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Trajectory and uniqueness of mutational signatures in yeast mutators.
The acquisition of mutations plays critical roles in adaptation, evolution, senescence, and tumorigenesis. Massive genome sequencing has allowed extraction of specific features of many mutational landscapes but it remains difficult to retrospectively determine the mechanistic origin(s), selective forces, and trajectories of transient or persistent mutations and genome rearrangements. Here, we conducted a prospective reciprocal approach to inactivate 13 single or multiple evolutionary conserved genes involved in distinct genome maintenance processes and characterize de novo mutations in 274 diploid Saccharomyces cerevisiae mutation accumulation lines. This approach revealed the diversity, complexity, and ultimate uniqueness of mutational landscapes, differently composed of base substitutions, small insertions/deletions (InDels), structural variants, and/or ploidy variations. Several landscapes parallel the repertoire of mutational signatures in human cancers while others are either novel or composites of subsignatures resulting from distinct DNA damage lesions. Notably, the increase of base substitutions in the homologous recombination-deficient Rad51 mutant, specifically dependent on the Polζ translesion polymerase, yields COSMIC signature 3 observed in BRCA1/BRCA2-mutant breast cancer tumors. Furthermore, "mutome" analyses in highly polymorphic diploids and single-cell bottleneck lineages revealed a diverse spectrum of loss-of-heterozygosity (LOH) signatures characterized by interstitial and terminal chromosomal events resulting from interhomolog mitotic cross-overs. Following the appearance of heterozygous mutations, the strong stimulation of LOHs in the rad27/FEN1 and tsa1/PRDX1 backgrounds leads to fixation of homozygous mutations or their loss along the lineage. Overall, these mutomes and their trajectories provide a mechanistic framework to understand the origin and dynamics of genome variations that accumulate during clonal evolution
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