To modulate or to skip: De-escalating PARP inhibitor maintenance therapy in ovarian cancer using adaptive therapy.

Toxicity and emerging drug resistance pose important challenges in poly-adenosine ribose polymerase inhibitor (PARPi) maintenance therapy of ovarian cancer. We propose that adaptive therapy, which dynamically reduces treatment based on the tumor dynamics, might alleviate both issues. Utilizing in vitro time-lapse microscopy and stepwise model selection, we calibrate and validate a differential equation mathematical model, which we leverage to test different plausible adaptive treatment schedules. Our model indicates that adjusting the dosage, rather than skipping treatments, is more effective at reducing drug use while maintaining efficacy due to a delay in cell kill and a diminishing dose-response relationship. In vivo pilot experiments confirm this conclusion. Although our focus is toxicity mitigation, reducing drug use may also delay resistance. This study enhances our understanding of PARPi treatment scheduling and illustrates the first steps in developing adaptive therapies for new treatment settings. A record of this paper's transparent peer review process is included in the supplemental information.

Peristromal niches protect lung cancers from targeted therapies through a combined effect of multiple molecular mediators.

Targeted therapies directed against oncogenic signaling addictions, such as inhibitors of ALK in ALK+ NSCLC often induce strong and durable clinical responses. However, they are not curative in metastatic cancers, as some tumor cells persist through therapy, eventually developing resistance. Therapy sensitivity can reflect not only cell-intrinsic mechanisms but also inputs from stromal microenvironment. Yet, the contribution of tumor stroma to therapeutic responses in vivo remains poorly defined. To address this gap of knowledge, we assessed the contribution of stroma-mediated resistance to therapeutic responses to the frontline ALK inhibitor alectinib in xenograft models of ALK+ NSCLC. We found that stroma-proximal tumor cells are partially protected against cytostatic effects of alectinib. This effect is observed not only in remission, but also during relapse, indicating the strong contribution of stroma-mediated resistance to both persistence and resistance. This therapy-protective effect of the stromal niche reflects a combined action of multiple mechanisms, including growth factors and extracellular matrix components. Consequently, despite improving alectinib responses, suppression of any individual resistance mechanism was insufficient to fully overcome the protective effect of stroma. Focusing on shared collateral sensitivity of persisters offered a superior therapeutic benefit, especially when using an antibody-drug conjugate with bystander effect to limit therapeutic escape. These findings indicate that stroma-mediated resistance might be the major contributor to both residual and progressing disease and highlight the limitation of focusing on suppressing a single resistance mechanism at a time.

Modeling tumors as species-rich ecological communities.

Many advanced cancers resist therapeutic intervention. This process is fundamentally related to intra-tumor heterogeneity: multiple cell populations, each with different mutational and phenotypic signatures, coexist within a tumor and its metastatic nodes. Like species in an ecosystem, many cancer cell populations are intertwined in a complex network of ecological interactions. Most mathematical models of tumor ecology, however, cannot account for such phenotypic diversity nor are able to predict its consequences. Here we propose that the Generalized Lotka-Volterra model (GLV), a standard tool to describe complex, species-rich ecological communities, provides a suitable framework to describe the ecology of heterogeneous tumors. We develop a GLV model of tumor growth and discuss how its emerging properties, such as outgrowth and multistability, provide a new understanding of the disease. Additionally, we discuss potential extensions of the model and their application to three active areas of cancer research, namely phenotypic plasticity, the cancer-immune interplay and the resistance of metastatic tumors to treatment. Our work outlines a set of questions and a tentative road map for further research in cancer ecology.

Mathematical Model-Driven Deep Learning Enables Personalized Adaptive Therapy.

