In a recent article published in the journal Cell, researchers found fungal deoxyribonucleic acid (DNA) and cells at low abundances across many human cancers, with cancer type-dependent variations in fungus community composition and fungal-bacteriome interactions.
Study: Pan-cancer analyses reveal cancer-type-specific fungal ecologies and bacteriome interactions. Image Credit: Kateryna Kon / Shutterstock
Studies have shown that tumors have spatially heterogeneous, intracellular, and polymicrobial communities. Sepich-Poore et al. showed that nutrient limitations in the tumor microenvironment (TME) and antibiotics induce selection pressures that affect fungal-bacterial-cancer-immune cell compositions.
Fungi seem like significant opportunistic pathogens that shape host immunity and infect cancer patients; however, they are understudied. It also remains unknown whether they could be part of polymorphic microbiomes representing cancer. This provided enough motivation to explore cancer clonal evolution as a multi-species process and characterize the pan-cancer mycobiome. Furthermore, since bacteria and fungi share symbiotic and antagonistic relationships in nature, studying their interactions in tumors could also potentially provide synergistic diagnostic performance for specific cancer(s).
About the study
In the present study, researchers profiled fungal DNA in two large cohorts of cancer samples, the Weizmann (WIS) and Cancer Genome Atlas (TCGA) cohorts. They screened patients with 35 different cancers, obtained 17,401 patient tissue, blood, and plasma samples, and proceeded to characterize their cancer mycobiome.
The WIS cohort comprised 1,183 formalin-fixed paraffin-embedded (FFPE) or frozen tumor samples and normal adjacent tissue [(NAT); often paired)] from eight types of tissues retrieved from lung, melanoma, ovary, breast, colon, brain, bone, and pancreas, as well as non-cancer normal breast tissue. The second cohort encompassed whole-genome sequencing (WGS) and ribonucleic acid sequencing (RNA-seq) data.
The team examined all cancer samples for the fungal presence and characterized them using internal transcribed spacer 2 (ITS2) amplicon sequencing. Further, they quantified fungal DNA using quantitative polymerase chain reaction (qPCR) of the fungal 5.8S ribosomal gene in a random subset of the WIS cohort comprising 261 tumor and 137 negative control samples. Furthermore, the team compared the fungal presence (or absence) data at different taxonomic levels to estimate the normalized mutual intradomain information for the WIS cohort.
Studies have shown that bacteriomes, immunomes, and mycobiomes demonstrate cancer-type specificity. It is, thus, likely that multi-domain fungal clusters vary across cancer types. The team compared WIS-overlapping fungal and bacterial genera in TCGA using a neural network method previously developed to estimate microbiome-metabolite co-occurrences.
The team also tested whether mycotypes were associated with immune responses, C1 to C6, previously identified in TCGA patients and patient survival. Further, they determined whether machine learning (ML) discriminated mycobiomes between and within cancer types. Finally, the researchers applied differential abundance (DA) testing and ML between stage I and IV tumor mycobiomes.
All tested tumors had higher fungal loads than negative controls, but fungal loads differed across tumor types, with the highest fungal DNA loads in breast and bone cancers. ITS2 amplicon and sequencing also found more fungal reads in all tumor types than in negative controls. Notably, colon and lung tumors had markedly higher fungal load than NAT. The researchers noted a similar trend in breast tumors versus NAT and normal tissues.
Compared to matched bacteriomes, tumor-specific fungi had lower diversities and abundances. Interestingly, although fungi were present in all examined cancer types, not all tumors showed a positive fungal signal. However, imaging revealed that most fungi were intracellular, like intratumoral bacteria. In addition, Mycobiome richness was lower for WIS (amplicon) cohort than the TCGA (shotgun metagenomic) cohort. Interestingly, four of seven cancers shared by WIS and TCGA showed significant positive correlations between intratumoral fungal and bacterial richness.
Unlike bacteria, there is a shortage of published fungal genomes that limit gene content inference from amplicon data. Moreover, low fungal abundances in tumors make their functional characterization more difficult. However, the study findings pointed at Malassezia globosa, a fungal species that promotes pancreatic oncogenesis. The researchers also noted substantial correlations between some fungal species and other parameters, such as age, tumor subtypes, and immunotherapy response. However, the researchers could not determine the precise nature of these associations.
