Gabriella Estevam

All Publications

• MET variants with activating N-lobe mutations identified in hereditary papillary renal cell carcinomas still require ligand stimulation. Molecular oncology Guérin, C., Vinchent, A., Fernandes, M., Damour, I., Laratte, A., Tellier, R., Estevam, G. O., Meneboo, J. P., Villenet, C., Descarpentries, C., Fraser, J. S., Figeac, M., Cortot, A. B., Rouleau, E., Tulasne, D. 2025; 19 (8): 2366-2387

  Abstract

  In hereditary papillary renal cell carcinoma (HPRCC), the hepatocyte growth factor receptor (MET) receptor tyrosine kinase (RTK) mutations recorded to date are located in the kinase domain and lead to constitutive MET activation. This contrasts with MET mutations identified in non-small-cell lung cancer (NSCLC), which lead to exon 14 skipping and deletion of a regulatory domain: In this latter case, the mutated receptor still requires ligand stimulation. Sequencing of MET in samples from 158 HPRCC and 2808 NSCLC patients revealed 10 uncharacterized mutations. Four of these, all found in HPRCC and leading to amino acid substitutions in the N-lobe of the MET kinase, proved able to induce cell transformation, which was further enhanced by hepatocyte growth factor (HGF) stimulation: His1086Leu, Ile1102Thr, Leu1130Ser, and Cis1125Gly. Similar to the variant resulting in MET exon 14 skipping, the two N-lobe MET variants His1086Leu and Ile1102Thr were found to require stimulation by HGF in order to strongly activate downstream signaling pathways and epithelial cell motility. The Ile1102Thr mutation also displayed transforming potential, promoting tumor growth in a xenograft model. In addition, the N-lobe-mutated MET variants were found to trigger a common HGF-stimulation-dependent transcriptional program, consistent with an observed increase in cell motility and invasion. Altogether, this functional characterization revealed that N-lobe variants still require ligand stimulation, in contrast to other RTK variants. This suggests that HGF expression in the tumor microenvironment is important for tumor growth. The sensitivity of these variants to MET inhibitors opens the way for use of targeted therapies for patients harboring the corresponding mutations.

  View details for DOI 10.1002/1878-0261.13806

  View details for PubMedID 39980226

  View details for PubMedCentralID PMC12330938

• Mapping kinase domain resistance mechanisms for the MET receptor tyrosine kinase via deep mutational scanning. eLife Estevam, G. O., Linossi, E., Rao, J., Macdonald, C. B., Ravikumar, A., Chrispens, K. M., Capra, J. A., Coyote-Maestas, W., Pimentel, H., Collisson, E. A., Jura, N., Fraser, J. S. 2025; 13

  Abstract

  Mutations in the kinase and juxtamembrane domains of the MET Receptor Tyrosine Kinase are responsible for oncogenesis in various cancers and can drive resistance to MET-directed treatments. Determining the most effective inhibitor for each mutational profile is a major challenge for MET-driven cancer treatment in precision medicine. Here, we used a deep mutational scan (DMS) of ~5764 MET kinase domain variants to profile the growth of each mutation against a panel of 11 inhibitors that are reported to target the MET kinase domain. We validate previously identified resistance mutations, pinpoint common resistance sites across type I, type II, and type I ½ inhibitors, unveil unique resistance and sensitizing mutations for each inhibitor, and verify non-cross-resistant sensitivities for type I and type II inhibitor pairs. We augment a protein language model with biophysical and chemical features to improve the predictive performance for inhibitor-treated datasets. Together, our study demonstrates a pooled experimental pipeline for identifying resistance mutations, provides a reference dictionary for mutations that are sensitized to specific therapies, and offers insights for future drug development.

  View details for DOI 10.7554/eLife.101882

  View details for PubMedID 39960754

  View details for PubMedCentralID PMC11832172

• Engineering of CRISPR-Cas PAM recognition using deep learning of vast evolutionary data. bioRxiv : the preprint server for biology Nayfach, S., Bhatnagar, A., Novichkov, A., Estevam, G. O., Kim, N., Hill, E., Ruffolo, J. A., Silverstein, R., Gallagher, J., Kleinstiver, B., Meeske, A. J., Cameron, P., Madani, A. 2025

  Abstract

  CRISPR-Cas enzymes must recognize a protospacer-adjacent motif (PAM) to edit a genomic site, significantly limiting the range of targetable sequences in a genome. Machine learning-based protein engineering provides a powerful solution to efficiently generate Cas protein variants tailored to recognize specific PAMs. Here, we present Protein2PAM, an evolution-informed deep learning model trained on a dataset of over 45,000 CRISPR-Cas PAMs. Protein2PAM rapidly and accurately predicts PAM specificity directly from Cas proteins across Type I, II, and V CRISPR-Cas systems. Using in silico deep mutational scanning, we demonstrate that the model can identify residues critical for PAM recognition in Cas9 without utilizing structural information. As a proof of concept for protein engineering, we employ Protein2PAM to computationally evolve Nme1Cas9, generating variants with broadened PAM recognition and up to a 50-fold increase in PAM cleavage rates compared to the wild-type under in vitro conditions. This work represents the first successful application of machine learning to achieve customization of Cas enzymes for alternate PAM recognition, paving the way for personalized genome editing.

