicating that physicians make treatment decisions in clinical practice based on the 70-GS result. In clinically high risk, 70-GS Low Risk patients, there was a 60.0% reduction in treatment recommendations that include CT. Additionally, physicians reported having greater confidence in treatment decisions for their patients in 72% (n = 258/358) of cases after receiving 70-GS results.Trial registration“Measuring the Impact of MammaPrint on Adjuvant and Neoadjuvant Treatment in Breast Cancer Patients: A Prospective Registry” (NCT02670577) retrospectively registered on Jan 27, 2016.

The role of comorbidity assessment in guiding treatment decision-making for women with early breast cancer: a systematic literature review.

Comorbidity in breast cancer patients impacts treatment choice, toxicity, and outcomes. While comorbidity measurement tools are frequently used by researchers, little is known about their use in clinical practice. The aim of this review was to examine the use of comorbidity measurement tools in clinical practice and their role in treatment decision-making in breast cancer. Six electronic databases were searched from inception to 21 March 2019. Quantitative or mixed methods studies addressing primary treatment of breast cancer and identifying a comorbidity measurement tool used in clinical practice treatment decision-making were included. Data was extracted on tool utilized, impact on treatment decisions or outcomes, pattern of use, and psychometric properties. A systematic search of literature yielded 752 studies. Of the four studies that met inclusion criteria, each utilized a comprehensive geriatric assessment tool, though only in a subset of patients. No studies found direct comorbidity measurement tools utilized independently of geriatric assessment. Assessment results had variable impact on treatment decisions. Impacts on patient mortality and treatment toxicity, cost-effectiveness, and psychometric characteristics of the tools were not identified. There is little evidence on use of specific comorbidity tools in clinical decision-making in breast cancer outside of comorbidity assessment as part of geriatric assessment tools. There was limited impact on decision-making or patient outcomes when these were utilized. Further research is needed to identify barriers to comorbidity assessment in clinical practice and identify comorbidity tools that have the potential to improve patient outcomes.

Treatment decision-making and financial toxicity in metastatic breast cancer.

97 Background: Shared decision-making (SDM), a process where patients partner with their physician to incorporate personal preferences into treatment decisions, is a tenet of high-value healthcare. It is unknown if high-value care associated with SDM manifests in the form of decreased out-of-pocket costs. Therefore, this study analyzes the relationship between patient preference for SDM and financial toxicity in metastatic breast cancer (MBC). Methods: This cross-sectional study utilized surveys of women age ≥ 18 with MBC who received care at two academic hospitals in Alabama between 2017 and 2019. SDM preference and financial toxicity were measured using the Control Preferences Scale and the Comprehensive Score for Financial Toxicity (COST) tool (11-item scale from 0-44, with lower scores indicating worse FT), respectively. Patient demographic and clinical data were abstracted from the electronic medical record. Effect sizes were calculated using Cohen’s d or Cramer’s V. Differences in financial toxicity by SDM preference were estimated using mixed models clustered by site and treating medical oncologist. Results: In 79 women with MBC, 41% preferred SDM, 33% preferred provider-driven decision making, and 22% preferred patient-driven decision making. Patients preferring SDM were more often college educated (48% vs. 40%; V = .15), higher income (52% vs. 44%; V = .09), and privately insured (47% vs. 41%; V = .11). Overall median COST score was 23 (interquartile range 16-30), which varied modestly by SDM preference. After adjusting for patient demographic and clinical characteristics, similar financial toxicity levels were found in patients who preferred SDM (COST 22, 95% confidence interval [CI] 19-25), patient-driven decision making (COST 22, 95% CI 18-26), and provider-driven decision making (COST 24, 95% CI 20-27). Conclusions: Similar levels of financial toxicity were found in patients with differing decision–making preferences regarding their MBC treatment, which may be secondary to lack of discussions about cost. Further research is needed to determine if and how financial toxicity is being identified or included within decision-making.

"Waiting for breast reconstruction": An interpretative phenomenological analysis of heterosexual couples' experiences of mastectomy for breast cancer.

Treatment decisions, such as breast reconstruction is made by the dyad patient-physician, but close others can have an influence on the process. Shared decision-making models include close others but current studies generally investigate physician-patient interactions only. Moreover, little is known about couples' interactions throughout the breast cancer journey and treatment decision-making. Thus, the aim of the present study was to explore couples' experiences of mastectomy and breast reconstruction decision-making, the month following the mastectomy. Ten unstructured individual interviews, with five couples facing mastectomy for breast cancer, were conducted. A dyadic interpretative phenomenological analysis (IPA) was carried on the data set. Eight super-ordinate themes emerged from the data. Couples' experiences were marked by the experience of the disease, the mastectomy and the impact they produced. The relationships with health professionals and partners were also important. Partners were not only supportive and protective, but directly (i.e., couples talk about treatments) and indirectly influenced (i.e., women make a decision taking into consideration how it will impact their couple) the women in their decision to have or not a breast reconstruction. The interpretative accounts underline how women and partners cope with the disease thinking about the aftertreatment. This study explains for the first time how intimate partners influence breast reconstruction decision-making and how couples cope individually with the disease immediately after mastectomy. Health professionals need to involve partners more in the treatment decision-making process as the women consider and talks to them about the choices they have to make.

