bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improving differential diagnosis of pulmonary large cell neuroendocrine carcinoma (LCNEC) and small cell lung cancer via a transcriptomic, biological pathway-based ridge regression model.
Authors: Hong Jun , Hou Likun , Zhang Wei , Dong Zhengwei , Huang Zhan , Yuan Wuzhou , Zhang Lei , Wu Chunyan 
Published: June 2021

3065 Background: In clinics, it can be challenging to make correct diagnosis of LCNEC, Small cell lung cancer (SCLC), if tissues, like needle biopsies, are insufficient or morphology was poorly preserved. In this study, a reliable classifier was constructed based on transcriptome data and machine learning (Ridge regression) to improve the diagnostic accuracy for LCNEC and SCLC. Methods: RNA-Seq data obtained from 3 public cohorts were collected as training set, including 60 NSCLC cases from The Cancer Genome Atlas (TCGA), 66 LCNEC cases from Julie George et al., Nature Communications 2018, and 33 SCLC cases from Julie George et al., Nature 2015. Another 80 NSCLC, 30 LCNEC and 15 SCLC cases published by Martin Peifer et al., Nature Genetics 2012 were used as validation set. Additionally, RNA-Seq data of 27 borderline samples which were hard to make diagnosis based on histology and Immunohistochemistry were used to test the accuracy of the prediction model. Results: 13,959 genes mapped to 186 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were included. Gene Set Variation Analysis (GSVA) algorithm was used to enrich and score each KEGG pathway. A prediction model based on GSVA score of each pathway was constructed via Ridge regression. This GSVA Score Model achieved ROC-AUC 0.949 and concordant rate of 0.75 for the entire prediction efficiency. Of the 27 borderline samples which were hard to make confirmed diagnosis, 17/27 (63.0%) were predicted as LCNEC, 7/27 were predicted as SCLC, and the remainder were predicted as NSCLC. While only 8 (29.6%) cases with LCNEC were diagnosed by pathologists, which was significantly lower than the results predicted by the model. Furthermore, cases with model predicted LCNEC had a significant longer disease-free survival than that with model predicted SCLC (median DFS,59 months for LCNEC vs 5 months for SCLC, p = 0.0043), which was in parallel with currently known prognostic difference of these two types of neuroendocrine tumors. Conclusions: This GSVA algorithm-based prediction model was able to make accurate diagnosis of LCNEC and SCLC. And it may provide valuable information for clinics to choose optimal therapeutic approach for patients with pulmonary neuroendocrine tumors.[Table: see text]
TP53 and CHEK2 germline mutations in malignant solid tumors.
Authors: Zhang Zhiye , Wu Yaqing , Luo Ningning , Zhang Qin , Han Tiantian , Bu Xuesong , Shuai Qin , Qi Yingxue 
Published: June 2021

3119 Background: Li-Fraumeni syndrome (LFS, OMIM #151623) is an autosomal dominant cancer predisposition syndrome. Typical LFS tumors comprise adrenocortical carcinomas, sarcoma, breast cancer and central nervous system tumors. LFS is also associated with an increased risk of a multitude of other common types of cancer, such as prostate cancer, lung cancer, gastric cancer, colorectal cancer, ovarian cancer, melanoma, etc. TP53 germline mutations are the most common gene with LFS. Germline mutations of CHEK2 have been identified as another predisposing gene and associated with a range of cancer types. However, the pattern of TP53 and CHEK2 germline mutations in malignant tumors remains unknown. Methods: We identified 8535 malignant solid tumors patients without selecting age or family history in a retrospective cohort. Germline mutations were categorized based on ACMG (American College of Medical Genetics and Genomics) guidelines in pathogenicity. The patients were divided into three groups, P group (with pathogenic mutations), LP group (with likely-pathogenic mutations) and Non_P group (neither pathogenic nor likely-pathogenic mutation). Statistical significance was defined as P-value less than 0.05. Results: A total of 461 (461/8535) patients carried TP53 or CHEK2 germline mutations were identified, in which 15 patients with pathogenic mutations and 17 patients with likely-pathogenic mutations. One patient with lung cancer in LP group carried TP53 homozygous mutation ( p. Ser215Ile), and the remaining 31 patients all carried heterozygous mutations. Among these 31 carriers, 16 (51.6%) carried nonsense or missense mutations (10 for nonsense and 6 for missense mutations). 3 patients in the P group carried CHEK2 p. Y139* (one liver cancer patient and two lung cancer patients) were identified. The median age of group P, LP and Non_P was 55 (39 for TP53, 61 for CHEK2), 63 (52 for TP53, 66 for CHEK2) and 59, respectively. Somatic mutation analysis found no significant difference in tumor mutation burden (TMB) among three groups. The SNV/INDEL mutation frequency of LRP1B in the P or LP group was significantly lower than the Non_P group. Conclusions: Our data showed that 0.375% (32/8535) malignant solid tumor patients carried TP53 (16/8535) or CHEK2 (16/8535) germline pathogenic or likely-pathogenic mutations. The relationship between germline mutations and cancer susceptibility will be studied in the future.[Table: see text]
Analysis of the MOSAIC correlative cancer database integrating molecular cancer classification and tumor profiling to identify targeted treatment options for metastatic cancer.
Authors: Ma Li , Treuner Kai , Wong Jenna , Spigel David R. , Schnabel Catherine A. , Greco F Anthony 
Published: June 2021

