Biology subjects

Michielin, O.

Publications and source records attributed to Michielin, O..

3 recordsLinked to original sources

bioRxiv2023

Tumor-reactive clonotype dynamics underlying clinical response to TIL therapy in melanoma

The profiles, specificity and dynamics of tumor-specific clonotypes that are associated with clinical response to adoptive cell therapy (ACT) using tumor-infiltrating lymphocytes (TILs) remain unclear. Using single-cell RNA/TCR-sequencing, we tracked TIL clonotypes from baseline tumors to ACT products and post-ACT blood and tumor samples in melanoma patients treated with TIL-ACT. Patients with clinical responses had baseline tumors enriched in tumor-reactive TILs, which were more effectively mobilized upon in vitro expansion, yielding products with higher numbers of tumor-specific CD8+ cells, which also preferentially infiltrated tumors post-ACT. Conversely, lack of clinical responses was associated with tumors devoid of tumor-reactive resident clonotypes, and with cell products mostly composed of blood-borne clonotypes mainly persisting in blood but not in tumors post-ACT. Upon expansion, tumor-specific TILs lost the specific signatures of states originally exhibited in tumors, including exhaustion, and in responders acquired an intermediate exhausted effector state after tumor engraftment, revealing important functional cell reinvigoration.

cancer biology↗

bioRxiv2023

Combination of NY-ESO-1-TCR-T-cells coengineered to secrete SiRPalpha decoys with anti-tumor antibodies to augment macrophage phagocytosis

The adoptive transfer of T cell receptor (TCR)-engineered T cells (ACT) targeting the HLA-A2 restricted cancer-testis epitope NY-ESO-1157-165 (A2/NY) has yielded favorable clinical responses against a variety of cancers. Two promising approaches to improve ACT efficacy are TCR affinity-optimization and combinatorial treatment strategies to reprogram the tumor microenvironment (TME). By computational design, we previously developed a panel of affinity-enhanced A2/NY-TCRs. Here, we have demonstrated improved tumor control and engraftment by T cells gene-modified to express one such TCR comprising a single amino acid replacement in CDR3{beta} (A97L). To harness macrophages in the TME, we coengineered TCR-T cells to constitutively or inducibly secrete a high-affinity signal regulatory protein alpha (SiRP) decoy (CV1) to block the CD47 dont eat me signal. We demonstrated better control of tumor outgrowth by CV1-Fc coengineered TCR-T cells but in subcutaneous xenograft tumor models we observed depletion of both CV1-Fc and CV1 coengineered T cells. Importantly, CV1 coengineered T cells were not depleted by human macrophages in vitro. Moreover, Avelumab and Cetuximab enhanced macrophage-mediated phagocytosis in vitro in the presence of CV1, and augmented tumor control upon ACT. Taken together, our study indicates important clinical promise for harnessing macrophages by combining CV1 coengineered TCR-T cells with tumor-targeting monoclonal antibodies.

bioengineering↗

bioRxiv2019

Machine learning predicts immunoglobulin light chain toxicity through somatic mutations

In systemic light chain amyloidosis (AL), pathogenic monoclonal immunoglobulin light chains (LCs) form toxic aggregates and amyloid fibrils in target organs. Prompt diagnosis is crucial to avoid permanent organ damage. However, delays in diagnosis are common, with a consequent poor patients prognosis, as symptoms usually appear only after strong organ involvement. Here, we present LICTOR, a machine learning approach predicting LC toxicity in AL, based on the distribution of somatic mutations acquired during clonal selection. LICTOR achieved a specificity and a sensitivity of 0.82 and 0.76, respectively, with an area under the receiver operating characteristic curve (AUC) of 0.87. Tested on an independent set of 12 LCs sequences with known clinical phenotypes, LICTOR achieved a prediction accuracy of 83%. Furthermore, we were able to abolish the toxic phenotype of an LC by in silico reverting two germline-specific somatic mutations identified by LICTOR and by experimentally assessing the loss of in vivo toxicity in a Caenorhabditis elegans model. Therefore, LICTOR represents a promising strategy for AL diagnosis and reducing high mortality rates in AL.

bioinformatics↗