Metastatic renal cell carcinoma with occult primary: a multicenter prospective cohort.

Metastatic carcinoma of presumed renal origin (rCUP) has recently emerged as a new entity within the heterogeneous entity of Cancers of Unknown Primary (CUP) but their biological features and optimal therapeutic management remain unknown. We report the molecular characteristics and clinical outcome of a series of 25 rCUP prospectively identified within the French National Multidisciplinary Tumor Board for CUP. This cohort strongly suggests that rCUP share similarities with common RCC subtypes and benefit from renal-tailored systemic treatment. This study highlights the importance of integrating clinical and molecular data for optimal diagnosis and management of CUP.

Tumor-Infiltrating Lymphocytes in Triple-Negative Breast Cancer.

Importance: The association of tumor-infiltrating lymphocyte (TIL) abundance in breast cancer tissue with cancer recurrence and death in patients with early-stage triple-negative breast cancer (TNBC) who are not treated with adjuvant or neoadjuvant chemotherapy is unclear. Objective: To study the association of TIL abundance in breast cancer tissue with survival among patients with early-stage TNBC who were treated with locoregional therapy but no chemotherapy. Design, Setting, and Participants: Retrospective pooled analysis of individual patient-level data from 13 participating centers in North America (Rochester, Minnesota; Vancouver, British Columbia, Canada), Europe (Paris, Lyon, and Villejuif, France; Amsterdam and Rotterdam, the Netherlands; Milan, Padova, and Genova, Italy; Gothenburg, Sweden), and Asia (Tokyo, Japan; Seoul, Korea), including 1966 participants diagnosed with TNBC between 1979 and 2017 (with follow-up until September 27, 2021) who received treatment with surgery with or without radiotherapy but no adjuvant or neoadjuvant chemotherapy. Exposure: TIL abundance in breast tissue from resected primary tumors. Main Outcomes and Measures: The primary outcome was invasive disease-free survival [iDFS]. Secondary outcomes were recurrence-free survival [RFS], survival free of distant recurrence [distant RFS, DRFS], and overall survival. Associations were assessed using a multivariable Cox model stratified by participating center. Results: This study included 1966 patients with TNBC (median age, 56 years [IQR, 39-71]; 55% had stage I TNBC). The median TIL level was 15% (IQR, 5%-40%). Four-hundred seventeen (21%) had a TIL level of 50% or more (median age, 41 years [IQR, 36-63]), and 1300 (66%) had a TIL level of less than 30% (median age, 59 years [IQR, 41-72]). Five-year DRFS for stage I TNBC was 94% (95% CI, 91%-96%) for patients with a TIL level of 50% or more, compared with 78% (95% CI, 75%-80%) for those with a TIL level of less than 30%; 5-year overall survival was 95% (95% CI, 92%-97%) for patients with a TIL level of 50% or more, compared with 82% (95% CI, 79%-84%) for those with a TIL level of less than 30%. At a median follow-up of 18 years, and after adjusting for age, tumor size, nodal status, histological grade, and receipt of radiotherapy, each 10% higher TIL increment was associated independently with improved iDFS (hazard ratio [HR], 0.92 [0.89-0.94]), RFS (HR, 0.90 [0.87-0.92]), DRFS (HR, 0.87 [0.84-0.90]), and overall survival (0.88 [0.85-0.91]) (likelihood ratio test, P < 10e-6). Conclusions and Relevance: In patients with early-stage TNBC who did not undergo adjuvant or neoadjuvant chemotherapy, breast cancer tissue with a higher abundance of TIL levels was associated with significantly better survival. These results suggest that breast tissue TIL abundance is a prognostic factor for patients with early-stage TNBC.

Assessment of Pathology Domain-Specific Knowledge of ChatGPT and Comparison to Human Performance.

