Respiratory complex I regulates dendritic cell maturation in explant model of human tumor immune microenvironment.

BACKGROUND: Combining cytotoxic chemotherapy or novel anticancer drugs with T-cell modulators holds great promise in treating advanced cancers. However, the response varies depending on the tumor immune microenvironment (TIME). Therefore, there is a clear need for pharmacologically tractable models of the TIME to dissect its influence on mono- and combination treatment response at the individual level. METHODS: Here we establish a patient-derived explant culture (PDEC) model of breast cancer, which retains the immune contexture of the primary tumor, recapitulating cytokine profiles and CD8+T cell cytotoxic activity. RESULTS: We explored the immunomodulatory action of a synthetic lethal BCL2 inhibitor venetoclax+metformin drug combination ex vivo, discovering metformin cannot overcome the lymphocyte-depleting action of venetoclax. Instead, metformin promotes dendritic cell maturation through inhibition of mitochondrial complex I, increasing their capacity to co-stimulate CD4+T cells and thus facilitating antitumor immunity. CONCLUSIONS: Our results establish PDECs as a feasible model to identify immunomodulatory functions of anticancer drugs in the context of patient-specific TIME.

Well-being trajectories in breast cancer and their predictors: A machine-learning approach.

OBJECTIVE: This study aimed to describe distinct trajectories of anxiety/depression symptoms and overall health status/quality of life over a period of 18 months following a breast cancer diagnosis, and identify the medical, socio-demographic, lifestyle, and psychological factors that predict these trajectories. METHODS: 474 females (mean age = 55.79 years) were enrolled in the first weeks after surgery or biopsy. Data from seven assessment points over 18 months, at 3-month intervals, were used. The two outcomes were assessed at all points. Potential predictors were assessed at baseline and the first follow-up. Machine-Learning techniques were used to detect latent patterns of change and identify the most important predictors. RESULTS: Five trajectories were identified for each outcome: stably high, high with fluctuations, recovery, deteriorating/delayed response, and stably poor well-being (chronic distress). Psychological factors (i.e., negative affect, coping, sense of control, social support), age, and a few medical variables (e.g., symptoms, immune-related inflammation) predicted patients' participation in the delayed response and the chronic distress trajectories versus all other trajectories. CONCLUSIONS: There is a strong possibility that resilience does not always reflect a stable response pattern, as there might be some interim fluctuations. The use of machine-learning techniques provides a unique opportunity for the identification of illness trajectories and a shortlist of major bio/behavioral predictors. This will facilitate the development of early interventions to prevent a significant deterioration in patient well-being.

Predictors of successful neoadjuvant treatment in HER2­positive breast cancer.

The prognosis of local or locally advanced human epidermal growth factor receptor 2 (HER2)-positive breast cancer after a complete response from neoadjuvant systemic treatment (NAT) is excellent. However, some of the patients succumb to their disease, so novel predictive factors to identify these patients at risk are needed. Retrospective data from 119 patients treated at the Helsinki University Hospital Comprehensive Cancer Centre (Helsinki, Finland) were collected. All patients had in situ hybridization-confirmed HER2-positive breast cancer and underwent NAT with a curative intention. The primary tumours were relatively large, most patients had cytologically confirmed lymph node metastases and the treatments used were current regimens. A total of 63 (52.1%) patients had a pathological complete response (pCR) to neoadjuvant therapy. Achieving pCR predicted longer disease-free survival (DFS; P=0.0083) but not overall survival (P=0.061). The patients with a pCR had an estimated DFS rate of 96.8% at 5 years, compared with only 59.7% of the patients with non-pCR. Radiological complete response (CR) was associated with pCR (P=0.00033), although imaging yielded 30.4% false-negative and 36.9% false-positive results. The association between the radiological CR and pCR was more obvious in oestrogen receptor-negative tumours. Moderate (compared with strong) immunohistochemical HER2 expression predicted a lower chance of pCR (P=0.0078) and worse breast cancer-specific survival (P=0.0015). In conclusion, pCR after NAT served as an important prognostic factor in women with high-risk HER2-positive breast cancer. The patients with only moderate immunohistochemical HER2 expression had a lower chance of reaching a pCR, as well as a shorter breast cancer-specific survival.

