A deep-learning framework to predict cancer treatment response from histopathology images through imputed transcriptomics.

Advances in artificial intelligence have paved the way for leveraging hematoxylin and eosin-stained tumor slides for precision oncology. We present ENLIGHT-DeepPT, an indirect two-step approach consisting of (1) DeepPT, a deep-learning framework that predicts genome-wide tumor mRNA expression from slides, and (2) ENLIGHT, which predicts response to targeted and immune therapies from the inferred expression values. We show that DeepPT successfully predicts transcriptomics in all 16 The Cancer Genome Atlas cohorts tested and generalizes well to two independent datasets.

Prediction of cancer treatment response from histopathology images through imputed transcriptomics.

Advances in artificial intelligence have paved the way for leveraging hematoxylin and eosin (H&E)-stained tumor slides for precision oncology. We present ENLIGHT-DeepPT, an approach for predicting response to multiple targeted and immunotherapies from H&E-slides. In difference from existing approaches that aim to predict treatment response directly from the slides, ENLIGHT-DeepPT is an indirect two-step approach consisting of (1) DeepPT, a new deep-learning framework that predicts genome-wide tumor mRNA expression from slides, and (2) ENLIGHT, which predicts response based on the DeepPT inferred expression values.

The new world of RNA diagnostics and therapeutics.

The 5th Workshop IRE on Translational Oncology was held in Rome (Italy) on 27-28 March at the IRCCS Regina Elena National Cancer Institute. This meeting entitled "The New World of RNA diagnostics and therapeutics" highlightes the significant progress in the RNA field made over the last years. Research moved from pure discovery towards the development of diagnostic biomarkers or RNA-base targeted therapies seeking validation in several clinical trials.

Clinically oriented prediction of patient response to targeted and immunotherapies from the tumor transcriptome.

Background: Precision oncology is gradually advancing into mainstream clinical practice, demonstrating significant survival benefits. However, eligibility and response rates remain limited in many cases, calling for better predictive biomarkers.

Methods: We present ENLIGHT, a transcriptomics-based computational approach that identifies clinically relevant genetic interactions and uses them to predict a patient's response to a variety of therapies in multiple cancer types without training on previous treatment response data.

An autonomous debating system.

Artificial intelligence (AI) is defined as the ability of machines to perform tasks that are usually associated with intelligent beings. Argument and debate are fundamental capabilities of human intelligence, essential for a wide range of human activities, and common to all human societies. The development of computational argumentation technologies is therefore an important emerging discipline in AI research.

Microbes can help explain the evolution of host altruism.

The evolution of altruistic behaviour, which is costly to the donor but beneficial for the recipient, is among the most intriguing questions in evolutionary biology. Several theories have been proposed to explain it, including kin selection, group selection and reciprocity. Here we propose that microbes that manipulate their hosts to act altruistically could be favoured by selection, and may play a role in the widespread occurrence of altruism.

Identifying and Investigating Unexpected Response to Treatment: A Diabetes Case Study.

The availability of electronic health records creates fertile ground for developing computational models of various medical conditions. We present a new approach for detecting and analyzing patients with unexpected responses to treatment, building on machine learning and statistical methodology. Given a specific patient, we compute a statistical score for the deviation of the patient's response from responses observed in other patients having similar characteristics and medication regimens.

Paradoxical Hypersusceptibility of Drug-resistant Mycobacteriumtuberculosis to β-lactam Antibiotics.

Mycobacterium tuberculosis (M. tuberculosis) is considered innately resistant to β-lactam antibiotics. However, there is evidence that susceptibility to β-lactam antibiotics in combination with β-lactamase inhibitors is variable among clinical isolates, and these may present therapeutic options for drug-resistant cases.

Changing the approach to treatment choice in epilepsy using big data.

Purpose: A UCB-IBM collaboration explored the application of machine learning to large claims databases to construct an algorithm for antiepileptic drug (AED) choice for individual patients.

