Introduction to Computational Cancer Genomic PDF

INTRODUCTION TO COMPUTATIONAL CANCER GENOMIC Seminario 1 Dr Fall 02/12/2024 Genomic Architecture of Diseases and Cancer Genomics Personalized/Precision Medicine Discover genetic mechanisms and risk factors for diseas...

INTRODUCTION TO COMPUTATIONAL CANCER GENOMIC Seminario 1 Dr Fall 02/12/2024 Genomic Architecture of Diseases and Cancer Genomics Personalized/Precision Medicine Discover genetic mechanisms and risk factors for diseases (e.g., inherited conditions, cancer, autoimmune disorders). Provide DNA-based predictions of individual susceptibility to future diseases. Use precision diagnostics to accurately detect and classify existing conditions. Tailor treatments and prevention strategies to individual genetic profiles. Enhance drug efficacy and specificity through genetic insights. Genetic Penetrance Definition: The proportion of individuals with a specific genetic variant (genotype) who exhibit the associated trait or condition (phenotype). - High penetrance: The genetic variant almost always leads to the condition. - Low penetrance: Other factors (e.g., environment, additional genes) play a larger role in condition manifestation. Examples of Highly Penetrant Conditions: - Sickle Cell Disease: Caused by mutations in the HBB gene. - Color Blindness: Often due to mutations in X-linked genes. - Cystic Fibrosis: Recessive condition caused by mutations in the CFTR gene. Challenges for Risk Prediction - Low impact of individual variants: o Most common complex traits have small effect sizes. o Odds Ratios (OR) indicate limited predictive power for individual-level risks. - Difficulty with complex trait prediction: o Combining multiple variants for risk prediction remains challenging. o Genetic tick score are still not fully effective for many disease - Missing heritability: o Large portions of heritability remain unexplained by known genetic factors How Are Genetic Variants Discovered? - Genome-Wide Association Studies (GWAS): o Compares genetic variants across large populations to identify statistical associations with traits. o Requires thousands to millions of participants to detect small effect sizes. o Results visualized as a Manhattan plot, showing the strength of associations across the genome. - Family-Based Studies and Linkage Analysis: o Focuses on identifying genetic variants shared among affected family members. 179  o Effective for discovering rare, high-penetrance mutations associated with Mendelian diseases. o Relies on mapping genetic markers inherited along with the disease trait in families. Frequency and Effect Size - Rare variants tend to have larger effects: o Highly penetrant mutations (e.g., cystic fibrosis): rare but strong impact (large odds ratios). - Common variants usually have smaller effects: o Genetic variants identified in GWAS studies (e.g., type 2 diabetes): frequent but modest odds ratios. - Continuum from rare, high-impact variants to common, low-impact variants shaped by evolutionary forces: o Rare, high-impact mutations under strong negative selection. o Some common variants (e.g., associated with type 2 diabetes) may have conferred an advantage in the past (e.g., fat storage during scarce food periods). GWAS Limitations - Small effect sizes: o Most variants explain little of the disease risk. o Missing heritability problem persists. - Focus on common variants: o Rare variants with larger effects are often missed. - Gene-gene and gene-environment interactions neglected. - Limited functional insights: o Causal genes/mechanisms unclear. - Overrepresentation of European populations: o Findings are not generalizable to diverse groups. - Large investments with limited return. 180 Cancer - Cancer is a complex disease: o The field is moving from cancer cells to understanding the cancer ecosystem. - Cancer genomics: o Shifts away from “driver mutations” to the molecular makeup of the tumor ecosystem in its entirety. o Strongly driven by technology, both wet lab and computational. Cancer is a Disease of the Genome - Somatic Mutations: o Cancer arises from acquired mutations in cells during a person’s lifetime. o These mutations drive uncontrolled cell growth and survival. o Not heritable: not passed from parents to offspring. o Unlike genetic diseases, occur in somatic cells, not germline cells. - Cancer Susceptibility Genes: o Some hereditary mutations (e.g., BRCA1, BRCA2) increase the risk of cancer. o These predispositions interact with somatic mutations to initiate cancer. - Focus of Cancer Genomics: o Understanding somatic mutations and their role in tumor biology. o Identifying actionable targets for therapy and precision medicine. Genetics of Cancer - Tumor Suppressor Genes o Protect cells from uncontrolled growth. Function is lost when both alleles are inactivated (recessive loss). o Examples: RB1, BRCA1. - Oncogenes o Promote cell growth and division, become cancerous when mutated or overactivated (dominant gain). o Examples: KRAS, EGFR. - Knudson’s Two-Hit Hypothesis 181  o Explains how tumor suppressor genes are inactivated in cancer. o First “hit”: Inherited or acquired mutation in one allele. o Second “hit”: Mutation or loss of the second allele in somatic cells. o Often seen in hereditary cancers (e.g., retinoblastoma). Cancer Cells Accumulate Mutations Over Time  Mutation burden varies by cancer type, exposure, age of onset, and DNA repair ability. Cancers Are a Mix of Subclones That Can Respond Differently to Therapy Identify Genomic Alterations: Variant Calling Algorithms General DNA Sequencing Workflow 1. Genomic DNA or RNA 2. Library of DNA fragments 3. Sequencing device 4. Computational analysis 182 Basecalling  Prediction of the DNA sequence from the images.  Output format: FASTQ. Reference Mapping - Why do we map reads to the reference? o By comparing the reads from a sequenced individual to a reference genome, we can identify variants. o This involves identifying where in the reference genome a read might have come from. - Output format: FASTA. Reference Genome - Maintained by the Genome Reference Consortium (GRC). - 70% derived from a single male from Buffalo, NY. - Current version: GRCh38 (2013). - Europeans differ from the reference in ~3–4 million sites. - Latest version (hg38) includes alternative configurations (ALT) for highly polymorphic regions, e.g., HLA genes on chromosome 6. Reference Mapping Challenges - Needs to be accurate: o Misaligned reads are a source of false positive variant calls. - The reads contain sequencing errors: o Must account for differences between the individual and reference. - The genome is very large and repetitive. - Mapping needs to be fast What Are Paired Reads?  Paired-end sequencing generates two reads from opposite ends of a DNA fragment.  Used to improve accuracy and facilitate alignment in complex genomes. 183 What Is a Mutation? - In NGS, a mutation is a position where we detect the presence of a non-reference allele. - Always relative to the reference genome: o Not necessarily unusual; sometimes the reference allele is extremely rare. o Not necessarily matching the ethnicity of your samples. Types of variants Germline Variants - For European individuals relative to the reference genome: o 3–4M single-nucleotide variants:  1.2M homozygous variants. o 500k short insertions and deletions. o 22k coding variants:  10k non-synonymous.  200 loss-of-function variants. o ~100s of copy number variants (totaling 5Mb). o ~1000s of structural variations. Germline vs Somatic Variants - Germline Variants: o Common: ~1/kb = 1000/Mb compared to the reference genome. o "De novo" germline mutations (from a parent’s germ cells) are very rare: ~100/genome. 184  - Somatic Variants: Rare: ~1/Mb. o Exome (~50Mb):  50 somatic mutations.  50,000 germline mutations. o Genome (~3Gb):  3,000 somatic mutations.  3,000,000 germline mutations. Key Note: Germline mutations are also present when sequencing tumor DNA. Cancer Genomes Have Specific Properties That Require Specialized Analytical Strategies - Tumor/Normal Admixture: o Tumor DNA is often contaminated with DNA from non-malignant cells, o diluting biological signals. - Intra-Tumoral Heterogeneity: o Cancer is often a mosaic of genetically distinct cellular populations. - Genomic Instability: o Copy number changes, loss of heterozygosity, and genomic rearrangements distort expected allelic distributions. - Expected Variant Allelic Fraction (VAF): o Differences in VAF caused by somatic variants versus sequencing errors. Tumor-Normal pair - Data are generated from a pair of DNA samples from the same patient: tumour and normal (preferably blood). - Most common study design, routinely used. Works really well for AF >10%. - Allows classification of variants in somatic/germline status. - Requires matched tumor/normal pairs: higher sequencing cost. 185 Statistical issue - When sequencing a tumor, 1/1000 variant found is actually somatic - Need very high sensitivity to detect variants from matched germline DNA: o 99.9% sensitivity → 1 missed /Mb → 2 somatic detected /Mb → False Discovery Rate = 50% Mutation detection sensitivity is dictated by sequencing depth and is limited by sequencer & sequencing error 186 Read pair orientation and structural variants (SV) Expected orientation:  One read on the forward strand, one read on the reverse strand Fragment size distribution - Fragment/insert size is determined by library preparation - Pairs that match the expected orientation and distance are called concordant - Discordant read pairs give evidence of structural variation Structural variant summary VCF Format for Variant Calling - Consists of Metadata and a precise definition of the variant in tab-delimited fields o Precise definitions: https://samtools.github.io/hts-specs/ VCF File Format - Not a very human-readable format, but flexible and many tools exist to manage/convert them: o BCFtools: https://samtools.github.io/bcftools/ o R VariantAnnotation package: bioconductor.org/packages/VariantAnnotation - Generally, a final annotation step also converts them into a TAB-delimited text file (e.g., ANNOVAR) 187 NON CODING