Standard-of-care treatment regimens have long been designed for maximal cell killing, yet these strategies often fail when applied to metastatic cancers due to the emergence of drug resistance. Adaptive treatment strategies have been developed as an alternative approach, dynamically adjusting treatment to suppress the growth of treatment-resistant populations and thereby delay, or even prevent, tumor progression. Promising clinical results in prostate cancer indicate the potential to optimize adaptive treatment protocols. Here, we applied deep reinforcement learning (DRL) to guide adaptive drug scheduling and demonstrated that these treatment schedules can outperform the current adaptive protocols in a mathematical model calibrated to prostate cancer dynamics, more than doubling the time to progression. The DRL strategies were robust to patient variability, including both tumor dynamics and clinical monitoring schedules. The DRL framework could produce interpretable, adaptive strategies based on a single tumor burden threshold, replicating and informing optimal treatment strategies. The DRL framework had no knowledge of the underlying mathematical tumor model, demonstrating the capability of DRL to help develop treatment strategies in novel or complex settings. Finally, a proposed five-step pathway, which combined mechanistic modeling with the DRL framework and integrated conventional tools to improve interpretability compared to traditional "black-box" DRL models, could allow translation of this approach to the clinic. Overall, the proposed framework generated personalized treatment schedules that consistently outperformed clinical standard-of-care protocols.

Cementless total knee arthroplasty in young patients using tantalum trabecular implants results in significantly lower rates of aseptic loosening.

Aims: The mean age of patients undergoing total knee arthroplasty (TKA) has reduced with time. Younger patients have increased expectations following TKA. Aseptic loosening of the tibial component is the most common cause of failure of TKA in the UK. Interest in cementless TKA has re-emerged due to its encouraging results in the younger patient population. We review a large series of tantalum trabecular metal cementless implants in patients who are at the highest risk of revision surgery. Methods: A total of 454 consecutive patients who underwent cementless TKA between August 2004 and December 2021 were reviewed. The mean follow-up was ten years. Plain radiographs were analyzed for radiolucent lines. Patients who underwent revision TKA were recorded, and the cause for revision was determined. Data from the National Joint Registry for England, Wales, Northern Island, the Isle of Man and the States of Guernsey (NJR) were compared with our series. Results: No patients in our series had evidence of radiolucent lines on their latest radiological assessment. Only eight patients out of 454 required revision arthroplasty, and none of these revisions were indicated for aseptic loosening of the tibial baseplate. When compared to data from the NJR annual report, Kaplan-Meier estimates from our series (2.94 (95% confidence interval (CI) 1.24 to 5.87)) show a significant reduction in cumulative estimates of revision compared to all cemented (4.82 (95% CI 4.69 to 4.96)) or cementless TKA (5.65 (95% CI 5.23 to 6.10)). Our data (2.94 (95% CI 1.24 to 5.87)) also show lower cumulative revision rates compared to the most popular implant (PFC Sigma Cemented Knee implant fixation, 4.03 (95% CI 3.75 to 4.33)). The prosthesis time revision rate (PTIR) estimates for our series (2.07 (95% CI 0.95 to 3.83)) were lower than those of cemented cases (4.53 (95% CI 4.49 to 4.57)) from NJR. Conclusion: The NexGen trabecular (tantalum) cementless implant has lower revision rates in our series compared to all cemented implants and other types of cementless implants, and its use in younger patients should be encouraged.

Skin Cancer Referrals by Non-Medical Practitioners: A Prospective Observational Study.

The aim of this study was to investigate the appropriateness of suspected skin cancer referrals made by non-medical practitioners (NMPs) and compare this to referrals made by local General Practitioners (GPs). Data were collected prospectively from patients referred to a UK hospital dermatology department from primary care. The profession of the referrer was ascertained from review of referral letters and direct questioning. Patient records and subsequent histology reports were reviewed to determine ultimate diagnosis. 668/753 patients (88.7%) were referred by GPs versus 85/753 (11.3%) by NMPs. 340/668 (50.1%) of patients in the GP group and 47/85 (55.3%) in the NMP group were discharged without intervention (p = 0.45). An ultimate diagnosis of skin malignancy was made in 196/668 (29.3%) patients in the GP and 25/85 (29.4%)) in the NMP group (p = 0.99). These early data suggest significant potential for NMPs to become more involved in skin lesion assessment.

