Genome profiling is an efficient tool to avoid the STUMP classification of uterine smooth muscle lesions: a comprehensive array-genomic hybridization analysis of 77 tumors.

The diagnosis of a uterine smooth muscle lesion is, in the majority of cases, straightforward. However, in a small number of cases, the morphological criteria used in such lesions cannot differentiate with certainty a benign from a malignant lesion and a diagnosis of smooth muscle tumor with uncertain malignant potential (STUMP) is made. Uterine leiomyosarcomas are often easy to diagnose but it is difficult or even impossible to identify a prognostic factor at the moment of the diagnosis with the exception of the stage. We hypothesize, for uterine smooth muscle lesions, that there is a gradient of genomic complexity that correlates to outcome. We first tested this hypothesis on STUMP lesions in a previous study and demonstrated that this 'gray category' could be split according to genomic index into two groups. A benign group, with a low to moderate alteration rate without recurrence and a malignant group, with a highly rearranged profile akin to uterine leiomyosarcomas. Here, we analyzed a large series of 77 uterine smooth muscle lesions (from 76 patients) morphologically classified as 19 leiomyomas, 14 STUMP and 44 leiomyosarcomas with clinicopathological and genomic correlations. We confirmed that genomic index with a cut-off=10 is a predictor of recurrence (P<0.0001) and with a cut-off=35 is a marker for poor overall survival (P=0.035). For the tumors confined to the uterus, stage as a prognostic factor was not useful in survival prediction. At stage I, among the tumors reclassified as molecular leiomyosarcomas (ie, genomic index ≥10), the poor prognostic markers were: 5p gain (overall survival P=0.0008), genomic index at cut-off=35 (overall survival P=0.0193), 13p loss including RB1 (overall survival P=0.0096) and 17p gain including MYOCD gain (overall survival P=0.0425). Based on these findings (and the feasibility of genomic profiling by array-comparative genomic hybridization), genomic index, 5p and 17p gains prognostic value could be evaluated in future prospective chemotherapy trials.

Uterine smooth muscle tumor analysis by comparative genomic hybridization: a useful diagnostic tool in challenging lesions.

The diagnosis and management of uterine smooth muscle tumors with uncertain malignant potential (STUMP) is often challenging, and genomic data on these lesions as well as on uterine smooth muscle lesions are limited. We tested the hypothesis that genomic profile determination by array-CGH could split STUMP into a benign group with scarce chromosomal alterations akin to leiomyoma and a malignant group with high chromosomal instability akin to leiomyosarcoma. Array-CGH genomic profile analysis was conducted for a series of 29 cases of uterine STUMP. A group of ten uterine leiomyomas and ten uterine leiomyosarcomas served as controls. The mean age was 50 years (range, 24-85) and the follow-up ranged from 12 to 156 months (average 70 months). Since STUMP is a heterogenous group of tumors with genomic profiles that can harbor few to many chromosomal alterations, we compared genomic indices in leiomyomas and leiomyosarcomas and set a genomic index=10 threshold. Tumors with a genomic index <10 were classified as nonrecurring STUMPs and those with a genomic index >10 represented STUMPs with recurrences and unfavorable outcomes. Hence, the genomic index threshold splits the STUMP category into two groups of tumors with different outcomes: a group comparable to leiomyomas and another similar to leiomyosarcomas, but more indolent. In our STUMP series, genomic analysis by array-CGH is an innovative diagnostic tool for problematic smooth muscle uterine lesions, complementary to the morphological evaluation approach. We provide an improved classification method for distinguishing truly malignant tumors from benign lesions within the category of STUMP, especially those with equivocal morphological features.

Ovarian cancer molecular pathology.

Ovarian cancer (OVC) is the fourth leading cause of cancer mortality among women in Europe and the United States. Its early detection is difficult due to the lack of specificity of clinical symptoms. Unfortunately, late diagnosis is a major contributor to the poor survival rates for OVC, which can be attributed to the lack of specific sets of markers. Aside from patients sharing a strong family history of ovarian and breast cancer, including the BRCA1 and BRCA2 tumor suppressor genes mutations, the most used biomarker is the Cancer-antigen 125 (CA-125). CA-125 has a sensitivity of 80 % and a specificity of 97 % in epithelial cancer (stage III or IV). However, its sensitivity is 30 % in stage I cancer, as its increase is linked to several physiological phenomena and benign situations. CA-125 is particularly useful for at-risk population diagnosis and to assess response to treatment. It is clear that alone, CA-125 is inadequate as a biomarker for OVC diagnosis. There is an unmet need to identify additional biomarkers. Novel and more sensitive proteomic strategies such as MALDI mass spectrometry imaging studies are well suited to identify better markers for both diagnosis and prognosis. In the present review, we will focus on such proteomic strategies in regards to OVC signaling pathways, OVC development and escape from the immune response.

