Genomic evolution of cancer models: perils and opportunities

Establishment and characterization of NCC-SS6-C1: a novel patient-derived cell line of synovial sarcoma

Synovial sarcoma (SS) is identified as a sarcoma with monomorphic blue spindle cells that display variable epithelial differentiation and is characterized by the SS18::SSX fusion gene. SS accounts for approximately 5-10% of all soft tissue sarcomas, making it a relatively common type within this group of tumors. Since SS is generally sensitive to chemotherapy, the standard treatment for SS includes extensive surgical resection, complemented by neoadjuvant chemotherapy with several approved anticancer drugs. However, in advanced and metastatic cases, the efficacy of these drugs is limited, resulting in poor prognoses. This underscores the need for innovative therapeutic strategies. Patient-derived cancer cell lines are essential tools for basic and preclinical research, yet only four SS cell lines are publicly available. To facilitate the studies of SS, we have developed a novel SS cell line, named NCC-SS6-C1, derived from surgically excised tumor tissue of an SS patient. NCC-SS6-C1 cells preserve the SS18::SSX1 fusion gene, consistent with the genetic characteristics of the original tumor. The cells exhibit continuous proliferation, invasiveness, and the ability to form spheroids. Additionally, we confirmed that this cell line was useful for evaluating the efficacy of anticancer drugs. Our results suggest that NCC-SS6-C1 is a useful tool for basic and pre-clinical studies of SS.

Aneuploidy as a driver of human cancer

Aneuploidy, an abnormal chromosome composition, is a major contributor to cancer development and progression and an important determinant of cancer therapeutic responses and clinical outcomes. Despite being recognized as a hallmark of human cancer, the exact role of aneuploidy as a 'driver' of cancer is still largely unknown. Identifying the specific genetic elements that underlie the recurrence of common aneuploidies remains a major challenge of cancer genetics. In this Review, we discuss recurrent aneuploidies and their function as drivers of tumor development. We then delve into the context-dependent identification and functional characterization of the driver genes underlying driver aneuploidies and examine emerging strategies to uncover these driver genes using cancer genomics data and cancer models. Lastly, we explore opportunities for targeting driver aneuploidies in cancer by leveraging the functional consequences of these common genetic alterations.

Integrating frontiers: a holistic, quantum and evolutionary approach to conquering cancer through systems biology and multidisciplinary synergy

Cancer therapy is facing increasingly significant challenges, marked by a wide range of techniques and research efforts centered around somatic mutations, precision oncology, and the vast amount of big data. Despite this abundance of information, the quest to cure cancer often seems more elusive, with the "war on cancer" yet to deliver a definitive victory. A particularly pressing issue is the development of tumor treatment resistance, highlighting the urgent need for innovative approaches. Evolutionary, Quantum Biology and System Biology offer a promising framework for advancing experimental cancer research. By integrating theoretical studies, translational methods, and flexible multidisciplinary clinical research, there's potential to enhance current treatment strategies and improve outcomes for cancer patients. Establishing stronger links between evolutionary, quantum, entropy and chaos principles and oncology could lead to more effective treatments that leverage an understanding of the tumor's evolutionary dynamics, paving the way for novel methods to control and mitigate cancer. Achieving these objectives necessitates a commitment to multidisciplinary and interprofessional collaboration at the heart of both research and clinical endeavors in oncology. This entails dismantling silos between disciplines, encouraging open communication and data sharing, and integrating diverse viewpoints and expertise from the outset of research projects. Being receptive to new scientific discoveries and responsive to how patients react to treatments is also crucial. Such strategies are key to keeping the field of oncology at the forefront of effective cancer management, ensuring patients receive the most personalized and effective care. Ultimately, this approach aims to push the boundaries of cancer understanding, treating it as a manageable chronic condition, aiming to extend life expectancy and enhance patient quality of life.

Understanding the interplay between dNTP metabolism and genome stability in cancer

The size and composition of the intracellular DNA precursor pool is integral to the maintenance of genome stability, and this relationship is fundamental to our understanding of cancer. Key aspects of carcinogenesis, including elevated mutation rates and induction of certain types of DNA damage in cancer cells, can be linked to disturbances in deoxynucleoside triphosphate (dNTP) pools. Furthermore, our approaches to treat cancer heavily exploit the metabolic interplay between the DNA and the dNTP pool, with a long-standing example being the use of antimetabolite-based cancer therapies, and this strategy continues to show promise with the development of new targeted therapies. In this Review, we compile the current knowledge on both the causes and consequences of dNTP pool perturbations in cancer cells, together with their impact on genome stability. We outline several outstanding questions remaining in the field, such as the role of dNTP catabolism in genome stability and the consequences of dNTP pool expansion. Importantly, we detail how our mechanistic understanding of these processes can be utilised with the aim of providing better informed treatment options to patients with cancer.

Tumor Cell Stemness and Stromal Cell Features Contribute to Oral Cancer Outcome Disparity in Black Americans

Black Americans (BAs) with head and neck cancer (HNC) have worse survival outcomes compared to the White patients. While HNC disparities in patient outcomes for BAs have been well recognized, the specific drivers of the inferior outcomes remain poorly understood. Here, we investigated the biologic features of patient tumor specimens obtained during the surgical treatment of oral cancers and performed a follow-up study of the patients' post-surgery recurrences and metastases with the aim to explore whether tumor biologic features could be associated with the poorer outcomes among BA patients compared with White American (WA) patients. We examined the tumor stemness traits and stromal properties as well as the post-surgery recurrence and metastasis of oral cancers among BA and WA patients. It was found that high levels of tumor self-renewal, invasion, tumorigenesis, metastasis, and tumor-promoting stromal characteristics were linked to post-surgery recurrence and metastasis. There were more BA than WA patients demonstrating high stemness traits and strong tumor-promoting stromal features in association with post-surgery tumor recurrences and metastases, although the investigated cases displayed clinically comparable TNM stages and histological grades. These findings demonstrated that the differences in tumor stemness and stromal property among cancers with comparable clinical diagnoses contribute to the outcome disparity in HNCs. More research is needed to understand the genetic and molecular basis of the biologic characteristics underlying the inferior outcomes among BA patients, so that targeting strategies can be developed to reduce HNC disparity.

Genomic profiling of a multi-lineage and multi-passage patient-derived xenograft biobank reflects heterogeneity of ovarian cancer

Ovarian cancer (OC) manifests as a complex disease characterized by inter- and intra-patient heterogeneity. Despite enhanced biological and genetic insights, OC remains a recalcitrant malignancy with minimal survival improvement. Based on multi-site sampling and a multi-lineage patient-derived xenograft (PDX) establishment strategy, we present herein the establishment of a comprehensive PDX biobank from histologically and molecularly heterogeneous OC patients. Comprehensive profiling of matched PDX and patient samples demonstrates that PDXs closely recapitulate parental tumors. By leveraging multi-lineage models, we reveal that the previously reported genomic disparities of PDX could be mainly attributed to intra-patient spatial heterogeneity instead of substantial model-independent genomic evolution. Moreover, DNA damage response pathway inhibitor (DDRi) screening uncovers heterogeneous responses across models. Prolonged iterative drug exposure recapitulates acquired drug resistance in initially sensitive models. Meanwhile, interrogation of induced drug-resistant (IDR) models reveals that suppressed interferon (IFN) response and activated Wnt/β-catenin signaling contribute to acquired DDRi drug resistance.

