Neoadjuvant chemo- or chemo-radiation-therapy of pancreatic ductal adenocarcinoma differentially shift ECM composition, complement activation, energy metabolism and ribosomal proteins of the residual tumor mass.

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer, often diagnosed at stages that dis-qualify for surgical resection. Neoadjuvant therapies offer potential tumor regression and improved resectability. Although features of the tumor biology (e.g., molecular markers) may guide adjuvant therapy, biological alterations after neoadjuvant therapy remain largely unexplored. We performed mass spectrometry to characterize the proteomes of 67 PDAC resection specimens of patients who received either neoadjuvant chemo (NCT) or chemo-radiation (NCRT) therapy. We employed data-independent acquisition (DIA), yielding a proteome coverage in excess of 3500 proteins. Moreover, we successfully integrated two publicly available proteome datasets of treatment-naïve PDAC to unravel proteome alterations in response to neoadjuvant therapy, highlighting the feasibility of this approach. We found highly distinguishable proteome profiles. Treatment-naïve PDAC was characterized by enrichment of immunoglobulins, complement and extracellular matrix (ECM) proteins. Post-NCT and post-NCRT PDAC presented high abundance of ribosomal and metabolic proteins as compared to treatment-naïve PDAC. Further analyses on patient survival and protein expression identified treatment-specific prognostic candidates. We present the first proteomic characterization of the residual PDAC mass after NCT and NCRT, and potential protein candidate markers associated with overall survival. We conclude that residual PDAC exhibits fundamentally different proteome profiles as compared to treatment-naïve PDAC, influenced by the type of neoadjuvant treatment. These findings may impact adjuvant or targeted therapy options.

AI-Based Detection of Oral Squamous Cell Carcinoma with Raman Histology.

Stimulated Raman Histology (SRH) employs the stimulated Raman scattering (SRS) of photons at biomolecules in tissue samples to generate histological images. Subsequent pathological analysis allows for an intraoperative evaluation without the need for sectioning and staining. The objective of this study was to investigate a deep learning-based classification of oral squamous cell carcinoma (OSCC) and the sub-classification of non-malignant tissue types, as well as to compare the performances of the classifier between SRS and SRH images. Raman shifts were measured at wavenumbers k1 = 2845 cm-1 and k2 = 2930 cm-1. SRS images were transformed into SRH images resembling traditional H&E-stained frozen sections. The annotation of 6 tissue types was performed on images obtained from 80 tissue samples from eight OSCC patients. A VGG19-based convolutional neural network was then trained on 64 SRS images (and corresponding SRH images) and tested on 16. A balanced accuracy of 0.90 (0.87 for SRH images) and F1-scores of 0.91 (0.91 for SRH) for stroma, 0.98 (0.96 for SRH) for adipose tissue, 0.90 (0.87 for SRH) for squamous epithelium, 0.92 (0.76 for SRH) for muscle, 0.87 (0.90 for SRH) for glandular tissue, and 0.88 (0.87 for SRH) for tumor were achieved. The results of this study demonstrate the suitability of deep learning for the intraoperative identification of tissue types directly on SRS and SRH images.

Non-invasive monitoring of neoadjuvant radiation therapy response in soft tissue sarcomas by multiparametric MRI and quantification of circulating tumor DNA-A study protocol.

