Molecular phenotyping using networks, diffusion, and topology: soft tissue sarcoma

Cancer Informatics for Cancer Centers: Sharing Ideas on How to Build an Artificial Intelligence-Ready Informatics Ecosystem for Radiation Oncology

In August 2022, the Cancer Informatics for Cancer Centers brought together cancer informatics leaders for its biannual symposium, Precision Medicine Applications in Radiation Oncology, co-chaired by Quynh-Thu Le, MD (Stanford University), and Walter J. Curran, MD (GenesisCare). Over the course of 3 days, presenters discussed a range of topics relevant to radiation oncology and the cancer informatics community more broadly, including biomarker development, decision support algorithms, novel imaging tools, theranostics, and artificial intelligence (AI) for the radiotherapy workflow. Since the symposium, there has been an impressive shift in the promise and potential for integration of AI in clinical care, accelerated in large part by major advances in generative AI. AI is now poised more than ever to revolutionize cancer care. Radiation oncology is a field that uses and generates a large amount of digital data and is therefore likely to be one of the first fields to be transformed by AI. As experts in the collection, management, and analysis of these data, the informatics community will take a leading role in ensuring that radiation oncology is prepared to take full advantage of these technological advances. In this report, we provide highlights from the symposium, which took place in Santa Barbara, California, from August 29 to 31, 2022. We discuss lessons learned from the symposium for data acquisition, management, representation, and sharing, and put these themes into context to prepare radiation oncology for the successful and safe integration of AI and informatics technologies.

The topology of data: Opportunities for cancer research

Motivation

Topological methods have recently emerged as a reliable and interpretable framework for extracting information from high-dimensional data, leading to the creation of a branch of applied mathematics called Topological Data Analysis (TDA). Since then, TDA has been progressively adopted in biomedical research. Biological data collection can result in enormous datasets, comprising thousands of features and spanning diverse datatypes. This presents a barrier to initial data analysis as the fundamental structure of the dataset becomes hidden, obstructing the discovery of important features and patterns. TDA provides a solution to obtain the underlying shape of datasets over continuous resolutions, corresponding to key topological features independent of noise. TDA has the potential to support future developments in healthcare as biomedical datasets rise in complexity and dimensionality. Previous applications extend across the fields of neuroscience, oncology, immunology and medical image analysis. TDA has been used to reveal hidden subgroups of cancer patients, construct organizational maps of brain activity and classify abnormal patterns in medical images. The utility of TDA is broad and to understand where current achievements lie, we have evaluated the present state of TDA in cancer data analysis.

Results

This article aims to provide an overview of TDA in Cancer Research. A brief introduction to the main concepts of TDA is provided to ensure that the article is accessible to readers who are not familiar with this field. Following this, a focussed literature review on the field is presented, discussing how TDA has been applied across heterogeneous datatypes for cancer research.

Molecular Biomarkers of Response to Eribulin in Patients with Leiomyosarcoma

PURPOSE

A randomized phase III study evaluated the efficacy of eribulin versus dacarbazine in patients with advanced liposarcoma and leiomyosarcoma. Improved overall survival (OS) led to approval of eribulin for liposarcoma, but not for leiomyosarcoma.

EXPERIMENTAL DESIGN

We explored the molecular profile of 77 archival leiomyosarcoma samples from this trial to identify potential predictive biomarkers, utilizing low-coverage whole-genome and whole-exome sequencing. Tumor molecular profiles were correlated with clinical data, and disease control was defined as complete/partial response or stable disease (RECIST v1.1).

RESULTS

Overall, 111 focal copy-number alterations were observed in leiomyosarcoma. Gain of chromosome 17q12 was the most common event, present in 43 of 77 cases (56%). In the eribulin-treated group, gains of 4q26, 20p12.2, 13q13.3, 8q22.2, and 8q13.2 and loss of 1q44 had a negative impact on progression-free survival (PFS), while loss of 2p12 correlated with better prognosis. Gains of 4q22.1 and losses of 3q14.2, 2q14.1, and 11q25 had a negative impact on OS in patients with leiomyosarcoma receiving eribulin. The most commonly mutated genes were TP53 (38%), MUC16 (32%), and ATRX (17%). The presence of ATRX mutations had a negative impact on PFS in both treatment arms; however, the correlation with worse OS was observed only in the eribulin-treated patients. TP53 mutations were associated with longer PFS on eribulin.

CONCLUSIONS

Leiomyosarcoma has a complex genetic background, with multiple copy-number alterations and mutations affecting genes implicated in tumorigenesis. We identified several molecular changes with potential impact on survival of patients with leiomyosarcoma when treated with eribulin.

Risk score based on expression of five novel genes predicts survival in soft tissue sarcoma

In this study, The Cancer Genome Atlas and Genotype-Tissue Expression databases were used to identify potential biomarkers of soft tissue sarcoma (STS) and construct a prognostic model. The model was used to calculate risk scores based on the expression of five key genes, among which MYBL2 and FBN2 were upregulated and TSPAN7, GCSH, and DDX39B were downregulated in STS patients. We also examined gene signatures associated with the key genes and evaluated the model’s clinical utility. The key genes were found to be involved in the cell cycle, DNA replication, and various cancer pathways, and gene alterations were associated with a poor prognosis. According to the prognostic model, risk scores negatively correlated with infiltration of six types of immune cells. Furthermore, age, margin status, presence of metastasis, and risk score were independent prognostic factors for STS patients. A nomogram that incorporated the risk score and other independent prognostic factors accurately predicted survival in STS patients. These findings may help to improve prognostic prediction and aid in the identification of effective treatments for STS patients.