Demographic, clinical and antibody characteristics of patients with digital ulcers in systemic sclerosis: data from the DUO Registry

OBJECTIVES: The Digital Ulcers Outcome (DUO) Registry was designed to describe the clinical and antibody characteristics, disease course and outcomes of patients with digital ulcers associated with systemic sclerosis (SSc). METHODS: The DUO Registry is a European, prospective, multicentre, observational, registry of SSc patients with ongoing digital ulcer disease, irrespective of treatment regimen. Data collected included demographics, SSc duration, SSc subset, internal organ manifestations, autoantibodies, previous and ongoing interventions and complications related to digital ulcers. RESULTS: Up to 19 November 2010 a total of 2439 patients had enrolled into the registry. Most were classified as either limited cutaneous SSc (lcSSc; 52.2%) or diffuse cutaneous SSc (dcSSc; 36.9%). Digital ulcers developed earlier in patients with dcSSc compared with lcSSc. Almost all patients (95.7%) tested positive for antinuclear antibodies, 45.2% for anti-scleroderma-70 and 43.6% for anticentromere antibodies (ACA). The first digital ulcer in the anti-scleroderma-70-positive patient cohort occurred approximately 5 years earlier than the ACA-positive patient group. CONCLUSIONS: This study provides data from a large cohort of SSc patients with a history of digital ulcers. The early occurrence and high frequency of digital ulcer complications are especially seen in patients with dcSSc and/or anti-scleroderma-70 antibodies

Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group

Funder: U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)Funder: National Center for Research Resources under award number 1 C06 RR12463-01, VA Merit Review Award IBX004121A from the United States Department of Veterans Affairs Biomedical Laboratory Research and Development Service, the DOD Prostate Cancer Idea Development Award (W81XWH-15-1-0558), the DOD Lung Cancer Investigator-Initiated Translational Research Award (W81XWH-18-1-0440), the DOD Peer Reviewed Cancer Research Program (W81XWH-16-1-0329), the Ohio Third Frontier Technology Validation Fund, the Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering and the Clinical and Translational Science Award Program (CTSA) at Case Western Reserve University.Funder: Susan G Komen Foundation (CCR CCR18547966) and a Young Investigator Grant from the Breast Cancer Alliance.Funder: The Canadian Cancer SocietyFunder: Breast Cancer Research Foundation (BCRF), Grant No. 17-194Abstract: Assessment of tumor-infiltrating lymphocytes (TILs) is increasingly recognized as an integral part of the prognostic workflow in triple-negative (TNBC) and HER2-positive breast cancer, as well as many other solid tumors. This recognition has come about thanks to standardized visual reporting guidelines, which helped to reduce inter-reader variability. Now, there are ripe opportunities to employ computational methods that extract spatio-morphologic predictive features, enabling computer-aided diagnostics. We detail the benefits of computational TILs assessment, the readiness of TILs scoring for computational assessment, and outline considerations for overcoming key barriers to clinical translation in this arena. Specifically, we discuss: 1. ensuring computational workflows closely capture visual guidelines and standards; 2. challenges and thoughts standards for assessment of algorithms including training, preanalytical, analytical, and clinical validation; 3. perspectives on how to realize the potential of machine learning models and to overcome the perceptual and practical limits of visual scoring

Pitfalls in assessing stromal tumor infiltrating lymphocytes (sTILs) in breast cancer

Abstract: Stromal tumor-infiltrating lymphocytes (sTILs) are important prognostic and predictive biomarkers in triple-negative (TNBC) and HER2-positive breast cancer. Incorporating sTILs into clinical practice necessitates reproducible assessment. Previously developed standardized scoring guidelines have been widely embraced by the clinical and research communities. We evaluated sources of variability in sTIL assessment by pathologists in three previous sTIL ring studies. We identify common challenges and evaluate impact of discrepancies on outcome estimates in early TNBC using a newly-developed prognostic tool. Discordant sTIL assessment is driven by heterogeneity in lymphocyte distribution. Additional factors include: technical slide-related issues; scoring outside the tumor boundary; tumors with minimal assessable stroma; including lymphocytes associated with other structures; and including other inflammatory cells. Small variations in sTIL assessment modestly alter risk estimation in early TNBC but have the potential to affect treatment selection if cutpoints are employed. Scoring and averaging multiple areas, as well as use of reference images, improve consistency of sTIL evaluation. Moreover, to assist in avoiding the pitfalls identified in this analysis, we developed an educational resource available at www.tilsinbreastcancer.org/pitfalls

Application of a risk-management framework for integration of stromal tumor-infiltrating lymphocytes in clinical trials

Funder: Breast Cancer Research Foundation (BCRF); doi: https://doi.org/10.13039/100001006Abstract: Stromal tumor-infiltrating lymphocytes (sTILs) are a potential predictive biomarker for immunotherapy response in metastatic triple-negative breast cancer (TNBC). To incorporate sTILs into clinical trials and diagnostics, reliable assessment is essential. In this review, we propose a new concept, namely the implementation of a risk-management framework that enables the use of sTILs as a stratification factor in clinical trials. We present the design of a biomarker risk-mitigation workflow that can be applied to any biomarker incorporation in clinical trials. We demonstrate the implementation of this concept using sTILs as an integral biomarker in a single-center phase II immunotherapy trial for metastatic TNBC (TONIC trial, NCT02499367), using this workflow to mitigate risks of suboptimal inclusion of sTILs in this specific trial. In this review, we demonstrate that a web-based scoring platform can mitigate potential risk factors when including sTILs in clinical trials, and we argue that this framework can be applied for any future biomarker-driven clinical trial setting

