IMPRESS: Improved methylation profiling using restriction enzymes and smMIP sequencing, combined with a new biomarker panel, creating a multi-cancer detection assay.

BACKGROUND: Despite the worldwide progress in cancer diagnostics, more sensitive diagnostic biomarkers are needed. The methylome has been extensively investigated in the last decades, but a low-cost, bisulfite-free detection method for multiplex analysis is still lacking. METHODS: We developed a methylation detection technique called IMPRESS, which combines methylation-sensitive restriction enzymes and single-molecule Molecular Inversion Probes. We used this technique for the development of a multi-cancer detection assay for eight of the most lethal cancer types worldwide. We selected 1791 CpG sites that can distinguish tumor from normal tissue based on DNA methylation. These sites were analysed with IMPRESS in 35 blood, 111 tumor and 114 normal samples. Finally, a classifier model was built. RESULTS: We present the successful development of IMPRESS and validated it with ddPCR. The final classifier model discriminating tumor from normal samples was built with 358 CpG target sites and reached a sensitivity of 0.95 and a specificity of 0.91. Moreover, we provide data that highlight IMPRESS's potential for liquid biopsies. CONCLUSIONS: We successfully created an innovative DNA methylation detection technique. By combining this method with a new multi-cancer biomarker panel, we developed a sensitive and specific multi-cancer assay, with potential use in liquid biopsies.

Murine orthotopic lung transplant models: A comprehensive overview of genetic mismatch degrees and histopathological insights into chronic lung allograft dysfunction.

Currently, lung transplantation outcome remains inferior compared to other solid organ transplantations. A major cause for limited survival after lung transplantation is chronic lung allograft dysfunction. Numerous animal models have been developed to investigate chronic lung allograft dysfunction to discover adequate treatments. The murine orthotopic lung transplant model has been further optimized over the last years. However, different degrees of genetic mismatch between donor and recipient mice have been used, applying a single, minor, moderate, and major genetic mismatch. This review aims to reassess the existing murine mismatch models and provide a comprehensive overview, with a specific focus on their eventual histopathological presentation. This will be crucial to leverage this model and tailor it according to specific research needs.

Reproducible Reporting of the Collection and Evaluation of Annotations for Artificial Intelligence Models.

This work puts forth and demonstrates the utility of a reporting framework for collecting and evaluating annotations of medical images used for training and testing artificial intelligence (AI) models in assisting detection and diagnosis. AI has unique reporting requirements, as shown by the AI extensions to the Consolidated Standards of Reporting Trials (CONSORT) and Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) checklists and the proposed AI extensions to the Standards for Reporting Diagnostic Accuracy (STARD) and Transparent Reporting of a Multivariable Prediction model for Individual Prognosis or Diagnosis (TRIPOD) checklists. AI for detection and/or diagnostic image analysis requires complete, reproducible, and transparent reporting of the annotations and metadata used in training and testing data sets. In an earlier work by other researchers, an annotation workflow and quality checklist for computational pathology annotations were proposed. In this manuscript, we operationalize this workflow into an evaluable quality checklist that applies to any reader-interpreted medical images, and we demonstrate its use for an annotation effort in digital pathology. We refer to this quality framework as the Collection and Evaluation of Annotations for Reproducible Reporting of Artificial Intelligence (CLEARR-AI).

Training pathologists to assess stromal tumour-infiltrating lymphocytes in breast cancer synergises efforts in clinical care and scientific research.

A growing body of research supports stromal tumour-infiltrating lymphocyte (TIL) density in breast cancer to be a robust prognostic and predicive biomarker. The gold standard for stromal TIL density quantitation in breast cancer is pathologist visual assessment using haematoxylin and eosin-stained slides. Artificial intelligence/machine-learning algorithms are in development to automate the stromal TIL scoring process, and must be validated against a reference standard such as pathologist visual assessment. Visual TIL assessment may suffer from significant interobserver variability. To improve interobserver agreement, regulatory science experts at the US Food and Drug Administration partnered with academic pathologists internationally to create a freely available online continuing medical education (CME) course to train pathologists in assessing breast cancer stromal TILs using an interactive format with expert commentary. Here we describe and provide a user guide to this CME course, whose content was designed to improve pathologist accuracy in scoring breast cancer TILs. We also suggest subsequent steps to translate knowledge into clinical practice with proficiency testing.

