External validation and clinical utility assessment of PREDICT breast cancer prognostic model in young, systemic treatment-naïve women with node-negative breast cancer

BACKGROUND

The validity of the PREDICT breast cancer prognostic model is unclear for young patients without adjuvant systemic treatment. This study aimed to validate PREDICT and assess its clinical utility in young women with node-negative breast cancer who did not receive systemic treatment.

METHODS

We selected all women from the Netherlands Cancer Registry who were diagnosed with node-negative breast cancer under age 40 between 1989 and 2000, a period when adjuvant systemic treatment was not standard practice for women with node-negative disease. We evaluated the calibration and discrimination of PREDICT using the observed/expected (O/E) mortality ratio, and the area under the receiver operating characteristic curve (AUC), respectively. Additionally, we compared the potential clinical utility of PREDICT for selectively administering chemotherapy to the chemotherapy-to-all strategy using decision curve analysis at predefined thresholds.

RESULTS

A total of 2264 women with a median age at diagnosis of 36 years were included. Of them, 71.2% had estrogen receptor (ER)-positive tumors and 44.0% had grade 3 tumors. Median tumor size was 16 mm. PREDICT v2.2 underestimated 10-year all-cause mortality by 33% in all women (O/E ratio:1.33, 95%CI:1.22-1.43). Model discrimination was moderate overall (AUC10-year:0.65, 95%CI:0.62-0.68), and poor for women with ER-negative tumors (AUC10-year:0.56, 95%CI:0.51-0.62). Compared to the chemotherapy-to-all strategy, PREDICT only showed a slightly higher net benefit in women with ER-positive tumors, but not in women with ER-negative tumors.

CONCLUSIONS

PREDICT yields unreliable predictions for young women with node-negative breast cancer. Further model updates are needed before PREDICT can be routinely used in this patient subset.

Determinants of SARS-CoV-2 nasopharyngeal testing in a rural community sample susceptible of first infection: the CHRIS COVID-19 study

To characterize COVID-19 epidemiology, numerous population-based studies have been undertaken to model the risk of SARS-CoV-2 infection. Less is known about what may drive the probability to undergo testing. Understanding how much testing is driven by contextual or individual conditions is important to delineate the role of individual behavior and to shape public health interventions and resource allocation. In the Val Venosta/Vinschgau district (South Tyrol, Italy), we conducted a population-representative longitudinal study on 697 individuals susceptible to first infection who completed 4,512 repeated online questionnaires at four-week intervals between September 2020 and May 2021. Mixed-effects logistic regression models were fitted to investigate associations of self-reported SARS-CoV-2 testing with individual characteristics (social, demographic, and biological) and contextual determinants. Testing was associated with month of reporting, reflecting the timing of both the pandemic intensity and public health interventions, COVID-19-related symptoms (odds ratio, OR:8.26; 95% confidence interval, CI:6.04-11.31), contacts with infected individuals within home (OR:7.47, 95%CI:3.81-14.62) or outside home (OR:9.87, 95%CI:5.78-16.85), and being retired (OR:0.50, 95%CI:0.34-0.73). Symptoms and next within- and outside-home contacts were the leading determinants of swab testing predisposition in the most acute phase of the pandemics. Testing was not associated with age, sex, education, comorbidities, or lifestyle factors. In the study area, contextual determinants reflecting the course of the pandemic were predominant compared to individual sociodemographic characteristics in explaining the SARS-CoV-2 probability of testing. Decision makers should evaluate whether the intended target groups were correctly prioritized by the testing campaign.

Trends and symptoms of SARS-CoV-2 infection: a longitudinal study on an Alpine population representative sample

OBJECTIVES

The continuous monitoring of SARS-CoV-2 infection waves and the emergence of novel pathogens pose a challenge for effective public health surveillance strategies based on diagnostics. Longitudinal population representative studies on incident events and symptoms of SARS-CoV-2 infection are scarce. We aimed at describing the evolution of the COVID-19 pandemic during 2020 and 2021 through regular monitoring of self-reported symptoms in an Alpine community sample.

DESIGN

To this purpose, we designed a longitudinal population representative study, the Cooperative Health Research in South Tyrol COVID-19 study.

PARTICIPANTS AND OUTCOME MEASURES

A sample of 845 participants was retrospectively investigated for active and past infections with swab and blood tests, by August 2020, allowing adjusted cumulative incidence estimation. Of them, 700 participants without previous infection or vaccination were followed up monthly until July 2021 for first-time infection and symptom self-reporting: COVID-19 anamnesis, social contacts, lifestyle and sociodemographic data were assessed remotely through digital questionnaires. Temporal symptom trajectories and infection rates were modelled through longitudinal clustering and dynamic correlation analysis. Negative binomial regression and random forest analysis assessed the relative importance of symptoms.

RESULTS

At baseline, the cumulative incidence of SARS-CoV-2 infection was 1.10% (95% CI 0.51%, 2.10%). Symptom trajectories mimicked both self-reported and confirmed cases of incident infections. Cluster analysis identified two groups of high-frequency and low-frequency symptoms. Symptoms like fever and loss of smell fell in the low-frequency cluster. Symptoms most discriminative of test positivity (loss of smell, fatigue and joint-muscle aches) confirmed prior evidence.

CONCLUSIONS

Regular symptom tracking from population representative samples is an effective screening tool auxiliary to laboratory diagnostics for novel pathogens at critical times, as manifested in this study of COVID-19 patterns. Integrated surveillance systems might benefit from more direct involvement of citizens' active symptom tracking.

