The COMET (Comparison of Operative versus Monitoring and Endocrine Therapy) trial: a phase III randomised controlled clinical trial for low-risk ductal carcinoma in situ (DCIS)

Introduction Ductal carcinoma in situ (DCIS) is a noninvasive non-obligate precursor of invasive breast cancer. With guideline concordant care (GCC), DCIS outcomes are at least as favourable as some other early stage cancer types such as prostate cancer, for which active surveillance (AS) is a standard of care option. However, AS has not yet been tested in relation to DCIS. The goal of the COMET (Comparison of Operative versus Monitoring and Endocrine Therapy) trial for low-risk DCIS is to gather evidence to help future patients consider the range of treatment choices for low-risk DCIS, from standard therapies to AS. The trial will determine whether there may be some women who do not substantially benefit from current GCC and who could thus be safely managed with AS. This protocol is version 5 (11 July 2018). Any future protocol amendments will be submitted to Quorum Centralised Institutional Review Board/local institutional review boards for approval via the sponsor of the study (Alliance Foundation Trials). Methods and analysis COMET is a phase III, randomised controlled clinical trial for patients with low-risk DCIS. The primary outcome is ipsilateral invasive breast cancer rate in women undergoing GCC compared with AS. Secondary objectives will be to compare surgical, oncological and patient-reported outcomes. Patients randomised to the GCC group will undergo surgery as well as radiotherapy when appropriate; those in the AS group will be monitored closely with surgery only on identification of invasive breast cancer. Patients in both the GCC and AS groups will have the option of endocrine therapy. The total planned accrual goal is 1200 patients. Ethics and dissemination The COMET trial will be subject to biannual formal review at the Alliance Foundation Data Safety Monitoring Board meetings. Interim analyses for futility/safety will be completed annually, with reporting following Consolidated Standards of Reporting Trials (CONSORT) guidelines for noninferiority trials

Application of deep learning on mammographies to discriminate between low and high-risk DCIS for patient participation in active surveillance trials

Background: Ductal Carcinoma In Situ (DCIS) can progress to invasive breast cancer, but most DCIS lesions never will. Therefore, four clinical trials (COMET, LORIS, LORETTA, AND LORD) test whether active surveillance for women with low-risk Ductal carcinoma In Situ is safe (E. S. Hwang et al., BMJ Open, 9: e026797, 2019, A. Francis et al., Eur J Cancer. 51: 2296–2303, 2015, Chizuko Kanbayashi et al. The international collaboration of active surveillance trials for low-risk DCIS (LORIS, LORD, COMET, LORETTA), L. E. Elshof et al., Eur J Cancer, 51, 1497–510, 2015). Low-risk is defined as grade I or II DCIS. Because DCIS grade is a major eligibility criteria in these trials, it would be very helpful to assess DCIS grade on mammography, informed by grade assessed on DCIS histopathology in pre-surgery biopsies, since surgery will not be performed on a significant number of patients participating in these trials. Objective: To assess the performance and clinical utility of a convolutional neural network (CNN) in discriminating high-risk (grade III) DCIS and/or Invasive Breast Cancer (IBC) from low-risk (grade I/II) DCIS based on mammographic features. We explored whether the CNN could be used as a decision support tool, from excluding high-risk patients for active surveillance. Methods: In this single centre retrospective study, 464 patients diagnosed with DCIS based on pre-surgery biopsy between 2000 and 2014 were included. The collection of mammography images was partitioned on a patient-level into two subsets, one for training containing 80% of cases (371 cases, 681 images) and 20% (93 cases, 173 images) for testing. A deep learning model based on the U-Net CNN was trained and validated on 681 two-dimensional mammograms. Classification performance was assessed with the Area Under the Curve (AUC) receiver operating characteristic and predictive values on the test set for predicting high risk DCIS-and high-risk DCIS and/ or IBC from low-risk DCIS. Results: When classifying DCIS as high-risk, the deep learning network achieved a Positive Predictive Value (PPV) of 0.40, Negative Predictive Value (NPV) of 0.91 and an AUC of 0.72 on the test dataset. For distinguishing high-risk and/or upstaged DCIS (occult invasive breast cancer) from low-risk DCIS a PPV of 0.80, a NPV of 0.84 and an AUC of 0.76 were achieved. Conclusion: For both scenarios (DCIS grade I/II vs. III, DCIS grade I/II vs. III and/or IBC) AUCs were high, 0.72 and 0.76, respectively, concluding that our convolutional neural network can discriminate low-grade from high-grade DCIS.</p