Tumor-immune metaphenotypes orchestrate an evolutionary bottleneck that promotes metabolic transformation.

Introduction: Metabolism plays a complex role in the evolution of cancerous tumors, including inducing a multifaceted effect on the immune system to aid immune escape. Immune escape is, by definition, a collective phenomenon by requiring the presence of two cell types interacting in close proximity: tumor and immune. The microenvironmental context of these interactions is influenced by the dynamic process of blood vessel growth and remodelling, creating heterogeneous patches of well-vascularized tumor or acidic niches. Methods: Here, we present a multiscale mathematical model that captures the phenotypic, vascular, microenvironmental, and spatial heterogeneity which shapes acid-mediated invasion and immune escape over a biologically-realistic time scale. The model explores several immune escape mechanisms such as i) acid inactivation of immune cells, ii) competition for glucose, and iii) inhibitory immune checkpoint receptor expression (PD-L1). We also explore the efficacy of anti-PD-L1 and sodium bicarbonate buffer agents for treatment. To aid in understanding immune escape as a collective cellular phenomenon, we define immune escape in the context of six collective phenotypes (termed "meta-phenotypes"): Self-Acidify, Mooch Acid, PD-L1 Attack, Mooch PD-L1, Proliferate Fast, and Starve Glucose. Results: Fomenting a stronger immune response leads to initial benefits (additional cytotoxicity), but this advantage is offset by increased cell turnover that leads to accelerated evolution and the emergence of aggressive phenotypes. This creates a bimodal therapy landscape: either the immune system should be maximized for complete cure, or kept in check to avoid rapid evolution of invasive cells. These constraints are dependent on heterogeneity in vascular context, microenvironmental acidification, and the strength of immune response. Discussion: This model helps to untangle the key constraints on evolutionary costs and benefits of three key phenotypic axes on tumor invasion and treatment: acid-resistance, glycolysis, and PD-L1 expression. The benefits of concomitant anti-PD-L1 and buffer treatments is a promising treatment strategy to limit the adverse effects of immune escape.

Second-Order Effects of Chemotherapy Pharmacodynamics and Pharmacokinetics on Tumor Regression and Cachexia.

Drug dose response curves are ubiquitous in cancer biology, but these curves are often used to measure differential response in first-order effects: the effectiveness of increasing the cumulative dose delivered. In contrast, second-order effects (the variance of drug dose) are often ignored. Knowledge of second-order effects may improve the design of chemotherapy scheduling protocols, leading to improvements in tumor response without changing the total dose delivered. By considering treatment schedules with identical cumulative dose delivered, we characterize differential treatment outcomes resulting from high variance schedules (e.g. high dose, low dose) and low variance schedules (constant dose). We extend a previous framework used to quantify second-order effects, known as antifragility theory, to investigate the role of drug pharmacokinetics. Using a simple one-compartment model, we find that high variance schedules are effective for a wide range of cumulative dose values. Next, using a mouse-parameterized two-compartment model of 5-fluorouracil, we show that schedule viability depends on initial tumor volume. Finally, we illustrate the trade-off between tumor response and lean mass preservation. Mathematical modeling indicates that high variance dose schedules provide a potential path forward in mitigating the risk of chemotherapy-associated cachexia by preserving lean mass without sacrificing tumor response.

Spatial interactions modulate tumor growth and immune infiltration.

