Antibody Drug Conjugate Sacituzumab Govitecan Enables A Sequential TOP1/PARP Inhibitor Cancer Therapy Strategy in Breast Cancer Patients.

PURPOSE: The Antibody-Drug Conjugate (ADC) Sacituzumab govitecan (SG) comprises the topoisomerase 1 (TOP1) inhibitor SN-38, coupled to a monoclonal antibody targeting trophoblast cell surface antigen 2 (TROP-2). Poly (ADP-ribose) polymerase (PARP) inhibition may synergize with TOP1 inhibitors and SG, but previous studies combining systemic PARP and TOP1 inhibitors failed due to dose-limiting myelosuppression. Here, we assess proof-of-mechanism and clinical feasibility for SG and talazoparib employing an innovative sequential dosing schedule. PATIENTS AND METHODS: In vitro models tested pharmacodynamic endpoints, and in a phase 1b clinical trial (NCT04039230) 30 patients with metastatic Triple-Negative Breast Cancer (mTNBC) received SG and talazoparib using a concurrent (N=7) or sequential (N=23) schedule. Outcome measures included safety, tolerability, preliminary efficacy and establishment of a recommended phase 2 dose (RP2D). RESULTS: We hypothesized that tumor-selective delivery of TOP1i via SG would reduce non-tumor toxicity and create a temporal window, enabling sequential dosing of SG and PARP inhibition. In vitro, sequential SG followed by talazoparib delayed TOP1 cleavage complex clearance, increased DNA damage and promoted apoptosis. In the clinical trial, sequential SG/talazoparib successfully met primary objectives and demonstrated median PFS of 7.6 months without Dose-Limiting Toxicities (DLTs), while concurrent dosing yielded 2.3 months PFS and multiple DLTs including severe myelosuppression. CONCLUSIONS: While SG dosed concurrently with talazoparib is not tolerated clinically due to an insufficient therapeutic window, sequential dosing of SG then talazoparib proved a viable strategy. These findings support further clinical development of the combination and suggest that ADC-based therapy may facilitate novel, mechanism-based dosing strategies.

Parallel Genomic Alterations of Antigen and Payload Targets Mediate Polyclonal Acquired Clinical Resistance to Sacituzumab Govitecan in Triple-Negative Breast Cancer.

Sacituzumab govitecan (SG), the first antibody-drug conjugate (ADC) approved for triple-negative breast cancer, incorporates the anti-TROP2 antibody hRS7 conjugated to a topoisomerase-1 (TOP1) inhibitor payload. We sought to identify mechanisms of SG resistance through RNA and whole-exome sequencing of pretreatment and postprogression specimens. One patient exhibiting de novo progression lacked TROP2 expression, in contrast to robust TROP2 expression and focal genomic amplification of TACSTD2/TROP2 observed in a patient with a deep, prolonged response to SG. Analysis of acquired genomic resistance in this case revealed one phylogenetic branch harboring a canonical TOP1 E418K resistance mutation and subsequent frameshift TOP1 mutation, whereas a distinct branch exhibited a novel TACSTD2/TROP2 T256R missense mutation. Reconstitution experiments demonstrated that TROP2T256R confers SG resistance via defective plasma membrane localization and reduced cell-surface binding by hRS7. These findings highlight parallel genomic alterations in both antibody and payload targets associated with resistance to SG. SIGNIFICANCE: These findings underscore TROP2 as a response determinant and reveal acquired SG resistance mechanisms involving the direct antibody and drug payload targets in distinct metastatic subclones of an individual patient. This study highlights the specificity of SG and illustrates how such mechanisms will inform therapeutic strategies to overcome ADC resistance.This article is highlighted in the In This Issue feature, p. 2355.

Radiomic and radiogenomic modeling for radiotherapy: strategies, pitfalls, and challenges.

The power of predictive modeling for radiotherapy outcomes has historically been limited by an inability to adequately capture patient-specific variabilities; however, next-generation platforms together with imaging technologies and powerful bioinformatic tools have facilitated strategies and provided optimism. Integrating clinical, biological, imaging, and treatment-specific data for more accurate prediction of tumor control probabilities or risk of radiation-induced side effects are high-dimensional problems whose solutions could have widespread benefits to a diverse patient population-we discuss technical approaches toward this objective. Increasing interest in the above is specifically reflected by the emergence of two nascent fields, which are distinct but complementary: radiogenomics, which broadly seeks to integrate biological risk factors together with treatment and diagnostic information to generate individualized patient risk profiles, and radiomics, which further leverages large-scale imaging correlates and extracted features for the same purpose. We review classical analytical and data-driven approaches for outcomes prediction that serve as antecedents to both radiomic and radiogenomic strategies. Discussion then focuses on uses of conventional and deep machine learning in radiomics. We further consider promising strategies for the harmonization of high-dimensional, heterogeneous multiomics datasets (panomics) and techniques for nonparametric validation of best-fit models. Strategies to overcome common pitfalls that are unique to data-intensive radiomics are also discussed.

