mkdir -p /home/daytona/workspace/cancer-pdf && ls /home/daytona/workspace/cancer…

~/cancer-pdf/generate_cancer_pdf.py

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

OUTPUT = "/home/daytona/workspace/cancer-pdf/Cancer_Overview_Medical_Students.pdf"

# ── Color palette ──────────────────────────────────────────────────────────────
DARK_BLUE   = HexColor("#1A3A5C")
MED_BLUE    = HexColor("#2E6DA4")
LIGHT_BLUE  = HexColor("#D6E8F7")
ACCENT      = HexColor("#C0392B")  # red accent
TABLE_HEADER= HexColor("#1A3A5C")
TABLE_ROW1  = HexColor("#EAF2FB")
TABLE_ROW2  = HexColor("#FFFFFF")
LIGHT_GRAY  = HexColor("#F5F5F5")
MID_GRAY    = HexColor("#555555")
GREEN       = HexColor("#1A6B3C")

# ── Document setup ─────────────────────────────────────────────────────────────
doc = SimpleDocTemplate(
    OUTPUT,
    pagesize=A4,
    rightMargin=2*cm, leftMargin=2*cm,
    topMargin=2.5*cm, bottomMargin=2.5*cm,
    title="Cancer: A Comprehensive Overview for Medical Students",
    author="Orris Medical Library",
)

# ── Styles ─────────────────────────────────────────────────────────────────────
base = getSampleStyleSheet()

def S(name, **kw):
    s = ParagraphStyle(name, **kw)
    return s

cover_title = S("CoverTitle",
    fontName="Helvetica-Bold", fontSize=32, textColor=HexColor("#FFFFFF"),
    alignment=TA_CENTER, leading=38, spaceAfter=10)

cover_sub = S("CoverSub",
    fontName="Helvetica", fontSize=15, textColor=HexColor("#D6E8F7"),
    alignment=TA_CENTER, leading=20, spaceAfter=6)

cover_meta = S("CoverMeta",
    fontName="Helvetica", fontSize=11, textColor=HexColor("#AACCE0"),
    alignment=TA_CENTER, leading=16)

h1 = S("H1",
    fontName="Helvetica-Bold", fontSize=17, textColor=DARK_BLUE,
    spaceBefore=18, spaceAfter=6, leading=22,
    borderPad=4)

h2 = S("H2",
    fontName="Helvetica-Bold", fontSize=13, textColor=MED_BLUE,
    spaceBefore=12, spaceAfter=4, leading=17)

h3 = S("H3",
    fontName="Helvetica-Bold", fontSize=11, textColor=HexColor("#2c5f8a"),
    spaceBefore=8, spaceAfter=3, leading=14)

body = S("Body",
    fontName="Helvetica", fontSize=10, textColor=HexColor("#222222"),
    leading=15, spaceAfter=6, alignment=TA_JUSTIFY)

body_bold = S("BodyBold",
    fontName="Helvetica-Bold", fontSize=10, textColor=HexColor("#222222"),
    leading=15, spaceAfter=4)

callout = S("Callout",
    fontName="Helvetica", fontSize=9.5, textColor=HexColor("#1A3A5C"),
    leading=14, spaceAfter=4, alignment=TA_JUSTIFY,
    leftIndent=8, rightIndent=8)

ref_style = S("RefStyle",
    fontName="Helvetica", fontSize=8.5, textColor=MID_GRAY,
    leading=13, spaceAfter=2)

footer_style = S("FooterStyle",
    fontName="Helvetica", fontSize=8, textColor=MID_GRAY,
    alignment=TA_CENTER)

# ── Helper builders ────────────────────────────────────────────────────────────

def section_header(title, subtitle=None):
    elems = []
    elems.append(HRFlowable(width="100%", thickness=3, color=MED_BLUE, spaceAfter=4))
    elems.append(Paragraph(title, h1))
    if subtitle:
        elems.append(Paragraph(subtitle, h2))
    elems.append(Spacer(1, 3))
    return elems

def callout_box(text, bg=LIGHT_BLUE, border=MED_BLUE):
    data = [[Paragraph(text, callout)]]
    t = Table(data, colWidths=[16.5*cm])
    t.setStyle(TableStyle([
        ("BACKGROUND", (0,0), (-1,-1), bg),
        ("BOX",        (0,0), (-1,-1), 1.5, border),
        ("LEFTPADDING",(0,0),(-1,-1), 10),
        ("RIGHTPADDING",(0,0),(-1,-1), 10),
        ("TOPPADDING", (0,0),(-1,-1), 8),
        ("BOTTOMPADDING",(0,0),(-1,-1), 8),
        ("ROWBACKGROUNDS",(0,0),(-1,-1),[bg]),
    ]))
    return t

def two_col_table(headers, rows, col_widths=None):
    if col_widths is None:
        col_widths = [8*cm, 8.5*cm]
    all_rows = [[Paragraph(h, S("TH", fontName="Helvetica-Bold", fontSize=10,
                  textColor=HexColor("#FFFFFF"), leading=13)) for h in headers]]
    for i, row in enumerate(rows):
        bg = TABLE_ROW1 if i % 2 == 0 else TABLE_ROW2
        all_rows.append([Paragraph(str(c), S(f"TD{i}", fontName="Helvetica",
                fontSize=9.5, leading=13, textColor=HexColor("#222222"))) for c in row])
    t = Table(all_rows, colWidths=col_widths, repeatRows=1)
    style = [
        ("BACKGROUND",  (0,0), (-1,0), TABLE_HEADER),
        ("ROWBACKGROUNDS", (0,1), (-1,-1), [TABLE_ROW1, TABLE_ROW2]),
        ("BOX",         (0,0), (-1,-1), 1, MED_BLUE),
        ("INNERGRID",   (0,0), (-1,-1), 0.5, HexColor("#CCDDEE")),
        ("TOPPADDING",  (0,0), (-1,-1), 5),
        ("BOTTOMPADDING",(0,0),(-1,-1), 5),
        ("LEFTPADDING", (0,0), (-1,-1), 7),
        ("RIGHTPADDING",(0,0),(-1,-1), 7),
        ("VALIGN",      (0,0), (-1,-1), "TOP"),
    ]
    t.setStyle(TableStyle(style))
    return t

def bullet_list(items, style=body):
    return ListFlowable(
        [ListItem(Paragraph(item, style), bulletColor=MED_BLUE, leftIndent=18) for item in items],
        bulletType="bullet", bulletFontName="Helvetica", bulletFontSize=10,
        leftIndent=12, spaceBefore=2, spaceAfter=2
    )

def numbered_list(items, style=body):
    return ListFlowable(
        [ListItem(Paragraph(item, style), leftIndent=18) for item in items],
        bulletType="1", bulletFontName="Helvetica", bulletFontSize=10,
        leftIndent=12, spaceBefore=2, spaceAfter=2
    )

