Digital pathology for reporting histopathology samples, including cancer screening samples - definitive evidence from a multisite study.

AIMS: To conduct a definitive multicentre comparison of digital pathology (DP) with light microscopy (LM) for reporting histopathology slides including breast and bowel cancer screening samples. METHODS: A total of 2024 cases (608 breast, 607 GI, 609 skin, 200 renal) were studied, including 207 breast and 250 bowel cancer screening samples. Cases were examined by four pathologists (16 study pathologists across the four speciality groups), using both LM and DP, with the order randomly assigned and 6 weeks between viewings. Reports were compared for clinical management concordance (CMC), meaning identical diagnoses plus differences which do not affect patient management. Percentage CMCs were computed using logistic regression models with crossed random-effects terms for case and pathologist. The obtained percentage CMCs were referenced to 98.3% calculated from previous studies. RESULTS: For all cases LM versus DP comparisons showed the CMC rates were 99.95% [95% confidence interval (CI) = 99.90-99.97] and 98.96 (95% CI = 98.42-99.32) for cancer screening samples. In speciality groups CMC for LM versus DP showed: breast 99.40% (99.06-99.62) overall and 96.27% (94.63-97.43) for cancer screening samples; [gastrointestinal (GI) = 99.96% (99.89-99.99)] overall and 99.93% (99.68-99.98) for bowel cancer screening samples; skin 99.99% (99.92-100.0); renal 99.99% (99.57-100.0). Analysis of clinically significant differences revealed discrepancies in areas where interobserver variability is known to be high, in reads performed with both modalities and without apparent trends to either. CONCLUSIONS: Comparing LM and DP CMC, overall rates exceed the reference 98.3%, providing compelling evidence that pathologists provide equivalent results for both routine and cancer screening samples irrespective of the modality used.

Integrated tumor identification and automated scoring minimizes pathologist involvement and provides new insights to key biomarkers in breast cancer.

Digital image analysis (DIA) is becoming central to the quantitative evaluation of tissue biomarkers for discovery, diagnosis and therapeutic selection for the delivery of precision medicine. In this study, automated DIA using a new purpose-built software platform (QuPath) is applied to a cohort of 293 breast cancer patients to score five biomarkers in tissue microarrays (TMAs): ER, PR, HER2, Ki67 and p53. This software is able to measure IHC expression following fully automated tumor recognition in the same immunohistochemical (IHC)-stained tissue section, as part of a rapid workflow to ensure objectivity and accelerate biomarker analysis. The digital scores produced by QuPath were compared with manual scores by a pathologist and shown to have a good level of concordance in all cases (Cohen's κ>0.6), and almost perfect agreement for the clinically relevant biomarkers ER, PR and HER2 (κ>0.86). To assess prognostic value, cutoff thresholds could be applied to both manual and automated scores using the QuPath software, and survival analysis performed for 5-year overall survival. DIA was shown to be capable of replicating the statistically significant stratification of patients achieved using manual scoring across all biomarkers (P<0.01, log-rank test). Furthermore, the image analysis scores were shown to consistently lead to statistical significance across a wide range of potential cutoff thresholds, indicating the robustness of the method, and identify sub-populations of cases exhibiting different expression patterns within the p53 and Ki67 data sets that warrant further investigation. These findings have demonstrated QuPath's suitability for fast, reproducible, high-throughput TMA analysis across a range of important biomarkers. This was achieved using our tumor recognition algorithms for IHC-stained sections, trained interactively without the need for any additional tumor recognition markers, for example, cytokeratin, to obtain greater insight into the relationship between biomarker expression and clinical outcome applicable to a range of cancer types.

Stratified mucin-producing intraepithelial lesion (SMILE): report of a case series with associated pathological findings.

