Chad J. Creighton

Proteome and transcriptome data combined can help assess the relevance of non-coding germline variants. Here, we combine germline Structural Variants (SVs) with mass spectrometry-based proteomics on tumors from 1637 cancer patients spanning various tumor tissues of origin to determine the extent SV breakpoint patterns involve differential protein expression of nearby genes. Rare and singleton SVs disrupting protein expression of known cancer susceptibility genes collectively involve 6% of patients. About 24% of the hundreds of genes with SV-associated non-coding cis-regulatory alterations at the mRNA level are similarly associated at the protein level. Both rare and common SVs may associate with differential protein expression within a specific tumor type or across multiple tissue types, including SVs differentially represented by patient ancestry. SVs involving altered methylation of CpG Islands or enhancers are also implicated in differential protein expression. Our results emphasize the contribution of germline SVs to cancer heterogeneity at the proteome level.

EGFR-mutant lung adenocarcinomas (LUADs) that are vulnerable to the EGFR antagonist osimertinib (Osi) eventually relapse, owing in part to the emergence of drug-tolerant persister (DTP) cells that arise through epigenetic mechanisms. Intratumoral DTP cells can herald a worse clinical outcome, but the way in which DTP cells influence LUAD progression remains unclear. Osi-resistant (OR) cells exhibit typical DTP cell features, including a propensity to undergo senescence and epithelial-mesenchymal transition (EMT), which can activate heightened secretory states. Therefore, we postulated that OR cells influence LUAD progression through paracrine mechanisms. To test this hypothesis, we utilized congenic pairs of EGFR-mutant LUAD cell lines in which drug-naive (DN) cells were rendered OR by chronic exposure to escalating doses of Osi. Cocultured in vitro or coinjected into mice, paracrine signals from OR cells enhanced the growth and metastatic properties of DN cells. EMT and senescence activated nonoverlapping secretomes, and OR cells governed DN cells by undergoing EMT but not senescence. Mechanistically, Osi rapidly increased TGF-β2 levels to initiate EMT, which triggered a Golgi remodeling process that accelerated the biogenesis and anterograde trafficking of secretory vesicles. The protumorigenic activity of OR cells was diminished by depletion of EMT-dependent secreted proteins or the EMT-activating transcription factor ZEB1. These findings identify paracrine mechanisms by which OR cells drive LUAD progression.

Wnt signaling is a complex pathway consisting of numerous ligands and frizzled (FZD) receptors. These signaling components are widely expressed in human prostate tissues and often undergo upregulation or mutation in advanced prostate cancers. Enhanced Wnt signaling promotes prostate cancer cell proliferation, metastasis, and resistance to therapy. However, targeting pan-Wnt signaling poses challenges due to tissue toxicity. We show that FZD6 is the most highly expressed and frequently amplified Wnt receptor in advanced human prostate cancers. Knockdown of FZD6 suppresses both in vitro and in vivo growth of various prostate cancer cell lines and patient-derived xenograft models. FZD6 knockdown impairs DNA double-strand break (DSB) repair, as determined by both resolution of γH2AX foci and DNA DSB repair reporter assays. Mechanistically, FZD6 knockdown-induced growth suppression is linked to reduced activities of SRC kinase and STAT3, while DNA damage repair deficiency is mediated through WEE1 downregulation via PLK1. Knockdown of FZD6 enhances the therapeutic efficacy of genotoxic agents for prostate cancer cells. A kinome-wide CRISPR-Cas9 knockout screen reveals that FZD6 inhibition sensitizes prostate cancer cells to the inhibition of PKMYT1, a WEE kinase family member. Collectively, we demonstrate that targeting a single FZD receptor highly expressed in prostate cancers can yield significant therapeutic efficacy, and uncover therapeutic vulnerabilities associated with FZD6 inhibition.

Germline Structural Variants (SVs) represent an important source of genetic diversity, in large part due to their influence on gene transcription. It is necessary to systematically catalog germline SVs and their associated impacted genes across different cohorts and tissue and cellular contexts, including pediatric brain or Central Nervous System (CNS) tumors. We combined RNA with whole genome sequencing across 1430 pediatric brain or CNS tumor patients from the Children’s Brain Tumor Network. We set out to systematically identify genes for which the proximity of germline SVs was recurrently and significantly associated with differential expression in the tumor sample across multiple patients. For hundreds of genes, recurrent and common germline SV breakpoints within 1 Mb were associated with higher or lower expression in tumors spanning various histologic types. Some germline SV-expression associations involved gene deletion or disruption, while others represented cis-regulatory alterations. Rare and singleton SVs disrupting DNA repair-related and mitochondrial-related genes collectively involved 2.7 and 4.7% of patients, respectively. Genes with germline SV breakpoint patterns and expression associated with patients of African ancestry included ACOT1 and CRYBB2P1. Genes with germline SV breakpoint patterns and expression associated with patient survival included ACTG1 and AHRR. Genes altered in association with both somatic and germline SVs included HGF and BCOR. Our results capture a class of phenotypic variation at work in the setting of pediatric brain tumors, including genes with cancer roles.