# ── Page callbacks ─────────────────────────────────────────────────────────────
def cover_page_cb(canvas, doc):
    w, h = A4
    # Navy gradient background (simulated with rect)
    canvas.setFillColor(HexColor("#0D2340"))
    canvas.rect(0, 0, w, h, fill=1, stroke=0)
    # Lighter top band
    canvas.setFillColor(HexColor("#1A3A5C"))
    canvas.rect(0, h*0.55, w, h*0.45, fill=1, stroke=0)
    # Accent stripe
    canvas.setFillColor(ACCENT)
    canvas.rect(0, h*0.52, w, 0.5*cm, fill=1, stroke=0)
    # Bottom DNA-like decorative stripe
    canvas.setFillColor(MED_BLUE)
    canvas.rect(0, 2*cm, w, 0.3*cm, fill=1, stroke=0)

def normal_page_cb(canvas, doc):
    """Header + footer for normal pages."""
    w, h = A4
    # Top header bar
    canvas.setFillColor(DARK_BLUE)
    canvas.rect(0, h - 1.5*cm, w, 1.5*cm, fill=1, stroke=0)
    canvas.setFillColor(HexColor("#FFFFFF"))
    canvas.setFont("Helvetica-Bold", 9)
    canvas.drawString(2*cm, h - 1.0*cm, "CANCER: A COMPREHENSIVE OVERVIEW")
    canvas.setFont("Helvetica", 9)
    canvas.drawRightString(w - 2*cm, h - 1.0*cm, "For Medical Students & Clinicians")
    # Footer
    canvas.setFillColor(LIGHT_GRAY)
    canvas.rect(0, 0, w, 1.3*cm, fill=1, stroke=0)
    canvas.setFillColor(MID_GRAY)
    canvas.setFont("Helvetica", 8)
    canvas.drawCentredString(w/2, 0.6*cm, f"Page {doc.page}  |  Sources: Robbins & Kumar Basic Pathology; Robbins, Cotran & Kumar Pathologic Basis of Disease  |  © Orris {datetime.date.today().year}")
    canvas.setFillColor(MED_BLUE)
    canvas.rect(0, 1.3*cm, w, 0.15*cm, fill=1, stroke=0)

# ── Build content ──────────────────────────────────────────────────────────────
story = []

# ═══════════════════════════════════════════════════════════════════════════════
# COVER PAGE
# ═══════════════════════════════════════════════════════════════════════════════
story.append(Spacer(1, 4.5*cm))
story.append(Paragraph("CANCER", cover_title))
story.append(Spacer(1, 0.5*cm))
story.append(Paragraph("A Comprehensive Overview", cover_sub))
story.append(Spacer(1, 0.3*cm))
story.append(Paragraph("Types · Causes · Pathogenesis · Diagnosis · Treatment", cover_sub))
story.append(Spacer(1, 1.5*cm))
story.append(Paragraph("For Medical Students & Clinicians", cover_meta))
story.append(Spacer(1, 0.5*cm))
story.append(Paragraph(f"Compiled July {datetime.date.today().year}", cover_meta))
story.append(Spacer(1, 0.4*cm))
story.append(Paragraph("Sources: Robbins & Kumar Basic Pathology | Robbins, Cotran & Kumar Pathologic Basis of Disease", cover_meta))
story.append(PageBreak())

# ═══════════════════════════════════════════════════════════════════════════════
# 1. INTRODUCTION
# ═══════════════════════════════════════════════════════════════════════════════
story += section_header("1.  Introduction to Cancer")

story.append(Paragraph(
    "Cancer is the second leading cause of death worldwide; only cardiovascular diseases take a greater toll. "
    "Cancer is not a single disease but a heterogeneous group of disorders, all sharing a profound dysregulation "
    "of cell growth and behaviour. Some types of cancer are curable, while others remain virtually always fatal. "
    "Advances in diagnosis, treatment, and prognosis depend on a deeper understanding of the molecular and cellular "
    "basis of each cancer type.", body))

story.append(callout_box(
    "<b>Key concept:</b> Cancer is fundamentally a <b>genetic disorder</b> caused by DNA mutations that may arise "
    "from environmental mutagens, spontaneous replication errors, or inherited germline variants. "
    "These mutations confer selective growth or survival advantages on affected cells, which then expand clonally "
    "and, through stepwise Darwinian selection, give rise to malignant tumours.",
    bg=LIGHT_BLUE, border=MED_BLUE))
story.append(Spacer(1, 8))

story.append(Paragraph("Fundamental Shared Features of All Cancers", h2))
story.append(bullet_list([
    "<b>Genetic origin:</b> DNA mutations (somatic or germline) plus epigenetic alterations.",
    "<b>Clonal expansion:</b> Tumours arise from a single cell and expand clonally.",
    "<b>Tumour progression:</b> Continued Darwinian selection favours genetically aggressive subclones.",
    "<b>Disruption of normal growth controls:</b> Oncogene activation and tumour-suppressor loss.",
    "<b>Ability to invade and metastasise:</b> The hallmark distinguishing malignant from benign.",
]))

story.append(PageBreak())

# ═══════════════════════════════════════════════════════════════════════════════
# 2. NOMENCLATURE & CLASSIFICATION
# ═══════════════════════════════════════════════════════════════════════════════
story += section_header("2.  Nomenclature &amp; Classification")

story.append(Paragraph(
    "Tumours (neoplasms) are classified primarily by their <b>cell of origin</b> and by their "
    "<b>biological behaviour</b> — benign or malignant.", body))

story.append(Paragraph("Benign vs. Malignant Tumours", h2))
story.append(two_col_table(
    ["Feature", "Benign", "Malignant"],
    [
        ["Differentiation", "Well-differentiated; resembles parent tissue", "Variable; may be poorly differentiated (anaplastic)"],
        ["Growth rate", "Slow; mitoses rare and normal", "Rapid; mitoses frequent and abnormal"],
        ["Local invasion", "Non-invasive; remains localised", "Invasive; infiltrates adjacent structures"],
        ["Capsule", "Usually present (fibrous capsule)", "Absent or incomplete"],
        ["Metastasis", "Absent", "Frequently present"],
        ["Effect on host", "Usually not life-threatening", "Can be fatal without treatment"],
        ["Recurrence after excision", "Rare", "Common"],
    ],
    col_widths=[4.5*cm, 6*cm, 6*cm]
))
story.append(Spacer(1, 10))

story.append(Paragraph("Naming Conventions", h2))
story.append(two_col_table(
    ["Cell / Tissue of Origin", "Benign Name", "Malignant Name"],
    [
        ["Epithelial (squamous)", "Squamous cell papilloma", "Squamous cell carcinoma"],
        ["Epithelial (glandular)", "Adenoma", "Adenocarcinoma"],
        ["Smooth muscle", "Leiomyoma", "Leiomyosarcoma"],
        ["Striated muscle", "Rhabdomyoma", "Rhabdomyosarcoma"],
        ["Fat", "Lipoma", "Liposarcoma"],
        ["Bone", "Osteoma", "Osteosarcoma"],
        ["Cartilage", "Chondroma", "Chondrosarcoma"],
        ["Haemopoietic (WBC)", "—", "Leukaemia / Lymphoma"],
        ["Melanocyte", "Melanocytic naevus", "Melanoma"],
        ["Nerve sheath", "Neurofibroma / Schwannoma", "Malignant peripheral nerve sheath tumour"],
    ],
    col_widths=[5.5*cm, 5.5*cm, 5.5*cm]
))
story.append(Spacer(1, 10))

story.append(callout_box(
    "<b>Carcinoma in situ (CIS):</b> Cytologically malignant cells occupying the full thickness of the epithelium "
    "with an <i>intact basement membrane</i> — the earliest recognisable form of malignant epithelial neoplasia. "
    "CIS of the cervix (CIN III) and bladder are classic examples.",
    bg=HexColor("#FEF9E7"), border=HexColor("#D4AC0D")))

story.append(PageBreak())

