Characterization of Depressive Symptoms Trajectories After Breast Cancer Diagnosis in Women in France

IMPORTANCE

Breast cancer (BC) diagnosis and treatment expose patients to a 5-fold higher risk of depression compared with the general population, with an estimated prevalence of 10% to 25%. A depressive episode in patients with BC has implications for the tolerance of and adherence to treatment, impairing quality of life and reducing life expectancy.

OBJECTIVE

To identify and characterize distinct longitudinal patterns of depressive symptoms in patients with BC from diagnosis to 3 years after treatment.

DESIGN, SETTINGS, AND PARTICIPANTS

The CANTO-DEePRESS (Deeper in the Understanding and Prevention of Depression in Breast Cancer Patients) cohort study included women in the French multicenter CANTO (CANcer TOxicities) cohort study (conducted between March 20, 2012 and December 11, 2018), who were 18 years or older with invasive stage I to III BC and no previous BC treatment. The study aimed to characterize toxicities over a 5-year period following stage I to III primary BC treatment. Assessments of depressive symptoms were performed on a subset of patients with available data at diagnosis and at least 2 other time points. All data were extracted from the CANTO database on October 1, 2020.

MAIN OUTCOMES AND MEASURES

The primary outcome was the level of depressive symptoms at each assessment time point measured with the Hospital Anxiety and Depression Scale and depression subscale at BC diagnosis and at 3 to 6, 12, and 36 months after the end of treatment. The group-based trajectory modeling was used to identify trajectory groups, and multinomial logistic regression models were used to characterize the following factors associated with trajectory group affiliation: demographic, socioeconomic, clinical, lifestyle, and quality-of-life data.

RESULTS

A total of 4803 women (mean [SD] age, 56.2 [11.2] years; 2441 patients [50.8%] with stage I BC) were included in the study. Six trajectory groups that described the heterogeneity in the expression of depressive symptoms were identified: noncases with no expression of symptoms (n = 2634 [54.8%]), intermediate worsening (1076 [22.4%]), intermediate improvement (480 [10.0%]), remission (261 [5.4%]), delayed occurrence (200 [4.2%]), and stable depression (152 [3.2%]). HADS-D scores at diagnosis were consistently associated with the 5 depressive trajectory group affiliations, with an estimated higher probability per point increase of experiencing subthreshold or clinically significant depressive symptoms between diagnosis and the 3 years after the end of BC treatment. The higher probabilities ranged from 1.49 (95% CI, 1.43-1.54) for the intermediate worsening group to 10.53 (95% CI, 8.84-12.55) for the stable depression group. Trajectory groups with depressive symptoms differed from the noncases group without symptoms by demographic and clinical factors, such as having dependent children, lower household income, cancer stage, family history of BC, previous psychiatric hospitalizations, obesity, smoking status, higher levels of fatigue, and depression at diagnosis.

CONCLUSIONS AND RELEVANCE

In this cohort study, nearly a third of patients with BC experienced temporary or lasting significant depressive symptoms during and after treatment. Improving early identification of women at risk of developing long-term or delayed depression is therefore critical to increase quality of life and overall survival. Subjected to validation, this study is an important first step toward personalized care of patients with BC at risk of depression.

Prognostic Value of Stromal Tumor-Infiltrating Lymphocytes in Young, Node-Negative, Triple-Negative Breast Cancer Patients Who Did Not Receive (neo)Adjuvant Systemic Therapy

PURPOSE

Triple-negative breast cancer (TNBC) is considered aggressive, and therefore, virtually all young patients with TNBC receive (neo)adjuvant chemotherapy. Increased stromal tumor-infiltrating lymphocytes (sTILs) have been associated with a favorable prognosis in TNBC. However, whether this association holds for patients who are node-negative (N0), young (< 40 years), and chemotherapy-naïve, and thus can be used for chemotherapy de-escalation strategies, is unknown.

METHODS

We selected all patients with N0 TNBC diagnosed between 1989 and 2000 from a Dutch population–based registry. Patients were age < 40 years at diagnosis and had not received (neo)adjuvant systemic therapy, as was standard practice at the time. Formalin-fixed paraffin-embedded blocks were retrieved (PALGA: Dutch Pathology Registry), and a pathology review including sTILs was performed. Patients were categorized according to sTILs (< 30%, 30%-75%, and ≥ 75%). Multivariable Cox regression was performed for overall survival, with or without sTILs as a covariate. Cumulative incidence of distant metastasis or death was analyzed in a competing risk model, with second primary tumors as competing risk.

RESULTS

sTILs were scored for 441 patients. High sTILs (≥ 75%; 21%) translated into an excellent prognosis with a 15-year cumulative incidence of a distant metastasis or death of only 2.1% (95% CI, 0 to 5.0), whereas low sTILs (< 30%; 52%) had an unfavorable prognosis with a 15-year cumulative incidence of a distant metastasis or death of 38.4% (32.1 to 44.6). In addition, every 10% increment of sTILs decreased the risk of death by 19% (adjusted hazard ratio: 0.81; 95% CI, 0.76 to 0.87), which are an independent predictor adding prognostic information to standard clinicopathologic variables (χ² = 46.7, P < .001).

CONCLUSION

Chemotherapy-naïve, young patients with N0 TNBC with high sTILs (≥ 75%) have an excellent long-term prognosis. Therefore, sTILs should be considered for prospective clinical trials investigating (neo)adjuvant chemotherapy de-escalation strategies.

Young cancer patients with TNBC and high sTILs have an excellent outcome, even without systemic treatment

Long-Term Longitudinal Patterns of Patient-Reported Fatigue After Breast Cancer: A Group-Based Trajectory Analysis

PURPOSE

Fatigue is recognized as one of the most burdensome and long-lasting adverse effects of cancer and cancer treatment. We aimed to characterize long-term fatigue trajectories among breast cancer survivors.

METHODS

We performed a detailed longitudinal analysis of fatigue using a large ongoing national prospective clinical study (CANcer TOxicity, ClinicalTrials.gov identifier: NCT01993498) of patients with stage I-III breast cancer treated from 2012 to 2015. Fatigue was assessed at diagnosis and year 1, 2, and 4 postdiagnosis. Baseline clinical, sociodemographic, behavioral, tumor-related, and treatment-related characteristics were available. Trajectories of fatigue and risk factors of trajectory-group membership were identified by iterative estimates of group-based trajectory models.

RESULTS

Three trajectory groups were identified for severe global fatigue (n = 4,173). Twenty-one percent of patients were in the high-risk group, having risk estimates of severe global fatigue of 94.8% (95% CI, 86.6 to 100.0) at diagnosis and 64.6% (95% CI, 59.2 to 70.1) at year 4; 19% of patients clustered in the deteriorating group with risk estimates of severe global fatigue of 13.8% (95% CI, 6.7 to 20.9) at diagnosis and 64.5% (95% CI, 57.3 to 71.8) at year 4; 60% were in the low-risk group with risk estimates of 3.6% (95% CI, 2.5 to 4.7) at diagnosis and 9.6% (95% CI, 7.5 to 11.7) at year 4. The distinct dimensions of fatigue clustered in different trajectory groups than those identified by severe global fatigue, being differentially affected by sociodemographic, clinical, and treatment-related factors.

CONCLUSION

Our findings highlight the multidimensional nature of cancer-related fatigue and the complexity of its risk factors. This study helps to identify patients with increased risk of severe fatigue and to inform personalized interventions to ameliorate this problem.

Individual-specific networks for prediction modelling – A scoping review of methods

Background

Recent advances in biotechnology enable the acquisition of high-dimensional data on individuals, posing challenges for prediction models which traditionally use covariates such as clinical patient characteristics. Alternative forms of covariate representations for the features derived from these modern data modalities should be considered that can utilize their intrinsic interconnection. The connectivity information between these features can be represented as an individual-specific network defined by a set of nodes and edges, the strength of which can vary from individual to individual. Global or local graph-theoretical features describing the network may constitute potential prognostic biomarkers instead of or in addition to traditional covariates and may replace the often unsuccessful search for individual biomarkers in a high-dimensional predictor space.

Methods

We conducted a scoping review to identify, collate and critically appraise the state-of-art in the use of individual-specific networks for prediction modelling in medicine and applied health research, published during 2000–2020 in the electronic databases PubMed, Scopus and Embase.

Results

Our scoping review revealed the main application areas namely neurology and pathopsychology, followed by cancer research, cardiology and pathology (N = 148). Network construction was mainly based on Pearson correlation coefficients of repeated measurements, but also alternative approaches (e.g. partial correlation, visibility graphs) were found. For covariates measured only once per individual, network construction was mostly based on quantifying an individual’s contribution to the overall group-level structure. Despite the multitude of identified methodological approaches for individual-specific network inference, the number of studies that were intended to enable the prediction of clinical outcomes for future individuals was quite limited, and most of the models served as proof of concept that network characteristics can in principle be useful for prediction.

Conclusion

The current body of research clearly demonstrates the value of individual-specific network analysis for prediction modelling, but it has not yet been considered as a general tool outside the current areas of application. More methodological research is still needed on well-founded strategies for network inference, especially on adequate network sparsification and outcome-guided graph-theoretical feature extraction and selection, and on how networks can be exploited efficiently for prediction modelling.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01544-6.

A phase III randomized trial of weight loss to reduce cancer-related fatigue among overweight and obese breast cancer patients: MEDEA Study design

Background

Elevated body mass index (BMI) represents a risk factor for cancer-related fatigue (CRF). Weight loss interventions are feasible and safe in cancer survivors, leading to improved cardio-metabolic and quality of life (QOL) outcomes and modulating inflammatory biomarkers. Randomized data are lacking showing that a lifestyle intervention aimed at weight loss, combining improved diet, exercise, and motivational counseling, reduces CRF. Motivating to Exercise and Diet, and Educating to healthy behaviors After breast cancer (MEDEA) is a multi-center, randomized controlled trial evaluating the impact of weight loss on CRF in overweight or obese survivors of breast cancer. Herein, we described the MEDEA methodology.

Methods

Patients (N = 220) with stage I–III breast cancer and BMI ≥ 25 kg/m², within 12 months of primary treatment, and able to walk ≥ 400 m are eligible to enroll. Participants are randomized 1:1 to health education alone vs. a personalized telephone-based weight loss intervention plus health education. Both arms receive a health education program focusing on healthy living. Patients in the intervention arm are paired with an individual lifestyle coach, who delivers the intervention through 24 semi-structured telephone calls over 1 year. Intervention goals include weight loss ≥ 10% of baseline, caloric restriction of 500–1000 Kcal/day, and increased physical activity (PA) to 150 (initial phase) and 225–300 min/week (maintenance phase). The intervention is based on the social cognitive theory and is adapted from the Breast Cancer Weight Loss trial (BWEL, A011401). The primary endpoint is the difference in self-reported CRF (EORTC QLQ-C30) between arms. Secondary endpoints include the following: QOL (EORTC QLQ-C30, -BR45, -FA12), anxiety, and depression (HADS); weight and BMI, dietary habits and quality, PA, and sleep; health care costs (hospital-admissions, all-drug consumption, sick leaves) and cost-effectiveness (cost per quality-adjusted life-year); and patient motivation and satisfaction. The primary analysis of MEDEA will compare self-reported CRF at 12 months post-randomization between arms, with 80.0% power (two-sided α = 0.05) to detect a standardized effect size of 0.40.

Discussion

MEDEA will test the impact of a weight loss intervention on CRF among overweight or obese BC survivors, potentially providing additional management strategies and contributing to establish weight loss support as a new standard of clinical care.