Molecular correlates of cancer patient overall survival (OS) can provide new insights. Here, we systematically cataloged pan-cancer, multi-omic correlates of OS transcending tumor lineage across 11,019 patients, involving multiple cancer types while correcting for cancer-type-intrinsic OS differences. Significant fractions of genes with mRNA associated with OS in pan-cancer analyses showed concordant associations at the levels of DNA copy number alteration or methylation. Pan-cancer gene signatures of T-cell and macrophage tumor infiltrates were associated with better and worse OS, respectively. Pathways implicated by molecular OS associations included metabolism, PI3K/Akt, Wnt, and TGF-beta receptor. Significant fractions of worse OS-associated genes were essential for cell growth. A pan-cancer RNA signature of aggressive cancers associated with greater sensitivity in vitro to inhibitors of MEK1/2, glycolysis pathway, and HSP90, and with chemotherapy response in patient breast tumors. With therapeutic implications, pan-cancer molecular associations with patient survival reveal genes and pathways underlying more aggressive diseases.

The molecular landscape of human cancers involves multiple omics layers of complexity, from genome to proteome and beyond. Cancer proteogenomics involves the integration of protein expression patterns with somatic DNA alterations. Recently, advances in mass spectrometry-based proteomic profiling technologies have enabled the generation of combined proteomic and multi-omic data for thousands of human tumors across dozens of studies. These data in the public domain can be utilized to give us a more complete picture of cancer-specific pathways and processes and identify gene candidates for therapeutic targeting. Many proteogenomic studies are ongoing involving various cancer types according to tissue or cell of origin, including studies to predict response to therapy. In addition, pan-cancer analyses across multiple studies can identify molecular commonalities, differences, and emergent themes across tumor lineages. Data integration can determine which gene alterations at the transcriptome level are translated to the protein level. A wealth of knowledge and analytical approaches developed historically to integrate gene transcription with genomic data can be readily applied to proteogenomic analyses. Here is provided an overview of higher-level analyses of proteogenomic datasets. Such analyses include defining proteomic subtypes of cancer, exploring the impact of somatic mutations and epigenetic modifications on protein expression, cataloging proteomic correlates of more aggressive disease or drug response, and identifying enriched pathways.

The extent of genetic variation and its influence on gene expression across multiple tissue and cellular contexts is still being characterized, with germline Structural Variants (SVs) being historically understudied. DNA methylation also represents a component of normal germline variation across individuals. Here, we combine germline SVs (by short-read sequencing) with tumor DNA methylation across 1292 pediatric brain tumor patients. For thousands of methylation probes for CpG Islands (CGIs) or enhancers, rare and common SV breakpoints upstream or downstream associate with differential methylation in tumors spanning various histologic types, a significant subset involving genes with SV-associated differential expression. Cancer predisposition genes involving SV-associated differential methylation and expression include MSH2, RSPA, and PALB2. SV breakpoints falling within CGIs or histone marks H3K36me3 or H3K9me3 associate with differential CGI methylation. Genes with SVs and CGI methylation associated with patient survival include POLD4. Our results capture a class of normal phenotypic variation having disease implications.

Breast cancer (BCa), a leading malignancy among women, is characterized by morphological and molecular heterogeneity. While early-stage, hormone receptor, and HER2-positive BCa are treatable, triple-negative BCa and metastatic BCa remains largely untreatable. Advances in sequencing and proteomic technologies have improved our understanding of the molecular alterations that occur during BCa initiation and progression and enabled identification of subclass-specific biomarkers and therapeutic targets. Despite the availability of abundant omics data in public repositories, user-friendly tools for multi-omics data analysis and integration are scarce. To address this, we developed a comprehensive BCa data analysis platform called MammOnc-DB (http://resource.path.uab.edu/MammOnc-Home.html), comprising data from more than 20,000 BCa samples. MammOnc-DB facilitates hypothesis generation and testing, biomarker discovery, and therapeutic targets identification. The platform also includes pre- and post-treatment data, which can help users identify treatment resistance markers and support combination therapy strategies, offering researchers and clinicians a comprehensive tool for BCa data analysis and visualization.