Development of a deep-learning model tailored for HER2 detection in breast cancer to aid pathologists in interpreting HER2-low cases.

AIMS: Over 50% of breast cancer cases are "Human epidermal growth factor receptor 2 (HER2) low breast cancer (BC)", characterized by HER2 immunohistochemistry (IHC) scores of 1+ or 2+ alongside no amplification on fluorescence in situ hybridization (FISH) testing. The development of new anti-HER2 antibody-drug conjugates (ADCs) for treating HER2-low breast cancers illustrates the importance of accurately assessing HER2 status, particularly HER2-low breast cancer. In this study we evaluated the performance of a deep-learning (DL) model for the assessment of HER2, including an assessment of the causes of discordances of HER2-Null between a pathologist and the DL model. We specifically focussed on aligning the DL model rules with the ASCO/CAP guidelines, including stained cells' staining intensity and completeness of membrane staining. METHODS AND RESULTS: We trained a DL model on a multicentric cohort of breast cancer cases with HER2-IHC scores (n = 299). The model was validated on two independent multicentric validation cohorts (n = 369 and n = 92), with all cases reviewed by three senior breast pathologists. All cases underwent a thorough review by three senior breast pathologists, with the ground truth determined by a majority consensus on the final HER2 score among the pathologists. In total, 760 breast cancer cases were utilized throughout the training and validation phases of the study. The model's concordance with the ground truth (ICC = 0.77 [0.68-0.83]; Fisher P = 1.32e-10) is higher than the average agreement among the three senior pathologists (ICC = 0.45 [0.17-0.65]; Fisher P = 2e-3). In the two validation cohorts, the DL model identifies 95% [93% - 98%] and 97% [91% - 100%] of HER2-low and HER2-positive tumours, respectively. Discordant results were characterized by morphological features such as extended fibrosis, a high number of tumour-infiltrating lymphocytes, and necrosis, whilst some artefacts such as nonspecific background cytoplasmic stain in the cytoplasm of tumour cells also cause discrepancy. CONCLUSION: Deep learning can support pathologists' interpretation of difficult HER2-low cases. Morphological variables and some specific artefacts can cause discrepant HER2-scores between the pathologist and the DL model.

PD-1 defines a distinct, functional, tissue-adapted state in Vδ1+ T cells with implications for cancer immunotherapy.

Checkpoint inhibition (CPI), particularly that targeting the inhibitory coreceptor programmed cell death protein 1 (PD-1), has transformed oncology. Although CPI can derepress cancer (neo)antigen-specific αß T cells that ordinarily show PD-1-dependent exhaustion, it can also be efficacious against cancers evading αß T cell recognition. In such settings, γδ T cells have been implicated, but the functional relevance of PD-1 expression by these cells is unclear. Here we demonstrate that intratumoral TRDV1 transcripts (encoding the TCRδ chain of Vδ1+ γδ T cells) predict anti-PD-1 CPI response in patients with melanoma, particularly those harboring below average neoantigens. Moreover, using a protocol yielding substantial numbers of tissue-derived Vδ1+ cells, we show that PD-1+Vδ1+ cells display a transcriptomic program similar to, but distinct from, the canonical exhaustion program of colocated PD-1+CD8+ αß T cells. In particular, PD-1+Vδ1+ cells retained effector responses to TCR signaling that were inhibitable by PD-1 engagement and derepressed by CPI.

A cohort profile of the Graham Roberts study cohort.

Purpose: The Graham Roberts Study was initiated in 2018 and is the first Trials Within Cohorts (TwiCs) study for bladder cancer. Its purpose is to provide an infrastructure for answering a breadth of research questions, including clinical, mechanistic, and supportive care centred questions for bladder cancer patients. Participants: All consented patients are those aged 18 or older, able to provide signed informedconsent and have a diagnosis of new or recurrent bladder cancer. All patients are required to have completed a series of baseline questionnaires. The questionnaires are then sent out every 12 months and include information on demographics and medical history as well as questionnaires to collect information on quality of life, fatigue, depression, overall health, physical activity, and dietary habits. Clinical information such as tumor stage, grade and treatment has also been extracted for each patient. Findings to date: To date, a total of 125 bladder cancer patients have been consented onto the study with 106 filling in the baseline questionnaire. The cohort is made up of 75% newly diagnosed bladder cancer patients and 66% non-muscle invasive bladder cancer cases. At present, there is 1-year follow-up information for 70 patients, 2-year follow-up for 57 patients, 3-year follow-up for 47 patients and 4-year follow-up for 19 patients. Future plans: We plan to continue recruiting further patients into the cohort study. Using the data collected within the study, we hope to carry out independent research studies with a focus on quality of life. We are also committed to utilizing the Roberts Study Cohort to set up and commence an intervention. The future studies and trials carried out using the Roberts Cohort have the potential to identify and develop interventions that could improve the prevention, diagnosis, and treatment of bladder cancer.