Direct observation of immune cell trafficking patterns and tumor-immune interactions is unlikely in human tumors with currently available technology, but computational simulations based on clinical data can provide insight to test hypotheses. It is hypothesized that patterns of collagen formation evolve as a mechanism of immune escape, but the exact nature of the interaction between immune cells and collagen is poorly understood. Spatial data quantifying the degree of collagen fiber alignment in squamous cell carcinomas indicates that late stage disease is associated with highly aligned fibers. Here, we introduce a computational modeling framework (called Lenia) to discriminate between two hypotheses: immune cell migration that moves 1) parallel or 2) perpendicular to collagen fiber orientation. The modeling recapitulates immune-ECM interactions where collagen patterns provide immune protection, leading to an emergent inverse relationship between disease stage and immune coverage. We also illustrate the capabilities of Lenia to model the evolution of tumor progression and immune predation. Lenia provides a flexible framework for considering a spectrum of local (cell-scale) to global (tumor-scale) dynamics by defining a kernel cell-cell interaction function that governs tumor growth dynamics under immune predation with immune cell migration. Mathematical modeling provides important mechanistic insights into cell interactions. Short-range interaction kernels provide a mechanism for tumor cell survival under conditions with strong Allee effects, while asymmetric tumor-immune interaction kernels lead to poor immune response. Thus, the length scale of tumor-immune interactions drives tumor growth and infiltration.

CLPB disaggregase dysfunction impacts the functional integrity of the proteolytic SPY complex.

CLPB is a mitochondrial intermembrane space AAA+ domain-containing disaggregase. CLPB mutations are associated with 3-methylglutaconic aciduria and neutropenia; however, the molecular mechanism underscoring disease and the contribution of CLPB substrates to disease pathology remains unknown. Interactions between CLPB and mitochondrial quality control (QC) factors, including PARL and OPA1, have been reported, hinting at dysregulation of organelle QC in disease. Utilizing proteomic and biochemical approaches, we show a stress-specific aggregation phenotype in a CLPB-null environment and define the CLPB substrate profile. We illustrate an interplay between intermembrane space proteins including CLPB, HAX1, HTRA2, and the inner membrane quality control proteins (STOML2, PARL, YME1L1; SPY complex), with CLPB deficiency impeding SPY complex function by virtue of protein aggregation in the intermembrane space. We conclude that there is an interdependency of mitochondrial QC components at the intermembrane space/inner membrane interface, and perturbations to this network may underscore CLPB disease pathology.

Testing Adaptive Therapy Protocols Using Gemcitabine and Capecitabine in a Preclinical Model of Endocrine-Resistant Breast Cancer.

Adaptive therapy, an ecologically inspired approach to cancer treatment, aims to overcome resistance and reduce toxicity by leveraging competitive interactions between drug-sensitive and drug-resistant subclones, prioritizing patient survival and quality of life instead of killing the maximum number of cancer cells. In preparation for a clinical trial, we used endocrine-resistant MCF7 breast cancer to stimulate second-line therapy and tested adaptive therapy using capecitabine, gemcitabine, or their combination in a mouse xenograft model. Dose modulation adaptive therapy with capecitabine alone increased survival time relative to MTD but not statistically significantly (HR = 0.22, 95% CI = 0.043-1.1, p = 0.065). However, when we alternated the drugs in both dose modulation (HR = 0.11, 95% CI = 0.024-0.55, p = 0.007) and intermittent adaptive therapies, the survival time was significantly increased compared to high-dose combination therapy (HR = 0.07, 95% CI = 0.013-0.42, p = 0.003). Overall, the survival time increased with reduced dose for both single drugs (p < 0.01) and combined drugs (p < 0.001), resulting in tumors with fewer proliferation cells (p = 0.0026) and more apoptotic cells (p = 0.045) compared to high-dose therapy. Adaptive therapy favors slower-growing tumors and shows promise in two-drug alternating regimens instead of being combined.

Testing Adaptive Therapy Protocols using Gemcitabine and Capecitabine on a Mouse Model of Endocrine-Resistant Breast Cancer.

