Transcriptomic analysis reveals a tissue-specific loss of identity during ageing and cancer.

INTRODUCTION: Understanding changes in cell identity in cancer and ageing is of great importance. In this work, we analyzed how gene expression changes in human tissues are associated with tissue specificity during cancer and ageing using transcriptome data from TCGA and GTEx. RESULTS: We found significant downregulation of tissue-specific genes during ageing in 40% of the tissues analyzed, which suggests loss of tissue identity with age. For most cancer types, we have noted a consistent pattern of downregulation in genes that are specific to the tissue from which the tumor originated. Moreover, we observed in cancer an activation of genes not usually expressed in the tissue of origin as well as an upregulation of genes specific to other tissues. These patterns in cancer were associated with patient survival. The age of the patient, however, did not influence these patterns. CONCLUSION: We identified loss of cellular identity in 40% of the tissues analysed during human ageing, and a clear pattern in cancer, where during tumorigenesis cells express genes specific to other organs while suppressing the expression of genes from their original tissue. The loss of cellular identity observed in cancer is associated with prognosis and is not influenced by age, suggesting that it is a crucial stage in carcinogenesis.

Smart Sensors and Microtechnologies in the Precision Medicine Approach against Lung Cancer.

BACKGROUND AND RATIONALE: The therapeutic interventions against lung cancer are currently based on a fully personalized approach to the disease with considerable improvement of patients' outcome. Alongside continuous scientific progresses and research investments, massive technologic efforts, innovative challenges, and consolidated achievements together with research investments are at the bases of the engineering and manufacturing revolution that allows a significant gain in clinical setting. AIM AND METHODS: The scope of this review is thus to focus, rather than on the biologic traits, on the analysis of the precision sensors and novel generation materials, as semiconductors, which are below the clinical development of personalized diagnosis and treatment. In this perspective, a careful revision and analysis of the state of the art of the literature and experimental knowledge is presented. RESULTS: Novel materials are being used in the development of personalized diagnosis and treatment for lung cancer. Among them, semiconductors are used to analyze volatile cancer compounds and allow early disease diagnosis. Moreover, they can be used to generate MEMS which have found an application in advanced imaging techniques as well as in drug delivery devices. CONCLUSIONS: Overall, these issues represent critical issues only partially known and generally underestimated by the clinical community. These novel micro-technology-based biosensing devices, based on the use of molecules at atomic concentrations, are crucial for clinical innovation since they have allowed the recent significant advances in cancer biology deciphering as well as in disease detection and therapy. There is an urgent need to create a stronger dialogue between technologists, basic researchers, and clinicians to address all scientific and manufacturing efforts towards a real improvement in patients' outcome. Here, great attention is focused on their application against lung cancer, from their exploitations in translational research to their application in diagnosis and treatment development, to ensure early diagnosis and better clinical outcomes.

A mainstreaming oncogenomics model: improving the identification of Lynch syndrome.

Introduction: "Mainstreaming" is a proposed strategy to integrate genomic testing into oncology. The aim of this paper is to develop a mainstreaming oncogenomics model by identifying health system interventions and implementation strategies for mainstreaming Lynch syndrome genomic testing. Methods: A rigorous theoretical approach inclusive of conducting a systematic review and qualitative and quantitative studies was undertaken using the Consolidated Framework for Implementation Research. Theory-informed implementation data were mapped to the Genomic Medicine Integrative Research framework to generate potential strategies. Results: The systematic review identified a lack of theory-guided health system interventions and evaluation for Lynch syndrome and other mainstreaming programs. The qualitative study phase included 22 participants from 12 health organizations. The quantitative Lynch syndrome survey included 198 responses: 26% and 66% from genetic and oncology health professionals, respectively. Studies identified the relative advantage and clinical utility of mainstreaming to improve genetic test access and to streamline care, and adaptation of current processes was recognized for results delivery and follow-up. Barriers identified included funding, infrastructure and resources, and the need for process and role delineation. The interventions to overcome barriers were as follows: embedded mainstream genetic counselors, electronic medical record genetic test ordering, results tracking, and mainstreaming education resources. Implementation evidence was connected through the Genomic Medicine Integrative Research framework resulting in a mainstreaming oncogenomics model. Discussion: The proposed mainstreaming oncogenomics model acts as a complex intervention. It features an adaptable suite of implementation strategies to inform Lynch syndrome and other hereditary cancer service delivery. Implementation and evaluation of the model are required in future research.