The effect of Oncotype DX® on adjuvant chemotherapy treatment decisions in early breast cancer.

The aim of this study was to objectively establish the value of the Oncotype DX® (ODX) gene assay in adjuvant treatment decisions for intermediate risk patients with early, oestrogen receptor positive, human epidermal growth factor receptor 2 negative, lymph node negative breast cancer at a district general hospital. All patients who underwent surgery for breast cancer between January 2015 and December 2017 at Queen Elizabeth Hospital in King's Lynn were considered for inclusion in the study. Those who did not meet the criteria for ODX referral were excluded. Patients were divided into two cohorts based on whether they were treated before or after the introduction of ODX testing in this hospital (June 2016): the pre-ODX and post-ODX groups. The primary outcome was the percentage of patients for whom adjuvant chemotherapy (AC) was recommended in each group. Of the 462 patients who underwent surgery during the study period, 43 met the eligibility criteria for ODX testing: 18 in the pre-ODX group and 25 in the post-ODX group. AC was recommended and given to 11 (61%) of the patients in the pre-ODX group. In the post-ODX group, AC was recommended for seven patients with an ODX Recurrence Score® (RS) of >25; this was given to six patients (24%). One patient (with a RS of 26) declined AC. ODX testing led to a significant reduction in the proportion of patients who received AC (p=0.015). In intermediate risk patients with breast cancer, the results of the ODX gene assay may change the decision for adjuvant treatment. It represents a valuable tool to assist patients' and clinicians' decision making regarding adjuvant chemotherapy.

Gene Expression Profiling of Carcinoma Breast and its, Prognostic Signature: A Review

Breast cancer research and clinical care have reached a new era due to the development of modern genetic technologies. In the past, breast cancer diagnosis, prognosis and treatment decisions were based on, clinical and pathological analysis of the breast cancer tissues and axillary lymph nodes. It has been observed that the prognosis and recommendation about treatment based on these features, imperfectly predict the outcome and results in excessive treatment and chemotherapy, with marginal benefits. Hence new methods are needed to understand breast cancer properly, to optimize and individualize the breast cancer treatment and prognosis. The development of the gene microarray techniques has enabled scientists to detect the different gene expression array of thousands of gene simultaneously and thus create a gene expression profile for different types of breast cancers. Gene expression profiling of the breast cancers has improved our understanding of the heterogeneity of breast cancer on the genomic level; challenged the clinical classification of breast cancer; served as an important prognostic indicator and most importantly, begin to guide our treatment in women with early breast cancer. Incorporation of molecular assays into the treatment and planning strategy of breast cancer continues to be a work in progress. This approach is evolving quickly due to strong scientific pieces of evidence to become a standard of practice in the near future. This article provides an overview of the development and application of molecular assays as applied to breast cancer.

HER2 testing: Insights from pathologists’ perspective on challenging HER2 cases.

e12512 Background: HER2 status dictates treatment decisions in breast cancer making the precision and accuracy of laboratory testing essential. While most HER2 testing is straightforward using ASCO-CAP interpretation guidelines, 5% of cases fall into the less common categories (groups 2-4) and can present unusual diagnostic dilemmas. 6 high volume HER2 FISH laboratories evaluated 10 challenging HER2 cases. The results highlight the challenges and potential for variability in interpreting rare cases with unusual results. Methods: Ten diagnostically challenging HER2 FISH cases were selected from our breast cancer archives (41 months/3863 cases). Unstained slides were evaluated by 6 high volume laboratories. Laboratories performed HER2 FISH testing (along with HER2 IHC as needed) and rendered an interpretation. Participants discussed results during a conference call. Cases included the following situations: low-level amplification (ratio ≥ 2.0, mean HER2 signals/cell between 4.0-5.9), HER2 mean copy number or ratio near a clinically actionable threshold, complex heterogeneity or clones, and a technical challenge (signal clustering). Results: All laboratories elected to perform HER2 IHC in conjunction with FISH in all cases. Only 3/10 cases resulted in 100% agreement between the 6 institutions, all of which were in the category addressing complex heterogeneity. During the study, we identified a problematic subset (low amplified FISH cases) which may have discordant (negative) IHC results. Another problematic scenario is the finding of a small, highly amplified HER2 clone. Lastly, we identified cases with high cell to cell variability, and we saw discordant values resulting in disparate overall HER2 interpretations. Conclusions: Oncologists and pathologists should be aware of these challenges and the potential for variability in interpretation of these rare case scenarios. Evaluation for concordance between IHC and FISH results, correlation with other clinico-pathologic features and additional steps to ensure the accuracy of test results can help resolve unusual results, but grey zones may still remain and should be discussed when making clinical decisions based on HER2 test results.