Impute-then-exclude versus exclude-then-impute: Lessons when imputing a variable used both in cohort creation and as an independent variable in the analysis model

We examined the setting in which a variable that is subject to missingness is used both as an inclusion/exclusion criterion for creating the analytic sample and subsequently as the primary exposure in the analysis model that is of scientific interest. An example is cancer stage, where patients with stage IV cancer are often excluded from the analytic sample, and cancer stage (I to III) is an exposure variable in the analysis model. We considered two analytic strategies. The first strategy, referred to as "exclude-then-impute," excludes subjects for whom the observed value of the target variable is equal to the specified value and then uses multiple imputation to complete the data in the resultant sample. The second strategy, referred to as "impute-then-exclude," first uses multiple imputation to complete the data and then excludes subjects based on the observed or filled-in values in the completed samples. Monte Carlo simulations were used to compare five methods (one based on "exclude-then-impute" and four based on "impute-then-exclude") along with the use of a complete case analysis. We considered both missing completely at random and missing at random missing data mechanisms. We found that an impute-then-exclude strategy using substantive model compatible fully conditional specification tended to have superior performance across 72 different scenarios. We illustrated the application of these methods using empirical data on patients hospitalized with heart failure when heart failure subtype was used for cohort creation (excluding subjects with heart failure with preserved ejection fraction) and was also an exposure in the analysis model.

Assessing Performance and Clinical Usefulness in Prediction Models With Survival Outcomes: Practical Guidance for Cox Proportional Hazards Models

Risk prediction models need thorough validation to assess their performance. Validation of models for survival outcomes poses challenges due to the censoring of observations and the varying time horizon at which predictions can be made. This article describes measures to evaluate predictions and the potential improvement in decision making from survival models based on Cox proportional hazards regression.

As a motivating case study, the authors consider the prediction of the composite outcome of recurrence or death (the "event") in patients with breast cancer after surgery. They developed a simple Cox regression model with 3 predictors, as in the Nottingham Prognostic Index, in 2982 women (1275 events over 5 years of follow-up) and externally validated this model in 686 women (285 events over 5 years). Improvement in performance was assessed after the addition of progesterone receptor as a prognostic biomarker.

The model predictions can be evaluated across the full range of observed follow-up times or for the event occurring by the end of a fixed time horizon of interest. The authors first discuss recommended statistical measures that evaluate model performance in terms of discrimination, calibration, or overall performance. Further, they evaluate the potential clinical utility of the model to support clinical decision making according to a net benefit measure. They provide SAS and R code to illustrate internal and external validation.

The authors recommend the proposed set of performance measures for transparent reporting of the validity of predictions from survival models.

PredictCBC-2.0: a contralateral breast cancer risk prediction model developed and validated in ~ 200,000 patients

BACKGROUND

Prediction of contralateral breast cancer (CBC) risk is challenging due to moderate performances of the known risk factors. We aimed to improve our previous risk prediction model (PredictCBC) by updated follow-up and including additional risk factors.

METHODS

We included data from 207,510 invasive breast cancer patients participating in 23 studies. In total, 8225 CBC events occurred over a median follow-up of 10.2 years. In addition to the previously included risk factors, PredictCBC-2.0 included CHEK2 c.1100delC, a 313 variant polygenic risk score (PRS-313), body mass index (BMI), and parity. Fine and Gray regression was used to fit the model. Calibration and a time-dependent area under the curve (AUC) at 5 and 10 years were assessed to determine the performance of the models. Decision curve analysis was performed to evaluate the net benefit of PredictCBC-2.0 and previous PredictCBC models.

RESULTS

The discrimination of PredictCBC-2.0 at 10 years was higher than PredictCBC with an AUC of 0.65 (95% prediction intervals (PI) 0.56-0.74) versus 0.63 (95%PI 0.54-0.71). PredictCBC-2.0 was well calibrated with an observed/expected ratio at 10 years of 0.92 (95%PI 0.34-2.54). Decision curve analysis for contralateral preventive mastectomy (CPM) showed the potential clinical utility of PredictCBC-2.0 between thresholds of 4 and 12% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers.

CONCLUSIONS

Additional genetic information beyond BRCA1/2 germline mutations improved CBC risk prediction and might help tailor clinical decision-making toward CPM or alternative preventive strategies. Identifying patients who benefit from CPM, especially in the general breast cancer population, remains challenging.

Predicting 10-year survival after resection of colorectal liver metastases; an international study including biomarkers and perioperative treatment

BACKGROUND

The aim of this study was to develop a prediction model for 10-year overall survival (OS) after resection of colorectal liver metastasis (CRLM) based on patient, tumour and treatment characteristics.

METHODS

Consecutive patients after complete resection of CRLM were included from two centres (1992-2019). A prediction model providing 10-year OS probabilities was developed using Cox regression analysis, including KRAS, BRAF and histopathological growth patterns. Discrimination and calibration were assessed using cross-validation. A web-based calculator was built to predict individual 10-year OS probabilities.

RESULTS

A total of 4112 patients were included. The estimated 10-year OS was 30% (95% CI 29-32). Fifteen patient, tumour and treatment characteristics were independent prognostic factors for 10-year OS; age, gender, location and nodal status of the primary tumour, disease-free interval, number and diameter of CRLM, preoperative CEA, resection margin, extrahepatic disease, KRAS and BRAF mutation status, histopathological growth patterns, perioperative systemic chemotherapy and hepatic arterial infusion pump chemotherapy. The discrimination at 10-years was 0.73 for both centres. A simplified risk score identified four risk groups with a 10-year OS of 57%, 38%, 24%, and 12%.

CONCLUSIONS

Ten-year OS after resection of CRLM is best predicted with a model including 15 patient, tumour, and treatment characteristics. The web-based calculator can be used to inform patients. This model serves as a benchmark to determine the prognostic value of novel biomarkers.

Graphical calibration curves and the integrated calibration index (ICI) for competing risk models

BACKGROUND

Assessing calibration-the agreement between estimated risk and observed proportions-is an important component of deriving and validating clinical prediction models. Methods for assessing the calibration of prognostic models for use with competing risk data have received little attention.

