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.
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.
Immunotherapy with checkpoint inhibitors, such as the programmed death-1 (PD-1) antibodies pembrolizumab and nivolumab, are effective in a variety of tumors, yet not all patients respond. Tumor microsatellite instability-high (MSI-H) has emerged as a biomarker of response to checkpoint blockade, leading to the tissue agnostic approval of pembrolizumab in MSI-H cancers. Here we describe a patient with MSI-H colorectal cancer that was treated with this immune checkpoint inhibitor and exhibited progression of disease. We examined this intrinsic resistance through genomic, transcriptional, and pathologic characterization of the patient's tumor and the associated immune microenvironment. The tumor had typical MSI-H molecular features, including a high neoantigen load. We also identified biallelic loss of the gene for β2-microglobulin (B2M), whose product is critical for antigen presentation. Immune infiltration deconvolution analysis of bulk transcriptome data from this anti-PD-1–resistant tumor and hundreds of other colorectal cancer specimens revealed a high natural killer cell and M2 macrophage infiltration in the patient's cancer. This was confirmed by single-cell transcriptome analysis and multiplex immunofluorescence. Our study provides insight into resistance in MSI-H tumors and suggests immunotherapeutic strategies in additional genomic contexts of colorectal cancer.
Background: Cancer genomes are peppered with somatic mutations imprinted by different mutational processes. The mutational pattern of a cancer genome can be used to identify and understand the etiology of the underlying mutational processes. A plethora of prior research has focused on examining mutational signatures and mutational patterns from single base substitutions and their immediate sequencing context. We recently demonstrated that further classification of small mutational events (including substitutions, insertions, deletions, and doublet substitutions) can be used to provide a deeper understanding of the mutational processes that have molded a cancer genome. However, there has been no standard tool that allows fast, accurate, and comprehensive classification for all types of small mutational events.
Results: Here, we present SigProfilerMatrixGenerator, a computational tool designed for optimized exploration and visualization of mutational patterns for all types of small mutational events. SigProfilerMatrixGenerator is written in Python with an R wrapper package provided for users that prefer working in an R environment. SigProfilerMatrixGenerator produces fourteen distinct matrices by considering transcriptional strand bias of individual events and by incorporating distinct classifications for single base substitutions, doublet base substitutions, and small insertions and deletions. While the tool provides a comprehensive classification of mutations, SigProfilerMatrixGenerator is also faster and more memory efficient than existing tools that generate only a single matrix.
Conclusions: SigProfilerMatrixGenerator provides a standardized method for classifying small mutational events that is both efficient and scalable to large datasets. In addition to extending the classification of single base substitutions, the tool is the first to provide support for classifying doublet base substitutions and small insertions and deletions.
There is ample evidence that inflammatory processes and signaling play a critical role in the progression of most cancers (1), especially as potent initiators at sites of chronic injury (2). In these chronic inflammation–associated cancers (CIACs), tissue injury is caused by sustained or recurrent infection, irritation, or trauma. This distinct class of malignancies, which includes acid reflux–associated esophageal adenocarcinoma (EAC), smoking-associated lung squamous cell carcinoma, Helicobacter pylori infection–associated gastric adenocarcinoma, and inflammatory bowel disease–associated colon adenocarcinoma, is responsible for 20 to 25% of all cancer deaths worldwide, killing more than 2 million people annually. Compared with other tumor types, CIACs tend to have a poorer prognosis, attributable to a high propensity for metastasis and drug resistance. The underlying mechanisms by which chronic inflammation promotes the development of such lethal cancers are still poorly understood.