Exam Molecular Methods PDF

407.2 58 577.3 270.2 478.3 Liquid Biopsy qPCR 147 157 246 Aims and objectives: 567.4 454.3 666.4 - To extract DNA 317.2fr...

407.2 58 577.3 270.2 478.3 Liquid Biopsy qPCR 147 157 246 Aims and objectives: 567.4 454.3 666.4 - To extract DNA 317.2from lymphocytes and cell-free DNA from patient plasma - To analyze cell-free DNA levels and determine whether they reflect disease kinetics - To analyze ERBB2 amplification in cell-free DNA to determine whether the liquid biopsy can detect acquired amplification prior to clinical disease progression Learning Outcomes: - Describe how cell-free DNA is extracted from blood and lymphocytes - Explain how real-time qPCR can be used to quantify DNA and calculate copy number - Understand how this can be used clinically to guide treatment Background: PCR vs. qPCR In conventional PCR, the amplified DNA product is detected in an end-point analysis In real-time PCR that accumulation of amplification product is measured as the reaction progresses in real time with product quantification after each cycle Conventional PCR The DNA amplification product is detected at the end of the PCR cycles. Usually done by running the amplified DNA on agarose gel  and staining it with Ethidium Bromide (or dye) which fluoresces under UV light The primary goal is to determine the absence or presence of the target DNA, the intensity of the bands on the gel can give a rough estimate of the amount of DNA but it is not very precise (Qualitative detection) Conventional PCR is less sensitive and less quantitative than qPCR, it does not provide information about the dynamics of the amplification process Real-time qPCR Real-time detection of PCR products is enabled by the inclusion of a fluorescent reporter molecule in each reaction that yields increased fluorescence with an increasing amount of product DNA The fluorescence chemistries employed for this purpose include DNA-binding dyes (SYBR Green) and fluorescently labelled sequence-specific probes (TaqMan). Thermal cyclers equipped with fluorescence detection are used to monitor the fluorescence signal as amplification occurs. The measured fluorescence is proportional to the total amount of amplicon; the change in fluorescence over time is used to calculate the amount of amplicon produced in each cycle. The accumulation of the amplified product is measured in real time during each PCR cycle, achieved by using fluorescent dyes (SYBR Green) or fluorescently labelled probes (TaqMan) that emit fluorescence when bound to amplified DNA RT-qPCR allows for the quantification of the DNA starting material. The fluorescence intensity, which is directly proportional to the amount of PCR product is measured at the end of each cycle, enabling precise quantification The cycle threshold value is the cycle number at which the  fluorescence signal exceeds the background level. The Ct value is inversely proportional to the amount of target DNA, lower Ct values indicate higher amounts of target DNA It offers high sensitivity, specificity, and the ability to detect low levels of target DNA RT-qPCR can be multiplexed allowing simultaneous detection of multiple targets in a single reaction by using different fluorescent dyes for each target In SYBR Green-based real-time PCR, a melt curve analysis can be performed after the amplification cycles. This analysis distinguishes between specific and non-specific products based on their melting temperatures, enhancing the assay's accuracy Advantages of RT-qPCR Real-time PCR allows you to determine the initial number of copies of template DNA (the amplification target sequence) with accuracy and high sensitivity over a wide dynamic range. Real-time PCR results can either be qualitative (the presence or absence of a sequence) or quantitative (copy number). Liquid biopsy qPCR qPCR is a method that can be utilized for analysis of circulating tumor DNA (ctDNA), the tumor-derived fraction of cell-free DNA circulating in the blood. Several studies have shown that qPCR can be used to detect gene amplifications in ctDNA that drive cancer progression and metastasis, such as ERBB2 (also known as HER2). Detection of these gene amplifications could potentially have huge clinical implications, especially in HER2 positive tumors that can be treated with targeted  therapies such as Herceptin. This practical will determine