Synthesis and anticancer properties of novel dolastatin 10 analogs featuring five-membered heterocyclic rings with a linkable group at the C-terminus

Dolastatin 10 (Dol-10), a natural marine-source pentapeptide, is a powerful antimitotic agent regarded as one of the most potent anticancer compounds found to date. Dol-10 however, lacks chemical conjugation capabilities, which restricts the feasibility of its application in targeted drug therapy. This limitation has spurred the prospect that chemical structure of the parent molecule might allow conjugation of the derivatives to drug carriers such as antibodies. By first employing docking studies, we designed and prepared a series of novel Dol-10 analogs with a modified C-terminus, preserving high potency of the parent compound while enhancing conjugation capability. The modifications involved the introduction of a methyleneamine functionality at position 4 of the 1,3-thiazole ring, along with the substitution of the thiazole ring with a 1,2,3-triazole moiety, furnished with methylenehydroxy, carboxy, methyleneamine, and N(Me)-methyleneamine tethering functionalities at position 4. Among the synthesized pentapeptides, DA-1 exhibited the highest potency in prostate cancer (PC-3) cells, eliciting apoptosis (IC50 0.2 ± 0.1 nm) and cell cycle arrest at the mitotic stage after at least 6 days of culture. This delayed response suggests the accumulation of cellular stress or significant physiological alterations that profoundly impact the cell cycle. We believe that these novel Dol-10 derivates represent a new and straightforward route for the development of C-terminus modified Dol-10-based microtubule inhibitors, thereby advancing targeted anticancer therapy.

Translation of Epigenetics in Cell-Free DNA Liquid Biopsy Technology and Precision Oncology

Technological advancements in cell-free DNA (cfDNA) liquid biopsy have triggered exponential growth in numerous clinical applications. While cfDNA-based liquid biopsy has made significant strides in personalizing cancer treatment, the exploration and translation of epigenetics in liquid biopsy to clinical practice is still nascent. This comprehensive review seeks to provide a broad yet in-depth narrative of the present status of epigenetics in cfDNA liquid biopsy and its associated challenges. It highlights the potential of epigenetics in cfDNA liquid biopsy technologies with the hopes of enhancing its clinical translation. The momentum of cfDNA liquid biopsy technologies in recent years has propelled epigenetics to the forefront of molecular biology. We have only begun to reveal the true potential of epigenetics in both our understanding of disease and leveraging epigenetics in the diagnostic and therapeutic domains. Recent clinical applications of epigenetics-based cfDNA liquid biopsy revolve around DNA methylation in screening and early cancer detection, leading to the development of multi-cancer early detection tests and the capability to pinpoint tissues of origin. The clinical application of epigenetics in cfDNA liquid biopsy in minimal residual disease, monitoring, and surveillance are at their initial stages. A notable advancement in fragmentation patterns analysis has created a new avenue for epigenetic biomarkers. However, the widespread application of cfDNA liquid biopsy has many challenges, including biomarker sensitivity, specificity, logistics including infrastructure and personnel, data processing, handling, results interpretation, accessibility, and cost effectiveness. Exploring and translating epigenetics in cfDNA liquid biopsy technology can transform our understanding and perception of cancer prevention and management. cfDNA liquid biopsy has great potential in precision oncology to revolutionize conventional ways of early cancer detection, monitoring residual disease, treatment response, surveillance, and drug development. Adapting the implementation of liquid biopsy workflow to the local policy worldwide and developing point-of-care testing holds great potential to overcome global cancer disparity and improve cancer outcomes.

A genomic instability-associated lncRNA signature for predicting prognosis and biomarkers in lung adenocarcinoma

Genomic instability (GI) was associated with tumorigenesis. However, GI-related lncRNA signature (GILncSig) in lung adenocarcinoma (LUAD) is still unknown. In this study, the lncRNA expression data, somatic mutation information and clinical survival information of LUAD were downloaded from The Cancer Genome Atlas (TCGA) and performed differential analysis. Functional and prognosis analysis revealed that multiple GI-related pathways were enriched. By using univariate and multivariate Cox regression analysis, 5 GI-associated lncRNAs (AC012085.2, FAM83A-AS1, MIR223HG, MIR193BHG, LINC01116) were identified and used to construct a GILncSig model. Mutation burden analysis indicated that the high-risk GI group had much higher somatic mutation count and the risk score constructed by the 5 GI-associated lncRNAs was an independent predictor for overall survival (OS) (P < 0.05). Overall, our study provides valuable insights into the involvement of GI-associated lncRNAs in LUAD and highlights their potential as therapeutic targets.

Establishment, characterization, and genetic profiling of patient-derived osteosarcoma cells from a patient with retinoblastoma

Osteosarcoma is the most common malignant bone cancer in pediatric patients. Patients who respond poorly to chemotherapy experience worse clinical outcomes with a high mortality rate. The major challenge is the lack of effective drugs for these patients. To introduce new drugs for clinical approval, preclinical studies based on in vitro models must demonstrate the potency of the tested drugs, enabling the drugs to enter phase 1 clinical trials. Patient-derived cell culture is a promising testing platform for in vitro studies, as they more accurately recapitulate cancer states and genetic profiles compared to cell lines. In the present study, we established patient-derived osteosarcoma cells (PDC) from a patient who had previously been diagnosed with retinoblastoma. We identified a new variant of a germline mutation in the RB1 gene in the tissue of the patient. The biological effects of this PDC were studied to observe whether the cryopreserved PDC retained a feature of fresh PDC. The cryopreserved PDC preserved the key biological effects, including cell growth, invasive capability, migration, and mineralization, that define the conserved phenotypes compared to fresh PDC. From whole genome sequencing analysis of osteosarcoma tissue and patient-derived cells, we found that cryopreserved PDC was a minor population in the origin tissue and was selectively grown under the culture conditions. The cryopreserved PDC has a high resistance to conventional chemotherapy. This study demonstrated that the established cryopreserved PDC has the aggressive characteristics of osteosarcoma, in particular the chemoresistance phenotype that might be used for further investigation in the chemoresistant mechanism of osteosarcoma. In conclusion, the approach we applied for primary cell culture might be a promising method to generate in vitro models for functional testing of osteosarcoma.

Measurement of chromosomal instability and level of DNA damage in peripheral blood mononuclear cells of endometrial cancer patients

Endometrial cancer is one of the most common invasive gynecologic malignancies in developed countries. The aim of this study was to evaluate chromosomal instability and level of DNA damage in peripheral blood mononuclear cells (PBMCs) of newly diagnosed endometrial cancer patients in relation to health status (diagnosis), age, histological grade of cancer, residence, smoking, number of pregnancies, miscarriages, and abortions. The analyzed sample consisted of 60 individuals, 30 endometrial cancer patients with an average age of 64.37 ± 7.08, and 30 healthy control women with an average age of 60.23 ± 11.55. Chromosomal instability was evaluated by the cytokinesis-block micronucleus (CBMN) assay, and the level of DNA damage by the single-cell gel electrophoresis (comet) assay in PBMCs. The average frequencies of micronuclei (MNi), nucleoplasmic bridges (NPBs) as well as nuclear buds (NBUDs) were significantly higher in cancer patients compared to controls (P < .0005). There was no difference in the nuclear division index (NDI) among the analyzed samples. The comet assay showed that the patients had a significantly increased genetic damage index (GDI) compared with controls (P < .0005). Using linear regression analysis, we found that health status (diagnosis) had the strongest influence on the MN frequency as well as GDI (P < .0005). Our results indicated that there is a high level of genetic damage in both the level of DNA and the level of chromosomes in the PBMCs of newly diagnosed patients with endometrial cancer, where the frequency and level of damage were significantly affected by health status, grade of cancer, residence, number of pregnancies, miscarriages, and abortions.

