Effects of phlebotomy on the growth of ferric nitrilotriacetate-induced renal cell carcinoma.

The ferric nitrilotriacetate-induced carcinogenesis model is unique in that reactive oxygen species-free radicals are involved in the carcinogenic process. But the effects of iron-withdrawal in the progression of renal cell carcinoma are not well understood. We performed repeated phlebotomies on animals that had been administered ferric nitrilotriacetate in the initiation stage of renal cell carcinoma (phlebotomy group), and compared the development of renal tumors with those not receiving repeated phlebotomies (non-phlebotomy group). Ferric nitrilotriacetate-treated male Wistar rats were randomly divided into 2 groups: a phlebotomy group (21 rats) and a non-phlebotomy group (17 rats). Ten age-adjusted normal rats were also observed as a normal group. Hematocrit was maintained under 25% in the phlebotomy group. Hematocrit levels in the normal group and in the non-phlebotomy group were not significantly different. As a result, the incidence of renal cell carcinoma was not significantly different between phlebotomy and non-phlebotomy animals. However, the total weight of the renal cell carcinoma was significantly heavier in the animals from non-phlebotomy group than in those from the phlebotomy group (23.64 g +/- 18.54 vs. 54.40 g +/- 42.40, P &#60; 0.05). The present study demonstrated that phlebotomy after the administration of ferric nitrilotriacetate did not reduce the incidence of renal cell carcinoma. In addition, we showed that iron withdrawal at the promotion stage of carcinogenesis will retard tumor growth.</p

On a Network Model of Localization in a Random Magnetic Field

We consider a network model of snake states to study the localization problem of non-interacting fermions in a random magnetic field with zero average. After averaging over the randomness, the network of snake states is mapped onto $M$ coupled SU$(2N)$ spin chains in the $N \rightarrow 0$ limit. The number of snake states near the zero-field contour, $M$, is an even integer. In the large conductance limit $g = M {e^2 \over 2 \pi \hbar}$ ($M \gg 2$), it turns out that this system is equivalent to a particular representation of the ${\rm U}(2N) / {\rm U}(N) \times {\rm U}(N)$ sigma model ($N \rightarrow 0$) {\it without} a topological term. The beta function $\beta (1/M)$ of this sigma model in the $1/M$ expansion is consistent with the previously known $\beta (g)$ of the unitary ensemble. These results and further plausible arguments support the conclusion that all the states are localized.Comment: Revtex, 6 pages, 3 figures appended as an uuencoded fil

Pitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: a report of the international immuno‐oncology biomarker working group

The clinical significance of the tumor-immune interaction in breast cancer (BC) has been well established, and tumor-infiltrating lymphocytes (TILs) have emerged as a predictive and prognostic biomarker for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2 negative) breast cancer (TNBC) and HER2-positive breast cancer. How computational assessment of TILs can complement manual TIL-assessment in trial- and daily practices is currently debated and still unclear. Recent efforts to use machine learning (ML) for the automated evaluation of TILs show promising results. We review state-of-the-art approaches and identify pitfalls and challenges by studying the root cause of ML discordances in comparison to manual TILs quantification. We categorize our findings into four main topics; (i) technical slide issues, (ii) ML and image analysis aspects, (iii) data challenges, and (iv) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns, or design choices in the computational implementation. To aid the adoption of ML in TILs assessment, we provide an in-depth discussion of ML and image analysis including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial- and routine clinical management of patients with TNBC

Consensus guidelines for the use and interpretation of angiogenesis assays

The formation of new blood vessels, or angiogenesis, is a complex process that plays important roles in growth and development, tissue and organ regeneration, as well as numerous pathological conditions. Angiogenesis undergoes multiple discrete steps that can be individually evaluated and quantified by a large number of bioassays. These independent assessments hold advantages but also have limitations. This article describes in vivo, ex vivo, and in vitro bioassays that are available for the evaluation of angiogenesis and highlights critical aspects that are relevant for their execution and proper interpretation. As such, this collaborative work is the first edition of consensus guidelines on angiogenesis bioassays to serve for current and future reference

The tale of TILs in breast cancer : a report from the International Immuno-Oncology Biomarker Working Group

The advent of immune-checkpoint inhibitors (ICI) in modern oncology has significantly improved survival in several cancer settings. A subgroup of women with breast cancer (BC) has immunogenic infiltration of lymphocytes with expression of programmed deathligand 1 (PD-L1). These patients may potentially benefit from ICI targeting the programmed death 1 (PD-1)/PD-L1 signaling axis. The use of tumor-infiltrating lymphocytes (TILs) as predictive and prognostic biomarkers has been under intense examination. Emerging data suggest that TILs are associated with response to both cytotoxic treatments and immunotherapy, particularly for patients with triple-negative BC. In this review from The International Immuno-Oncology Biomarker Working Group, we discuss (a) the biological understanding of TILs, (b) their analytical and clinical validity and efforts toward the clinical utility in BC, and (c) the current status of PD-L1 and TIL testing across different continents, including experiences from low-to-middle-income countries, incorporating also the view of a patient advocate. This information will help set the stage for future approaches to optimize the understanding and clinical utilization of TIL analysis in patients with BC.The National Health and Medical Research Council of Australia; the Cure; the Royal Australasian College of Physicians; the NIH/NCI ; the National Breast Cancer Foundation of Australia Endowed Chair; the Breast Cancer Research Foundation, New York and the Breast Cancer Research Foundation (BCRF).www.nature.com/npjbcanceram2022Immunolog

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