METHODS

We propose a method for graphically assessing the calibration of competing risk regression models. Our proposed method can be used to assess the calibration of any model for estimating incidence in the presence of competing risk (e.g., a Fine-Gray subdistribution hazard model; a combination of cause-specific hazard functions; or a random survival forest). Our method is based on using the Fine-Gray subdistribution hazard model to regress the cumulative incidence function of the cause-specific outcome of interest on the predicted outcome risk of the model whose calibration we want to assess. We provide modifications of the integrated calibration index (ICI), of E50 and of E90, which are numerical calibration metrics, for use with competing risk data. We conducted a series of Monte Carlo simulations to evaluate the performance of these calibration measures when the underlying model has been correctly specified and when the model was mis-specified and when the incidence of the cause-specific outcome differed between the derivation and validation samples. We illustrated the usefulness of calibration curves and the numerical calibration metrics by comparing the calibration of a Fine-Gray subdistribution hazards regression model with that of random survival forests for predicting cardiovascular mortality in patients hospitalized with heart failure.

RESULTS

The simulations indicated that the method for constructing graphical calibration curves and the associated calibration metrics performed as desired. We also demonstrated that the numerical calibration metrics can be used as optimization criteria when tuning machine learning methods for competing risk outcomes.

CONCLUSIONS

The calibration curves and numeric calibration metrics permit a comprehensive comparison of the calibration of different competing risk models.

Long-term risk of subsequent ipsilateral lesions after surgery with or without radiotherapy for ductal carcinoma in situ of the breast

BACKGROUND

Radiotherapy (RT) following breast-conserving surgery (BCS) for ductal carcinoma in situ (DCIS) reduces ipsilateral breast event rates in clinical trials. This study assessed the impact of DCIS treatment on a 20-year risk of ipsilateral DCIS (iDCIS) and ipsilateral invasive breast cancer (iIBC) in a population-based cohort.

METHODS

The cohort comprised all women diagnosed with DCIS in the Netherlands during 1989-2004 with follow-up until 2017. Cumulative incidence of iDCIS and iIBC following BCS and BCS + RT were assessed. Associations of DCIS treatment with iDCIS and iIBC risk were estimated in multivariable Cox models.

RESULTS

The 20-year cumulative incidence of any ipsilateral breast event was 30.6% (95% confidence interval (CI): 28.9-32.6) after BCS compared to 18.2% (95% CI 16.3-20.3) following BCS  +  RT. Women treated with BCS compared to BCS + RT had higher risk of developing iDCIS and iIBC within 5 years after DCIS diagnosis (for iDCIS: hazard ratio (HR)_age < 50 3.2 (95% CI 1.6-6.6); HR_{age ≥ 50} 3.6 (95% CI 2.6-4.8) and for iIBC: HR_age<50 2.1 (95% CI 1.4-3.2); HR_{age ≥ 50} 4.3 (95% CI 3.0-6.0)). After 10 years, the risk of iDCIS and iIBC no longer differed for BCS versus BCS + RT (for iDCIS: HR_age < 50 0.7 (95% CI 0.3-1.5); HR_{age ≥ 50} 0.7 (95% CI 0.4-1.3) and for iIBC: HR_age < 50 0.6 (95% CI 0.4-0.9); HR_{age ≥ 50} 1.2 (95% CI 0.9-1.6)).

CONCLUSION

RT is associated with lower iDCIS and iIBC risk up to 10 years after BCS, but this effect wanes thereafter.

Development and validation of the PORTRET tool to predict recurrence, overall survival, and other-cause mortality in older patients with breast cancer in the Netherlands: a population-based study

BACKGROUND

Current prediction tools for breast cancer outcomes are not tailored to the older patient, in whom competing risk strongly influences treatment effects. We aimed to develop and validate a prediction tool for 5-year recurrence, overall mortality, and other-cause mortality for older patients (aged ≥65 years) with early invasive breast cancer and to estimate individualised expected benefits of adjuvant systemic treatment.

METHODS

We selected surgically treated patients with early invasive breast cancer (stage I-III) aged 65 years or older from the population-based FOCUS cohort in the Netherlands. We developed prediction models for 5-year recurrence, overall mortality, and other-cause mortality using cause-specific Cox proportional hazard models. External validation was performed in a Dutch Cancer registry cohort. Performance was evaluated with discrimination accuracy and calibration plots.

FINDINGS

We included 2744 female patients in the development cohort and 13631 female patients in the validation cohort. Median age was 74·8 years (range 65-98) in the development cohort and 76·0 years (70-101) in the validation cohort. 5-year follow-up was complete for more than 99% of all patients. We observed 343 and 1462 recurrences, and 831 and 3594 deaths, of which 586 and 2565 were without recurrence, in the development and validation cohort, respectively. The area under the receiver-operating-characteristic curve at 5 years in the external dataset was 0·76 (95% CI 0·75-0·76) for overall mortality, 0·76 (0·76-0·77) for recurrence, and 0·75 (0·74-0·75) for other-cause mortality.

INTERPRETATION

The PORTRET tool can accurately predict 5-year recurrence, overall mortality, and other-cause mortality in older patients with breast cancer. The tool can support shared decision making, especially since it provides individualised estimated benefits of adjuvant treatment.

FUNDING

Dutch Cancer Foundation and ZonMw.

Abstract PS6-08: The PORTRET-tool: A prediction tool for older patients with breast cancer that predicts recurrence, survival and other-cause mortality