Interobserver Variation in the Assessment of Immunohistochemistry Expression Levels in HER2-Negative Breast Cancer: Can We Improve the Identification of Low Levels of HER2 Expression by Adjusting the Criteria? An International Interobserver Study.

The classification of human epidermal growth factor receptor 2 (HER2) expression is optimized to detect HER2-amplified breast cancer (BC). However, novel HER2-targeting agents are also effective for BCs with low levels of HER2. This raises the question whether the current guidelines for HER2 testing are sufficiently reproducible to identify HER2-low BC. The aim of this multicenter international study was to assess the interobserver agreement of specific HER2 immunohistochemistry scores in cases with negative HER2 results (0, 1+, or 2+/in situ hybridization negative) according to the current American Society of Clinical Oncology/College of American Pathologists (ASCO/CAP) guidelines. Furthermore, we evaluated whether the agreement improved by redefining immunohistochemistry (IHC) scoring criteria or by adding fluorescent in situ hybridization (FISH). We conducted a 2-round study of 105 nonamplified BCs. During the first assessment, 16 pathologists used the latest version of the ASCO/CAP guidelines. After a consensus meeting, the same pathologists scored the same digital slides using modified IHC scoring criteria based on the 2007 ASCO/CAP guidelines, and an extra "ultralow" category was added. Overall, the interobserver agreement was limited (4.7% of cases with 100% agreement) in the first round, but this was improved by clustering IHC categories. In the second round, the highest reproducibility was observed when comparing IHC 0 with the ultralow/1+/2+ grouped cluster (74.3% of cases with 100% agreement). The FISH results were not statistically different between HER2-0 and HER2-low cases, regardless of the IHC criteria used. In conclusion, our study suggests that the modified 2007 ASCO/CAP criteria were more reproducible in distinguishing HER2-0 from HER2-low cases than the 2018 ASCO/CAP criteria. However, the reproducibility was still moderate, which was not improved by adding FISH. This could lead to a suboptimal selection of patients eligible for novel HER2-targeting agents. If the threshold between HER2 IHC 0 and 1+ is to be clinically actionable, there is a need for clearer, more reproducible IHC definitions, training, and/or development of more accurate methods to detect this subtle difference in protein expression levels.

Circulating tumor cell detection: A prospective comparison between CellSearch® and RareCyte® platforms in patients with progressive metastatic breast cancer.

PURPOSE: Circulating tumor cells (CTCs) are prognostic in patients with breast cancer. Several technical platforms exist for their enumeration and characterization. Comparative studies between these platforms are scarce. The RareCyte CTC detection is theoretically more sensitive than the established CellSearch platform, which identifies only CTCs that express EpCAM and cytokeratin. This study prospectively compares CTC enumeration in patients with breast cancer in a paired analysis using these two platforms. It investigates survival outcomes in groups defined by a CTC count threshold. DESIGN: CTC enumeration was performed on 100 samples obtained from 86 patients with progressive metastatic breast cancer (MBC) in two independent laboratories each blinded to the clinical data and the results from the other platform. RESULTS: One hundred paired samples were collected and CTC counts were determined using the CellSearch and RareCyte CTC platforms. In total, 65% and 75% of samples had at least one detectable CTC in 7.5 mL blood with the CellSearch and the RareCyte systems, respectively. CTC counts with the CellSearch system ranged from 0 to 2289 with a median of 3 CTCs, the RareCyte CTC counts ranged from 0 to 1676 with a median of 3 CTCs. The number of samples with 5 or more CTCs in 7.5 mL of blood (the poor prognosis cut-off validated with the CellSearch system) blood was 45% with the CellSearch test and 48% with the RareCyte test. CTC counts quantified with the CellSearch and the RareCyte systems were strongly correlated (Spearman's r = 0.8235 (0.7450-0.8795) p < 0.001). 86 patients were included for Kaplan-Meier survival analysis. An increased mortality risk in patients with CellSearch of 5 CTCs or more per 7.5 mL blood, with a log-rank hazard ratio of 5.164 (2.579-10.34) (p < 0.001) was confirmed. The survival analysis with RareCyte CTC counts with the identical cut-off showed a significantly impaired survival with a hazard ratio of 4.213 (2.153-8.244) (p < 0.001). CONCLUSION: Our data demonstrate the analytical and prognostic equivalence of CellSearch and RareCyte CTC enumeration platforms in patients with MBC using the CellSearch cut-off. This is the first demonstration of prognostic significance using the RareCyte platform.

