Statistical Methods for Integrated Cancer Genomic Data Using a Joint Latent Variable Model

Inspired by the TCGA (The Cancer Genome Atlas), we explore multimodal genomic datasets with integrative methods using a joint latent variable approach. We use iCluster+, an existing clustering method for integrative data, to identify potential subtypes within TCGA sarcoma and mesothelioma tumors, and across a large cohort of 33 dierent TCGA cancer datasets. For classication, motivated to improve the prediction of platinum resistance in high grade serous ovarian cancer (HGSOC) treatment, we propose novel integrative methods, iClassify to perform classication using a joint latent variable model. iClassify provides eective data integration and classication while handling heterogeneous data types, while providing a natural framework to incorporate covariate risk factors and examine genomic driver by covariate risk factor interaction. Feature selection is performed through a thresholding parameter that combines both latent variable and feature coecients. We demonstrate increased accuracy in classication over methods that assume homogeneous data type, such as linear discriminant analysis and penalized logistic regression, and improved feature selection. We apply iClassify to a TCGA cohort of HGSOC patients with three types of genomic data and platinum response data. This methodology has broad applications beyond predicting treatment outcomes and disease progression in cancer, including predicting prognosis and diagnosis in other diseases with major public health implications

Artificial intelligence in cancer imaging: Clinical challenges and applications

Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care

Microarray Enriched Gene Rank

We develop a new concept that reflects how genes are connected based on microarray data using the coefficient of determination (the squared Pearson correlation coefficient). Our gene rank combines a priori knowledge about gene connectivity, say, from the Gene Ontology (GO) database, and the microarray expression data at hand, called the microarray enriched gene rank, or simply gene rank (GR). GR, similarly to Google PageRank, is defined in a recursive fashion and is computed as the left maximum eigenvector of a stochastic matrix derived from microarray expression data. An efficient algorithm is devised that allows computation of GR for 50 thousand genes with 500 samples within minutes on a personal computer using the public domain statistical package R

EPMA position paper in cancer:current overview and future perspectives

At present, a radical shift in cancer treatment is occurring in terms of predictive, preventive, and personalized medicine (PPPM). Individual patients will participate in more aspects of their healthcare. During the development of PPPM, many rapid, specific, and sensitive new methods for earlier detection of cancer will result in more efficient management of the patient and hence a better quality of life. Coordination of the various activities among different healthcare professionals in primary, secondary, and tertiary care requires well-defined competencies, implementation of training and educational programs, sharing of data, and harmonized guidelines. In this position paper, the current knowledge to understand cancer predisposition and risk factors, the cellular biology of cancer, predictive markers and treatment outcome, the improvement in technologies in screening and diagnosis, and provision of better drug development solutions are discussed in the context of a better implementation of personalized medicine. Recognition of the major risk factors for cancer initiation is the key for preventive strategies (EPMA J. 4(1):6, 2013). Of interest, cancer predisposing syndromes in particular the monogenic subtypes that lead to cancer progression are well defined and one should focus on implementation strategies to identify individuals at risk to allow preventive measures and early screening/diagnosis. Implementation of such measures is disturbed by improper use of the data, with breach of data protection as one of the risks to be heavily controlled. Population screening requires in depth cost-benefit analysis to justify healthcare costs, and the parameters screened should provide information that allow an actionable and deliverable solution, for better healthcare provision

Nat Cancer

The molecular characterization of tumors now informs clinical cancer care for many patients. This advent of molecular oncology has been driven by the expanding number of therapeutic biomarkers that can predict sensitivity to both approved agents and investigational agents. Beyond its role in driving clinical-trial enrollments and guiding therapy in individual patients, large-scale clinical genomics in oncology also represents a rapidly expanding research resource for translational scientific discovery. Here we review the progress, opportunities, and challenges of scientific and translational discovery from prospective clinical genomic screening programs now routinely conducted for patients with cancer.U54 OD020355/OD/NIH HHSUnited States/U54 OD020355/CD/ODCDC CDC HHSUnited States/R01 CA207244/CA/NCI NIH HHSUnited States/P30 CA008748/CA/NCI NIH HHSUnited States/R01 CA245069/CA/NCI NIH HHSUnited States/R01 CA204749/CA/NCI NIH HHSUnited States/2022-04-06T00:00:00Z35122052PMC898517511193vault:4135

