Cancer, a leading cause of death globally, occurs due to genomic changes and manifests heterogeneously across patients. To advance research on personalized treatment strategies, the effectiveness of various drugs on cells derived from cancers (`cell lines') is experimentally determined in laboratory settings. Nevertheless, variations in the distribution of genomic data and drug responses between cell lines and humans arise due to biological and environmental differences. Moreover, while genomic profiles of many cancer patients are readily available, the scarcity of corresponding drug response data limits the ability to train machine learning models that can predict drug response in patients effectively. Recent cancer drug response prediction methods have largely followed the paradigm of unsupervised domain-invariant representation learning followed by a downstream drug response classification step. Introducing supervision in both stages is challenging due to heterogeneous patient response to drugs and limited drug response data. This paper addresses these challenges through a novel representation learning method in the first phase and weak supervision in the second. Experimental results on real patient data demonstrate the efficacy of our method (WISER) over state-of-the-art alternatives on predicting personalized drug response.

With the rapid advances in high-throughput sequencing technologies, the focus of survival analysis has shifted from examining clinical indicators to incorporating genomic profiles with pathological images. However, existing methods either directly adopt a straightforward fusion of pathological features and genomic profiles for survival prediction, or take genomic profiles as guidance to integrate the features of pathological images. The former would overlook intrinsic cross-modal correlations. The latter would discard pathological information irrelevant to gene expression. To address these issues, we present a Cross-Modal Translation and Alignment (CMTA) framework to explore the intrinsic cross-modal correlations and transfer potential complementary information. Specifically, we construct two parallel encoder-decoder structures for multi-modal data to integrate intra-modal information and generate cross-modal representation. Taking the generated cross-modal representation to enhance and recalibrate intra-modal representation can significantly improve its discrimination for comprehensive survival analysis. To explore the intrinsic crossmodal correlations, we further design a cross-modal attention module as the information bridge between different modalities to perform cross-modal interactions and transfer complementary information. Our extensive experiments on five public TCGA datasets demonstrate that our proposed framework outperforms the state-of-the-art methods.

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.

Doctors have a new tool with which to diagnose your illnesses -- artificial intelligence. If Dr. Gregory House were a real person, he might've finally met his match in the diagnostics field in the form of Sophia Genetics. The Swiss company employs artificial intelligence to help doctors and other medical professionals diagnose and treat patients by way of genomic analysis. And now, the firm has raised $30 million to continue on its life-saving quest. Though just six years old, Sophia Genetics has already made quite a name for itself in the health world.

Sophia Genetics, a big data analytics company that's using artificial intelligence (AI) to help medical professionals diagnose and treat patients through genomic analysis, has raised $30 million in a round of funding led by Balderton Capital, with participation from 360 Capital Partners, Invoke Capital, and Alychlo. Founded out of Switzerland in 2011, Sophia Genetics sells itself as "the most advanced artificial intelligence AI for data-driven medicine." Its platform learns from thousands of patients' genomic profiles to improve and expedite patient diagnosis across oncology, hereditary cancer, metabolic disorders, pediatrics, and cardiology. The company said that its AI technology is currently being used by more than 300 hospitals in 53 countries, and it has already analyzed the genomic profiles of more than 125,000 patients. Prior to now, Sophia Genetics had raised around $30 million in funding, and with its latest cash injection the company said that it plans to further develop its technology, recruit top talent, and grow hospitals' adoption of clinical genomics testing.

Jurgi Camblong is a self-confessed dreamer. He believes it's possible to "democratise data-driven medicine" by standardising data analytics around the world. Founded in 2011, Camblong's Swiss-based Sophia Genetics is trying to do this with artificial intelligence and machine learning. Camblong, speaking at the WIRED Health conference in London, announced that in the next few weeks the firm will have analysed the genomic profiles of 100,000 people. Genome sequencing is the practice of decoding a person's DNA, a process that creates colossal amounts of data.

The Bonassama District Hospital in Douala, Cameroon and six other African hospitals are adopting SOPHiA to – no matter their experience in genomic testing – get up to speed and analyze genomic data to identify disease-causing mutations in patients' genomic profiles, and decide on the most effective care. A release from the global leader in Data-Driven Medicine, Sophia Genetics, says in addition to the Bonassama district hospital, the modern technology is being adopted by Pharma Process in Casablanca, Morocco; ImmCell in Rabat, Morocco; The Al Azhar Oncology Center in Rabat, Morocco; The Riad Biology Center in Rabat, Morocco; The Oudayas, Medical Analysis Laboratory, Morocco;and The Center for Proteomic & Genomic Research (CPGR) in Cape Town, South Africa. As new users of SOPHiA, they become part of a larger network of 260 hospitals in 46 countries that share clinical insights across patient cases and patient populations, which feeds a knowledgebase of biomedical findings to accelerate diagnostics and care. Speaking about the adoption of SOPHiA in Africa, Jurgi Camblong, Sophia Genetics' CEO and co-founder, declared: "Since inception, our vision has been to develop innovative technological solutions that analyze patients' genomic profiles to offer better diagnosis and care to the greatest number of patients, wherever they live. Today, I am very proud that SOPHiA is triggering a technological leapfrog movement in healthcare across Africa."

The advent of ground-breaking genomic technologies in the clinical environment, such as Next Generation DNA Sequencing (NGS), bioinformatics and artificial intelligence, has ushered in a new era in healthcare. Every day, a gigantic amount of genomic information is generated and analysed to identify pathogenic mutations in patient DNA. This new paradigm, known as'Data-Driven Medicine', already allows clinicians to treat the causes of disease rather than its consequences and opens up new treatment opportunities for patients. This has been exemplified in cancer care where therapeutic decisions do not solely rely on morphology, but also on a patient's unique molecular profile. However, the production and analysis of patient genomic profiles is only the first part of the Data-Driven Medicine story.