Trial registration

ClinicalTrials.govNCT04304924

Abstract P4-11-01: Development of a clinico-bio-behavioral model for cancer-related fatigue (CRF) incorporating inflammatory biomarkers and proteomic data

Background: We previously developed a clinico-behavioral model of CRF and reported an increased risk of severe CRF among survivors of breast cancer (BC) receiving adjuvant hormonal therapy (HT) (Di Meglio A, ASCO 2021). We now aim to comprehensively explore the contribution of relevant serum proteins in explaining CRF. We adopted a multimodal approach, both (1) hypothesis-driven, based on the rationale that deregulation of systemic inflammatory processes and mediators of immunologic or neuroendocrine activation are associated with vulnerability to CRF, and (2) discovery-driven, based on proteomic analyses. Methods: Women with stage I-III HR+/HER2- tumors receiving HT (N=1153) were included from the multicenter, prospective CANTO cohort (NCT01993498). The primary outcome of interest was severe post-treatment global CRF at year-2 (Y2) after diagnosis (score ≥ 40/100, EORTC QLQ-C30). Secondary outcomes included CRF dimensions (physical, emotional, cognitive; EORTC QLQ-FA12). For the hypothesis-driven analyses, pre-treatment blood samples were profiled (Randox Laboratories Limited, UK) at diagnosis of BC, using a multi-biomarker panel assessing IL6, TNFα, IL1RA, CRP, IL2, IL1β, IFNγ, IL10, IL1A, IL4, and IL8. Pre-specified pre-treatment clinico-behavioral covariates (age, BMI, smoking status, psychological, and pre-treatment symptom burden, based on previously developed models) were forced into a multivariable logistic regression. Biomarkers were retained by Augmented Backwards Elimination (p&lt;0.05) only if significantly associated with CRF. For the discovery approach, we used hyper-reaction monitoring mass spectrometry for the unbiased quantification of all detectable peptides and proteins in human plasma samples at diagnosis (Biognosys, CH), among a discovery subset (N=462). We then aimed to identify a proteomic signature associated with severe CRF at Y2. Log-transformed protein intensities were analyzed in terms of differential expression. The proteins that were identified to be significantly different among the patients reporting and not reporting severe CRF were then used to train a logistic regression model. Results: Prevalence of severe global CRF increased from 21.6% at diagnosis to 34.8% at Y2. In the final model, higher pre-treatment levels of IL6 and lower levels of IFNγ and IL10 were significant predictors of severe global CRF at Y2 (Table). The AUC of this clinico-bio-behavioral model was 0.78 (95%CI 0.75 - 0.82) and was suggestive of an improved performance as compared to clinico-behavioral models. Among CRF dimensions, a significant association emerged only between CRP and severe cognitive CRF (outcome prevalence at Y2 14.2%; adjusted OR per CRP log-unit increase 1.40 [95%CI 1.01-1.93]).

In the discovery subset, several proteins were identified as differentially regulated (p&lt;0.05) among patients reporting and not reporting severe CRF at Y2. Most of these were related to coagulation pathways (including C4BPA, C4BPB, HABP2, PLF4, PROS). However, models incorporating proteomic data did not seem to augment the predictive ability compared to clinico-behavioral models. Conclusions: Using clinical and biological pre-treatment measurements, it may be possible to identify a subset of BC patients at high risk for increased post-treatment CRF while on HT. This provides the possibility of testing dedicated preventive interventions.

Table. Clinico-bio-behavioral model of pre-treatment predictors of severe global CRF at Y2, incorporating circulating inflammatory biomarkers.Adjusted OR§ (95% CI)Age, per additional 1 year0.98 (0.96-0.99)BMI, per additional unit1.02 (0.99-1.06)Current smoker, vs never2.27 (1.47-3.51)Former smoker, vs never0.97 (0.64-1.46)Anxiety case*, vs normal1.13 (0.75-1.70)Doubtful anxiety*, vs normal1.11 (0.73-1.68)Pre-treatment Insomnia**, per additional 10 points1.09 (1.04-1.15)Pre-treatment Pain**, per additional 10 points1.10 (1.01-1.18)Severe pre-treatment CRF**, vs no4.70 (3.13-7.05)IL6***1.72 (1.25-2.36)IL1RA***1.24 (0.85-1.81)IL2***1.43 (0.99-2.08)IFNγ***0.54 (0.30-0.95)IL10***0.40 (0.18-0.87)IL4***1.47 (0.67-3.20)IL8***1.15 (0.83-1.60)OR= Odds Ratio; CI= Confidence Interval; §by all factors in Table; *HADS; **QLQ-C30; ***per log-unit increase

Citation Format: Antonio Di Meglio, Stergios Christodoulidis, Davide Soldato, Antonin Della Noce, Daniele Presti, Julie Havas, Florine Dubuisson, Barbara Pistilli, Valerie Camara-Clayette, Cecile Charles, Patricia A Ganz, Julienne Bower, Ann H Partridge, Alexandra Jacquet, Sibille Everhard, Sandrine Boyault, Fabrice André, Paul-Henry Cournede, Stefan Michiels, Caroline Pradon, Ines Vaz-Luis. Development of a clinico-bio-behavioral model for cancer-related fatigue (CRF) incorporating inflammatory biomarkers and proteomic data [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P4-11-01.

Abstract P4-11-34: An integrated clinical, behavioral and biological model to predict the risk of weight gain among breast cancer survivors (BCS)

Background: Weight management is an integral part of survivorship care. Excess weight in BCS is associated with worse clinical outcomes and quality of life. Early identification of BCS at risk of gaining substantial weight could lead to prompt and tailored interventions. We aimed at developing a predictive model of weight gain that integrates clinical, behavioral and biological data. Methods: We included patients with stage I-III BC from the CANTO cohort (NCT01993498). CANTO collects longitudinal data, including objective anthropometric measures, at diagnosis (dx), 1 (T1), 2 (T2) and 4 (T3) years after dx. In addition, profiling of blood samples obtained at dx was performed for two sub-cohorts with HR+/HER2- BC for quantification of: (1) inflammatory and metabolic biomarkers (IL6, TNFα, IL1RA, CRP, IL2, IL1β, IFNγ, IL10, IL1A, IL4, IL8, ADPN, LEPT, INS, RETN) and (2) detectable proteins using hyper reaction mass spectrometry (Biognosys). Our outcome of interest was weight gain (increase ≥ 5%) compared to dx. First, multivariable logistic regression with bootstrapped Augmented Backwards Elimination (ABE) retained associations between weight gain and clinico-behavioral covariates. To assess contribution of biologic data, ABE retained associations between weight gain and biomarkers, correcting for significant covariates. Models were validated using internal cross-validation and overoptimism-correction. For proteomics, proteins relative intensity was calculated, and a bootstrapped differential protein expression analysis identified proteins associated with weight gain that were then included in logistic regression. Models performance was assessed in terms of Area Under the Curve (AUC). Results: In the overall cohort (N=9541) mean age was 56.8 (SD 11.4), mean BMI was 25.9 Kg/m2 (SD 5.4), 48.9% of pts were overweight or obese, and 52.9% received chemotherapy (CT). Overall, 16.9% (T1), 23.4% (T2), and 27.2% (T3) BCS gained weight (absolute mean change (95% CI): 6.1 kg (5.9-6.2), 6.7 kg (6.5-6.9) and 7.2 kg (6.9-7.3) at T1, T2, T3, respectively). In clinico-behavioral models, younger age, current smoking, lower income and education, receipt of CT and radiotherapy were associated with increased risk of weight gain (Table). Among 1261 BCS with biomarkers data, higher levels of IL1α (OR for 1-unit log increase [95%CI] 0.11 [0.02 - 0.65]) and of ADPN (1.36 [1.01 - 1.85]) were associated with lower and higher risk of weight gain at T2 and T3, respectively. Performance of models integrating these biomarkers was similar to clinico-behavioral models. Among 462 BCS with proteomic profiling, preliminary data showed that higher relative abundance of IgG Fc Binding Protein (OR 0.44, p&lt;.05) and Tubulin-1 (OR 0.73, p&lt;.05) was associated with lower risk of weight gain at T1. AUC of model integrating clinical and proteomics data was 0.74 (0.58-0.90). Conclusions: Over one-in-four BCS in the CANTO cohort experienced meaningful weight gain 4 years after dx. This large, multidimensional study confirms the role of clinico-behavioral risk factors for weight gain. However, the predictive ability of clinico-behavioral models seems suboptimal. The exploitation of additional data dimensions, including serum proteins and proteomic data, may help improve predictive ability and inform underlying biological processes implicated in weight gain after BC. Further studies will aim at improving model stability, particularly for proteomics analyses.

Table. Models of weight gain in the overall cohort.T1 (N= 8397)T2 (N= 7663)T3 (N= 5802)Clinical predictors OR* (95% CI)OR* (95% CI)OR* (95% CI)Age, 1-year increase0.96 (0.94 - 0.97)0.96 (0.95 - 0.97)0.96 (0.95 - 0.97)BMI, 1-unit increaseNRNS0.97 (0.94 - 0.99)Education, primary vs collegeNS1.57 (1.04 - 2.39)NREducation, high school vs college1.38 (1.04 - 1.83)1.54 (1.21 - 1.98)NRIncome, ≥ 1500 and &lt;3000 vs &gt;3000NRNR1.29 (1.00 - 1.66)Smoke, current vs never1.70 (1.24 - 2.33)NR1.53 (1.12 - 2.08)Chemotherapy, yes vs no1.40 (1.07 - 1.82)1.31 (1.01 - 1.69)NRRadiotherapy, yes vs no2.10 (1.10 - 3.99)NR1.83 (1.08 - 3.12)AUC (95% CI) - clinical models0.65 (0.63 - 0.68)0.64 (0.61 - 0.67)0.65 (0.63 - 0.68)AUC (95% CI) - clinical + inflammatory and metabolic biomarkers models, [N]0.65 (0.60 - 0.70), [1179]0.66 (0.62 - 0.70), [948]0.67 (0.63 - 0.71), [1017]AUC (95% CI) - clinical and proteomics models, [N]0.74 (0.58 - 0.90), [462]0.65 (0.50 - 0.81), [462]NEOR= Odds Ratio, CI= Confidence Interval, NR= Not Retained, NS= Not significant, NE= Not evaluated *Adjusted by age, menopause, smoke, socioeconomic, psychological, tumor and treatments **Significant covariates from previous models were forced and ABE selected significant variables among all circulating biomarkers.

Citation Format: Davide Soldato, Antonio Di Meglio, Caroline Pradon, Antonin Della Noce, Daniele Presti, Julie Havas, Florine Dubuisson, Barbara Pistilli, Valerie Camara-Clayette, Fabrice André, Alexandra Jacquet, Sibille Everhard, Sandrine Boyault, Paul-Henry Cournede, Stefan Michiels, Ines Vaz-Luis, Stergios Christodoulidis. An integrated clinical, behavioral and biological model to predict the risk of weight gain among breast cancer survivors (BCS) [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P4-11-34.