Interobserver variability in the assessment of stromal tumor-infiltrating lymphocytes (sTILs) in triple-negative invasive breast carcinoma influences the association with pathological complete response: the IVITA study.

High stromal tumor-infiltrating lymphocytes (sTILs) in triple-negative breast cancer (TNBC) are associated with pathological complete response (pCR) after neoadjuvant chemotherapy (NAC). Histopathological assessment of sTILs in TNBC biopsies is characterized by substantial interobserver variability, but it is unknown whether this affects its association with pCR. Here, we aimed to investigate the degree of interobserver variability in an international study, and its impact on the relationship between sTILs and pCR. Forty pathologists assessed sTILs as a percentage in digitalized biopsy slides, originating from 41 TNBC patients who were treated with NAC followed by surgery. Pathological response was quantified by the MD Anderson Residual Cancer Burden (RCB) score. Intraclass correlation coefficients (ICCs) were calculated per pathologist duo and Bland-Altman plots were constructed. The relation between sTILs and pCR or RCB class was investigated. The ICCs ranged from -0.376 to 0.947 (mean: 0.659), indicating substantial interobserver variability. Nevertheless, high sTILs scores were significantly associated with pCR for 36 participants (90%), and with RCB class for eight participants (20%). Post hoc sTILs cutoffs at 20% and 40% resulted in variable associations with pCR. The sTILs in TNBC with RCB-II and RCB-III were intermediate to those of RCB-0 and RCB-I, with lowest sTILs observed in RCB-I. However, the limited number of RCB-I cases precludes any definite conclusions due to lack of power, and this observation therefore requires further investigation. In conclusion, sTILs are a robust marker for pCR at the group level. However, if sTILs are to be used to guide the NAC scheme for individual patients, the observed interobserver variability might substantially affect the chance of obtaining a pCR. Future studies should determine the 'ideal' sTILs threshold, and attempt to fine-tune the patient selection for sTILs-based de-escalation of NAC regimens. At present, there is insufficient evidence for robust and reproducible sTILs-guided therapeutic decisions.

The path to a better biomarker: application of a risk management framework for the implementation of PD-L1 and TILs as immuno-oncology biomarkers in breast cancer clinical trials and daily practice.

Immune checkpoint inhibitor therapies targeting PD-1/PD-L1 are now the standard of care in oncology across several hematologic and solid tumor types, including triple negative breast cancer (TNBC). Patients with metastatic or locally advanced TNBC with PD-L1 expression on immune cells occupying ≥1% of tumor area demonstrated survival benefit with the addition of atezolizumab to nab-paclitaxel. However, concerns regarding variability between immunohistochemical PD-L1 assay performance and inter-reader reproducibility have been raised. High tumor-infiltrating lymphocytes (TILs) have also been associated with response to PD-1/PD-L1 inhibitors in patients with breast cancer (BC). TILs can be easily assessed on hematoxylin and eosin-stained slides and have shown reliable inter-reader reproducibility. As an established prognostic factor in early stage TNBC, TILs are soon anticipated to be reported in daily practice in many pathology laboratories worldwide. Because TILs and PD-L1 are parts of an immunological spectrum in BC, we propose the systematic implementation of combined PD-L1 and TIL analyses as a more comprehensive immuno-oncological biomarker for patient selection for PD-1/PD-L1 inhibition-based therapy in patients with BC. Although practical and regulatory considerations differ by jurisdiction, the pathology community has the responsibility to patients to implement assays that lead to optimal patient selection. We propose herewith a risk-management framework that may help mitigate the risks of suboptimal patient selection for immuno-therapeutic approaches in clinical trials and daily practice based on combined TILs/PD-L1 assessment in BC. © 2020 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Interobserver variability in upfront dichotomous histopathological assessment of ductal carcinoma in situ of the breast: the DCISion study.

