Functional Genomic Complexity Defines Intratumor Heterogeneity and Tumor Aggressiveness in Liver Cancer

The landscape of etiological patterns of hepatocellular carcinoma and intrahepatic cholangiocarcinoma in Thailand

Thailand is among countries with the highest global incidence and mortality rates of hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (iCCA). While viral hepatitis and liver fluke infections have been associated with HCC and iCCA, respectively, other environmental risk factors, overall risk factor commonality and combinatorial roles, and effects on survival have not been systematically examined. We conducted a TIGER-LC consortium-based population study covering all high-incidence areas of both malignancies across Thailand: 837 HCC, 1474 iCCA, and 1112 controls (2011-2019) were comprehensively queried on lifelong environmental exposures, lifestyle, and medical history. Multivariate logistic regression and Cox proportional hazards analyses were used to evaluate risk factors and associated survival patterns. Our models identified shared risk factors between HCC and iCCA, such as viral hepatitis infection, liver fluke infection, and diabetes, including novel and shared associations of agricultural pesticide exposure (OR range of 1.50; 95% CI: 1.06-2.11 to 2.91; 95% CI: 1.82-4.63) along with vulnerable sources of drinking water. Most patients had multiple risk factors, magnifying their risk considerably. Patients with lower risk levels had better survival in both HCC (HR 0.78; 95% CI: 0.64-0.96) and iCCA (HR 0.84; 95% CI: 0.70-0.99). Risk factor co-exposures and their common associations with HCC and iCCA in Thailand emphasize the importance for future prevention and control measures, especially in its large agricultural sector. The observed mortality patterns suggest ways to stratify patients for anticipated survivorship and develop plans to support medical care of longer-term survivors, including behavioral changes to reduce exposures.

Dissecting the tumor ecosystem of liver cancers in the single-cell era

Primary liver cancers (PLCs) are a broad class of malignancies that include HCC, intrahepatic cholangiocarcinoma, and combined hepatocellular and intrahepatic cholangiocarcinoma. PLCs are often associated with a poor prognosis due to their high relapse and low therapeutic response rates. Importantly, PLCs exist within a dynamic and complex tumor ecosystem, which includes malignant, immune, and stromal cells. It is critical to dissect the PLC tumor ecosystem to uncover the underlying mechanisms associated with tumorigenesis, relapse, and treatment resistance to facilitate the discovery of novel therapeutic targets. Single-cell and spatial multi-omics sequencing techniques offer an unprecedented opportunity to elucidate spatiotemporal interactions among heterogeneous cell types within the complex tumor ecosystem. In this review, we describe the latest advances in single-cell and spatial technologies and review their applications with respect to dissecting liver cancer tumor ecosystems.

Establish an assessment model to characterized metastasis ability Single-cell multiomics reveals heterogeneous cell states linked to metastatic potential in liver cancer cell lines

Hepatocellular carcinoma (HCC) is the most common liver cancer with a high rate of metastasis. However, the molecular mechanisms that drive metastasis remain unclear. We combined single-cell transcriptomic, proteomic, and chromatin accessibility data to investigate how heterogeneous phenotypes contribute to metastatic potential in five HCC cell lines. We confirmed that the prevalence of a mesenchymal state and levels of cell proliferation are linked to the metastatic potential. We also identified a rare hypoxic subtype that has a higher capacity for glycolysis and exhibits dormant, invasive, and malignant characteristics. This subtype has increased metastatic potential. We further identified a robust 14-gene panel representing this hypoxia signature and this hypoxia signature could serve as a prognostic index. Our data provide a valuable data resource, facilitate a deeper understanding of metastatic mechanisms, and may help diagnosis of metastatic potential in individual patients, thus supporting personalized medicine.