CONTEXT.­: Artificial intelligence algorithms hold the potential to fundamentally change many aspects of society. Application of these tools, including the publicly available ChatGPT, has demonstrated impressive domain-specific knowledge in many areas, including medicine. OBJECTIVES.­: To understand the level of pathology domain-specific knowledge for ChatGPT using different underlying large language models, GPT-3.5 and the updated GPT-4. DESIGN.­: An international group of pathologists (n = 15) was recruited to generate pathology-specific questions at a similar level to those that could be seen on licensing (board) examinations. The questions (n = 15) were answered by GPT-3.5, GPT-4, and a staff pathologist who recently passed their Canadian pathology licensing exams. Participants were instructed to score answers on a 5-point scale and to predict which answer was written by ChatGPT. RESULTS.­: GPT-3.5 performed at a similar level to the staff pathologist, while GPT-4 outperformed both. The overall score for both GPT-3.5 and GPT-4 was within the range of meeting expectations for a trainee writing licensing examinations. In all but one question, the reviewers were able to correctly identify the answers generated by GPT-3.5. CONCLUSIONS.­: By demonstrating the ability of ChatGPT to answer pathology-specific questions at a level similar to (GPT-3.5) or exceeding (GPT-4) a trained pathologist, this study highlights the potential of large language models to be transformative in this space. In the future, more advanced iterations of these algorithms with increased domain-specific knowledge may have the potential to assist pathologists and enhance pathology resident training.

Spatial Transcriptomics Reveal Pitfalls and Opportunities for the Detection of Rare High-Plasticity Breast Cancer Subtypes.

Breast cancer is one of the most prominent types of cancers, in which therapeutic resistance is a major clinical concern. Specific subtypes, such as claudin-low and metaplastic breast carcinoma (MpBC), have been associated with high nongenetic plasticity, which can facilitate resistance. The similarities and differences between these orthogonal subtypes, identified by molecular and histopathological analyses, respectively, remain insufficiently characterized. Furthermore, adequate methods to identify high-plasticity tumors to better anticipate resistance are lacking. Here, we analyzed 11 triple-negative breast tumors, including 3 claudin-low and 4 MpBC, via high-resolution spatial transcriptomics. We combined pathological annotations and deconvolution approaches to precisely identify tumor spots, on which we performed signature enrichment, differential expression, and copy number analyses. We used The Cancer Genome Atlas and Cancer Cell Line Encyclopedia public databases for external validation of expression markers. By focusing our spatial transcriptomic analyses on tumor cells in MpBC samples, we bypassed the negative impact of stromal contamination and identified specific markers that are neither expressed in other breast cancer subtypes nor expressed in stromal cells. Three markers (BMPER, POPDC3, and SH3RF3) were validated in external expression databases encompassing bulk tumor material and stroma-free cell lines. We unveiled that existing bulk expression signatures of high-plasticity breast cancers are relevant in mesenchymal transdifferentiated compartments but can be hindered by abundant stromal cells in tumor samples, negatively impacting their clinical applicability. Spatial transcriptomic analyses constitute powerful tools to identify specific expression markers and could thus enhance diagnosis and clinical care of rare high-plasticity breast cancers.

Exploring the added value of pretherapeutic MR descriptors in predicting breast cancer pathologic complete response to neoadjuvant chemotherapy.

