Christine Sers’s research while affiliated with Humboldt-Universität zu Berlin and other places

GPA33 is a promising surface antigen for targeted therapy in colorectal cancer (CRC). It is expressed almost exclusively in CRC and intestinal epithelia. However, previous clinical studies have not achieved expected response rates. We investigated GPA33 expression and regulation in CRC and developed a GPA33-targeted cellular therapy. We examined GPA33 expression in CRC cohorts using immunohistochemistry and immunofluorescence. We analyzed GPA33 regulation by interference with oncogenic signaling in vitro and in vivo using inhibitors and conditional inducible regulators. Furthermore, we engineered anti-GPA33-CAR T cells and assessed their activity in vitro and in vivo. GPA33 expression showed consistent intratumoral heterogeneity in CRC with antigen loss at the infiltrative tumor edge. This pattern was preserved at metastatic sites. GPA33-positive cells had a differentiated phenotype and low WNT activity. Low GPA33 expression levels were linked to tumor progression in patients with CRC. Downregulation of WNT activity induced GPA33 expression in vitro and in GPA33-negative tumor cell subpopulations in xenografts. GPA33-CAR T cells were activated in response to GPA33 and reduced xenograft growth in mice after intratumoral application. GPA33-targeted therapy may be improved by simultaneous WNT inhibition to enhance GPA33 expression. Furthermore, GPA33 is a promising target for cellular immunotherapy in CRC.

Single‐cell analyses can be confounded by assigning unrelated groups of cells to common developmental trajectories. For instance, cancer cells and admixed normal epithelial cells could adopt similar cell states thus complicating analyses of their developmental potential. Here, we develop and benchmark CCISM (for Cancer Cell Identification using Somatic Mutations) to exploit genomic single nucleotide variants for the disambiguation of cancer cells from genomically normal non‐cancer cells in single‐cell data. We find that our method and others based on gene expression or allelic imbalances identify overlapping sets of colorectal cancer versus normal colon epithelial cells, depending on molecular characteristics of individual cancers. Further, we define consensus cell identities of normal and cancer epithelial cells with higher transcriptome cluster homogeneity than those derived using existing tools. Using the consensus identities, we identify significant shifts of cell state distributions in genomically normal epithelial cells developing in the cancer microenvironment, with immature states increased at the expense of terminal differentiation throughout the colon, and a novel stem‐like cell state arising in the left colon. Trajectory analyses show that the new cell state extends the pseudo‐time range of normal colon stem‐like cells in a cancer context. We identify cancer‐associated fibroblasts as sources of WNT and BMP ligands potentially contributing to increased plasticity of stem cells in the cancer microenvironment. Our analyses advocate careful interpretation of cell heterogeneity and plasticity in the cancer context and the consideration of genomic information in addition to gene expression data when possible.

Secondary resistance limits the clinical effectiveness of mutation-specific RAS inhibitors in colorectal cancer. It is unknown whether broad-spectrum RAS inhibitors meet similar limitations. Here, we identify and categorize mechanisms of resistance to the broad-spectrum active-state RAS inhibitor RMC-7977 in colorectal cancer cell lines. We found that KRAS-mutant colorectal cancer cell lines are universally sensitive to RMC-7977, inhibiting the RAS-RAF-MEK-ERK axis, halting proliferation and in some cases inducing apoptosis. To monitor KRAS downstream effector pathway activity, we developed a compartment-specific dual-color ERK activity reporter system. RMC-7977 treatment reduced reporter activity. However, long-term dose escalation with RMC-7977 revealed multiple patterns of reporter reactivation in emerging resistant cell populations that correlated with phosphorylation states of compartment-specific ERK targets. Cells sorted for high, low, or cytoplasmic reporter activity exhibited distinct patterns of genomic mutations, phospho-protein, and transcriptional activities. Notably, all resistant subpopulations showed dynamic ERK regulation in the presence of the RAS inhibitor, unlike the parental sensitive cell lines. High levels of RAS downstream activities were observed in cells characterized by a KRAS Y71H resistance mutation. In contrast, RAS inhibitor-resistant populations with low, or cytoplasmic ERK reporter reactivation displayed different genetic alterations, among them RAF1 S257L and S259P mutations. Colorectal cancer cells resistant to RMC-7977 and harboring the RAF1 mutation specifically exhibited synergistic sensitivity to concurrent RAS and RAF inhibition. Our findings endorse reporter-assisted screening together with single-cell analyses as a powerful approach for dissecting the complex landscape of therapy resistance. The strategy offers opportunities to develop clinically relevant combinatorial treatments to counteract emergence of resistant cancer cells.