The Power and Promise of Patient-Derived Xenografts of Human Breast Cancer

In 2016, a group of researchers engaged in the development of patient-derived xenografts (PDXs) of human breast cancer provided a comprehensive review of the state of the field. In that review, they summarized the clinical problem that PDXs might address, the technical approaches to their generation (including a discussion of host animals and transplant conditions tested), and presented transplantation success (take) rates across groups and across transplantation conditions. At the time, there were just over 500 unique PDX models created by these investigators representing all three clinically defined subtypes (ER+, HER2+, and TNBC). Today, many of these PDX resources have at least doubled in size, and several more PDX development groups now exist, such that there may be well upward of 1000 PDX models of human breast cancer in existence worldwide. They also presented a series of open questions for the field. Many of these questions have been addressed. However, several remain open, or only partially addressed. Herein, we revisit these questions, and recount the progress that has been made in a number of areas with respect to generation, characterization, and use of PDXs in translational research, and re-present questions that remain open. These open questions, and others, are now being addressed not only by individual investigators, but also large, well-funded consortia including the PDXNet program of the National Cancer Institute in the United States, and the EuroPDX Consortium, an organization of PDX developers across Europe. Finally, we discuss the new opportunities in PDX-based research.

Modelling the micro- and macro- environment of pancreatic cancer: from patients to pre-clinical models and back

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy with very low survival rates. Over the past 50 years, improvements in PDAC survival have significantly lagged behind the progress made in other cancers. PDAC's dismal prognosis is due to typical late-stage diagnosis combined with lack of effective treatments and complex mechanisms of disease. We propose that improvements in survival are partly hindered by the current focus on largely modelling and targeting PDAC as one disease, despite it being heterogeneous. Implementing new disease-representative pre-clinical mouse models that capture this complexity could enable the development of transformative therapies. Specifically, these models should recapitulate human PDAC late-stage biology, heterogeneous genetics, extensive non-malignant stroma, and associated risk factors and comorbidities. In this Perspective, we focus on how pre-clinical mouse models could be improved to exemplify key features of PDAC micro- and macro- environments, which would drive clinically relevant patient stratification, tailored treatments and improved survival.

A Workflow for Meaningful Interpretation of Classification Results from Handheld Ambient Mass Spectrometry Analysis Probes

While untargeted analysis of biological tissues with ambient mass spectrometry analysis probes has been widely reported in the literature, there are currently no guidelines to standardize the workflows for the experimental design, creation, and validation of molecular models that are utilized in these methods to perform class predictions. By drawing parallels with hurdles that are faced in the field of food fraud detection with untargeted mass spectrometry, we provide a stepwise workflow for the creation, refinement, evaluation, and assessment of the robustness of molecular models, aimed at meaningful interpretation of mass spectrometry-based tissue classification results. We propose strategies to obtain a sufficient number of samples for the creation of molecular models and discuss the potential overfitting of data, emphasizing both the need for model validation using an independent cohort of test samples, as well as the use of a fully characterized feature-based approach that verifies the biological relevance of the features that are used to avoid false discoveries. We additionally highlight the need to treat molecular models as "dynamic" and "living" entities and to further refine them as new knowledge concerning disease pathways and classifier feature noise becomes apparent in large(r) population studies. Where appropriate, we have provided a discussion of the challenges that we faced in our development of a 10 s cancer classification method using picosecond infrared laser mass spectrometry (PIRL-MS) to facilitate clinical decision-making at the bedside.

Multiple reaction monitoring assays for large-scale quantitation of proteins from 20 mouse organs and tissues

Mouse is the mammalian model of choice to study human health and disease due to its size, ease of breeding and the natural occurrence of conditions mimicking human pathology. Here we design and validate multiple reaction monitoring mass spectrometry (MRM-MS) assays for quantitation of 2118 unique proteins in 20 murine tissues and organs. We provide open access to technical aspects of these assays to enable their implementation in other laboratories, and demonstrate their suitability for proteomic profiling in mice by measuring normal protein abundances in tissues from three mouse strains: C57BL/6NCrl, NOD/SCID, and BALB/cAnNCrl. Sex- and strain-specific differences in protein abundances are identified and described, and the measured values are freely accessible via our MouseQuaPro database: http://mousequapro.proteincentre.com . Together, this large library of quantitative MRM-MS assays established in mice and the measured baseline protein abundances represent an important resource for research involving mouse models.

Pediatric Tumors as Disorders of Development: The Case for In Vitro Modeling Based on Human Stem Cells

Despite improvements in patient outcomes, pediatric cancer remains a leading cause of non-accidental death in children. Recent genetic analysis of patients with pediatric cancers indicates an important role for both germline genetic predisposition and cancer-specific somatic driver mutations. Increasingly, evidence demonstrates that the developmental timepoint at which the cancer cell-of-origin transforms is critical to tumor identity and therapeutic response. Therefore, future therapeutic development would be bolstered by the use of disease models that faithfully recapitulate the genetic context, cell-of-origin, and developmental window of vulnerability in pediatric cancers. Human stem cells have the potential to incorporate all of these characteristics into a pediatric cancer model, while serving as a platform for rapid genetic and pharmacological testing. In this review, we describe how human stem cells have been used to model pediatric cancers and how these models compare to other pediatric cancer model modalities.

Exploring Novel Genome Instability-associated lncRNAs and their Potential Function in Pan-Renal Cell Carcinoma

OBJECTIVE

Genomic instability can drive clonal evolution, continuous modification of tumor genomes, and tumor genomic heterogeneity. The molecular mechanism of genomic instability still needs further investigation. This study aims to identify novel genome instabilityassociated lncRNAs (GI-lncRNAs) and investigate the role of genome instability in pan-Renal cell carcinoma (RCC).

MATERIALS AND METHODS

A mutator hypothesis was employed, combining the TCGA database of somatic mutation (SM) information, to identify GI-lncRNAs. Subsequently, a training cohort (n = 442) and a testing cohort (n = 439) were formed by randomly dividing all RCC patients. Based on the training cohort dataset, a multivariate Cox regression analysis lncRNAs risk model was created. Further validations were performed in the testing cohort, TCGA cohort, and different RCC subtypes. To confirm the relative expression levels of lncRNAs in HK-2, 786-O, and 769-P cells, qPCR was carried out. Functional pathway enrichment analyses were performed for further investigation.

RESULTS

A total of 170 novel GI-lncRNAs were identified. The lncRNA prognostic risk model was constructed based on LINC00460, AC073218.1, AC010789.1, and COLCA1. This risk model successfully differentiated patients into distinct risk groups with significantly different clinical outcomes. The model was further validated in multiple independent patient cohorts. Additionally, functional and pathway enrichment analyses revealed that GI-lncRNAs play a crucial role in GI. Furthermore, the assessments of immune response, drug sensitivity, and cancer stemness revealed a significant relationship between GI-lncRNAs and tumor microenvironment infiltration, mutational burden, microsatellite instability, and drug resistance.

CONCLUSIONS

In this study, we discovered four novel GI-lncRNAs and developed a novel signature that effectively predicted clinical outcomes in pan-RCC. The findings provide valuable insights for pan-RCC immunotherapy and shed light on potential underlying mechanisms.

Revolutionizing Cancer Treatment: Unveiling New Frontiers by Targeting the (Un)Usual Suspects

This Special Issue includes original articles and reviews on both established and innovative approaches to cancer targeting, showcased at the 29th IGB Workshop titled "Targeting the (un)usual suspects in cancer" "https://29thigbworkshop [...].

Chromosome instability and aneuploidy in the mammalian brain

This review investigates the role of aneuploidy and chromosome instability (CIN) in the aging brain. Aneuploidy refers to an abnormal chromosomal count, deviating from the normal diploid set. It can manifest as either a deficiency or excess of chromosomes. CIN encompasses a broader range of chromosomal alterations, including aneuploidy as well as structural modifications in DNA. We provide an overview of the state-of-the-art methodologies utilized for studying aneuploidy and CIN in non-tumor somatic tissues devoid of clonally expanded populations of aneuploid cells.CIN and aneuploidy, well-established hallmarks of cancer cells, are also associated with the aging process. In non-transformed cells, aneuploidy can contribute to functional impairment and developmental disorders. Despite the importance of understanding the prevalence and specific consequences of aneuploidy and CIN in the aging brain, these aspects remain incompletely understood, emphasizing the need for further scientific investigations.This comprehensive review consolidates the present understanding, addresses discrepancies in the literature, and provides valuable insights for future research efforts.