whether liquid biopsy (and more specifically qPCR) can detect relapse earlier than scans in a patient undergoing therapy with anti-hormone receptor therapy (Tamoxifen), by analyzing ERBB2 amplification. Tumor biopsy vs. Liquid biopsy - Tumor biopsy is invasive, extracting tissue using needle of surgical excision, provide detailed view of tumor at a specific time and location, essential for initial diagnosis, type and grade of cancer guiding treatment decisions, gold standard for initial diagnosis and pathological assessment, but it is not representative of all subclones within tumor. Also in the metastatic setting, the patient is too sick of repeated biopsies so it dependents on their disease burden - Liquid biopsy is non-invasive and requires analysis of saliva, urine, blood samples to detect cf-DNA and CTC, allows for repeated sampling overtime providing dynamic view of tumor evolution and response to treatment (longitudinal sampling), used for early detection, MRD monitoring, monitor of disease progression and for ID genetic mutations for targeted therapy, it is representative of all subclones within the tumor, captures intratumor heterogeneity in a metastatic setting 4 Main candidates for liquid biopsy - CTC: these are cancer cells that have detached from the primary tumor and circulate in the bloodstream, they provide info about presence and characteristics of cancer and their levels can be indicative of disease progression and treatment response - Cell-free nucleotides: cell-free DNA represents all DNA fragments in the bloodstream derived from normal and cancer cells, CT-DNA is a subset of cf-DNA originating  from cancer cells, they are shorter fragments. Cf-DNA is derived from normal cell turnover like apoptosis, necrosis or secretion less specific for cancer detections, ctDNA carry tumor specific genetic alterations and released from tumor cells undergoing apoptosis necrosis or secretion to bloodstream, used for characterizing tumor specific mutations, MRD monitoring and disease relapse. - Exosomes: small extracellular vesicles released by cells, including cancer cells into the blood, involved in cell-cell communication and shed by tumor DNA, they carry info about resistance to therapies and provides dynamic view of treatment response and disease progression - Platelets: second most abundant blood components that can be educated by tumor RNA to carry tumor-specific info, helps predict response to treatment and change in platelet profile indicate disease progression Blood draw - Blood drawn from patient must be placed in EDTA tube because cheap and readily available and processed within 2 hours to prevent from WBC lysis which release contaminants like cellular components and genomic DNA which dilutes ct-DNA compromising sample integrity and making samples useless - Centrifugation at 1000g for 10 min at 4C to separate plasma from other blood components - Blood divided into plasma (55%, taken and centrifuged at 2000g then stored (plasma contains ct-dna/cf-dna, CTC and fats, this is why it is centrifuged again, need ultracentrifuge to remove important DNA so no problem), WBC & platelets (4% taken and stored, contains germline DNA because genes will have CN of 2 used as a comparator for blood samples), RBC (41%, taken and stored) Principle of silica-column based DNA extraction 1) Lysis  o Breaks down membrane of WBC, proteins to release DNA/RNA o Plasma contains nucleic acids (DNA/RNA), proteins, platelets, cells/cellular debris - Addition of lysis buffer AL and protease, buffer AL breaks open cell and nuclear membrane, protease denatures the proteins and keeps the DNA intact - Buffer AL contain chaotropic salt and guanidinium chloride, it inactivates nucleases and promotes nucleic acid binding to pure silica material - Incubation at 56C for 10 min to promote protease activity and prevents proteins breaking down DNA - Addition of 100% ethanol to precipitate DNA out of solution 2) Binding to silica column - DNA separated from proteins using selective binding to silica and centrifugation, high salt > pH 7, DNA binds to silica 3) Wash x2 - Colum washed with buffer AW1 (high (low) of guanidinium salt and high EtOH, then buffer AW2 very low salt and lower 80% EtOH, reducing concentration of salt and ethanol to remove residual proteins without interfering with DNA 4) Membrane drying - High centrifugal speed to