Image analysis for bright-field HER2 in situ hybridization: validation for clinical use.

The aim of the present study was to develop and validate a quantitative image analysis (IA) algorithm to aid pathologists in assessing bright-field HER2 in situ hybridization (ISH) tests in solid cancers. A cohort of 80 sequential cases (40 HER2-negative and 40 HER2-positive) were evaluated for HER2 gene amplification with bright-field ISH. We developed an IA algorithm using the ISH Module from HALO software to automatically quantify HER2 and CEP17 copy numbers per cell as well as the HER2/CEP17 ratio. We observed a high correlation of HER2/CEP17 ratio, an average of HER2 and CEP17 copy number per cell between visual and IA quantification (Pearson's correlation coefficient of 0.842, 0.916, and 0.765, respectively). IA was able to count from 124 cells to 47,044 cells (median of 5565 cells). The margin of error for the visual quantification of the HER2/CEP17 ratio and of the average of HER2 copy number per cell decreased from a median of 0.23 to 0.02 and from a median of 0.49 to 0.04, respectively, in IA. Curve estimation regression models showed that a minimum of 469 or 953 invasive cancer cells per case is needed to reach an average margin of error below 0.1 for the HER2/CEP17 ratio or for the average of HER2 copy number per cell, respectively. Lastly, on average, a case took 212.1 s to execute the IA, which means that it evaluates about 130 cells/s and requires 6.7 s/mm2. The concordance of the IA software with the visual scoring was 95%, with a sensitivity of 90% and a specificity of 100%. All four discordant cases were able to achieve concordant results after the region of interest adjustment. In conclusion, this validation study underscores the usefulness of IA in HER2 ISH testing, displaying excellent concordance with visual scoring and significantly reducing margins of error.

Somatic Mutations in KEAP1-NRF2 Complex in Breast Cancer.

Breast cancer remains the leading cause of cancer deaths for women. Long-term estrogen exposure is considered carcinogenic due to semiquinone production and to compromised detoxification. Metabolic regulator polymorphisms, such as KEAP1 (rs1048290) and NRF2 (rs35652124, rs6721961, rs6706649), can be valuable in understanding the individual cytoprotection profile. Thus, we aim to genotype these polymorphisms in blood, tumours and surrounding tissue, to identify somatic mutations and correlate it to prognoses. A total of 23 controls and 69 women with histological confirmed breast cancer were recruited, and DNA from blood/surrounding/tumour tissue was genotyped. Genotyping and clinicopathological data were correlated. We verified that rs35652124 presents different genotype distribution between the blood/surrounding tissue (p-value = 0.023) and tumour/surrounding tissues (p-value = 0.041). Apart from rs35652124 and considering the histological grade, the other four polymorphisms have different distributions among different tissues. There is a tendency towards the loss of heterozygosity in the surrounding tissue when compared to blood and tumour tissues, and higher genotype variability in histologic grade 2. These somatic mutations and different distribution patterns may indicate a heterogeneous and active microenvironment, influencing breast cancer outcome. Additionally, it would be pertinent to evaluate the predictive value of the histologic grade 2 considering somatic mutation profiles and distributions.

Evaluation of Artificial Intelligence-Based Gleason Grading Algorithms "in the Wild".

The biopsy Gleason score is an important prognostic marker for prostate cancer patients. It is, however, subject to substantial variability among pathologists. Artificial intelligence (AI)-based algorithms employing deep learning have shown their ability to match pathologists' performance in assigning Gleason scores, with the potential to enhance pathologists' grading accuracy. The performance of Gleason AI algorithms in research is mostly reported on common benchmark data sets or within public challenges. In contrast, many commercial algorithms are evaluated in clinical studies, for which data are not publicly released. As commercial AI vendors typically do not publish performance on public benchmarks, comparison between research and commercial AI is difficult. The aims of this study are to evaluate and compare the performance of top-ranked public and commercial algorithms using real-world data. We curated a diverse data set of whole-slide prostate biopsy images through crowdsourcing containing images with a range of Gleason scores and from diverse sources. Predictions were obtained from 5 top-ranked public algorithms from the Prostate cANcer graDe Assessment (PANDA) challenge and 2 commercial Gleason grading algorithms. Additionally, 10 pathologists (A.C., C.R., J.v.I., K.R.M.L., P.R., P.G.S., R.G., S.F.K.J., T.v.d.K., X.F.) evaluated the data set in a reader study. Overall, the pairwise quadratic weighted kappa among pathologists ranged from 0.777 to 0.916. Both public and commercial algorithms showed high agreement with pathologists, with quadratic kappa ranging from 0.617 to 0.900. Commercial algorithms performed on par or outperformed top public algorithms.

