Correlation between biopsy type and insufficient tissue availability for biomarker testing in five solid cancer types.

e22136 Background: Limited tissue availability is a known barrier to conducting biomarker testing on cancer tissue specimens. It has been assumed that the frequency of insufficient tissue being available is correlated with type of tissue biopsy performed, but this has not previously been quantified across multiple cancer types. Methods: A panel of pathologists in the US (N=90) were asked to report on recent cancer tissues they prepared/handled for biomarker testing. The physicians were asked to report on the cancer type, biopsy method, molecular diagnostics tests conducted, and whether sufficient tissue was available for all requested tests. If insufficient tissue was available, they were asked to report on which tests they were unable to perform as a result. All data was collected through an online survey between August and November 2012. Results: In total, 99 breast cancer samples, 85 NSCLC samples, 60 CRC samples, 30 gastric cancer samples and 40 melanoma samples were reported on. Insufficient tissue was available in 5 (6%) of all NSCLC samples, 2 of which were obtained through fine needle aspirations (14% of all FNAs), a further 2 through core biopsies (5%), and a further 1 through a bronchoscopy (8%). Insufficient tissue was available in 1 (1%) of all breast cancer samples (core biopsy, 1% of all core biopsies), 1 (3%) of all melanoma samples (core biopsy, 13% of all core biopsies) and 1 (2%) of all CRC samples (core biopsy, 11% of all core biopsies). Conclusions: Insufficient cancer tissue for biomarker testing occurred across 4 out of 5 cancer types reported on, though was rare (<5%) in cancers other than NSCLC. The majority of tissue samples in which insufficient tissue was present were acquired through core biopsies (67% of all cases) or FNAs (22% of all cases).

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Diagonistic Value of ARFI in Breast Lesions

Journal of Pharmaceutical Research International ◽

10.9734/jpri/2021/v33i50b33421 ◽

2021 ◽

pp. 24-30

Author(s):

J. Jayapriya ◽

S. Arul Murugan

Keyword(s):

Breast Cancer ◽

Acoustic Radiation ◽

Radiation Force ◽

Acoustic Radiation Force Impulse ◽

Stiffness Index ◽

Cancer Tissue ◽

Cancer Type ◽

Tissue Samples ◽

Biopsy Confirmation ◽

Index Value

Breast cancer became the most prominent cancer type in women worldwide. Its prevalence increased in recent years due to changes in life style and relapse among the patients seemed to be higher. Acoustic radiation force impulse (ARFI) imaging in based on the principle of the ultrasonic elasticity and the elestography accurately predict and measure the changes in breast cancer tissue compared to the normal tissue. It is a technical alternative to the palpation and able to measure lesser than 10 mm size. In contrast to biopsy, where the reduced deformability would occur and lead to biopsy failing. In fibroadenoma, due to its complications, many false positives could be detected and the ARFI elastography serve as an effective alternative method for breast cancer confirmation. The tissue stiffness index value is used to differentiate the benign and malignant tissue samples. ARFI further, use B- mode elasticity and help in recommending the biopsy confirmation.

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Pan-cancer analysis reveals complex tumor-specific alternative polyadenylation

10.1101/160960 ◽

2017 ◽

Author(s):

Zhuyi Xue ◽

René L Warren ◽

Ewan A Gibb ◽

Daniel MacMillan ◽

Johnathan Wong ◽

...