Application of Deep Learning on Mammographies To Discriminate Between Low and High-Risk Dcis for Patient Participation in Active Surveillance Trials

BACKGROUND: Ductal Carcinoma In Situ (DCIS) can progress to invasive breast cancer, but most DCIS lesions never will. Therefore, four clinical trials (COMET, LORIS, LORETTA, AND LORD) test whether active surveillance for women with low-risk Ductal carcinoma In Situ is safe (E. S. Hwang et al., BMJ Open, 9: e026797, 2019, A. Francis et al., Eur J Cancer. 51: 2296-2303, 2015, Chizuko Kanbayashi et al. The international collaboration of active surveillance trials for low-risk DCIS (LORIS, LORD, COMET, LORETTA), L. E. Elshof et al., Eur J Cancer, 51, 1497-510, 2015). Low-risk is defined as grade I or II DCIS. Because DCIS grade is a major eligibility criteria in these trials, it would be very helpful to assess DCIS grade on mammography, informed by grade assessed on DCIS histopathology in pre-surgery biopsies, since surgery will not be performed on a significant number of patients participating in these trials. OBJECTIVE: To assess the performance and clinical utility of a convolutional neural network (CNN) in discriminating high-risk (grade III) DCIS and/or Invasive Breast Cancer (IBC) from low-risk (grade I/II) DCIS based on mammographic features. We explored whether the CNN could be used as a decision support tool, from excluding high-risk patients for active surveillance. METHODS: In this single centre retrospective study, 464 patients diagnosed with DCIS based on pre-surgery biopsy between 2000 and 2014 were included. The collection of mammography images was partitioned on a patient-level into two subsets, one for training containing 80% of cases (371 cases, 681 images) and 20% (93 cases, 173 images) for testing. A deep learning model based on the U-Net CNN was trained and validated on 681 two-dimensional mammograms. Classification performance was assessed with the Area Under the Curve (AUC) receiver operating characteristic and predictive values on the test set for predicting high risk DCIS-and high-risk DCIS and/ or IBC from low-risk DCIS. RESULTS: When classifying DCIS as high-risk, the deep learning network achieved a Positive Predictive Value (PPV) of 0.40, Negative Predictive Value (NPV) of 0.91 and an AUC of 0.72 on the test dataset. For distinguishing high-risk and/or upstaged DCIS (occult invasive breast cancer) from low-risk DCIS a PPV of 0.80, a NPV of 0.84 and an AUC of 0.76 were achieved. CONCLUSION: For both scenarios (DCIS grade I/II vs. III, DCIS grade I/II vs. III and/or IBC) AUCs were high, 0.72 and 0.76, respectively, concluding that our convolutional neural network can discriminate low-grade from high-grade DCIS

Ductal carcinoma in situ: to treat or not to treat, that is the question

Abstract: Ductal carcinoma in situ (DCIS) now represents 20–25% of all ‘breast cancers’ consequent upon detection by population-based breast cancer screening programmes. Currently, all DCIS lesions are treated, and treatment comprises either mastectomy or breast-conserving surgery supplemented with radiotherapy. However, most DCIS lesions remain indolent. Difficulty in discerning harmless lesions from potentially invasive ones can lead to overtreatment of this condition in many patients. To counter overtreatment and to transform clinical practice, a global, comprehensive and multidisciplinary collaboration is required. Here we review the incidence of DCIS, the perception of risk for developing invasive breast cancer, the current treatment options and the known molecular aspects of progression. Further research is needed to gain new insights for improved diagnosis and management of DCIS, and this is integrated in the PRECISION (PREvent ductal Carcinoma In Situ Invasive Overtreatment Now) initiative. This international effort will seek to determine which DCISs require treatment and prevent the consequences of overtreatment on the lives of many women affected by DCIS

TBCRC 039: a phase II study of preoperative ruxolitinib with or without paclitaxel for triple-negative inflammatory breast cancer

