Biology subjects

Lin-Rahardja, K.

Publications and source records attributed to Lin-Rahardja, K..

3 recordsLinked to original sources

bioRxiv2025

Leveraging Experimental Evolution to Extract Predictive Collateral Drug Response Signatures in Ewings Sarcoma

Therapeutic options for patients with relapsed or refractory Ewings sarcoma (EWS) remain limited. Collateral sensitivity, where resistance to one drug confers sensitivity to another, could be leveraged to optimize chemotherapy for EWS. Gene expression signatures that predict collateral sensitivity states can be used to guide treatment selection in an evolution-informed manner. We experimentally evolved resistance to first-line EWS chemotherapy in cell lines. Throughout, we measured collateral responses across a panel of anticancer drugs and quantified transcriptomic changes. Collateral drug responses varied across replicate evolutionary trajectories, but convergent states of collateral sensitivity emerged across different replicates at different times. By associating these convergent phenotypes with gene expression patterns, we derived a library of predictive signatures for numerous drugs. These signatures accurately distinguished states of collaterally sensitivity from states of collateral resistance within our dataset and were further validated in an independent EWS cell line. Our findings demonstrate that gene expression signatures can predict collateral sensitivity in EWS, providing a foundation for personalized therapeutic strategies. This approach also provides a generalizable workflow for developing predictive biomarkers to guide chemotherapy selection in patients with rare diseases that lack reliable second-line chemotherapy regimens.

cancer biology↗

bioRxiv2024

Personalizing chemotherapy drug selection using a novel transcriptomic chemogram

Gene expression signatures predictive of chemotherapeutic response have the potential to greatly extend the reach of precision medicine by allowing medical providers to plan treatment regimens on an individual basis for patients with and without actionable mutations. Most published gene signatures are only capable of predicting response for individual drugs, but currently, a majority of chemotherapy regimens utilize combinations of different agents. We propose a unified framework, called the chemogram, that uses predictive gene signatures to rank the relative predicted sensitivity of different drugs for individual tumor samples. Using this approach, providers could efficiently screen against many therapeutics to identify the drugs that would fit best into a patients treatment plan at any given time. This can be easily reassessed at any point in time if treatment efficacy begins to decline due to therapeutic resistance. To demonstrate the utility of the chemogram, we first extract predictive gene signatures using a previously established method for extracting pan-cancer signatures inspired by convergent evolution. We derived 3 signatures for 3 commonly used cytotoxic drugs (cisplatin, gemcitabine, and 5-fluorouracil). We then used these signatures in our framework to predict and rank sensitivity among the drugs within individual cell lines. To assess the accuracy of our method, we compared the rank order of predicted response to the rank order of observed response (fraction of surviving cells at a standardized dose) against each of the 3 chemotherapies. Across a majority of cancer types, chemogram-generated predictions were consistently more accurate than randomized prediction rankings, as well as prediction rankings made by randomly generated gene signatures. In addition to the chemograms ability to rank relative sensitivity for any given tumor, this framework is easily scalable for any number of drugs for which a predictive signature exists. We repeated the process described above for 10 drugs and found that the accuracy of the predicted sensitivity rankings was maintained as the number of drugs in the chemograms screen increased. Our proposed framework demonstrates the ability of transcriptomic signatures to not only predict chemotherapeutic response but correctly assign rankings of drug sensitivity on an individual basis. With further validation, the chemogram could be easily integrated in a clinical setting, as it only requires gene expression data, which is less expensive than an extensive drug screen and can be performed at scale.

cancer biology↗

bioRxiv2022

TP53 mutations in endometrial cancer contribute to clinically-relevant radiation resistance and highlight the potential for biomarker-driven radiosensitization strategies.

Endometrial cancer (EC) is the most common type of gynecologic malignancy in the United States, with over 66, 200 new cases expected in 2023. The number of mortalities per year now approximate that of ovarian cancer. Despite our ability to identify different biologic clusters of EC, we have yet to understand the functional impact of key genomic alterations associated with discrepant prognoses and exploit this knowledge for therapeutic benefit. Given this, we set out to determine how alterations in p53 signaling, as conferred my TP53 mutations, impact radiotherapy response in EC. We also explored if manipulation of this signaling pathway could be utilized as a radio-sensitization strategy in EC. Our work demonstrates that p53 signaling plays a significant role in radiotherapy response for EC and that leveraging this genomic data may allow us to exploit this pathway as a viable radiotherapeutic target in a significant number of EC cases.

cancer biology↗