Tryptophan metabolism is dysregulated in individuals with Fanconi anemia.

Fanconi anemia (FA) is a complex genetic disorder associated with progressive marrow failure and a strong predisposition to malignancy. FA is associated with metabolic disturbances such as short stature, insulin resistance, thyroid dysfunction, abnormal body mass index (BMI), and dyslipidemia. We studied tryptophan metabolism in FA by examining tryptophan and its metabolites before and during the stress of hematopoietic stem cell transplant (HSCT). Tryptophan is an essential amino acid that can be converted to serotonin and kynurenine. We report here that serotonin levels are markedly elevated 14 days after HSCT in individuals with FA, in contrast to individuals without FA. Kynurenine levels are significantly reduced in individuals with FA compared with individuals without FA, before and after HSCT. Most peripheral serotonin is made in the bowel. However, serotonin levels in stool decreased in individuals with FA after transplant, similar to individuals without FA. Instead, we detected serotonin production in the skin in individuals with FA, whereas none was seen in individuals without FA. As expected, serotonin and transforming growth factor ß (TGF-ß) levels were closely correlated with platelet count before and after HSCT in persons without FA. In FA, neither baseline serotonin nor TGF-B correlated with baseline platelet count (host-derived platelets), only TGF-B correlated 14 days after transplant (blood bank-derived platelets). BMI was negatively correlated with serotonin in individuals with FA, suggesting that hyperserotonemia may contribute to growth failure in FA. Serotonin is a potential therapeutic target, and currently available drugs might be beneficial in restoring metabolic balance in individuals with FA.

MLCD: A Unified Software Package for Cancer Diagnosis.

PURPOSE: Machine Learning Package for Cancer Diagnosis (MLCD) is the result of a National Institutes of Health/National Cancer Institute (NIH/NCI)-sponsored project for developing a unified software package from state-of-the-art breast cancer biopsy diagnosis and machine learning algorithms that can improve the quality of both clinical practice and ongoing research. METHODS: Whole-slide images of 240 well-characterized breast biopsy cases, initially assembled under R01 CA140560, were used for developing the algorithms and training the machine learning models. This software package is based on the methodology developed and published under our recent NIH/NCI-sponsored research grant (R01 CA172343) for finding regions of interest (ROIs) in whole-slide breast biopsy images, for segmenting ROIs into histopathologic tissue types and for using this segmentation in classifiers that can suggest final diagnoses. RESULT: The package provides an ROI detector for whole-slide images and modules for semantic segmentation into tissue classes and diagnostic classification into 4 classes (benign, atypia, ductal carcinoma in situ, invasive cancer) of the ROIs. It is available through the GitHub repository under the Massachusetts Institute of Technology license and will later be distributed with the Pathology Image Informatics Platform system. A Web page provides instructions for use. CONCLUSION: Our tools have the potential to provide help to other cancer researchers and, ultimately, to practicing physicians and will motivate future research in this field. This article describes the methodology behind the software development and gives sample outputs to guide those interested in using this package.

Assessment of Machine Learning of Breast Pathology Structures for Automated Differentiation of Breast Cancer and High-Risk Proliferative Lesions.

Importance: Following recent US Food and Drug Administration approval, adoption of whole slide imaging in clinical settings may be imminent, and diagnostic accuracy, particularly among challenging breast biopsy specimens, may benefit from computerized diagnostic support tools. Objective: To develop and evaluate computer vision methods to assist pathologists in diagnosing the full spectrum of breast biopsy samples, from benign to invasive cancer. Design, Setting, and Participants: In this diagnostic study, 240 breast biopsies from Breast Cancer Surveillance Consortium registries that varied by breast density, diagnosis, patient age, and biopsy type were selected, reviewed, and categorized by 3 expert pathologists as benign, atypia, ductal carcinoma in situ (DCIS), and invasive cancer. The atypia and DCIS cases were oversampled to increase statistical power. High-resolution digital slide images were obtained, and 2 automated image features (tissue distribution feature and structure feature) were developed and evaluated according to the consensus diagnosis of the expert panel. The performance of the automated image analysis methods was compared with independent interpretations from 87 practicing US pathologists. Data analysis was performed between February 2017 and February 2019. Main Outcomes and Measures: Diagnostic accuracy defined by consensus reference standard of 3 experienced breast pathologists. Results: The accuracy of machine learning tissue distribution features, structure features, and pathologists for classification of invasive cancer vs noninvasive cancer was 0.94, 0.91, and 0.98, respectively; the accuracy of classification of atypia and DCIS vs benign tissue was 0.70, 0.70, and 0.81, respectively; and the accuracy of classification of DCIS vs atypia was 0.83, 0.85, and 0.80, respectively. The sensitivity of both machine learning features was lower than that of the pathologists for the invasive vs noninvasive classification (tissue distribution feature, 0.70; structure feature, 0.49; pathologists, 0.84) but higher for the classification of atypia and DCIS vs benign cases (tissue distribution feature, 0.79; structure feature, 0.85; pathologists, 0.72) and the classification of DCIS vs atypia (tissue distribution feature, 0.88; structure feature, 0.89; pathologists, 0.70). For the DCIS vs atypia classification, the specificity of the machine learning feature classification was similar to that of the pathologists (tissue distribution feature, 0.78; structure feature, 0.80; pathologists, 0.82). Conclusion and Relevance: The computer-based automated approach to interpreting breast pathology showed promise, especially as a diagnostic aid in differentiating DCIS from atypical hyperplasia.

Histopathologic Features of Postablation Tubal Sterilization Syndrome.

Postablation tubal sterilization syndrome (PATSS) is an uncommon complication of endometrial ablation in patients with antecedent tubal ligation characterized by cyclic pelvic pain. Recurrent tubal distention resulting from retrograde menstruation into occluded proximal fallopian tube segments by residual/regenerated cornual endometrial tissue is postulated to be the cause. Reports of PATSS have largely focused on the clinicoradiologic and operative findings. Detailed descriptions of the gross pathologic findings of PATSS are sparse and rarer still are examples in which the histologic manifestations are discussed. Three patients with a history of tubal ligation and subsequent endometrial ablation who underwent hysterectomy and bilateral salpingo-oophorectomy for pelvic pain were identified. A clinical suspicion of PATSS was conveyed to the pathologist at the time of initial pathologic examination in only 2 of the 3 cases. Pathologic findings in all 3 cases were similar and included hematosalpinx of the proximal fallopian tubes, intraluminal hemosiderotic material, mural hemosiderosis, and pseudoxanthomatous salpingitis featuring plical and mural lipofuscin-laden macrophages, along with inactive to attenuated endometrium with variable submucosal myometrial hyalinization/scarring compatible with postablative changes. The pathologic features, in conjunction with the appropriate clinicoradiologic findings, were interpreted as consistent with PATSS. PATSS complicates an estimated 5% to 10% of endometrial ablations, but is likely underreported due to a lack of awareness. Pathologists should consider PATSS in hysterectomy specimens that show postablative endometrial changes accompanied by hematosalpinx and pseudoxanthomatous salpingitis of the proximal segments of ligated fallopian tubes. To our knowledge, this is the first study to depict the histopathologic features of PATSS.