Highly effective cancer therapies often face limitations due to acquired resistance and toxicity. Adaptive therapy, an ecologically inspired approach, seeks to control therapeutic resistance and minimize toxicity by leveraging competitive interactions between drug-sensitive and drug-resistant subclones, prioritizing patient survival and quality of life over maximum cell kill. In preparation for a clinical trial in breast cancer, we used large populations of MCF7 cells to rapidly generate endocrine-resistance breast cancer cell line. We then mimicked second line therapy in ER+ breast cancers by treating the endocrine-resistant MCF7 cells in a mouse xenograft model to test adaptive therapy with capecitabine, gemcitabine, or the combination of those two drugs. Dose-modulation adaptive therapy with capecitabine alone increased survival time relative to MTD, but not statistically significant (HR: 0.22, 95% CI 0.043- 1.1 P = 0.065). However, when we alternated the drugs in both dose modulation (HR = 0.11, 95% CI: 0.024 - 0.55, P = 0.007) and intermittent adaptive therapies significantly increased survival time compared to high dose combination therapy (HR = 0.07, 95% CI: 0.013 - 0.42; P = 0.003). Overall, survival time increased with reduced dose for both single drugs (P < 0.01) and combined drugs (P < 0.001). Adaptive therapy protocols resulted in tumors with lower proportions of proliferating cells (P = 0.0026) and more apoptotic cells (P = 0.045). The results show that Adaptive therapy outperforms high-dose therapy in controlling endocrine-resistant breast cancer, favoring slower-growing tumors, and showing promise in two-drug alternating regimens.

Virtual alignment of pathology image series for multi-gigapixel whole slide images.

Interest in spatial omics is on the rise, but generation of highly multiplexed images remains challenging, due to cost, expertise, methodical constraints, and access to technology. An alternative approach is to register collections of whole slide images (WSI), generating spatially aligned datasets. WSI registration is a two-part problem, the first being the alignment itself and the second the application of transformations to huge multi-gigapixel images. To address both challenges, we developed Virtual Alignment of pathoLogy Image Series (VALIS), software which enables generation of highly multiplexed images by aligning any number of brightfield and/or immunofluorescent WSI, the results of which can be saved in the ome.tiff format. Benchmarking using publicly available datasets indicates VALIS provides state-of-the-art accuracy in WSI registration and 3D reconstruction. Leveraging existing open-source software tools, VALIS is written in Python, providing a free, fast, scalable, robust, and easy-to-use pipeline for registering multi-gigapixel WSI, facilitating downstream spatial analyses.

Human Tim8a, Tim8b and Tim13 are auxiliary assembly factors of mature Complex IV.

Human Tim8a and Tim8b are paralogous intermembrane space proteins of the small TIM chaperone family. Yeast small TIMs function in the trafficking of proteins to the outer and inner mitochondrial membranes. This putative import function for hTim8a and hTim8b has been challenged in human models, but their precise molecular function(s) remains undefined. Likewise, the necessity for human cells to encode two Tim8 proteins and whether any potential redundancy exists is unclear. We demonstrate that hTim8a and hTim8b function in the assembly of cytochrome c oxidase (Complex IV). Using affinity enrichment mass spectrometry, we define the interaction network of hTim8a, hTim8b and hTim13, identifying subunits and assembly factors of the Complex IV COX2 module. hTim8-deficient cells have a COX2 and COX3 module defect and exhibit an accumulation of the Complex IV S2 subcomplex. These data suggest that hTim8a and hTim8b function in assembly of Complex IV via interactions with intermediate-assembly subcomplexes. We propose that hTim8-hTim13 complexes are auxiliary assembly factors involved in the formation of the Complex IV S3 subcomplex during assembly of mature Complex IV.

Intermetastatic and Intrametastatic Heterogeneity Shapes Adaptive Therapy Cycling Dynamics.