Can AI predict epithelial lesion categories via automated analysis of cervical biopsies: The TissueNet challenge?

The French Society of Pathology (SFP) organized its first data challenge in 2020 with the help of the Health Data Hub (HDH). The organization of this event first consisted of recruiting nearly 5000 cervical biopsy slides obtained from 20 pathology centers. After ensuring that patients did not refuse to include their slides in the project, the slides were anonymized, digitized, and annotated by expert pathologists, and finally uploaded to a data challenge platform for competitors from around the world. Competing teams had to develop algorithms that could distinguish 4 diagnostic classes in cervical epithelial lesions. Among the many submissions from competitors, the best algorithms achieved an overall score close to 95%. The final part of the competition lasted only 6 weeks, and the goal of SFP and HDH is now to allow for the collection to be published in open access for the scientific community. In this report, we have performed a "post-competition analysis" of the results. We first described the algorithmic pipelines of 3 top competitors. We then analyzed several difficult cases that even the top competitors could not predict correctly. A medical committee of several expert pathologists looked for possible explanations for these erroneous results by reviewing the images, and we present their findings here targeted for a large audience of pathologists and data scientists in the field of digital pathology.

Radical fimbriectomy: a reasonable temporary risk-reducing surgery for selected women with a germ line mutation of BRCA 1 or 2 genes? Rationale and preliminary development.

OBJECTIVE: Bilateral salpingo-oophorectomy (BSO) is the gold standard prophylactic surgery for BRCA1 or 2 mutation carriers. However, due to the resulting early menopause and fertility desires, young women are reluctant to undergo this procedure. In view of the recent literature on ovarian carcinogenesis, we wish to report a novel conceptual surgical procedure we called "radical fimbriectomy." This procedure is aimed to protect this subset of high-risk women from high-grade serous pelvic carcinoma, while preserving their ovarian function. METHODS: Women with BRCA mutation, who were scheduled for BSO, were informed of the procedure approved by our local review board. Radical fimbriectomy consists of removing all the tube and the fimbrio-ovarian junction, step immediately followed in this developmental phase by completion oophorectomy. Four methods of partial ovarian transsection were prospectively compared: sharp division, stapler, bipolar division and harmonic scalpel. Surgical safety and pathological alterations were assessed. All specimens underwent extensive pathological evaluation using both SEE-FIM protocol and serial sections. RESULTS: Fourteen women were enrolled in the study. Sharp and EndoGIA® appeared to be the safest methods of ovarian resection providing the best specimen quality for pathological examination. CONCLUSION: We believe this technique could be suggested to young mutation carriers reluctant to undergo BSO. This approach is preferable to no prophylactic surgery at all. However, until the safety and validity of this procedure is confirmed by a multi-institutional study, women who undergo radical fimbriectomy should continue to receive regular multimodal evaluation and be advised of the risks involved until they finally accept secondary castration.

A Partial Label-Based Machine Learning Approach For Cervical Whole-Slide Image Classification: The Winning TissueNet Solution.

Cervical cancer is the fourth most common cancer in women worldwide. To determine early treatment for patients, it is critical to accurately classify the cervical intraepithelial lesion status based on a microscopic biopsy. Lesion classification is a 4-class problem, with biopsies being designated as benign or increasingly malignant as class 1-3, with 3 being invasive cancer. Unfortunately, traditional biopsy analysis by a pathologist is time-consuming and subject to intra- and inter-observer variability. For this reason, it is of interest to develop automatic analysis pipelines to classify lesion status directly from a digitalized whole slide image (WSI). The recent TissueNet Challenge was organized to find the best automatic detection pipeline for this task, using a dataset of 1015 annotated WSI slides. In this work, we present our winning end-to-end solution for cervical slide classification composed of a two-step classification model: First, we classify individual slide patches using an ensemble CNN, followed by an SVM-based slide classification using statistical features of the aggregated patch-level predictions. Importantly, we present the key innovation of our approach, which is a novel partial label-based loss function that allows us to supplement the supervised WSI patch annotations with weakly supervised patches based on the WSI class. This led to us not requiring additional expert tissue annotation, while still reaching the winning score of 94.7%. Our approach is a step towards the clinical inclusion of automatic pipelines for cervical cancer treatment planning.Clinical relevance- The explanation of the winning Tis-sueNet AI algorithm for automated cervical cancer classification, which may provide insights for the next generation of computer assisted tools in digital pathology.

