High-confidence calling of normal epithelial cells allows identification of a novel stem-like cell state in the colorectal cancer microenvironment.

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.

On minimising tumoural growth under treatment resistance.

Drug resistance is a major challenge for curative cancer treatment, representing the main reason of death in patients. Evolutionary biology suggests pauses between treatment rounds as a way to delay or even avoid resistance emergence. Indeed, this approach has already shown promising preclinical and early clinical results, and stimulated the development of mathematical models for finding optimal treatment protocols. Due to their complexity, however, these models do not lend themself to a rigorous mathematical analysis, hence so far clinical recommendations generally relied on numerical simulations and ad-hoc heuristics. Here, we derive two mathematical models describing tumour growth under genetic and epigenetic treatment resistance, respectively, which are simple enough for a complete analytical investigation. First, we find key differences in response to treatment protocols between the two modes of resistance. Second, we identify the optimal treatment protocol which leads to the largest possible tumour shrinkage rate. Third, we fit the "epigenetic model" to previously published xenograft experiment data, finding excellent agreement, underscoring the biological validity of our approach. Finally, we use the fitted model to calculate the optimal treatment protocol for this specific experiment, which we demonstrate to cause curative treatment, making it superior to previous approaches which generally aimed at stabilising tumour burden. Overall, our approach underscores the usefulness of simple mathematical models and their analytical examination, and we anticipate our findings to guide future preclinical and, ultimately, clinical research in optimising treatment regimes.

Combination of tumor asphericity and an extracellular matrix-related prognostic gene signature in non-small cell lung cancer patients.

One important aim of precision oncology is a personalized treatment of patients. This can be achieved by various biomarkers, especially imaging parameters and gene expression signatures are commonly used. So far, combination approaches are sparse. The aim of the study was to independently validate the prognostic value of the novel positron emission tomography (PET) parameter tumor asphericity (ASP) in non small cell lung cancer (NSCLC) patients and to investigate associations between published gene expression profiles and ASP. This was a retrospective evaluation of PET imaging and gene expression data from three public databases and two institutional datasets. The whole cohort comprised 253 NSCLC patients, all treated with curative intent surgery. Clinical parameters, standard PET parameters and ASP were evaluated in all patients. Additional gene expression data were available for 120 patients. Univariate Cox regression and Kaplan-Meier analysis was performed for the primary endpoint progression-free survival (PFS) and additional endpoints. Furthermore, multivariate cox regression testing was performed including clinically significant parameters, ASP, and the extracellular matrix-related prognostic gene signature (EPPI). In the whole cohort, a significant association with PFS was observed for ASP (p < 0.001) and EPPI (p = 0.012). Upon multivariate testing, EPPI remained significantly associated with PFS (p = 0.018) in the subgroup of patients with additional gene expression data, while ASP was significantly associated with PFS in the whole cohort (p = 0.012). In stage II patients, ASP was significantly associated with PFS (p = 0.009), and a previously published cutoff value for ASP (19.5%) was successfully validated (p = 0.008). In patients with additional gene expression data, EPPI showed a significant association with PFS, too (p = 0.033). The exploratory combination of ASP and EPPI showed that the combinatory approach has potential to further improve patient stratification compared to the use of only one parameter. We report the first successful validation of EPPI and ASP in stage II NSCLC patients. The combination of both parameters seems to be a very promising approach for improvement of risk stratification in a group of patients with urgent need for a more personalized treatment approach.

In Silico Analysis Predicts Nuclear Factors NR2F6 and YAP1 as Mesenchymal Subtype-Specific Therapeutic Targets for Ovarian Cancer Patients.