Adaptive therapies that alternate between drug applications and drug-free vacations can exploit competition between sensitive and resistant cells to maximize the time to progression. However, optimal dosing schedules depend on the properties of metastases, which are often not directly measurable in clinical practice. Here, we proposed a framework for estimating features of metastases through tumor response dynamics during the first adaptive therapy treatment cycle. Longitudinal prostate-specific antigen (PSA) levels in 16 patients with metastatic castration-resistant prostate cancer undergoing adaptive androgen deprivation treatment were analyzed to investigate relationships between cycle dynamics and clinical variables such as Gleason score, the change in the number of metastases over a cycle, and the total number of cycles over the course of treatment. The first cycle of adaptive therapy, which consists of a response period (applying therapy until 50% PSA reduction), and a regrowth period (removing treatment until reaching initial PSA levels), delineated several features of the computational metastatic system: larger metastases had longer cycles; a higher proportion of drug-resistant cells slowed the cycles; and a faster cell turnover rate sped up drug response time and slowed regrowth time. The number of metastases did not affect cycle times, as response dynamics were dominated by the largest tumors rather than the aggregate. In addition, systems with higher intermetastasis heterogeneity responded better to continuous therapy and correlated with dynamics from patients with high or low Gleason scores. Conversely, systems with higher intrametastasis heterogeneity responded better to adaptive therapy and correlated with dynamics from patients with intermediate Gleason scores. SIGNIFICANCE: Multiscale mathematical modeling combined with biomarker dynamics during adaptive therapy helps identify underlying features of metastatic cancer to inform treatment decisions.

Collateral Changes in Cell Physiology Associated with ADC-7 ß-Lactamase Expression in Acinetobacter baumannii.

The ADC (AmpC) ß-lactamase is universally present in the Acinetobacter baumannii chromosome, suggesting it may have a yet-to-be-identified cellular function. Using peptidoglycan composition analysis, we show that overexpressing the ADC-7 ß-lactamase in A. baumannii drives changes consistent with altered l,d-transpeptidase activity. Based on this, we tested whether cells overexpressing ADC-7 would exhibit new vulnerabilities. As proof of principle, a screen of transposon insertions revealed that an insertion in the distal 3' end of canB, encoding carbonic anhydrase, resulted in a significant loss of viability when the adc-7 gene was overexpressed. A canB deletion mutant exhibited a more pronounced loss of viability than the transposon insertion, and this became amplified when cells overexpressed ADC-7. Interestingly, overexpression of the OXA-23 or TEM-1 ß-lactamases also led to a pronounced loss of viability in cells with reduced carbonic anhydrase activity. In addition, we demonstrate that reduced CanB activity led to increased sensitivity to peptidoglycan synthesis inhibitors and to the carbonic anhydrase inhibitor ethoxzolamide. Furthermore, this strain exhibited a synergistic interaction with the peptidoglycan inhibitor fosfomycin and ethoxzolamide. Our results highlight the impact of ADC-7 overexpression on cell physiology and reveal that the essential carbonic anhydrase CanB may represent a novel target for antimicrobial agents that would exhibit increased potency against ß-lactamase-overexpressing A. baumannii. IMPORTANCE Acinetobacter baumannii has become resistant to all classes of antibiotics, with ß-lactam resistance responsible for the majority of treatment failures. New classes of antimicrobials are needed to treat this high-priority pathogen. This study had uncovered a new genetic vulnerability in ß-lactamase-expressing A. baumannii, where reduced carbonic anhydrase activity becomes lethal. Inhibitors of carbonic anhydrase could represent a new method for treating A. baumannii infections.

Modeling collective cell behavior in cancer: Perspectives from an interdisciplinary conversation.

Collective cell behavior contributes to all stages of cancer progression. Understanding how collective behavior emerges through cell-cell interactions and decision-making will advance our understanding of cancer biology and provide new therapeutic approaches. Here, we summarize an interdisciplinary discussion on multicellular behavior in cancer, draw lessons from other scientific disciplines, and identify future directions.