AIMS: Stratified mucin-producing intraepithelial lesion (SMILE) is an uncommon premalignant cervical lesion showing morphological overlap with cervical intraepithelial neoplasia (CIN) and adenocarcinoma in situ (AIS)/cervical glandular intraepithelial neoplasia. Given the limited literature on SMILE, we aimed to document its frequency and assess the associated pathology in a large series of cases. METHODS AND RESULTS: Over a 6-year period, we documented all cases of SMILE and the associated findings. SMILE was diagnosed in 69 specimens from 60 patients (50 loop excisions and 19 punch biopsy specimens), constituting 0.6% of cervical specimens. Overall, SMILE coexisted with high-grade CIN in 93% of cases, AIS in 42%, and a carcinoma in 10%. When SMILE was diagnosed on a cervical punch biopsy, there was high-grade CIN in the follow-up loop in most cases. SMILE was consistently diffusely p16-positive and IMP3-negative. CONCLUSIONS: Given the morphological overlap and significant association with high-grade CIN and AIS, we believe that SMILE is best regarded as a form of high-grade reserve cell dysplasia, probably arising from reserve or stem cells with the capacity for multidirectional differentiation. We recommend that management of SMILE should be as for AIS.

Molecular mapping the presence of druggable targets in preinvasive and precursor breast lesions: a comprehensive review of biomarkers related to therapeutic interventions.

The analysis of clinical breast samples using biomarkers is integral to current breast cancer management. Currently, a limited number of targeted therapies are standard of care in breast cancer treatment. However, these targeted therapies are only suitable for a subset of patients and resistance may occur. Strategies to prevent the occurrence of invasive lesions are required to reduce the morbidity and mortality associated with the development of cancer. In theory, application of targeted therapies to pre-invasive lesions will prevent their progression to invasive lesions with full malignant potential. The diagnostic challenge for pathologists is to make interpretative decisions on early detected pre-invasive lesions. Overall, only a small proportion of these pre-invasive lesions will progress to invasive carcinoma and morphological assessment is an imprecise and subjective means to differentiate histologically identical lesions with varying malignant potential. Therefore differential biomarker analysis in pre-invasive lesions may prevent overtreatment with surgery and provide a predictive indicator of response to therapy. There follows a review of established and emerging potential druggable targets in pre-invasive lesions and correlation with lesion morphology.

The prognostic significance of the aberrant extremes of p53 immunophenotypes in breast cancer.

AIMS: The utility of p53 as a prognostic assay has been elusive. The aims of this study were to describe a novel, reproducible scoring system and assess the relationship between differential p53 immunohistochemistry (IHC) expression patterns, TP53 mutation status and patient outcomes in breast cancer. METHODS AND RESULTS: Tissue microarrays were used to study p53 IHC expression patterns: expression was defined as extreme positive (EP), extreme negative (EN), and non-extreme (NE; intermediate patterns). Overall survival (OS) was used to define patient outcome. A representative subgroup (n = 30) showing the various p53 immunophenotypes was analysed for TP53 hotspot mutation status (exons 4-9). Extreme expression of any type occurred in 176 of 288 (61%) cases. As compared with NE expression, EP expression was significantly associated (P = 0.039) with poorer OS. In addition, as compared with NE expression, EN expression was associated (P = 0.059) with poorer OS. Combining cases showing either EP or EN expression better predicted OS than either pattern alone (P = 0.028). This combination immunophenotype was significant in univariate but not multivariate analysis. In subgroup analysis, six substitution exon mutations were detected, all corresponding to extreme IHC phenotypes. Five missense mutations corresponded to EP staining, and the nonsense mutation corresponded to EN staining. No mutations were detected in the NE group. CONCLUSIONS: Patients with extreme p53 IHC expression have a worse OS than those with NE expression. Accounting for EN as well as EP expression improves the prognostic impact. Extreme expression positively correlates with nodal stage and histological grade, and negatively with hormone receptor status. Extreme expression may relate to specific mutational status.

Quantification of HER2 heterogeneity in breast cancer-implications for identification of sub-dominant clones for personalised treatment.

Breast cancer is a heterogeneous disease, at both an inter- and intra-tumoural level. Appreciating heterogeneity through the application of biomarkers and molecular signatures adds complexity to tumour taxonomy but is key to personalising diagnosis, treatment and prognosis. The extent to which heterogeneity exists, and its interpretation remains a challenge to pathologists. Using HER2 as an exemplar, we have developed a simple reproducible heterogeneity index. Cell-to-cell HER2 heterogeneity was extensive in a proportion of both reported 'amplified' and 'non-amplified' cases. The highest levels of heterogeneity objectively identified occurred in borderline categories and higher ratio non-amplified cases. A case with particularly striking heterogeneity was analysed further with an array of biomarkers in order to assign a molecular diagnosis. Broad biological complexity was evident. In essence, interpretation, depending on the area of tumour sampled, could have been one of three distinct phenotypes, each of which would infer different therapeutic interventions. Therefore, we recommend that heterogeneity is assessed and taken into account when determining treatment options.

