Complete remission in a patient with widely metastatic HER2-amplified pancreatic adenocarcinoma following multimodal therapy informed by tumor sequencing and organoid profiling

This work, "Complete remission in a patient with widely metastatic HER2-amplified pancreatic adenocarcinoma following multimodal therapy informed by tumor sequencing and organoid profiling" highlights the power of multi-institution collaboration, combining strengths in organoid profiling (Kuo group at Stanford), personalized vaccine therapy (Gillanders group at WUSTL), in vitro drug testing and drug sensitivity (SEngine, MSK, and Mprobe), clinical trials (Dr Ari Baron at CPMC), and the Canopy Health learning network. Here, we demonstrate a complete clinical response achieved in a patient with HER2+ metastatic pancreatic ductal adenocarcinoma to a coordinated barrage of anti-HER2, personalized vaccine and checkpoint inhibition immunotherapy, radiation, and chemotherapy. Comprehensive organoid profiling with drug sensitivity screening and drug testing suggested a vulnerability to anti-HER2 directed therapy, facilitating personalized treatment selection for our patient, which contributed to her clinical benefit. Immune response monitoring following personalized vaccine, radiation and checkpoint inhibition showed a sustained increase in neoantigen specific T cell response.

Weakly-Supervised Tumor Purity Prediction From Frozen H&E Stained Slides

Estimating tumor purity is especially important in the age of precision medicine. Purity estimates have been shown to be critical for correction of tumor sequencing results, and higher purity samples allow for more accurate interpretations from next-generation sequencing results. In addition, tumor purity has been shown to be correlated with survival outcomes for several diseases. Molecular-based purity estimates using computational approaches require sequencing of tumors, which is both time-consuming and expensive. Here we propose an approach, weakly-supervised purity (wsPurity), which can accurately quantify tumor purity within a slide, using multiple and different types of cancer. This approach allows for a flexible analysis of tumors from whole slide imaging (WSI) of histology hematoxylin and eosin (H&E) slides. Our model predicts tumor type with high accuracy (greater than 80% on an independent test cohort), and tumor purity at a higher accuracy compared to a comparable fully-supervised approach (0.1335 MAE on an independent test cohort). In addition to tumor purity prediction, our approach can identify high resolution tumor regions within a slide, to enrich tumor cell selection for downstream analyses. This model could also be used in a clinical setting, to stratify tumors into high and low tumor purity, using different thresholds, in a cancer-dependent manner, depending on what purity levels correlate with worse disease outcomes. In addition, this approach could be used in clinical practice to select the best tissue block for sequencing. Overall, this approach can be used in several different ways to analyze WSIs of tumor H&E sections.

Clonal hematopoiesis detection in cancer patients using cell free DNA sequencing

In the context of cancer, clonal hematopoiesis of indeterminate potential (CHIP) is associated with the development of therapy-related myeloid neoplasms and shorter overall survival. Cell-free DNA (cfDNA) sequencing is becoming widely adopted for genomic screening of cancer patients but has not been used extensively to determine CHIP status due to a requirement for matched blood and tumor sequencing. Here we present an accurate machine learning approach to determine clonal hematopoiesis (CH) status from cfDNA sequencing alone and apply our model to 4,096 oncology clinical cfDNA samples. Using this method, we determine that 26% of patients in this cohort have evidence of CH and CH is most common in lung cancer patients. Matched RNAseq data shows signals of increased inflammation, especially neutrophil activation, within the tumor microenvironment of CH-positive patients. Additionally, CH patients showed evidence of neutrophil activation systemically, pointing to a potential mechanism of action for the worse outcomes associated with CH status. Neutrophil activation may be one of many mechanisms however, as estrogen positive breast cancer patients harboring TET2 frameshift mutations had worse outcomes but similar neutrophil levels to CH-negative patients.