RNA IN CANCER Lezione 16 Groupwork What is a Non-Coding RNA? Overview of Human Genome Transcription 75% of the human genome is transcribed into RNA. Only 3% of the genome is transcribed into protein-coding mRNAs. Non-coding RNAs (ncRNAs) are RNA molecules that do not code for proteins. Non-coding RNAs are categorized based on their: Length: Long ncRNAs (lncRNAs) vs. short ncRNAs (e.g., miRNAs, siRNAs). Shape: Linear or circular. Location: Cytoplasmic, nuclear, or mitochondrial. MAJOR TYPES OF NCRNA S IN CANCER MicroRNA (miRNA) Size: Small RNA, approximately 22 nucleotides (nt) in length. Function: Binds to complementary sequences in mRNAs, forming the RNA-induced silencing complex (RISC), leading to mRNA degradation. PIWI-Interacting RNA (piRNA) Length: 24–30 nt. Location: Found primarily in germline cells. Function: Binds to PIWI proteins and regulates chromatin via epigenetic mechanisms. Long Non-Coding RNAs (lncRNAs) and Circular RNAs (circRNAs) Length: Greater than 200 nt. Structure: o lncRNAs: Linear in shape. o circRNAs: Circular or ring-like. Origin: Transcribed from exons, introns, intergenic regions, or untranslated regions. Function: Fold into complex secondary structures, enabling interactions with DNA, RNA, and proteins. ROLES OF NCRNA S Regulatory Roles in Cancer Gene Expression Control: ncRNAs regulate gene expression at transcriptional, post- transcriptional, and epigenetic levels. Chromatin Remodeling: ncRNAs interact with chromatin remodelers to influence cancer progression. Key Roles of ncRNAs in the Hallmarks of Cancer Proliferation: miRNAs like miR-21 promote uncontrolled cell growth. Apoptosis Evasion: lncRNAs like MALAT1 suppress apoptosis, supporting cancer cell survival. 188  Metastasis: ncRNAs like HOTAIR modulate epithelial-mesenchymal transition (EMT) and facilitate cell migration. Angiogenesis: ncRNAs regulate angiogenic factors such as VEGF, driving tumor vascularization. ncRNAs as Biomarkers Non-Invasive Detection: ncRNAs can be detected in blood, urine, and other body fluids. Cancer-Specific Patterns: o Example: miR-141 serves as a marker for prostate cancer. ncRNAs and Therapy Resistance Resistance Mechanisms Modulation of Drug Resistance: ncRNAs can influence resistance mechanisms by modulating: Drug efflux pumps, DNA repair mechanisms, Apoptosis evasion, Immune evasion Overcoming Resistance o Designing Strategies: Targeting the pathways affected by ncRNAs to overcome resistance. Therapeutic Potential of ncRNAs o Intervention Targets: Suppress oncogenic ncRNAs or restore tumor-suppressive ncRNAs to inhibit cancer progression. o Therapeutic Molecules: ncRNAs themselves regulate multiple target genes and can serve as therapeutic agents. o Drug Delivery: ncRNAs can be encapsulated in nanoparticles or exosomes for targeted delivery in cancer therapy. Evolutionary and Personalized Medicine Perspectives Evolutionary Insight Highly conserved ncRNAs provide insight into robust regulatory mechanisms that are essential across species. Personalized Medicine o Tailored Therapies: Treatment strategies can be customized based on ncRNA expression profiles to improve therapeutic outcomes. o Predictive Analytics: ncRNA expression can be used to predict therapy response and assess the risk of metastasis, enabling more accurate and personalized treatment plans. Non-coding RNA types and their function MICRORNA S (MIRNAS) Regulators of Gene Expression miRNAs regulate gene expression by binding to the 3' untranslated regions (UTRs) of messenger RNAs (mRNAs). This interaction leads to either the degradation of the mRNA or the inhibition of its translation, thus controlling protein production OncomiRs Some miRNAs, such as miR-21, act as oncomiRs by promoting cancer. They do this by downregulating tumor suppressor genes, aiding cancer cell growth and survival. Tumor-Suppressor miRNAsOther miRNAs, like let-7, function as tumor suppressors by inhibiting oncogenes, thereby suppressing cancer development. Aberrant miRNA Expression Abnormal miRNA expression plays a significant role in cancer initiation, progression, and metastasis, contributing to the malignancy of tumors. 189 LONG N ONCODING RNAS ( LNCRNAS ) lncRNAs are a class of RNA molecules typically greater than 200 nucleotides in length and do not encode proteins. They can also be transcribed from introns and are involved in various biological processes, including cancer development and progression. Epigenetic Regulation: lncRNAs can interact with chromatin-modifying complexes to influence gene expression. For example: o HOTAIR: Promotes metastasis by reprogramming chromatin, contributing to cancer progression. Scaffolding: lncRNAs act as scaffolds, bringing together proteins and other molecules to regulate transcriptional activity. Sponging Activity: lncRNAs can bind and sequester miRNAs, preventing them from interacting with their target mRNAs, thus regulating gene expression indirectly. PIWI-INTERACTING RNA S (PIRNA S) Transposon Silencing: piRNAs play a crucial role in suppressing transposable elements, helping maintain genomic integrity by preventing the movement of these elements that could disrupt the genome. Epigenetic Changes: Aberrant piRNA pathways are associated with cancer-related epigenetic modifications, influencing gene expression and potentially contributing to tumorigenesis. 190 piRNA Biogenesis Transcription: Long single-stranded precursors are transcribed from piRNA clusters. Export: The precursor piRNAs are transported from the nucleus to the cytoplasm. Primary Processing: Cleavage by the enzyme Zucchini generates intermediate fragments, which are then loaded onto PIWI proteins. 3′ End Trimming: Exonucleases trim the piRNAs to their mature size (24–31 nt). 3′ End Methylation: The enzyme HEN1 adds a 2′-O- methylation to the 3′ end of piRNAs, enhancing their stability. Ping-Pong Amplification: A secondary amplification cycle increases piRNA levels, characterized by a 10-nt overlap between primary and secondary piRNAs. Piwi Proteins  PIWI proteins are a specialized subgroup of the Argonaute protein family that specifically interact with piRNAs (piwi-interacting RNAs). They are essential for several biological processes, including: Germline development, Genome integrity Silencing of transposable elements  PIWIL2 (MILI) and PIWIL4 (MIWI2) are two crucial members of the PIWI protein family. These proteins are vital for the piRNA pathway, particularly in the silencing of transposons and supporting germline development. 191 NGS and its role in identification of ncRNAs Next-generation sequencing (NGS) is a technology for determining the sequence of DNA or RNA to study genetic variation associated with diseases or other biological phenomena RNA-Seq A high-throughput technique used to measure RNA expression levels, providing comprehensive insights into transcriptomes. Steps Involved: 1. Sample Preparation: a. Extract RNA from both normal and cancerous tissues. b. Enrich specific RNA types, such as miRNAs and lncRNAs. 2. Library Preparation: a. Prepare libraries tailored to target specific RNA types and sizes for sequencing. 3. Sequencing: a. Platforms: i. Illumina: Commonly used for sequencing miRNAs. ii. Oxford Nanopore: Preferred for sequencing lncRNAs. 4. Bioinformatics Analysis: a. Quality Control: Use tools like FastQC to assess the quality of the FastQ files. b. Alignment: Map sequences to a reference genome. c. Annotation: Utilize databases such as Ensembl to annotate the sequences. Novel Discovery: Identify new RNA species or previously uncharacterized transcripts. Differential Expression Analysis Objective: Compare RNA expression in cancerous vs. normal cells. Tools: We can use different bioinformatic tools that one of the most usable tools is DESeq2 to identify differentially expressed ncRNAs. Outcome: Discover ncRNAs associated with cancer phenotypes. Functional characterization: Objective: Find out the contribution of this identified ncRNAs in cancer biology. WNT /Β-CATENIN P ATHWAY A critical signaling pathway in embryonic development, tissue regeneration, and cancer Key Players Wnt Ligands: Secreted glycoproteins that initiate the pathway. Frizzled Receptors: Bind Wnt ligands at the cell surface. β-Catenin: Central effector that regulates gene transcription. Active Pathway Wnt binds to Frizzled and co-receptor LRP5/6, stabilizing β-catenin. β-Catenin accumulates in the cytoplasm and translocates to the nucleus. It activates target genes by interacting with TCF/LEF transcription factors. Inactive Pathway Without Wnt, β-catenin is degraded by the destruction complex (AXIN, APC, GSK-3β). Role of ncRNAs in Wnt/β-Catenin Pathway Regulation 192  ncRNAs: Regulate Wnt signaling, either promoting or inhibiting pathway activity. Types: o miRNAs: Directly target Wnt components, regulating their expression and activity. o lncRNAs: Modulate chromatin and transcription factors, influencing Wnt pathway gene expression. o circRNAs: Act as miRNA sponges, sequestering miRNAs and affecting their regulatory roles in Wnt signaling. MICRORNAS (MIRNAS) Activating Wnt/β-Catenin miRNAs downregulate inhibitors, thereby enhancing Wnt signaling. Example: miR-17-92: Suppresses DKK1 and SFRP1 in colorectal cancer. Inhibiting Wnt/β-Catenin miRNAs target activators to reduce pathway activity. Example: miR-200a: Suppresses β-catenin, reducing metastasis in breast cancer. LONG NONCODING RNAS (LNCRNAS) Activating Wnt Signaling Interact with chromatin or transcription factors to enhance Wnt activity. Example: LncRNA HOTAIR: Silences Wnt inhibitors, driving metastasis. Inhibiting Wnt Signaling Stabilize Wnt inhibitors to reduce pathway activity. Example: LncRNA APCDD1L-AS1: Stabilizes APCDD1L, lowering β-catenin signaling in gastric cancer. CIRCULAR RNAS (CIRCRNAS) Promoting Wnt Signaling Act as sponges for miRNAs that inhibit Wnt components. Example: CircRNA CDR1as: Sponges miR-7, enhancing β-catenin activity. Inhibiting Wnt Signaling Interfere with Wnt ligand binding or activator recruitment. Example: CircRNA CircITCH: Suppresses Dishevelled (Dvl) interaction, blocking the pathway. WNT PATHWAY AND CANCER VIA NCRNAS Tumor InitiationDysregulated ncRNAs activate aberrant Wnt signaling. o Example: miR-19b overexpression drives Wnt in hepatocellular carcinoma. 