Abstract P4-07-17: Association between plasma-based sequential windowed acquisition mass spectrometry (SWATH-MS) and invasive disease free survival (iDFS) in HR+/HER2- early breast cancer in the CANTO cohort

Background: The definition of breast cancer (BC) prognosis has historically relied on clinico-pathological factors. Novel omics markers including proteomic analyses could improve our understanding of the biological host drivers of breast cancer recurrence and survival. We aimed at identifying patients (pts) at high risk of recurrence based on proteomic markers in plasma.Methods: CANTO is a multicenter, prospective cohort study of stage I-III BCS (NCT01993498). Plasma samples were collected on HR+/HER2- pts at diagnosis (dx) and analyzed by SWATH-MS, implemented by Biognosys AG (Schlieren, Switzerland), resulting in a relative quantification of the abundance of 500 proteins in the plasma. A Cox model was fitted to estimate to associate proteomic and clinical variables with the primary endpoint IDFS Clinical covariates consisted of age, stage and grade. An adaptive Lasso method was used to perform model selection. The discrimination performances of the model were assessed on 100 random train-test partitions of the cohort. Results: There were 457 pts with analyzed plasma samples. The median age at dx was 59.3 years, and the repartition of cancer stage was 52% for stage I, 37% for stage II and 11% for stage III. The mean duration of follow-up was 5.4 years, and 53 (11.5%) IDFS events (non local recurrences, second primary cancers and deaths) were reported. In total, 7 proteins were selected by the adaptive Lasso process; associated with the age, the stage and the grade at dx, 3 proteins were retained as having a significant impact on the IDFS: GTP-binding nuclear protein Ran (RAN), involved in cell division and GTP metabolic process, C4b-binding protein alpha-chain (C4BPA), involved in complement activation, and prothrombin (THRB), involved in acute-phase response and blood activation. Concordance indices were computed on 100 random test subsets of the cohort for the model with clinical variables only (0.67+/- 0.08), for the model with selected protein features only (0.74 +/- 0.07) and for the model with both proteomic and clinical covariates (0.75 +/-0.06). Conclusion: The discrimination performances of the estimated model suggest that proteomics provide relevant markers associated with BC prognosis. Validation on an independent validation set is required. Host related plasma proteins represent an avenue worth exploring to improve our understanding of BC relapse risk

Table 1.Estimated hazard ratios of the linear Cox model.FeaturesHR* (95% CI)p-valuesRAN (for 1 SD increase)0.66 (0.51-0.85)&lt;0.005THRB (for 1 SD increase)1.43 (0.99-2.06)0.05C4BPA (for 1 SD increase)1.44 (1.02-2.02)0.04stage--II vs I1.68 (0.82-3.46)0.16III vs I4.29 (1.88-9.75)&lt;0.005HR = hazard ratio CI = confidence interval * adjusted by age and grade

Citation Format: Antonin Della Noce, Stergios Christodoulidis, Antonio Di Meglio, Julie Havas, Alicia Tran-Dien, Fabrice André, Ines Vaz-Luis, Paul-Henry Cournède, Stefan Michiels. Association between plasma-based sequential windowed acquisition mass spectrometry (SWATH-MS) and invasive disease free survival (iDFS) in HR+/HER2- early breast cancer in the CANTO cohort [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P4-07-17.

Abstract OT2-10-02: Mypebs: An international randomized study comparing personalized, risk-stratified to standard breast cancer screening in women aged 40-70

Background Currently, mammographic-based breast cancer screening (BCS) using age as the single criterion for population selection, apart from rare high-risk indications, is being questioned for its imperfect sensitivity (interval cancers) and specificity (false positive recalls), as well as the risk of over-diagnoses. BC risk scores incorporating personal and family history, breast mammographic density and genetic information based on a polygenic score (PRS) give a promisingly accurate likelihood of a woman developing invasive BC in the next 5 years. MyPeBS, a European Commission H2020-funded randomized clinical trial (NCT03672331) conducted in 6 countries (Belgium, France, Israel, Italy, Spain and UK) aims to demonstrate the usefulness of a risk-based screening approach to improve BCS in the general population. Methods MyPeBS’s primary objective is to show non-inferiority of the risk-stratified BCS arm in terms of incidence rate of breast cancer of stage 2 and higher, compared to the standard BCS arm. The key secondary objective, if non-inferiority is shown, is to demonstrate superiority of the risk-based screening arm. MyPeBS also assesses whether this strategy allows reduces morbidity (less false positives); is equally or more cost-effective, and impacts women’s understanding, awareness and emotional responses as compared to standard of care. Women aged 40 to 70 are eligible if they did not have prior BC or high risk-condition, and live in a participating country and area. Participants are randomized 1:1 between standard BCS according to country’s/region’s ongoing policy, or risk-stratified BCS, where screening frequency and method depend on their individual 5 year predicted risk of invasive BC (Table 1). Stratification factors include age, prior mammogram and country. Risk assessment uses a centralized genotyping on a saliva sample to assess PRS 313 (Mavaddat et al, Am J Hum Genet 2019), which is embedded in either a BCSC-derived score for women with at most one first-degree relative with BC; or Tyrer-Cuzick score for women with &gt; 1 family member with breast cancer. Women participate for 4 years. Planned accrual is 85000. On June 30th, 2021, 13882 women have been randomized.

Table 1.BC Screening schedule in MyPeBS’ Risk-based armRisk levelLow riskAverage riskHigh riskVery high riskNumerical definition (invasive breast cancer risk at 5 years)&lt;1%1-1.66%≥ 1.66% and &lt; 6%≥ 6%Mammogram1 at end of study (4 years)Every 2 yearsYearlyYearlyAdditionalYearly breast cancer awareness reminderHigh density: US or ABUS every 2 yearsHigh density: US or ABUS every 2 yearsAnnual MRI until age 60

Citation Format: Suzette Delaloge, Paolo Giorgi Rossi, Michal Guindy, Fiona Gilbert, Jean-Benoit Burrion, Corinne Balleyguier, Marta Roman Exposito, Livia Giordano, Harry De Koning, Sandrine de Montgolfier, Stephane Ragusa, Damien Drubay, Marie-Eve Rouge-Bugat, Gareth D Evans, Debbie Keatley, Helene Blanche, Anne Boland, Emilien Gauthier, Aloys Dubois d'Aische, Cécile Vissac-Sabatier, Daniel Couch, Camille Baron, Jean-François Deleuze, Paul Pharoah, Stefan Michiels. Mypebs: An international randomized study comparing personalized, risk-stratified to standard breast cancer screening in women aged 40-70 [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr OT2-10-02.

Abstract P4-11-09: Cancer-related cognitive impairment (CRCI) in early breast cancer (BC) survivors

Background: Up to 35% BC survivors who receive adjuvant treatment (tx) experience severe CRCI, which has a significant impact on quality of life, disrupting daily functioning as well as self-esteem, self-confidence, and work ability. However, limited tools exist to predict the risk of CRCI. We aimed to develop a comprehensive model of severe CRCI, including clinical and serum inflammatory protein data. Methods: We included 8875 patients (pts) with stage I-III BC from the multicenter, prospective CANTO cohort (NCT01993498). Longitudinal data were collected at diagnosis (dx), 1 (T1), 2 (T2) and 4 (T3) years post-dx. Our outcome of interest was severe cognitive impairment at T1, T2, and T3 (score &lt; 75/100, EORTC QLQ-C30, Giesinger JM 2020). Multivariable logistic regression models retained associations between baseline clinical variables (sociodemographic, psychological, tumor, and tx-related) with severe CRCI by bootstrapped Augmented Backwards Elimination (ABE). Among a subset of patients with HR+/HER2- BC (N= 1151), blood samples were profiled at dx using a multi-biomarker inflammatory panel assessing IL6, TNFα, IL1RA, CRP, IL2, IL1β, IFNγ, IL10, IL1A, IL4, IL8, and monocyte chemoattractant protein-1 (MCP-1). All biomarkers were incorporated simultaneously into a model of severe CRCI and retained only if significantly associated with CRCI by ABE (p&lt;0.05). Previously retained clinical associations were forced into the model. Results: In the overall cohort, mean age at dx was 56.7 years (SD 11.3), and 52.7% and 81.3% of pts received chemotherapy and hormonal therapy, respectively. Prevalence rates of severe CRCI were 31.2% (dx), 31.4% (T1), 30.9% (T2), and 29.9% (T3). Severe post-tx CRCI was consistently associated with severe pre-tx pain and severe pre-tx CRCI. Severe pre-tx fatigue, younger age, anxiety symptoms and hot flashes at dx were also associated with increased odds of severe CRCI at some post-dx time-points (Table 1). Models Area Under the Curve (AUC) were 0.73 (95% confidence intervals [CI] 0.70-0.76) at T1, 0.69 (CI 0.65-0.72) at T2, and 0.68 (CI 0.63-0.72) at T3. Among pts with available serum biomarkers, no significant associations were observed between inflammatory proteins and CRCI at any time point. Performance of models incorporating inflammatory biomarkers was similar to clinical-only models (Table 2). Conclusions: Almost 1/3 of BC survivors in this cohort reported severe CRCI. This rate was stable throughout the survivorship period and did not seem to be affected by cancer-specific or tx-related factors, or inflammatory biomarkers. Pts age and concomitant symptom burden at dx emerged as consistent associations with severe CRCI. A description of the average population risk of CRCI using a self-reported, global evaluation scale may not fully describe the granularity of this phenomenon. Further studies building on dedicated, objective measurements, may help identify latent classes of pts experiencing a major decline in cognitive function following BC tx, and for whom a contribution of biology may help explain inter-individual variability and underlying biological processes.

Table 1.Models of severe CRCI in the overall cohort: clinical predictors.T1 (N=7724)T2 (N=6825)T3 (N=4706)OR* (95% CI)OR* (95% CI)OR* (95% CI)Severe Pain**, vs no1.50 (1.09-2.07)1.93 (1.39-2.69)1.55 (1.03-2.34)Severe pre-tx CRCI**, vs no3.69 (2.70-5.05)2.53 (1.85-3.46)2.21 (1.47-3.32)Severe Fatigue**, vs no1.50 (1.06-2.11)1.61 (1.13-2.28)1.08 (0.69-1.70)Age (continous)0.98 (0.97-0.99)NR0.98 (0.96-0.99)Menopause, post- vs pre-NR0.73 (0.54-0.98)NRAnxiety, case vs normalNRNR1.82 (1.13-2.92)Anxiety, borderline vs normalNRNR1.84 (1.17-2.91)Hot flashes, vs no1.25 (0.92-1.69)1.20 (0.87-1.65)1.64 (1.10-2.43)Corrected AUC0.73 (0.70-0.76)0.69 (0.65-0.72)0.68 (0.63-0.72)OR= Odds Ratio, CI= Confidence Interval, NR= Not Retained; *Adjusted by BMI, alcohol, smoke, socioeconomic, psychological, tumor and tx; **QLQ-C30

Table 2.Models of severe CRCI in the overall cohort**: biological biomarkers.T1 (N=1094)T2 (N=1091)T3 (N=870)OR* (95% CI)OR* (95% CI)OR* (95% CI)IL6NR0.80 (0.46-1.40)1.01 (0.64-1.60)IL1RA0.66 (0.37-1.17)0.88 (0.50-1.55)NRCRP0.94 (0.60-1.48)1.44 (0.92-2.27)NRIL20.93 (0.55-1.57)1.10 (0.61-1.97)NRIL1βNR1.55 (0.71-3.40)NRIFNγ1.86 (0.69-5.01)0.75 (0.25-2.22)NRIL101.05 (0.34-3.27)1.27 (0.58-2.78)NRIL1A0.71 (0.15-3.33)0.80 (0.17-3.66)NRIL80.96 (0.58-1.58)NRNRTNFαNR1.35 (0.67-2.73)NRMCP-11.07 (0.64-1.78)0.83 (0.51-1.35)0.80 (0.48-1.31)Corrected AUC0.72 (0.67-0.77)0.70 (0.65-0.75)0.67 (0.62-0.72)OR= Odds Ratio, CI= Confidence Interval, NR= Not Retained; *Adjusted by BMI, alcohol, smoke, socioeconomic, psychological, tumor and tx; ** Clinical predictors from previous models were forced in the models at each time-point

Citation Format: Daniele Presti, Florence Joly, Davide Soldato, Stergios Christodoulidis, Antonin Della Noce, Julie Havas, Florine Dubuisson, Barbara Pistilli, Valerie Camara-Clayette, Fabrice André, Anne-Laure Martin, Alexandra Jacquet, Sandrine Boyault, Ivan Bièche, Charles Coutant, Paul-Henry Cournede, Stefan Michiels, Caroline Pradon, Ines Vaz-Luis, Antonio Di Meglio. Cancer-related cognitive impairment (CRCI) in early breast cancer (BC) survivors [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P4-11-09.

Uptake of Recommendations for Posttreatment Cancer-Related Fatigue Among Breast Cancer Survivors

BACKGROUND

Physical activity (PA) and psychosocial interventions are recommended management strategies for cancer-related fatigue (CRF). Randomized trials support the use of mind-body techniques, whereas no data show benefit for homeopathy or naturopathy.

METHODS

We used data from CANTO (ClinicalTrials.gov identifier: NCT01993498), a multicenter, prospective study of stage I-III breast cancer (BC). CRF, evaluated after primary treatment completion using the EORTC QLQ-C30 (global CRF) and QLQ-FA12 (physical, emotional, and cognitive dimensions), served as the independent variable (severe [score of ≥40/100] vs nonsevere). Outcomes of interest were adherence to PA recommendations (≥10 metabolic equivalent of task [MET] h/week [GPAQ-16]) and participation in consultations with a psychologist, psychiatrist, acupuncturist, or other complementary and alternative medicine (CAM) practitioner (homeopath and/or naturopath) after CRF assessment. Multivariable logistic regression examined associations between CRF and outcomes, adjusting for sociodemographic, psychologic, tumor, and treatment characteristics.

