Biology subjects

Beckman, R. A.

Publications and source records attributed to Beckman, R. A..

7 recordsLinked to original sources

bioRxiv2025

Probability Distribution for Rare Neutral Mutations in Cancers and Application to Dynamic Precision Medicine of Cancer

Cancers exhibit substantial genetic diversity between individual cells. Previous work shows greater overall diversity that also increases more quickly during a patients clinical course than heretofore expected. Rare subclones will harbor pre-existing resistance to any single agent and may cause medium to late term relapse, which may evolve further variants that have simultaneous resistance to non-cross resistant therapies. In this work, we present a probability distribution function (PDF) of the variant allele fraction (VAF), or prevalence, of a rare subclone, derived from previous evolutionary theory. We show that current clinical sequencing protocols fail to detect the vast majority of these rare subclones and that by the time of detection, simultaneous multiple resistance may already evolve. We apply the PDF to simulation of dynamic precision medicine (DPM), an evolutionary guided precision medicine paradigm that attempts to proactively eliminate singly-resistant subclones before they evolve multiple resistance, with significant potential to extend survival. We show the simulated benefit of DPM with perfect information is hampered by an inability to detect rare subclones if they are assumed to be absent when undetectable. However, the DPM benefit is restored if the PDF is used to calculate the likelihood of the subclone being present below the level of detection and incorporated into the DPM simulation and therapy recommendations in a probabilistic fashion. Two other common statistical distributions are less effective. This theoretical advance facilitates DPM and potentially other evolutionary guided approaches to precision cancer medicine in spite of the limitations of clinical sequencing. Significance StatementCancers contain many cells, each genetically unique. These variations can include pre-existing resistance to therapy, enabling relapse. DNA sequencing cannot detect minority cellular populations (subclones) below a certain size, by which time they may have already evolved simultaneous resistance to multiple therapies. We present a mathematical approach that assigns a risk that a subclone is present, and at what prevalence in the cellular population, even when undetected. We simulate applying this approach to dynamic precision medicine (DPM), which attempts to proactively eliminate singly resistant cells before they become resistant to multiple therapies. Using this probability distribution, we can retain the benefit of DPM even when most rare subclones are undetectable, in contrast to just assuming undetected subclones are absent.

systems biology↗

bioRxiv2024

Personalized cancer treatment strategies incorporating irreversible and reversible drug resistance mechanisms

Despite advances in targeted cancer therapy, the promise of precision medicine has been limited by resistance to these treatments. In this study, we propose a mathematical modelling framework incorporating cellular heterogeneity, genetic evolutionary dynamics, and non-genetic plasticity, accounting for both irreversible and reversible drug resistance. Previously we proposed Dynamic Precision Medicine (DPM), a personalized treatment strategy that designed individualized treatment sequences by simulations of irreversible genetic evolutionary dynamics in a heterogeneous tumor. Here we apply DPM to the joint model of reversible and irreversible drug resistance mechanisms, analyze the simulation results and compare the efficacy of various treatment strategies. The results indicate that this enhanced version of DPM results in superior patient outcomes compared with current personalized medicine treatment approaches. Our results provide insights into cancer treatment strategies for heterogeneous tumors with genetic evolutionary dynamics and non-genetic cellular plasticity, potentially leading to improvements in survival time for cancer patients.

systems biology↗

bioRxiv2023

Unraveling Vulnerabilities in Endocrine Therapy-Resistant HER2+/ER+ Breast Cancer

