The growth of multiparametric imaging protocols has paved the way for quantitative imaging phenotypes that predict treatment response and clinical outcome, reflect underlying cancer molecular characteristics and spatiotemporal heterogeneity, and can guide personalized treatment planning. This growth has underlined the need for efficient quantitative analytics to derive high-dimensional imaging signatures of diagnostic and predictive value in this emerging era of integrated precision diagnostics. This paper presents cancer imaging phenomics toolkit (CaPTk), a new and dynamically growing software platform for analysis of radiographic images of cancer, currently focusing on brain, breast, and lung cancer. CaPTk leverages the value of quantitative imaging analytics along with machine learning to derive phenotypic imaging signatures, based on two-level functionality. First, image analysis algorithms are used to extract comprehensive panels of diverse and complementary features, such as multiparametric intensity histogram distributions, texture, shape, kinetics, connectomics, and spatial patterns. At the second level, these quantitative imaging signatures are fed into multivariate machine learning models to produce diagnostic, prognostic, and predictive biomarkers. Results from clinical studies in three areas are shown: (i) computational neuro-oncology of brain gliomas for precision diagnostics, prediction of outcome, and treatment planning; (ii) prediction of treatment response for breast and lung cancer, and (iii) risk assessment for breast cancer.

BACKGROUND. Poly(ADP-ribose) polymerase (PARP) inhibitors are effective in a broad population of patients with ovarian cancer; however, resistance caused by low enzyme expression of the drug target PARP-1 remains to be clinically evaluated in this context. We hypothesize that PARP-1 expression is variable in ovarian cancer and can be quantified in primary and metastatic disease using a novel PET imaging agent. METHODS. We used a translational approach to describe the significance of PET imaging of PARP-1 in ovarian cancer. First, we produced PARP1-KO ovarian cancer cell lines using CRISPR/Cas9 gene editing to test the loss of PARP-1 as a resistance mechanism to all clinically used PARP inhibitors. Next, we performed preclinical microPET imaging studies using ovarian cancer patient–derived xenografts in mouse models. Finally, in a phase I PET imaging clinical trial we explored PET imaging as a regional marker of PARP-1 expression in primary and metastatic disease through correlative tissue histology. RESULTS. We found that deletion of PARP1 causes resistance to all PARP inhibitors in vitro, and microPET imaging provides proof of concept as an approach to quantify PARP-1 in vivo. Clinically, we observed a spectrum of standard uptake values (SUVs) ranging from 2–12 for PARP-1 in tumors. In addition, we found a positive correlation between PET SUVs and fluorescent immunohistochemistry for PARP-1 (r ² = 0.60). CONCLUSION. This work confirms the translational potential of a PARP-1 PET imaging agent and supports future clinical trials to test PARP-1 expression as a method to stratify patients for PARP inhibitor therapy. TRIAL REGISTRATION. Clinicaltrials.gov NCT02637934. FUNDING. Research reported in this publication was supported by the Department of Defense OC160269, a Basser Center team science grant, NIH National Cancer Institute R01CA174904, a Department of Energy training grant DE-SC0012476, Abramson Cancer Center Radiation Oncology pilot grants, the Marsha Rivkin Foundation, Kaleidoscope of Hope Foundation, and Paul Calabresi K12 Career Development Award 5K12CA076931.

BACKGROUND. Several molecular imaging strategies can identify bacterial infections in humans. PET affords the potential for sensitive infection detection deep within the body. Among PET-based approaches, antibiotic-based radiotracers, which often target key bacterial-specific enzymes, have considerable promise. One question for antibiotic radiotracers is whether antimicrobial resistance (AMR) reduces specific accumulation within bacteria, diminishing the predictive value of the diagnostic test. METHODS. Using a PET radiotracer based on the antibiotic trimethoprim (TMP), [¹¹C]-TMP, we performed in vitro uptake studies in susceptible and drug-resistant bacterial strains and whole-genome sequencing (WGS) in selected strains to identify TMP resistance mechanisms. Next, we queried the NCBI database of annotated bacterial genomes for WT and resistant dihydrofolate reductase (DHFR) genes. Finally, we initiated a first-in-human protocol of [¹¹C]-TMP in patients infected with both TMP-sensitive and TMP-resistant organisms to demonstrate the clinical feasibility of the tool. RESULTS. We observed robust [¹¹C]-TMP uptake in our panel of TMP-sensitive and -resistant bacteria, noting relatively variable and decreased uptake in a few strains of P. aeruginosa and E. coli. WGS showed that the vast majority of clinically relevant bacteria harbor a WT copy of DHFR, targetable by [¹¹C]-TMP, and that despite the AMR, these strains should be “imageable.” Clinical imaging of patients with [¹¹C]-TMP demonstrated focal radiotracer uptake in areas of infectious lesions. CONCLUSION. This work highlights an approach to imaging bacterial infection in patients, which could affect our understanding of bacterial pathogenesis as well as our ability to better diagnose infections and monitor response to therapy. TRIAL REGISTRATION. ClinicalTrials.gov NCT03424525. FUNDING. Institute for Translational Medicine and Therapeutics, Burroughs Wellcome Fund, NIH Office of the Director Early Independence Award (DP5-OD26386), and University of Pennsylvania NIH T32 Radiology Research Training Grant (5T32EB004311-12).