remove residual ethanol as it interferes with downstream analysis and inhibit pcr or incubate column at 56C 5) Elution - DNA eluted in water or Tris-HCL buffer (Buffer AE), low salt ph7 - Unmasks charges and rehydrates DNA and surface of the silica membrane, DNA unbound and flows out of column during centrifugation step - Tris HCl used if you want to store sample, if you want to use  immediately elute in water Cf-DNA - Major sources of cf-DNA: blood, urine, saliva, cerebral spinal fluid, pleural fluid - Derived from: o Apoptosis: programmed cell death, during apoptosis DNA is fragmented and released into bloodstream o Necrosis: cell injury results in premature cell death, causing cell to rupture and releasing DNA fragments into bloodstream o Active Secretion: active secretion and release of DNA from living cells, some cells actively secrete DNA into EC env CtDNA - Tumor derived fraction of cell-free DNA, forms part of cell- free DNA but it come specifically from the tumor, cell-free DNA is derived from WBC Quantification of cfDNA using different kits cfDNA extraction A) the y-axis represents the average DNA concentration, and the x-axis represents different commercial kits to measure the plasma DNA The bars represent the DNA quantified using different methods, GADPH, ACTBL2, HPRT1 are genes commonly quantified using pcr to estimate DNA yield as housekeeping genes. Qubit 2.0 is a spectrophotometric method to measure total DNA. DNA blood mini and CNA kits yield high cfDNA conc, with consistent results across all detection methods, while Nucleospin XS and Epigentek kits yield a much lower DNA conc, with some detection methods resulting in non-detectable DNA. Qubit 2.0 indicate higher DNA concentrations than the qpcr methods likely due to measuring total DNA, including non-specific DNA fragments. B) DNA blood mini kit has HMW DNA coming through while CAN kit you get less, so that is better Y axis shows the % recovery of DNA, X-axis shows the kit and dilution concentrations of DNA (0.05-50ng), the DNA fragments tested include Lambda 564bp and 23kb The DNA blood mini kit achieves the highest recovery rates across all fragment sizes, but especially for shorter fragments. The CNA kit also performs well but has lower recovery rates for larger fragments. NucleoSpin XS shows the lowest recovery rates, particularly for smaller DNA concentrations and larger fragments. The performance for smaller DNA concentrations declines in each kit, but blood mini kit outperforms the other in shorter and larger fragments So Blood mini is best for recovery of cfDNA across different concentrations and fragment size, it is best for smaller fragments which is essential for cfDNA and ctDNA analysis as they are short fragments themselves Larger fragments usually represent that WBC lysis. cfDNA in plasma is typically fragmented due to apoptosis or necrosis, resulting in small DNA fragments, most commonly around 150-200 bp (the size of DNA wrapped around a nucleosome). However, HMW DNA may originate from lysed cells (e.g., white blood cells or tumor cells), and its presence can indicate contamination during sample processing. Tape station plot Looks at sample and pump out data from different fragment sizes A) X-axis is BP sizes, y-ais is FU, we get more HMW DNA ranging from 500-10000 which is not what we want, also shows there is LMW around 150bp in this samples, area under hump gives idea of amount of DNA as well as FU does the same This sample is contaminated with HMW DNA, likely from cell lysis or poor plasma preparation. B) Less DNA overall but the vast majority of it is LMW so that sample is more pure for cf-dna, the 35 is LMW marker and 10380 is the HMW marker used to calculate DNA concentrations. Additionally, the purity (LMW-to-HMW ratio) in Panel B makes it better for cfDNA-focused applications. The scenario - Patient with stage 2 BC, N0, M0, ER+, PR+, HER2- - Tamoxifen treatment, sample taken from p1 cancerous, p2 non-cancerous, p3 relapse every 6 months Analysis of CNA by qPCR Conventional PCR  - End point reaction, poor precision, low sensitivity, short dynamic range 5cm) , T4 (tumor broken through skin or attached to chest wall, locally invaded) o Lymph node status (N): N0 (no nodes) N1 (swollen nodes) N2 (nodes swollen and lumpy) N3 (located near collarbone) o Metastasis (M): M0 (nodes cancer-free) M1 (nodes cancerous or micro