Background Cancer patients transitioning to survivorship after completing cancer treatments need psychosocial interventions to manage stressors such as anxiety, depression, and fear of cancer recurrence. Mindfulness-based interventions (MBIs) are effective for treating these symptoms; however, cancer survivors are often unable to participate in face-to-face interventions because of difficulties such as work and family commitments, treatment-related side-effects, scheduling conflicts, and geography. Smartphone app–based MBIs are an innovative way to deliver psychosocial cancer care and can overcome several such difficulties, since patients can participate at their own convenience. Objective The SEAMLESS (Smartphone App–Based Mindfulness Intervention for Cancer Survivors) study aims to evaluate the efficacy of a tailored app-based mindfulness intervention for cancer survivors (the Am Mindfulness-Based Cancer Survivorship—MBCS—Journey) for treating (1) symptoms of stress (primary outcome), as well as (2) fear of cancer recurrence, anxiety, depression, fatigue, and overall physical functioning (secondary outcomes). This is the first Canadian efficacy trial of a tailored mindfulness app intervention in cancer survivors. Methods This is a randomized waitlist-controlled trial, which will evaluate the effectiveness of Am MBCS for impacting the primary and secondary outcomes in cancer survivors who have completed all their cancer treatments. Outcomes will be assessed using web-based surveys with validated psychometric instruments at (1) baseline, (2) mid-intervention (2 weeks later), (3) immediately postintervention (4 weeks), (4) 3 months postbaseline, (5) 6 months postbaseline, and (6) 12 months postbaseline. The waitlist group will complete all assessments and will cross over to the intervention condition after the 3-month assessment. In addition, data will be obtained by the smartphone app itself, which includes users’ engagement with the app-based intervention, their emotional state (eg, angry and elated) from a user-inputted digital emotion-mapping board, and psychobiometric data using photoplethysmography technology. Results The study received ethics approval in September 2018 and recruitment commenced in January 2019. Participants are being recruited through a provincial cancer registry, and the majority of participants currently enrolled are breast (44/83, 53%) or colorectal (17/83, 20%) cancer survivors, although some survivors of other cancer are also present. Data collection for analysis of the primary outcome time-point will be complete by September 2019, and the follow-up data will be collected and analyzed by September 2020. Data will be analyzed to determine group differences using linear mixed modelling statistical techniques. Conclusions Cancer care providers are uncertain about the efficacy of app-based mindfulness interventions for patients, which are available in great supply in today’s digital world. This study will provide rigorously evaluated efficacy data for an app-based mindfulness intervention for cancer survivors, which if helpful, could be made available for psychosocial care at cancer centers worldwide. Trial Registration ClinicalTrials.gov NCT03484000; https://clinicaltrials.gov/ct2/show/NCT03484000 International Registered Report Identifier (IRRID) DERR1-10.2196/15178

Background: Real-world outcomes for patients with human epidermal growth factor receptor-2 (HER2)-positive metastatic breast cancer (MBC) treated with pertuzumab in combination with taxane chemotherapy plus trastuzumab (TaxTP) in the first line setting and trastuzumab emtansine (TE) in any line of treatment are lacking. Methods: Cohorts of patients treated with (1) TaxTP and (2) TE from January 1, 2013 through December 31, 2016 were retrospectively obtained from a population-based database. Cohorts were described according to age, hormone receptor (HR) status, prior systemic therapies, event-free survival (EFS) defined as time from start of treatment to start of next line of treatment or death, and overall survival (OS). Results: A total of 122 patients were treated with TaxTP and 104 with TE. In the TaxTP cohort, EFS was significantly longer in the trastuzumab-naïve group compared with the adjuvant trastuzumab group (median EFS = 27.0 vs 12.4 months; P = .002). In the TaxTP cohort, median OS was not reached. In the TE cohort, EFS was significantly longer in the pertuzumab-naïve group compared with pertuzumab-exposed group (median time to treatment failure [TTF] = 18.7 vs 5.5 months; P < .001). Overall survival was also significantly longer in the pertuzumab-naïve group compared with the pertuzumab-exposed group (median OS = 23.2 vs 14.1 months; P = .022). In multivariable analyses, adjuvant trastuzumab and prior pertuzumab exposure in the metastatic setting remained significant predictors of inferior EFS for patients treated with TaxTP and TE, respectively. Conclusions: New anti-HER2 therapies appear to be clinically relevant in the real-world.

The reduced cost of trastuzumab biosimilars has led to increased adoption for HER2-positive breast cancer. This review of trastuzumab biosimilars encompasses this development and real world clinical data in early breast cancer. In addition, we present a retrospective study evaluating the total pathological complete response (tpCR) rates (lack of residual invasive cancer in resected breast tissue and axillary nodes), of MYL-1401O to reference trastuzumab (TRZ) in the neoadjuvant setting for HER2+ early breast cancer (EBC) in Alberta, Canada. Neoadjuvant patients with HER2+ EBC treated with TRZ from November 2018–October 2019 and MYL-1401O from December 2019–September 2020 were identified. Logistic regression was used to control for variables potentially associated with tpCR: trastuzumab product, age, pre-operative T- and N-stage, grade, hormone receptor (HR)-status, HER2-status, chemotherapy regimen, and chemotherapy completion. tpCR was 35.6% in the MYL-1401O group (n = 59) and 40.3% in the TRZ (n = 77) group, p = 0.598. After controlling for clinically relevant variables, there was no significant difference in the odds of achieving tpCR in patients treated with TRZ versus MYL-1401O (OR 1.1, 95% CI 0.5–2.4, p = 0.850). tpCR rates were similar for patients treated with MYL-1401O compared to trastuzumab in our real world study of HER2+ neoadjuvant EBC and comparable to pivotal phase 3 trials.

PURPOSE The optimal characteristics among patients with breast cancer to recommend neoadjuvant chemotherapy is an active area of clinical research. We developed and compared several approaches to developing prediction models for pathologic complete response (pCR) among patients with breast cancer in Alberta. METHODS The study included all patients with breast cancer who received neoadjuvant chemotherapy in Alberta between 2012 and 2014 identified from the Alberta Cancer Registry. Patient, tumor, and treatment data were obtained through primary chart review. pCR was defined as no residual invasive tumor at surgical excision in breast or axilla. Two types of prediction models for pCR were built: (1) expert model: variables selected on the basis of oncologists' opinions and (2) data-driven model: variables selected by trained machine. These model types were fit using logistic regression (LR), random forests (RF), and gradient-boosted trees (GBT). We compared the models using area under the receiver operating characteristic curve and integrated calibration index, and internally validated using bootstrap resampling. RESULTS A total of 363 cases were included in the analyses, of which 86 experienced pCR. The RF and GBT fits yielded higher optimism-corrected area under the receiver operating characteristic curves compared with LR for the expert (RF: 0.70; GBT: 0.69; LR: 0.65) and data-driven models (RF: 0.71; GBT: 0.68; LR: 0.64). The LR fit yielded the lowest integrated calibration indices for the expert (LR: 0.037; GBT: 0.05; RF: 0.10) and data-driven models (LR: 0.026; GBT: 0.06; RF: 0.099). CONCLUSION Our models demonstrated predictive ability for pCR using routinely collected clinical and demographic variables. We show that machine learning fit methods can be used to optimize models for pCR prediction. We also show that additional variables beyond clinical expertise do not considerably improve predictive ability and may not be of value on the basis of the burden of data collection.