Cancer Risk Stratification Research Articles

Colorectal cancer risk stratification using a polygenic risk score in symptomatic primary care patients-a UK Biobank retrospective cohort study.

Colorectal cancer (CRC) is a leading cause of cancer mortality worldwide. Accurate cancer risk assessment approaches could increase rates of early CRC diagnosis, improve health outcomes for patients and reduce pressure on diagnostic services. The faecal immunochemical test (FIT) for blood in stool is widely used in primary care to identify symptomatic patients with likely CRC. However, there is a 6-16% noncompliance rate with FIT in clinic and ~90% of patients over the symptomatic 10 µg/g test threshold do not have CRC. A polygenic risk score (PRS) quantifies an individual's genetic risk of a condition based on many common variants. Existing PRS for CRC have so far been used to stratify asymptomatic populations. We conducted a retrospective cohort study of 50,387 UK Biobank participants with a CRC symptom in their primary care record at age 40+. A PRS based on 201 variants, 5 genetic principal components and 22 other risk factors and markers for CRC were assessed for association with CRC diagnosis within 2 years of first symptom presentation using logistic regression. Associated variables were included in an integrated risk model and trained in 80% of the cohort to predict CRC diagnosis within 2 years. An integrated risk model combining PRS, age, sex, and patient-reported symptoms was predictive of CRC development in a testing cohort (receiver operating characteristic area under the curve, ROCAUC: 0.76, 95% confidence interval: 0.71-0.81). This model has the potential to improve early diagnosis of CRC, particularly in cases of patient noncompliance with FIT.

Prognostic significance of metastatic lymph node size and extra-nodal extension in papillary thyroid carcinoma according to the 9th edition of general rules for the description of thyroid cancer risk stratification: a single center validation study.

In the 9th edition of general rules for the description of thyroid cancer (GRDTC), the N factor was subdivided according to the maximum diameter of metastatic lymph nodes, presence of extra-nodal extension (ENE), and location of mediastinal lymph nodes. This study aimed to investigate the clinical usefulness of the 9th GRDTC risk stratification in papillary thyroid carcinoma (PTC) patients with lymph node metastasis. A total of 703 PTC patients with lymph node metastasis who underwent initial thyroidectomy at our institution between January 2000 and October 2023 were included. Among the 703 patients with PTC, the 10-year cause specific survival rates of patients with pN1a-1 (n = 383), pN1a-2 (n = 13), pN1b-1 (n = 234), and pN1b-2 (n = 73) were 97.9%, 100%, 95.4%, and 76.2%, respectively (p < 0.001). Therefore, the pN1b-2 classification identified patients with a worse prognosis among those with pN1b. Among the 664 patients with M0 PTC, the 10-year disease free survival (DFS) rates of the patients with pN1a-1 (n = 378), pN1a-2 (n = 13), pN1b-1 (n = 215), and pN1b-2 (n = 58) were 86.9%, 62.5%, 79.9%, and 59.4%, respectively (p < 0.001). The pN1b-2 category was associated with worse DFS in pN1b patients. The 9th edition of the GRDTC may be useful for stratifying the prognosis of patients with PTC. The risk assessment of PTC-related death and recurrence will be more accurate by considering the size of lymph node metastasis and ENE in GRDTC.

Do Prostate Imaging-Reporting and Data System (PIRADS) lesions predict holmium laser enucleation of prostate outcomes?

Prostate magnetic resonance imaging (MRI) is used for prostate cancer (PCa) screening and risk stratification and is helpful for surgical planning for patients undergoing holmium laser enucleation of the prostate (HoLEP). There are few studies investigating the correlation between MRI Prostate Imaging-Reporting and Data System (PIRADS) lesion characteristics and HoLEP pathology and outcomes. We performed retrospective review of patients who underwent HoLEP between January 2021 and August 2023 by a single surgeon. Preoperative, intraoperative, and postoperative characteristics and outcomes were analyzed for all patients who had a documented preoperative prostate MRI. There were 334 patients without a pre-existing diagnosis of PCa and with a preoperative prostate MRI, of which 140 (42%) had at least one PIRADS lesion. There was a total of 203 PIRADS lesions: 91 (45%) in the peripheral zone (PZ), 106 (52%) in the transition zone (TZ), and 6 (2%) not specified. Incidental PCa was noted in 44 (13%) patients at time of HoLEP. Presence or location of lesion was not significantly associated with rate or grade of incidental PCa on pathology. Greater number of lesions and lesion size correlated with longer procedure times. Lesion number, size, or grade were not found to correlate with cancer grade or rate of cancer. Grade, presence, location, size, and number of PIRADS lesions on preoperative prostate MRI for patients with an appropriate prior PCa workup were not significantly associated with incidental PCa or higher PCa grade on HoLEP pathology.