The expression of mutated RAS genes drives extensive transcriptome alterations. Perturbation experiments have shown that the transcriptional responses to downstream effector pathways are partially unique and non-overlapping, suggesting a modular organization of the RAS-driven expression program. However, the relationship between individual deregulated transcription factors and the entire cancer cell-specific genetic program is poorly understood. To identify potential regulators of the RAS/MAPK-dependent fraction of the genetic program, we monitored transcriptome and proteome changes following conditional, time-resolved expression of mutant HRASG12V in human epithelial cells during neoplastic conversion. High mobility group AT hook2 (HMGA2), an architectural chromatin modulating protein and oncofetal tumour marker, was recovered as the earliest upregulated transcription factor. Knock-down of HMGA2 reverted anchorage-independent growth and epithelial-mesenchymal transition not only in HRAS-transformed cells but also in an independent, KRASG12V-driven rat epithelial model. Moreover, HMGA2 silencing reverted the deregulated expression of 60% of RAS-responsive target genes. These features qualify HMGA2 as a master regulator of mutant RAS-driven expression patterns. The delayed deregulation of FOSL1, ZEB1 and other transcription factors with known oncogenic activity suggests that HMGA2 acts in concert with a network of regulatory factors to trigger full neoplastic conversion. Although transcription factors are considered difficult to drug, the central role of HMGA2 in the transcription factor network as well as its relevance for cancer prognosis has motivated attempts to block its function using small molecular weight compounds. The further development of direct HMGA2 antagonists may prove useful in cancer cells that have developed resistance to signalling chain inhibition.

Single-cell analyses can be confounded by assigning unrelated groups of cells to common developmental trajectories. For instance, cancer cells and admixed normal epithelial cells could potentially adopt similar cell states thus complicating analyses of their developmental potential. Here, we develop and benchmark CCISM (for Cancer Cell Identification using Somatic Mutations) to exploit genomic single nucleotide variants for the disambiguation of cancer cells from genomically normal non-cancer epithelial cells in single-cell data. In colorectal cancer datasets, we find that our method and others based on gene expression or allelic imbalances identify overlapping sets of cancer versus normal epithelial cells, depending on molecular characteristics of individual cancers. Further, we define consensus cell identities of normal and cancer epithelial cells with higher transcriptome cluster homogeneity than those derived using existing tools. Using the consensus identities, we identify significant shifts of cell state distributions in genomically normal epithelial cells developing in the cancer microenvironment, with immature states increased at the expense of terminal differentiation throughout the colon, and a novel stem-like cell state arising in the left colon. Trajectory analyses show that the new cell state extends the pseudo-time range of normal colon stem-like cells in a cancer context. We identify cancer-associated fibroblasts as sources of WNT and BMP ligands potentially contributing to increased plasticity of stem cells in the cancer microenvironment. Our analyses advocate careful interpretation of cell heterogeneity and plasticity in the cancer context and the consideration of genomic information in addition to gene expression data when possible. Novelty and Impact Single-cell analyses have become standard to assess cell heterogeneity and developmental hierarchies in cancer tissues. However, these datasets are complex and contain cancer and non-cancer lineage cells. Here, we develop and systematically benchmark tools to distinguish between cancer and non-cancer single-cell transcriptomes, based on gene expression or different levels of genomic information. We provide strategies to combine results of different tools into consensus calls tailored to the biology and genetic characteristics of the individual cancer.

Background The aim of this study was to analyse transcriptomic differences between primary and recurrent high-grade serous ovarian carcinoma (HGSOC) to identify prognostic biomarkers. Methods We analysed 19 paired primary and recurrent HGSOC samples using targeted RNA sequencing. We selected the best candidates using in silico survival and pathway analysis and validated the biomarkers using immunohistochemistry on a cohort of 44 paired samples, an additional cohort of 504 primary HGSOCs and explored their function. Results We identified 233 differential expressed genes. Twenty-three showed a significant prognostic value for PFS and OS in silico. Seven markers ( AHRR, COL5A2, FABP4, HMGCS2, ITGA5, SFRP2 and WNT9B ) were chosen for validation at the protein level. AHRR expression was higher in primary tumours ( p < 0.0001) and correlated with better patient survival ( p < 0.05). Stromal SFRP2 expression was higher in recurrent samples ( p = 0.009) and protein expression in primary tumours was associated with worse patient survival ( p = 0.022). In multivariate analysis, tumour AHRR and SFRP2 remained independent prognostic markers. In vitro studies supported the anti-tumorigenic role of AHRR and the oncogenic function of SFRP2. Conclusions Our results underline the relevance of AHRR and SFRP2 proteins in aryl-hydrocarbon receptor and Wnt-signalling, respectively, and might lead to establishing them as biomarkers in HGSOC.