Introduction Previous studies have shown that available tools such as ‘Adjuvant Online!” are not able to accurately predict the prognosis of patients aged 65 years or older with breast cancer. Furthermore, all available tools predict prognosis in terms of recurrence-free survival or overall survival, whilst the risk of other-cause mortality is often high in the older patient with breast cancer. This is highly relevant as it may influence treatment decisions. Patient characteristics such as comorbidity and various geriatric variables have shown to be predictive for these outcomes and could enhance the precision of prognostic tools for this target population. The objective of this study was to develop a prediction tool for recurrence, survival and other-cause mortality for older patients with breast cancer who received locoregional treatment, with incorporation of patient-, tumor- and geriatric variables. The tool additionally predicts expected benefits of systemic treatment. Methods Data from the large population-based FOCUS cohort was used, consisting of consecutive breast cancer patients in the South-Western part of the Netherlands, diagnosed between 1997 and 2004, aged 65 years and older. It contains detailed information on tumor characteristics, treatment, comorbidity and geriatric parameters. We developed a risk prediction model using a Cox proportional hazards regression model for overall survival and cause-specific Cox proportional hazards models for recurrence and other-cause mortality (defined as mortality without recurrence). The included predictors were derived from the PREDICT tool (consisting of age and various tumor variables), since this tool was previously shown to have the best performance in older adults so far. Predictors were complemented with comorbidity and geriatric variables. Discrimination accuracy was evaluated using time-dependent area under the curve (AUC). The potential annual benefit of chemotherapy was calculated assuming a relative risk of chemotherapy on recurrence of 0.7, derived from data from the most recent updates of the Early Breast Cancer Trialists’ Collaborative Group (EBCTCG). Additional benefit of endocrine treatment will be included in further development of the tool. Results A total of 2,744 patients were included for the initial development. For all patients, 5-year follow-up was complete with a high event-rate including 343 recurrences and 831 total deaths of which 586 without recurrence. The strongest predictors for overall survival and non-recurrence mortality were age (HR = 2.14, 95% CI: 1.89 - 2.43 and HR = 2.87, 95% CI: 2.46 - 3.35, respectively) and dementia (HR = 1.52, 95% CI: 1.16 - 1.99 and HR = 1.9, 95% CI: 1.49 - 2.65, respectively), and for recurrence, nodal status (HR = 1.80, 95% CI: 1.45 - 2.24) and tumor grade (HR = 2.96, 95% CI: 1.88 - 4.66). The time-dependent AUC at 5 years for recurrence-specific and other-cause mortality were 0.78 (95% CI: 0.76 - 0.81), and 0.75 (95% CI: 0.72 - 0.78), respectively. The AUC for overall survival was 0.75 (95% CI: 0.72 - 0.78). External validation is currently being performed in a large dataset retrieved from the national cancer registry (N= 13,631). These results will be presented during the symposium. Conclusion We have developed a model for predicting 5-year recurrence, other-cause mortality and overall survival, including expected benefits of adjuvant treatment, for older patients with breast cancer, with a good discrimination performance within a large-population based cohort. To our knowledge, this is the first model specifically designed for the older population, including competing risk as a predicted outcome and with incorporation of geriatric variables.

Citation Format: Willeke van der Plas-Krijgsman, Daniele Giardiello, Hein Putter, Ewout W Steyerberg, Esther Bastiaannet, Anne M Stiggelbout, Simon P Mooijaart, Johanneke EA Portielje, Gerrit J Liefers, Nienke A de Glas. The PORTRET-tool: A prediction tool for older patients with breast cancer that predicts recurrence, survival and other-cause mortality [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS6-08.

Back to simplicity: a four-marker blood cell score to quantify prognostically relevant myeloid cells in melanoma patients

Background

Myeloid-derived suppressor cells (MDSC), a cornerstone of cancer-related immunosuppression, influence response to therapy and disease outcomes in melanoma patients. Nevertheless, their quantification is far from being integrated into routine clinical practice mostly because of the complex and still evolving phenotypic signatures applied to define the cell subsets. Here, we used a multistep downsizing process to verify whether a core of few markers could be sufficient to capture the prognostic potential of myeloid cells in peripheral blood mononuclear cells (PBMC) of metastatic melanoma patients.

Methods

In baseline frozen PBMC from a total of 143 stage IIIc to IV melanoma patients, we first assessed the relevant or redundant expression of myeloid and MDSC-related markers by flow cytometry (screening set, n=23 patients). Subsequently, we applied the identified panel to the development set samples (n=59 patients undergoing first/second-line therapy) to obtain prognostic variables associated with overall survival (OS) and progression-free survival (PFS) by machine learning adaptive index modeling. Finally, the identified score was confirmed in a validation set (n=61) and compared with standard clinical prognostic factors to assess its additive value in patient prognostication.

Results

This selection process led to the identification of what we defined myeloid index score (MIS), which is composed by four cell subsets (CD14⁺, CD14⁺HLA-DR^neg, CD14⁺PD-L1⁺ and CD15⁺ cells), whose frequencies above cut-offs stratified melanoma patients according to progressively worse prognosis. Patients with a MIS=0, showing no over-threshold value of MIS subsets, had the best clinical outcome, with a median survival of >33.6 months, while in patients with MIS 1→3, OS deteriorated from 10.9 to 6.8 and 6.0 months as the MIS increased (p<0.0001, c-index=0.745). MIS clustered patients into risk groups also according to PFS (p<0.0001). The inverse correlation between MIS and survival was confirmed in the validation set, was independent of the type of therapy and was not interfered by clinical prognostic factors. MIS HR was remarkably superior to that of lactate dehydrogenase, tumor burden and neutrophil-to-lymphocyte ratio.

Conclusion

The MIS >0 identifies melanoma patients with a more aggressive disease, thus acting as a simple blood biomarker that can help tailoring therapeutic choices in real-life oncology.

Breast Cancer Polygenic Risk Score and Contralateral Breast Cancer Risk

Previous research has shown that polygenic risk scores (PRSs) can be used to stratify women according to their risk of developing primary invasive breast cancer. This study aimed to evaluate the association between a recently validated PRS of 313 germline variants (PRS313) and contralateral breast cancer (CBC) risk. We included 56,068 women of European ancestry diagnosed with first invasive breast cancer from 1990 onward with follow-up from the Breast Cancer Association Consortium. Metachronous CBC risk (N = 1,027) according to the distribution of PRS313 was quantified using Cox regression analyses. We assessed PRS313 interaction with age at first diagnosis, family history, morphology, ER status, PR status, and HER2 status, and (neo)adjuvant therapy. In studies of Asian women, with limited follow-up, CBC risk associated with PRS313 was assessed using logistic regression for 340 women with CBC compared with 12,133 women with unilateral breast cancer. Higher PRS313 was associated with increased CBC risk: hazard ratio per standard deviation (SD) = 1.25 (95%CI = 1.18-1.33) for Europeans, and an OR per SD = 1.15 (95%CI = 1.02-1.29) for Asians. The absolute lifetime risks of CBC, accounting for death as competing risk, were 12.4% for European women at the 10th percentile and 20.5% at the 90th percentile of PRS313. We found no evidence of confounding by or interaction with individual characteristics, characteristics of the primary tumor, or treatment. The C-index for the PRS313 alone was 0.563 (95%CI = 0.547-0.586). In conclusion, PRS313 is an independent factor associated with CBC risk and can be incorporated into CBC risk prediction models to help improve stratification and optimize surveillance and treatment strategies.

