Objective: We evaluated the ability of minimally invasive, image-guided vacuum-assisted biopsy (VAB) to reliably diagnose a pathologic complete response in the breast (pCR-B). Summary Background Data: Neoadjuvant systemic treatment (NST) elicits a pathologic complete response in up to 80% of women with breast cancer. In such cases, breast surgery, the gold standard for confirming pCR-B, may be considered overtreatment. Methods: This multicenter, prospective trial enrolled 452 women presenting with initial stage 1-3 breast cancer of all biological subtypes. Fifty-four women dropped out; 398 were included in the full analysis. All participants had an imaging-confirmed partial or complete response to NST and underwent studyspecific image-guided VAB before guideline-adherent breast surgery. The primary endpoint was the false-negative rate (FNR) of VAB-confirmed pCR-B.Results: Image-guided VAB alone did not detect surgically confirmed residual tumor in 37 of 208 women [FNR, 17.8%; 95% confidence interval (CI), 12.8-23.7%]. Of these 37 women, 12 (32.4%) had residual DCIS only, 20 (54.1%) had minimal residual tumor (<5 mm), and 19 of 25 (76.0%) exhibited invasive cancer cellularity of 10%. In 19 of the 37 cases (51.4%), the false-negative result was potentially avoidable. Exploratory analysis showed that performing VAB with the largest needle by volume (7-gauge) resulted in no false-negative results and that combining imaging and imageguided VAB into a single diagnostic test lowered the FNR to 6.2% (95% CI, 3.4%-10.5%). Conclusions: Image-guided VAB missed residual disease more often than expected. Refinements in procedure and patient selection seem possible and necessary before omitting breast surgery.

Invasive lobular carcinoma (ILC) is an understudied subtype of breast cancer that requires novel therapies in the advanced setting. To study acquired resistance to endocrine therapy in ILC, we have recently performed RNA-Sequencing on long-term estrogen deprived cell lines and identified FGFR4 overexpression as a top druggable target. Here, we show that FGFR4 expression also increases dramatically in endocrine-treated distant metastases, with an average fold change of 4.8 relative to the paired primary breast tumor for ILC, and 2.4-fold for invasive ductal carcinoma (IDC). In addition, we now report that FGFR4 hotspot mutations are enriched in metastatic breast cancer, with an additional enrichment for ILC, suggesting a multimodal selection of FGFR4 activation. These data collectively support the notion that FGFR4 is an important mediator of endocrine resistance in ILC, warranting future mechanistic studies on downstream signaling of overexpressed wild-type and mutant FGFR4.

PURPOSE Neoadjuvant systemic treatment (NST) elicits a pathologic complete response in 40%-70% of women with breast cancer. These patients may not need surgery as all local tumor has already been eradicated by NST. However, nonsurgical approaches, including imaging or vacuum-assisted biopsy (VAB), were not able to accurately identify patients without residual cancer in the breast or axilla. We evaluated the feasibility of a machine learning algorithm (intelligent VAB) to identify exceptional responders to NST. METHODS We trained, tested, and validated a machine learning algorithm using patient, imaging, tumor, and VAB variables to detect residual cancer after NST (ypT+ or in situ or ypN+) before surgery. We used data from 318 women with cT1-3, cN0 or +, human epidermal growth factor receptor 2–positive, triple-negative, or high-proliferative Luminal B–like breast cancer who underwent VAB before surgery ( NCT02948764 , RESPONDER trial). We used 10-fold cross-validation to train and test the algorithm, which was then externally validated using data of an independent trial ( NCT02575612 ). We compared findings with the histopathologic evaluation of the surgical specimen. We considered false-negative rate (FNR) and specificity to be the main outcomes. RESULTS In the development set (n = 318) and external validation set (n = 45), the intelligent VAB showed an FNR of 0.0%-5.2%, a specificity of 37.5%-40.0%, and an area under the receiver operating characteristic curve of 0.91-0.92 to detect residual cancer (ypT+ or in situ or ypN+) after NST. Spiegelhalter's Z confirmed a well-calibrated model ( z score –0.746, P = .228). FNR of the intelligent VAB was lower compared with imaging after NST, VAB alone, or combinations of both. CONCLUSION An intelligent VAB algorithm can reliably exclude residual cancer after NST. The omission of breast and axillary surgery for these exceptional responders may be evaluated in future trials.