Background Patients with inflammatory breast cancer (IBC) have overall poor clinical outcomes, with triple-negative IBC (TN-IBC) being associated with the worst survival, warranting the investigation of novel therapies. Preclinical studies implied that ruxolitinib (RUX), a JAK1/2 inhibitor, may be an effective therapy for TN-IBC. Methods We conducted a randomized phase II study with nested window-of-opportunity in TN-IBC. Treatment-naïve patients received a 7-day run-in of RUX alone or RUX plus paclitaxel (PAC). After the run-in, those who received RUX alone proceeded to neoadjuvant therapy with either RUX + PAC or PAC alone for 12 weeks; those who had received RUX + PAC continued treatment for 12 weeks. All patients subsequently received 4 cycles of doxorubicin plus cyclophosphamide prior to surgery. Research tumor biopsies were performed at baseline (pre-run-in) and after run-in therapy. Tumors were evaluated for phosphorylated STAT3 (pSTAT3) by immunostaining, and a subset was also analyzed by RNA-seq. The primary endpoint was the percent of pSTAT3-positive pre-run-in tumors that became pSTAT3-negative. Secondary endpoints included pathologic complete response (pCR). Results Overall, 23 patients were enrolled, of whom 21 completed preoperative therapy. Two patients achieved pCR (8.7%). pSTAT3 and IL-6/JAK/STAT3 signaling decreased in post-run-in biopsies of RUX-treated samples, while sustained treatment with RUX + PAC upregulated IL-6/JAK/STAT3 signaling compared to RUX alone. Both treatments decreased GZMB+ T cells implying immune suppression. RUX alone effectively inhibited JAK/STAT3 signaling but its combination with PAC led to incomplete inhibition. The immune suppressive effects of RUX alone and in combination may negate its growth inhibitory effects on cancer cells. Conclusion In summary, the use of RUX in TN-IBC was associated with a decrease in pSTAT3 levels despite lack of clinical benefit. Cancer cell-specific-targeting of JAK2/STAT3 or combinations with immunotherapy may be required for further evaluation of JAK2/STAT3 signaling as a cancer therapeutic target.Trial registrationwww.clinicaltrials.gov, NCT02876302. Registered 23 August 2016

NeuriteQuant: An open source toolkit for high content screens of neuronal Morphogenesis

<p>Abstract</p> <p>Background</p> <p>To date, some of the most useful and physiologically relevant neuronal cell culture systems, such as high density co-cultures of astrocytes and primary hippocampal neurons, or differentiated stem cell-derived cultures, are characterized by high cell density and partially overlapping cellular structures. Efficient analytical strategies are required to enable rapid, reliable, quantitative analysis of neuronal morphology in these valuable model systems.</p> <p>Results</p> <p>Here we present the development and validation of a novel bioinformatics pipeline called NeuriteQuant. This tool enables fully automated morphological analysis of large-scale image data from neuronal cultures or brain sections that display a high degree of complexity and overlap of neuronal outgrowths. It also provides an efficient web-based tool to review and evaluate the analysis process. In addition to its built-in functionality, NeuriteQuant can be readily extended based on the rich toolset offered by ImageJ and its associated community of developers. As proof of concept we performed automated screens for modulators of neuronal development in cultures of primary neurons and neuronally differentiated P19 stem cells, which demonstrated specific dose-dependent effects on neuronal morphology.</p> <p>Conclusions</p> <p>NeuriteQuant is a freely available open-source tool for the automated analysis and effective review of large-scale high-content screens. It is especially well suited to quantify the effect of experimental manipulations on physiologically relevant neuronal cultures or brain sections that display a high degree of complexity and overlap among neurites or other cellular structures.</p

Impact of an Online Decision Support tool for Ductal Carcinoma In Situ (DCIS) Using a Pre-Post Design (AFT-25)

BACKGROUND: The heterogeneous biology of ductal carcinoma in situ (DCIS), as well as the variable outcomes, in the setting of numerous treatment options have led to prognostic uncertainty. Consequently, making treatment decisions is challenging and necessitates involved communication between patient and provider about the risks and benefits. We developed and investigated an interactive decision support tool (DST) designed to improve communication of treatment options and related long-term risks for individuals diagnosed with DCIS. FINDINGS: The DST was developed for use by individuals aged \u3e 40 years with DCIS and is based on a disease simulation model that integrates empirical data and clinical characteristics to predict patient-specific impacts of six DCIS treatment choices. Personalized risk predictions for each treatment option were communicated using icon arrays and percentages for each outcome. Users of the DST were asked before and after interacting with the DST about: (1) awareness of DCIS treatment options, (2) willingness to consider these options, (3) knowledge of risks associated with DCIS, and (4) helpfulness of the DST. Data were collected from January 2019 to April 2022. Users\u27 median estimated risk of dying from DCIS in 10 years decreased from 9% pre-tool to 3% post-tool (p \u3c 0.0001). 76% (n = 101/132) found the tool helpful. CONCLUSIONS: Information about DCIS treatment options and related risk predictions was effectively communicated, and a large majority participants found the DST to be helpful. Successfully informing patients about their treatment options and how their individual risks affect those options is a critical step in the decision-making process. CLINICALTRIALS: gov Identifier NCT02926911