Multiple myeloma (MM) is a cancer of plasma cells that is associated with low blood counts, bone and calcium disorders and infections. Despite advances in treatment in the past decade, the number of deaths associated with MM has not decreased. The pros and cons of bone marrow analysis are discussed: Cytogenetics (Next Gen. Sequencing), flow cytometry and FISH. Three markers and their interactions are used to identify MM cells by flow cytometry: CD138, the gold standard for detection, CD38 and CD45. CD138 is not a reliable marker because hypoxia leads to decreased expression and missed myeloma cells. The findings for a new model that can detect a new subset of myeloma with significant clonality using CD38 as a more specific marker for cell types other than MM cells are discussed. The definition and assessment of Minimal Residual Disease has changed the way we view therapeutic response in MM, and, with refinement, it can be a force to reform classifications, alter management paradigms, and identify those patients who may yet achieve a functional cure. There is no known way to detect circulating myeloma cells due to hypoxia. However, the new model is promising as it detected >80% of circulating cells, suggesting detecting myeloma without biopsies is possible using the right markers.

Learning Objectives:

• Understand the benefits and disadvantage of bone marrow analysis to diagnose Multiple Myeloma
• Understand the role CD138 plays to improve Multiple Myeloma screening

The use of biomarkers in screening and optimization of the diagnostic strategy may avoid unnecessary biopsies and improve the prostate biopsy performance. Screening for prostate cancer (Pca) may provide a small benefit in disease-specific mortality over 10 years but it does not improve overall mortality. These benefits should be weighed against the potential complications from biopsies and subsequent treatment, and the risk of overdiagnosis and overtreatment. PSA, if used alone, is insufficient. Integration of genomic tests may advance the diagnosis of Pca. Prostate health index, 4 kallicrein score combining PSA isoforms improve the AUC in the diagnosis setting. The genomic rearrangement occurs in about 30-50% of prostate cancer patients having a specifity of 93% and a PPV of 94%. Mi prostate score combines PCA3 with TMPRSS2 ERG to estimate risk of Pca and thus to help decision making for prostate biopsy. miRNAs seems useful tools for diagnosis and prediction of clinical outcome of Pca patients. PSA screening has to be associated with the mpMRI in order to avoid un-necessary biopsies. The circulating tumor cells identification using immuno cyto chemistry techniques seems the most promising screening test but it needs further evaluation. Integration with genomic tests (such as micro RNA) might turn out imperative in the prediction of clinical outcome.

Learning Objectives:

• Understand the benefits of prostate cancer screening
• Understand the benefits of multi analyte assay panel vs one assay panel

CA19-9 is widely known as being the best marker for pancreatic cancer having, on average, a 75% specificity and sensitivity. Three versions of a candidate biomarker related to CA 19-9 (sTRA), detected through screening via on-chip glycon profiling and their value in pancreatic cancer diagnosis are described. The distinct structural components of the sTRA versions, contrary to CA19-9, gives them a stem characteristic, indicating a potential role as subtype markers. Plasma levels of the biomarkers correlated with 45 matched samples, indicating the blood sample can be a good indicator of the primary tumor level. Combining the biomarkers together can detect more types of cancers than each alone, leading to improved sensitivity from 47% to 63%. Additionally, a statistically significant sensitivity increases over CA19-9 alone was noted in a blinded set. This is most rigorous validation so far of improvement upon CA 19-9 using upfront predefined thresholds and classification rules with statistically significant improvement. CA 19-9 provides prognosis value but lacks treatment prediction value. In contract, sTRA, demonstrates value for differentiating subtypes of cancers that is more resistant to chemo.

Learning Objectives:

• Understand the gaps in pancreatic cancer screening and how to overcome those gaps
• sTRA demonstrates differentiating value when compared to CA19-9

Hepatocellular carcinoma (HCC) is a primary liver malignancy that typically develops in the setting of chronic liver disease. It is the 4th leading cause of cancer Worldwide with a five-year survival of only 18%. The diagnosis of HCC can be difficult and often requires several imaging modalities and biopsies. It is important for tumors to be detected when they are < 2cm in size for an early diagnosis, prevention of stage progression and ultimately a favorable outcome. Surveillance plays a key role in early diagnosis while ultrasound and CT technologies scans have limitations. The tumor markers AFP, AFP-L3 and PIVKA may be useful in early diagnose of HCC. It has been shown that combining the use of the three markers improves sensitivity significantly and has the potential to change the outcome of patients with HCC. Practice guidelines for HCC are discussed for US (AASLD), Asia (APASL) and Japan (JSH). Several models that combine clinical features and HCC biomarkers in order to predict HCC are discussed: GALAD, MoRAL, ASAP. Integrating these biomarkers in HCC treatment pathways as a surrogate for treatment response or disease recurrence is extremely important for early diagnosis and change of outcome.

