Publications
The highest frequencies of KRAS mutations occur in colorectal carcinoma (CRC) and pancreatic ductal adenocarcinoma (PDAC). The ability to target downstream pathways mediating KRAS oncogenicity is limited by an incomplete understanding of the contextual cues modulating the signaling output of activated K-RAS. We performed mass spectrometry on mouse tissues expressing wild-type or mutant Kras to determine how tissue context and genetic background modulate oncogenic signaling. Mutant Kras dramatically altered the proteomes and phosphoproteomes of preneoplastic and neoplastic colons and pancreases in a context-specific manner. We developed an approach to statistically humanize the mouse networks with data from human cancer and identified genes within the humanized CRC and PDAC networks synthetically lethal with mutant KRAS. Our studies demonstrate the context-dependent plasticity of oncogenic signaling, identify non-canonical mediators of KRAS oncogenicity within the KRAS-regulated signaling network, and demonstrate how statistical integration of mouse and human datasets can reveal cross-species therapeutic insights.
The highest frequencies of KRAS mutations occur in colorectal carcinoma (CRC) and pancreatic ductal adenocarcinoma (PDAC). The ability to target downstream pathways mediating KRAS oncogenicity is limited by an incomplete understanding of the contextual cues modulating the signaling output of activated K-RAS. We performed mass spectrometry on mouse tissues expressing wild-type or mutant Kras to determine how tissue context and genetic background modulate oncogenic signaling. Mutant Kras dramatically altered the proteomes and phosphoproteomes of preneoplastic and neoplastic colons and pancreases in a context-specific manner. We developed an approach to statistically humanize the mouse networks with data from human cancer and identified genes within the humanized CRC and PDAC networks synthetically lethal with mutant KRAS. Our studies demonstrate the context-dependent plasticity of oncogenic signaling, identify non-canonical mediators of KRAS oncogenicity within the KRAS-regulated signaling network, and demonstrate how statistical integration of mouse and human datasets can reveal cross-species therapeutic insights.
Single-cell RNA sequencing has enabled the decomposition of complex tissues into functionally distinct cell types. Often, investigators wish to assign cells to cell types through unsupervised clustering followed by manual annotation or via ‘mapping’ to existing data. However, manual interpretation scales poorly to large datasets, mapping approaches require purified or pre-annotated data and both are prone to batch effects. To overcome these issues, we present CellAssign, a probabilistic model that leverages prior knowledge of cell-type marker genes to annotate single-cell RNA sequencing data into predefined or de novo cell types. CellAssign automates the process of assigning cells in a highly scalable manner across large datasets while controlling for batch and sample effects. We demonstrate the advantages of CellAssign through extensive simulations and analysis of tumor microenvironment composition in high-grade serous ovarian cancer and follicular lymphoma.
Single-cell RNA sequencing has enabled the decomposition of complex tissues into functionally distinct cell types. Often, investigators wish to assign cells to cell types through unsupervised clustering followed by manual annotation or via ‘mapping’ to existing data. However, manual interpretation scales poorly to large datasets, mapping approaches require purified or pre-annotated data and both are prone to batch effects. To overcome these issues, we present CellAssign, a probabilistic model that leverages prior knowledge of cell-type marker genes to annotate single-cell RNA sequencing data into predefined or de novo cell types. CellAssign automates the process of assigning cells in a highly scalable manner across large datasets while controlling for batch and sample effects. We demonstrate the advantages of CellAssign through extensive simulations and analysis of tumor microenvironment composition in high-grade serous ovarian cancer and follicular lymphoma.
Purpose: The future of non-operative management of DCIS relies on distinguishing lesions requiring treatment from those needing only active surveillance. More accurate preoperative staging and grading of DCIS would be helpful. We identified determinants of upstaging preoperative breast biopsies showing ductal carcinoma in situ (DCIS) to invasive breast cancer (IBC), or of upgrading them to higher-grade DCIS, following examination of the surgically excised specimen.
Methods: We studied all women with DCIS at preoperative biopsy in a large specialist cancer centre during 2000–2014. Information from clinical records, mammography, and pathology specimens from both preoperative biopsy and excised specimen were abstracted. Women suspected of having IBC during biopsy were excluded.
Results: Among 606 preoperative biopsies showing DCIS, 15.0% (95% confidence interval 12.3–18.1) were upstaged to IBC and a further 14.6% (11.3–18.4) upgraded to higher-grade DCIS. The risk of upstaging increased with presence of a palpable lump (21.1% vs 13.0%, pdifference = 0.04), while the risk of upgrading increased with presence of necrosis on biopsy (33.0% vs 9.5%, pdifference < 0.001) and with use of 14G core-needle rather than 9G vacuum-assisted biopsy (22.8% vs 7.0%, pdifference < 0.001). Larger mammographic size increased the risk of both upgrading (pheterogeneity = 0.01) and upstaging (pheterogeneity = 0.004).
Conclusions: The risk of upstaging of DCIS in preoperative biopsies is lower than previously estimated and justifies conducting randomized clinical trials testing the safety of active surveillance for lower grade DCIS. Selection of women with low grade DCIS for such trials, or for active surveillance, may be improved by consideration of the additional factors identified in this study.