Genetic mapping of pancreatic cancer by targeted next-generation sequencing in a cohort of patients managed with nab-paclitaxel-based chemotherapy or agents targeting the EGFR axis: a retrospective analysis of the Hellenic Cooperative Oncology Group (HeCOG)

Evaluation of pancreatic cancer specimens for comprehensive genomic profiling

Inadequate specimen quality or quantity hinders comprehensive genomic profiling in identifying actionable mutations and guiding treatment strategies. We investigated the optimal conditions for pancreatic cancer specimen selection for comprehensive genomic profiling. We retrospectively analyzed 213 pancreatic cancer cases ordered for comprehensive genomic profiling and compared results from pancreatic biopsy, liver biopsy of pancreatic cancer metastases, pancreatectomy, liquid, and nonliver metastatic organ specimens. We examined preanalytical conditions, including cellularity (tumor cell count/size). The successfully tested cases were those that underwent comprehensive genomic profiling tests without any issues. The successfully tested case ratio was 72.8%. Pancreatic biopsy had the highest successfully tested case ratio (87%), with a high tumor cell percentage, despite the small number of cells (median, 3425). Pancreatic biopsy, liver biopsy of pancreatic cancer metastases, and non-liver metastatic organ had higher successfully tested case ratios than that for pancreatectomy. Liver biopsy of pancreatic cancer metastases and pancreatectomy cases with tumor size (mm2) × tumor ratio (%) > 150 and >3000, respectively, had high successfully tested case ratios. The success of comprehensive genomic profiling is significantly influenced by the tumor cell ratio, and pancreatic biopsy is a potentially suitable specimen for comprehensive genomic profiling.

Genetic and therapeutic landscapes in cohort of pancreatic adenocarcinomas: next-generation sequencing and machine learning for full tumor exome analysis

About 7% of all cancer deaths are caused by pancreatic cancer (PCa). PCa is known for its lowest survival rates among all oncological diseases and heterogenic molecular profile. Enormous amount of genetic changes, including somatic mutations, exceeds the limits of routine clinical genetic laboratory tests and further stagnates the development of personalized treatments. We aimed to build a mutational landscape of PCa in the Russian population based on full exome next-generation sequencing (NGS) of the limited group of patients. Applying a machine learning model on full exome individual data we received personalized recommendations for targeted treatment options for each clinical case and summarized them in the unique therapeutic landscape.

Personalized matched targeted therapy in advanced pancreatic cancer: a pilot cohort analysis

Despite progress, 2-year pancreatic cancer survival remains dismal. We evaluated a biomarker-driven, combination/N-of-one strategy in 18 patients (advanced/metastatic pancreatic cancer) (from Molecular Tumor Board). Targeted agents administered/patient = 2.5 (median) (range, 1-4); first-line therapy (N = 5); second line, (N = 13). Comparing patients (high versus low degrees of matching) (matching score ≥50% versus <50%; reflecting number of alterations matched to targeted agents divided by number of pathogenic alterations), survival was significantly longer (hazard ratio [HR] 0.24 (95% confidence interval [CI], 0.078-0.76, P = 0.016); clinical benefit rates (CBR) (stable disease ≥6 months/partial/complete response) trended higher (45.5 vs 0.0%, P = 0.10); progression-free survival, HR, 95% CI, 0.36 (0.12-1.10) (p = 0.075). First versus ≥2nd-line therapy had higher CBRs (80.0 vs 7.7%, P = 0.008). No grade 3-4 toxicities occurred. The longest responder achieved partial remission (17.5 months) by co-targeting MEK and CDK4/6 alterations (chemotherapy-free). Therefore, genomically matched targeted agent combinations were active in these advanced pancreatic cancers. Larger prospective trials are warranted.

Comprehensive Analysis of Somatic Mutations in Driver Genes of Resected Pancreatic Ductal Adenocarcinoma Reveals KRAS G12D and Mutant TP53 Combination as an Independent Predictor of Clinical Outcome

BACKGROUND

Prognosis in pancreatic ductal adenocarcinoma (PDAC) remains poor despite improved systemic therapies and surgical techniques. The identification of biomarkers to advance insight in tumor biology and achieve better individualized prognostication could help improve outcomes. Our aim was to elucidate the prognostic role of the four main driver mutations (KRAS, TP53, SMAD4, CDKN2A) and their combinations in resected PDAC.