Predictive factors for prolonged sick leave in breast cancer patients treated with adjuvant therapies: a retrospective registry study.

BACKGROUND: Being able to work during and after breast cancer treatments is important for patients to have a sense of normalcy, financial security, and improved quality of life, and for society due to the economic burden of sick leave. Factors influencing the length of sick leave can be sociodemographic factors, workplace adaptations, recurrences, symptoms, and type of treatment. The aim of this study is to analyse factors associated with prolonged sick leave after adjuvant breast cancer treatments. METHODS: The population of this registry study consists of 1333 early breast cancer patients diagnosed and treated in Helsinki University Hospital between 2016 and 2018. Data on patient demographics, disease characteristics, treatment, and healthcare resource utilization were obtained from Helsinki University Hospital and data on income level and sick leave were obtained from Kela sickness benefits registry. Prolonged sick leave was determined as the patient accumulating 30 or more reimbursed sick leave days during a 60-day follow-up period after the end of active oncological treatment. Univariate analysis and multivariate analysis were conducted. RESULTS: A total of 26% of the patients in this study were on sick leave for 30 or more days after the active treatments ended. Study findings show that chemotherapy, triple-negative breast cancer, reconstructive surgery, amount of outpatient visits, and income are associated with prolonged sick leave. Independent predictors of prolonged sick leave were treatment line, number of outpatient contacts, reconstruction, and triple-negative breast cancer. CONCLUSIONS: Our study shows that prolonged sick leave affects a substantial number of working-age women with early breast cancer. Independent predictors for prolonged sick leave were all treatment-related. Targeted support for treatment-related side-effects already during the treatment period could lead to better recovery and earlier return to work.

Effects of letrozole on serum estradiol and estrone in postmenopausal breast cancer patients and tolerability of treatment: a prospective trial using a highly sensitive LC-MS/MS (liquid chromatography-tandem mass spectrometry) method for estrogen measurement.

PURPOSE: To analyze serum estradiol (E2) and estrone (E1) during letrozole treatment and their association to Quality of Life (QoL) and side-effects. METHODS: Postmenopausal breast cancer patients starting adjuvant letrozole were eligible. Serum samples were taken at baseline, three, and 12 months. E2 and FSH were measured with routine chemiluminescent immunoassays. E2 and E1 were analyzed after trial completion with a highly sensitive liquid chromatography-tandem mass spectrometry method (LC-MS/MS) with lower limits of quantification (LLOQ) of 5 pmol/L. QoL was measured at baseline and at 12 months with the EORTC QLQ-C30 and QLQ-BR23 and the Women's Health questionnaires, and menopause-related symptoms with the modified Kupperman Index. RESULTS: Of 100 screened patients 90 completed the trial. Baseline mean LC-MS/MS E2 and E1 were 12 pmol/L (range < 5-57) and 66 pmol/L (< 5-226), respectively. E2 levels measured by immunoassay and LC-MS/MS showed no correlation. E2 and E1 were completely suppressed by letrozole except for one occasion (E1 11 pmol/L at 3 months). Pain, side effects of systemic therapy, vasomotor symptoms, joint and muscle aches, and vaginal dryness increased during letrozole treatment. A high baseline E2 was significantly associated with increased aching joints and muscles, but not with the other side effects. CONCLUSIONS: Letrozole supresses E2 and E1 completely below the LLOQ of the LC-MS/MS in postmenopausal women. High pre-treatment E2 levels were associated with more joint and muscle pain during letrozole. Automated immunoassays are unsuitable for E2 monitoring during letrozole therapy due to poor sensitivity.

Personalized Risk Analysis to Improve the Psychological Resilience of Women Undergoing Treatment for Breast Cancer: Development of a Machine Learning-Driven Clinical Decision Support Tool.

