Method of detecting increase in risk of suffering from or developing RCC

Abstract

There is provided a method of detecting an increase in risk of suffering from or developing renal cell carcinoma (RCC), comprising the steps of: a) determining a level of Enterocter asparaagiforms in a fecal sample of a human subject, b) determining a level of Enterocter asparaagiforms, c) determining a level of Enterocter asparaagiforms in a fecal sample of the human subject; b) comparing the determined level with a reference level; and c) determining that the human subject has an increased risk of suffering from or developing RCC if the determined level is above the reference level.

Description

Technical Field

[0001] This disclosure relates to the field of cancer screening, and more particularly to screening for renal cell carcinoma (RCC). Background Technology

[0002] Globally, there are over 400,000 new cases of renal cell carcinoma (RCC) each year, and over 170,000 people die from it (https: / / gco.iarc.fr). To date, the gut microbiota has not been proven to be associated with RCC.

[0003] RCC is typically diagnosed through imaging studies, such as computed tomography (CT) scans with contrast or ultrasound, or combined multimodal imaging. These imaging tests help physicians identify any abnormalities or suspicious lumps that may be signs of cancer.

[0004] In addition to imaging studies, a needle biopsy procedure can be used to obtain abnormal cell samples to confirm the diagnosis. During a needle biopsy, a needle is inserted through the skin and down to the kidney mass. This procedure allows physicians to extract cell samples, which can then be examined under a microscope for definitive diagnosis.

[0005] Other tests that may be used to diagnose kidney cancer include urinalysis, blood tests, and chest X-rays. These tests help doctors determine the health of other organs in the body and identify other signs of the disease.

[0006] Mohan et al. (Syst Appl Microbiol. 2006 Jun;29(4):292-9) isolated an obligate anaerobic, Gram-positive rod-shaped organism from the feces of a healthy human donor and proposed classifying this new bacterium as a new species, *Clostridium asparaginis*. Clostridium asparagiforme Later, this bacterium was reclassified as *Clostridium asparagus*. Enterocloster asparagiformis )(Haas and Blanchard, Int J SystEvol Microbiol 2020;70:23-34). Summary of the Invention

[0007] This disclosure is based on an analysis of the association between bacteria and RCC classification using interpretable machine learning methods.

[0008] Using bacterial species abundance data, a machine learning classification model has been trained, tested, and interpreted to distinguish RCC samples from healthy samples, and RCC characteristics of 31 species have been characterized. Among these species, enrichment of *Clostridium asparagus* is a unique predictor of RCC compared to other disease characteristics.

[0009] Therefore, this disclosure provides a method for detecting an increased risk of developing or progressing to renal cell carcinoma (RCC), comprising the following steps: a) Determine the level of *Clostridium asparagus* in fecal samples from human subjects; b) Compare the determined level with the reference level; and c) If the determined level is higher than the reference level, the subject is deemed to have an increased risk of developing or having RCC. Attached Figure Description

[0010] Figure 1 The PCoA scatter plot shows the difference in β diversity between the PRJEB22863 RCC sample and the PRJEB6070 healthy sample.

[0011] Figure 2 The curve is the AUCROC curve, showing a higher predictive score between RCC samples and healthy samples (positive label is RCC).

[0012] Figure 3 The characteristics of 31 species of RCC are described. It is a bar chart showing the SHAP score used to predict RCC (positive and negative values ​​represent increases and decreases in abundance predictable for RCC, respectively), filtered by an average directional SHAP score higher than 0.004. The species highlighted is *Clostridium asparagus*: an enriched species specific to RCC.

[0013] Figure 4 The most interpretive species for RCC classification is shown. Left: Doramectin-producing formic acid-producing species, characterized by depletion. Dorea formicigenerans Right panel: Band plot of relative abundance of the enriched characteristic species Clostridium asparagus (FDR adjusted MWW = 7.11e-11).

[0014] Figure 5 The log2 fold change in RCC versus health (“Log(RCC / health)”) is shown compared with its significance (FDR-adjusted MWW, labeled “Log10 (FDR)”), where the SHAP score for each species is expressed as the size of the dot. The labeled dots represent *Clostridium asparagus*. Detailed Implementation

[0015] This disclosure provides a method for detecting an increased risk of developing or progressing to renal cell carcinoma (RCC), comprising the following steps: a) Determine the level of *Clostridium asparagus* in fecal samples from human subjects; b) Compare the determined level with the reference level; and c) If the determined level is higher than the reference level, the subject is deemed to have an increased risk of developing or having RCC.

[0016] Those skilled in the art will understand that this method is typically performed ex vivo.

[0017] The reference level for this method is, for example, a pre-determined level based on the determination of the level of Clostridium asparagus in fecal samples from subjects who do not have RCC, such as healthy subjects and / or subjects with other cancers but not RCC.

[0018] In one embodiment, the method further includes the step of conducting further examinations on subjects with an increased risk of developing or suffering from RCC to establish an RCC diagnosis. This further examination may include microscopic examination and / or imaging of cells in a biopsy.

[0019] Step a) may include quantitative real-time PCR (qPCR). To facilitate qPCR, bacterial DNA is typically extracted from a fecal sample. This is then followed by the use of primers specific to *Clostridium asparagus*.

[0020] In one implementation, step a) identifies additional bacteria (such as...) Figure 3 The level of another bacterium with a positive directional mean absolute shap score is determined in step b), which is compared with a relevant reference level in step b), and in step c), if the level of Clostridium asparagus and the level of the other bacterium are both higher than their respective reference levels, the subject is determined to have an increased risk of developing or having RCC.