BACKGROUND: Tumour heterogeneity in high-grade serous ovarian cancer (HGSOC) is a proposed cause of acquired resistance to treatment and high rates of relapse. Among the four distinct molecular subtypes of HGSOC, the mesenchymal subtype (MES) has been observed with high frequency in several study cohorts. Moreover, it exhibits aggressive characteristics with poor prognosis. The failure to adequately exploit such subtypes for treatment results in high mortality rates, highlighting the need for effective targeted therapeutic strategies that follow the idea of personalized medicine (PM). METHODS: As a proof-of-concept, bulk and single-cell RNA data were used to characterize the distinct composition of the tumour microenvironment (TME), as well as the cell-cell communication and its effects on downstream transcription of MES. Moreover, transcription factor activity contextualized with causal inference analysis identified novel therapeutic targets with potential causal impact on transcription factor dysregulation promoting the malignant phenotype. FINDINGS: Fibroblast and macrophage phenotypes are of utmost importance for the complex intercellular crosstalk of MES. Specifically, tumour-associated macrophages were identified as the source of interleukin 1 beta (IL1B), a signalling molecule with significant impact on downstream transcription in tumour cells. Likewise, signalling molecules tumour necrosis factor (TNF), transforming growth factor beta (TGFB1), and C-X-C motif chemokine 12 (CXCL12) were prominent drivers of downstream gene expression associated with multiple cancer hallmarks. Furthermore, several consistently hyperactivated transcription factors were identified as potential sources for treatment opportunities. Finally, causal inference analysis identified Yes-associated protein 1 (YAP1) and Nuclear Receptor Subfamily 2 Group F Member 6 (NR2F6) as novel therapeutic targets in MES, verified in an independent dataset. INTERPRETATION: By utilizing a sophisticated bioinformatics approach, several candidates for treatment opportunities, including YAP1 and NR2F6 were identified. These candidates represent signalling regulators within the cellular network of the MES. Hence, further studies to confirm these candidates as potential targeted therapies in PM are warranted.

Live-attenuated vaccine sCPD9 elicits superior mucosal and systemic immunity to SARS-CoV-2 variants in hamsters.

Vaccines play a critical role in combating the COVID-19 pandemic. Future control of the pandemic requires improved vaccines with high efficacy against newly emerging SARS-CoV-2 variants and the ability to reduce virus transmission. Here we compare immune responses and preclinical efficacy of the mRNA vaccine BNT162b2, the adenovirus-vectored spike vaccine Ad2-spike and the live-attenuated virus vaccine candidate sCPD9 in Syrian hamsters, using both homogeneous and heterologous vaccination regimens. Comparative vaccine efficacy was assessed by employing readouts from virus titrations to single-cell RNA sequencing. Our results show that sCPD9 vaccination elicited the most robust immunity, including rapid viral clearance, reduced tissue damage, fast differentiation of pre-plasmablasts, strong systemic and mucosal humoral responses, and rapid recall of memory T cells from lung tissue after challenge with heterologous SARS-CoV-2. Overall, our results demonstrate that live-attenuated vaccines offer advantages over currently available COVID-19 vaccines.

Oncogenic signaling is coupled to colorectal cancer cell differentiation state.

Colorectal cancer progression is intrinsically linked to stepwise deregulation of the intestinal differentiation trajectory. In this process, sequential mutations of APC, KRAS, TP53, and SMAD4 enable oncogenic signaling and establish the hallmarks of cancer. Here, we use mass cytometry of isogenic human colon organoids and patient-derived cancer organoids to capture oncogenic signaling, cell phenotypes, and differentiation states in a high-dimensional single-cell map. We define a differentiation axis in all tumor progression states from normal to cancer. Our data show that colorectal cancer driver mutations shape the distribution of cells along the differentiation axis. In this regard, subsequent mutations can have stem cell promoting or restricting effects. Individual nodes of the cancer cell signaling network remain coupled to the differentiation state, regardless of the presence of driver mutations. We use single-cell RNA sequencing to link the (phospho-)protein signaling network to transcriptomic states with biological and clinical relevance. Our work highlights how oncogenes gradually shape signaling and transcriptomes during tumor progression.

Single-cell gene regulatory network prediction by explainable AI.

The molecular heterogeneity of cancer cells contributes to the often partial response to targeted therapies and relapse of disease due to the escape of resistant cell populations. While single-cell sequencing has started to improve our understanding of this heterogeneity, it offers a mostly descriptive view on cellular types and states. To obtain more functional insights, we propose scGeneRAI, an explainable deep learning approach that uses layer-wise relevance propagation (LRP) to infer gene regulatory networks from static single-cell RNA sequencing data for individual cells. We benchmark our method with synthetic data and apply it to single-cell RNA sequencing data of a cohort of human lung cancers. From the predicted single-cell networks our approach reveals characteristic network patterns for tumor cells and normal epithelial cells and identifies subnetworks that are observed only in (subgroups of) tumor cells of certain patients. While current state-of-the-art methods are limited by their ability to only predict average networks for cell populations, our approach facilitates the reconstruction of networks down to the level of single cells which can be utilized to characterize the heterogeneity of gene regulation within and across tumors.

