Find a gentic signature to predict the chemotherapy for ATIP3 deficient Triple Negative Breast Cancer

Presentation at CBIO meeting 23/10/2023

Transcript

1. Gwenn Guichaoua, CBIO meeting 23/10/2023 Find a genetic signature to predict the chemotherapy response for Triple Negative Breast Cancer ATIP3-deficient Supervisors : Véronique Stoven, Chloé Azencott, Olivier Collier (Modal’X Nanterre), Clara Nahmias (IGR) 1
2. Bad Subtype Luminal A HER2- positif Chemoterapy Hormonotherapy monoclonal antibodies Luminal B Triple Negatif Phenotype Prognosis Treatment ER+ PR+ ER+ PR+ HER2+ ER- PR- HER2- Good ATIP3 protein: a new marker for a category of TNBC 2 Biological sub-typing of the breast cancers Breast cancer: 1 of the 3 most common cancers worldwide A candidate biomarker to define a new breast cancer subtype, identified by Clara Nahmias’s team •Low expression of ATIP3 in TNBC [Rodriguez&al, 2009] •Poorer prognosis for tumors that not express ATIP3 (called ATIP3- tumors) [Rodriguez&al, 2019] •70% of ATIP3- tumors resistance to the chemotherapy •ATIP3- resistant tumors more agressive than ATIP3+ tumors resistant Important unmet need for new therapies and therapeutic target Lack of knowledge for understanding the mechanism of ATIP3 ATIP3-
3. Roadmap of the thesis New sub-type of patients: ATIP3 deficient TNBC Part 1: Find a genetic signature To predict the chemotherapy response Part 2: Chemogenomics Find a new treatment To increase the survival rate 70%, avoid chemotherapy 30%, chemotherapy 3
4. Methods : Construct ATIP3- databases: extracted from Public transcriptomic Databases Find few differentially expressed genes: supervised learning (Multitasks Group Lasso [Tozzo, 2021]) ⌃t`(Xtwt, Yt) + ||(wt)t || <latexit sha1_base64="ucc44/lpv05X6qR7W7DBWiy7hwc=">AAAC+HicjVHLbtNAFD1xaRtCW0xZshmRVkpVFNllUZYR3bBMBUlTNZE1diZhlPFD9hhUEj6EXXeILT/AtnwB4g/gL7gzdaWWqmrHsn3m3HvOzL03zJQstOf9rjlLD5ZXVusPG4/W1jceu082+0Va5pHoRalK80HIC6FkInpaaiUGWS54HCpxFM4OTPzog8gLmSbv9GkmRjGfJnIiI66JCtyXW8O3chrzQLOhUKo1CPTHQL84DvQO22VDRU5jvli0iNwJ9GLBthqNwG16bc8udhP4FWiiWt3U/YUhxkgRoUQMgQSasAJHQc8JfHjIiBthTlxOSNq4wGc0SFtSlqAMTuyMvlPanVRsQnvjWVh1RKcoenNSMmyTJqW8nLA5jdl4aZ0Ne5v33Hqau53SP6y8YmI13hN7l+4y8746U4vGBK9sDZJqyixjqosql9J2xdycXalKk0NGnMFjiueEI6u87DOzmsLWbnrLbfyPzTSs2UdVbom/5pY0YP//cd4E/b2277X9w71m53U16jqe4TlaNM99dPAGXfTI+wt+4Bw/nU/OmfPV+XaR6tQqzVNcW873f1b6oiE=</latexit> <latexit sha1_base64="ucc44/lpv05X6qR7W7DBWiy7hwc=">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</latexit> <latexit sha1_base64="ucc44/lpv05X6qR7W7DBWiy7hwc=">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</latexit> <latexit sha1_base64="ucc44/lpv05X6qR7W7DBWiy7hwc=">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</latexit> Goal : Find small list of genes which allows to predict chemotherapy resistance For TNBC tumors, deficient in ATIP3 Blocking points : Non specific Databases Part 1 : Find a genetic signature (with Olivier Collier) 4 Public Transcriptomic studies pathologic Complete Response (pCR) to neoadjuvant chemotherapy
5. Plan 5 General study: Microtubule-related genes in Breast Cancer (BC) Microtubules and chemotherapy Choice of 17 MT-rel genes and their partners in BC Article Most Random Gene Expression Signatures Are Significantly Associated with Breast Cancer Outcome Venet et al, PLOS 2011 How to assess the predictive potential of a signature in BC ? Cofounding effect of the proliferation in BC Prediction of the chemotherapy resistance (pCR) with the 17 MT-rel genes or other deregulated genes in the network for all breast cancer Method Results
6. Microtubule-related genes and chemotherapy 6 Hypothesis : 411 MT-related genes, including ATIP3, play a role in the taxane resistance Taxane (reference chemotherapy) : poison of the mitotic spindle Taxane Microtubule polymerisation Microtubule depolymerisation Cellular division
7. VIM RACGAP1 KIF23 AURKB KIF18B TPX2 MTUS1 KIF11 COMPLEX:_KIF2C_KIF18B MAPT COMPLEX:_CCNB1_CDK1 CDK5 ASPM AURKA KIF15 GTSE1 MAST4 KIF4A KIF2A PLK1 CDK4 ATM TUBB3 PRC1 EPHB2 TP53 DIAPH2 FYN TTBK1 CAMK2A KIFC1 CENPE KIF14 CIT CAMK2B LRRK2 STMN1 PRKAA1 KIF20A PRKACA MARK1 CDK1 KIF2C Network of 17 Microtubule-Related Genes in BC 7 Accepted article A Network of 17 Microtubule-Related Genes Highlights Functional Deregulations in BC Rodrigues et al, Cancers, October 2023 17 MT-related genes extracted [Rodriguez et al, PNAS 2019] Among the 411 MT-related genes, 17 MT-related genes DE sensible/resistant in BC In 3 independent transcriptomic studies
8. Plan 8 General study: Microtubule-related genes in Breast Cancer (BC) Microtubules and chemotherapy Choice of 17 MT-rel genes and their partners in BC Article Most Random Gene Expression Signatures Are Significantly Associated with Breast Cancer Outcome Venet et al, PLOS 2011 How to assess the predictive potential of a signature in BC ? Cofounding effect of the proliferation in BC Prediction of the chemotherapy resistance (pCR) with the 17 MT-rel genes or other deregulated genes in the network for all breast cancer Method Results