[0021] In another implementation, step a) identifies at least two additional bacteria (such as...) Figure 3 The levels of other bacteria (with a positive directional mean absolute shap score) are compared with relevant reference levels in step b), and in step c), if the levels of *Clostridium asparagus* and these other bacteria are both higher than their respective reference levels, the subject is determined to have an increased risk of developing or having RCC.

[0022] Example Materials and methods Metagenomic sequencing data collection Metagenomic species abundance data and metadata were obtained from [sources of information for interpreting the microbiome map].

[0023] Using pandas in Python, and the ICG2 mapping database (available at https: / / entrepot.recherche.data.gouv.fr / dataset.xhtml?persistentId=doi:10.15454 / FLANUP), taxonomic information was annotated on the MSP.

[0024] Statistical analysis Differences in species abundance were calculated using the Python scipy package, and p-values ​​were adjusted using Benjamini-Hochberg multiple test correction, where a q-value less than 0.05 was considered significant.

[0025] The p-value of PERMANOVA is calculated using the "permanova" function in skbio.stats.distance based on Bray-Curtis dissimilarity.

[0026] Machine Learning and Model Exploration All machine learning operations were performed using Python packages. Before feature selection and machine learning analysis, the MSP species abundance was normalized using the "StandardScaler" function from the sklearn.preprocessing package.

[0027] After preprocessing, the entire dataset is split into 70% training set and 30% test set using "train_test_split" in sklearn.model_selection.

[0028] The RandomForestClassifier from sklearn.ensemble was used as the model, and its hyperparameters are listed in Table 1.

[0029] Table 1. Hyperparameter fine-tuning of the random forest classification model.

[0030]

[0031] Use sklearn.metrics to calculate model metrics.

[0032] Use the "shap" package to explore and interpret the model.

[0033] Species with an average absolute SHAP score higher than 0.004 were selected as members of the disease characteristics.

[0034] result No matched healthy controls were found in PRJEB22863. However, using healthy samples from the matched geographical cohort PRJEB6070 (French CRC cohort), we found a significant difference in Bray-Curtisβ diversity between the two groups (PERMANOVA p=0.001). Figure 1 ).

[0035] The random forest model from the Python Scikitlearn module was trained using the hyperparameters listed in Table 1.

[0036] These models are highly predictive of RCC, with an AUCROC of 0.994. Figure 2 ).

[0037] By applying SHAP scores to the species characteristics of the model, species biomarkers in the RCC can be classified. Figure 3 ).

[0038] Thirty-one species had an average absolute SHAP score higher than 0.004, namely the following species: Synergistic butyric acid mononucleosis type 2 (… Butyricimonas synergistica 2 Timon Rombutz ( Romboutsia timonensis ), Collins gas-producing bacteria ( Collinsella aerofaciens ), vestibular streptococci ( Streptococcus vestibularis Enterobacter butyrate-producing bacteria ( Intestinimonas butyriciproducens ), Long-chain Dorsetella type 2 ( Dorea longicatena 2 ), Broutbacterium ovale ( Blautia obeum ), Bacillus spp. Coprobacillus sp. CAG:235, Salmonella allergens ( Alistipes shahii ), Visceral Odor Bacteria ( Odoribacter splanchnicus ), blood twin cocci ( Gemella sanguinis ), Ruminococcus brucellosis type 2 ( Ruminococcus bromii 2 Bartlett's Enterobacter ( Intestinibacter bartlettii Unclassified butyric cocci ( Butyricicoccus ), Doremus genus ( Dorea sp. CAG:105, Asparagus-shaped Enterobacteriaceae ( Enterocloster asparagiformis (Original name: Clostridium asparagine) Clostridium asparagiforme )), Masseybroutella ( Blautia massiliensis fecal cocci () Coprococcus catus ), Broutella spp. Blautia sp. )2789STDY5608844, Protruding Belly Eubacterium ( Eubacterium ventriosum ), Rumenococcus ( Ruminococcus sp. )2789STDY5608882, Mycobacterium tumefaciens ( Eubacterium ramulus ), Streptococcus salivarius ( Streptococcus salivarius Oral Actinomycetes ( Actinomyces oris ), Streptococcus sanguinis ( Streptococcus sanguinis ), Harvey anaerobic bacillus type 1 ( Anaerostipes hadrus 1 ), accompanied by fecal cocci ( Coprococcus comes ), Rumenococcus ( Ruminococcus sp. )2789STDY5608817, Wexlerbroutella ( Blautia wexlerae ), Hodgkin's Eubacterium ( Eubacterium hallii ) and formic acid-producing Dorsetella ( Dorea formicigenerans ).

[0039] The abundance of these species differed significantly between RCC samples and healthy samples. Figure 4 and Figure 5 ).

[0040] These species are further subdivided into exhaustion signals (whose depletion can predict a variety of diseases) and pan-cancer signals (whose enrichment can predict a variety of cancers).

[0041] Among these species, the enrichment of one species, Clostridium asparagine (formerly known as Clostridium asparagine), is a unique predictor for RCC.

Claims

1. A method for detecting an increased risk of developing or progressing to renal cell carcinoma (RCC), comprising the following steps: a) Identify *Clostridium asparagus* in fecal samples from human subjects. Enterocloster asparagiformis ) level; b) Compare the determined level with the reference level; and c) If the determined level is higher than the reference level, the subject is deemed to have an increased risk of developing or having RCC.

2. The method according to claim 1, wherein, The reference level is a pre-determined level based on the determination of the level of Clostridium asparagus in fecal samples from subjects who do not have RCC, such as healthy subjects and / or subjects with other cancers other than RCC.

3. The method of claim 1 or 2, further comprising the step of conducting further examinations on the subject who has an increased risk of developing or suffering from RCC to establish an RCC diagnosis.

4. The method according to any one of the preceding claims, wherein, Step a) includes quantitative real-time PCR (qPCR).

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