•

Provide a high-resolution single-cell triple-omics data of five liver cancer cell lines

•

Identify a robust 14-gene set representing hypoxia signature

•

The hypoxia signature is associated with prognosis

•

Establish an assessment model to characterized metastasis ability

Single-cell atlas of tumor cell evolution in response to therapy in hepatocellular carcinoma and intrahepatic cholangiocarcinoma

BACKGROUND & AIMS

Intratumor molecular heterogeneity is a key feature of tumorigenesis and is linked to treatment failure and patient prognosis. Herein, we aimed to determine what drives tumor cell evolution by performing single-cell transcriptomic analysis.

METHODS

We analyzed 46 hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (iCCA) biopsies from 37 patients enrolled in interventional studies at the NIH Clinical Center, with 16 biopsies collected before and after treatment from 7 patients. We developed a novel machine learning-based consensus clustering approach to track cellular states of 57,000 malignant and non-malignant cells including tumor cell transcriptome-based functional clonality analysis. We determined tumor cell relationships using RNA velocity and reverse graph embedding. We also studied longitudinal samples from 4 patients to determine tumor cellular state and its evolution. We validated our findings in bulk transcriptomic data from 488 patients with HCC and 277 patients with iCCA.

RESULTS

Using transcriptomic clusters as a surrogate for functional clonality, we observed an increase in tumor cell state heterogeneity which was tightly linked to patient prognosis. Furthermore, increased functional clonality was accompanied by a polarized immune cell landscape which included an increase in pre-exhausted T cells. We found that SPP1 expression was tightly associated with tumor cell evolution and microenvironmental reprogramming. Finally, we developed a user-friendly online interface as a knowledge base for a single-cell atlas of liver cancer.

CONCLUSIONS

Our study offers insight into the collective behavior of tumor cell communities in liver cancer as well as potential drivers of tumor evolution in response to therapy.

LAY SUMMARY

Intratumor molecular heterogeneity is a key feature of tumorigenesis that is linked to treatment failure and patient prognosis. In this study, we present a single-cell atlas of liver tumors from patients treated with immunotherapy and describe intratumoral cell states and their hierarchical relationship. We suggest osteopontin, encoded by the gene SPP1, as a candidate regulator of tumor evolution in response to treatment.

Intratumor Heterogeneity in Hepatocellular Carcinoma: Challenges and Opportunities

Hepatocellular carcinoma (HCC) represents a leading cause of cancer-related death, but it remains difficult to treat. Intratumor genetic and phenotypic heterogeneity are inherent properties of breast, skin, lung, prostate, and brain tumors, and intratumor heterogeneity (ITH) helps define prognosis and therapeutic response in these cancers. Several recent studies estimate that ITH is inherent to HCC and attribute the clinical intractability of HCC to this heterogeneity. In this review, we examine the evidence for genomic, phenotypic, and tumor microenvironment ITH in HCC, with a focus on two of the top molecular drivers of HCC: β-catenin (CTNNB1) and Telomerase reverse transcriptase (TERT). We discuss the influence of ITH on HCC diagnosis, prognosis, and therapy, while highlighting the gaps in knowledge and possible future directions.

Studying the Role of Chromosomal Instability (CIN) in GI Cancers Using Patient-derived Organoids

Chromosomal instability (CIN) is associated with the initiation and progression of gastrointestinal (GI) tract cancers. Cancers of the GI tract are typically characterized by altered chromosome numbers. While the dynamics of CIN have been extensively characterized in 2D monolayer cell cultures derived from GI tumors, the tumor microenvironment and 3D tumor architecture also contribute to the progression of CIN, which is not captured in 2D cell culture systems. To overcome these limitations, self-organizing cellular structures that retain organ-specific 3D architecture, namely organoids, have been derived from various tissues of the GI tract. Organoids derived from normal tissue and patient tumors serve as a useful paradigm to study the crosstalk between tumor cells in the context of a tissue microenvironment and its impact on chromosomal stability. Such a paradigm, therefore, has a considerable advantage over 2D cell culture systems in drug screening and personalized medicine. Here, we review the importance of patient-derived tumor organoids (PDTOs) as a model to study CIN in cancers of the GI tract.