9. Prediction of the pCR with different lists of genes 9 Method for each transcriptomic study, Split samples between train (80%) and test (20%) Train a Logistic regression to predict pCR RocAUC calculated for 5 fold-Cross Validation Prediction performance for different lists of genes All genes 411 MT-rel genes DE genes 17 MT-rel DE genes ER gene AURKB ATIP3
10. Prediction with different lists of genes and lists of random genes of identical size 10 All genes 411 MT-rel genes DE genes 17 MT-rel DE genes ER gene AURKB ATIP3
11. Results for more transcriptomic studies 11 Conclusion Not significant differences between different lists of genes and random lists from a certain number of genes Questions the specificity of the signatures and therefore their biological interpretability of the genes which contain it For each study (microarray)
12. Plan 12 General study: Microtubule-related genes in Breast Cancer (BC) Microtubules and chemotherapy Choice of 17 MT-rel genes and their partners in BC Article Most Random Gene Expression Signatures Are Significantly Associated with Breast Cancer Outcome Venet et al, PLOS 2011 How to assess the predictive potential of a signature in BC ? Cofounding effect of the proliferation in BC Prediction of the chemotherapy resistance (pCR) with the 17 MT-rel genes or other deregulated genes in the network for all breast cancer Method Results
13. How most oncology studies evaluate the predictive power of signature or cancer-driving mechanisms ? 13 Kaplan-Meier plot : show how the marker expression correlates with survival outcome in patients HR: Hazard ratio between 2 groups and its associated p-value HR > 1 : better survival and HR < 1 : poorer survival p < 0.05 statistically significant association between the marker expression and the survival event Many mechanics found and signatures (list of genes) derived Overall survival (fraction of patients alive) ATIP3 Expression Blocking points for the authors Diversity of mechanisms and signatures found in BC Meta-analyses of several outcome signatures * equivalent prognostic performances * highly correlated with proliferation, a predictor of BC outcome that has been used for decades
14. How the authors of the article evaluate the predictive power of signature ? 14 Public transcriptomic study Cohort of 295 patients and the overall survival end-point For different signatures, Binary stratification of the cohort Compute the first principal component (PC1) of the signature Split the cohort according to the median of PC1. Compute the hazard ratio (HR) and the related log-rank p-values with the standard Cox procedure
15. Which are significant BC outcome predictors ? 15 Most signatures not biologically related to cancer Most published breast cancer signatures are not more strongly associated with BC outcome than sets of random genes Compared 47 published BC outcome signatures to signatures made of random genes 60% were not significantly better 23% were worst predictors More than 90% of random signatures >100 genes were significant outcome predictors A signature of social defeat in mice p-values for association with BC outcome 67% are associated with BC outcome at p < 0.05, 23% at p < 0.00001 77% of random signatures are associated with outcome at p < 0.05, and 30% at p < 0.00001 26% of individual genes associated with outcome at p < 0.05
16. Investigation of the confounding effect of proliferation in BC 16 Proliferation a well-known breast cancer prognostic marker PCNA (proliferating cell nuclear antigen) a protein that encircles DNA and regulates several processes leading to DNA replication Meta-PCNA : a proliferation signature Compute a signature composed of the 1% genes the most positively correlated with PCNA expression across these 36 tissues from normal, healthy, individuals encompassing 27 organs 131 genes including canonical proliferation markers (MKI67, TOP2A, MCM2 …) including 6 of our 17 MT-rel genes (TPX2, KIF20A, AURKA, AURKB, RACGAP1, KIF4A)
17. Meta-PCNA integrates most of the outcome-related signal contained in the BC transcriptome 17 Meta-PCNA adjustment decreases the prognostic abilities of published BC signatures Most prognostic transcriptional signals are correlated with meta-PCNA Each point is a signature Distribution of the correlations of individual genes with meta-PCNA for genes significantly associated with overall survival (red) More than 50% of the BC transcriptome is correlated with meta-PCNA Any predictor resting on a linear combination of genes associated with outcome has a high probability to be confounded by proliferation Removing cell cycle genes from a signature cannot rule out proliferation as a confounder
18. Conclusion and perspectives 18 Conclusion Most signatures are not significantly different from random predictors, especially large ones Random single- and multiple-genes expression markers have a high probability to be associated with BC outcome The proliferation signature is correlated with more than 50% of BC transcriptome and cannot be removed by purging known cell-cycle genes from a signature. Perspectives Take into account proliferation in our tests New approach + robust and + biologically interpretable: Search for deregulated pathways instead of deregulated genes Finding ATIP3- vs ATIP3+ therapies Part 2 : chemogenomics Make sys-bio approach based on transcriptomic data