On tumoural growth and treatment under cellular dedifferentiation.

Differentiated cancer cells may regain stem cell characteristics; however, the effects of such a cellular dedifferentiation on tumoural growth and treatment are currently understudied. Thus, we here extend a mathematical model of cancer stem cell (CSC) driven tumour growth to also include dedifferentiation. We show that dedifferentiation increases the likelihood of tumorigenesis and the speed of tumoural growth, both modulated by the proliferative potential of the non-stem cancer cells (NSCCs). We demonstrate that dedifferentiation also may lead to treatment evasion, especially when a treatment solely targets CSCs. Conversely, targeting both CSCs and NSCCs in parallel is shown to be more robust to dedifferentiation. Despite dedifferentiation, perturbing CSC-related parameters continues to exert the largest relative effect on tumoural growth; however, we show the existence of synergies between specific CSC- and NSCC-directed treatments which cause superadditive reductions of tumoural growth. Overall, our study demonstrates various effects of dedifferentiation on growth and treatment of tumoural lesions, and we anticipate our results to be helpful in guiding future molecular and clinical research on limiting tumoural growth in vivo.

Helicobacter pylori shows tropism to gastric differentiated pit cells dependent on urea chemotaxis.

The human gastric epithelium forms highly organized gland structures with different subtypes of cells. The carcinogenic bacterium Helicobacter pylori can attach to gastric cells and subsequently translocate its virulence factor CagA, but the possible host cell tropism of H. pylori is currently unknown. Here, we report that H. pylori preferentially attaches to differentiated cells in the pit region of gastric units. Single-cell RNA-seq shows that organoid-derived monolayers recapitulate the pit region, while organoids capture the gland region of the gastric units. Using these models, we show that H. pylori preferentially attaches to highly differentiated pit cells, marked by high levels of GKN1, GKN2 and PSCA. Directed differentiation of host cells enable enrichment of the target cell population and confirm H. pylori preferential attachment and CagA translocation into these cells. Attachment is independent of MUC5AC or PSCA expression, and instead relies on bacterial TlpB-dependent chemotaxis towards host cell-released urea, which scales with host cell size.

Identifying mutant-specific multi-drug combinations using comparative network reconstruction.

Targeted inhibition of aberrant signaling is an important treatment strategy in cancer, but responses are often short-lived. Multi-drug combinations have the potential to mitigate this, but to avoid toxicity such combinations must be selective and given at low dosages. Here, we present a pipeline to identify promising multi-drug combinations. We perturbed an isogenic PI3K mutant and wild-type cell line pair with a limited set of drugs and recorded their signaling state and cell viability. We then reconstructed their signaling networks and mapped the signaling response to changes in cell viability. The resulting models, which allowed us to predict the effect of unseen combinations, indicated that no combination selectively reduces the viability of the PI3K mutant cells. However, we were able to validate 25 of the 30 combinations that we predicted to be anti-selective. Our pipeline enables efficient prioritization of multi-drug combinations from the enormous search space of possible combinations.

Mutations in ALK signaling pathways conferring resistance to ALK inhibitor treatment lead to collateral vulnerabilities in neuroblastoma cells.

BACKGROUND: Development of resistance to targeted therapies has tempered initial optimism that precision oncology would improve poor outcomes for cancer patients. Resistance mechanisms, however, can also confer new resistance-specific vulnerabilities, termed collateral sensitivities. Here we investigated anaplastic lymphoma kinase (ALK) inhibitor resistance in neuroblastoma, a childhood cancer frequently affected by activating ALK alterations. METHODS: Genome-wide forward genetic CRISPR-Cas9 based screens were performed to identify genes associated with ALK inhibitor resistance in neuroblastoma cell lines. Furthermore, the neuroblastoma cell line NBLW-R was rendered resistant by continuous exposure to ALK inhibitors. Genes identified to be associated with ALK inhibitor resistance were further investigated by generating suitable cell line models. In addition, tumor and liquid biopsy samples of four patients with ALK-mutated neuroblastomas before ALK inhibitor treatment and during tumor progression under treatment were genomically profiled. RESULTS: Both genome-wide CRISPR-Cas9-based screens and preclinical spontaneous ALKi resistance models identified NF1 loss and activating NRASQ61K mutations to confer resistance to chemically diverse ALKi. Moreover, human neuroblastomas recurrently developed de novo loss of NF1 and activating RAS mutations after ALKi treatment, leading to therapy resistance. Pathway-specific perturbations confirmed that NF1 loss and activating RAS mutations lead to RAS-MAPK signaling even in the presence of ALKi. Intriguingly, NF1 loss rendered neuroblastoma cells hypersensitive to MEK inhibition. CONCLUSIONS: Our results provide a clinically relevant mechanistic model of ALKi resistance in neuroblastoma and highlight new clinically actionable collateral sensitivities in resistant cells.

