Whole-genome and transcriptome sequencing were performed to identify potential therapeutic strategies in the absence of viable treatment options for a patient initially diagnosed with vulvar adenocarcinoma. Genomic events were prioritized by comparison against variant distributions in the TCGA pan-cancer data set and complemented with detailed transcriptome sequencing and copy-number analysis. These findings were considered against published scientific literature in order to evaluate the functional effects of potentially relevant genomic events. Analysis of the transcriptome against a background of 27 TCGA cancer types led to reclassification of the tumor as a primary HER2+ mammary-like adenocarcinoma of the vulva. This revised diagnosis was subsequently confirmed by follow-up immunohistochemistry for a mammary-like adenocarcinoma. The patient was treated with chemotherapy and targeted therapies for HER2+ breast cancer. The detailed pathology and genomic findings of this case are presented herein.

BACKGROUND: Artificial intelligence (AI) is rapidly fuelling a fundamental transformation in the practice of pathology. However, clinical integration remains challenging, with no AI algorithms to date in routine adoption within typical anatomic pathology (AP) laboratories. This survey gathered current expert perspectives and expectations regarding the role of AI in AP from those with first-hand computational pathology and AI experience. METHODS: Perspectives were solicited using the Delphi method from 24 subject matter experts between December 2020 and February 2021 regarding the anticipated role of AI in pathology by the year 2030. The study consisted of three consecutive rounds: 1) an open-ended, free response questionnaire generating a list of survey items; 2) a Likert-scale survey scored by experts and analysed for consensus; and 3) a repeat survey of items not reaching consensus to obtain further expert consensus. FINDINGS: Consensus opinions were reached on 141 of 180 survey items (78.3%). Experts agreed that AI would be routinely and impactfully used within AP laboratory and pathologist clinical workflows by 2030. High consensus was reached on 100 items across nine categories encompassing the impact of AI on (1) pathology key performance indicators (KPIs) and (2) the pathology workforce and specific tasks performed by (3) pathologists and (4) AP lab technicians, as well as (5) specific AI applications and their likelihood of routine use by 2030, (6) AIs role in integrated diagnostics, (7) pathology tasks likely to be fully automated using AI, and (8) regulatory/legal and (9) ethical aspects of AI integration in pathology. INTERPRETATION: This systematic consensus study details the expected short-to-mid-term impact of AI on pathology practice. These findings provide timely and relevant information regarding future care delivery in pathology and raise key practical, ethical, and legal challenges that must be addressed prior to AIs successful clinical implementation. FUNDING: No specific funding was provided for this study.

Risk stratification is critical in the care of patients with myelodysplastic syndromes (MDS). Approximately 10% have a complex karyotype (CK), defined as more than two cytogenetic abnormalities, which is a highly adverse prognostic marker. However, CK-MDS can carry a wide range of chromosomal abnormalities and somatic mutations. To refine risk stratification of CK-MDS patients, we examined data from 359 CK-MDS patients shared by the International Working Group for MDS. Mutations were underrepresented with the exception of TP53 mutations, identified in 55% of patients. TP53 mutated patients had even fewer co-mutated genes but were enriched for the del(5q) chromosomal abnormality (p < 0.005), monosomal karyotype (p < 0.001), and high complexity, defined as more than 4 cytogenetic abnormalities (p < 0.001). Monosomal karyotype, high complexity, and TP53 mutation were individually associated with shorter overall survival, but monosomal status was not significant in a multivariable model. Multivariable survival modeling identified severe anemia (hemoglobin < 8.0 g/dL), NRAS mutation, SF3B1 mutation, TP53 mutation, elevated blast percentage (>10%), abnormal 3q, abnormal 9, and monosomy 7 as having the greatest survival risk. The poor risk associated with CK-MDS is driven by its association with prognostically adverse TP53 mutations and can be refined by considering clinical and karyotype features.