The 1000 Mitoses Project: A Consensus-Based International Collaborative Study on Mitotic Figures Classification.

Introduction. The identification of mitotic figures is essential for the diagnosis, grading, and classification of various different tumors. Despite its importance, there is a paucity of literature reporting the consistency in interpreting mitotic figures among pathologists. This study leverages publicly accessible datasets and social media to recruit an international group of pathologists to score an image database of more than 1000 mitotic figures collectively. Materials and Methods. Pathologists were instructed to randomly select a digital slide from The Cancer Genome Atlas (TCGA) datasets and annotate 10-20 mitotic figures within a 2 mm2 area. The first 1010 submitted mitotic figures were used to create an image dataset, with each figure transformed into an individual tile at 40x magnification. The dataset was redistributed to all pathologists to review and determine whether each tile constituted a mitotic figure. Results. Overall pathologists had a median agreement rate of 80.2% (range 42.0%-95.7%). Individual mitotic figure tiles had a median agreement rate of 87.1% and a fair inter-rater agreement across all tiles (kappa = 0.284). Mitotic figures in prometaphase had lower percentage agreement rates compared to other phases of mitosis. Conclusion. This dataset stands as the largest international consensus study for mitotic figures to date and can be utilized as a training set for future studies. The agreement range reflects a spectrum of criteria that pathologists use to decide what constitutes a mitotic figure, which may have potential implications in tumor diagnostics and clinical management.

HER2 and PD-L1 Expression in Gastric and Gastroesophageal Junction Cancer: Insights for Combinatorial Targeting Approaches.

Gastric and gastroesophageal junction adenocarcinomas (GA/GEJA) are associated with a poor prognosis, primarily due to late disease diagnosis. Human Epidermal Growth Factor Receptor 2 (HER2) overexpression and programmed death-ligand 1 (PD-L1) expression are important biomarkers for treatment selection in locally advanced unresectable and metastatic GA/GEJA, and there is increasing interest in their role in earlier stages of disease. In this study, we aimed to evaluate HER2 and PD-L1 expression in a curative-intent GA/GEJA cohort to describe their expression patterns and analyze the association between HER2 expression and clinicopathological features. HER2 expression was evaluated in surgical and endoscopic submucosal dissection tumor samples, and PD-L1 was evaluated in HER2-positive cases. The clinical cohort included 107 patients, with 8.4% testing positive for HER2 (seven of whom also exhibited a PD-L1 combined positive score of ≥1. HER2 status was not significantly associated with survival outcomes. A pathologist-guided, region-specific analysis revealed that PD-L1 expression rarely overlaps with HER2-positive tumor areas. While the therapeutic implications of these observations remain unknown, these findings suggest that combination strategies targeting HER2 and PD-L1 might be directed toward distinct tumor subclones. The herein disclosed region-specific biomarker expression patterns may have important therapeutic and prognostic impacts, warranting further evaluation.

Optimizing the management of thyroid specimens to efficiently generate whole slide images for diagnosis.