RESULTS

Among 7,902 women diagnosed from 2012 through 2017, 36.4% reported severe global CRF, and 35.8%, 22.6%, and 14.1% reported severe physical, emotional, and cognitive CRF, respectively. Patients reporting severe global CRF were less likely to adhere to PA recommendations (60.4% vs 66.7%; adjusted odds ratio [aOR], 0.82; 95% CI, 0.71-0.94; P=.004), and slightly more likely to see a psychologist (13.8% vs 7.5%; aOR, 1.29; 95% CI, 1.05-1.58; P=.014), psychiatrist (10.4% vs 5.0%; aOR, 1.39; 95% CI, 1.10-1.76; P=.0064), acupuncturist (9.8% vs 6.5%; aOR, 1.46; 95% CI, 1.17-1.82; P=.0008), or CAM practitioner (12.5% vs 8.2%; aOR, 1.49; 95% CI, 1.23-1.82; P<.0001). There were differences in recommendation uptake by CRF dimension, including that severe physical CRF was associated with lower adherence to PA (aOR, 0.74; 95% CI, 0.63-0.86; P=.0001) and severe emotional CRF was associated with higher likelihood of psychologic consultations (aOR, 1.37; 95% CI, 1.06-1.79; P=.017).

CONCLUSIONS

Uptake of recommendations to improve CRF, including adequate PA and use of psychosocial services, seemed suboptimal among patients with early-stage BC, whereas there was a nonnegligible interest in homeopathy and naturopathy. Findings of this large study indicate the need to implement recommendations for managing CRF in clinical practice.

Development and Validation of a Predictive Model of Severe Fatigue After Breast Cancer Diagnosis: Toward a Personalized Framework in Survivorship Care

PURPOSE

Fatigue is common and troublesome among breast cancer survivors; however, limited tools exist to predict its risk.

PATIENTS AND METHODS

Participants with stage I-III breast cancer were prospectively included from CANTO (ClinicalTrials.gov identifier: NCT01993498), collecting longitudinal data at diagnosis (before the initiation of any cancer treatment) and 1 (T1), 2 (T2), and 4 (T3) years after diagnosis. The main outcome was severe global fatigue at T2 (score ≥ 40/100, European Organisation for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire-C30). Analyses at T3 were exploratory. Secondary outcomes included physical, emotional, and cognitive fatigue (EORTC Quality of Life Questionnaire-FA12). Multivariable logistic regression models retained associations with severe fatigue by bootstrapped Augmented Backward Elimination. Validation methods included 10-fold internal cross-validation, overoptimism-corrected area under the receiver operating characteristic curves, and external validation.

RESULTS

Among 5,640, 5,000, and 3,400 patients at T1, T2, and T3, respectively, the prevalence of post-treatment severe global fatigue was 35.6%, 34.0%, and 31.5% in the development cohort. Retained risk factors for severe global fatigue at T2 were severe pretreatment fatigue (adjusted odds ratio v no 3.191 [95% CI, 2.704 to 3.767]); younger age (for 1-year decrement 1.015 [1.009 to 1.022]), higher body mass index (for unit increment 1.025 [1.012 to 1.038]), current smoking behavior (v never 1.552 [1.291 to 1.866]), worse anxiety (v noncase 1.265 [1.073 to 1.492]), insomnia (for unit increment 1.005 [1.003 to 1.007]), and pain at diagnosis (for unit increment 1.014 [1.010 to 1.017]), with an area under the receiver operating characteristic curve of 0.73 (95% CI, 0.72 to 0.75). Receipt of hormonal therapy was a risk factor for severe fatigue at T3 (v no 1.448 [1.165 to 1.799]). Dimension-specific risk factors included body mass index for physical fatigue and emotional distress for emotional and cognitive fatigue.

CONCLUSION

We propose a predictive model to assess fatigue among breast cancer survivors, within a personalized survivorship care framework. This may help clinicians to provide early management interventions or to correct modifiable risk factors and offer more tailored monitoring and education to patients at risk of severe post-treatment fatigue.

Role of peer support in a hepatitis C elimination programme

Many people with chronic hepatitis C infection don't engage in treatment. To eliminate hepatitis C and avoid health inequalities therapy must be provided to everyone. In other diseases peers with lived experience of the condition have improved care but, for hepatitis C, studies have not shown unequivocal benefit. We completed a retrospective analysis of the English National Health Service treatment registry comparing treatment networks with and without peers using Bayesian Poisson (for count outcomes) or Bayesian Binomial (for proportion outcomes) mixed effects models with time fixed effects. For each outcome, we estimated relative ratio (RR-Poisson model) or odds ratio (Odds Ratio (OR)-Binomial model) between peer and non-peer networks. We analysed 30,729 patients within 20 operational delivery networks. In networks with peers there was an increase in the number of people initiating therapy (RR 1.12 95%, credible interval 1.02-1.21) and an increase in the proportion completing therapy (OR 2.45 95%, credible interval 1.49-3.84). However, we saw no change in proportions of people using drugs who initiated therapy nor any significant change in virological response (OR 1.14 95% credible interval 0.979-1.36). We repeated the analysis looking at the impact of peers two months after they had been introduced, when they had established networks of contacts, and saw an increase in the proportion of people treated in addiction services. In treating patients with chronic hepatitis C infection the inclusion of peer supporters may increase the number of people who initiate and complete antiviral therapy.

The tale of TILs in breast cancer: A report from The International Immuno-Oncology Biomarker Working Group

The advent of immune-checkpoint inhibitors (ICI) in modern oncology has significantly improved survival in several cancer settings. A subgroup of women with breast cancer (BC) has immunogenic infiltration of lymphocytes with expression of programmed death-ligand 1 (PD-L1). These patients may potentially benefit from ICI targeting the programmed death 1 (PD-1)/PD-L1 signaling axis. The use of tumor-infiltrating lymphocytes (TILs) as predictive and prognostic biomarkers has been under intense examination. Emerging data suggest that TILs are associated with response to both cytotoxic treatments and immunotherapy, particularly for patients with triple-negative BC. In this review from The International Immuno-Oncology Biomarker Working Group, we discuss (a) the biological understanding of TILs, (b) their analytical and clinical validity and efforts toward the clinical utility in BC, and (c) the current status of PD-L1 and TIL testing across different continents, including experiences from low-to-middle-income countries, incorporating also the view of a patient advocate. This information will help set the stage for future approaches to optimize the understanding and clinical utilization of TIL analysis in patients with BC.

A frequentist one-step model for a simple network meta-analysis of time-to-event data in presence of an effect modifier

Introduction

Individual patient data (IPD) present particular advantages in network meta-analysis (NMA) because interactions may lead an aggregated data (AD)-based model to wrong a treatment effect (TE) estimation. However, fewer works have been conducted for IPD with time-to-event contrary to binary outcomes. We aimed to develop a general frequentist one-step model for evaluating TE in the presence of interaction in a three-node NMA for time-to-event data.

Methods

One-step, frequentist, IPD-based Cox and Poisson generalized linear mixed models were proposed. We simulated a three-node network with or without a closed loop with (1) no interaction, (2) covariate-treatment interaction, and (3) covariate distribution heterogeneity and covariate-treatment interaction. These models were applied to the NMA (Meta-analyses of Chemotherapy in Head and Neck Cancer [MACH-NC] and Radiotherapy in Carcinomas of Head and Neck [MARCH]), which compared the addition of chemotherapy or modified radiotherapy (mRT) to loco-regional treatment with two direct comparisons. AD-based (contrast and meta-regression) models were used as reference.

Results

In the simulated study, no IPD models failed to converge. IPD-based models performed well in all scenarios and configurations with small bias. There were few variations across different scenarios. In contrast, AD-based models performed well when there were no interactions, but demonstrated some bias when interaction existed and a larger one when the modifier was not distributed evenly. While meta-regression performed better than contrast-based only, it demonstrated a large variability in estimated TE. In the real data example, Cox and Poisson IPD-based models gave similar estimations of the model parameters. Interaction decomposition permitted by IPD explained the ecological bias observed in the meta-regression.

Conclusion

The proposed general one-step frequentist Cox and Poisson models had small bias in the evaluation of a three-node network with interactions. They performed as well or better than AD-based models and should also be undertaken whenever possible.

Individual patient data meta-analysis of neoadjuvant chemotherapy followed by surgery versus upfront surgery for carcinoma of the oesophagus or the gastro-oesophageal junction

INTRODUCTION

Which neoadjuvant treatment for locally advanced thoracic oesophagus (TE) or gastro-oesophageal junction carcinoma is best remains an open question. Randomised controlled trials variously accrued patients with adenocarcinoma and squamous cell carcinoma, making strong conclusions hard to obtain. The primary objective of this individual participant data meta-analysis was to investigate the effect of neoadjuvant chemotherapy on overall survival (OS).

PATIENTS AND METHODS

Eligible trials should have closed to accrual before 2016 and compared neoadjuvant chemotherapy and surgery (CS) to surgery alone. All relevant published and unpublished trials were identified via searches of electronic databases, conference proceedings and clinical trial registers. The main end-point was OS. Investigators were contacted to obtain the individual patient data, which was recorded, harmonised and checked. A random-effects Cox model, stratified by trial, was used for meta-analysis and subgroup analyses were preplanned.

RESULTS

16 trials were identified as eligible. Individual patient data were obtained from 12 trial and 2478 patients. CS was associated with an improved OS versus surgery, hazard ratio (HR) = 0.83 [0.72-0.96], p < 0.0001, translating to an absolute benefit of 5.7% at 5-years from 16.8% to 22.5%. Treatment effects did not vary substantially between adenocarcinoma (HR = 0.73 [0.62-0.87]) and squamous cell carcinoma (HR = 0.91 [0.76-1.08], interaction p = 0.26). A somewhat more pronounced effect was observed in gastro-oesophageal junction (HR = 0.68 [0.50-0.93]) versus TE (HR = 0.87 [0.75-1.00], interaction p = 0.07). CS was also associated with a greater disease-free survival (HR = 0.74 [0.64-0.85], p < 0.001).

CONCLUSIONS

Neoadjuvant chemotherapy conferred a better OS than surgery alone and should be considered in all anatomical location and histological subtypes.

Penalized Poisson model for network meta-analysis of individual patient time-to-event data

Network meta-analysis (NMA) allows the combination of direct and indirect evidence from a set of randomized clinical trials. Performing NMA using individual patient data (IPD) is considered as a "gold standard" approach as it provides several advantages over NMA based on aggregate data. For example, it allows to perform advanced modeling of covariates or covariate-treatment interactions. An important issue in IPD NMA is the selection of influential parameters among terms that account for inconsistency, covariates, covariate-by-treatment interactions or nonproportionality of treatments effect for time to event data. This issue has not been deeply studied in the literature yet and in particular not for time-to-event data. A major difficulty is to jointly account for between-trial heterogeneity which could have a major influence on the selection process. The use of penalized generalized mixed effect model is a solution, but existing implementations have several shortcomings and an important computational cost that precludes their use for complex IPD NMA. In this article, we propose a penalized Poisson regression model to perform IPD NMA of time-to-event data. It is based only on fixed effect parameters which improve its computational cost over the use of random effects. It could be easily implemented using existing penalized regression package. Computer code is shared for implementation. The methods were applied on simulated data to illustrate the importance to take into account between trial heterogeneity during the selection procedure. Finally, it was applied to an IPD NMA of overall survival of chemotherapy and radiotherapy in nasopharyngeal carcinoma.