# ═══════════════════════════════════════════════════════════════════════════════
# 3. EPIDEMIOLOGY
# ═══════════════════════════════════════════════════════════════════════════════
story += section_header("3.  Epidemiology")

story.append(Paragraph("Incidence &amp; Mortality", h2))
story.append(Paragraph(
    "Cancer represents a major global health burden. The most common cancers globally include lung, breast, "
    "colorectal, prostate, and stomach cancers. Lung cancer is the leading cause of cancer-related mortality in "
    "both sexes combined. Geographic and demographic variation in incidence reflects differences in environmental "
    "exposures, lifestyle, genetic susceptibility, and access to screening.", body))

story.append(Paragraph("Risk Factors", h2))
story.append(two_col_table(
    ["Category", "Examples"],
    [
        ["Tobacco", "Lung, oral, laryngeal, oesophageal, bladder, renal, pancreatic cancers"],
        ["Ionising radiation", "Leukaemia, thyroid, breast, lung carcinoma"],
        ["Ultraviolet radiation", "Basal cell carcinoma, squamous cell carcinoma, melanoma"],
        ["Chemical carcinogens", "Aflatoxin (liver), benzene (leukaemia), asbestos (mesothelioma), aniline dyes (bladder)"],
        ["Oncogenic viruses", "HPV (cervical/oropharyngeal), EBV (Burkitt lymphoma, NPC), HBV/HCV (hepatocellular), HTLV-1 (adult T-cell leukaemia/lymphoma), HHV-8 (Kaposi sarcoma)"],
        ["Chronic inflammation", "H. pylori (gastric), IBD (colorectal), HBV/HCV (hepatocellular), chronic cholecystitis (gallbladder)"],
        ["Age", "Incidence rises sharply after age 50; multi-hit model requires years for mutation accumulation"],
        ["Hereditary factors", "BRCA1/2 (breast, ovarian), APC (FAP/colorectal), RB1 (retinoblastoma), TP53 (Li-Fraumeni), MLH1/MSH2/MSH6 (Lynch syndrome)"],
        ["Immunodeficiency", "HIV/AIDS, transplant recipients — increased incidence of virus-associated cancers"],
    ],
    col_widths=[5*cm, 11.5*cm]
))

story.append(PageBreak())

# ═══════════════════════════════════════════════════════════════════════════════
# 4. MOLECULAR BASIS — HALLMARKS OF CANCER
# ═══════════════════════════════════════════════════════════════════════════════
story += section_header("4.  Molecular Basis &amp; Hallmarks of Cancer")

story.append(Paragraph(
    "Hanahan and Weinberg described a set of acquired functional capabilities — the <b>hallmarks of cancer</b> — "
    "that together dictate malignant growth. These are enabled by two underlying characteristics: "
    "<b>genomic instability</b> and <b>tumour-promoting inflammation</b>.", body))

story.append(Paragraph("The Hallmarks (Hanahan &amp; Weinberg)", h2))
hallmarks = [
    ("1. Self-sufficiency in growth signals",
     "Oncogene activation (RAS, BRAF, HER2/ERBB2, EGFR). Cancer cells produce their own growth factors "
     "(autocrine loops) or constitutively activate downstream signalling. "
     "RAS point mutations occur in ~15–20% of all human tumours; ~90% of pancreatic adenocarcinomas."),
    ("2. Insensitivity to growth inhibitory signals",
     "Loss of tumour-suppressor genes (TSGs). RB (retinoblastoma protein) is the 'governor of the cell cycle': "
     "when phosphorylated, it releases E2F transcription factors, driving S-phase entry. "
     "TP53 is the 'guardian of the genome': sensing DNA damage, it triggers cell-cycle arrest or apoptosis. "
     "TP53 is mutated in >50% of all human cancers."),
    ("3. Evasion of apoptosis",
     "BCL-2 overexpression (follicular lymphoma t[14;18]). Loss of pro-apoptotic signals. "
     "Survival signals from PI3K/AKT pathway inhibit BAD and caspase-9."),
    ("4. Limitless replicative potential (immortality)",
     "Upregulation of telomerase (TERT) prevents telomere shortening, allowing indefinite cell division. "
     "Normal somatic cells have ~60–70 doublings before replicative senescence."),
    ("5. Sustained angiogenesis",
     "Tumours >1–2 mm require new blood vessels to supply O2 and nutrients. "
     "VEGF (vascular endothelial growth factor) is the primary driver. "
     "An 'angiogenic switch' tips the balance from anti-angiogenic to pro-angiogenic factors."),
    ("6. Invasion and metastasis",
     "Epithelial-to-mesenchymal transition (EMT), basement membrane degradation by MMPs, "
     "intravasation, survival in circulation, extravasation at distant sites, and colonisation."),
    ("7. Evasion of immune surveillance",
     "PD-L1 overexpression, loss of MHC class I, secretion of immunosuppressive cytokines (IL-10, TGF-β), "
     "recruitment of regulatory T cells and MDSCs into the tumour microenvironment."),
    ("8. Reprogramming of energy metabolism (Warburg effect)",
     "Aerobic glycolysis — glucose converted to lactate even in the presence of O2 — "
     "provides biosynthetic precursors for rapid proliferation. IDH1/2 mutations produce the oncometabolite "
     "2-hydroxyglutarate, which inhibits epigenetic enzymes."),
]
for title_h, desc in hallmarks:
    story.append(KeepTogether([
        Paragraph(title_h, h3),
        Paragraph(desc, body),
    ]))

story.append(Paragraph("Key Genetic Lesions in Cancer", h2))
story.append(two_col_table(
    ["Mechanism", "Examples in Cancer"],
    [
        ["Point mutations", "KRAS G12D (pancreatic, colorectal); TP53 R248W (breast, lung); BRAF V600E (melanoma, hairy cell leukaemia)"],
        ["Gene amplification", "HER2/ERBB2 amplification in breast cancer (~20%); MYCN in neuroblastoma; EGFR in glioblastoma"],
        ["Gene rearrangement / fusion", "BCR-ABL (Philadelphia chromosome, CML); EML4-ALK (lung adenocarcinoma); PML-RARα (AML M3)"],
        ["Deletion / LOH", "RB1 deletion (retinoblastoma); CDKN2A/p16 (bladder, lung); VHL (renal cell carcinoma)"],
        ["Epigenetic silencing", "MLH1 promoter methylation (microsatellite instability, colorectal cancer); CDKN2A methylation"],
        ["MicroRNA dysregulation", "miR-21 overexpression (oncomiR); miR-15a/16-1 deletion (CLL)"],
    ],
    col_widths=[5.5*cm, 11*cm]
))

story.append(PageBreak())