193  Cancer Stem Cell Maintenance: Wnt signaling maintains stemness in cancer cells. o Example: LncRNA LINC00958 enhances Wnt signaling in glioblastoma. Metastasis: ncRNAs promote Epithelial-to-Mesenchymal Transition (EMT) via Wnt. o Example: miR-21 facilitates β-catenin nuclear translocation in colon cancer. P53 PATHWAY  p53 o A transcription factor activated by cellular stress (e.g., hypoxia, DNA damage). o Mutated in 50% of all cancers. o Activates downstream genes involved in DNA repair or apoptosis.  Regulation by MDM2 o MDM2 is a key negative regulator of p53. o Binds to p53’s N-terminal transactivation domain, inhibiting transcriptional activation of apoptosis and cell cycle arrest genes. o Activates downstream genes involved in DNA repair or apoptosis.  Apoptosis Transactivation o Intrinsic Apoptosis Activates pro-apoptotic factors (Noxa, Bax, Puma) to degrade anti- apoptotic Bcl-2 proteins. o Extrinsic Apoptosis Upregulates death receptors (FAS, PDD1) for external apoptosis signaling.  Cell Cycle Arrest o Activates Gadd45 to halt the cell cycle. o Transcribes p21, an inhibitor of CDK complexes and cMYC, blocking cell proliferation. P53 PATHWAY AND NCRNAS IN CANCER 1. MicroRNAs (miRNAs) MiRNAs regulate the p53 pathway at multiple levels, influencing its upstream regulators, p53 itself, and its downstream targets.  Regulation of p53 Expression o miR-125b: A negative regulator of p53; its overexpression reduces p53 levels, promoting cancer cell survival. o miR-504: Directly targets p53 mRNA, suppressing its expression in cancers like glioblastoma.  Regulation of p53 Regulators o miR-605: Activates p53 by suppressing MDM2. o miR-192/215: Enhance p53 activity by targeting MDM2.  Regulation of p53 Targets o miR-34 Family: A direct transcriptional target of p53. o Targets cell cycle and apoptosis regulators (e.g., CDK4, SIRT1, and BCL2). o miR-34a: Represses Sirtuin 1 (SIRT1), enhancing p53 activity through increased acetylation. This drives apoptosis via a positive feedback loop. o c-MYC: Regulated by the miR-34b seed sequence, which binds to the c-MYC 3′-UTR. 2. LncRNAs interact with p53, its cofactors, or downstream effectors to modulate the pathway  Positive Regulation of p53 Activity o LncRNA MEG3: Enhances p53 stability and transcriptional activity by suppressing MDM2 expression. o LincRNA-p21: A direct transcriptional target of p53, it mediates transcriptional repression of genes involved in cell proliferation. 194   Negative Regulation of p53 Activity o LncRNA MALAT1: Suppresses p53-mediated apoptosis and promotes cancer progression in lung and breast cancers. 3. Circular RNAs (circRNAs)  CircRNAs influence the p53 pathway by acting as sponges for miRNAs or interacting with proteins.  Circ-FOXO3 o Acts as a tumor suppressor by interacting with p53 and other proteins involved in cell cycle control. o Binds to MDM2. ncRNAs and hallmarks of cancer 1. Proliferation o EPigenetically Induced lncRNA 1 (EPIC1) and MYC activation  Frequently hypomethylated in various cancers, leading to its overexpression. o EPIC1 and MYC activation  MYC oncogenic pathway  MYC-MAX complex   EPIC1 binds to MYC and enhances the MYC-MAX binding to DNA 2. Non mutational epigenetic reprogramming and EMT : o HOTAIR lncRNA  Located at the HOXC locus of the genome.  Involved in regulating chromatin dynamics. o Mechanism of action in metastasis by epigenetic regulation:  Interacts with PRC2 and LSD1, which are involved in gene silencing.  Represses transcription of key tumor suppressor genes (e.g., HOXA5, p21). o HOTAIR  Significant role in colorectal cancer progression.  Induces epithelial-to-mesenchymal transition (EMT) in cells. 195   Mechanism of action: Suppresses the expression of HNF4α by recruiting SNAIL, leading to EMT. o E-cadherins levels lowered  SLUG and SNAIL  Expression depending on the tissue 3. Cell Metabolism: o Circ-ENO1 in lung adenocarcinoma:  Circular RNA leading to tumor propagation via the glycolysis pathway.  ENO1 (Enolase 1) is a metabolic enzyme involved in glycolysis.  Upregulation of ENO1 is correlated with cancer progression through the Warburg effect, characterized by increased glycolysis even in the presence of oxygen. - Rapid Proliferation: - Accumulation of lactate in the tumor environment. - Tumor survival under hypoxic conditions. o Circ-ENO1 mechanism: Functions as a sponge for miR-22-3p, which inhibits the expression of ENO1 (a tumor suppressor). o Silencing circ-ENO1 downregulates the glycolysis pathway. 4. Immune Evasion: o miR-34a in immune evasion: Acts on key elements involved in immune evasion, including PD-L1 and SIRT1. o Immune checkpoint upregulation:  PD-L1 and CTLA-4 are upregulated, inhibiting CD8 cytotoxic T cell activity.  Reduced antigen presentation.  Prevents recognition by CD8 T cells.  Secretion of immunosuppressive cytokines such as IL-10 and TGF-β. o Tregs Functions:  Tregs (Regulatory T cells) maintain immune tolerance and prevent autoimmune diseases.  They avoid excessive immune responses. 196   Inhibit activation and proliferation of immune cells.  In cancer, Tregs help tumor cells escape recognition by the immune system. - miR-34a and Treg cell recruitment: - miR-34a regulates Treg recruitment by modulating CCL22 expression. - Inhibition of miR-34a increases Treg migratory activity. - Decreased miR-34a levels lead to increased Treg recruitment. 5. Metastasis: o NKILA (NF-KappaB Interacting LncRNA):  Critical in regulating NF-κB signaling.  Acts as a suppressor of NF-κB activation in cancer. o NF-κB in Cancer:  NF-κB promotes the expression of matrix metalloproteinases (MMPs):  MMP-2 and MMP-9 degrade the extracellular matrix. o NF-κB regulates genes involved in actin cytoskeleton reorganization, enhancing cell motility. o Alters integrins to promote migration. o NKILA Mechanism of Action:  NKILA binds to the NF-κB/IκB complex, blocking IκB phosphorylation by masking IKK phosphorylation sites.  Prevents NF-κB activation, inhibiting breast cancer metastasis. o Downregulation in Cancer:  NKILA is downregulated in invasive cancer, leading to overexpression of NF-κB.  In invasive breast cancer cells, NKILA expression is significantly reduced due to miR- 103/107-mediated degradation. 6. Angiogenesis: o miR-29c in Lung Cancer: o VEGFA (Vascular Endothelial Growth Factor A):  The most potent proangiogenic factor in the VEGF family.  VEGFA overexpression is common in various tumors. o Secreted in response to hypoxia. Binding to receptors VEGFR-1 and VEGFR-2 triggers the formation of new blood vessels. o miR-29c Function:  Acts as a tumor suppressor in lung carcinoma.  Targeting VEGFA: miR-29c binds directly to the 3’-UTR (untranslated region) of VEGFA mRNA, suppressing its expression.  miR-29c inhibits VEGFA-mediated angiogenesis. 197 198 CANCER EPIGENOMICS: FROM MECHANISM TO THERAPY Lezione 19 6/12/2024 EPIGENETICS AND CANCER Gene expression is deregulated in cancer cells  Activation of oncogenes  Inactivation of tumour suppressor genes And disrupts key cellular pathways:  Constitutive growth signals  Insensitivity to anti-growth signals  Evasion of cell death (apoptosis)  Immortalization  Impaired DNA-repair capacity  Increased genomic instability  Tissue invasion and metastasis Epigenetic alterations involving DNA methylation can lead to cancer by various mechanisms Three major routes have been identified by which CpG methylation can contribute to the oncogenic phenotype: 1. General hypomethylation of the cancer genome. 2. Focal hypermethylation at TSG promoters. 3. Direct mutagenesis of 5mC-containing sequences by deamination, UV irradiation, or exposure to other carcinogens. It is significant that all three of these alterations generally occur simultaneously to contribute to cancer, suggesting that altered homeostasis of epigenetic mechanisms is central to the evolution of human cancer. Chromatin structural changes in cancer cells 199 Alteration of DNA methylation in cancer Key epigentic regulators of DNA methylation 200 EXAMPLE 1: DNA METHYLATION ALTERATIONS IN COLORECTAL CANCER (CRC) DNA methylation alterations in colorectal cancer (BOTH GENETIC AND EPIGENETIC EVENTS) Global hypomethylation  Adenomas  Carcinomas  First step in CRC development  Associated with genomic instability and proto-oncogenes’ activation CIMP: CPG ISLANDS METHYLATOR PHENOTYPE CIMP tumors are classified into two groups:  CIMP-low (CIMP-L)  CIMP-high (CIMP-H) About 20% of CRCs are CIMP- H associated significantly with:  Older age  Mostly female genders  Proximal colon  Poor differentiation  MSI (microsatellite instability)  KRAS and BRAF mutations EXAMPLE 2: TET-TDG -MEDIATED ACTIVE DNA DEMETHYLATION AND CANCER Active DNA demethylation pathway mediated by TET-TDG  5fC & 5caC o 5fC and 5caC are present at very low levels in mammalian genomes. o 5fC and 5caC localize on CGI promoters, gene bodies, and enhancers.  5hmC o Accounts for 1–10% of 5mC. o 5hmC levels vary among cell types and tissues. o 5hmC localizes on CGI promoters, gene bodies, and enhancers. Ten-Eleven Translocation (TET) genes and cancer o Loss-of-function mutations in TET genes, predominantly TET2, are frequent in hematological cancers: o Associated with impaired 5mC oxidation and decreased genomic 5hmC levels. o Low incidence of TET mutations in solid tumors. 