NEOPLASIA - Comprehensive Guide

from reportlab.lib.pagesizes import A4
from reportlab.lib import colors
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.lib.units import cm, mm
from reportlab.platypus import (
    SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle,
    HRFlowable, PageBreak, KeepTogether
)
from reportlab.lib.enums import TA_CENTER, TA_LEFT, TA_JUSTIFY
from reportlab.platypus import BaseDocTemplate, Frame, PageTemplate
from reportlab.lib.colors import HexColor

OUTPUT = "/tmp/workspace/neoplasia-guide/Oncogenes_TumorSuppressors_Hallmarks.pdf"

# ── Colour palette ──────────────────────────────────────────────────────────
NAVY      = HexColor("#1a2e4a")
TEAL      = HexColor("#1a7f7a")
ORANGE    = HexColor("#e07b39")
LIGHT_BG  = HexColor("#f0f4f8")
CARD_BG   = HexColor("#eaf6f5")
WARN_BG   = HexColor("#fff3e0")
RED_DARK  = HexColor("#c0392b")
GREY_TEXT = HexColor("#4a5568")
WHITE     = colors.white
BLACK     = colors.black
YELLOW_HL = HexColor("#fff9c4")

W, H = A4

# ── Styles ───────────────────────────────────────────────────────────────────
def build_styles():
    base = getSampleStyleSheet()
    s = {}

    s["cover_title"] = ParagraphStyle(
        "cover_title", fontSize=30, leading=36, textColor=WHITE,
        fontName="Helvetica-Bold", alignment=TA_CENTER, spaceAfter=6)
    s["cover_sub"] = ParagraphStyle(
        "cover_sub", fontSize=14, leading=18, textColor=HexColor("#cce8e6"),
        fontName="Helvetica", alignment=TA_CENTER, spaceAfter=4)
    s["cover_meta"] = ParagraphStyle(
        "cover_meta", fontSize=10, leading=14, textColor=HexColor("#a0c4c2"),
        fontName="Helvetica", alignment=TA_CENTER)

    s["h1"] = ParagraphStyle(
        "h1", fontSize=18, leading=22, textColor=WHITE,
        fontName="Helvetica-Bold", alignment=TA_LEFT,
        spaceAfter=2, spaceBefore=0)
    s["h2"] = ParagraphStyle(
        "h2", fontSize=13, leading=17, textColor=NAVY,
        fontName="Helvetica-Bold", spaceBefore=12, spaceAfter=4)
    s["h3"] = ParagraphStyle(
        "h3", fontSize=11, leading=14, textColor=TEAL,
        fontName="Helvetica-Bold", spaceBefore=8, spaceAfter=3)
    s["body"] = ParagraphStyle(
        "body", fontSize=9.5, leading=14, textColor=GREY_TEXT,
        fontName="Helvetica", spaceBefore=2, spaceAfter=4,
        alignment=TA_JUSTIFY)
    s["bullet"] = ParagraphStyle(
        "bullet", fontSize=9.5, leading=14, textColor=GREY_TEXT,
        fontName="Helvetica", leftIndent=14, spaceBefore=1, spaceAfter=1,
        bulletIndent=4, alignment=TA_LEFT)
    s["note"] = ParagraphStyle(
        "note", fontSize=9, leading=13, textColor=HexColor("#7b341e"),
        fontName="Helvetica-Oblique", spaceBefore=2, spaceAfter=2,
        leftIndent=10, borderPad=4)
    s["tag"] = ParagraphStyle(
        "tag", fontSize=8.5, leading=12, textColor=WHITE,
        fontName="Helvetica-Bold", alignment=TA_CENTER)
    s["section_intro"] = ParagraphStyle(
        "section_intro", fontSize=9.5, leading=14, textColor=GREY_TEXT,
        fontName="Helvetica-Oblique", spaceBefore=2, spaceAfter=8,
        alignment=TA_JUSTIFY)
    s["table_header"] = ParagraphStyle(
        "table_header", fontSize=8.5, leading=11, textColor=WHITE,
        fontName="Helvetica-Bold", alignment=TA_CENTER)
    s["table_cell"] = ParagraphStyle(
        "table_cell", fontSize=8.5, leading=11.5, textColor=GREY_TEXT,
        fontName="Helvetica", alignment=TA_LEFT)
    s["table_cell_bold"] = ParagraphStyle(
        "table_cell_bold", fontSize=8.5, leading=11.5, textColor=NAVY,
        fontName="Helvetica-Bold", alignment=TA_LEFT)
    s["hallmark_title"] = ParagraphStyle(
        "hallmark_title", fontSize=10.5, leading=13, textColor=NAVY,
        fontName="Helvetica-Bold", spaceBefore=0, spaceAfter=2)
    s["hallmark_body"] = ParagraphStyle(
        "hallmark_body", fontSize=9, leading=13, textColor=GREY_TEXT,
        fontName="Helvetica", spaceBefore=0, spaceAfter=0)
    return s

S = build_styles()