Factors influencing surgical treatment decisions for breast cancer: a qualitative exploration of surgeon and patient perspectives.

The rate of mastectomy is much higher in Newfoundland and Labrador than in any other province in Canada, even for women diagnosed at an early stage. In this paper, we present qualitative data from women who have made a decision for surgical treatment and from breast surgeons in an effort to better explicate factors influencing breast cancer (bca) surgical decision-making. The study's descriptive, qualitative design involved holding interviews with breast surgeons and holding focus groups and interviews with women who were offered the choice of breast-conserving surgery (bcs) or mastectomy (mt). Participants included 35 women and 13 surgeons. High interest in mt and increasing requests for prophylactic contralateral mt were evident. A host of factors-clinical, demographic, psychosocial, education-related, and cultural-influenced the decisions. A key factor for women was fear of recurrence and a need to "just get rid of it," but the experiences of others also influenced the decisions. Life stage and family considerations also factored prominently into women's decisions. Women with early-stage bca more often chose mt and often demanded prophylactic removal of the healthy breast. Findings highlight the importance of ensuring that women at average risk are appropriately counselled about the low likelihood of a subsequent contralateral bca and the lack of survival benefit associated with prophylactic contralateral mt. Findings also revealed other areas of presurgical discussion that might help women think through their personal circumstances and values so as to encourage informed surgical decisions.

What Is Important When Making Treatment Decisions in Metastatic Breast Cancer? A Qualitative Analysis of Decision-Making in Patients and Oncologists.

Metastatic breast cancer (MBC) is an ideal environment for shared decision-making because of the large number of guideline-based treatment options with similar efficacy but different toxicity profiles. This qualitative analysis describes patient and provider factors that influence decision-making in treatment of MBC. Patients and community oncologists completed in-person interviews. Academic medical oncologists participated in focus groups. Interviews and focus groups were audio-recorded, transcribed, and analyzed using NVivo. Using an a priori model based on the Ottawa Framework, two independent coders analyzed transcripts using a constant comparative method. Major themes and exemplary quotes were extracted. Participants included 20 patients with MBC, 6 community oncologists, and 5 academic oncologists. Analysis of patient interviews revealed a decision-making process characterized by the following themes: decision-making style, contextual factors, and preferences. Patient preference subthemes include treatment efficacy, physical side effects of treatment, emotional side effects of treatment, cognitive side effects of treatment, cost and financial toxicity, salience of cutting-edge treatment options (clinical trial or newly approved medication), treatment logistics and convenience, personal and family responsibilities, treatment impact on daily activities, participation in self-defining endeavors, attending important events, and pursuing important goals. Physician decisions emphasized drug-specific characteristics (treatment efficacy, side effects, cost) rather than patient preferences, which might impact treatment choice. Although both patients with MBC and oncologists considered treatment characteristics when making decisions, patients' considerations were broader than oncologists', incorporating contextual factors such as the innovative value of the treatment and life responsibilities. Differences in perspectives between patients and oncologists suggests the value of tools to facilitate systematic communication of preferences in the setting of MBC. Both patients with metastatic breast cancer (MBC) and oncologists emphasized importance of efficacy and physical side effects when making treatment decisions. However, other patient considerations for making treatment decisions were broader, incorporating contextual factors such as the logistics of treatments, personal and family responsibilities, and ability to attend important events. Furthermore, individual patients varied substantially in priorities that they want considered in treatment decisions. Differences in perspectives between patients and oncologists suggest the value of tools to facilitate systematic elicitation of preferences and communication of those preferences to oncologists for integration into decision-making in MBC.

