Applications of spatial transcriptomics and artificial intelligence to develop integrated management of pancreatic cancer.

Cancer is a complex disease intrinsically associated with cellular processes and gene expression. With the development of techniques such as single-cell sequencing and sequential fluorescence in situ hybridization (seqFISH), it was possible to map the location of cells based on their gene expression with more precision. Moreover, in recent years, many tools have been developed to analyze these extensive datasets by integrating machine learning and artificial intelligence in a comprehensive manner. Since these tools analyze sequencing data, they offer the chance to analyze any tissue regardless of its origin. By applying this to cancer settings, spatial transcriptomic analysis based on artificial intelligence may help us understand cell-cell communications within the tumor microenvironment. Another advantage of this analysis is the identification of new biomarkers and therapeutic targets. The integration of such analysis with other omics data and with routine exams such as magnetic resonance imaging can help physicians with the earlier diagnosis of tumors as well as establish a more personalized treatment for pancreatic cancer patients. In this review, we give an overview description of pancreatic cancer, describe how spatial transcriptomics and artificial intelligence have been used to study pancreatic cancer and provide examples of how integrating these tools may help physicians manage pancreatic cancer in a more personalized approach.

CellNeighborEX: deciphering neighbor-dependent gene expression from spatial transcriptomics data.

Cells have evolved their communication methods to sense their microenvironments and send biological signals. In addition to communication using ligands and receptors, cells use diverse channels including gap junctions to communicate with their immediate neighbors. Current approaches, however, cannot effectively capture the influence of various microenvironments. Here, we propose a novel approach to investigate cell neighbor-dependent gene expression (CellNeighborEX) in spatial transcriptomics (ST) data. To categorize cells based on their microenvironment, CellNeighborEX uses direct cell location or the mixture of transcriptome from multiple cells depending on ST technologies. For each cell type, CellNeighborEX identifies diverse gene sets associated with partnering cell types, providing further insight. We found that cells express different genes depending on their neighboring cell types in various tissues including mouse embryos, brain, and liver cancer. Those genes are associated with critical biological processes such as development or metastases. We further validated that gene expression is induced by neighboring partners via spatial visualization. The neighbor-dependent gene expression suggests new potential genes involved in cell-cell interactions beyond what ligand-receptor co-expression can discover.

Tumor heterogeneity: An oncogenic driver of PDAC progression and therapy resistance under stress conditions.

Pancreatic ductal adenocarcinoma (PDAC) is a clinically challenging disease usually diagnosed at advanced or metastasized stage. By this year end, there are an expected increase in 62,210 new cases and 49,830 deaths in the United States, with 90% corresponding to PDAC subtype alone. Despite advances in cancer therapy, one of the major challenges combating PDAC remains tumor heterogeneity between PDAC patients and within the primary and metastatic lesions of the same patient. This review describes the PDAC subtypes based on the genomic, transcriptional, epigenetic, and metabolic signatures observed among patients and within individual tumors. Recent studies in tumor biology suggest PDAC heterogeneity as a major driver of disease progression under conditions of stress including hypoxia and nutrient deprivation, leading to metabolic reprogramming. We therefore advance our understanding in identifying the underlying mechanisms that interfere with the crosstalk between the extracellular matrix components and tumor cells that define the mechanics of tumor growth and metastasis. The bilateral interaction between the heterogeneous tumor microenvironment and PDAC cells serves as another important contributor that characterizes the tumor-promoting or tumor-suppressing phenotypes providing an opportunity for an effective treatment regime. Furthermore, we highlight the dynamic reciprocating interplay between the stromal and immune cells that impact immune surveillance or immune evasion response and contribute towards a complex process of tumorigenesis. In summary, the review encapsulates the existing knowledge of the currently applied treatments for PDAC with emphasis on tumor heterogeneity, manifesting at multiple levels, impacting disease progression and therapy resistance under stress.

Tumor microenvironment interactions with cancer stem cells in pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer in the United States. Additionally, the low survival rate makes PDAC the third-leading cause of cancer-related mortality in the United States, and it is projected that by 2030, it will become the second-leading cause of cancer mortality. Several biological factors contribute to PDAC aggressiveness, and their understanding will narrow the gap from biology to clinical care of PDAC, leading to earlier diagnoses and the development of better treatment options. In this review, we describe the origins of PDAC highlighting the role of cancer stem cells (CSC). CSC, also known as tumor initiating cells, which exhibit a unique metabolism that allows them to maintain a highly plastic, quiescent, immune- and therapy-evasive state. However, CSCs can exit quiescence during proliferation and differentiation, with the capacity to form tumors while constituting a small population in tumor tissues. Tumorigenesis depends on the interactions between CSCs and other cellular and non-cellular components in the microenvironment. These interactions are fundamental to support CSC stemness and are maintained throughout tumor development and metastasis. PDAC is characterized by a massive desmoplastic reaction, which result from the deposition of high amounts of extracellular matrix components by stromal cells. Here we review how this generates a favorable environment for tumor growth by protecting tumor cells from immune responses and chemotherapy and inducing tumor cell proliferation and migration, leading to metastasis formation ultimately leading to death. We emphasize the interactions between CSCs and the tumor microenvironment leading to metastasis formation and posit that better understanding and targeting of these interactions will improve patient outcomes.