Contralateral breast cancer risk in patients with ductal carcinoma in situ and invasive breast cancer

We aimed to assess contralateral breast cancer (CBC) risk in patients with ductal carcinoma in situ (DCIS) compared with invasive breast cancer (BC). Women diagnosed with DCIS (N = 28,003) or stage I-III BC (N = 275,836) between 1989 and 2017 were identified from the nationwide Netherlands Cancer Registry. Cumulative incidences were estimated, accounting for competing risks, and hazard ratios (HRs) for metachronous invasive CBC. To evaluate effects of adjuvant systemic therapy and screening, separate analyses were performed for stage I BC without adjuvant systemic therapy and by mode of first BC detection. Multivariable models including clinico-pathological and treatment data were created to assess CBC risk prediction performance in DCIS patients. The 10-year cumulative incidence of invasive CBC was 4.8% for DCIS patients (CBC = 1334). Invasive CBC risk was higher in DCIS patients compared with invasive BC overall (HR = 1.10, 95% confidence interval (CI) = 1.04-1.17), and lower compared with stage I BC without adjuvant systemic therapy (HR = 0.87; 95% CI = 0.82-0.92). In patients diagnosed ≥2011, the HR for invasive CBC was 1.38 (95% CI = 1.35-1.68) after screen-detected DCIS compared with screen-detected invasive BC, and was 2.14 (95% CI = 1.46-3.13) when not screen-detected. The C-index was 0.52 (95% CI = 0.50-0.54) for invasive CBC prediction in DCIS patients. In conclusion, CBC risks are low overall. DCIS patients had a slightly higher risk of invasive CBC compared with invasive BC, likely explained by the risk-reducing effect of (neo)adjuvant systemic therapy among BC patients. For support of clinical decision making more information is needed to differentiate CBC risks among DCIS patients.

Prediction of contralateral breast cancer: external validation of risk calculators in 20 international cohorts

BACKGROUND

Three tools are currently available to predict the risk of contralateral breast cancer (CBC). We aimed to compare the performance of the Manchester formula, CBCrisk, and PredictCBC in patients with invasive breast cancer (BC).

METHODS

We analyzed data of 132,756 patients (4682 CBC) from 20 international studies with a median follow-up of 8.8 years. Prediction performance included discrimination, quantified as a time-dependent Area-Under-the-Curve (AUC) at 5 and 10 years after diagnosis of primary BC, and calibration, quantified as the expected-observed (E/O) ratio at 5 and 10 years and the calibration slope.

RESULTS

The AUC at 10 years was: 0.58 (95% confidence intervals [CI] 0.57-0.59) for CBCrisk; 0.60 (95% CI 0.59-0.61) for the Manchester formula; 0.63 (95% CI 0.59-0.66) and 0.59 (95% CI 0.56-0.62) for PredictCBC-1A (for settings where BRCA1/2 mutation status is available) and PredictCBC-1B (for the general population), respectively. The E/O at 10 years: 0.82 (95% CI 0.51-1.32) for CBCrisk; 1.53 (95% CI 0.63-3.73) for the Manchester formula; 1.28 (95% CI 0.63-2.58) for PredictCBC-1A and 1.35 (95% CI 0.65-2.77) for PredictCBC-1B. The calibration slope was 1.26 (95% CI 1.01-1.50) for CBCrisk; 0.90 (95% CI 0.79-1.02) for PredictCBC-1A; 0.81 (95% CI 0.63-0.99) for PredictCBC-1B, and 0.39 (95% CI 0.34-0.43) for the Manchester formula.

CONCLUSIONS

Current CBC risk prediction tools provide only moderate discrimination and the Manchester formula was poorly calibrated. Better predictors and re-calibration are needed to improve CBC prediction and to identify low- and high-CBC risk patients for clinical decision-making.

Abstract P5-07-12: Prediction and clinical utility of a contralateral breast cancer risk model

Background: Breast cancer survivors are at risk for contralateral breast cancer (CBC), with the consequent burden of further treatment and potentially less favorable prognosis. We aimed to develop and validate a CBC risk prediction model, and evaluate its applicability for clinical decision-making.

Methods: We included data of 132,756 invasive non-metastatic breast cancer patients from 20 studies with 4,682 CBC events and a median follow-up of 8.8 years. We developed a multivariable Fine and Gray prediction model (PredictCBC-1A) including patient, primary tumor, and treatment characteristics, and BRCA1/2 germline mutation status, accounting for the competing risks of death and distant metastasis. We also developed a model without BRCA1/2 mutation status (PredictCBC-1B). Prediction performance was evaluated using calibration and discrimination, calculated by a time-dependent Area-Under-the-Curve (AUC) at 5 and 10 years after diagnosis of primary breast cancer, and an internal-external cross-validation procedure. Decision curve analysis was performed to evaluate the net benefit of the model to quantify clinical utility.

Results: In the multivariable model, BRCA1/2 germline mutation status, family history and systemic adjuvant treatment showed the strongest associations with CBC risk. The AUC of PredictCBC-1A was 0.63 (95% prediction interval (PI) at 5 years: 0.52–0.74; at 10 years: 0.53–0.72). Calibration in-the-large was -0.13 (95%PI: -1.62–1.37) and the calibration slope was 0.90 (95%PI: 0.73–1.08). The AUC of Predict-1B at 10 years was 0.59 (95% PI: 0.52–0.66); calibration was slightly lower. Decision curve analysis for preventive contralateral mastectomy showed potential clinical utility of PredictCBC-1A between thresholds of 4-10% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers.