Transition from optical to digital observation requires an additional procedure in the pathology laboratory, the scanning of glass slides, leading to increased time and digital archive consumption. Thyroid surgical samples often carry the need to collect several tissue fragments that generate many slides to be scanned. This study evaluated the impact of using different inking colours for the surgical margin, section thickness, and glass slide type, in the consumption of time and archive. The series comprehended 40 nodules from 30 patients, including 34 benign nodules in follicular nodular disease, 1 NIFTP, and 5 papillary carcinomas. In 12 nodules, the dominant pattern was microfollicular/solid and in 28 it was macrofollicular. Scanning times/mm2 were longer in red-inked fragments in comparison to green (p = 0.04) and black ones (p = 0.024), and in blue-inked in comparison to green ones (p = 0.043). File sizes/mm2 were larger in red-inked fragments in comparison to green (p = 0.008) and black ones (p = 0.002). The dominant pattern microfollicular/solid was associated with bigger file size/mm2 in comparison with the macrofollicular one (p < 0.001). All scanner outputs increase significantly with the thickness of the section. All scanning outputs increase with the usage of adhesive glass slides in comparison to non-adhesive ones. Small interventions in thyroid sample management that can help optimizing the digital workflow include to prefer black and green inking colours for the surgical margins and 2 µm section in non-adhesive glass slides for increased efficiency.

One nodule-one punction-one slide: Optimizing thyroid fine-needle aspiration for a digital workflow.

OBJECTIVE: Interventional pathologists have expanded their expertise by acquiring proficiency in ultrasound-guided thyroid fine-needle aspiration biopsy (FNAB) and are now required to optimize punction procedures due to low resources and digital workflows. The aim of this study is to compare FNAB sample adequacy in two series with one versus two slides available for cytopathological analysis and its influence on diagnosis categorization, time taken to reach a final diagnosis, scanning time and size of the digital files produced. METHODS: Patients were retrospectively selected based on the sampling of thyroid nodules using either two glass slides (two-slide group) or one slide only (one-slide group) and cytological diagnosis was performed using the second edition of the Bethesda system. For each group, the initial 15 cases were sorted to be scanned. RESULTS: From a total of 713 procedures, 328 were sampled into two slides and 385 on one slide only. No significant differences were found regarding nodule size, location or EU-TIRADS classification between the two groups. The one-slide group did not exhibit a higher prevalence of non-diagnostic or atypia of undetermined significance (AUS) categories. As expected, the mean time taken to finalize diagnoses in cases where only one slide was prepared was 1.2 days faster. Scanning time and total file size were also significantly smaller in the one-slide group. CONCLUSIONS: Adopting the 'one nodule-one puncture-one slide' strategy for thyroid FNAB optimization enhances procedural efficiency in digital workflows, leading to cost savings without compromising diagnostic accuracy.

Inking cell blocks improves scanner detection for diagnosis in pathology.

Cell blocks may be hard to be totally automatically detected by the scanner (ADS), generating incomplete whole slide images (WSIs), with areas that are not scanned, leading to possible false negative diagnosis. The aim of this study is to test if inking the cell blocks helps increasing ADS. Test 1: 15 cell blocks were sectioned, one half inked black (1HB) and the other inked green (1HG). Each of the halves was individually processed to generate a WSI stained by the H&E. 1HBs and 1HGs had similar scanning time (median 59 s vs. 65 s, p = .126) and file sizes (median 382 Mb vs. 381 Mb, p = .567). The black ink interfered less in the observation (2.2% vs. 44.4%; p < .001) than in the green one. Test 2: 15 cell blocks were sectioned, one half inked black (2HB) and the other left unstained/null (2HN). Each of the halves was individually processed to generate three WSIs-one HE, one periodic-acid Schiff (PAS), and one immunostained by cytokeratin AE1&AE3 (CKAE1AE3). HE and PAS WSIs from both 2HN and 2HB groups were all totally ADS and had similar scanning times and file sizes. Concerning immunostaining with CKAE1AE3: ADS (46.7% vs. 93.3%; p = .014), median time for scanning (57 s vs. 83 s; p < .001) and file size (178 Mb vs. 338 Mb; p < .001) were reduced significantly in the 2HN group in comparison with the 2HB. Although increasing scanning time and file size, inking the cell blocks helps increasing ADS after immunostaining, improving the safety and efficiency of the digital pathology workflow.