Validation of a new fully automated software for 2D digital mammographic breast density evaluation in predicting breast cancer risk

We compared accuracy for breast cancer (BC) risk stratification of a new fully automated system (DenSeeMammo-DSM) for breast density (BD) assessment to a non-inferiority threshold based on radiologists' visual assessment. Pooled analysis was performed on 14,267 2D mammograms collected from women aged 48-55 years who underwent BC screening within three studies: RETomo, Florence study and PROCAS. BD was expressed through clinical Breast Imaging Reporting and Data System (BI-RADS) density classification. Women in BI-RADS D category had a 2.6 (95% CI 1.5-4.4) and a 3.6 (95% CI 1.4-9.3) times higher risk of incident and interval cancer, respectively, than women in the two lowest BD categories. The ability of DSM to predict risk of incident cancer was non-inferior to radiologists' visual assessment as both point estimate and lower bound of 95% CI (AUC 0.589; 95% CI 0.580-0.597) were above the predefined visual assessment threshold (AUC 0.571). AUC for interval (AUC 0.631; 95% CI 0.623-0.639) cancers was even higher. BD assessed with new fully automated method is positively associated with BC risk and is not inferior to radiologists' visual assessment. It is an even stronger marker of interval cancer, confirming an appreciable masking effect of BD that reduces mammography sensitivity.

Interobserver Agreement of PD-L1/SP142 Immunohistochemistry and Tumor-Infiltrating Lymphocytes (TILs) in Distant Metastases of Triple-Negative Breast Cancer: A Proof-of-Concept Study. A Report on Behalf of the International Immuno-Oncology Biomarker Working Group

Patients with advanced triple-negative breast cancer (TNBC) benefit from treatment with atezolizumab, provided that the tumor contains ≥1% of PD-L1/SP142-positive immune cells. Numbers of tumor-infiltrating lymphocytes (TILs) vary strongly according to the anatomic localization of TNBC metastases. We investigated inter-pathologist agreement in the assessment of PD-L1/SP142 immunohistochemistry and TILs. Ten pathologists evaluated PD-L1/SP142 expression in a proficiency test comprising 28 primary TNBCs, as well as PD-L1/SP142 expression and levels of TILs in 49 distant TNBC metastases with various localizations. Interobserver agreement for PD-L1 status (positive vs. negative) was high in the proficiency test: the corresponding scores as percentages showed good agreement with the consensus diagnosis. In TNBC metastases, there was substantial variability in PD-L1 status at the individual patient level. For one in five patients, the chance of treatment was essentially random, with half of the pathologists designating them as positive and half negative. Assessment of PD-L1/SP142 and TILs as percentages in TNBC metastases showed poor and moderate agreement, respectively. Additional training for metastatic TNBC is required to enhance interobserver agreement. Such training, focusing on metastatic specimens, seems worthwhile, since the same pathologists obtained high percentages of concordance (ranging from 93% to 100%) on the PD-L1 status of primary TNBCs.

Determinants of use of oral complementary-alternative medicine among women with early breast cancer: a focus on cancer-related fatigue

BACKGROUND

Despite the questionable effectiveness of oral complementary and alternative medicine (OCAM) in relieving cancer-related symptoms, including fatigue (CRF), many patients use it aiming to improve their quality of life. We assessed factors associated with OCAM use, focusing on CRF.

METHODS

Women with stage I-III breast cancer (BC) were included from CANTO (NCT01993498). OCAM use was defined as taking homeopathy, vitamins/minerals, or herbal/dietary supplements. Multivariable multinomial logistic regressions evaluated associations of CRF (EORTC QLQ-C30), patient, and treatment characteristics with OCAM use.

RESULTS

Among 5237 women, 23.0% reported OCAM use overall (49.3% at diagnosis, 50.7% starting post-diagnosis), mostly homeopathy (65.4%). Mean (SD) CRF score was 27.6 (24.0) at diagnosis and 35.1 (25.3) at post-diagnosis. More intense CRF was consistently associated with OCAM use at diagnosis and post-diagnosis [adjusted odds ratio (aOR) for 10-point increase 1.05 (95% Confidence interval 1.01-1.09) and 1.04 (1.01-1.09) vs. never use, respectively]. Odds of using OCAM at diagnosis were higher among older [for 5-year increase, 1.09 (1.04-1.14)] and more educated patients [college vs. primary 1.80 (1.27-2.55)]. Women with income > 3000 [vs. < 1500 euros/month, 1.44 (1.02-2.03)], anxiety [vs. not, 1.25 (1.01-1.54)], and those receiving chemotherapy [vs. not, 1.32 (1.04-1.68)] had higher odds of using OCAM post-diagnosis.

CONCLUSION

One-in-four patients reported use of OCAM. More severe CRF was consistently associated with its use. Moreover, older, better educated, wealthier, more anxious women, and those receiving chemotherapy seemed more prone to use OCAM. Characterizing profiles of BC patients more frequently resorting to OCAM may help deliver targeted information about its benefits and potential risks.

[Surgery or not on an atypical breast lesion? Taking anxiety into account in shared decision support from a prospective cohort of 300 patients]

INTRODUCTION

Organized and individual breast screening have been accompanied by an increase in the detection of "atypical breast lesions (ABL)". Recently, the NOMAT multicenter study proposed a predictive model of the risk of developing breast cancer after detection of an ABL in order to avoid surgical removal of "low-risk" lesions. It also aimed to provide information on psychological experience, in particularly anxiety, to assist in the shared medical decision process.

METHODS

Three hundred women undergoing surgery for ABL were included between 2015 and 2018 at 18 French centers. Women completed questionnaires before and after surgery assessing their level of anxiety (STAI-State, STAI-Trait), their level of tolerance to uncertainty, their perceived risk of developing a breast cancer, and their satisfaction with the management care.

RESULTS

One hundred nighty nine patients completed the STAI-Status before and after surgery. Overall, a decrease in anxiety level (35.4 vs 42.7, P<0.001) was observed. Anxious temperament and greater intolerance to uncertainty were significantly associated swith decreased anxiety (33%), whereas younger age was associated with increased anxiety (8%).

CONCLUSION

Surgery for ABL seems to be associated with only a few cases with an increase in anxiety and seems to increase the perception of the risk of developing breast cancer. Taking into account the psychological dimension remains in all cases essential in the process of shared therapeutic decision.

A two-stage drop-the-losers design for time-to-event outcome using a historical control arm

Phase II immuno-oncology clinical trials screen for efficacy an increasing number of treatments. In rare cancers, using historical control data is a pragmatic approach for speeding up clinical trials. The drop-the-losers design allows dropping off ineffective arms at interim analyses. We extended the original drop-the-losers design for a time-to-event outcome using a historical control through the one-sample log-rank statistic. Simulated trials featured three arms at the first stage, one at the second stage, nine scenarios, eight sample sizes with 5%- and 10%- nominal family-wise error rate (FWER). A numerical algorithm is provided to solve power calculations at the design stage. Our design was compared with a group of three independent single-arm trials (fixed design) with and without correction for multiplicity. Our design allowed strict control of the FWER at nominal levels while the misspecification of survival distribution and fixed design inflated the FWER up to three times the nominal level. The empirical power of our design increased with the sample size, the treatment effect and the number of effective treatments and dropped when more patients were recruited at the second stage. The fixed design with correction showed comparable power, while our design advantageously included more patients to the most promising arm. Recommendations for future applications are given. By taking advantage of the use of historical control data and a time-to-event outcome, the drop-the-losers design is a promising tool to meet the challenge of improving phase II clinical trials in immuno-oncology.

Body weight and return to work among survivors of early-stage breast cancer

BACKGROUND

Many breast cancer (BC) survivors are employed at diagnosis and are expected to return to work after treatment. Among them, around 50% are overweight or obese. There are limited data about the impact of body weight on their ability to return to work.

METHODS

We used data from CANcer TOxicity (NCT01993498), a prospective, multicentre cohort of women with stage I-III BC. Professionally active women who were ≥5 years younger than retirement age were identified. Multivariable logistic regression models examined associations of body mass index (BMI) at diagnosis and subsequent weight changes with non-return to work 2 years after diagnosis, adjusting for psychosocial, treatment and behavioural characteristics.

RESULTS

Among 1869 women, 689 were overweight or obese. Overall, 398 patients (21.3%) had not returned to work 2 years after diagnosis. Non-return to work was more likely for overweight or obese than underweight or normal weight patients (adjusted OR (aOR) 1.32; 95% CI, 1.01 to 1.75; p=0.045). Weight loss (≥5%) was observed in 15.7% overweight or obese and 8.7% underweight or normal weight patients and was associated with significant increases in physical activity only among overweight or obese patients (mean change, +4.7 metabolic-equivalent-of-task-hour/week; 95% CI +1.9 to +7.5). Overweight or obese patients who lost weight were more likely to return to work compared with those who did not lose weight (aOR of non-return-to-work, 0.48; 95% CI 0.24 to 0.97, p=0.0418), whereas weight loss was associated with increased odds of non-return to work among underweight or normal weight women (aOR 2.07; 95% CI 1.20 to 3.56, p=0.0086) (p_interactionBMI×weight changes=0.0002). The continuous trend of weight gain on non-return to work was significant for overweight or obese patients (aOR for one-percent-unit difference, 1.03; 95% CI 1.01 to 1.06, p=0.030).

CONCLUSIONS

Excess weight may be a barrier to return to work. Among overweight or obese BC survivors, weight loss was associated with higher rates of return to work, whereas further weight gain was associated with lower likelihood of return to work. Employment outcomes should be evaluated in randomised studies of weight management.

Impact of COVID-19 on healthcare organisation and cancer outcomes

BACKGROUND

Changes in the management of patients with cancer and delays in treatment delivery during the COVID-19 pandemic may impact the use of hospital resources and cancer mortality.

PATIENTS AND METHODS

Patient flows, patient pathways and use of hospital resources during the pandemic were simulated using a discrete event simulation model and patient-level data from a large French comprehensive cancer centre's discharge database, considering two scenarios of delays: massive return of patients from November 2020 (early-return) or March 2021 (late-return). Expected additional cancer deaths at 5 years and mortality rate were estimated using individual hazard ratios based on literature.

RESULTS

The number of patients requiring hospital care during the simulation period was 13,000. In both scenarios, 6-8% of patients were estimated to present a delay of >2 months. The overall additional cancer deaths at 5 years were estimated at 88 in early-return and 145 in late-return scenario, with increased additional deaths estimated for sarcomas, gynaecological, liver, head and neck, breast cancer and acute leukaemia. This represents a relative additional cancer mortality rate at 5 years of 4.4 and 6.8% for patients expected in year 2020, 0.5 and 1.3% in 2021 and 0.5 and 0.5% in 2022 for each scenario, respectively.

CONCLUSIONS

Pandemic-related diagnostic and treatment delays in patients with cancer are expected to impact patient survival. In the perspective of recurrent pandemics or alternative events requiring an intensive use of limited hospital resources, patients should be informed not to postpone care, and medical resources for patients with cancer should be sanctuarised.

The journey of tumor-infiltrating lymphocytes as a biomarker in breast cancer: clinical utility in an era of checkpoint inhibition

In 2014, we described a method to quantify percentage of tumor-infiltrating lymphocytes (TILs) on hematoxylin and eosin-stained slides of breast cancer samples using light microscopy that could be performed easily by pathologists with no extra stains. The aim of detailing the method was to facilitate independent research groups replicating our prognostic findings using TIL quantity in early-stage breast cancers. A global working group of breast pathologists was convened to standardize, test reproducibility, and refine the method. A website was also established which allowed free training (www.tilsinbreastcancer.org). As a result of this work, TIL data have been collected in over 20 000 primary breast cancer samples worldwide and the robust associations with better prognoses in triple-negative breast cancer (TNBC) and HER2+ BC have been confirmed. This has resulted in the inclusion of the TIL biomarker in several international breast cancer guidelines as well as in national criteria for routine pathology reporting. TIL therefore represents the first biological prognostic biomarker for early-stage TNBCs, and here its prognostic effect is linear, with values of 30%-50% being suggested as suitable for use in potential chemotherapy de-escalation studies. The efficacy of immune checkpoint-targeted agents in breast cancer now provides direct evidence that host immune responses can modify tumor growth in some patients. With the recent granting of accelerated approvals for the first PD-1/PD-L1 targeting agents in early and advanced TNBC, our focus has now moved to investigating the clinical utility of TIL in the setting of immune checkpoint agents, with or without PD-L1 protein assessment. Emerging data suggest that TIL quantity can help clinicians identify patients with breast cancer who benefit most from PD-1/PD-L1 inhibition. In patients with advanced TNBC and HER2+ disease a TIL cut-off of 5% or 10%, with PD-L1 expression can define 'immune-enriched' tumors and currently seems to have the most clinical relevance in this context.