NKG2D-CAR memory T cells target pediatric T-cell acute lymphoblastic leukemia in vitro and in vivo but fail to eliminate leukemia initiating cells

Introduction

Refractory/relapsed pediatric acute leukemia are still clinically challenging and new therapeutic strategies are needed. Interactions between Natural Killer Group 2D (NKG2D) receptor, expressed in cytotoxic immune cells, and its ligands (NKG2DL), which are upregulated in leukemic blasts, are important for anti-leukemia immunosurveillance. Nevertheless, leukemia cells may develop immunoescape strategies as NKG2DL shedding and/or downregulation.

Methods

In this report, we analyzed the anti-leukemia activity of NKG2D chimeric antigen receptor (CAR) redirected memory (CD45RA-) T cells in vitro and in a murine model of T-cell acute lymphoblastic leukemia (T-ALL). We also explored in vitro how soluble NKG2DL (sNKG2DL) affected NKG2D-CAR T cells' cytotoxicity and the impact of NKG2D-CAR T cells on Jurkat cells gene expression and in vivo functionality.

Results

In vitro, we found NKG2D-CAR T cells targeted leukemia cells and showed resistance to the immunosuppressive effects exerted by sNKG2DL. In vivo, NKG2D-CAR T cells controlled T cell leukemia burden and increased survival of the treated mice but failed to cure the animals. After CAR T cell treatment, Jurkat cells upregulated genes related to proliferation, survival and stemness, and in vivo, they exhibited functional properties of leukemia initiating cells.

Discussion

The data here presented suggest, that, in combination with other therapeutic approaches, NKG2D-CAR T cells could be a novel treatment for pediatric T-ALL.

The role of matrix stiffness in breast cancer progression: a review

The significance of matrix stiffness in cancer development has been investigated in recent years. The gradual elastic force the extracellular matrix imparts to cells, known as matrix stiffness, is one of the most important types of mechanical stimulation. Increased matrix stiffness alters the biological activity of cells, which promotes the growth of numerous malignancies, including breast cancer. Comprehensive studies have demonstrated that increasing matrix stiffness activates molecular signaling pathways that are closely linked to breast cancer progression. There are many articles exploring the relationship between mechanism hardness and breast cancer, so we wanted to provide a systematic summary of recent research advances. In this review, we briefly introduce the mechanism of matrix stiffness in breast cancer, elaborate on the effect of extracellular matrix stiffness on breast cancer biological behavior and signaling pathways, and finally, we will talk about breast cancer treatment that focuses on matrix stiffness.

Spatio-temporal heterogeneity in cancer evolution and tumor microenvironment of renal cell carcinoma with tumor thrombus

Metastasis is the most fatal aspect of cancer, often preceded by a tumor thrombus (TT) which forms within the vascular system. Renal cell carcinoma (RCC), the predominant form of kidney cancer, witnesses a venous system invasion in 4-10% of cases, resulting in venous tumor thrombus (RCC-TT). This variant represents a formidable clinical challenge due to its escalated surgical complexity, heightened risk of progression and metastasis, and an adverse prognosis. However, recent trials addressing RCC-TT face significant barriers stemming from the profound inter- and intra-tumoral heterogeneity, patient-specific treatment variations, and distinct therapeutic resistance patterns between the primary tumor (PT) and the TT. This review delves into the unique evolutionary pathway of RCC-TT, the relationship between the staging and grading of RCC-TT invasion patterns, and the spatial molecular profiling of RCC-TT. Additionally, we assess the temporal heterogeneity among TT, PT, and distant metastases, as well as the functional phenotypes of TME components. An outlook for future research on RCC-TT is also provided.

KS-EMPD-1: a novel cell line of primary extramammary Paget's disease

Extramammary Paget's disease (EMPD) is a rare skin cancer that mainly occurs in apocrine sweat gland-rich areas in elderly people. The prognosis of metastatic EMPD is unfavorable because of the lack of fully effective systemic therapies. However, the difficulty in establishing a model of EMPD has hampered basic research for exploring its pathogenesis and optimal treatments. Here, we established for the first time an EMPD cell line (named KS-EMPD-1) from a primary tumor on the left inguinal region of an 86-year-old Japanese male. The cells were successfully maintained for more than 1 year, with a doubling time of 31.2 ± 0.471 h. KS-EMPD-1 exhibited constant growth, spheroid formation, and invasiveness, and was confirmed to be identical to the original tumor by short tandem repeat analyses, whole exome sequencing, and immunohistochemistry (CK7+CK20-GCDFP15+). Western blotting of the cells revealed the protein expression of HER2, NECTIN4, and TROP2, which have recently attracted attention as potential therapeutic targets for EMPD. KS-EMPD-1 was highly sensitive to docetaxel and paclitaxel on chemosensitivity test. The KS-EMPD-1 cell line is a promising resource for basic and preclinical research on EMPD to better define the tumor characteristics and treatment strategy of this rare cancer.

Patient-Derived Ex Vivo Cultures and Endpoint Assays with Surrogate Biomarkers in Functional Testing for Prediction of Therapeutic Response

Prediction of therapeutic outcomes is important for cancer patients in order to reduce side effects and improve the efficacy of anti-cancer drugs. Currently, the most widely accepted method for predicting the efficacy of anti-cancer drugs is gene panel testing based on next-generation sequencing. However, gene panel testing has several limitations. For example, only 10% of cancer patients are estimated to have druggable mutations, even if whole-exome sequencing is applied. Additionally, even if optimal drugs are selected, a significant proportion of patients derive no benefit from the indicated drug treatment. Furthermore, most of the anti-cancer drugs selected by gene panel testing are molecularly targeted drugs, and the efficacies of cytotoxic drugs remain difficult to predict. Apart from gene panel testing, attempts to predict chemotherapeutic efficacy using ex vivo cultures from cancer patients have been increasing. Several groups have retrospectively demonstrated correlations between ex vivo drug sensitivity and clinical outcome. For ex vivo culture, surgically resected tumor tissue is the most abundant source. However, patients with recurrent or metastatic tumors do not usually undergo surgery, and chemotherapy may be the only option for those with inoperable tumors. Therefore, predictive methods using small amounts of cancer tissue from diagnostic materials such as endoscopic, fine-needle aspirates, needle cores and liquid biopsies are needed. To achieve this, various types of ex vivo culture and endpoint assays using effective surrogate biomarkers of drug sensitivity have recently been developed. Here, we review the variety of ex vivo cultures and endpoint assays currently available.

Fanconi anemia-isogenic head and neck cancer cell line pairs: A basic and translational science resource

Fanconi anemia (FA) is a heritable malformation, bone marrow failure and cancer predisposition syndrome that confers an exceptionally high risk of squamous carcinomas. These carcinomas originate in epithelia lining the mouth, proximal esophagus, vulva and anus: their origins are not understood, and no effective ways have been identified to prevent or delay their appearance. Many FA-associated carcinomas are also therapeutically challenging: they may be multi-focal and stage-advanced at diagnosis, and most individuals with FA cannot tolerate standard-of-care systemic therapies such as DNA cross-linking drugs or ionizing radiation due to constitutional DNA damage hypersensitivity. We developed the Fanconi Anemia Cancer Cell Line Resource (FA-CCLR) to foster new work on the origins, treatment and prevention of FA-associated carcinomas. The FA-CCLR consists of Fanconi-isogenic head and neck squamous cell carcinoma (HNSCC) cell line pairs generated from five individuals with FA-associated HNSCC, and five individuals with sporadic HNSCC. Sporadic, isogenic HNSCC cell line pairs were generated in parallel with FA patient-derived isogenic cell line pairs to provide comparable experimental material to use to identify cell and molecular phenotypes driven by germline or somatic loss of Fanconi pathway function, and the subset of these FA-dependent phenotypes that can be modified, complemented or suppressed. All 10 FANC-isogenic cell line pairs are available to academic, non-profit and industry investigators via the "Fanconi Anemia Research Materials" Resource and Repository at Oregon Health & Sciences University, Portland OR.

