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Circulating tumor DNA (ctDNA) is an emerging biomarker for the treatment of early breast cancer (EBC). We sought to evaluate a highly sensitive tumor-informed ctDNA assay in a real-world cohort of patients receiving neoadjuvant therapy (NAT) to assess clinical validity and explore prognostic outcomes. ctDNA is detected in 77.2% (88/114) of participants at baseline, with 18/88 (20.5%) having a baseline estimated variant allele frequency (eVAF) of <0.01%. Persistent detection of ctDNA, measured midway through NAT (mid-NAT), is associated with disease recurrence in all participants, reaching statistical significance in those with HER2-negative disease. Stratified analyses demonstrate that ctDNA detected mid-NAT enhances the prognostic accuracy of the residual cancer burden (RCB) score for disease recurrence. Postoperative or follow-up detection of ctDNA demonstrates a 100% positive predictive value for disease recurrence, with a median lead time of 374 days (range: 13-1010 days). These data suggest that assays with high analytical sensitivity may improve baseline ctDNA detection in patients with EBC. The ability to replicate the prognostic association of ctDNA dynamics in a real-world cohort supports further investigation. Prospective trials incorporating ctDNA testing are warranted to assess and develop the clinical utility of ctDNA-guided treatment strategies. Tumour-informed ctDNA is a sensitive potential biomarker for treatment response in breast cancer. Here, the authors use longitudinal sampling to predict disease recurrence during neoadjuvant and adjuvant treatment.

Improved methods to monitor treatment response may enhance patient management and clinical outcomes. This study assessed the feasibility and performance of a tumor-informed circulating tumor DNA (ctDNA) assay in metastatic HR+/HER2− breast cancer patients receiving endocrine and CDK4/6 inhibitor therapy. By conducting whole exome sequencing on archival tumors, highly sensitive personalized ctDNA panels were designed for blood monitoring. The assay showed high detection sensitivity (91% baseline, 70% all timepoints) and associations between higher baseline estimated variant allele fractions, liver metastases, and shorter time to treatment failure (TTF) and overall survival (OS). Complete molecular response, defined as ctDNA clearance, was observed in 28% of patients and correlated with improved TTF (HR 0.07) and OS (HR 0.07). The last cleared timepoint predated treatment failure by a median 14.3 months. ctDNA rises or limited decreases preceded radiographic progression. Molecular metrics may facilitate plasma-first monitoring and innovative strategies for clinical practice and trial design.

Deep learning has proven capable of automating key aspects of histopathologic analysis. However, its context-specific nature and continued reliance on large expert-annotated training datasets hinders the development of a critical mass of applications to garner widespread adoption in clinical/research workflows. Here, we present an online collaborative platform that streamlines tissue image annotation to promote the development and sharing of custom computer vision models for PHenotyping And Regional Analysis Of Histology (PHARAOH; https://www.pathologyreports.ai/ ). Specifically, PHARAOH uses a weakly supervised, human-in-the-loop learning framework whereby patch-level image features are leveraged to organize large swaths of tissue into morphologically-uniform clusters for batched annotation by human experts. By providing cluster-level labels on only a handful of cases, we show how custom PHARAOH models can be developed efficiently and used to guide the quantification of cellular features that correlate with molecular, pathologic and patient outcome data. Moreover, by using our PHARAOH pipeline, we showcase how correlation of cohort-level cytoarchitectural features with accompanying biological and outcome data can help systematically devise interpretable morphometric models of disease. Both the custom model design and feature extraction pipelines are amenable to crowdsourcing, positioning PHARAOH to become a fully scalable, systems-level solution for the expansion, generalization and cataloging of computational pathology applications. Faust, Chen, and colleagues present PHARAOH, a collaborative computational pathology platform that allows histologists to quickly develop custom labelled image datasets to train and catalogue a variety of machine learning models for histopathological analysis.

Deep learning has proven to be capable of automating key aspects of histopathologic analysis, but its continual reliance on large expert-annotated training datasets hinders widespread adoption. Here, we present an online collaborative portal that streamlines tissue image annotation to promote the development and sharing of custom computer vision models for PHenotyping And Regional Analysis Of Histology (PHARAOH; https://www.pathologyreports.ai/). PHARAOH uses a weakly supervised active learning framework whereby patch-level image features are leveraged to organize large swaths of tissue into morphologically-uniform clusters for batched human annotation. By providing cluster-level labels on only a handful of cases, we show how custom PHARAOH models can be developed and used to guide the quantification of cellular features that correlate with molecular, pathologic and patient outcome data. Both custom model design and feature extraction pipelines are amenable to crowdsourcing making PHARAOH a fully scalable systems-level solution for the systematic expansion and cataloging of computational pathology applications.

Rock glaciers—ice-rich creeping landforms typical of permafrost mountain ranges—can develop an anomalous landslide-like behaviour called destabilisation. This behaviour is characterised by failure mechanisms (including cracks and crevasses) and increases in displacement rates by one to two orders of magnitude. Existing studies of this phenomenon have been limited to a small number of landforms and short time spans. Here, we systematically investigate the evolution of rock glacier kinematics over the past seven decades for the entire French Alps by combining observations of landform features indicative of the onset of destabilisation with data on displacements rates using aerial orthoimagery. We show that rock glacier velocities have significantly increased since the 1990s, concurrent with the development of destabilisation in 18 landforms that represent 5% of the 337 active rock glaciers. This pattern of activity correlates with rising air temperatures in the region, which suggests that a warming climate may play a role in this process.

Self-regulating processes are stochastic processes whose local regularity, as measured by the pointwise Hölder exponent, is a function of amplitude. They seem to provide relevant models for various signals arising for example in geophysics or biomedicine. We propose in this work an estimator of the self-regulating function (that is, the function relating amplitude and Hölder regularity) of the self-regulating midpoint displacement process and study some of its properties. We prove that it is almost surely convergent and obtain a central limit theorem. Numerical simulations show that the estimator behaves well in practice.