Purpose: PARP inhibitors have become the standard-of-care treatment for homologous recombination deficient (HRD) high-grade serous ovarian cancer (HGSOC). However, not all HRD tumors respond to PARPi. Biomarkers to predict response are needed. [18F]FluorThanatrace ([18F]FTT) is a PARPi-analog PET radiotracer that noninvasively measures PARP-1 expression. Herein, we evaluate [18F]FTT as a biomarker to predict response to PARPi in patient-derived xenograft (PDX) models and subjects with HRD HGSOC. Experimental Design: In PDX models, [18F]FTT-PET was performed before and after PARPi (olaparib), ataxia-telangiectasia inhibitor (ATRi), or both (PARPi-ATRi). Changes in [18F]FTT were correlated with tumor volume changes. Subjects were imaged with [18F]FTT-PET at baseline and after ∼1 week of PARPi. Changes in [18F]FTT-PET uptake were compared with changes in tumor size (RECISTv1.1), CA-125, and progression-free survival (PFS). Results: A decrease in [18F]FTT tumor uptake after PARPi correlated with response to PARPi, or PARPi-ATRi treatment in PARPi-resistant PDX models (r = 0.77–0.81). In subjects (n = 11), percent difference in [18F]FTT-PET after ∼7 days of PARPi compared with baseline correlated with best RECIST response (P = 0.01), best CA-125 response (P = 0.033), and PFS (P = 0.027). All subjects with >50% reduction in [18F]FTT uptake had >6-month PFS and >50% reduction in CA-125. Utilizing only baseline [18F]FTT uptake did not predict such responses. Conclusions: The decline in [18F]FTT uptake shortly after PARPi initiation provides a measure of drug-target engagement and shows promise as a biomarker to guide PARPi therapies in this pilot study. These results support additional preclinical mechanistic and clinical studies in subjects receiving PARPi ± combination therapy. See related commentary by Liu and Zamarin, p. 1384

The process to develop and evaluate fluorine 18 fluorthanatrace as a PET radiotracer for cancer imaging requires substantial time and resources, but it may improve the diagnosis and clinical manage... Fluorine 18 (18F) fluorthanatrace (18F-FTT) is a PET radiotracer for imaging poly (adenosine diphosphate–ribose) polymerase-1 (PARP-1), an important target for a class of drugs known as PARP inhibi...

BACKGROUND: [ ¹⁸ F]FluorThanatrace ([ ¹⁸ F]FTT) is a radiolabeled poly (adenosine diphosphate-ribose) polymerase inhibitor (PARPi) that enables noninvasive quantification of PARP with potential to serve as a biomarker for patient selection for PARPi therapy. Here we report for the first time to our knowledge noninvasive in vivo visualization of drug-target engagement during PARPi treatment. METHODS: Two single-arm, prospective, nonrandomized clinical trials were conducted at the University of Pennsylvania from May 2017 to March 2020. PARP expression in breast cancer was assessed in vivo via [ ¹⁸ F]FTT PET before and after initiation of PARPi treatment and in vitro via [ ¹²⁵ I]KX1 (an analog of [ ¹⁸ F]FTT) binding to surgically removed breast cancer. RESULTS: Thirteen patients had baseline [ ¹⁸ F]FTT PET. Nine of these then had resection and in vitro evaluation of [ ¹⁸ F]FTT uptake with an analog and uptake was blocked with PARPi. Of the other 4 patients, 3 had [ ¹⁸ F]FTT PET uptake, and all had uptake blocked with treatment with a therapeutic PARPi. Initial in vivo [ ¹⁸ F]FTT tumor uptake ranged from undetectable to robust. Following initiation of PARPi therapy, [ ¹⁸ F]FTT uptake was not detectable above background in all cases. In vitro tumor treatment with a PARPi resulted in 82% reduction in [ ¹²⁵ I]KX1 binding. CONCLUSION: [ ¹⁸ F]FTT noninvasively quantifies PARP-1 expression. Early results indicate ability to visualize PARPi drug-target engagement in vivo and suggest the utility of further study to test [ ¹⁸ F]FTT PET as a predictive and pharmacodynamic biomarker. TRIAL REGISTRATION: ClinicalTrials.gov identifiers NCT03083288 and NCT03846167. FUNDING: Metavivor Translational Research Award, Susan G. Komen for the Cure (CCR 16376362), Department of Defense BC190315, and Abramson Cancer Center Breakthrough Bike Challenge.