The fungus has been detected in 35 types of cancer, most often intracellular


In a recent article in the journal cellthe researchers found fungal deoxyribonucleic acid (DNA) and cells in low amounts across several human cancers, with cancer-type-dependent differences in fungal community composition and fungal-bacterial interactions.

Study: Pan-cancer analyzes reveal cancer-type-specific fungal environments and microbiota interactions.  Image credit: Kateryna Kon/Shutterstock

Stady: Pan-cancer analyzes reveal cancer-type-specific fungal environments and microbe interactions. Image credit: Kateryna Kon/Shutterstock

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Studies have shown that tumors have spatially heterogeneous, intracellular, and polymicrobial communities. Sepich-Poore et al. showed that nutrient restrictions in the tumor microenvironment (TME) and antibiotics induce selection pressures affecting fungal, bacterial, cancerous, and immune cell compositions.

Fungi look like opportunistic pathogens that modulate host immunity and infect cancer patients; However, they have been studied. It is also still unknown whether they could be part of the polymorphic microbiomes that represent cancer. This provided sufficient motivation to explore phylogenetic phylogeny as a multispecies process and to characterize the universal phylogenetic mycobacteria of phylogenesis. Furthermore, since bacteria and fungi share relationships that are both symbiotic and antagonistic in nature, studying their interactions in tumors could also provide synergistic diagnostic performance for specific cancer(s).

about studying

In this study, the researchers profiled the DNA of fungi in two large collections of cancer samples, the Weismann Collection (WIS) and the Cancer Genome Atlas (TCGA). They examined patients with 35 different cancer types, obtained 17,401 tissue, blood and plasma samples, and proceeded to characterize their mycobacteria.

The WIS collection consisted of 1183 formalin-fixed paraffin-embedded (FFPE) or frozen tumor specimens and normal adjacent tissues [(NAT); often paired)] From eight types of tissue retrieved from the lung, skin cancerand ovaries, breast, colon, brain, bone, and pancreas, as well as normal, non-cancerous breast tissue. The second set included whole genome sequencing (WGS) and RNA sequencing (RNA-seq) data.

The team screened all cancer samples for the presence of the fungus and characterized it using internal patterned amplicon sequencing 2 (ITS2). Furthermore, they quantified mycobacterial DNA using quantitative polymerase chain reaction (qPCR) of the fungal 5.8S ribosomal gene in a random subset of the WIS cohort of 261 tumors and 137 negative control samples. Furthermore, the team compared data for fungal presence (or absence) at different taxonomic levels to estimate mutual intra-range natural information for the WIS group.

Studies have shown that bacteria, immunogens, and mycobacteria show cancer type specificity. Thus, multidomain fungal populations are likely to differ in different types of cancer. The team compared fungal and bacterial genera overlapping in the WIS at TCGA using a previously developed neural network method for estimating microbiome and metabolite co-occurrence.

The team also tested whether the innate phenotypes were associated with previously identified immune responses, C1 through C6, in TCGA patients and patient survival. Furthermore, they determined whether machine learning (ML) discriminates fungi between and within cancer types. Finally, the researchers applied the differential abundance test (DA) and ML between stage I and IV mycorrhiza.

Results

All tumors tested had higher fungal loads than the negative control group, but fungal loads varied across tumor types, with the highest mycobacterial DNA loads found in breast and bone cancers. ITS2 amplicon and sequencing also found more inbred reads in all types of tumors compared to negative controls. Notably, colon and lung tumors carried a significantly higher fungal load than NAT. The researchers observed a similar trend in breast tumors versus NAT and normal tissue.

Compared with matched bacteria, mycobacteria had lower diversity and abundance. Interestingly, although fungi were present in all cancers examined, not all tumors showed a positive fungal signal. However, imaging showed that most of the fungi were intracellular, like bacteria inside a tumor. In addition, Mycobiome richness was lower for the WIS (amplicon) group than for the TCGA (metagenomic shotgun) group. Interestingly, four of the seven carcinomas shared between WIS and TCGA showed significant positive correlations between intratumoral fungal richness and bacterial richness.

In contrast to bacteria, there is a lack of published fungal genomes that limit gene content inference from amplicon data. Moreover, the low abundance of mycobacteria in tumors makes their functional characterization more challenging. However, the results of the study indicated in spherical Malassezia, a type of fungus that promotes the formation of tumors in the pancreas. The researchers also noted significant associations between some innate types and other parameters, such as age, tumor subtypes and immunotherapy response. However, the researchers were unable to determine the exact nature of these associations.

The researchers noted positive associations between the microbiome and fungi across several cancers. However, their diversity, abundance, and co-occurrence vary with the type of cancer. It raises the possibility that, unlike the gut, the TME is a non-competitive space for microbial colonization, which the researchers called a “permissive” phenotype. They referred to these distinct groups of fungi, bacteria, and immunity that are driven by fungal iterations as mycotypes. For example, breast cancer had the highest co-occurrence of fungi and bacteria (96.5%), with Aspergillus fungus And the Malassezia as an interlocutor.

Unsupervised analyzes revealed three types of fungi, that is, F1 (Malassezia – Ramularia – Trichosporon), F2 (Aspergillus and Candida), and F3 (polygenous, incl narrated). Interestingly, mycotype log ratios varied across TCGA and WIS cancer types. Six of the nine TCGA scoring ratios between domains are significantly correlated (eg, fungal F1/F2 versus bacterial F1/F2), suggesting similar shifts within multidomain ecologies among diverse human cancers and validating the inferred recurrence. Moreover, the log ratios of immune cells co-occurring with F1, F2 or F3 staphylococcus aureus discriminate subtypes of the immune response.

The mycotypes driven by fungi and carcinogens had distinct immune responses that resulted in patient survival. Although rare, this fungus was effective immunogenically, similar to programmed dead (PD) 1+ cells in immunotherapy. Correlations of fungi with clinical criteria can help detect cancers in the early stages, supporting their clinical usefulness as biomarkers and potential therapeutic targets. Finally, DA assay detected cancer stage-specific mycobacteria in gastric, rectal, and renal carcinomas among RNA-seq samples, while ML data supported gastric and renal carcinoma stage differentiation.

conclusions

The study provided the first analysis of plasmodesmata in early-stage cancers. The researchers discovered the fungi in 35 types of cancer, and most of the fungi were inside cancer cells and immune cells, similar to bacteria inside a tumor. Although they could not identify the sources of plasma-derived cell-free fungi, these species could help diagnose cancer in its early stages. Moreover, they detected multiple innate-bacterial immune environments across the tumors. Interestingly, intratumoral mycoses categorize clinical outcomes, including immunotherapy response.

Journal reference:

  • Pan-Cancer Analyzes Reveal Cancer-Specific Fungal Environments and Bacteria Interactions, Lian Naronsky-Haziza, Gregory D. Sipic Bohr, Ilana Leviathan, Omar Asraf, Cameron Martino, Deborah Najman, Nancy Javert, Jason E. Stagich, Guy Amit, Antonio Gonzalez, Steven Andrew, Jelly Berry, Ruthie Ariel, Arnon Melcer, Justin B. Shaffer, Keon Zhou, Nora Balint Lahat, Iris Barshak, Maya Dadiani, Inav N Gal-Yam, Sandeep Praveen Patel, Amir Bashan, Austin Swafford, Yitzhak Belbel, Rob Knight, Ravid Straussman, Cell 2022, DOI: https://doi.org/10.1016/j.cell.2022.09.005And the https://www.cell.com/cell/fulltext/S0092-8674(22)01127-8



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