201  o Solid tumors often show markedly reduced 5hmC levels, suggesting loss of TET activity. o New findings reveal roles of deregulated TET functions through various mechanisms in solid malignancies. Mechanism underlying deregulation of TET function in cancer TET1 as a tumor suppressor in CRC o TET1 and 5hmC are strongly reduced in primary colon cancers with respect to the surrounding healthy tissues. o TET1 expression in human colon tissues and in normal epithelial colon cells (CCD), which were positive for 5hmC modification but were almost undetectable in all the analyzed colon cancer cell lines. o TET1 re-expression in TET silenced cell induced a full recovery of the level of 5hmC as well as a reduction of the cell proliferation rate. o TET1 expression blocked the growth of the tumours in murine models o WNT pathway inhibitors DKKs and SFRPs are epigenetically inactivated by DNA methylation in colorectal cancer cells, and this event is able to sustain tumour development. o TET1 targeting the DKK and SFRP genes induced a reduction of the 5mC and an increase of the 5hmC marks on their promoter regions leading to the expression of these genes. o Loss of TET1 promotes DNA methylation of DKK and SFRP promoters leading to a full repression of the gene. 202 TET1 as an oncogene in lung cancer o TET1 gene expression is elevated in human lung tumor samples and NSCLC-derived cell lines o TET1 down-regulation induces a reduction of the cell proliferation rate in lung cancer cells o TET1 elevated expression confers oncogenic properties to lung cancer cells by contributing to sustained growth. o TET1 downregulation induces senescence in lung cancer cells o The mechanism of TET1 overexpression identifies a new oncosuppressive function for the p53 gene through its repression of TET1 transcription via direct binding to the proximal promoter that is disrupted by p53 mutation. o TET1 effects on lung cancer cell fate are mediated by regulation of genes that prevent genomic instability–associated cellular senescence in the context of mutant p53. o Simultaneous TET1 depletion in conjunction with cisplatin (CDDP) or doxorubicin (Dox) treatment have additive or synergistic effects on cell growth inhibition. TET2 as a tumor suppressor melanoma o Loss of 5-hmC is an epigenetic hallmark of melanoma, with diagnostic/prognostic value o Genome-wide mapping reveals a demolished 5-hmC landscape in human melanoma epigenome o Downregulating IDH2 and TETs suggests a mechanism underlying 5-hmC loss in melanoma o TET2 and IDH2 set the 5-hmC landscape, suppress melanoma growth, and increase survival TET1-2-3 as a oncogenes in gastric cancer o TET1-2-3 (TETs) transcripts are upregulated in primary gastric cancer specimens and associated with poor clinical outcomes of gastric cancer patients. o Knockdown of TETs inhibits gastric cancer cell proliferation and growth in vivo and in vitro. o TETs RNA functioned as a ceRNA to sequester the miR26 family, leading to EZH2 overexpression in gastric cancer. Competing endogenous RNAs (abbreviated ceRNAs) regulate other RNA transcripts by competing for shared microRNAs. EXAMPLE 3: ALTERED HISTONE MODIFICATION PATTERNS IN CANCER Histone modifications aka post- translational modifications (PTMs) and cancer 203  o Recurrent mutation or trascritptional deregulation of histone modifier o Common overexpression of the gens involved in chromatin condensation o Lowered expression and requent inactivating mutation for genes involved in chromatin relaxation Myeloid/lymphoid or mixed-lineage leukemia 1 (MLL) and cancer  The MLL family of KMTs is implicated in many forms of cancer, either by loss of function or, in the case of MLL1, through dysregulation following translocation or rearrangement.  MLL1 is a histone methyltransferase.  The N terminus of MLL1 (also known as KMT2A) physically interacts with cofactors required for genomic targeting such as menin.  Rearrangement of the MLL1 gene in acute myelogenous leukaemia (AML) or acute lymphoblastic leukaemia (ALL).  More than 100 MLL1 fusion partners have been reported to date, with the most frequent being AF4, ENL, AF9, AF10, and ELL. EZH2 in cancer  Enhancer of zeste homolog 2 (EZH2) is a histone-lysine N-methyltransferase enzyme.  EZH2 is the functional enzymatic component of the Polycomb Repressive Complex 2 (PRC2), which is responsible for healthy embryonic development through the epigenetic maintenance of genes responsible for regulating development and differentiation.  EZH2 is responsible for the methylation activity of PRC2.  PRC2 primarily methylates histone H3 on lysine 27 (H3K27me3).  Mutation or over-expression of EZH2 has been linked to many forms of cancers. HISTONE ACETYLTRANSFERASES AND CANCER CREBBP and EP300  CBP and p300 belong to the p300-CBP coactivator family and are associated with more than 16,000 genes in humans. 204   CREB-binding protein (CREBBP or CBP) and histone acetyltransferase p300 (also known as p300 HAT and encoded by the EP300 gene) induce histone H3 lysine 27 acetylation (H3K27ac) at target gene promoters and enhancers, promoting transcription activation.  The p300-CBP coactivator binds to transcription factors such as BRD4, facilitating gene activation.  CBP can add acetyl groups to both transcription factors and histone lysines. In cancer  Loss or inactivation mutations of CREBBP or EP300 result in decreased H3K27ac at enhancers and reduced expression of genes involved in tumor suppression, cell differentiation, and/or antitumor immunity.  Alterations in CREBBP and EP300 account for approximately: 60–70% of follicular lymphomas (FLs). 25–30% of diffuse large B-cell lymphomas (DLBCLs). and represent a common and early event during the pathogenesis of these lymphomas.  Mutations in EP300 have also been reported in patients with solid cancers, such as glioma and melanoma.  Somatic mutations within EP300 or CREBBP predominantly include truncation and missense substitutions clustered within the HAT domain, impairing the acetylation process on chromatin. Oncohistones  Missense mutations in histone variants are named oncohistones  Most commonly H3.3 or H1 missense mutations alter key post-translational modification sites leading to a tumorigenic gene expression program driving specific malignancies in children or young adults  Oncohistones including H3K27M, H3K36M and H3G34V/R/W/L mutations are common in pediatric cancers  Many oncohistones are tumour type-specific, influencing histone modification patterns globally  The mechanisms underlying oncohistone-induced oncogenesis are complex. A simple model is that oncohistones influence histone methyltransferases in trans or in cis 205 Chromatin Remodeling and Cancer ATP-dependent chromatin-remodelling mechanisms Chromatin structure can be modified locally by chromatin-remodeling complexes, which transiently dislocate DNA/nucleosome interactions by utilizing the energy of ATP hydrolysis to reposition nucleosomes, modulating accessibility of specific genes to the transcriptional machinery. These nucleosome remodelling complexes all belong to the SWI2/SNF2 family. This family is divided into several subfamilies according to the degree of homology within the conserved ATPase domain and the presence of protein motifs in adjacent regions. The most common complexes are SNF2 and ISW. An additional group includes the hNURD complex. The SWI/SNF complex SWI/SNF complexes are recruited to cis-regulatory elements such as promoters and enhancers, where they contribute to the establishment and maintenance of chromatin accessibility at transcription factor (TF) binding sites The SWI/SNF complex has 3 main variants:  The ARID1A-containing BAF complex (BAF)  The Poly-bromo-associated BAF (PBAF) complex  The bromodomain-containing protein 9 (BRD9)-associated BAF complex, also known as the ncBAF or noncanonical BAF complex. EXAMPLE 4: ALTERED CHROMATIN REMODELLING IN CANCER Chromatin remodeling and cancer o Chromatin remodelers are large multi-subunit complexes containing:  An enzymatic ATPase  Core subunits  Accessory subunits o Chromatin remodelers load, slide, or eject nucleosomes o Chromatin remodeling complexes involved in cancer: SWI/SNF and cancer  Loss-of-function mutations in genes encoding SWI/SNF subunits are found in >20% of human cancers, with point mutations occurring about twice as often as deletions.  SWI/SNF genes are also amplified in many cancers.  hSWI/SNF subunits are deregulated in cancer 206 EXAMPLE 5: EPIGENETIC AND METABOLISM Mutations in IDH1/IDH2 in Acute Myeloid Leukemia (AML) and gliomas The genes for isocitrate dehydrogenase (IDH1 and IDH2) are frequently mutated in adult AML.IDH1 and IDH2 encode enzymes involved in citrate metabolism that convert isocitrate to α-ketoglutarate (αKG), which is required for the biological activity of diverse dioxygenases (including TET2). 207 EPIMUTATIONS IN CANCER There are four major types of epimutations affecting cancer:  DNA hypermethylation at promoters  Genome-wide DNA hypomethylation  Abnormal modification of histones and/or their recognition  Abnormal chromatin structures caused by mal-functional chromatin remodelers INTERPLAY BETWEEN GENOME AND EPIGENOME IN CANCER.  Changes in the genome can influence the epigenome and vice versa  This forms a network that produces genetically or epigenetically encoded variations in the phenotype that are subject to Darwinian selection for growth advantage and thus eventually achieving the hallmarks of cancer Epigenetic cancer therapies  Epigentic events in cancer are early and reversible  Strategy: reactivation of silenced genes Epigenetic therapy  Stringent definitionA treatment which targets a chromatin modifier or other factors, which leads to stable epigenetic changes and to an anti-tumor phenotype  Broad definitionAny treatment directed against a chromatin modifier, which leads to an anti- tumor phenotype 208 Epigenetic anticancer strategies  Epigenetic heterogeneity in tumors o Tumor cells exhibit varying epigenetic modifications, indicating cellular-level heterogeneity.  Reversibility of epigenetic alterations o Abnormal DNA methylation and acetylation patterns are key targets in cancer treatment due to their reversible nature.  Small-molecule inhibitors o Target chromatin- and histone-modifying enzymes. o Aim to reverse epigenetic changes and restore normal states. o Have shown success in clinical trials as cancer therapeutics. Early epigenetic therapies CLINICAL EPIGENTIC DRUGS (EPIDRUGS) DNA methyltransferases (DNTMs) inhibitors or DNA hypomethylating agents (HMAs)  Role of DNA methylationCancer cells accumulate aberrant DNA methylation and gene silencing.  Pharmacological inhibitors Developed to reverse these changes by inhibiting DNMTs.  The FDA-approved first-generation EpiDrugs targeting DNA methylation (DNMT inhibitors, DNMTi): o 5′-Azacytidine (5-Aza or Vidaza®) o 5-Aza-2-deoxycytidine (5-Aza-2dC or Decitabine or Dacogen®) 209   FDA-approved for myelodysplastic syndromes (MDS).  