BACKGROUND: Health professionals are often faced with the need to identify women at risk of manifesting poor psychological resilience following the diagnosis and treatment of breast cancer. Machine learning algorithms are increasingly used to support clinical decision support (CDS) tools in helping health professionals identify women who are at risk of adverse well-being outcomes and plan customized psychological interventions for women at risk. Clinical flexibility, cross-validated performance accuracy, and model explainability permitting person-specific identification of risk factors are highly desirable features of such tools. OBJECTIVE: This study aimed to develop and cross-validate machine learning models designed to identify breast cancer survivors at risk of poor overall mental health and global quality of life and identify potential targets of personalized psychological interventions according to an extensive set of clinical recommendations. METHODS: A set of 12 alternative models was developed to improve the clinical flexibility of the CDS tool. All models were validated using longitudinal data from a prospective, multicenter clinical pilot at 5 major oncology centers in 4 countries (Italy, Finland, Israel, and Portugal; the Predicting Effective Adaptation to Breast Cancer to Help Women to BOUNCE Back [BOUNCE] project). A total of 706 patients with highly treatable breast cancer were enrolled shortly after diagnosis and before the onset of oncological treatments and were followed up for 18 months. An extensive set of demographic, lifestyle, clinical, psychological, and biological variables measured within 3 months after enrollment served as predictors. Rigorous feature selection isolated key psychological resilience outcomes that could be incorporated into future clinical practice. RESULTS: Balanced random forest classifiers were successful at predicting well-being outcomes, with accuracies ranging between 78% and 82% (for 12-month end points after diagnosis) and between 74% and 83% (for 18-month end points after diagnosis). Explainability and interpretability analyses built on the best-performing models were used to identify potentially modifiable psychological and lifestyle characteristics that, if addressed systematically in the context of personalized psychological interventions, would be most likely to promote resilience for a given patient. CONCLUSIONS: Our results highlight the clinical utility of the BOUNCE modeling approach by focusing on resilience predictors that can be readily available to practicing clinicians at major oncology centers. The BOUNCE CDS tool paves the way for personalized risk assessment methods to identify patients at high risk of adverse well-being outcomes and direct valuable resources toward those most in need of specialized psychological interventions.

Digital solution in the follow-up of early breast cancer a randomized study.

BACKGROUND: After primary treatment, patients with early breast cancer (EBC) are followed-up for at least 5 years. At the Helsinki University Hospital (HUS) surveillance includes appointments at 1, 3 and 5 years, and between pre-planned visits a phone call service operated by a nurse practitioner for counseling about symptoms related to side-effects or potential recurrence. In 2015 HUS launched a digital solution for cancer patients. This study was designed to find out patient preference, Health related (HR) quality of life (QOL) and satisfaction with a digital solution compared to a phone call service during the first year of follow-up. MATERIAL AND METHODS: Patients with EBC were randomized at the final visit of radiotherapy to surveillance by phone calls or by the digital Noona solution during the first year outside pre-planned visits. After six months the groups were crossed over to the other arm. Primary endpoint was patient preference for either follow-up method among those who had contacted the study nurse at least once by both phone service and digital solution. RESULTS: Out of the 765 patients randomized, 142 had contacted the hospital with both methods and were eligible for inclusion in the analyses of the present study. Out of the 142 patients, 56 preferred phone calls, 43 the digital solution while 43 considered both modalities equal. Preference for the digital solution was higher among patients aged 65 or less. There were no differences in HR QoL or overall satisfaction between the modalities. However, the patients rated the timeliness of response better while using the digital solution. CONCLUSION: Of the patients 30% preferred the digital solution, 40% phone calls while 30% found them equal as the primary follow-up method for EBC during the first year outside pre-planned visits. There is a need to include also digital solutions in surveillance of EBC. CLINICALTRIALS.GOV IDENTIFIER: NCT04980989.

Personalized prediction of one-year mental health deterioration using adaptive learning algorithms: a multicenter breast cancer prospective study.