Optimising the use of colonoscopy to improve risk stratification for colorectal cancer in symptomatic patients: A decision-curve analysis.

Pressured healthcare resources make risk stratification and patient prioritisation fundamental issues for the investigation of colorectal cancer (CRC) in symptomatic patients. The present study uses machine learning algorithms and decision strategies to improve the appropriate use of colonoscopy. All symptomatic patients in a single health board (2018-2021) proceeding to colonoscopy to investigate for CRC were included. Machine learning algorithms (NeuralNetwork, randomForest, Logistic regression, Naïve-Bayes and Adaboost) were used to risk-stratify patients for CRC using demographics, symptoms, quantitative faecal immunochemical test (qFIT) and haematological tests. Decision curve analyses were performed to determine the optimal decision strategies. 3776 patients were included (median age, 65; M:F,0.9:1.0) and CRC was identified in 217 patients (5.7%). qFIT > 400 μg Hb/g was the most important variable (%IncMSE = 78.5). RandomForrest had the highest area under curve (0.91) and accuracy (0.80) for CRC. When utilising decision curve analysis (DCA), 30%, 46% and 54% of colonoscopies were saved at accepted CRC probabilities of 1%, 2% and 3%, respectively. RandomForrest modelling had superior net clinical benefit compared to default colonoscopy strategies. MLA-derived decision strategies that account for patient and referrer risk preference reduce colonoscopy demand and carry net clinical benefit compared to default colonoscopy strategies.

Machine learning-based autophagy-related prognostic signature for personalized risk stratification and therapeutic approaches in bladder cancer

ObjectiveBladder cancer (BCa) is a highly lethal urological malignancy characterized by its notable histological heterogeneity. Autophagy has swiftly emerged as a diagnostic and prognostic biomarker in diverse cancer types. Nonetheless, the currently accessible autophagy-related signature specific to BCa remains limited. MethodsA refined autophagy-related signature was developed through a 10-fold cross-validation framework, incorporating 101 combinations of machine learning algorithms. The performance of this signature in predicting prognosis and response to immunotherapy was thoroughly evaluated, along with an exploration of potential drug targets and compounds. In vitro and in vivo experiments were conducted to verify the regulatory mechanism of hub gene. ResultsThe autophagy-related prognostic signature (ARPS) has exhibited superior performance in predicting the prognosis of BCa compared to the majority of clinical features and other developed markers. Higher ARPS is associated with poorer prognosis and reduced sensitivity to immunotherapy. Four potential targets and five therapeutic agents were screened for patients in the high-ARPS group. In vitro and vivo experiments have confirmed that FKBP9 promotes the proliferation, invasion, and metastasis of BCa. ConclusionsOverall, our study developed a valuable tool to optimize risk stratification and decision-making for BCa patients.

Improving the Risk Prediction of the 2015 ATA Recurrence Risk Stratification in Papillary Thyroid Cancer.

Various prognostic factors are expected to refine the American Thyroid Association (ATA) recurrence risk stratification for patients with papillary thyroid cancer (PTC). However, it remains unclear to what extent integrating these factors improves patient treatment decision-making. We developed two predictive models for structural incomplete response (SIR) at the one-year follow-up visit, based on comprehensive clinical data from a retrospective cohort of 2539 patients. Model 1 included the recurrence risk stratification and lymph node features (i.e., number and ratio of metastatic lymph nodes, N stage). Model 2 further incorporated preablation stimulated thyroglobulin (s-Tg). An independent cohort of 746 patients was used for validation analysis. We assessed the models' predictive performance compared to the recurrence risk stratification using the integrated discrimination improvement (IDI) and the continuous net reclassification improvement (NRI). The clinical utility of the models was evaluated using decision curve analysis. Both Model 1 and Model 2 outperformed the recurrence risk stratification in predicting SIR, with improved correct classification rates (Model 1: IDI=0.02, event NRI=42.31%; Model 2: IDI=0.07, event NRI=53.54%). The decision curves indicated that both models provided greater benefits over the risk stratification system in clinical decision-making. In the validation set, Model 2 maintained similar performance while Model 1 did not significantly improve correct reclassification. The inclusion of lymph node features and s-Tg showed potential to enhance the predictive accuracy and clinical utility of the existing risk stratification system for PTC patients.

