The pursuit of highly sensitive and specific cancer diagnostics based on cell‐free nucleic acids isolated from minimally invasive liquid biopsies has been an area of intense research and commercial effort for at least two decades. Most of these tests detect cancer‐specific mutations or epigenetic modifications on circulating DNA derived from tumor cells (ctDNA). Although recent FDA approvals of both single and multianalyte liquid biopsy companion diagnostic assays are proof of the tremendous progress made in this domain, using ctDNA for the diagnosis of early‐stage (stage I/II) cancers remains challenging due to several factors such as low mutational allele frequency in circulation, overlapping profiles in genomic alterations among diverse cancers, and clonal hematopoiesis. This review discusses these analytical challenges, interim solutions, and the opportunity to complement ctDNA diagnostics with microbiome‐aware analyses that may mitigate several existing ctDNA assay limitations.

We investigated the diet of the squirrel glider (Petaurus norfolcensis) from within a highly fragmented landscape in the northern (tropical) part of its geographic range where information was absent. We analysed 86 faecal samples of 53 gliders from 11 locations and obtained 97 observations of 10 gliders feeding at two locations. Pollen of Eucalyptus/Corymbia was present in 70% and Melaleuca in 20% of faecal samples. Indicators of sap feeding were present in 44% of samples and seeds were present in 14% of samples. Invertebrates, mostly moth larvae, were present in 54% of samples. Observations of gliders feeding revealed that the main food types were invertebrates (36% of observations), nectar and pollen (27% of observations) and sap (26% of observations) of five tree species. Differences in the use of the major food types revealed by the two methods of diet analysis partly reflect site-based differences. Qualitatively, the diet was similar to that described in southern Australia, with confirmation that sap may be important at some locations. Our results reveal the contribution that different tree species make to the diet, which should be used to guide habitat restoration for the squirrel glider in this fragmented landscape.

Introduction: Links between cancer and microbes date back four millennia (Sepich-Poore et al. 2021. Science). Recently, we found that microbial DNA is detectable in tumor tissues and patient blood from many human cancer types (Poore et al. 2020. Nature). These intratumoral and bloodborne microbiomes were distinct between cancer types, between normal and malignant tissues, and present in cell-free plasma samples. However, the practical utility of cell-free microbial DNA (cf-mbDNA) as a bona fide liquid biopsy diagnostic, including its applicability in early-stage disease in treatment-naïve individuals, distinguishing histological subtypes, and discriminating against non-cancer-but-diseased patients remains unknown. Thus, we constructed an age and sex-matched cohort of >1000 individuals with lung cancer, lung disease, and no disease (healthy) to evaluate the utility of a cf-mbDNA-driven liquid biopsy diagnostic. Methods: Shallow shotgun metagenomic sequencing with gold-standard positive and negative controls was performed using 400 µL of patient plasma. Direct genome alignments separated human and microbial reads, and generated genome-wide binned and species-level abundances, respectively. Novel taxonomic diversity was captured by additionally performing de novo co-assemblies in tandem with tumor and blood samples from The Cancer Genome Atlas (TCGA). Multi-modal, stacked machine learning classifiers then evaluated the diagnostic performance of microbial-only and multi-species (microbial + human) information. Results: Cf-mbDNA provides strong diagnostic performance in treatment-naïve, cancer-bearing individuals versus age and sex-matched healthy controls, as early as stage I disease (AUROCs≥0.90). Furthermore, cf-mbDNA outperforms histological classification compared to human genomic information. Multi-species models paired with routinely-available clinicodemographic information provided robust discrimination of lung cancer versus lung diseases (AUROC≥0.80). Importantly, the addition of cell-free microbial information produced an integrated model surpassing the diagnostic performance of PET-CT and clinical risk models for lung nodule malignancy determination in a blinded validation cohort of Stage I lung cancer and non-cancer lung disease samples. Conclusion: Cf-mbDNA features comprise a novel class of biomarkers that are combinable with host analytes, and show promise for real-world, early-stage, lung cancer diagnosis. Citation Format: Serena Fraraccio, Stephen Wandro, Akanksha Singh-Taylor, Sandrine Miller-Montgomery, Eddie Adams, Rob Knight, Leopoldo N. Segal, Harvey I. Pass, Gregory D. Sepich-Poore. Assessing the real-world utility of cell-free microbial DNA in diagnosing early-stage lung cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5713.