Histopathological assessment of ductal carcinoma in situ, a nonobligate precursor of invasive breast cancer, is characterized by considerable interobserver variability. Previously, post hoc dichotomization of multicategorical variables was used to determine the "ideal" cutoffs for dichotomous assessment. The present international multicenter study evaluated interobserver variability among 39 pathologists who performed upfront dichotomous evaluation of 149 consecutive ductal carcinomas in situ. All pathologists independently assessed nuclear atypia, necrosis, solid ductal carcinoma in situ architecture, calcifications, stromal architecture, and lobular cancerization in one digital slide per lesion. Stromal inflammation was assessed semiquantitatively. Tumor-infiltrating lymphocytes were quantified as percentages and dichotomously assessed with a cutoff at 50%. Krippendorff's alpha (KA), Cohen's kappa and intraclass correlation coefficient were calculated for the appropriate variables. Lobular cancerization (KA = 0.396), nuclear atypia (KA = 0.422), and stromal architecture (KA = 0.450) showed the highest interobserver variability. Stromal inflammation (KA = 0.564), dichotomously assessed tumor-infiltrating lymphocytes (KA = 0.520), and comedonecrosis (KA = 0.539) showed slightly lower interobserver disagreement. Solid ductal carcinoma in situ architecture (KA = 0.602) and calcifications (KA = 0.676) presented with the lowest interobserver variability. Semiquantitative assessment of stromal inflammation resulted in a slightly higher interobserver concordance than upfront dichotomous tumor-infiltrating lymphocytes assessment (KA = 0.564 versus KA = 0.520). High stromal inflammation corresponded best with dichotomously assessed tumor-infiltrating lymphocytes when the cutoff was set at 10% (kappa = 0.881). Nevertheless, a post hoc tumor-infiltrating lymphocytes cutoff set at 20% resulted in the highest interobserver agreement (KA = 0.669). Despite upfront dichotomous evaluation, the interobserver variability remains considerable and is at most acceptable, although it varies among the different histopathological features. Future studies should investigate its impact on ductal carcinoma in situ prognostication. Forthcoming machine learning algorithms may be useful to tackle this substantial diagnostic challenge.

Dichotomous histopathological assessment of ductal carcinoma in situ of the breast results in substantial interobserver concordance.

AIMS: Robust prognostic markers for ductal carcinoma in situ (DCIS) of the breast require high reproducibility and thus low interobserver variability. The aim of this study was to compare interobserver variability among 13 pathologists, in order to enable the identification of robust histopathological characteristics. METHODS AND RESULTS: One representative haematoxylin and eosin-stained slide was selected for 153 DCIS cases. All pathologists independently assessed nuclear grade, intraductal calcifications, necrosis, solid growth, stromal changes, stromal inflammation, and apocrine differentiation. All characteristics were assessed categorically. Krippendorff's alpha was calculated to assess overall interobserver concordance. Cohen's kappa was calculated for every observer duo to further explore interobserver variability. The highest concordance was observed for necrosis, calcifications, and stromal inflammation. Assessment of solid growth, nuclear grade and stromal changes resulted in lower concordance. Poor concordance was observed for apocrine differentiation. Kappa values for each observer duo identified the 'ideal' cut-off for dichotomisation of multicategory variables. For instance, concordance was higher for 'non-high versus high' nuclear grade than for 'low versus non-low' nuclear grade. 'Absent/mild' versus 'moderate/extensive' stromal inflammation resulted in substantially higher concordance than other dichotomous cut-offs. CONCLUSIONS: Dichotomous assessment of the histopathological features of DCIS resulted in moderate to substantial agreement among pathologists. Future studies on prognostic markers in DCIS should take into account this degree of interobserver variability to define cut-offs for categorically assessed histopathological features, as reproducibility is paramount for robust prognostic markers in daily clinical practice. A new prognostic index for DCIS might be considered, based on two-tier grading of histopathological features. Future research should explore the prognostic potential of such two-tier assessment.