Clinical trial designs for evaluating and exploiting cancer evolution

The evolution of drug-resistant cell subpopulations causes cancer treatment failure. Current preclinical evidence shows that it is possible to model herding of clonal evolution and collateral sensitivity where an initial treatment could favourably influence the response to a subsequent one. Novel therapy strategies exploiting this understanding are being considered, and clinical trial designs for steering cancer evolution are needed. Furthermore, preclinical evidence suggests that different subsets of drug-sensitive and resistant clones could compete between themselves for nutrients/blood supply, and clones that populate a tumour do so at the expense of other clones. Treatment paradigms based on this clinical application of exploiting cell-cell competition include intermittent dosing regimens or cycling different treatments before progression. This will require clinical trial designs different from the conventional practice of evaluating responses to individual therapy regimens. Next-generation sequencing to assess clonal dynamics longitudinally will improve current radiological assessment of clinical response/resistance and be incorporated into trials exploiting evolution. Furthermore, if understood, clonal evolution can be used to therapeutic advantage, improving patient outcomes based on a new generation of clinical trials.

Glioblastoma Spheroid Invasion through Soft, Brain-Like Matrices Depends on Hyaluronic Acid-CD44 Interactions

Increased secretion of hyaluronic acid (HA), a glycosaminoglycan abundant in the brain extracellular matrix (ECM), correlates with worse clinical outcomes for glioblastoma (GBM) patients. GBM cells aggressively invade the brain parenchyma while encountering spatiotemporal changes in their local ECM, including HA concentration. To investigate how varying HA concentrations affect GBM invasion, patient-derived GBM cells are cultured within a soft, 3D matrix in which HA concentration is precisely varied and cell migration observed. Data demonstrate that HA concentration can determine the invasive activity of patient-derived GBM cells in a biphasic and highly sensitive manner, where the absolute concentration of HA at which cell migration peaked is specific to each patient-derived line. Furthermore, evidence that this response relies on phosphorylated ezrin, which interacts with the intracellular domain of HA-engaged CD44 to effectively link the actin cytoskeleton to the local ECM is provided. Overall, this study highlights CD44-HA binding as a major mediator of GBM cell migration that acts independently of integrins and focal adhesion complexes and suggests that targeting HA-CD44-ezrin interactions represents a promising therapeutic strategy to prevent tumor cell invasion in the brain.

Patient-derived models: Promising tools for accelerating the clinical translation of breast cancer research findings

Breast cancer has become the highest incidence of cancer in women. It was extensively and deeply studied by biologists and medical workers worldwide. However, the meaningful results in lab researches cannot be realized in clinical, and a part of new drugs in clinical experiments do not obtain as good results as the preclinical researches. It is urgently that promote a kind of breast cancer research models that can get study results closer to the physiological condition of the human body. Patient-derived models (PDMs) originating from clinical tumor, contain primary elements of tumor and maintain key clinical features of tumor. So they are promising research models to facilitate laboratory researches translate to clinical application, and predict the treatment outcome of patients. In this review, we summarize the establishment of PDMs of breast cancer, reviewed the application of PDMs in clinical translational researches and personalized precision medicine with breast cancer as an example, to improve the understanding of PDMs among researchers and clinician, facilitate them to use PDMs on a large scale of breast cancer researches and promote the clinical translation of laboratory research and new drug development.

Breast cancer cells interact with tumor-derived extracellular matrix in a molecular subtype-specific manner

Mimicking the native microenvironment is vital for tumor engineering. Breast cancer is a highly heterogeneous disease with various molecular subtypes exhibiting distinct biological behaviors and treatment responsiveness. The heterogeneity of extracellular matrix (ECM) of breast cancer has remained largely unexplored and underestimated. The present study addressed this issue by comparing the composition, architecture, and functional roles of ECMs derived from breast cancers of two molecular subtypes, which are luminal-A breast cancer (less aggressive, ERα+)-derived ECM (LA-ECM) and triple-negative breast cancer (high aggressive, ERα-)-derived ECM (TN-ECM). Compared with normal breast tissue-derived ECMs (B-ECM), tumor-derived ECMs showed higher contents of pro-collagen I, fibronectin, and laminin, in addition with a significantly altered architecture. Transcriptome sequencing revealed that, compared with those cultured with B-ECM, MCF7 cells (an estrogen receptor (ER)α + luminal-A breast cancer cell line) cultured with LA-ECM and TN-ECM showed approximately 9.65 % and 9.04 % changes in the expression of all detected genes, respectively. The TN-ECM induced proliferation, promoted epithelial-to-mesenchymal transition, downregulated ERα expression, and reduced endocrine treatment sensitivity of MCF7. Above results have elucidated the role of phenotype-specific tumor ECM in cell phenotype maintenance, treatment sensitivity, and cancer progression, which highlighted the importance of ECM heterogeneity as well as its role in tumor microenvironment engineering and drug screening.

In vivo PDX CRISPR/Cas9 screens reveal mutual therapeutic targets to overcome heterogeneous acquired chemo-resistance

Resistance towards cancer treatment represents a major clinical obstacle, preventing cure of cancer patients. To gain mechanistic insights, we developed a model for acquired resistance to chemotherapy by treating mice carrying patient derived xenografts (PDX) of acute lymphoblastic leukemia with widely-used cytotoxic drugs for 18 consecutive weeks. In two distinct PDX samples, tumors initially responded to treatment, until stable disease and eventually tumor re-growth evolved under therapy, at highly similar kinetics between replicate mice. Notably, replicate tumors developed different mutations in TP53 and individual sets of chromosomal alterations, suggesting independent parallel clonal evolution rather than selection, driven by a combination of stochastic and deterministic processes. Transcriptome and proteome showed shared dysregulations between replicate tumors providing putative targets to overcome resistance. In vivo CRISPR/Cas9 dropout screens in PDX revealed broad dependency on BCL2, BRIP1 and COPS2. Accordingly, venetoclax re-sensitized derivative tumors towards chemotherapy, despite genomic heterogeneity, demonstrating direct translatability of the approach. Hence, despite the presence of multiple resistance-associated genomic alterations, effective rescue treatment for polychemotherapy-resistant tumors can be identified using functional testing in preclinical models.

Microfluidic-based dynamic BH3 profiling predicts anticancer treatment efficacy

Precision medicine is starting to incorporate functional assays to evaluate anticancer agents on patient-isolated tissues or cells to select for the most effective. Among these new technologies, dynamic BH3 profiling (DBP) has emerged and extensively been used to predict treatment efficacy in different types of cancer. DBP uses synthetic BH3 peptides to measure early apoptotic events ('priming') and anticipate therapy-induced cell death leading to tumor elimination. This predictive functional assay presents multiple advantages but a critical limitation: the cell number requirement, that limits drug screening on patient samples, especially in solid tumors. To solve this problem, we developed an innovative microfluidic-based DBP (µDBP) device that overcomes tissue limitations on primary samples. We used microfluidic chips to generate a gradient of BIM BH3 peptide, compared it with the standard flow cytometry based DBP, and tested different anticancer treatments. We first examined this new technology's predictive capacity using gastrointestinal stromal tumor (GIST) cell lines, by comparing imatinib sensitive and resistant cells, and we could detect differences in apoptotic priming and anticipate cytotoxicity. We then validated µDBP on a refractory GIST patient sample and identified that the combination of dactolisib and venetoclax increased apoptotic priming. In summary, this new technology could represent an important advance for precision medicine by providing a fast, easy-to-use and scalable microfluidic device to perform DBP in situ as a routine assay to identify the best treatment for cancer patients.