Assessing interventions promoting the uptake of cancer-related genomic services within the Latino community: A scoping review using the RE-AIM framework.

Cancer genomic services (CGS) can support genetic risk-stratified cancer prevention and treatment. Racial/ethnic minority groups are less likely to access and utilize CGS compared with non-Hispanic Whites. Little research has described characteristics of interventions targeted at CGS among Latinos. This scoping review aimed to (1) describe interventions promoting uptake of CGS among Latinos in the United States and Latin America, (2) describe intervention adaptations for Latino participants, and (3) summarize intervention implementation factors suggested by reach, effectiveness, adoption, implementation, and maintenance (RE-AIM) framework. We conducted a search in English and Spanish of literature published between 2005 and 2022 across PubMed and Latin American and Caribbean Health Sciences Literature databases. Sixteen of 2344 papers met the inclusion criteria of the analysis. Efforts to promote CGS among Latino communities were limited in the US and lower in Latin America. This review highlights the need for in-depth exploration of acculturation-informed interventions and better reporting on implementation factors to enhance their scalability across diverse settings.

Misrepresented multiple endocrine neoplasia 2: Do the British Thyroid Association guidelines accurately predict thyroid cancer risk in high-risk groups with multiple endocrine neoplasia 2? A case series

Introduction: The incidence of thyroid nodules in the general population is around 40%. The British Thyroid Association U-grading has high sensitivity for identifying the common thyroid cancer subtypes (papillary and follicular). However, ultrasound features of the rarer medullary thyroid cancer differ, with lower sensitivity for ultrasound detection. Hereditary medullary thyroid cancer accounts for 25% of cases, forming part of the multiple endocrine neoplasia syndromes (multiple endocrine neoplasia 2) and is associated with RET proto-oncogene mutation, for which gene testing is increasingly available. This study aims to evaluate British Thyroid Association U-grading for thyroid cancer risk stratification in this high-risk population. Case report: This was a retrospective review of four multiple endocrine neoplasia 2 patients referred for thyroid ultrasound. A total of 10 thyroid nodules were graded as part of routine evaluation, taken from an endocrine and genetics tertiary referral centre. Patients with identifiable RET mutation from March 2017 to February 2023 were reviewed. Discussion: Six patients had 10 thyroid nodules, of which 8 were graded as U2, 2 graded U3–5 and 8 confirmed as medullary thyroid cancer. However, two patients had no pathology data at the time of writing. For this cohort, U-grading and genetics were discordant, with RET gene testing more effective than ultrasound in cancer detection. All nodules should be considered high risk for medullary thyroid cancer, regardless of U-grade. Conclusion: Our data demonstrate that British Thyroid Association U-score has limited value for medullary thyroid cancer detection in this high-risk group and cannot be used for risk stratification or surveillance. As a rarer thyroid cancer subtype, medullary thyroid cancer and the high-risk multiple endocrine neoplasia 2 population are under-represented in British Thyroid Association 2014 guidance and deserve consideration in future editions.

The REMAR (Rhein-Main-Registry) real-world study: prospective evaluation of the 21-gene breast recurrence score® assay in addition to Ki-67 for adjuvant treatment decisions in early-stage breast cancer