# ── Helper flowables ─────────────────────────────────────────────────────────
def rule(color=TEAL, thickness=1.0):
    return HRFlowable(width="100%", thickness=thickness, color=color,
                      spaceAfter=6, spaceBefore=4)

def sp(n=6):
    return Spacer(1, n)

def h1_banner(text):
    """Dark navy banner for section heading."""
    data = [[Paragraph(text, S["h1"])]]
    t = Table(data, colWidths=[W - 4*cm])
    t.setStyle(TableStyle([
        ("BACKGROUND", (0,0), (-1,-1), NAVY),
        ("TOPPADDING",    (0,0), (-1,-1), 8),
        ("BOTTOMPADDING", (0,0), (-1,-1), 8),
        ("LEFTPADDING",   (0,0), (-1,-1), 12),
        ("RIGHTPADDING",  (0,0), (-1,-1), 12),
        ("ROUNDEDCORNERS", [6]),
    ]))
    return t

def key_fact_box(text):
    """Orange-bordered key fact box."""
    data = [[Paragraph(f"<b>KEY FACT:</b> {text}", S["note"])]]
    t = Table(data, colWidths=[W - 4*cm])
    t.setStyle(TableStyle([
        ("BACKGROUND",    (0,0), (-1,-1), WARN_BG),
        ("LINEAFTER",     (0,0), (0,-1), 3, ORANGE),
        ("TOPPADDING",    (0,0), (-1,-1), 6),
        ("BOTTOMPADDING", (0,0), (-1,-1), 6),
        ("LEFTPADDING",   (0,0), (-1,-1), 10),
        ("RIGHTPADDING",  (0,0), (-1,-1), 10),
    ]))
    return t

def info_box(text):
    """Teal info box."""
    data = [[Paragraph(text, ParagraphStyle("ib", fontSize=9, leading=13,
        fontName="Helvetica", textColor=HexColor("#1a5c59")))]]
    t = Table(data, colWidths=[W - 4*cm])
    t.setStyle(TableStyle([
        ("BACKGROUND",   (0,0), (-1,-1), CARD_BG),
        ("LINEBEFORE",   (0,0), (0,-1), 3, TEAL),
        ("TOPPADDING",   (0,0), (-1,-1), 7),
        ("BOTTOMPADDING",(0,0), (-1,-1), 7),
        ("LEFTPADDING",  (0,0), (-1,-1), 10),
        ("RIGHTPADDING", (0,0), (-1,-1), 10),
    ]))
    return t

def bullet_list(items):
    return [Paragraph(f"• {i}", S["bullet"]) for i in items]

def make_table(headers, rows, col_widths, stripe=True):
    th = [Paragraph(h, S["table_header"]) for h in headers]
    data = [th]
    for i, row in enumerate(rows):
        data.append([Paragraph(str(c), S["table_cell_bold"] if j == 0 else S["table_cell"])
                     for j, c in enumerate(row)])
    ts = [
        ("BACKGROUND",   (0,0), (-1,0), NAVY),
        ("GRID",         (0,0), (-1,-1), 0.5, HexColor("#d1d5db")),
        ("LINEBELOW",    (0,0), (-1,0), 1.5, TEAL),
        ("TOPPADDING",   (0,0), (-1,-1), 5),
        ("BOTTOMPADDING",(0,0), (-1,-1), 5),
        ("LEFTPADDING",  (0,0), (-1,-1), 6),
        ("RIGHTPADDING", (0,0), (-1,-1), 6),
        ("VALIGN",       (0,0), (-1,-1), "TOP"),
    ]
    if stripe:
        for i in range(1, len(data)):
            bg = LIGHT_BG if i % 2 == 0 else WHITE
            ts.append(("BACKGROUND", (0,i), (-1,i), bg))
    t = Table(data, colWidths=col_widths)
    t.setStyle(TableStyle(ts))
    return t

# ── Page callbacks ───────────────────────────────────────────────────────────
def cover_page_cb(canvas, doc):
    canvas.saveState()
    # Gradient-like background: draw two rectangles
    canvas.setFillColor(NAVY)
    canvas.rect(0, 0, W, H, fill=1, stroke=0)
    canvas.setFillColor(TEAL)
    canvas.rect(0, H*0.55, W, H*0.45, fill=1, stroke=0)
    # Decorative stripe
    canvas.setFillColor(ORANGE)
    canvas.rect(0, H*0.54, W, 8, fill=1, stroke=0)
    # Footer
    canvas.setFillColor(HexColor("#0d2135"))
    canvas.rect(0, 0, W, 40, fill=1, stroke=0)
    canvas.setFont("Helvetica", 8)
    canvas.setFillColor(HexColor("#8cb3cc"))
    canvas.drawCentredString(W/2, 15, "Based on Robbins & Kumar Basic Pathology (9th ed.) • For Study Use Only")
    canvas.restoreState()

def normal_page_cb(canvas, doc):
    canvas.saveState()
    # Header bar
    canvas.setFillColor(NAVY)
    canvas.rect(0, H - 28, W, 28, fill=1, stroke=0)
    canvas.setFont("Helvetica-Bold", 8)
    canvas.setFillColor(WHITE)
    canvas.drawString(2*cm, H - 18, "ONCOGENES · TUMOR SUPPRESSORS · HALLMARKS OF CANCER")
    canvas.setFont("Helvetica", 8)
    canvas.drawRightString(W - 2*cm, H - 18, f"Page {doc.page}")
    # Footer
    canvas.setFillColor(LIGHT_BG)
    canvas.rect(0, 0, W, 22, fill=1, stroke=0)
    canvas.setFillColor(GREY_TEXT)
    canvas.setFont("Helvetica-Oblique", 7.5)
    canvas.drawCentredString(W/2, 7, "Robbins & Kumar Basic Pathology (9th ed.) • Study Guide • Orris Medical")
    canvas.restoreState()

# ── Build document ────────────────────────────────────────────────────────────
def build():
    doc = BaseDocTemplate(
        OUTPUT, pagesize=A4,
        leftMargin=2*cm, rightMargin=2*cm,
        topMargin=1.5*cm, bottomMargin=1.5*cm,
    )

    cover_frame  = Frame(0, 0, W, H, leftPadding=3*cm, rightPadding=3*cm,
                         topPadding=H*0.2, bottomPadding=60)
    normal_frame = Frame(2*cm, 1.5*cm, W-4*cm, H-3.8*cm,
                         leftPadding=0, rightPadding=0,
                         topPadding=8, bottomPadding=8)

    doc.addPageTemplates([
        PageTemplate(id="Cover",  frames=[cover_frame],  onPage=cover_page_cb),
        PageTemplate(id="Normal", frames=[normal_frame], onPage=normal_page_cb),
    ])

    story = []