PATIENTS AND METHODS

A retrospective analysis was conducted utilizing the cBioPortal database and National Cancer Institute's Cancer Genomic Atlas (TCGA) on patients in whom next-generation sequencing was performed on upfront resected PDAC from 2012 to 2020. Multivariable Cox regression was implemented to elucidate risk-adjusted predictors of overall (OS) and recurrence-free survival (RFS). Results were validated employing a Johns Hopkins Hospital (JHH) cohort.'

RESULTS

In the discovery cohort (n = 587), increased number of mutated driver genes was associated with worse OS (p = 0.047). Specifically, patients with mutations in ≥ 2 driver genes had worse OS than ≤ 1 mutated gene (18.2 versus 32.3 months, p = 0.033). Co-occurrence of mutant (mt)KRAS p.G12D with mtTP53 (median OS, 25.9 months) conferred better prognosis than co-occurrence of other mtKRAS variants (p.G12V/R/other) with mtTP53 (median OS, 16.9 months, p = 0.038). The findings were validated using a JHH cohort. Multivariable risk-adjustment found co-occurrence of mtKRAS p.G12D with mtTP53 to be an independent predictor of beneficial OS and RFS [HR (95% CI): 0.18 (0.03-0.81) and 0.31 (0.11-0.89) respectively].

CONCLUSION

In chemo-naïve resected PDAC, combinations of mutations in the four driver genes are associated with prognosis. In patients with combined mtKRAS and mtTP53, KRAS p.G12D variant confers a better OS and RFS.

The Landscape of Genetic Alterations Stratified Prognosis in Oriental Pancreatic Cancer Patients

Background

Pancreatic cancer is a life-threatening malignant disease with significant diversity among geographic regions and races leading to distinct carcinogenesis and prognosis. Previous studies mainly focused on Western patients, while the genomic landscape of Oriental patients, especially Chinese, remained less investigated.

Methods

A total of 408 pancreatic cancer patients were enrolled. A panel containing 436 cancer-related genes was used to detect genetic alterations in tumor samples.

Results

We profiled the genomic alteration landscape of pancreatic duct adenocarcinoma (PDAC), intraductal papillary mucinous neoplasm (IPMN), periampullary carcinoma (PVC), and solid-pseudopapillary tumor (SPT). Comparison with a public database revealed specific gene mutations in Oriental PDAC patients including higher mutation rates of DNA damage repair-related genes. Analysis of mutational signatures showed potential heterogenous carcinogenic factors caused by diabetes mellitus. KRAS mutation, especially KRAS G12D mutation, was associated with poor survival, while patients not harboring the 17 significant copy number variations (CNVs) had a better prognosis. We further identified multiple correlations between clinicopathologic variables and genetic mutations, as well as CNVs. Finally, by network-based stratification, three classes of PDAC patients were robustly clustered. Among these, class 1 (characterized by the Fanconi anemia pathway) achieved the best outcome, while class 2 (involved in the platinum drug resistance pathway) suffered from the worst prognosis.

Conclusions

In this study, we reported for the first time the genetic alteration landscape of Oriental PDAC patients identifying many Oriental-specific alterations. The relationship between genetic alterations and clinicopathological factors as well as prognosis demonstrated important genomic impact on tumor biology. This study will help to optimize clinical treatment of Oriental PDAC patients and improve their survival.

Artificial intelligence (AI) and big data in cancer and precision oncology

Artificial intelligence (AI) and machine learning have significantly influenced many facets of the healthcare sector. Advancement in technology has paved the way for analysis of big datasets in a cost- and time-effective manner. Clinical oncology and research are reaping the benefits of AI. The burden of cancer is a global phenomenon. Efforts to reduce mortality rates requires early diagnosis for effective therapeutic interventions. However, metastatic and recurrent cancers evolve and acquire drug resistance. It is imperative to detect novel biomarkers that induce drug resistance and identify therapeutic targets to enhance treatment regimes. The introduction of the next generation sequencing (NGS) platforms address these demands, has revolutionised the future of precision oncology. NGS offers several clinical applications that are important for risk predictor, early detection of disease, diagnosis by sequencing and medical imaging, accurate prognosis, biomarker identification and identification of therapeutic targets for novel drug discovery. NGS generates large datasets that demand specialised bioinformatics resources to analyse the data that is relevant and clinically significant. Through these applications of AI, cancer diagnostics and prognostic prediction are enhanced with NGS and medical imaging that delivers high resolution images. Regardless of the improvements in technology, AI has some challenges and limitations, and the clinical application of NGS remains to be validated. By continuing to enhance the progression of innovation and technology, the future of AI and precision oncology show great promise.