Automated tumor analysis for molecular profiling in lung cancer.

The discovery and clinical application of molecular biomarkers in solid tumors, increasingly relies on nucleic acid extraction from FFPE tissue sections and subsequent molecular profiling. This in turn requires the pathological review of haematoxylin & eosin (H&E) stained slides, to ensure sample quality, tumor DNA sufficiency by visually estimating the percentage tumor nuclei and tumor annotation for manual macrodissection. In this study on NSCLC, we demonstrate considerable variation in tumor nuclei percentage between pathologists, potentially undermining the precision of NSCLC molecular evaluation and emphasising the need for quantitative tumor evaluation. We subsequently describe the development and validation of a system called TissueMark for automated tumor annotation and percentage tumor nuclei measurement in NSCLC using computerized image analysis. Evaluation of 245 NSCLC slides showed precise automated tumor annotation of cases using Tissuemark, strong concordance with manually drawn boundaries and identical EGFR mutational status, following manual macrodissection from the image analysis generated tumor boundaries. Automated analysis of cell counts for % tumor measurements by Tissuemark showed reduced variability and significant correlation (p < 0.001) with benchmark tumor cell counts. This study demonstrates a robust image analysis technology that can facilitate the automated quantitative analysis of tissue samples for molecular profiling in discovery and diagnostics.

PICan: An integromics framework for dynamic cancer biomarker discovery.

Modern cancer research on prognostic and predictive biomarkers demands the integration of established and emerging high-throughput technologies. However, these data are meaningless unless carefully integrated with patient clinical outcome and epidemiological information. Integrated datasets hold the key to discovering new biomarkers and therapeutic targets in cancer. We have developed a novel approach and set of methods for integrating and interrogating phenomic, genomic and clinical data sets to facilitate cancer biomarker discovery and patient stratification. Applied to a known paradigm, the biological and clinical relevance of TP53, PICan was able to recapitulate the known biomarker status and prognostic significance at a DNA, RNA and protein levels.

Molecular and clinicopathological markers of prognosis in breast cancer.

A vast body of research in breast cancer prognostication has accumulated. Yet despite this, patients within current prognostic categories may have significantly different outcomes. There is a need to more accurately divide those cancer types associated with an excellent prognosis from those requiring more aggressive therapy. Gene expression array studies have revealed the numerous molecular breast cancer subtypes that are associated with differing outcomes. Furthermore, as next generation technologies evolve and further reveal the complexities of breast cancer, it is likely that existing prognostic approaches will become progressively refined. Future prognostication in breast cancer requires a morphomolecular, multifaceted approach involving the assessment of anatomical disease extent and levels of protein, DNA and RNA expression. One of the major challenges in prognostication will be the integration of potential assays into existing clinical systems and identification of appropriate patient subgroups for analysis.

Pseudoinvasion of benign squamous epithelium following cervical biopsy: a pseudoneoplastic phenomenon mimicking invasive squamous carcinoma.

AIMS: To describe an unusual and hitherto unreported pseudoneoplastic phenomenon that is characterised by the entrapment of benign squamous epithelium following cervical loop excision or punch biopsy and that may mimic invasive squamous carcinoma. METHODS AND RESULTS: The authors report six cases in patients aged 25-45 years in whom benign squamous epithelium is incorporated within the cervical stroma as a result of the healing process following prior loop excision (four cases) or punch biopsy (two cases). The entrapped nests of squamous epithelium typically have a hypereosinophilic appearance and are associated with a granulation tissue-like and inflammatory stromal response and sometimes with retraction artefact mimicking lymphovascular permeation. CONCLUSIONS: The overall morphological appearances, especially in the context of a prior or current diagnosis of cervical intraepithelial neoplasia, may result in misdiagnosis as invasive squamous carcinoma. Pathologists should be aware of this pseudoneoplastic artefact so that an erroneous diagnosis of malignancy is not made.