Mathematical modelling identifies conditions for maintaining and escaping feedback control in the intestinal epithelium.

The intestinal epithelium is one of the fastest renewing tissues in mammals. It shows a hierarchical organisation, where intestinal stem cells at the base of crypts give rise to rapidly dividing transit amplifying cells that in turn renew the pool of short-lived differentiated cells. Upon injury and stem-cell loss, cells can also de-differentiate. Tissue homeostasis requires a tightly regulated balance of differentiation and stem cell proliferation, and failure can lead to tissue extinction or to unbounded growth and cancerous lesions. Here, we present a two-compartment mathematical model of intestinal epithelium population dynamics that includes a known feedback inhibition of stem cell differentiation by differentiated cells. The model shows that feedback regulation stabilises the number of differentiated cells as these become invariant to changes in their apoptosis rate. Stability of the system is largely independent of feedback strength and shape, but specific thresholds exist which if bypassed cause unbounded growth. When dedifferentiation is added to the model, we find that the system can recover faster after certain external perturbations. However, dedifferentiation makes the system more prone to losing homeostasis. Taken together, our mathematical model shows how a feedback-controlled hierarchical tissue can maintain homeostasis and can be robust to many external perturbations.

DNA methylation reveals distinct cells of origin for pancreatic neuroendocrine carcinomas and pancreatic neuroendocrine tumors.

BACKGROUND: Pancreatic neuroendocrine neoplasms (PanNENs) fall into two subclasses: the well-differentiated, low- to high-grade pancreatic neuroendocrine tumors (PanNETs), and the poorly-differentiated, high-grade pancreatic neuroendocrine carcinomas (PanNECs). While recent studies suggest an endocrine descent of PanNETs, the origin of PanNECs remains unknown. METHODS: We performed DNA methylation analysis for 57 PanNEN samples and found that distinct methylation profiles separated PanNENs into two major groups, clearly distinguishing high-grade PanNECs from other PanNETs including high-grade NETG3. DNA alterations and immunohistochemistry of cell-type markers PDX1, ARX, and SOX9 were utilized to further characterize PanNECs and their cell of origin in the pancreas. RESULTS: Phylo-epigenetic and cell-type signature features derived from alpha, beta, acinar, and ductal adult cells suggest an exocrine cell of origin for PanNECs, thus separating them in cell lineage from other PanNENs of endocrine origin. CONCLUSIONS: Our study provides a robust and clinically applicable method to clearly distinguish PanNECs from G3 PanNETs, improving patient stratification.

The single-cell transcriptional landscape of lung carcinoid tumors.

Lung carcinoid tumors, also referred to as pulmonary neuroendocrine tumors or lung carcinoids, are rare neoplasms of the lung with a more favorable prognosis than other subtypes of lung cancer. Still, some patients suffer from relapsed disease and metastatic spread. Several recent single-cell studies have provided detailed insights into the cellular heterogeneity of more common lung cancers, such as adeno- and squamous cell carcinoma. However, the characteristics of lung carcinoids on the single-cell level are yet completely unknown. To study the cellular composition and single-cell gene expression profiles in lung carcinoids, we applied single-cell RNA sequencing to three lung carcinoid tumor samples and normal lung tissue. The single-cell transcriptomes of carcinoid tumor cells reflected intertumoral heterogeneity associated with clinicopathological features, such as tumor necrosis and proliferation index. The immune microenvironment was specifically enriched in noninflammatory monocyte-derived myeloid cells. Tumor-associated endothelial cells were characterized by distinct gene expression profiles. A spectrum of vascular smooth muscle cells and pericytes predominated the stromal microenvironment. We found a small proportion of myofibroblasts exhibiting features reminiscent of cancer-associated fibroblasts. Stromal and immune cells exhibited potential paracrine interactions which may shape the microenvironment via NOTCH, VEGF, TGFß and JAK/STAT signaling. Moreover, single-cell gene signatures of pericytes and myofibroblasts demonstrated prognostic value in bulk gene expression data. Here, we provide first comprehensive insights into the cellular composition and single-cell gene expression profiles in lung carcinoids, demonstrating the noninflammatory and vessel-rich nature of their tumor microenvironment, and outlining relevant intercellular interactions which could serve as future therapeutic targets.