Microfluidics guided by deep learning for cancer immunotherapy screening

Immunocyte infiltration and cytotoxicity play critical roles in both inflammation and immunotherapy. However, current cancer immunotherapy screening methods overlook the capacity of the T cells to penetrate the tumor stroma, thereby significantly limiting the development of effective treatments for solid tumors. Here, we present an automated high-throughput microfluidic platform for simultaneous tracking of the dynamics of T cell infiltration and cytotoxicity within the 3D tumor cultures with a tunable stromal makeup. By recourse to a clinical tumor-infiltrating lymphocyte (TIL) score analyzer, which is based on a clinical data-driven deep learning method, our platform can evaluate the efficacy of each treatment based on the scoring of T cell infiltration patterns. By screening a drug library using this technology, we identified an epigenetic drug (lysine-specific histone demethylase 1 inhibitor, LSD1i) that effectively promoted T cell tumor infiltration and enhanced treatment efficacy in combination with an immune checkpoint inhibitor (anti-PD1) in vivo. We demonstrated an automated system and strategy for screening immunocyte-solid tumor interactions, enabling the discovery of immuno- and combination therapies.

Clinical prospects of WRN inhibition as a treatment for MSI tumours

The discovery of synthetic lethal interactions with genetic deficiencies in cancers has highlighted several candidate targets for drug development, with variable clinical success. Recent work has unveiled a promising synthetic lethal interaction between inactivation/inhibition of the WRN DNA helicase and tumours with microsatellite instability, a phenotype that arises from DNA mismatch repair deficiency. While these and further studies have highlighted the therapeutic potential of WRN inhibitors, compounds with properties suitable for clinical exploitation remain to be described. Furthermore, the complexities of MSI development and its relationship to cancer evolution pose challenges for clinical prospects. Here, we discuss possible paths of MSI tumour development, the viability of WRN inhibition as a strategy in different scenarios, and the necessary conditions to create a roadmap towards successful implementation of WRN inhibitors in the clinic.

Generation and Integrated Analysis of Advanced Patient-Derived Orthoxenograft Models (PDOX) for the Rational Assessment of Targeted Therapies in Endometrial Cancer

Clinical management of endometrial cancer (EC) is handicapped by the limited availability of second line treatments and bona fide molecular biomarkers to predict recurrence. These limitations have hampered the treatment of these patients, whose survival rates have not improved over the last four decades. The advent of coordinated studies such as The Cancer Genome Atlas Uterine Corpus Endometrial Carcinoma (TCGA_UCEC) has partially solved this issue, but the lack of proper experimental systems still represents a bottleneck that precludes translational studies from successful clinical testing in EC patients. Within this context, the first study reporting the generation of a collection of endometrioid-EC-patient-derived orthoxenograft (PDOX) mouse models is presented that is believed to overcome these experimental constraints and pave the way toward state-of-the-art precision medicine in EC. The collection of primary tumors and derived PDOXs is characterized through an integrative approach based on transcriptomics, mutational profiles, and morphological analysis; and it is demonstrated that EC tumors engrafted in the mouse uterus retain the main molecular and morphological features from analogous tumor donors. Finally, the molecular properties of these tumors are harnessed to assess the therapeutic potential of trastuzumab, a human epidermal growth factor receptor 2 (HER2) inhibitor with growing interest in EC, using patient-derived organotypic multicellular tumor spheroids and in vivo experiments.

Systematic characterization of cancer transcriptome at transcript resolution

Transcribed RNAs undergo various regulation and modification to become functional transcripts. Notably, cancer transcriptome has not been fully characterized at transcript resolution. Herein, we carry out a reference-based transcript assembly across >1000 cancer cell lines. We identify 498,255 transcripts, approximately half of which are unannotated. Unannotated transcripts are closely associated with cancer-related hallmarks and show clinical significance. We build a high-confidence RNA binding protein (RBP)-transcript regulatory network, wherein most RBPs tend to regulate transcripts involved in cell proliferation. We identify numerous transcripts that are highly associated with anti-cancer drug sensitivity. Furthermore, we establish RBP-transcript-drug axes, wherein PTBP1 is experimentally validated to affect the sensitivity to decitabine by regulating KIAA1522-a6 transcript. Finally, we establish a user-friendly data portal to serve as a valuable resource for understanding cancer transcriptome diversity and its potential clinical utility at transcript level. Our study substantially extends cancer RNA repository and will facilitate anti-cancer drug discovery.

A novel DNA damage repair-related signature for predicting prognositc and treatment response in non-small lung cancer

DNA damage repair (DDR) is essential for maintaining genome integrity and modulating cancer risk, progression, and therapeutic response. DDR defects are common among non-small lung cancer (NSCLC), resulting in new challenge and promise for NSCLC treatment. Thus, a thorough understanding of the molecular characteristics of DDR in NSCLC is helpful for NSCLC treatment and management. Here, we systematically analyzed the relationship between DDR alterations and NSCLC prognosis, and successfully established and validated a six-DDR gene prognostic model via LASSO Cox regression analysis based on the expression of prognostic related DDR genes, CDC25C, NEIL3, H2AFX, NBN, XRCC5, RAD1. According to this model, NSCLC patients were classified into high-risk subtype and low-risk subtype, each of which has significant differences between the two subtypes in clinical features, molecular features, immune cell components, gene mutations, DDR pathway activation status and clinical outcomes. The high-risk patients was characterized with worse prognosis, lower proportion and number of DDR mutations, unique immune profile and responsive to immunetherapy. And the low-risk patients tend to have superior survival, while being less responsive to immunotherapy and more sensitive to treatment with DNA-damaging chemotherapy drugs. Overall, this molecular classification based on DDR expression profile enables hierarchical management of patients and personalized clinical treatment, and provides potential therapeutic targets for NSCLC.

Estimating intraclonal heterogeneity and subpopulation changes from bulk expression profiles in CMap

Premnas is a computational framework that provides a new perspective to interpret perturbational data in LINC L1000 CMap by learning an ad hoc subpopulation representation from scRNA-seq and performing the digital cytometry to estimate the abundance of undetermined subpopulations.

The connectivity among signatures upon perturbations curated in the CMap library provides a valuable resource for understanding therapeutic pathways and biological processes associated with the drugs and diseases. However, because of the nature of bulk-level expression profiling by the L1000 assay, intraclonal heterogeneity and subpopulation compositional change that could contribute to the responses to perturbations are largely neglected, hampering the interpretability and reproducibility of the connections. In this work, we proposed a computational framework, Premnas, to estimate the abundance of undetermined subpopulations from L1000 profiles in CMap directly according to an ad hoc subpopulation representation learned from a well-normalized batch of single-cell RNA-seq datasets by the archetypal analysis. By recovering the information of subpopulation changes upon perturbation, the potentials of drug-resistant/susceptible subpopulations with CMap L1000 were further explored and examined. The proposed framework enables a new perspective to understand the connectivity among cellular signatures and expands the scope of the CMAP and other similar perturbation datasets limited by the bulk profiling technology.