Identifying individual patient characteristics that contribute to long-term mental health deterioration following diagnosis of breast cancer (BC) is critical in clinical practice. The present study employed a supervised machine learning pipeline to address this issue in a subset of data from a prospective, multinational cohort of women diagnosed with stage I-III BC with a curative treatment intention. Patients were classified as displaying stable HADS scores (Stable Group; n = 328) or reporting a significant increase in symptomatology between BC diagnosis and 12 months later (Deteriorated Group; n = 50). Sociodemographic, life-style, psychosocial, and medical variables collected on the first visit to their oncologist and three months later served as potential predictors of patient risk stratification. The flexible and comprehensive machine learning (ML) pipeline used entailed feature selection, model training, validation and testing. Model-agnostic analyses aided interpretation of model results at the variable- and patient-level. The two groups were discriminated with a high degree of accuracy (Area Under the Curve = 0.864) and a fair balance of sensitivity (0.85) and specificity (0.87). Both psychological (negative affect, certain coping with cancer reactions, lack of sense of control/positive expectations, and difficulties in regulating negative emotions) and biological variables (baseline percentage of neutrophils, thrombocyte count) emerged as important predictors of mental health deterioration in the long run. Personalized break-down profiles revealed the relative impact of specific variables toward successful model predictions for each patient. Identifying key risk factors for mental health deterioration is an essential first step toward prevention. Supervised ML models may guide clinical recommendations toward successful illness adaptation.

Psychological and somatic symptoms among breast cancer patients in four European countries: A cross-lagged panel model.

Psychological and physical health among women with breast cancer are linked. However, more research is needed to test the interrelations between psychological and somatic symptoms, over time and throughout the different phases of breast cancer treatment, to determine when and which interventions should be prioritized. Six hundred and eighty nine women from four countries (Finland, Israel, Italy and Portugal) completed questionnaires during their first clinical consultation following diagnosis with breast cancer, and again after 3 and 6 months. The questionnaires included self-reported measures of psychological symptoms (Hospital Anxiety and Depression Scale; the Positive and Negative Affect Schedule Short Form) and somatic symptoms [selected items from the International European Organization for Research and Treatment of Cancer (EORTC) questionnaires]. Psychological and somatic symptoms were relatively stable across the three time-points. Cross-lagged paths leading from somatic to psychological symptoms (beta coefficients of 0.08-0.10), as well as vice-versa (beta 0.11-0.12), were found to be significant. No evidence was found for cross-cultural differences in mutual effects of psychological and somatic symptoms. The findings of this study call for tailoring personal interventions for breast cancer patients-either from a somatic perspective or a psychological perspective-and adjust them to the specific experiences of the individual patient.

Associations between Physical Exercise, Quality of Life, Psychological Symptoms and Treatment Side Effects in Early Breast Cancer.

Background: Identifying and understanding modifiable factors for the well-being of cancer patients is critical in survivorship research. We studied variables associated with the exercise habits of breast cancer patients and investigated if the achievement of exercise recommendations was associated with enhanced quality of life and/or psychological well-being. Material and Methods. 311 women from Finland, Portugal, Israel, and Italy receiving adjuvant therapy for stage I-III breast cancer answered questions about sociodemographic factors and physical exercise. Quality of life was assessed by the EORTC C30 and BR23 questionnaires. Anxiety and depression were evaluated using the HADS scale. Results: At the beginning of adjuvant therapy and after twelve months, 32% and 26% of participants were physically inactive, 27% and 30% exercised between 30 and 150 minutes per week, while 41% and 45% exercised the recommended 150 minutes or more per week. Relative to other countries, Finnish participants were more likely to be active at baseline and at twelve months (89% vs. 50%, p < 0.001 and 87% vs. 64%, p < 0.001). Participants with stage I cancer were more likely to be active at twelve months than those with a higher stage (80% vs. 70%,p < 0.05). The inactive participants reported more anxiety (p < 0.05) and depression (p < 0.001), lower global quality of life (p < 0.001), and more side effects (p < 0.05) than the others at twelve months. Accordingly, those who remained inactive or decreased their level of exercise from baseline to twelve months reported more anxiety (p < 0.01) and depression (p < 0.001), lower global quality of life (p < 0.001), and more side effects (p < 0.05) than those with the same or increased level of exercise. Conclusion: For women with early breast cancer, exercise was associated with a better quality of life, less depression and anxiety, and fewer adverse events of adjuvant therapy. Trial registration number: NCT05095675. Paula Poikonen-Saksela on behalf of Bounce consortium (https://www.bounce-project.eu/).

Predicting Effective Adaptation to Breast Cancer to Help Women BOUNCE Back: Protocol for a Multicenter Clinical Pilot Study.

