Technical Challenges for CTC Implementation in Breast Cancer

Breast cancer is the most common neoplasm in women worldwide. Tissue biopsy, currently the gold standard to obtain tumor molecular information, is invasive and might be affected by tumor heterogeneity rendering it incapable to portray the complete dynamic picture by the absence of specific genetic changes during the evolution of the disease. In contrast, liquid biopsy can provide unique opportunities for real-time monitoring of disease progression, treatment response and for studying tumor heterogeneity combining the information of DNA that tumors spread in the blood (circulating tumor DNA) with CTCs analysis. In this review, we analyze the technical and biological challenges for isolation and characterization of circulating tumor cells from breast cancer patients. Circulating tumor cell (CTC) enumeration value is included in numerous clinical studies due to the prognostic’s role of these cells. Despite this, there are so many questions pending to answer. How to manage lymphocytes background, how to distinguish the CTCs subtypes or how to work with frozen samples, are some of the issues that will discuss in this review. Based on our experience, we try to address these issues and other technical limitations that should be solved to optimize the standardization of protocols, sample extraction procedures, circulating-tumor material isolation (CTCs vs. ctDNA) and the very diverse methodologies employed, aiming to consolidate the use of CTCs in the clinic. Furthermore, we think that new approaches focusing on isolation CTCs in other body fluids such as cerebrospinal or ascitic fluid are necessary to increase the opportunities of circulating tumor cells in the practice clinic as well as to study the promising role of CTC clusters and their prognostic value in metastatic breast cancer

study protocol

Funding Information: AJO-M was national coordinator for Portugal of a non-interventional study (EDMS-ERI-143085581, 4.0) to characterize a Treatment-Resistant Depression Cohort in Europe, sponsored by Janssen-Cilag, Ltd (2019–2020), is recipient of a grant from Schuhfried GmBH for norming and validation of cognitive tests, and is national coordinator for Portugal of trials of psilocybin therapy for treatment-resistant depression, sponsored by Compass Pathways, Ltd (EudraCT number 2017–003288-36), and of esketamine for treatment-resistant depression, sponsored by Janssen-Cilag, Ltd (EudraCT NUMBER: 2019–002992-33). Funding Information: The FAITH project is funded under the European Commission (EC) Horizon Europe Programme, ‘H2020-EU.3.1.—SOCIETAL CHALLENGES—Health, demographic change, and well-being’. It is funded to the value €4.8 M, under the specific topic ‘SC1-DTH-01–2019—Big data and Artificial Intelligence for monitoring health status and quality of life after the cancer treatment’ with Grant agreement ID: 875358. The funder has no influence in the design, collection, analysis, data interpretation, or manuscript writing. Funding Information: RL is supported by an individual Scientific Employment Stimulus from Fundação para a Ciência e Tecnologia, Portugal (CEECIND/04157/2018). Publisher Copyright: © 2022, The Author(s).Background: Depression is a common condition among cancer patients, across several points in the disease trajectory. Although presenting higher prevalence rates than the general population, it is often not reported or remains unnoticed. Moreover, somatic symptoms of depression are common in the oncological context and should not be dismissed as a general symptom of cancer. It becomes even more challenging to track psychological distress in the period after the treatment, where connection with the healthcare system typically becomes sporadic. The main goal of the FAITH project is to remotely identify and predict depressive symptoms in cancer survivors, based on a federated machine learning (ML) approach, towards optimization of privacy. Methods: FAITH will remotely analyse depression markers, predicting their negative trends. These markers will be treated in distinct categories, namely nutrition, sleep, activity and voice, assessed in part through wearable technologies. The study will include 300 patients who have had a previous diagnosis of breast or lung cancer and will be recruited 1 to 5 years after the end of primary cancer. The study will be organized as a 12-month longitudinal prospective observational cohort study, with monthly assessments to evaluate depression symptoms and quality of life among cancer survivors. The primary endpoint is the severity of depressive symptoms as measured by the Hamilton Depression Rating Scale (Ham-D) at months 3, 6, 9 and 12. Secondary outcomes include self-reported anxiety and depression symptoms (HADS scale), and perceived quality of life (EORTC questionnaires), at baseline and monthly. Based on the predictive models gathered during the study, FAITH will also aim at further developing a conceptual federated learning framework, enabling to build machine learning models for the prediction and monitoring of depression without direct access to user’s personal data. Discussion: Improvements in the objectivity of psychiatric assessment are necessary. Wearable technologies can provide potential indicators of depression and anxiety and be used for biofeedback. If the FAITH application is effective, it will provide healthcare systems with a novel and innovative method to screen depressive symptoms in oncological settings. Trial registration: Trial ID: ISRCTN10423782. Date registered: 21/03/2022.publishersversionpublishe