    # ═══════════════════════════════════════════════════════════════════════
    # COVER PAGE
    # ═══════════════════════════════════════════════════════════════════════
    story.append(sp(40))
    story.append(Paragraph("Oncogenes,", S["cover_title"]))
    story.append(Paragraph("Tumor Suppressors &", S["cover_title"]))
    story.append(Paragraph("Hallmarks of Cancer", S["cover_title"]))
    story.append(sp(14))
    story.append(rule(WHITE, 1.5))
    story.append(sp(10))
    story.append(Paragraph("A Comprehensive Medical Study Guide", S["cover_sub"]))
    story.append(sp(6))
    story.append(Paragraph("Pathology | Oncology | Molecular Biology", S["cover_meta"]))
    story.append(sp(6))
    story.append(Paragraph("Robbins & Kumar Basic Pathology, 9th Edition", S["cover_meta"]))

    story.append(PageBreak())

    # Switch to Normal template
    from reportlab.platypus import NextPageTemplate
    story.append(NextPageTemplate("Normal"))
    story.append(PageBreak())

    # ═══════════════════════════════════════════════════════════════════════
    # SECTION 1: ONCOGENES
    # ═══════════════════════════════════════════════════════════════════════
    story.append(h1_banner("SECTION 1 — ONCOGENES"))
    story.append(sp(10))

    story.append(Paragraph("1.1  Proto-oncogenes vs Oncogenes", S["h2"]))
    story.append(rule(TEAL, 0.5))
    story.append(Paragraph(
        "Proto-oncogenes are normal cellular genes that regulate cell growth, differentiation, "
        "and apoptosis. When mutated or aberrantly expressed, they become <b>oncogenes</b> — "
        "dominantly acting drivers of neoplastic transformation. Oncogenes require mutation of "
        "only <b>one allele</b> to exert their effect (dominant gain-of-function).", S["body"]))
    story.append(sp(4))
    story.append(info_box(
        "<b>How proto-oncogenes become oncogenes:</b><br/>"
        "① Point mutation → constitutively active protein (e.g., RAS Gly12→Val)<br/>"
        "② Gene amplification → massive overexpression (e.g., N-MYC in neuroblastoma)<br/>"
        "③ Chromosomal translocation → fusion protein or deregulated expression<br/>"
        "④ Insertional mutagenesis → retroviral LTR drives overexpression"
    ))
    story.append(sp(10))

    story.append(Paragraph("1.2  Categories of Oncoproteins", S["h2"]))
    story.append(rule(TEAL, 0.5))
    headers = ["Category", "Mechanism", "Key Example(s)", "Cancer Association"]
    rows = [
        ["Growth factors", "Autocrine/paracrine stimulation", "PDGF-β (sis), FGF family", "Gliomas, sarcomas"],
        ["Growth factor receptors", "Constitutive kinase activation", "ERBB1 (EGFR), ERBB2 (HER2), RET", "Lung, breast, thyroid"],
        ["Signal transducers (GTPase)", "Locked in GTP-bound 'on' state", "RAS (KRAS, NRAS, HRAS)", "Pancreas (90%), colon (45%), lung (35%)"],
        ["Signal transducers (kinase)", "Constitutive tyrosine kinase", "BCR-ABL (t9;22), SRC", "CML, ALL"],
        ["Nuclear transcription factors", "Uncontrolled target gene activation", "MYC, N-MYC, L-MYC", "Burkitt lymphoma, neuroblastoma"],
        ["Cell cycle regulators", "Unrestrained cyclin/CDK activity", "Cyclin D1, CDK4", "Mantle cell lymphoma, melanoma"],
        ["Anti-apoptotic proteins", "Block programmed cell death", "BCL-2", "Follicular lymphoma t(14;18)"],
    ]
    story.append(make_table(headers, rows,
        [3.2*cm, 3.8*cm, 4.2*cm, 4.2*cm]))
    story.append(sp(8))

    story.append(Paragraph("1.3  RAS — The Most Commonly Mutated Oncogene", S["h2"]))
    story.append(rule(TEAL, 0.5))
    story.append(Paragraph(
        "RAS proteins (KRAS, NRAS, HRAS) are small GTPases that act as molecular switches. "
        "Normally, RAS cycles between inactive GDP-bound and active GTP-bound states, with "
        "GTPase activity returning it to the OFF state.", S["body"]))
    story += bullet_list([
        "<b>Mutation hotspot:</b> Codon 12 (Gly→Val) — prevents GTP hydrolysis → RAS locked in active state",
        "<b>Frequency:</b> Mutated in ~30% of all human tumors",
        "<b>Downstream effects:</b> Activates RAF→MEK→ERK (proliferation) and PI3K→AKT (survival)",
        "<b>Top KRAS cancers:</b> Pancreatic (90%), Colorectal (45%), Lung adenocarcinoma (35%)",
        "<b>Therapeutic targeting:</b> KRAS G12C inhibitors (sotorasib, adagrasib) — approved for NSCLC",
    ])
    story.append(sp(6))
    story.append(key_fact_box(
        "RAS mutations are the most common single-gene oncogenic event in human cancer (~30% overall). "
        "KRAS is mutated in 90% of pancreatic ductal adenocarcinomas."
    ))
    story.append(sp(10))