Demonstrating the interference of tissue processing in the evaluation of tissue biomarkers: The case of PD-L1.

The immunohistochemical (IHC) expression of PD-L1 in cancer models is used as a predictive biomarker of response to immunotherapy. We aimed to evaluate the impact of the usage of 3 different tissue processors in the IHC expression of PD-L1 antibody clones: 22C3 and SP142. Three different topographies of samples (n = 73) were selected at the macroscopy room: 39 uterine leiomyomas, 17 placentas and 17 palatine tonsils. Three fragments were collected from each sample and were inked with a specific color that represented their separate processing in a different tissue processor (A, B or C). During embedding, the 3 fragments with distinct processing were ensemble in the same cassette for sectioning of 3 slides/each: hematoxylin-eosin, 22C3 PDL1 IHC staining and SP142 PD-L1 IHC staining, that were blindly observed by 2 pathologists under digital environment. All but one set of 3 fragments were considered adequate for observation even in the presence of artifacts associated with processing issues that were recorded as high as 50.7 % for processor C. The occurrence of background non-specific staining and the presence of false positive results appear to be unrelated with the PD-L1 clone or the type of tissue processing. 22C3 PD-L1 was more frequently considered adequate for evaluation than SP142 PD-L1 that, in 29.2 % of WSIs (after tissue processor C) were considered not adequate for observation due to lack of the typical pattern of expression. Similarly, the intensity of PD-L1 staining was significantly decreased in fragments processed by C (both PD-L1 clones) in tonsil and placenta specimens, and by A (both clones) in comparison with those processed by B. This study demonstrates the need to standardize the tissue processing in pathology to cope with the growing needs of precision medicine quantifications and the production of high-quality material necessary for computational pathology usage.

Syndecan-4 is a maestro of gastric cancer cell invasion and communication that underscores poor survival.

Gastric cancer is a dominating cause of cancer-associated mortality with limited therapeutic options. Here, we show that syndecan-4 (SDC4), a transmembrane proteoglycan, is highly expressed in intestinal subtype gastric tumors and that this signature associates with patient poor survival. Further, we mechanistically demonstrate that SDC4 is a master regulator of gastric cancer cell motility and invasion. We also find that SDC4 decorated with heparan sulfate is efficiently sorted in extracellular vesicles (EVs). Interestingly, SDC4 in EVs regulates gastric cancer cell-derived EV organ distribution, uptake, and functional effects in recipient cells. Specifically, we show that SDC4 knockout disrupts the tropism of EVs for the common gastric cancer metastatic sites. Our findings set the basis for the molecular implications of SDC4 expression in gastric cancer cells and provide broader perspectives on the development of therapeutic strategies targeting the glycan-EV axis to limit tumor progression.

Metastatic solid tumours to the penis: a clinicopathologic evaluation of 109 cases from an international collaboration.

AIMS: To elucidate the spectrum of metastatic tumours to the penis and their clinicopathologic features. METHODS: The databases and files of 22 pathology departments from eight countries on three continents were queried to identify metastatic solid tumours of the penis and to characterize their clinical and pathologic features. RESULTS: We compiled a series of 109 cases of metastatic solid tumours that secondarily involved the penis. The mean patient age at diagnosis was 71 years (range, 7-94 years). Clinical presentation commonly included a penile nodule/mass (48/95; 51%) and localised pain (14/95; 15%). A prior history of malignancy was known in 92/104 (89%) patients. Diagnosis was made mainly on biopsy (82/109; 75%), or penectomy (21/109; 19%) specimens. The most common penile locations were the glans (45/98; 46%) and corpus cavernosum (39/98; 39%). The most frequent histologic type was adenocarcinoma (56%). Most primary carcinomas originated in the genitourinary (76/108; 70%) and gastrointestinal (20/108; 18%) tracts, including prostate (38/108; 35%), urinary bladder (27/108; 25%), and colon/rectum (18/108; 17%). Concurrent or prior extrapenile metastases were identified in 50/78 (64%) patients. Clinical follow-up (mean 22 months, range 0-171 months) was available for 87/109 (80%) patients, of whom 46 (53%) died of disease. CONCLUSION: This is the largest study to date of metastatic solid tumours secondarily involving the penis. The most frequent primaries originated from the genitourinary and gastrointestinal tracts. Metastatic penile tumours usually presented with penile nodules/masses and pain, and they often occurred in the setting of advanced metastatic disease, portending poor clinical outcomes.