A prospective observational study for a Federated Artificial Intelligence solution for monitoring mental health status after cancer treatment (FAITH): study protocol

Background: Depression is a common condition among cancer patients, across several points in the disease trajec‑ tory. Although presenting higher prevalence rates than the general population, it is often not reported or remains unnoticed. Moreover, somatic symptoms of depression are common in the oncological context and should not be dismissed as a general symptom of cancer. It becomes even more challenging to track psychological distress in the period after the treatment, where connection with the healthcare system typically becomes sporadic. The main goal of the FAITH project is to remotely identify and predict depressive symptoms in cancer survivors, based on a federated machine learning (ML) approach, towards optimization of privacy. Methods: FAITH will remotely analyse depression markers, predicting their negative trends. These markers will be treated in distinct categories, namely nutrition, sleep, activity and voice, assessed in part through wearable technolo‑ gies. The study will include 300 patients who have had a previous diagnosis of breast or lung cancer and will be recruited 1 to 5 years after the end of primary cancer. The study will be organized as a 12-month longitudinal pro‑ spective observational cohort study, with monthly assessments to evaluate depression symptoms and quality of life among cancer survivors. The primary endpoint is the severity of depressive symptoms as measured by the Hamilton Depression Rating Scale (Ham-D) at months 3, 6, 9 and 12. Secondary outcomes include self-reported anxiety and depression symptoms (HADS scale), and perceived quality of life (EORTC questionnaires), at baseline and monthly. Based on the predictive models gathered during the study, FAITH will also aim at further developing a conceptual fed‑ erated learning framework, enabling to build machine learning models for the prediction and monitoring of depres‑ sion without direct access to user’s personal data. Discussion: Improvements in the objectivity of psychiatric assessment are necessary. Wearable technologies can provide potential indicators of depression and anxiety and be used for biofeedback. If the FAITH application isinfo:eu-repo/semantics/publishedVersio

Efficacy of Neoadjuvant Carboplatin plus Docetaxel in Triple-Negative Breast Cancer: Combined Analysis of Two Cohorts

Recent studies demonstrate that addition of neoadjuvant (NA) carboplatin (Cb) to anthracycline/taxane chemotherapy improves pathological complete response (pCR) in triple negative breast cancer (TNBC). Effectiveness of anthracycline-free, platinum combinations in TNBC is not well known. Here we report efficacy of NA carboplatin + docetaxel (CbD) in TNBC

Efficacy of Neoadjuvant Carboplatin plus Docetaxel in Triple-Negative Breast Cancer: Combined Analysis of Two Cohorts

PURPOSE: Recent studies demonstrate that addition of neoadjuvant (NA) carboplatin (Cb) to anthracycline/taxane chemotherapy improves pathological complete response (pCR) in triple negative breast cancer (TNBC). Effectiveness of anthracycline-free, platinum combinations in TNBC is not well known. Here we report efficacy of NA carboplatin + docetaxel (CbD) in TNBC. PATIENTS AND METHODS: The study population includes 190 patients with stage I-III TNBC treated uniformly on two independent prospective cohorts. All patients were prescribed NA chemotherapy regimen of Cb (AUC 6) + D (75mg/m2) given every 21 days × 6 cycles. Pathological complete response (pCR: no evidence of invasive tumor in the breast and axilla) and Residual Cancer Burden (RCB) were evaluated. RESULTS: Among 190 patients, median tumor size was 35mm, 52% Lymph Node positive and 16% had germline BRCA1/2 mutation. The overall pCR and RCB 0+1 rates were 55% and 68%, respectively. pCR in patients with BRCA associated and wild-type TNBC were 59% and 56%, respectively (p=0.83). On multivariable analysis stage III disease was the only factor associated with a lower likelihood of achieving a pCR. 21% and 7% of patients, respectively, experienced at least one grade 3 or 4 adverse event. CONCLUSION: The CbD regimen was well tolerated and yielded high pCR rates in both BRCA associated and wildtype TNBC. These results are comparable to pCR achieved with addition of Cb to anthracycline-taxane chemotherapy. Our study adds to the existing data on the efficacy of platinum agents in TNBC and supports further exploration of the CbD regimen in randomized studies