An integrative approach for the identification of prognostic and predictive biomarkers in rectal cancer
Bibliographic record
Abstract
// Marco Agostini 1, 2, 3 , Klaus-Peter Janssen 4 , ll-Jin Kim 5 , Edoardo D’Angelo 1, 2 , Silvia Pizzini 6 , Andrea Zangrando 2, 7 , Carlo Zanon 8 , Chiara Pastrello 9 , Isacco Maretto 1 , Maura Digito 1 , Chiara Bedin 1, 3 , Igor Jurisica 9 , Flavio Rizzolio 10, 11 , Antonio Giordano 11 , Stefania Bortoluzzi 12 , Donato Nitti 1, * , Salvatore Pucciarelli 1, * 1 Department of Surgical, Oncological and Gastroenterological Sciences, Section of Surgery, University of Padova, Padua, Italy 2 Istituto di Ricerca Pediatrica-Città della Speranza, Padua, Italy 3 The Methodist Hospital Research Institute, Houston, USA 4 Department of Surgery, UCSF, San Francisco, CA, USA 5 Department of Surgery, UCSF, San Francisco, CA 94143, California, CA 6 Department of Biology, University of Padua, Padua, Italy 7 Department of Woman and Child Health, University of Padua, Padua, Italy 8 Neuroblastoma Laboratory, Istituto di Ricerca Pediatrica-Città della Speranza, Padua, Italy 9 Ontario Cancer Institute, the Campbell Family Institute for Cancer Research, and Techna Institute, University Health Network, Toronto, ON, Canada 10 Department of Translational Research, National Cancer Institute – CRO-IRCSS, Aviano, Italy 11 Sbarro Institute for Cancer Research and Molecular Medicine, Center for Biotechnology, College of Science and Technology, Temple University, Philadelphia, PA, USA 12 Department of Molecular Medicine, University of Padua, Padua, Italy * These authors have contributed equally to this work Correspondence to: Marco Agostini, e-mail: m.agostini@unipd.it Keywords: rectal cancer, integrated approach, biological network, prognostic, predictive Received: April 28, 2015 Accepted: August 20, 2015 Published: September 02, 2015 ABSTRACT Introduction: Colorectal cancer is the third most common cancer in the world, a small fraction of which is represented by locally advanced rectal cancer (LARC). If not medically contraindicated, preoperative chemoradiotherapy, represent the standard of care for LARC patients. Unfortunately, patients shows a wide range of response rates in which approximately 20% has a complete pathological response, whereas in 20 to 40% the response is poor or absent. Results: The following specific gene signature, able to discriminate responders’ patients from non-responders, were founded: AKR1C3, CXCL11, CXCL10, IDO1, CXCL9, MMP12 and HLA-DRA. These genes are mainly involved in immune system pathways and interact with drugs traditionally used in the adjuvant treatment of rectal cancer. Discussion: The present study suggests that new ideas for therapy could be found not only limited to studying genes differentially expressed between the two groups of patients but deepening the mechanisms, associated to response, in which they are involved. Methods: Gene expression studies performed by: Agostini et al ., Rimkus et al . and Kim et al . have been merged through a meta-analysis of the raw data. Gene expression data-sets have been processed using A-MADMAN. Common differentially expressed gene (DEG) were identified through SAM analysis. To further characterize the identified DEG we deeply investigated its biological role using an integrative computational biology approach.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".