The anti-malarial drug atovaquone potentiates platinum-mediated cancer cell death by increasing oxidative stress.

Platinum chemotherapies are highly effective cytotoxic agents but often induce resistance when used as monotherapies. Combinatorial strategies limit this risk and provide effective treatment options for many cancers. Here, we repurpose atovaquone (ATQ), a well-tolerated & FDA-approved anti-malarial agent by demonstrating that it potentiates cancer cell death of a subset of platinums. We show that ATQ in combination with carboplatin or cisplatin induces striking and repeatable concentration- and time-dependent cell death sensitization in vitro across a variety of cancer cell lines. ATQ induces mitochondrial reactive oxygen species (mROS), depleting intracellular glutathione (GSH) pools in a concentration-dependent manner. The superoxide dismutase mimetic MnTBAP rescues ATQ-induced mROS production and pre-loading cells with the GSH prodrug N-acetyl cysteine (NAC) abrogates the sensitization. Together, these findings implicate ATQ-induced oxidative stress as key mediator of the sensitizing effect. At physiologically achievable concentrations, ATQ and carboplatin furthermore synergistically delay the growth of three-dimensional avascular spheroids. Clinically, ATQ is a safe and specific inhibitor of the electron transport chain (ETC) and is concurrently being repurposed as a candidate tumor hypoxia modifier. Together, these findings suggest that ATQ is deserving of further study as a candidate platinum sensitizing agent.

Genomics models in radiotherapy: From mechanistic to machine learning.

Machine learning (ML) provides a broad framework for addressing high-dimensional prediction problems in classification and regression. While ML is often applied for imaging problems in medical physics, there are many efforts to apply these principles to biological data toward questions of radiation biology. Here, we provide a review of radiogenomics modeling frameworks and efforts toward genomically guided radiotherapy. We first discuss medical oncology efforts to develop precision biomarkers. We next discuss similar efforts to create clinical assays for normal tissue or tumor radiosensitivity. We then discuss modeling frameworks for radiosensitivity and the evolution of ML to create predictive models for radiogenomics.

Tumor pH and Protein Concentration Contribute to the Signal of Amide Proton Transfer Magnetic Resonance Imaging.

Abnormal pH is a common feature of malignant tumors and has been associated clinically with suboptimal outcomes. Amide proton transfer magnetic resonance imaging (APT MRI) holds promise as a means to noninvasively measure tumor pH, yet multiple factors collectively make quantification of tumor pH from APT MRI data challenging. The purpose of this study was to improve our understanding of the biophysical sources of altered APT MRI signals in tumors. Combining in vivo APT MRI measurements with ex vivo histological measurements of protein concentration in a rat model of brain metastasis, we determined that the proportion of APT MRI signal originating from changes in protein concentration was approximately 66%, with the remaining 34% originating from changes in tumor pH. In a mouse model of hypopharyngeal squamous cell carcinoma (FaDu), APT MRI showed that a reduction in tumor hypoxia was associated with a shift in tumor pH. The results of this study extend our understanding of APT MRI data and may enable the use of APT MRI to infer the pH of individual patients' tumors as either a biomarker for therapy stratification or as a measure of therapeutic response in clinical settings. SIGNIFICANCE: These findings advance our understanding of amide proton transfer magnetic resonance imaging (APT MRI) of tumors and may improve the interpretation of APT MRI in clinical settings.

Targeting tumour hypoxia: shifting focus from oxygen supply to demand.

Tumour hypoxia is a well-recognised barrier to anti-cancer therapy and represents one of the best validated targets in oncology. Previous attempts to tackle hypoxia have focussed primarily on increasing tumour oxygen supply; however, clinical studies using this approach have yielded only modest clinical benefit, with often significant toxicity and practical limitations. Therefore, there are currently no anti-hypoxia treatments in widespread clinical use. As an emerging alternative strategy, we discuss the relevance of inhibiting tumour oxygen metabolism to alleviate hypoxia and highlight recently initiated clinical trials using this approach.

Radiogenomics and radiotherapy response modeling.

Advances in patient-specific information and biotechnology have contributed to a new era of computational medicine. Radiogenomics has emerged as a new field that investigates the role of genetics in treatment response to radiation therapy. Radiation oncology is currently attempting to embrace these recent advances and add to its rich history by maintaining its prominent role as a quantitative leader in oncologic response modeling. Here, we provide an overview of radiogenomics starting with genotyping, data aggregation, and application of different modeling approaches based on modifying traditional radiobiological methods or application of advanced machine learning techniques. We highlight the current status and potential for this new field to reshape the landscape of outcome modeling in radiotherapy and drive future advances in computational oncology.