Strong impact of MammaPrint and BluePrint on treatment decisions in luminal early breast cancer: results of the WSG-PRIMe study

PurposeThe WSG-PRIMe Study prospectively evaluated the impact of the 70-gene signature MammaPrint® (MP) and the 80-gene molecular subtyping assay BluePrint® on clinical therapy decisions in luminal early breast cancer.Methods452 hormone receptor (HR)-positive and HER2-negative patients were recruited (N0, N1). Physicians provided initial therapy recommendations based on clinicopathological factors. After prospective risk classification by MammaPrint/BluePrint was revealed, post-test treatment recommendations and actual treatment were recorded. Decisional Conflict and anxiety were measured by questionnaires.ResultsPost-test switch (in chemotherapy (CT) recommendation) occurred in 29.1% of cases. Overall, physician adherence to MP risk assessment was 92.3% for low-risk and 94.3% for high-risk MP scores. Adherence was remarkably high in “discordant” groups: 74.7% of physicians initially recommending CT switched to CT omission following low-risk MP scores; conversely, 88.9% of physicians initially recommending CT omission switched to CT recommendations following high-risk MP scores. Most patients (99.2%) recommended to forgo CT post-test and 21.3% of patients with post-test CT recommendations did not undergo CT; among MP low-risk patients with pre-test and post-test CT recommendations, 40% did not actually undergo CT. Luminal subtype assessment by BluePrint was discordant with IHC assessment in 34% of patients. Patients’ State Anxiety scores improved significantly overall, particularly in MP low-risk patients. Trait Anxiety scores increased slightly in MP high risk and decreased slightly in MP low-risk patients.ConclusionsMammaPrint and BluePrint test results strongly impacted physicians’ therapy decisions in luminal EBC with up to three involved lymph nodes. The high adherence to genetically determined risk assessment represents a key prerequisite for achieving a personalized cost-effective approach to disease management of early breast cancer.

Abstract P3-08-23: Prosigna assay for treatment decisions in early breast cancer: A single center, decision impact study

Abstract Background: Therapeutic decisions in early breast cancer (EBC) are based on clinical and pathological features, which are subject to intra- and inter-observer variability. Hence, in the era of precision medicine, there is growing need for predictive biomarkers. The Prosigna assay utilizes Prediction Analysis of Microarray, a test based on the analysis of 50 intrinsic subtype-linked gene clusters. This single center decision impact study aimed to evaluate the effect of Prosigna test results on physicians' adjuvant treatment choices. Methods: Between September 2017 and February 2018, FFPE tumor samples from 53 newly diagnosed, postmenopausal, hormone receptor-positive, HER2-negative EBC (T1-T2; pN0-N1a) patients were analyzed. Pre-test clinical judgments and Prosigna test results were compared. Results: Mean age was 59 (42-77). Invasive ductal carcinoma (79.2%), grade 2 (52.8%) and T1c-N0 tumors (43.4%) represented the majority. Before the Prosigna test, 65.4% of the patients were classified as luminal A and 34.6% as luminal B. Of the pre-test risk groups, 40.4% were low-risk, 40.4% were intermediate risk and 19.2% were high risk. Prosigna assay grouped 50% of patients as luminal A, 44.2% as luminal B, 3.8% as basal type and 1.9% as HER2-expressing. Post-test ROR score-based groups were distributed as 25% low-risk, 40.4% intermediate risk and 34.6% high risk. There was a statistically significant correlation between clinically defined and molecularly assessed intrinsic BC subtypes (kappa:0.334, p=0.007). Similarly, pre-test and post-test recurrence risk groups were correlated (kappa:0.397, p=0.001). Before the Prosigna test, endocrine treatment was physicians' primary choice in 20 patients (39.2%), chemotherapy was recommended to 31 patients (60.8%). Overall, the Prosigna assay led to a change in choice of treatment for one patient (2%). There was 40.4% discordance between pre- and post-test recurrence risk groups. In addition, intrinsic subtypes were 34.6% discordant, which is largely driven by the reclassification of pre-test luminal A tumors into Prosigna luminal B group. Table 1.Impact of Prosigna Results on Final Treatment Decision Prosigna low risk N=13 (%24.5)Prosigna intermediate risk N=21 (%39.6)Prosigna high risk N=18 (%34)Total N=51 (%100)Treatment choice before Prosigna CT+HT0121729 (56.9)HT only127120 (39.2)CT offered, not accepted by the patient0202 (3.9)Treatment choice after Prosigna CT+HT0121830 (58.8)HT only127019 (37.3)CT offered, not accepted by the patient0202 (3.9)Change in treatment choice HT to CT001100 (0)CT to HT0000 (0)CT: Chemotherapy HT: Hormonotherapy Conclusions: Although conventional risk assessment methods are relatively inexpensive with shorter turnaround times, their accuracy for risk assessment and value for risk reduction are suboptimal. According to our results, Prosigna assay was found as a relevant tool for clinical decision-making process. In cases where there is a discrepancy between the clinical assessment results and the Prosigna assay, tumor boards may guide treatment recommendations. Long term follow-up of these patients will elucidate the potential benefits of using multigene molecular tests as biomarkers for EBC treatment. Citation Format: Esin E, Oksuzoglu BO, Markoc F, Bilgetekin I, Yildiz F, Guntekin S, Yukruk F, Atalay R. Prosigna assay for treatment decisions in early breast cancer: A single center, decision impact study [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P3-08-23.