Conclusions: We developed a reasonably calibrated model to predict the risk of CBC in women of European-descent, however, prediction accuracy was moderate. Our model shows potential for improved risk counseling, but decision regarding contralateral preventive mastectomy, especially in the general breast cancer population, remains challenging.

Citation Format: Daniele Giardiello, Ewout W Steyerberg, Michael Hauptmann, Muriel A Adank, Agnes Jager, Hester OA Oldenburg, Maartje J Hooning, Marjanka K Schmidt, Breast Cancer Association Consortium. Prediction and clinical utility of a contralateral breast cancer risk model [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 P5-07-12.

Prediction and clinical utility of a contralateral breast cancer risk model

BACKGROUND

Breast cancer survivors are at risk for contralateral breast cancer (CBC), with the consequent burden of further treatment and potentially less favorable prognosis. We aimed to develop and validate a CBC risk prediction model and evaluate its applicability for clinical decision-making.

METHODS

We included data of 132,756 invasive non-metastatic breast cancer patients from 20 studies with 4682 CBC events and a median follow-up of 8.8 years. We developed a multivariable Fine and Gray prediction model (PredictCBC-1A) including patient, primary tumor, and treatment characteristics and BRCA1/2 germline mutation status, accounting for the competing risks of death and distant metastasis. We also developed a model without BRCA1/2 mutation status (PredictCBC-1B) since this information was available for only 6% of patients and is routinely unavailable in the general breast cancer population. Prediction performance was evaluated using calibration and discrimination, calculated by a time-dependent area under the curve (AUC) at 5 and 10 years after diagnosis of primary breast cancer, and an internal-external cross-validation procedure. Decision curve analysis was performed to evaluate the net benefit of the model to quantify clinical utility.

RESULTS

In the multivariable model, BRCA1/2 germline mutation status, family history, and systemic adjuvant treatment showed the strongest associations with CBC risk. The AUC of PredictCBC-1A was 0.63 (95% prediction interval (PI) at 5 years, 0.52-0.74; at 10 years, 0.53-0.72). Calibration-in-the-large was -0.13 (95% PI: -1.62-1.37), and the calibration slope was 0.90 (95% PI: 0.73-1.08). The AUC of Predict-1B at 10 years was 0.59 (95% PI: 0.52-0.66); calibration was slightly lower. Decision curve analysis for preventive contralateral mastectomy showed potential clinical utility of PredictCBC-1A between thresholds of 4-10% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers.

CONCLUSIONS

We developed a reasonably calibrated model to predict the risk of CBC in women of European-descent; however, prediction accuracy was moderate. Our model shows potential for improved risk counseling, but decision-making regarding contralateral preventive mastectomy, especially in the general breast cancer population where limited information of the mutation status in BRCA1/2 is available, remains challenging.

Impact of Comorbidities and Age on Cause-Specific Mortality in Postmenopausal Patients with Breast Cancer

BACKGROUND

The aim was to study the impact of comorbidities and age on breast cancer mortality, taking into account competing causes of death.

SUBJECTS, MATERIALS, AND METHODS

Cohort analysis of Dutch and Belgian patients with postmenopausal, early hormone receptor-positive breast cancer included in the Tamoxifen and Exemestane Adjuvant Multinational (TEAM) trial between 2001 and 2006. This is a randomized controlled trial of patients who had completed local treatment with curative intent and were randomized to receive exemestane for 5 years, or sequential treatment of tamoxifen followed by exemestane for a duration of 5 years. Patients were categorized by number of comorbidities (no comorbidities, 1-2 comorbidities, and >2 comorbidities) and age (<70 years and ≥70 years). Main outcome was breast cancer mortality considering other-cause mortality as competing event; cumulative incidences were calculated using the Cumulative Incidence Competing Risk Methods, and the Fine and Gray model was used to calculate the effect of age and comorbidities for the cause-specific incidences of breast cancer death, taking into account the effect of competing causes of death.

RESULTS

Overall, 3,159 patients were included, of which 2,203 (69.7%) were aged <70 years and 956 (30.3%) were aged ≥70 years at diagnosis. Cumulative incidence of breast cancer mortality was higher among patients ≥70 without comorbidities (22.2%, 95% CI, 17.5-26.9) compared with patients <70 without comorbidities (15.6%, 95% CI, 13.6-17.7, reference group), multivariable subdistribution hazard ratio (sHR) 1.49 (95% CI, 1.12-1.97, p = .005) after a median follow-up of 10 years. Use of chemotherapy was lower in older patients (1%, irrespective of the number of comorbidities) compared with younger patients (50%, 44%, and 38% for patients with no, 1-2, or >2 comorbidities, p < .001).

CONCLUSION

Older patients without comorbidities have a higher risk of dying due to breast cancer than younger counterparts, even when taking into account higher competing mortality, while use of chemotherapy in this group was low. These findings underline the need to take into account comorbidities, age, and competing mortality in the prognosis of breast cancer for accurate decision making.

IMPLICATIONS FOR PRACTICE

Older patients without comorbidity are at increased risk of dying from breast cancer, despite a higher other-cause mortality. This study shows that including age and comorbidity for the assessment of breast cancer mortality and other-cause mortality is indispensable for treatment decision making in older patients. Future prognostic tools for breast cancer prognosis should incorporate these items as well as risk of toxicity of adjuvant chemotherapy to adequately predict outcomes to optimize personalized treatment for older patients with early breast cancer.

Risk factors for metachronous contralateral breast cancer: A systematic review and meta-analysis

BACKGROUND

The risk of developing metachronous contralateral breast cancer (CBC) is a recurrent topic at the outpatient clinic. We aimed to provide CBC risk estimates of published patient, pathological, and primary breast cancer (PBC) treatment-related factors.

METHODS

PubMed was searched for publications on factors associated with CBC risk. Meta-analyses were performed with grouping of studies by mutation status (i.e., BRCA1, BRCA2, CHEK2 c.1100delC), familial cohorts, and general population-based cohorts.

