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Abstract Background Metastatic breast cancer is a deadly disease with a low 5-year survival rate. Tracking metastatic spread in living patients is difficult and thus poorly understood. Methods Via rapid autopsy, we have collected 30 tumor samples over 3 timepoints and across 8 organs from a triple-negative metastatic breast cancer patient. The large number of sites sampled, together with deep whole-genome sequencing and advanced computational analysis, allowed us to comprehensively reconstruct the tumor’s evolution at subclonal resolution. Results The most unique, previously unreported aspect of the tumor’s evolution that we observed in this patient was the presence of “subclone incubators,” defined as metastatic sites where substantial tumor evolution occurs before colonization of additional sites and organs by subclones that initially evolved at the incubator site. Overall, we identified four discrete waves of metastatic expansions, each of which resulted in a number of new, genetically similar metastasis sites that also enriched for particular organs (e.g., abdominal vs bone and brain). The lung played a critical role in facilitating metastatic spread in this patient: the lung was the first site of metastatic escape from the primary breast lesion, subclones at this site were likely the source of all four subsequent metastatic waves, and multiple sites in the lung acted as subclone incubators. Finally, functional annotation revealed that many known drivers or metastasis-promoting tumor mutations in this patient were shared by some, but not all metastatic sites, highlighting the need for more comprehensive surveys of a patient’s metastases for effective clinical intervention. Conclusions Our analysis revealed the presence of substantial tumor evolution at metastatic incubator sites in a patient, with potentially important clinical implications. Our study demonstrated that sampling of a large number of metastatic sites affords unprecedented detail for studying metastatic evolution.

Abstract Advances in imaging and early cancer detection have increased interest in magnetic resonance (MR) guided focused ultrasound (MRgFUS) technologies for cancer treatment. MRgFUS ablation treatments could reduce surgical risks, preserve organ tissue and function, and improve patient quality of life. However, surgical resection and histological analysis remain the gold standard to assess cancer treatment response. For non-invasive ablation therapies such as MRgFUS, the treatment response must be determined through MR imaging biomarkers. However, current MR biomarkers are inconclusive and have not been rigorously evaluated against histology via accurate registration. Existing registration methods rely on anatomical features to directly register in vivo MR and histology. For MRgFUS applications in anatomies such as liver, kidney, or breast, anatomical features that are not caused by the treatment are often insufficient to drive direct registration. We present a novel MR to histology registration workflow that utilizes intermediate imaging and does not rely on anatomical MR features being visible in histology. The presented workflow yields an overall registration accuracy of 1.00 ± 0.13 mm. The developed registration pipeline is used to evaluate a common MRgFUS treatment assessment biomarker against histology. Evaluating MR biomarkers against histology using this registration pipeline will facilitate validating novel MRgFUS biomarkers to improve treatment assessment without surgical intervention. While the presented registration technique has been evaluated in a MRgFUS ablation treatment model, this technique could be potentially applied in any tissue to evaluate a variety of therapeutic options.

Context: Telepathology is a digital, microscope-independent method of diagnosing pathology from scanned slides. Frozen sections (FS) can be performed and read by a pathologist at any site. At our institution, telepathology is used for diagnosis of frozen sections of sentinel lymph nodes (SLN) in patients who have undergone neoadjuvant chemotherapy and are enrolled in a clinical trial. Objective: We investigated the accuracy of diagnosing SLN frozen sections in the neoadjuvant setting using telepathology. Design: SLN were entirely submitted for frozen section. A pathology assistant prepared the frozen and scanned the slides using VisionTek M6 digital microscope ecosystem (East Dundee, IL). Cases were interpreted by trained, board-certified pathologists. All frozen sections remnants were submitted for formalin-fixed paraffin-embedded permanent sections. Frozen section diagnoses using telepathology were compared to final pathology. Turn-around time from specimen collection to frozen section diagnosis was recorded. Results: 54 SLN from 22 breast neoadjuvant cases were diagnosed via telepathology from March 2017 to July 2019. 95% of SLNs interpreted as negative on frozen section and on permanents. A definitive diagnosis could not be rendered on six SLNs; diagnosed “atypical” at frozen. Sensitivity and specificity were 80% and 100% respectively with accuracy of 95.8%. The false-negative rate was 5%. There were no false positives. The average turn-around time was over an hour. Conclusions: Telepathology is an accurate method of diagnosing SLN frozen sections in the neoadjuvant setting, but lobular carcinomas and treatment effect pose diagnostic challenges and the time to report results is increased compared to standard microscopy.

In metastatic breast cancer, subclonal evolution can drive drug resistance. Here, the authors genetically and transcriptionally follow the evolution of four breast cancers over time and treatment, and suggest a phenotype-targeted treatment strategy to adapt to cancer as it evolves.

The originally published version of this Article contained an error in Figure 4. In panel a, grey boxes surrounding the subclones associated with patients #2 and #4 obscured adjacent portions of the heatmap. This error has now been corrected in both the PDF and HTML versions of the Article.