    story.append(Paragraph("1.4  Key Oncogenes Quick Reference", S["h2"]))
    story.append(rule(TEAL, 0.5))
    headers2 = ["Oncogene", "Activation", "Cancer Type", "Clinical Relevance"]
    rows2 = [
        ["KRAS", "Point mutation (codon 12)", "Pancreas, colon, lung", "Predict resistance to EGFR inhibitors"],
        ["ERBB2 (HER2)", "Amplification", "Breast (~20%), gastric (~15%)", "Trastuzumab, pertuzumab target"],
        ["EGFR (ERBB1)", "Mutation/amplification", "Lung (exon 19/21), GBM", "Erlotinib, osimertinib target"],
        ["BCR-ABL", "t(9;22) translocation", "CML, Ph+ ALL", "Imatinib/TKI — targeted therapy"],
        ["N-MYC", "Gene amplification (>10 copies)", "Neuroblastoma", "Poor prognosis marker"],
        ["C-MYC", "t(8;14) translocation", "Burkitt lymphoma", "IgH enhancer drives MYC overexpression"],
        ["BCL-2", "t(14;18) translocation", "Follicular lymphoma", "Venetoclax (BCL-2 inhibitor)"],
        ["ALK", "Translocation/inversion", "Lung NSCLC, ALCL", "Crizotinib, alectinib target"],
        ["RET", "Point mutation", "MEN2A/2B, medullary thyroid Ca", "Germline testing; vandetanib"],
        ["CDK4", "Amplification/mutation", "Glioblastoma, sarcoma, melanoma", "CDK4/6 inhibitors (palbociclib)"],
    ]
    story.append(make_table(headers2, rows2,
        [2.8*cm, 3.3*cm, 4*cm, 5.3*cm]))

    story.append(PageBreak())

    # ═══════════════════════════════════════════════════════════════════════
    # SECTION 2: TUMOR SUPPRESSOR GENES
    # ═══════════════════════════════════════════════════════════════════════
    story.append(h1_banner("SECTION 2 — TUMOR SUPPRESSOR GENES"))
    story.append(sp(10))

    story.append(Paragraph(
        "Tumor suppressor genes (TSGs) encode proteins that restrain cell growth, promote apoptosis, "
        "or maintain genomic integrity. Both alleles must be inactivated for loss of function — "
        "consistent with Knudson's <b>Two-Hit Hypothesis</b>. The first hit may be inherited "
        "(germline) or somatic; the second hit is always somatic.", S["body"]))
    story.append(sp(4))
    story.append(info_box(
        "<b>Knudson's Two-Hit Hypothesis (1971):</b><br/>"
        "• <b>Sporadic retinoblastoma:</b> Both RB allele mutations acquired somatically → unilateral, late onset<br/>"
        "• <b>Familial retinoblastoma:</b> First hit inherited (germline), second hit somatic → bilateral, early onset<br/>"
        "• Generalised to all tumor suppressors: loss of heterozygosity (LOH) is the common final pathway"
    ))
    story.append(sp(10))

    story.append(Paragraph("2.1  RB — Governor of the Cell Cycle", S["h2"]))
    story.append(rule(TEAL, 0.5))
    story.append(Paragraph(
        "The retinoblastoma protein (RB) is the master regulator of the G1→S checkpoint. "
        "Its phosphorylation state dictates whether a cell proceeds through the cell cycle.", S["body"]))

    rb_data = [
        [Paragraph("<b>State</b>", S["table_header"]),
         Paragraph("<b>RB Status</b>", S["table_header"]),
         Paragraph("<b>Effect</b>", S["table_header"])],
        [Paragraph("Growth inhibition", S["table_cell_bold"]),
         Paragraph("Hypophosphorylated (active)", S["table_cell"]),
         Paragraph("Binds E2F → blocks S-phase genes → ARREST", S["table_cell"])],
        [Paragraph("Mitogenic signal", S["table_cell_bold"]),
         Paragraph("Hyperphosphorylated by cyclin D/CDK4/6, cyclin E/CDK2", S["table_cell"]),
         Paragraph("Releases E2F → S-phase transcription → PROLIFERATION", S["table_cell"])],
        [Paragraph("Cancer", S["table_cell_bold"]),
         Paragraph("Lost / mutated (both alleles)", S["table_cell"]),
         Paragraph("E2F constitutively free → uncontrolled proliferation", S["table_cell"])],
    ]
    rb_t = Table(rb_data, colWidths=[3.5*cm, 5.5*cm, 6.4*cm])
    rb_t.setStyle(TableStyle([
        ("BACKGROUND",   (0,0), (-1,0), NAVY),
        ("BACKGROUND",   (0,1), (-1,1), LIGHT_BG),
        ("BACKGROUND",   (0,2), (-1,2), WHITE),
        ("BACKGROUND",   (0,3), (-1,3), HexColor("#ffe8e8")),
        ("GRID",         (0,0), (-1,-1), 0.5, HexColor("#d1d5db")),
        ("TOPPADDING",   (0,0), (-1,-1), 6),
        ("BOTTOMPADDING",(0,0), (-1,-1), 6),
        ("LEFTPADDING",  (0,0), (-1,-1), 6),
        ("RIGHTPADDING", (0,0), (-1,-1), 6),
        ("VALIGN",       (0,0), (-1,-1), "TOP"),
    ]))
    story.append(rb_t)
    story.append(sp(6))
    story += bullet_list([
        "<b>Cancers with RB loss:</b> Retinoblastoma, osteosarcoma, small cell lung carcinoma, bladder Ca",
        "<b>G1-S checkpoint disrupted in ALL cancers</b> — via RB loss, CDK4/6 amplification, cyclin D overexpression, or p16(INK4a)/CDKN2A deletion",
        "<b>p16 (CDKN2A):</b> CDK inhibitor — prevents cyclin D/CDK4 from phosphorylating RB; commonly deleted in melanoma",
    ])
    story.append(sp(8))