Keyword(s):

Alternative Polyadenylation ◽

Length Change ◽

The Cancer Genome Atlas ◽

Cancer Type ◽

Rna Seq ◽

Multiple Cancer ◽

Tissue Samples ◽

Cancer Genome Atlas ◽

Specific Alternative ◽

Cancer Types

AbstractAlternative polyadenylation (APA) of 3’ untranslated regions (3’ UTRs) has been implicated in cancer development. Earlier reports on APA in cancer primarily focused on 3’ UTR length modifications, and the conventional wisdom is that tumor cells preferentially express transcripts with shorter 3’ UTRs. Here, we analyzed the APA patterns of 114 genes, a select list of oncogenes and tumor suppressors, in 9,939 tumor and 729 normal tissue samples across 33 cancer types using RNA-Seq data from The Cancer Genome Atlas, and we found that the APA regulation machinery is much more complicated than what was previously thought. We report 77 cases (gene-cancer type pairs) of differential 3’ UTR cleavage patterns between normal and tumor tissues, involving 33 genes in 13 cancer types. For 15 genes, the tumor-specific cleavage patterns are recurrent across multiple cancer types. While the cleavage patterns in certain genes indicate apparent trends of 3’ UTR shortening in tumor samples, over half of the 77 cases imply 3’ UTR length change trends in cancer that are more complex than simple shortening or lengthening. This work extends the current understanding of APA regulation in cancer, and demonstrates how large volumes of RNA-seq data generated for characterizing cancer cohorts can be mined to investigate this process.

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Survival after bone metastasis by primary cancer type: a Danish population-based cohort study

BMJ Open ◽

10.1136/bmjopen-2017-016022 ◽

2017 ◽

Vol 7 (9) ◽

pp. e016022 ◽

Cited By ~ 33

Author(s):

Elisabeth Svensson ◽

Christian F Christiansen ◽

Sinna P Ulrichsen ◽

Mikael R Rørth ◽

Henrik T Sørensen

Keyword(s):

Breast Cancer ◽

Bone Metastasis ◽

Bone Metastases ◽

Population Based ◽

Solid Cancer ◽

Cancer Type ◽

Adjusted Relative Risk ◽

Primary Cancer ◽

Cancer Types ◽

Synchronous Metastases

ObjectiveIn the 10 most common primary types with bone metastases, we aimed to examine survival, further stratifying on bone metastases only or with additional synchronous metastases.MethodsWe included all patients aged 18 years and older with incident hospital diagnosis of solid cancer between 1994 and 2010, subsequently diagnosed with BM until 2012. We followed patients from date of bone metastasis diagnosis until death, emigration or 31 December 2012, whichever came first. We computed 1-year, 3-year and 5-year survival (%) and the corresponding 95% CIs stratified on primary cancer type. Comparing patients with bone metastasis only and patients with other synchronous metastases, we estimated crude and adjusted HRs and corresponding 95% CI for mortality.ResultsWe included 17 251 patients with bone metastasis. The most common primary cancer types with bone metastasis were prostate (34%), breast (22%) and lung (20%). One-year survival after bone metastasis diagnosis was lowest in patients with lung cancer (10%, 95% CI 9% to 11%) and highest in patients with breast cancer (51%, 50% to 53%). At 5 years of follow-up, only patients with breast cancer had over 10% survival (13%, 11% to 14%). The risk of mortality was increased for the majority of cancer types among patients with bone and synchronous metastases compared with bone only (adjusted relative risk 1.29–1.57), except for cervix, ovarian and bladder cancer.ConclusionsWhile patients with bone metastases after most primary cancers have poor survival, one of ten patients with bone metastasis from breast cancer survived 5 years.

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Attention to diet, exercise, and weight in the oncology clinic: Results of a national patient survey.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.10549 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. 10549-10549

Author(s):

Jennifer A. Ligibel ◽

Lori J. Pierce ◽

Catherine M. Bender ◽

Tracy E Crane ◽

Christina Marie Dieli-Conwright ◽

...