Block DNMT activity, leading to genome-wide DNA hypomethylation and re-expression of silenced tumor genes.  DNMTi are cytosine analogs that work by incorporating into newly synthesized DNA and irreversibly trapping DNMTs to DNA, resulting in global demethylation and DNA damage.  GSK3685032: A novel DNMTi, a non-covalent DNMTi with more durable DNA hypomethylation and lower toxicity than nucleoside analogs. Mechanism of DNMT1 inhibition  GSK3685032 o Competitive inhibitor, competing with the DNMT1 active-site loop for hemimethylated DNA. o Selective and reversible inhibition of DNMT1 activity.  Nucleoside analogs (AZA or DAC) o Cytidine analogs are incorporated into the new strand of replicating DNA and covalently trap DNMT1 to DNA. o This leads to DNA damage. Targeting TET activity in cancer The addition of low-dose Vitamin C to decitabine appeared to improve overall survival in elderly AML patients 210 HDAC INHIBITORS Different classes of HDACs Histone deacetylases (HDACs) are a family of enzymes grouped into four classes in humans based on their homology to yeast HDACs analogs HDAC INHIBITORS  Epigenetic dysregulation in cancer: o Abnormal acetylation modifications lead to the silencing of tumor suppressor genes. o HDAC inhibitors (HDACis) block HDAC deacetylase activity, restoring acetylation homeostasis.  Mechanism of action: o Increased gene transcription due to unrestricted HAT activity. o Triggers biological responses such as chromatin remodeling, tumor suppressor gene transcription, growth inhibition, and apoptosis. o Main mechanism: Activation of intrinsic apoptosis pathways.  Selective targeting: o HDACis selectively target tumor cells, showing effectiveness in preclinical studies. o Several HDACis have been approved for hematologic malignancies. HDAC inhibitors induce cancer cell cycle arrest, differentiation, and cell death, reduce angiogenesis, and modulate immune response.  SAHA (Vorinostat), a pan-HDAC inhibitor, was the first FDA-approved HDAC inhibitor and is clinically effective in the treatment of refractory primary cutaneous T-cell lymphoma.  Others are now available. KMT INHIBITORS  KMTs and KDMs: o KMTs: Lysine methyltransferases, enzymes adding methyl groups. o KDMs: Lysine demethylases, enzymes removing methyl groups. o Inhibitors targeting these enzymes show promise in treating cancer.  Key KMTs inhibitors: o ORY-1001 (LSD1/KDM1A inhibitor): Evaluated in blood disorders. 211  o Pinometostat (EPZ-5676): First-in-class DOT1L inhibitor for adult acute leukemia.  Clinical Implications: o KMT inhibitors hold potential for cancer diagnosis and treatment. o Ongoing studies and clinical trials demonstrate their effectiveness. Targeted epigenetic therapies  Mutation or over-expression of EZH2 has been linked to many forms of cancer.  EZH2 inhibits genes responsible for suppressing tumor development, and blocking EZH2 activity may slow tumor growth.  EZH2 has been targeted for inhibition because it is upregulated in multiple cancers.  GSK126 (EZH2 inhibitor): Inhibits growth of immune-deficient tumor cells. EZH2 is the functional enzymatic component of the Polycomb Repressive Complex 2 COMBINATION THERAPY STRATEGIES  Challenges in monotherapy: o Resistance: Single-agent treatments can lead to resistance. o Limited activity: Often insufficient to target all cancer cells effectively.  Combination therapy benefits: o Enhanced efficacy: Combining different epigenetic markers provides better results. o Examples:  Vorinostat and AZA: More effective in MDS and CMML than monotherapy.  DAC with KDM1A, EHMT2, or EZH2 inhibitors: Increases gene regulation while maintaining selectivity.  AZA and Entinostat: Improves antitumor activity in CRC mouse models. Co-targeting cancer with DNMTi and HDACi 212   Synergistic Effects: o DNMTi and HDACi: Synergistically activate tumor suppressor genes. o Examples:  DNMTi and HDACi in MM: Induce expression of tumor suppressor genes and inhibit oncogenes like MYC and IRF4.  DNMTis and Histone Methylation Inhibitors: Achieve synergistic effects targeting cancer-associated genes.  Epigenetic and Immunotherapy: o DNMTis: Sensitize tumors to immunotherapy.  Multi-target Strategies: o Dual DNMT and HDAC inhibitor, effective in breast cancer. o Dual LSD1/HDAC inhibitor, antiproliferative in melanoma and squamous cell carcinoma. o Targets HDAC and EZH2, effective in various cancers. Combination therapies: Emerging as a powerful approach to modern cancer treatment by targeting multiple pathways and improving therapeutic outcomes. Precision medicine integration  Tailored treatments: Based on individual patient characteristics to maximize therapeutic effects and minimize side effects.  Epigenetic and genetic testing: Essential for personalized medicine.  Multi-Omics and AI: Key in identifying epigenetic biomarkers for early screening, diagnosis, and personalized treatment.  Technological advances: o Identify complex epigenetic patterns in cancer cells.  Future Prospects: o Integration of Multi-Omics: Comprehensive insights into genomic and epigenomic abnormalities. o AI Applications: Improve precision medicine through advanced image analysis and genomic data interpretation. 213 Targeting epigenetic regulators to overcome drug resistance in cancers Case study: Resensitization of chemoresistant ovarian tumors to carboplatin by epigenetic drug therapy o DNMTIs or HDACIs, alone or in combination, can reverse platinum resistance in chemoresistant ovarian cancer. o Additive or synergistic effects of DNMTI and HDACI combinations on silenced gene re- expression have been demonstrated. o This chemosensitization is hypothesized to be due to the derepression of tumor suppressor genes (TSG) that were previously silenced by promoter DNA methylation or a transcriptionally repressed (closed) chromatin environment. 214  SEXUAL DIMORPHISM IN CANCER Groupwork 13/12/2024 lezione EPIDEMIOLOGY Trends in incidence rates for selected cancers by sex, United States, 1975–2020. Rates are age adjusted to the 2000 US standard population and adjusted for delays in reporting. Incidence data for 2020 are shown separate from trend lines. aLiver includes intrahepatic bile duct. The X Chromosome X-CHROMOSOME INACTIVATION (XCI) Molecular Mechanism  XCI is a mechanism that inactivates one of the two X chromosomes in females to balance gene expression with males who have only one X chromosome. Thus, it is mammals' strategy for dosage compensation.  XCI is composed of 3 steps: o INITIATION o ESTABLISHMENT o MAINTENANCE  The XCI locus produces a lncRNA, called XIST.  XIST interacts with specific proteins to cover and silence an X chromosome in each female cell. XCI involvement in cancer  XCI process silences several genes, including oncogenes. Thus, loss of Xist expression promotes tumor development. 215   Downregulation of Xist expression is commonly observed in breast cancer and in ovarian cancer.  Female tissues are mosaic for most X-linked genes. Skewed X-Chromosome inactivation can protect women against the consequences of these genes’ mutations. o Skewed X-Chromosome inactivation occurs when females have unequal proportions of cells with the paternal or maternal X-chromosome inactivated. o Skewing might be caused by selective pressure or might be purely stochastic in nature. In the context of cancer, skewed X chromosome inactivation amplifies or reduces the effect of inherited mutations in female individuals, influencing the sex-biased phenotypes arising from mutations in sex chromosomes’ genes. Regions that escape XCI (PARs) Almost 15% of X-linked genes evade XCI and continue to be expressed in both Xa (active) and Xi (inactive).  Genes located in PseudalAutosomal Regions (PARs) evade XCI.  PARs are regions homologous to autosomes, located at the two ends of sex mammal chromosomes. They play a crucial role in the pairing and genetic recombination of X and Y chromosomes during meiosis. EXITS GENES Almost 15% of X-linked genes evade XCI and continue to be expressed in both Xa (active) and Xi (inactive).  Many X-chromosome regions have been proposed as loci for Tumor Suppressor Genes that can escape XCI. Thus, they can be expressed by both Xa and Xi and have tumor suppressor function. These genes are called “Escape from X-Inactivation Tumor Suppressor (EXITS)” genes. 216 EXITS genes in sexual dimorphism in cancer  The biallelic expression of female EXITS genes is one of the reasons why men have a greater propensity to develop cancer.  This biallelic expression may explain the reduction in cancer incidence in women.  Male cells need only one harmful mutation in a tumor suppressor gene to cause cancer, while female cells need both copies to be mutated to cause cancer. KDM5C IN CLEAR CELL RENAL CELL CARCINOMA (CCRCC)  KDM5C regulates glycogenesis, glycogenolysis, and PPP, through its histone demethylase activity.  The loss of KDM5C activity leads to cancer metabolic reprogramming and might contribute to the male predominance in ccRCC.  KDM5C knockout suppresses lipid peroxidation and ferroptosis, which normally kills malignant cells.  KDM5C deficiency elicits ccRCC-specific metabolic phenotypes and confers resistance to ferroptosis.  In females, there are two active alleles of KDM5C; therefore, they are protected from complete gene loss after a single gene alteration.  In males, one renal cell mutation inactivated the only allele of the KDM5C gene, thereby probably promoting tumorigenesis KDM6A IN UROTHELIAL BLADDER CARCINOMA Urothelial bladder carcinoma is the most common type of bladder cancer and one of the most frequent cancers in men in developed regions. TUMOR SUPPRESSOR ACTIVITY  It has been demonstrated that the loss of KDM6A in bladder carcinoma cells line improves the cell proliferation rate by 25%, by activating EZH2-dependent transcriptional repression.  In contrast to KDM6A, EZH2 is a histone methyltransferase that adds a methyl group to H3K27 down-regulating gene transcription.  