Gastrointestinal stromal tumour of the stomach in an 8-year-old boy.

We report a case of an 8-year-old boy who underwent an ultrasound for investigation of a left upper quadrant mass and who was subsequently diagnosed with gastrointestinal stromal tumour of his stomach. Abdominal ultrasound showed a large cystic mass in the left upper abdomen which was further characterised by magnetic resonance imaging. At laparotomy, a large cystic lesion was excised from his stomach, which was confirmed at histology to be a gastrointestinal stromal tumour. We briefly discuss the presentation and imaging findings of paediatric gastrointestinal stromal tumour and how it differs from the adult form of the disease and treatments.

Big Data Analytics for Prostate Radiotherapy.

Radiation therapy is a first-line treatment option for localized prostate cancer and radiation-induced normal tissue damage are often the main limiting factor for modern radiotherapy regimens. Conversely, under-dosing of target volumes in an attempt to spare adjacent healthy tissues limits the likelihood of achieving local, long-term control. Thus, the ability to generate personalized data-driven risk profiles for radiotherapy outcomes would provide valuable prognostic information to help guide both clinicians and patients alike. Big data applied to radiation oncology promises to deliver better understanding of outcomes by harvesting and integrating heterogeneous data types, including patient-specific clinical parameters, treatment-related dose-volume metrics, and biological risk factors. When taken together, such variables make up the basis for a multi-dimensional space (the "RadoncSpace") in which the presented modeling techniques search in order to identify significant predictors. Herein, we review outcome modeling and big data-mining techniques for both tumor control and radiotherapy-induced normal tissue effects. We apply many of the presented modeling approaches onto a cohort of hypofractionated prostate cancer patients taking into account different data types and a large heterogeneous mix of physical and biological parameters. Cross-validation techniques are also reviewed for the refinement of the proposed framework architecture and checking individual model performance. We conclude by considering advanced modeling techniques that borrow concepts from big data analytics, such as machine learning and artificial intelligence, before discussing the potential future impact of systems radiobiology approaches.

Outcome modeling techniques for prostate cancer radiotherapy: Data, models, and validation.

Prostate cancer is a frequently diagnosed malignancy worldwide and radiation therapy is a first-line approach in treating localized as well as locally advanced cases. The limiting factor in modern radiotherapy regimens is dose to normal structures, an excess of which can lead to aberrant radiation-induced toxicities. Conversely, dose reduction to spare adjacent normal structures risks underdosing target volumes and compromising local control. As a result, efforts aimed at predicting the effects of radiotherapy could invaluably optimize patient treatments by mitigating such toxicities and simultaneously maximizing biochemical control. In this work, we review the types of data, frameworks and techniques used for prostate radiotherapy outcome modeling. Consideration is given to clinical and dose-volume metrics, such as those amassed by the QUANTEC initiative, and also to newer methods for the integration of biological and genetic factors to improve prediction performance. We furthermore highlight trends in machine learning that may help to elucidate the complex pathophysiological mechanisms of tumor control and radiation-induced normal tissue side effects.

Detrusor after-contraction on ambulatory urodynamics in symptomatic women.

OBJECTIVES: To investigate the association between detrusor after-contraction and urodynamic parameters in a cohort of patients undergoing urodynamic studies by ambulatory monitoring. METHODS: All symptomatic adult female patients with non-neurogenic lower urinary tract dysfunction having ambulatory monitoring over the period January 1998 to January 2014 were included. Urodynamic traces were reviewed to identify detrusor after-contraction. Measured urodynamic variables were Qmax (mL/s), V(void) (mL) and P(det.Qmax) (cmH(2)O). Student's unpaired t-test was used to compare the mean of the variable in the detrusor after-contraction and non-detrusor after-contraction groups. RESULTS: We identified 331 women with a median age of 50 years (range 16-82). Detrusor after-contraction was seen after at least one void in 122 patients giving a prevalence of 37%. A total of 167 (51%) patients had detrusor overactivity. Diagnosis of detrusor overactivity was associated with the presence of detrusor after-contraction (P < 0.05). Overall, patients with detrusor after-contraction had a statistically higher mean P(det.Qmax) (32 vs 28 cmH(2)O, P = 0.04) and lower mean voided volume (300 vs 378 mL, P < 0.001). CONCLUSION: These findings suggest a relatively high prevalence of detrusor after-contraction during ambulatory monitoring, and an association between detrusor overactivity, V(void), P(det.Qmax) and detrusor after-contraction recorded during ambulatory monitoring. Therefore, a link between detrusor after-contractions and the syndrome of overactive bladder can be postulated.

Contrasting analytical and data-driven frameworks for radiogenomic modeling of normal tissue toxicities in prostate cancer.

