MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response

Predicting clinical outcome is remarkably important but challenging. Research efforts have been paid on seeking significant biomarkers associated with the therapy response or/and patient survival. However, these biomarkers are generally costly and invasive, and possibly dissatifactory for novel therapy. On the other hand, multi-modal, heterogeneous, unaligned temporal data is continuously generated in clinical practice. This paper aims at a unified deep learning approach to predict patient prognosis and therapy response, with easily accessible data, e.g., radiographics, laboratory and clinical information. Prior arts focus on modeling single data modality, or ignore the temporal changes. Importantly, the clinical time series is asynchronous in practice, i.e., recorded with irregular intervals. In this study, we formalize the prognosis modeling as a multi-modal asynchronous time series classification task, and propose a MIA-Prognosis framework with Measurement, Intervention and Assessment (MIA) information to predict therapy response, where a Simple Temporal Attention (SimTA) module is developed to process the asynchronous time series. Experiments on synthetic dataset validate the superiory of SimTA over standard RNN-based approaches. Furthermore, we experiment the proposed method on an in-house, retrospective dataset of real-world non-small cell lung cancer patients under anti-PD-1 immunotherapy. The proposed method achieves promising performance on predicting the immunotherapy response. Notably, our predictive model could further stratify low-risk and high-risk patients in terms of long-term survival.Comment: MICCAI 2020 (Early Accepted; Student Travel Award

Is EC class predictable from reaction mechanism?

We thank the Scottish Universities Life Sciences Alliance (SULSA) and the Scottish Overseas Research Student Awards Scheme of the Scottish Funding Council (SFC) for financial support.Background: We investigate the relationships between the EC (Enzyme Commission) class, the associated chemical reaction, and the reaction mechanism by building predictive models using Support Vector Machine (SVM), Random Forest (RF) and k-Nearest Neighbours (kNN). We consider two ways of encoding the reaction mechanism in descriptors, and also three approaches that encode only the overall chemical reaction. Both cross-validation and also an external test set are used. Results: The three descriptor sets encoding overall chemical transformation perform better than the two descriptions of mechanism. SVM and RF models perform comparably well; kNN is less successful. Oxidoreductases and hydrolases are relatively well predicted by all types of descriptor; isomerases are well predicted by overall reaction descriptors but not by mechanistic ones. Conclusions: Our results suggest that pairs of similar enzyme reactions tend to proceed by different mechanisms. Oxidoreductases, hydrolases, and to some extent isomerases and ligases, have clear chemical signatures, making them easier to predict than transferases and lyases. We find evidence that isomerases as a class are notably mechanistically diverse and that their one shared property, of substrate and product being isomers, can arise in various unrelated ways. The performance of the different machine learning algorithms is in line with many cheminformatics applications, with SVM and RF being roughly equally effective. kNN is less successful, given the role that non-local information plays in successful classification. We note also that, despite a lack of clarity in the literature, EC number prediction is not a single problem; the challenge of predicting protein function from available sequence data is quite different from assigning an EC classification from a cheminformatics representation of a reaction.Publisher PDFPeer reviewe

Liquid Biopsy in Non-Small Cell Lung Cancer (NSCLC)

Lung cancer is the leading cause of cancer deaths worldwide. To date, the gold standard for the molecular analysis of a patient affected by NSCLC is the tissue biopsy. The discovery of activating mutations and rearrangements in specific genes has revolutionized the therapeutic approaches of lung cancer over the last years. For this reason, a strict \u201cmolecular follow-up\u201d is mandatory to evaluate patient\u2019s disease evolution. Indeed, liquid biopsy has raised as the \u201cnew ambrosia of researchers\u201d as it could help clinicians to identify both prognostic and predictive biomarkers in a more accessible way. Liquid biopsy analysis can be used in different moments starting from diagnosis to relapse, earning multiple clinical meanings, offering thus a noninvasive but valid method to detect actionable mutations. Although the implementation of both exosomes and CTCs in clinical practice is several steps back, new advances and discoveries make them, together with the ctDNA, a very promising tool. In the following chapter we will discuss the recent advances of liquid biopsy in NSCLC highlighting the possible clinical utility of CTCs, ctDNA and exosomes

Patient, tumor, and healthcare factors associated with regional variability in lung cancer survival: a Spanish high‑resolution population‑based study