3121 Background: Unless tumor type and genetic alterations can be identified, metastatic cancer patients with unknown or uncertain diagnoses may be limited to empiric chemotherapy. The 92-gene assay (CancerTYPE ID) is a validated gene expression classifier of 50 tumor types and subtypes for patients with cancer of unknown primary (CUP) or ambiguous diagnoses. Multimodal molecular biomarker testing by next-generation sequencing (NGS), tumor mutational burden (TMB), fluorescent in situ hybridization (FISH), microsatellite instability (MSI), and immunohistochemistry (IHC) can identify genetic targets. A database integrating tumor typing with biomarker analysis in metastatic cases was utilized to identify the most prevalent genetic alterations by tumor type. Methods: MOSAIC (Molecular Synergy to Advance Individualized Cancer Care) is an IRB-approved database of cases with CancerTYPE ID testing plus multimodal biomarker testing. The current study determined molecular tumor type followed by molecular profiling by NGS for up to 323 genes, (NeoTYPE profiles, Neogenomics). Results: Tumor type was determined in 1992 of 2151 cases (92.7%), comprised of 27 different tumor types. 72% of cases were comprised of the 7 tumor types shown in the table,which also shows the frequency of the 10 most commonly mutated genes by tumor type. Bolded are genes with actionable genetic mutations for which FDA-approved therapies are not currently indicated in the identified tumor type. Conclusions: Precise targeted treatment for many patients with CUP or ambiguous diagnoses requires accurate diagnosis of the cancer origin combined with multimodal molecular testing to identify actionable genetic alterations in the appropriate cellular context. Future studies will evaluate additional biomarker profiles, including TMB, FISH, MSI, and IHC for cases in the MOSAIC database.[Table: see text]
Temporal and spatial topography of cell proliferation in cancer.
Authors: Kabraji Sheheryar Kairas , Gaglia Giorgio , Argyropoulou Danae , Dai Yang , Wang Shu , Bergholz Johann , Coy Shannon , Lin Jia-Ren , Jeselsohn Rinath , Metzger Otto , Winer Eric P. , Dillon Deborah , Zhao Jean , Sorger Peter , Santagata Sandro 
Published: June 2021

