Survival after Locoregional Recurrence or Second Primary Breast Cancer: Impact of the Disease-Free Interval

The association between the disease-free interval (DFI) and survival after a locoregional recurrence (LRR) or second primary (SP) breast cancer remains uncertain. The objective of this study is to clarify this association to obtain more information on expected prognosis. Women first diagnosed with early breast cancer between 2003–2006 were selected from the Netherlands Cancer Registry. LRRs and SP tumours within five years of first diagnosis were examined. The five-year period was subsequently divided into three equal intervals. Prognostic significance of the DFI on survival after a LRR or SP tumour was determined using Kaplan-Meier estimates and multivariable Cox regression analysis. Follow-up was complete until January 1, 2014. A total of 37,278 women was included in the analysis. LRRs or SP tumours were diagnosed in 890 (2,4%) and 897 (2,4%) respectively. Longer DFI was strongly and independently related to an improved survival after a LRR (long versus short: HR 0.65, 95% CI 0.48–0.88; medium versus short HR 0.81, 95% CI 0.65–1.01). Other factors related to improved survival after LRR were younger age (<70 years) and surgical removal of the recurrence. No significant association was found between DFI and survival after SP tumours. This is the first study to explore the association between the DFI and survival after recurrence in a nationwide population-based cancer registry. The DFI before a LRR is an independent prognostic factor for survival, with a longer DFI predicting better prognosi

Socioeconomic status and its relation with breast cancer recurrence and survival in young women in the Netherlands

BACKGROUND: Associations between socioeconomic status (SES) and breast cancer survival are most pronounced in young patients. We further investigated the relation between SES, subsequent recurrent events and mortality in breast cancer patients < 40 years. Using detailed data on all recurrences that occur between date of diagnosis of the primary tumor and last observation, we provide a unique insight in the prognosis of young breast cancer patients according to SES. METHODS: All women < 40 years diagnosed with primary operated stage I-III breast cancer in 2005 were selected from the nationwide population-based Netherlands Cancer Registry. Data on all recurrences within 10 years from primary tumor diagnosis were collected directly from patient files. Recurrence patterns and absolute risks of recurrence, contralateral breast cancer (CBC) and mortality - accounting for competing risks - were analysed according to SES. Relationships between SES, recurrence patterns and excess mortality were estimated using a multivariable joint model, wherein the association between recurrent events and excess mortality (expected mortality derived from the general population) was included. RESULTS: We included 525 patients. The 10-year recurrence risk was lowest in high SES (18.1%), highest in low SES (29.8%). Death and CBC as first events were rare. In high, medium and low SES 13.2%, 15.3% and 19.1% died following a recurrence. Low SES patients had shorter median time intervals between diagnosis, first recurrence and 10-year mortality (2.6 and 2.7 years, respectively) compared to high SES (3.5 and 3.3 years, respectively). In multivariable joint modeling, high SES was significantly related to lower recurrence rates over 10-year follow-up, compared to low SES. A strong association between the recurrent event process and excess mortality was found. CONCLUSIONS: High SES is associated with lower recurrence risks, less subsequent events and better prognosis after recurrence over 10 years than low SES. Breast cancer risk factors, adjuvant treatment adherence and treatment of recurrence may possibly play a role in this association

Health state utility and health-related quality of life measures in patients with advanced ovarian cancer

Purpose: Measuring health-related quality of life (HRQoL) in ovarian cancer patients is critical to understand the impact of disease and treatment. Preference-based HRQoL measures, called health state utilities, are used specifically in health economic evaluations. Real-world patient-reported data on HRQoL and health state utilities over the long-term course of ovarian cancer are limited. This study aims to determine HRQoL and health state utilities in different health states of ovarian cancer. Methods: This cross-sectional, multicenter study included patients with stage III-IV ovarian cancer in six health states: at diagnosis, during chemotherapy, after cytoreductive surgery (CRS), after chemotherapy, in remission, and at first recurrence. HRQoL was measured using the European Organization for Research and Treatment of Cancer Core Quality of Life Questionnaire C30, and the ovarian cancer-specific module OV28. Health state utilities were assessed using the EuroQol five-dimension five-level (EQ-5D-5L) questionnaire. Descriptive analyses were performed for each health state. Results: Two hundred thirty-two patients participated, resulting in 319 questionnaires. Median age was 66 years. The lowest HRQoL was observed during chemotherapy and shortly after CRS. Physical and role functioning were most affected and the highest symptom prevalence was observed in the fatigue, nausea, pain, dyspnea, gastrointestinal, neuropathy, attitude, and sexuality domains. Patients in remission had the best HRQoL. Mean utility values ranged from 0.709 (±0.253) at diagnosis to 0.804 (±0.185) after chemotherapy.Conclusions: This study provides clinicians with a valuable resource to aid in patient counseling and clinical decision-making. The utilities, in particular, are crucial for researchers conducting economic analyses to inform policy decisions.</p

