# Picosecond infrared laser mass spectrometry for 10-second identification of lymphoproliferative imposter tumours in patient-derived xenografts

## TL;DR

A new 10-second method using laser mass spectrometry can quickly and accurately identify fake tumors in preclinical cancer models.

## Contribution

PIRL-MS enables rapid, high-accuracy identification of lymphoproliferative imposter tumors in PDXs with minimal preparation.

## Key findings

- PIRL-MS achieves >99% sensitivity and specificity in identifying imposter tumors.
- The method requires only 10 seconds of data collection and minimal processing time.
- PIRL-MS can be used in situ and does not require tissue preparation.

## Abstract

Patient derived xenografts (PDXs) are widely used in preclinical research. However, lymphoproliferative ‘outgrowths’ at the site of tumour xenoplantation are a common source of failure in the creation of the disease model. In this work, we assessed the performance of 10-second molecular profiling of xenoplanted tissue with picosecond infrared laser mass spectrometry (PIRL-MS) as a new method for rapid identification of lymphoproliferative ‘outgrowths’ in serial passages to streamline the quality control workflow. PIRL-MS can identify ‘imposter’ lymphoproliferative tumours with sensitivity and specificity values of > 99%. This observation is established over n = 258 independent PDX specimens and n = 3,393 ten-second mass spectral data points used for building and validating (blind assessment) a classifier multivariate model to enable discrimination. We first established a classifier model based on principal component analysis coupled with linear discriminant analysis (PCA-LDA) to discriminate between true solid tumour PDXs (of 5 common epithelioid cancer types originating from lung, pancreas, ovarian, colon and head & neck as well as imposter tumours of lymphoproliferative origin. Implementation of the classifier only requires 10 seconds of mass spectral data collection (using a hand-held probe) and less than an additional second for data processing and evaluation against the model towards a classification. In addition, PIRL-MS analysis does not require any tissue preparation before analysis, and from previous research can also be deployed in situ/in vivo to save time. These attributes, coupled with its reported high sensitivity and specificity for identification of imposter lymphoproliferative tumours, position PIRL-MS as a rapid quality control method for fidelity assessment of xenoplanted tissues. These observations motivate follow-on work to reduce the cost and the footprint of the PIRL-MS platform towards lowering the adoption barrier for routine use.

The online version contains supplementary material available at 10.1038/s41598-025-33064-w.