Nanoinformatics and Personalized Medicine: An Advanced Cumulative Approach for Cancer Management.

BACKGROUND: Even though the battle against one of the deadliest diseases, cancer, has advanced remarkably in the last few decades and the survival rate has improved significantly; the search for an ultimate cure remains a utopia. Nanoinformatics, which is bioinformatics coupled with nanotechnology, endows many novel research opportunities in the preclinical and clinical development of personalized nanosized drug carriers in cancer therapy. Personalized nanomedicines serve as a promising treatment option for cancer owing to their noninvasiveness and their novel approach. Explicitly, the field of personalized medicine is expected to have an enormous impact soon because of its many advantages, namely its versatility to adapt a drug to a cohort of patients. OBJECTIVE: The current review explains the application of this newly emerging field called nanoinformatics to the field of precision medicine. This review also recapitulates how nanoinformatics could hasten the development of personalized nanomedicine for cancer, which is undoubtedly the need of the hour. CONCLUSION: This approach has been facilitated by a humongous impending field named Nanoinformatics. These breakthroughs and advances have provided insight into the future of personalized medicine. Imperatively, they have been enabling landmark research to merge all advances, creating nanosized particles that contain drugs targeting cell surface receptors and other potent molecules designed to kill cancerous cells. Nanoparticle- based medicine has been developing and has become a center of attention in recent years, focusing primely on proficient delivery systems for various chemotherapy drugs.

Analysis of single-nucleotide polymorphisms in genes associated with triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is a rare variant of breast cancer (BC) known to be aggressive and refractory. TNBC lacks effective early diagnostic and therapeutic options leading to poorer outcomes. The genomic landscape and alterations leading to BC and TNBC are vast and unclear. Single nucleotide polymorphisms (SNPs) are a widespread form of genetic alterations with a multi-faceted impact on multiple diseases, including BC and TNBC. In this study, we attempted to construct a framework that could identify genes associated with TNBC and screen the SNPs reported in these genes using a set of computational predictors. This framework helped identify BRCA1, BRCA2, EGFR, PIK3CA, PTEN, and TP53 as recurrent genes associated with TNBC. We found 2%-29% of reported SNPs across genes to be typed pathogenic by all the predictors in the framework. We demonstrate that our framework prediction on BC samples identifies 99% of alterations as pathogenic by at least one predictor and 32% as pathogenic by all the predictors. Our framework could be an initial step in developing an early diagnosis of TNBC and potentially help improve the understanding of therapeutic resistance and sensitivity.

Oncogenomic Changes in Pancreatic Cancer and Their Detection in Stool.

Pancreatic cancer (PC) is an aggressive malignancy with a dismal prognosis. To improve patient survival, the development of screening methods for early diagnosis is pivotal. Oncogenomic alterations present in tumor tissue are a suitable target for non-invasive screening efforts, as they can be detected in tumor-derived cells, cell-free nucleic acids, and extracellular vesicles, which are present in several body fluids. Since stool is an easily accessible source, which enables convenient and cost-effective sampling, it could be utilized for the screening of these traces. Herein, we explore the various oncogenomic changes that have been detected in PC tissue, such as chromosomal aberrations, mutations in driver genes, epigenetic alterations, and differentially expressed non-coding RNA. In addition, we briefly look into the role of altered gut microbiota in PC and their possible associations with oncogenomic changes. We also review the findings of genomic alterations in stool of PC patients, and the potentials and challenges of their future use for the development of stool screening tools, including the possible combination of genomic and microbiota markers.

Current status of precision medicine in pediatric oncology in Spain: a consensus report by the Spanish Society of Paediatric Haematology and Oncology (SEHOP)

The study analyzes the current status of personalized medicine in pediatric oncology in Spain. It gathers national data on the tumor molecular studies and genomic sequencing carried out at diagnosis and at relapse, the centers that perform these studies, the technology used and the interpretation and clinical applicability of the results. Current challenges and future directions to achieve a coordinated national personalized medicine strategy in pediatric oncology are also discussed. Next generation sequencing-based (NGS) gene panels are the technology used in the majority of centers and financial limitations are the main reason for not incorporating these studies into routine care. Nowadays, the application of precision medicine in pediatric oncology is a reality in a great number of Spanish centers. However, its implementation is uneven and lacks standardization of protocols; therefore, national coordination to overcome the inequalities is required. Collaborative work within the Personalized Medicine Group of SEHOP is an adequate framework for encouraging a step forward in the effort to move precision medicine into the national healthcare system.