BACKGROUND: Despite the continued progress of medicine, dealing with breast cancer is becoming a major socioeconomic challenge, particularly due to its increasing incidence. The ability to better manage and adapt to the entire care process depends not only on the type of cancer but also on the patient's sociodemographic and psychological characteristics as well as on the social environment in which a person lives and interacts. Therefore, it is important to understand which factors may contribute to successful adaptation to breast cancer. To our knowledge, no studies have been performed on the combination effect of multiple psychological, biological, and functional variables in predicting the patient's ability to bounce back from a stressful life event, such as a breast cancer diagnosis. Here we describe the study protocol of a multicenter clinical study entitled "Predicting Effective Adaptation to Breast Cancer to Help Women to BOUNCE Back" or, in short, BOUNCE. OBJECTIVE: The aim of the study is to build a quantitative mathematical model of factors associated with the capacity for optimal adjustment to cancer and to study resilience through the cancer continuum in a population of patients with breast cancer. METHODS: A total of 660 women with breast cancer will be recruited from five European cancer centers in Italy, Finland, Israel, and Portugal. Biomedical and psychosocial variables will be collected using the Noona Healthcare platform. Psychosocial, sociodemographic, lifestyle, and clinical variables will be measured every 3 months, starting from presurgery assessment (ie, baseline) to 18 months after surgery. Temporal data mining, time-series prediction, sequence classification methods, clustering time-series data, and temporal association rules will be used to develop the predictive model. RESULTS: The recruitment process stared in January 2019 and ended in November 2021. Preliminary results have been published in a scientific journal and are available for consultation on the BOUNCE project website. Data analysis and dissemination of the study results will be performed in 2022. CONCLUSIONS: This study will develop a predictive model that is able to describe individual resilience and identify different resilience trajectories along the care process. The results will allow the implementation of tailored interventions according to patients' needs, supported by eHealth technologies. TRIAL REGISTRATION: ClinicalTrials.gov NCT05095675; https://clinicaltrials.gov/ct2/show/NCT05095675. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/34564.

Comparison of the mutational profiles of neuroendocrine breast tumours, invasive ductal carcinomas and pancreatic neuroendocrine carcinomas.

The pathophysiology and the optimal treatment of breast neuroendocrine tumours (NETs) are unknown. We compared the mutational profiles of breast NETs (n = 53) with those of 724 publicly available invasive ductal carcinoma (IDC) and 98 pancreatic NET (PNET) cases. The only significantly different pathogenetic or unknown variant rate between breast NETs and IDCs was detected in the TP53 (11.3% in breast NETs and 41% in IDCs, adjusted p value 0.027) and ADCK2 (9.4% in breast NETs vs. 0.28% in IDCs, adjusted p value 0.045) genes. Between breast NETs and PNETs, different pathogenetic or unknown variant frequencies were detected in 30 genes. For example, MEN1 was mutated in only 6% of breast NETs and 37% in PNETs (adjusted p value 0.00050), and GATA3 pathogenetic or unknown variants were only found in 17.0% of breast NETs and 0% in PNETs (adjusted p value 0.0010). The most commonly affected oncogenic pathways in the breast NET cases were PI3K/Akt/mTOR, NOTCH and RTK-RAS pathways. Breast NETs had typically clock-like mutational signatures and signatures associated with defective DNA mismatch repair in their mutational landscape. Our results suggest that the breast NET mutational profile more closely resembles that of IDCs than that of PNETs. These results also revealed several potentially druggable targets, such as MMRd, in breast NETs. In conclusion, breast NETs are indeed a separate breast cancer entity, but their optimal treatment remains to be elucidated.

Delay in the initiation of adjuvant chemotherapy in patients with breast cancer with mastectomy with or without immediate breast reconstruction.