3122 Background: Tumors are complex ecosystems where exogenous and endogenous cues are integrated to either stimulate or inhibit cancer cell proliferation. However, the nature of these complex cell cycle states, their spatial organization, response to perturbation, and implications for clinical outcomes, are poorly characterized in tumor tissues. Methods: We used multiplexed tissue imaging to develop a robust classifier of proliferation, the multivariate proliferation index (MPI), using 513 unique tumors across five cancer types. Next, we used dimensionality reduction analysis to assess how the patterns of cell cycle protein expression in tumors were altered in response to perturbation. Results: The MPI outperforms single markers, like Ki67, when classifying proliferative index across diverse tumor types and reveals the proliferative architecture of tumors in situ. We find that proliferative and non-proliferative cancer cells are organized across microscopic (cell-to-cell) and macroscopic (tissue-level) scales. Both domains are reshaped by therapy, and local clusters of proliferative and non-proliferative tumor cells preferentially neighbor distinct tumor-infiltrating immune cells. We further phenotyped non-proliferating cancer cells using markers of quiescent cancer cells, cancer stem cells, and dormant cancer cells. We found that these types of non-proliferating cancer cells can occupy distinct regions within the same primary tumor. In high-dimensional marker space, populations of proliferative cancer cells express canonical patterns of cell cycle protein markers, a property we refer to as “cell cycle coherence”. Untreated tumors exist in a continuum of coherence states, ranging from optimal coherence, akin to freely cycling cells in culture, to reduced coherence characterized by either cell cycle polarization or non-canonical marker expression. Coherence can be stereotypically altered by induction and abrogation of mitogen signaling in a HER2-driven model of breast cancer. Cell cycle coherence is modulated by neoadjuvant therapy in patients with localized breast cancer, and coherence is associated with disease-free survival after adjuvant therapy in patients with colorectal cancer, mesothelioma and glioblastoma. Conclusions: The MPI robustly defines proliferating and non-proliferating cells in tissues, with immediate implications for clinical practice and research. The coherence metrics capture the diversity of post-treatment cell cycle states directly in clinical samples, a fundamental step in advancing precision medicine. More broadly, replacing binary metrics with multivariate traits provides a quantitative framework to study temporal processes from fixed static images and to investigate the rich spatial biology of human cancers.
Frequency of longitudinal changes in TP53 mutation status from gene sequencing of serial tumor biopsies from a large cohort of cancer patients.
Authors: Annis D. Allen Allen , Pavlick Dean C. , Frampton Garrett M. , Albacker Lee A. , Vukovic Vojislav M. , Aivado Manuel 
Published: June 2021

3124 Background: The p53 pathway is one of the most important in cancer biology, with mutation of the TP53 gene that encodes the p53 tumor suppressor protein observed in ≈50% of all cancers. We evaluated the frequency of changes in TP53 mutation status in a large cohort of serial tumor biopsies. Methods: From a database of >200,000 next-generation gene sequencing results we identified 16,592 samples arising from repeat biopsies from 7840 patients (pts), average 2.12 per pt, 1007 pts with ≥3, max 11; over an interval up to 234 months (mos), average 11.0 mos. TP53 mutations with known or unknown significance in successive biopsies and changes in assignment from TP53-Wild-Type (WT) to Mutant (Mut), or Mut to WT, were evaluated vs. cancer type and time between biopsies. Results: Table: N (%) of samples vs. change in TP53 status from previous biopsy vs. mos from initial biopsy in all samples (7840 initial + 8752 successive, 46% TP53-Mut) and the three most represented cancers: non-small cell lung cancer (NSCLC, 1189 initial + 1268 successive, 60% Mut), breast (947 initial + 993 successive, 55% Mut), multiple myeloma (MM, 578 initial + 981 successive, 18% Mut). Conclusions: Changes in TP53 status were rare (<10% of samples). Differences may occur in serial biopsy samples for pathophysiological reasons, e.g., a mutant clone becoming dominant and/or heterogeneity at different tumor biopsy sites, or analytical differences in biopsy tumor content or assay sensitivity between samples. In this analysis, WT-to-Mut changes were more frequent (5.9%) than Mut-to-WT changes (3.3%), suggesting a small selection pressure for TP53 alterations later in oncogenesis and indicating that these alterations are truncal. Mut-to-WT changes are not readily explained physiologically and may suggest these infrequent changes are mostly due to sampling or analytical variability, and genuine changes in TP53 mutation status are quite rare.[Table: see text]
The value of defining molecular resistance in patients with progressive EGFR and ALK-driven lung cancer in a public system.
Authors: Barron Carly C. , Stockley Tracy , Law Jennifer H. , Shabir Muqdas , Fernandes Roxanne , Zhang Tong , Le Lisa W. , Tsao Ming Sound , Kamel-Reid Suzanne , Pal Prodipto , Cabanero Michael , Schwock Joerg , Ko Hyangmi , Liu Geoffrey , Bradbury Penelope Ann , Sacher Adrian G. , Shepherd Frances A. , Leighl Natasha B. , Perdrizet Kirstin 
Published: June 2021