Keyword(s):

Breast Cancer ◽

Cancer Patients ◽

Online Survey ◽

Early Stage ◽

Cancer Type ◽

Breast Cancer Patients ◽

Related Factors ◽

Increased Risk ◽

Diet And Exercise ◽

To Receive

10549 Background: Obesity and related factors are increasingly associated with increased risk of developing and dying from cancer. The American Society of Clinical Oncology (ASCO) conducted a survey of cancer patients to assess their experience in receiving recommendations and referrals related to weight, diet and exercise as a part of their cancer care. Methods: An online survey was distributed to potential participants between March and June 2020 via ASCO channels and patient advocacy organizations, with an estimated reach of over 25,000 individuals. Eligibility criteria included being 18 years, living in the US, and having been diagnosed with cancer. Logistic regression was used to determine factors associated with recommendation and referral patterns. Results: In total, 2419 individuals responded to the survey. Most respondents were female (75.5%), 61.8% had an early-stage malignancy, 38.2% had advanced disease, and 49.0% were currently receiving treatment. Breast cancer was the most common cancer type (36.0%). Average BMI was 25.8 kg/m2. The majority of respondents consumed £2 servings of fruits and vegetables per day (50.9%) and exercised £2 times per week (50.4%). Exercise was addressed at most or some oncology visits in 57.5% of respondents, diet in 50.7%, and weight in 28.4%. Referrals were less common: 14.9% of respondents were referred to an exercise program, 25.6% to a dietitian and 4.5% to a weight management program. In multiple regression analyses, racial and ethnicity minority respondents were more likely to receive advice about diet (Odds Ratio [OR] 1.92, 95% CI 1.56-2.38) and weight (OR 1.64, 95% CI 1.23-2.17) compared to non-Hispanic whites, individuals diagnosed with cancer in the past 5 yrs (vs > 5 yrs) were more likely to receive advice about exercise (OR 1.48, 95% CI 1.23-1.79), and breast cancer patients were more likely to receive advice about exercise (OR 1.37, 95% CI 1.11-1.68) and weight (OR 1.46, 95% CI 1.03-2.07) than other cancer patients. Overall, 74% of survey respondents had changed their diet or exercise after cancer diagnosis. Respondents reporting that their oncologist spoke to them about increasing exercise or eating healthier foods were more likely to report a change in behavior than those whose oncologists did not (exercise: 79.6% vs 69.0%, P < 0.001; diet 81.1% vs 71.4%, P < 0.001). Respondents whose oncologist had spoken to them about exercise were more likely to exercise > 2 times per week compared to respondents whose oncologists did not address exercise (53.5% vs 44.1%, P < 0.001). Conclusions: In a national survey of oncology patients, slightly more than half of respondents reported attention to diet and exercise during oncology visits. Provider recommendations for diet and exercise were associated with positive changes in these behaviors. Additional attention to diet and exercise as part of oncology visits is needed to help support healthy lifestyle change in cancer patients.

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Strategy for SRM-based Verification of Biomarker Candidates Discovered by iTRAQ Method in Limited Breast Cancer Tissue Samples

Journal of Proteome Research ◽

10.1021/pr300322q ◽

2012 ◽

Vol 11 (8) ◽

pp. 4201-4210 ◽

Cited By ~ 52

Author(s):

Satoshi Muraoka ◽

Hideaki Kume ◽

Shio Watanabe ◽

Jun Adachi ◽

Masayoshi Kuwano ◽

...

Keyword(s):

Breast Cancer ◽

Breast Cancer Tissue ◽

Cancer Tissue ◽

Tissue Samples

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RNA Sequencing in Comparison to Immunohistochemistry for Measuring Cancer Biomarkers in Breast Cancer and Lung Cancer Specimens

Biomedicines ◽

10.3390/biomedicines8050114 ◽

2020 ◽

Vol 8 (5) ◽

pp. 114

Author(s):

Maxim Sorokin ◽

Kirill Ignatev ◽

Elena Poddubskaya ◽

Uliana Vladimirova ◽

Nurshat Gaifullin ◽

...

