Collaborating Authors

First timeline of a cancer tracks tumours from origin to spread

New Scientist

But one man's illness has led to the first precise tracing of a cancer's evolution. Knowing the exact time at which a particular tumour developed in the patient's body allowed scientists to create a timeline for how his cancer evolved from a few cells, all the way through to the tumours that caused his eventual death. The study provides clues about what makes some cancers spread rapidly, and may in the future help doctors estimate how a tumour might respond to therapies. The analysis was carried out on a man diagnosed with bowel cancer in 2008, which later metastasised, spreading into other areas of his body. He suspected this might also be cancerous but decided to keep an eye on it before performing any further surgery.

Blood test shows promise for detecting the deadliest cancers early

New Scientist

A blood test developed and checked using blood samples from 4000 people can accurately detect more than 50 cancer types, often before any symptoms appear. It was most accurate at identifying 12 especially dangerous types, including pancreatic cancers that are usually diagnosed only at a very late stage. Many groups around the world are trying to develop blood tests for cancer, often referred to as "liquid biopsies". Michael Seiden at US Oncology, a company involved in cancer care, and his team explored several ways of testing for cancer based on sequencing the DNA that dying cells release into the bloodstream. The team found that looking at methylation patterns at around a million sites was the most promising.

An evaluation of machine learning techniques to predict the outcome of children treated for Hodgkin-Lymphoma on the AHOD0031 trial: A report from the Children's Oncology Group Machine Learning

In this manuscript we analyze a data set containing information on children with Hodgkin Lymphoma (HL) enrolled on a clinical trial. Treatments received and survival status were collected together with other covariates such as demographics and clinical measurements. Our main task is to explore the potential of machine learning (ML) algorithms in a survival analysis context in order to improve over the Cox Proportional Hazard (CoxPH) model. We discuss the weaknesses of the CoxPH model we would like to improve upon and then we introduce multiple algorithms, from well-established ones to state-of-the-art models, that solve these issues. We then compare every model according to the concordance index and the brier score. Finally, we produce a series of recommendations, based on our experience, for practitioners that would like to benefit from the recent advances in artificial intelligence.

The Lancet Oncology, December 2019, Volume 20, Issue 12, Pages 1615-1772, e654-e729


Artificial intelligence in gastrointestinal endoscopy: how intelligent can it get? Are neutralising anti-VEGF or VEGFR2 antibodies necessary in the treatment of EGFR-mutated non-small-cell lung cancer? Carlos Pedraz-Valdunciel Quality of life with durvalumab in stage III non-small-cell lung cancer Untapped potential: recognising CNS opportunities in early oncology drug development Lorlatinib: a new treatment option for ROS1-positive lung cancer Tumour Treating Fields for mesothelioma: controversy versus opportunity Quality of life and CAR-T cell therapy in children, adolescents, and young adults with haematological malignancies Denosumab for giant cell tumour of bone: success and limitations Androgen receptor-targeted agents in the management of advanced prostate cancer Post-operative salvage androgen deprivation and radiotherapy for prostate cancer Endocrine-based therapy versus chemotherapy in advanced breast cancer Reducing infection-related morbidity and mortality in patients with myeloma Cancer prevention and treatment in humanitarian settings: an urgent and unmet need Shedding light on dabrafenib-induced fevers in patients with melanoma Multiplicity in oncology randomised controlled trials: a threat to medical evidence? Artificial intelligence in gastrointestinal endoscopy: how intelligent can it get? Artificial intelligence in gastrointestinal endoscopy: how intelligent can it get?

Ushering in the next generation of precision trials for pediatric cancer


Cancer treatment decisions are increasingly based on the genomic profile of the patient's tumor, a strategy called "precision oncology." Over the past few years, a growing number of clinical trials and case reports have provided evidence that precision oncology is an effective approach for at least some children with cancer. Here, we review key factors influencing pediatric drug development in the era of precision oncology. We describe an emerging regulatory framework that is accelerating the pace of clinical trials in children as well as design challenges that are specific to trials that involve young cancer patients. Last, we discuss new drug development approaches for pediatric cancers whose growth relies on proteins that are difficult to target therapeutically, such as transcription factors. The landscape of genomic alterations in cancers that arise in children, adolescents, and young adults is slowly becoming clearer as a result of dedicated pediatric cancer genome-sequencing projects conducted over the past decade. Of particular note are two recent studies that produced a comprehensive picture of the genomic features that characterize many of the more common pediatric cancers (1, 2). Two major themes have emerged.