Genetic and molecular mechanisms of sarcomas

Sarcomas are heterogeneous malignant mesenchymal tumors with diverse biological features and unique clinical characteristics, the genetic alterations of sarcomas are highly variable. With the development of sequencing technologies, efficient and practical approaches to detect gene expressions and gene variants contribute to the prediction of patient prognosis and the choice of treatment modalities. Given the rarity of sarcomas, the comprehensive transcriptomic or genomic profiles are still lacking for many subtypes. In the present thesis, by applying sequencing technology in sarcoma cohorts, combined with bioinformatics data analysis and molecular biology experiments, we have revealed new biological mechanisms dictating sarcoma behavior and provided insights for clinical applications. In Paper I, we characterized the gene signatures related to poor prognosis, first-line treatment failure, and chemotherapy resistance in Ewing sarcoma (ES). High expression of IGF2 was associated with shorter overall survival in ES patients and promoted cell proliferation, radiation resistance, and apoptosis inhibition in vitro. The transcriptome analysis of clinical samples and cell lines uncovered an IGF-dependent signature and potentially related to stem cell-like signatures in ES. Paper II continued to highlight the transcriptome signatures in ES. Here, we identified prognosis-related RNA-binding proteins (RBPs) and constructed an RBP-based prognostic risk model that showed stable predictive power for evaluating overall survival in clinical samples. Within the model, NSUN7 is considered an independent prognostic favorable prognostic marker, which was also validated by immunohistochemistry. In Paper III, we discovered that TERT promoter mutations were present in 45% of patients in a cohort of 190 patients with conventional chondrosarcoma (CHS). The mutation was significantly associated with recurrence, distant metastasis, and high tumor grade. The heterogeneity of primary tumors and the altered mutational status between asynchronous metastatic lesions revealed that CHS is a multiclonal disease that progresses through branching evolution. In Paper IV, we identified three clusters with distinct transcriptomic and genomic patterns in synovial sarcoma (SS), of which SS cluster I (SSC-I) was characterized by hyperproliferation, immune cell silencing, and poor prognosis; SSC-II was characterized by high vascularity and stromal component with the better clinical outcome; SSC-III was characterized by epithelial components with genomic complexity and checkpoint-mediated immune suppression. Collectively, the present thesis illustrated the pathogenic mechanisms of ES, CHS, and SS through the analysis of transcriptomic and genomic data, identified prognostic biomarkers, and at the clinical application-level provided strong evidence for patient stratification, risk prediction, and personalized treatment assessment

Emerging role of circulating tumor cells in immunotherapy.

Over the last few years, immunotherapy, in particular, immune checkpoint inhibitor therapy, has revolutionized the treatment of several types of cancer. At the same time, the uptake in clinical oncology has been slow owing to the high cost of treatment, associated toxicity profiles and variability of the response to treatment between patients. In response, personalized approaches based on predictive biomarkers have emerged as new tools for patient stratification to achieve effective immunotherapy. Recently, the enumeration and molecular analysis of circulating tumor cells (CTCs) have been highlighted as prognostic biomarkers for the management of cancer patients during chemotherapy and for targeted therapy in a personalized manner. The expression of immune checkpoints on CTCs has been reported in a number of solid tumor types and has provided new insight into cancer immunotherapy management. In this review, we discuss recent advances in the identification of immune checkpoints using CTCs and shed light on the potential applications of CTCs towards the identification of predictive biomarkers for immunotherapy

Identifikation von ABCC1 als potenziellen Angriffspunkt in Actinomycin D-resistenten, pädiatrischen Hochrisiko-Rhabdomyosarkomen: ein CRISPR-Aktivationsscreen

Rhabdomyosarcomas (RMS) are the most common childhood soft tissue tumors. Despite multimodal treatment regimen, around 20% of pediatric sarcoma patients still suffer from local relapses, indicating underlying resistance to common therapy. Translocation of PAX3 and FOXO1, and the expression of the respective fusion protein, is known to predict adverse prognosis, but no significant improvement in patient outcome of these high-risk patients has been achieved in the last three decades. Here, we performed a genome-wide CRISPR activation screen in the fusion-positive cell line Rh4 to search for genes associated with resistance to actinomycin D (ActD), a drug commonly used in the treatment of RMS and other pediatric cancers such as nephroblastoma and Ewing’s sarcoma. The top-ranking hit was the gene ABCC1, encoding for a drug efflux-pump. The expression of ATP-binding cassette (ABC) transporters like ABCC1 has long been suspected to play a role in multidrug resistance, however small molecule inhibitors targeting them are still not approved for clinical use. In this study, we were able to observe a synergistic relationship between ActD and the ABCC1 inhibitor tetrandrine, implying a possible benefit of the inhibition of ABCC1 in ActD-resistant RMS and highlighting the necessity of further preclinical and clinical investigations into the role of ABCC1 in RMS in response to treatment.Rhabdomyosarkome (RMS) sind die häufigsten pädiatrischen Weichteilsarkome. Trotz eines multimodalen Behandlungsansatzes leiden 20% aller pädiatrischen Sarkompatient*innen unter lokalen Rezidiven, was auf zugrundeliegende Resistenzmechanismen schließen lässt. Die Translokation von PAX3 und FOXO1 und die Expression des entsprechenden Fusionsproteins sind bekannte negative Prädiktoren für die Prognose von RMS. In den letzten 30 Jahren konnte jedoch keine Verbesserung des Therapieerfolges von Hochrisikopatienten erzielt werden. In dieser Studie nutzten wir einen genomweiten CRISPR-Aktivationsscreen in der fusionspositiven RMS-Zelllinie Rh4, um nach Genen zu suchen, die mit einer Resistenz gegenüber Actinomycin D (ActD) assoziiert sind. Dieses Chemotherapeutikum findet nicht nur bei RMS-Patient*innen, sondern auch in der Behandlung anderer pädiatrischer Tumoren wie dem Nephroblastom und dem Ewing-Sarkom Verwendung. Das Gen mit dem höchsten Rang war die Effluxpumpe ABCC1. Obwohl die Expression von ATP-binding cassette (ABC)-Transportern wie ABCC1 schon lange unter dem Verdacht steht, zu Multiresistenzen von Tumoren beizutragen, sind gezielte small molecule-Therapien noch nicht für den klinischen Gebrauch zugelassen. In dieser Studie konnten wir eine synergistische Beziehung zwischen ActD und dem ABCC1-Inhibitor Tetrandrine nachweisen, welches auf einen möglichen Vorteil einer Kombinationstherapie in ActD-resistenten RMS hinweist. Zudem unterstreicht unsere Studie die Notwendigkeit, die Rolle von ABCC1 in RMS in präklinischen und klinischen Studien weiter zu erforschen