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Patients with metastatic breast cancer (MBC) can develop malignant effusions (MEs) when excess fluid and tumor cells accumulate in the pleural or peritoneal space. MEs are associated with significant morbidity and mortality. Tropism of tumor cells in these spaces is poorly understood and tumor cells residing in these fluids are difficult to study with traditional methods due to low abundance. To overcome this limitation and understand the pathobiology of MEs in MBCs, we applied a label-free method to isolate tumor cells based on morphology using an artificial intelligence (AI) model This method enabled isolation of live cells for downstream molecular analysis and characterization of morphological heterogeneity. We collected ME samples from 9 MBC patients with cytology-confirmed MEs, including 3 triple negative and 6 hormone receptor-positive/HER2-negative breast cancers. ME cells were imaged and isolated using a Deepcell AI model trained on a separate cohort of MEs to isolate and enrich tumor cells based on deep learning representations of morphological features. Quantitative morphological features were then extracted from cell images to perform cluster analysis and visualized by dimensionality reduction projections. In parallel, flow cytometry (FC) was used to quantify % EpCAM+/Claudin4+ tumor cells in ME samples. Copy number (CN) analysis was performed before and after enrichment with as few as 200 cells from each sample. Tumor cell fraction (TCF) estimates were calculated from the AI classifier, FC, and CN analyses. CN profiling of malignant cells sorted by the AI classifier revealed genomic alterations common in breast cancer in 7 of 9 patients. We observed increased amplitude of genomic alterations in tumor-enriched vs. unenriched samples, indicating successful tumor cell enrichment in 14 out of 16 samples. We observed a significant correlation in TCF between the AI classifier and FC with a Spearman test (rho=0.568, p0.008). In some cases, the AI classifier reported higher TCF than FC. This could indicate that the AI model detected tumor cells missed by antibody labeling or false positives from morphologically similar cell types, such as macrophages and mesothelial cells. Remarkably, analysis of serial ME samples from the same patient revealed morphological shifts in cell populations which were consistent with changes in TCF by CN analysis. We demonstrate the feasibility of label-free identification and isolation of tumor cells in MEs using an AI classifier. CN profiling of isolated tumor cells verified enrichment from low initial frequencies. Future work will include expression analysis of ME tumor and immune cells, and analysis of organoids derived from MEs. Combining molecular and morphological profiling of MEs will enable new insights into their pathobiology and inform targeted treatment strategies for patients with this condition. Citation Format: Mark J. Magbanua, Laura Huppert, Janifer Cruz, Jackson Goudreau, Shaopu Zhou, Keyi Yin, Pavel Stejskal, Ryan Carelli, Julie Kim, Linda Hsie, Maggie Wang, Zhouyang Lian, Michael Phelan, Manisha Ray, Chassidy Johnson, Amy L. Delson, Ron Balassanian, Jennifer Rosenbluth, Laura van't Veer, Michelle Melisko, Hope S. Rugo, Mahyar Salek, Maddison Masaeli, Hani Goodarzi. Artificial intelligence-assisted morphology-based detection and enrichment of malignant effusion tumor cells as a method for molecular profiling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB170.