The influence of adjuvant systemic regimens on contralateral breast cancer risk and receptor subtype

Background: An increasing number of breast cancer (BC) survivors are at risk of developing contralateral breast cancer (CBC). We aimed to investigate the influence of various adjuvant systemic regimens on, subtype-specific, risk of CBC. Methods: This population-based cohort study included female patients diagnosed with first invasive BC between 2003 and 2010; follow-up was complete until 2016. Clinico-pathological data were obtained from the Netherlands Cancer Registry and additional data on receptor status through linkage with PALGA: the Dutch Pathology Registry. Cumulative incidences (death and distant metastases as competing risk) and hazard ratios (HRs) were estimated for all invasive metachronous CBC and CBC subtypes. Results: Of 83 144 BC patients, 2816 developed a CBC; the 10-year cumulative incidence was 3.8% (95% confidence interval [CI]=3.7% to 4.0%). Overall, adjuvant chemotherapy (HR=0.70, 95% CI=0.62 to 0.80), endocrine therapy (HR=0.46, 95% CI=0.41 to 0.52), and trastuzumab with chemotherapy (HR=0.57, 95% CI=0.45 to 0.73) were strongly associated with a reduced CBC risk. Specifically, taxane-containing chemotherapy (HR=0.48, 95% CI=0.36 to 0.62) and aromatase inhibitors (HR=0.32, 95% CI=0.23 to 0.44) were associated with a large CBC risk reduction. More detailed analyses showed that endocrine therapy statistically significantly decreased the risk of estrogen receptor (ER)-positive CBC (HR=0.41, 95% CI=0.36 to 0.47) but not ER-negative CBC (HR=1.32, 95% CI=0.90 to 1.93) compared with no endocrine therapy. Patients receiving chemotherapy for ER-negative first BC had a higher risk of ER-negative CBC from 5years of follow-up (HR=2.84, 95% CI=1.62 to 4.99) compared with patients not receiving chemotherapy for ER-negative first BC. Conclusion: Endocrine therapy, chemotherapy, as well as trastuzumab with chemotherapy reduce CBC risk. However, each adjuvant therapy regimen had a different impact on the CBC subtype distribution. Taxane-containing chemotherapy and aromatase inhibitors were associated with the largest CBC risk reduction

Machine learning-based somatic variant calling in cell-free DNA of metastatic breast cancer patients using large NGS panels

Abstract Next generation sequencing of cell-free DNA (cfDNA) is a promising method for treatment monitoring and therapy selection in metastatic breast cancer (MBC). However, distinguishing tumor-specific variants from sequencing artefacts and germline variation with low false discovery rate is challenging when using large targeted sequencing panels covering many tumor suppressor genes. To address this, we built a machine learning model to remove false positive variant calls and augmented it with additional filters to ensure selection of tumor-derived variants. We used cfDNA of 70 MBC patients profiled with both the small targeted Oncomine breast panel (Thermofisher) and the much larger Qiaseq Human Breast Cancer Panel (Qiagen). The model was trained on the panels’ common regions using Oncomine hotspot mutations as ground truth. Applied to Qiaseq data, it achieved 35% sensitivity and 36% precision, outperforming basic filtering. For 20 patients we used germline DNA to filter for somatic variants and obtained 245 variants in total, while our model found seven variants, of which six were also detected using the germline strategy. In ten tumor-free individuals, our method detected in total one (potentially germline) variant, in contrast to 521 variants detected without our model. These results indicate that our model largely detects somatic variants