Biology subjects

Tomasetti, C.

Publications and source records attributed to Tomasetti, C..

8 recordsLinked to original sources

bioRxiv2026

General prediction of T cell receptor antigen specificity from sequence using AlphaFold 3

The Major Histocompatibility Complex (MHC):peptide:T Cell Receptor (TCR) complex is the most diverse trimolecular interface known in nature and a key trigger for adaptive immunity. TCRs are now routinely sequenced at scale, however decoding their specificity for antigens remains a bottleneck. While in silico approaches have advanced considerably, none enables prediction against "unseen" epitopes, limiting their applicability to a tiny fraction of cases. Here, we show that AlphaFold 3 (AF3) predicts the structures of MHC:peptide:TCR triads with unprecedented accuracy. By applying AF3 to >9,000 TCRs mapped to >1,000 distinct epitopes restricted by >70 MHC class I / II alleles, we identify features that distinguish cognate triads from controls. The resulting model achieves median AUCs of 0.81-0.92 on validation triads unseen by AF3 or during feature selection. Our results reveal that generalized prediction of TCR specificity from sequence is possible, with the potential to greatly accelerate the decoding of immune responses.

immunology↗

bioRxiv2025

Learned Geometry, Predicted Binding: Structurally-BasedPrediction of Peptide:MHC Binding Using AlphaFold 3 EnablesCD4 T Cell Epitope Prediction

The accurate prediction of T cell epitope peptides within proteins of interest has a wide range of applications, but is complicated by the multiple determinants of antigenicity, the polymorphism of the Major Histocompatibility Complex (MHC) locus, and the great diversity of possible peptide antigens. Leading in silico methods use a variety of statistical approaches to learn from sequences identified through both in vitro MHC binding and peptide elution studies, but their performance remains imperfect, particularly for MHC II-restricted responses. Here we present MHCIIFold-GNN, an entirely orthogonal solution to this problem that combines three new elements: (i) a highly-multiplexed peptide:MHCII binding assay, (ii) generalizable structural modeling using AlphaFold3, and (iii) transfer learning with a Graph-based Neural Network. Trained exclusively on newly-generated in vitro binders, we show that MHCIIFold-GNN enables state-of-the-art prediction of CD4 T cell epitopes presented by diverse MHC II proteins, on par with non-structural methods that rely on much larger datasets including naturally-processed ligands. Moreover, when MHCIIFold-GNN and a leading non-structural method are combined, we observe unparalleled performance on a held-out test set (11 % boost), underscoring the orthogonality of the methods. These results highlight the power of a new class of structure-informed approaches to the CD4 T cell epitope prediction problem.

bioinformatics↗

bioRxiv2023

Functional characterization of all CDKN2A missense variants and comparison to in silico models of pathogenicity

Interpretation of variants identified during genetic testing is a significant clinical challenge. In this study, we developed a high-throughput CDKN2A functional assay and characterized all possible CDKN2A missense variants. We found that 17.7% of all missense variants were functionally deleterious. We also used our functional classifications to assess the performance of in silico models that predict the effect of variants, including recently reported models based on machine learning. Notably, we found that all in silico models performed similarly when compared to our functional classifications with accuracies of 39.5-85.4%. Furthermore, while we found that functionally deleterious variants were enriched within ankyrin repeats, we did not identify any residues where all missense variants were functionally deleterious. Our functional classifications are a resource to aid the interpretation of CDKN2A variants and have important implications for the application of variant interpretation guidelines, particularly the use of in silico models for clinical variant interpretation.

genetics↗

bioRxiv2020

Larger organ size caused by obesity is a mechanism for higher cancer risk.

Obesity increases significantly cancer risk in various organs. Although this has been recognized for decades, the mechanism through which this happens has never been explained. Here, we show that obese people (BMI [≥]30) have on average 55% (95%CI: 46%-66%), 68% (95%CI: 59%-76%), and 39% (95%CI: 29%-49%) larger kidneys, liver, and pancreas, respectively. We also find a significant linear relationship between the increase in organ volume and the increase in cancer risk (P-value<10-12). These results provide a mechanism explaining why obese individuals have higher cancer risk in several organs: the larger the organ volume the more cells at risk of becoming cancerous. These findings are important for a better understanding of the effects that obesity has on cancer risk and, more generally, for the development of better preventive strategies to limit the mortality caused by obesity.

cancer biology↗

bioRxiv2019

Assessing aneuploidy with repetitive element sequencing.