BACKGROUND AND PURPOSE: We explore analytical and data-driven approaches to investigate the integration of genetic variations (single nucleotide polymorphisms [SNPs] and copy number variations [CNVs]) with dosimetric and clinical variables in modeling radiation-induced rectal bleeding (RB) and erectile dysfunction (ED) in prostate cancer patients. MATERIALS AND METHODS: Sixty-two patients who underwent curative hypofractionated radiotherapy (66 Gy in 22 fractions) between 2002 and 2010 were retrospectively genotyped for CNV and SNP rs5489 in the xrcc1 DNA repair gene. Fifty-four patients had full dosimetric profiles. Two parallel modeling approaches were compared to assess the risk of severe RB (Grade⩾3) and ED (Grade⩾1); Maximum likelihood estimated generalized Lyman-Kutcher-Burman (LKB) and logistic regression. Statistical resampling based on cross-validation was used to evaluate model predictive power and generalizability to unseen data. RESULTS: Integration of biological variables xrcc1 CNV and SNP improved the fit of the RB and ED analytical and data-driven models. Cross-validation of the generalized LKB models yielded increases in classification performance of 27.4% for RB and 14.6% for ED when xrcc1 CNV and SNP were included, respectively. Biological variables added to logistic regression modeling improved classification performance over standard dosimetric models by 33.5% for RB and 21.2% for ED models. CONCLUSION: As a proof-of-concept, we demonstrated that the combination of genetic and dosimetric variables can provide significant improvement in NTCP prediction using analytical and data-driven approaches. The improvement in prediction performance was more pronounced in the data driven approaches. Moreover, we have shown that CNVs, in addition to SNPs, may be useful structural genetic variants in predicting radiation toxicities.

Non-invasive whole-body plethysmograph for assessment and prediction of radiation-induced lung injury using simultaneously acquired nitric oxide and lung volume measurements.

Radiation-induced lung injury (RILI) is a prevalent side effect in patients who undergo thoracic irradiation as part of their cancer treatment. Preclinical studies play a major role in understanding disease onset under controlled experimental conditions. The aim of this work is to develop a single-chambered optimized, non-invasive, whole-body plethysmograph prototype for unrestrained small animal lung volume measurements for preclinical RILI studies. The system is also designed to simultaneously obtain nitric oxide (NO) measurements of the expired breath. The device prototype was tested using computer simulations, phantom studies and in vivo measurements in experimental animal models of RILI. The system was found to improve resemblance to true breathing signal characteristics as measured by improved skewness (21.83%) and kurtosis (51.94%) in addition to increased overall signal sensitivity (3.61%) of the acquired breath signal, when compared to matching control data. NO concentration data was combined with breath measurements in order to predict early RILI onset. The system was evaluated using serial weekly measurements in hemi-thorax irradiated rats (n = 8) yielding a classification performance of 50.0%, 62.5%, 87.5% using lung volume only, NO only, and combined measurements of both, respectively. Our results indicate that improved performance could be achieved when measurements of lung volume are combined with those of NO. This would provide the overall plethysmography system with the ability to provide useful diagnostic and prognostic information for preclinical and, potentially, clinical thoracic dose escalation studies.

Hypoxia-inducible factor-1α and its role in the proliferation of retinoblastoma cells.

In order to better understand the role of HIF-1α in the proliferation of the retinoblastoma cells, a siRNA knockdown of HIF-1α followed by a proliferation assay was performed. Further sequencing was then carried out in order to assess knockdown efficiency and expression of HIF-1α. Upregulation of HIF-1α gene expression in CoCl2-treated retinoblastoma cells was demonstrated via melting curve analysis from PCR tests and was further analyzed using western blot and densitometry analysis. Reduction of HIF-1α expression in retinoblastoma, post HIF-1α knockdown, was observed after siRNA transfection into Y-79 cells. Knockdown of HIF-1α resulted in a significant decrease in proliferation thereby demonstrating that HIF-1α is involved in promoting survival and proliferation in retinoblastoma cells. Stabilization of HIF-1α in retinoblastoma cells using CoCl2 was unsuccessful.

Local chemotherapeutic agents for the treatment of ocular malignancies.

We critically analyze available peer-reviewed literature, including clinical trials and case reports, on local ocular cancer treatments. Recent innovations in many areas of ocular oncology have introduced promising new therapies, but, for the most part, the optimal treatment of ocular malignancies remains elusive.

Standardization of terminology for episodes of hematopoietic stem cell patient transplant care.

The nomenclature describing hematopoietic stem cell transplantation has evolved, adding precision and definition in research and regulation. The lack of coordination and standardization in terminology has left some gaps in the definition of episodes of clinical care. These voids have caused particular problems in contracting for payment and billing for services rendered. The purpose of this report is to propose definitions for cell products, cell infusions, and transplantation episodes.