Current status of precision medicine in pediatric oncology in Spain: a consensus report by the Spanish Society of Paediatric Haematology and Oncology (SEHOP).

The study analyzes the current status of personalized medicine in pediatric oncology in Spain. It gathers national data on the tumor molecular studies and genomic sequencing carried out at diagnosis and at relapse, the centers that perform these studies, the technology used and the interpretation and clinical applicability of the results. Current challenges and future directions to achieve a coordinated national personalized medicine strategy in pediatric oncology are also discussed. Next generation sequencing-based (NGS) gene panels are the technology used in the majority of centers and financial limitations are the main reason for not incorporating these studies into routine care. Nowadays, the application of precision medicine in pediatric oncology is a reality in a great number of Spanish centers. However, its implementation is uneven and lacks standardization of protocols; therefore, national coordination to overcome the inequalities is required. Collaborative work within the Personalized Medicine Group of SEHOP is an adequate framework for encouraging a step forward in the effort to move precision medicine into the national healthcare system.

Microbiota Alterations and Their Association with Oncogenomic Changes in Pancreatic Cancer Patients.

Pancreatic cancer (PC) is an aggressive disease with a high mortality and poor prognosis. The human microbiome is a key factor in many malignancies, having the ability to alter host metabolism and immune responses and participate in tumorigenesis. Gut microbes have an influence on physiological functions of the healthy pancreas and are themselves controlled by pancreatic secretions. An altered oral microbiota may colonize the pancreas and cause local inflammation by the action of its metabolites, which may lead to carcinogenesis. The mechanisms behind dysbiosis and PC development are not completely clear. Herein, we review the complex interactions between PC tumorigenesis and the microbiota, and especially the question, whether and how an altered microbiota induces oncogenomic changes, or vice versa, whether cancer mutations have an impact on microbiota composition. In addition, the role of the microbiota in drug efficacy in PC chemo- and immunotherapies is discussed. Possible future scenarios are the intentional manipulation of the gut microbiota in combination with therapy or the utilization of microbial profiles for the noninvasive screening and monitoring of PC.

Horizon Scanning in Cancer Genomics: How Advances in Genomic Medicine Will Change Cancer Care Over the Next Decade.

PURPOSE OF REVIEW: Advances in genomic medicine have the potential to revolutionise cancer patient care by driving forwards the clinical practice of precision oncology. This review aims to outline how genomic medicine advances may alter the care of cancer patients and their families over the next 10 years. RECENT FINDINGS: The translation of oncogenomic advances into the clinical environment will likely be facilitated by the increasing availability of next-generation sequencing technologies and the increasing genomic literacy of healthcare professionals. The implementation of the centralised, nationwide NHS Genomic Medicine Service promises to improve equity of cancer care and to facilitate personalisation of almost every stage of the care pathway, from informing population screening and how we diagnose cancer to delivering prognoses and surveillance. Advances in cancer pharmacogenomics, and other "omics" technologies, have a tremendous potential to optimise patient care. Genomic medicine advances will also enhance the care offered to cancer patients' families. SUMMARY: Genomic medicine advances are likely to transform almost every aspect of a cancer patient's care pathway. Cancer care will profoundly improve over the next decade, increasing UK cancer survival rates and improving patient outcomes.

Long-term disease control and survival observed after stereotactic ablative body radiotherapy for oligometastatic breast cancer.

PURPOSE: We examined the characteristics of breast cancer patients with oligometastases (OM) treated with stereotactic ablative body radiotherapy (SABR) to identify factors associated with local progression, distant metastasis progression, time to subsequent therapy, progression-free survival (PFS), and overall survival (OS). METHODS: We retrospectively reviewed a single-institution database of patients treated with radiotherapy between 2008 and 2018 and identified 79 patients who received SABR to OM. Twenty-seven patients had genetic testing of metastatic tumors using an institutional targeted sequencing platform. Kaplan-Meier analysis, Cox regression, and competing risk models were used to compare clinical and genetic correlates with outcomes. RESULTS: Median follow-up was 50 months (IQR: 29-66) with 67% of patients alive at the last follow-up. Of the 65% of patients who progressed, 82% progressed outside of the radiation field, 18% experienced local failure, and 80% had oligoprogression. Median OS was 86 months (IQR: 29-66), and PFS was 33 months (IQR: 10-38). Less than 5 years from diagnosis to SABR and triple-negative breast cancer (TNBC) were associated with worse OS. Advanced T stage, any prior chemotherapy, and TNBC were associated with worse PFS. Alterations in CEBPB, RB1, TBX3, PTEN, and CDK4 were associated with worse survival outcomes. CONCLUSION: Long-term systemic disease control and survival can be achieved with SABR for oligometastatic breast cancer. Hormone receptor-positive patients with a long disease interval from initial diagnosis and limited systemic progression history may be ideal for SABR to all sites of disease.