An 8-gene mRNA expression profile in circulating tumor cells predicts response to aromatase inhibitors in metastatic breast cancer patients.

BACKGROUND: Molecular characterization of circulating tumor cells (CTC) is promising for personalized medicine. We aimed to identify a CTC gene expression profile predicting outcome to first-line aromatase inhibitors in metastatic breast cancer (MBC) patients. METHODS: CTCs were isolated from 78 MBC patients before treatment start. mRNA expression levels of 96 genes were measured by quantitative reverse transcriptase polymerase chain reaction. After applying predefined exclusion criteria based on lack of sufficient RNA quality and/or quantity, the data from 45 patients were used to construct a gene expression profile to predict poor responding patients, defined as disease progression or death <9 months, by a leave-one-out cross validation. RESULTS: Of the 45 patients, 19 were clinically classified as poor responders. To identify them, the 75% most variable genes were used to select genes differentially expressed between good and poor responders. An 8-gene CTC predictor was significantly associated with outcome (Hazard Ratio [HR] 4.40, 95% Confidence Interval [CI]: 2.17-8.92, P < 0.001). This predictor identified poor responding patients with a sensitivity of 63% and a positive predictive value of 75%, while good responding patients were correctly predicted in 85% of the cases. In multivariate Cox regression analysis, including CTC count at baseline, the 8-gene CTC predictor was the only factor independently associated with outcome (HR 4.59 [95% CI: 2.11-9.56], P < 0.001). This 8-gene signature was not associated with outcome in a group of 71 MBC patients treated with systemic treatments other than AI. CONCLUSIONS: An 8-gene CTC predictor was identified which discriminates good and poor outcome to first-line aromatase inhibitors in MBC patients. Although results need to be validated, this study underscores the potential of molecular characterization of CTCs.

Adult suprapatellar pleiomorphic rhabdomyosarcoma with jejunal metastasis causing intussusception: a case report.

Jejuno-jejunal intussusception is rarely encountered in adults. Management depends on the viability of the involved bowel. Exploration is favored because in adults generally an underlying 'lead point' is found to be present. Pleimorphic rhabdomyosarcoma (pRMS) arises from striated muscle cells. They are usually diagnosed during childhood and can occur virtually all over the body, controversially in places were few striated cells are found. In adults, these tumors are rare and are mostly encountered in the head-and-neck region. We present the case of a 48-year-old woman with a jejunal metastasis from a suprapatellar pRMS diagnosed 2.5 years earlier resulting in a jejuno-jejunal intussusception.

Clinical validity of circulating tumour cells in patients with metastatic breast cancer: a pooled analysis of individual patient data.