Asymptomatic pelvic metastasis from thymic carcinoma: a case report.

Thymic epithelial tumors are rare and often occur somewhere local. Metastatic sites of thymic carcinomas (Masaoka-Koga stage IVb) are mostly seen in the lung, liver and brain. We report a 64-year-old female with an initial diagnosis of thymoma B3 who first showed thoracic recurrences and then an asymptomatic isolated pelvic metastasis from her thymic carcinoma.

Spectroimmunohistochemistry: a novel form of MALDI mass spectrometry imaging coupled to immunohistochemistry for tracking antibodies.

MALDI mass spectrometry imaging (MALDI-MSI) is currently used for clinical applications, such as biomarker identification, particularly for the study of solid tumors. The ability to map specific compounds that have been determined to be biomarkers and therapeutic targets is relevant for the evaluation of the efficacy of targeted therapies. This article describes a new method called Spectro-ImmunoHistoChemistry (SIHC), which combines the use of specific antibodies against markers and mass spectrometric imaging in the MS/MS mode. SIHC is based on direct primary antibody-antigen recognition, trypsin digestion of the antibody overlaying the markers of interest in the tissue section, and MALDI-MSI of the tryptic peptides generated from the antibody. This approach has both clinical and pharmacological applications. First, it can be used as a cross-validation method to monitor the presence specifically of a marker in a tissue section. Second, SIHC could potentially be used as a novel technology for tracking specific antibodies after in vivo injection for anti-cancer treatments. Additionally, SIHC could enable novel clinical applications of MSI, such as monitoring the efficacy of cytotoxic antibody treatments.

Detection and quantification of CSF malignant cells by the CellSearch technology in patients with melanoma leptomeningeal metastasis.

Melanoma is the most frequent solid tumor associated with leptomeningeal metastasis (LM). The usual diagnostic tools, that is, cytomorphological assessment of cerebro-spinal fluid (CSF) and gadolinium-enhanced MRI of the entire neuraxis both lack effectiveness. The CellSearch Veridex technology for the detection of circulating tumor cells (CTC) in blood was designed for the follow-up and prognosis of breast, prostate, colorectal, and lung cancer, which express EpCAM markers. We have previously adapted this technology to detect malignant cells in the CSF of breast cancer LM. Our objective here was to check if this technology would also allow the detection and the enumeration of CTC in the CSF of melanoma patients presenting with LM although melanoma does not express EpCAM markers. On the occasion of the intrathecal treatment of LM in 2 melanoma patients, 5 mL of CSF and 7.5 mL of blood were collected on CellSave Preservative Tubes and analyzed within 3 days after CSF sampling using a melanoma-dedicated kit. The CellSearch Veridex technology was then adapted to direct enrichment, enumeration, and visualization of melanoma cells in the CSF. CD146+, HMW-MAA+, CD34-, and CD45- cells with typical morphology could be observed and enumerated sequentially with reproducible results, corresponding to CSF melanoma cells (CSFMC). In contrast to the current gold standard cytomorphological analysis, this new approach allowed a precise quantification of CSFMC in all samples concomitantly analyzed. This methodology, established on a limited volume of sample and allowing delayed processing, could prove of great interest in the diagnosis and follow-up of melanoma patients with LM.

Microproteomics by liquid extraction surface analysis: application to FFPE tissue to study the fimbria region of tubo-ovarian cancer.

PURPOSE: We have developed a new method for rapid analysis of a specific region on formalin fixed and paraffin embedded (FFPE) tissue sections. This method combines advantages of direct tissue MS analysis keeping histological information and conventional proteomics approaches for confident identification of proteins in complex sample. EXPERIMENTAL DESIGN: After histological annotation, heat-induced antigen retrieval is performed on FFPE tissue. Using a chemical inkjet printer, trypsin is deposited on discrete regions of less than 1 mm². After protein digestion, a liquid extraction is performed to retrieve all the peptides. Data coming from identification of proteins in cancer and benign region are compared. RESULTS: In total, 3649 unique peptides were identified (with a peptide strict false discovery rate less than 1%) corresponding to 983 and 792 nonredundant protein groups identified in benign and cancer region, respectively. A total of 123 protein groups are found only in cancer region and 315 are specific to the benign part. From these data, it has been possible to obtain different important signaling pathways involved in cancer processes and some proteins already known as biomarkers. CONCLUSIONS AND CLINICAL RELEVANCE: This new approach using a combination of localized on-tissue protein digestion and liquid microextraction followed by LC-MS/MS analysis is useful for advancing our understanding of cancer biology. It is a rapid and innovative technique that will contribute positively to clinical proteomics.