BACKGROUND: Wide resection remains the cornerstone of localized soft-tissue sarcomas (STS) treatment. Neoadjuvant radiation therapy (NRT) may decrease the risk of local recurrences; however, its effectiveness for different histological STS subtypes has not been systematically investigated. The proposed prospective study evaluates the NRT response in STS using liquid biopsies and the correlation of multiparametric magnetic resonance imaging (mpMRI) with histopathology and immunohistochemistry. METHODS: Patients with localized high-grade STS, who qualify for NRT, are included in this study. LIQUID BIOPSIES: Quantification of circulating tumor DNA (ctDNA) in patient blood samples is performed by targeted next-generation sequencing. Soft-tissue sarcoma subtype-specific panel sequencing in combination with patient-specific exome sequencing allows the detection of individual structural variants and point mutations. Circulating free DNA is isolated from peritherapeutically collected patient plasma samples and ctDNA quantified therein. Identification of breakpoints is carried out using FACTERA. Bioinformatic analysis is performed using samtools, picard, fgbio, and the MIRACUM Pipeline. MPMRI: Combination of conventional MRI sequences with diffusion-weighted imaging, intravoxel-incoherent motion, and dynamic contrast enhancement. Multiparametric MRI is performed before, during, and after NRT. We aim to correlate mpMRI data with the resected specimen's macroscopical, histological, and immunohistochemical findings. RESULTS: Preliminary data support the notion that quantification of ctDNA in combination with tumor mass characterization through co-registration of mpMRI and histopathology can predict NRT response of STS. CLINICAL RELEVANCE: The methods presented in this prospective study are necessary to assess therapy response in heterogeneous tumors and lay the foundation of future patient- and tumor-specific therapy concepts. These methods can be applied to various tumor entities. Thus, the participation and support of a wider group of oncologic surgeons are needed to validate these findings on a larger patient cohort.

Multiparametric magnetic resonance imaging for radiation therapy response monitoring in soft tissue sarcomas: a histology and MRI co-registration algorithm.

Rationale: To establish a spatially exact co-registration procedure between in vivo multiparametric magnetic resonance imaging (mpMRI) and (immuno)histopathology of soft tissue sarcomas (STS) to identify imaging parameters that reflect radiation therapy response of STS. Methods: The mpMRI-Protocol included diffusion-weighted (DWI), intravoxel-incoherent motion (IVIM), and dynamic contrast-enhancing (DCE) imaging. The resection specimen was embedded in 6.5% agarose after initial fixation in formalin. To ensure identical alignment of histopathological sectioning and in vivo imaging, an ex vivo MRI scan of the specimen was rigidly co-registered with the in vivo mpMRI. The deviating angulation of the specimen to the in vivo location of the tumor was determined. The agarose block was trimmed accordingly. A second ex vivo MRI in a dedicated localizer with a 4 mm grid was performed, which was matched to a custom-built sectioning machine. Microtomy sections were stained with hematoxylin and eosin. Immunohistochemical staining was performed with anti-ALDH1A1 antibodies as a radioresistance and anti-MIB1 antibodies as a proliferation marker. Fusion of the digitized microtomy sections with the in vivo mpMRI was accomplished through nonrigid co-registration to the in vivo mpMRI. Co-registration accuracy was qualitatively assessed by visual assessment and quantitatively evaluated by computing target registration errors (TRE). Results: The study sample comprised nine tumor sections from three STS patients. Visual assessment after nonrigid co-registration showed a strong morphological correlation of the histopathological specimens with ex vivo MRI and in vivo mpMRI after neoadjuvant radiation therapy. Quantitative assessment of the co-registration procedure using TRE analysis of different pairs of pathology and MRI sections revealed highly accurate structural alignment, with a total median TRE of 2.25 mm (histology - ex vivo MRI), 2.22 mm (histology - in vivo mpMRI), and 2.02 mm (ex vivo MRI - in vivo mpMRI). There was no significant difference between TREs of the different pairs of sections or caudal, middle, and cranial tumor parts, respectively. Conclusion: Our initial results show a promising approach to obtaining accurate co-registration between histopathology and in vivo MRI for STS. In a larger cohort of patients, the method established here will enable the prospective identification and validation of in vivo imaging biomarkers for radiation therapy response prediction and monitoring in STS patients via precise molecular and cellular correlation.

Multiple Immunostainings with Different Epitope Retrievals-The FOLGAS Protocol.

We describe a sequential multistaining protocol for immunohistochemistry, immunofluorescence and CyTOF imaging for formalin-fixed, paraffin-embedded specimens (FFPE) in the formalin gas-phase (FOLGAS), enabling sequential multistaining, independent from the primary and secondary antibodies and retrieval. Histomorphologic details are preserved, and crossreactivity and loss of signal intensity are not detectable. Combined with a DAB-based hydrophobic masking of metal-labeled primary antibodies, FOLGAS allows the extended use of CyTOF imaging in FFPE sections.