BACKGROUND: Patients with breast cancer undergoing mastectomy should be offered the option of immediate breast reconstruction (IBR). The aim of this retrospective study was to assess whether there is a delay in the initiation of adjuvant chemotherapy in patients undergoing mastectomy with or without IBR. METHOD: The study included patients aged 70 years or younger with clinically node-negative breast cancer who underwent unilateral mastectomy with IBR (IBR group) or mastectomy alone (no-IBR group) followed by adjuvant chemotherapy at the Helsinki University Hospital between January 2012 to July 2018. RESULTS: A total of 645 patients were included; 186 in the IBR group and 459 in the no-IBR group. Sixty-six (35.5 per cent) patients in the IBR group and 102 (22.2 per cent) patients in the no-IBR group received their first chemotherapy cycle later than 6 weeks after surgery (P < 0.001). The respective numbers for later than 8 weeks were 17 (9.1 per cent) and 14 (3.1 per cent) (P = 0.001). Among all 645 patients, postoperative complications were a significant risk factor for a delay in the initiation of chemotherapy. Sixty-seven (39.9 per cent) patients with and 101 (21.2 per cent) patients without complications had a delay in chemotherapy (P < 0.001). The delay in chemotherapy was due to complications in 39 (59.1 per cent) in the IBR group and in 28 (27.5 per cent) in the no-IBR group (P < 0.001). CONCLUSION: Patients undergoing mastectomy alone were more likely to receive adjuvant chemotherapy within 6 weeks after surgery compared with the IBR patients. IBR significantly increased the risk of postoperative complications in comparison with mastectomy alone. The complications, in turn, were a significant risk factor for delay in adjuvant chemotherapy.

A graphical LASSO analysis of global quality of life, sub scales of the EORTC QLQ-C30 instrument and depression in early breast cancer.

We aimed to (a) investigate the interplay between depression, symptoms and level of functioning, and (b) understand the paths through which they influence health related quality of life (QOL) during the first year of rehabilitation period of early breast cancer. A network analysis method was used. The population consisted of 487 women aged 35-68 years, who had recently completed adjuvant chemotherapy or started endocrine therapy for early breast cancer. At baseline and at the first year from randomization QOL, symptomatology and functioning by the EORTC QLQ-C30 and BR-23 questionnaires, and depression by the Finnish version of Beck's 13-item depression scale, were collected. The multivariate interplay between the related scales was analysed via regularized partial correlation networks (graphical LASSO). The median global quality of life (gQoL) at baseline was 69.9 ± 19.0 (16.7-100) and improved to 74.9 ± 19.0 (0-100) after 1 year. Scales related to mental health (emotional functioning, cognitive functioning, depression, insomnia, body image, future perspective) were clustered together at both time points. Fatigue was mediated through a different route, having the strongest connection with physical functioning and no direct connection with depression. Multiple paths existed connecting symptoms and functioning types with gQoL. Factors with the strongest connections to gQoL included: social functioning, depression and fatigue at baseline; emotional functioning and fatigue at month 12. Overall, the most important nodes were depression, gQoL and fatigue. The graphical LASSO network analysis revealed that scales related to fatigue and emotional health had the strongest associations to the EORTC QLQ-C30 gQoL score. When we plan interventions for patients with impaired QOL it is important to consider both psychological support and interventions that improve fatigue and physical function like exercise.Trial registration: http://www.clinicaltrials.gov/ (identifier number NCT00639210).

Compressive stress-mediated p38 activation required for ERα + phenotype in breast cancer.

Breast cancer is now globally the most frequent cancer and leading cause of women's death. Two thirds of breast cancers express the luminal estrogen receptor-positive (ERα + ) phenotype that is initially responsive to antihormonal therapies, but drug resistance emerges. A major barrier to the understanding of the ERα-pathway biology and therapeutic discoveries is the restricted repertoire of luminal ERα + breast cancer models. The ERα + phenotype is not stable in cultured cells for reasons not fully understood. We examine 400 patient-derived breast epithelial and breast cancer explant cultures (PDECs) grown in various three-dimensional matrix scaffolds, finding that ERα is primarily regulated by the matrix stiffness. Matrix stiffness upregulates the ERα signaling via stress-mediated p38 activation and H3K27me3-mediated epigenetic regulation. The finding that the matrix stiffness is a central cue to the ERα phenotype reveals a mechanobiological component in breast tissue hormonal signaling and enables the development of novel therapeutic interventions. Subject terms: ER-positive (ER + ), breast cancer, ex vivo model, preclinical model, PDEC, stiffness, p38 SAPK.