    story.append(Paragraph("2.2  TP53 — Guardian of the Genome", S["h2"]))
    story.append(rule(TEAL, 0.5))
    story.append(Paragraph(
        "p53 (encoded by <i>TP53</i>) is the most commonly mutated gene in all human cancers. "
        "It functions as a transcription factor that responds to cellular stress to prevent "
        "propagation of damaged cells.", S["body"]))
    story.append(sp(4))

    p53_items = [
        ["DNA damage / anoxia / oncogene activation", "ATM/ATR kinases phosphorylate p53 → released from MDM2 → p53 accumulates"],
        ["Normal unstressed cell", "MDM2 binds p53 → ubiquitinates it → proteasomal degradation (t½ = 20 min)"],
        ["p53 activation outcome 1", "G1 arrest: transcribes CDKN1A (p21) → inhibits CDK2 → RB stays hypophosphorylated"],
        ["p53 activation outcome 2", "Senescence: permanent cell cycle arrest; global chromatin restructuring"],
        ["p53 activation outcome 3", "Apoptosis: transcribes BAX, PUMA, NOXA → mitochondrial pathway → caspase activation"],
        ["If p53 mutated/lost", "No arrest, no repair, no apoptosis → mutations accumulate → malignant transformation"],
    ]
    p53_t = Table([[Paragraph(r[0], S["table_cell_bold"]), Paragraph(r[1], S["table_cell"])] for r in p53_items],
                  colWidths=[5*cm, 10.4*cm])
    p53_t.setStyle(TableStyle([
        ("GRID",        (0,0), (-1,-1), 0.5, HexColor("#d1d5db")),
        ("BACKGROUND",  (0,0), (-1,-1), WHITE),
        ("ROWBACKGROUNDS", (0,0), (-1,-1), [LIGHT_BG, WHITE]),
        ("TOPPADDING",  (0,0), (-1,-1), 5),
        ("BOTTOMPADDING",(0,0), (-1,-1), 5),
        ("LEFTPADDING", (0,0), (-1,-1), 6),
        ("VALIGN",      (0,0), (-1,-1), "TOP"),
    ]))
    story.append(p53_t)
    story.append(sp(6))
    story.append(key_fact_box(
        "TP53 is mutated in >50% of ALL human cancers. "
        "Li-Fraumeni syndrome = germline TP53 mutation → multiple early-onset cancers (sarcoma, breast, brain, adrenal). "
        "HPV E6 protein targets p53 for degradation."
    ))
    story.append(sp(10))

    story.append(Paragraph("2.3  Other Major Tumor Suppressors", S["h2"]))
    story.append(rule(TEAL, 0.5))
    headers3 = ["Gene", "Normal Function", "Loss → Cancer", "Hereditary Syndrome"]
    rows3 = [
        ["APC", "Degrades β-catenin; inhibits WNT signaling", "Colorectal cancer (>70%)", "Familial adenomatous polyposis (FAP)"],
        ["BRCA1/2", "Homologous recombination DNA repair", "Breast, ovarian, pancreatic", "Hereditary breast/ovarian cancer syndrome"],
        ["PTEN", "Phosphatase; inhibits PI3K/AKT pathway", "Endometrial, prostate, GBM", "Cowden syndrome"],
        ["VHL", "Targets HIF-1α for degradation", "Clear cell renal cell carcinoma", "Von Hippel-Lindau syndrome"],
        ["NF1", "RAS-GAP; deactivates RAS", "Neurofibroma, MPNST, leukemia", "Neurofibromatosis type 1"],
        ["NF2", "Merlin — links cytoskeleton to cell membrane", "Schwannoma, meningioma, ependymoma", "Neurofibromatosis type 2"],
        ["CDKN2A (p16)", "Inhibits CDK4/6; prevents RB phosphorylation", "Melanoma, pancreatic Ca", "Familial melanoma (FAMMM)"],
        ["SMAD2/4", "TGF-β signal transduction", "Pancreatic, colorectal carcinoma", "Juvenile polyposis"],
        ["WT1", "Transcription factor in kidney development", "Wilms tumor (nephroblastoma)", "WAGR, Denys-Drash syndromes"],
        ["MLH1/MSH2", "Mismatch repair (MMR)", "Colorectal (microsatellite instability)", "Lynch syndrome (HNPCC)"],
    ]
    story.append(make_table(headers3, rows3,
        [2.4*cm, 4*cm, 4*cm, 5*cm]))

    story.append(PageBreak())

    # ═══════════════════════════════════════════════════════════════════════
    # SECTION 3: HALLMARKS OF CANCER
    # ═══════════════════════════════════════════════════════════════════════
    story.append(h1_banner("SECTION 3 — HALLMARKS OF CANCER"))
    story.append(sp(10))
    story.append(Paragraph(
        "Hanahan & Weinberg described 8 fundamental hallmarks acquired by virtually all cancers, "
        "plus 2 enabling characteristics that facilitate their acquisition. Each hallmark is driven "
        "by specific oncogene/TSG alterations.", S["section_intro"]))