3126 Background: Repeat molecular profiling, except to detect EGFR T790M, is not routinely performed in Canadian patients with lung cancer progressing on EGFR tyrosine kinase inhibitors (TKIs). We performed genomic profiling on post-progression biopsies in patients with stage IV non-small cell lung cancer (NSCLC) and known EGFR/ ALK aberrations treated with TKIs to identify resistance mechanisms, evaluate options for subsequent treatment, and to assess clinical trial eligibility and costs. Methods: From Feb 2018-Aug 2020, post-progression tumour biopsies from consenting patients at a major cancer centre underwent genomic profiling (ThermoFisher OCA v3.0 including hotspots, fusions, and copy number variations in 161 cancer-associated genes). Outcomes of interest were the identification of resistance mutations, actionable targets, clinical trial eligibility (per clinicaltrials.gov), and costs. Results: Thirty-two patients consented to the study. Most, 84% (n = 27), had successful testing completed while 16% (n = 5) had insufficient tissue. Median age of the cohort was 56 yrs, 59% (n = 16) were female, 74% (n = 20) were never-smokers, 81% (n = 22) had ECOG performance status 0-1, and 67% (n = 18) were Asian. The majority, 81% (n = 22) had EGFR mutated NSCLC, and had progressed on EGFR TKIs (15 with previously identified T790M had progressed on osimertinib), and 19% (n = 5) had ALK fusions. Patients had received a median of 2 prior lines of targeted therapy prior to re-biopsy (IQR 1.5,3). One patient had evidence of small cell transformation and associated TP53 and RB1 mutations, 11% (n = 3) had acquired EGFR C797S mutations, and 11% (n = 3) had acquired ALK resistance point mutations (G1202R n = 2, I1171N n = 1). Genomic profiling identified additional actionable targets in 19% of patients (n = 5: MET exon 14 skip mutation n = 1, MET amplification n = 2, BRAF V600E n = 2). Overall, 33% (n = 9) patients had management-changing resistance mechanisms identified (small cell transformation n = 1, actionable targets n = 5, ALK inhibitor resistance = 3). New clinical trial options based on genomic profiling results were identified for 67% (n = 18) of patients. Incremental costs for repeat genomic profiling were approximately $880 CAD per case. Conclusions: Molecular profiling upon development of resistance to targeted therapy in our cohort revealed actionable resistance mechanisms for over a third of patients and clinical trial options for 67%. These incremental benefits for patients highlight the importance of routine molecular profiling in the setting of acquired TKI resistance in lung cancer.
Clinically advanced pelvic squamous cell carcinomas (pSCC) in men and women: A comprehensive genomic profiling (CGP) study.
Authors: Spiess Philippe E. , Grivas Petros , Mata Douglas A. , Bratslavsky Gennady , Jacob Joseph M , Necchi Andrea , Danziger Natalie , Lin Douglas I. , Decker Brennan , Sokol Ethan , Huang Richard S.P. , Ross Jeffrey S. 
Published: June 2021

3130 Background: Given that the clinical manifestations, disease course, and treatment options for pSCC differ between tumor types, we performed CGP to examine possible genomic differences. Methods: 1,741 clinically advanced pSCCs including 230 penile (penSCC), 17 male urethral (murthSCC), 125 male anal (manSCC), 7 female urethral (furthSCC), 263 vulvar (vulSCC), 822 cervical (crvSCC), and 277 female anal SCCs (fanSCC) underwent hybrid capture-based CGP to evaluate all classes of genomic alterations (GAs). Tumor mutational burden (TMB) was determined on up to 1.1 Mb of sequenced DNA and microsatellite instability (MSI) was determined on up to 114 loci. PD-L1 expression was determined by IHC (Dako 22C3). Results: HPV-16/18 detection was lowest in murthSCC and vulSCC and highest in manSCC, fanSCC, and crvSCC. TP53 GAs were inversely associated with HPV status. PIK3CA GA frequency varied (22-43%). DNA-damage response (DDR) GAs (e.g., BRCA1/2, ATM, others) were low ( < 1-3%) throughout. Cell-cycle GAs were most frequent in external cases (penSCC, furthSCC, vulSCC). MTOR pathway GAs ( PTEN, FBXW7) were the most frequently identified “actionable” GAs. FGFR3 GA were present in >5% of murthSCC, crvSCC, and fanSCC; other receptor-tyrosine kinase (RTK) targeted options were 1% in BRAF/ ERBB2. NOTCH1 GAs were present in > 15% of penSCC and vulvSCC. TMB ≥10 mut/Mb was >15% in manSCC, fanSCC, and crvSCC. PD-L1 low expression was > 25% in all pSCC except crvSCC and high expression was > 18% in all pSCC except urthSCC and manSCC. Conclusions: Despite similar histology, pSCC differ widely in GAs and HPV status. PIK3CA is the most frequent “targetable” GA followed by MTOR pathway and cell cycle; RTK targets are extremely rare. PARP inhibitor options appear low given the infrequent finding of DDR GAs. Anti-PD(L)1 could be considered in a number of cases based on TMB>10 mut/Mb and PD-L1 expression.[Table: see text]
Cancer and COVID-19: A proposed mechanism with therapeutic interventions.
Authors: Leyfman Yan , Emmanuel Nancy , Tentler Aleksey , Cappelli Jared , Erick Timothy K , Sharma Pushpa , Park Chandler H. 
Published: June 2021