A genomic instability-related lncRNA model for predicting prognosis and immune checkpoint inhibitor efficacy in breast cancer

Breast cancer has overtaken lung cancer as the most frequently diagnosed cancer type and is the leading cause of death for women worldwide. It has been demonstrated in published studies that long non-coding RNAs (lncRNAs) involved in genomic stability are closely associated with the progression of breast cancer, and remarkably, genomic stability has been shown to predict the response to immune checkpoint inhibitors (ICIs) in cancer therapy, especially colorectal cancer. Therefore, it is of interest to explore somatic mutator-derived lncRNAs in predicting the prognosis and ICI efficacy in breast cancer patients. In this study, the lncRNA expression data and somatic mutation data of breast cancer patients from The Cancer Genome Atlas (TCGA) were downloaded and analyzed thoroughly. Univariate and multivariate Cox proportional hazards analyses were used to generate the genomic instability-related lncRNAs in a training set, which was subsequently used to analyze a testing set and combination of the two sets. The qRT-PCR was conducted in both normal mammary and breast cancer cell lines. Furthermore, the Kaplan–Meier and receiver operating characteristic (ROC) curves were applied to validate the predictive effect in the three sets. Finally, the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to evaluate the association between genomic instability-related lncRNAs and immune checkpoints. As a result, a six-genomic instability-related lncRNA signature (U62317.4, MAPT-AS1, AC115837.2, EGOT, SEMA3B-AS1, and HOTAIR) was identified as the independent prognostic risk model for breast cancer patients. Compared with the normal mammary cells, the qRT-PCR showed that HOTAIR was upregulated while MAPT-AS1, EGOT, and SEMA3B-AS1 were downregulated in breast cancer cells. The areas under the ROC curves at 3 and 5 years were 0.711 and 0.723, respectively. Moreover, the patients classified in the high-risk group by the prognostic model had abundant negative immune checkpoint molecules. In summary, this study suggested that the prognostic model comprising six genomic instability-related lncRNAs may provide survival prediction. It is necessary to identify patients who are suitable for ICIs to avoid severe immune-related adverse effects, especially autoimmune diseases. This model may predict the ICI efficacy, facilitating the identification of patients who may benefit from ICIs.

X-box Binding Protein 1 is a Potential Immunotherapy Target in Ovarian Cancer

The allure of potentially dramatic and durable responses to immunotherapy has driven the study of several immune checkpoint inhibitor (ICI) agents in ovarian cancer. However, the results of ICI therapy in ovarian cancer have been rather disappointing. It is important to understand the reasons for the poor efficacy of ICI in ovarian cancer and to look for new targets for immunotherapy. To solve this problem, ovarian cancer–associated datasets were individually collected from The Cancer Genome Atlas (TCGA)、International Cancer Genome Consortium (ICGC)、Genotype-Tissue Expression (GTEx), and comprehensively performed to expression, prognostic, pathological correlation, genomic and immunologic analyses of reported all immune checkpoints by Gene Expression Profiling Interactive Analysis 2 (GEPIA2), Tumor and Immune System Interaction Database (TISIDB), cBio Cancer Genomics Portal (cBioPortal), and Kaplan-Meier Plotter. We concluded that those well-identified immune checkpoints might not be ideal targets for ovarian cancer immunotherapy. Intriguingly, the genomic alteration of X-box binding protein 1 (XBP1), the important mediator of chemotherapy-induced cancer immunogenic cell death, was found to be a potential coregulator of immune checkpoints in ovarian cancer. Importantly, XBP1 was detected to be highly expressed in ovarian cancer compared with normal ovarian tissue, and high XBP1 expression significantly benefits both overall survival (OS) and disease-free survival (DFS) of ovarian cancer patients. More importantly, XBP1 was further observed to be closely related to anti-tumor immunity in ovarian cancer, including multiple T-cell signatures and immunity-killing molecules. In conclusion, upregulating XBP1 rather than targeting immune checkpoints represents a potentially more efficient approach for ovarian cancer therapy.

Tumor microenvironment responsive nanocarriers for gene therapy

Stimuli responsive nanocarriers are important non-viral gene carriers for gene therapy. We discuss the stimulus conditions and then highlight various stimuli responsive nanocarriers in the tumor microenvironment for cancer gene therapy. We hope that this review will inspire readers to develop more effective stimuli responsive nanocarriers for delivering genes.

Computational estimation of quality and clinical relevance of cancer cell lines

Immortal cancer cell lines (CCLs) are the most widely used system for investigating cancer biology and for the preclinical development of oncology therapies. Pharmacogenomic and genome-wide editing screenings have facilitated the discovery of clinically relevant gene-drug interactions and novel therapeutic targets via large panels of extensively characterised CCLs. However, tailoring pharmacological strategies in a precision medicine context requires bridging the existing gaps between tumours and in vitro models. Indeed, intrinsic limitations of CCLs such as misidentification, the absence of tumour microenvironment and genetic drift have highlighted the need to identify the most faithful CCLs for each primary tumour while addressing their heterogeneity, with the development of new models where necessary. Here, we discuss the most significant limitations of CCLs in representing patient features, and we review computational methods aiming at systematically evaluating the suitability of CCLs as tumour proxies and identifying the best patient representative in vitro models. Additionally, we provide an overview of the applications of these methods to more complex models and discuss future machine-learning-based directions that could resolve some of the arising discrepancies.

Translational research: A patient-centered approach to bridge the valley of death

Animal models have evolved to be a key component of translational research of cancer, and they are now extensively used to test drug candidates, predict drug responses, and essentially drive discovery of cancer biology. However, the model-centric approach has not yielded the expected abundance of treatment advances. We propose that the focus of translational research needs to shift from animal models to human patients, and the goal should be to understand why tumor responses and outcomes are so variable between patients and how it can be predicted at the individual level and thus to generate hypotheses that are more relevant to people than to atypically sensitive animal models.

Regulation of protein complex partners as a compensatory mechanism in aneuploid tumors

Aneuploidy, a state of chromosome imbalance, is a hallmark of human tumors, but its role in cancer still remains to be fully elucidated. To understand the consequences of whole-chromosome-level aneuploidies on the proteome, we integrated aneuploidy, transcriptomic, and proteomic data from hundreds of The Cancer Genome Atlas/Clinical Proteomic Tumor Analysis Consortium tumor samples. We found a surprisingly large number of expression changes happened on other, non-aneuploid chromosomes. Moreover, we identified an association between those changes and co-complex members of proteins from aneuploid chromosomes. This co-abundance association is tightly regulated for aggregation-prone aneuploid proteins and those involved in a smaller number of complexes. On the other hand, we observed that complexes of the cellular core machinery are under functional selection to maintain their stoichiometric balance in aneuploid tumors. Ultimately, we provide evidence that those compensatory and functional maintenance mechanisms are established through post-translational control, and that the degree of success of a tumor to deal with aneuploidy-induced stoichiometric imbalance impacts the activation of cellular protein degradation programs and patient survival.

Whole-Genome Duplication Shapes the Aneuploidy Landscape of Human Cancers

Aneuploidy is a hallmark of cancer with tissue-specific prevalence patterns that suggest it plays a driving role in cancer initiation and progression. However, the contribution of aneuploidy to tumorigenesis depends on both cellular and genomic contexts. Whole-genome duplication (WGD) is a common macroevolutionary event that occurs in more than 30% of human tumors early in tumorigenesis. Although tumors that have undergone WGD are reported to be more permissive to aneuploidy, it remains unknown whether WGD also affects aneuploidy prevalence patterns. Here we analyzed clinical tumor samples from 5,586 WGD- tumors and 3,435 WGD+ tumors across 22 tumor types and found distinct patterns of aneuploidy in WGD- and WGD+ tumors. WGD+ tumors were characterized by more promiscuous aneuploidy patterns, in line with increased aneuploidy tolerance. Moreover, the genetic interactions between chromosome arms differed between WGD- and WGD+ tumors, giving rise to distinct cooccurrence and mutual exclusivity aneuploidy patterns. The proportion of whole-chromosome aneuploidy compared with arm-level aneuploidy was significantly higher in WGD+ tumors, indicating distinct dominant mechanisms for aneuploidy formation. Human cancer cell lines successfully reproduced these WGD/aneuploidy interactions, confirming the relevance of studying this phenomenon in culture. Finally, induction of WGD and assessment of aneuploidy in isogenic WGD-/WGD+ human colon cancer cell lines under standard or selective conditions validated key findings from the clinical tumor analysis, supporting a causal link between WGD and altered aneuploidy landscapes. We conclude that WGD shapes the aneuploidy landscape of human tumors and propose that this interaction contributes to tumor evolution.