    hallmarks = [
        ("1", TEAL, "Self-Sufficiency in Growth Signals",
         "Cancer cells generate their own mitogenic signals or are hypersensitive to low-level signals.",
         ["Oncoproteins mimic growth factor signaling",
          "RAS mutations lock the pathway 'ON'",
          "EGFR/HER2 overexpression → ligand-independent signaling",
          "MYC drives transcription of proliferation genes"]),
        ("2", NAVY, "Insensitivity to Growth-Inhibitory Signals",
         "Cancer cells are blind or resistant to anti-mitogenic signals (e.g., TGF-β, contact inhibition).",
         ["RB loss: E2F no longer restrained at G1/S",
          "p16/CDKN2A deletion: CDK4/6 cannot be inhibited",
          "SMAD4 loss: TGF-β tumor-suppressive signaling lost",
          "All cancers dysregulate the G1-S checkpoint"]),
        ("3", ORANGE, "Evasion of Apoptosis",
         "Cancer cells survive despite pro-apoptotic signals by upregulating survival pathways.",
         ["BCL-2 overexpression (t14;18 in follicular lymphoma)",
          "TP53 loss removes apoptotic trigger",
          "PI3K/AKT activation suppresses BAD/caspases",
          "Survivin and IAP family proteins block caspases"]),
        ("4", HexColor("#6b46c1"), "Limitless Replicative Potential",
         "Normal cells senesce after ~60-70 divisions (Hayflick limit) due to telomere shortening. Cancer cells bypass this.",
         ["Telomerase (hTERT) reactivated in >90% of cancers",
          "Telomerase adds TTAGGG repeats to chromosome ends",
          "Prevents replicative senescence and crisis",
          "Some cancers use ALT (Alternative Lengthening of Telomeres)"]),
        ("5", HexColor("#2d6a4f"), "Sustained Angiogenesis",
         "Tumors >1–2 mm require new blood vessel formation (neovascularization) to receive O₂ and nutrients.",
         ["VEGF (vascular endothelial growth factor) is the master angiogenic switch",
          "HIF-1α (activated by hypoxia or VHL loss) drives VEGF transcription",
          "Tumor vessels: leaky, tortuous, poorly perfused",
          "Anti-VEGF therapy: bevacizumab; anti-VEGFR: sunitinib, sorafenib"]),
        ("6", HexColor("#9b2335"), "Invasion and Metastasis",
         "The deadliest hallmark. Responsible for ~90% of cancer mortality.",
         ["E-cadherin loss → epithelial-mesenchymal transition (EMT)",
          "MMP secretion degrades basement membrane and ECM",
          "Integrins bind matrix for migration",
          "Intravasation → circulation → extravasation → colonization",
          "Organotropism: 'seed and soil' — colon→liver, breast→bone/brain"]),
        ("7", HexColor("#c05621"), "Altered Cellular Metabolism (Warburg Effect)",
         "Cancer cells prefer aerobic glycolysis (glucose→lactate) even in the presence of O₂.",
         ["Provides biosynthetic precursors: nucleotides, lipids, amino acids",
          "Hexosamine pathway: glycosylation for growth factor receptors",
          "IDH1/IDH2 mutations → 2-hydroxyglutarate (oncometabolite) → epigenetic dysregulation",
          "Basis for PET scanning (18F-FDG uptake in metabolically active tumors)"]),
        ("8", HexColor("#1a365d"), "Evasion of Immune Surveillance",
         "Tumors express antigens but evade destruction by adaptive and innate immunity.",
         ["Downregulate MHC class I → invisible to CD8+ CTLs",
          "PD-L1 expression on tumor cells → binds PD-1 on T cells → T cell exhaustion",
          "Recruit immunosuppressive cells: Tregs, M2 macrophages, MDSCs",
          "Secrete TGF-β, IL-10, IDO to create immunosuppressive TME",
          "Basis for checkpoint immunotherapy: anti-PD-1 (nivolumab), anti-CTLA-4 (ipilimumab)"]),
    ]

    for num, color, title, summary, points in hallmarks:
        # Badge + title row
        badge_data = [[
            Paragraph(num, ParagraphStyle("badge_num", fontSize=14, fontName="Helvetica-Bold",
                                          textColor=WHITE, alignment=TA_CENTER)),
            Paragraph(title, ParagraphStyle("h_title", fontSize=11.5, fontName="Helvetica-Bold",
                                             textColor=color, leading=14)),
        ]]
        badge_t = Table(badge_data, colWidths=[1*cm, 14.4*cm])
        badge_t.setStyle(TableStyle([
            ("BACKGROUND",   (0,0), (0,0), color),
            ("BACKGROUND",   (1,0), (1,0), WHITE),
            ("TOPPADDING",   (0,0), (-1,-1), 5),
            ("BOTTOMPADDING",(0,0), (-1,-1), 5),
            ("LEFTPADDING",  (0,0), (0,0), 0),
            ("LEFTPADDING",  (1,0), (1,0), 8),
            ("VALIGN",       (0,0), (-1,-1), "MIDDLE"),
            ("LINEBELOW",    (1,0), (1,0), 1, color),
        ]))

        content_rows = [[
            Paragraph(""),
            Paragraph(summary, ParagraphStyle("h_sum", fontSize=9, fontName="Helvetica-Oblique",
                                              textColor=GREY_TEXT, leading=13)),
        ]]
        for pt in points:
            content_rows.append([Paragraph(""), Paragraph(f"▸ {pt}",
                ParagraphStyle("h_pt", fontSize=9, fontName="Helvetica",
                               textColor=GREY_TEXT, leading=13, leftIndent=6))])

        content_t = Table(content_rows, colWidths=[1*cm, 14.4*cm])
        content_t.setStyle(TableStyle([
            ("BACKGROUND",  (0,0), (-1,-1), HexColor("#f8fafc")),
            ("LINEAFTER",   (0,0), (0,-1), 3, color),
            ("TOPPADDING",  (0,0), (-1,-1), 2),
            ("BOTTOMPADDING",(0,0), (-1,-1), 2),
            ("LEFTPADDING", (1,0), (1,-1), 8),
            ("VALIGN",      (0,0), (-1,-1), "TOP"),
        ]))