PurposeKi-67 is recommended by international/national guidelines for risk stratification in early breast cancer (EBC), particularly for defining “intermediate risk,” despite inter-laboratory/inter-observer variability and cutoff uncertainty. We investigated Ki-67 (> 10%– < 40%, determined locally) as a prognostic marker for intermediate/high risk in EBC, pN0-1 patients.MethodsThis prospective, non-interventional, real-world study included females ≥ 18 years, with pN0/pN1mi/pN1, HR+ , HER2-negative EBC, and locally determined Ki-67 ranging 10%–40%. The primary outcome was changes in treatment recommendations after disclosing the Oncotype DX Breast Recurrence Score®(RS) assay result.ResultsThe analysis included 567 patients (median age, 57 [range, 29–83] years; 70%/1%/29%/ with pN0/pN1mi/pN1 disease; 81% and 19% with RS results 0–25 and 26–100, respectively). The correlations between local and central Ki-67, local Ki-67, and the RS, and central Ki-67 and the RS results were weak (r = 0.35, r = 0.3, and r = 0.46, respectively), and discrepancies were noted in both directions (e.g., local Ki-67 was lower or higher than central Ki-67). After disclosing the RS, treatment recommendations changed for 190 patients (34%). Changes were observed in pN0 and pN1mi/pN1 patients and in patients with centrally determined Ki-67 ≤ 10% and > 10%. Treatment changes were aligned with RS results (adding chemotherapy for patients with higher RS results, omitting it for lower RS results), and their net result was 8% reduction in adjuvant chemotherapy use (from 32% pre-RS results to 24% post-RS results).ConclusionThe Oncotype DX® assay is a tool for individualizing treatments that adds to classic treatment decision factors. The RS result and Ki-67 are not interchangeable, and Ki-67, as well as nodal status, should not be used as gatekeepers for testing eligibility, to avoid under and overtreatment.

Genome wide-scale CRISPR-Cas9 knockout screens identify a fitness score for optimized risk stratification in colorectal cancer

BackgroundThe molecular complexity of colorectal cancer poses a significant challenge to the clinical implementation of accurate risk stratification. There is still an urgent need to find better biomarkers to enhance established risk stratification and guide risk-adapted treatment decisions.Methodswe systematically analyzed cancer dependencies of 17 colorectal cancer cells and 513 other cancer cells based on genome-scale CRISPR-Cas9 knockout screens to identify colorectal cancer-specific fitness genes. A regression model was built using colorectal cancer-specific fitness genes, which was validated in other three independent cohorts. 30 published gene expression signatures were also retrieved.FindingsWe defined a total of 1828 genes that were colorectal cancer-specific fitness genes and identified a 22 colorectal cancer-specific fitness gene (CFG22) score. A high CFG22 score represented unfavorable recurrence and mortality rates, which was validated in three independent cohorts. Combined with age, and TNM stage, the CFG22 model can provide guidance for the prognosis of colorectal cancer patients. Analysis of genomic abnormalities and infiltrating immune cells in the CFG22 risk stratification revealed molecular pathological difference between the subgroups. Besides, drug analysis found that CFG22 high patients were more sensitive to clofibrate.InterpretationThe CFG22 model provided a powerful auxiliary prediction tool for identifying colorectal cancer patients with high recurrence risk and poor prognosis, optimizing precise treatment and improving clinical efficacy.

Unlocking Clinical Insights: Lymphocyte-Specific Protein Tyrosine Kinase Candidates as Promising Therapeutic Targets for Pancreatic Cancer Risk Stratification.

Background: Uncover the pivotal link between lymphocyte-specific protein tyrosine kinase (Lck)-related genes and clinical risk stratification in pancreatic cancer. Methods: This study identifies shared genes between differentially expressed genes (DEGs) and Lck-related genes in pancreatic cancer using a methodological framework rooted in The Cancer Genome Atlas database. Feature gene selection is accomplished and a signature model is constructed. Statistical significant clinical endpoints such as overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) were defined. Results: After performing random survival forest, Lasso regression, and multivariate Cox regression model, 7 trait genes out of 272 Lck-associated DEGs are selected to create a signature model that is independent of other clinical factors and can predict OS and DSS. It appears that high-risk patients have activated the TP53 signaling pathway and the cell cycle signaling pathway. LAMA3 turned out to be the hub gene of the signature with high expression in pancreatic cancer. Patients with increased expression of LAMA3 had a short OS, DSS, and PFI in comparison. The candidate competing endogenous RNA network of LAMA3 turned out to be OPI5-AS1/hsa-miR-186-5p/LAMA3 axis. Conclusions: A characteristic signature of seven Lck-related genes, especially LAMA3, has been shown to be a key factor in clinical risk stratification for pancreatic cancer.

Prospective of Mitochondrial DNA Variations in Cancer on Genomic Medicine.