RESULTS

Sixty-eight papers satisfied our inclusion criteria. Strong associations with CBC were found for carrying a BRCA1 (RR = 3.7; 95%CI:2.8-4.9), BRCA2 (RR = 2.8; 95%CI:1.8-4.3) or CHEK2 c.1100delC (RR = 2.7; 95%CI:2.0-3.7) mutation. In population-based cohorts, PBC family history (RR = 1.8; 95%CI:1.2-2.6), body mass index (BMI) ≥30 kg/m² (RR = 1.5; 95%CI:1.3-1.9), lobular PBC (RR = 1.4; 95%CI:1.1-1.8), estrogen receptor-negative PBC (RR = 1.5; 95%CI:1.0-2.3) and treatment with radiotherapy <40 years (RR = 1.4; 95%CI:1.1-1.7) was associated with increased CBC risk. Older age at PBC diagnosis (RR per decade = 0.93; 95%CI:0.88-0.98), and treatment with chemotherapy (RR = 0.7; 95%CI:0.6-0.8) or endocrine therapy (RR = 0.6; 95%CI:0.5-0.7) were associated with decreased CBC risk.

CONCLUSIONS

Mutation status, family history, and PBC treatment are key factors for CBC risk. Age at PBC diagnosis, BMI, lobular histology and hormone receptor status have weaker associations and should be considered in combination with key factors to accurately predict CBC risk.

Nomogram-based Prediction of Overall Survival in Patients with Metastatic Urothelial Carcinoma Receiving First-line Platinum-based Chemotherapy: Retrospective International Study of Invasive/Advanced Cancer of the Urothelium (RISC)

BACKGROUND

The available prognostic models for overall survival (OS) in patients with metastatic urothelial carcinoma (UC) have been derived from clinical trial populations of cisplatin-treated patients.

OBJECTIVE

To develop a new model based on real-world patients.

DESIGN, SETTING, AND PARTICIPANTS

Individual patient-level data from 29 centers were collected, including metastatic UC and first-line cisplatin- or carboplatin-based chemotherapy administered between January 2006 and January 2011.

INTERVENTION

First-line, platinum-based, combination chemotherapy.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The population was randomly split into a development and a validation cohort. Generalized boosted regression modelling was used to screen out irrelevant variables and address multivariable analyses. Two nomograms were built to estimate OS probability, the first based on baseline factors and platinum agent, the second incorporating objective response (OR). The performance of the above nomograms and that of other available models was assessed. We plotted decision curves to evaluate the clinical usefulness of the two nomograms.

RESULTS AND LIMITATIONS

A total of 1020 patients were analyzed (development: 687, validation: 333). In a platinum-stratified Cox model, significant variables for OS were performance status (p<0.001), white blood cell count (p=0.013), body mass index (p=0.003), ethnicity (p=0.012), lung, liver, or bone metastases (p<0.001), and prior perioperative chemotherapy (p=0.012). The c-index was 0.660. The distribution of the nomogram scores was associated with OR (p<0.001), and incorporating OR into the model further improved the c-index in the validation cohort (0.670).

CONCLUSIONS

We developed and validated two nomograms for OS to be used before and after completion of first-line chemotherapy for metastatic UC.

PATIENT SUMMARY

We proposed two models for estimating overall survival of patients with metastatic urothelial carcinoma receiving first-line, platinum-based chemotherapy. These nomograms have been developed on real-world patients who were treated outside of clinical trials and may be used irrespective of the chemotherapeutic platinum agent used.

Immunomodulatory Factors Control the Fate of Melanoma Tumor Initiating Cells

Melanoma is a highly heterogeneous tumor for which recent evidence supports a model of dynamic stemness. Melanoma cells might temporally acquire tumor-initiating properties or switch from a status of tumor-initiating cells (TICs) to a more differentiated one depending on the tumor context. However, factors driving these functional changes are still unknown. We focused on the role of cyto/chemokines in shaping TICs isolated directly from tumor specimens of two melanoma patients, namely Me14346S and Me15888S. We analyzed the secretion profile of TICs and of their corresponding melanoma differentiated cells and we tested the ability of cyto/chemokines to influence TIC self-renewal and differentiation. We found that TICs, grown in vitro as melanospheres, had a complex secretory profile as compared to their differentiated counterparts. Some factors, such as CCL-2 and IL-8, also produced by adherent melanoma cells and melanocytes did not influence TIC properties. Conversely, IL-6, released by differentiated cells, reduced TIC self-renewal and induced TIC differentiation while IL-10, produced by Me15888S, strongly promoted TIC self-renewal through paracrine/autocrine actions. Complete neutralization of IL-10 activity by gene silencing and antibody-mediated blocking of the IL-10Rα was required to sensitize Me15888S to IL-6-induced differentiation. For the first time these results show that functional heterogeneity of melanoma could be directly influenced by inflammatory and suppressive soluble factors, with IL-6 favoring TIC differentiation, and IL-10 supporting TIC self-renewal. Thus, understanding the tumor microenvironment (TME) role in modulating melanoma TIC phenotype is fundamental to identifying novel therapeutic targets to achieve long-lasting regression of metastatic melanoma. Stem Cells 2016;34:2449-2460.

Dynamics of Mycoplasma hyopneumoniae seroconversion and infection in pigs in the three main production systems

In this study, we investigated the dynamics of Mycoplasma hyopneumoniae infections in 66 pig farms, with different production systems (one-, two-, and three-site systems), and considered different risk factors. Serological assay was used to detect serum antibodies against M. hyopneumoniae and real time polymerase chain reaction (RT-PCR) was performed to detect M. hyopneumoniae DNA in tracheobronchial swabs. Results demonstrated that M. hyopneumoniae infection status was predominantly influenced by the age of the animals and the type of production system. Infection rates were higher in older animals and the prevalence was higher in the one- and two-site systems than in the three-site systems. Dynamics of infection by RT-PCR showed that earlier M. hyopneumoniae infection on one-site farms occurs earlier, while on two- and three-site farms occurs later but spreads faster, suggesting that contact between animals of different age favors the transmission.