BACKGROUND: We aimed to assess the clinical validity of circulating tumour cell (CTC) quantification for prognostication of patients with metastatic breast cancer by undertaking a pooled analysis of individual patient data. METHODS: We contacted 51 European centres and asked them to provide reported and unreported anonymised data for individual patients with metastatic breast cancer who participated in studies between January, 2003, and July, 2012. Eligible studies had participants starting a new line of therapy, data for progression-free survival or overall survival, or both, and CTC quantification by the CellSearch method at baseline (before start of new treatment). We used Cox regression models, stratified by study, to establish the association between CTC count and progression-free survival and overall survival. We used the landmark method to assess the prognostic value of CTC and serum marker changes during treatment. We assessed the added value of CTCs or serum markers to prognostic clinicopathological models in a resampling procedure using likelihood ratio (LR) χ(2) statistics. FINDINGS: 17 centres provided data for 1944 eligible patients from 20 studies. 911 patients (46·9%) had a CTC count of 5 per 7·5 mL or higher at baseline, which was associated with decreased progression-free survival (hazard ratio [HR] 1·92, 95% CI 1·73-2·14, p<0·0001) and overall survival (HR 2·78, 95% CI 2·42-3·19, p<0·0001) compared with patients with a CTC count of less than 5 per 7·5 mL at baseline. Increased CTC counts 3-5 weeks after start of treatment, adjusted for CTC count at baseline, were associated with shortened progression-free survival (HR 1·85, 95% CI 1·48-2·32, p<0·0001) and overall survival (HR 2·26, 95% CI 1·68-3·03) as were increased CTC counts after 6-8 weeks (progression-free survival HR 2·20, 95% CI 1·66-2·90, p<0·0001; overall survival HR 2·91, 95% CI 2·01-4·23, p<0·0001). Survival prediction was significantly improved by addition of baseline CTC count to the clinicopathological models (progression-free survival LR 38·4, 95% CI 21·9-60·3, p<0·0001; overall survival LR 64·9, 95% CI 41·3-93·4, p<0·0001). This model was further improved by addition of CTC change at 3-5 weeks (progression-free survival LR 8·2, 95% CI 0·78-20·4, p=0·004; overall survival LR 11·5, 95% CI 2·6-25·1, p=0·0007) and at 6-8 weeks (progression-free survival LR 15·3, 95% CI 5·2-28·3; overall survival LR 14·6, 95% CI 4·0-30·6; both p<0·0001). Carcinoembryonic antigen and cancer antigen 15-3 concentrations at baseline and during therapy did not add significant information to the best baseline model. INTERPRETATION: These data confirm the independent prognostic effect of CTC count on progression-free survival and overall survival. CTC count also improves the prognostication of metastatic breast cancer when added to full clinicopathological predictive models, whereas serum tumour markers do not. FUNDING: Janssen Diagnostics, the Nuovo-Soldati foundation for cancer research.

Simultaneous detection of eight cancer types using a multiplex droplet digital PCR assay.

DNA methylation biomarkers have emerged as promising tools for cancer detection. Common methylation patterns across tumor types allow multi-cancer detection. Droplet digital PCR (ddPCR) has gained considerable attention for methylation detection. However, multi-cancer detection using multiple targets in ddPCR has never been performed before. Therefore, we developed a multiplex ddPCR assay for multi-cancer detection. Based on previous data analyses using The Cancer Genome Atlas (TCGA), we selected differentially methylated targets for eight frequent tumor types (lung, breast, colorectal, prostate, pancreatic, head and neck, liver, and esophageal cancer). Three targets were validated using ddPCR in 103 tumor and 109 normal adjacent fresh frozen samples. Two distinct ddPCR assays were successfully developed. Output data from both assays is combined to obtain a read-out from the three targets together. Our overall ddPCR assay has a cross-validated area under the curve (cvAUC) of 0.948. Performance between distinct cancer types varies, with sensitivities ranging from 53.8% to 100% and specificities ranging from 80% to 100%. Compared to previously published single-target parameters, we show that combining targets can drastically increase sensitivity and specificity, while lowering DNA input. In conclusion, we are the first to report a multi-cancer methylation ddPCR assay, which allows for highly accurate tumor predictions.

Expression profiling of cancerous and normal breast tissues identifies microRNAs that are differentially expressed in serum from patients with (metastatic) breast cancer and healthy volunteers.