Mitochondrial DNA (mtDNA) has emerged as a pivotal component in understanding the etiology and susceptibility of cancer. A recent study by Chen and colleagues delineated the germline genetic effect of mtDNA single-nucleotide polymorphisms (SNP) and haplogroups across pan-cancer risk. They identified a subset of mtSNPs and the corresponding risk score, as well as haplogroups A and M7 alongside their genetic interactions, conferring a protective effect against various cancers. These findings underscored the value of mtDNA variations as biomarkers for cancer etiology and as tools for cancer risk stratification. Future investigations are encouraged to integrate comprehensive omics data of genomics, transcriptomics, proteomics, and metabolomics, etc., from nuclear DNA with mtDNA variations, alongside single-cell and spatial technologies, to unravel the tumor mechanism and identify the drug targets. Moreover, the incorporation of polygenic risk score, that included mtDNA variations with both rare and common frequencies, and liquid biopsy-based biomarkers would enhance the predictive performance of cancer risk assessment and refine the risk stratification of population-based cancer screening. This commentary advocates for the validation across diverse populations to harness the full potential of mitochondrial genomics, and ultimately paves the prospective way for advancements in personalized cancer therapeutics and prevention strategies. See related article by Chen and colleagues, Cancer Epidemiol Biomarkers Prev 2024;33:381-8.

Development of a Longitudinal Prostate Cancer Transcriptomic and Clinical Data Linkage

Although tissue-based gene expression testing has become widely used for prostate cancer risk stratification, its prognostic performance in the setting of clinical care is not well understood. To develop a linkage between a prostate genomic classifier (GC) and clinical data across payers and sites of care in the US. In this cohort study, clinical and transcriptomic data from clinical use of a prostate GC between 2016 and 2022 were linked with data aggregated from insurance claims, pharmacy records, and electronic health record (EHR) data. Participants were anonymously linked between datasets by deterministic methods through a deidentification engine using encrypted tokens. Algorithms were developed and refined for identifying prostate cancer diagnoses, treatment timing, and clinical outcomes using diagnosis codes, Common Procedural Terminology codes, pharmacy codes, Systematized Medical Nomenclature for Medicine clinical terms, and unstructured text in the EHR. Data analysis was performed from January 2023 to January 2024. Diagnosis of prostate cancer. The primary outcomes were biochemical recurrence and development of prostate cancer metastases after diagnosis or radical prostatectomy (RP). The sensitivity of the linkage and identification algorithms for clinical and administrative data were calculated relative to clinical and pathological information obtained during the GC testing process as the reference standard. A total of 92 976 of 95 578 (97.2%) participants who underwent prostate GC testing were successfully linked to administrative and clinical data, including 53 871 who underwent biopsy testing and 39 105 who underwent RP testing. The median (IQR) age at GC testing was 66.4 (61.0-71.0) years. The sensitivity of the EHR linkage data for prostate cancer diagnoses was 85.0% (95% CI, 84.7%-85.2%), including 80.8% (95% CI, 80.4%-81.1%) for biopsy-tested participants and 90.8% (95% CI, 90.5%-91.0%) for RP-tested participants. Year of treatment was concordant in 97.9% (95% CI, 97.7%-98.1%) of those undergoing GC testing at RP, and 86.0% (95% CI, 85.6%-86.4%) among participants undergoing biopsy testing. The sensitivity of the linkage was 48.6% (95% CI, 48.1%-49.1%) for identifying RP and 50.1% (95% CI, 49.7%-50.5%) for identifying prostate biopsy. This study established a national-scale linkage of transcriptomic and longitudinal clinical data yielding high accuracy for identifying key clinical junctures, including diagnosis, treatment, and early cancer outcome. This resource can be leveraged to enhance understandings of disease biology, patterns of care, and treatment effectiveness.