metastatic) Tumor Grade - Grade 1: cancer cells look normal and are growing slowly (low grade) well differentiated only minor differences - Grade 2: cells do not look like normal cells; cells may be growing more quickly than normal (intermediate grade) mod differentiated - Grade 3: cancer cels look very abnormal and are growing quickly (high grade) poorly differntated, high levels of proliferation like ki67 respond well to traditional chemo Microsatellite status - Two types of microsatellite status o Microsatellite instability (MSI): genetic condition characterized by changes in length of microsatellite (short/repetitive DNA sequences) due to defects in DNA repairs (most seen in endometrial and CRC), this is driven by alterations in mismatch repair proteins which results in loss of MMR protein expression o Microsatellite stable (MSS): cancers have normal expression of MMR genes  - Main MMR proteins: o MLH1 (MutL Homolog 1) o MSH2 (MutS Homolog 2) o MSH6 (Muts Homolog 6) o PMS2 (postmeitotic segregation increased 2) o Responsible for repairing DSB and SSB in DNA repair - Patients with MSI cancer have a better prognosis, if the MMR proteins have a loss of expression - So if MMR protein expression is lost, DNA repair is lost and as a result mutations increase, however MSI patients have a better prognosis Microsatellite Instability - MSI drives tumorigenesis by: o Creating a hypermutator phenotype through loss of MMR protein expression, mutations accumulate and causes genomic instability o Increased accumulation of mutations in critical genes involved in cell cycle regulation (TGFB RECEPTOR TYPE 2) and apoptosis (BAX) ▪ This results in inhibition of cancer cell apoptosis and increased cancer cell cycle division and growth - MSI tumors are associated with a strong immune response as they have a high mutational burden, thus allowing for a increased recognition and attack by the immune system due to tumor release of neoantigens, this leads to o Lower rates of recurrence o Better overall survival o More likely to respond to immunotherapy - However, these cancer may start to inhibit the immune system by upregulation PDL1, PDL1 binds to PD-1 receptors on t cells, inactivating immune response as an  evasion mechanism - MSS tumor respond better to chemo, MSI tumor respond better to immunotherapy so MS status can effect survival based on what drug is used - Chemo for CRC MSS tumors: FOLFOX: Folate, Fluorouracil and Oxaliplatin Consensus Molecular Subtypes in CRC - 4 different CMS in CRC o CMS1-4 - CMS1 is MSI type CIMP+. BRAF mutant, immune activated, most common in ascending colon o Good early-stage prognosis, poor late-stage prognosis o Responsive to immunotherapies - CMS2: WNT/MYC activation APC deletion, TP53 MSS, good prognosis and responsive to chemo - CMS3: KRAS mutation characterized by metabllic dysregulation, intermediate prognosis, targeted therapies (Kras mutant therapies) MSS/MSI - CMS4: Mesenchymal, EMT, Stromal infiltration, poor prognosis, limited, possible angiogenic therapies MSS CpG Island Methylator Phenotype (CIMP) Status - In most early cancers, there is re-methylation of the entire cancer genome - CRC CMS1 MSI tumors show widespread hyper- methylation of CpG islands in gene promoter regions leading to transcriptional silencing in tumor suppressor genes which results in tumorigenesis - Associated with MSI tumors and BRAF mutations - BRAF mutations: MSI tumors harbor mutation in the BRAF gene which further drives hypermethylation of CpG islands o Most common mutation is V600E where valine is replated by glutamic acid at position 600, BRAF become oncogene and drives continuous signaling for  division of cancer cells P53 tumor suppressor - P53 expressed when cell is stressed and apoptosis activated - In tumor there is lots of p53 in the nucleus, however cells don’t die - P53 accumulate in CRC cells, however they evade apoptosis - Normal function of P53 o P53 activated by hypoxia, redox DNA damage (intrinsic or extrinsic factors), translocates to nucleus and starts a transcriptional pathway and it transcribes and results in translation of MDM2 forming a complex (MDM2 is an E3 ubiquitin ligase), then the complex is ubiquitinated and targeted for proteasomal degradation (controlled feedback loop) o P53 when activated promotes transcription of apoptotic proteins BAX, NOXA, PUMA and upregulates them. P53 regulates