CD8+ T cell infiltration in breast and colon cancer: A histologic and statistical analysis

<div><p>The prevalence of cytotoxic tumor infiltrating lymphocytes (TILs) has demonstrated prognostic value in multiple tumor types. In particular, CD8 counts (in combination with CD3 and CD45RO) have been shown to be superior to traditional UICC staging in colon cancer patients and higher total CD8 counts have been associated with better survival in breast cancer patients. However, immune infiltrate heterogeneity can lead to potentially significant misrepresentations of marker prevalence in routine histologic sections. We examined step sections of breast and colorectal cancer samples for CD8+ T cell prevalence by standard chromogenic immunohistochemistry to determine marker variability and inform practice of T cell biomarker assessment in formalin-fixed, paraffin-embedded (FFPE) tissue samples. Stained sections were digitally imaged and CD8+ lymphocytes within defined regions of interest (ROI) including the tumor and surrounding stroma were enumerated. Statistical analyses of CD8+ cell count variability using a linear model/ANOVA framework between patients as well as between levels within a patient sample were performed. Our results show that CD8+ T-cell distribution is highly homogeneous within a standard tissue sample in both colorectal and breast carcinomas. As such, cytotoxic T cell prevalence by immunohistochemistry on a single level or even from a subsample of biopsy fragments taken from that level can be considered representative of cytotoxic T cell infiltration for the entire tumor section within the block. These findings support the technical validity of biomarker strategies relying on CD8 immunohistochemistry.</p></div

CD8 IHC repeated measures staining results for 25 breast (red) and colorectal (blue) carcinoma samples.

<p>Each tumor block was sectioned 8 times or until the sample was exhausted.</p

Slide-level biopsy simulation results.

<p>(A) Percent of times over 1000 rounds of simulations that the values obtained from sampling increased numbers of biopsy fragments produced a result within 1SD of the mean CD8 staining for a given slide. Calculating the mean over increased numbers of biopsies led to better estimates of the mean, while calculating maxima over the sample biopsies led to overestimates of a slide’s CD8 levels. Samples are sorted by increasing performance in terms of being able to produce an estimate within 1SD of total CD8 staining for that slide. (B) Estimates of the standard deviation of the difference between the mean or maximum of selected core biopsies and the mean or maximum of the out-of-bag or unselected cores on a given slide. Increased sampling leads to improvements in variability for when using means but not when using order statistics. (C) Estimates of the standard deviation of the difference between the mean or maximum of selected core biopsies and the mean or maximum of the out-of-bag or unselected cores on a given slide as a function of the mean CD8 percent positive staining for a slide. Loess fits are used to highlight mean performance over the observed dynamic range on both the log₂ and observed percent staining scales. Increased sampling leads to improvements in variability when using means but not when using order statistics.</p

Sample workflow for immunohistochemistry and image analysis.

<p>Tumor blocks were sectioned and levels for CD8 immunohistochemistry taken at 25 μm intervals. Stained slides were scanned and the tumor area and immediately adjacent stroma was manually designated by a pathologist on all slides. All nucleated cells as well as CD8+ cells within the defined area were identified and counted by image analysis. Simulated core biopsies were identified by creating a grid of rectangular regions over the entire image, each approx. 2mm². in size. Rectangular regions that overlapped with at least 0.7 mm² of manually identified region were analyzed. Scale bar illustrated in “Level 1” panel equals 500 μm.</p

Block-level biopsy simulation results.

<p>(A) Percent of times over 1000 rounds of simulations that the values obtained from sampling increased numbers of biopsy fragments produced a result within 1SD of the mean CD8 staining for an entire tumor block. Calculating the mean over increased numbers of fragments led to better estimates of the mean, while calculating maxima over the sample biopsies led to overestimates of a sample’s CD8 levels. Samples are sorted by increasing performance in terms of being able to produce an estimate within 1SD of total CD8 staining for that tumor block. (B) Estimates of the standard deviation of the difference between the mean or maximum of selected core biopsies and the mean or maximum of the out-of-bag or unselected cores for a given tumor sample. Increased sampling leads to improvements in variability for when using means but not when using order statistics. (C) Estimates of the standard deviation of the difference between the mean or maximum of selected core biopsies and the mean or maximum of the out-of-bag or unselected cores for a given tumor sample as a function of the mean CD8 percent positive staining for that block. Loess fits are used to highlight mean performance over the observed dynamic range on both the log₂ and observed percent staining scales. Increased sampling leads to improvements in variability when using means but not when using order statistics.</p

Simulation receiver-operator characteristic (ROC) curves.

<p>With increased sampling, there is increased performance of the CD8 IHC assay to classify as positive or negative for a given cut-off (different colors) when using the means of different numbers of sampled biopsies (left) and decreased performance when using maxima (right). Over the set of cut-offs (1%, 2%, 5%, 10%), the analysis treated the mean CD8 staining as the true intensity against which the results of the subsamples of biopsy fragments were evaluated. Similar results were obtained when benchmarking the staining result from core biopsies against the mean staining of the entire block (data not shown).</p