# ═══════════════════════════════════════════════════════════════════════════════
# 5. CARCINOGENESIS — MULTISTEP MODEL
# ═══════════════════════════════════════════════════════════════════════════════
story += section_header("5.  Carcinogenesis: The Multistep Model")

story.append(Paragraph(
    "Carcinogenesis is a <b>multistep process</b> involving the stepwise accumulation of complementary mutations "
    "over years or decades. No single mutation is sufficient to produce a fully malignant tumour.", body))

story.append(Paragraph("Colorectal Cancer as a Model: The Adenoma-Carcinoma Sequence", h2))
story.append(two_col_table(
    ["Step", "Molecular Event", "Morphology"],
    [
        ["1", "Loss of APC (tumour suppressor)", "Normal epithelium → Hyperproliferative epithelium"],
        ["2", "Activation of KRAS oncogene", "Early adenoma (dysplastic crypt focus)"],
        ["3", "Loss of SMAD2/4 (TGF-β signalling)", "Intermediate adenoma"],
        ["4", "Loss of TP53", "Late adenoma (villous features)"],
        ["5", "Additional mutations; telomerase activation", "Invasive carcinoma"],
    ],
    col_widths=[1.5*cm, 7*cm, 7*cm]
))
story.append(Spacer(1, 8))

story.append(Paragraph("Chemical Carcinogens", h2))
story.append(two_col_table(
    ["Type", "Mechanism", "Examples"],
    [
        ["Direct-acting", "Electrophilic; react directly with DNA", "Alkylating agents (cyclophosphamide), acylating agents"],
        ["Indirect-acting (procarcinogens)", "Require metabolic activation by P-450 enzymes", "Polycyclic aromatic hydrocarbons (benzo[a]pyrene in tobacco smoke), aflatoxin B1"],
        ["Promoters", "Not mutagenic; enhance cell proliferation after initiation", "Phorbol esters, hormones, saccharin (in rodents)"],
    ],
    col_widths=[4*cm, 6.5*cm, 6*cm]
))
story.append(Spacer(1, 8))

story.append(Paragraph("Radiation Carcinogenesis", h2))
story.append(bullet_list([
    "<b>Ionising radiation</b> (X-rays, gamma-rays, alpha/beta particles): causes double-strand DNA breaks, base damage, and chromosomal aberrations. Classic association with leukaemia (post-Hiroshima), thyroid carcinoma (post-Chernobyl), and mesothelioma (radon in miners).",
    "<b>Ultraviolet radiation</b> (UV-B, 280–320 nm): forms pyrimidine dimers (cyclobutane dimers and 6-4 photoproducts). Key mutation signature: C→T transitions at dipyrimidine sites. Xeroderma pigmentosum patients (defective nucleotide excision repair) have a 2000-fold elevated skin cancer risk.",
]))

story.append(PageBreak())

# ═══════════════════════════════════════════════════════════════════════════════
# 6. MAJOR CANCER TYPES
# ═══════════════════════════════════════════════════════════════════════════════
story += section_header("6.  Major Cancer Types")

story.append(Paragraph("6a.  Carcinomas (epithelial origin, ~85% of all cancers)", h2))
story.append(two_col_table(
    ["Type", "Common Sites", "Key Molecular Features / Notes"],
    [
        ["Adenocarcinoma", "Lung, breast, colon, prostate, endometrium, pancreas", "Gland-forming; commonly expresses mucin; KRAS, EGFR, HER2 mutations depending on site"],
        ["Squamous cell carcinoma (SCC)", "Lung, head & neck, oesophagus, cervix, skin", "Keratin pearls on histology; EGFR overexpression; HPV-related in oropharynx/cervix"],
        ["Urothelial (transitional cell) carcinoma", "Bladder, ureter, renal pelvis", "Associated with aniline dyes, cyclophosphamide, smoking; frequent FGFR3 mutations in low-grade"],
        ["Hepatocellular carcinoma", "Liver", "Associated with HBV, HCV, aflatoxin, cirrhosis; AFP elevation; TERT promoter mutations"],
        ["Renal cell carcinoma (clear cell)", "Kidney", "VHL mutation/deletion → HIF stabilisation → VEGF overproduction; treated with anti-VEGF and mTOR inhibitors"],
    ],
    col_widths=[4*cm, 5*cm, 7.5*cm]
))
story.append(Spacer(1, 10))

story.append(Paragraph("6b.  Sarcomas (mesenchymal origin, ~1% of cancers)", h2))
story.append(bullet_list([
    "<b>Osteosarcoma:</b> Most common primary bone malignancy; metaphysis of long bones; bimodal peak (adolescents, elderly with Paget's disease); RB1 and TP53 mutations; alkaline phosphatase elevated.",
    "<b>Ewing sarcoma:</b> Diaphysis of long bones; t(11;22) translocation producing EWS-FLI1 fusion; highly aggressive; small round blue cell tumour on histology.",
    "<b>Leiomyosarcoma:</b> Smooth muscle origin; uterus, retroperitoneum; must be distinguished from leiomyoma by mitotic index and necrosis.",
    "<b>Liposarcoma:</b> Most common soft-tissue sarcoma in adults; retroperitoneum and extremities; MDM2 amplification in well-differentiated/dedifferentiated subtypes.",
    "<b>Gastrointestinal stromal tumour (GIST):</b> c-KIT (CD117) mutation in ~85%; responds dramatically to imatinib (KIT/PDGFRA inhibitor).",
]))
story.append(Spacer(1, 8))

story.append(Paragraph("6c.  Haematological Malignancies", h2))
story.append(two_col_table(
    ["Disease", "Cell of Origin", "Key Features"],
    [
        ["Acute myeloid leukaemia (AML)", "Myeloid progenitor", "FAB M3 (APL): t(15;17) PML-RARα; treated with ATRA + arsenic. FLT3, NPM1 mutations are common."],
        ["Chronic myeloid leukaemia (CML)", "Pluripotent HSC", "t(9;22) Philadelphia chromosome; BCR-ABL; imatinib (tyrosine kinase inhibitor) is curative in most."],
        ["Acute lymphoblastic leukaemia (ALL)", "B- or T-lymphoblast", "Most common childhood cancer; Ph+ ALL has worse prognosis; treated with induction/consolidation/maintenance chemo ± TKI."],
        ["Chronic lymphocytic leukaemia (CLL)", "Mature B cell", "Most common adult leukaemia in the West; CD5+/CD19+/CD23+; del(17p) = TP53 loss = poor prognosis; BTK inhibitors (ibrutinib)."],
        ["Diffuse large B-cell lymphoma (DLBCL)", "Germinal centre / activated B cell", "Most common aggressive NHL; BCL-2 and BCL-6 translocations; R-CHOP is standard first-line therapy."],
        ["Hodgkin lymphoma", "Reed-Sternberg cells (B-cell origin)", "Bimodal age distribution; CD15+/CD30+; EBV association in mixed cellularity subtype; ABVD chemotherapy."],
        ["Multiple myeloma", "Plasma cell", "M-protein on SPEP; lytic bone lesions; hypercalcaemia, renal failure, anaemia, bone pain (CRAB). t(4;14), del(17p) = high risk."],
    ],
    col_widths=[4.5*cm, 3.5*cm, 8.5*cm]
))

story.append(PageBreak())