The researchers observed positive correlations between micro- and mycobiomes across several cancers. However, their diversities, abundances, and co-occurrences varied with cancer type. It raises the possibility that TMEs, unlike gut, might be a non-competitive space for microbial colonization, which the researchers termed a “permissive” phenotype. They referred to these distinct fungi-bacteria-immune clusters driven by fungal co-occurrences as mycotypes. For instance, breast cancer had the most significant fungi-bacteria co-occurrences (96.5%), with Aspergillus and Malassezia as hubs.
Unsupervised analyses revealed three mycotypes, viz., F1 (Malassezia-Ramularia-Trichosporon), F2 (Aspergillus-Candida), and F3 (multi-genera, including Yarrowia). Interestingly, mycotype log ratios varied across TCGA and WIS cancer types. Six of nine TCGA log-ratios between domains significantly correlated (e.g., fungal F1/F2 vs. bacterial F1/F2), suggesting similar shifts within multi-domain ecologies among diverse human cancers and validating inferred co-occurrences. Furthermore, log ratios of immune cells co-occurring with F1, F2, or F3-clustered fungi distinguished immune response subtypes.
Fungal-driven, pan-cancer mycotypes had distinct immune responses that stratified patient survival. Though sparse, these fungi were immunologically potent, analogous to programmed death (PD)1+ cells in immunotherapy. The associations of fungi with clinical parameters could enable the detection of early-stage cancers, supporting their clinical utility as potential biomarkers and therapeutic targets. Finally, DA testing revealed cancer stage-specific fungi for the stomach, rectal, and renal cancers among RNA-seq samples, whereas ML data supported stomach and renal cancer stage differentiation.
The study provided the first analysis of plasma mycobiomes in early-stage cancers. The researchers detected fungi in 35 types of cancer, and most fungi were intracellular within cancer and immune cells, analogous to intratumoral bacteria. Although they could not locate the sources of cell-free plasma-derived fungi, these species could help diagnose cancer in its early stages. Further, they detected multiple fungal-bacterial-immune ecologies across tumors. Intriguingly, intratumoral fungi stratified clinical outcomes, including immunotherapy response.
- Pan-cancer analyses reveal cancer-type-specific fungal ecologies and bacteriome interactions, Lian Narunsky-Haziza, Gregory D. Sepich-Poore, Ilana Livyatan, Omer Asraf, Cameron Martino, Deborah Nejman, Nancy Gavert, Jason E. Stajich, Guy Amit, Antonio González, Stephen Wandro, Gili Perry, Ruthie Ariel, Arnon Meltser, Justin P. Shaffer, Qiyun Zhu, Nora Balint-Lahat, Iris Barshack, Maya Dadiani, Einav N. Gal-Yam, Sandip Pravin Patel, Amir Bashan, Austin D. Swafford, Yitzhak Pilpel, Rob Knight, Ravid Straussman, Cell 2022, DOI: https://doi.org/10.1016/j.cell.2022.09.005, https://www.cell.com/cell/fulltext/S0092-8674(22)01127-8
Posted in: Medical Science News | Medical Research News | Medical Condition News | Disease/Infection News
Tags: Bacteria, Blood, Bone, Brain, Breast Cancer, Cancer, Candida, Cell, Diagnostic, DNA, Evolution, fungi, Gene, Genome, Imaging, Immune Response, immunity, Immunotherapy, Intracellular, Machine Learning, Melanoma, Metabolite, micro, Microbiome, Oncogenesis, Pancreas, Phenotype, Polymerase, Polymerase Chain Reaction, Renal Cancer, Ribonucleic Acid, RNA, Stomach, Tumor
Neha is a digital marketing professional based in Gurugram, India. She has a Master’s degree from the University of Rajasthan with a specialization in Biotechnology in 2008. She has experience in pre-clinical research as part of her research project in The Department of Toxicology at the prestigious Central Drug Research Institute (CDRI), Lucknow, India. She also holds a certification in C++ programming.
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