  View details for DOI 10.1101/2025.01.06.631536

  View details for PubMedID 39829748

  View details for PubMedCentralID PMC11741284

• Conserved regulatory motifs in the juxtamembrane domain and kinase N-lobe revealed through deep mutational scanning of the MET receptor tyrosine kinase domain. eLife Estevam, G. O., Linossi, E. M., Macdonald, C. B., Espinoza, C. A., Michaud, J. M., Coyote-Maestas, W., Collisson, E. A., Jura, N., Fraser, J. S. 2024; 12

  Abstract

  MET is a receptor tyrosine kinase (RTK) responsible for initiating signaling pathways involved in development and wound repair. MET activation relies on ligand binding to the extracellular receptor, which prompts dimerization, intracellular phosphorylation, and recruitment of associated signaling proteins. Mutations, which are predominantly observed clinically in the intracellular juxtamembrane and kinase domains, can disrupt typical MET regulatory mechanisms. Understanding how juxtamembrane variants, such as exon 14 skipping (METΔEx14), and rare kinase domain mutations can increase signaling, often leading to cancer, remains a challenge. Here, we perform a parallel deep mutational scan (DMS) of the MET intracellular kinase domain in two fusion protein backgrounds: wild-type and METΔEx14. Our comparative approach has revealed a critical hydrophobic interaction between a juxtamembrane segment and the kinase ⍺C-helix, pointing to potential differences in regulatory mechanisms between MET and other RTKs. Additionally, we have uncovered a β5 motif that acts as a structural pivot for the kinase domain in MET and other TAM family of kinases. We also describe a number of previously unknown activating mutations, aiding the effort to annotate driver, passenger, and drug resistance mutations in the MET kinase domain.

  View details for DOI 10.7554/eLife.91619

  View details for PubMedID 39268701

  View details for PubMedCentralID PMC11398868

• Rosace: a robust deep mutational scanning analysis framework employing position and mean-variance shrinkage. Genome biology Rao, J., Xin, R., Macdonald, C., Howard, M. K., Estevam, G. O., Yee, S. W., Wang, M., Fraser, J. S., Coyote-Maestas, W., Pimentel, H. 2024; 25 (1): 138

  Abstract

  Deep mutational scanning (DMS) measures the effects of thousands of genetic variants in a protein simultaneously. The small sample size renders classical statistical methods ineffective. For example, p-values cannot be correctly calibrated when treating variants independently. We propose Rosace, a Bayesian framework for analyzing growth-based DMS data. Rosace leverages amino acid position information to increase power and control the false discovery rate by sharing information across parameters via shrinkage. We also developed Rosette for simulating the distributional properties of DMS. We show that Rosace is robust to the violation of model assumptions and is more powerful than existing tools.

  View details for DOI 10.1186/s13059-024-03279-7

  View details for PubMedID 38789982

  View details for PubMedCentralID PMC11127319

• State of the structure address on MET receptor activation by HGF. Biochemical Society transactions Linossi, E. M., Estevam, G. O., Oshima, M., Fraser, J. S., Collisson, E. A., Jura, N. 2021; 49 (2): 645-661

  Abstract

  The MET receptor tyrosine kinase (RTK) and its cognate ligand hepatocyte growth factor (HGF) comprise a signaling axis essential for development, wound healing and tissue homeostasis. Aberrant HGF/MET signaling is a driver of many cancers and contributes to drug resistance to several approved therapeutics targeting other RTKs, making MET itself an important drug target. In RTKs, homeostatic receptor signaling is dependent on autoinhibition in the absence of ligand binding and orchestrated set of conformational changes induced by ligand-mediated receptor dimerization that result in activation of the intracellular kinase domains. A fundamental understanding of these mechanisms in the MET receptor remains incomplete, despite decades of research. This is due in part to the complex structure of the HGF ligand, which remains unknown in its full-length form, and a lack of high-resolution structures of the complete MET extracellular portion in an apo or ligand-bound state. A current view of HGF-dependent MET activation has evolved from biochemical and structural studies of HGF and MET fragments and here we review what these findings have thus far revealed.

  View details for DOI 10.1042/BST20200394

  View details for PubMedID 33860789

  View details for PubMedCentralID PMC8711257