The delivery of appropriate targeted therapies to cancer patients requires the complete analysis of the molecular profiling of tumors and the patient's clinical characteristics in the context of existing knowledge and recent findings described in biomedical literature and several other sources. We evaluated the potential contributions of specific natural language processing solutions to support knowledge discovery from biomedical literature. Two models from the Bidirectional Encoder Representations from Transformers (BERT) family, two Large Language Models, and PubTator 3.0 were tested for their ability to support the named entity recognition (NER) and the relation extraction (RE) tasks. PubTator 3.0 and the BioBERT model performed best in the NER task (best F1-score equal to 0.93 and 0.89, respectively), while BioBERT outperformed all other solutions in the RE task (best F1-score 0.79) and a specific use case it was applied to by recognizing nearly all entity mentions and most of the relations.

Cancer drug response prediction (DRP) models present a promising approach towards precision oncology, tailoring treatments to individual patient profiles. While deep learning (DL) methods have shown great potential in this area, models that can be successfully translated into clinical practice and shed light on the molecular mechanisms underlying treatment response will likely emerge from collaborative research efforts. This highlights the need for reusable and adaptable models that can be improved and tested by the wider scientific community. In this study, we present a scoring system for assessing the reusability of prediction DRP models, and apply it to 17 peer-reviewed DL-based DRP models. As part of the IMPROVE (Innovative Methodologies and New Data for Predictive Oncology Model Evaluation) project, which aims to develop methods for systematic evaluation and comparison DL models across scientific domains, we analyzed these 17 DRP models focusing on three key categories: software environment, code modularity, and data availability and preprocessing. While not the primary focus, we also attempted to reproduce key performance metrics to verify model behavior and adaptability. Our assessment of 17 DRP models reveals both strengths and shortcomings in model reusability. To promote rigorous practices and open-source sharing, we offer recommendations for developing and sharing prediction models. Following these recommendations can address many of the issues identified in this study, improving model reusability without adding significant burdens on researchers. This work offers the first comprehensive assessment of reusability and reproducibility across diverse DRP models, providing insights into current model sharing practices and promoting standards within the DRP and broader AI-enabled scientific research community.

The essence of precision oncology lies in its commitment to tailor targeted treatments and care measures to each patient based on the individual characteristics of the tumor. The inherent heterogeneity of tumors necessitates gathering information from diverse data sources to provide valuable insights from various perspectives, fostering a holistic comprehension of the tumor. Over the past decade, multimodal data integration technology for precision oncology has made significant strides, showcasing remarkable progress in understanding the intricate details within heterogeneous data modalities. These strides have exhibited tremendous potential for improving clinical decision-making and model interpretation, contributing to the advancement of cancer care and treatment. Given the rapid progress that has been achieved, we provide a comprehensive overview of about 300 papers detailing cutting-edge multimodal data integration techniques in precision oncology. In addition, we conclude the primary clinical applications that have reaped significant benefits, including early assessment, diagnosis, prognosis, and biomarker discovery. Finally, derived from the findings of this survey, we present an in-depth analysis that explores the pivotal challenges and reveals essential pathways for future research in the field of multimodal data integration for precision oncology.

Cancer is a significant threat worldwide. In a study published in ACS Cancer, authors Bray et al. stated that cancer may overtake cardiovascular disease as the leading cause of premature death worldwide over the course of this century. Cancer biomarkers are the biological molecules produced by the tumor in a person; this helps characterize the disease's state and prognosis. Prognostic markers give insight into the survival and disease progression. Despite the biomarker testing and targetting, varied treatment responses have been observed across patients.

In 2019, there were approximately 23.6 million new cancer cases and 10 million cancer deaths globally, which represents a 26.3% increase in new cases and a 20.9% increase in fatalities compared with 2010. Furthermore, COVID-19 has had devastating effects on patients with cancer, with massive numbers of delayed diagnoses and treatments due to the constraints COVID-19 has put on health systems. As the pandemic normalizes, global communities look to re-prioritize – ensuring quality and equitable access of cancer diagnostics, treatment and care. We believe the technologies of the fourth industrial revolution (4IR) can address some of the most significant challenges that humanity faces today. The World Economic Forum is working with partners globally to close the gap in premature death by lung cancer and to leverage new technologies to improve and transform cancer care in India.

