LiquidCNA: Tracking subclonal evolution from longitudinal liquid biopsies using somatic copy number alterations

Adaptive Control of Tumor Growth

Cancer treatment optimizations select the most optimum combinations of drugs, sequencing schedules, and appropriate doses that would limit toxicity and yield an improved patient quality of life. However, these optimizations often lack an adequate consideration of cancer's near-infinite potential for evolutionary adaptation to therapeutic interventions. Adapting cancer therapy based on monitored tumor burden and clonal composition is an intuitively sound approach to the treatment of cancer as an inherently complex and adaptive system. The adaptation would be driven by clinical outcome setpoints embodying the aims to thwart therapeutic resistance and maintain a long-term management of the disease or even a cure. However, given the nonlinear, stochastic dynamics of tumor response to therapeutic interventions, adaptive therapeutic strategies may at least need a one-step-ahead prediction of tumor burden to maintain their control over tumor growth dynamics. The article explores the feasibility of adaptive cancer treatment driven by tumor state feedback assuming cell adaptive fitness to be the underlying source of phenotypic plasticity and pathway entropy as a biomarker of tumor growth trajectory. The exploration is undertaken using deterministic and stochastic models of tumor growth dynamics.

Adaptive Cancer Therapy in the Age of Generative Artificial Intelligence

Therapeutic resistance is a major challenge facing the design of effective cancer treatments. Adaptive cancer therapy is in principle the most viable approach to manage cancer's adaptive dynamics through drug combinations with dose timing and modulation. However, there are numerous open issues facing the clinical success of adaptive therapy. Chief among these issues is the feasibility of real-time predictions of treatment response which represent a bedrock requirement of adaptive therapy. Generative artificial intelligence has the potential to learn prediction models of treatment response from clinical, molecular, and radiomics data about patients and their treatments. The article explores this potential through a proposed integration model of Generative Pre-Trained Transformers (GPTs) in a closed loop with adaptive treatments to predict the trajectories of disease progression. The conceptual model and the challenges facing its realization are discussed in the broader context of artificial intelligence integration in oncology.

Circulating tumour DNA testing in metastatic breast cancer: Integration with tissue testing

Breast cancer biomarker profiling predominantly relies on tissue testing (surgical and/or biopsy samples). However, the field of liquid biopsy, particularly the analysis of circulating tumour DNA (ctDNA), has witnessed remarkable progress and continues to evolve rapidly. The incorporation of ctDNA-based testing into clinical practice is creating new opportunities for patients with metastatic breast cancer (MBC). ctDNA offers advantages over conventional tissue analyses, as it reflects tumour heterogeneity and enables multiple serial biopsies in a minimally invasive manner. Thus, it serves as a valuable complement to standard tumour tissues and, in certain instances, may even present a potential alternative approach. In the context of MBC, ctDNA testing proves highly informative in the detection of disease progression, monitoring treatment response, assessing actionable biomarkers, and identifying mechanisms of resistance. Nevertheless, ctDNA does exhibit inherent limitations, including its generally low abundance, necessitating timely blood samplings and rigorous management of the pre-analytical phase. The development of highly sensitive assays and robust bioinformatic tools has paved the way for reliable ctDNA analyses. The time has now come to establish how ctDNA and tissue analyses can be effectively integrated into the diagnostic workflow of MBC to provide patients with the most comprehensive and accurate profiling. In this manuscript, we comprehensively analyse the current methodologies employed in ctDNA analysis and explore the potential benefits arising from the integration of tissue and ctDNA testing for patients diagnosed with MBC.

Adaptive therapy achieves long-term control of chemotherapy resistance in high grade ovarian cancer

Drug resistance results in poor outcomes for most patients with metastatic cancer. Adaptive Therapy (AT) proposes to address this by exploiting presumed fitness costs incurred by drug-resistant cells when drug is absent, and prescribing dose reductions to allow fitter, sensitive cells to re-grow and re-sensitise the tumour. However, empirical evidence for treatment-induced fitness change is lacking. We show that fitness costs in chemotherapy-resistant ovarian cancer cause selective decline and apoptosis of resistant populations in low-resource conditions. Moreover, carboplatin AT caused fluctuations in sensitive/resistant tumour population size in vitro and significantly extended survival of tumour-bearing mice. In sequential blood-derived cell-free DNA and tumour samples obtained longitudinally from ovarian cancer patients during treatment, we inferred resistant cancer cell population size through therapy and observed it correlated strongly with disease burden. These data have enabled us to launch a multicentre, phase 2 randomised controlled trial (ACTOv) to evaluate AT in ovarian cancer.

Tracking early lung cancer metastatic dissemination in TRACERx using ctDNA

Circulating tumour DNA (ctDNA) can be used to detect and profile residual tumour cells persisting after curative intent therapy1. The study of large patient cohorts incorporating longitudinal plasma sampling and extended follow-up is required to determine the role of ctDNA as a phylogenetic biomarker of relapse in early-stage non-small-cell lung cancer (NSCLC). Here we developed ctDNA methods tracking a median of 200 mutations identified in resected NSCLC tissue across 1,069 plasma samples collected from 197 patients enrolled in the TRACERx study2. A lack of preoperative ctDNA detection distinguished biologically indolent lung adenocarcinoma with good clinical outcome. Postoperative plasma analyses were interpreted within the context of standard-of-care radiological surveillance and administration of cytotoxic adjuvant therapy. Landmark analyses of plasma samples collected within 120 days after surgery revealed ctDNA detection in 25% of patients, including 49% of all patients who experienced clinical relapse; 3 to 6 monthly ctDNA surveillance identified impending disease relapse in an additional 20% of landmark-negative patients. We developed a bioinformatic tool (ECLIPSE) for non-invasive tracking of subclonal architecture at low ctDNA levels. ECLIPSE identified patients with polyclonal metastatic dissemination, which was associated with a poor clinical outcome. By measuring subclone cancer cell fractions in preoperative plasma, we found that subclones seeding future metastases were significantly more expanded compared with non-metastatic subclones. Our findings will support (neo)adjuvant trial advances and provide insights into the process of metastatic dissemination using low-ctDNA-level liquid biopsy.

Circulating Tumor DNA in Gastric and Gastroesophageal Junction Cancer

Tumor cells shed DNA into the plasma. “Liquid biopsy” analysis of mutations or other genomic alterations in circulating cell-free DNA (cfDNA) may provide us with a tool to detect minimal residual cancer, comprehensively profile the genomic tumor landscape in search of druggable targets, and monitor cancers non-invasively over time for treatment failure or emerging treatment-resistant tumor subclones. While liquid biopsies have not yet entered routine clinical management in patients with gastric and gastroesophageal junction cancers, this group of diseases may benefit from such advanced diagnostic tools due to their pronounced genetic spatiotemporal heterogeneity and limitations in imaging sensitivity. Moreover, as the armamentarium of targeted treatment approaches and immunotherapies expands, cfDNA analyses may reveal their utility not only as a biomarker of response but also for precision monitoring. In this review, we discuss the different applications of cfDNA analyses in patients with gastric and gastroesophageal junction cancer and the technical challenges that such liquid biopsies have yet to overcome.