Early senescence and production of senescence-associated cytokines are major determinants of radioresistance in head-and-neck squamous cell carcinoma.

Resistance against radio(chemo)therapy-induced cell death is a major determinant of oncological treatment failure and remains a perpetual clinical challenge. The underlying mechanisms are manifold and demand for comprehensive, cancer entity- and subtype-specific examination. In the present study, resistance against radiotherapy was systematically assessed in a panel of human head-and-neck squamous cell carcinoma (HNSCC) cell lines and xenotransplants derived thereof with the overarching aim to extract master regulators and potential candidates for mechanism-based pharmacological targeting. Clonogenic survival data were integrated with molecular and functional data on DNA damage repair and different cell fate decisions. A positive correlation between radioresistance and early induction of HNSCC cell senescence accompanied by NF-κB-dependent production of distinct senescence-associated cytokines, particularly ligands of the CXCR2 chemokine receptor, was identified. Time-lapse microscopy and medium transfer experiments disclosed the non-cell autonomous, paracrine nature of these mechanisms, and pharmacological interference with senescence-associated cytokine production by the NF-κB inhibitor metformin significantly improved radiotherapeutic performance in vitro and in vivo. With regard to clinical relevance, retrospective analyses of TCGA HNSCC data and an in-house HNSCC cohort revealed that elevated expression of CXCR2 and/or its ligands are associated with impaired treatment outcome. Collectively, our study identifies radiation-induced tumor cell senescence and the NF-κB-dependent production of distinct senescence-associated cytokines as critical drivers of radioresistance in HNSCC whose therapeutic targeting in the context of multi-modality treatment approaches should be further examined and may be of particular interest for the subgroup of patients with elevated expression of the CXCR2/ligand axis.

Neuroblastoma signalling models unveil combination therapies targeting feedback-mediated resistance.

Very high risk neuroblastoma is characterised by increased MAPK signalling, and targeting MAPK signalling is a promising therapeutic strategy. We used a deeply characterised panel of neuroblastoma cell lines and found that the sensitivity to MEK inhibitors varied drastically between these cell lines. By generating quantitative perturbation data and mathematical modelling, we determined potential resistance mechanisms. We found that negative feedbacks within MAPK signalling and via the IGF receptor mediate re-activation of MAPK signalling upon treatment in resistant cell lines. By using cell-line specific models, we predict that combinations of MEK inhibitors with RAF or IGFR inhibitors can overcome resistance, and tested these predictions experimentally. In addition, phospho-proteomic profiling confirmed the cell-specific feedback effects and synergy of MEK and IGFR targeted treatment. Our study shows that a quantitative understanding of signalling and feedback mechanisms facilitated by models can help to develop and optimise therapeutic strategies. Our findings should be considered for the planning of future clinical trials introducing MEKi in the treatment of neuroblastoma.

Single-cell RNA sequencing reveals distinct tumor microenvironmental patterns in lung adenocarcinoma.

Recent developments in immuno-oncology demonstrate that not only cancer cells, but also the tumor microenvironment can guide precision medicine. A comprehensive and in-depth characterization of the tumor microenvironment is challenging since its cell populations are diverse and can be important even if scarce. To identify clinically relevant microenvironmental and cancer features, we applied single-cell RNA sequencing to ten human lung adenocarcinomas and ten normal control tissues. Our analyses revealed heterogeneous carcinoma cell transcriptomes reflecting histological grade and oncogenic pathway activities, and two distinct microenvironmental patterns. The immune-activated CP²E microenvironment was composed of cancer-associated myofibroblasts, proinflammatory monocyte-derived macrophages, plasmacytoid dendritic cells and exhausted CD8+ T cells, and was prognostically unfavorable. In contrast, the inert N³MC microenvironment was characterized by normal-like myofibroblasts, non-inflammatory monocyte-derived macrophages, NK cells, myeloid dendritic cells and conventional T cells, and was associated with a favorable prognosis. Microenvironmental marker genes and signatures identified in single-cell profiles had progonostic value in bulk tumor profiles. In summary, single-cell RNA profiling of lung adenocarcinoma provides additional prognostic information based on the microenvironment, and may help to predict therapy response and to reveal possible target cell populations for future therapeutic approaches.