The Cancer Research Center of Marseille (CRCM) is the basic science and translational research unit of the IPC (http://www.institutpaolicalmettes.fr/en/research/). Also affiliated to INSERM, CNRS and Aix-Marseille University, the 400 researchers working at the CRCM form a strongly multi-disciplinary research environment characterized by frequent and close collaborations with IPC clinicians. IPC is a member of the UNICANCER network (http://www.unicancer.fr/en/unicancer). This project will investigate the application of Artificial Intelligence, and especially its Machine Learning (ML) component, to Precision Oncology (PO). PO is a form of medicine that uses personal information to prevent, diagnose and treat cancers.

This paper describes an initial dataset and automatic natural language processing (NLP) method for extracting concepts related to precision oncology from biomedical research articles. We extract five concept types: Cancer, Mutation, Population, Treatment, Outcome. A corpus of 250 biomedical abstracts were annotated with these concepts following standard double-annotation procedures. We then experiment with BERT-based models for concept extraction. The best-performing model achieved a precision of 63.8%, a recall of 71.9%, and an F1 of 67.1. Finally, we propose additional directions for research for improving extraction performance and utilizing the NLP system in downstream precision oncology applications.

Analysis of somatic mutation profiles from cancer patients is essential in the development of cancer research. However, the low frequency of most mutations and the varying rates of mutations across patients makes the data extremely challenging to statistically analyze as well as difficult to use in classification problems, for clustering, visualization or for learning useful information. Thus, the creation of low dimensional representations of somatic mutation profiles that hold useful information about the DNA of cancer cells will facilitate the use of such data in applications that will progress precision medicine. In this paper, we talk about the open problem of learning from somatic mutations, and present Flatsomatic: a solution that utilizes variational autoencoders (VAEs) to create latent representations of somatic profiles. The work done in this paper shows great potential for this method, with the VAE embeddings performing better than PCA for a clustering task, and performing equally well to the raw high dimensional data for a classification task. We believe the methods presented herein can be of great value in future research and in bringing data-driven models into precision oncology.

As part of Microsoft's Project Hanover, biomedical researchers from the Jackson Laboratory are refining an artificial intelligence tool that "reads" medical documents to inform the development of precision cancer treatments, per an Oct. 27 Microsoft blog post. The Bar Harbor, Maine-based Jackson Laboratory developed a searchable database of complex genomic information that can be sorted and interpreted to improve outcomes and share information about clinical trials and treatments. To speed this process, the lab's researchers are applying Microsoft's machine reading AI, which automatically extracts from thousands of medical and research documents only the most relevant information about cancer mutations, drugs and patient responses. The partnership is mutually beneficial: Microsoft's AI tool is increasing the lab team's efficiency in curating their Clinical Knowledgebase, while their usage is simultaneously validating the AI's accuracy and effectiveness in "reading" documents. "Our goal is to make the human curators superpowered," said Hoifung Poon, Project Hanover's lead researcher and director of precision health natural language processing with Microsoft's research organization.

In the past decade, advances in precision oncology have resulted in an increased demand for predictive assays that enable the selection and stratification of patients for treatment. The enormous divergence of signalling and transcriptional networks mediating the crosstalk between cancer, stromal and immune cells complicates the development of functionally relevant biomarkers based on a single gene or protein. However, the result of these complex processes can be uniquely captured in the morphometric features of stained tissue specimens. The possibility of digitizing whole-slide images of tissue has led to the advent of artificial intelligence (AI) and machine learning tools in digital pathology, which enable mining of subvisual morphometric phenotypes and might, ultimately, improve patient management. In this Perspective, we critically evaluate various AI-based computational approaches for digital pathology, focusing on deep neural networks and'hand-crafted' feature-based methodologies. We aim to provide a broad framework for incorporating AI and machine learning tools into clinical oncology, with an emphasis on biomarker development.