Methods: Claims data were collected between January 2006 and September 2011 for patients with epilepsy > 16 years of age. A subset of patient claims with a valid index date of AED treatment change (new, add, or switch) were used to train the AED prediction model by retrospectively evaluating an index date treatment for subsequent treatment change.

A system for identifying and investigating unexpected response to treatment.

The availability of electronic health records creates fertile ground for developing computational models for various medical conditions. Using machine learning, we can detect patients with unexpected responses to treatment and provide statistical testing and visualization tools to help further analysis. The new system was developed to help researchers uncover new features associated with reduced response to treatment, and to aid physicians in identifying patients that are not responding to treatment as expected and hence deserve more attention.

Novel microRNA-based assay demonstrates 92% agreement with diagnosis based on clinicopathologic and management data in a cohort of patients with carcinoma of unknown primary.

Background: Cancer of unknown or uncertain primary is a major diagnostic and clinical challenge, since identifying the tissue-of-origin of metastases is crucial for selecting optimal treatment. MicroRNAs are a family of non-coding, regulatory RNA molecules that are tissue-specific, with a great potential to be excellent biomarkers.

Methods: In this study we tested the performance of a microRNA-based assay in formalin-fixed paraffin-embedded samples from 84 CUP patients.

Predicting progression of bladder urothelial carcinoma using microRNA expression.

Unlabelled: WHAT'S KNOWN ON THE SUBJECT? AND WHAT DOES THE STUDY ADD?: Recurrence and progression prediction in urothelial cancer is currently based on clinical and pathological factors: tumour grade, tumour stage, number of lesions, tumour size, previous recurrence rate, and presence of concomitant carcinoma in situ. These factors are not specific enough to predict progression and ∼50% of patients diagnosed as high risk in fact do not progress within 3 years. Patient follow-up is both expensive and unpleasant (frequent invasive cystoscopies).

Global microRNA profiling in favorable prognosis subgroups of cancer of unknown primary (CUP) demonstrates no significant expression differences with metastases of matched known primary tumors.

No data exist on biologic differences between Cancer of unknown primary (CUP) and metastatic solid tumors of known primary site. We assigned a primary tissue of origin in 40 favorable CUP patients (A: serous peritoneal carcinomatosis n = 14, B: axillary adenocarcinoma n = 8, C: upper squamous cervical adenopathy n = 18) by means of a 64-microRNA assay. Subsequently, we profiled the expression of 733 microRNAs (miRs) in the CUP cases and compared results with metastases from 20 ovarian carcinomas, 10 breast adenocarcinomas, 20 squamous head neck or lung tumors.

Classification of the four main types of lung cancer using a microRNA-based diagnostic assay.

For patients with primary lung cancer, accurate determination of the tumor type significantly influences treatment decisions. However, techniques and methods for lung cancer typing lack standardization. In particular, owing to limited tumor sample amounts and the poor quality of some samples, the classification of primary lung cancers using small preoperative biopsy specimens presents a diagnostic challenge using current tools.

A second-generation microRNA-based assay for diagnosing tumor tissue origin.

Background: Cancers of unknown primary origin (CUP) constitute 3%-5% (50,000 to 70,000 cases) of all newly diagnosed cancers per year in the United States. Including cancers of uncertain primary origin, the total number increases to 12%-15% (180,000 to 220,000 cases) of all newly diagnosed cancers per year in the United States. Cancers of unknown/uncertain primary origins present major diagnostic and clinical challenges because the tumor tissue of origin is crucial for selecting optimal treatment.

Tumor microRNA-29a expression and the risk of recurrence in stage II colon cancer.

There is emerging evidence for the prognostic role of various microRNA (miRNA) molecules in colon cancer. The aim of this study was therefore to compare the miRNA profiles in the primary tumor of patients with recurrent and non-recurrent colon cancer. The study population included 110 patients, 51 (46%) with stage I and 59 (54%) with stage II disease, who underwent curative colectomies between 1995 and 2005 without adjuvant therapy and for whom reliable miRNA expression data were available.