Abstract 358: A prospective study of prostate cancer metastases identifies an androgen receptor activity-low, stemness program associated with resistance to androgen receptor axis inhibitors and unveils mechanisms of clonal evolution

Background: The androgen receptor axis inhibitors (ARi) (e.g, enzalutamide, abiraterone acetate) are administered in daily practice for men with metastatic castration-resistant prostate cancer (mCRPC). However, not all patients respond, and mechanisms of both primary and acquired resistance remain largely unknown.

Methods: In a prospective trial MATCH-R (NCT02517892), 55 mCRPC patients underwent whole exome sequencing (WES) (n=45) and RNA-sequencing (RNA-seq) (n=52) of metastatic biopsies before starting ARi. Also, 16 mCRPC patients underwent biopsy at time of resistance (WES=14, RNA-seq = 14). The objectives were to identify genomic alterations associated with resistance to ARi as well as to describe clonal evolution. Primary resistance was determined at 4 months of treatment using composite criteria for progression that included serum prostate specific antigen measurements, bone scan, CT imaging and symptom assessments. Acquired resistance was defined by occurrence of progressive disease after initial response or stable disease. Associations of genomic and transcriptomic alterations with primary resistance were determined using Wilcoxon and Fisher's exact tests.

Results: At 4 months, 22/55 patients in the cohort had disease progression (primary resistance). No genomic alterations from WES analysis were significantly associated with primary resistance. Analysis of sequential biopsies suggests that mCRPC follows mainly a parallel evolution model and involve DNA-repair related mutational processes. At time of acquired resistance to ARi, most tumors acquired new drivers affecting AR pathway (e.g, AR, NCOR1/2) or lineage switching (e.g, RB1, PTEN, TP53). Using computational methods, we measured AR transcriptional function and performed gene set enrichment analysis to identify pathways whose activity state correlated with resistance. AR gene alterations and AR expression were similar between responding and non-responding patients. Transcriptional analysis demonstrated that multiple specific gene sets — including those linked to low AR transcriptional activity, stemness program, RB loss and homologous repair deficiency — were activated in both primary and acquired resistance.

Conclusion: Resistance to AR axis inhibitors results from multiple transcriptional programs already activated in pre-treatment samples. Clonal evolution analysis along with RNA-seq data indicate the role of genomic instability and lineage switching in driving acquired resistance

Citation Format: Naoual Menssouri, Loic Poiraudeau, Carole Helissey, Ludovic Bigot, Jonathan Sabio, Tony Ibrahim, Claudio Nicotra, Maud Ngocamus, Lambros Tselikas, Thierry De Baere, Etienne Rouleau, Ludovic Lacroix, Anne Chaucherau, Luc Friboulet, Ronan Flippot, Giulia Baciarello, Laurence Albiges, Emeline Colomba, Pernelle Lavaud, Stefan Michiels, Aline Maillard, Antoine Italiano, Fabrice Barlesi, Jean-Charles Soria, Jean-Yves Scoazec, christophe Massard, Benjamin Besse, Fabrice André, Karim Fizazi, Daniel Gautheret, Yohann Loriot. A prospective study of prostate cancer metastases identifies an androgen receptor activity-low, stemness program associated with resistance to androgen receptor axis inhibitors and unveils mechanisms of clonal evolution [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 358.

Abstract 448: High prevalence of pathogenic germline variants in patients with oncogene-driven non-small cell lung cancer

BACKGROUND: Preliminary data has highlighted inherited predisposition to lung cancer related to certain genes. The frequency of pathogenic germline variants (PGV) PGV in patients (pts) with lung cancer according to the presence of an oncogenic driver is unknown. We studied the PGV of genes predisposing to cancer in pts with non-small cell lung cancer (NSCLC), and the somatic molecular profile of lung tumors.

METHODS: Retrospective study of whole exome sequencing (WES) from tissue biopsies performed in pts with advanced NSCLC enrolled, after signature of the inform consent, in the MOSCATO/MATCH-R trials between 2012 and 2018. Variants were considered as PGVs in the cancer predisposing genes (PMID: 29625052) if they satisfied the following criteria: (i) they had a “PASS” flag in HaplotypeCaller, (ii) were annotated as “Pathogenic” or “Likely Pathogenic” in ClinVar (PMID: 29165669) or InterVar (PMID: 28132688), or (iii) were truncating variants. Somatic driver mutations and Loss of Heterozygocity (LOH) of PGV harboring genes were further evaluated. The overlap to loss of heterozygocity regions was reported only when the variant allele frequency of the PGV was significantly higher than in the normal tissue. Cancer history, clinical and molecular data were retrospectively collected. The somatic mutations (m) in EGFR/BRAF/MET/HER2/KRAS and fusions in ALK/ROS1/RET were also considered for analysis.

RESULTS: Among 134 pts, 48% were women, median age was 61 (range 24-83), 45% were nonsmokers, 74% had adenocarcinoma. The most common somatic oncogenic driver alterations were: EGFRm in 44 pts (33%), KRASm in 19 pts (14%), BRAFm in 12 pts (9%) and ALK in 12 pts (9%).PGV were found in 22 out of 152 (15%) cancer-predisposing genes; 4 pts had additional somatic mutations (2) or LOHs (2) in the same genes. 77% of PGVs were in genes which are part of DNA repair pathways including 3.6% nucleotide excision repair (ERCC1/2/3, XPA), 6.5% homologous recombination/Fanconi Anemia: (FANC/A/C/M/D2, BRCA1, RECQL), 2.1% base excision repair (MUTYH, NTHL1), while the others were represented by genes related to cell signaling and metabolism (NF1, MET, ELANE, PRDM9, TRIM37).In the 22 PGV-carriers, 68% had a somatic oncogene-driven alteration (15/22) : EGFRm (n=7; 5 ex19del, 2 ex21(L858R)), KRASm (n=3; 2 G12D, 1 G12V), METm (n=2), HER2m (n=1), ROS1 (n=1) and RET (n=1). PGV were observed in 16% of EGFRm (7/44), 67% of METm (2/3), 15% in KRASm (3/19), 33% of HER2m (1/3), 25% of ROS1 (1/4), 50% of RET (1/2); but no PGV was identified in pts with BRAFVm (12) or ALK (12).

CONCLUSION: In our cohort, 15% of pts with NSCLC were PGV-carriers; 68% of PGV-carriers had oncogene-driven tumors, particularly with somatic EGFR mutations. PGV and oncogene-driven lung carcinogenesis need further evaluation.

Citation Format: Laura Mezquita, Andrei Iurchenko, Jose Carlos Benitez, Maria Baz, Sergey Nikolaev, David Planchard, Felix Blanc-Durand, Mihaela Aldea, Patricia Martín-Romano, Yohann Loriot, Claudio Nicotra, Maud Ngocamus, Jean-Yves Scoazec, Stefan Michiels, Sophie Postel-Vinay, Julien Viot, Luc Friboulet, Antoine Italiano, Fabrice Andre, Christophe Massard, Jean-Charles Soria, Etienne Rouleau, Daniel Gautheret, Benjamin Besse. High prevalence of pathogenic germline variants in patients with oncogene-driven non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 448.

Abstract 2230: Integrative genomic analysis of refractory metastatic cancer

Metastatic cancer refractory to treatment has a dismal prognosis. While genetic mechanisms of primary tumors and to a lesser extent of metastatic cancers have been studied on large cohorts, it is still not well understood if metastatic tumors refractory to systemic treatment contain specific signatures at genomic or transcriptional levels. Molecular characterization of metastatic cancers at terminal stage is indispensable for understanding the mechanisms of resistance to treatment and for prediction of tumor aggressiveness at earlier stages. Here, we present a large pan-cancer cohort of 914 tumors resistant to systemic therapies. Comprehensive clinical information is available for all the patients and includes tumor subtype, biopsy site, treatment, age, overall survival, etc. Biopsies of tumor metastases were taken after the diagnosed resistance. For this cohort we performed whole exome sequencing of a subset of 427 tumors with a median depth of 123X and matching blood, and transcriptome sequencing of all 914 tumors. We will discuss the variation in this cohort along 3 axes. Firstly we will describe the somatic mutations and indels by types of driver mutations, mutational burden, level of subclonality and mutational signatures. Additionally we will elaborate on the genomic instability and quantity of somatic copy number alterations (SCNAs). Secondly, we will present the annotation of germline DNA, frequency and classification of pathogenic variants predisposing to cancer. Germline variants are analyzed in the context of somatic genome focusing on the detection of the second hit by a mutation or a SCNA. Thirdly, we will present results of the analysis of differentially expressed genes and transcripts, activation of oncogenic and treatment resistance pathways, and fusion genes reported by multiple tools. The aforementioned genetic parameters are fed into a multi-omics model for identification of the key factors associated with the clinical features in the cohort. Furthermore this cohort is compared to primary tumors and to another cohort of metastatic tumors in order to unravel genetic markers of refractory disease. To this aim, we harmonized DNA- and RNA-derived variables from The Cancer Genome Atlas (TCGA) and MET500 (Robinson et al., Nature 2017). Such integrative analysis unravels genetic hallmarks of aggressive and treatment-resistant metastatic cancers highlighting the importance of multi-level tumor profiling to inform on therapeutic strategies.

Citation Format: Julien Viot, Yoann Pradat, Ismael Padioleau, Andrey Yurchenko, Loic Verlingues, Rebecca Clodion, Stefan Michiels, Marc Deloger, Gerome Jules-Clément, Yohann Loriot, Benjamin Besse, Fabrice Andre, Paul-Henry Cournedes, Daniel Gautheret, Sergey Nikolaev. Integrative genomic analysis of refractory metastatic cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2230.

Assessing the risk of severe post-treatment (tx) cancer-related fatigue (CRF) among breast cancer survivors (BCS) in the CANcer TOxicity (CANTO) cohort

12022

Background: CRF is among the most common and troublesome symptoms experienced by BCS. While preventing severe post-tx CRF is a major survivorship need, limited tools exist to predict this risk. We aimed to describe the long-term prevalence rates and to identify BCS that are more likely to develop severe CRF. Methods: CANTO is a multicenter, prospective clinical study of stage I-III BCS (NCT01993498). Longitudinal data were collected at diagnosis (dx), 0.5 (T1), 1 (T2) and 3 (T3) years post-tx. The primary outcome of interest was severe post-tx global CRF (score ≥ 40/100, EORTC QLQ-C30). Secondary outcomes were physical, emotional and cognitive dimensions of CRF (QLQ-FA12). Multivariable logistic regression models retained associations with severe CRF by bootstrapped Augmented Backwards Elimination, validated using 10-fold internal cross-validation and overoptimism-corrected AUC. Results: Among 6619 BCS, mean age at dx was 56.5 years (SD 11.5), mean BMI was 25.9 Kg/m2 (SD 5.4), 53.3% and 80.8% received chemotherapy (CT) and hormonotherapy (HT), respectively. Prevalence rates of severe global CRF were 25.0% (dx), 35.6% (T1), 34.0% (T2) and 31.6% (T3). Severe post-tx global CRF was consistently associated with higher BMI, worse insomnia and pain, and severe pre-tx CRF. Receipt of CT increased odds of severe CRF at T1, whereas associations of HT with CRF emerged at T2 and T3 (Table). The estimated risk of severe CRF at T3 was 14% for a BCS with BMI 23.0 Kg/m2 and no concomitant symptoms at dx, whereas it was 82% for a BCS with BMI 32.0 Kg/m2, severe insomnia, pain and pre-tx CRF, receiving HT. Anxiety and depression at dx were consistently retained in models of severe post-tx emotional and cognitive CRF (all p <.05). Conclusions: Over 1/3 BCS endured persistent, severe global CRF, particularly those that were medically more fragile and reported heavier pre-tx symptom burden. A transient impact of CT on CRF was evident on the short aftermath of tx, whereas exposure to HT seemed to affect CRF on the longer run. Consistent factors flag BCS whose risk of severe CRF is high and who should be upfront targeted and aggressively helped. Dimension-specific risk factors can guide prevention of distinct CRF symptoms. Clinical trial information: NCT01993498. [Table: see text]

