Citations

Aims Thymic epithelial tumours (TET), including thymomas and thymic carcinomas and thymic neuroendocrine neoplasms, are malignant neoplasms that can be associated with morbidity and mortality. Recently, an updated version of the World Health Organization (WHO) Classification of Thoracic Tumours 5th Edition, 2021 has been released, which included various changes to the classification of these neoplasms. In addition, in 2017 the Union for International Cancer Control (UICC) / American Joint Committee on Cancer (AJCC) published the 8th Edition Staging Manual which, for the first time, includes a TNM staging that is applicable to thymomas, thymic carcinomas, and thymic neuroendocrine neoplasms. Methods and Results To standardize reporting of resected TET and thymic neuroendocrine neoplasms the accrediting bodies updated their reporting protocols. The International Collaboration on Cancer Reporting (ICCR), which represents a collaboration between various National Associations of Pathology, updated its 2017 histopathology reporting guide on TET and thymic neuroendocrine neoplasms accordingly. This report will highlight important changes in the reporting of TET and thymic neuroendocrine neoplasms based on the 2021 WHO, emphasize the 2017 TNM staging, and also comment on the rigour and various uncertainties for the pathologist when trying to follow that staging. Conclusion The ICCR dataset provides a comprehensive, standardized template for reporting of resected TET and thymic neuroendocrine neoplasms.

Abstract Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting and analyzing spatial data (raster versus vector‐based). We then provide a compendium of spatial immune cell metrics that have been reported in the literature, summarizing prognostic associations in the context of a variety of cancers. We conclude by discussing two well‐described clinical biomarkers, the breast cancer stromal tumor infiltrating lymphocytes score and the colon cancer Immunoscore, and describe investigative opportunities to improve clinical utility of these spatial biomarkers. © 2023 The Pathological Society of Great Britain and Ireland.

AbstractThe clinical significance of the tumor‐immune interaction in breast cancer is now established, and tumor‐infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple‐negative (estrogen receptor, progesterone receptor, and HER2‐negative) breast cancer and HER2‐positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state‐of‐the‐art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) 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 for TIL 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 triple‐negative breast cancer. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

198 Background: HER2 overexpression/amplification (HER2+) occurs in 3%-5% of patients (pts) w/ metastatic colorectal cancer (mCRC). Rates of HER2+ can increase to ~10% in pts w/ RAS/BRAF wild-type mCRC tumors. The MOUNTAINEER trial (NCT03043313) evaluated the efficacy and safety of the investigational combination of tucatinib with trastuzumab in pts with HER2+ and RAS wild-type mCRC. Established regional guidelines for mCRC recommend HER2 testing and HER2-directed treatment options; however, there is currently no established best practice for HER2 testing and interpretation in mCRC. Here, we present data from a concordance analysis comparing breast and gastric HER2 testing algorithms in the mCRC setting. Methods: The MOUNTAINEER trial enrolled pts w/ HER2+ mCRC identified using ≥1 method: tissue-based local immunohistochemistry (IHC), in situ hybridization (ISH), and/or next-generation sequencing (NGS) testing. Archival or fresh tumor tissue was submitted to a sponsor-designated central laboratory for confirmatory HER2 testing w/ IHC/FISH per the package insert of the FDA approved assay and scored by both the breast and gastric algorithms for HER2 IHC. A positive result per the breast scoring criteria for IHC requires circumferential membrane staining for HER2, while the gastric criteria allows for circumferential, basolateral, or lateral staining patterns. Results: A total of 114 pts were enrolled with HER2+ tumors per ≥1 local testing methods; 69 pts were HER2+ by NGS, 46 by IHC 3+, and 36 by ISH. Of 105 pts who had tissue available for central HER2 testing w/IHC/FISH, 98 had valid HER2 results; 82/98 (83.7%) of pts had tumors centrally confirmed as HER2+ using both the breast and gastric algorithms. Tissue samples from pts in the MOUNTAINEER trial had 100% concordance between breast and gastric algorithms in HER2 status and 99% concordance in HER2 IHC score. Conclusions: Central pathology testing using both the breast and gastric criteria showed high concordance between these two commonly used algorithms. A high central confirmation rate of local HER2+ results was also observed. These data support the use of either the breast or gastric algorithms to identify HER2+ mCRC tumors until an FDA-approved HER2 assay is available for mCRC. Clinical trial information: NCT03043313 . [Table: see text]