Further analysis showed that H3K27m3 signals are enriched at promoter regions of EZH2 target genes, including: o PIP5K1B, involved in constitutive signaling by aberrant PI3K in cancer. o GHR, growth hormone receptor.  JmjC is a domain of the protein encoded by KDM6A. 217   Mutations in this domain are associated with different penetrance in males and females.  In addition to mutations located in the JmjC domain, there are several truncating mutations throughout the protein.  Truncating mutations lead to a non-functional protein, which is compensated with biallelic expression in females. DDX3X TUMOR-PROMOTIVE AND TUMOR-SUPPRESSIVE ROLES (CASE STUDY) TUMOR SUPPRESSOR ACTIVITY In hepatocyte-specific DDX3X knockout mouse model, loss of DDX3X expression causes liver cell proliferation due to a decrease in the expression of DNA repair factors. PRO-ONCOGENIC ROLE In HNSCC, DDX3X promotes cell migration, invasion, and increases the translation of metastasis- promoting factors.  Given DDX3X is an EXITS gene, females have two active copies of this gene, providing protection against complete gene loss after a single mutation.  In contrast, males have only one copy, making them more susceptible to DDX3X mutations.  Moreover, skewing can protect females against the effect of pathogenic mutations due to the presence of more cells with wt allele on their active X chromosome, leading to less expression of the mutant allele ATRX in Gastric Cancer  In this study, the frequencies of somatic mutations from the TCGA cohort between female and male GC patients were compared.  Only ATRX deleterious missense mutations were found to occur more in female patients rather than male patients.  To avoid accidental bias caused by a single cohort, sex bias of ATRX mutation was further verified in three different cohorts: o TCGA o GACA-CN o GACA-JP  Further analysis suggested that patients with ATRX mutations, especially female GC patients, were significantly associated with higher TMB (Tumor Mutation Burden). 218   Despite this, ICI-treated patients with ATRX mutations had a better overall survival compared to those without this gene mutation.  This study speculated that the ATRX mutation in female GC patients might impact the related DNA damage repair, accounting for higher TMB and corresponding enhanced anticancer immunity and overall survival.  However, the precise mechanism of ATRX mutation in regulating anticancer immunity in female GC patients requires further research. DIFFERENTIAL EXPRESSION Here is the text with the bullet points added as requested:  To test the EXITS hypothesis, researchers performed an unbiased analysis of paired tumor/germline exome sequencing from 4126 patients across 21 tumor types from TCGA and Broad Institute datasets. Moreover, in 1994 cases, CNV data was available based on high- density SNP arrays.  The analysis was performed for LOF mutations (SNVs and InDels) in 4126 patients with exome data and then for LOF mutation or CN loss in 1994 patients with both exome and copy number data available.  These findings showed that ATRX, DDX3X, KDM5C, and KDM6A have been implicated as TSGs.  The robustness of these findings was assessed by a further analysis by which DDX3X, KDM5C, and KDM6A were significantly more frequently mutated across all male cancers. o Each dot represents one tumor, red ones have mutation of the indicated type.  DDX3X, KDM5C, and KDM6A had higher expression in non-mutated female compared to male tumors across these two types tested.  They also found that female tumors with LOF/CN mutations were more likely to lose the X chromosome than male tumors with LOF/CN mutations were to lose the Y chromosome. These results suggest that the Y chromosome homologue in males might not have equivalent tumor suppressor activity to EXITS gene alleles on the inactivated X chromosome in females 219 KEY CONCEPT  The loss of XIST expression might promote tumor development, due to incorrect XCI process in silencing oncogenes.  Skewed X chromosome inactivation could reduce the effect of inherited mutations in female individuals, influencing the sex-biased phenotypes arising from mutations in sex chromosome genes.  Biallelic expression of EXITS genes could protect females by pathogenic mutations in one of the two alleles, while their monoallelic expression in males makes them more susceptible to pathogenic mutations.  In males, Y chromosome homologue of EXITS genes might not have equivalent tumor suppressor activity to EXITS gene alleles on the inactivated X chromosome in females, making males more prone to tumorigenesis. The Y chromosome  PseudoAutosomal Regions (PARs): o High homology with X chromosome. o Allows for recombination during meiosis. o Higher recombination rates for PAR1.  Male Specific Y regions (MSY): o Used as the starting point for the YCC parsimony tree. o Exclusive to this chromosome. o No homologous recombination (HR) available. LOY AND MLOY  Loss Of Y chromosome (LOY): o The total or partial loss of genetic information contained in the Y chromosome.  Mosaic form is the most common somatic mutation found in Y (mLOY).  Theories that point to meiosis errors: o CENP-A defects and CENP-B absence. o p53 deficiency.  Other theories: o Secondary structures deriving from AT-rich regions. o  Known correlations to lifestyle and age. Consequences of mLOY  mLOY has emerged as a genetic marker linked to a variety of health conditions: o Alzheimer Disease → higher mLOY rates in glial cells of AD patients. o Kidney Diseases → found in neoplasms and end-stage diseases. o Cardiovascular diseases → increased mortality for heart failure. 220  o Developmental disorders. o Infertility.  Cytogenetically visible Yq deletions are associated with male infertility. o From the observation of the microdeletions, sequencing of chr. Y was conducted.  To better understand which regions are involved in male infertility, a Y-Deletion Detection System (YDDS) was developed. o YDDS uses Multiplex PCR to detect all the previously known deletions linked to male infertility, comprising 57 Sequence Tagged Sites (STS):  STS: Short region along the genome (200 to 300 bases long) whose exact sequence is found nowhere else in the genome, making it uniquely amplifiable by PCR.  Main deleted regions involved in male infertility: AZFa, AZFb, AZFc. o AZF: AZoospermia Factors, regions encoding genes involved in spermatogenesis. Here is your text with bullet points added for clarity: The AZF regions  These regions contain genes that encode different proteins.  Some of these genes are testis-specific (exclusive to men), while others are ubiquitous. Consequences of mLOY in AZF regions: Male Infertility  Microdeletions in the AZFa region are associated with mild or severe oligospermia: o Oligospermia: Low sperm count in males.  Entire AZFc deletions are the most frequent form of Y microdeficiencies and are usually associated with severe oligozoospermia. Not only male infertility  Studies on male infertility have demonstrated that the deletion of both testis-specific and ubiquitous genes can lead to sex- specific issues such as oligospermia.  Some homologs of the genes deleted in cases of male infertility are known to be linked with tumors: o KDM5C in ccRCC (Clear Cell Renal Cell Carcinoma). o DDX3X in HNSCC (Head and Neck Squamous Cell Carcinoma). 221 Can the deletion of the Y homologs be linked with male-specific tumors? The community started from previous studies on mLOY in male infertility to find a correlation between this condition and testicular cancer. SEX RELATED CANCER TESTICULAR CANCER  sex specific cancer  Forms from tissues in one or more testicles  Most of them begin in germ cells (testicular germ cell tumors)  Different classes of testicular cancer exist, with nonseminomatous germ cell tumors being the fasted growing and most aggressive 17 FINNISH CASES FROM THE TCGA  17 loci observed for each sample using multiplex-PCR  The loci selected were part of the ones used for the YDDS and are representative of the three AZF regions  Negative controls: Non- tumoral testicular tissue samples from the fathers of 11 subjects and from other individuals (no deletions detected) Results  15 out of 17 individuals had deletions at one or more loci  44 out of 189 total loci deleted (more than one per individual)  Observed interesting deletion patterns How can the tumoral cell present fragments of regions whereas the non-tumoral tissues have the same regions deleted?  Not actual definite mechanism, but there are Hypotheses: o AZF deletions in non-tumor tissues preceded the appearance of testicular malignancies. o These tissues were a mosaic containing two or more cell lineages showing deletions at different loci → mLOY. 222  o The malignant transformation of a cell from one of the mosaic lineages, followed by population expansion, may explain the appearance of this pattern. Extended Studies 35 Norwegian and 14 Argentinian Cases from the TCGA  New studies to validate the previous observations were carried out on a total of 49 new samples (49 loci including the previously observed 17).  Different percentages observed: deletions in 13 out of 49 loci with a frequency of 26.5%.  Deletion patterns observed confirmed the mosaicism behavior: o One Norwegian patient developed a secondary tumor in the remaining testicle after the other was treated with orchiectomy. o The secondary tumor had AZF deletions on multiple loci, while the primary one had none. Why is LOY so Important?  LOY is significantly related to age, so it can be a relevant biomarker for cancer surveillance in the elderly.  It is a marker for genomic instability, one of the hallmarks of cancer.  It can help better understand cancer development, growth, and treatment resistance mechanisms.  Deeper comprehension of the sex-dependent differences in cancer linked to the Y chromosome. Y GENE EXPRESSION IN SHARED T ISSUES Is the Y Chromosome Involved Only in Male Exclusive Functions?  As seen before, LOY is linked to conditions such as kidney and cardiovascular conditions.  It is not responsible only for male-exclusive functions.  Y chromosome genes were instigated in 36 shared human tissues.  First, only Y genes lacking homology with X genes in non-reproductive tissues were considered.  