Clinical and immunological characteristics of sarcomas patients with clonogenic tumors

Tumorigenesis is related to the generation of heterogeneous tumor cell population, which is the result of genetic and epigenetic alterations followed by clonal selections and subsequent expansion. In basic studies genetic, histological and morphological diversity of different clones within a patient's neoplasm and specifics of their interrelation with patient's immune system are investigated mostly on the models of tumors of epithelial origin. Mesenchymal tumors such as soft tissue and bone-derived sarcomas (STBS) have been poorly studied in this regard. The molecular genetic methods used to examine intratumoral heterogeneity do not currently provide insight into which portion of the identified subclones are able to grow autonomously. Limiting dilution cloning demonstrates the existence of self-regulating tumor cells in the population and can serve as an independent prognostic predictor of poor prognosis. Intratumoral heterogeneity results not only in differences in growth dynamics, gene expression, and phenotypic markers, but also in the resistance to treatment, especially immunotherapy, thus causing tumor eluding immune escape. The changes that accompany this process can be affected by the cellular immune system, resulting in an imbalance between populations. The variations in the population composition of immune system cells are now widely debated as a predictor of response to immunotherapy, which is of obvious interest for sarcomas, where the effectiveness of chemotherapy is low and the prognosis is unfavorable, especially in case of metastatic disease development. The search for new predictive markers of disease prognosis and treatment efficacy is an important task, to which this study is focused. Our results demonstrate that clonogenic tumor characteristics such as clonogenic potential is independent predictor of unfavorable prognosis in cases of cancer and correlate with the clinical characteristics of the tumor such as overall survival (OS) and progression free survival (PFS). It was found that patients with clonogenic sarcomas had a lower content of activated cytotoxic T-lymphocytes (CTL) with the CD3⁺CD8⁺HLA^-DR⁺ phenotype and an increased number of natural NK killers (p < 0.05) compared to nonclonogenic tumors. In addition, according to our data, a high neutrophil to lymphocyte ratio (NLR), a low value of major T-lymphocyte populations, and a higher number of natural killer cells (NK) in the blood can be negative prognostic factors for the immunotherapy of this disease.

Precision Oncology Beyond Genomics: The Future Is Here-It Is Just Not Evenly Distributed

Cancer is a multifactorial disease with increasing incidence. There are more than 100 different cancer types, defined by location, cell of origin, and genomic alterations that influence oncogenesis and therapeutic response. This heterogeneity between tumors of different patients and also the heterogeneity within the same patient's tumor pose an enormous challenge to cancer treatment. In this review, we explore tumor heterogeneity on the longitudinal and the latitudinal axis, reviewing current and future approaches to study this heterogeneity and their potential to support oncologists in tailoring a patient's treatment regimen. We highlight how the ideal of precision oncology is reaching far beyond the knowledge of genetic variants to inform clinical practice and discuss the technologies and strategies already available to improve our understanding and management of heterogeneity in cancer treatment. We will focus on integrating multi-omics technologies with suitable in vitro models and their proficiency in mimicking endogenous tumor heterogeneity.

Understanding tumour cell heterogeneity and its implication for immunotherapy in liver cancer using single-cell analysis

Over the last decade, precision medicine and immunotherapeutic approaches have become increasingly popular in oncology. Early clinical trials reported promising results, but response rates in phase III clinical trials have been suboptimal. Knowledge gained from subsequent translational studies indicates the importance of targeting the tumour microenvironment to overcome resistance to immunotherapy. In this era of precision medicine, it is crucial to consider inter- as well as intratumoural heterogeneity. Single-cell analysis is a cutting-edge technology that enables us to better define the tumour cell community and to identify potential targets for immunotherapy or combination treatments. This review focuses on single-cell analysis in the context of immunotherapy in liver cancer, including the rationale behind studying hepatocellular carcinoma biology at a single-cell level. Single-cell technologies have the potential to revolutionise our understanding of resistance mechanisms and to guide drug discovery efforts, leading to further advances in personalised medicine.