        story.append(KeepTogether([badge_t, content_t, sp(8)]))

    story.append(sp(6))
    story.append(Paragraph("Enabling Characteristics", S["h2"]))
    story.append(rule(TEAL, 0.5))
    enabling = [
        ["Genomic Instability",
         "Defects in DNA damage sensing, repair, or checkpoint activation accelerate driver mutation acquisition. "
         "Examples: MMR defects (Lynch syndrome → MSI-H CRC), BRCA1/2 defects (HRR loss → breast/ovarian Ca), "
         "nucleotide excision repair defects (XP → skin Ca)."],
        ["Tumor-Promoting Inflammation",
         "Chronic inflammation supplies growth factors (EGF, FGF, HGF), pro-angiogenic factors (VEGF), "
         "ECM-remodeling enzymes (MMPs), and survival signals (NF-κB pathway) to tumor cells. "
         "Classic examples: H. pylori → gastric Ca; HBV/HCV → hepatocellular Ca; IBD → colonic Ca."],
    ]
    for title, desc in enabling:
        en_data = [[Paragraph(title, S["h3"]),
                    Paragraph(desc, S["body"])]]
        en_t = Table(en_data, colWidths=[4*cm, 11.4*cm])
        en_t.setStyle(TableStyle([
            ("BACKGROUND",  (0,0), (0,0), CARD_BG),
            ("BACKGROUND",  (1,0), (1,0), WHITE),
            ("TOPPADDING",  (0,0), (-1,-1), 8),
            ("BOTTOMPADDING",(0,0),(-1,-1), 8),
            ("LEFTPADDING", (0,0), (-1,-1), 8),
            ("GRID",        (0,0), (-1,-1), 0.5, HexColor("#d1d5db")),
            ("VALIGN",      (0,0), (-1,-1), "TOP"),
        ]))
        story.append(en_t)
        story.append(sp(5))

    story.append(PageBreak())

    # ═══════════════════════════════════════════════════════════════════════
    # SECTION 4: QUICK REVIEW TABLES
    # ═══════════════════════════════════════════════════════════════════════
    story.append(h1_banner("SECTION 4 — QUICK REVIEW & HIGH-YIELD TABLES"))
    story.append(sp(10))

    story.append(Paragraph("4.1  Oncogene vs Tumor Suppressor — Key Differences", S["h2"]))
    story.append(rule(TEAL, 0.5))
    headers_cmp = ["Feature", "Oncogene", "Tumor Suppressor Gene"]
    rows_cmp = [
        ["Normal role", "Promote cell growth/survival", "Inhibit cell growth / promote apoptosis"],
        ["Mutation type", "Gain-of-function", "Loss-of-function"],
        ["Alleles required", "One (dominant)", "Two (recessive — both hits needed)"],
        ["Inheritance in familial Ca", "Rare", "Common (one hit inherited)"],
        ["Examples", "RAS, MYC, BCL-2, HER2, BCR-ABL", "TP53, RB, APC, BRCA1/2, PTEN, VHL"],
        ["Drug targeting", "Often targetable (TKIs, antibodies)", "Harder to target; restore function strategies"],
    ]
    story.append(make_table(headers_cmp, rows_cmp,
        [3.5*cm, 6.1*cm, 5.8*cm]))
    story.append(sp(10))

    story.append(Paragraph("4.2  Hereditary Cancer Syndromes", S["h2"]))
    story.append(rule(TEAL, 0.5))
    headers_hc = ["Syndrome", "Gene(s)", "Cancers", "Inheritance"]
    rows_hc = [
        ["Familial Retinoblastoma", "RB1", "Retinoblastoma, osteosarcoma", "AD"],
        ["Li-Fraumeni", "TP53", "Sarcoma, breast, brain, adrenal, leukemia", "AD"],
        ["FAP", "APC", "Colorectal (100% by age 40), duodenal, gastric", "AD"],
        ["Lynch (HNPCC)", "MLH1, MSH2, MSH6, PMS2", "Colorectal, endometrial, ovarian, gastric", "AD"],
        ["HBOC", "BRCA1, BRCA2", "Breast, ovarian, pancreatic, prostate", "AD"],
        ["MEN2A/2B", "RET (oncogene)", "Medullary thyroid Ca, pheochromocytoma, hyperparathyroidism", "AD"],
        ["VHL disease", "VHL", "Clear cell RCC, hemangioblastoma, pheochromocytoma", "AD"],
        ["Cowden syndrome", "PTEN", "Breast, thyroid, endometrial, hamartomas", "AD"],
        ["Neurofibromatosis 1", "NF1", "Neurofibroma, MPNST, leukemia (JMML)", "AD"],
        ["Xeroderma Pigmentosum", "XPC, XPA, etc.", "Skin cancers (SCC, BCC, melanoma)", "AR"],
    ]
    story.append(make_table(headers_hc, rows_hc,
        [3.8*cm, 3.5*cm, 5.5*cm, 2.6*cm]))
    story.append(sp(10))