Neighbor-specific gene expression revealed from physically interacting cells during mouse embryonic development.

Development of multicellular organisms is orchestrated by persistent cell-cell communication between neighboring partners. Direct interaction between different cell types can induce molecular signals that dictate lineage specification and cell fate decisions. Current single-cell RNA-seq technology cannot adequately analyze cell-cell contact-dependent gene expression, mainly due to the loss of spatial information. To overcome this obstacle and resolve cell-cell contact-specific gene expression during embryogenesis, we performed RNA sequencing of physically interacting cells (PIC-seq) and assessed them alongside similar single-cell transcriptomes derived from developing mouse embryos between embryonic day (E) 7.5 and E9.5. Analysis of the PIC-seq data identified gene expression signatures that were dependent on the presence of specific neighboring cell types. Our computational predictions, validated experimentally, demonstrated that neural progenitor (NP) cells upregulate Lhx5 and Nkx2-1 genes, when exclusively interacting with definitive endoderm (DE) cells. Moreover, there was a reciprocal impact on the transcriptome of DE cells, as they tend to upregulate Rax and Gsc when in contact with NP cells. Using individual cell transcriptome data, we formulated a means of computationally predicting the impact of one cell type on the transcriptome of its neighboring cell types. We have further developed a distinctive spatial-t-distributed stochastic neighboring embedding to display the pseudospatial distribution of cells in a 2-dimensional space. In summary, we describe an innovative approach to study contact-specific gene regulation during embryogenesis.

Cell Competition in Carcinogenesis.

The majority of human cancers evolve over time through the stepwise accumulation of somatic mutations followed by clonal selection akin to Darwinian evolution. However, the in-depth mechanisms that govern clonal dynamics and selection remain elusive, particularly during the earliest stages of tissue transformation. Cell competition (CC), often referred to as 'survival of the fittest' at the cellular level, results in the elimination of less fit cells by their more fit neighbors supporting optimal organism health and function. Alternatively, CC may allow an uncontrolled expansion of super-fit cancer cells to outcompete their less fit neighbors thereby fueling tumorigenesis. Recent research discussed herein highlights the various non-cell-autonomous principles, including interclonal competition and cancer microenvironment competition supporting the ability of a tumor to progress from the initial stages to tissue colonization. In addition, we extend current insights from CC-mediated clonal interactions and selection in normal tissues to better comprehend those factors that contribute to cancer development.

Flower lose, a cell fitness marker, predicts COVID-19 prognosis.

Risk stratification of COVID-19 patients is essential for pandemic management. Changes in the cell fitness marker, hFwe-Lose, can precede the host immune response to infection, potentially making such a biomarker an earlier triage tool. Here, we evaluate whether hFwe-Lose gene expression can outperform conventional methods in predicting outcomes (e.g., death and hospitalization) in COVID-19 patients. We performed a post-mortem examination of infected lung tissue in deceased COVID-19 patients to determine hFwe-Lose's biological role in acute lung injury. We then performed an observational study (n = 283) to evaluate whether hFwe-Lose expression (in nasopharyngeal samples) could accurately predict hospitalization or death in COVID-19 patients. In COVID-19 patients with acute lung injury, hFwe-Lose is highly expressed in the lower respiratory tract and is co-localized to areas of cell death. In patients presenting in the early phase of COVID-19 illness, hFwe-Lose expression accurately predicts subsequent hospitalization or death with positive predictive values of 87.8-100% and a negative predictive value of 64.1-93.2%. hFwe-Lose outperforms conventional inflammatory biomarkers and patient age and comorbidities, with an area under the receiver operating characteristic curve (AUROC) 0.93-0.97 in predicting hospitalization/death. Specifically, this is significantly higher than the prognostic value of combining biomarkers (serum ferritin, D-dimer, C-reactive protein, and neutrophil-lymphocyte ratio), patient age and comorbidities (AUROC of 0.67-0.92). The cell fitness marker, hFwe-Lose, accurately predicts outcomes in COVID-19 patients. This finding demonstrates how tissue fitness pathways dictate the response to infection and disease and their utility in managing the current COVID-19 pandemic.

Cell competition in intratumoral and tumor microenvironment interactions.