Individual patient data meta-analysis of neoadjuvant chemotherapy followed by surgery versus upfront surgery in esophageal or gastro-esophageal carcinoma

4067

Background: Defining the optimal neoadjuvant treatment for resectable locally advanced esophageal carcinoma remains an open question. The debate is fuelled by the fact that most of the available randomized clinical trials (RCT) accrued two histological subtypes (adenocarcinoma (AC) and squamous cell carcinoma (SCC)) and two anatomical locations (TE and GEJ). The aim of this individual patient data (IPD) meta-analysis was to investigate the effect of preoperative chemotherapy on survival with a specific focus on histological subtypes and anatomical locations. Methods: Were eligible published or unpublished RCT closed to accrual before December 2015 and comparing neoadjuvant chemotherapy (CS) to primary surgery (S), identified by electronic database, conference proceedings and clinical trial register. All analyses were conducted on IPD obtained from trial Investigators. The Primary endpoint was overall survival (OS), Secondary endpoints were disease-free survival (DFS) with a 6-months landmark time, local/distant relapse/death without relapse as competing events. Two subgroup analyses were pre-planned one on the histological subtype and another on the anatomical location. A stratified logrank test was used for OS and DFS, and a stratified fine and gray model for competing events. HR, and risk ratios (RR) were combined using a random effect model. Results: IPD were obtained from 12 RCT (2601 patients) out of 16 identified (2863 patients) When compared to S, CS was associated with a significantly increased OS, (HR = 0.85[0.78-0.92], p < 0.0001), with a 5-year absolute OS benefit of 5.7%. However, the subgroup analysis by histological subtype showed an OS benefit from CS higher for AC (HR = 0.80[0.72-0.91], p < 0.01), when compared to SCC (HR = 0.90[0.80-1.01], p = 0.06), but with p for interaction = 0.2. In the subgroup analysis by anatomical location CS benefit was seen across both anatomical location with a trend in favor of GEJ (TE: HR = 0.89[0.81-0.98], p = 0.02 GEJ: HR = 0.71[0.57-0.88]), p < 0.01, p for interaction = 0.057). CS also improved DFS (HR = 0.81[0.74-0.88], p < 0.0001), with the same trend for the subgroup analyses, with apparent significant benefit for AC HR = 0.80[0.72-0.91] when compared to SCC HR = 0.90[0.80-1.01], (p for interaction 0.045) and a similar benefit for both location (TE: HR = 0.89[0.81-0.98] p < 0.01, GEJ: HR = 0.71[0.57-0.88], p = 0.095, P for interaction 0.11). Local (HR = 0.76[0.63-0.92], p = 0.0045) and distant (HR = 0.87[0.76-0.99], p = 0.04) relapses were also significantly lower in the CS arm, with no significant variation according to histological subtypes or tumor location. Conclusions: Neoadjuvant chemotherapy significantly improves OS when added to upfront surgery and was equally effective in AC and SCC. A slightly more pronounced effect was observed for overall survival in the GEJ location vs. the TE.

EPAC-lung: European pooled analysis of the prognostic value of circulating tumour cells in small cell lung cancer

BACKGROUND

Circulating tumour cell (CTC) number is an independent prognostic factor in patients with small cell lung cancer (SCLC) but there is no consensus on the CTC threshold for prognostic significance. We undertook a pooled analysis of individual patient data to clinically validate CTC enumeration and threshold for prognostication.

METHODS

Four European cancer centres, experienced in CellSearch CTC enumeration for SCLC provided pseudo anonymised data for patients who had undergone pre-treatment CTC count. Data was collated, and Cox regression models, stratified by centre, explored the relationship between CTC count and survival. The added value of incorporating CTCs into clinico-pathological models was investigated using likelihood ratio tests.

RESULTS

A total of 367 patient records were evaluated. A one-unit increase in log-transformed CTC counts corresponded to an estimated hazard ratio (HR) of 1.24 (95% CI: 1.19-1.29, P<0.0001) for progression free survival (PFS) and 1.23 (95% CI: 1.18-1.28, P<0.0001) for overall survival (OS). CTC count of ≥15 or ≥50 was significantly associated with an increased risk of progression (CTC ≥15: HR 3.20, 95% CI: 2.50-4.09, P<0.001; CTC ≥50: HR 2.56, 95% CI: 2.01-3.25, P<0.001) and an increased risk of death (CTC ≥15: HR 2.90, 95% CI: 2.28-3.70, P<0.001; CTC ≥50: HR 2.47, 95% CI: 1.95-3.13, P<0.001). There was no significant inter-centre heterogeneity observed. Addition of CTC count to clinico-pathological models as a continuous log-transformed variable, offers further prognostic value (both likelihood ratio P<0.001 for OS and PFS).

CONCLUSIONS

Higher pre-treatment CTC counts are a negative independent prognostic factor in SCLC when considered as a continuous variable or dichotomised counts of ≥15 or ≥50. Incorporating CTC counts, as a continuous variable, improves clinic-pathological prognostic models.

Abstract OT-38-01: MEDEA: A randomized trial of weight loss to reduce cancer-related fatigue (CRF) among overweight and obese breast cancer (BC) patients

Rationale: Overweight and obesity are highly prevalent among BC patients and are linked to poorer prognosis and worse patient-reported outcomes (PROs). Weight loss interventions, based on caloric restriction, increased physical activity (PA) and behavioral counselling, are safe and feasible among BC survivors and hold the promise to improve BC-specific outcomes. MEDEA: Motivating to Exercise and Diet, and Educating to healthy behaviors After breast cancer (ClinicalTrials.gov NCT04304924) will evaluate the impact of weight loss on CRF.

Trial design: French multi-center 1:1 randomized controlled trial comparing a 12-month personalized, telephone-based weight loss program + health education intervention vs health education alone in overweight or obese BC patients.

Endpoints and measures: Primary endpoint: difference in self-reported CRF 12 months post-randomization between arms, measured using the EORTC QLQ-C30 CRF subscale. Secondary endpoints: 1) PROs (EORTC QLQ-C30, -B45, -FA12), anxiety and depression (Hospital Anxiety and Depression Scale); 2) weight and body mass index (BMI), diet habits and quality, PA, sleep; 3) cost-effectiveness (number and length of hospital-admissions, all-drug consumption, number and duration of sick leaves). Accelerometer data will be collected to track PA and sleep measures. Qualitative analyses will evaluate patient motivation and satisfaction.

Main eligibility criteria: stage I-II-III BC, primary BC treatment completed within the prior 12 months (definitive surgery, adjuvant chemo-, and/or radio-therapy, if administered), BMI ≥25 kg/m2, ability to walk at least 400 meters at any pace, ECOG PS 0-1, not participating in another weight loss, dietary or PA intervention clinical trial.

Intervention and Control arms: The intervention and health education program are adapted from the BWEL: Breast Cancer WEight Loss study (ClinicalTrials.gov NCT02750826; PI Ligibel JA). The behavior change program is based on the Social Cognitive Theory. Patients in the intervention arm are paired with an individual lifestyle coach, who delivers the intervention through 24 semi-structured telephone calls of 30-60 minutes, supplemented by a detailed participant workbook and scheduled as follows: 1) intensive phase (weeks 1-12), 12 weekly calls; 2) consolidation phase (weeks 13-24), 6 bi-weekly calls; 3) maintenance phase (weeks 25-52), 1 monthly call. Coaches were hired and trained specifically for MEDEA, they are located at a centralized call center and receive support from coordinating nutrition, PA, and behavioral experts. Regular meetings with study team and investigators assure standardized delivery of the intervention and troubleshooting. Intervention goals include weight loss ≥10% of baseline weight, caloric restriction of 500-1000 Kcal/day, increased PA to 150 minutes/week in the initial phase and 225-300 minutes/week in the maintenance phase. Toolbox solutions are offered to tailor the intervention and meet the needs of specific ethnic, socioeconomic or other patient populations with difficulties in achieving intervention goals. All participants in both arms receive a health education program focusing on healthy living.

Accrual: MEDEA will enroll 220 patients overall. Recruitment started in June 2020.

Statistical considerations: The primary analysis of MEDEA will compare the primary endpoint of CRF scores at the 12-month post-randomization time point between arms. The study has 90.0% power at two-sided α=0.05 to detect a standardized effect size of 0.40 (sample size inflated for drop outs). For interpreting the clinical significance of effects, 0.2, 0.5 and 0.8 standard deviation effects will be considered as small, moderate, and large (Cohen, 1988). All other measures, time points and analyses will be considered secondary or exploratory.

Citation Format: Antonio Di Meglio, Elise Martin, Stefan Michiels, Cecile Charles, Tracy E. Crane, Aude Barbier, Bruno Raynard, Anthony Mangin, Olivier Tredan, Paul H. Cottu, Laurence Vanlemmens, Carine Segura-Djezzar, Anne Lesur, Barbara Pistilli, Florence Joly, Thomas Ginsbourger, Bernadette Coquet, Guillemette Jacob, Aude Sirven, Julia Bonastre, Jennifer A. Ligibel, Ines Vaz-Luis. MEDEA: A randomized trial of weight loss to reduce cancer-related fatigue (CRF) among overweight and obese breast cancer (BC) patients [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 OT-38-01.

Prognostic and predictive effect of KRAS gene copy number and mutation status in early stage non-small cell lung cancer patients

Background

In the current analysis, we characterize the prognostic significance of KRAS mutations with concomitant copy number aberrations (CNA) in early stage non-small cell lung cancer (NSCLC), and evaluate the ability to predict survival benefit from adjuvant chemotherapy.

Methods

Clinical and genomic data from the LACE (Lung Adjuvant Cisplatin Evaluation)-Bio consortium was utilized. CNAs were categorized as Gain (CN ≥2) or Neutral (Neut)/Loss; KRAS status was defined as wild type (WT) or mutant (MUT). The following groups were compared in all patients and the adenocarcinoma subgroup, and were correlated to survival endpoints using a Cox proportional hazards model: WT + Neut/Loss (reference), WT + Gain, MUT + Gain and MUT + Neut/Loss. A treatment-by-variable interaction was added to evaluate predictive effect.

Results

Of the 946 (399 adenocarcinoma) NSCLC patients, 41 [30] had MUT + Gain, 145 [99] MUT + Neut/Loss, 125 [16] WT + Gain, and 635 [254] WT + Neut/Loss. A non-significant trend towards worse lung cancer-specific survival (LCSS; HR =1.34; 95% CI, 0.83-2.17, P=0.232), DFS (HR =1.34; 95% CI, 0.86-2.09, P=0.202) and OS (HR =1.59; 95% CI, 0.99-2.54, P=0.055) was seen in KRAS MUT + Gain patients relative to KRAS WT + Neut/Loss patients. A negative prognostic effect of KRAS MUT + Neut/Loss was observed for LCSS (HR =1.32; 95% CI, 1.01-1.71, P=0.038) relative to KRAS WT + Neut/Loss on univariable analysis, but to a lesser extent after adjusting for covariates (HR =1.28; 95% CI, 0.97-1.68, P=0.078). KRAS MUT + Gain was associated with a greater beneficial effect of chemotherapy on DFS compared to KRAS WT + Neut/Loss patients (rHR =0.33; 95% CI, 0.11-0.99, P=0.048), with a non-significant trend also seen for LCSS (rHR =0.41; 95% CI, 0.13-1.33, P=0.138) and OS (rHR =0.40; 95% CI, 0.13-1.26, P=0.116) in the adenocarcinoma subgroup.

