Proteomic profiling identifies muscle-invasive bladder cancers with distinct biology and responses to platinum-based chemotherapy.

Platinum-based neoadjuvant chemotherapy prior to radical cystectomy is the preferred treatment for muscle-invasive bladder cancer despite modest survival benefit and significant associated toxicities. Here, we profile the global proteome of muscle-invasive bladder cancers pre- and post-neoadjuvant chemotherapy treatment using archival formalin-fixed paraffin-embedded tissue. We identify four pre-neoadjuvant chemotherapy proteomic clusters with distinct biology and response to therapy and integrate these with transcriptomic subtypes and immunohistochemistry.

Learning generalizable AI models for multi-center histopathology image classification.

Investigation of histopathology slides by pathologists is an indispensable component of the routine diagnosis of cancer. Artificial intelligence (AI) has the potential to enhance diagnostic accuracy, improve efficiency, and patient outcomes in clinical pathology. However, variations in tissue preparation, staining protocols, and histopathology slide digitization could result in over-fitting of deep learning models when trained on the data from only one center, thereby underscoring the necessity to generalize deep learning networks for multi-center use.

A systematic review of nanocarriers for treatment of urologic cancers.

Nanocarriers (NCs) are a form of nanotechnology widely investigated in cancer treatment to improve the safety and efficacy of systemic therapies by increasing tumor specificity. Numerous clinical trials have explored the use of NCs in urologic cancers since the approval of the first NCs for cancer treatment over 20 years ago. The objective of this systematic review is to examine the effectiveness and safety of NCs in treating urological cancers.