# ═══════════════════════════════════════════════════════════════════════════════
# 7. CHARACTERISTICS OF MALIGNANCY — INVASION & METASTASIS
# ═══════════════════════════════════════════════════════════════════════════════
story += section_header("7.  Invasion &amp; Metastasis")

story.append(Paragraph(
    "Metastasis is defined as the spread of tumour cells to sites <b>physically discontinuous</b> with the primary tumour. "
    "Approximately 30% of patients with newly diagnosed solid tumours have clinically evident metastases, "
    "and a further 20% have occult micrometastases at diagnosis.", body))

story.append(Paragraph("Routes of Spread", h2))
story.append(two_col_table(
    ["Route", "Mechanism", "Typical Cancers"],
    [
        ["Lymphatic (most common for carcinomas)", "Tumour cells enter lymphatics and travel to regional nodes, then to the thoracic duct and systemic circulation", "Breast, colorectal, lung, melanoma"],
        ["Haematogenous", "Tumour cells invade blood vessels (especially thin-walled veins); portal system → liver; inferior vena cava → lung; Batson's plexus → vertebrae", "Sarcomas, renal cell carcinoma, hepatocellular carcinoma"],
        ["Transcoelomic (cavity seeding)", "Direct seeding across peritoneum, pleura, or pericardium", "Ovarian carcinoma (peritoneum), lung cancer (pleura)"],
        ["Perineural spread", "Tumour cells migrate along nerve sheaths", "Adenoid cystic carcinoma, prostate cancer"],
    ],
    col_widths=[4.5*cm, 7.5*cm, 4.5*cm]
))
story.append(Spacer(1, 8))

story.append(Paragraph("Steps of Metastasis (Invasion-Metastasis Cascade)", h2))
story.append(numbered_list([
    "<b>Local invasion:</b> E-cadherin downregulation → epithelial-to-mesenchymal transition (EMT); MMP secretion degrades basement membrane and extracellular matrix.",
    "<b>Intravasation:</b> Tumour cells enter blood or lymphatic vessels.",
    "<b>Survival in circulation:</b> Cancer cells form emboli with platelets; evade NK cells.",
    "<b>Arrest and extravasation:</b> Cells adhere to endothelium at distant site using selectins and integrins.",
    "<b>Colonisation:</b> Establishment of a metastatic niche; requires tumour-stroma interactions and evasion of local immunity.",
]))
story.append(Spacer(1, 8))

story.append(callout_box(
    "<b>Organ tropism (Seed and Soil hypothesis — Paget, 1889):</b> Certain tumours preferentially metastasise to "
    "specific organs: breast → bone/liver/lung/brain; prostate → bone (osteoblastic); lung → brain/adrenal/bone; "
    "colorectal → liver (via portal circulation); melanoma → brain/liver/lung.",
    bg=HexColor("#F9EBEA"), border=ACCENT))

story.append(PageBreak())

# ═══════════════════════════════════════════════════════════════════════════════
# 8. CLINICAL ASPECTS
# ═══════════════════════════════════════════════════════════════════════════════
story += section_header("8.  Clinical Aspects of Cancer")

story.append(Paragraph("Local &amp; Systemic Effects of Tumours", h2))
story.append(two_col_table(
    ["Effect", "Mechanism / Examples"],
    [
        ["Mass effect / obstruction", "Colonic carcinoma causing obstruction; pituitary adenoma compressing optic chiasm"],
        ["Haemorrhage / ulceration", "GI bleeding from gastric carcinoma; haemoptysis from lung cancer"],
        ["Cancer cachexia", "TNF-α (cachectin), IL-1, IL-6, IFN-γ secreted by tumour and immune cells → lipolysis, proteolysis, anorexia, fatigue; associated with poor prognosis"],
        ["Infection", "Neutropenia, mucosal disruption, immunosuppression from tumour or treatment"],
        ["Bone marrow replacement", "Myelophthisic anaemia; leukoerythroblastic blood picture"],
    ],
    col_widths=[5*cm, 11.5*cm]
))
story.append(Spacer(1, 8))

story.append(Paragraph("Paraneoplastic Syndromes", h2))
story.append(Paragraph(
    "Paraneoplastic syndromes are symptom complexes not attributable to tumour mass, metastasis, or metabolic "
    "disturbances. They result from <b>ectopic hormone production</b>, <b>autoimmune/cross-reactive mechanisms</b>, "
    "or secretion of <b>bioactive peptides</b>.", body))
story.append(two_col_table(
    ["Syndrome", "Mediator / Mechanism", "Associated Tumour"],
    [
        ["SIADH (hyponatraemia)", "Ectopic ADH secretion", "Small cell lung carcinoma (SCLC)"],
        ["Cushing syndrome", "Ectopic ACTH secretion", "SCLC, bronchial carcinoid, pancreatic neuroendocrine tumour"],
        ["Hypercalcaemia of malignancy", "PTHrP (parathyroid hormone-related peptide) or osteolytic metastases", "Squamous cell carcinoma (lung, head & neck), renal cell carcinoma, multiple myeloma"],
        ["Eaton-Lambert syndrome", "Auto-antibodies against voltage-gated calcium channels (VGCC)", "Small cell lung carcinoma"],
        ["Cerebellar degeneration", "Anti-Yo (anti-Purkinje cell) antibodies", "Ovarian and breast carcinoma"],
        ["Acanthosis nigricans", "Ectopic insulin-like / TGF-α signalling", "Gastric, lung, uterine carcinoma"],
        ["Migratory thrombophlebitis (Trousseau sign)", "Tumour mucin activates clotting; hypercoagulability", "Pancreatic adenocarcinoma, other mucin-secreting carcinomas"],
        ["Carcinoid syndrome", "Serotonin, histamine, bradykinin secretion", "Carcinoid tumour (especially hepatic metastases); pancreatic NET"],
    ],
    col_widths=[5*cm, 6*cm, 5.5*cm]
))

story.append(PageBreak())