Expression of markers of stem cell characteristics, epithelial-mesenchymal transition, basal-like phenotype, proliferation, and androgen receptor in metaplastic breast cancer and their prognostic impact.

BACKGROUND: Metaplastic breast cancer (MpBC) is a heterogeneous subtype of invasive mammary carcinoma associated with epithelial-mesenchymal transition (EMT) and cancer stem cell characteristics. Data regarding prognostic markers and potentially actionable targets for therapy are still limited. The present study aimed to characterize the immunohistochemical landscape of this rare malignancy and to identify potential prognostic factors and targets for therapy. MATERIAL AND METHODS: A total of 75 patients diagnosed with MpBC over a 15-year period were included in the study. We performed immunohistochemical analyses for Ki-67 (MIB-1), epidermal growth factor receptor (EGFR), cytokeratin 5/6, vimentin, CD44, and androgen receptor (AR) and correlated their expression with clinicopathologic features and clinical outcomes. The p-values for survival analyses were corrected for multiple testing (threshold 0.01). RESULTS: Most tumors expressed CK5/6 (73%), EGFR (59%), CD44 (81%), and vimentin (87%). Eighty-nine percent had a high Ki-67 index. Eighty-four percent were classified as basal-like (CK 5/6 or EGFR positive). AR was expressed in 21% of the tumors. The basal-like phenotype was significantly (p = 0.009) associated with inferior disease-free (DFS) and breast-cancer-specific overall survival (BCOS) with borderline significance (p = 0.01). In addition, a low Ki-67 index was associated with improved DFS (p = 0.033) and BCOS (p = 0.03). CONCLUSION: Most MpBCs express basal markers (CK5/6, EGFR), epithelial-mesenchymal transition marker vimentin, and the stem cell marker CD44. Expression of basal-like markers was significantly related to inferior DFS. All the 11 patients with a lack of expression of basal markers survived without relapse.

Cognitive, emotional, and behavioral mediators of the impact of coping self-efficacy on adaptation to breast cancer: An international prospective study.

OBJECTIVE: The main objective of this prospective multicenter study was to examine whether illness representations of control, affect, and coping behaviors mediate the effects of self-efficacy to cope with cancer on psychological symptoms and overall quality of life, in breast cancer patients. METHOD: Data from 413 women (Mean age = 54.87; SD = 8.01), coming from four countries (i.e., Finland, Israel, Italy, Portugal), who received medical therapy for their early breast cancer, were analyzed. Coping self-efficacy was assessed at baseline. Potential mediators were assessed three months later, and outcomes after six months. RESULTS: Coping self-efficacy was related to all mediators and outcomes. Illness representations of treatment control, positive and negative affect, and certain coping behaviors (mostly, anxiety preoccupation) mediated the effects of coping self-efficacy. Coping self-efficacy was related to each outcome through a different combination of mediators. CONCLUSIONS: Coping self-efficacy is a major self-regulation factor which is linked to well-being through multiple cognitive, emotional, and behavioral pathways. Enhancement of coping self-efficacy should be a central intervention goal for patients with breast cancer, towards promotion of their well-being.

A Novel Digital Patient-Reported Outcome Platform (Noona) for Clinical Use in Patients With Cancer: Pilot Study Assessing Suitability.

BACKGROUND: As the incidence of cancer is on the rise, there is a need to develop modern communication tools between patients and the medical personnel. Electronic patient-reported outcome (ePRO) measures increase the safety of cancer treatments and may have an impact on treatment outcome as well. ePRO may also provide a cost-efficient way to organize follow-up for patients with cancer. Noona is an internet-based system for patients to self-report symptoms and adverse events of cancer treatments from home via a computer or a smart device (eg, smartphone, tablet). OBJECTIVE: In this pilot study, we assessed the suitability of a novel ePRO application (Noona) for patients with cancer, nurses, and doctors at the Helsinki University Hospital, Finland. METHODS: The study included 44 patients with cancer (different solid tumor types) and 17 health care professionals (nurses or medical doctors). Patients were either operated or received systemic treatment or radiotherapy. Patients reported their symptoms to the medical staff via Noona. In addition, patients and clinicians answered a questionnaire, based on which Noona's suitability for clinical use was evaluated in terms of usability (ease of use, operability, and learnability), reliability (subjective opinion of the participant), and incidence of harmful events reported by the participants. RESULTS: A total of 41/44 (93%) patients and 15/17 (88%) professionals reported that the program was easy or quite easy to use; 38/44 (86%) patients and 11/17 (65%) professionals found Noona reliable, and 38/44 (86%) patients and 10/17 (59%) professionals would recommend Noona to other patients or their colleagues. No harmful incidences caused by the use of Noona were reported by the patients; however, 1 harmful incidence was reported by one of the professionals. CONCLUSIONS: The majority of the participants felt that Noona appeared reliable and it was easy to use. Noona seems to be a useful tool for monitoring patient's symptoms during cancer therapy. Future studies will determine the impact of this ePRO platform in routine clinical practice.