BackgroundBreast tumors overexpressing human epidermal growth factor receptor (HER2) confer intrinsic resistance to endocrine therapy (ET), and patients with HER2/ estrogen receptor-positive (HER2+/HR+) breast cancer (BCa) are less responsive to ET than HER2-/ER+. However, real-world evidence reveals that a large subset of HER2+/ER+ patients receive ET as monotherapy, positioning this treatment pattern as a clinical challenge. In the present study, we developed and characterized two distinct in vitro models of ET-resistant (ETR) HER2+/ER+ BCa to identify possible therapeutic vulnerabilities. MethodsTo mimic ETR to aromatase inhibitors (AI), we developed two long-term estrogen-deprived (LTED) cell lines from BT-474 (BT474) and MDA-MB-361 (MM361). Growth assays, PAM50 molecular subtyping, genomic and transcriptomic analyses, followed by validation and functional studies, were used to identify targetable differences between ET-responsive parental and ETR-LTED HER2+/ER+ cells. ResultsCompared to their parental cells, MM361 LTEDs grew faster, lost ER, and increased HER2 expression, whereas BT474 LTEDs grew slower and maintained ER and HER2 expression. Both LTED variants had reduced responsiveness to fulvestrant. Whole-genome sequencing of the more aggressive MM361 LTED model system identified exonic mutations in genes encoding transcription factors and chromatin modifiers. Single-cell RNA sequencing demonstrated a shift towards non-luminal phenotypes, and revealed metabolic remodeling of MM361 LTEDs, with upregulated lipid metabolism and antioxidant genes associated with ferroptosis, including GPX4. Combining the GPX4 inhibitor RSL3 with anti-HER2 agents induced significant cell death in both the MM361 and BT474 LTEDs. ConclusionsThe BT474 and MM361 AI-resistant models capture distinct phenotypes of HER2+/ER+ BCa and identify altered lipid metabolism and ferroptosis remodeling as vulnerabilities of this type of ETR BCa.

cancer biology↗

bioRxiv2020

Efficiency of a Randomized Confirmatory Basket Trial Design Constrained to Control the False Positive Rate by Indication

PURPOSEMolecular oncology determines biomarker-defined niche indications. Basket trials pool histologic indications sharing molecular pathophysiology, potentially improving development efficiency. Currently basket trials have been confirmatory only for exceptional therapies. Our previous randomized basket design may be generally suitable in the resource-intensive confirmatory phase, maintains high power, and provides nearly k-fold increased efficiency for k indications, but controls false positives for the pooled result only. Since false positive control by indications (FWER) may sometimes be required, we now simulate a variant of this basket design controlling FWER at 0.025k, the total FWER of k separate randomized trials. METHODSThe previous design eliminated indications at an interim analysis, conducting a final pooled analysis of remaining indications. To control FWER, we rechecked individual indications at a prospectively defined level of statistical significance after any positive pooled result. We simulated this modified design under numerous scenarios varying design parameters. Only designs controlling FWER and minimizing estimation bias were allowable. RESULTSSequential analyses (interim, pooled, and post-individual tests)) result in cumulative power losses. Optimal performance results when k = 3,4. We report efficiency (expected # true positives/expected sample size) relative to k parallel studies, at 90% power ("uncorrected") or at the power achieved in the basket trial ("corrected", because conventional designs could also increase efficiency by sacrificing power). Efficiency and power (percentage active indications identified) improve with higher percentage of initial indications active. Up to 92% uncorrected and 38% corrected efficiency improvement is possible, with power {approx} 60%. CONCLUSIONSEven under FWER control, randomized confirmatory basket trials substantially improve development efficiency. Initial indication selection is critical. The design is particularly attractive when enrollment challenges preclude full powering of individual indications.

cancer biology↗

bioRxiv2019

Unexpectedly High Subclonal Mutational Diversity in Human Colorectal Cancer and Its Significance