Bringing Onco-Innovation to Europe's Healthcare Systems: The Potential of Biomarker Testing, Real World Evidence, Tumour Agnostic Therapies to Empower Personalised Medicine.

Rapid and continuing advances in biomarker testing are not being matched by uptake in health systems, and this is hampering both patient care and innovation. It also risks costing health systems the opportunity to make their services more efficient and, over time, more economical. The potential that genomics has brought to biomarker testing in diagnosis, prediction and research is being realised, pre-eminently in many cancers, but also in an ever-wider range of conditions-notably BRCA1/2 testing in ovarian, breast, pancreatic and prostate cancers. Nevertheless, the implementation of genetic testing in clinical routine setting is still challenging. Development is impeded by country-related heterogeneity, data deficiencies, and lack of policy alignment on standards, approval-and the role of real-world evidence in the process-and reimbursement. The acute nature of the problem is compellingly illustrated by the particular challenges facing the development and use of tumour agnostic therapies, where the gaps in preparedness for taking advantage of this innovative approach to cancer therapy are sharply exposed. Europe should already have in place a guarantee of universal access to a minimum suite of biomarker tests and should be planning for an optimum testing scenario with a wider range of biomarker tests integrated into a more sophisticated health system articulated around personalised medicine. Improving healthcare and winning advantages for Europe's industrial competitiveness and innovation require an appropriate policy framework-starting with an update to outdated recommendations. We show herein the main issues and proposals that emerged during the previous advisory boards organised by the European Alliance for Personalized Medicine which mainly focus on possible scenarios of harmonisation of both oncogenetic testing and management of cancer patients.

Evaluating genomic biomarkers associated with resistance or sensitivity to chemotherapy in patients with advanced breast and colorectal cancer.

INTRODUCTION: Carcinogenesis is driven by an array of complex genomic patterns; these patterns can render an individual resistant or sensitive to certain chemotherapy agents. The Personalized Oncogenomics (POG) project at BC Cancer has performed integrative genomic analysis of whole tumour genomes and transcriptomes for over 700 patients with advanced cancers, with an aim to predict therapeutic sensitivities. The aim of this study was to utilize the POG genomic data to evaluate a discrete set of biomarkers associated with chemo-sensitivity or-resistance in advanced stage breast and colorectal cancer POG patients. METHODS: This was a retrospective multi-centre analysis across all BC CANCER sites. All breast and colorectal cancer patients enrolled in the POG program between July 1, 2012 and November 30, 2016 were eligible for inclusion. Within the breast cancer population, those treated with capecitabine, paclitaxel, and everolimus were analyzed, and for the colorectal cancer patients, those treated with capecitabine, bevacizumab, irinotecan, and oxaliplatin were analyzed. The expression levels of the selected biomarkers of interest (EPHB4, FIGF, CD133, DICER1, DPYD, TYMP, TYMS, TAP1, TOP1, CKDN1A, ERCC1, GSTP1, BRCA1, PTEN, ABCB1, TLE3, and TXNDC17) were reported as mRNA percentiles. RESULTS: For the breast cancer population, there were 32 patients in the capecitabine cohort, 15 in the everolimus cohort, and 12 in the paclitaxel cohort. For the colorectal cancer population, there were 29 patients in the bevacizumab cohort, 12 in the oxaliplatin cohort, 29 in the irinotecan cohort, and 6 in the capecitabine cohort. Of the biomarkers evaluated, the strongest associations were found between Bevacizumab-based therapy and DICER1 (P = 0.0445); and between capecitabine therapy and TYMP (P = 0.0553). CONCLUSIONS: Among breast cancer patients, higher TYMP expression was associated with sensitivity to capecitabine. Among colorectal cancer patients, higher DICER1 expression was associated with sensitivity to bevacizumab-based therapy. This study supports further assessment of the potential predictive value of mRNA expression of these genomic biomarkers.