Artificial intelligence-assisted cancer diagnosis improves the efficiency of pathologists in prostatic biopsies.

Paige Prostate is a clinical-grade artificial intelligence tool designed to assist the pathologist in detecting, grading, and quantifying prostate cancer. In this work, a cohort of 105 prostate core needle biopsies (CNBs) was evaluated through digital pathology. Then, we compared the diagnostic performance of four pathologists diagnosing prostatic CNB unaided and, in a second phase, assisted by Paige Prostate. In phase 1, pathologists had a diagnostic accuracy for prostate cancer of 95.00%, maintaining their performance in phase 2 (93.81%), with an intraobserver concordance rate between phases of 98.81%. In phase 2, pathologists reported atypical small acinar proliferation (ASAP) less often (about 30% less). Additionally, they requested significantly fewer immunohistochemistry (IHC) studies (about 20% less) and second opinions (about 40% less). The median time required for reading and reporting each slide was about 20% lower in phase 2, in both negative and cancer cases. Lastly, the average total agreement with the software performance was observed in about 70% of the cases, being significantly higher in negative cases (about 90%) than in cancer cases (about 30%). Most of the diagnostic discordances occurred in distinguishing negative cases with ASAP from small foci of well-differentiated (less than 1.5 mm) acinar adenocarcinoma. In conclusion, the synergic usage of Paige Prostate contributes to a significant decrease in IHC studies, second opinion requests, and time for reporting while maintaining highly accurate diagnostic standards.

Decline of case reports in pathology and their renewal in the digital age: an analysis of publication trends over four decades.

AIMS: We investigated the trend in case reports (CRs) publication in a sample of pathology journals. Furthermore, we proposed an alternative publishing route through new digital communication platforms, represented by the 'social media case report'. METHODS: 28 pathology journals were selected from SCImago database and searched in PubMed to identify the number of published CRs. Four reference decades (1981-2020) were selected. The 5-year impact factor (IF) was retrieved from the Academic Accelerator database. RESULTS: CRs increased during the first three decades (6752, 8698 and 11148, respectively; mean values: 355, 27.3%; 334, 26.4%; 398, 28.8%) as the number of CR-publishing journals (19, 26 and 28, respectively). In the last decade, CRs significantly decreased (9341; mean 334, 23.6%) without variation in the number of CR-publishing journals (28). Half of the journals reduced CRs (from -1.1% to -37.9%; mean decreasing percentage -14.7%), especially if active since the first decade (11/14, 79%); the other half increased CRs (from +0.5% to +34.2%; mean increasing percentage +11.8%), with 8/14 (57%) starting publishing in the first decade. The 5-year IF ranged from 0.504 to 5.722. Most of the journals with IF ≥2 (10/14, 71%) reduced the CRs number, while 71% of journals with IF <2 increased CRs publication (especially journals with IF <1, +15.1%). CONCLUSIONS: CRs publication decreased during the last decade, especially for journals which are older or have higher IF. Social media CRs may represent a valid alternative and by using standardised templates to enter all relevant data may be organised in digital databases and/or transformed in traditional CRs.

Deep learning-based image analysis predicts PD-L1 status from H&E-stained histopathology images in breast cancer.