Characterizing the 70-gene MammaPrint risk stratification in Chinese luminal type breast cancer and its correlation with clinicopathological characteristics, immune and metabolic functions.

e12559 Background: The 70-gene MammaPrint is endorsed by the NCCN guidelines for predicting 5-year distant metastasis risk in HR+, HER2-, with 0-3 lymph nodes, early invasive breast cancer. Similarly, it is recommended by the CSCO breast cancer guidelines for prognostic assessment post breast cancer surgery. However, no prior studies have investigated its application in Chinese luminal-type breast patients or its correlation with immune and metabolic functions. Methods: This analysis encompasses 76 early-stage invasive Luminal breast cancer patients, registered at the First People's Hospital of Foshan between 2020 and 2023, with recorded clinical characteristics under informed consent. Patient-reported data include clinical features, pathology, and the 70-gene MammaPrint risk of recurrence. Additionally, RNA-seq analysis was conducted in 24 patients. Results: Among 76 enrolled patients,55.3% were categorized as MammaPrint low risk (MP-Low), and 44.7% as MammaPrint high risk (MP-High). Patients over 50 were more likely MP-Low (p=0.028, OR=2.860), while those with &gt;20% Ki67-stained cells were prone to MP-High (p&lt;0.001, OR=0.160). Comparing with MINDACT, there was 52.3% consistency, with 55.6% of MP-Low patients being C-Low (MINDACT risk Low) and 47.1% of MP-High patients being C-High (MINDACT risk High). Notably,21% of C-Low patients showed MP-High risk, and 27% of C-High patients exhibited MP-Low risk, of which 83.3% were ≥50 years old. Based on MINDACT and ISPY2 trial outcomes, patients were further categorized into four groups: MP-Ultralow, MP-Low but not ultralow (LNUL), MP-High but not ultrahigh (HNUH) and MP-Ultrahigh. The study identified an 18% MP-Ultralow population, suggesting that excessive adjuvant therapy might be avoided in these patients. Additionally, 41% of patients were identified as LNUL group, indicating potential benefit from adjuvant treatment or endocrine prolongation therapy. 4% were identified as MP-Ultrahigh group, suggesting potential benefits from neoadjuvant targeted therapy, albeit a poorer long-term benefit in the non-pCR portion. Further transcriptome analysis in 24 patients revealed significantly higher levels of Th2 cells in the MP-High group, with no significant difference observed in CD8 T cells between the two groups. Meanwhile, metabolic-related signatures also differed, with significantly higher levels of Dopamine, Norepinephrine, Heme, and Epinephrine biosynthesis in the MP-Low group. Conclusions: This study elucidates the intricate relationships between MammaPrint prediction results of 70 genes and clinical characteristics, immune and metabolic signatures in Chinese Luminal type breast cancer patients. The refined risk classification provides valuable insights for precision treatment, advancing personalized therapeutic strategies.

Leveraging Serial Low-Dose CT Scans in Radiomics-based Reinforcement Learning to Improve Early Diagnosis of Lung Cancer at Baseline Screening.

Purpose To evaluate the feasibility of leveraging serial low-dose CT (LDCT) scans to develop a radiomics-based reinforcement learning (RRL) model for improving early diagnosis of lung cancer at baseline screening. Materials and Methods In this retrospective study, 1951 participants (female patients, 822; median age, 61 years [range, 55-74 years]) (male patients, 1129; median age, 62 years [range, 55-74 years]) were randomly selected from the National Lung Screening Trial between August 2002 and April 2004. An RRL model using serial LDCT scans (S-RRL) was trained and validated using data from 1404 participants (372 with lung cancer) containing 2525 available serial LDCT scans up to 3 years. A baseline RRL (B-RRL) model was trained with only LDCT scans acquired at baseline screening for comparison. The 547 held-out individuals (150 with lung cancer) were used as an independent test set for performance evaluation. The area under the receiver operating characteristic curve (AUC) and the net reclassification index (NRI) were used to assess the performances of the models in the classification of screen-detected nodules. Results Deployment to the held-out baseline scans showed that the S-RRL model achieved a significantly higher test AUC (0.88 [95% CI: 0.85, 0.91]) than both the Brock model (AUC, 0.84 [95% CI: 0.81, 0.88]; P = .02) and the B-RRL model (AUC, 0.86 [95% CI: 0.83, 0.90]; P = .02). Lung cancer risk stratification was significantly improved by the S-RRL model as compared with Lung CT Screening Reporting and Data System (NRI, 0.29; P < .001) and the Brock model (NRI, 0.12; P = .008). Conclusion The S-RRL model demonstrated the potential to improve early diagnosis and risk stratification for lung cancer at baseline screening as compared with the B-RRL model and clinical models. Keywords: Radiomics-based Reinforcement Learning, Lung Cancer Screening, Low-Dose CT, Machine Learning © RSNA, 2024 Supplemental material is available for this article.