We report a sensitive PCR-based assay that can detect aneuploidy in samples containing as little as 3 picograms of DNA. Using a single primer pair, we amplified [~]750,000 amplicons distributed throughout the genome Aneuploidy was detected in 49% of liquid biopsies from a total of 883 non-metastatic cancers of eight different types. Combining aneuploidy with somatic mutation detection and eight standard protein biomarkers yielded a median sensitivity of 80% at 99% specificity.

cancer biology↗

bioRxiv2019

Tumor mutational landscape is a record of the pre-malignant state

Chromatin structure has a major influence on the cell-specific density of somatic mutations along the cancer genome. Here, we present a pan-cancer study in which we searched for the putative cancer cell-of-origin of 2,550 whole genomes, representing 32 cancer types by matching their mutational landscape to the regional patterns of chromatin modifications ascertained in 104 normal tissue types. We found that, in almost all cancer types, the cell-of-origin can be predicted solely from their DNA sequences. Our analysis validated the hypothesis that high-grade serous ovarian cancer originates in the fallopian tube and identified distinct origins of breast cancer subtypes. We also demonstrated that the technique is equally capable of identifying the cell-of-origin for a series of 2,044 metastatic samples from 22 of the tumor types available as primaries. Moreover, cancer drivers, whether inherited or acquired, reside in active chromatin regions in the respective cell-of-origin. Taken together, our findings highlight that many somatic mutations accumulate while the chromatin structure of the cell-of-origin is maintained and that this historical record, captured in the DNA, can be used to identify the often elusive cancer cell-of-origin.

genomics↗

bioRxiv2017

Non-invasive detection of upper tract urothelial carcinomas through the analysis of driver gene mutations and aneuploidy in urine

Upper tract urothelial carcinomas (UTUC) of the renal pelvis or ureter can be difficult to detect and challenging to diagnose. Here, we report the development and application of a non-invasive test for UTUC based on molecular analyses of DNA recovered from cells shed into the urine. The test, called UroSEEK, incorporates assays for mutations in eleven genes frequently mutated in urologic malignancies and for allelic imbalances on 39 chromosome arms. At least one genetic abnormality was detected in 75% of urinary cell samples from 56 UTUC patients but in only 0.5% of 188 samples from healthy individuals. The assay was considerably more sensitive than urine cytology, the current standard-of-care. UroSEEK therefore has the potential to be used for screening or to aid in diagnosis in patients at increased risk for UTUC, such as those exposed to herbal remedies containing the carcinogen aristolochic acid.

cancer biology↗

bioRxiv2017

Non-invasive detection of bladder cancer through the analysis of driver gene mutations and aneuploidy

Current non-invasive approaches for bladder cancer (BC) detection are suboptimal. We report the development of non-invasive molecular test for BC using DNA recovered from cells shed into urine. This \"UroSEEK\" test incorporates assays for mutations in 11 genes and copy number changes on 39 chromosome arms. We first evaluated 570 urine samples from patients at risk for BC (microscopic hematuria or dysuria). UroSEEK was positive in 83% of patients that developed BC, but in only 7% of patients who did not develop BC. Combined with cytology, 95% of patients that developed BC were positive. We then evaluated 322 urine samples from patients soon after their BCs had been surgically resected. UroSEEK detected abnormalities in 66% of the urine samples from these patients, sometimes up to 4 years prior to clinical evidence of residual neoplasia, while cytology was positive in only 25% of such urine samples. The advantages of UroSEEK over cytology were particularly evident in low-grade tumors, wherein cytology detected none while UroSEEK detected 67% of 49 cases. These results establish the foundation for a new, non-invasive approach to the detection of BC in patients at risk for initial or recurrent disease.

cancer biology↗