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Mediastinal structure measurements are important for the radiologist's review of computed tomography pulmonary angiography (CTPA) examinations. In the reporting process, radiologists make measurements of diameters, volumes, and organ densities for image quality assessment and risk stratification. However, manual measurement of these features is time consuming. Here, we sought to develop a time-saving automated algorithm that can accurately detect, segment and measure mediastinal structures in routine clinical CTPA examinations. In this study, 700 CTPA examinations collected and annotated. Of these, a training set of 180 examinations were used to develop a fully automated deterministic algorithm. On the test set of 520 examinations, two radiologists validated the detection and segmentation performance quantitatively, and ground truth was annotated to validate the measurement performance. External validation was performed in 47 CTPAs from two independent datasets. The system had 86-100% detection and segmentation accuracy in the different tasks. The automatic measurements correlated well to those of the radiologist (Pearson's r 0.68-0.99). Taken together, the fully automated algorithm accurately detected, segmented, and measured mediastinal structures in routine CTPA examinations having an adequate representation of common artifacts and medical conditions., (© 2023. The Author(s).)

Therapies targeting somatic bystander genetic events represent a new avenue for cancer treatment. We recently identified a subset of colorectal cancer (CRC) patients who are heterozygous for a wild-type and a low activity allele (NAT2*6) but lack the wild-type allele in their tumors due to loss of heterozygosity (LOH) at 8p22. These tumors were sensitive to treatment with a cytotoxic substrate of NAT2 (6-(4-aminophenyl)-N-(3,4,5-trimethoxyphenyl)pyrazin-2-amine, APA), and pointed to NAT2 loss being a therapeutically exploitable vulnerability of CRC tumors. To better estimate the total number of treatable CRC patients, we here determined whether tumor cells retaining also other NAT2 low activity variants after LOH respond to APA treatment. The prevalent low activity alleles NAT2*5 and NAT2*14, but not NAT2*7, were found to be low metabolizers with high sensitivity to APA. By analysis of two different CRC patient cohorts, we detected heterozygosity for NAT2 alleles targetable by APA, along with allelic imbalances pointing to LOH, in ~ 24% of tumors. Finally, to haplotype the NAT2 locus in tumor and patient-matched normal samples in a clinical setting, we develop and demonstrate a long-read sequencing based assay. In total, > 79.000 CRC patients per year fulfil genetic criteria for high sensitivity to a NAT2 LOH therapy and their eligibility can be assessed by clinical sequencing.

Breast cancer (BC) is a genetically heterogeneous disease with high prevalence in Northern Europe. However, there has been no detailed investigation into the Scandinavian somatic landscape. Here, in a homogeneous Swedish cohort, we describe the somatic events underlying BC, leveraging a targeted next-generation sequencing approach. We designed a 20.5 Mb array targeting coding and regulatory regions of genes with a known role in BC (n = 765). The selected genes were either from human BC studies (n = 294) or from within canine mammary tumor associated regions (n = 471). A set of predominantly estrogen receptor positive tumors (ER +  85%) and their normal tissue counterparts (n = 61) were sequenced to ~ 140 × and 85 × mean target coverage, respectively. MuTect2 and VarScan2 were employed to detect single nucleotide variants (SNVs) and copy number aberrations (CNAs), while MutSigCV (SNVs) and GISTIC (CNAs) algorithms estimated the significance of recurrent somatic events. The significantly mutated genes (q ≤ 0.01) were PIK3CA (28% of patients), TP53 (21%) and CDH1 (11%). However, histone modifying genes contained the largest number of variants (KMT2C and ARID1A, together 28%). Mutations in KMT2C were mutually exclusive with PI3KCA mutations (p ≤ 0. 001) and half of these affect the formation of a functional PHD domain. The tumor suppressor CDK10 was deleted in 80% of the cohort while the oncogene MDM4 was amplified. Mutational signature analyses pointed towards APOBEC deaminase activity (COSMIC signature 2) and DNA mismatch repair (COSMIC signature 6). We noticed two significantly distinct patterns related to patient age; TP53 being more mutated in the younger group (29% vs 9% of patients) and CDH23 mutations were absent from the older group. The increased somatic mutation prevalence in the histone modifying genes KMT2C and ARID1A distinguishes the Swedish cohort from previous studies. KMT2C regulates enhancer activation and assists tumor proliferation in a hormone-rich environment, possibly pointing to a role in ER + BC, especially in older cases. Finally, age of onset appears to affect the mutational landscape suggesting that a larger age-diverse population incorporating more molecular subtypes should be studied to elucidate the underlying mechanisms.

Multigene expression signatures provide a molecular subdivision of early breast cancer associated with patient outcome. A gap remains in the validation of such signatures in clinical treatment groups of patients within population-based cohorts of unselected primary breast cancer representing contemporary disease stages and current treatments. A cohort of 3520 resectable breast cancers with RNA sequencing data included in the population-based SCAN-B initiative (ClinicalTrials.gov ID NCT02306096) were selected from a healthcare background population of 8587 patients diagnosed within the years 2010-2015. RNA profiles were classified according to 19 reported gene signatures including both gene expression subtypes (e.g. PAM50, IC10, CIT) and risk predictors (e.g. Oncotype DX, 70-gene, ROR). Classifications were analyzed in nine adjuvant clinical assessment groups: TNBC-ACT (adjuvant chemotherapy, n = 239), TNBC-untreated (n = 82), HER2+/ER- with anti-HER2+ ACT treatment (n = 110), HER2+/ER+ with anti-HER2 + ACT + endocrine treatment (n = 239), ER+/HER2-/LN- with endocrine treatment (n = 1113), ER+/HER2-/LN- with endocrine + ACT treatment (n = 243), ER+/HER2-/LN+ with endocrine treatment (n = 423), ER+/HER2-/LN+ with endocrine + ACT treatment (n = 433), and ER+/HER2-/LN- untreated (n = 200). Gene signature classification (e.g., proportion low-, high-risk) was generally well aligned with stratification based on current immunohistochemistry-based clinical practice. Most signatures did not provide any further risk stratification in TNBC and HER2+/ER- disease. Risk classifier agreement (low-, medium/intermediate-, high-risk groups) in ER+ assessment groups was on average 50-60% with occasional pair-wise comparisons having <30% agreement. Disregarding the intermediate-risk groups, the exact agreement between low- and high-risk groups was on average ~80-95%, for risk prediction signatures across all assessment groups. Outcome analyses were restricted to assessment groups of TNBC-ACT and endocrine treated ER+/HER2-/LN- and ER+/HER2-/LN+ cases. For ER+/HER2- disease, gene signatures appear to contribute additional prognostic value even at a relatively short follow-up time. Less apparent prognostic value was observed in the other groups for the tested signatures. The current study supports the usage of gene expression signatures in specific clinical treatment groups within population-based breast cancer. It also stresses the need of further development to reach higher consensus in individual patient classifications, especially for intermediate-risk patients, and the targeting of patients where current gene signatures and prognostic variables provide little support in clinical decision-making.