the intrinsic pathway, and BAX translocates to the mitochondria and BAX/BAX homodimers or BAX/BAK heterodimers form creating a pore in the mitochondria of the cell which releases cytochrome c and this results in caspase-9 activation which results in apoptosis - Mutated p53 o Mutant P53 does not bind to MDM2 since MDM2 is an e3 ubiquitin ligase and target p53 for destruction, it cannot do this to mutant p53 o So as a result mutant p53 cannot induce a survival transcriptional profile because it loses its ability to promote transcription of pro-apoptotic proteins which impairs tumors suppressor function and drives oncogenesis o Mutant p53 outcompetes wild type p53, preventing  apoptosis and a large accumulation of p53 o Aso mutations can occur in MDM2 which prevents binding to mutant p53 o Most p53 mutations in CRC appear in the DNA binding domain spanning exon 5-8, so it cannot bind to its promotors and cannot induce survival transcriptional profile and activate apoptosis ▪ Codons: 175, 245, 248, 273, 282 (This is not an exhaustive list) o Mutations in the DND of P53 disrupt its ability to transactivate genes that mediate apoptosis o P53 mutations reduce: ▪ Bax ▪ Puma ▪ Noxa Proteomics Clinical experiments - Find biomarker of disease for diagnosis (pancreatic cancer) o Cell extraction from pancreas (however it is difficult to ask a patient who potentially does not have pancreatic cancer to undergo surgical excision), so diagnostic blood tests are better to extract blood plasma, urine, saliva, sputum (for respiratory disease analysis) (need consent) - Experimental design o Patients with determined pancreatic cancer as the test sample o Patients that are healthy without disease as the negative control ▪ Considerations: age and sex balance for statistical side ▪ Patients with pancreatic cancer are around 60-65 o Patients with precursor of pancreatic cancer (pancreatitis) which will not always progress to cancer as positive control - Collection samples o Blood extraction, biopsies - Pre-analytical variation o Extraction of tissue for biopsy (researcher could prepare one tissue biopsy earlier or later than other tissue biopsy) this results in variation in time and space, the time in getting it in the form of raw blood to sample needed (plasma) through centrifugation then freeze it to prevent lysis and contamination which can dilute plasma biomarkers and proteins can start to degrade (there are many thousands of proteins in a plasma sample that degrade at different speeds due to differences in protein stability, PTM, temperature and storage conditions, and protein concentration), so it is  essential to prepare the proteins for MS as soon as possible to account for pre-analytical variation this has to be pre-defined ▪ Pre-defined procedures refer to standardized protocols that must be established for identical sample collection, processing and storage to limit pre-analytical variation o Encompasses differences in sample collection, processing and storage ▪ Batch effects could result from pre-analytical variation, this is systematic differences between batches of samples processed at different times or slightly different conditions, to mitigate normalization using statistical methods and quality controls by including internal controls and replicates with each batch to adjust for batch variability - Ease of getting samples o Blood, urine, sputum easier to extract than tissue biopsies (ideal are fingerpick blood analysis) for easier monitoring of cancer - Numbers of samples o Wide range of pancreatic cancer patients to capture variability and to determine stat significance o Powering study – look to determine variants of analytical technique to determine differences seen in biomarkers, this gives number of samples needed to get results that are statistically significant and not due to chance ▪ Factors influencing power: effect size, sample size, sig level, variability Cut-offs - Altering cut-off of an analyte affect the specificity and sensitivity of a diagnostic test  - Sensitivity is the ability of the test to correctly identify individuals who are sick (true positives), higher sensitivity means lower false negatives - Specificity is the ability of the test to correctly identify healthy individuals who don’t have disease (true negatives), higher specificity means lower false positives - Left panel cut-off 400ug/L o