AbstractBackgroundReduced striatal uptake on ioflupane SPECT is considered an indicative biomarker for Dementia with Lewy Bodies (DLB), and has been proposed as an indicative biomarker for Mild Cognitive Impairment with Lewy Bodies (MCI‐LB). Little data exists on the profile of uptake on key striatal regions of interest (ROIs) in DLB or MCI‐LB.MethodParticipants with DLB or MCI‐LB at baseline based on the presence of ≥2 core clinical DLB features, and ≥2 ioflupane SPECT scans performed approximately annually, were identified from the Mayo ADRC database. Minimum brain laterality z‐score values for the putamen (whole, anterior and posterior), caudate and striatum were determined using DaTQUANT 2 software. Baseline clinical and scan data was used for all analyses, and the annualized minimum putamen z‐change was determined on serial scans. The scan was considered abnormal if the minimum putamen z‐score was <‐0.98 based on clinicopathologic data.Result27 participants were included (21 DLB and 6 MCI‐LB, 96% male). Compared to MCI‐LB, DLB had a lower mean MoCA score and a trend toward a higher UPDRS score compared to MCI‐LB; there were no statistically significant differences in age, sex, onset age or duration of cognitive decline, or presence of other core DLB features. The majority (89%) had an abnormal scan at baseline. All ROI z‐scores showed the expected general trend of DLB<MCI‐LB, and the annualized z‐change was more negative in DLB, but none of the findings reached statistical significance.ConclusionThese findings suggest in MCI‐LB and DLB that 1) an abnormal ioflupane SPECT scan is frequently present at diagnosis, 2) z‐scores become more negative over time, and 3) the annualized z‐change is similar. Similar findings in larger cohorts would support the use of ioflupane SPECT as a biomarker in both MCI‐LB and DLB clinical trials.

AbstractBackgroundSeveral hypotheses exist regarding the neurologic substrate underlying visual hallucinations (VH) in Dementia with Lewy Bodies (DLB). Studies have suggested an association with regional cerebral hypometabolism in the primary visual or visual association cortices on FDG‐PET. We compared regional cerebral glucose metabolism and posterior cingulate island sign (CIS) ratios in DLB and in Mild Cognitive Impairment with Lewy Bodies (MCI‐LB) participants with (VH+) and without (VH‐) VH.MethodParticipants with clinically probable DLB or MCI‐LB and an FDG‐PET scan were identified from the Mayo ADRC database. Responses on the Visual Hallucinations and Delusions Questionnaire and Neuropsychiatric Inventory were used to assess presence or absence of VH. Standardized uptake value ratios (SUVr) were used to calculate regional cerebral glucose hypometabolism in the primary visual cortex (PVC), lateral occipital cortex (LOC), and whole occipital cortex (WOC). The CIS ratio was defined as posterior cingulate divided by the sum of the precuneus plus cuneus FDG‐PET uptake ratio.ResultTwo cohorts were identified (27 DLB and 18 MCI‐LB, 87% male). Eighteen DLB participants were VH+ and 9 were VH‐, whereas 5 MCI‐LB were VH+ and 13 were VH‐. There were no statistically significant differences in demographic (age, sex, education) and clinical features (MoCA, CDR, presence of other core DLB features, and UPDRS) between VH+ vs VH‐ in those with DLB, and between VH+ vs VH‐ in those with MCI‐LB. Similarly, there were no statistically significant differences in DLB between VH+ vs VH‐ in PVC (1.55±0.14 vs 1.58±0.15), LOC (1.22±0.16 vs 1.28±0.12), WOC (1.29±0.15 vs 1.34±0.11), or CIS ratio (1.16±0.08 vs 1.14±0.10), nor in MCI‐LB between VH+ vs VH‐ in PVC (1.68±0.19 vs 1.58±0.14), LOC (1.40±0.21 vs 1.32±0.14), WOC (1.45±0.20 vs 1.37±0.14), or CIS ratio (1.17±0.08 vs 1.19±0.10) (P>0.05 for all comparisons).ConclusionThese findings do not support a direct correlation between regional cerebral glucose metabolism and VH in DLB or MCI‐LB. Further work in DLB and MCI‐LB with larger numbers is warranted. Other neurologic substrates and mechanisms, such as REM sleep intrusions during wakefulness, neurotransmitter dysfunction, and disruption of functional connectivity across brain regions should be explored as contributors to VH in DLB.