Learning Objectives:

• Understand the challenge and clinical unmet needs when it comes to HCC screening
• Understand the difference between using multi analytic panel approach vs one assay and how it impacts the accuracy of the results

The mechanism of HER2 protein expression in tumors is primarily gene amplification, primarily enabled via the process of dimerization of HER2 receptors. Anytime there is a discrepancy, which is exceedingly rare, between protein and gene status, we rely more on the protein status rather than gene status for treatment purposes because the drug doesn’t target the gene. There are common misconceptions that low grade tumors, lobular, mucinous “tubular” tumors and tumors with incomplete membranous signal are always HER2 negative. Additionally, there is the misconception that most HER2-positive tumors are high grade. ASCO/CAP 2018 update moved closer to performing Immunohistochemistry (IHC) & FISH (Fluorescence) on all tumors and recommends the use of dual-probe instead of single-probe IHC assays. 2018 Guidelines swing the pendulum in the “negative results” direction. As this will not solve the dilemma of the “gray zone” group, we propose a 3-tier scoring of HER2 status as either positive, negative or low as this will not solve the dilemma of the “gray zone” group. Our QA program includes tumor analysis, nuclear score, tumor histological grade, IHC score and FISH score. Correlation with IHC and tumor histopathology and performing metrics is mandatory in order to improve HER2 testing accuracy.

Learning Objectives:

• Understand the impact of HER2 between the protein and gene status
• Understand the role of IHC & FISH in breast cancer diagnosis

Biomarkers are essential to define breast cancer subtypes that predict prognosis and guide treatment and to guide selection of Rx in metastatic disease. 94% of breast cancer is curable and biomarkers play a critical role in identifying patients with early stage cancer who may benefit from novel therapies. Biomarkers can be useful in evaluation of symptoms in survivors but should not be used in asymptomatic survivors outside of a clinical trial. More research is needed to identify high risk patients who can be targeted for early intervention and improved outcomes. Biomarkers may be used as one of the tools to evaluate response to Rx and may be used to individualize therapy and guide decisions over chemotherapy. Examples of validated and commonly used tumor markers in oncology include PSA for prostate cancer, M protein for Myeloma, HCG for some testicular cancers. Biomarkers are used to guide therapy in metastatic breast cancer. ASCO guidelines recommend biopsy to confirm diagnosis and check standard clinical biomarkers (ER, PR, and HER2) to guide therapy. Decisions on changing to a new drug or discontinuing treatment should be based on the patient's goals for care and clinical evaluation of disease progression or response. There is no evidence that changing therapy solely based on biomarker results improves health outcome, quality of life, or cost effectiveness. Biomarkers may be used as one of the tools to evaluate response to Rx. Liquid biopsy as early screening of response and guide to precision therapy is the future.

Learning Objectives:

• The role of biomarkers in predicting breast cancer
• Learn about liquid biopsy and how it’s used in breast cancer screening and monitoring.

The COVID-19 pandemic has compelled oncology practices to hit the “reset” button for their care delivery model. Cancer is a Global burden projected to increase to 27 million by 2040 due to population growth, aging population and risk factors. Patients with cancer are one of the most affected groups by the pandemic because they are immunocompromised and require frequent admission to the hospital. There is significant interruption in delivering of care and the incident of cancer diagnoses may increase during and after pandemic due to halted screening. Accumulating data suggest that the likelihood of a severe illness and death from COVID-19 is higher among adult patients with cancer hematologic malignancies. Psychological impact on patients and staff is huge and communication, remote care and e-health are key. Balancing the risks of delaying cancer treatments versus the risks for SARS CoV-2 exposure is challenging during the pandemic as guidelines are constantly changing. Cancer research should be prioritized and funded and innovations in COVID and cancer testing, and screening technologies are needed. Clinicians should proactively discuss goals of care and advance care planning, especially for those with advanced cancer who are at elevated risk for COVID-19. In summary, cancer patients represent a special situation during this pandemic and a tiered approach to testing could help provide them with life-saving chemotherapy without jeopardizing their chances of benefit.

Learning Objectives:

• How COVID 19 reshaped the global oncology model
• Understand the areas of focus within healthcare systems during the pandemic, and how it relates to cancer patients.

Lung cancer is the #1 cancer killer Worldwide and accounts for 38% people dying of respiratory diseases. It is a big healthcare burden with costs of approximately $28 Billion per year. Early diagnosis is crucial for improvement of survival rate. Screening lung cancer with low dose chest CT screening effectively could save approximately 8% of the population dying yearly from cancer. Nodules detected by chest CT are categorized proportionally by risk level based on size from low to high. The biggest problem represents the intermediate zone, the largest category, which needs improved diagnostics in a noninvasive way. Imaging and biomarkers may be helpful in stratifying the risk. Biomarkers have multiple layers of evidence and add value, cost effective and eventually save lives. In collaboration with a group in the UK, 15,000 benign lung nodules and 932 cancers from NLST were analyzed. The artificial intelligent model LCP-CNN reclassifies about one third of nodules into a lower or higher risk group, decreasing number of unnecessary procedures and decreases time to diagnosis. Biomarker research of early detection should reach ‘evidence’ based randomized controlled clinical trials observational studies clinical expertise. Additionally, we need to make sure patients are involved in the design of the studies, need informed consent and we need to work with Statistical models who help assess the strength and robustness of biomarkers. We need to look at the continuous risk factors and risk prediction models, molecular and imaging, to address the shortcomings of the clinical prediction models to find a solution of our current evaluation of lung nodule.

Learning Objectives:

• Understand the difference between LDCT vs Multi Analyte Assay Analysis
• Identify risk factors and risk predictions models to address the lung cancer screening gap