bims-ovdlit 2024-04-21 papers

Issue of 2024‒04‒21
three papers selected by
Lara Paracchini, Humanitas Research

Gynecol Oncol. 2024 Apr 18. pii: S0090-8258(24)00192-6. [Epub ahead of print]186 110-116

The karyometric signature is altered in fallopian tubes with serous tubal intraepithelial carcinoma.

Gustavo C Rodriguez, Michael Yozwiak, Omar L Nelson, Hao Helen Zhang, Ahyoung Amy Kim, William Watkin, Jennifer K Barton, David S Alberts.

OBJECTIVE: Recent evidence suggests that the fimbriated end of the fallopian tube harbors the precursor cells for many high-grade ovarian cancers, opening the door for development of better screening methods that directly assess the fallopian tube in women at risk for malignancy. Previously we have shown that the karyometric signature is abnormal in the fallopian tube epithelium in women at hereditary risk of ovarian cancer. In this study, we sought to determine whether the karyometric signature in serous tubal intraepithelial carcinoma (STIC) is significantly different from normal, and whether an abnormal karyometric signature can be detected in histologically normal tubal epithelial cells adjacent to STIC lesions.METHODS: The karyometric signature was measured in epithelial cells from the proximal and fimbriated portion of the fallopian tube in fallopian tube specimens removed from women at: 1) average risk for ovarian cancer undergoing surgery for benign gynecologic indications (n = 37), 2) hereditary risk of ovarian cancer (germline BRCA alterations) undergoing risk-reducing surgery (n = 44), and 3) diagnosed with fimbrial STICs (n = 17).
RESULTS: The karyometric signature in tubes with fimbrial STICs differed from that of tubes with benign histology. The degree of karyometric alteration increased with increasing proximity to fimbrial STICs, ranging from moderate in the proximal portion of the tube, to greatest in both normal appearing fimbrial cells near STICs as well as in fimbrial STIC lesions.
CONCLUSION: These data demonstrate an abnormal karyometric signature in STICs that may extend beyond the STIC, potentially providing an opportunity for early detection of fallopian tube neoplasia.

Keywords: Early detection; Fallopian tube cancer; Karyometry; Ovarian cancer; Screening

DOI: https://doi.org/10.1016/j.ygyno.2024.04.007

Nat Commun. 2024 Apr 17. 15(1): 3292

A cfDNA methylation-based tissue-of-origin classifier for cancers of unknown primary.

Alicia-Marie Conway, Simon P Pearce, Alexandra Clipson, Steven M Hill, Francesca Chemi, Dan Slane-Tan, Saba Ferdous, A S Md Mukarram Hossain, Katarzyna Kamieniecka, Daniel J White, Claire Mitchell, Alastair Kerr, Matthew G Krebs, Gerard Brady, Caroline Dive, Natalie Cook, Dominic G Rothwell.

Cancers of Unknown Primary (CUP) remains a diagnostic and therapeutic challenge due to biological heterogeneity and poor responses to standard chemotherapy. Predicting tissue-of-origin (TOO) molecularly could help refine this diagnosis, with tissue acquisition barriers mitigated via liquid biopsies. However, TOO liquid biopsies are unexplored in CUP cohorts. Here we describe CUPiD, a machine learning classifier for accurate TOO predictions across 29 tumour classes using circulating cell-free DNA (cfDNA) methylation patterns. We tested CUPiD on 143 cfDNA samples from patients with 13 cancer types alongside 27 non-cancer controls, with overall sensitivity of 84.6% and TOO accuracy of 96.8%. In an additional cohort of 41 patients with CUP CUPiD predictions were made in 32/41 (78.0%) cases, with 88.5% of the predictions clinically consistent with a subsequent or suspected primary tumour diagnosis, when available (23/26 patients). Combining CUPiD with cfDNA mutation data demonstrated potential diagnosis re-classification and/or treatment change in this hard-to-treat cancer group.

DOI: https://doi.org/10.1038/s41467-024-47195-7

bioRxiv. 2024 Apr 03. pii: 2024.04.03.587939. [Epub ahead of print]

CINner: modeling and simulation of chromosomal instability in cancer at single-cell resolution.

Khanh N Dinh, Ignacio Vázquez-García, Andrew Chan, Rhea Malhotra, Adam Weiner, Andrew W McPherson, Simon Tavaré.

Cancer development is characterized by chromosomal instability, manifesting in frequent occurrences of different genomic alteration mechanisms ranging in extent and impact. Mathematical modeling can help evaluate the role of each mutational process during tumor progression, however existing frameworks can only capture certain aspects of chromosomal instability (CIN). We present CINner, a mathematical framework for modeling genomic diversity and selection during tumor evolution. The main advantage of CINner is its flexibility to incorporate many genomic events that directly impact cellular fitness, from driver gene mutations to copy number alterations (CNAs), including focal amplifications and deletions, missegregations and whole-genome duplication (WGD). We apply CINner to find chromosome-arm selection parameters that drive tumorigenesis in the absence of WGD in chromosomally stable cancer types. We found that the selection parameters predict WGD prevalence among different chromosomally unstable tumors, hinting that the selective advantage of WGD cells hinges on their tolerance for aneuploidy and escape from nullisomy. Direct application of CINner to model the WGD proportion and fraction of genome altered (FGA) further uncovers the increase in CNA probabilities associated with WGD in each cancer type. CINner can also be utilized to study chromosomally stable cancer types, by applying a selection model based on driver gene mutations and focal amplifications or deletions. Finally, we used CINner to analyze the impact of CNA probabilities, chromosome selection parameters, tumor growth dynamics and population size on cancer fitness and heterogeneity. We expect that CINner will provide a powerful modeling tool for the oncology community to quantify the impact of newly uncovered genomic alteration mechanisms on shaping tumor progression and adaptation.

DOI: https://doi.org/10.1101/2024.04.03.587939