OBJECTIVES: To evaluate the association between pretreatment MRI descriptors and breast cancer (BC) pathological complete response (pCR) to neoadjuvant chemotherapy (NAC). MATERIALS AND METHODS: Patients with BC treated by NAC with a breast MRI between 2016 and 2020 were included in this retrospective observational single-center study. MR studies were described using the standardized BI-RADS and breast edema score on T2-weighted MRI. Univariable and multivariable logistic regression analyses were performed to assess variables association with pCR according to residual cancer burden. Random forest classifiers were trained to predict pCR on a random split including 70% of the database and were validated on the remaining cases. RESULTS: Among 129 BC, 59 (46%) achieved pCR after NAC (luminal (n = 7/37, 19%), triple negative (n = 30/55, 55%), HER2 + (n = 22/37, 59%)). Clinical and biological items associated with pCR were BC subtype (p < 0.001), T stage 0/I/II (p = 0.008), higher Ki67 (p = 0.005), and higher tumor-infiltrating lymphocytes levels (p = 0.016). Univariate analysis showed that the following MRI features, oval or round shape (p = 0.047), unifocality (p = 0.026), non-spiculated margins (p = 0.018), no associated non-mass enhancement (p = 0.024), and a lower MRI size (p = 0.031), were significantly associated with pCR. Unifocality and non-spiculated margins remained independently associated with pCR at multivariable analysis. Adding significant MRI features to clinicobiological variables in random forest classifiers significantly increased sensitivity (0.67 versus 0.62), specificity (0.69 versus 0.67), and precision (0.71 versus 0.67) for pCR prediction. CONCLUSION: Non-spiculated margins and unifocality are independently associated with pCR and can increase models performance to predict BC response to NAC. CLINICAL RELEVANCE STATEMENT: A multimodal approach integrating pretreatment MRI features with clinicobiological predictors, including tumor-infiltrating lymphocytes, could be employed to develop machine learning models for identifying patients at risk of non-response. This may enable consideration of alternative therapeutic strategies to optimize treatment outcomes. KEY POINTS: • Unifocality and non-spiculated margins are independently associated with pCR at multivariable logistic regression analysis. • Breast edema score is associated with MR tumor size and TIL expression, not only in TN BC as previously reported, but also in luminal BC. • Adding significant MRI features to clinicobiological variables in machine learning classifiers significantly increased sensitivity, specificity, and precision for pCR prediction.

[Treatments for rare ovarian tumors: What's new?] / Avancées thérapeutiques dans la prise en charge des tumeurs rares malignes ovariennes.

Even if each rare ovarian tumor (ROT) has a low incidence, the sum of all these entities represents almost the half of all ovarian neoplasms. Thus, development of dedicated clinical trial emerged as a prerequisite to improve their managements. Owing to the spreading of dedicated institutional networks and (supra)national collaborations, the number of clinical trials has increased the past few years, with different types of trials; while some focused on specific molecular features, others assessed innovative molecules. Furthermore, relevant randomized clinical trials were designed as a mean to position new treatment options. Currently, innovative molecular-driven trials, based on master protocol trials are emerging and may shed light towards the improvement of personalized medicine regarding ROT.

[Application in France of the 2021 European recommendations on endometrial cancer]. / Application en France des recommandations européennes 2021 sur le cancer de l'endomètre.

The latest European recommendations of the European Societies of Gynecological Oncology (ESGO), Radiotherapy and Oncology (ESTRO) and Anatomopathology (ESP) concerning the management of patients with endometrial cancer were published in 2021. On behalf of the French Society of Gynecologic Oncology (SFOG) and the SFOG campus, we wish to summarize for the French-speaking readership the main measures with a more specific application for France. We also incorporate data from a Delphi survey conducted with a panel of French and French-speaking Swiss experts. The data presented in this article relate to histo-molecular characteristics, radiological data of endometrial cancer, and management of low-risk, intermediate-risk, intermediate-high-risk, and metastatic cancers. The aim of this review article is to show the application of the latest international recommendations to clinicians and pathologists for the implementation of these recommendations.

Management of Endometrial Cancer: French Society of Onco-Gynecology's Evaluation through a Delphi Survey.

Our aim was to assess the opinion of a panel of experts and obtain a consensus on the management of endometrial cancer in France and French Switzerland. A Delphi survey was carried out among a panel of French and French-speaking Swiss experts. The first questionnaire included 65 questions divided into eight categories: characterization of experts, histo-molecular characteristics and radiological data of endometrial cancer, and management of low-risk, intermediate-risk, intermediate-high-risk, high-risk, and metastatic cancers. The experts were asked to reply on a 9-point scale, both on the validity and the clarity of each question. After the answers were analyzed, a second questionnaire was sent to the same experts. The study took place between December 2021 and March 2022. Further, 58 (57.4%) of the 101 experts responded in the first round, and 39 recommendations were obtained (60%). Six questions were voted redundant and 20 discordant. These questions were reformulated, and, at the end of the second round, 17 recommendations were validated (85%). In total, the study presents an analysis of 56 questions and related responses. Expert advice helps to clarify non-consensual issues, standardize the management of endometrial cancer, and optimize clinical practices.