Everyone in sick group is above this cutoff, so all sick individuals are correctly ID (100% sensitivity), however some healthy individuals fall above this cutoff resulting in false positives and lower specificity at 54% o This setting prioritize sensitivity over specificity ensuring no sick individuals are missed, however it sacrifices specificity leading to higher range of false positives in healthy individual - Right panel cutoff 500ug/L o Not all individuals in the sick group are identified as above this cutoff, sensitivity drops to 92% more false negatives are identified o specificity above this cutoff has increased to 79%, meaning that more individuals are identified correctly as healthy (true negatives) o This cutoff helps balance sensitivity to specificity however, this lowers the sensitivity to ID sick individuals as some are missed, however sensitivity increases which identifies more accurately individuals who are healthy true negative - No assay is 100% most are around 90% ROC curve - The specificity and sensitivity of an assay test can be used to create a ROC curve by creating a series of cut-offs that have various sensitivity and specificity  - The y axis shows sensitivity (=true positive fraction = TP/(TP+FN) - The x axis shows 1-specificty (=false positive fraction = FP/(FP+TN) - Plot Sensitivity (y axis) vs 1-specificity (x axis) - Area under arc is called area under curve - Most cancers are defined by stage, so numbers need to be balanced per stage - Early disease is low symptom, when symptoms become aggressive patients go to hospital so most studies are on late stage - In stage 1 or 2 the two biomarkers give area under the curve from 0.8 to 0.9 fold, in late stage 3/4 the area under curve increases from 0.95 to 0.96 increasing ability to predict accurately the disease or health using area under curve, closer to 1 more accurate - Proteomics offers clinical diagnosis by having multiple markers to more accurately define and diagnose the disease Sequencing of peptides - C terminus is AA = residue + 19 and N terminus is AA = reside +1 - We can figure out mutation in cancer from peptide sequencing - Glycine Valine leucine histidine alanine valine lysine  - These are tryptic peptides so you can assume either lysine or arginine are at end of peptide. Selecting peptides for quantitative MS - Rules for peptide selection are critical o Longer than 6-7 aa for increased specificity and shorter than 22 aa because with LC/MS it will be too big to pass through the beads o Should not contain PTM to lower complexity o No reactive residues, combo of aa can react with nearby o Should be unique aa sequence o No N or C terminus peptides to prevent digestion o Not susceptible to missed cleavage because it can cause variable issues - This can limit number of tryptic peptides such as methionine because it oxidizes, cystine gets alkylated - Tryptic peptides cleave at lysine (K) and arginine (R) except when followed by proline (P) Western blot interpretation  - Bands give info about presence or absence of proteins Target Protein 1: First Panel: The strong bands in the "+" treatment lanes suggest that the HSP90 inhibitor did not fully inhibit the stability or expression of this protein. This indicates that Target Protein 1 may not be highly dependent on HSP90 for its stabilization. Alternatively, it may indicate compensatory mechanisms or that the protein is resistant to degradation even when HSP90 is inhibited. Second Panel: The weaker bands in the "+" treatment lanes suggest that the inhibitor partially destabilized this target protein, leading to a reduction in its levels. This discrepancy between the two panels could reflect variability in experimental conditions or differences in the exposure time, antibody sensitivity, or specific treatment effects on distinct isoforms or post-translationally modified forms of the protein. Interpretation: The results suggest that Target Protein 1 may have partial dependency on HSP90, with some degree of destabilization in response to the treatment. However, the incomplete inhibition implies that the HSP90 inhibitor is either not fully effective or that additional pathways may be compensating for the loss of HSP90 function. Target Protein 2: First 3 Bands ("+" Treatment): The strong bands in the first half of the "+" lanes suggest that the initial treatment with the HSP90 inhibitor did