Programmed death ligand-1 (PD-L1) has been recently adopted for breast cancer as a predictive biomarker for immunotherapies. The cost, time, and variability of PD-L1 quantification by immunohistochemistry (IHC) are a challenge. In contrast, hematoxylin and eosin (H&E) is a robust staining used routinely for cancer diagnosis. Here, we show that PD-L1 expression can be predicted from H&E-stained images by employing state-of-the-art deep learning techniques. With the help of two expert pathologists and a designed annotation software, we construct a dataset to assess the feasibility of PD-L1 prediction from H&E in breast cancer. In a cohort of 3,376 patients, our system predicts the PD-L1 status in a high area under the curve (AUC) of 0.91 - 0.93. Our system is validated on two external datasets, including an independent clinical trial cohort, showing consistent prediction performance. Furthermore, the proposed system predicts which cases are prone to pathologists miss-interpretation, showing it can serve as a decision support and quality assurance system in clinical practice.

Deep learning for fully-automated nuclear pleomorphism scoring in breast cancer.

To guide the choice of treatment, every new breast cancer is assessed for aggressiveness (i.e., graded) by an experienced histopathologist. Typically, this tumor grade consists of three components, one of which is the nuclear pleomorphism score (the extent of abnormalities in the overall appearance of tumor nuclei). The degree of nuclear pleomorphism is subjectively classified from 1 to 3, where a score of 1 most closely resembles epithelial cells of normal breast epithelium and 3 shows the greatest abnormalities. Establishing numerical criteria for grading nuclear pleomorphism is challenging, and inter-observer agreement is poor. Therefore, we studied the use of deep learning to develop fully automated nuclear pleomorphism scoring in breast cancer. The reference standard used for training the algorithm consisted of the collective knowledge of an international panel of 10 pathologists on a curated set of regions of interest covering the entire spectrum of tumor morphology in breast cancer. To fully exploit the information provided by the pathologists, a first-of-its-kind deep regression model was trained to yield a continuous scoring rather than limiting the pleomorphism scoring to the standard three-tiered system. Our approach preserves the continuum of nuclear pleomorphism without necessitating a large data set with explicit annotations of tumor nuclei. Once translated to the traditional system, our approach achieves top pathologist-level performance in multiple experiments on regions of interest and whole-slide images, compared to a panel of 10 and 4 pathologists, respectively.

The impact of different coverslipping methods in the quality of the whole slide images used for diagnosis in pathology.

Digital pathology workflow aims to create whole slide images (WSIs) for diagnosis. The quality of the WSIs depends primarily on the quality of the glass slides produced by the pathology laboratory, where the coverslipping method plays an important role. In this study we compare the glass, the film, and the liquid coverslipping methods to evaluate which ones are suitable to create WSIs for diagnosis. The study included 18 formalin-fixed paraffin-embedded tissue blocks. Of each block, 3 consecutive sections were covered using 1 of the 3 methods. The slides were scanned and evaluated for quality criteria by 2 pathologists experienced in digital pathology. The coverslipping method interferes with the quality of the WSIs, as well as with the scanning time and the file size of the WSIs. All coverslipping methods were found suitable for diagnosis. The glass and liquid methods were manual and had similar results concerning the presence of air bubbles/polymer accumulation, air drying artefacts, tissue exposed, and staining alterations. The glass method was the one with more air bubbles. The liquid method was associated with more alterations on the WSIs, but with the lowest file sizes. Automation of coverslipping and calibration of the scanner for the coverslipping method chosen by the pathology laboratory are relevant for the final quality of the WSIs.

HEROHE Challenge: Predicting HER2 Status in Breast Cancer from Hematoxylin-Eosin Whole-Slide Imaging.