3135 Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel betacoronavirus that causes the respiratory illness coronavirus disease 2019 (COVID-19). COVID-19 ranges in severity from an asymptomatic viral infection to life-threatening cases of pneumonia, acute respiratory distress syndrome (ARDS), multi-organ damage and sepsis. Cancer patients are at an increased risk of severe SARS-CoV-2 infection due to their immunocompromised status. We propose a mechanism by which SARS-CoV-2 infection causes multiple organ damage through IL-6-mediated inflammation and hypoxia-induced cellular metabolic alterations leading to cell death. Hypoxia is also induced by malignancy due to alterations in metabolism, resulting in greater IL-6 secretion. Methods: To highlight the possible effect of active cancer on the likelihood of hypoxia in COVID-19, we analyzed the correlation between cancer status and the severity of COVID-19 from the COVID-19 and Cancer Consortium data registry. For cancer status, we looked at progressive cancer and remission of cancer only -- those being the two extremes of presence and absence of uncontrolled cancer. Similar to prior studies, the severity of COVID-19 was used as an indication of hypoxia. Results: We observed a 24% positive deviation between expected and actual number of patients with actively progressing cancer who had hypoxic COVID-19 (moderate to severe), and a 26.9% negative deviation between expected and actual number of patients with active cancer who had no hypoxia with COVID-19 (p<0.0001). Conversely, for patients with cancer in remission, there was only a +5.8% and -5.1% deviation between expected and actual number of patients who did not have hypoxia and who had hypoxia, respectively. Conclusions: These results suggest that in the presence of poorly controlled malignancy, there is an increased likelihood of hypoxia in patients with COVID-19, thereby exacerbating downstream cytokine release syndrome and contributing to prolonged systemic inflammatory injury. Appreciating this pathway, future therapies can be developed to target the pathogenesis of both diseases and prevent progression, as seen with mesenchymal stem cells, which demonstrated a 91% overall survival and 100% survival in patients younger than 85 years old at one month after a single treatment.[Table: see text]
Genome-wide cfDNA fragmentation in patients with cancer and other diseases.
Authors: Carey Jacob , Jones Sian , Leal Alessandro , Chesnick Bryan , Butler Denise , Rongione Michael , Scharpf Robert B. , Johansen Julia S. , Velculescu Victor E. , Dracopoli Nicholas C 
Published: June 2021

3136 Background: Genome-wide cfDNA fragmentation patterns have previously been demonstrated to distinguish with high sensitivity and specificity between plasma samples from individuals with and without cancer. Methods: To further evaluate cfDNA fragmentation as a blood-based screening test for cancer, we have evaluated these genomic features in patients with and without cancer who have been diagnosed with other common comorbidities. For this study, we have used low coverage (1-2x) whole genome sequencing to analyze plasma samples from 412 patients referred to an advanced diagnostic center due to unexplained signs and symptoms associated with cancer. Results: Seventy-seven of these patients were ultimately diagnosed with one of sixteen different solid cancers, 68 with cardiovascular disease, 48 with diabetes, as well as other comorbidities. We used the DELFI approach (DNA evaluation of fragments for early interception) to measure the cfDNA fragmentation in plasma from all 412 patients included in the study using a cross-validated machine learning model. Patients with cancer could be distinguished from non-cancer individuals with high sensitivity and specificity (AUC = 0.92), including for common cancers such as colorectal and lung cancer. Conclusions: These data support the hypothesis that abnormal cfDNA fragmentation is a consequence of unregulated mitoses in cancer cells and can be distinguished from fragmentation patterns of individuals with co-morbidities.