Expression differences between 20 X and Y homologs were then investigated. Expression Comparison Results - Higher Gene Expression  Key genes: AMELY, NLGN4Y, PCDH11Y, TXLNGY, all demonstrated higher gene expression than that of their X chromosome homolog across all 36 analyzed tissues. 223 Expression Comparison Results - Similarly Expressed Genes  Key genes: EIF1AY, KDM5D, UTY, all demonstrated expression rates similar to their X homologs across the 36 analyzed tissues. What Are the Results of This Study?  X and Y homologous genes are expressed with remarkable variability among different tissues.  In some cases, a Y-biased increase in Y genes expression up to 3.6-fold.  Many Y chromosome genes are expressed as much as their X homologous genes in shared tissues. Non-Sex Related Cancer: Bladder Cancer Cancer Overview  Comprises approximately 3.0% of new cancer cases and 2.1% of cancer deaths, with higher incidence in men.  It is known how androgen receptor (AR) signaling contributes to the male-biased susceptibility of CD8+ T cells to their functional exhaustion in the tumor microenvironment (TME), resulting in an immune evasive TME.  Different types of bladder cancer: o Non-muscle invasive bladder cancer (NMIBC): Transurethral resection of the tumor o Muscle invasive bladder cancer (MIBC): Cisplatin chemotherapy + radical cystectomy o Metastatic BC: Chemotherapy + immune checkpoint inhibitors (ICIs) Chromosome Y Gene Expression and LOY Effects in Bladder Cancer  To better understand the contribution of the Y chromosome in bladder cancer, 18 genes expressed in normal bladder endothelium were studied to stratify the overall survival of 300 male patients with MIBC.  Two main expression groups were identified: o Yhigh: Higher expression rates o Ylow: Lower expression rates due to LOY Results  Overall survival (OS) of patients with BC was assessed for both expression profiles.  Individuals with Ylow had worse survival rates.  Similar results were observed in those with NMIBC, suggesting the presence of LOY in earlier stages of the disease. 224 How Can LOY and Ylow Influence the Disease Outcome?  To understand why Ylow cells had worse outcomes, MB49 BC cells were injected into wild-type (wt) mice. MB49 cells tend to spontaneously lose the Y chromosome.  A female BC cell line and a breast cancer cell line were used as controls.  The contribution of the host’s immune response to the aggressive phenotype was assessed via Rag2-/- Il2rg-/- immunocompromised male mice. LOY-Induced Immune Evasion  The candidate genes for this aggressive phenotype were looked for by assessing the expression of seven genes normally expressed in both mice and human bladder endothelium in the MB49 line.  Only three genes (KDM5D, UTY, and DDX3Y) were also expressed in the MB49 line, with KDM5D and UTY associated with an unfavorable prognosis in human BC.  The molecular drivers that contribute to immune evasion in LOY (Y-) cells was verified using cellular lines with KDM5D and UTY either knocked out or overexpressed in both Y- and Y+ (Y chromosome retained):  The sequencing of the transcriptome of Y- and Y+ MB49 tumors, along with high-dimensional spectral flow cytometry, helped delineating the intratumoral immune cell populations;  Y- tumors were enriched in the proportion of total CD8+ T cells and immunosuppressive macrophages;  Also, the following genes were overexpressed in Ylow cells: o CD274, encoding PD-L1; o LAG3 and HAVCR2, encoding TIM3; 225   PD-L1: checkpoint protein that normally helps prevent excessive immune activation by binding to PD-1 receptor on T cells and inhibiting their action;  TIM3: suppresses T cell activation and promotes apoptosis LOY-Induced Immune Evasion - KDM5D’s Role  AR signaling contributes to the male-biased susceptibility of CD8+ T cells to their functional exhaustion in the BC TME.  Among the genes analyzed, KDM5D normally downregulates AR transcriptional activity by demethylating H3K4me3 active transcription marks.  The involvement of KDM5D in the immune evasive TME, along with its effect on AR transcriptional activity, confirms its key role in determining the more aggressive male BC phenotype. Conclusions on the Influence of LOY on the Immuno-Evasive TME  LOY causes the downregulation of UTY and KDM5D, which then leads to chromatin rearrangements responsible for the overexpression of PD-L1 and TIM3, promoting exhaustion of CD8+ T cells;  The resulting lack of functional CD8+ T cells in the BC TME can be exploited to design treatments that use Immune Checkpoint Inhibitors (ICIs): o ICIs: molecules that target inhibitors of the immune system (such as TIM3 and PD- L1) allowing it to more effectively recognize and attack cancer cells.  Nowadays, studies on how to implement ICIs treatments to male LOY BC patients standard procedures are making progress to deliver better results EXTREME DOWNREGULATION OF THE Y CHROMOSOME (EDY)  Instances of cancer free LOY condition (male infertility )  LOY might not be the best indicator of cancer.  Extreme Downregulation of Y genes (EDY) is a consequence of LOY.  EDY was first explored via RNA-seq data from 371 individuals in non-diseased tissues to better understand its basic characteristics. o More common than LOY in non-diseased individuals. o Found frequently in two different tissues. o May have genetic biases linked to other autosomal loci. EDY in 12 TCGA Cancer Tissues  Investigated LOY-EDY-cancer connections through TCGA 12 studies o LOY → genomic analyses, o EDY → transcription analyses.  Results o significant correlation between LOY-age, but no correlation between EDY-age. o Downregulation of DDX3Y, EIF1AY, KDM5D, RPS4Y1, UTY, and ZFY in all cancer samples.  Four interesting features of the downregulated genes: o Located in pairs in three distant regions of MEY and may share regulatory elements; o Encode proteins relevant in the cell cycle regulation; o They have X-inactivation escaping homologs; 226  o Male-biased LoF somatic mutations have been found in four of the X chromosome homologs of these genes across many cancers in co-occurrence with LOY.  Other analyses suggest the most probable causal sequence for cancer is: aging → LOY → EDY → cancer. EDY in 3034 TCGA Cancer Tissues  Analysis of CNVs differences between different EDY statuses;  Higher proportions of copy number gains in LOY-free cases;  EGFR = Epidermal Growth Factor Receptor, one of the four members of the ErbB family of tyrosine kinase receptors whose catalytic activation leads to an increase in DNA methyltransferase activity;  EGFR overexpression causes an increase in DNA methylation. Why is EDY so important?  Overall, EDY is more significantly correlated with cancer than LOY;  One can have a genetic predisposition to EDY;  LOY-free EDY conditions represent additional risk for cancer, indicating how the downregulation is more relevant than mere deletions;  It can be linked to epigenetic patterns seen in cancer (EGFR overexpression);  Helps better understand the steps that lead to cancer development. KEY CONCEPTS o Y chromosome genes are involved in both sex-specific and non-specific traits. o mLOY is common in aged men and is linked to many conditions. o LOY in young people can lead to severe developmental defects and conditions. o LOY and Y chromosome defects are linked to sex-specific cancers: o In testicular cancer, AZF deletions in the long arm of the chromosome are leading causes. o The same genes are also linked to non-sex-specific cancers: o In bladder cancer, LOY leads to the formation of a tumor-evasive TME. o KDM5D and UTY play central roles in these conditions. o EDY is a better predictor and marker for cancer than LOY. o Understanding LOY and EDY will help better understand mechanisms related to cancer development and lead to new therapies. 227 Sex disparities in Autosomal Cancer Genes The complexity of X-chromosome inactivation (XCI) has influenced research to focus more on autosomes. DNA sequencing does not distinguish between the inactive (Xi) and active (Xa) X chromosomes, which complicates the identification of specific alleles being transcribed into mRNA. In contrast, non-sex chromosomes allow for straightforward correlations between coding genes and their expressed mRNA products. This technical limitation has resulted in preferential studies of autosomal genes SEX DISPARITIES IN AUTOSOMAL CANCER GENES TP53 Among autosomal tumor suppressors, TP53 is the most well-known, with mutations occurring in nearly half of all tumors.  Mutation Overview: o TP53 is the most commonly mutated gene in cancer, found in 50% of cases.  Sex Differences in TP53 Mutations: o Cancers with TP53 mutations are more frequent in men than women. o TP53-mutated cancers are linked to poorer survival rates. The reason behind the poorer survival rates 1. Hormonal Influences: a. Estrogen: Enhances p53 activity, aiding DNA repair and reducing mutation rates in females. b. Androgens: Contribute to oxidative stress, increasing mutation risk in males. 2. X-Linked Gene Interactions: a. X-linked genes like MDM4 and RCHY1 modulate p53 stability. b. Females benefit from having two X chromosomes, offering potential redundancy. 3. Epigenetic Differences: a. Sex-specific DNA methylation and histone modifications may impact TP53 expression differently in males and females. 4. Environmental and Lifestyle Factors: a. Males often face higher exposure to carcinogens, contributing to elevated TP53 mutation rates. Impact of DAXX and ATRX Mutations on TP53 Signaling and Telomere Dynamics:  Mutations in DAXX and ATRX: o Lead to Alternative Lengthening of Telomeres (ALT):  ALT causes structural changes in chromatin, reducing accessibility at TP53 binding sites. 228   This disruption weakens TP53's ability to suppress tumor growth and regulate cell death. Sex Differences in Immune Checkpoint Genes Functions:  Females: o Genes like HLA-DRA and HLA-C influence cancer risk and protection exclusively in women.  