THE UKRAINIAN STEPPE AS A REGION OF INTERCULTURAL CONTACTS BETWEEN ATLANTIC AND MEDITERRANEAN ZONES OF EUROPEAN MESOLITHIC

his volume contains the majority of the papers presented during a conference that took place on 16th-21st May, 1997 in Łódź, Poland. The conference was organized by the Institute of Archaeology, University of Łódź and Département d'anthropologie, Université de Montreal (Canada). The conference was funded by the University of Łódź and by IREX (International Research & Exchanges Board), which also supported this publication. The publication was partly founded by the University of Łódź and by the Foundation of Adam Mickiewicz University, too. The major questions of the conference were, 1) what is the current evidence for eastern or southern influences in the development of eastern European Mesolithic and Neolithic populations, and 2) to what extent are current political trends, especially the reassertion or, in some cases, the creation of ethnic and national identities, influencing our interpretations of the prehistoric data. The idea for such a conference came into being through the co-organizers' long-term studies of the development of those prehistoric human populations which inhabited the vast region stretching north and east from the Oder river and Carpathian Mountains to the foothills of the Urals. In a tradition established in modern times by Gordon Childe, virtually all of the transformations of Eastern Europe's Neolithic Age human landscape have been assumed to be responses to prior developments in the Balkan peninsula and Danube basin. We think that a body of new evidence requires a renewed analysis of the distributions of cultural products, peoples, and ideas across Eastern Europe during the Mesolithic through the Early Metal Age within a much wider geographic context than previously has been the case. This includes giving adequate attention to the far-ranging interactions of communities between the Pontic and Baltic area with those located in both the Caucasus and the Aralo-Caspian regions. We hope that this volume will contribute to such a redirection of future analyses

Artificial intelligence-based pathology as a biomarker of sensitivity to atezolizumab–bevacizumab in patients with hepatocellular carcinoma: a multicentre retrospective study

Background Clinical benefits of atezolizumab plus bevacizumab (atezolizumab–bevacizumab) are observed only in a subset of patients with hepatocellular carcinoma and the development of biomarkers is needed to improve therapeutic strategies. The atezolizumab–bevacizumab response signature (ABRS), assessed by molecular biology profiling techniques, has been shown to be associated with progression-free survival after treatment initiation. The primary objective of our study was to develop an artificial intelligence (AI) model able to estimate ABRS expression directly from histological slides, and to evaluate if model predictions were associated with progression-free survival. Methods In this multicentre retrospective study, we developed a model (ABRS-prediction; ABRS-P), which was derived from the previously published clustering-constrained attention multiple instance learning (or CLAM) pipeline. We trained the model fit for regression analysis using a multicentre dataset from The Cancer Genome Atlas (patients treated by surgical resection, n=336). The ABRS-P model was externally validated on two independent series of samples from patients with hepatocellular carcinoma (a surgical resection series, n=225; and a biopsy series, n=157). The predictive value of the model was further tested in a series of biopsy samples from a multicentre cohort of patients with hepatocellular carcinoma treated with atezolizumab–bevacizumab (n=122). All samples in the study were from adults (aged ≥18 years). The validation sets were sampled between Jan 1, 2008, to Jan 1, 2023. For the multicentre validation set, the primary objective was to assess the association of high versus low ABRS-P values, defined relative to cross-validation median split thresholds in the first biopsy series, with progression-free survival after treatment initiation. Additionally, we performed spatial transcriptomics and matched prediction heatmaps with in situ expression profiles. Findings Of the 840 patients sampled, 641 (76%) were male and 199 (24%) were female. Across the development and validation datasets, hepatocellular carcinoma risk factors included alcohol intake, hepatitis B and C virus infections, and non-alcoholic steatohepatitis. Using cross-validation in the development series, the mean Pearson’s correlation between ABRS-P values and ABRS score (mean expression of ABRS genes) was 0·62 (SD 0·09; mean p<0·0001, SD<0·0001). The ABRS-P generalised well on the external validation series (surgical resection series, r=0·60 [95% CI 0·51–0·68], p<0·0001; biopsy series, r=0·53 [0·40–0·63], p<0·0001). In the 122 patients treated with atezolizumab–bevacizumab, those with ABRS-P-high tumours (n=74) showed significantly longer median progression-free survival than those with ABRS-P-low tumours (n=48) after treatment initiation (12 months [95% CI 7–not reached] vs 7 months [4–9]; p=0·014). Spatial transcriptomics showed significantly higher ABRS score, along with upregulation of various other immune effectors, in tumour areas with high ABRS-P values versus areas with low ABRS-P values. Interpretation Our study indicates that AI applied on hepatocellular carcinoma digital slides is able to serve as a biomarker for progression-free survival in patients treated with atezolizumab–bevacizumab. This approach could be used in the development of inexpensive and fast biomarkers for targeted therapies. The combination of AI heatmaps with spatial transcriptomics provides insight on the molecular features associated with predictions. This methodology could be applied to other cancers or diseases and improve understanding of the biological mechanisms that drive responses to treatments