    story.append(Paragraph("4.3  Viruses & Cancer", S["h2"]))
    story.append(rule(TEAL, 0.5))
    headers_v = ["Virus", "Type", "Cancer(s)", "Key Mechanism"]
    rows_v = [
        ["HPV 16/18", "DNA (Papillomavirus)", "Cervical, oropharyngeal, anal", "E6 degrades p53; E7 inactivates RB"],
        ["EBV", "DNA (Herpesvirus)", "Burkitt lymphoma, Hodgkin lymphoma, NPC, PTLD", "LMP-1 activates NF-κB; EBERs block apoptosis"],
        ["HBV", "DNA (Hepadnavirus)", "Hepatocellular carcinoma", "HBx activates proto-oncogenes; chronic hepatitis"],
        ["HCV", "RNA (Flavivirus)", "Hepatocellular carcinoma", "Cirrhosis-mediated; NS5A activates WNT/β-catenin"],
        ["HTLV-1", "RNA retrovirus", "Adult T-cell leukemia/lymphoma", "Tax activates NF-κB and IL-2/IL-2R (autocrine)"],
        ["HHV-8/KSHV", "DNA (Herpesvirus)", "Kaposi sarcoma, PEL, MCD", "vIL-6, vCCL1/2; FLIP blocks apoptosis"],
        ["H. pylori", "Bacterium", "Gastric adenocarcinoma, MALT lymphoma", "CagA activates SRC; chronic NF-κB inflammation"],
    ]
    story.append(make_table(headers_v, rows_v,
        [2.6*cm, 2.8*cm, 4.2*cm, 5.8*cm]))

    story.append(PageBreak())

    # ═══════════════════════════════════════════════════════════════════════
    # SECTION 5: CLINICAL TARGETS
    # ═══════════════════════════════════════════════════════════════════════
    story.append(h1_banner("SECTION 5 — THERAPEUTIC TARGETING OF CANCER HALLMARKS"))
    story.append(sp(10))
    story.append(Paragraph(
        "Understanding cancer's molecular mechanisms has enabled targeted therapies. "
        "Each hallmark can potentially be countered by one or more drug classes.", S["section_intro"]))

    headers_tx = ["Hallmark", "Target/Pathway", "Drug Class / Example", "Cancer"]
    rows_tx = [
        ["Growth signals", "HER2", "Monoclonal Ab — Trastuzumab", "Breast, gastric"],
        ["Growth signals", "EGFR", "TKI — Osimertinib, erlotinib", "Lung NSCLC"],
        ["Growth signals", "BCR-ABL", "TKI — Imatinib, dasatinib", "CML"],
        ["Growth signals", "KRAS G12C", "Covalent inhibitor — Sotorasib", "Lung NSCLC"],
        ["Growth signals", "BRAF V600E", "BRAF inhibitor — Vemurafenib", "Melanoma, CRC"],
        ["Cell cycle (TSG loss)", "CDK4/6", "CDK4/6i — Palbociclib, ribociclib", "HR+ breast Ca"],
        ["Apoptosis evasion", "BCL-2", "BCL-2 inhibitor — Venetoclax", "CLL, AML"],
        ["Angiogenesis", "VEGF/VEGFR", "Bevacizumab (anti-VEGF mAb); sunitinib", "CRC, RCC, NSCLC"],
        ["Immune evasion", "PD-1/PD-L1", "Nivolumab, pembrolizumab, atezolizumab", "Melanoma, NSCLC, many"],
        ["Immune evasion", "CTLA-4", "Ipilimumab", "Melanoma"],
        ["DNA repair defect", "PARP", "PARP inhibitors — Olaparib, niraparib", "BRCA1/2+ breast/ovarian"],
        ["Metabolism", "IDH1/IDH2", "Ivosidenib (IDH1), enasidenib (IDH2)", "AML, cholangiocarcinoma"],
    ]
    story.append(make_table(headers_tx, rows_tx,
        [3.3*cm, 3.3*cm, 5.2*cm, 3.6*cm]))
    story.append(sp(12))

    # High-yield summary box
    summary_text = (
        "<b>HIGH-YIELD EXAM SUMMARY</b><br/><br/>"
        "• Most commonly mutated gene in ALL cancers: <b>TP53</b><br/>"
        "• Most commonly mutated oncogene: <b>RAS</b> (~30% of all cancers)<br/>"
        "• Hallmark responsible for 90% of cancer deaths: <b>Invasion &amp; Metastasis</b><br/>"
        "• Both alleles must be lost for TSG effect: <b>Two-Hit Hypothesis (Knudson)</b><br/>"
        "• Oncogenes: dominant (1 allele); TSGs: recessive (2 alleles)<br/>"
        "• G1-S checkpoint disrupted in ALL cancers (RB, p16, CDK4, cyclin D pathway)<br/>"
        "• p53 is stabilised by DNA damage via ATM → MDM2 released<br/>"
        "• Warburg effect = aerobic glycolysis → basis for FDG-PET scanning<br/>"
        "• Telomerase reactivated in &gt;90% of cancers → immortality<br/>"
        "• HPV E6 degrades p53; E7 inactivates RB<br/>"
        "• BCL-2 t(14;18) in follicular lymphoma = prototype of apoptosis evasion"
    )
    summary_data = [[Paragraph(summary_text, ParagraphStyle(
        "sumbox", fontSize=9.5, leading=15, fontName="Helvetica",
        textColor=NAVY))]]
    summary_t = Table(summary_data, colWidths=[W - 4*cm])
    summary_t.setStyle(TableStyle([
        ("BACKGROUND",   (0,0), (-1,-1), YELLOW_HL),
        ("LINEABOVE",    (0,0), (-1,0), 3, ORANGE),
        ("LINEBELOW",    (0,-1),(-1,-1), 3, ORANGE),
        ("TOPPADDING",   (0,0), (-1,-1), 12),
        ("BOTTOMPADDING",(0,0), (-1,-1), 12),
        ("LEFTPADDING",  (0,0), (-1,-1), 14),
        ("RIGHTPADDING", (0,0), (-1,-1), 14),
    ]))
    story.append(summary_t)

    # Build
    doc.build(story)
    print(f"PDF created: {OUTPUT}")

build()