Purpose Single-cell transcriptional profiling reveals cell heterogeneity and clinically relevant traits in intra-operatively collected patient-derived tissue. So far, single-cell studies have been constrained by the requirement for prospectively collected fresh or cryopreserved tissue. This limitation might be overcome by recent technical developments enabling single-cell analysis of FFPE tissue. Methods We benchmark single-cell profiles from patient-matched fresh, cryopreserved and archival FFPE cancer tissue. Results We find that fresh tissue and FFPE routine blocks can be employed for the robust detection of clinically relevant traits on the single-cell level. Specifically, single-cell maps of fresh patient tissues and corresponding FFPE tissue blocks could be integrated into common low-dimensional representations, and cell subtype clusters showed highly correlated transcriptional strengths of signaling pathway, hallmark, and clinically useful signatures, although expression of single genes varied due to technological differences. FFPE tissue blocks revealed higher cell diversity compared to fresh tissue. In contrast, single-cell profiling of cryopreserved tissue was prone to artifacts in the clinical setting. Conclusion Our analysis highlights the potential of single-cell profiling in the analysis of retrospectively and prospectively collected archival pathology cohorts and increases the applicability in translational research.

Purpose We evaluated additional mutations in RAS wild-type (WT) metastatic colorectal cancer (mCRC) as prognostic and predictive biomarkers for the efficacy of added panitumumab to a 5-fluorouracil plus folinic acid (FU/FA) maintenance as pre-specified analysis of the randomized PanaMa trial. Patients and Methods Mutations (MUT) were identified using targeted next-generation sequencing (NGS; Illumina Cancer Hotspot Panel v2) and IHC. RAS/BRAF V600E/PIK3CA/AKT1/ALK1/ERBB2/PTEN MUT and HER2/neu overexpressions were negatively hyperselected and correlated with median progression-free survival (PFS) and overall survival (OS) since start of maintenance treatment, and objective response rates (ORR). Univariate/multivariate Cox regression estimated hazard ratios (HR) and 95% confidence intervals (CI). Results 202 of 248 patients (81.5%) of the full analysis set (FAS) had available NGS data: hyperselection WT, 162 (80.2%); MUT, 40 (19.8%). From start of maintenance therapy, hyperselection WT tumors were associated with longer median PFS as compared with hyperselection MUT mCRC (7.5 vs. 5.4 months; HR, 0.75; 95% CI, 0.52–1.07; P = 0.11), OS (28.7 vs. 22.2 months; HR, 0.53; 95% CI, 0.36–0.77; P = 0.001), and higher ORR (35.8% vs. 25.0%, P = 0.26). The addition of panitumumab to maintenance was associated with significant benefit in hyperselection WT tumors for PFS (9.2 vs. 6.0 months; HR, 0.66; 95% CI, 0.47–0.93; P = 0.02) and numerically also for OS (36.9 vs. 24.9 months; HR, 0.91; 95% CI, 0.61–1.36; P = 0.50), but not in hyperselection MUT tumors. Hyperselection status interacted with maintenance treatment arms in terms of PFS (P = 0.06) and OS (P = 0.009). Conclusions Extended molecular profiling beyond RAS may have the potential to improve the patient selection for anti-EGFR containing maintenance regimens.

Motivation Cell fate decisions, such as apoptosis or proliferation, are communicated via signaling pathways. The pathways are heavily intertwined and often consist of sequential interaction of proteins (kinases). Information integration takes place on the protein level via n-to-1 interactions. A state-of-the-art procedure to quantify information flow (edges) between signaling proteins (nodes) is network inference. However, edge weight calculation typically refers to 1-to-1 interactions only and relies on mean protein phosphorylation levels instead of single cell distributions. Information theoretic measures such as the mutual information (MI) have the potential to overcome these shortcomings but are still rarely used. Results This work proposes a Bayesian nearest neighbor-based MI estimator (BannMI) to quantify n-to-1 kinase dependency in signaling pathways. BannMI outperforms the state-of-the-art MI estimator on protein-like data in terms of mean squared error and Pearson correlation. Using BannMI, we analyze apoptotic signaling in phosphoproteomic cancerous and noncancerous breast cell line data. Our work provides evidence for cooperative signaling of several kinases in programmed cell death and identifies a potential key role of the mitogen-activated protein kinase p38. Availability and implementation Source code and applications are available at: https://github.com/zuiop11/nn_info and can be downloaded via Pip as Python package: nn-info.