# ═══════════════════════════════════════════════════════════════════════════════
# 9. GRADING & STAGING
# ═══════════════════════════════════════════════════════════════════════════════
story += section_header("9.  Grading &amp; Staging")

story.append(two_col_table(
    ["Parameter", "Grading", "Staging"],
    [
        ["Definition", "Pathological assessment of tumour differentiation (cytological atypia, mitotic rate, necrosis)", "Clinical/pathological extent of disease spread"],
        ["Basis", "Microscopic morphology of tumour cells", "Tumour size, lymph node involvement, distant metastases"],
        ["Purpose", "Indicates biological aggressiveness of individual tumour cells", "Guides treatment selection and predicts prognosis"],
        ["Examples", "WHO Grade 1–4 (brain); Gleason score (prostate); Scarff-Bloom-Richardson (breast)", "TNM system (T = primary tumour, N = nodes, M = metastasis); FIGO for gynaecological cancers"],
        ["Limitation", "May not reflect overall tumour behaviour in all sites", "Does not capture tumour biology or molecular features"],
    ],
    col_widths=[3.5*cm, 7*cm, 6*cm]
))
story.append(Spacer(1, 10))

story.append(Paragraph("TNM Staging System", h2))
story.append(two_col_table(
    ["Stage", "Definition"],
    [
        ["Stage 0", "Carcinoma in situ; no invasion"],
        ["Stage I", "Small tumour localised to organ of origin; no node involvement"],
        ["Stage II", "Larger tumour and/or regional node involvement (N1)"],
        ["Stage III", "Extensive local/regional disease; multiple node groups involved"],
        ["Stage IV", "Distant metastases (M1) — any T, any N"],
    ],
    col_widths=[3.5*cm, 13*cm]
))

story.append(PageBreak())

# ═══════════════════════════════════════════════════════════════════════════════
# 10. LABORATORY DIAGNOSIS
# ═══════════════════════════════════════════════════════════════════════════════
story += section_header("10.  Laboratory Diagnosis")

story.append(Paragraph("Morphological Methods", h2))
story.append(two_col_table(
    ["Method", "Description / Use"],
    [
        ["Excisional / incisional biopsy", "Gold standard for solid tumours; provides architecture and margin status"],
        ["Core needle biopsy", "Preserves architecture; used for breast, liver, kidney, prostate"],
        ["Fine needle aspiration cytology (FNAC)", "Cytology only; useful for thyroid, lymph nodes, salivary gland; cannot assess invasion"],
        ["Frozen section", "Intraoperative; guides extent of surgical resection; less accurate than paraffin"],
        ["Immunohistochemistry (IHC)", "Identifies cell lineage, receptor status (ER/PR/HER2 in breast), prognostic markers; essential for lymphoma classification"],
        ["Electron microscopy", "Ultrastructural characterisation; Birbeck granules (Langerhans histiocytosis); dense-core granules (neuroendocrine tumours)"],
        ["Flow cytometry", "Immunophenotyping of haematological malignancies; determines T vs B cell, clonality"],
    ],
    col_widths=[5*cm, 11.5*cm]
))
story.append(Spacer(1, 8))

story.append(Paragraph("Tumour Markers", h2))
story.append(two_col_table(
    ["Marker", "Cancer Association", "Clinical Use"],
    [
        ["AFP (alpha-fetoprotein)", "Hepatocellular carcinoma, germ cell tumours", "Diagnosis, monitoring; raised in yolk sac tumour"],
        ["CEA (carcinoembryonic antigen)", "Colorectal, gastric, breast, lung carcinoma", "Monitoring recurrence; not screening (low specificity)"],
        ["PSA (prostate-specific antigen)", "Prostate carcinoma", "Screening (controversial), staging, monitoring treatment response"],
        ["hCG (beta-human chorionic gonadotropin)", "Choriocarcinoma, germ cell tumours", "Highly sensitive marker; used for staging and monitoring"],
        ["CA 125", "Ovarian carcinoma (especially serous)", "Monitoring response; elevated in other conditions (low specificity for screening)"],
        ["CA 19-9", "Pancreatic adenocarcinoma, biliary tract", "Monitoring; not useful as screening test"],
        ["LDH (lactate dehydrogenase)", "Lymphoma, germ cell tumours, melanoma", "Reflects tumour bulk and disease activity"],
        ["Calcitonin", "Medullary thyroid carcinoma", "Diagnosis and follow-up; elevated in familial MEN2"],
        ["Chromogranin A", "Neuroendocrine tumours (carcinoids, phaeochromocytoma)", "Diagnosis and monitoring NETs"],
    ],
    col_widths=[4*cm, 5.5*cm, 7*cm]
))
story.append(Spacer(1, 8))

story.append(Paragraph("Molecular &amp; Genomic Diagnosis", h2))
story.append(bullet_list([
    "<b>PCR / RT-PCR:</b> Detection of fusion transcripts (BCR-ABL, PML-RARα); minimal residual disease (MRD) monitoring.",
    "<b>Fluorescence in situ hybridisation (FISH):</b> Gene amplifications (HER2, ALK, ROS1) and chromosomal translocations.",
    "<b>Next-generation sequencing (NGS) / comprehensive genomic profiling:</b> Simultaneous detection of SNVs, indels, CNVs, fusions, and TMB/MSI status across hundreds of genes.",
    "<b>Liquid biopsy (cell-free DNA / ctDNA):</b> Non-invasive monitoring of tumour evolution, treatment response, and resistance mutations.",
    "<b>Microsatellite instability (MSI) / mismatch repair (MMR) testing:</b> Predicts response to immune checkpoint inhibitors; required for Lynch syndrome screening in colorectal cancers.",
]))

story.append(PageBreak())

# ═══════════════════════════════════════════════════════════════════════════════
# 11. TREATMENT
# ═══════════════════════════════════════════════════════════════════════════════
story += section_header("11.  Treatment Modalities")

story.append(Paragraph("Overview of Treatment Strategies", h2))
story.append(two_col_table(
    ["Modality", "Mechanism", "Key Indications / Examples"],
    [
        ["Surgery", "Physical removal of primary tumour ± regional nodes; potentially curative for localised disease", "Most solid tumours (Stage I–III); also palliative (bypass, decompression)"],
        ["Radiotherapy (RT)", "Ionising radiation causes DNA damage → apoptosis; kills proliferating cells in tumour and margin", "Head & neck SCC (definitive RT), breast (adjuvant), cervical cancer (chemoRT), brain metastases (WBRT/SRS), palliative bone pain"],
        ["Conventional chemotherapy", "Cytotoxic drugs target rapidly dividing cells: alkylating agents, antimetabolites, topoisomerase inhibitors, spindle poisons (taxanes, vinca alkaloids)", "Haematological malignancies, germ cell tumours, small cell lung cancer; adjuvant in breast, colon cancers"],
        ["Targeted therapy", "Small molecules or antibodies block specific oncogenic drivers", "Imatinib → BCR-ABL (CML); trastuzumab → HER2 (breast); erlotinib/osimertinib → EGFR (lung); vemurafenib → BRAF V600E (melanoma); venetoclax → BCL-2 (CLL); ibrutinib → BTK (CLL/MCL)"],
        ["Immunotherapy", "Restore or amplify anti-tumour immune responses", "PD-1/PD-L1 checkpoint inhibitors (pembrolizumab, nivolumab, atezolizumab) — melanoma, NSCLC, MSI-H tumours; CTLA-4 (ipilimumab); CAR-T cell therapy (DLBCL, ALL, multiple myeloma)"],
        ["Hormone therapy", "Deprive hormone-sensitive tumours of trophic signals", "Tamoxifen / aromatase inhibitors (ER+ breast cancer); ADT/enzalutamide/abiraterone (prostate cancer); progestins (endometrial cancer)"],
        ["Stem cell transplantation (SCT)", "High-dose chemotherapy ± TBI followed by autologous or allogeneic haematopoietic reconstitution", "Autologous SCT: multiple myeloma, lymphoma. Allogeneic SCT: AML, ALL, MDS — graft-vs-leukaemia (GVL) effect is therapeutic."],
    ],
    col_widths=[4*cm, 5.5*cm, 7*cm]
))
story.append(Spacer(1, 8))

story.append(callout_box(
    "<b>Precision oncology:</b> Comprehensive molecular profiling of each patient's tumour enables selection of "
    "agents targeting that specific tumour's driver alterations. Examples: KRAS G12C inhibitors (sotorasib) in "
    "NSCLC; NTRK inhibitors (larotrectinib) in TRK fusion-positive cancers; PARP inhibitors (olaparib) in "
    "BRCA1/2-mutated breast and ovarian cancers; RET inhibitors (selpercatinib) in RET-fusion lung and thyroid cancers.",
    bg=HexColor("#E8F8F5"), border=GREEN))

story.append(PageBreak())