Background: Previous studies have independently validated the prognostic relevance of residual cancer burden (RCB) after neoadjuvant chemotherapy. We used results from several independent cohorts in a pooled patient-level analysis to evaluate the relationship of RCB with long-term prognosis across different phenotypic subtypes of breast cancer, to assess generalisability in a broad range of practice settings. Methods: In this pooled analysis, 12 institutes and trials in Europe and the USA were identified by personal communications with site investigators. We obtained participant-level RCB results, and data on clinical and pathological stage, tumour subtype and grade, and treatment and follow-up in November, 2019, from patients (aged ≥18 years) with primary stage I–III breast cancer treated with neoadjuvant chemotherapy followed by surgery. We assessed the association between the continuous RCB score and the primary study outcome, event-free survival, using mixed-effects Cox models with the incorporation of random RCB and cohort effects to account for between-study heterogeneity, and stratification to account for differences in baseline hazard across cancer subtypes defined by hormone receptor status and HER2 status. The association was further evaluated within each breast cancer subtype in multivariable analyses incorporating random RCB and cohort effects and adjustments for age and pretreatment clinical T category, nodal status, and tumour grade. Kaplan-Meier estimates of event-free survival at 3, 5, and 10 years were computed for each RCB class within each subtype. Findings: We analysed participant-level data from 5161 patients treated with neoadjuvant chemotherapy between Sept 12, 1994, and Feb 11, 2019. Median age was 49 years (IQR 20–80). 1164 event-free survival events occurred during follow-up (median follow-up 56 months [IQR 0–186]). RCB score was prognostic within each breast cancer subtype, with higher RCB score significantly associated with worse event-free survival. The univariable hazard ratio (HR) associated with one unit increase in RCB ranged from 1·55 (95% CI 1·41–1·71) for hormone receptor-positive, HER2-negative patients to 2·16 (1·79–2·61) for the hormone receptor-negative, HER2-positive group (with or without HER2-targeted therapy; p

ImportancePathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer strongly correlates with overall survival and has become the standard end point in neoadjuvant trials. However, there is controversy regarding whether the definition of pCR should exclude or permit the presence of residual ductal carcinoma in situ (DCIS).ObjectiveTo examine the association of residual DCIS in surgical specimens after neoadjuvant chemotherapy for breast cancer with survival end points to inform standards for the assessment of pathologic complete response.Design, Setting, and ParticipantsThe study team analyzed the association of residual DCIS after NAC with 3-year event-free survival (EFS), distant recurrence-free survival (DRFS), and local-regional recurrence (LRR) in the I-SPY2 trial, an adaptive neoadjuvant platform trial for patients with breast cancer at high risk of recurrence. This is a retrospective analysis of clinical specimens and data from the ongoing I-SPY2 adaptive platform trial of novel therapeutics on a background of standard of care for early breast cancer. I-SPY2 participants are adult women diagnosed with stage II/III breast cancer at high risk of recurrence.InterventionsParticipants were randomized to receive taxane and anthracycline-based neoadjuvant therapy with or without 1 of 10 investigational agents, followed by definitive surgery.Main Outcomes and MeasuresThe presence of DCIS and EFS, DRFS, and LRR.ResultsThe study team identified 933 I-SPY2 participants (aged 24 to 77 years) with complete pathology and follow-up data. Median follow-up time was 3.9 years; 337 participants (36%) had no residual invasive disease (residual cancer burden 0, or pCR). Of the 337 participants with pCR, 70 (21%) had residual DCIS, which varied significantly by tumor-receptor subtype; residual DCIS was present in 8.5% of triple negative tumors, 15.6% of hormone-receptor positive tumors, and 36.6% of ERBB2-positive tumors. Among those participants with pCR, there was no significant difference in EFS, DRFS, or LRR based on presence or absence of residual DCIS.Conclusions and RelevanceThe analysis supports the definition of pCR as the absence of invasive disease after NAC regardless of the presence or absence of DCIS.Trial RegistrationClinicalTrials.gov Identifier NCT01042379.

Ductal carcinoma in situ (DCIS) of the breast represents a heterogeneous group of neoplastic lesions in the breast ducts. The goal for management of DCIS is to prevent the development of invasive breast cancer. This manuscript focuses on the NCCN Guidelines Panel recommendations for the workup, primary treatment, risk reduction strategies, and surveillance specific to DCIS.

These NCCN Guideline Insights highlight the important updates to the systemic therapy recommendations in the 2016 NCCN Guidelines for Breast Cancer. In the most recent version of these guidelines, the NCCN Breast Cancer Panel included a new section on the principles of preoperative systemic therapy. In addition, based on new evidence, the panel updated systemic therapy recommendations for women with hormone receptor-positive breast cancer in the adjuvant and metastatic disease settings and for patients with HER2-positive metastatic breast cancer. This report summarizes these recent updates and discusses the rationale behind them.