SIGNIFICANCE

These findings suggest that the interactions between whole-genome duplication and aneuploidy are important for tumor evolution, highlighting the need to consider genome status in the analysis and modeling of cancer aneuploidy.

DNA-based copy number analysis confirms genomic evolution of PDX models

Genomic evolution of patient-derived xenografts (PDXs) may lead to their gradual divergence away of their tumors of origin. We previously reported the genomic evolution of the copy number (CN) landscapes of PDXs during their engraftment and passaging¹. However, whether PDX models are highly stable throughout passaging², or can evolve CNAs rapidly^1,3, remains controversial. Here, we reassess the genomic evolution of PDXs using DNA-based CN profiles. We find strong evidence for genomic evolution in the DNA-based PDX data: a median of ~10% of the genome is differentially altered between matched primary tumors (PTs) and PDXs across cohorts (range, 0% to 73% across all models). In 24% of the matched PT-PDX samples, over a quarter of the genome is differentially affected by CN alterations. Moreover, in matched analyses of PTs and their derived PDXs at multiple passages, later-passage PDXs are significantly less similar to their parental PTs than earlier-passage PDXs, indicative of genomic divergence. We conclude that PDX models indeed evolve throughout their derivation and propagation, and that the phenotypic consequences of this evolution ought to be assessed in order to determine its relevance to the proper application of these valuable cancer models.

Identification of LncRNA Prognostic Signature Associated With Genomic Instability in Pancreatic Adenocarcinoma

Background

Genomic instability (GI) is a critical feature of cancer which plays a key role in the occurrence and development of pancreatic adenocarcinoma (PAAD). Long non-coding RNA (LncRNA) is an emerging prognostic biomarker because it is involved in regulating GI. Recently, researchers used such GI-related LncRNAs (GILncRNAs) to establish a prognostic signature for patients with cancer and helped in predicting the overall prognosis of the patients. However, it is evident that patients with PAAD still lack such prognostic signature constructed with GILncRNA.

Methods

The present study screened GILncRNAs from 83 patients with PAAD. Prognosis-related GILncRNAs were identified by univariate Cox regression analysis. The correlation coefficients of these GILncRNAs were obtained by multivariate Cox regression analysis and used to construct a signature. The signature in the present study was then assessed through survival analysis, mutation correlation analysis, independent prognostic analysis, and clinical stratification analysis in the training set and validated in the testing as well as all TCGA set. The current study performed external clinical relevance validation of the signature and validated the effect of AC108134.2 in GILncSig on PAAD using in vitro experiments. Finally, the function of GILncRNA signature (GILncSig) dependent on Gene Ontology enrichment analysis was explored and chemotherapeutic drug sensitivity analysis was also performed.

Results

Results of the present study found that a total of 409 GILncRNAs were identified, 5 of which constituted the prognostic risk signature in this study, namely, AC095057.3, AC108134.2, AC124798.1, AL606834.1, and AC104695.4. It was found that the signature of the present study was better than others in predicting the overall survival and applied to patients with PAAD of all ages, genders, and tumor grades. Further, it was noted that the signature of the current study in the GSE102238, was correlated with tumor length, and tumor stage of patients with PAAD. In vitro, functional experiments were used in the present study to validate that AC108134.2 is associated with PAAD genomic instability and progression. Notably, results of the pRRophetic analysis in the current study showed that the high-risk group possessed reverse characteristics and was sensitive to chemotherapy.

Conclusions

In conclusion, it was evident that the GILncSig used in the present study has good prognostic performance. Therefore, the signature may become a potential sensitive biological indicator of PAAD chemotherapy, which may help in clinical decision-making and management of patients with cancer.

Genetic Clonality as the Hallmark Driving Evolution of Non-Small Cell Lung Cancer

Data indicate that many driver alterations from the primary tumor of non-small cell lung cancer (NSCLC) are predominantly shared across all metastases; however, disseminating cells may also acquire a new genetic landscape across their journey. By comparing the constituent subclonal mutations between pairs of primary and metastatic samples, it is possible to derive the ancestral relationships between tumor clones, rather than between tumor samples. Current treatment strategies mostly rely on the theory that metastases are genetically similar to the primary lesions from which they arise. However, intratumor heterogeneity (ITH) affects accurate diagnosis and treatment decisions and it is considered the main hallmark of anticancer therapy failure. Understanding the genetic changes that drive the metastatic process is critical for improving the treatment strategies of this deadly condition. Application of next generation sequencing (NGS) techniques has already created knowledge about tumorigenesis and cancer evolution; however, further NGS implementation may also allow to reconstruct phylogenetic clonal lineages and clonal expansion. In this review, we discuss how the clonality of genetic alterations influence the seeding of primary and metastatic lesions of NSCLC. We highlight that wide genetic analyses may reveal the phylogenetic trajectories of NSCLC evolution, and may pave the way to better management of follow-up and treatment.

Progenitor cells derived from gene-engineered human induced pluripotent stem cells as synthetic cancer cell alternatives for in vitro pharmacology

Limitations in genetic stability and recapitulating accurate physiological disease properties challenge the utility of patient-derived (PD) cancer models for reproducible and translational research. We have genetically engineered a portfolio of isogenic human induced pluripotent stem cells (hiPSCs) with different pan-cancer relevant oncoprotein signatures followed by differentiation into lineage-committed progenitor cells. Characterization on molecular and biological level validated successful stable genetic alterations in pluripotency state as well as upon differentiation to prove the functionality of our approach Meanwhile proposing core molecular networks possibly involved in early dysregulation of stem cell homeostasis, the application of our cell systems in comparative substance testing indicates the potential for cancer research such as identification of augmented therapy resistance of stem cells in response to activation of distinct oncogenic signatures. This article is protected by copyright. All rights reserved.

Preclinical modeling of leiomyosarcoma identifies susceptibility to transcriptional CDK inhibitors through antagonism of E2F-driven oncogenic gene expression

PURPOSE

Leiomyosarcoma (LMS) is a neoplasm characterized by smooth muscle differentiation, complex copy-number alterations, tumor suppressor loss and the absence of recurrent driver mutations. Clinical management for advanced disease relies on the use of empiric cytotoxic chemotherapy with limited activity, and novel targeted therapies supported by preclinical research on LMS biology are urgently needed. A lack of fidelity of established LMS cell lines to their mesenchymal neoplasm of origin has limited translational understanding of this disease, and few other preclinical models have been established. Here, we characterize LMS patient derived xenograft (PDX) models of LMS, assessing fidelity to their tumors of origin and performing preclinical evaluation of candidate therapies.

EXPERIMENTAL DESIGN

We implanted 49 LMS surgical samples into immunocompromised mice. Engrafting tumors were characterized by histology, targeted next-generation sequencing, RNA-seq and ultra-low passage whole-genome sequencing. Candidate therapies were selected based on prior evidence of pathway activation or high-throughput dynamic BH3 profiling.

RESULTS

We show that LMS PDX maintain the histologic appearance, copy-number alterations and transcriptional program of their parental tumors across multiple xenograft passages. Transcriptionally, LMS PDX co-cluster with paired LMS patient-derived samples and differ primarily in host-related immunologic and microenvironment signatures. We identify susceptibility of LMS PDX to transcriptional CDK inhibition, which disrupts an E2F-driven oncogenic transcriptional program and inhibits tumor growth.

CONCLUSIONS

Our results establish LMS PDX as valuable preclinical models and identify strategies to discover novel vulnerabilities in this disease. These data support the clinical assessment of transcriptional CDK inhibitors as a therapeutic strategy for LMS patients.