Breast cancer is the most common malignancy in women worldwide, and is responsible for more than half a million deaths each year. The appropriate therapy depends on the evaluation of the expression of various biomarkers, such as the human epidermal growth factor receptor 2 (HER2) transmembrane protein, through specialized techniques, such as immunohistochemistry or in situ hybridization. In this work, we present the HER2 on hematoxylin and eosin (HEROHE) challenge, a parallel event of the 16th European Congress on Digital Pathology, which aimed to predict the HER2 status in breast cancer based only on hematoxylin-eosin-stained tissue samples, thus avoiding specialized techniques. The challenge consisted of a large, annotated, whole-slide images dataset (509), specifically collected for the challenge. Models for predicting HER2 status were presented by 21 teams worldwide. The best-performing models are presented by detailing the network architectures and key parameters. Methods are compared and approaches, core methodologies, and software choices contrasted. Different evaluation metrics are discussed, as well as the performance of the presented models for each of these metrics. Potential differences in ranking that would result from different choices of evaluation metrics highlight the need for careful consideration at the time of their selection, as the results show that some metrics may misrepresent the true potential of a model to solve the problem for which it was developed. The HEROHE dataset remains publicly available to promote advances in the field of computational pathology.

p40/PD-L1 and TTF1/PD-L1 Immunohistochemical Double Staining Contributes to Intraindividual Variability Associated With PD-L1 Staining Interpretation in Lung Cancer Samples.

The important developments achieved in recent years with a consequent paradigm shift in the treatment of non-small cell lung cancer (NSCLC), including the latest immune checkpoint inhibitors, have led to an increasing need to optimize the scarce material usually available in the diagnosis of these tumors. In this sense, this study intends to evaluate the performance of double immunohistochemistry (IHC) in comparison to simple IHC for programmed death-ligand 1 (PD-L1) evaluation with 22C3 clone for selection to therapy with pembrolizumab. For that, 38 histologic samples of NSCLC small biopsies sent to our laboratory were selected. Double IHC were performed with the doublets TTF1/PD-L1 and p40/PD-L1, after all the usual diagnostic routine and molecular study was performed. The slides were interpreted by 2 independent pathologists and the results obtained were compared with each other and with the results obtained at diagnosis. A perfect agreement was observed when comparing the immunoexpression of TTF1 and p40 in double IHC in relation to single IHC. Although the agreement was substantial in the analysis of the positive/negative PD-L1 IHC (81.6% to 92.1%; κ=0.610 to 0.829) and in the analysis of the 50% cut-off (86.8% to 89.5%; κ=0.704 to 0.759), it fell short of the expected and desirable agreement for a biomarker such as PD-L1, since this result will have a major role in the institution of a treatment. In conclusion, this small series does not allow us to recommend this methodology for the evaluation of the PD-L1 biomarker in double staining IHC with the 22C3 clone for therapy selection.

The spectrum of HER2 expression in breast cancer: linking immunohistochemistry quantification with in situ hybridization assay.

We aimed to document the pathological characteristics of breast cancer (BC) cases with different scores of HER2 by immunohistochemistry (IHC), as well as to establish a relationship between HER2 expression and HER2 amplification by in situ hybridization (ISH). A cohort of 258 primary BC cases was evaluated for HER2 gene amplification with bright-field ISH. All HER2-negative and HER2-positive cases by IHC were concordant with the ISH classification. BC cases with score of 0 had lower average of HER2 copy number compared to cases with score of 1 + . HER2-equivocal cases by IHC had intermediate pathological characteristics between HER2-negative and HER2-positive cases. About 12% of HER2-equivocal cases were classified as ISH-positive. HER2-equivocal cases with HER2 gene amplification had proliferation index, HER2/CEP17 ratio, and average of HER2 copy number between HER2-equivocal cases without HER2 gene amplification and HER2-positive cases by IHC. Additionally, HER2-equivocal cases with HER2 amplification had score of 2 + in at least 50% of the total tumor area, with a proportion of ISH-positive cases increasing with the amount of score of 2 + present in the tumor. The quantification of score of 2 + in the tumor predicted the ISH classification with an AUC of 0.902. A logistic regression model using the same HER2 quantification and the nuclear score was able to increase the abovementioned prediction to an AUC of 0.929. As such, we were able to link HER2 quantification by IHC and morphological analysis with HER2 amplification by ISH.