INTRODUCTION: MicroRNAs (miRNAs) are a group of small noncoding RNAs involved in the regulation of gene expression. As such, they regulate a large number of cellular pathways, and deregulation or altered expression of miRNAs is associated with tumorigenesis. In the current study, we evaluated the feasibility and clinical utility of circulating miRNAs as biomarkers for the detection and staging of breast cancer. METHODS: miRNAs were extracted from a set of 84 tissue samples from patients with breast cancer and eight normal tissue samples obtained after breast-reductive surgery. After reverse transcription and preamplification, 768 miRNAs were profiled by using the TaqMan low-density arrays. After data normalization, unsupervised hierarchical cluster analysis (UHCA) was used to investigate global differences in miRNA expression between cancerous and normal samples. With fold-change analysis, the most discriminating miRNAs between both tissue types were selected, and their expression was analyzed on serum samples from 20 healthy volunteers and 75 patients with breast cancer, including 16 patients with untreated metastatic breast cancer. miRNAs were extracted from 200 µl of serum, reverse transcribed, and analyzed in duplicate by using polymerase chain reaction (qRT-PCR). RESULTS: UHCA showed major differences in miRNA expression between tissue samples from patients with breast cancer and tissue samples from breast-reductive surgery (P < 0.0001). Generally, miRNA expression in cancerous samples tends to be repressed when compared with miRNA expression in healthy controls (P = 0.0685). The four most discriminating miRNAs by fold-change (miR-215, miR-299-5p, miR-411, and miR-452) were selected for further analysis on serum samples. All miRNAs at least tended to be differentially expressed between serum samples from patients with cancer and serum samples from healthy controls (miR-215, P = 0.094; miR-299-5P, P = 0.019; miR-411, P = 0.002; and miR-452, P = 0.092). For all these miRNAs, except for miR-452, the greatest difference in expression was observed between serum samples from healthy volunteers and serum samples from untreated patients with metastatic breast cancer. CONCLUSIONS: Our study provides a basis for the establishment of miRNAs as biomarkers for the detection and eventually staging of breast cancer through blood-borne testing. We identified and tested a set of putative biomarkers of breast cancer and demonstrated that altered levels of these miRNAs in serum from patients with breast cancer are particularly associated with the presence of metastatic disease.

Interrogating breast cancer heterogeneity using single and pooled circulating tumor cell analysis.

Single cell technologies allow the interrogation of tumor heterogeneity, providing insights into tumor evolution and treatment resistance. To better understand whether circulating tumor cells (CTCs) could complement metastatic biopsies for tumor genomic profiling, we characterized 11 single CTCs and 10 pooled CTC samples at the mutational and copy number aberration (CNA) levels, and compared these results with matched synchronous tumor biopsies from 3 metastatic breast cancer patients with triple-negative (TNBC), HER2-positive and estrogen receptor-positive (ER+) tumors. Similar CNA profiles and the same patient-specific driver mutations were found in bulk tissue and CTCs for the HER2-positive and TNBC tumors, whereas different CNA profiles and driver mutations were identified for the ER+ tumor, which presented two distinct clones in CTCs defined by mutations in ESR1 Y537N and TP53, respectively. Furthermore, de novo mutational signatures derived from CTCs described patient-specific biological processes. These data suggest that tumor tissue and CTCs provide complementary clinically relevant information to map tumor heterogeneity and tumor evolution.

Development of Training Materials for Pathologists to Provide Machine Learning Validation Data of Tumor-Infiltrating Lymphocytes in Breast Cancer.

The High Throughput Truthing project aims to develop a dataset for validating artificial intelligence and machine learning models (AI/ML) fit for regulatory purposes. The context of this AI/ML validation dataset is the reporting of stromal tumor-infiltrating lymphocytes (sTILs) density evaluations in hematoxylin and eosin-stained invasive breast cancer biopsy specimens. After completing the pilot study, we found notable variability in the sTILs estimates as well as inconsistencies and gaps in the provided training to pathologists. Using the pilot study data and an expert panel, we created custom training materials to improve pathologist annotation quality for the pivotal study. We categorized regions of interest (ROIs) based on their mean sTILs density and selected ROIs with the highest and lowest sTILs variability. In a series of eight one-hour sessions, the expert panel reviewed each ROI and provided verbal density estimates and comments on features that confounded the sTILs evaluation. We aggregated and shaped the comments to identify pitfalls and instructions to improve our training materials. From these selected ROIs, we created a training set and proficiency test set to improve pathologist training with the goal to improve data collection for the pivotal study. We are not exploring AI/ML performance in this paper. Instead, we are creating materials that will train crowd-sourced pathologists to be the reference standard in a pivotal study to create an AI/ML model validation dataset. The issues discussed here are also important for clinicians to understand about the evaluation of sTILs in clinical practice and can provide insight to developers of AI/ML models.