Integrative Network Fusion: A Multi-Omics Approach in Molecular Profiling.

Recent technological advances and international efforts, such as The Cancer Genome Atlas (TCGA), have made available several pan-cancer datasets encompassing multiple omics layers with detailed clinical information in large collection of samples. The need has thus arisen for the development of computational methods aimed at improving cancer subtyping and biomarker identification from multi-modal data. Here we apply the Integrative Network Fusion (INF) pipeline, which combines multiple omics layers exploiting Similarity Network Fusion (SNF) within a machine learning predictive framework. INF includes a feature ranking scheme (rSNF) on SNF-integrated features, used by a classifier over juxtaposed multi-omics features (juXT). In particular, we show instances of INF implementing Random Forest (RF) and linear Support Vector Machine (LSVM) as the classifier, and two baseline RF and LSVM models are also trained on juXT. A compact RF model, called rSNFi, trained on the intersection of top-ranked biomarkers from the two approaches juXT and rSNF is finally derived. All the classifiers are run in a 10x5-fold cross-validation schema to warrant reproducibility, following the guidelines for an unbiased Data Analysis Plan by the US FDA-led initiatives MAQC/SEQC. INF is demonstrated on four classification tasks on three multi-modal TCGA oncogenomics datasets. Gene expression, protein expression and copy number variants are used to predict estrogen receptor status (BRCA-ER, N = 381) and breast invasive carcinoma subtypes (BRCA-subtypes, N = 305), while gene expression, miRNA expression and methylation data is used as predictor layers for acute myeloid leukemia and renal clear cell carcinoma survival (AML-OS, N = 157; KIRC-OS, N = 181). In test, INF achieved similar Matthews Correlation Coefficient (MCC) values and 97% to 83% smaller feature sizes (FS), compared with juXT for BRCA-ER (MCC: 0.83 vs. 0.80; FS: 56 vs. 1801) and BRCA-subtypes (0.84 vs. 0.80; 302 vs. 1801), improving KIRC-OS performance (0.38 vs. 0.31; 111 vs. 2319). INF predictions are generally more accurate in test than one-dimensional omics models, with smaller signatures too, where transcriptomics consistently play the leading role. Overall, the INF framework effectively integrates multiple data levels in oncogenomics classification tasks, improving over the performance of single layers alone and naive juxtaposition, and provides compact signature sizes.

Biomarkers for Early Cancer Diagnosis: Prospects for Success through the Lens of Tumor Genetics.

Thousands of candidate cancer biomarkers have been proposed, but so far, few are used in cancer screening. Failure to implement these biomarkers is attributed to technical and design flaws in the discovery and validation phases, but a major obstacle stems from cancer biology itself. Oncogenomics has revealed broad genetic heterogeneity among tumors of the same histology and same tissue (or organ) from different patients, while tumors of different tissue origins also share common genetic mutations. Moreover, there is wide intratumor genetic heterogeneity among cells within any single neoplasm. These findings seriously limit the prospects of finding a single biomarker with high specificity for early cancer detection. Current research focuses on developing biomarker panels, with data assessment by machine-learning algorithms. Whether such approaches will overcome the inherent limitations posed by tumor biology and lead to tests with true clinical value remains to be seen.

The Many Faces of Gene Regulation in Cancer: A Computational Oncogenomics Outlook.

Cancer is a complex disease at many different levels. The molecular phenomenology of cancer is also quite rich. The mutational and genomic origins of cancer and their downstream effects on processes such as the reprogramming of the gene regulatory control and the molecular pathways depending on such control have been recognized as central to the characterization of the disease. More important though is the understanding of their causes, prognosis, and therapeutics. There is a multitude of factors associated with anomalous control of gene expression in cancer. Many of these factors are now amenable to be studied comprehensively by means of experiments based on diverse omic technologies. However, characterizing each dimension of the phenomenon individually has proven to fall short in presenting a clear picture of expression regulation as a whole. In this review article, we discuss some of the more relevant factors affecting gene expression control both, under normal conditions and in tumor settings. We describe the different omic approaches that we can use as well as the computational genomic analysis needed to track down these factors. Then we present theoretical and computational frameworks developed to integrate the amount of diverse information provided by such single-omic analyses. We contextualize this within a systems biology-based multi-omic regulation setting, aimed at better understanding the complex interplay of gene expression deregulation in cancer.

Mouse models in the era of large human tumour sequencing studies.