Value of a confirmatory re-biopsy as part of a modern risk stratified cancer surveillance programme for early prostate cancer.

Value of a confirmatory re-biopsy as part of a modern risk stratified cancer surveillance programme for early prostate cancer.

Association of a polygenic risk score with risk of abnormal ultrasound findings and ovarian cancer in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial.

10542 Background: Several susceptibility loci have been identified in genome-wide association studies (GWAS) of single nucleotide polymorphisms (SNPs) that are associated with ovarian cancers. Polygenic risk scores (PRS) combining certain loci have shown limited performance at predicting ovarian cancer risk. However, the utilization of PRS with ovarian cancer screening tests, like transvaginal ultrasound (TVU), warrants further evaluation. This study investigated the association between a polygenic risk score and ovarian cancer risk and abnormal TVU screening in a large-scale randomized controlled ovarian cancer screening trial. Methods: A polygenic risk score was constructed using weighted sums of risk alleles of 15 ovarian cancer SNPs identified in previous GWAS to evaluate the association with abnormal TVU screening results and ovarian cancer outcomes in female participants of the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. Complete genotyping information, TVU results, and ovarian cancer outcomes were available for 15722 females (mean age 62.2 years +/- 5.3) with European ancestry, including 116 ovarian cancer cases. Abnormal TVU was defined as ovarian volume or cyst volume &gt; 10 cm3, any solid/cystic component within a cystic ovarian tumor, or any solid area or papillary projection extending into the cavity of a cystic ovarian tumor. Polygenic risk scores were categorized as quartiles and odds ratios (OR) were calculated to assess risk of ovarian cancer or abnormal TVU result using the lowest PRS quartile as reference category. Results: TVU screening results were abnormal for 732 (732/15722, 4.66%) participants and normal for 14990 (14990/15722, 95.3%) participants at trial baseline. PRS within the fourth quartile demonstrated increased risk of ovarian cancer with reference to the lowest quartile (OR 1.77, 95% confidence interval: 1.04-3.08). Similarly, women in the highest quartile of the PRS had increased odds of an abnormal TVU screening result (OR 1.46, 95% confidence interval: 1.17-1.83) compared to women in the lowest PRS quartile. Conclusions: A polygenic risk score including 15 loci previously shown to be associated with ovarian cancer risk showed an association with increased ovarian cancer risk in the PLCO trial. Furthermore, the PRS was also associated with abnormal ultrasound results. Our findings support further evaluation of polygenic risk scores and transvaginal ultrasound for ovarian cancer risk stratification and screening approaches.

Prostate Cancer Risk Stratification by Digital Histopathology and Deep Learning.

Prostate cancer (PCa) represents a highly heterogeneous disease that requires tools to assess oncologic risk and guide patient management and treatment planning. Current models are based on various clinical and pathologic parameters including Gleason grading, which suffers from a high interobserver variability. In this study, we determine whether objective machine learning (ML)-driven histopathology image analysis would aid us in better risk stratification of PCa. We propose a deep learning, histopathology image-based risk stratification model that combines clinicopathologic data along with hematoxylin and eosin- and Ki-67-stained histopathology images. We train and test our model, using a five-fold cross-validation strategy, on a data set from 502 treatment-naïve PCa patients who underwent radical prostatectomy (RP) between 2000 and 2012. We used the concordance index as a measure to evaluate the performance of various risk stratification models. Our risk stratification model on the basis of convolutional neural networks demonstrated superior performance compared with Gleason grading and the Cancer of the Prostate Risk Assessment Post-Surgical risk stratification models. Using our model, 3.9% of the low-risk patients were correctly reclassified to be high-risk and 21.3% of the high-risk patients were correctly reclassified as low-risk. These findings highlight the importance of ML as an objective tool for histopathology image assessment and patient risk stratification. With further validation on large cohorts, the digital pathology risk classification we propose may be helpful in guiding administration of adjuvant therapy including radiotherapy after RP.

