High throughput assessment of biomarkers in tissue microarrays using artificial intelligence: PTEN loss as a proof-of-principle in multi-center prostate cancer cohorts.

Phosphatase and tensin homolog (PTEN) loss is associated with adverse outcomes in prostate cancer and has clinical potential as a prognostic biomarker. The objective of this work was to develop an artificial intelligence (AI) system for automated detection and localization of PTEN loss on immunohistochemically (IHC) stained sections. PTEN loss was assessed using IHC in two prostate tissue microarrays (TMA) (internal cohort, n = 272 and external cohort, n = 129 patients). TMA cores were visually scored for PTEN loss by pathologists and, if present, spatially annotated. Cores from each patient within the internal TMA cohort were split into 90% cross-validation (N = 2048) and 10% hold-out testing (N = 224) sets. ResNet-101 architecture was used to train core-based classification using a multi-resolution ensemble approach (×5, ×10, and ×20). For spatial annotations, single resolution pixel-based classification was trained from patches extracted at ×20 resolution, interpolated to ×40 resolution, and applied in a sliding-window fashion. A final AI-based prediction model was created from combining multi-resolution and pixel-based models. Performance was evaluated in 428 cores of external cohort. From both cohorts, a total of 2700 cores were studied, with a frequency of PTEN loss of 14.5% in internal (180/1239) and external 13.5% (43/319) cancer cores. The final AI-based prediction of PTEN status demonstrated 98.1% accuracy (95.0% sensitivity, 98.4% specificity; median dice score = 0.811) in internal cohort cross-validation set and 99.1% accuracy (100% sensitivity, 99.0% specificity; median dice score = 0.804) in internal cohort test set. Overall core-based classification in the external cohort was significantly improved in the external cohort (area under the curve = 0.964, 90.6% sensitivity, 95.7% specificity) when further trained (fine-tuned) using 15% of cohort data (19/124 patients). These results demonstrate a robust and fully automated method for detection and localization of PTEN loss in prostate cancer tissue samples. AI-based algorithms have potential to streamline sample assessment in research and clinical laboratories.

Risk Stratification of Prostate Cancer Through Quantitative Assessment of PTEN Loss (qPTEN).

BACKGROUND: Phosphatase and tensin homolog (PTEN) loss has long been associated with adverse findings in early prostate cancer. Studies to date have yet to employ quantitative methods (qPTEN) for measuring of prognostically relevant amounts of PTEN loss in postsurgical settings and demonstrate its clinical application. METHODS: PTEN protein levels were measured by immunohistochemistry in radical prostatectomy samples from training (n = 410) and validation (n = 272) cohorts. PTEN loss was quantified per cancer cell and per tissue microarray core. Thresholds for identifying clinically relevant PTEN loss were determined using log-rank statistics in the training cohort. Univariate (Kaplan-Meier) and multivariate (Cox proportional hazards) analyses on various subpopulations were performed to assess biochemical recurrence-free survival (BRFS) and were independently validated. All statistical tests were two-sided. RESULTS: PTEN loss in more than 65% cancer cells was most clinically relevant and had statistically significant association with reduced BRFS in training (hazard ratio [HR] = 2.48, 95% confidence interval [CI] = 1.59 to 3.87; P < .001) and validation cohorts (HR = 4.22, 95% CI = 2.01 to 8.83; P < .001). The qPTEN scoring method identified patients who recurred within 5.4 years after surgery (P < .001). In men with favorable risk of biochemical recurrence (Cancer of the Prostate Risk Assessment - Postsurgical scores <5 and no adverse pathological features), qPTEN identified a subset of patients with shorter BRFS (HR = 5.52, 95% CI = 2.36 to 12.90; P < .001) who may be considered for intensified monitoring and/or adjuvant therapy. CONCLUSIONS: Compared with previous qualitative approaches, qPTEN improves risk stratification of postradical prostatectomy patients and may be considered as a complementary tool to guide disease management after surgery.

The breast cancer immunophenotype of TP53-p.R337H carriers is different from that observed among other pathogenic TP53 mutation carriers.

Germline TP53 mutations are associated with Li-Fraumeni syndrome, an autosomal dominant disorder characterized by a predisposition to multiple early-onset cancers including breast cancer (BC), the most prevalent tumor among women. The majority of germline TP53 mutations are clustered within the DNA-binding domain of the gene, disrupting the structure and function of the protein. A specific germline mutation in the tetramerization domain of p53, p.R337H, was reported at a high frequency in Southern and Southeastern Brazil. This mutation appears to result in a more subtle defect in the protein, which becomes functionally deficient only under particular conditions. Recent studies show that the BC phenotype in TP53 mutation carriers is often HER2 positive (63-83%). Considering that the immunophenotype of BC among p.R337H carriers has not been reported, we reviewed immunohistochemistry data of 66 p.R337H carriers in comparison with 12 patients with other non-functional TP53 germline mutation. Although 75% of carriers of these mutations showed significant HER2 overexpression (3+), corroborating previous studies, only 22.7% of p.R337H patients had BC overexpressing HER2. These results reinforce the notion that different germline mutations in TP53 may predispose to BC via different mechanisms.

Hypothenar hammer syndrome: case report and literature review.

Case report of a 69 year-old patient, with history of repetitive trauma events in the wrist, clinically simulating tenosynovitis, being held with Doppler Ultrasound and Magnetic Nuclear Resonance, which showed ulnar artery thrombosis. The accurate diagnosis of the hammer hypothenar disease through those tests enable an early intervention, improving the prognosis of patients affected by this rare disease.

Hypothenar hammer syndrome: case report and literature review / Síndrome do martelo hipotenar: relato de caso e revisão da literatura

Case report of a 69 year-old patient, with history of repetitive trauma events in the wrist, clinically simulating tenosynovitis, being held with Doppler Ultrasound and Magnetic Nuclear Resonance, which showed ulnar artery thrombosis. The accurate diagnosis of the hammer hypothenar disease through those tests enable an early intervention, improving the prognosis of patients affected by this rare disease.

Paciente de 69 anos, com história de traumas repetitivos no punho, simulando clinicamente tenossinovite, sendo feitas ultrassonografia com doppler e ressonância nuclear magnética, que evidenciaram trombose da artéria ulnar. O diagnóstico preciso da doença do martelo hipotenar por meio desses exames possibilita uma intervenção precoce e melhora o prognóstico dos pacientes acometidos por essa rara afecção.