# ═══════════════════════════════════════════════════════════════════════════════
# 12. IMMUNE SURVEILLANCE & IMMUNOTHERAPY
# ═══════════════════════════════════════════════════════════════════════════════
story += section_header("12.  Tumour Immunology &amp; Immunotherapy")

story.append(Paragraph("Tumour Antigens", h2))
story.append(bullet_list([
    "<b>Tumour-specific antigens (TSAs) / neoantigens:</b> Arise from somatic mutations; unique to tumour cells; recognised by cytotoxic T lymphocytes (CTLs). High TMB (tumour mutational burden) tumours — melanoma, NSCLC — have many neoantigens.",
    "<b>Tumour-associated antigens (TAAs):</b> Overexpressed in tumours but also present on normal cells (e.g., HER2, WT1, MAGE, NY-ESO-1).",
    "<b>Oncofetal antigens:</b> AFP, CEA — re-expressed in malignancy from foetal gene programmes.",
    "<b>Differentiation antigens:</b> CD20 (B-cell lymphoma → rituximab); prostate-specific antigen (PSA); melanocyte differentiation antigens (MART-1, gp100).",
]))
story.append(Spacer(1, 8))

story.append(Paragraph("Mechanisms of Immune Evasion", h2))
story.append(bullet_list([
    "Loss of MHC class I expression → invisible to CD8+ CTLs.",
    "Expression of PD-L1 (CD274) → binds PD-1 on T cells → T-cell exhaustion.",
    "CTLA-4 engagement → downregulates early T-cell activation.",
    "Secretion of TGF-β, IL-10, VEGF → immunosuppressive tumour microenvironment.",
    "Recruitment of regulatory T cells (Tregs) and myeloid-derived suppressor cells (MDSCs).",
    "IDO (indoleamine-2,3-dioxygenase) production → tryptophan depletion → suppresses T-cell function.",
]))
story.append(Spacer(1, 8))

story.append(Paragraph("Immune Checkpoint Inhibitors (ICIs)", h2))
story.append(two_col_table(
    ["Agent", "Target", "Approved Indications (selected)"],
    [
        ["Pembrolizumab (Keytruda)", "PD-1", "NSCLC, melanoma, MSI-H/dMMR solid tumours, HNSCC, urothelial, cervical, TMB-high"],
        ["Nivolumab (Opdivo)", "PD-1", "Melanoma, NSCLC, renal cell, HCC, HNSCC, gastric, colorectal (dMMR)"],
        ["Atezolizumab (Tecentriq)", "PD-L1", "NSCLC, urothelial carcinoma, triple-negative breast cancer (PD-L1+)"],
        ["Durvalumab (Imfinzi)", "PD-L1", "NSCLC (consolidation after CRT), biliary tract cancer"],
        ["Ipilimumab (Yervoy)", "CTLA-4", "Melanoma, RCC (combo with nivolumab), colorectal (dMMR)"],
        ["Tremelimumab (Imjudo)", "CTLA-4", "HCC (combo with durvalumab)"],
    ],
    col_widths=[5*cm, 3*cm, 8.5*cm]
))
story.append(Spacer(1, 8))

story.append(callout_box(
    "<b>Immune-related adverse events (irAEs):</b> Autoimmune toxicity is the principal side-effect of ICIs. "
    "Common irAEs include colitis (anti-CTLA-4 > anti-PD-1), pneumonitis, hepatitis, thyroiditis, and hypophysitis. "
    "Managed with corticosteroids (prednisolone 1–2 mg/kg/day for grade ≥3); permanent discontinuation for "
    "severe (grade 4) events.",
    bg=HexColor("#FEF9E7"), border=HexColor("#D4AC0D")))

story.append(PageBreak())

# ═══════════════════════════════════════════════════════════════════════════════
# 13. CANCER PREVENTION & SCREENING
# ═══════════════════════════════════════════════════════════════════════════════
story += section_header("13.  Cancer Prevention &amp; Screening")

story.append(Paragraph("Primary Prevention", h2))
story.append(bullet_list([
    "<b>Tobacco cessation:</b> Single most impactful intervention; reduces risk of lung, head & neck, bladder, renal, oesophageal, and pancreatic cancers.",
    "<b>Vaccination:</b> HPV vaccine (Gardasil 9) — prevents ~90% of cervical cancer and HPV-associated oropharyngeal SCC; HBV vaccine — prevents hepatocellular carcinoma.",
    "<b>Diet & obesity:</b> Obesity is an independent risk factor for colorectal, endometrial, breast (post-menopausal), renal, and oesophageal cancers; associated with elevated oestrogen, insulin, and IGF-1.",
    "<b>H. pylori eradication:</b> Reduces risk of gastric adenocarcinoma and MALT lymphoma.",
    "<b>Chemoprevention:</b> Tamoxifen/raloxifene in high-risk women for breast cancer; aspirin/NSAIDs reduce colorectal adenoma recurrence.",
    "<b>Sun protection:</b> SPF ≥30 sunscreen, protective clothing to reduce UV-associated skin cancers.",
]))
story.append(Spacer(1, 8))

story.append(Paragraph("Screening Programmes", h2))
story.append(two_col_table(
    ["Cancer", "Recommended Test", "Target Population (general)"],
    [
        ["Colorectal", "Colonoscopy (every 10 years) or annual FIT/FOBT; flexible sigmoidoscopy", "Age 45–75 years (ACS); 50–75 (USPSTF)"],
        ["Breast", "Mammography (± DBT)", "Women 40–74 years (ACS: annual 40–54; biennial 55+)"],
        ["Cervical", "HPV testing ± Pap smear", "Women 25–65 years; HPV-vaccinated populations may extend interval"],
        ["Lung", "Low-dose CT (LDCT)", "Age 50–80, ≥20 pack-year smoking history, current or quit <15 years"],
        ["Prostate", "PSA ± DRE (shared decision-making)", "Men ≥50 (average risk); ≥40–45 (high risk: Black men, BRCA2)"],
        ["Hepatocellular", "Liver ultrasound ± AFP every 6 months", "Cirrhosis; chronic HBV (with active disease or family history)"],
    ],
    col_widths=[3.5*cm, 6*cm, 7*cm]
))

story.append(PageBreak())

