BACKGROUND: Understanding tumor heterogeneity is essential for advancing cancer treatment. Clonal reconstruction methods play a pivotal role in deciphering this heterogeneity. Our goal is to develop a clonal reconstruction approach that is clinically applicable, easy to implement, and capable of delivering both high-speed performance and excellent reconstruction accuracy. RESULTS: We present MyClone, a probabilistic method designed to reconstruct the clonal composition of tumors using deep sequencing genomic data. MyClone processes read counts and copy number information of single nucleotide variants derived from deep sequencing data, enabling it to determine the mutational composition of clones and the cancer cell fractions of these mutations. Compared to existing clonal reconstruction methods, MyClone enhances clustering accuracy and cancer cell fraction prediction when applied to deep-targeted sequencing data and bulk tumor sequencing data with deep sequencing coverage. Additionally, MyClone achieves a substantial improvement in computational speed. We rigorously validated MyClone's performance using both simulated and real clinical datasets and applied it to analyze a circulating tumor DNA sequencing dataset from 139 metastatic breast cancer patients. In this analysis, we explored mechanisms of drug resistance in metastatic breast cancer and identified 10 mutated genes potentially associated with drug resistance or sensitivity. CONCLUSIONS: For deeply sequenced data, MyClone outperforms existing methods on both targeted sequencing and bulk tumor data. Its high computational efficiency and reconstruction accuracy position MyClone as a promising tool for broad clinical application in cancer treatment. The source code is publicly available at https://github.com/Hansen0413/myclone-code .

Generalist Large Language Models (LLMs), such as GPT-4, have shown considerable promise in various domains, including medical diagnosis. Rare diseases, affecting approximately 300 million people worldwide, often have unsatisfactory clinical diagnosis rates primarily due to a lack of experienced physicians and the complexity of differentiating among many rare diseases. In this context, recent news such as "ChatGPT correctly diagnosed a 4-year-old's rare disease after 17 doctors failed" underscore LLMs' potential, yet underexplored, role in clinically diagnosing rare diseases. To bridge this research gap, we introduce RareBench, a pioneering benchmark designed to systematically evaluate the capabilities of LLMs on 4 critical dimensions within the realm of rare diseases. Meanwhile, we have compiled the largest open-source dataset on rare disease patients, establishing a benchmark for future studies in this domain. To facilitate differential diagnosis of rare diseases, we develop a dynamic few-shot prompt methodology, leveraging a comprehensive rare disease knowledge graph synthesized from multiple knowledge bases, significantly enhancing LLMs' diagnostic performance. Moreover, we present an exhaustive comparative study of GPT-4's diagnostic capabilities against those of specialist physicians. Our experimental findings underscore the promising potential of integrating LLMs into the clinical diagnostic process for rare diseases. This paves the way for exciting possibilities in future advancements in this field.