Keyword(s):

Breast Cancer ◽

Lung Cancer ◽

Rna Sequencing ◽

Expression Profiles ◽

Correlation Study ◽

Cancer Tissue ◽

Transcriptional Level ◽

Tissue Samples ◽

Protein Levels ◽

Formalin Fixed Paraffin Embedded

RNA sequencing is considered the gold standard for high-throughput profiling of gene expression at the transcriptional level. Its increasing importance in cancer research and molecular diagnostics is reflected in the growing number of its mentions in scientific literature and clinical trial reports. However, the use of different reagents and protocols for RNA sequencing often produces incompatible results. Recently, we published the Oncobox Atlas of RNA sequencing profiles for normal human tissues obtained from healthy donors killed in road accidents. This is a database of molecular profiles obtained using uniform protocol and reagents settings that can be broadly used in biomedicine for data normalization in pathology, including cancer. Here, we publish new original 39 breast cancer (BC) and 19 lung cancer (LC) RNA sequencing profiles obtained for formalin-fixed paraffin-embedded (FFPE) tissue samples, fully compatible with the Oncobox Atlas. We performed the first correlation study of RNA sequencing and immunohistochemistry-measured expression profiles for the clinically actionable biomarker genes in FFPE cancer tissue samples. We demonstrated high (Spearman’s rho 0.65–0.798) and statistically significant (p < 0.00004) correlations between the RNA sequencing (Oncobox protocol) and immunohistochemical measurements for HER2/ERBB2, ER/ESR1 and PGR genes in BC, and for PDL1 gene in LC; AUC: 0.963 for HER2, 0.921 for ESR1, 0.912 for PGR, and 0.922 for PDL1. To our knowledge, this is the first validation that total RNA sequencing of archived FFPE materials provides a reliable estimation of marker protein levels. These results show that in the future, RNA sequencing can complement immunohistochemistry for reliable measurements of the expression biomarkers in FFPE cancer samples.

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Multi-omic data helps improve prediction of personalised tumor suppressors and oncogenes

10.1101/2022.01.13.476163 ◽

2022 ◽

Author(s):

Malvika Sudhakar ◽

Raghunathan Rengaswamy ◽

Karthik Raman

Keyword(s):

Tumour Progression ◽

Suppressor Gene ◽

Tumour Suppressor Gene ◽

Driver Mutations ◽

Cancer Type ◽

Expression Data ◽

Multiple Cancer ◽

Driver Genes ◽

Cancer Types ◽

Omic Data

The progression of tumorigenesis starts with a few mutational and structural driver events in the cell. Various cohort-based computational tools exist to identify driver genes but require a large number of samples to produce reliable results. Many studies use different methods to identify driver mutations/genes from mutations that have no impact on tumour progression; however, a small fraction of patients show no mutational events in any known driver genes. Current unsupervised methods map somatic and expression data onto a network to identify the perturbation in the network. Our method is the first machine learning model to classify genes as tumour suppressor gene (TSG), oncogene (OG) or neutral, thus assigning the functional impact of the gene in the patient. In this study, we develop a multi-omic approach, PIVOT (Personalised Identification of driVer OGs and TSGs), to train on experimentally or computationally validated mutational and structural driver events. Given the lack of any gold standards for the identification of personalised driver genes, we label the data using four strategies and, based on classification metrics, show gene-based labelling strategies perform best. We build different models using SNV, RNA, and multi-omic features to be used based on the data available. Our models trained on multi-omic data improved predictions compared to mutation and expression data, achieving an accuracy >0.99 for BRCA, LUAD and COAD datasets. We show network and expression-based features contribute the most to PIVOT. Our predictions on BRCA, COAD and LUAD cancer types reveal commonly altered genes such as TP53, and PIK3CA, which are predicted drivers for multiple cancer types. Along with known driver genes, our models also identify new driver genes such as PRKCA, SOX9 and PSMD4. Our multi-omic model labels both CNV and mutations with a more considerable contribution by CNV alterations. While predicting labels for genes mutated in multiple samples, we also label rare driver events occurring in as few as one sample. We also identify genes with dual roles within the same cancer type. Overall, PIVOT labels personalised driver genes as TSGs and OGs and also identifies rare driver genes. PIVOT is available at https://github.com/RamanLab/PIVOT.

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