Introduction: Human tissues, including tumors, are extensively colonized by taxonomically diverse microbes. Intra-tumoral microbial activity and events of cellular turnover and trafficking contribute to shedding of microbial nucleic acids into the blood stream. Here we characterized microbial signatures (mbDNA) present in primary-tumor tissue and in the blood of patients affected with different cancer types, with particular focus on lung cancer, and we demonstrated the discriminatory power of such microbial signatures for the identification and classification of lung cancer versus other cancer types. We further validated our findings using plasma-derived cell-free microbial DNA (cf-mbDNA) to discriminate between lung cancer and cancer-free control samples. Methods: We examined The Cancer Genome Atlas (TCGA) compendium of treatment-naïve, whole genome and transcriptomic sequencing datasets to extrapolate genetic signatures of microbial origin associated with 33 different tumor types collected from 10,481 patients, which included non-neoplastic tumor-adjacent tissue and blood samples. 7.2% of TCGA sequencing reads were classified as non-human, of which 35.2% could be taxonomically classified using a reference database containing 59,974 total microbial genomes. These taxonomically assigned data sets were then used to train machine learning models (using a 70/30 train/test split for all cancers) to discriminate between and within types and stages of cancer. Results: We demonstrated that mbDNA signatures from whole blood can be used to accurately classify the tissue of origin of 20 unique cancer types, including lung adenocarcinoma and lung squamous cell carcinoma. For lung adenocarcinoma we reported high discrimination between paired tumor tissue and normal-adjacent tissue (Avg. {AUROC,AUPR}={0.85,0.95}) and between primary tumor tissue and all-other cancer types (Avg. {AUROC,AUPR}={0.96,0.69}, n=32 cancer-types). We also demonstrated the high performance of blood-derived mbDNA when discriminating among TCGA cancer types: Avg. {AUROC,AUPR}={0.97,0.80}. Subsequent liquid biopsy results using plasma-derived cf-mbDNA offer compelling evidence that cf-mbDNA signatures can robustly discriminate adenocarcinoma lung-cancer samples from non-cancer controls. Conclusion: mbDNA holds considerable promise as a truly orthogonal means of detecting and classifying lung cancer independently from host genomic alternations. Using only mbDNA signatures we have demonstrated robust discrimination between cancer-free controls and lung cancer samples and have provided early evidence of the applicability of this approach to liquid biopsy. Our present efforts analyzing plasma cf-mbDNA with an expanded sample cohort will serve to fully validate this new class of liquid biopsy biomarkers for lung cancer detection. Citation Format: Gregory D. Sepich-Poore, Serena Fraraccio, Stephen Wandro, Rob Knight, Sandrine Miller-Montgomery, Eddie Adams. Early-stage lung cancer detection via circulating microbial DNA biomarkers and machine learning classification [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1184.

For decades the method for generating antibodies with high avidity and affinity relied upon monoclonal antibody generation from immunized mice or polyclonal antibodies from rabbits. Both methods have their shortcomings. The murine immune system—specifically the high degree of immunological tolerance—prevents robust humoral responses to antigens that closely reasonable self-antigens in the mouse. Additionally, the cost and time necessary to generate a monoclonal antibody from mice can be prohibitively expensive, particularly if multiple attempts at hybridoma generation are required. A work around is to generate polyclonal antibodies in rabbits. Rabbits are further evolutionarily removed from humans than mice and mount a stronger immunological response to immunized human proteins or antigens. Unfortunately, the source of polyclonal antibodies from each rabbit is finite. To combat this problem, here we describe the development of a method to isolate single rabbit plasma B cells after immunization. Once isolated the VH and VL regions of the antibody are sequenced and the sequences are cloned into a recombinant rabbit IgG and IgK backbone that enables the recombinant expression of the single cell-derived, cloned rabbit antibodies. In doing so we have harnessed the immunological response of the rabbit host while eliminating the supply problem of finite reactive serum volumes and the cost of traditional murine monoclonal antibody generation.