Collaborating Authors

Advance articles Annals of Oncology


Accepted Manuscript Editorial 28 May 2018 Artificial intelligence for melanoma diagnosis: How can we deliver on the promise? Published: 28 May 2018 Section: Editorial Melanoma Corrected Proof Research Article 28 May 2018 Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists H A Haenssle; C Fink; R Schneiderbauer; F Toberer; T Buhl ... Annals of Oncology, mdy166, Published: 28 May 2018 Section: Original Article Corrected Proof Review Article 28 May 2018 Gastrointestinal stromal tumours: ESMO–EURACAN Clinical Practice Guidelines for diagnosis, treatment and follow-up P G Casali; N Abecassis; S Bauer; R Biagini; S Bielack ... Annals of Oncology, mdy095, Published: 28 May 2018 Section: clinical practice guidelines Corrected Proof Review Article 28 May 2018 Soft tissue and visceral sarcomas: ESMO–EURACAN Clinical Practice Guidelines for diagnosis, treatment and follow-up P G Casali; N Abecassis; S Bauer; R Biagini; S Bielack ... Annals of Oncology, mdy096, Published: 28 May 2018 Section: clinical practice guidelines Corrected Proof Review Article 23 May 2018 Hodgkin lymphoma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up D A Eichenauer; B M P Aleman; M André; M Federico; M Hutchings ... Annals of Oncology, mdy080, Published: 23 May 2018 Section: clinical practice guidelines Accepted Manuscript Review Article 22 May 2018 Advances in the systemic treatment of melanoma brain metastases I C Glitza Oliva; G Schvartsman; H Tawbi Annals of Oncology, mdy185,

Innovative AI Breath Analyzer Diagnoses Diseases by "Smell"


Imagine being able to know if you have Parkinson's disease, multiple sclerosis, liver failure, Crohn's diseases, pulmonary hypertension, chronic kidney disease, or any number of cancers based on a simple, non-invasive test of your breath. Breath analyzers to detect alcohol have been around for well over half a century--why not apply the same concept to detect diseases? A global team of scientists from universities in Israel, France, Latvia, China and the United States have developed an artificial intelligence (AI) system to detect 17 diseases from exhaled breath with 86 percent accuracy. The research team led by Professor Hassam Haick of the Technion-Israel Institute of Technology collected breath samples from 1404 subjects with either no disease (healthy control) or one of 17 different diseases. The disease conditions include lung cancer, colorectal cancer, head and neck cancer, ovarian cancer, bladder cancer, prostate cancer, kidney cancer, gastric cancer, Crohn's disease, ulcerative colitis, irritable bowel syndrome, idiopathic Parkinson's, atypical Parkinson ISM, multiple sclerosis, pulmonary hypertension, pre-eclampsia toxemia, and chronic kidney disease.

Lynparza Phase III PAOLA-1 trial met primary endpoint as 1st-line maintenance treatment with bevacizumab for advanced ovarian cancer


AstraZeneca and MSD Inc., Kenilworth, N.J., US (MSD: known as Merck & Co., Inc. inside the US and Canada) today announced positive results from the Phase III PAOLA-1 trial in women with advanced ovarian cancer. The trial, in the 1st-line maintenance setting, compared Lynparza (olaparib) added to standard-of-care (SoC) bevacizumab vs. bevacizumab alone in women with or without BRCA gene mutations. The trial met its primary endpoint in the intent-to-treat* population with a statistically-significant and clinically-meaningful improvement in progression-free survival (PFS), increasing the time women taking Lynparza plus bevacizumab lived without disease progression or death vs. those taking bevacizumab alone. The results, including biomarker sub-group analyses, will be presented at a forthcoming medical meeting. The safety and tolerability profiles observed in PAOLA-1 were generally consistent with those known for each medicine.

Predicting drug response of tumors from integrated genomic profiles by deep neural networks Machine Learning

The study of high-throughput genomic profiles from a pharmacogenomics viewpoint has provided unprecedented insights into the oncogenic features modulating drug response. A recent screening of ~1,000 cancer cell lines to a collection of anti-cancer drugs illuminated the link between genotypes and vulnerability. However, due to essential differences between cell lines and tumors, the translation into predicting drug response in tumors remains challenging. Here we proposed a DNN model to predict drug response based on mutation and expression profiles of a cancer cell or a tumor. The model contains a mutation and an expression encoders pre-trained using a large pan-cancer dataset to abstract core representations of high-dimension data, followed by a drug response predictor network. Given a pair of mutation and expression profiles, the model predicts IC50 values of 265 drugs. We trained and tested the model on a dataset of 622 cancer cell lines and achieved an overall prediction performance of mean squared error at 1.96 (log-scale IC50 values). The performance was superior in prediction error or stability than two classical methods and four analog DNNs of our model. We then applied the model to predict drug response of 9,059 tumors of 33 cancer types. The model predicted both known, including EGFR inhibitors in non-small cell lung cancer and tamoxifen in ER+ breast cancer, and novel drug targets. The comprehensive analysis further revealed the molecular mechanisms underlying the resistance to a chemotherapeutic drug docetaxel in a pan-cancer setting and the anti-cancer potential of a novel agent, CX-5461, in treating gliomas and hematopoietic malignancies. Overall, our model and findings improve the prediction of drug response and the identification of novel therapeutic options.

The Blessings of Multiple Causes Machine Learning

Causal inference from observation data often assumes "strong ignorability," that all confounders are observed. This assumption is standard yet untestable. However, many scientific studies involve multiple causes, different variables whose effects are simultaneously of interest. We propose the deconfounder, an algorithm that combines unsupervised machine learning and predictive model checking to perform causal inference in multiple-cause settings. The deconfounder infers a latent variable as a substitute for unobserved confounders and then uses that substitute to perform causal inference. We develop theory for when the deconfounder leads to unbiased causal estimates, and show that it requires weaker assumptions than classical causal inference. We analyze its performance in three types of studies: semi-simulated data around smoking and lung cancer, semi-simulated data around genomewide association studies, and a real dataset about actors and movie revenue. The deconfounder provides a checkable approach to estimating close-to-truth causal effects.