Karin A. Oien

TP53 mutation occurs in 50-75% of human pancreatic ductal adenocarcinomas (PDAC) following an initiating activating mutation in the KRAS gene. These p53 mutations frequently result in expression of a stable protein, p53(R175H), rather than complete loss of protein expression. In this study we elucidate the functions of mutant p53 (Trp53(R172H)), compared to knockout p53 (Trp53(fl)), in a mouse model of PDAC. First we find that although Kras(G12D) is one of the major oncogenic drivers of PDAC, most Kras(G12D)-expressing pancreatic cells are selectively lost from the tissue, and those that remain form premalignant lesions. Loss, or mutation, of Trp53 allows retention of the Kras(G12D)-expressing cells and drives rapid progression of these premalignant lesions to PDAC. This progression is consistent with failed growth arrest and/or senescence of premalignant lesions, since a mutant of p53, p53(R172P), which can still induce p21 and cell cycle arrest, is resistant to PDAC formation. Second, we find that despite similar kinetics of primary tumor formation, mutant p53(R172H), as compared with genetic loss of p53, specifically promotes metastasis. Moreover, only mutant p53(R172H)-expressing tumor cells exhibit invasive activity in an in vitro assay. Importantly, in human PDAC, p53 accumulation significantly correlates with lymph node metastasis. In summary, by using 'knock-in' mutations of Trp53 we have identified two critical acquired functions of a stably expressed mutant form of p53 that drive PDAC; first, an escape from Kras(G12D)-induced senescence/growth arrest and second, the promotion of metastasis.

PURPOSE: Patients with metastatic adenocarcinoma of unknown origin are a common clinical problem. Knowledge of the primary site is important for their management, but histologically, such tumors appear similar. Better diagnostic markers are needed to enable the assignment of metastases to likely sites of origin on pathologic samples. EXPERIMENTAL DESIGN: Expression profiling of 27 candidate markers was done using tissue microarrays and immunohistochemistry. In the first (training) round, we studied 352 primary adenocarcinomas, from seven main sites (breast, colon, lung, ovary, pancreas, prostate and stomach) and their differential diagnoses. Data were analyzed in Microsoft Access and the Rosetta system, and used to develop a classification scheme. In the second (validation) round, we studied 100 primary adenocarcinomas and 30 paired metastases. RESULTS: In the first round, we generated expression profiles for all 27 candidate markers in each of the seven main primary sites. Data analysis led to a simplified diagnostic panel and decision tree containing 10 markers only: CA125, CDX2, cytokeratins 7 and 20, estrogen receptor, gross cystic disease fluid protein 15, lysozyme, mesothelin, prostate-specific antigen, and thyroid transcription factor 1. Applying the panel and tree to the original data provided correct classification in 88%. The 10 markers and diagnostic algorithm were then tested in a second, independent, set of primary and metastatic tumors and again 88% were correctly classified. CONCLUSIONS: This classification scheme should enable better prediction on biopsy material of the primary site in patients with metastatic adenocarcinoma of unknown origin, leading to improved management and therapy.

Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer-related mortality. Despite significant advances made in the treatment of other cancers, current chemotherapies offer little survival benefit in this disease. Pancreaticoduodenectomy offers patients the possibility of a cure, but most will die of recurrent or metastatic disease. Hence, preventing metastatic disease in these patients would be of significant benefit. Using principal component analysis (PCA), we identified a LOX/hypoxia signature associated with poor patient survival in resectable patients. We found that LOX expression is upregulated in metastatic tumors from Pdx1-Cre Kras(G12D/+) Trp53(R172H/+) (KPC) mice and that inhibition of LOX in these mice suppressed metastasis. Mechanistically, LOX inhibition suppressed both migration and invasion of KPC cells. LOX inhibition also synergized with gemcitabine to kill tumors and significantly prolonged tumor-free survival in KPC mice with early-stage tumors. This was associated with stromal alterations, including increased vasculature and decreased fibrillar collagen, and increased infiltration of macrophages and neutrophils into tumors. Therefore, LOX inhibition is able to reverse many of the features that make PDAC inherently refractory to conventional therapies and targeting LOX could improve outcome in surgically resectable disease.

Deep learning (DL) can accelerate the prediction of prognostic biomarkers from routine pathology slides in colorectal cancer (CRC). However, current approaches rely on convolutional neural networks (CNNs) and have mostly been validated on small patient cohorts. Here, we develop a new transformer-based pipeline for end-to-end biomarker prediction from pathology slides by combining a pre-trained transformer encoder with a transformer network for patch aggregation. Our transformer-based approach substantially improves the performance, generalizability, data efficiency, and interpretability as compared with current state-of-the-art algorithms. After training and evaluating on a large multicenter cohort of over 13,000 patients from 16 colorectal cancer cohorts, we achieve a sensitivity of 0.99 with a negative predictive value of over 0.99 for prediction of microsatellite instability (MSI) on surgical resection specimens. We demonstrate that resection specimen-only training reaches clinical-grade performance on endoscopic biopsy tissue, solving a long-standing diagnostic problem.