The Sooner the Better? How Symptom Interval Correlates With Outcome in Children and Adolescents With Solid Tumors: Regression Tree Analysis of the Findings of a Prospective Study

BACKGROUND

The potential impact of diagnostic delays on patients' outcomes is a debated issue in pediatric oncology and discordant results have been published so far. We attempted to tackle this issue by analyzing a prospective series of 351 consecutive children and adolescents with solid malignancies using innovative statistical tools.

METHODS

To address the nonlinear complexity of the association between symptom interval and overall survival (OS), a regression tree algorithm was constructed with sequential binary splitting rules and used to identify homogeneous patient groups vis-à-vis functional relationship between diagnostic delay and OS.

RESULTS

Three different groups were identified: group A, with localized disease and good prognosis (5-year OS 85.4%); group B, with locally or regionally advanced, or metastatic disease and intermediate prognosis (5-year OS 72.9%), including neuroblastoma, Wilms tumor, non-rhabdomyosarcoma soft tissue sarcoma, and germ cell tumor; and group C, with locally or regionally advanced, or metastatic disease and poor prognosis (5-year OS 45%), including brain tumors, rhabdomyosarcoma, and bone sarcoma. The functional relationship between symptom interval and mortality risk differed between the three subgroups, there being no association in group A (hazard ratio [HR]: 0.96), a positive linear association in group B (HR: 1.48), and a negative linear association in group C (HR: 0.61).

CONCLUSIONS

Our analysis suggests that at least a subset of patients can benefit from an earlier diagnosis in terms of survival. For others, intrinsic aggressiveness may mask the potential effect of diagnostic delays. Based on these findings, early diagnosis should remain a goal for pediatric cancer patients.

Clinical outcomes of intermediate risk metastatic germ cell tumors (IRGCT): Results from a single-institution series

380

Background: IRGCT comprises a consistent category of metastatic patients (pts), and information on the recommended management of these pts should be updated. Usually they enter clinical trials for poor prognosis GCT. We aimed to address the heterogeneity of this category and to identify clinical prognostic factors for sub-stratification of pts. Methods: Data on consecutive pts with IRGCT and who received treatment at Fondazione INT Milano in the time-frame 02/1980-03/2014 were collected. Cox regression analyses were done evaluating potential prognostic factors for overall survival (OS, primary endpoint) to first-line therapy. Each factor was evaluated in a multivariable model. An exploratory OS comparison between outlier groups was undertaken with Kaplan Meier curves and logrank test. Results: Data on 181 pts were collected. Median age was 27 yrs (IQR 22-32), 10 pts had a retroperitoneal (RP) primary, 6 had pure seminoma. 72 (39.8%) had lung metastases and 54 (32.3%) bulky (i.e. ≥10cm) RP lymph-nodes (LN). Pts received cisplatin, bleomycin and etoposide (PEB, n=156) or vinblastine (PVB, n=23), 2 other treatments. Median follow up was 173 months (IQR: 87-237). Globally, 5-y PFS and OS were 66.8% (95%CI: 60.1-74.2) and 83.3% (77.8-89.2). However, 5-y OS for pts with AFP 5,000-10,000 IU/ml (N=19) was 61.8% (95%CI: 43.0-88.7) while it was 89.1% (95%CI: 81.2-97.7) for nonseminomas with elevated LDH only (N=57) and similar for elevated HCG only (N=22); overall p<0.001. Multivariable analysis for OS is shown in the table (c-index= 0.63). Distribution of variables over time: bulky RP LN and elevated LDH were more frequent in earlier series (p=0.003 and 0.011). Conclusions: The prognostic heterogeneity of IRGCT category is a matter of fact and should be addressed by clinical trials. Pts with highly elevated AFP have an OS similar to poor prognostic category, while those categorized by elevated HCG or LDH only are close to good risk ones. [Table: see text]

Spatiotemporal and Ecological Patterns of Mycobacterium microti Infection in Wild Boar (Sus scrofa)

Mycobacterium microti has recently been described as the causative agent of tuberculosis-like lesions in wild boar (Sus scrofa), a reservoir specie of Mycobacterium tuberculosis complex (MTBC) in some European Mediterranean ecosystem. Through a five-year survey on tuberculosis in free-living wild boars, the epidemiological trend of M. microti infections and the host and population risk factors linked with its occurrence were described. Retropharyngeal and mandibular lymph nodes of 3041 hunted wild boars from six different districts were macroscopically inspected. The sex and age of each animal were registered, as well as the animal abundance in each district. Lesions compatible with tuberculosis (190) were collected and analysed using a gyrB PCR-RFLP assay. M. microti was identified directly in 99 tissue samples (Prev = 3.26%; 95% CI: 2.67-3.97%), while neither Mycobacterium bovis, nor other members of the MTBC were detected. The probability of being M. microti positive showed spatio-temporal variability, with 26% of increase of risk of being infected for each year. Moreover, a positive effect of wild boar abundance and age on the prevalence was detected. The generalized increase in the European wild boar population, coupled with its sensitivity to M. microti infection, poses a future concern for the identification and management of MTBC members in wild boar.

Isolation and identification of Salmonella spp. from red foxes (Vulpes vulpes) and badgers (Meles meles) in northern Italy

Background

Salmonella spp. have been isolated from a wide range of wild animals. Opportunistic wild carnivores such as red foxes (Vulpes vulpes) and badgers (Meles meles) may act as environmental indicators or as potential sources of salmonellosis in humans. The present study characterizes Salmonella spp. isolated from the intestinal contents of hunted or dead red foxes (n = 509) and badgers (n = 17) in northern Italy.

Findings

Thirty-one strains of Salmonella belonging to 3 Salmonella enterica subspecies were isolated. Fourteen different serovars of S. enterica subsp. enterica were identified, among which were serovars often associated with human illness.

Conclusions

Wild opportunistic predators can influence the probability of infection of both domestic animals and humans through active shedding of the pathogen to the environment. The epidemiological role of wild carnivores in the spread of salmonellosis needs to be further studied.