Tumors are complex cellular and acellular environments within which cancer clones are under continuous selection pressures. Cancer cells are in a permanent mode of interaction and competition with each other as well as with the immediate microenvironment. In the course of these competitive interactions, cells share information regarding their general state of fitness, with less-fit cells being typically eliminated via apoptosis at the hands of those cells with greater cellular fitness. Competitive interactions involving exchange of cell fitness information have implications for tumor growth, metastasis, and therapy outcomes. Recent research has highlighted sophisticated pathways such as Flower, Hippo, Myc, and p53 signaling, which are employed by cancer cells and the surrounding microenvironment cells to achieve their evolutionary goals by means of cell competition mechanisms. In this review, we discuss these recent findings and explain their importance and role in evolution, growth, and treatment of cancer. We further consider potential physiological conditions, such as hypoxia and chemotherapy, that can function as selective pressures under which cell competition mechanisms may evolve differently or synergistically to confer oncogenic advantages to cancer.

SPARC-p53: The double agents of cancer.

Cancer is a complex disease with high incidence and mortality rates. The important role played by the tumor microenvironment in regulating oncogenesis, tumor growth, and metastasis is by now well accepted in the scientific community. SPARC is known to participate in tumor-stromal interactions and impact cancer growth in ambiguous ways, which either enhance or suppress cancer aggressiveness, in a context-dependent manner. p53 transcription factor, a well-established tumor suppressor, has been reported to promote tumor growth in certain situations, such as hypoxia, thus displaying a duality in its action. Although both proteins are being tested in clinical trials, the synergistic relation between them is yet to be explored in clinical practice. In this review, we address the controversial roles of SPARC and p53 as double agents in cancer, briefly summarizing the interaction found between these two molecules and its importance in cancer.

HIF-transcribed p53 chaperones HIF-1α.

Chronic hypoxia is associated with a variety of physiological conditions such as rheumatoid arthritis, ischemia/reperfusion injury, stroke, diabetic vasculopathy, epilepsy and cancer. At the molecular level, hypoxia manifests its effects via activation of HIF-dependent transcription. On the other hand, an important transcription factor p53, which controls a myriad of biological functions, is rendered transcriptionally inactive under hypoxic conditions. p53 and HIF-1α are known to share a mysterious relationship and play an ambiguous role in the regulation of hypoxia-induced cellular changes. Here we demonstrate a novel pathway where HIF-1α transcriptionally upregulates both WT and MT p53 by binding to five response elements in p53 promoter. In hypoxic cells, this HIF-1α-induced p53 is transcriptionally inefficient but is abundantly available for protein-protein interactions. Further, both WT and MT p53 proteins bind and chaperone HIF-1α to stabilize its binding at its downstream DNA response elements. This p53-induced chaperoning of HIF-1α increases synthesis of HIF-regulated genes and thus the efficiency of hypoxia-induced molecular changes. This basic biology finding has important implications not only in the design of anti-cancer strategies but also for other physiological conditions where hypoxia results in disease manifestation.

Flower isoforms promote competitive growth in cancer.

In humans, the adaptive immune system uses the exchange of information between cells to detect and eliminate foreign or damaged cells; however, the removal of unwanted cells does not always require an adaptive immune system1,2. For example, cell selection in Drosophila uses a cell selection mechanism based on 'fitness fingerprints', which allow it to delay ageing3, prevent developmental malformations3,4 and replace old tissues during regeneration5. At the molecular level, these fitness fingerprints consist of combinations of Flower membrane proteins3,4,6. Proteins that indicate reduced fitness are called Flower-Lose, because they are expressed in cells marked to be eliminated6. However, the presence of Flower-Lose isoforms at a cell's membrane does not always lead to elimination, because if neighbouring cells have similar levels of Lose proteins, the cell will not be killed4,6,7. Humans could benefit from the capability to recognize unfit cells, because accumulation of damaged but viable cells during development and ageing causes organ dysfunction and disease8-17. However, in Drosophila this mechanism is hijacked by premalignant cells to gain a competitive growth advantage18. This would be undesirable for humans because it might make tumours more aggressive19-21. It is unknown whether a similar mechanism of cell-fitness comparison is present in humans. Here we show that two human Flower isoforms (hFWE1 and hFWE3) behave as Flower-Lose proteins, whereas the other two isoforms (hFWE2 and hFWE4) behave as Flower-Win proteins. The latter give cells a competitive advantage over cells expressing Lose isoforms, but Lose-expressing cells are not eliminated if their neighbours express similar levels of Lose isoforms; these proteins therefore act as fitness fingerprints. Moreover, human cancer cells show increased Win isoform expression and proliferate in the presence of Lose-expressing stroma, which confers a competitive growth advantage on the cancer cells. Inhibition of the expression of Flower proteins reduces tumour growth and metastasis, and induces sensitivity to chemotherapy. Our results show that ancient mechanisms of cell recognition and selection are active in humans and affect oncogenic growth.