Males: o More likely to lose immune checkpoint genes like MICA, CD52, and PDCD1, which play a critical role in modulating immune responses. Sex-Specific Genetic Mutations in Liver Cancer:  Females: o Mutations in BAP1, a gene involved in DNA repair, are more frequent in women (14%) compared to men (1.6%), highlighting a sex-specific genetic pattern in liver cancer.  Males: o Mutations in CTNNB1, affecting β-catenin, are significantly more common in men (33%) compared to women (12%). o This aligns with liver cancer being three times more prevalent and deadly in men. Lung and Kidney Cancer:  Females: o In lung adenocarcinoma, the NF1 gene is inactivated more often. o In kidney cancer (KIRC), PIK3CA amplifications are more common, reflecting different pathway activations compared to men.  Males: o In kidney cancer (KIRC), MTOR and PTEN deletions are more frequent, which may drive tumor progression and alter treatment responses. SEX DISPARITIES IN CANCER METABOLIC PATHWAYS Glucose Metabolism:  Male cancers: o Male tumors primarily rely on glycolysis (the Warburg effect) for energy production. o Key Enzymes:  HK2 (Hexokinase 2)  LDHA (Lactate Dehydrogenase A) o High lactate production acidifies the tumor microenvironment, driving tumor invasion, immune suppression, and metastasis.  Female cancers: o Female tumors utilize both glycolysis and oxidative phosphorylation (OXPHOS) for energy. o Key Regulators:  PGC-1α (PPARGC1A): Drives mitochondrial biogenesis and enhances energy efficiency.  SDH (Succinate Dehydrogenase): Links the TCA cycle to the electron transport chain. o Effects:  Reduces lactate buildup, limiting tumor invasiveness.  Supports more sustainable and less aggressive tumor growth. 229 Lipid Metabolism:  Male cancers: o Lipid metabolism is less prominent but critical in androgen-driven cancers (e.g., prostate cancer). o Key Regulators:  SREBF1 (Sterol Regulatory Element-Binding Protein 1): Activates genes for fatty acid and cholesterol synthesis.  FASN (Fatty Acid Synthase): Produces palmitate for membrane biosynthesis. o Effects:  Enhances oxidative stress resistance.  Increases tumor proliferation and aggressiveness.  Female cancers: o Female tumors depend heavily on fatty acid oxidation (FAO). o Key Enzymes:  CPT1A (Carnitine Palmitoyltransferase 1A): Regulates mitochondrial FAO.  ACOX1 (Acyl-CoA Oxidase 1): Catalyzes the first step of peroxisomal FAO, generating NADPH. o Effects:  Provides energy for tumor growth.  Protects hormone-driven cancers from oxidative stress. Amino Acid Metabolism  Arginine Metabolism:  Male cancers: o ARG1 depletes arginine, suppressing the immune system and helping tumors grow.  Female cancers: o ASS1 maintains arginine levels, supporting immune activity and reducing tumor immune evasion.  Serine/Glycine Metabolism: Glycine supports DNA production by contributing to purine synthesis and one-carbon metabolism, both of which are essential for creating and maintaining the DNA structure.  Male cancers: o Low PHGDH activity limits nucleotide production, causing genomic instability.  Female cancers: o SHMT2, influenced by estrogen, converts serine to glycine, fueling DNA production and regulating genes through epigenetics. Oxidative Stress and Reactive Oxygen Species (ROS): o Male cancers:  Key Players:  NOX1: Produces high levels of ROS.  NF-κB: Stabilized by ROS, promoting inflammation and cancer growth.  Effects: High ROS levels cause DNA damage, leading to mutations and aggressive tumors 230  o Female cancers:  Key Players:  SOD2: Converts harmful superoxide radicals into less harmful hydrogen peroxide.  CAT: Breaks down hydrogen peroxide into water and oxygen.  Effects: Strong antioxidant defenses lower ROS (reactive oxygen species), reduce DNA damage, and enhance responses to treatment in estrogen-driven cancers. Summary of Sex Disparities in Autosomal Cancer Genes and Metabolism Male Cancers: Males exhibit a higher mutation load and accumulate somatic mutations approximately 10 years earlier than females. This early accumulation increases the likelihood of developing truncal mutations that drive cancer initiation. TP53 mutations are more frequent in men, associated with poorer survival outcomes and higher chromosomal instability, which accelerates tumor progression. Men are also more likely to lose immune checkpoint genes such as MICA, CD52, and PDCD1, reducing immune surveillance and enhancing tumor evasion. Metabolically, male tumors rely heavily on glycolysis (Warburg effect), producing high lactate levels that acidify the tumor microenvironment, promoting invasion and immune suppression. Additionally, male cancers show increased glutamine uptake via GLS and ASCT2, arginine depletion through ARG1, and elevated ROS levels driven by NOX1 and NF-κB, contributing to tumor aggressiveness. Female Cancers: Female cancers show distinct advantages due to epigenetic regulation and enhanced antioxidant defenses. Women benefit from better proteasome activity and the ability to regulate oxidative stress through enzymes like SOD2 and CAT, which reduce DNA damage and improve therapeutic responses. Protective immune checkpoint genes, such as HLA-DRA and HLA-C, play sex-specific roles, reducing cancer risks in women. Metabolically, female tumors balance glycolysis and oxidative phosphorylation (OXPHOS), reducing lactate production and supporting less invasive growth. Dependence on fatty acid oxidation (FAO), regulated by CPT1A and ACOX1, provides energy while protecting against oxidative stress. Female tumors are also more adaptable, synthesizing glutamine internally via GLUL, which supports survival in nutrient-deprived environments The Role of Biomarkers in Cancer Research What are biomarkers?  Measurable indicators of biological processes, disease states, or responses to treatments:  Powerful tools for early diagnosis, monitoring, and predicting treatment response in cancer.  Example: o HER2 amplification in breast cancer. o Use of targeted therapy like trastuzumab effectiveness. 231 Why are biomarkers relevant in sexual dimorphism?  Male and female patients exhibit distinct tumor characteristics:  Influences include sex chromosomes, hormone regulation, and immune responses.  Recognizing these differences improves therapy outcomes. Emerging Technologies in Biomarker Detection:  Liquid Biopsies: o Detect circulating tumor DNA from a blood test. o Monitor therapy response and predict resistance.  Minimal Residual Disease: o Detect cancer presence at the molecular level post-treatment. o Enable real-time and less invasive monitoring. Epigenetic Biomarkers and Sex Differences  Specific patterns of epigenetic changes that can indicate the presence of disease, predict disease progression, or determine response to treatments.  Sex-specific patterns influence on cancer development, therapy resistance,and sensitivity. o Example in lung cancer  Woman the tumor cell is hypermethylation and Associated with EGFR mutations.  Man the tumor call is hypomethylation and associated with KRAS muatation. IMPACT OF DNA METHYLATION ON THERAPY Therapy Resistance  Methylation in tumor suppressor genes, such as PTEN, can contribute to therapy resistance:  In Men: o Higher methylation of PTEN suppresses its function, affecting tumor growth regulation and immune responses. o Methylation reduces PTEN’s role in immune surveillance, leading to reduced effectiveness of immune checkpoint inhibitors (ICIs) (e.g., PD-1/PD-L1 inhibitors). o Results in a resistant tumor microenvironment where ICIs are less effective.  In Women: o Estrogen-regulated methylation of PTEN alters the immune landscape and enhances immune editing, reducing tumor immunogenicity. o Methylation-induced changes in gene expression, including PTEN silencing, impair immune activation, decreasing ICI effectiveness. o Similar therapy resistance mechanisms to men, but driven by estrogen influences. 232 Methylation Role in Therapy Sensitivity: Methylation also creates opportunities for enhancing sensitivity to treatments:  Hypomethylating agents like 5-azacytidine and decitabine can reverse aberrant methylation, reactivating tumor suppressor genes.  These drugs reactivate silenced tumor suppressor genes by removing aberrant methylation at CpG islands within promoter regions.  Combining demethylating agents with immune checkpoint inhibitors (ICIs) can help re-sensitize tumors to immunotherapy, particularly in resistant male cancers.  In women, targeting estrogen-driven methylation pathways opens possibilities for new therapeutic strategies, particularly in hormone-sensitive cancers like breast and ovarian cancers. Histone Modifications and Sex-Specific Chromatin States in Cancer:  Role of Histone Modifications: o Histone modifications (e.g., acetylation, methylation) regulate chromatin accessibility, impacting cancer progression and therapy. o Sex hormones (estrogen, androgen) influence histone modifications differently in males and females. o Focus on lung cancer and melanoma for their sexually dimorphic responses to therapy. Sex-Specific Effects of Histone Modifications in Lung Cancer:  Estrogen Effects in Women: Enhances histone acetylation at genes driving tumor growth and immune regulation, altering therapy responses.  Androgen Effects in Men: Promotes histone methylation, suppressing immune checkpoints and driving resistance to immune checkpoint inhibitors (ICIs).  Clinical Insight: o Female lung cancer patients show varied immune responses. o Men often experience therapy resistance due to immune-suppressive gene expression. 233 Therapeutic Implications of Histone Modifiers  What They Do: EZH2 is a histone methyltransferase that adds repressive methylation marks on histones, leading to gene silencing.  Play significant role in antitumor immunity Histone Modifications and Immune Microenvironment in Melanoma  EZH2-Mediated Methylation in Men: o Suppresses genes essential for immune cell recruitment, reducing tumor-infiltrating lymphocytes (TILs).  Estrogen Effects in Women: o Influenced by estrogen signaling, may exhibit distinct patterns of histone acetylation that alter the tumor immune microenvironment.  Clinical Insight: o Males experience lower TIL activity and resistance to anti-PD-1 therapies. o Estrogen-related chromatin changes in females impact immune responses differently. The