Monitoring serum estradiol levels in breast cancer patients during extended adjuvant letrozole treatment after five years of tamoxifen: a prospective trial.

PURPOSE: To analyze whether monitoring serum estradiol (E2) levels using a highly sensitive and specific liquid chromatography tandem mass spectrometry (LC-MS/MS) method may identify patients with AI failure with E2 levels below the lower limit of quantification (LLOQ) after schwitching from tamoxifen to letrozole. METHODS: In a prospective study of breast cancer patients switching to letrozole treatment after previous tamoxifen, plasma estrogen levels were measured at baseline and after 3- and 12-months using LC-MS/MS. RESULTS: Forty-six patients were classified postmenopausal and entered into the final analysis. Thirty-nine (85%) patients had three- and 12-month E2 concentrations below the LLOQ (5 pmol/L). In the seven patients classified as AI-failures during letrozole treatment, serum E2-MS level rose above 5 pmol/L at 3 months with a mean E2-MS 77.5 pmol/L or 12 months with a mean E2-MS 21 pmol/L. None of the baseline variables i.e., age at diagnosis, age at study entry, age at menarche, BMI, endometrial thickness, total ovarian volume, baseline FSH, E2-IA, or E2-MS were significantly associated with the risk of AI failure in logistic regression. E2 levels at baseline measured by E2-IA did not significantly correlate to the levels measured by E2-MS. CONCLUSIONS: There is a relatively high risk of inadequate estrogen suppression in patients who switch from tamoxifen treatment to AIs. The use of sensitive and specific assays, such as LC-MS/MS methods, to monitor estrogen levels during AI treatment is essential to minimize the risk of a proceeding inefficient endocrine therapy.

A machine learning-based pipeline for modeling medical, socio-demographic, lifestyle and self-reported psychological traits as predictors of mental health outcomes after breast cancer diagnosis: An initial effort to define resilience effects.

Displaying resilience following a diagnosis of breast cancer is crucial for successful adaptation to illness, well-being, and health outcomes. Several theoretical and computational models have been proposed toward understanding the complex process of illness adaptation, involving a large variety of patient sociodemographic, lifestyle, medical, and psychological characteristics. To date, conventional multivariate statistical methods have been used extensively to model resilience. In the present work we describe a computational pipeline designed to identify the most prominent predictors of mental health outcomes following breast cancer diagnosis. A machine learning framework was developed and tested on the baseline data (recorded immediately post diagnosis) from an ongoing prospective, multinational study. This fully annotated dataset includes socio-demographic, lifestyle, medical and self-reported psychological characteristics of women recently diagnosed with breast cancer (N = 609). Nine different feature selection and cross-validated classification schemes were compared on their performance in classifying patients into low vs high depression symptom severity. Best-performing approaches involved a meta-estimator combined with a Support Vector Machines (SVMs) classification algorithm, exhibiting balanced accuracy of 0.825, and a fair balance between sensitivity (90%) and specificity (74%). These models consistently identified a set of psychological traits (optimism, perceived ability to cope with trauma, resilience as trait, ability to comprehend the illness), and subjective perceptions of personal functionality (physical, social, cognitive) as key factors accounting for concurrent depression symptoms. A comprehensive supervised learning pipeline is proposed for the identification of predictors of depression symptoms which could severely impede adaptation to illness.