not inhibit Target Protein 2 effectively. This could be due to insufficient inhibitor concentration, short treatment duration, or a slower response of this protein to destabilization. Last 3 Bands ("+" Treatment): The weaker bands in the later "+" lanes indicate that the treatment eventually destabilized this target protein, reducing its expression or  stability. This aligns with the known mechanism of HSP90 inhibitors, which disrupt protein folding, leading to degradation via the proteasome. Biological Implication: Target Protein 2’s response suggests it is an HSP90 client protein, and its destabilization by the inhibitor prevents survivin stabilization. Since survivin is critical for cell survival and anti- apoptotic activity, its destabilization likely triggers apoptosis, as indicated by the progressive weakening of the bands. Loading Control: The uneven bands in the loading control suggest variability in protein loading, likely due to pipetting errors or inconsistent sample preparation. This is a significant limitation, as it affects the reliability of the data and the validity of comparing band intensities between lanes. Recommendations: Use densitometry to normalize the target protein band intensities to the loading control signal. Repeat the experiment with careful pipetting and sample preparation to ensure equal loading across lanes. Running technical replicates would also help validate the results. Protocol Survivin is a cancer target and has two functions, it inhibits apoptosis through incact Histology: Understanding Cancer Through Tissue Examination What is Histology? Histology is the study of the microscopic structure of tissues. In cancer research, it is essential for identifying the type of cancer, its grade, and the molecular markers expressed within cells. Tissue samples are taken (via biopsy), processed, stained, and analyzed under a microscope to evaluate their structure, cellular organization, and protein expression. Key Steps in Histology 1. Tissue Preparation: A biopsy sample is embedded in paraffin wax to stabilize the tissue. Thin sections, about 4 microns thick, are sliced and placed on glass slides for staining. 2. Staining: The primary stain in histology is hematoxylin and eosin (H&E): Hematoxylin stains nuclei blue, highlighting the DNA content. Eosin stains the cytoplasm and extracellular proteins pink, revealing cell structure and organization. 3. Immunohistochemistry (IHC): IHC uses antibodies to detect specific proteins in tissue sections. For example, in breast cancer: Estrogen receptor (ER) staining identifies ER+ cancers. HER2 staining detects HER2-positive cancers, guiding targeted therapy decisions. Histology in Cancer Progression 1. Cellular Localization: Proteins are distributed differently depending on cellular function or disease state. Example: Beta-catenin is a protein that normally localizes to the cell membrane, where it helps in cell adhesion. In colorectal cancer, beta-catenin translocates to the nucleus, activating Wnt signaling and driving tumor growth. 2. Markers of Proliferation: Ki67 is a nuclear marker that highlights dividing cells. In normal tissue, Ki67 staining is limited to areas of active cell division, like crypts in the intestine. In cancer, Ki67 staining becomes widespread, indicating uncontrolled cell proliferation. 3. Structural Changes: Cancer disrupts normal tissue architecture. In colorectal cancer: Normal crypt-villus structures are replaced by disorganized clusters of malignant cells. Markers like p53 and MSI status (microsatellite instability) help refine the diagnosis and predict treatment responses. Applications of Histology in Cancer: Diagnosis: Identifies cancer type, grade, and stage. Treatment Planning: Determines molecular markers (e.g., HER2, ER, and PR) that influence therapy decisions. Research: Studies tumor microenvironments, including the interaction of cancer cells with immune cells and the stroma. Liquid Biopsy: A Revolution in Non-Invasive Cancer Monitoring What is a Liquid Biopsy? A liquid biopsy is a technique for analyzing cancer-related molecules from bodily fluids like blood, saliva, or urine. It offers a non-invasive alternative to tissue biopsies and allows for repeated sampling to track disease over time. Key Biomarkers Analyzed 1. Cell-Free DNA (cfDNA): cfDNA is released into the bloodstream by both normal and cancer cells during processes like apoptosis (programmed cell death) or necrosis (uncontrolled cell death). cfDNA is short (~150-200 base pairs) due to fragmentation. 