Conclusions

A small prognostic effect of KRAS mutation was identified for LCSS, and a trend towards worse LCSS, DFS and OS was noted for KRAS MUT + Gain. A potential predictive effect of concomitant KRAS mutation and copy number gain was observed for DFS in adenocarcinoma patients. These results could be driven by the small number of patients and require validation.

Inhibition of RANK signaling in breast cancer induces an anti-tumor immune response orchestrated by CD8+ T cells

Most breast cancers exhibit low immune infiltration and are unresponsive to immunotherapy. We hypothesized that inhibition of the receptor activator of nuclear factor-κB (RANK) signaling pathway may enhance immune activation. Here we report that loss of RANK signaling in mouse tumor cells increases leukocytes, lymphocytes, and CD8⁺ T cells, and reduces macrophage and neutrophil infiltration. CD8⁺ T cells mediate the attenuated tumor phenotype observed upon RANK loss, whereas neutrophils, supported by RANK-expressing tumor cells, induce immunosuppression. RANKL inhibition increases the anti-tumor effect of immunotherapies in breast cancer through a tumor cell mediated effect. Comparably, pre-operative single-agent denosumab in premenopausal early-stage breast cancer patients from the Phase-II D-BEYOND clinical trial (NCT01864798) is well tolerated, inhibits RANK pathway and increases tumor infiltrating lymphocytes and CD8⁺ T cells. Higher RANK signaling activation in tumors and serum RANKL levels at baseline predict these immune-modulatory effects. No changes in tumor cell proliferation (primary endpoint) or other secondary endpoints are observed. Overall, our preclinical and clinical findings reveal that tumor cells exploit RANK pathway as a mechanism to evade immune surveillance and support the use of RANK pathway inhibitors to prime luminal breast cancer for immunotherapy.

Receptor activator of nuclear factor-κB (RANK)/RANK-ligand (RANKL) signaling regulates the tumor-immune crosstalk. Here the authors show that systemic RANKL inhibition promotes CD8 + T cell infiltration in patients with early breast cancer and that loss of RANK signaling in tumor cells drives a T cell-dependent anti-tumor response in preclinical models.

Recommendations for the use of next-generation sequencing (NGS) for patients with metastatic cancers: a report from the ESMO Precision Medicine Working Group

Next-generation sequencing (NGS) allows sequencing of a high number of nucleotides in a short time frame at an affordable cost. While this technology has been widely implemented, there are no recommendations from scientific societies about its use in oncology practice. The European Society for Medical Oncology (ESMO) is proposing three levels of recommendations for the use of NGS. Based on the current evidence, ESMO recommends routine use of NGS on tumour samples in advanced non-squamous non-small-cell lung cancer (NSCLC), prostate cancers, ovarian cancers and cholangiocarcinoma. In these tumours, large multigene panels could be used if they add acceptable extra cost compared with small panels. In colon cancers, NGS could be an alternative to PCR. In addition, based on the KN158 trial and considering that patients with endometrial and small-cell lung cancers should have broad access to anti-programmed cell death 1 (anti-PD1) antibodies, it is recommended to test tumour mutational burden (TMB) in cervical cancers, well- and moderately-differentiated neuroendocrine tumours, salivary cancers, thyroid cancers and vulvar cancers, as TMB-high predicted response to pembrolizumab in these cancers. Outside the indications of multigene panels, and considering that the use of large panels of genes could lead to few clinically meaningful responders, ESMO acknowledges that a patient and a doctor could decide together to order a large panel of genes, pending no extra cost for the public health care system and if the patient is informed about the low likelihood of benefit. ESMO recommends that the use of off-label drugs matched to genomics is done only if an access programme and a procedure of decision has been developed at the national or regional level. Finally, ESMO recommends that clinical research centres develop multigene sequencing as a tool to screen patients eligible for clinical trials and to accelerate drug development, and prospectively capture the data that could further inform how to optimise the use of this technology.

Predictive classifier for intensive treatment of head and neck cancer

BACKGROUND

This study was designed to test the hypothesis that the effectiveness of intensive treatment for locoregionally advanced head and neck cancer (LAHNC) depends on the proportion of patients' overall event risk attributable to cancer.

METHODS

This study analyzed 22,339 patients with LAHNC treated in 81 randomized trials testing altered fractionation (AFX; Meta-Analysis of Radiotherapy in Squamous Cell Carcinomas of Head and Neck [MARCH] data set) or chemotherapy (Meta-Analysis of Chemotherapy in Head and Neck Cancer [MACH-NC] data set). Generalized competing event regression was applied to the control arms in MARCH, and patients were stratified by tertile according to the ω score, which quantified the relative hazard for cancer versus competing events. The classifier was externally validated on the MACH-NC data set. The study tested for interactions between the ω score and treatment effects on overall survival (OS).

RESULTS

Factors associated with a higher ω score were a younger age, a better performance status, an oral cavity site, higher T and N categories, and a p16-negative/unknown status. The effect of AFX on OS was greater in patients with high ω scores (hazard ratio [HR], 0.92; 95% confidence interval [CI], 0.85-0.99) and medium ω scores (HR, 0.91; 95% CI, 0.84-0.98) versus low ω scores (HR, 0.97; 95% CI, 0.90-1.05; P for interaction = .086). The effect of chemotherapy on OS was significantly greater in patients with high ω scores (HR, 0.81; 95% CI, 0.75-0.88) and medium ω scores (HR, 0.86; 95% CI, 0.78-0.93) versus low ω scores (HR, 0.96; 95% CI, 0.86-1.08; P for interaction = .011).

CONCLUSIONS

LAHNC patients with a higher risk of cancer progression relative to competing mortality, as reflected by a higher ω score, selectively benefit from more intensive treatment.

Prediction of Breast Cancer Treatment-Induced Fatigue by Machine Learning Using Genome-Wide Association Data

BACKGROUND

We aimed at predicting fatigue after breast cancer treatment using machine learning on clinical covariates and germline genome-wide data.

METHODS

We accessed germline genome-wide data of 2799 early-stage breast cancer patients from the Cancer Toxicity study (NCT01993498). The primary endpoint was defined as scoring zero at diagnosis and higher than quartile 3 at 1 year after primary treatment completion on European Organization for Research and Treatment of Cancer quality-of-life questionnaires for Overall Fatigue and on the multidimensional questionnaire for Physical, Emotional, and Cognitive fatigue. First, we tested univariate associations of each endpoint with clinical variables and genome-wide variants. Then, using preselected clinical (false discovery rate < 0.05) and genomic (P < .001) variables, a multivariable preconditioned random-forest regression model was built and validated on a hold-out subset to predict fatigue. Gene set enrichment analysis identified key biological correlates (MetaCore). All statistical tests were 2-sided.

RESULTS

Statistically significant clinical associations were found only with Emotional and Cognitive Fatigue, including receipt of chemotherapy, anxiety, and pain. Some single nucleotide polymorphisms had some degree of association (P < .001) with the different fatigue endpoints, although there were no genome-wide statistically significant (P < 5.00 × 10^-8) associations. Only for Cognitive Fatigue, the predictive ability of the genomic multivariable model was statistically significantly better than random (area under the curve = 0.59, P = .01) and marginally improved with clinical variables (area under the curve = 0.60, P = .005). Single nucleotide polymorphisms found to be associated (P < .001) with Cognitive Fatigue belonged to genes linked to inflammation (false discovery rate adjusted P = .03), cognitive disorders (P = 1.51 × 10^-12), and synaptic transmission (P = 6.28 × 10^-8).

CONCLUSIONS

Genomic analyses in this large cohort of breast cancer survivors suggest a possible genetic role for severe Cognitive Fatigue that warrants further exploration.

Prediction of Breast Cancer Treatment-Induced Fatigue by Machine Learning Using Genome-Wide Association Data

BACKGROUND

We aimed at predicting fatigue after breast cancer treatment using machine learning on clinical covariates and germline genome-wide data.

METHODS

We accessed germline genome-wide data of 2799 early-stage breast cancer patients from the Cancer Toxicity study (NCT01993498). The primary endpoint was defined as scoring zero at diagnosis and higher than quartile 3 at 1 year after primary treatment completion on European Organization for Research and Treatment of Cancer quality-of-life questionnaires for Overall Fatigue and on the multidimensional questionnaire for Physical, Emotional, and Cognitive fatigue. First, we tested univariate associations of each endpoint with clinical variables and genome-wide variants. Then, using preselected clinical (false discovery rate < 0.05) and genomic (P < .001) variables, a multivariable preconditioned random-forest regression model was built and validated on a hold-out subset to predict fatigue. Gene set enrichment analysis identified key biological correlates (MetaCore). All statistical tests were 2-sided.

RESULTS

Statistically significant clinical associations were found only with Emotional and Cognitive Fatigue, including receipt of chemotherapy, anxiety, and pain. Some single nucleotide polymorphisms had some degree of association (P < .001) with the different fatigue endpoints, although there were no genome-wide statistically significant (P < 5.00 × 10-8) associations. Only for Cognitive Fatigue, the predictive ability of the genomic multivariable model was statistically significantly better than random (area under the curve = 0.59, P = .01) and marginally improved with clinical variables (area under the curve = 0.60, P = .005). Single nucleotide polymorphisms found to be associated (P < .001) with Cognitive Fatigue belonged to genes linked to inflammation (false discovery rate adjusted P = .03), cognitive disorders (P = 1.51 × 10-12), and synaptic transmission (P = 6.28 × 10-8).

CONCLUSIONS

Genomic analyses in this large cohort of breast cancer survivors suggest a possible genetic role for severe Cognitive Fatigue that warrants further exploration.

Prospective Multicenter Study Validate a Prediction Model for Surgery Uptake Among Women with Atypical Breast Lesions

BACKGROUND

Diagnosis of atypical breast lesions (ABLs) leads to unnecessary surgery in 75-90% of women. We have previously developed a model including age, complete radiological target excision after biopsy, and focus size that predicts the probability of cancer at surgery. The present study aimed to validate this model in a prospective multicenter setting.

- METHODS

Women with a recently diagnosed ABL on image-guided biopsy were recruited in 18 centers, before wire-guided localized excisional lumpectomy. Primary outcome was the negative predictive value (NPV) of the model.

RESULTS

The NOMAT model could be used in 287 of the 300 patients included (195 with ADH). At surgery, 12 invasive (all grade 1), and 43 in situ carcinomas were identified (all ABL: 55/287, 19%; ADH only: 49/195, 25%). The area under the receiving operating characteristics curve of the model was 0.64 (95% CI 0.58-0.69) for all ABL, and 0.63 for ADH only (95% CI 0.56-0.70). For the pre-specified threshold of 20% predicted probability of cancer, NPV was 82% (77-87%) for all ABL, and 77% (95% CI 71-83%) for patients with ADH. At a 10% threshold, NPV was 89% (84-94%) for all ABL, and 85% (95% CI 78--92%) for the ADH. At this threshold, 58% of the whole ABL population (and 54% of ADH patients) could have avoided surgery with only 2 missed invasive cancers.

CONCLUSION

The NOMAT model could be useful to avoid unnecessary surgery among women with ABL, including for patients with ADH.

CLINICAL TRIAL REGISTRATION

NCT02523612.

Integrating expert's knowledge constraint of time dependent exposures in structure learning for Bayesian networks

Learning a Bayesian network is a difficult and well known task that has been largely investigated. To reduce the number of candidate graphs to test, some authors proposed to incorporate a priori expert knowledge. Most of the time, this a priori information between variables influences the learning but never contradicts the data. In addition, the development of Bayesian networks integrating time such as dynamic Bayesian networks allows identifying causal graphs in the context of longitudinal data. Moreover, in the context where the number of strongly correlated variables is large (i.e. oncology) and the number of patients low; if a biomarker has a mediated effect on another, the learning algorithm would associate them wrongly and vice versa. In this article we propose a method to use the a priori expert knowledge as hard constraints in a structure learning method for Bayesian networks with a time dependant exposure. Based on a simulation study and an application, where we compared our method to the state of the art PC-algorithm, the results showed a better recovery of the true graphs when integrating hard constraints a priori expert knowledge even for small level of information.