Cancer is a complex disease in which cells progressively accumulate mutations disrupting their cellular processes. A fraction of these mutations drive tumourigenesis by affecting oncogenes or tumour suppressor genes, but many mutations are passengers with no clear contribution to tumour development. The advancement of DNA and RNA sequencing technologies has enabled in-depth analysis of thousands of human tumours from various tissues to perform systematic characterization of their (epi)genomes and transcriptomes in order to identify (epi)genetic changes associated with cancer. Combined with considerable progress in algorithmic development, this expansion in scale has resulted in the identification of many cancer-associated mutations, genes and pathways that are considered to be potential drivers of tumour development. However, it remains challenging to systematically identify drivers affected by complex genomic rearrangements and drivers residing in non-coding regions of the genome or in complex amplicons or deletions of copy-number driven tumours. Furthermore, functional characterization is challenging in the human context due to the lack of genetically tractable experimental model systems in which the effects of mutations can be studied in the context of their tumour microenvironment. In this respect, mouse models of human cancer provide unique opportunities for pinpointing novel driver genes and their detailed characterization. In this review, we provide an overview of approaches for complementing human studies with data from mouse models. We also discuss state-of-the-art technological developments for cancer gene discovery and validation in mice.

BioBankWarden: A web-based system to support translational cancer research by managing clinical and biomaterial data.

BACKGROUND: Researchers of translational medicine face numerous challenges in attempting to bring research results to the bedside. This field of research covers a wide range of resources, including blood and tissue samples, which are processed for isolation of RNA and DNA to study cancer omics data (genomics, proteomics and metabolomics). Clinical information about patients׳ habits, family history, physical examinations, remissions, etc., is also important to underpin studies aimed at identifying patterns that lead to the development of cancer and to its successful treatment. PURPOSE: Development of a web-based computer system-BioBankWarden-to manage, consolidate and integrate these diversified data, enabling cancer research groups to retrieve and analyze clinical and biomolecular data within an integrative environment. The system has a three-tier architecture comprising database, logic and user-interface layers. RESULTS: The system׳s integrated database and user-friendly interface allow for the control of patient records, biomaterial storage, research groups, research projects, users and biomaterial exchange. CONCLUSIONS: BioBankWarden can be used to store and retrieve specific information from different clinical fields linked to biomaterials collected from patients, providing the functionalities required to support translational research in the field of cancer.

Sleeping Beauty transposition: from biology to applications.

Sleeping Beauty (SB) is the first synthetic DNA transposon that was shown to be active in a wide variety of species. Here, we review studies from the last two decades addressing both basic biology and applications of this transposon. We discuss how host-transposon interaction modulates transposition at different steps of the transposition reaction. We also discuss how the transposon was translated for gene delivery and gene discovery purposes. We critically review the system in clinical, pre-clinical and non-clinical settings as a non-viral gene delivery tool in comparison with viral technologies. We also discuss emerging SB-based hybrid vectors aimed at combining the attractive safety features of the transposon with effective viral delivery. The success of the SB-based technology can be fundamentally attributed to being able to insert fairly randomly into genomic regions that allow stable long-term expression of the delivered transgene cassette. SB has emerged as an efficient and economical toolkit for safe and efficient gene delivery for medical applications.

Pathway-Informed Classification System (PICS) for Cancer Analysis Using Gene Expression Data.

We introduce Pathway-Informed Classification System (PICS) for classifying cancers based on tumor sample gene expression levels. PICS is a computational method capable of expeditiously elucidating both known and novel biological pathway involvement specific to various cancers and uses that learned pathway information to separate patients into distinct classes. The method clearly separates a pan-cancer dataset by tissue of origin and also sub-classifies individual cancer datasets into distinct survival classes. Gene expression values are collapsed into pathway scores that reveal which biological activities are most useful for clustering cancer cohorts into subtypes. Variants of the method allow it to be used on datasets that do and do not contain noncancerous samples. Activity levels of all types of pathways, broadly grouped into metabolic, cellular processes and signaling, and immune system, are useful for separating the pan-cancer cohort. In the clustering of specific cancer types, certain pathway types become more valuable depending on the site being studied. For lung cancer, signaling pathways dominate; for pancreatic cancer, signaling and metabolic pathways dominate; and for melanoma, immune system pathways are the most useful. This work suggests the utility of pathway-level genomic analysis and points in the direction of using pathway classification for predicting the efficacy and side effects of drugs and radiation.