Purpose The third most frequently diagnosed cancer in Europe in 2018 was lung cancer; it is also the leading cause of cancer death in Europe. We studied patient and tumor characteristics, and patterns of healthcare provision explaining regional variability in lung cancer survival in southern Spain. Methods A population-based cohort study included all 1196 incident first invasive primary lung cancer (C33–C34 according to ICD-10) cases diagnosed between 2010 and 2011 with follow-up until April 2015. Data were drawn from local population-based cancer registries and patients’ hospital medical records from all public and private hospitals from two regions in southern Spain. Results There was evidence of regional differences in lung cancer late diagnosis (58% stage IV in Granada vs. 65% in Huelva, p value < 0.001). Among patients with stage I, only 67% received surgery compared with 0.6% of patients with stage IV. Patients treated with a combination of radiotherapy, chemotherapy, and surgery had a 2-year mortality risk reduction of 94% compared with patients who did not receive any treatment (excess mortality risk 0.06; 95% CI 0.02–0.16). Geographical differences in survival were observed between the two regions: 35% vs. 26% at 1-year since diagnosis. Conclusions The observed geographic differences in survival between regions are due in part to the late cancer diagnosis which determines the use of less effective therapeutic options. Results from our study justify the need for promoting lung cancer early detection strategies and the harmonization of the best practice in lung cancer management and treatment.Maria Jose Sanchez Perez is supported by the Andalusian Department of Health: Research, Development, and Innovation Office project grant PI-0152/2017. Miguel Angel Luque-Fernandez is supported by the Spanish National Institute of Health, Carlos III Miguel Servet I Investigator Award (CP17/00206)

Advances in structure elucidation of small molecules using mass spectrometry

The structural elucidation of small molecules using mass spectrometry plays an important role in modern life sciences and bioanalytical approaches. This review covers different soft and hard ionization techniques and figures of merit for modern mass spectrometers, such as mass resolving power, mass accuracy, isotopic abundance accuracy, accurate mass multiple-stage MS(n) capability, as well as hybrid mass spectrometric and orthogonal chromatographic approaches. The latter part discusses mass spectral data handling strategies, which includes background and noise subtraction, adduct formation and detection, charge state determination, accurate mass measurements, elemental composition determinations, and complex data-dependent setups with ion maps and ion trees. The importance of mass spectral library search algorithms for tandem mass spectra and multiple-stage MS(n) mass spectra as well as mass spectral tree libraries that combine multiple-stage mass spectra are outlined. The successive chapter discusses mass spectral fragmentation pathways, biotransformation reactions and drug metabolism studies, the mass spectral simulation and generation of in silico mass spectra, expert systems for mass spectral interpretation, and the use of computational chemistry to explain gas-phase phenomena. A single chapter discusses data handling for hyphenated approaches including mass spectral deconvolution for clean mass spectra, cheminformatics approaches and structure retention relationships, and retention index predictions for gas and liquid chromatography. The last section reviews the current state of electronic data sharing of mass spectra and discusses the importance of software development for the advancement of structure elucidation of small molecules

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)1.

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field

Liquid biopsies come of age: towards implementation of circulating tumour DNA

Improvements in genomic and molecular methods are expanding the range of potential applications for circulating tumour DNA (ctDNA), both in a research setting and as a ‘liquid biopsy’ for cancer management. Proof-of-principle studies have demonstrated the translational potential of ctDNA for prognostication, molecular profiling and monitoring. The field is now in an exciting transitional period in which ctDNA analysis is beginning to be applied clinically, although there is still much to learn about the biology of cell-free DNA. This is an opportune time to appraise potential approaches to ctDNA analysis, and to consider their applications in personalized oncology and in cancer research.We would like to acknowledge the support of The University of Cambridge, Cancer Research UK (grant numbers A11906, A20240, A15601) (to N.R., J.D.B.), the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement n. 337905 (to N.R.), the Cambridge Experimental Cancer Medicine Centre, and Hutchison Whampoa Limited (to N.R.), AstraZeneca (to R.B., S.P.), the Cambridge Experimental Cancer Medicine Centre (ECMC) (to R.B., S.P.), and NIHR Biomedical Research Centre (BRC) (to R.B., S.P.). J.G.C. acknowledges clinical fellowship support from SEOM