Human colorectal cancers (CRC) contain both clonal and subclonal mutations. Clonal mutations are positively selected, present in most cells and drive malignant progression. Subclonal mutations are randomly dispersed throughout the genome; they provide a vast reservoir of mutant cells that can expand, repopulate the tumor and result in the rapid emergence of resistance, as well as being a major contributor to tumor heterogeneity. Here, we apply Duplex Sequencing (DS) methodology to quantify subclonal mutations in CRC tumor with unprecedented depth (104) and accuracy (<10-7). We measured mutation frequencies in genes encoding replicative DNA polymerases and in genes frequently mutated in CRC, and found an unexpectedly high effective mutation rate, 7.1 x 10-7. The curve of subclonal mutation accumulation as a function of sequencing depth, using DNA obtained from five different tumors, is in accord with a neutral model of tumor evolution. We present a new theoretical approach to model neutral evolution independent of the infinite sites assumption (which states that a particular mutation arises only in one tumor cell at any given time). Our analysis indicates that the infinite sites assumption is not applicable once the number of tumor cells exceeds the reciprocal of the mutation rate, a circumstance relevant to even the smallest clinically diagnosable tumor. Our methods allow accurate estimation of the total mutation burden in clinical cancers. Our results indicate that no DNA locus is wild type in every malignant cell within a tumor at the time of diagnosis (probability of all cells wild type = 10-308).\n\nSignificance StatementCancers evolve many mutations. Clonal mutations are selected early. Subsequent evolution occurs in a branching fashion, possibly without selection (\"neutral evolution\"). Rarer mutations occur later on smaller branches of the evolutionary tree. Using a DNA sequencing method, duplex sequencing, with unprecedented accuracy and sensitivity, we quantified very rare unique subclonal mutations in diagnostic specimens from five human colorectal cancers. Rarer subclones probe later evolutionary timepoints than previously possible. We confirm neutral evolution at later times and find many more subclonal mutations than expected. A novel theoretical method allowed us to extrapolate further forward in time to diagnosis. At diagnosis, every base in DNA is mutated in at least one cancer cell. In particular any therapy resistance mutation would be present.

cancer biology↗

bioRxiv2019

An evidence-based network approach to recommending targeted cancer therapies

In this work, we introduce CDGnet, an evidence-based network approach for recommending targeted cancer therapies, available as a user-friendly informatics tool. Our approach can be used to expand the range of options of targeted therapies for cancer patients who undergo molecular profiling. It considers biological pathway information specifically by looking at downstream targets of oncogenes and is personalized for individual patients via the user-inputted molecular alterations and cancer type. CDGnet integrates disparate sources of knowledge and provides results in a number of easily-accessible and usable forms, while separating targeted cancer therapies into categories in an evidence-based manner.

bioinformatics↗

bioRxiv2018

Modelling bistable tumour population dynamics to design effective treatment strategies

Despite recent advances in targeted drugs and immunotherapy, cancer remains \"the emperor of all maladies\" due to inevitable emergence of resistance. Drug resistance is thought to be driven by mutations and/or dynamic plasticity that deregulate pathway activities and regulatory programs of a highly heterogeneous tumour. In this study, we propose a modelling framework to simulate population dynamics of heterogeneous tumour cells with reversible drug resistance. Drug sensitivity of a tumour cell is determined by its internal states, which are demarcated by coordinated activities of multiple interconnected oncogenic pathways. Transitions between cellular states depend on the effects of targeted drugs and regulatory relations between the pathways. Under this framework, we build a simple model to capture drug resistance characteristics of BRAF-mutant melanoma, where two cell states are described by two mutually inhibitory - main and alternative - pathways. We assume that cells with an activated main pathway are proliferative yet sensitive to the BRAF inhibitor, and cells with an activated alternative pathway are quiescent but resistant to the drug. We describe a dynamical process of tumour growth under various drug regimens using the explicit solution of mean-field equations. Based on these solutions, we compare efficacy of three treatment strategies: static treatments with continuous and constant dosages, periodic treatments with regular intermittent phases and drug holidays, and treatments derived from optimal control theory (OCT). Based on these analysis, periodic treatments outperform static treatments with a considerable margin, while treatments based on OCT outperform the best periodic treatment. Our results provide insights regarding optimal cancer treatment modalities for heterogeneous tumours, and may guide the development of optimal therapeutic strategies to circumvent drug resistance and due to tumour plasticity.

cancer biology↗