# ═══════════════════════════════════════════════════════════════════════════════
# 14. EMERGING CONCEPTS
# ═══════════════════════════════════════════════════════════════════════════════
story += section_header("14.  Emerging Concepts in Oncology")

story.append(Paragraph("Cancer Stem Cells (CSCs)", h2))
story.append(Paragraph(
    "A subpopulation of tumour cells (cancer stem cells or tumour-initiating cells) possesses self-renewal capacity, "
    "resistance to conventional chemotherapy and radiotherapy, and the ability to regenerate the tumour mass. "
    "CSCs may arise from normal tissue stem cells or from differentiated cells that acquire stemness through EMT. "
    "Markers include CD44+/CD24- (breast), CD133+ (colon, brain), EpCAM+ (liver).", body))

story.append(Paragraph("Tumour Microenvironment (TME)", h2))
story.append(Paragraph(
    "The TME comprises cancer-associated fibroblasts (CAFs), tumour-associated macrophages (TAMs), "
    "endothelial cells, pericytes, immune cells, and the extracellular matrix. "
    "TAMs typically adopt an M2 immunosuppressive phenotype, promoting angiogenesis and immune evasion. "
    "CAFs remodel the ECM and secrete paracrine survival signals. The TME is a therapeutic target: "
    "VEGF inhibition (bevacizumab), FAK inhibition, and stromal reprogramming strategies are under investigation.", body))

story.append(Paragraph("Liquid Biopsy", h2))
story.append(Paragraph(
    "Analysis of cell-free circulating tumour DNA (ctDNA), circulating tumour cells (CTCs), and exosomes "
    "in blood provides real-time tumour profiling. Applications include early detection (GRAIL Galleri test), "
    "monitoring treatment response, detecting minimal residual disease, and identifying mechanisms of acquired "
    "drug resistance (e.g., EGFR T790M/C797S in osimertinib-resistant lung cancer).", body))

story.append(Paragraph("Artificial Intelligence in Oncology", h2))
story.append(Paragraph(
    "AI/deep learning models are being applied to: histopathological image analysis (automated grading, "
    "tumour detection); radiomics (extracting quantitative features from CT/MRI/PET); genomic data integration; "
    "drug response prediction; and clinical outcome modelling. "
    "Several FDA-cleared AI tools are now available for digital pathology (e.g., Paige Prostate, Lunit SCOPE).", body))

story.append(PageBreak())

# ═══════════════════════════════════════════════════════════════════════════════
# 15. QUICK-REFERENCE SUMMARY TABLE
# ═══════════════════════════════════════════════════════════════════════════════
story += section_header("15.  Quick-Reference: Top 10 Cancers")

story.append(two_col_table(
    ["Cancer", "Major RF", "Key Molecular Marker", "First-line Treatment Overview"],
    [
        ["Lung adenocarcinoma", "Smoking, radon, asbestos", "EGFR, ALK, ROS1, KRAS G12C, PD-L1", "TKI if driver mutation; pembrolizumab if PD-L1 ≥50% / chemo-IO combo"],
        ["Breast (ER+)", "Age, obesity, BRCA1/2, HRT", "ER/PR+, HER2-, Ki-67", "Endocrine therapy ± CDK4/6 inhibitor (palbociclib)"],
        ["Breast (HER2+)", "HER2 amplification", "HER2 3+ / ISH amplified", "Pertuzumab + trastuzumab + docetaxel (neoadjuvant / adjuvant); T-DM1 / T-DXd"],
        ["Colorectal", "Diet, IBD, Lynch, FAP", "KRAS/NRAS/BRAF WT; MSI-H", "FOLFOX/FOLFIRI ± bevacizumab or EGFR antibody; pembrolizumab (MSI-H)"],
        ["Prostate", "Age, Black race, BRCA2", "PSA, AR, BRCA1/2", "ADT ± enzalutamide/abiraterone; PARP inhibitor (BRCA2 mutated)"],
        ["Gastric", "H. pylori, diet, EBV", "HER2, MSI, PD-L1, CLDN18.2", "Perioperative FLOT or FOLFOX; trastuzumab (HER2+); nivolumab + chemo"],
        ["Hepatocellular", "HBV, HCV, cirrhosis, aflatoxin", "AFP, VEGF, PD-L1", "Sorafenib or lenvatinib (systemic); atezolizumab + bevacizumab (first-line IO)"],
        ["Pancreatic", "Smoking, obesity, BRCA2, familial", "KRAS G12D/V, SMAD4, TP53", "FOLFIRINOX or gemcitabine + nab-paclitaxel; olaparib (BRCA1/2 maintenance)"],
        ["Melanoma", "UV, fair skin, CDKN2A, BRCA2", "BRAF V600E (~50%), PD-L1, TMB", "Nivolumab + ipilimumab; BRAF + MEK inhibitor (dabrafenib + trametinib)"],
        ["CML", "BCR-ABL (Ph chromosome)", "BCR-ABL fusion transcript / FISH", "Imatinib, dasatinib, or nilotinib (first-line TKI); asciminib (T315I resistant)"],
    ],
    col_widths=[3.5*cm, 4*cm, 4.5*cm, 4.5*cm]
))

story.append(PageBreak())

# ═══════════════════════════════════════════════════════════════════════════════
# 16. REFERENCES
# ═══════════════════════════════════════════════════════════════════════════════
story += section_header("References")
refs = [
    "Kumar V, Abbas AK, Aster JC. <i>Robbins &amp; Kumar Basic Pathology</i>. 11th ed. Philadelphia: Elsevier; 2023. Chapter 6: Neoplasia, pp. 187–229.",
    "Kumar V, Abbas AK, Aster JC, et al. <i>Robbins and Cotran Pathologic Basis of Disease</i>. 10th ed. Philadelphia: Elsevier; 2021. Chapter 7: Neoplasia, pp. 259–330.",
    "Hanahan D. Hallmarks of Cancer: New Dimensions. <i>Cancer Discovery</i>. 2022;12(1):31–46. doi:10.1158/2159-8290.CD-21-1059",
    "Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. <i>Cell</i>. 2011;144(5):646–674.",
    "World Health Organization. <i>World Cancer Report 2022</i>. Geneva: WHO; 2022.",
    "NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines). National Comprehensive Cancer Network. Available at: www.nccn.org [Accessed July 2026].",
    "Siegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. <i>CA Cancer J Clin</i>. 2024;74(1):12–49.",
]
for i, r in enumerate(refs, 1):
    story.append(Paragraph(f"{i}. {r}", ref_style))
    story.append(Spacer(1, 3))

# ── Build PDF ──────────────────────────────────────────────────────────────────
def make_doc():
    # We use onFirstPage / onLaterPages callbacks
    doc.build(story,
              onFirstPage=cover_page_cb,
              onLaterPages=normal_page_cb)

make_doc()
print(f"PDF saved to {OUTPUT}")

cd /home/daytona/workspace/cancer-pdf && python generate_cancer_pdf.py 2>&1

ls -lh /home/daytona/workspace/cancer-pdf/Cancer_Overview_Medical_Students.pdf