Prospective gene signature study using microRNA to identify the tissue of origin in patients with carcinoma of unknown primary.

Purpose: Accurate identification of tissue of origin (ToO) for patients with carcinoma of unknown primary (CUP) may help customize therapy to the putative primary and thereby improve the clinical outcome. We prospectively studied the performance of a microRNA-based assay to identify the ToO in CUP patients.

Experimental Design: Formalin-fixed paraffin-embedded (FFPE) metastatic tissue from 104 patients was reviewed and 87 of these contained sufficient tumor for testing.

Accurate classification of metastatic brain tumors using a novel microRNA-based test.

Background: Identification of the tissue of origin of a brain metastatic tumor is vital to its management. Carcinoma of unknown primary (CUP) is common in oncology, representing 3%-5% of all invasive malignancies. We aimed to validate a recently developed microRNA-based quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) test for identifying the tumor tissue of origin, first in a consecutive cohort of metastatic tumors of known origin and then in a cohort of CUP cases resected from the central nervous system (CNS).

Validation of a microRNA-based qRT-PCR test for accurate identification of tumor tissue origin.

Identification of the tissue of origin of a tumor is vital to its management. Previous studies showed tissue-specific expression patterns of microRNA and suggested that microRNA profiling would be useful in addressing this diagnostic challenge. MicroRNAs are well preserved in formalin-fixed, paraffin-embedded (FFPE) samples, further supporting this approach.

hsa-miR-29c* is linked to the prognosis of malignant pleural mesothelioma.

The inability to forecast outcomes for malignant mesothelioma prevents clinicians from providing aggressive multimodality therapy to the most appropriate individuals who may benefit from such an approach. We investigated whether specific microRNAs (miR) could segregate a largely surgically treated group of mesotheliomas into good or bad prognosis categories. A training set of 44 and a test set of 98 mesothelioma tumors were analyzed by a custom miR platform, along with 9 mesothelioma cell lines and 3 normal mesothelial lines.

Diagnostic assay based on hsa-miR-205 expression distinguishes squamous from nonsquamous non-small-cell lung carcinoma.

Purpose: Recent advances in treatment of lung cancer require greater accuracy in the subclassification of non-small-cell lung cancer (NSCLC). Targeted therapies which inhibit tumor angiogenesis pose higher risk for adverse response in cases of squamous cell carcinoma. Interobserver variability and the lack of specific, standardized assays limit the current abilities to adequately stratify patients for such treatments.

MicroRNAs accurately identify cancer tissue origin.

MicroRNAs (miRNAs) belong to a class of noncoding, regulatory RNAs that is involved in oncogenesis and shows remarkable tissue specificity. Their potential for tumor classification suggests they may be used in identifying the tissue in which cancers of unknown primary origin arose, a major clinical problem. We measured miRNA expression levels in 400 paraffin-embedded and fresh-frozen samples from 22 different tumor tissues and metastases.

Identification of hundreds of conserved and nonconserved human microRNAs.

MicroRNAs are noncoding RNAs of approximately 22 nucleotides that suppress translation of target genes by binding to their mRNA and thus have a central role in gene regulation in health and disease. To date, 222 human microRNAs have been identified, 86 by random cloning and sequencing, 43 by computational approaches and the rest as putative microRNAs homologous to microRNAs in other species. To prove our hypothesis that the total number of microRNAs may be much larger and that several have emerged only in primates, we developed an integrative approach combining bioinformatic predictions with microarray analysis and sequence-directed cloning.

Dynamic model of the octopus arm. I. Biomechanics of the octopus reaching movement.

The octopus arm requires special motor control schemes because it consists almost entirely of muscles and lacks a rigid skeletal support. Here we present a 2D dynamic model of the octopus arm to explore possible strategies of movement control in this muscular hydrostat. The arm is modeled as a multisegment structure, each segment containing longitudinal and transverse muscles and maintaining a constant volume, a prominent feature of muscular hydrostats.