2. Circulating Tumor DNA (ctDNA): A subset of cfDNA derived specifically from tumor cells. Contains tumor-specific mutations, amplifications, or methylation patterns. 3. Circulating Tumor Cells (CTCs): Intact cancer cells shed by the tumor into the bloodstream. Provide information about tumor characteristics and metastatic potential. 4. Exosomes: Small vesicles secreted by cells that carry DNA, RNA, and proteins. Tumor-derived exosomes can reveal resistance mechanisms or metastatic activity. 5. Tumor-Educated Platelets (TEPs): Platelets modified by cancer to carry tumor-specific RNA. Changes in platelet profiles can indicate the presence and progression of cancer. Liquid Biopsy Protocol 1. Blood Collection and Plasma Isolation: Blood is drawn into EDTA tubes, which prevent clotting and minimize contamination. Samples are processed within 2 hours to prevent white blood cell lysis, which could release genomic DNA and dilute cfDNA. 2. Centrifugation: Blood is centrifuged at 1,000g for 10 minutes to separate plasma from cellular components. Plasma is re-centrifuged at 2,000g to remove residual debris. 3. cfDNA Extraction: The plasma is processed using a silica-column-based kit: Lysis buffer breaks open cells and protects DNA. Ethanol precipitates DNA, which binds to silica in the column. Wash steps with Buffer AW1 and AW2 remove contaminants. DNA is eluted in a buffer for downstream analysis. 4. Quantification and Analysis: qPCR or sequencing methods are used to detect mutations (e.g., HER2 amplifications, ESR1 mutations) and monitor treatment responses. Applications of Liquid Biopsy 1. Early Detection: ctDNA mutations can reveal cancer before clinical symptoms appear. 2. Therapy Monitoring: Tracks treatment efficacy by monitoring levels of ctDNA or CTCs. 3. Predicting Resistance: Identifies mutations like ESR1 (endocrine resistance) or HER2 amplifications, guiding therapy adjustments. What is Proteomics? Proteomics is the large-scale study of proteins, including their structure, function, and interactions. In cancer research, proteomics identifies biomarkers for diagnosis, monitors disease progression, and helps discover therapeutic targets. Key Steps in Proteomics 1. Sample Collection and Preparation: Bodily fluids like blood, urine, or saliva are preferred for non-invasive biomarker discovery. Tissue samples may also be used for in-depth analysis. 2. Protein Isolation: Proteins are extracted and digested into peptides using enzymes like trypsin. 3. Mass Spectrometry (MS): Peptides are analyzed using liquid chromatography-mass spectrometry (LC-MS). MS separates peptides based on mass and charge, identifying their sequences. 4. Data Analysis: Quantifies proteins and compares their expression between cancer and normal samples. Statistical tools correct for batch effects and normalize data. Applications of Proteomics 1. Biomarker Discovery: Proteins like CA19-9 for pancreatic cancer or HER2 for breast cancer can guide early diagnosis. 2. Therapeutic Targets: Drugs targeting proteins in key pathways, like HER2 inhibitors or beta-catenin inhibitors, are developed using proteomic insights. 3. Understanding Drug Resistance: Proteomics identifies resistance mechanisms, such as alternative signaling pathways that cancer uses to bypass treatment. Challenges in Proteomics 1. Pre-Analytical Variability: Differences in sample handling, storage, or preparation can introduce errors. Standardized protocols are essential to ensure reproducibility. 2. Batch Effects: Variability between sample processing batches can skew results. Internal controls help correct for this. 3. Complexity of Cancer Biology: Proteins undergo post-translational modifications (PTMs) like phosphorylation or glycosylation, adding layers of complexity to analysis. Conclusion Histology, liquid biopsy, and proteomics are revolutionizing cancer research and treatment. By combining detailed tissue analysis, non-invasive biomarker detection, and protein-level insights, researchers are paving the way for personalized medicine, where treatments are tailored to the molecular profile of each patient’s cancer. Together, these tools are unlocking new frontiers in understanding, diagnosing, and fighting cancer.