Autoimmune conditions and gastric cancer risk in a population-based study in the United Kingdom

BackgroundAlthough overall incidence of gastric cancer is decreasing, incidence has been increasing among young people in some Western countries. This trend may stem from the increase in autoimmune conditions.MethodsA nested case-control study of gastric cancer in UK Clinical Practice Research Datalink. Up to ten cancer-free controls were matched to cases by age and sex. Conditional logistic regression was used to calculate odds ratios (OR) and 95% confidence intervals (CI) for associations between analyzable autoimmune conditions (n = 34) and gastric cancer with Bonferroni correction. We evaluated associations between pernicious anaemia and other conditions. A meta-analysis of published prospective studies and ours was conducted.ResultsAmong 6586 cases (1156 cardia, 1104 non-cardia, and 4334 overlapping/unspecified tumours) and 65,687 controls, any autoimmune condition was associated with gastric cancer (OR = 1.10; 95% CI: 1.01–1.20). Individuals with pernicious anaemia had higher gastric cancer risk than those without (OR = 2.75; 2.19–3.44). Among controls, pernicious anaemia was associated with seven other conditions (OR range: 2.21–29.80). The pooled estimate for any autoimmune condition and gastric cancer was 1.17 (1.14–1.21; n = 47,126 cases).ConclusionAutoimmunity increases gastric cancer risk. Some autoimmune conditions may be indirectly associated with gastric cancer via pernicious anaemia. Pernicious anaemia could be considered for gastric cancer risk stratification and screening.

Abstract A025: Integrating genomic alterations and histopathological features for enhanced risk stratification in non-muscle invasive bladder cancer

Abstract Urothelial carcinoma is the most prevalent type of bladder cancer, where non-muscle invasive bladder cancer (NMIBC) accounts for about 75% of the initial diagnosis. Recognizing the limitations of current WHO04/16 grading and TNM staging systems, this study investigates genomic alterations, mitotic activity index (MAI) and immunohistochemical (IHC) markers CK20, p53, and CD25 to identify better prognostic biomarkers in NMIBC. In this retrospective population-based study of NMIBC patients (n=287), a targeted next-generation sequencing (NGS) panel analysis was performed using Oncomine™ Focus Assay on Ion GeneStudio S5 platform to identify genomic changes, including single nucleotide variations (SNVs) and copy number variations (CNVs) across 52 genes. The results revealed that the most frequent gene variants were identified in FGFR3 (n=183, 45%) and PIK3CA (n=98, 24%), while AKT1 (n=39, 20%), ERBB2 (n=24, 12%), MYCN (n=23, 12%) and CCND1 (n=22, 11%) were among the most amplified genes. Distinct genetic associations were observed among Ta, T1, low-grade (LG), and high-grade (HG) tumors. Ta tumors were significantly associated with FGFR3 and PIK3CA mutations, while T1 tumors were linked to ERBB2 mutations (p&amp;lt;0.001). Additionally, LG tumors were enriched with FGFR3 mutations, while HG tumors were significantly associated with FGFR1, CCND1, and ERBB2 mutations (p&amp;lt;0.001). In the analysis of tumor recurrence, patients with FGFR3 mutations had significantly shorter recurrence-free survival (RFS) (p=0.033). Analysis of stage progression revealed a significant association between FGFR3 mutations and improved PFS (p&amp;lt;0.001), while MYC, ERBB2 (p&amp;lt;0.001), and MYCN (p=0.011) were linked to shorter PFS. In gene set enrichment analysis, gene sets like PIK3CA and ERBB2 (p=0.003), and PIK3CA and MYC (p=0.005), were inversely associated with progression-free survival (PFS). In the multivariate Cox regression analysis, MAI was the strongest predictor for PFS (p&amp;lt;0.001). The study emphasizes the potential of combining genomic alterations with histopathological features for enhanced risk stratification in NMIBC patients. Citation Format: Melinda Lillesand, Vebjørn Kvikstad, Einar Gudlaugsson, Ivar Skaland, Aida Slewa Johannessen, Almaz Nigatu Tesfahun, Sigmund Vegard Sperstad, Emiel A.M. Janssen, Marie Austdal. Integrating genomic alterations and histopathological features for enhanced risk stratification in non-muscle invasive bladder cancer [abstract]. In: Proceedings of the AACR Special Conference on Bladder Cancer: Transforming the Field; 2024 May 17-20; Charlotte, NC. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(10_Suppl):Abstract nr A025.