Early IFN-α signatures and persistent dysfunction are distinguishing features of NK cells in severe COVID-19.

Longitudinal analyses of the innate immune system, including the earliest time points, are essential to understand the immunopathogenesis and clinical course of coronavirus disease (COVID-19). Here, we performed a detailed characterization of natural killer (NK) cells in 205 patients (403 samples; days 2 to 41 after symptom onset) from four independent cohorts using single-cell transcriptomics and proteomics together with functional studies. We found elevated interferon (IFN)-α plasma levels in early severe COVD-19 alongside increased NK cell expression of IFN-stimulated genes (ISGs) and genes involved in IFN-α signaling, while upregulation of tumor necrosis factor (TNF)-induced genes was observed in moderate diseases. NK cells exert anti-SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) activity but are functionally impaired in severe COVID-19. Further, NK cell dysfunction may be relevant for the development of fibrotic lung disease in severe COVID-19, as NK cells exhibited impaired anti-fibrotic activity. Our study indicates preferential IFN-α and TNF responses in severe and moderate COVID-19, respectively, and associates a prolonged IFN-α-induced NK cell response with poorer disease outcome.

Mitogen-activated protein kinase activity drives cell trajectories in colorectal cancer.

In colorectal cancer, oncogenic mutations transform a hierarchically organized and homeostatic epithelium into invasive cancer tissue lacking visible organization. We sought to define transcriptional states of colorectal cancer cells and signals controlling their development by performing single-cell transcriptome analysis of tumors and matched non-cancerous tissues of twelve colorectal cancer patients. We defined patient-overarching colorectal cancer cell clusters characterized by differential activities of oncogenic signaling pathways such as mitogen-activated protein kinase and oncogenic traits such as replication stress. RNA metabolic labeling and assessment of RNA velocity in patient-derived organoids revealed developmental trajectories of colorectal cancer cells organized along a mitogen-activated protein kinase activity gradient. This was in contrast to normal colon organoid cells developing along graded Wnt activity. Experimental targeting of EGFR-BRAF-MEK in cancer organoids affected signaling and gene expression contingent on predictive KRAS/BRAF mutations and induced cell plasticity overriding default developmental trajectories. Our results highlight directional cancer cell development as a driver of non-genetic cancer cell heterogeneity and re-routing of trajectories as a response to targeted therapy.

Classification of Molecular Subtypes of High-Grade Serous Ovarian Cancer by MALDI-Imaging.

Despite the correlation of clinical outcome and molecular subtypes of high-grade serous ovarian cancer (HGSOC), contemporary gene expression signatures have not been implemented in clinical practice to stratify patients for targeted therapy. Hence, we aimed to examine the potential of unsupervised matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) to stratify patients who might benefit from targeted therapeutic strategies. Molecular subtyping of paraffin-embedded tissue samples from 279 HGSOC patients was performed by NanoString analysis (ground truth labeling). Next, we applied MALDI-IMS paired with machine-learning algorithms to identify distinct mass profiles on the same paraffin-embedded tissue sections and distinguish HGSOC subtypes by proteomic signature. Finally, we devised a novel approach to annotate spectra of stromal origin. We elucidated a MALDI-derived proteomic signature (135 peptides) able to classify HGSOC subtypes. Random forest classifiers achieved an area under the curve (AUC) of 0.983. Furthermore, we demonstrated that the exclusion of stroma-associated spectra provides tangible improvements to classification quality (AUC = 0.988). Moreover, novel MALDI-based stroma annotation achieved near-perfect classifications (AUC = 0.999). Here, we present a concept integrating MALDI-IMS with machine-learning algorithms to classify patients according to distinct molecular subtypes of HGSOC. This has great potential to assign patients for personalized treatment.